Add new tests for seven modules. Include GH workflow.

.github/workflows/ci.yml

@@ -0,0 +1,69 @@
+name: CI
+
+on: 
+  pull_request:
+  push:
+    branches: [main, develop]
+    tags: ['*']
+
+jobs:
+  test:
+    name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }} - ${{ github.event_name}}
+    runs-on: ${{ matrix.os }}
+    strategy: 
+      fail-fast: false
+      matrix:
+        version:
+          - "1.6" # LTS (64-bit Linux)
+          - "1.7"
+          - "1.8" 
+          - '1.9' # latest stable Julia release (Linux)
+        os:
+          - ubuntu-latest
+        arch:
+          - x64
+        include: # additional tests [Julia-nightly] (Linux) 
+          - os: ubuntu-latest
+            version: 'nightly'
+            arch: x64
+            allow_failure: true
+    steps:
+      - uses: actions/checkout@v3
+      - name: Create a new branch and pull test logs from repo # only push logs if PR is not from a fork
+        if: ${{ ! github.event.pull_request.head.repo.fork }}   
+        run: |
+          git checkout -b ${GITHUB_REF#refs/heads/}-testlogs
+          mkdir test/Logs; cd test/Logs
+          git config --global init.defaultBranch main
+          git init
+          git config --local user.name GenXProject
+          git config --local user.email "[email protected]"
+          git remote add testlog https://[email protected]/GenXProject/GenX-testlog.git
+          git pull testlog main
+          if [ -d Logs_v${{ matrix.version }} ]; then
+            mv Logs_v${{ matrix.version }}/*.log .
+          else
+            mkdir Logs_v${{ matrix.version }}
+          fi
+        env:
+          LOG_TOKEN: ${{ secrets.LOG_TOKEN }}
+      - uses: julia-actions/setup-julia@v1
+        with:
+          version: ${{ matrix.version }}
+          arch: ${{ matrix.arch }}
+      - uses: julia-actions/cache@v1
+      - uses: julia-actions/julia-buildpkg@v1
+      - uses: julia-actions/julia-runtest@v1
+      - name: Commit logs and push back to repo # only push logs if PR is not from a fork
+        if: ${{ ! github.event.pull_request.head.repo.fork }}   
+        run: |
+          cd ${GITHUB_WORKSPACE}/test/Logs
+          mv *.log Logs_v${{ matrix.version }}
+          git add -f Logs_v${{ matrix.version }}
+          git commit -m "Update test logs for Julia v${{ matrix.version }}"
+          git pull --rebase testlog main # pull again to avoid conflicts from other versions
+          git push testlog main
+      - uses: julia-actions/julia-processcoverage@v1
+      - uses: codecov/codecov-action@v3
+        with:
+          file: lcov.info

.gitignore

@@ -19,6 +19,8 @@ gurobi.log
 cplex.log
 Highs.log
 
+test/Logs
+
 /docs/build/
 
 # Microsoft office temporary files
@@ -42,3 +44,7 @@ Example_Systems/SmallNewEngland/ThreeZones_full/*
 
 Example_Systems/SmallNewEngland/ThreeZones_MultiStage/Inputs/*
 test_get_retirement_period.jl
+
+# Test files
+test/TDR/TDR_Results_test
+test/MethodofMorris/morris.csv

CHANGELOG.md

@@ -22,6 +22,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Add PR template (#516)
 - Validation ensures that resource flags (THERM, HYDRO, LDS etc) are self-consistent (#513).
 - Maintenance formulation for thermal-commit plants (#556).
+- Add new tests for GenX: three-zone, multi-stage, electrolyzer, VRE+storage, 
+  piecewise_fuel+CO2, TDR, and Method of Morris (#563).
+
 
 ### Fixed
 - Set MUST_RUN=1 for RealSystemExample/small_hydro plants (#517).

Project.toml

@@ -38,8 +38,10 @@ YAML = "0.4.7"
 julia = "1"
 
 [extras]
+JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
+LoggingExtras = "e6f89c97-d47a-5376-807f-9c37f3926c36"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "Logging"]
+test = ["Test", "JLD2", "Logging", "LoggingExtras"]

src/GenX.jl

@@ -18,6 +18,7 @@ export choose_output_dir
 export run_ddp
 export configure_multi_stage_inputs
 export load_inputs_multi_stage
+export compute_cumulative_min_retirements!
 export write_multi_stage_outputs
 export run_genx_case!
 export run_timedomainreduction!

