nikcleju@0: # -*- coding: utf-8 -*-
nikcleju@0: """
nikcleju@0: Created on Wed Nov 09 12:28:55 2011
nikcleju@0: 
nikcleju@0: @author: ncleju
nikcleju@0: """
nikcleju@0: 
nikcleju@0: import numpy
nikcleju@0: import scipy.io
nikcleju@0: import matplotlib.pyplot as plt
nikcleju@0: import matplotlib.cm as cm
nikcleju@0: import matplotlib.colors as mcolors
nikcleju@0: 
nikcleju@0: # Sample call
nikcleju@0: #utils.loadshowmatrices_multipixels('H:\\CS\\Python\\Results\\pt_std1\\approx_pt_std1.mat', dosave=True, saveplotbase='approx_pt_std1_',saveplotexts=('png','eps','pdf'))
nikcleju@0: 
nikcleju@0: #def loadshowmatrices(filename, algonames = None):
nikcleju@0: #    mdict = scipy.io.loadmat(filename)
nikcleju@0: #    if algonames == None:
nikcleju@0: #      algonames = mdict['algonames']
nikcleju@0: #    
nikcleju@0: #    for algonameobj in algonames:
nikcleju@0: #        algoname = algonameobj[0][0]
nikcleju@0: #        print algoname
nikcleju@0: #        if mdict['meanmatrix'][algoname][0,0].ndim == 2:
nikcleju@0: #            plt.figure()
nikcleju@0: #            plt.imshow(mdict['meanmatrix'][algoname][0,0], cmap=cm.gray, interpolation='nearest',origin='lower')            
nikcleju@0: #        elif mdict['meanmatrix'][algoname][0,0].ndim == 3:
nikcleju@0: #            for matrix in mdict['meanmatrix'][algoname][0,0]:
nikcleju@0: #                plt.figure()
nikcleju@0: #                plt.imshow(matrix, cmap=cm.gray, interpolation='nearest',origin='lower')
nikcleju@0: #    plt.show()
nikcleju@0: #    print "Finished."
nikcleju@0: #
nikcleju@0: #
nikcleju@0: #def loadsavematrices(filename, saveplotbase, saveplotexts, algonames = None):
nikcleju@0: #    
nikcleju@0: #    mdict = scipy.io.loadmat(filename)
nikcleju@0: #    lambdas = mdict['lambdas']
nikcleju@0: #
nikcleju@0: #    if algonames is None:
nikcleju@0: #      algonames = mdict['algonames']
nikcleju@0: #    
nikcleju@0: #    for algonameobj in algonames:
nikcleju@0: #        algoname = algonameobj[0][0]
nikcleju@0: #        print algoname
nikcleju@0: #        if mdict['meanmatrix'][algoname][0,0].ndim == 2:
nikcleju@0: #            plt.figure()
nikcleju@0: #            plt.imshow(mdict['meanmatrix'][algoname][0,0], cmap=cm.gray, interpolation='nearest',origin='lower')
nikcleju@0: #            for ext in saveplotexts:
nikcleju@0: #              plt.savefig(saveplotbase + algoname + '.' + ext, bbox_inches='tight')
nikcleju@0: #        elif mdict['meanmatrix'][algoname][0,0].ndim == 3:
nikcleju@0: #            ilbd = 0
nikcleju@0: #            for matrix in mdict['meanmatrix'][algoname][0,0]:
nikcleju@0: #                plt.figure()
nikcleju@0: #                plt.imshow(matrix, cmap=cm.gray, interpolation='nearest',origin='lower')
nikcleju@0: #                for ext in saveplotexts:
nikcleju@0: #                  plt.savefig(saveplotbase + algoname + ('_lbd%.0e' % lambdas[ilbd]) + '.' + ext, bbox_inches='tight')
nikcleju@0: #                ilbd = ilbd + 1
nikcleju@0: #    print "Finished."  
