Add decision matrix visualisation test to /test/mcda.jl

Should be moved to `site_selection.jl` later when all tests are passing Add additional testing to decision matrix visualisation tests Formatting Fix tests
open-AIMS · Jan 19, 2025 · bb6b35e · bb6b35e
1 parent 3b6aeda
commit bb6b35e
Show file tree

Hide file tree

Showing 4 changed files with 66 additions and 54 deletions.
diff --git a/ext/AvizExt/viz/spatial.jl b/ext/AvizExt/viz/spatial.jl
@@ -303,7 +303,7 @@ function ADRIA.viz.map!(
     opts::OPT_TYPE=DEFAULT_OPT_TYPE(),
     axis_opts::OPT_TYPE=set_axis_defaults(DEFAULT_OPT_TYPE())
 )
-    if length(rs.loc_data.site_id) != size(M, 1)
+    if length(rs.loc_data.site_id) != size(outputs_matrix, 1)
         error("Only unfiltered decision matrices can be plotted.")
     end
 

diff --git a/src/scenario.jl b/src/scenario.jl
@@ -1105,5 +1105,6 @@ function cyclone_mortality!(
     return nothing
 end
 
-_loc_coral_cover(C_cover_t::Array{Float64,3}) =
-    dropdims(sum(C_cover_t; dims=(1, 2)); dims=(1, 2))
+function _loc_coral_cover(C_cover_t::Array{Float64,3})
+    return dropdims(sum(C_cover_t; dims=(1, 2)); dims=(1, 2))
+end
diff --git a/test/mcda.jl b/test/mcda.jl
@@ -3,6 +3,11 @@ using ADRIA.Distributions
 using ADRIA.decision.JMcDM
 using ADRIA.decision: subtypes
 
+if !@isdefined(ADRIA_DIR)
+    const ADRIA_DIR = pkgdir(ADRIA)
+    const TEST_DOMAIN_PATH = joinpath(ADRIA_DIR, "test", "data", "Test_domain")
+end
+
 @testset "Validate included MCDA methods" begin
     """
     Identifies MCDA methods that pass a simple test to inform whether they should be included
@@ -155,3 +160,60 @@ end
 
     @test dm.data == new_vals || "Failed to update criteria values by name"
 end
+
+@testset "Test decision matrix spatial plotting" begin
+    mcda_funcs = ADRIA.decision.mcda_methods()
+
+    dom = ADRIA.load_domain(TEST_DOMAIN_PATH, 45)
+    N = 2^3
+    scens = ADRIA.sample_selection(dom, N)  # get scenario dataframe
+    scen = scens[1, :]
+
+    # Get seeding preferences
+    seed_pref = ADRIA.decision.SeedPreferences(dom, scen)
+
+    # Calculate criteria vectors
+    # Cover
+    sum_cover = vec(sum(dom.init_coral_cover; dims=1).data)
+    # DHWS
+    dhw_scens = dom.dhw_scens[:, :, Int64(scen["dhw_scenario"])]
+    plan_horizon = Int64(scen["plan_horizon"])
+    decay = 0.99 .^ (1:(plan_horizon + 1)) .^ 2
+    dhw_projection = ADRIA.decision.weighted_projection(
+        dhw_scens, 1, plan_horizon, decay, 75
+    )
+    # Connectivity
+    area_weighted_conn = dom.conn.data .* ADRIA.site_k_area(dom)
+    conn_cache = similar(area_weighted_conn)
+    in_conn, out_conn, network = ADRIA.connectivity_strength(
+        area_weighted_conn, sum_cover, conn_cache
+    )
+
+    # Create decision matrix
+    seed_decision_mat = ADRIA.decision.decision_matrix(
+        dom.loc_ids,
+        seed_pref.names;
+        seed_in_connectivity=in_conn,
+        seed_out_connectivity=out_conn,
+        seed_heat_stress=dhw_projection,
+        seed_coral_cover=sum_cover
+    )
+
+    # Get results from applying MCDA algorithm
+    crit_agg = ADRIA.decision.criteria_aggregated_scores(
+        seed_pref, seed_decision_mat, mcda_funcs[1]
+    )
+
+    # Don't plot constant criteria
+    is_const = Bool[length(x) == 1 for x in unique.(eachcol(seed_decision_mat.data))]
+
+    @test all(.!isnan.(crit_agg.scores)) || "Criteria aggregate score contains NaNs."
+    @test all(.!isnan.(seed_decision_mat)) || "Decision matrix contains NaNs."
+    @test all(seed_decision_mat .>= 0.0) || "Decision matrix contains negative values."
+
+    # Plot normalized scores and criteria as map
+    decision_mat_fig = ADRIA.viz.selection_criteria_map(
+        dom, seed_decision_mat[criteria=.!is_const],
+        crit_agg.scores ./ maximum(crit_agg.scores)
+    )
+end
diff --git a/test/site_selection.jl b/test/site_selection.jl
@@ -89,54 +89,3 @@ end
         rank in 1:size(s_order, 1)
     ]) || "Ranking does not match mcda score ordering"
 end
-
-@testset "Test decision matrix spatial plotting" begin
-    dom = ADRIA.load_domain(TEST_DOMAIN_PATH, 45)
-    N = 2^3
-    scens = ADRIA.sample_selection(dom, N)  # get scenario dataframe
-    scen = scens[1, :]
-
-    # Get seeding preferences
-    seed_pref = ADRIA.decision.SeedPreferences(dom, scen)
-
-    # Calculate criteria vectors
-    # Cover
-    sum_cover = vec(sum(dom.init_coral_cover; dims=1).data)
-    # DHWS
-    dhw_scens = dom.dhw_scens[:, :, Int64(scen["dhw_scenario"])]
-    plan_horizon = Int64(scen["plan_horizon"])
-    decay = 0.99 .^ (1:(plan_horizon + 1)) .^ 2
-    dhw_projection = ADRIA.decision.weighted_projection(
-        dhw_scens, 1, plan_horizon, decay, 75
-    )
-    # Connectivity
-    area_weighted_conn = dom.conn.data .* ADRIA.site_k_area(dom)
-    conn_cache = similar(area_weighted_conn)
-    in_conn, out_conn, network = ADRIA.connectivity_strength(
-        area_weighted_conn, sum_cover, conn_cache
-    )
-
-    # Create decision matrix
-    seed_decision_mat = ADRIA.decision.decision_matrix(
-        dom.loc_ids,
-        seed_pref.names;
-        seed_in_connectivity=in_conn,
-        seed_out_connectivity=out_conn,
-        seed_heat_stress=dhw_projection,
-        seed_coral_cover=sum_cover
-    )
-
-    # Get results from applying MCDA algorithm
-    crit_agg = ADRIA.decision.criteria_aggregated_scores(
-        seed_pref, seed_decision_mat, mcda_funcs[1]
-    )
-
-    # Don't plot constant criteria
-    is_const = Bool[length(x) == 1 for x in unique.(eachcol(seed_decision_mat.data))]
-
-    # Plot normalized scores and criteria as map
-    decision_mat_fig = ADRIA.viz.selection_criteria_map(
-        dom, seed_decision_mat[criteria=.!is_const],
-        crit_agg.scores ./ maximum(crit_agg.scores)
-    )
-end