parameters: (chart_series, **config_dict)
return_statement: return all_change_points
docstring: Finds the same alerts as would be found normally as points are added.
  
    Each time a new point is added to a data series on dashboard, the
    FindChangePoints function is called with some points from that series.
    In order to simulate this here, we need to repeatedly call FindChangePoints.
  
    Args:
      chart_series: A list of (x, y) pairs.
      **config_dict: An alert threshold config dict.
  
    Returns:
      A list of find_change_points.ChangePoint objects, one for each alert found.
docstring_summary: Finds the same alerts as would be found normally as points are added.
docstring_tokens: Finds the same alerts as would be found normally as points are added .
function: def SimulateAlertProcessing(chart_series, **config_dict):
    """Finds the same alerts as would be found normally as points are added.
  
    Each time a new point is added to a data series on dashboard, the
    FindChangePoints function is called with some points from that series.
    In order to simulate this here, we need to repeatedly call FindChangePoints.
  
    Args:
      chart_series: A list of (x, y) pairs.
      **config_dict: An alert threshold config dict.
  
    Returns:
      A list of find_change_points.ChangePoint objects, one for each alert found.
    """
    all_change_points = []
    highest_x = None  # This is used to avoid finding duplicate alerts.
    # The number of points that are passed in to FindChangePoints normally may
    # depend on either the specific "max_window_size" value or another default
    # used in find_anomalies.
    window = config_dict.get('max_window_size', find_anomalies.DEFAULT_NUM_POINTS)
    for end in range(1, len(chart_series)):
      start = max(0, end - window)
      series = chart_series[start:end]
      change_points = find_change_points.FindChangePoints(series, **config_dict)
      change_points = [c for c in change_points if c.x_value > highest_x]
      if change_points:
        highest_x = max(c.x_value for c in change_points)
        all_change_points.extend(change_points)
    return all_change_points
function_tokens: def SimulateAlertProcessing ( chart_series , * * config_dict ) : all_change_points = [ ] highest_x = None # This is used to avoid finding duplicate alerts. # The number of points that are passed in to FindChangePoints normally may # depend on either the specific "max_window_size" value or another default # used in find_anomalies. window = config_dict . get ( 'max_window_size' , find_anomalies . DEFAULT_NUM_POINTS ) for end in range ( 1 , len ( chart_series ) ) : start = max ( 0 , end - window ) series = chart_series [ start : end ] change_points = find_change_points . FindChangePoints ( series , * * config_dict ) change_points = [ c for c in change_points if c . x_value > highest_x ] if change_points : highest_x = max ( c . x_value for c in change_points ) all_change_points . extend ( change_points ) return all_change_points