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Libs/mat2tikz/test/compareTimings.m
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256
Libs/mat2tikz/test/compareTimings.m
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function compareTimings(statusBefore, statusAfter)
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% COMPARETIMINGS compare timing of matlab2tikz test suite runs
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%
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% This function plots some analysis plots of the timings of different test
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% cases. When the test suite is run repeatedly, the median statistics are
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% reported as well as the individual runs.
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%
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% Usage:
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% COMPARETIMINGS(statusBefore, statusAfter)
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%
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% Parameters:
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% - statusBefore and statusAfter are expected to be
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% N x R cell arrays, each cell contains a status of a test case
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% where there are N test cases, repeated R times each.
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%
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% You can build such cells, e.g. with the following snippet.
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%
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% suite = @ACID
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% N = numel(suite(0)); % number of test cases
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% R = 10; % number of repetitions of each test case
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%
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% statusBefore = cell(N, R);
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% for r = 1:R
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% statusBefore(:, r) = testHeadless;
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% end
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%
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% % now check out the after commit
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%
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% statusAfter = cell(N, R);
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% for r = 1:R
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% statusAfter(:, r) = testHeadless;
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% end
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%
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% compareTimings(statusBefore, statusAfter)
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%
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% See also: testHeadless
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%% Extract timing information
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time_cf = extract(statusBefore, statusAfter, @(s) s.tikzStage.cleanfigure_time);
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time_m2t = extract(statusBefore, statusAfter, @(s) s.tikzStage.m2t_time);
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%% Construct plots
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hax(1) = subplot(3,2,1);
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histograms(time_cf, 'cleanfigure');
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legend('show')
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hax(2) = subplot(3,2,3);
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histograms(time_m2t, 'matlab2tikz');
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legend('show')
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linkaxes(hax([1 2]),'x');
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hax(3) = subplot(3,2,5);
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histogramSpeedup('cleanfigure', time_cf, 'matlab2tikz', time_m2t);
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legend('show');
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hax(4) = subplot(3,2,2);
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plotByTestCase(time_cf, 'cleanfigure');
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legend('show')
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hax(5) = subplot(3,2,4);
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plotByTestCase(time_m2t, 'matlab2tikz');
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legend('show')
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hax(6) = subplot(3,2,6);
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plotSpeedup('cleanfigure', time_cf, 'matlab2tikz', time_m2t);
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legend('show');
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linkaxes(hax([4 5 6]), 'x');
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% ------------------------------------------------------------------------------
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end
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%% Data processing
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function timing = extract(statusBefore, statusAfter, func)
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otherwiseNaN = {'ErrorHandler', @(varargin) NaN};
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timing.before = cellfun(func, statusBefore, otherwiseNaN{:});
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timing.after = cellfun(func, statusAfter, otherwiseNaN{:});
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end
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function [names,timings] = splitNameTiming(vararginAsCell)
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names = vararginAsCell(1:2:end-1);
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timings = vararginAsCell(2:2:end);
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end
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%% Plot subfunctions
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function [h] = histograms(timing, name)
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% plot histogram of time measurements
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colors = colorscheme;
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histostyle = {'DisplayStyle', 'bar',...
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'Normalization','pdf',...
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'EdgeColor','none',...
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'BinWidth',0.025};
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hold on;
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h{1} = myHistogram(timing.before, histostyle{:}, ...
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'FaceColor', colors.before, ...
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'DisplayName', 'Before');
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h{2} = myHistogram(timing.after , histostyle{:}, ...
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'FaceColor', colors.after,...
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'DisplayName', 'After');
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xlabel(sprintf('%s runtime [s]',name))
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ylabel('Empirical PDF');
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end
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function [h] = histogramSpeedup(varargin)
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% plot histogram of observed speedup
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histostyle = {'DisplayStyle', 'bar',...
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'Normalization','pdf',...
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'EdgeColor','none'};
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[names,timings] = splitNameTiming(varargin);
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nData = numel(timings);
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h = cell(nData, 1);
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minTime = NaN; maxTime = NaN;
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for iData = 1:nData
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name = names{iData};
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timing = timings{iData};
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hold on;
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speedup = computeSpeedup(timing);
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color = colorOptionsOfName(name, 'FaceColor');
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h{iData} = myHistogram(speedup, histostyle{:}, color{:},...
