old_contents: from robust_fit import RLM, bisquare
  
  
  # Possible coefficients
  design_coefs = ['all',
                  'intercept', 'slope',
                  'seasonality', 'categorical',
                  'rmse']
  
  
  def _key_lookup_ignorecase(d, key):
      """ Search dict for key, ignoring case
  
      Args:
        d (dict): dict to search
        key (str): key to search for
  
      Returns:
        str or None: key in dict `d` matching `key` if found; else None
  
      """
      key = [k for k in d.keys() if key.lower() == k.lower()]
      if key:
          return key[0]
      else:
          return None
  
  
  def design_to_indices(design_matrix, features):
      """ Return indices of coefficients for features in design matrix
  
      Args:
        design_matrix (OrderedDict): OrderedDict containing design features keys
          and indices of coefficient matrix as values
        features (list): list of feature coefficients to extract
  
      Return:
        tuple: list of indices and names for each feature specified in `features`
  
      """
      if 'all' in features:
          features = design_coefs[1:]
  
      i_coefs = []
      coef_names = []
      for c in features:
          if c == 'intercept':
              k = _key_lookup_ignorecase(design_matrix, 'intercept')
              i_coefs.append(design_matrix.get(k))
              coef_names.append(k)
          elif c == 'slope':
              k = _key_lookup_ignorecase(design_matrix, 'x')
              i_coefs.append(design_matrix.get(
                  _key_lookup_ignorecase(design_matrix, 'x')))
              coef_names.append(k)
          elif c == 'seasonality':
              i = [k for k in design_matrix.keys() if 'harm' in k]
              i_coefs.extend([design_matrix[_i] for _i in i])
              coef_names.extend(i)
          elif c == 'categorical':
              i = [k for k in design_matrix.keys() if 'C' in k]
              i_coefs.extend([design_matrix[_i] for _i in i])
              coef_names.extend(i)
  
      i_coefs = [i for i in i_coefs if i is not None]
      coef_names = [n for n in coef_names if n is not None]
  
      return i_coefs, coef_names
  
new_contents: from . import packaged
  from .robust_fit import RLM, bisquare
  
  
  # Possible coefficients
  design_coefs = ['all',
                  'intercept', 'slope',
                  'seasonality', 'categorical',
                  'rmse']
  
  
  def _key_lookup_ignorecase(d, key):
      """ Search dict for key, ignoring case
  
      Args:
        d (dict): dict to search
        key (str): key to search for
  
      Returns:
        str or None: key in dict `d` matching `key` if found; else None
  
      """
      key = [k for k in d.keys() if key.lower() == k.lower()]
      if key:
          return key[0]
      else:
          return None
  
  
  def design_to_indices(design_matrix, features):
      """ Return indices of coefficients for features in design matrix
  
      Args:
        design_matrix (OrderedDict): OrderedDict containing design features keys
          and indices of coefficient matrix as values
        features (list): list of feature coefficients to extract
  
      Return:
        tuple: list of indices and names for each feature specified in `features`
  
      """
      if 'all' in features:
          features = design_coefs[1:]
  
      i_coefs = []
      coef_names = []
      for c in features:
          if c == 'intercept':
              k = _key_lookup_ignorecase(design_matrix, 'intercept')
              i_coefs.append(design_matrix.get(k))
              coef_names.append(k)
          elif c == 'slope':
              k = _key_lookup_ignorecase(design_matrix, 'x')
              i_coefs.append(design_matrix.get(
                  _key_lookup_ignorecase(design_matrix, 'x')))
              coef_names.append(k)
          elif c == 'seasonality':
              i = [k for k in design_matrix.keys() if 'harm' in k]
              i_coefs.extend([design_matrix[_i] for _i in i])
              coef_names.extend(i)
          elif c == 'categorical':
              i = [k for k in design_matrix.keys() if 'C' in k]
              i_coefs.extend([design_matrix[_i] for _i in i])
              coef_names.extend(i)
  
      i_coefs = [i for i in i_coefs if i is not None]
      coef_names = [n for n in coef_names if n is not None]
  
      return i_coefs, coef_names
  
subject: Make packaged regressors avail in submodule import