old_contents: """
  The ```nanshe``` package is an image processing package that contains a variety
  of different techniques, which are used primarily to assemble the ADINA
  algorithm proposed by Diego, et al.
  ( http://dx.doi.org/10.1109/ISBI.2013.6556660 ) to extract active neurons from
  an image sequence. This algorithm uses online dictionary learning (a form of
  matrix factorization) at its heart as implemented by Marial, et al.
  ( http://dx.doi.org/10.1145/1553374.1553463 ) to find a set of atoms (or basis
  images) that are representative of an image sequence and can be used to
  approximately reconstruct the sequence. However, it is designed in a modular
  way so that a different matrix factorization could be swapped in and
  appropriately parameterized. Other portions of the algorithm include a
  preprocessing phase that has a variety of different techniques that can be
  applied optionally. For example, removing registration artifacts from
  a line-by-line registration algorithm, background subtraction, and a wavelet
  transform to filter objects in a particular size.
  """
  __author__ = "John Kirkham <kirkhamj@janelia.hhmi.org>"
  __date__ = "$Dec 22, 2014 08:46:12 EST$"
  
  
  __all__ = [
      "converter", "io", "imp", "learner" "registerer", "spams_sandbox",
      "synthetic_data", "util",  # "viewer"
  ]
  
  import converter
  import io
  import imp
  import learner
  import registerer
  import spams_sandbox
  import synthetic_data
  import util
  # import viewer           # Must be commented as there is some segfault coming from Volumina.
  
new_contents: """
  The ```nanshe``` package is an image processing package that contains a variety
  of different techniques, which are used primarily to assemble the ADINA
  algorithm proposed by Diego, et al.
  ( doi:`10.1109/ISBI.2013.6556660`_ ) to extract active neurons from
  an image sequence. This algorithm uses online dictionary learning (a form of
  matrix factorization) at its heart as implemented by Marial, et al.
  ( doi:`10.1145/1553374.1553463`_ ) to find a set of atoms (or basis
  images) that are representative of an image sequence and can be used to
  approximately reconstruct the sequence. However, it is designed in a modular
  way so that a different matrix factorization could be swapped in and
  appropriately parameterized. Other portions of the algorithm include a
  preprocessing phase that has a variety of different techniques that can be
  applied optionally. For example, removing registration artifacts from
  a line-by-line registration algorithm, background subtraction, and a wavelet
  transform to filter objects in a particular size.
  
  
  .. _`10.1109/ISBI.2013.6556660`: http://dx.doi.org/10.1109/ISBI.2013.6556660
  .. _`10.1145/1553374.1553463`: http://dx.doi.org/10.1145/1553374.1553463
  """
  __author__ = "John Kirkham <kirkhamj@janelia.hhmi.org>"
  __date__ = "$Dec 22, 2014 08:46:12 EST$"
  
  
  __all__ = [
      "converter", "io", "imp", "learner" "registerer", "spams_sandbox",
      "synthetic_data", "util",  # "viewer"
  ]
  
  import converter
  import io
  import imp
  import learner
  import registerer
  import spams_sandbox
  import synthetic_data
  import util
  # import viewer           # Must be commented as there is some segfault coming from Volumina.
  
subject: Use DOI format to simplify links.