mainfolder='';

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

%%

% GENERAL DIFFERENCE BETWEEN MODELS DURING DELAY (CUED UNCUED) (cat cardinal model vs
% anti cardinal model)

clear all

  mainfolder='';
  
  %% Uncomment to load the data
% 
% %1 cued
% folder{1}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/single_trial_RSA_first_check_object_1_cued_cat_udlr_Pearson/']
% folder{2}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/single_trial_RSA_first_check_object_1_cued_cat_udlr45_Pearson/']
% 
% %1 uncued
% folder{3}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/single_trial_RSA_first_check_object_1_uncued_cat_udlr_Pearson/']
% folder{4}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/single_trial_RSA_first_check_object_1_uncued_cat_udlr45_Pearson/']
% 
% %2 cued
% folder{5}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/single_trial_RSA_first_check_object_2_cued_cat_udlr_Pearson/']
% folder{6}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/single_trial_RSA_first_check_object_2_cued_cat_udlr45_Pearson/']
% 
% %2 uncued
% folder{7}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/single_trial_RSA_first_check_object_2_uncued_cat_udlr_Pearson/']
% folder{8}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/single_trial_RSA_first_check_object_2_uncued_cat_udlr45_Pearson/']
% 
% % 1 and 2 cued - circle
% folder{9}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/single_trial_RSA_first_check_object_1_cued_Pearson/']
% folder{10}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/single_trial_RSA_first_check_object_2_cued_Pearson/']
% 
% 
% load([mainfolder, 'good_partis.mat']) %list of participant with a good performance
% partis=good_partis';
% 
% 
% [RHO_id_group,time]=loadingrsa(folder,partis)
% 
% 
% 
% %% Loading 2 delay
% 
% %1 cued
% folder{1}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_RSA_first_check_object_1_cued_cat_udlr_Pearson/']
% folder{2}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_RSA_first_check_object_1_cued_cat_udlr45_Pearson/']
% 
% %1 uncued
% folder{3}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_RSA_first_check_object_1_uncued_cat_udlr_Pearson/']
% folder{4}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_RSA_first_check_object_1_uncued_cat_udlr45_Pearson/']
% 
% %2 cued
% folder{5}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_RSA_first_check_object_2_cued_cat_udlr_Pearson/']
% folder{6}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_RSA_first_check_object_2_cued_cat_udlr45_Pearson/']
% 
% %2 uncued
% folder{7}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_RSA_first_check_object_2_uncued_cat_udlr_Pearson/']
% folder{8}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_RSA_first_check_object_2_uncued_cat_udlr45_Pearson/']
% 
% % 1 2 cued circle
% folder{9}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_RSA_first_check_object_1_uncued_Pearson/']
% folder{10}=[mainfolder 'results/mean_recentered_th100/euclideanmodels/2nd_cue/single_trial_RSA_first_check_object_2_uncued_Pearson/']
% 
% load([mainfolder, 'good_partis.mat']) %list of participant with a good performance
% partis=good_partis';
% 
% [RHO_id_group2,time2]=loadingrsa(folder,partis)

%% Optional -> Loading the data ready to be plotted

load('data_for_plots/Figure_3/data_n41_2022.mat');

%% delay 1

%ori1 cued
a1cued{1}=RHO_id_group{1}; %  model model 1
a1cued{2}=RHO_id_group{2}; %  model model 2 (anticardinal)

%ori1 uncued
a1uncued{1}=RHO_id_group{3}; %  model model 1
a1uncued{2}=RHO_id_group{4}; %  model model 2 (anticardinal)

%ori2 cued
a2cued{1}=RHO_id_group{5}; %  model model 1
a2cued{2}=RHO_id_group{6}; %  model model 2 (anticardinal)

%ori2 uncued
a2uncued{1}=RHO_id_group{7}; %  model model 1
a2uncued{2}=RHO_id_group{8}; %  model model 2 (anticardinal)

a1circle=RHO_id_group{9}; %  model circle 1
a2circle=RHO_id_group{10}; %  model cicle 2 


%% delay 2

%ori1 cued
a1cued2{1}=RHO_id_group2{1}; %  model model 1
a1cued2{2}=RHO_id_group2{2}; %  model model 2 (anticardinal)

%ori1 uncued
a1uncued2{1}=RHO_id_group2{3}; %  model model 1
a1uncued2{2}=RHO_id_group2{4}; %  model model 2 (anticardinal)

%ori2 cued
a2cued2{1}=RHO_id_group2{5}; %  model model 1
a2cued2{2}=RHO_id_group2{6}; %  model model 2 (anticardinal)

%ori2 uncued
a2uncued2{1}=RHO_id_group2{7}; %  model model 1
a2uncued2{2}=RHO_id_group2{8}; %  model model 2 (anticardinal)

a1circle2=RHO_id_group2{9}; %  model circle 1
a2circle2=RHO_id_group2{10}; %  model cicle 2 

