Add JuliaFormatter (#480)

.JuliaFormatter.toml

@@ -0,0 +1,8 @@
+# Configuration file for JuliaFormatter.jl
+# For more information, see: https://domluna.github.io/JuliaFormatter.jl/stable/config/
+
+always_for_in = true
+always_use_return = true
+margin = 80
+remove_extra_newlines = true
+short_to_long_function_def = true

.github/workflows/format_check.yml

@@ -0,0 +1,30 @@
+name: format-check
+on:
+  push:
+    branches:
+      - master
+      - release-*
+  pull_request:
+    types: [opened, synchronize, reopened]
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: julia-actions/setup-julia@latest
+        with:
+          version: '1'
+      - uses: actions/checkout@v1
+      - name: Format check
+        shell: julia --color=yes {0}
+        run: |
+          using Pkg
+          Pkg.add(PackageSpec(name="JuliaFormatter", version="0.22.4"))
+          using JuliaFormatter
+          format(".", verbose=true)
+          out = String(read(Cmd(`git diff`)))
+          if isempty(out)
+              exit(0)
+          end
+          @error "Some files have not been formatted !!!"
+          write(stdout, out)
+          exit(1)

benchmark/benchmarks.jl

@@ -1,7 +1,7 @@
 using Pkg
 tempdir = mktempdir()
 Pkg.activate(tempdir)
-Pkg.develop(PackageSpec(path=joinpath(@__DIR__, "..")))
+Pkg.develop(PackageSpec(path = joinpath(@__DIR__, "..")))
 Pkg.add(["BenchmarkTools", "PkgBenchmark", "MathOptInterface"])
 Pkg.resolve()
 
@@ -13,26 +13,26 @@ const MOIU = MOI.Utilities
 
 const SUITE = BenchmarkGroup()
 
-problems =  [   
-                "constant_fix!_with_complex_numbers",
-                "affine_dot_multiply_atom",
-                "affine_hcat_atom",
-                "affine_trace_atom",
-                "exp_entropy_atom",
-                "exp_log_perspective_atom",
-                "socp_norm_2_atom",
-                "socp_quad_form_atom",
-                "socp_sum_squares_atom",
-                "lp_norm_inf_atom",
-                "lp_maximum_atom",
-                "sdp_and_exp_log_det_atom",
-                "sdp_norm2_atom",
-                "sdp_lambda_min_atom",
-                "sdp_sum_largest_eigs",
-                "mip_integer_variables",
-            ]
+problems = [
+    "constant_fix!_with_complex_numbers",
+    "affine_dot_multiply_atom",
+    "affine_hcat_atom",
+    "affine_trace_atom",
+    "exp_entropy_atom",
+    "exp_log_perspective_atom",
+    "socp_norm_2_atom",
+    "socp_quad_form_atom",
+    "socp_sum_squares_atom",
+    "lp_norm_inf_atom",
+    "lp_maximum_atom",
+    "sdp_and_exp_log_det_atom",
+    "sdp_norm2_atom",
+    "sdp_lambda_min_atom",
+    "sdp_sum_largest_eigs",
+    "mip_integer_variables",
+]
 
 SUITE["formulation"] = ProblemDepot.benchmark_suite(problems) do problem
     model = MOIU.MockOptimizer(MOIU.Model{Float64}())
-    Convex.load_MOI_model!(model, problem)
+    return Convex.load_MOI_model!(model, problem)
 end

benchmark/pprinthelper.jl

@@ -7,16 +7,16 @@ function displayresult(result)
     md = sprint(export_markdown, result)
     md = replace(md, ":x:" => "❌")
     md = replace(md, ":white_check_mark:" => "✅")
-    display(Markdown.parse(md))
+    return display(Markdown.parse(md))
 end
 
 function printnewsection(name)
     println()
     println()
     println()
-    printstyled("▃" ^ displaysize(stdout)[2]; color=:blue)
-    println()
-    printstyled(name; bold=true)
+    printstyled("▃"^displaysize(stdout)[2]; color = :blue)
     println()
+    printstyled(name; bold = true)
     println()
+    return println()
 end

benchmark/pprintjudge.jl

@@ -2,8 +2,10 @@
 # which is available under an MIT license (see LICENSE).
 using PkgBenchmark
 include("pprinthelper.jl")
-group_target = PkgBenchmark.readresults(joinpath(@__DIR__, "result-target.json"))
-group_baseline = PkgBenchmark.readresults(joinpath(@__DIR__, "result-baseline.json"))
+group_target =
+    PkgBenchmark.readresults(joinpath(@__DIR__, "result-target.json"))
+group_baseline =
+    PkgBenchmark.readresults(joinpath(@__DIR__, "result-baseline.json"))
 judgement = judge(group_target, group_baseline)
 
 displayresult(judgement)

benchmark/runbenchmarks.jl

@@ -3,10 +3,6 @@
 using PkgBenchmark
 benchmarkpkg(
     dirname(@__DIR__),
-    BenchmarkConfig(
-        env = Dict(
-            "JULIA_NUM_THREADS" => "1",
-        ),
-    ),
+    BenchmarkConfig(env = Dict("JULIA_NUM_THREADS" => "1")),
     resultfile = joinpath(@__DIR__, "result.json"),
 )

benchmark/runjudge.jl

@@ -2,13 +2,9 @@
 # which is available under an MIT license (see LICENSE).
 using PkgBenchmark
 
