code: def sliding_window(time_series, width, step, order='F'):
      '''
      Segments univariate time series with sliding window
  
      Parameters
      ----------
      time_series : array like shape [n_samples]
          time series or sequence
      width : int > 0
          segment width in samples
      step : int > 0
          stepsize for sliding in samples
  
      Returns
      -------
      w : array like shape [n_segments, width]
          resampled time series segments
      '''
      w = np.hstack([time_series[i:1 + i - width or None:step] for i in range(0, width)])
      result = w.reshape((int(len(w) / width), width), order='F')
      if order == 'F':
          return result
      else:
          return np.ascontiguousarray(resultf sliding_window(time_series, width, step, order='F'):
      '''
      Segments univariate time series with sliding window
  
      Parameters
      ----------
      time_series : array like shape [n_samples]
          time series or sequence
      width : int > 0
          segment width in samples
      step : int > 0
          stepsize for sliding in samples
  
      Returns
      -------
      w : array like shape [n_segments, width]
          resampled time series segments
      '''
      w = np.hstack([time_series[i:1 + i - width or None:step] for i in range(0, width)])
      result = w.reshape((int(len(w) / width), width), order='F')
      if order == 'F':
          return result
      else:
          return np.ascontiguousarray(result)
docs: Segments univariate time series with sliding window
  
      Parameters
      ----------
      time_series : array like shape [n_samples]
          time series or sequence
      width : int > 0
          segment width in samples
      step : int > 0
          stepsize for sliding in samples
  
      Returns
      -------
      w : array like shape [n_segments, width]
          resampled time series segments