currentFile = mfilename('fullpath');
[pathstr,~,~] = fileparts(currentFile);
cd(fullfile(pathstr,'..'))
rootpath = pwd;

pn.data_eeg = fullfile(rootpath, '..', 'eegmp_preproc', 'data', 'outputs', 'eeg');
pn.data_erp = fullfile(rootpath, 'data', 'erp');
pn.data_erf = fullfile(rootpath, 'data', 'erf');
pn.tools = fullfile(rootpath, 'tools');
    addpath(fullfile(rootpath, '..', 'eegmp_preproc', 'tools', 'fieldtrip')); ft_defaults
    addpath(fullfile(pn.tools, 'BrewerMap'));
    addpath(fullfile(pn.tools, 'shadedErrorBar'));
    
%% load erp

for ind_id = 1:33
    id = sprintf('sub-%03d', ind_id); disp(id)
    load(fullfile(pn.data_erp, [id,'_erp_bl.mat']));
    for ind_option = 2:3
        if ind_id == 1
             erpgroup.subsequent_memory.(conds.subsequent_memory{ind_option}) = erp_bl.subsequent_memory{ind_option};
             erpgroup.subsequent_memory.(conds.subsequent_memory{ind_option}) = ...
                 rmfield(erpgroup.subsequent_memory.(conds.subsequent_memory{ind_option}), {'avg', 'var', 'dof'});
             erpgroup.subsequent_memory.(conds.subsequent_memory{ind_option}).dimord = 'sub_chan_time';
        end
        erpgroup.subsequent_memory.(conds.subsequent_memory{ind_option}).avg(ind_id,:,:) = erp_bl.subsequent_memory{ind_option}.avg;
    end
end

time = erpgroup.subsequent_memory.subsequent_remembered.time;
elec = erpgroup.subsequent_memory.subsequent_remembered.elec;
channels = erpgroup.subsequent_memory.subsequent_remembered.label;

mergeddata = cat(4, erpgroup.subsequent_memory.subsequent_forgotten.avg, ...
    erpgroup.subsequent_memory.subsequent_remembered.avg);

smoothdur = 10; % 5 = 10 ms

%% visualize differences

idx_chans = [44,14,9,15];
% avg across channels and conditions
condAvg = squeeze(nanmean(nanmean(mergeddata(:,idx_chans,:,1:2),2),4));

h = figure('units','centimeters','position',[0 0 10 8]);
cla; hold on;

% new value = old value ? subject average + grand average
curData = squeeze(nanmean(mergeddata(:,idx_chans,:,1),2));
curData = curData-condAvg+repmat(nanmean(condAvg,1),size(condAvg,1),1);
standError = nanstd(curData,1)./sqrt(size(curData,1));
l1 = shadedErrorBar(time,smoothts(nanmean(curData,1),'b',smoothdur),smoothts(standError,'b',smoothdur),...
    'lineprops', {'color', 'k','linewidth', 2}, 'patchSaturation', .1);
curData = squeeze(nanmean(mergeddata(:,idx_chans,:,2),2));
curData = curData-condAvg+repmat(nanmean(condAvg,1),size(condAvg,1),1);
standError = nanstd(curData,1)./sqrt(size(curData,1));
l2 = shadedErrorBar(time,smoothts(nanmean(curData,1),'b',smoothdur),smoothts(standError,'b',smoothdur),...
    'lineprops', {'color', 'r','linewidth', 2}, 'patchSaturation', .1);
% ax = gca; ax.YDir = 'reverse';
legend([l1.mainLine, l2.mainLine],{'forgotten', 'remembered'}, ...
    'location', 'southwest'); legend('boxoff')
xlabel('Time (s) from stim onset')
xlim([-.25 1.9]); %ylim(YLim)
ylabel({'ERP';'(microVolts)'});
xlabel({'Time (s)'});    
set(findall(gcf,'-property','FontSize'),'FontSize',14)