Dice_Rolls___10x.m

animateFrames();
function animateFrames()
    animFilename = 'Dice_Rolls___10x.gif'; % Output file name
    firstFrame = true;
    framesPerSecond = 24;
    delayTime = 1/framesPerSecond;

    % Create the gif
    for frame = 1:48
        drawframe(frame);
        fig = gcf();
        fig.Units = 'pixels';
        fig.Position(3:4) = [300,300];
        im = getframe(fig);
        [A,map] = rgb2ind(im.cdata,256);

        if firstFrame
            firstFrame = false;
            imwrite(A,map,animFilename, 'LoopCount', Inf, 'DelayTime', delayTime);
        else
            imwrite(A,map,animFilename, 'WriteMode', 'append', 'DelayTime', delayTime);
        end
    end
end
function drawframe(f) 
    % Visualization of the law of large numbers showing how the
    % experimental distribution of dice rolls approaches the expected
    % distribution as number of rolls increases
    
    close(gcf)

    % Internal variables
    n=10; % number of dice at each throw
    f=n*f; % total number of dice thrown af each round/frame
    s=6; % number of sides on dice

    % Seed random generator and roll dice
    rng(1);
    rolls=randi([1,s],1,f); % vector of f dice rolls
    counts=histcounts(rolls,1:s+1);
    probs=counts/f; % probabilities/distributions of each roll value, expected to converge to 1/s (1/6)

    % Top segment of figure for histogram of dice roll frequencies
    subplot(4,1,1:3)
    bar(probs)
    annotation('line',[.136 .9],[.56 .56],Color='r',LineStyle='--',LineWidth=2)
    annotation('textbox',[.14 .4 .5 .5],'String','Expected probability is: 1/6 or 0.1667...',Color='r',FitBoxToText='on',LineStyle='none',FontSize=8)
    ylim([0 0.4])
    title(['Dice Roll Simulation - ',num2str(f),' Rolls'])

    % Bottom segment of figure for visualizing dice rolls
    subplot(4,1,4)
    text(0.5,0.7,'Current Round: ','FontSize',12,'HorizontalAlignment','center')
    text(0.5,0.25,char(9855+rolls(end-(n-1):end)),'FontSize',24,'HorizontalAlignment','center')
    axis off
end