nikcleju@0:     
nikcleju@0: def loadmatrices(filename, algonames=None, doshow=True, dosave=False, saveplotbase=None, saveplotexts=None):
nikcleju@0:     
nikcleju@0:     if dosave and (saveplotbase is None or saveplotexts is None):
nikcleju@0:       print('Error: please specify name and extensions for saving')
nikcleju@0:       raise Exception('Name or extensions for saving not specified')
nikcleju@0:     
nikcleju@0:     mdict = scipy.io.loadmat(filename)
nikcleju@0: 
nikcleju@0:     if dosave:
nikcleju@0:       lambdas = mdict['lambdas']
nikcleju@0: 
nikcleju@0:     if algonames is None:
nikcleju@0:       algonames = mdict['algonames']
nikcleju@0:     
nikcleju@0:     for algonameobj in algonames:
nikcleju@0:         algoname = algonameobj[0][0]
nikcleju@0:         print algoname
nikcleju@0:         if mdict['meanmatrix'][algoname][0,0].ndim == 2:
nikcleju@0:             plt.figure()
nikcleju@0:             plt.imshow(mdict['meanmatrix'][algoname][0,0], cmap=cm.gray, norm=mcolors.Normalize(0,1), interpolation='nearest',origin='lower')
nikcleju@0:             if dosave:
nikcleju@0:               for ext in saveplotexts:
nikcleju@0:                 plt.savefig(saveplotbase + algoname + '.' + ext, bbox_inches='tight')
nikcleju@0:         elif mdict['meanmatrix'][algoname][0,0].ndim == 3:
nikcleju@0:             if dosave:
nikcleju@0:               ilbd = 0
nikcleju@0:             for matrix in mdict['meanmatrix'][algoname][0,0]:
nikcleju@0:                 plt.figure()
nikcleju@0:                 plt.imshow(matrix, cmap=cm.gray, norm=mcolors.Normalize(0,1), interpolation='nearest',origin='lower')
nikcleju@0:                 if dosave:
nikcleju@0:                   for ext in saveplotexts:
nikcleju@0:                     plt.savefig(saveplotbase + algoname + ('_lbd%.0e' % lambdas[ilbd]) + '.' + ext, bbox_inches='tight')
nikcleju@0:                   ilbd = ilbd + 1
nikcleju@0:     
nikcleju@0:     if doshow:
nikcleju@0:       plt.show()
nikcleju@0:     print "Finished."    
nikcleju@0: 
nikcleju@0:     
nikcleju@0: def loadshowmatrices_multipixels(filename, algonames = None, doshow=True, dosave=False, saveplotbase=None, saveplotexts=None):
nikcleju@0:   
nikcleju@0:     if dosave and (saveplotbase is None or saveplotexts is None):
nikcleju@0:       print('Error: please specify name and extensions for saving')
nikcleju@0:       raise Exception('Name or extensions for saving not specified')
nikcleju@0:       
nikcleju@0:     mdict = scipy.io.loadmat(filename)
nikcleju@0:     
nikcleju@0:     if dosave:
nikcleju@0:       lambdas = mdict['lambdas']
nikcleju@0:       
nikcleju@0:     if algonames == None:
nikcleju@0:       algonames = mdict['algonames']
nikcleju@0:     
nikcleju@0: #    thresh = 0.90
nikcleju@0:     N = 10
nikcleju@0: #    border = 2
nikcleju@0: #    bordercolor = 0 # black
nikcleju@0:     
nikcleju@0:     for algonameobj in algonames:
nikcleju@0:         algoname = algonameobj[0][0]
nikcleju@0:         print algoname
nikcleju@0:         if mdict['meanmatrix'][algoname][0,0].ndim == 2:
nikcleju@0:             
nikcleju@0:             # Prepare bigger matrix
nikcleju@0:             rows,cols = mdict['meanmatrix'][algoname][0,0].shape
nikcleju@0:             bigmatrix = numpy.zeros((N*rows,N*cols))
nikcleju@0:             for i in numpy.arange(rows):
