Add testing mcda visualisations to mcda tests

test/site_selection.jl

@@ -89,3 +89,51 @@ 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