-# Copyright (c) 2017 Cisco and/or its affiliates.
+# Copyright (c) 2018 Cisco and/or its affiliates.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at:
import pandas as pd
from string import replace
-from math import isnan
from collections import OrderedDict
-from numpy import nan
+from numpy import nan, isnan
from xml.etree import ElementTree as ET
from errors import PresentationError
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
- header = [" Test Case",
+ header = ["Test Case",
"Trend [Mpps]",
- " Short-Term Change [%]",
- " Long-Term Change [%]",
- " Regressions [#]",
- " Progressions [#]",
- " Outliers [#]"
+ "Short-Term Change [%]",
+ "Long-Term Change [%]",
+ "Regressions [#]",
+ "Progressions [#]",
+ "Outliers [#]"
]
header_str = ",".join(header) + "\n"
"-".join(tst_data["name"].
split("-")[1:]))
tbl_dict[tst_name] = {"name": name,
- "data": dict()}
+ "data": OrderedDict()}
try:
tbl_dict[tst_name]["data"][str(build)] = \
tst_data["result"]["throughput"]
if len(tbl_dict[tst_name]["data"]) > 2:
pd_data = pd.Series(tbl_dict[tst_name]["data"])
- last_key = pd_data.keys()[-1]
- win_size = min(pd_data.size, table["window"])
- win_first_idx = pd_data.size - win_size
- key_14 = pd_data.keys()[win_first_idx]
- long_win_size = min(pd_data.size, table["long-trend-window"])
-
data_t, _ = split_outliers(pd_data, outlier_const=1.5,
- window=win_size)
-
+ window=table["window"])
+ last_key = data_t.keys()[-1]
+ win_size = min(data_t.size, table["window"])
+ win_first_idx = data_t.size - win_size
+ key_14 = data_t.keys()[win_first_idx]
+ long_win_size = min(data_t.size, table["long-trend-window"])
median_t = data_t.rolling(window=win_size, min_periods=2).median()
stdev_t = data_t.rolling(window=win_size, min_periods=2).std()
- median_first_idx = pd_data.size - long_win_size
+ median_first_idx = median_t.size - long_win_size
try:
max_median = max(
[x for x in median_t.values[median_first_idx:-win_size]
except KeyError:
median_t_14 = nan
- # Test name:
- name = tbl_dict[tst_name]["name"]
-
# Classification list:
classification_lst = list()
- for build_nr, value in pd_data.iteritems():
-
- if isnan(data_t[build_nr]) \
- or isnan(median_t[build_nr]) \
+ for build_nr, value in data_t.iteritems():
+ if isnan(median_t[build_nr]) \
or isnan(stdev_t[build_nr]) \
or isnan(value):
classification_lst.append("outlier")
rel_change_long = round(
((last_median_t - max_median) / max_median) * 100, 2)
- logging.info("rel_change_last : {}".format(rel_change_last))
- logging.info("rel_change_long : {}".format(rel_change_long))
-
tbl_lst.append(
- [name,
+ [tbl_dict[tst_name]["name"],
'-' if isnan(last_median_t) else
round(last_median_t / 1000000, 2),
'-' if isnan(rel_change_last) else rel_change_last,