fmri_plot_brain_scores.m

绝对经典,老外制作的功能强大的matlab实现PLS_TOOBOX

   selected_data = popup_string{selected_idx};

   last_datamat = getappdata(gcf,'PlottedDatamat');

   if (strcmp(last_datamat,selected_data) == 0)

       set(findobj(gcf,'Tag','MessageLine'),'String','Loading data ... ');

       [st_datamat, st_coords, st_win_size, st_evt_list] = ...
                                    load_plotted_datamat(selected_idx);
       if isempty(st_datamat)
           set(gcf,'Pointer',old_pointer);
           return;
       end;

       setappdata(gcf,'PlottedDatamat',selected_data);
       setappdata(gcf,'STDatamat',st_datamat);
       setappdata(gcf,'STWinSize',st_win_size);
       setappdata(gcf,'STEvtList',st_evt_list);

       set(findobj(gcf,'Tag','MessageLine'),'String','');
   else
       set(gcf,'Pointer',old_pointer);
       status = 1;
       return;
   end;


   brainlv = getappdata(gcf,'BrainLV');
   curr_lv_state = getappdata(gcf,'CurrLVState');
   pls_coords = getappdata(gcf,'PLSCoords');

   num_lvs = length(curr_lv_state);
   num_voxels = length(pls_coords);
   num_evts = length(st_evt_list);
   
   blv = reshape(brainlv,[st_win_size,num_voxels,num_lvs]);
   bs = zeros(st_win_size,num_evts,num_lvs);

   for i=1:st_win_size,
      if ndims(blv)==3
         bs(i,:,:) = squeeze(st_datamat(:,i,:)) * squeeze(blv(i,:,:)); 
      else
         bs(i,:,:) = squeeze(st_datamat(:,i,:)) * blv(i,:)'; 
      end
   end;

   setappdata(gcf,'CurrBrainScores',bs);

   status = 1;

   set(gcf,'Pointer',old_pointer);

   return;                                              % get_brain_scores


%--------------------------------------------------------------------------
%
function  plot_brain_scores()

   if (get_brain_scores ~= 1);
      return;
   end;

   st_evt_list = getappdata(gcf,'STEvtList');

   conditions = getappdata(gcf,'Conditions');
   curr_lv_state = getappdata(gcf,'CurrLVState');
   select_lv = find(curr_lv_state == 1);

   h = findobj(gcf,'Tag','PlotButton');
   if strcmp(lower(get(h,'Enable')),'off'),  return; end;

   bs = getappdata(gcf,'CurrBrainScores');
   plotted_data = squeeze(bs(:,:,select_lv))';

   num_conditions = length(getappdata(gcf,'Conditions'));
   condition = cell(1,num_conditions);  

   max_num_stim = 0;
   for i=1:num_conditions,
      condition{i}.st_row_idx  = find(st_evt_list == i);
      condition{i}.num_stim = length(condition{i}.st_row_idx);
      condition{i}.name = conditions{i};
      if (max_num_stim < condition{i}.num_stim)
          max_num_stim = condition{i}.num_stim;
      end;
   end;


   % generate the plots
   %
   plot_cond_idx = [1:num_conditions];

   plot_dims = getappdata(gcf,'PlotDims');
   if isempty(plot_dims) 
       if (num_conditions < 5)
         num_rows = num_conditions;
       else
         num_rows = 5;
       end;
       if (max_num_stim < 4),
         num_cols = max_num_stim;
       else
         num_cols = 4;
       end;
       plot_dims = [num_rows num_cols];
   end;

   f_pos = get(gcf,'Position');
   axes_margin = [.37 .13 .15 .1];

   fmri_plot_cond_stim_ui('STARTUP', plotted_data, condition,  ...
           	                    axes_margin, plot_dims, plot_cond_idx);

   setappdata(gcf,'PLS_PLOT_BS_ACTIVE',1);
%   save('PLS_plotted_bs','plotted_data','condition');
   setappdata(gcf,'PLS_BS_PLOTTED_DATA',plotted_data);
   setappdata(gcf,'PLS_BS_PLOTTED_CONDITON',condition);
%   set(findobj(gcf,'Tag','PlotMenu'),'Enable','on');
%   set(findobj(gcf,'Tag','OptionMenu'),'Enable','on');

   return;


%--------------------------------------------------------------------------
%
function  save_response_fn()

   if (get_brain_scores ~= 1);
      return;
   end;

   st_evt_list = getappdata(gcf,'STEvtList');
   curr_lv_state = getappdata(gcf,'CurrLVState');
   select_lv = find(curr_lv_state == 1);
   bs = getappdata(gcf,'CurrBrainScores');
   bs_data = squeeze(bs(:,:,select_lv))';
   selected_files = get_selected_filename;


