new_contents: 
  import healpy as hp
  import numpy as np
  
  import matplotlib.pyplot as plt
  
  def load_ply(path):
      
      lines = []
      verts = []
      norms = []
          
      
      f = open(path, "r")
      for line in f:
          lines.append(line)
          
      if (lines[0] != "ply\n"):
          return 0
      
      i = 1
      #get number of vertices
      while (lines[i].split()[0] != 'element'):
          i += 1
          
      if (lines[i].split()[1] == 'vertex'):
          nbV = int(lines[i].split()[2])
          print str(nbV) + " vertices"
          
          i += 1
          
          #count number of properties: if 3 no normals, if 6 normals
          nbP = 0
  #        while (lines[i].split()[0] == 'property'):
  #            nbP += 1
  #            i += 1
      
      #if ((lines[i].split()[0] == "element") & (lines[i].split()[1] == "face")):
      #    nbF = int(lines[i].split()[2])
      #    print str(nbF) + " faces"
          
      while (lines[i].split()[0] != 'end_header'):
          i += 1
      
      vstart = i + 1
      
      invertedIndex = [[] for x in xrange(nbV)]
      
      #read vertices and normals
      for i in range(vstart,vstart+nbV):
          vals = lines[i].split()
          flist = map(float, vals)
          verts.append(flist[0:3])
          #if (nbP > 3):
          norms.append(flist[0:3])
          #if (nbP > 6):
          #    curvatures.append(flist[6])
       
      f.close()
      
      return verts, np.swapaxes(norms,0,1)
  
  def normalized(a):
      return a / np.linalg.norm(a, 2, 0)
  
  
  
  nside = 32
  
  verts, norms = load_ply( "/export/home/kandinsky/nmellado/git/globOpt/data/scenes-paper/bu_lansFull/cloudRGBNormal_patches_gaussSphere.ply")
  
  mmap = np.zeros((hp.nside2npix(nside)))
  mmap[ hp.vec2pix(nside, norms[0], norms[1], norms[2]) ] += 1
  
  mmap = mmap
  
  hp.cartview(mmap, nest=True, title="Mollview image NESTED")
  plt.savefig('out_patches.png')
  
subject: Add script to display normal distribution