old_contents: # Released under The MIT License (MIT)
  # http://opensource.org/licenses/MIT
  # Copyright (c) 2013-2016 SCoT Development Team
  
  """Use internally implemented functions as backend."""
  
  from __future__ import absolute_import
  
  from . import backend
  
  
  def generate():
      import scipy as sp
  
      from . import datatools, pca, csp
      from .var import VAR
      from .external.infomax_ import infomax
  
      def wrapper_infomax(data):
          """Call Infomax (adapted from MNE) for ICA calculation."""
          u = infomax(datatools.cat_trials(data).T).T
          m = sp.linalg.pinv(u)
          return m, u
  
      def wrapper_pca(x, reducedim):
          """Call SCoT's PCA algorithm."""
          c, d = pca.pca(datatools.cat_trials(x),
                         subtract_mean=False, reducedim=reducedim)
          y = datatools.dot_special(c.T, x)
          return c, d, y
  
      def wrapper_csp(x, cl, reducedim):
          c, d = csp.csp(x, cl, numcomp=reducedim)
          y = datatools.dot_special(c.T, x)
          return c, d, y
  
      return {'ica': wrapper_infomax, 'pca': wrapper_pca, 'csp': wrapper_csp,
              'var': VAR}
  
  
  backend.register('builtin', generate)
  
new_contents: # Released under The MIT License (MIT)
  # http://opensource.org/licenses/MIT
  # Copyright (c) 2013-2016 SCoT Development Team
  
  """Use internally implemented functions as backend."""
  
  from __future__ import absolute_import
  import scipy as sp
  
  from . import backend
  from . import datatools, pca, csp
  from .var import VAR
  from .external.infomax_ import infomax
  
  
  def generate():
      def wrapper_infomax(data):
          """Call Infomax (adapted from MNE) for ICA calculation."""
          u = infomax(datatools.cat_trials(data).T).T
          m = sp.linalg.pinv(u)
          return m, u
  
      def wrapper_pca(x, reducedim):
          """Call SCoT's PCA algorithm."""
          c, d = pca.pca(datatools.cat_trials(x),
                         subtract_mean=False, reducedim=reducedim)
          y = datatools.dot_special(c.T, x)
          return c, d, y
  
      def wrapper_csp(x, cl, reducedim):
          c, d = csp.csp(x, cl, numcomp=reducedim)
          y = datatools.dot_special(c.T, x)
          return c, d, y
  
      return {'ica': wrapper_infomax, 'pca': wrapper_pca, 'csp': wrapper_csp,
              'var': VAR}
  
  
  backend.register('builtin', generate)
  
subject: Move imports to top