last plot updates
This commit is contained in:
151
plot_1.m
151
plot_1.m
@@ -1,23 +1,27 @@
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clear;
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% Load data
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%% Load data
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dataPath = fullfile('.', 'sandbox', 'plot1');
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simHists = dir(dataPath); simHists = simHists(3:end);
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simInits = simHists(endsWith({simHists.name}, 'miSimInits.mat'));
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simHists = simHists(endsWith({simHists.name}, 'miSimHist.mat'));
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dataFiles = dir(dataPath);
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dataFiles = dataFiles(~startsWith({dataFiles.name}, '.'));
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simInits = dataFiles(endsWith({dataFiles.name}, 'miSimInits.mat'));
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simHists = dataFiles(endsWith({dataFiles.name}, 'miSimHist.mat'));
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assert(length(simHists) == length(simInits), "input data availability mismatch");
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% Initialize plotting data
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%% Aggregate run data
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nRuns = length(simHists);
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Cfinal = NaN(nRuns, 1);
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n = NaN(nRuns, 1);
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nAgents = NaN(nRuns, 1);
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doubleIntegrator = NaN(nRuns, 1);
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numObjective = NaN(nRuns, 1);
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positions = cell(6, nRuns);
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commsRadius = NaN(nRuns, 1);
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collisionRadius = NaN(nRuns, 1);
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maxAgents = 6;
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alphaDist = NaN(maxAgents, nRuns);
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positions = cell(maxAgents, nRuns);
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adjacency = cell(nRuns, 1);
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% Aggregate relevant data
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for ii = 1:length(simHists)
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for ii = 1:nRuns
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initName = strrep(simInits(ii).name, "_miSimInits.mat", "");
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histName = strrep(simHists(ii).name, "_miSimHist.mat", "");
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assert(initName == histName);
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@@ -25,38 +29,29 @@ for ii = 1:length(simHists)
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init = load(fullfile(simInits(ii).folder, simInits(ii).name));
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hist = load(fullfile(simHists(ii).folder, simHists(ii).name));
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% Stash relevant data
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Cfinal(ii) = hist.out.perf(end) / init.objectiveIntegral;
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n(ii) = init.numAgents;
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nAgents(ii) = init.numAgents;
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doubleIntegrator(ii) = init.useDoubleIntegrator;
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numObjective(ii) = size(init.objectivePos, 1);
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commsRadius(ii) = unique(init.comRange);
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collisionRadius(ii) = unique(init.collisionRadius);
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for jj = 1:length(hist.out.agent)
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adjacency{ii} = hist.out.constraintAdjacency(:, :, 1);
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for jj = 1:nAgents(ii)
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alphaDist(jj, ii) = hist.out.agent(jj).sensor.alphaDist;
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positions{jj, ii} = hist.out.agent(jj).pos;
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assert(hist.out.agent(jj).commsRadius == commsRadius(ii));
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assert(hist.out.agent(jj).collisionRadius == collisionRadius(ii));
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end
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alphaDist2 = unique(alphaDist);
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if length(alphaDist2) > 1
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alphaDist2 = alphaDist2(1);
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end
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if doubleIntegrator(ii) && all(alphaDist(1:n(ii), ii) == alphaDist2) && numObjective(ii) == 1
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a2betaIdx = ii;
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a2beta = struct("init", init, "hist", hist.out);
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end
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end
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commsRadius = unique(commsRadius); assert(isscalar(commsRadius));
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collisionRadius = unique(collisionRadius); assert(isscalar(collisionRadius));
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sensors = flip(unique(alphaDist(1, :)));
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n_unique = sort(unique(n));
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nGroups = length(n_unique);
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sensorTypes = flip(unique(alphaDist(1, :)));
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nValues = sort(unique(nAgents));
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nGroups = length(nValues);
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% Build config label for each run
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config = strings(nRuns, 1);
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%% Build config labels
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baseConfig = strings(nRuns, 1);
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for ii = 1:nRuns
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s = "";
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@@ -65,9 +60,9 @@ for ii = 1:nRuns
