def average_values_for_plot(self, metric_store, data, averaging_factor):
    """
    Create the time series for the various metrics, averaged over the aggregation period being used for plots

    :param dict metric_store: The metric store used to store all the parsed jmeter log data
    :param dict data: Dict with all the metric data to be output to csv
    :param float averaging_factor: averaging factor to be used for calculating the average per second metrics
    :return: None
    """
    for metric, transaction_store in metric_store.items():
      for transaction, time_store in transaction_store.items():
        for time_stamp, metric_data in sorted(time_store.items()):
          if metric in ['t', 'by']:
            data[self.get_csv(transaction, metric)].append(','.join([str(time_stamp), str(sum(map(float, metric_data)) / float(len(metric_data)))]))
            if metric == 'by':
              metric_store['thr'][transaction][time_stamp] = sum(map(float, metric_data)) / float(averaging_factor * 1024 * 1024 / 8.0)
              data[self.get_csv(transaction, 'thr')].append(','.join([str(time_stamp), str(metric_store['thr'][transaction][time_stamp])]))
          elif metric in ['qps', 'eqps']:
            data[self.get_csv(transaction, metric)].append(','.join([str(time_stamp), str(metric_data / float(averaging_factor))]))
    return None
def average_values_for_plot(self, metric_store, data, averaging_factor):
    """
    Create the time series for the various metrics, averaged over the aggregation period being used for plots

    :param dict metric_store: The metric store used to store all the parsed jmeter log data
    :param dict data: Dict with all the metric data to be output to csv
    :param float averaging_factor: averaging factor to be used for calculating the average per second metrics
    :return: None
    """
    for metric, transaction_store in metric_store.items():
      for transaction, time_store in transaction_store.items():
        for time_stamp, metric_data in sorted(time_store.items()):
          if metric in ['t', 'by']:
            data[self.get_csv(transaction, metric)].append(','.join([str(time_stamp), str(sum(map(float, metric_data)) / float(len(metric_data)))]))
            if metric == 'by':
              metric_store['thr'][transaction][time_stamp] = sum(map(float, metric_data)) / float(averaging_factor * 1024 * 1024 / 8.0)
              data[self.get_csv(transaction, 'thr')].append(','.join([str(time_stamp), str(metric_store['thr'][transaction][time_stamp])]))
          elif metric in ['qps', 'eqps']:
            data[self.get_csv(transaction, metric)].append(','.join([str(time_stamp), str(metric_data / float(averaging_factor))]))
    return None
def average_values_for_plot ( self , metric_store , data , averaging_factor ) : for metric , transaction_store in metric_store . items ( ) : for transaction , time_store in transaction_store . items ( ) : for time_stamp , metric_data in sorted ( time_store . items ( ) ) : if metric in [ 't' , 'by' ] : data [ self . get_csv ( transaction , metric ) ] . append ( ',' . join ( [ str ( time_stamp ) , str ( sum ( map ( float , metric_data ) ) / float ( len ( metric_data ) ) ) ] ) ) if metric == 'by' : metric_store [ 'thr' ] [ transaction ] [ time_stamp ] = sum ( map ( float , metric_data ) ) / float ( averaging_factor * 1024 * 1024 / 8.0 ) data [ self . get_csv ( transaction , 'thr' ) ] . append ( ',' . join ( [ str ( time_stamp ) , str ( metric_store [ 'thr' ] [ transaction ] [ time_stamp ] ) ] ) ) elif metric in [ 'qps' , 'eqps' ] : data [ self . get_csv ( transaction , metric ) ] . append ( ',' . join ( [ str ( time_stamp ) , str ( metric_data / float ( averaging_factor ) ) ] ) ) return None
Create the time series for the various metrics, averaged over the aggregation period being used for plots

    :param dict metric_store: The metric store used to store all the parsed jmeter log data
    :param dict data: Dict with all the metric data to be output to csv
    :param float averaging_factor: averaging factor to be used for calculating the average per second metrics
    :return: None
Create the time series for the various metrics averaged over the aggregation period being used for plots