old_contents: from numpy.testing import assert_equal, assert_almost_equal
  import numpy as np
  
  from nipy.neurospin.group import routines, _onesample, _twosample
  
  def slow_add_lines(A, B, I):
      for i in xrange(len(I)):
          B[I[i]] += A[i]
  
  
  def test_add_lines():
      nlines = 100000
      ncols = 1
      A = np.random.rand(nlines, ncols)
      B = np.random.rand(nlines, ncols)
      I = np.asarray(np.random.randint(nlines, size=nlines)
              ).astype(np.int)
      C1 = B.copy()
      C2 = B.copy()
      routines.add_lines(A, C1, I)
      slow_add_lines(A, C2, I)
      assert_almost_equal(C1, C2)         
  
  def test_add_lines2():
      nlines = 1e5
      ncols = 1
      A = np.random.rand(nlines, ncols)
      B = np.random.rand(nlines, ncols)
      I = np.asarray(np.random.randint(nlines, size=nlines)
              ).astype(np.int)
      C1 = B.copy()
      C2 = B.copy()
      routines.add_lines(A, C1, I)
      slow_add_lines(A, C2, I)
      assert_almost_equal(C1, C2)
  
  
  def test_onesample_stat():
      dx, dy, dz = 3, 4, 2
      nvox = dx*dy*dz
      nsub = 12
      # Make surrogate data 
      aux = np.arange(nvox)
      x = np.reshape(aux.repeat(nsub), [dx, dy, dz, nsub])
      # Gold standard 
      y_target = np.inf * np.ones(nvox)
      y_target[0] = 0.0
      # Test: input C-contiguous, data owner, axis=3
      y = _onesample.stat(x, axis=3).reshape(nvox)
      assert_equal(y, y_target)
      # Test: input F-contiguous, not owner, axis=0 
      y = _onesample.stat(x.T, axis=0).reshape(nvox)
      assert_equal(y, y_target)
      # Test: input C-contiguous, data owner, axis=0
      xT = x.T.copy()
      y = _onesample.stat(xT, axis=0).reshape(nvox)
      assert_equal(y, y_target)
      
  
      
  if __name__ == "__main__":
      import nose
      nose.run(argv=['', __file__])
  
new_contents: from numpy.testing import assert_equal, assert_almost_equal
  import numpy as np
  
  from nipy.neurospin.group import routines, onesample, twosample
  
  def slow_add_lines(A, B, I):
      for i in xrange(len(I)):
          B[I[i]] += A[i]
  
  
  def test_add_lines():
      nlines = 100000
      ncols = 1
      A = np.random.rand(nlines, ncols)
      B = np.random.rand(nlines, ncols)
      I = np.asarray(np.random.randint(nlines, size=nlines)
              ).astype(np.int)
      C1 = B.copy()
      C2 = B.copy()
      routines.add_lines(A, C1, I)
      slow_add_lines(A, C2, I)
      assert_almost_equal(C1, C2)         
  
  def test_add_lines2():
      nlines = 1e5
      ncols = 1
      A = np.random.rand(nlines, ncols)
      B = np.random.rand(nlines, ncols)
      I = np.asarray(np.random.randint(nlines, size=nlines)
              ).astype(np.int)
      C1 = B.copy()
      C2 = B.copy()
      routines.add_lines(A, C1, I)
      slow_add_lines(A, C2, I)
      assert_almost_equal(C1, C2)
  
  
  def test_onesample_stat():
      dx, dy, dz = 3, 4, 2
      nvox = dx*dy*dz
      nsub = 12
      # Make surrogate data 
      aux = np.arange(nvox)
      x = np.reshape(aux.repeat(nsub), [dx, dy, dz, nsub])
      # Gold standard 
      y_target = np.inf * np.ones(nvox)
      y_target[0] = 0.0
      # Test: input C-contiguous, data owner, axis=3
      y = onesample.stat(x, axis=3).reshape(nvox)
      assert_equal(y, y_target)
      # Test: input F-contiguous, not owner, axis=0 
      y = onesample.stat(x.T, axis=0).reshape(nvox)
      assert_equal(y, y_target)
      # Test: input C-contiguous, data owner, axis=0
      xT = x.T.copy()
      y = onesample.stat(xT, axis=0).reshape(nvox)
      assert_equal(y, y_target)
      
  
      
  if __name__ == "__main__":
      import nose
      nose.run(argv=['', __file__])
  
subject: Fix modified import in tests.