plots 3 and 4

plot_1.m

@@ -1,6 +1,6 @@
 clear;
 % Load data
-dataPath = fullfile('.', 'sandbox', 'plot1_3');
+dataPath = fullfile('.', 'sandbox', 'plot1');
 simHists = dir(dataPath); simHists = simHists(3:end);
 simInits = simHists(endsWith({simHists.name}, 'miSimInits.mat'));
 simHists = simHists(endsWith({simHists.name}, 'miSimHist.mat'));
@@ -99,7 +99,7 @@ ylabel("Final coverage (normalized)");
 title("Final performance of parameterizations");
 legend(["$AI\alpha$"; "$AI\beta$"; "$AII\alpha$"; "$BI\beta$"], "Interpreter", "latex", "Location", "northwest");
 grid("on");
-ylim([0, 1]);
+ylim([0, 1/2]);
 
 %%
 f2 = figure;
@@ -120,6 +120,14 @@ for ii = 1:nRuns
     end
 end
 
+% Cap pairwise distances at communications range
+for ii = 1:nRuns
+    nPairs = nchoosek(n(ii), 2);
+    for pp = 1:nPairs
+        pairDist{pp, ii} = min(pairDist{pp, ii}, commsRadius);
+    end
+end
+
 % Compute mean, min, max pairwise distance across all pairs and timesteps per run
 meanPairDist = NaN(nRuns, 1);
 minPairDist = NaN(nRuns, 1);
@@ -164,19 +172,4 @@ grid(x2, "on");
 
 yline(collisionRadius, 'r--', "Label", "Collision Radius", "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
 yline(commsRadius, 'r--', "Label", "Communications Radius", "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
-
+ylim([0, commsRadius + 5]);
-ylim([0, inf]);
-
-f3 = figure;
-x3 = axes;
-assert(size(a2beta.init.objectivePos, 1) == 1)
-assert(a2beta.hist.useDoubleIntegrator);
-assert(a2beta.hist.agent(1).sensor.alphaDist == sensors(2))
-
-plot(a2beta.hist.perf./a2beta.init.objectiveIntegral);
-hold("on");
-for ii = 1:length(a2beta.hist.agent)
-    plot(a2beta.hist.agent(ii).perf./a2beta.init.objectiveIntegral);
-end
-grid("on");
-xlabel("Performance");

