code: def plot_full(candsfile, cands, mode='im'):
      
  
      loc, prop, d = read_candidates(candsfile, returnstate=True)
      npixx, npixy = prop[0][4].shape
      nints, nchan, npol = prop[0][5].shape
  
      bin = 10
      plt.figure(1)
      for i in cands:
          if mode == 'spec':
              rr = np.array([np.abs(prop[i][5][:,i0:i0+bin,0].mean(axis=1)) for i0 in range(0,nchan,bin)])
              ll = np.array([np.abs(prop[i][5][:,i0:i0+bin,1].mean(axis=1)) for i0 in range(0,nchan,bin)])
              sh = ll.shape
              data = np.concatenate( (rr, np.zeros(shape=(sh[0], sh[1]/2)), ll), axis=1)
          elif mode == 'im':
              data = prop[i][4]
          plt.subplot(np.sqrt(len(cands)), np.sqrt(len(cands)), cands.index(i))
          plt.imshow(data, interpolation='nearest')
      plt.show()
docs: Plot 'full' features, such as cutout image and spectrum.