mpib_memoreye/Figure_1_paneld.m

clear all;

mainfolder='U:/'

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

partis=good_partis';
partis_sub=good_partis';

%% 1st Panel

figure;   

oris=[11.25;33.75;56.25;78.75;101.25;123.75;146.25;168.75;191.25;213.75;236.25;258.75;281.25;303.75;326.25;348.75];

colores=(colormap((hsv(16))));
colores=[colores(13:16,:);colores(1:4,:);colores(5:8,:);colores(9:12,:);]

rrr=ones(1,16)*1;
for ooo=1:16
polarplot(deg2rad(oris(ooo)),rrr(ooo),'wo','MarkerSize',18,'LineWidth',2,'MarkerFaceColor',colores(ooo,:));hold on,
end

rlim([0.5 1.1])
oris1=[0 90 180 270]'
ost=[num2str(oris1)]
ost(:,4)='°'

thetaticks(oris1)
thetaticklabels(ost)

ax=gca; 
ax.ThetaGrid='on';
ax.RGrid='off';
ax.RTickLabel=[]; 

set(gca,'FontSize',20);
set(gca,'color','none');

%% 2nd Panel

%% Uncomment to run from scratch

% %% Parameters to be modify per loop
% 
% tobefound{1} = 'find(data_epoch_thr.trialinfo.object_1_rot==objlist(k)&data_epoch_thr.trialinfo.retrocue==1)';
% tobefound{2} = 'find(data_epoch_thr.trialinfo.object_2_rot==objlist(k)&data_epoch_thr.trialinfo.retrocue==2)';
% tobefound{3} = 'find(data_epoch_thr.trialinfo.object_2_rot==objlist(k)&data_epoch_thr.trialinfo.retrocue==2)';
% tobefound{4} = 'find(data_epoch_thr.trialinfo.object_1_rot==objlist(k)&data_epoch_thr.trialinfo.retrocue_uncued==1)';
% 
% %%
% foldlo{1}='eyelink_preprocessed/mean_recentered_th100/object_1_onset_11_seconds/';
% foldlo{2}='eyelink_preprocessed/mean_recentered_th100/object_1_onset_11_seconds/';
% foldlo{3}='eyelink_preprocessed/mean_recentered_th100/object_1_onset_11_seconds/';
% foldlo{4}='eyelink_preprocessed/mean_recentered_th100/2nd_cue_onset_17_5__seconds/';
% 
% %%
% item_id_rsa{1}=nan(length(partis),16,3,5501);
% item_id_rsa{2}=nan(length(partis),16,3,5501);
% item_id_rsa{3}=nan(length(partis),16,3,5501);
% item_id_rsa{4}=nan(length(partis),16,3,8751);
% %%
% for fff=1:4
%     
%     for ppp=1:length(partis)
%         
%         %% Loading dataset
%         toload =[mainfolder foldlo{fff} partis{ppp,:} '.mat'];
%         load(toload)
%         
%         %Loading logfiles
%         
%         subtable=[];
%         
%         if strcmp(partis_sub{ppp,:},'p11')
%             load([mainfolder 'logfiles/resultfile_p11_table_repaired.mat'])
%             subtable=sub
%         else
%             
%             sub = tdfread([mainfolder 'logfiles/resultfile_' partis_sub{ppp,:} '.txt'],'tab'); %Logfile for this participant
%             subtable=struct2table(sub);
%         end
%         
%         
%         
%         %% Selecting trials per rot only (not ID)
%         
%         objlistid=unique(data_epoch_thr.trialinfo.object_1_id);
%         counter=1;
%         
%         objlist=unique(data_epoch_thr.trialinfo.object_1_rot);
%         
%         for k =1:length(objlist)
%             
%             triales = eval(tobefound{fff});
%             [t_s_ave{counter}] = squeeze(nanmean(data_epoch_thr.eyedat(triales,:,:),1));
%             counter=counter+1;
%             
%         end
%         
%         %% Substracting the mean  per condition (not in this case)
%               
%         for k = 1:length(t_s_ave);
%             
%             if ~isempty(t_s_ave{k})
%                 
%                 rr=(t_s_ave{k});
%                 item_id_rsa{fff}(ppp,k,:,:)=[rr];
%                 
%             elseif isnan(t_s_ave{k})
%                 item_id_rsa{fff}(ppp,k,1:3,1:size(data_epoch_thr.eyedat,3))=NaN;
%             end
%         end
%         
%         time{fff}=data_epoch_thr.time;
%         
%         %%
%         
%     end
%     
% end
% %%
% 
% save('circledata.mat','objlist','item_id_rsa', 'time')

%% Load results

load('circledata.mat')

%% Plots

IDs = {'1',' ',' ','4','5',' ',' ','8','9',' ',' ','12','13',' ',' ','16'}

colores=(colormap(flip(hsv(16))));

toi{1} = [400 1000];
toi{2} = [1400 2000];
toi{3} = [5350 5850];
toi{4} = [2350 2850];

titu={'stimulus 1';'stimulus 2';'delay 1';'delay 2';}

figure

for fff=1:4
    
    subplot(1,4,fff)
    
    ave_item_id_rsa=squeeze(nanmedian(item_id_rsa{fff},1));
    std_item_id_rsa=squeeze(std(item_id_rsa{fff},1,1));
    avg_std_item_id_rsa=squeeze(nanmean(std_item_id_rsa(:,1:2,:),2));
     
    t1=find(time{1}==toi{fff}(1));
    t2=find(time{1}==toi{fff}(2));
    
    for ppp=1:size(item_id_rsa{fff},1)
        
        x=squeeze(mean(item_id_rsa{fff}(ppp,:,1,t1:t2),4));
        y=squeeze(mean(item_id_rsa{fff}(ppp,:,2,t1:t2).*-1,4));
        
        sct=scatter(x,y,60,colores,'filled','MarkerFaceAlpha',.25);hold on;
        
    end
    
    x=mean(ave_item_id_rsa(:,1,t1:t2),3);
    y=mean(ave_item_id_rsa(:,2,t1:t2).*-1,3);
    z=(mean(avg_std_item_id_rsa(:,t1:t2),2)/40)*250;
    
    sct=scatter(x,y,z,colores,'filled','MarkerEdgeColor',[1 1 1],...
        'LineWidth',1.5)

    set(gca,'Color','k')
   
    ao=0.8/0.0217;
    xlim([-ao ao])
    ylim([-ao ao])
    
    pixtovd=0.0217; %pixel to visual degrees
    
    set(gca,'XTick',-ao:(ao/2):ao)
    set(gca,'XTickLabel',(-ao:(ao/2):ao)*pixtovd)
    set(gca,'YTick',-ao:(ao/2):ao)
    set(gca,'YTickLabel',(-ao:(ao/2):ao)*pixtovd)
      
    title(titu{fff})
    % ylabel('° vis. angle')
    xlabel('° vis. angle')   
    set(gca,'FontSize',20);    
    
end