src/additional_tools/method_of_morris.jl

@@ -1,3 +1,6 @@
+const SEED = 1234
+
+
 @doc raw"""
 	morris(EP::Model, path::AbstractString, setup::Dict, inputs::Dict, outpath::AbstractString, OPTIMIZER)
 
@@ -14,6 +17,7 @@ struct MorrisResult{T1,T2}
     variances::T1
     elementary_effects::T2
 end
+
 function generate_design_matrix(p_range, p_steps, rng;len_design_mat,groups)
     ps = [range(p_range[i][1], stop=p_range[i][2], length=p_steps[i]) for i in 1:length(p_range)]
     indices = [rand(rng, 1:i) for i in p_steps]
@@ -74,8 +78,9 @@ function sample_matrices(p_range,p_steps, rng;num_trajectory,total_num_trajector
     reduce(hcat,matrices)
 end
 
-function my_gsa(f, p_steps, num_trajectory, total_num_trajectory, p_range::AbstractVector,len_design_mat,groups)
+function my_gsa(f, p_steps, num_trajectory, total_num_trajectory, p_range::AbstractVector, len_design_mat, groups, random)
     rng = Random.default_rng()
+    if !random; Random.seed!(SEED);  end
     design_matrices_original = sample_matrices(p_range, p_steps, rng;num_trajectory,
                                         total_num_trajectory,len_design_mat,groups)
     println(design_matrices_original)
@@ -158,7 +163,7 @@ function my_gsa(f, p_steps, num_trajectory, total_num_trajectory, p_range::Abstr
     end
     MorrisResult(reduce(hcat, means),reduce(hcat, means_star),reduce(hcat, variances),effects)
 end
-function morris(EP::Model, path::AbstractString, setup::Dict, inputs::Dict, outpath::AbstractString, OPTIMIZER)
+function morris(EP::Model, path::AbstractString, setup::Dict, inputs::Dict, outpath::AbstractString, OPTIMIZER; random=true)
 
     # Reading the input parameters
     Morris_range = load_dataframe(joinpath(path, "Method_of_morris_range.csv"))
@@ -197,7 +202,7 @@ function morris(EP::Model, path::AbstractString, setup::Dict, inputs::Dict, outp
     end
 
     # Perform the method of morris analysis
-    m = my_gsa(f1,p_steps,num_trajectory,total_num_trajectory,p_range,len_design_mat,groups)
+    m = my_gsa(f1,p_steps,num_trajectory,total_num_trajectory,p_range,len_design_mat,groups,random)
     println(m.means)
     println(DataFrame(m.means', :auto))
     #save the mean effect of each uncertain variable on the objective fucntion
@@ -207,5 +212,5 @@ function morris(EP::Model, path::AbstractString, setup::Dict, inputs::Dict, outp
     Morris_range[!,:variance] = DataFrame(m.variances', :auto)[!,:x1]
 
     CSV.write(joinpath(outpath, "morris.csv"), Morris_range)
-
+    return Morris_range
 end

src/time_domain_reduction/time_domain_reduction.jl

@@ -18,6 +18,9 @@ using Distances
 using CSV
 using GenX
 
+
+const SEED = 1234
+
 @doc raw"""
     rmse_score(y_true, y_pred)
 
@@ -202,7 +205,7 @@ function get_worst_period_idx(R)
 end
 
 @doc raw"""
-    cluster(ClusterMethod, ClusteringInputDF, NClusters, nIters)
+    cluster(ClusterMethod, ClusteringInputDF, NClusters, nIters, v=false, random=true)
 
 Get representative periods using cluster centers from kmeans or kmedoids.
 
