function[mat trial_ind] = getTrialMatrix(obj, varargin)
%% getTrialMatrix returns traces of all selected trials in selection, surviving the Quality Threshold
%
% Usage 
%   [mat trial_ind] = PDSession.getTrialMatrix([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
%  - 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:
%
%   *) without parameters    : session.getTrialMatrix;
%   *) with one parameter    : session.getTrialMatrix(1:10);
%   *) 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);

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

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

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

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

if ~ismember(eye,{'left', 'right', 'both'});
	eye = 'left';
end
    
if isempty(selection)
    selection = 1:length(obj.trials);
end

%% Quality check

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;
for i = 1:length(data_objs)
    if max_trial_length < length(data_objs(i).(signal_type));
        max_trial_length = length(data_objs(i).(signal_type));
    end
end


%% define empty output matrix with zeros
mat = nan(length(valid_trials), max_trial_length);

for t = 1:length(trials)
    trace = trials(t).(eye).(data_field).(signal_type);
    mat(t,1:length(trace)) = trace;   
end