% === Analysis pipeline for the Visual Search project === 
% this file contains references to the main functions and comments about
% how they should be used. 

%% specify the base directory for this system so that the code works ====
% temporarily here to make it easier for CK to switch systems @home
switch computer
    case 'MACI64'
        ThisSystem = 'CK-MBP';
    case 'GLNXA64'
        ThisSystem = 'CK-Home-Lin';
    % specify for your own system: add case
end

switch ThisSystem
    case 'CK-NIN-Lin'
        fld.basedir = '/home/chris/Desktop/VISUAL_SEARCH/MANUSCRIPT/Data_and_Code/DATA';
    case 'CK-Home-Lin'
        fld.basedir = '/home/chris/Desktop/VISUAL_SEARCH/MANUSCRIPT/Data_and_Code/DATA';
    case 'CK-MBP'
        fld.basedir = ['/Volumes/OSX/Users/chris/Dropbox/CURRENT_PROJECTS/'...
            'MONKEY_EPHYS/VISUAL_SEARCH/MANUSCRIPT/Data_and_Code/DATA'];
    % specify for your own system: add case
end 

fld.procdatadir = fullfile(fld.basedir,'Processed_Data');

fprintf('=============================================================\n')   
fprintf(['System used: ' ThisSystem '\n'])
fprintf(['Basedir used: ' fld.basedir '\n'])      
fprintf('=============================================================\n')      

% info --
addpath(genpath(fullfile(pwd,'info')));

% subfunctions --
addpath(genpath(fullfile(pwd,'functions')));
addpath(genpath(fullfile(pwd,'figfunctions')));


%% individual session analysis ==========================================
% Store individual % channels from individual sessions as individual files, 
% such that they can easily be concatenated. 
% LOG data are stored as LUTs (tables) that can be concatenated easily. 
if false % avoid running this redundantly
    % the following function returns a master list of all dates, that can be
    % used to load data
    datainfo = session_info(); %#ok<*UNRCH>
    
    % the following function loads the data, extracts all necessary 
    % information and stores it in a more flexible format (a LUT). 
    % It also preprocesses env data. The data is stored per channel.
    sessions = datainfo(1).dates;
    analyse_individual_sessions(fld, 'M1',sessions);
    sessions = datainfo(2).dates;
    analyse_individual_sessions(fld, 'M2',sessions); 
end

%% adjust saved data ====================================================
% we added goodtrials as an array, but it needs to be a cell array because
% it doesn't have the same number of fields between sessions (so it can't
% be concatenated). let's fix it once, using this function, so we don't
% have to re-run analyse_individual_sessions. 
if false % avoid running this redundantly
    modify_mp(fld,'M1',datainfo(1).dates);
    modify_mp(fld,'M2',datainfo(2).dates);
    
    % there is an issue with the LUTs, namely that sometimes stimonset and
    % targonset are empty (we created the LUTs by converting log files into a
    % table. let's fix those.
    fix_lut(fld,'M1');
    fix_lut(fld,'M2');
end

%% extra info ===========================================================
% this script loads data from the usable channels (i.e. channels that at
% some point have a stimulus in their RF) and compares the SNR, to get a
% better idea of which channels we can use.
if false % avoid running this redundantly
    check_snr_over_sessions(fld,'M1',false);
    check_snr_over_sessions(fld,'M2',false);
end

%% figure behavior ======================================================
% behavior
figure_behavior(fld);

%% output some general numbers for the methods section ==================
methods_numbers(fld)

%% figure neurotrace ====================================================
% this figure shows the main result, namely that responses to targets are
% enhanced and responses to distractors are supressed, both compared to
% nontargets. this function also estimates the latency.

if false % run prep-script
    for si = 1:2
        figure_neurotrace_prepare(fld,['M' num2str(si)],true);
    end
end

figure_neurotrace(fld)

%% figure binmod ========================================================
% do a binned version of the modulation plots
figure_binmod(fld);

%% figure neurobar ======================================================
% this figure is a sort of extension of figure_neurotrace we show the
% responses for when the monkey is correct (i.e. he chose the target, not
% the nontarget or the distractor). Here we only look at bar plots
% of averages in a window, but we add conditions in which the monkey was
% incorrect (he chose either nontarget or distractor), and if he chose 
% nontarget, also we compare when that particular nontarget was in the RF 
% vs when it wasn't. 

% limit trials to include -------------------------------------------------

% % do all possible combination in a loop ---
% % by reactiontime
% for rt=[0 -1 1]
%     limit.ReactionTime = rt; 
%     % set to -1 (for lower) or +1 (for higher) or 0 for none at all
%     limit.ReactimeTimePerc = 30; % percentile
%     for dct = [0 1]
%         % by distance from target on error trials
%         limit.DistChoiceToTarget = dct; 
%         
%         dctr=[1 1; 2 3];
%         for i=1:2
%             limit.DistChoiceToTarget_range = dctr(i,:); % [min max]
% 
%             for dcs = [0 1]
%                 % by distance from SD on error trials
%                 limit.DistChoiceToSD = dcs; 
%                 
%                 dcsr=[1 1; 2 3];
%                 for j=1:2
%                     limit.DistChoiceToSD_range = dcsr(j,:); % [min max]
%          
%                     limit.PlotBehavior = false;
% % -------------------------------------------------------------------------
% 
%                     figure_neurobar_prepare(fld,'M1', limit); 
%                     figure_neurobar_prepare(fld,'M2', limit);
%                     figure_neurobar(fld, limit);
%                 end
%             end
%         end
%     end
% end

limit.ReactionTime = 0; 
% set to -1 (for lower) or +1 (for higher) or 0 for none at all
limit.ReactimeTimePerc = 30; % percentile

% by distance from target on error trials
limit.DistChoiceToTarget = 0; 
limit.DistChoiceToTarget_range = [1 1]; % [min max]

% by distance from SD on error trials
limit.DistChoiceToSD = 0; 
limit.DistChoiceToSD_range = [1 1]; % [min max]
         
limit.PlotBehavior = false;

% -------------------------------------------------------------------------
figure_neurobar_prepare(fld,'M1', limit); 
figure_neurobar_prepare(fld,'M2', limit);
figure_neurobar(fld, limit);

%% figure priming (behavior and RT color same/swap high/low reward) =====
figure_priming_color(fld)
figure_priming_shape(fld)
figure_priming_col_vs_shape(fld)

%% figure neuropriming (neuronal effect of shape/color swap =============
figure_neuropriming_prepare(fld,'M1')
figure_neuropriming_prepare(fld,'M2')
figure_neuropriming(fld)

%% figure saccadic response times (SRT) =================================
figure_SRT(fld)