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'DisplayName', name);
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[minTime, maxTime] = minAndMax(speedup, minTime, maxTime);
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end
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xlabel('Speedup')
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ylabel('Empirical PDF');
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set(gca,'XScale','log', 'XLim', [minTime, maxTime].*[0.9 1.1]);
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end
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function [h] = plotByTestCase(timing, name)
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% plot all time measurements per test case
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colors = colorscheme;
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hold on;
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if size(timing.before, 2) > 1
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h{3} = plot(timing.before, '.',...
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'Color', colors.before, 'HandleVisibility', 'off');
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h{4} = plot(timing.after, '.',...
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'Color', colors.after, 'HandleVisibility', 'off');
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end
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h{1} = plot(median(timing.before, 2), '-',...
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'LineWidth', 2, ...
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'Color', colors.before, ...
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'DisplayName', 'Before');
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h{2} = plot(median(timing.after, 2), '-',...
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'LineWidth', 2, ...
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'Color', colors.after,...
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'DisplayName', 'After');
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ylabel(sprintf('%s runtime [s]', name));
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set(gca,'YScale','log')
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end
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function [h] = plotSpeedup(varargin)
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% plot speed up per test case
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[names, timings] = splitNameTiming(varargin);
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nDatasets = numel(names);
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minTime = NaN;
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maxTime = NaN;
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h = cell(nDatasets, 1);
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for iData = 1:nDatasets
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name = names{iData};
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timing = timings{iData};
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color = colorOptionsOfName(name);
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hold on
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[speedup, medSpeedup] = computeSpeedup(timing);
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if size(speedup, 2) > 1
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plot(speedup, '.', color{:}, 'HandleVisibility','off');
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end
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h{iData} = plot(medSpeedup, color{:}, 'DisplayName', name, ...
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'LineWidth', 2);
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[minTime, maxTime] = minAndMax(speedup, minTime, maxTime);
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end
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nTests = size(speedup, 1);
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plot([-nTests nTests*2], ones(2,1), 'k','HandleVisibility','off');
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legend('show', 'Location','NorthWest')
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set(gca,'YScale','log','YLim', [minTime, maxTime].*[0.9 1.1], ...
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'XLim', [0 nTests+1])
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xlabel('Test case');
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ylabel('Speed-up (t_{before}/t_{after})');
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end
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%% Histogram wrapper
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function [h] = myHistogram(data, varargin)
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% this is a very crude wrapper that mimics Histogram in R2014a and older
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if ~isempty(which('histogram'))
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h = histogram(data, varargin{:});
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else % no "histogram" available
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options = struct(varargin{:});
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minData = min(data(:));
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maxData = max(data(:));
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if isfield(options, 'BinWidth')
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numBins = ceil((maxData-minData)/options.BinWidth);
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elseif isfield(options, 'NumBins')
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numBins = options.NumBins;
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else
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numBins = 10;
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end
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[counts, bins] = hist(data(:), numBins);
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if isfield(options,'Normalization') && strcmp(options.Normalization,'pdf')
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binWidth = mean(diff(bins));
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counts = counts./sum(counts)/binWidth;
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end
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h = bar(bins, counts, 1);
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% transfer properties as well
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names = fieldnames(options);
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for iName = 1:numel(names)
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option = names{iName};
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if isprop(h, option)
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set(h, option, options.(option));
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end
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end
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set(allchild(h),'FaceAlpha', 0.75); % only supported with OpenGL renderer
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% but this should look a bit similar with matlab2tikz then...
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end
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end
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%% Calculations
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function [speedup, medSpeedup] = computeSpeedup(timing)
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% computes the timing speedup (and median speedup)
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dRep = 2; % dimension containing the repeated tests
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speedup = timing.before ./ timing.after;
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medSpeedup = median(timing.before, dRep) ./ median(timing.after, dRep);
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end
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function [minTime, maxTime] = minAndMax(speedup, minTime, maxTime)
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% calculates the minimum/maximum time in an array and peviously
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% computed min/max times
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minTime = min([minTime; speedup(:)]);
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maxTime = min([maxTime; speedup(:)]);
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end
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%% Color scheme
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function colors = colorscheme()
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% defines the color scheme
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colors.matlab2tikz = [161 19 46]/255;
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colors.cleanfigure = [ 0 113 188]/255;
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colors.before = [236 176 31]/255;
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colors.after = [118 171 47]/255;
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end
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function color = colorOptionsOfName(name, keyword)
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% returns a cell array with a keyword (default: 'Color') and a named color
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% if it exists in the colorscheme
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if ~exist('keyword','var') || isempty(keyword)
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keyword = 'Color';
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end
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colors = colorscheme;
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if isfield(colors,name)
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color = {keyword, colors.(name)};
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else
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color = {};
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end
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end
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