def calc_trim_mean_throughput(self, samples=8):
"""
Calculate the cluster average throughput out of a few samples
Args:
samples (int): The number of samples to take
Returns:
float: The average cluster throughput
"""
throughput_vals = [self.get_cluster_throughput() for _ in range(samples)]
return round(get_trim_mean(throughput_vals), 3)
def calc_trim_metric_mean(self, metric=constants.LATENCY_QUERY, samples=5):
"""
Get the trimmed mean of a given metric
Args:
metric (str): The metric to calculate the average result for
samples (int): The number of samples to take
Returns:
float: The average result for the metric
"""
vals = list()
for i in range(samples):
vals.append(round(self.get_query(metric), 5))
if i == samples - 1:
break
time.sleep(5)
return round(get_trim_mean(vals), 5)
def calc_trim_metric_mean(self, metric, samples=5, mute_logs=False):
"""
Get the trimmed mean of a given metric
Args:
metric (str): The metric to calculate the average result for
samples (int): The number of samples to take
mute_logs (bool): True for muting the logs, False otherwise
Returns:
float: The average result for the metric
"""
vals = list()
for i in range(samples):
vals.append(round(self.get_query(metric, mute_logs), 5))
if i == samples - 1:
break
time.sleep(5)
return round(get_trim_mean(vals), 5)
