-# 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
format(table.get("title", "")))
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table)
# Prepare the header of the tables
format(table.get("title", "")))
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table)
data = input_data.merge_data(data)
data.sort_index(inplace=True)
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
suites = input_data.filter_data(table, data_set="suites")
suites = input_data.merge_data(suites)
return None
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table)
# Prepare the header of the tables
format(table.get("title", "")))
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
for tst_name in tbl_dict.keys():
item = [tbl_dict[tst_name]["name"], ]
if history:
- for hist_list in tbl_dict[tst_name]["history"].values():
- for hist_data in hist_list:
+ if tbl_dict[tst_name].get("history", None) is not None:
+ for hist_data in tbl_dict[tst_name]["history"].values():
if hist_data:
data_t = remove_outliers(
hist_data, outlier_const=table["outlier-const"])
item.extend([None, None])
else:
item.extend([None, None])
+ else:
+ item.extend([None, None])
if tbl_dict[tst_name]["ref-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
outlier_const=table["outlier-const"])
item.extend([None, None])
else:
item.extend([None, None])
- if item[-5] is not None and item[-3] is not None and item[-5] != 0:
- item.append(int(relative_change(float(item[-5]), float(item[-3]))))
+ if item[-4] is not None and item[-2] is not None and item[-4] != 0:
+ item.append(int(relative_change(float(item[-4]), float(item[-2]))))
if len(item) == len(header):
tbl_lst.append(item)
format(table.get("title", "")))
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
format(table.get("title", "")))
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
for job, builds in table["data"].items():
for build in builds:
for tst_name, tst_data in data[job][str(build)].iteritems():
+ if tst_name.lower() in table["ignore-list"]:
+ continue
if tbl_dict.get(tst_name, None) is None:
name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
"-".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:]
- if not isnan(x)])
+ max_median = max(
+ [x for x in median_t.values[median_first_idx:-win_size]
+ if not isnan(x)])
except ValueError:
max_median = nan
try:
except KeyError:
median_t_14 = nan
- # Test name:
- name = tbl_dict[tst_name]["name"]
-
- logging.info("{}".format(name))
- logging.info("pd_data : {}".format(pd_data))
- logging.info("data_t : {}".format(data_t))
- logging.info("median_t : {}".format(median_t))
- logging.info("last_median_t : {}".format(last_median_t))
- logging.info("median_t_14 : {}".format(median_t_14))
- logging.info("max_median : {}".format(max_median))
-
# 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")
- elif value < (median_t[build_nr] - 2 * stdev_t[build_nr]):
+ elif value < (median_t[build_nr] - 3 * stdev_t[build_nr]):
classification_lst.append("regression")
- elif value > (median_t[build_nr] + 2 * stdev_t[build_nr]):
+ elif value > (median_t[build_nr] + 3 * stdev_t[build_nr]):
classification_lst.append("progression")
else:
classification_lst.append("normal")
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,
for nrp in range(table["window"], -1, -1):
tbl_pro = [item for item in tbl_reg if item[5] == nrp]
for nro in range(table["window"], -1, -1):
- tbl_out = [item for item in tbl_pro if item[5] == nro]
+ tbl_out = [item for item in tbl_pro if item[6] == nro]
+ tbl_out.sort(key=lambda rel: rel[2])
tbl_sorted.extend(tbl_out)
file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
th.text = item
# Rows:
+ colors = {"regression": ("#ffcccc", "#ff9999"),
+ "progression": ("#c6ecc6", "#9fdf9f"),
+ "outlier": ("#e6e6e6", "#cccccc"),
+ "normal": ("#e9f1fb", "#d4e4f7")}
for r_idx, row in enumerate(csv_lst[1:]):
- background = "#D4E4F7" if r_idx % 2 else "white"
+ if int(row[4]):
+ color = "regression"
+ elif int(row[5]):
+ color = "progression"
+ elif int(row[6]):
+ color = "outlier"
+ else:
+ color = "normal"
+ background = colors[color][r_idx % 2]
tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background))
# Columns:
Code Review / csit.git / blobdiff