macaque_MST_map1/code/example_trial_histogram.m

clc; clear; close all;
% Note that this histogram could easily be plotted with information that is
% available from the meta data alone. However, this script is supposed to
% demonstrate how to easily loop through the individual files and therefore
% takes an approach that is slightly more time consuming.
% This script takes a few seconds. On a Late 2013 iMac with a 3.4 GHz 
% Quad-Core Intel Core i5, running Matlab 2018b it takes around 7s.


set(0, 'DefaultLineLineWidth', 2)
set(0,'defaultAxesFontSize', 18)

%% specify path
codeDir = fileparts(mfilename('fullpath'));
mainDir = codeDir(1:find(codeDir==filesep,1,'last'));
dataDir = [mainDir,'data',filesep];
% add all folders with code and data to the path
addpath(genpath(mainDir));

param2read  = {'/event_value', '/event_time'};
events2read = {'TRIAL_start', 'TRIAL_end'};
sun_color = [254,94,105]/255;
igg_color = [7, 182, 75]/255;
edg_color = [0, 169, 255]/255;


% read in meta data
meta_data = readtable([dataDir,'meta_data.txt']);
monkeys = cell2mat(meta_data.monkey);

% Prepare Figure
figure('Color',[1,1,1],'Units','centimeters','Position',[1, 1, 56, 24]);

experiments = {'MSTm', 'MSTt', 'MSTn'};
for exp = 1 : length(experiments)
    % the short way would be 
    % trial_total = eval(['meta_data.Exp_',experiments{exp},'_tt']);
    % but we actually want to loop through all files and load them, maybe
    % because we don't trust the information in the meta data
    trial_total = [];
    for rec = 1:height(meta_data) % loop through recording sessions
        % construct the filename
        filename = ['amm-',experiments{exp},'-',meta_data.monkey{rec},...
                '-',strip(meta_data.session_number{rec},'both','"'),'-',...
                meta_data.daily_count{rec},'-task.h5'];
            try
                file_info = h5info(filename, '/event_value');
                % extract values and times of each event
                event_value = h5_extract(filename, param2read, events2read);
                % We perform a sanity check whether the TRIAL numbers are
                % equal (this is actually done in the technical validation
                % script, but repeated here for illustrative purposes)
                if (max(event_value.TRIAL_start) == max(event_value.TRIAL_end)) & (max(event_value.TRIAL_start) == length(event_value.TRIAL_start))
                    trial_total = cat(1,trial_total,max(event_value.TRIAL_start));
                else
                    error('Different trial numbers for TRIAL_start and TRIAL_end or length of TRIAL_start does not equal max');
                end
            catch
                fprintf("File %s does not seem to exist.\n", filename);
                trial_total = cat(1,trial_total,0);
            end
    end
    
    
    %% plot a distribution of the trial counts across recording sessions
    subplot(1,4,exp)
    box('off')
    sun_data = trial_total(sum(monkeys=='sun',2)==3);
    igg_data = trial_total(sum(monkeys=='igg',2)==3);
    edg_data = trial_total(sum(monkeys=='edg',2)==3);
    [~,edges] = histcounts(trial_total);
    Nsun = histcounts(sun_data,edges);
    Nigg = histcounts(igg_data,edges);
    Nedg = histcounts(edg_data,edges);
    ctrs = (edges(1:end-1)+edges(2:end))/2;
    b = bar(ctrs, [ Nsun', Nigg' Nedg'],1,'stacked');
    b(1).FaceColor = sun_color;
    b(2).FaceColor = igg_color;
    b(3).FaceColor = edg_color;
    set(gca,'XTick',ctrs(1:2:length(ctrs)));
    
    
    %histogram(trial_total,'BinWidth',75);
    xlabel('Number of trials');
    if exp==1
        ylabel('Count of recording sessions');
    end
    title(experiments{exp});
    
    %xlim([0,1400]);
end
% add legend
sp4 = subplot(1,4,4);
sp4.Visible = 'off';
hold on
LH(1) = plot(nan, nan, '*', 'color', sun_color);
L{1} = 'sun';
LH(2) = plot(nan, nan, '*', 'color', igg_color);
L{2} = 'igg';
LH(3) = plot(nan, nan, '*', 'color', edg_color);
L{3} = 'edg';
legend(LH, L, 'FontSize', 20); legend('box', 'off')
legend('Location', 'west')