nikcleju@0:               for j in numpy.arange(cols):
nikcleju@0:                 bigmatrix[i*N:i*N+N,j*N:j*N+N] = mdict['meanmatrix'][algoname][0,0][i,j]
nikcleju@5:             bigmatrix = int_drawseparation(mdict['meanmatrix'][algoname][0,0],bigmatrix,10,0.95,2,0)
nikcleju@6:             #bigmatrix = int_drawseparation(mdict['meanmatrix'][algoname][0,0],bigmatrix,10,0.9, 2,0.2)
nikcleju@5:             bigmatrix = int_drawseparation(mdict['meanmatrix'][algoname][0,0],bigmatrix,10,0.8, 2,0.4)
nikcleju@5:             bigmatrix = int_drawseparation(mdict['meanmatrix'][algoname][0,0],bigmatrix,10,0.5, 2,1)
nikcleju@0: #                # Mark 95% border
nikcleju@0: #                if mdict['meanmatrix'][algoname][0,0][i,j] > thresh:
nikcleju@0: #                  # Top border
nikcleju@0: #                  if mdict['meanmatrix'][algoname][0,0][i-1,j] < thresh and i>0:
nikcleju@0: #                    bigmatrix[i*N:i*N+border,j*N:j*N+N] = bordercolor
nikcleju@0: #                  # Bottom border
nikcleju@0: #                  if mdict['meanmatrix'][algoname][0,0][i+1,j] < thresh and i<rows-1:
nikcleju@0: #                    bigmatrix[i*N+N-border:i*N+N,j*N:j*N+N] = bordercolor                
nikcleju@0: #                  # Left border
nikcleju@0: #                  if mdict['meanmatrix'][algoname][0,0][i,j-1] < thresh and j>0:
nikcleju@0: #                    bigmatrix[i*N:i*N+N,j*N:j*N+border] = bordercolor
nikcleju@0: #                  # Right border (not very probable)
nikcleju@0: #                  if j<cols-1 and mdict['meanmatrix'][algoname][0,0][i,j+1] < thresh:
nikcleju@0: #                    bigmatrix[i*N:i*N+N,j*N+N-border:j*N+N] = bordercolor
nikcleju@0:                     
nikcleju@0:             plt.figure()
nikcleju@0:             #plt.imshow(mdict['meanmatrix'][algoname][0,0], cmap=cm.gray, interpolation='nearest',origin='lower')            
nikcleju@5:             plt.imshow(bigmatrix, cmap=cm.gray, norm=mcolors.Normalize(0,1), interpolation='nearest',origin='lower')        
nikcleju@0:             if dosave:
nikcleju@0:               for ext in saveplotexts:
nikcleju@0:                 plt.savefig(saveplotbase + algoname + '.' + ext, bbox_inches='tight')            
nikcleju@0:         elif mdict['meanmatrix'][algoname][0,0].ndim == 3:
nikcleju@0:             if dosave:
nikcleju@0:               ilbd = 0          
nikcleju@0:               
nikcleju@0:             for matrix in mdict['meanmatrix'][algoname][0,0]:
nikcleju@0:               
nikcleju@0:                 # Prepare bigger matrix
nikcleju@0:                 rows,cols = matrix.shape
nikcleju@0:                 bigmatrix = numpy.zeros((N*rows,N*cols))
nikcleju@0:                 for i in numpy.arange(rows):
nikcleju@0:                   for j in numpy.arange(cols):
nikcleju@0:                     bigmatrix[i*N:i*N+N,j*N:j*N+N] = matrix[i,j]
nikcleju@5:                 bigmatrix = int_drawseparation(matrix,bigmatrix,10,0.95,2,0)
nikcleju@6:                 #bigmatrix = int_drawseparation(matrix,bigmatrix,10,0.9, 2,0.2)
nikcleju@5:                 bigmatrix = int_drawseparation(matrix,bigmatrix,10,0.8, 2,0.4)
nikcleju@5:                 bigmatrix = int_drawseparation(matrix,bigmatrix,10,0.5, 2,1)
nikcleju@0: #                    # Mark 95% border
