First attempt at scripter

test/Manifest.toml

@@ -2,7 +2,7 @@
 
 julia_version = "1.10.7"
 manifest_format = "2.0"
-project_hash = "0952182c7949199c353768e12df781ad89450b36"
+project_hash = "79646ec35824621c96f288d3984b6af1018d4656"
 
 [[deps.ASL_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]

test/Project.toml

@@ -1,4 +1,5 @@
 [deps]
+Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
 JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
 Match = "7eb4fadd-790c-5f42-8a69-bfa0b872bfbf"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"

test/test_mhmc.jl

@@ -20,7 +20,7 @@ function quick_setup(agent_ids, agent_conns, nᵩ; testing=true)
     end
     δ_w = 0.001 # represents temporal system dynamics (shift from A=identity)
 
-    gt_params=LGSFModelParameters(μ=μ,σ=[0.5],τ=[1.],ϕ₀=ϕ₀,
+    gt_params=LGSFModelParameters(μ=μ,σ=[1.],τ=[1.],ϕ₀=ϕ₀,
                                   A=Matrix{Float64}(I(nᵩ) .* (1- δ_w)),
                                   Q=0.000001*Matrix{Float64}(I(nᵩ)))
     gt_model=[initialize_SCRIBEModel_from_parameters(gt_params)]
@@ -29,10 +29,10 @@ function quick_setup(agent_ids, agent_conns, nᵩ; testing=true)
     a = 0
     for aid in agent_ids
         a += 1
-        ag_params=LGSFModelParameters(μ=μ,σ=[0.5],τ=[1.],
+        ag_params=LGSFModelParameters(μ=μ,σ=[1.],τ=[1.],
                                       ϕ₀=zeros(nᵩ), A=Matrix{Float64}(I(nᵩ)),
                                       Q=0.0001*Matrix{Float64}(I(nᵩ)))
-        observer=LGSFObserverBehavior(0.01)
+        observer=LGSFObserverBehavior(0.1)
         init_agent_loc=[0. 0.; -0.5 -0.5] + [0. 0.; a-1 a-1]
         lg_Fs = initialize_KF(ag_params, observer, copy(init_agent_loc), gt_model[1])
         ng.vertices[aid] = initialize_agent(aid, lg_Fs, ng)
@@ -210,9 +210,9 @@ end
 function simple_print_results(gt_model::Vector{T} where T<:SCRIBEModel, ng::NetworkGraph)
     agent_ids = collect(keys(ng.vertices))
 
-    fests_m = [(aid*"m", ng.vertices[aid].agent.estimates[end].estimate.ϕ) for aid in agent_ids]
-    fests_v = [(aid*"v", inv(ng.vertices[aid].agent.information[end].Y)) for aid in agent_ids]
-    fvals = Dict([(:k, ng.vertices["agent1"].agent.k), (:ϕ, gt_model[end].ϕ), fests_m..., fests_v...])
+    fests_m = [(aid*"m", ng.vertices[aid].agent.estimates[end].estimate.ϕ) for aid in agent_ids] # Final Mean Estimates
+    fests_v = [(aid*"v", inv(ng.vertices[aid].agent.information[end].Y)) for aid in agent_ids] # Final Variance Estimates
+    fvals = Dict([(:k, ng.vertices["agent1"].agent.k), (:ϕ, gt_model[end].ϕ), fests_m..., fests_v...]) # collate
 
     println("Results:")
     println("k: ", fvals[:k], "\nϕ(t=final):  ", fvals[:ϕ])
@@ -242,7 +242,8 @@ function frechet_dist_eval_plot(run_name::String; layout_size=(800,500))
     p2 = plot(size=layout_size, xlabel="Time (in discretized steps)",
               title="Close-up of performance of agent learned environment models \nafter gathering initial observations", margin=(10, :mm))
     for aid in agent_ids
-        plot!(p2, 1:length(gt_model), frechet_dists[aid], label="Agent "*aid[end], lw=2, yrange=[0., 1.0])
+        plot!(p2, (1:length(gt_model))[3:end], frechet_dists[aid][3:end], label="Agent "*aid[end], lw=2)
+        println(aid*" FDists: ", frechet_dists[aid])
     end
 
     plot(p1, p2, layout=(1,2), size=(layout_size[1]*2, layout_size[2]))
@@ -257,39 +258,67 @@ function error_mapf(gt::SCRIBEModel, m::SCRIBEModel, x::Vector; mode=:norm)
     end
 end
 
-function error_map_plots(run_name::String; layout_size=(1200,1000), mode=:tane)
+function error_map_plots(run_name::String; layout_size=(2700,1500), mode=:tane)
     png_name = "test/res_plots/"*run_name*"_err_map.png"
 
