Update format for JuliaFormatter v1.0.15

docs/Project.toml

@@ -25,7 +25,7 @@ CSV = "0.10"
 DataFrames = "1"
 Documenter = "0.27.9"
 GLPK = "=1.1.0"
-HTTP = "1"
+HTTP = "=1.5.2"
 HiGHS = "=1.2.0"
 Interpolations = "0.14"
 Ipopt = "=1.1.0"

docs/src/tutorials/linear/callbacks.jl

@@ -88,12 +88,12 @@ function example_user_cut_constraint()
     function my_callback_function(cb_data)
         callback_called = true
         x_vals = callback_value.(Ref(cb_data), x)
-        accumulated = sum(item_weights[i] for i = 1:N if x_vals[i] > 1e-4)
+        accumulated = sum(item_weights[i] for i in 1:N if x_vals[i] > 1e-4)
         println("Called with accumulated = $(accumulated)")
-        n_terms = sum(1 for i = 1:N if x_vals[i] > 1e-4)
+        n_terms = sum(1 for i in 1:N if x_vals[i] > 1e-4)
         if accumulated > 10
             con = @build_constraint(
-                sum(x[i] for i = 1:N if x_vals[i] > 0.5) <= n_terms - 1
+                sum(x[i] for i in 1:N if x_vals[i] > 0.5) <= n_terms - 1
             )
             println("Adding $(con)")
             MOI.submit(model, MOI.UserCut(cb_data), con)

src/lp_sensitivity2.jl

@@ -95,7 +95,7 @@ function lp_sensitivity_report(model::Model; atol::Float64 = 1e-8)
         # We call `collect` here because some Julia versions are missing sparse
         # matrix \ sparse vector fallbacks.
         j => B_fact \ collect(std_form.A[:, j]) for
-        j = 1:length(basis.basic_cols) if basis.basic_cols[j] == false
+        j in 1:length(basis.basic_cols) if basis.basic_cols[j] == false
     )
 
     report = SensitivityReport(

test/constraint.jl

@@ -649,15 +649,15 @@ function test_sum_constraint(ModelType, ::Any)
     C = [1 2 3; 4 5 6; 7 8 9]
 
     @test_expression sum(C[i, j] * x[i, j] for i in 1:2, j in 2:3)
-    @test_expression sum(C[i, j] * x[i, j] for i = 1:3, j in 1:3 if i != j) - y
+    @test_expression sum(C[i, j] * x[i, j] for i in 1:3, j in 1:3 if i != j) - y
     @test JuMP.isequal_canonical(
         @expression(model, sum(C[i, j] * x[i, j] for i in 1:3, j in 1:i)),
         sum(C[i, j] * x[i, j] for i in 1:3 for j in 1:i),
     )
     @test_expression sum(C[i, j] * x[i, j] for i in 1:3 for j in 1:i)
-    @test_expression sum(C[i, j] * x[i, j] for i = 1:3 if true for j in 1:i)
+    @test_expression sum(C[i, j] * x[i, j] for i in 1:3 if true for j in 1:i)
     @test_expression sum(
-        C[i, j] * x[i, j] for i = 1:3 if true for j = 1:i if true
+        C[i, j] * x[i, j] for i in 1:3 if true for j in 1:i if true
     )
     @test_expression sum(0 * x[i, 1] for i in 1:3)
     @test_expression sum(0 * x[i, 1] + y for i in 1:3)

test/hygiene.jl

@@ -20,30 +20,30 @@ JuMP.@variable(model, z[i = 1:2, j = 1:2], Symmetric)
 @test z isa LinearAlgebra.Symmetric
 
 JuMP.@constraint(model, x + sum(j * y[j] for j in r) <= 1)
-JuMP.@constraint(model, sum(y[j] for j = r if j == 4) <= 1)
+JuMP.@constraint(model, sum(y[j] for j in r if j == 4) <= 1)
 JuMP.@constraint(model, -1 <= x + y[3] <= 1)
 JuMP.@constraints(model, begin
     x + sum(j * y[j] for j in r) <= 1
-    sum(y[j] for j = r if j == 4) <= 1
+    sum(y[j] for j in r if j == 4) <= 1
 end)
 
 JuMP.@constraint(model, [x x; -x x] >= 0, JuMP.PSDCone())
 
 JuMP.@objective(model, sense, y[4])
 JuMP.@objective(model, Min, x + sum(j * y[j] for j in r))
-JuMP.@objective(model, Max, sum(y[j] for j = r if j == 4))
+JuMP.@objective(model, Max, sum(y[j] for j in r if j == 4))
 
 JuMP.@NLconstraint(model, y[3] == 1)
 JuMP.@NLconstraint(model, x + sum(j * y[j] for j in r) <= 1)
-JuMP.@NLconstraint(model, sum(y[j] for j = r if j == 4) <= 1)
+JuMP.@NLconstraint(model, sum(y[j] for j in r if j == 4) <= 1)
 JuMP.@NLconstraints(model, begin
     x + sum(j * y[j] for j in r) <= 1
-    sum(y[j] for j = r if j == 4) <= 1
+    sum(y[j] for j in r if j == 4) <= 1
 end)
 
 JuMP.@NLobjective(model, sense, y[4])
 JuMP.@NLobjective(model, Min, x + sum(j * y[j] for j in r))
-JuMP.@NLobjective(model, Max, sum(y[j] for j = r if j == 4))
+JuMP.@NLobjective(model, Max, sum(y[j] for j in r if j == 4))
 
 # TODO: Add tests for the content of the model.
 

test/macros.jl

@@ -137,7 +137,7 @@ let id = 0
 end
 
 function test_Check_Julia_generator_expression_parsing()
-    sumexpr = :(sum(x[i, j] * y[i, j] for i = 1:N, j in 1:M if i != j))
+    sumexpr = :(sum(x[i, j] * y[i, j] for i in 1:N, j in 1:M if i != j))
     @test sumexpr.head == :call
     @test sumexpr.args[1] == :sum
     @test sumexpr.args[2].head == :generator

test/perf/JuMPBenchmarks.jl

@@ -221,12 +221,12 @@ function _macro_linear(N::Int)
                 sum(
                     sum(
                         N * i * j * k * y[i, j, k] + x[i, j] for
-                        k = 1:N if i != j && j != k
+                        k in 1:N if i != j && j != k
                     ) for j in 1:N
                 ) for i in 1:N
             ) + sum(
-                sum(x[i, j] for j = 1:5N if j % i == 3) for
-                i = 1:10N if i <= N * z
+                sum(x[i, j] for j in 1:5N if j % i == 3) for
+                i in 1:10N if i <= N * z
             )
         )
     end
@@ -252,12 +252,12 @@ function _macro_quad(N::Int)
                 sum(
                     sum(
                         N * i * j * k * y[i, j, k] * x[i, j] for
-                        k = 1:N if i != j && j != k
+                        k in 1:N if i != j && j != k
                     ) for j in 1:N
                 ) for i in 1:N
             ) + sum(
-                sum(x[i, j] for j = 1:5N if j % i == 3) for
-                i = 1:10N if i <= N * z
+                sum(x[i, j] for j in 1:5N if j % i == 3) for
+                i in 1:10N if i <= N * z
             )
         )
     end