code: def _lowess_wt_standardize(weights, new_entries, x_copy_i, width):
      """
      The initial phase of creating the weights.
      Subtract the current x_i and divide by the width.
  
      Parameters
      ----------
      weights : ndarray
          The memory where (new_entries - x_copy_i)/width will be placed
      new_entries : ndarray
          The x-values of the k closest points to x[i]
      x_copy_i : float
          x[i], the i'th point in the (sorted) x values
      width : float
          The maximum distance between x[i] and any point in new_entries
  
      Returns
      -------
      Nothing. The modifications are made to weight in place.
      """
      weights[:] = new_entries
      weights -= x_copy_i
      weights /= width
  
file_path: statsmodels/statsmodels/nonparametric/smoothers_lowess_old.py
imports: import numpy
  from numpy.linalg import lstsq
label_desc: Use image node_id 3 for calling a global function with example usage: _lowess_wt_standardize(weights, new_entries, x_copy_i, width) without return types