function ret = p_analyze(varargin)
%	
%	varargin = fname1, rows1, fname2, rows2, ...
%

%cellplot(varargin)

temp_file = 'temp.mat';
max_rows = 0;
try
	load(temp_file);
  [files, stored_index] = size(ret);
  for i = 1:files
    rows = size(ret(i, 1).p);
    rows = rows(1);
    max_rows = max(max_rows, rows);
  end
catch
	files = size(varargin);
	files = files(2) / 2;
	for f_index = 1:files
		[fname, rows] = deal(varargin{(f_index * 2 - 1):(f_index * 2)});
		max_rows = max(max_rows, rows);
		fid = fopen(fname);
		sprintf('target file => %s\n', fname)
		a = fscanf(fid, '%e ',[((rows * rows) + 1) inf]);
		fclose(fid);
		[row_column, items] = size(a);
		items = items - 1;
		sprintf('total data => %d\n', items)
		
		stored_index = 0;
		for loop = 1:items
			if 0 ~= mod(loop, 100)
				continue
			end
			stored_index = stored_index + 1;
			
			v = zeros(rows, rows);
		  num = 1;
		  ret(f_index, stored_index).ts = a(num, loop); %#ok<AGROW>
			for i = 1:rows
				for j = 1:rows
		      num = num + 1;
					v(i,j) = a(num, loop);
				end
		  end
			ret(f_index, stored_index).p = v; %#ok<AGROW>
		end
		clear a;
	end

	save(temp_file, 'ret');
end

% 対角要素のグラフ
if 0
for target_index = 1:max_rows
	figure;
	set(gca,'fontsize',16,'fontname','Helvetica');
  hold on;
  %c = colormap;
  for f_index = 1:files
  	t = [];
  	y = [];
    try
      for i = 1:stored_index
      		t(i) = ret(f_index, i).ts;
          y(i) = ret(f_index, i).p(target_index, target_index); 
      end
    catch
    end
    %semilogy(t, y, 'color', c(f_index*16,:));
    c = [0 0 0];
    switch(f_index)
      case 1
        c = [1 0 0];
      case 2
        c = [0 1 0];
      case 3
        c = [0 0 1];
    end
    semilogy(t, y, 'color', c);
  end
  hold off;
end
end

% 姿勢(8:10)について主成分分析
for target_index = 8:10
	figure;
	set(gca,'fontsize',16,'fontname','Helvetica');
  hold on;
  for f_index = 1:files
    t = [];
    y = [];
    
    t_s = [];
    v_s = [];
    d_s = [];
    
    t_y_index = 1;
    for i = 1:stored_index
    	try
        % 固有値、固有ベクトルを求める
        [v, d] = eig(ret(f_index, i).p);
        t_s = cat(1, t_s, ret(f_index, i).ts);
        v_s = cat(2, v_s, v);
        d_s = cat(2, d_s, d);
        
        % target_indexが最大となる固有ベクトルの位置
        [rows, columns] = size(v);
        j = 1;
        max_i2 = 0;
        second_i2 = 0;
        while j <= columns
          max_elm = 0;
          max_i2 = 0;
          second_elm = 0;
          second_i2 = 0;
          for i2 = 1:rows
            if max_elm < abs(v(i2, j))
            	second_elm = max_elm;
            	second_i2 = max_i2;
              max_elm = abs(v(i2, j));
              max_i2 = i2;
            elseif second_elm < abs(v(i2, j))
            	second_elm = abs(v(i2, j));
            	second_i2 = i2;
            end
          end
          if target_index == max_i2
            break
          end
          j = j + 1;
        end

        % その固有値を記録
        y(t_y_index) = d(j, j); 
        t(t_y_index) = ret(f_index, i).ts;
        t_y_index = t_y_index + 1;
      catch
      end  
    end
    
    dlmwrite(sprintf('t_%d.csv', f_index), t_s, 'delimiter', ',', 'precision', 16);
    dlmwrite(sprintf('v_%d.csv', f_index), v_s, 'delimiter', ',', 'precision', 16);
    dlmwrite(sprintf('d_%d.csv', f_index), d_s, 'delimiter', ',', 'precision', 16);
    
    c = [0 0 0];
    switch(f_index)
      case 1
        c = [1 0 0];
      case 2
        c = [0 1 0];
      case 3
        c = [0 0 1];
    end
    plot(t, y, 'color', c);
  end
  hold off;
end