-mkconfig(; kwargs...) =
-    BenchmarkConfig(
-        env = Dict(
-            "JULIA_NUM_THREADS" => "1",
-        );
-        kwargs...
-    )
+function mkconfig(; kwargs...)
+    return BenchmarkConfig(env = Dict("JULIA_NUM_THREADS" => "1"); kwargs...)
+end
 
 group_target = benchmarkpkg(
     dirname(@__DIR__),

docs/examples_literate/general_examples/basic_usage.jl

@@ -33,8 +33,8 @@ p.constraints += A * x <= b
 p.constraints += [x >= 1; x <= 10; x[2] <= 5; x[1] + x[4] - x[2] <= 10]
 solve!(p, solver)
 
-println(round(p.optval, digits=2))
-println(round.(evaluate(x), digits=2))
+println(round(p.optval, digits = 2))
+println(round.(evaluate(x), digits = 2))
 println(evaluate(x[1] + x[4] - x[2]))
 
 # ### Matrix Variables and promotions
@@ -55,7 +55,7 @@ y = Variable()
 ## X is a 2 x 2 variable, and y is scalar. X' + y promotes y to a 2 x 2 variable before adding them
 p = minimize(norm(vec(X)) + y, 2 * X <= 1, X' + y >= 1, X >= 0, y >= 0)
 solve!(p, solver)
-println(round.(evaluate(X), digits=2))
+println(round.(evaluate(X), digits = 2))
 println(evaluate(y))
 p.optval
 
@@ -72,16 +72,20 @@ p.optval
 #
 
 x = Variable(4)
-p = satisfy(norm(x) <= 100, exp(x[1]) <= 5, x[2] >= 7, geomean(x[3], x[4]) >= x[2])
+p = satisfy(
+    norm(x) <= 100,
+    exp(x[1]) <= 5,
+    x[2] >= 7,
+    geomean(x[3], x[4]) >= x[2],
+)
 solve!(p, solver)
 println(p.status)
 evaluate(x)
 
 # ### SDP cone and Eigenvalues
 
-
 y = Semidefinite(2)
-p = maximize(eigmin(y), tr(y)<=6)
+p = maximize(eigmin(y), tr(y) <= 6)
 solve!(p, solver)
 p.optval
 

docs/examples_literate/general_examples/chebyshev_center.jl

@@ -11,9 +11,9 @@
 using Convex, LinearAlgebra, SCS
 
 # Generate the input data
-a1 = [ 2;  1];
-a2 = [ 2; -1];
-a3 = [-1;  2];
+a1 = [2; 1];
+a2 = [2; -1];
+a3 = [-1; 2];
 a4 = [-1; -2];
 b = ones(4, 1);
 
@@ -29,12 +29,19 @@ solve!(p, MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0))
 p.optval
 
 # Generate the figure
-x = range(-1.5, stop=1.5, length=100);
+x = range(-1.5, stop = 1.5, length = 100);
 theta = 0:pi/100:2*pi;
 using Plots
 plot(x, x -> -x * a1[1] / a1[2] + b[1] / a1[2])
-plot!(x, x -> -x * a2[1]/ a2[2] + b[2] / a2[2])
-plot!(x, x -> -x * a3[1]/ a3[2] + b[3] / a3[2])
-plot!(x, x -> -x * a4[1]/ a4[2] + b[4] / a4[2])
-plot!(evaluate(x_c)[1] .+ evaluate(r) * cos.(theta), evaluate(x_c)[2] .+ evaluate(r) * sin.(theta), linewidth = 2)
-plot!(title ="Largest Euclidean ball lying in a 2D polyhedron", legend = nothing)
+plot!(x, x -> -x * a2[1] / a2[2] + b[2] / a2[2])
+plot!(x, x -> -x * a3[1] / a3[2] + b[3] / a3[2])
+plot!(x, x -> -x * a4[1] / a4[2] + b[4] / a4[2])
+plot!(
+    evaluate(x_c)[1] .+ evaluate(r) * cos.(theta),
+    evaluate(x_c)[2] .+ evaluate(r) * sin.(theta),
+    linewidth = 2,
+)
+plot!(
+    title = "Largest Euclidean ball lying in a 2D polyhedron",
+    legend = nothing,
+)

docs/examples_literate/general_examples/control.jl

@@ -80,7 +80,6 @@
 #
 # The following code builds and solves our control example:
 
-
 using Convex, SCS, Plots
 
 ## Some constraints on our motion
@@ -103,12 +102,13 @@ force = Variable(2, T - 1)
 mu = 1
 
 ## Add constraints on our variables
-constraints = Constraint[ position[:, i + 1] == position[:, i] + h * velocity[:, i] for i in 1 : T - 1]
-
+constraints = Constraint[
+    position[:, i+1] == position[:, i] + h * velocity[:, i] for i in 1:T-1
+]
 
