code: def load_filter(filter_name):
      '''Retrieve a pre-computed filter.
  
      Parameters
      ----------
      filter_name : str
          The key of the filter, e.g., 'kaiser_fast'
  
      Returns
      -------
      half_window : np.ndarray
          The right wing of the interpolation filter
  
      precision : int > 0
          The number of samples between zero-crossings of the fitler
  
      rolloff : float > 0
          The roll-off frequency of the filter, as a fraction of Nyquist
      '''
  
      fname = os.path.join('data',
                           os.path.extsep.join([filter_name, 'npz']))
  
      data = np.load(pkg_resources.resource_filename(__name__, fname))
  
      return data['half_window'], data['precision'], data['rolloff'f load_filter(filter_name):
      '''Retrieve a pre-computed filter.
  
      Parameters
      ----------
      filter_name : str
          The key of the filter, e.g., 'kaiser_fast'
  
      Returns
      -------
      half_window : np.ndarray
          The right wing of the interpolation filter
  
      precision : int > 0
          The number of samples between zero-crossings of the fitler
  
      rolloff : float > 0
          The roll-off frequency of the filter, as a fraction of Nyquist
      '''
  
      fname = os.path.join('data',
                           os.path.extsep.join([filter_name, 'npz']))
  
      data = np.load(pkg_resources.resource_filename(__name__, fname))
  
      return data['half_window'], data['precision'], data['rolloff']
docs: Retrieve a pre-computed filter.
  
      Parameters
      ----------
      filter_name : str
          The key of the filter, e.g., 'kaiser_fast'
  
      Returns
      -------
      half_window : np.ndarray
          The right wing of the interpolation filter
  
      precision : int > 0
          The number of samples between zero-crossings of the fitler
  
      rolloff : float > 0
          The roll-off frequency of the filter, as a fraction of Nyquist