old_contents: # example one-step-ahead forecasting functions 
  # implementations based on https://www.otexts.org/fpp
  
  import numpy as np
  
  
  def naive(data, **kwargs):
      """The naive forecast for the next point is the value of the previous point. 
  
      In most forecasting situations, the naive model is a good baseline due to it's simplicity 
      and low computational overhead. 
  
      Args:
          data (np.array): Observed data, presumed to be ordered in time.
  
      Returns:
          float: a single-valued forecast for the next value in the series.
      """
      forecast = data[-1]
      return forecast
  
  
  def seasonal_naive(data, n=7, **kwargs):
      """The seasonal naive forecast for the next point is the value observed ``n`` points
      prior in the series. 
  
      The seasonal parameter (``n``) does not have units of time, but rather units of one 
      observation. For example, to account for weekly cycles within daily observations, ``n=7``.
  
      Args:
          data (np.array): Observed data, presumed to be ordered in time.
          n (int): period of data seasonality 
  
      Returns:
          float: a single-valued forecast for the next value in the series.
      """
      forecast = data[-n]
      return forecast
  
  
  def mean(data, n=3, **kwargs):
      """The mean forecast for the next point is the mean value of the previous ``n`` points in 
      the series.
  
      Args:
          data (np.array): Observed data, presumed to be ordered in time.
          n (int): period over which to calculate the mean 
  
      Returns:
          float: a single-valued forecast for the next value in the series.
      """
      # don't start averaging until we've seen n points
      if len(data[-n:]) < n:
          forecast = np.nan
      else:
          # nb: we'll keep the forecast as a float
          forecast = np.mean(data[-n:])
      return forecast
  
  
  def drift(data, n=3, **kwargs):
      """The drift forecast for the next point is a linear extrapolation from the previous ``n`` 
      points in the series.
  
      Args:
          data (np.array): Observed data, presumed to be ordered in time.
          n (int): period over which to calculate linear model for extrapolation 
      Returns a single-valued forecast for the next value in the series.
      """
      yi = data[-n]
      yf = data[-1]
      slope = (yf - yi) / (n - 1)
      forecast = yf + slope
      return forecast
  
new_contents: # example one-step-ahead forecasting functions 
  # implementations based on https://www.otexts.org/fpp
  
  import numpy as np
  
  
  def naive(data, **kwargs):
      """The naive forecast for the next point is the value of the previous point. 
  
      In most forecasting situations, the naive model is a good baseline due to it's simplicity 
      and low computational overhead. 
  
      Args:
          data (np.array): Observed data, presumed to be ordered in time.
  
      Returns:
          float: a single-valued forecast for the next value in the series.
      """
      forecast = data[-1]
      return forecast
  
  
  def seasonal_naive(data, n=7, **kwargs):
      """The seasonal naive forecast for the next point is the value observed ``n`` points
      prior in the series. 
  
      The seasonal parameter (``n``) does not have units of time, but rather units of one 
      observation. For example, to account for weekly cycles within daily observations, ``n=7``.
  
      Args:
          data (np.array): Observed data, presumed to be ordered in time.
          n (int): period of data seasonality 
  
      Returns:
          float: a single-valued forecast for the next value in the series.
      """
      forecast = data[-n]
      return forecast
  
  
  def mean(data, n=3, **kwargs):
      """The mean forecast for the next point is the mean value of the previous ``n`` points in 
      the series.
  
      Args:
          data (np.array): Observed data, presumed to be ordered in time.
          n (int): period over which to calculate the mean 
  
      Returns:
          float: a single-valued forecast for the next value in the series.
      """
      # don't start averaging until we've seen n points
      if len(data[-n:]) < n:
          forecast = np.nan
      else:
          # nb: we'll keep the forecast as a float
          forecast = np.mean(data[-n:])
      return forecast
  
  
  def drift(data, n=3, **kwargs):
      """The drift forecast for the next point is a linear extrapolation from the previous ``n`` 
      points in the series.
  
      Args:
          data (np.array): Observed data, presumed to be ordered in time.
          n (int): period over which to calculate linear model for extrapolation 
  
      Returns: 
          float: a single-valued forecast for the next value in the series.
      """
      yi = data[-n]
      yf = data[-1]
      slope = (yf - yi) / (n - 1)
      forecast = yf + slope
      return forecast
  
subject: Fix docstring format in forecast_func