Add option to use JMcDM functions in ADRIA site selection

Project.toml

@@ -25,8 +25,10 @@ GeoInterface = "cf35fbd7-0cd7-5166-be24-54bfbe79505f"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5"
 IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a"
+InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 ImageMagick = "6218d12a-5da1-5696-b52f-db25d2ecc6d1"
 Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
+JMcDM = "358108f5-d052-4d0a-8344-d5384e00c0e5"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"

src/ADRIA.jl

@@ -38,6 +38,7 @@ include("ExtInterface/ADRIA/Domain.jl")
 include("io/inputs.jl")
 
 include("sites/connectivity.jl")
+include("sites/mcda_methods.jl")
 include("sites/dMCDA.jl")
 
 include("interventions/seeding.jl")
@@ -82,12 +83,11 @@ if ccall(:jl_generating_output, Cint, ()) == 1
     Base.precompile(Tuple{typeof(combine_results),Vector{String}})   # time: 4.0178256
     Base.precompile(Tuple{typeof(growthODE),Matrix{Float64},Matrix{Float64},NamedTuple{(:r, :k, :mb, :comp, :sm_comp, :small_massives, :small, :mid, :large, :acr_5_11, :acr_6_12, :rec, :sigma, :M_sm, :sXr, :X_mb, :cover),Tuple{Matrix{Float64},Vector{Float64},Matrix{Float64},Float64,Matrix{Float64},SVector{3,Int64},SVector{6,Int64},SVector{19,Int64},SVector{4,Int64},SVector{2,Int64},SVector{2,Int64},Matrix{Float64},Matrix{Float64},Matrix{Float64},Matrix{Float64},Matrix{Float64},Vector{Float64}}},Float64})   # time: 1.4354926
     Base.precompile(Tuple{typeof(combine_results),ResultSet{String,Vector{Any},Vector{Any},Vector{Float64},NamedDimsArray{(:timesteps, :sites, :intervention, :scenarios),Float32,4,ZArray{Float32,4,Zarr.BloscCompressor,DirectoryStore}},NamedDimsArray{(:timesteps, :coral_id, :sites, :scenarios),Float32,4,ZArray{Float32,4,Zarr.BloscCompressor,DirectoryStore}},NamedDimsArray{(:timesteps, :sites, :scenarios),Float32,3,ZArray{Float32,3,Zarr.BloscCompressor,DirectoryStore}},Dict{String,AbstractArray},DataFrame}})   # time: 0.9439985
-    Base.precompile(Tuple{typeof(rank_sites!),Matrix{Float64},Vector{Float64},Matrix{Int64},Int64,typeof(topsis),Int64})   # time: 0.3518593
-    Base.precompile(Tuple{typeof(rank_sites!),Matrix{Float64},Vector{Float64},Matrix{Int64},Int64,typeof(vikor),Int64})   # time: 0.3170264
+    Base.precompile(Tuple{typeof(rank_sites!),Matrix{Float64},Vector{Float64},Matrix{Int64},Int64,typeof(adria_topsis),Int64})   # time: 0.3518593
+    Base.precompile(Tuple{typeof(rank_sites!),Matrix{Float64},Vector{Float64},Matrix{Int64},Int64,typeof(adria_vikor),Int64})   # time: 0.3170264
     Base.precompile(Tuple{typeof(scenario_attributes),String,String,Vector{String},String,EnvLayer{String,Vector{Int64}},SimConstants,Vector{String},Vector{Float64},Vector{Float64},Vector{Tuple{Float64,Float64}}})   # time: 0.2140636
     Base.precompile(Tuple{typeof(model_spec),Model})   # time: 0.1997914
     Base.precompile(Tuple{typeof(bleaching_mortality!),Matrix{Float64},Matrix{Float64},Vector{Float64},Int64,Vector{Float64},Vector{Float64},Vector{Float64},Vector{Float64},Float64})   # time: 0.1940948
-    Base.precompile(Tuple{typeof(rank_seed_sites!),Matrix{Float64},Vector{Float64},Matrix{Int64},Int64,Function})   # time: 0.1931881
     Base.precompile(Tuple{typeof(create_decision_matrix),Vector{Int64},Vector{Float64},Vector{Float64},Vector{Float64},Vector{Float64},Vector{Float64},Vector{Float64},Vector{Float64},Vector{Float64},Matrix{Float64},Matrix{Float64},Float64})   # time: 0.1929096
     Base.precompile(Tuple{typeof(scenario_attributes),String,String,Vector{String},String,EnvLayer{String,Vector{Any}},Dict{String,Any},Vector{Any},Vector{Float64},Vector{Float64},Vector{Any}})   # time: 0.1755622
     Base.precompile(Tuple{typeof(proportional_adjustment!),Matrix{Float64},Vector{Float64},Vector{Float64}})   # time: 0.1680073

src/ecosystem/Ecosystem.jl

@@ -45,7 +45,7 @@ Base.@kwdef struct Intervention{N,P,P2} <: EcoModel
     # Integer values have a +1 offset to allow for discrete value mapping
     # (see `set()` and `map_to_discrete()` methods)
     # Bounds are defined as floats to maintain type stability
-    guided::N = Param(0, ptype="integer", bounds=(-1.0, 3.0 + 1.0), dists="unif",
+    guided::N = Param(0, ptype="integer", bounds=(-1.0, 21.0 + 1.0), dists="unif",
         name="Guided", description="Choice of MCDA approach.")
     seed_TA::N = Param(0, ptype="integer", bounds=(0.0, 1000000.0 + 1.0), dists="unif",
         name="Seeded Tabular Acropora", description="Number of enhanced Tabular Acropora to seed per deployment year.")