function[pre_mat post_mat rel_time trial_ind] = getTimeLockedTrialMatrix(objs, varargin)
%% getTimeLockedTrialMatrix returns each trial trace shifted for a marker
%  or event timestamp
%
%  Usage 
%    [mat trial_ind] = PDSession.getTimeLockedTrialMatrix(event_code [,selection, eye, signal_type, corrected])
%
%  - mat           
%           Matrix of n x l, n = number of trials, l = maximum length of all traces
%  - trial_ind
%           Vector containing the selected trials after quality
%           thresholding
%  - event_code
%            Event code (or Label string that is matched.)
%  - selection 
%           Vector of selected / requested trials%
%  - eye
%           'left', 'right', or 'both' (default = 'left')
%  - signal_type
%           'raw', 'interpolated', 'filtered' (default = 'filtered');
%  - corrected
%           0 /1, use baseline corrected signals (default : 1)
%

% Examples:
%
%   *) with one parameter    : session.getTrialMatrix('stimulus start');
%   *) with two parameters   : session.getTrialMatrix('all', 'left');
%   *) with three parameters : session.getTrialMatrix('all', 'left', 'interpolated');
%   *) with four parameters  : session.getTrialMatrix(1:10, 'right', 'interpolated', 1);
%
% Usage :
%   function[mat trial_ind] = getTimeLockedTrialMatrix(obj, varargin)

%% determine input (this can be optimized...)
if nargin == 1
    error('No event code provided')
end

if nargin == 2
    event_code = varargin{1};
    selection = [];
    eye = 'left'; 
    signal_type =  'filtered'; 
    corrected = 1;        
end

if nargin == 3
    event_code = varargin{1};
    selection = varargin{2};
    eye = 'left'; 
    signal_type =  'filtered'; 
    corrected = 1;        
end

if nargin == 4
    event_code = varargin{1};
    selection = varargin{2};
    eye = varargin{3};
    signal_type =  'filtered';
    corrected = 1;        

end

if nargin == 5
    event_code = varargin{1};
    selection = varargin{2};
    eye = varargin{3};
    signal_type = varargin{4};
    corrected = 1;        
end

if nargin == 6
    event_code = varargin{1};
    selection = varargin{2};
    eye = varargin{3};
    signal_type = varargin{4};    
    corrected = varargin{5};
end    

if corrected == 1
    data_field = 'baseline_corrected';
else
    data_field = 'raw';
end

%%
for s = 1:length(objs)
    obj = objs(s);
  
    if isempty(selection)
        selection = 1:length(obj.trials);
    end
    
    %% incorporate trial quality
    q_objs = [obj.trials(selection).quality];
    q_vals = cat(1, q_objs.(eye));
    valid_trials = q_vals >= obj.settings.QualityThreshold;
    trial_ind = find(valid_trials);

    trials = [obj.trials(valid_trials).data];
    t_objs = [trials.(eye)]; data_objs = [t_objs.(data_field)];
    max_trial_length = 0;

    indices = obj.trials.getIndexForEvent(event_code);
    indices = indices(trial_ind);
    
    %% determine the maximum length of pre and post trial
    for i = 1:length(data_objs)
        if max_trial_length < length(data_objs(i).(signal_type)(1:indices(i)));
            max_trial_length_pre = length(data_objs(i).(signal_type)(1:indices(i)-1));
            max_trial_length_post = length(data_objs(i).(signal_type)(indices(i):end));
        end
    end
    
    %% predefine matrices (selected trials x max length)
    pre_mat = nan(length(trial_ind), max_trial_length_pre);
    post_mat = nan(length(trial_ind), max_trial_length_post);
    
    %% cut the trials in pre and post marker
    for t = 1:length(trials)

        pre_trace =  trials(t).(eye).(data_field).(signal_type)(1:indices(i)-1);                
        pre_mat(t,end-length(pre_trace)+1:end) = pre_trace;

        post_trace =  trials(t).(eye).(data_field).(signal_type)(indices(i):end);
        post_mat(t,1:length(post_trace)) = post_trace;

    end                
    
    rel_time = repmat(-length(pre_mat):length(post_mat)-1,length(trials),1);
end