function[e] = calculate_aggregate(e, index);
%
% Extract aggregated data based on parameters / selection of
% subjects/sessions/trials 
%
aggregate = e.aggregates(index);

try 
    tmp = aggregate.trials;
    aggregate.trials = str2num(aggregate.trials);
    if (isempty(aggregate.trials))
        aggregate.trials =  tmp;
    end
catch
end

%% rewrite all subjects to list of subjects...

if strcmp(aggregate.subjects, 'All subjects')
    e.aggregates(index).subjects = 1:length(e.subjects);    
end

if strcmp(aggregate.sessions, 'All sessions')
    e.aggregates(index).sessions = 1:length(e.subjects(1).sessions);    
end
aggregate = e.aggregates(index);

stimulus_onsets = {};

idx =1;
for sub = 1:length(aggregate.subjects)
   
    subject_id = aggregate.subjects(sub);


    for sess = 1:length(aggregate.sessions)
        
        sess_id = aggregate.sessions(sess);
        %trial_query = aggregate.trials;

        trials = aggregate.trials{subject_id}{sess_id};
        %aggregate.e.get_trials_based_on_label(e.subjects(subject_id).sessions(sess_id), trial_query);
            
        for t = 1:length(trials)            
            trial_id = trials(t);            
            trial_list(idx) = e.subjects(subject_id).sessions(sess_id).trials(trial_id).data;                    
            stimulus_onsets{idx} = e.subjects(subject_id).sessions(sess_id).trials(trial_id).stimulus_onset - ...
                                   e.subjects(subject_id).sessions(sess_id).trials(trial_id).trial_start;
            idx = idx + 1;
        end
    end
end


if isempty(find(~cellfun(@isempty,stimulus_onsets)));
    display('return?');
    return;
    
end
    
min_onset = min([stimulus_onsets{:}]);
max_onset = min([stimulus_onsets{:}]);

diffs = [stimulus_onsets{:}] - min_onset;

max_diff = max(diffs);

clear trials
for t = 1:length(trial_list)
    try
        trials{t} = [ nan(1,max_diff - diffs(t)) trial_list(t).baseline_corrected.filtered(diffs(t)+1:end-max_diff)' ];
    catch
        display('error');
    end
end


min_trial_length = min(cellfun(@length, trials));


for t = 1:length(trial_list)
    try
        trial_mat(t,:) = trials{t}(1:min_trial_length);
    catch
    end
end

mean_trial = mean(trial_mat);
std_trial  = std(trial_mat);
var_trial  = var(trial_mat);

l = length(mean_trial);

mean_trial([1:e.settings.FilterSize end-e.settings.FilterSize:end]) = NaN;
std_trial([1:e.settings.FilterSize end-e.settings.FilterSize:end]) = NaN;
var_trial([1:e.settings.FilterSize end-e.settings.FilterSize:end]) = NaN;

aggregate.mean = mean_trial;
aggregate.std = std_trial;
aggregate.var = var_trial;


e.aggregates(index).data = aggregate;