old_contents: """====================================================
                      DESCRIPTION
  
  =======================================================
  """
  __author__ = 'ronny'
  
  import numpy as np
  
  #===============================================================================
  #                                                              NORMALISE COLUMNS
  #===============================================================================
  def normalise_columns(x, mean, sd):
      """
      Normalise the elements of a 2D array to be Z-scores (standard normal values)
      scaled by some consistent mean and sd for each column(which should be the
      mean and sd of the training data)
  
      :param x: (2D Array)
          A 2D array, whose columns we wish to normalise
      :param mean: (1D array)
          The means to be used to normalise each of the columns of x
      :param sd: (1D array)
          The standard deviations to be used to normalise each of the columns of x
      :return: (numpy array)
          Returns an array where the elements are normalised along the columns.
      """
      # ==========================================================================
      return (np.array(x) - mean) / sd
  
  
  def un_normalise_columns(z, mean, sd):
      return z*sd + mean
  
new_contents: """====================================================
                      DESCRIPTION
  
  =======================================================
  """
  __author__ = 'ronny'
  
  import numpy as np
  
  #===============================================================================
  #                                                              NORMALISE COLUMNS
  #===============================================================================
  def normalise_columns(x, mean, sd):
      """
      Normalise the elements of a 2D array to be Z-scores (standard normal values)
      scaled by some consistent mean and sd for each column(which should be the
      mean and sd of the training data)
  
      :param x: (2D Array)
          A 2D array, whose columns we wish to normalise
      :param mean: (1D array)
          The means to be used to normalise each of the columns of x
      :param sd: (1D array)
          The standard deviations to be used to normalise each of the columns of x
      :return: (numpy array)
          Returns an array where the elements are normalised along the columns.
      """
      # ==========================================================================
      return (np.array(x) - mean) / sd
  
  
  
  #===============================================================================
  #                                                           UN_NORMALISE COLUMNS
  #===============================================================================
  def un_normalise_columns(z, mean, sd):
      """
      Takes a 2D array of Z-score values, along with the mean and standard
      deviations for each column used to calculate those z-scores. Returns the
      values in their original scale.
  
      :param z: (2D Array)
          A 2D array, containing z-score values
      :param mean: (1D array)
          The means that were used to normalise each of the columns of z
      :param sd: (1D array)
          The standard deviations that were used to normalise each of the columns
          of z
      :return: (numpy array)
          Returns an array where the elements are the values in their original
          scale. .
      """
      # ==========================================================================
      return z*sd + mean
  
  
  
subject: Add docstring to un_normalise_columns()