plot_3.m

@@ -0,0 +1,133 @@
+clear;
+% Load data
+dataPath = fullfile('.', 'sandbox', 'plot3');
+simHists = dir(dataPath); simHists = simHists(3:end);
+simInits = simHists(endsWith({simHists.name}, 'miSimInits.mat'));
+simHists = simHists(endsWith({simHists.name}, 'miSimHist.mat'));
+assert(length(simHists) == length(simInits), "input data availability mismatch");
+assert(isscalar(simHists));
+
+init = fullfile(simInits(1).folder, simInits(1).name);
+hist = fullfile(simHists(1).folder, simHists(1).name);
+
+init = load(init);
+hist = load(hist);
+hist = hist.out;
+
+f3 = figure;
+x3 = axes;
+assert(size(init.objectivePos, 1) == 1)
+assert(hist.useDoubleIntegrator);
+
+plot(hist.perf./init.objectiveIntegral);
+hold("on");
+for ii = 1:length(hist.agent)
+    plot(hist.agent(ii).perf./init.objectiveIntegral);
+end
+grid("on");
+ylabel("Performance (normalized)");
+xlabel("Timestep");
+legend(["Cumulative"; "Agent 1"; "Agent 2"; "Agent 3"; "Agent 4"], "Location", "northwest");
+title("$AII\beta$ Performance", "Interpreter", "latex");
+
+f4 = figure;
+x4 = axes;
+
+% Compute pairwise distances between agents over time
+nAgents = length(hist.agent);
+commsRadius = hist.agent(1).commsRadius;
+collisionRadius = hist.agent(1).collisionRadius;
+nPairs = nchoosek(nAgents, 2);
+T = size(hist.agent(1).pos, 1);
+pairDistMat = NaN(T, nPairs);
+pp = 0;
+for jj = 1:nAgents-1
+    for kk = jj+1:nAgents
+        pp = pp + 1;
+        pairDistMat(:, pp) = vecnorm(hist.agent(jj).pos - hist.agent(kk).pos, 2, 2);
+    end
+end
+
+% Cap at communications range
+% pairDistMat = min(pairDistMat, commsRadius);
+
+% Plot all pairwise distances over time
+hold(x4, 'on');
+hLeft = gobjects(nPairs, 1);
+for pp = 1:nPairs
+    hLeft(pp) = plot(x4, pairDistMat(:, pp), 'LineWidth', 1);
+end
+yline(x4, collisionRadius, 'r--', "Label", "Collision Radius", "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
+yline(x4, commsRadius, 'r--', "Label", "Communications Radius", "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
+hold(x4, 'off');
+xlabel(x4, "Timestep");
+ylabel(x4, "Pairwise distance");
+title(x4, "$AII\beta$ Pairwise Agent Distances and Barrier Function Values", "Interpreter", "latex");
+grid(x4, "on");
+ylim(x4, [0, commsRadius + 5]);
+
+% Build legend labels
+pairLabels = strings(nPairs, 1);
+pp = 0;
+for jj = 1:nAgents-1
+    for kk = jj+1:nAgents
+        pp = pp + 1;
+        pairLabels(pp) = sprintf("Agents %d-%d Distance", jj, kk);
+    end
+end
+% Left legend created after right-axis plots (see below)
+
+% Plot all barrier function values on right Y-axis
+nObs = init.numObstacles;
+nAA = nchoosek(nAgents, 2);
+nAO = nAgents * nObs;
+nAD = nAgents * 6;
+nComms = size(hist.barriers, 1) - nAA - nAO - nAD;
+
+yyaxis(x4, 'right');
+hold(x4, 'on');
+
+% Color palettes: pairs share colors across collision/comms,
+% agents share colors across obstacle/domain
+pairColors = lines(nAA);
+agentColors = lines(nAgents);
+
+% Row offsets in hist.barriers
+colStart = 1;
+obsStart = colStart + nAA;
+domStart = obsStart + nAO;
+comStart = domStart + nAD;
+
+% Collision + Comms barriers grouped per pair (same color)
+hRight = gobjects(0, 1);
+rightLabels = strings(0, 1);
+for pp = 1:nAA
+    hRight(end+1) = plot(x4, hist.barriers(colStart + pp - 1, :), '--', 'LineWidth', 1, 'Color', pairColors(pp, :));
+    rightLabels(end+1) = sprintf('h_{col} %d', pp);
+end
+for pp = 1:nComms
+    hRight(end+1) = plot(x4, hist.barriers(comStart + pp - 1, :), '-', 'LineWidth', 1.5, 'Color', pairColors(pp, :));
+    rightLabels(end+1) = sprintf('h_{com} %d', pp);
+end
+
+% Obstacle barriers — colored by agent
+idx = obsStart;
+for aa = 1:nAgents
+    for oo = 1:nObs
+        hRight(end+1) = plot(x4, hist.barriers(idx, :), ':', 'LineWidth', 1, 'Color', agentColors(aa, :));
+        rightLabels(end+1) = sprintf('h_{obs} a%d-o%d', aa, oo);
+        idx = idx + 1;
+    end
+end
+
+
+hold(x4, 'off');
+ylabel(x4, "Barrier function $h$", "Interpreter", "latex");
+
+% Clamp both Y-axes to start at 0
+yyaxis(x4, 'left');  ylim(x4, [0, 25]);
+yyaxis(x4, 'right'); ylim(x4, [0, inf]);
+x4.YAxis(2).Color = 'k';
+
+% Combined legend
+legend([hLeft(:); hRight(:)], [pairLabels(:); rightLabels(:)], "Location", "eastoutside");

resources/project/qaw0eS1zuuY1ar9TdPn1GMfrjbQ/Dt-U7DFYhio-3hILJY9YWPnda6sp.xml

@@ -1,2 +1,2 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<Info location="plot1.m" type="File"/>
+<Info location="plot_1.m" type="File"/>