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elseif numObjective(ii) == 2
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s = s + "B";
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end
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if alphaDist(1, ii) == sensors(1)
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if alphaDist(1, ii) == sensorTypes(1)
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s = s + "_I";
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elseif alphaDist(1, ii) == sensors(2)
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elseif alphaDist(1, ii) == sensorTypes(2)
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s = s + "_II";
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end
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if ~doubleIntegrator(ii)
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@@ -76,88 +71,77 @@ for ii = 1:nRuns
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s = s + "_beta";
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end
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baseConfig(ii) = s;
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config(ii) = n(ii) + "_" + s;
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end
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configOrder = unique(baseConfig(n == n_unique(1)), 'stable');
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nConfigsPerN = length(configOrder);
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configOrder = unique(baseConfig(nAgents == nValues(1)), 'stable');
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nConfigs = length(configOrder);
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configLabels = ["$AI\alpha$"; "$AI\beta$"; "$AII\alpha$"; "$BI\beta$"];
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%%
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%% Plot 1: Final normalized coverage
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close all;
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f1 = figure;
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x1 = axes;
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C_mean = NaN(nGroups, nConfigsPerN);
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C_var = NaN(nGroups, nConfigsPerN);
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C_mean = NaN(nGroups, nConfigs);
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C_var = NaN(nGroups, nConfigs);
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for ii = 1:nGroups
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for jj = 1:nConfigsPerN
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mask = (n == n_unique(ii)) & (baseConfig == configOrder(jj));
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for jj = 1:nConfigs
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mask = (nAgents == nValues(ii)) & (baseConfig == configOrder(jj));
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C_mean(ii, jj) = mean(Cfinal(mask));
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C_var(ii, jj) = var(Cfinal(mask));
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end
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end
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hBar = bar(x1, C_mean);
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hold(x1, 'on');
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for jj = 1:nConfigsPerN
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xPos = hBar(jj).XEndPoints;
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errorbar(x1, xPos, C_mean(:, jj), C_var(:, jj), 'k.', 'LineWidth', 1, 'HandleVisibility', 'off'); % disabled the error bars because they are small to the point of meaninglessness
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end
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hold(x1, 'off');
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set(x1, 'XTickLabel', string(n_unique));
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xlabel("Number of agents");
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ylabel("Final coverage (normalized)");
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title("Final performance of parameterizations");
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legend(["$AI\alpha$"; "$AI\beta$"; "$AII\alpha$"; "$BI\beta$"], "Interpreter", "latex", "Location", "northwest");
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grid("on");
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ylim([0, 1/2]);
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bar(x1, C_mean);
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set(x1, 'XTickLabel', string(nValues));
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xlabel(x1, "Number of agents");
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ylabel(x1, "Final coverage (normalized)");
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title(x1, "Final performance of parameterizations");
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legend(x1, configLabels, "Interpreter", "latex", "Location", "northwest");
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grid(x1, "on");
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ylim(x1, [0, 1/2]);
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savefig(f1, "plot1.fig");
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exportgraphics(f1, "plot1.png");
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%%
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%% Plot 2: Pairwise agent distances
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f2 = figure;
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x2 = axes;
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% Compute pairwise distances between agents
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maxPairs = nchoosek(6, 2);
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% Compute pairwise distances only for connected agents (static topology)
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maxPairs = nchoosek(maxAgents, 2);
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pairDist = cell(maxPairs, nRuns);
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for ii = 1:nRuns
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A = adjacency{ii};
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pp = 0;
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for jj = 1:n(ii)-1
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for kk = jj+1:n(ii)
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for jj = 1:nAgents(ii)-1
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for kk = jj+1:nAgents(ii)
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pp = pp + 1;
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pairDist{pp, ii} = vecnorm(positions{jj, ii} - positions{kk, ii}, 2, 2);
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if A(jj, kk)
