refactored performance plot data storage

@agent/run.m

@@ -25,8 +25,8 @@ function obj = run(obj, domain, partitioning, t)
     S(partitionMask) = sensorValues;
 
     % Find agent's performance
-    C = S.* F; % try gradient on this directly
+    C = S.* F;
-    obj.performance = [obj.performance  sum(C(~isnan(C)))];
+    obj.performance = [obj.performance, sum(C(~isnan(C)))];
 
     % Compute gradient on agent's performance
     [gradSensorPerformanceX, gradSensorPerformanceY] = gradient(S, domain.objective.discretizationStep); % grad S_n

@miSim/miSim.m

@@ -13,7 +13,7 @@ classdef miSim
         adjacency = NaN; % Adjacency matrix representing communications network graph
         sensorPerformanceMinimum = 1e-6; % minimum sensor performance to allow assignment of a point in the domain to a partition
         partitioning = NaN;
-        performance = NaN; % current cumulative sensor performance
+        performance = 0; % cumulative sensor performance
 
         fPerf; % performance plot figure
     end

@miSim/partition.m

@@ -24,18 +24,4 @@ function obj = partition(obj)
     [m, n, ~] = size(agentInds);
     [jj, kk] = ndgrid(1:m, 1:n);
     obj.partitioning = agentInds(sub2ind(size(agentInds), jj, kk, idx));
-
-    % Get individual agent sensor performance
-    nowIdx = [0; obj.partitioningTimes] == obj.t;
-    if isnan(obj.t)
-        nowIdx = 1;
-    end
-    for ii = 1:size(obj.agents, 1)
-        idx = obj.partitioning == ii;
-        agentPerformance = squeeze(agentPerformances(:, :, ii));
-        obj.perf(ii, nowIdx) = sum(agentPerformance(idx) .* obj.objective.values(idx));
-    end
-
-    % Current total performance
-    obj.perf(end, nowIdx) = sum(obj.perf(1:(end - 1), nowIdx));
 end

@miSim/plotPerformance.m

@@ -15,12 +15,14 @@ function obj = plotPerformance(obj)
     % Plot current cumulative performance
     hold(obj.fPerf.Children(1), 'on');
     o = plot(obj.fPerf.Children(1), obj.perf(end, :));
+    o.XData = NaN(size(o.XData)); % correct time will be set at runtime
     hold(obj.fPerf.Children(1), 'off');
 
     % Plot current agent performance
     for ii = 1:(size(obj.perf, 1) - 1)
         hold(obj.fPerf.Children(1), 'on');
         o = [o; plot(obj.fPerf.Children(1), obj.perf(ii, :))];
+        o(end).XData = NaN(size(o(end).XData)); % correct time will be set at runtime
         hold(obj.fPerf.Children(1), 'off');
     end
 

@miSim/run.m

@@ -28,6 +28,9 @@ function [obj] = run(obj)
             obj.agents{jj} = obj.agents{jj}.run(obj.domain, obj.partitioning, obj.t);
         end
 
+        % Update total performance
+        obj.performance = [obj.performance, sum(cellfun(@(x) x.performance(end), obj.agents))];
+
         % Update adjacency matrix
         obj = obj.updateAdjacency();
 

@miSim/updatePlots.m

@@ -39,16 +39,12 @@ function [obj] = updatePlots(obj, updatePartitions)
     drawnow;
     
     % Update performance plot
-    if updatePartitions
+    % Re-normalize performance plot
-        % find index corresponding to the current time
+    normalizingFactor = 1/max(obj.performance(end));
-        nowIdx = [0; obj.partitioningTimes] == obj.t;
+    obj.performancePlot(1).YData(1:length(obj.performance)) = obj.performance * normalizingFactor;
-        nowIdx = find(nowIdx);
+    obj.performancePlot(1).XData(find(isnan(obj.performancePlot(1).XData), 1, 'first')) = obj.t;
-
+    for ii = 2:(size(obj.agents, 1) + 1)
-        % Re-normalize performance plot
+        obj.performancePlot(ii).YData(1:length(obj.performance)) = obj.agents{ii - 1}.performance * normalizingFactor;
-        normalizingFactor = 1/max(obj.perf(end, 1:nowIdx));
+        obj.performancePlot(ii).XData(find(isnan(obj.performancePlot(ii).XData), 1, 'first')) = obj.t;
-        obj.performancePlot(1).YData(1:nowIdx) = obj.perf(end, 1:nowIdx) * normalizingFactor;
-        for ii = 2:size(obj.performancePlot, 1)
-            obj.performancePlot(ii).YData(1:nowIdx) = obj.perf(ii - 1, 1:nowIdx) * normalizingFactor;
-        end
     end
 end