def calculate_per_workload_wstats_per_stage(
self, workloads: Iterable[str], stage_index: int,
filter_nodes: List[str]) -> Dict[str, WStat]:
"""
Calculate WStat for all workloads in list for stage (stage_index).
Takes data from all nodes.
"""
workloads_wstats: Dict[str, WStat] = {}
for workload in workloads:
# filter tasks of a given workload
tasks = [
task for task in self.stages[stage_index].tasks.values()
if task.workload_name == workload
]
# filter out tasks which were run on >>filter_nodes<<
tasks = [task for task in tasks if task.node not in filter_nodes]
# avg but from 12 sec for a single task
throughputs_list = [
task.get_throughput('avg') for task in tasks
if task.get_throughput('avg') is not None
]
latencies_list = [
task.get_latency('avg') for task in tasks
if task.get_latency('avg') is not None
]
if len(throughputs_list) == 0:
exception_value = float('inf')
t_max, t_min, t_avg, t_stdev = [exception_value] * 4
l_max, l_min, l_avg, l_stdev = [exception_value] * 4
elif len(throughputs_list) == 1:
t_max, t_min, t_avg, t_stdev = [
throughputs_list[0], throughputs_list[0],
throughputs_list[0], 0
]
l_max, l_min, l_avg, l_stdev = [
throughputs_list[0], throughputs_list[0],
throughputs_list[0], 0
]
else:
t_max, t_min, t_avg, t_stdev = max(throughputs_list), min(throughputs_list), \
statistics.mean(throughputs_list), statistics.stdev(
throughputs_list)
l_max, l_min, l_avg, l_stdev = \
max(latencies_list), min(latencies_list),\
statistics.mean(latencies_list), statistics.stdev(latencies_list)
workloads_wstats[workload] = WStat(
latency=Stat(l_avg, l_min, l_max, l_stdev),
throughput=Stat(t_avg, t_min, t_max, t_stdev),
count=len(tasks),
name=workload)
return workloads_wstats
def transform(self, json: dict):
# variables
countries = json.keys()
all_data = []
# loop
for country in countries:
# build array of json
for i, stat in enumerate(json[country]):
date = datetime.strptime(stat["date"], "%Y-%m-%d").date()
data = Stat(
id=f"{country}{date}",
country=country,
date=date,
confirmed=stat["confirmed"],
deaths=stat["deaths"],
recovered=stat["recovered"],
)
all_data.append(data)
# return
return all_data