   [a1 b1]=sort(st_evt_list);
   c1 = length(unique(st_evt_list));
   b2 = reshape(b1, [length(st_evt_list)/c1, c1]);
   b2 = b2';
   b2 = b2(:)';
   st_evt_list = st_evt_list(b2);
   bs_data = bs_data(b2, :);


   bs_data_mean = ones(c1, size(bs_data,2));

   for i = 1:c1
      bs_data_mean(i,:) = mean(bs_data(find(st_evt_list==i),:),1);
   end


   fn = getappdata(gcf,'STFiles');
   fn = fn{1}.name;

   if findstr('BfMRIdatamat.mat', fn)
      [filename, pathname] = ...
	uiputfile('*_BfMRI_bs_plot.mat','Save the Response Functions');

      if ischar(filename) & isempty(findstr(lower(filename),'_bfmri_bs_plot'))
         [tmp filename] = fileparts(filename);
         filename = [filename, '_BfMRI_bs_plot.mat'];
      end
   else
      [filename, pathname] = ...
	uiputfile('*_fMRI_bs_plot.mat','Save the Response Functions');

      if ischar(filename) & isempty(findstr(lower(filename),'_fmri_bs_plot'))
         [tmp filename] = fileparts(filename);
         filename = [filename, '_fMRI_bs_plot.mat'];
      end
   end

   if isequal(filename,0)
	return;
   end;
   rf_plot_file = fullfile(pathname,filename);

   try
     save (rf_plot_file, 'selected_files', 'bs_data', 'bs_data_mean', 'st_evt_list', 'select_lv');
   catch
     msg = sprintf('Cannot save the response function data to %s',rf_plot_file);
     set(findobj(gcf,'Tag','MessageLine'),'String',['ERROR: ', msg]);
     status = 0;
     return;
   end;

   return;                                      % save_response_fn


%--------------------------------------------------------------------------
%
function  save_response_fn2()

   if (get_brain_scores ~= 1);
      return;
   end;

   curr_lv_state = getappdata(gcf,'CurrLVState');
   select_lv = find(curr_lv_state == 1);
   bs = getappdata(gcf,'CurrBrainScores');
   st_data = squeeze(bs(:,:,select_lv))';
   [nr nc] = size(st_data);

%  data_option = 1  - plot individual ST datamat
%  data_option = 2  - plot group data
%  data_option = 3  - plot all data

   popup_h = findobj(gcf,'Tag','STDatamatPopup');
   selected_idx = get(popup_h,'value');
   data_option = get(popup_h,'Userdata');

   st_filenames = getappdata(gcf,'STFiles');

   num_cond = sum(getappdata(gcf,'cond_selection'));
   evt_list = getappdata(gcf,'STEvtList');

   switch (data_option)

      case {1}					% plot individual data

         all_evt_length = {};			% evt length of all datamat

         %  find all_evt_length
         %
         for i = 1:length(st_filenames)
            filename = st_filenames{i}.fullname;
            group = st_filenames{i}.group;
            if group > length(all_evt_length)
               all_evt_length {group} = [];
               old_evt_length = [];
            else
               old_evt_length = all_evt_length{group};
            end

            load(filename, 'st_evt_list');
%            old_evt_length = [old_evt_length, length(st_evt_list)];
            cond_selection = getappdata(gcf,'cond_selection');
            [tmp1 tmp2 evt_length] = ...
		fmri_mask_evt_list(st_evt_list, cond_selection);
            old_evt_length = [old_evt_length, evt_length];
            all_evt_length{group} = old_evt_length;
         end

         group = st_filenames{selected_idx}.group;

         %  calc subj position
         %
         pre_group = 0;

         for i = 1:group - 1
            pre_group = pre_group + length(all_evt_length{i});
         end

         subj = selected_idx - pre_group;

         pattern = ...
            ['<ONLY INPUT PREFIX>*_fMRI_grp',num2str(group),'_subj', ...
		num2str(group),'_behavdata1.txt'];

         [fn, pn] = uiputfile(pattern,'Save Behav Data');

         if isequal(fn,0)
            return;
         end;

         [tmp fn] = fileparts(fn);

         first = 1;

         i = group;					% we know grp#
            j = subj;					% we know subj#

               behavdata = zeros(num_cond, nc);

               evt_length = all_evt_length{i}(j);
               last = first + evt_length - 1;
               subj_st_data = st_data(first:last,:);

               run = evt_length / num_cond;		% run# in subj

               if run ~= 1				% avg across run
                  head = 1;
                  for k = 1:num_cond
                     tail = head + run - 1;
                     behavdata(k,:) = mean(subj_st_data(head:tail,:), 1);
                     head = head + run;
                  end
               else
                  behavdata = subj_st_data;
               end

               first = first + evt_length;

