clear all
mainfolder=''

addpath(genpath(['subfunctions/']))
addpath(genpath(['subfunctions/rm_anova2']))

load('good_partis.mat') %list of participant with a good performance

partis_sub=good_partis'

%% Empty variables

accrot_cu_uncue_all{1,1}=nan(512,length(partis_sub));
accrot_cu_uncue_all{1,2}=nan(512,length(partis_sub));
accrot_cu_uncue_all{2,1}=nan(512,length(partis_sub));
accrot_cu_uncue_all{2,2}=nan(512,length(partis_sub));

%%

for ppp=1:length(partis_sub) %Per participant
        
    % Loading dataset
    
    if strcmp( partis_sub{ppp,:},'p11')
        load([mainfolder 'logfiles/resultfile_p11_table_repaired.mat'])
    else
        subtable=[];
        sub = tdfread([mainfolder 'logfiles/resultfile_' partis_sub{ppp,:} '.txt'],'tab'); %Logfile for this participant
        subtable=struct2table(sub);
    end
    
    for item=1:2
        
        
        % Cue (first test)
        
        toi=subtable.retro_cue==item&subtable.type_of_task==1 ...
            &subtable.acc_trial_concrete_cued~=2&subtable.acc_trial_concrete_cued~=NaN;
        
        toi2=subtable.retro_cue==item&subtable.type_of_task==1 ...
            &subtable.acc_trial_concrete_cued~=2; %to keep all trials it is fine if the value is NaN
        
        accrot_cu_uncue{item,1}(ppp)= nanmean(subtable.acc_trial_concrete_cued(toi));
        accrot_cu_uncue_all{item,1}(toi2,ppp)= (subtable.acc_trial_concrete_cued(toi2));
                
        
        % Uncue (second test)
        
        toi=subtable.retro_cue_uncued==item&subtable.type_of_task==1 ...
            &subtable.acc_trial_concrete_uncued~=2&subtable.acc_trial_concrete_uncued~=NaN;
        
        toi2=subtable.retro_cue_uncued==item&subtable.type_of_task==1 ...
            &subtable.acc_trial_concrete_uncued~=2; %to keep all trials it is fine if the value is NaN
               
        accrot_cu_uncue{item,2}(ppp)= nanmean(subtable.acc_trial_concrete_uncued(toi));
        accrot_cu_uncue_all{item,2}(toi2,ppp)=(subtable.acc_trial_concrete_uncued(toi2));
        
        
        
    end
    
end

%% Plot

all{1}= cat(2,accrot_cu_uncue{1,1}',accrot_cu_uncue{2,1}',accrot_cu_uncue{1,2}',accrot_cu_uncue{2,2}') %All data together

avg=nanmean(all{1},1);
sem=std(all{1})/sqrt(size(all{1},1));

figure;
er=errorbar([],avg,sem,sem,'o');
er.Color = [0 0 0];
er.LineStyle = 'none';
ylim([0.475 0.8])
xlim([0 5])
line([0,5],[0.5 0.5], 'Color', 'k','LineStyle','--');hold on;

xticks([1:4])
xticklabels({'ori 1 1st test','ori 2 1st test','ori 1 2nd test','ori 2 2nd test'})
ylabel('accuracy')

title(['General accuracy'])

%% Anova
     
a1= cat(1,accrot_cu_uncue{1,1}',accrot_cu_uncue{2,1}',accrot_cu_uncue{1,2}',accrot_cu_uncue{2,2}')
p=size(accrot_cu_uncue{1,1},2);

S=[1:p,1:p,1:p,1:p];
F1=[ones(p,1);ones(p,1)*2;ones(p,1);ones(p,1)*2]'
F2=[ones(p*2,1);ones(p*2,1)*2]'
FACTNAMES={'stim order', 'test order'}

stats = rm_anova2(a1,S,F1,F2,FACTNAMES)

totalSS=sum([stats{2,2},stats{3,2},stats{4,2},stats{5,2},stats{6,2},stats{7,2}]);
etaS=[stats{2,2}/totalSS,stats{3,2}/totalSS,stats{4,2}/totalSS]

%% Just to see means and sds

testone= cat(1,accrot_cu_uncue{1,1}',accrot_cu_uncue{2,1}');
testtwo= cat(1,accrot_cu_uncue{1,2}',accrot_cu_uncue{2,2}');
itemone= cat(1,accrot_cu_uncue{1,1}',accrot_cu_uncue{1,2}');
itemtwo= cat(1,accrot_cu_uncue{2,1}',accrot_cu_uncue{2,2}');

mean(testone)
std(testone)    

mean(testtwo)   
std(testtwo)    

mean(itemone)   
std(itemone)   

mean(itemtwo)    
std(itemtwo);   
        
%% Linear trend regarding distance

%from closest to longest disntace

distances(:,1)=all{1}(:,2) %stim 2 cued 1st
distances(:,2)=all{1}(:,1) %stim 1 cued 1st
distances(:,3)=all{1}(:,4)%stim 2 cued 2nd
distances(:,4)=all{1}(:,3) %stim 1 cued 2nd

%% Check linear trend

ball=[];
yfit = [];

for ppp = 1:size(distances,1)   
    
    tof=[];
    for c=1:size(distances,2);
    tof=[tof; distances(ppp,c)];    
    end
    [b,dev,stats] = glmfit(1:size(distances,2),tof,'normal');
    yfit(ppp,:) = polyval([b(2,1),b(1,1)],[1,2,3,4]);  
    ball(ppp) = b(2);
    
end

[h,p,ci,stats] = ttest(ball,0)
[p,h,stats] = signrank(ball,0)

%% Plotting accuracies + linear trends
all{1}= distances;
avg=nanmean(all{1},1);
sem=std(all{1})/sqrt(size(all{1},1));
figure;
er=errorbar([],avg,sem,sem,'o');
er.Color = [0 0 0];
er.LineStyle = 'none';
%  ylim([0.475 0.8])
xlim([0 5])
line([0,5],[0.5 0.5], 'Color', 'k','LineStyle','--');hold on;
xticks([1:4])
xticklabels({'ori 2 1st test','ori 1 1st test','ori 2 2nd test','ori 1 2nd test'});
hold on;
plot(yfit')
ylabel('accuracy')
title(['General accuracy based on distance'])