mpib_memoreye/check_percentage_of_NaN_eyes_2022.m

mainfolder='U:/LindeDomingoJuan/Memoreye_DATA/'
partis=good_partis_sub;

perNan=NaN(length(partis),2,2); %Preparing output variable

%%
for delay=1:2
    
    for ppp=1:length(partis)
        
        % Loading dataset
        
        if delay== 1
            toload =[mainfolder '/eyelink_preprocessed/mean_recentered_th100/object_1_onset_11_seconds/' partis{ppp,:} '.mat']; %for 1st delay
        elseif delay==2
            toload =[mainfolder '/eyelink_preprocessed/mean_recentered_th100/2nd_cue_onset_17_5__seconds/' partis{ppp,:} '.mat']; %for 2nd delay
        end
        load(toload)
        
        cx=squeeze(data_epoch_thr.eyedat(:,1,:)); %data of channel x
        cy=squeeze(data_epoch_thr.eyedat(:,2,:)); %data of channel y
        
        cxv=cx(:); %data as a vector
        cyv=cy(:); %data as a vector
        
        
        % Saving the percentage of NaN per epoch.
        
        perNan(ppp,1,delay)=(sum(isnan(cxv))/length(cxv))*100;
        perNan(ppp,2,delay)=(sum(isnan(cyv))/length(cyv))*100;       
        
    end

end


%%

baddelay1=excludingoutlier(perNan(:,1,1))
baddelay2=excludingoutlier(perNan(:,1,2))

index=(baddelay1+baddelay2)==0; %keeping only participants that are OK in both epochs


%% Making a final list of good participants and saving the file
    counter=1;
for ppp = 1:length(good_partis_sub)
    
    if index(ppp)==1
    good_partis{counter} =good_partis_sub{ppp}
    counter=counter+1;
    end
end

%% Plotting results

if figure_NaNs

figure; 

subplot(1,2,1)
boxplot(squeeze(perNan(:,:,1)))
ylabel('% of NaN')
xlabel('channel (x and y)')
title('delay 1 epoch')


subplot(1,2,2)
boxplot(squeeze(perNan(:,:,2)))
ylabel('% of NaN')
xlabel('channel (x and y)')
title('delay 2epoch')

end

m1=['the max NaN % in delay 1 is ' num2str(max(perNan(index,:,1))) ' and the min is ' num2str(min(perNan(index,:,1)))];
m2=['the max NaN % in delay 2 is ' num2str(max(perNan(index,:,2))) ' and the min is ' num2str(min(perNan(index,:,2)))];

disp(m1)
disp(m2)

%%

function output=excludingoutlier(data)

%excluding participants with values >1.5*IQR in the third quartile

ad=squeeze(data(:,1,1));
r = iqr(ad);
Q = quantile(ad,[0.75]);
output=ad>(1.5*r)+Q;

end