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pairDist{pp, ii} = vecnorm(positions{jj, ii} - positions{kk, ii}, 2, 2);
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end
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end
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end
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end
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% Cap pairwise distances at communications range
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for ii = 1:nRuns
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nPairs = nchoosek(n(ii), 2);
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for pp = 1:nPairs
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pairDist{pp, ii} = min(pairDist{pp, ii}, commsRadius);
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end
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end
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% Compute mean, min, max pairwise distance across all pairs and timesteps per run
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% Per-run statistics across all pairs and timesteps
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meanPairDist = NaN(nRuns, 1);
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minPairDist = NaN(nRuns, 1);
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maxPairDist = NaN(nRuns, 1);
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for ii = 1:nRuns
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nPairs = nchoosek(n(ii), 2);
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nPairs = nchoosek(nAgents(ii), 2);
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D = vertcat(pairDist{1:nPairs, ii});
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meanPairDist(ii) = mean(D);
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meanPairDist(ii) = mean(D, "omitmissing");
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minPairDist(ii) = min(D);
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maxPairDist(ii) = max(D);
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end
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% Group pairwise distance stats by (n, config), aggregating across reps
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meanD = NaN(nGroups, nConfigsPerN);
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minD = NaN(nGroups, nConfigsPerN);
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maxD = NaN(nGroups, nConfigsPerN);
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% Aggregate across trials per (n, config) group
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meanD = NaN(nGroups, nConfigs);
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minD = NaN(nGroups, nConfigs);
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maxD = NaN(nGroups, nConfigs);
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for ii = 1:nGroups
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for jj = 1:nConfigsPerN
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mask = (n == n_unique(ii)) & (baseConfig == configOrder(jj));
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for jj = 1:nConfigs
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mask = (nAgents == nValues(ii)) & (baseConfig == configOrder(jj));
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meanD(ii, jj) = mean(meanPairDist(mask));
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minD(ii, jj) = min(minPairDist(mask));
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maxD(ii, jj) = max(maxPairDist(mask));
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@@ -166,24 +150,25 @@ end
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% Plot whiskers (min to max) with mean markers
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barWidth = 0.8;
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groupWidth = barWidth / nConfigsPerN;
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groupWidth = barWidth / nConfigs;
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hold(x2, 'on');
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for jj = 1:nConfigsPerN
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xPos = (1:nGroups) + (jj - (nConfigsPerN + 1) / 2) * groupWidth;
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for jj = 1:nConfigs
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xPos = (1:nGroups) + (jj - (nConfigs + 1) / 2) * groupWidth;
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errorbar(x2, xPos, meanD(:, jj), meanD(:, jj) - minD(:, jj), maxD(:, jj) - meanD(:, jj), ...
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'o', 'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 10);
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end
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hold(x2, 'off');
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set(x2, 'XTick', 1:nGroups, 'XTickLabel', string(n_unique));
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set(x2, 'XTick', 1:nGroups, 'XTickLabel', string(nValues));
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xlabel(x2, "Number of agents");
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ylabel(x2, "Pairwise distance");
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title(x2, "Pairwise Agent Distances (min/mean/max)");
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legend(x2, ["$AI\alpha$"; "$AI\beta$"; "$AII\alpha$"; "$BI\beta$"], "Interpreter", "latex");
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legend(x2, configLabels, "Interpreter", "latex");
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grid(x2, "on");
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yline(collisionRadius, 'r--', "Label", "Collision Radius", "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
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yline(commsRadius, 'r--', "Label", "Communications Radius", "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
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ylim([0, commsRadius + 5]);
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yline(x2, collisionRadius, 'r--', "Label", "Collision Radius", ...
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"LabelHorizontalAlignment", "left", "HandleVisibility", "off");
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yline(x2, commsRadius, 'r--', "Label", "Communications Radius", ...
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"LabelHorizontalAlignment", "left", "HandleVisibility", "off");
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ylim(x2, [0, commsRadius + 5]);
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savefig(f2, "plot2.fig");
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exportgraphics(f2, "plot2.png");
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exportgraphics(f2, "plot2.png");
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