old_contents: #!/bin/env python
  ''' Overall mean
  
  Compile a list of pre-existing effect size images for the first 4
  subjects and the first run in the example dataset, and create an image
  giving the mean of these effect size images across subjects.
  
  Usage:
  
  overall_mean.py
  
  Creates overall_mean.img in working directory
  '''
  
  import numpy as N
  import pylab
  
  from neuroimaging.core.image.image import Image
  from neuroimaging.ui.visualization.viewer import BoxViewer
  from neuroimaging.utils.tests.data import repository
  
  def subj_run_effect_img(subject=0, run=1):
      ''' Return effect image from example dataset for subject and run no 
      '''
      return Image(
          'FIAC/fiac%d/fonc%d/fsl/fmristat_run/contrasts/overall/effect.img'
          % (subject,run), repository)
  
  def mean_overall(images):
      ''' Return mean over list of Images '''
      out = N.zeros(images[0].grid.shape, N.float64)
      for im in images:
          out += im[:]
      out /= len(images)
      return Image(out, grid=images[0].grid)
  
  # Collect subject images for first run
  subject_nos = range(4)
  effect_imgs = [subj_run_effect_img(subject=i, run=1) for i in subject_nos]
  
  # Create mean image and write to file
  overall_mean_img = mean_overall(effect_imgs)
  overall_mean_img.tofile('overall_mean.img', clobber=True)
  
  # Show in orthogonal viewer
  viewer = BoxViewer(overall_mean_img, colormap='spectral')
  viewer.draw()
  pylab.show()
  
new_contents: #!/bin/env python
  ''' Overall mean
  
  Compile a list of pre-existing effect size images for the first 4
  subjects and the first run in the example dataset, and create an image
  giving the mean of these effect size images across subjects.
  
  Usage:
  
  overall_mean.py
  
  Creates overall_mean.img in working directory
  '''
  
  import numpy as N
  import pylab
  
  from neuroimaging.core.image.image import Image
  from neuroimaging.ui.visualization.viewer import BoxViewer
  from neuroimaging.utils.tests.data import repository
  
  def subj_run_effect_img(subject=0, run=1):
      ''' Return effect image from example dataset for subject and run no 
      '''
      return Image(
          'FIAC/fiac%d/fonc%d/fsl/fmristat_run/contrasts/overall/effect.img'
          % (subject,run), repository)
  
  def mean_overall(images):
      ''' Return mean over list of Images '''
      out = N.zeros(images[0].grid.shape)
      for im in images:
          out += im[:]
      out /= len(images)
      return Image(out, grid=images[0].grid)
  
  # Collect subject images for first run
  subject_nos = range(4)
  effect_imgs = [subj_run_effect_img(subject=i, run=1) for i in subject_nos]
  
  # Create mean image and write to file
  overall_mean_img = mean_overall(effect_imgs)
  overall_mean_img.tofile('overall_mean.img', clobber=True)
  
  # Show in orthogonal viewer
  viewer = BoxViewer(overall_mean_img, colormap='spectral')
  viewer.draw()
  pylab.show()
  
subject: remove unneeded float64