               %  write behav data to file
               %
               rf_plot_file = fullfile(pn, ...
                  sprintf('%s_fMRI_grp%d_subj%d_behavdata1.txt',fn,i,j));
               behavdata = double(behavdata);
               save (rf_plot_file, '-ascii', 'behavdata');

      case {2}					% plot group data

         pattern = ...
            ['<ONLY INPUT PREFIX>*_fMRI_grp',num2str(selected_idx),'_subj1_behavdata2.txt'];

         [fn, pn] = uiputfile(pattern,'Save Behav Data');

         if isequal(fn,0)
            return;
         end;

         [tmp fn] = fileparts(fn);

         all_evt_length = {};			% evt length of all datamat

         %  find all_evt_length
         %
         for i = 1:length(st_filenames)
            filename = st_filenames{i}.fullname;
            group = st_filenames{i}.group;
            if group > length(all_evt_length)
               all_evt_length {group} = [];
               old_evt_length = [];
            else
               old_evt_length = all_evt_length{group};
            end

            load(filename, 'st_evt_list');
%            old_evt_length = [old_evt_length, length(st_evt_list)];
            cond_selection = getappdata(gcf,'cond_selection');
            [tmp1 tmp2 evt_length] = ...
		fmri_mask_evt_list(st_evt_list, cond_selection);
            old_evt_length = [old_evt_length, evt_length];
            all_evt_length{group} = old_evt_length;
         end

         %  find st_data for the subj
         %
         first = 1;

         i = selected_idx;				% we know grp#
            for j = 1:length(all_evt_length{i})		% all subj

               behavdata = zeros(num_cond, nc);

               evt_length = all_evt_length{i}(j);
               last = first + evt_length - 1;
               subj_st_data = st_data(first:last,:);

               run = evt_length / num_cond;		% run# in subj

               if run ~= 1				% avg across run
                  head = 1;
                  for k = 1:num_cond
                     tail = head + run - 1;
                     behavdata(k,:) = mean(subj_st_data(head:tail,:), 1);
                     head = head + run;
                  end
               else
                  behavdata = subj_st_data;
               end

               first = first + evt_length;

               %  write behav data to file
               %
               rf_plot_file = fullfile(pn, ...
                  sprintf('%s_fMRI_grp%d_subj%d_behavdata2.txt',fn,i,j));
               behavdata = double(behavdata);
               save (rf_plot_file, '-ascii', 'behavdata');
         
            end

      case {3}					% plot all data

         pattern = ...
            ['<ONLY INPUT PREFIX>*_fMRI_grp1_subj1_behavdata3.txt'];

         [fn, pn] = uiputfile(pattern,'Save Behav Data');

         if isequal(fn,0)
            return;
         end;

         [tmp fn] = fileparts(fn);

         all_evt_length = {};			% evt length of all datamat

         %  find all_evt_length
         %
         for i = 1:length(st_filenames)
            filename = st_filenames{i}.fullname;
            group = st_filenames{i}.group;
            if group > length(all_evt_length)
               all_evt_length {group} = [];
               old_evt_length = [];
            else
               old_evt_length = all_evt_length{group};
            end

            load(filename, 'st_evt_list');
%            old_evt_length = [old_evt_length, length(st_evt_list)];
            cond_selection = getappdata(gcf,'cond_selection');
            [tmp1 tmp2 evt_length] = ...
		fmri_mask_evt_list(st_evt_list, cond_selection);
            old_evt_length = [old_evt_length, evt_length];
            all_evt_length{group} = old_evt_length;
         end

         %  find st_data for the subj
         %
         first = 1;

         for i = 1:length(all_evt_length)		% all grp
            for j = 1:length(all_evt_length{i})		% all subj

               behavdata = zeros(num_cond, nc);

               evt_length = all_evt_length{i}(j);
               last = first + evt_length - 1;
               subj_st_data = st_data(first:last,:);

               run = evt_length / num_cond;		% run# in subj

               if run ~= 1				% avg across run
                  head = 1;
                  for k = 1:num_cond
                     tail = head + run - 1;
                     behavdata(k,:) = mean(subj_st_data(head:tail,:), 1);
                     head = head + run;
                  end
               else
                  behavdata = subj_st_data;
               end

               first = first + evt_length;

               %  write behav data to file
               %
               rf_plot_file = fullfile(pn, ...
                  sprintf('%s_fMRI_grp%d_subj%d_behavdata3.txt',fn,i,j));
               behavdata = double(behavdata);
               save (rf_plot_file, '-ascii', 'behavdata');
         
            end
         end

   end;

   return;                                      % save_response_fn2