nikcleju@0: #                    if matrix[i,j] > thresh:
nikcleju@0: #                      # Top border
nikcleju@0: #                      if matrix[i-1,j] < thresh and i>0:
nikcleju@0: #                        bigmatrix[i*N:i*N+border,j*N:j*N+N] = bordercolor
nikcleju@0: #                      # Bottom border
nikcleju@0: #                      if matrix[i+1,j] < thresh and i<rows-1:
nikcleju@0: #                        bigmatrix[i*N+N-border:i*N+N,j*N:j*N+N] = bordercolor                
nikcleju@0: #                      # Left border
nikcleju@0: #                      if matrix[i,j-1] < thresh and j>0:
nikcleju@0: #                        bigmatrix[i*N:i*N+N,j*N:j*N+border] = bordercolor
nikcleju@0: #                      # Right border (not very probable)
nikcleju@0: #                      if j<cols-1 and matrix[i,j+1] < thresh:
nikcleju@0: #                        bigmatrix[i*N:i*N+N,j*N+N-border:j*N+N] = bordercolor
nikcleju@0:                 
nikcleju@0:                 plt.figure()
nikcleju@0:                 #plt.imshow(matrix, cmap=cm.gray, interpolation='nearest',origin='lower')
nikcleju@5:                 plt.imshow(bigmatrix, cmap=cm.gray, norm=mcolors.Normalize(0,1), interpolation='nearest',origin='lower')
nikcleju@0:                 if dosave:
nikcleju@0:                   for ext in saveplotexts:
nikcleju@0:                     plt.savefig(saveplotbase + algoname + ('_lbd%.0e' % lambdas[ilbd]) + '.' + ext, bbox_inches='tight')
nikcleju@0:                   ilbd = ilbd + 1                
nikcleju@0:     if doshow:
nikcleju@0:       plt.show()
nikcleju@0:     print "Finished."    
nikcleju@0:     
nikcleju@0: def appendtomatfile(filename, toappend, toappendname):
nikcleju@0:   mdict = scipy.io.loadmat(filename)
nikcleju@0:   mdict[toappendname] = toappend
nikcleju@0:   try:
nikcleju@0:     scipy.io.savemat(filename, mdict)
nikcleju@0:   except:
nikcleju@0:     print "Save error"  
nikcleju@0:   
nikcleju@0:   # To save to a cell array, create an object array:
nikcleju@0:   #  >>> obj_arr = np.zeros((2,), dtype=np.object)
nikcleju@0:   #  >>> obj_arr[0] = 1
nikcleju@0:   #  >>> obj_arr[1] = 'a string'    
nikcleju@0:   
nikcleju@0: def int_drawseparation(matrix,bigmatrix,N,thresh,border,bordercolor):
nikcleju@0:   rows,cols = matrix.shape
nikcleju@0:   for i in numpy.arange(rows):
nikcleju@0:     for j in numpy.arange(cols):
nikcleju@0:       # Mark border
nikcleju@0:       # Use top-left corner of current square for reference
nikcleju@0:       if matrix[i,j] > thresh:
nikcleju@0:         # Top border
nikcleju@0:         if matrix[i-1,j] < thresh and i>0:
nikcleju@0:           bigmatrix[i*N:i*N+border,j*N:j*N+N] = bordercolor
nikcleju@0:         # Bottom border
nikcleju@0:         if i<rows-1 and matrix[i+1,j] < thresh:
nikcleju@0:           bigmatrix[i*N+N-border:i*N+N,j*N:j*N+N] = bordercolor                
nikcleju@0:         # Left border
nikcleju@0:         if matrix[i,j-1] < thresh and j>0:
nikcleju@0:           bigmatrix[i*N:i*N+N,j*N:j*N+border] = bordercolor
nikcleju@0:         # Right border (not very probable)
nikcleju@0:         if j<cols-1 and matrix[i,j+1] < thresh:
nikcleju@0:           bigmatrix[i*N:i*N+N,j*N+N-border:j*N+N] = bordercolor  
nikcleju@0:   
nikcleju@0:   return bigmatrix