%% This script highlights how the figures from the article were generated
n_ROI = 299;
n_conn = 299*298/2;
n_sw_tp = 167;
n_subjects = length(TC_MW);

% We select three connections of interest that we will compare across
% parameters
COI = [39,207; 148,207; 207,248];

COI_colors{1} = 1/255*[86,214,114;192,214,116;86,214,192];
COI_colors{2} = 1/255*[255,76,0;255,187,0;251,237,0];

% We will plot the same for the different connections
for con = 1:size(COI,1)
    
    %% 1. Sampling of the regions of interest and associated connection
    R1 = COI(con,1);
    R2 = COI(con,2);
    
    M = zeros(n_ROI,n_ROI);
    M(R1,R2) = 1;
    Mvec = logical(jUpperTriMatToVec(M));
    
    %% 2. Sampling of the related ISFC time courses
    for s = 1:n_subjects
    
        % W = 10
        ROI_ISFC_MW(s,:) = ISFC_MW{s}(Mvec,:);
        ROI_ISFC_RS(s,:) = ISFC_RS{s}(Mvec,:);
        
        % W = 5
        ROI_ISFC_MW_W5(s,:) = ISFC_MW_W5{s}(Mvec,:);
        ROI_ISFC_RS_W5(s,:) = ISFC_RS_W5{s}(Mvec,:);

    end

    t_MW = 19:2:352;
    t_MW_W5 = 9:2:352;
    
    t_RS = 404:2:678;
    t_RS_W5 = 394:2:678;
    
    %% 3. Plotting of the ISFC time courses for the movie-watching and 
    % resting-state cases
    figure; 
    set(gcf,'color','w');
    
    % For W = 10
    subplot(2,2,1);
    hold on;
    set(gca,'Box','off');
    plot(t_MW,ROI_ISFC_MW','LineWidth',0.5,'color',COI_colors{1}(con,:)); 
    hold on; ylim([-0.5 0.5]); xlim([t_MW_W5(1),t_MW_W5(end)]);
    %plot(t_MW,mean(ROI_ISFC_MW,1),'color','r','LineWidth',2); 
    subplot(2,2,2);
    hold on;
    set(gca,'Box','off');
    plot(t_RS,ROI_ISFC_RS','LineWidth',0.5,'color',COI_colors{2}(con,:)); 
    hold on;  ylim([-0.5 0.5]); xlim([t_RS_W5(1),t_RS_W5(end)]);
    %plot(t_RS,mean(ROI_ISFC_RS,1),'color','r','LineWidth',2);
    
    % For W = 5
    subplot(2,2,3);
    hold on;
    set(gca,'Box','off');
    plot(t_MW_W5,ROI_ISFC_MW_W5','LineWidth',0.5,'color',COI_colors{1}(con,:)); 
    hold on; ylim([-0.5 0.5]); xlim([t_MW_W5(1),t_MW_W5(end)]);
    %plot(t_MW_W5,mean(ROI_ISFC_MW_W5,1),'color','r','LineWidth',2); 
    subplot(2,2,4);
    hold on;
    set(gca,'Box','off');
    plot(t_RS_W5,ROI_ISFC_RS_W5','LineWidth',0.5,'color',COI_colors{2}(con,:)); 
    hold on;  ylim([-0.5 0.5]); xlim([t_RS_W5(1),t_RS_W5(end)]);
    %plot(t_RS_W5,mean(ROI_ISFC_RS_W5,1),'color','r','LineWidth',2);
    
    %% 4. Plotting of the ISFC excursion time courses
    Plot_Conn_TimeCourse(SignMat,R1,R2,19,352,2,COI_colors{1}(con,:),COI_colors{1}(con,:));
    Plot_Conn_TimeCourse(SignMat_W5,R1,R2,9,352,2,COI_colors{1}(con,:),COI_colors{1}(con,:));
    Plot_Conn_TimeCourse(SignMat_alphaverynice,R1,R2,19,352,2,COI_colors{1}(con,:),COI_colors{1}(con,:));
    Plot_Conn_TimeCourse(SignMat_alphaverynice_W5,R1,R2,9,352,2,COI_colors{1}(con,:),COI_colors{1}(con,:));
end

%% 5. Plotting of the spatial patterns at a salient time point
idx_W10 = 19;
idx_W5 = 24;


Spatial_OI = mean(squeeze(SignMat(:,idx_W10,:)),2);

tmp_MAT = jVecToUpperTriMat(Spatial_OI,n_ROI)+jVecToUpperTriMat(Spatial_OI,n_ROI)';
tmp_DEG = sum(abs(tmp_MAT),1)/2;

Factor_plots = 2*max(tmp_DEG);

PlotBrainGraph_Kmeans_withtitle(tmp_MAT,tmp_DEG,CodeBook,0,...
        Factor_plots,Factor_plots,2,1,'hot','jet',1,0.5,[-1 1],...
        ['W10_Alpha5'],'W5_Alpha5');
    

Spatial_OI = mean(squeeze(SignMat_W5(:,idx_W5,:)),2);

tmp_MAT = jVecToUpperTriMat(Spatial_OI,n_ROI)+jVecToUpperTriMat(Spatial_OI,n_ROI)';
tmp_DEG = sum(abs(tmp_MAT),1)/2;

PlotBrainGraph_Kmeans_withtitle(tmp_MAT,tmp_DEG,CodeBook,0,...
        Factor_plots,Factor_plots,2,1,'hot','jet',1,0.5,[-1 1],...
        ['W5_Alpha5'],['W5_Alpha5']);