import logging
import csv
-import pandas as pd
from string import replace
from collections import OrderedDict
"""Generate the table(s) with algorithm: table_performance_improvements
specified in the specification file.
+ # FIXME: Not used now.
+
:param table: Table to generate.
:param input_data: Data to process.
:type table: pandas.Series
"cmp-data": list()}
try:
tbl_dict[tst_name]["ref-data"].\
- append(tst_data["result"]["throughput"])
+ append(tst_data["result"]["receive-rate"].avg)
except TypeError:
pass # No data in output.xml for this test
for tst_name, tst_data in data[job][str(build)].iteritems():
try:
tbl_dict[tst_name]["cmp-data"].\
- append(tst_data["result"]["throughput"])
+ append(tst_data["result"]["receive-rate"].avg)
except KeyError:
pass
except TypeError:
"data": OrderedDict()}
try:
tbl_dict[tst_name]["data"][str(build)] = \
- tst_data["result"]["throughput"]
+ tst_data["result"]["receive-rate"]
except (TypeError, KeyError):
pass # No data in output.xml for this test
tbl_lst = list()
for tst_name in tbl_dict.keys():
- if len(tbl_dict[tst_name]["data"]) < 2:
+ data_t = tbl_dict[tst_name]["data"]
+ if len(data_t) < 2:
continue
- data_t = pd.Series(tbl_dict[tst_name]["data"])
-
classification_lst, avgs = classify_anomalies(data_t)
- win_size = min(data_t.size, table["window"])
- long_win_size = min(data_t.size, table["long-trend-window"])
+ win_size = min(len(data_t), table["window"])
+ long_win_size = min(len(data_t), table["long-trend-window"])
+
try:
max_long_avg = max(
[x for x in avgs[-long_win_size:-win_size]