original_docstring: 
      Computes probability of a word being recalled (in the appropriate recall list), given its presentation position
  
      Parameters
      ----------
      egg : quail.Egg
          Data to analyze
  
      match : str (exact, best or smooth)
          Matching approach to compute recall matrix.  If exact, the presented and
          recalled items must be identical (default).  If best, the recalled item
          that is most similar to the presented items will be selected. If smooth,
          a weighted average of all presented items will be used, where the
          weights are derived from the similarity between the recalled item and
          each presented item.
  
      distance : str
          The distance function used to compare presented and recalled items.
          Applies only to 'best' and 'smooth' matching approaches.  Can be any
          distance function supported by numpy.spatial.distance.cdist.
  
      Returns
      ----------
      prec : numpy array
        each number represents the probability of recall for a word presented in given position/index
  
      
docstring: Computes probability of a word being recalled (in the appropriate recall list), given its presentation position
  Parameters
  
  egg : quail.Egg
  Data to analyze
  
  match : str (exact, best or smooth)
  Matching approach to compute recall matrix.  If exact, the presented and
  recalled items must be identical (default).  If best, the recalled item
  that is most similar to the presented items will be selected. If smooth,
  a weighted average of all presented items will be used, where the
  weights are derived from the similarity between the recalled item and
  each presented item.
  
  distance : str
  The distance function used to compare presented and recalled items.
  Applies only to 'best' and 'smooth' matching approaches.  Can be any
  distance function supported by numpy.spatial.distance.cdist.
  
  Returns
  
  prec : numpy array
  each number represents the probability of recall for a word presented in given position/index
docstring_tokens: Computes probability of a word being recalled ( in the appropriate recall list ) given its presentation position Parameters egg : quail . Egg Data to analyze match : str ( exact best or smooth ) Matching approach to compute recall matrix . If exact the presented and recalled items must be identical ( default ) . If best the recalled item that is most similar to the presented items will be selected . If smooth a weighted average of all presented items will be used where the weights are derived from the similarity between the recalled item and each presented item . distance : str The distance function used to compare presented and recalled items . Applies only to ' best ' and ' smooth ' matching approaches . Can be any distance function supported by numpy . spatial . distance . cdist . Returns prec : numpy array each number represents the probability of recall for a word presented in given position / index
code: def spc_helper(egg, match='exact', distance='euclidean',
                 features=None):
      """
      Computes probability of a word being recalled (in the appropriate recall list), given its presentation position
  
      Parameters
      ----------
      egg : quail.Egg
          Data to analyze
  
      match : str (exact, best or smooth)
          Matching approach to compute recall matrix.  If exact, the presented and
          recalled items must be identical (default).  If best, the recalled item
          that is most similar to the presented items will be selected. If smooth,
          a weighted average of all presented items will be used, where the
          weights are derived from the similarity between the recalled item and
          each presented item.
  
      distance : str
          The distance function used to compare presented and recalled items.
          Applies only to 'best' and 'smooth' matching approaches.  Can be any
          distance function supported by numpy.spatial.distance.cdist.
  
      Returns
      ----------
      prec : numpy array
        each number represents the probability of recall for a word presented in given position/index
  
      """
  
      def spc(lst):
          d = np.zeros_like(egg.pres.values[0])
          inds = np.array(lst[~np.isnan(lst)]).astype(int)
          d[inds-1]=1
          return d
  
      opts = dict(match=match, distance=distance, features=features)
      if match is 'exact':
          opts.update({'features' : 'item'})
      recmat = recall_matrix(egg, **opts)
  
      if match in ['exact', 'best']:
          result = [spc(lst) for lst in recmat]
      elif match == 'smooth':
          result = np.nanmean(recmat, 2)
      else:
          raise ValueError('Match must be set to exact, best or smooth.')
      return np.mean(result, 0)
code_tokens: def spc_helper ( egg , match = 'exact' , distance = 'euclidean' , features = None ) : def spc ( lst ) : d = np . zeros_like ( egg . pres . values [ 0 ] ) inds = np . array ( lst [ ~ np . isnan ( lst ) ] ) . astype ( int ) d [ inds - 1 ] = 1 return d opts = dict ( match = match , distance = distance , features = features ) if match is 'exact' : opts . update ( { 'features' : 'item' } ) recmat = recall_matrix ( egg , ** opts ) if match in [ 'exact' , 'best' ] : result = [ spc ( lst ) for lst in recmat ] elif match == 'smooth' : result = np . nanmean ( recmat , 2 ) else : raise ValueError ( 'Match must be set to exact, best or smooth.' ) return np . mean ( result , 0 )
short_docstring: Computes probability of a word being recalled (in the appropriate recall list), given its presentation position
  Parameters
short_docstring_tokens: Computes probability of a word being recalled ( in the appropriate recall list ) given its presentation position Parameters
comment: """
      Computes probability of a word being recalled (in the appropriate recall list), given its presentation position
  
      Parameters
      ----------
      egg : quail.Egg
          Data to analyze
  
      match : str (exact, best or smooth)
          Matching approach to compute recall matrix.  If exact, the presented and
          recalled items must be identical (default).  If best, the recalled item
          that is most similar to the presented items will be selected. If smooth,
          a weighted average of all presented items will be used, where the
          weights are derived from the similarity between the recalled item and
          each presented item.
  
      distance : str
          The distance function used to compare presented and recalled items.
          Applies only to 'best' and 'smooth' matching approaches.  Can be any
          distance function supported by numpy.spatial.distance.cdist.
  
      Returns
      ----------
      prec : numpy array
        each number represents the probability of recall for a word presented in given position/index
  
      """