resources/project/qaw0eS1zuuY1ar9TdPn1GMfrjbQ/e-EiR3xDuOjis31Xf2_ZV1uoriEd.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<Info>
+    <Category UUID="FileClassCategory">
+        <Label UUID="design"/>
+    </Category>
+</Info>

resources/project/qaw0eS1zuuY1ar9TdPn1GMfrjbQ/e-EiR3xDuOjis31Xf2_ZV1uoriEp.xml

@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<Info location="plot_3.m" type="File"/>

test/results.m

@@ -59,21 +59,21 @@ classdef results < matlab.unittest.TestCase
         function c = makeConfigs()
             rng(results.seed);
             abMin = 6; % alpha*beta >= 6 ensures membership(0) = tanh(3) >= 0.995
-            alphaDist = rand(1, 2) .* [75, 40];
+            alphaDist = rand(1, 2) .* [75, 45];
-            betaDist = abMin ./ alphaDist + rand(1, 2) .* (20 - abMin ./ alphaDist);
+            betaDist = abMin ./ alphaDist + rand(1, 2) .* [1, 1/8] .* (20 - abMin ./ alphaDist);
             alphaTilt = 10 + rand(1, 2) .* [20, 20];
             betaTilt = abMin ./ alphaTilt + rand(1, 2) .* (50 - abMin ./ alphaTilt);
             sensors = struct('alphaDist', num2cell(alphaDist), 'alphaTilt', num2cell(alphaTilt), 'betaDist', num2cell(betaDist), 'betaTilt', num2cell(betaTilt));
-            % sensor1 = sigmoidSensor;
+            sensor1 = sigmoidSensor;
-            % sensor2 = sigmoidSensor;
+            sensor2 = sigmoidSensor;
-            % sensor1 = sensor1.initialize(sensors(1).alphaDist, sensors(1).betaDist, sensors(1).alphaTilt, sensors(1).betaTilt);
+            sensor1 = sensor1.initialize(sensors(1).alphaDist, sensors(1).betaDist, sensors(1).alphaTilt, sensors(1).betaTilt);
-            % sensor2 = sensor2.initialize(sensors(2).alphaDist, sensors(2).betaDist, sensors(2).alphaTilt, sensors(2).betaTilt);
+            sensor2 = sensor2.initialize(sensors(2).alphaDist, sensors(2).betaDist, sensors(2).alphaTilt, sensors(2).betaTilt);
-            % sensor1.plotParameters;
+            sensor1.plotParameters;
-            % sensor2.plotParameters;
+            sensor2.plotParameters;
             c = struct('A_1_alpha', struct('objectivePos', [3, 1] / 4 .* results.domainSize(1:2), 'sensor', sensors(1), 'doubleIntegrator', false), ...
                         'A_1_beta',  struct('objectivePos', [3, 1] / 4 .* results.domainSize(1:2), 'sensor', sensors(1), 'doubleIntegrator', true), ...
                         'A_2_alpha', struct('objectivePos', [3, 1] / 4 .* results.domainSize(1:2), 'sensor', sensors(2), 'doubleIntegrator', false), ...
-                        'B_1_beta',  struct('objectivePos', [[3, 1] / 4 .* results.domainSize(1:2); [3, 1] / 4 .* results.domainSize(1:2) + 12.5 .* [-1, 1] ./ sqrt(2)], 'sensor', sensors(1), 'doubleIntegrator', true));
+                        'B_1_beta',  struct('objectivePos', [[3, 1] / 4 .* results.domainSize(1:2); [3, 1] / 4 .* results.domainSize(1:2) + 25 .* [-1, 1] ./ sqrt(2)], 'sensor', sensors(1), 'doubleIntegrator', true));
         end
     end
 
@@ -267,10 +267,13 @@ classdef results < matlab.unittest.TestCase
         end
         function AIIbeta_plots_3_4(tc)
             % test-specific parameters
+            tc.makePlots = true;
+            tc.makeVideo = true;
             maxIters = 400;
 
             configs = results.makeConfigs();
             c = configs.A_2_alpha;
+            c.doubleIntegrator = true; % make a2alpha into a2beta
 
             % Set up fixed-size domain
             minAlt = tc.domainSize(3)/10 + rand * 1/10 * tc.domainSize(3);