%% Uncomment to run the cluster perm tests
% %% Cluster analysis of the difference (delay 1)
% 
% %Setting times (from -100 stim onset until the end of the delay period)
% t1=find(time==-100)
% t2=find(time==5850)
% t1_2=find(time==900)
% [timesignclust cluspval] = cluster_dif(a1cued,a2cued,time,t1,t2,t1_2)
% 
% % mean([timesignclust(1,1) timesignclust(2,1)]) %average of "starting" point;
% 
% %% Cluster analysis of the difference within-between (delay 2)
% 
% %Setting times (from -100 stim onset until the end of the delay period)
% t1=find(time2==-100)
% t2=find(time2==2850)
% t1_2=find(time2==-100)
% [timesignclust2 cluspval2] = cluster_dif(a1uncued2,a2uncued2,time2,t1,t2,t1_2)
% 
% % mean([timesignclust(1,1) timesignclust(2,1)]) %average of "starting" point;



%% Delay 1 fig

%% mode 3
coloresb=(colormap((parula(5))));
coloresb(6,:)=[0 0 0]; 
coloresb(1,:)=[1 0 0]; 
coloresb(3,:)=[1 0 0]; 

figure('Position',([200 800 750 700]))
subplot(2,1,1)

colores=coloresb([1 2 3 6 6],:)

leng1='repulsion (B=1)'
leng2='attraction (B=-1)'

titu=''
xl=[-500 5850];
yl=[-0.02 0.15];

pval=0.05
spa=0.1
toi=[2300 5000]

plot3lines_models(time,a1cued,colores,xl,yl,leng1,leng2,titu,0,'time (ms)',1,timesignclust(1,:),colores(1,:),nanmean(a1circle,2),colores(4,:),'unbiased (B=0)',1)
set(gca,'FontSize',20);

subplot(2,1,2)

titu=''

plot3lines_models(time,a2cued,colores,xl,yl,leng1,leng2,titu,0,'time (ms)',1,timesignclust(2,:),colores(1,:),nanmean(a2circle,2),colores(4,:),'unbiased (B=0)',0)
set(gca,'FontSize',20);

%% Delay 2 fig

figure('Position',([200 800 420 700]))
subplot(2,1,1)

leng1='repulsion (B=1)'
leng2='attraction (B=-1)'

titu=''
xl=[-300 2850];
yl=[-0.02 0.15];

pval=0.05
spa=0.1
toi=[2500 5000]

plot3lines_models2nd2(time2,a1uncued2,colores,xl,yl,leng1,leng2,titu,0,'time (ms) from Cue 2',1,timesignclust2(1,:),colores(1,:),nanmean(a1circle2,2),colores(4,:),'unbiased (B=0)',0)

set(gca,'FontSize',20);

subplot(2,1,2)

titu=''

plot3lines_models2nd2(time2,a2uncued2,colores,xl,yl,leng1,leng2,titu,0,'time (ms) from Cue 2',1,timesignclust2(2,:),colores(1,:),nanmean(a2circle2,2),colores(4,:),'unbiased (B=0)',0)

set(gca,'FontSize',20);

%% subfuction

function [RHO_id_group,time]=loadingrsa(folder,partis)

counter=1;

for ppp=1:length(partis)
    counterf=1;
    
    
    
     for fff=1:size(folder,2)
  toload=[folder{fff}, partis{ppp,:},'.mat']
  load(toload)
  
  RHO_id_group{counterf}(:,counter)=squeeze(nanmean(RSA_obj_rot.rho(:,:,:),1));
  counterf=counterf+1;

    end
     
  counter=counter+1;
   
end

time=RSA_obj_rot.time;

end


function [timesignclust cluspv] = cluster_dif(a1_1,a2_1,time_1,t1,t2,t1_2)

%ori1 cued
wi1 =a1_1{1}(t1:t2,:); %  with 1
bet1 =a1_1{2}(t1:t2,:); %  bet 1

%ori2 cued
wi2 =a2_1{1}(t1_2:t2,:); %  w 2
bet2 =a2_1{2}(t1_2:t2,:); %  betwen 2

RHOD_wi{1}=wi1();
RHOD_wi{2}=wi2();

RHOD_be{1}=bet1();
RHOD_be{2}=bet2();

pv_cluster=0.05; % p-vals for clust perm test
% az=zeros(size(RHOD_diff{1})); % matrix to be compare (in this case against 0);

timesall{1}=time_1(t1:t2);
timesall{2}=time_1(t1_2:t2);


[timesignclust cluspv]=allclustperm2(RHOD_wi,timesall,RHOD_be,pv_cluster) % output variable (rows -> lines; columns -> start and end (ms))

end

function [timesignclust cluspv]=allclustperm2(RHO_all,time,az,pv_cluster)
timesignclust=nan(size(RHO_all,2),2); % output variable (rows -> lines; columns -> start and end (ms))
for r=1:size(RHO_all,2);      
    [clusters, p_values, t_sums, permutation_distribution] =permutest(RHO_all{r},az{r},1,0.05,20000)
    if p_values(1)<pv_cluster
        
        timesignclust(r,1)=time{r}(clusters{1}(1))
        timesignclust(r,2)=time{r}(clusters{1}(end))
        
    end
    
    cluspv(r)=p_values(1);
end
end