     @load "test/res_data/"*run_name*".jld2" gt_model ng
     gt = gt_model[end]
 
-    num_plots = length(ng.vertices) + 1
+    num_plots = length(ng.vertices)
     needs_padding = num_plots%2==1
-    layout_num = (2,Integer(ceil(num_plots/2)))
+    # layout_num = (2,Integer(ceil(num_plots/2)))
+    layout_num = (num_plots, num_plots+1)
+    pred_cgrad = :thermal
+    gt_err_cgrad = :solar
+    rel_err_cgrad = :ice
 
     x_range = -5:0.1:5
     y_range = copy(x_range)
     gt_map = heatmap(x_range, y_range, [predict_SCRIBEModel(gt, [x,y]) for y in y_range, x in x_range],
-                     color=:viridis, xlabel="X", ylabel="Y",
-                     title="\nGround truth distribution of phenomena intensity",
-                     c = :thermal)
+                     color=:viridis, # xlabel="X", ylabel="Y",
+                     title="Ground truth distribution",
+                     c = pred_cgrad)
 
-    error_maps = Dict{String, Any}()
+    error_maps = Dict{String, Vector}()
     error_mapv = Any[gt_map]
 
-    for aid in keys(ng.vertices)
-        let m = ng.vertices[aid].agent.estimates[end].estimate, h=reduce(vcat, ng.vertices[aid].history)
-            error_maps[aid] = heatmap(x_range, y_range, [error_mapf(gt, m, [x,y]; mode=mode) for y in y_range, x in x_range],
-                                      color=:viridis, xlabel="X", ylabel="Y",
-                                      title="Error distribution of \nAgent "*aid[end]*"'s predictions of phenomena intensity",
-                                      c = :berlin)
-            plot!(error_maps[aid], copy(h[:,1]), copy(h[:,2]),
-                  linestyle=:dash, marker=:xcross, linecolor=:red, label="Sampling path and sites")
-            push!(error_mapv, error_maps[aid])
+    for (i, aid) in enumerate(keys(ng.vertices))
+        error_maps[aid] = Any[]
+        if i≠1
+            println("Blank because ground truth is already added, for "*aid[end])
+            push!(error_maps[aid], plot(legend=false,grid=false,foreground_color_subplot=:white))
         end
+        for (j, a2d) in enumerate(keys(ng.vertices))
+            let m1 = ng.vertices[aid].agent.estimates[end].estimate,
+                m2 = ng.vertices[a2d].agent.estimates[end].estimate,
+                h1=reduce(vcat, ng.vertices[aid].history),
+                h2=reduce(vcat, ng.vertices[a2d].history)
+
+                if i==j
+                    println("Ground comparison for agent "*aid[end])
+                    push!(error_maps[aid], heatmap(x_range, y_range, [error_mapf(gt, m1, [x,y]; mode=mode) for y in y_range, x in x_range],
+                                                   color=:viridis, # xlabel="X", ylabel="Y",
+                                                   title="% error between \nAgent "*aid[end]*"'s predictions and ground truth",
+                                                   c = gt_err_cgrad))
+                    plot!(error_maps[aid][end], copy(h1[:,1]), copy(h1[:,2]),
+                          linestyle=:dash, marker=:xcross, linecolor=:red, label="Agent "*aid[end]*" sampling sites")
+                else
+                    println("Comparison for agent "*aid[end]*" with agent "*a2d[end])
+                    push!(error_maps[aid], heatmap(x_range, y_range, [error_mapf(m1, m2, [x,y]; mode=mode) for y in y_range, x in x_range],
+                                                   color=:viridis, # xlabel="X", ylabel="Y",
+                                                   title="% error between \nAgent "*aid[end]*" and Agent "*a2d[end]*" predictions",
+                                                   c = rel_err_cgrad))
+                    # plot!(error_maps[aid][end], copy(h1[:,1]), copy(h1[:,2]),
+                    #       linestyle=:dash, marker=:xcross, linecolor=:red, label="Agent "*aid[end]*" sampling sites")
+                    # plot!(error_maps[aid][end], copy(h2[:,1]), copy(h2[:,2]),
+                    #       linestyle=:dash, marker=:xcross, linecolor=:red, label="Agent "*a2d[end]*" sampling sites")
+                end
+            end
+        end
+        append!(error_mapv, copy(error_maps[aid]))
     end
 
-    if needs_padding; push!(error_mapv, nothing); end
+    # if needs_padding; push!(error_mapv, nothing); end
     plot(error_mapv..., layout=layout_num, size=layout_size)
     savefig(png_name);
     println("Plot saved at "*png_name)