@@ -212,12 +215,13 @@ https://juliastats.org/Clustering.jl/dev/kmeans.html
 K-Medoids:
  https://juliastats.org/Clustering.jl/stable/kmedoids.html
 """
-function cluster(ClusterMethod, ClusteringInputDF, NClusters, nIters, v=false)
+function cluster(ClusterMethod, ClusteringInputDF, NClusters, nIters, v=false, random=true)
     if ClusterMethod == "kmeans"
         DistMatrix = pairwise(Euclidean(), Matrix(ClusteringInputDF), dims=2)
         R = kmeans(Matrix(ClusteringInputDF), NClusters, init=:kmcen)
 
         for i in 1:nIters
+            if !random; Random.seed!(SEED);  end
             R_i = kmeans(Matrix(ClusteringInputDF), NClusters)
 
             if R_i.totalcost < R.totalcost
@@ -243,6 +247,7 @@ function cluster(ClusterMethod, ClusteringInputDF, NClusters, nIters, v=false)
         R = kmedoids(DistMatrix, NClusters, init=:kmcen)
 
         for i in 1:nIters
+            if !random; Random.seed!(SEED);  end
             R_i = kmedoids(DistMatrix, NClusters)
             if R_i.totalcost < R.totalcost
                 R = R_i
@@ -257,7 +262,7 @@ function cluster(ClusterMethod, ClusteringInputDF, NClusters, nIters, v=false)
         M = R.medoids # get the cluster centers - M for Medoids
     else
         println("INVALID ClusterMethod. Select kmeans or kmedoids. Running kmeans instead.")
-        return cluster("kmeans", ClusteringInputDF, NClusters, nIters)
+        return cluster("kmeans", ClusteringInputDF, NClusters, nIters, v, random)
     end
     return [R, A, W, M, DistMatrix]
 end
@@ -485,7 +490,7 @@ end
 
 
 @doc raw"""
-    cluster_inputs(inpath, settings_path, v=false, norm_plot=false, silh_plot=false, res_plots=false, indiv_plots=false, pair_plots=false)
+    cluster_inputs(inpath, settings_path, mysetup, stage_id=-99, v=false; random=true)
 
 Use kmeans or kmedoids to cluster raw demand profiles and resource capacity factor profiles
 into representative periods. Use Extreme Periods to capture noteworthy periods or
@@ -538,7 +543,7 @@ to separate Vre_and_stor_solar_variability.csv and Vre_and_stor_wind_variability
 and wind profiles for co-located resources will be separated into different CSV files to be read by loading the inputs 
 after the clustering of the inputs has occurred. 
 """
-function cluster_inputs(inpath, settings_path, mysetup, stage_id=-99, v=false)
+function cluster_inputs(inpath, settings_path, mysetup, stage_id=-99, v=false; random=true)
     if v println(now()) end
 
     ##### Step 0: Load in settings and data
@@ -781,7 +786,7 @@ function cluster_inputs(inpath, settings_path, mysetup, stage_id=-99, v=false)
     cluster_results = []
 
     # Cluster once regardless of iteration decisions
-    push!(cluster_results, cluster(ClusterMethod, ClusteringInputDF, NClusters, nReps, v))
+    push!(cluster_results, cluster(ClusterMethod, ClusteringInputDF, NClusters, nReps, v, random))
 
     # Iteratively add worst periods as extreme periods OR increment number of clusters k
     #    until threshold is met or maximum periods are added (If chosen in inputs)
@@ -790,15 +795,15 @@ function cluster_inputs(inpath, settings_path, mysetup, stage_id=-99, v=false)
             if IterateMethod == "cluster"
                 if v println("Adding a new Cluster! ") end
                 NClusters += 1
-                push!(cluster_results, cluster(ClusterMethod, ClusteringInputDF, NClusters, nReps, v))
+                push!(cluster_results, cluster(ClusterMethod, ClusteringInputDF, NClusters, nReps, v, random))
             elseif (IterateMethod == "extreme") & (UseExtremePeriods == 1)
                 if v println("Adding a new Extreme Period! ") end
                 worst_period_idx = get_worst_period_idx(last(cluster_results)[1])
                 removed_period = string(names(ClusteringInputDF)[worst_period_idx])
                 select!(ClusteringInputDF, Not(worst_period_idx))
                 push!(ExtremeWksList, parse(Int, removed_period))
                 if v println(worst_period_idx, " (", removed_period, ") ", ExtremeWksList) end
-                push!(cluster_results, cluster(ClusterMethod, ClusteringInputDF, NClusters, nReps, v))
+                push!(cluster_results, cluster(ClusterMethod, ClusteringInputDF, NClusters, nReps, v, random))
             elseif IterateMethod == "extreme"
                 println("INVALID IterateMethod ", IterateMethod, " because UseExtremePeriods is off. Set to 1 if you wish to add extreme periods.")
                 break