julia formatter

.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 = 90
+ remove_extra_newlines = true
+ short_to_long_function_def = true

.github/workflows/format_check.yml

@@ -0,0 +1,31 @@
+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="1"))
+           using JuliaFormatter
+           format("src/MOI_wrapper", verbose=true)
+           format("test", 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)

CHANGELOG.md

@@ -1,6 +1,10 @@
 LaplacianOpt.jl Change Log
 =========================
 
+### v0.3.1
+- Minor change in `src/variables.jl`
+- Added JuliaFormatter.toml and formatting workfow
+
 ### v0.3.0
 - Includes adjacency of base and augments graphs in results dictionary
 - Constraints added to support cycle graphs with max algberaic connectivity using `hamiltonian_cycle` in `graph_type`

Project.toml

@@ -1,7 +1,7 @@
 name = "LaplacianOpt"
 uuid = "bb20392f-64fb-4001-92e8-14b3aedd5a9e"
 authors = ["Harsha Nagarajan"]
-version = "0.3.0"
+version = "0.3.1"
 
 [deps]
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"

docs/make.jl

@@ -5,27 +5,32 @@ using DocumenterTools: Themes
 for w in ("light", "dark")
     header = read(joinpath(@__DIR__, "src/assets/themes/style.scss"), String)
     theme = read(joinpath(@__DIR__, "src/assets/themes/$(w)defs.scss"), String)
-    write(joinpath(@__DIR__, "src/assets/themes/$(w).scss"), header*"\n"*theme)
+    write(joinpath(@__DIR__, "src/assets/themes/$(w).scss"), header * "\n" * theme)
 end
 
-Themes.compile(joinpath(@__DIR__, "src/assets/themes/light.scss"), joinpath(@__DIR__, "src/assets/themes/documenter-light.css"))
-Themes.compile(joinpath(@__DIR__, "src/assets/themes/dark.scss"), joinpath(@__DIR__, "src/assets/themes/documenter-dark.css"))
+Themes.compile(
+    joinpath(@__DIR__, "src/assets/themes/light.scss"),
+    joinpath(@__DIR__, "src/assets/themes/documenter-light.css"),
+)
+Themes.compile(
+    joinpath(@__DIR__, "src/assets/themes/dark.scss"),
+    joinpath(@__DIR__, "src/assets/themes/documenter-dark.css"),
+)
 
 makedocs(
     modules = [LaplacianOpt],
     sitename = "LaplacianOpt",
-    format = Documenter.HTML(mathengine = Documenter.MathJax(), 
-                             prettyurls = get(ENV, "CI", nothing) == "true"),
+    format = Documenter.HTML(
+        mathengine = Documenter.MathJax(),
+        prettyurls = get(ENV, "CI", nothing) == "true",
+    ),
     strict = true,
     authors = "Harsha Nagarajan",
     pages = [
-        "Introduction"        => "index.md",
-        "Quick Start guide"   => "quickguide.md",
+        "Introduction" => "index.md",
+        "Quick Start guide" => "quickguide.md",
         "Function References" => "function_references.md",
     ],
 )
 
-deploydocs(
-    repo = "github.com/harshangrjn/LaplacianOpt.jl.git",
-    push_preview = true
-)
+deploydocs(repo = "github.com/harshangrjn/LaplacianOpt.jl.git", push_preview = true)

examples/optimizer.jl

@@ -3,52 +3,57 @@
 #=====================================#
 
 function get_gurobi()
-    return JuMP.optimizer_with_attributes(Gurobi.Optimizer, 
-                                          MOI.Silent() => false, 
-                                          # "MIPFocus" => 3, # Focus on optimality over feasibility 
-                                          "Presolve" => 1) 
+    return JuMP.optimizer_with_attributes(
+        Gurobi.Optimizer,
+        MOI.Silent() => false,
+        # "MIPFocus" => 3, # Focus on optimality over feasibility 
+        "Presolve" => 1,
+    )
 end
 
 function get_cplex()
-     return JuMP.optimizer_with_attributes(CPLEX.Optimizer, 
-                                           MOI.Silent() => false, 
-                                           # "CPX_PARAM_EPGAP" => 1E-4,
-                                           # "CPX_PARAM_MIPEMPHASIS" => 2 # Focus on optimality over feasibility 
-                                           "CPX_PARAM_PREIND" => 1)
+    return JuMP.optimizer_with_attributes(
+        CPLEX.Optimizer,
+        MOI.Silent() => false,
+        # "CPX_PARAM_EPGAP" => 1E-4,
+        # "CPX_PARAM_MIPEMPHASIS" => 2 # Focus on optimality over feasibility 
+        "CPX_PARAM_PREIND" => 1,
+    )
 end
 
 #======================================#
 # MIP solvers (open-source, but slow)  #
 #======================================#
 
 function get_cbc()
-    return JuMP.optimizer_with_attributes(Cbc.Optimizer, 
-                                          MOI.Silent() => false) 
+    return JuMP.optimizer_with_attributes(Cbc.Optimizer, MOI.Silent() => false)
 end
 
 function get_glpk()
-    return JuMP.optimizer_with_attributes(GLPK.Optimizer, 
-                                          MOI.Silent() => false)
+    return JuMP.optimizer_with_attributes(GLPK.Optimizer, MOI.Silent() => false)
 end
 
