old_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  """
  
  .. autosummary::
      :toctree: generated/
  
  
  """
  
  import numpy
  
  def mcnemar(reference, estimated_a, estimated_b):
      """McNemar's test
  
      Wikipedia entry https://en.wikipedia.org/wiki/McNemar%27s_test
  
      Parameters
      ----------
      reference : list
          Reference value
  
      estimated_a : list
          System output A
  
      estimated_b : list
          System output B
  
      Returns
      -------
      float
          exact P-value
  
      """
  
      if len(reference) != len(estimated_a) or len(reference) != len(estimated_b):
          raise ValueError('Input arrays needs to be same length.')
  
      # Convert lists into numpy.array
      reference = numpy.array(reference)
      estimated_a = numpy.array(estimated_a)
      estimated_b = numpy.array(estimated_b)
  
      # Intermediate values
      correct_a = estimated_a == reference
      correct_b = estimated_b == reference
  
      incorrect_a = estimated_a != reference
      incorrect_b = estimated_b != reference
  
      # Contingency table values
      b = float( numpy.sum( numpy.logical_and(incorrect_a, correct_b) ) )
      c = float( numpy.sum( numpy.logical_and(correct_a, incorrect_b) ) )
  
      # Continuity corrected version of the McNemar test to approximate the binomial exact-P-value
      # Edwards, A (1948). "Note on the "correction for continuity" in testing the significance of the difference
      # between correlated proportions". Psychometrika. 13: 185–187
      if b + c > 0:
          return (numpy.abs(b - c) - 1)**2 / (b + c)
      else:
          return 0
  
new_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  """
  
  .. autosummary::
      :toctree: generated/
  
      mcnemar
  
  """
  
  import numpy
  
  def mcnemar(reference, estimated_a, estimated_b):
      """McNemar's test
  
      Wikipedia entry https://en.wikipedia.org/wiki/McNemar%27s_test
  
      Parameters
      ----------
      reference : list
          Reference value
  
      estimated_a : list
          System output A
  
      estimated_b : list
          System output B
  
      Returns
      -------
      float
          exact P-value
  
      """
  
      if len(reference) != len(estimated_a) or len(reference) != len(estimated_b):
          raise ValueError('Input arrays needs to be same length.')
  
      # Convert lists into numpy.array
      reference = numpy.array(reference)
      estimated_a = numpy.array(estimated_a)
      estimated_b = numpy.array(estimated_b)
  
      # Intermediate values
      correct_a = estimated_a == reference
      correct_b = estimated_b == reference
  
      incorrect_a = estimated_a != reference
      incorrect_b = estimated_b != reference
  
      # Contingency table values
      b = float( numpy.sum( numpy.logical_and(incorrect_a, correct_b) ) )
      c = float( numpy.sum( numpy.logical_and(correct_a, incorrect_b) ) )
  
      # Continuity corrected version of the McNemar test to approximate the binomial exact-P-value
      # Edwards, A (1948). "Note on the "correction for continuity" in testing the significance of the difference
      # between correlated proportions". Psychometrika. 13: 185–187
      if b + c > 0:
          return (numpy.abs(b - c) - 1)**2 / (b + c)
      else:
          return 0
  
subject: Add documentation