def getAverageTs(self): avg_ts = mu.median( gu.get_iats( [t[0] for t in gu.flatten_list(self.iat_length_tuples)])) log.debug("[bwdiff - %s] Average ts in the session is: %s.", self.end, avg_ts) return avg_ts
def estimate_rho(self, rho_star): """Estimate new value of rho based on past network performance.""" #log.debug("[cs-buflo] rho stats: %s" % self._rho_stats) time_intervals = gu.flatten_list([gu.apply_consecutive_elements(burst_list, lambda x, y: (y - x) * const.SCALE) for burst_list in self._rho_stats]) #log.debug("[cs-buflo] Time intervals = %s", time_intervals) if len(time_intervals) == 0: return rho_star else: return math.pow(2, math.floor(math.log(mu.median(time_intervals), 2)))
def estimate_rho(self, rho_star): """Estimate new value of rho based on past network performance.""" #log.debug("[cs-buflo] rho stats: %s" % self._rho_stats) time_intervals = gu.flatten_list([ gu.apply_consecutive_elements(burst_list, lambda x, y: (y - x) * const.SCALE) for burst_list in self._rho_stats ]) #log.debug("[cs-buflo] Time intervals = %s", time_intervals) if len(time_intervals) == 0: return rho_star else: return math.pow(2, math.floor(math.log(mu.median(time_intervals), 2)))
def getAverageTs(self): avg_ts = mu.median(gu.get_iats([t[0] for t in gu.flatten_list(self.iat_length_tuples)])) log.debug("[bwdiff - %s] Average ts in the session is: %s.", self.end, avg_ts) return avg_ts