from string import replace
from math import isnan
+from collections import OrderedDict
from numpy import nan
from xml.etree import ElementTree as ET
# Prepare the header of the tables
try:
- header = ["Test case",
- "{0} Throughput [Mpps]".format(table["reference"]["title"]),
- "{0} stdev [Mpps]".format(table["reference"]["title"]),
- "{0} Throughput [Mpps]".format(table["compare"]["title"]),
- "{0} stdev [Mpps]".format(table["compare"]["title"]),
- "Change [%]"]
+ header = ["Test case", ]
+
+ history = table.get("history", None)
+ if history:
+ for item in history:
+ header.extend(
+ ["{0} Throughput [Mpps]".format(item["title"]),
+ "{0} Stdev [Mpps]".format(item["title"])])
+ header.extend(
+ ["{0} Throughput [Mpps]".format(table["reference"]["title"]),
+ "{0} Stdev [Mpps]".format(table["reference"]["title"]),
+ "{0} Throughput [Mpps]".format(table["compare"]["title"]),
+ "{0} Stdev [Mpps]".format(table["compare"]["title"]),
+ "Change [%]"])
header_str = ",".join(header) + "\n"
except (AttributeError, KeyError) as err:
logging.error("The model is invalid, missing parameter: {0}".
pass
except TypeError:
tbl_dict.pop(tst_name, None)
+ if history:
+ for item in history:
+ for job, builds in item["data"].items():
+ for build in builds:
+ for tst_name, tst_data in data[job][str(build)].iteritems():
+ if tbl_dict.get(tst_name, None) is None:
+ continue
+ if tbl_dict[tst_name].get("history", None) is None:
+ tbl_dict[tst_name]["history"] = OrderedDict()
+ if tbl_dict[tst_name]["history"].get(item["title"],
+ None) is None:
+ tbl_dict[tst_name]["history"][item["title"]] = \
+ list()
+ try:
+ tbl_dict[tst_name]["history"][item["title"]].\
+ append(tst_data["throughput"]["value"])
+ except (TypeError, KeyError):
+ pass
tbl_lst = list()
for tst_name in tbl_dict.keys():
item = [tbl_dict[tst_name]["name"], ]
+ if history:
+ for hist_data in tbl_dict[tst_name]["history"].values():
+ if hist_data:
+ data_t = remove_outliers(
+ hist_data, outlier_const=table["outlier-const"])
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
+ 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[1] is not None and item[3] is not None:
- item.append(int(relative_change(float(item[1]), float(item[3]))))
- if len(item) == 6:
+ 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 len(item) == len(header):
tbl_lst.append(item)
# Sort the table according to the relative change
or isnan(stdev_t[build_nr]) \
or isnan(value):
classification_lst.append("outlier")
- elif value < (median_t[build_nr] - 3 * stdev_t[build_nr]):
+ elif value < (median_t[build_nr] - 2 * stdev_t[build_nr]):
classification_lst.append("regression")
- elif value > (median_t[build_nr] + 3 * stdev_t[build_nr]):
+ elif value > (median_t[build_nr] + 2 * stdev_t[build_nr]):
classification_lst.append("progression")
else:
classification_lst.append("normal")
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"])