old_contents: # Authors: Yousra Bekhti
  #          Mark Wronkiewicz <wronk.mark@gmail.com>
  #
  # License: BSD (3-clause)
  
  import numpy as np
  
  
  def _check_stc(stc1, stc2):
      # XXX What should we check? that the data is having the same size?
      if stc1.data.shape != stc2.data.shape:
          raise ValueError('Data in stcs must have the same size')
      if np.all(stc1.times != stc2.times):
          raise ValueError('Times of two stcs must match.')
  
  
  def source_estimate_quantification(stc1, stc2, metric='rms'):
      """Helper function to calculate matrix similarities.
  
      Parameters
      ----------
      stc1 : SourceEstimate
          First source estimate for comparison
      stc2 : SourceEstimate
          First source estimate for comparison
      metric : str
          Metric to calculate. 'rms', 'corr',
  
      Returns
      -------
      score : float
          Calculated metric
      """
      # TODO Add checks for source space
      _check_stc(stc1, stc2)
  
      score = _calc_metric(stc1.data, stc2.data, metric)
  
  
  def _calc_metric(data1, data2, metric):
      """Helper to calculate metric of choice.
  
      Parameters
      ----------
      data1 : ndarray, shape(n_sources, ntimes)
          Second data matrix
      data2 : ndarray, shape(n_sources, ntimes)
          Second data matrix
      metric : str
          Metric to calculate. 'rms', 'rms_normed', 'corr',
  
      Returns
      -------
      score : float
          Calculated metric
      """
  
      # Calculate root mean square difference between two matrices
      if metric == 'rms':
          return np.sqrt(np.mean((data1 - data2) ** 2))
  
      # Calculate root mean square difference between two normalized matrices
      if metric == 'rms_normed':
          data1 = data1 / np.max(data1)
          data2 = data2 / np.max(data2)
          return np.sqrt(np.mean((data1 - data2) ** 2))
  
      # Calculate correlation coefficient between matrix elements
      elif metric == 'corr':
          return np.correlate(data1.flatten(), data2.flatten())
  
new_contents: # Authors: Yousra Bekhti
  #          Mark Wronkiewicz <wronk.mark@gmail.com>
  #
  # License: BSD (3-clause)
  
  import numpy as np
  
  
  def _check_stc(stc1, stc2):
      # XXX What should we check? that the data is having the same size?
      if stc1.data.shape != stc2.data.shape:
          raise ValueError('Data in stcs must have the same size')
      if np.all(stc1.times != stc2.times):
          raise ValueError('Times of two stcs must match.')
  
  
  def source_estimate_quantification(stc1, stc2, metric='rms'):
      """Helper function to calculate matrix similarities.
  
      Parameters
      ----------
      stc1 : SourceEstimate
          First source estimate for comparison
      stc2 : SourceEstimate
          First source estimate for comparison
      metric : str
          Metric to calculate. 'rms', 'corr',
  
      Returns
      -------
      score : float
          Calculated metric
      """
      # TODO Add checks for source space
      _check_stc(stc1, stc2)
  
      score = _calc_metric(stc1.data, stc2.data, metric)
  
  
  def _calc_metric(data1, data2, metric):
      """Helper to calculate metric of choice.
  
      Parameters
      ----------
      data1 : ndarray, shape(n_sources, ntimes)
          Second data matrix
      data2 : ndarray, shape(n_sources, ntimes)
          Second data matrix
      metric : str
          Metric to calculate. 'rms', 'corr',
  
      Returns
      -------
      score : float
          Calculated metric
      """
  
      # Calculate root mean square difference between two matrices
      if metric == 'rms':
          return np.sqrt(np.mean((data1 - data2) ** 2))
  
      # Calculate root mean square difference between two normalized matrices
      if metric == 'rms_normed':
          data1 = data1 / np.max(data1)
          data2 = data2 / np.max(data2)
          return np.sqrt(np.mean((data1 - data2) ** 2))
  
      # Calculate correlation coefficient between matrix elements
      elif metric == 'corr':
          return np.correlate(stc1.data.flatten(), stc2.data.flatten())
  
subject: Add documentation, tests, fix corr