%--brain mask with the brain tissue

mask_name = 'C:\Users\Administrator\Desktop\workspace\preprocessed\masks\within_brain_mask.nii';

M   = load_untouch_nii( mask_name ); % load mask NIFTI

mask     = double(M.img>0);          % get 3d v

%--brain functional 4d data

data_4d = 'C:\Users\Administrator\Desktop\workspace\preprocessed\4d\func_3d.nii';

% data_4d = 'C:\Users\Administrator\Desktop\phycaa_workspace\phycaa_plus_2104_03_27\_PHYCAA_step1+2.nii';

V   = load_untouch_nii( data_4d ); 

%--transform 4d array to 2d array, using brain_mask

within_brain_voxels = nifti_to_mat(V,M); 

nt_matrix = within_brain_voxels;

n = size(nt_matrix,1);

t = size(nt_matrix,2);

mean_value = mean ( mean(nt_matrix,2) );

tmp_matrix = nt_matrix - mean_value;

mean_value2 = mean(tmp_matrix,2) ;

% normalise_data =  abs(( nt_matrix - repmat(  mean_value, 1 , t )) ./ repmat(  mean_value, 1 , t )) ;

normalise_data =  abs(( tmp_matrix - repmat(  mean_value2, 1 , t )) ./ repmat(  mean_value2, 1 , t )) ;

% normalise_data =  abs(( nt_matrix - mean_value) )/ mean_value ;

w_data = normalise_data;

 for i=1:n 

    tmp_rows = normalise_data(i,:);

    max_value = max(tmp_rows);

    tmp_rows_index =  (tmp_rows == max_value);

    tmp_rows1 = tmp_rows.* double(tmp_rows_index);

%     w_data(i,:) = double(tmp_rows_index);

    w_data(i,:) = tmp_rows1;

 end

 reference_waveform = sum(w_data);

 plot(reference_waveform);