old_contents: 
  import numpy as np
  
  
  def hellinger(data1, data2):
      '''
      Calculate the Hellinger Distance between two datasets.
  
      Parameters
      ----------
      data1 : numpy.ndarray
          1D array.
      data2 : numpy.ndarray
          1D array.
  
      Returns
      -------
      distance : float
          Distance value.
      '''
      distance = (1 / np.sqrt(2)) * \
          np.sqrt(np.nansum((np.sqrt(data1) - np.sqrt(data2)) ** 2.))
      return distance
  
  
  def standardize(x):
      return (x - np.nanmean(x)) / np.nanstd(x)
  
new_contents: 
  import numpy as np
  
  
  def hellinger(data1, data2):
      '''
      Calculate the Hellinger Distance between two datasets.
  
      Parameters
      ----------
      data1 : numpy.ndarray
          1D array.
      data2 : numpy.ndarray
          1D array.
  
      Returns
      -------
      distance : float
          Distance value.
      '''
      distance = (1 / np.sqrt(2)) * \
          np.sqrt(np.nansum((np.sqrt(data1) - np.sqrt(data2)) ** 2.))
      return distance
  
  
  def standardize(x):
      return (x - np.nanmean(x)) / np.nanstd(x)
  
  
  def kl_divergence(P, Q):
      '''
      Kullback Leidler Divergence
  
      Parameters
      ----------
  
      P,Q : numpy.ndarray
          Two Discrete Probability distributions
  
      Returns
      -------
  
      kl_divergence : float
      '''
      P = P[~np.isnan(P)]
      Q = Q[~np.isnan(Q)]
      P = P[np.isfinite(P)]
      Q = Q[np.isfinite(Q)]
      return np.nansum(np.where(Q != 0, P * np.log(P / Q), 0))
  
subject: Move KL Div to utils file