-for i in 1 : T - 1
-  acceleration = force[:, i]/mass + g - drag * velocity[:, i]
-  push!(constraints, velocity[:, i + 1] == velocity[:, i] + h * acceleration)
+for i in 1:T-1
+    acceleration = force[:, i] / mass + g - drag * velocity[:, i]
+    push!(constraints, velocity[:, i+1] == velocity[:, i] + h * acceleration)
 end
 
 ## Add position constraints
@@ -126,10 +126,10 @@ solve!(problem, MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0))
 # We can plot the trajectory taken by the object.
 
 pos = evaluate(position)
-plot([pos[1, 1]], [pos[2, 1]], st=:scatter, label="initial point")
-plot!([pos[1, T]], [pos[2, T]], st=:scatter, label="final point")
-plot!(pos[1, :], pos[2, :], label="trajectory")
+plot([pos[1, 1]], [pos[2, 1]], st = :scatter, label = "initial point")
+plot!([pos[1, T]], [pos[2, T]], st = :scatter, label = "final point")
+plot!(pos[1, :], pos[2, :], label = "trajectory")
 
 # We can also see how the magnitude of the force changes over time.
 
-plot(vec(sum(evaluate(force).^2, dims=1)), label="force (magnitude)")
+plot(vec(sum(evaluate(force) .^ 2, dims = 1)), label = "force (magnitude)")

docs/examples_literate/general_examples/huber_regression.jl

@@ -14,8 +14,8 @@ end
 # Generate data for Huber regression.
 using Random
 Random.seed!(1);
-number_samples = round(Int,1.5*n);
-beta_true = 5*randn(n);
+number_samples = round(Int, 1.5 * n);
+beta_true = 5 * randn(n);
 X = randn(n, number_samples);
 Y = zeros(number_samples);
 v = randn(number_samples);
@@ -28,43 +28,49 @@ using Convex, SCS, Distributions
 lsq_data = zeros(number_tests);
 huber_data = zeros(number_tests);
 prescient_data = zeros(number_tests);
-p_vals = range(0, stop=0.15, length=number_tests);
-for i=1:length(p_vals)
-    p = p_vals[i];
+p_vals = range(0, stop = 0.15, length = number_tests);
+for i in 1:length(p_vals)
+    p = p_vals[i]
     ## Generate the sign changes.
-    factor = 2 * rand(Binomial(1, 1-p), number_samples) .- 1;
-    Y = factor .* X' * beta_true + v;
+    factor = 2 * rand(Binomial(1, 1 - p), number_samples) .- 1
+    Y = factor .* X' * beta_true + v
 
     ## Form and solve a standard regression problem.
-    beta = Variable(n);
-    fit = norm(beta - beta_true) / norm(beta_true);
-    cost = norm(X' * beta - Y);
-    prob = minimize(cost);
-    solve!(prob, MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0));
-    lsq_data[i] = evaluate(fit);
+    beta = Variable(n)
+    fit = norm(beta - beta_true) / norm(beta_true)
+    cost = norm(X' * beta - Y)
+    prob = minimize(cost)
+    solve!(prob, MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0))
+    lsq_data[i] = evaluate(fit)
 
     ## Form and solve a prescient regression problem,
     ## i.e., where the sign changes are known.
-    cost = norm(factor .* (X'*beta) - Y);
-    solve!(minimize(cost), MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0))
-    prescient_data[i] = evaluate(fit);
+    cost = norm(factor .* (X' * beta) - Y)
+    solve!(
+        minimize(cost),
+        MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0),
+    )
+    prescient_data[i] = evaluate(fit)
 
     ## Form and solve the Huber regression problem.
-    cost = sum(huber(X' * beta - Y, 1));
-    solve!(minimize(cost), MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0))
-    huber_data[i] = evaluate(fit);
+    cost = sum(huber(X' * beta - Y, 1))
+    solve!(
+        minimize(cost),
+        MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0),
+    )
+    huber_data[i] = evaluate(fit)
 end
 
 #-
 using Plots
 
-plot(p_vals, huber_data, label="Huber", xlabel="p", ylabel="Fit" )
-plot!(p_vals, lsq_data, label="Least squares")
-plot!(p_vals, prescient_data, label="Prescient")
+plot(p_vals, huber_data, label = "Huber", xlabel = "p", ylabel = "Fit")
+plot!(p_vals, lsq_data, label = "Least squares")
+plot!(p_vals, prescient_data, label = "Prescient")
 #-
 
 ## Plot the relative reconstruction error for Huber and prescient regression,
 ## zooming in on smaller values of p.
 indices = findall(p_vals .<= 0.08);
-plot(p_vals[indices], huber_data[indices], label="Huber")
-plot!(p_vals[indices], prescient_data[indices], label="Prescient")
+plot(p_vals[indices], huber_data[indices], label = "Huber")
+plot!(p_vals[indices], prescient_data[indices], label = "Prescient")