 #========================================================#
 # Continuous nonlinear programming solver (open-source)  #
 #========================================================#
 
 function get_ipopt()
-     return JuMP.optimizer_with_attributes(Ipopt.Optimizer, 
-                                       MOI.Silent() => true, 
-                                       "sb" => "yes", 
-                                       "max_iter" => Int(1E4))
+    return JuMP.optimizer_with_attributes(
+        Ipopt.Optimizer,
+        MOI.Silent() => true,
+        "sb" => "yes",
+        "max_iter" => Int(1E4),
+    )
 end
 
-
 #=================================================================#
 # Local mixed-integer nonlinear programming solver (open-source)  #
 #=================================================================#
 function get_juniper()
-     return JuMP.optimizer_with_attributes(Juniper.Optimizer, 
-                                         MOI.Silent() => false, 
-                                         "mip_solver" => get_gurobi(), 
-                                         "nl_solver" => get_ipopt())
-end
+    return JuMP.optimizer_with_attributes(
+        Juniper.Optimizer,
+        MOI.Silent() => false,
+        "mip_solver" => get_gurobi(),
+        "nl_solver" => get_ipopt(),
+    )
+end

examples/run_examples.jl

@@ -18,14 +18,15 @@ lopt_optimizer = get_cplex()
 #= 
  Option I: Let the package parse a JSON file and obtain the data dictionary (data_dict)
  Sample JSON format: Check examples/instances
-=# 
+=#
 function data_I()
     num_nodes = 8
-    instance  = 1
+    instance = 1
     # Data format has to be as given in this JSON file
-    file_path = joinpath(@__DIR__, "instances/$(num_nodes)_nodes/$(num_nodes)_$(instance).json")
+    file_path =
+        joinpath(@__DIR__, "instances/$(num_nodes)_nodes/$(num_nodes)_$(instance).json")
     data_dict = LOpt.parse_file(file_path)
-    augment_budget = (num_nodes-1) # spanning tree constraint
+    augment_budget = (num_nodes - 1) # spanning tree constraint
     return data_dict, augment_budget
 end
 
@@ -34,7 +35,7 @@ end
             num_nodes, adjacency_base_graph and adjacency_augment_graph
 =#
 function data_II()
-    data_dict = Dict{String, Any}()
+    data_dict = Dict{String,Any}()
     data_dict["num_nodes"] = 4
     data_dict["adjacency_base_graph"] = [0 2 0 0; 2 0 3 0; 0 3 0 4; 0 0 4 0]
     data_dict["adjacency_augment_graph"] = [0 0 4 8; 0 0 0 7; 4 0 0 0; 8 7 0 0]
@@ -47,23 +48,25 @@ end
 #-------------------------------#
 data_dict, augment_budget = data_II()
 
-params = Dict{String, Any}(
-    "data_dict"           => data_dict,
-    "augment_budget"      => augment_budget,
-    "eigen_cuts_full"     => true,
+params = Dict{String,Any}(
+    "data_dict" => data_dict,
+    "augment_budget" => augment_budget,
+    "eigen_cuts_full" => true,
     "soc_linearized_cuts" => false,
-    "eigen_cuts_2minors"  => false,
-    "eigen_cuts_3minors"  => false,
-    "topology_flow_cuts"  => true,
+    "eigen_cuts_2minors" => false,
+    "eigen_cuts_3minors" => false,
+    "topology_flow_cuts" => true,
     # "time_limit"          => 3600,
-    )
+)
 
 #----------------------------------------------------------------#
 #      Optimization model and visualize solution (optional)      #
 #   Graph plots can be located inside `examples/plots` folder    #
 #----------------------------------------------------------------#
-result = LOpt.run_LOpt(params, 
-                       lopt_optimizer;
-                       visualize_solution   = false,  # Make this true to plot the graph solution
-                       visualizing_tool     = "tikz", # "graphviz" is another option
-                       display_edge_weights = false)
+result = LOpt.run_LOpt(
+    params,
+    lopt_optimizer;
+    visualize_solution = false,  # Make this true to plot the graph solution
+    visualizing_tool = "tikz", # "graphviz" is another option
+    display_edge_weights = false,
+)

src/LaplacianOpt.jl

@@ -9,10 +9,10 @@ import Graphs
 import TikzGraphs
 import TikzPictures
 
-const MOI  = MathOptInterface
-const LA   = LinearAlgebra
+const MOI = MathOptInterface
+const LA = LinearAlgebra
 const LOpt = LaplacianOpt
- 
+
 # Create our module level logger (this will get precompiled)
 const _LOGGER = Memento.getlogger(@__MODULE__)
 
@@ -22,13 +22,16 @@ __init__() = Memento.register(_LOGGER)
 
 "Suppresses information and warning messages output by LaplacianOpt, for fine grained control use the Memento package"
 function silence()
-    Memento.info(_LOGGER, "Suppressing information and warning messages for the rest of this session.  Use the Memento package for more fine-grained control of logging.")
-    Memento.setlevel!(Memento.getlogger(LaplacianOpt), "error")
+    Memento.info(
+        _LOGGER,
+        "Suppressing information and warning messages for the rest of this session.  Use the Memento package for more fine-grained control of logging.",
+    )
+    return Memento.setlevel!(Memento.getlogger(LaplacianOpt), "error")
 end
 
 "allows the user to set the logging level without the need to add Memento"
 function logger_config!(level)
-    Memento.config!(Memento.getlogger("LaplacianOpt"), level)
+    return Memento.config!(Memento.getlogger("LaplacianOpt"), level)
 end
 
 include("data.jl")