cd 'D:\Labor\Analyses\Toolbox\fMRI'

addpath('FSLNets')           
addpath('L1precision')      
addpath('pwling')            
addpath('../Tools/BCT')


folder_idx  = pwd; % get folder ID to store data
files_idx   = dir([folder_idx,'/*mat']); % list data2load
baseFileName = files_idx.name;
fullFileName = fullfile(folder_idx, baseFileName);
dirInfos = dir(fullFileName);
clear ('folder_idx','files_idx','baseFileName','fullFileName');

cd   'D:\Labor\Projects\Stroke_tDCS_rsfmri\B_rsfmri_stroke_tDCS_mice\data';
targetFolder=pwd;

clear 'data';
%load matrix data from .mat 
load data.mat;
load labels.mat;
% % load data_neu2.mat;
% % load data_full.mat;
% load labelsData.mat;
% load labelsROI.mat;
% load labelsROI_long.mat;

%% 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1) extract mNet/ROI and save in matrix
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

for ri = 1:size(data,2)
   for li = 1:size(data(ri).subj,2)
        for ti = 1:size(data(ri).subj(li).tp,2)
            
                mnx=data(ri).subj(li).tp(ti).mNet;
                if ~isempty(mnx)
                mNet(:,:,(ri-1)*4+ti,li)=mnx;
                end
            
        end
%         rnx=data(ri).subj(li).mNet;
%         ROI(:,:,li,ri)=rnx;
   end
end

mNet(mNet==0)=nan;
clear ('mnx','rnx','li','ri');
mNet_mean = squeeze(mean(mNet,4,'omitnan'));
% 
%%

DeltaTDCS = (mNet_mean(:,:,11)-mNet_mean(:,:,9))-(mNet_mean(:,:,7)-mNet_mean(:,:,5));
PTdiff=squeeze(mNet(:,:,7,:)-mNet_mean(:,:,5));
for i = 1:98
    for k = 1:98
DeltaSD(i,k) = std(PTdiff(i,k,:),'omitnan');
    end
end

DDelta=DeltaTDCS./DeltaSD;

%%

[X,Y,On] = grid_communities(M);

        dirInfo.name='';
        plotmat_values_rsfMRI(DeltaTDCS(On,On),[],dirInfo,[-.75 .75],0,[]);
        hold on
        axis square;
      plot(X,Y,'k','linewidth',2)

%%
NodeDelta=squeeze(mean(DDelta,1,'omitnan'));
ROIDelta(1,:)=NodeDelta(labels.nr14(1:7));
ROIDelta(2,:)=NodeDelta(labels.nr14(8:14));

ROI = cell2table(num2cell(ROIDelta),'VariableNames',lab)

h=heatmap(ROIDelta)
colormap('Parula')
h.YDisplayLabels = {'left','right'}
h.XDisplayLabels  = lab;
caxis([0 1])
title('Change in Node Strength (Z-score)')

%%

cd ('D:\Labor\Projekte\Stroke_tDCS_rsfmri\B_rsfmri_stroke_tDCS_mice\results\Old\plot_nodes')
atlas = niftiread("annotations_50_parent_splitted.nii.gz");
nodes = readtable("acronyms_parent_splitted.txt");
nodes = cell2mat(table2cell(nodes(:,1)));

atlas_mapped=nan(size(atlas,1),size(atlas,2),size(atlas,3));

NodesMean = squeeze(mean(group_delta(:,:,3,2),1,'omitnan'));
NodesStd = squeeze(std(group_delta(:,:,3,2),0,1,'omitnan'));
NodesDelta = NodesMean./NodesStd;
% % for l=1:length(nodes)
% %     
% %     z=find(atlas==nodes(l));
% %     if ~isempty(z)
% %        
% %         atlas_mapped(z)=NodeDelta(l);
% %        
% %     end
% %     end
% %     plot_map=atlas_mapped(:,:,124);
% %     imshow(plot_map,[-1 0])
% %     colormap('Parula')
% %     set(im, 'AlphaData', ~isnan(plot_map))
% % 
% % % niftiwrite(mask,'atlas_mapped.nii')
% % end
% % 
% % %%
% % 
% % cd ('D:\Labor\Projects\Stroke_tDCS_rsfmri\B_rsfmri_stroke_tDCS_mice\results\plot_nodes')
% % nodes = readtable("acronyms_parent_splitted.txt");
% % nodes = cell2mat(table2cell(nodes(:,1)));
%%
for k=1:3
clear ("mask_temp")
t=Tiff(strcat('mask',num2str(k),'.tiff'));
mask=read(t);
% l=Tiff(strcat('lesion_',num2str(k),'.tif'));
% lesion=read(l);


mask_temp=nan(size(mask,1),size(mask,2));
% mask_temp(:,:)=-10;

for l=1:length(nodes)
    isda=find(mask==nodes(l));
    if ~isempty(isda)
        mask_temp(isda)=NodesDelta(l);
    end
end

% lesion(lesion==0)=nan;
% lm = imshow(lesion);
% colormap(hot)
figure
clims = [-2 1];
imAlpha=ones(size(mask_temp));
imAlpha(isnan(mask_temp))=0;
im = imagesc(mask_temp,'AlphaData',imAlpha);
colorbar("off")
clim([-2 1])
c=colorbar;
c.TickLabels = '';
colormap('jet')
% set(im, 'AlphaData', ~isnan(mask_temp))
set(gca,'color',0*[1 1 1],'XTickLabel','','YTickLabel','');
% title('PT tDCS')

saveas(gca,strcat('Plot_node_change_tDCS_',num2str(k),'.png'))
end