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:
+ 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"])
+ 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])
+ 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[-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)
# Sort the table according to the relative change
pd_data = pd.Series(tbl_dict[tst_name]["data"])
last_key = pd_data.keys()[-1]
win_size = min(pd_data.size, table["window"])
- key_14 = pd_data.keys()[-(pd_data.size - win_size)]
+ 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,
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_idx = pd_data.size - long_win_size
+ median_first_idx = pd_data.size - long_win_size
try:
- max_median = max([x for x in median_t.values[median_idx:]
+ max_median = max([x for x in median_t.values[median_first_idx:]
if not isnan(x)])
except ValueError:
max_median = nan
else:
classification_lst.append("normal")
- if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0:
+ if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0.0:
rel_change_last = nan
else:
rel_change_last = round(
- (last_median_t - median_t_14) / median_t_14, 2)
+ ((last_median_t - median_t_14) / median_t_14) * 100, 2)
- if isnan(max_median) or isnan(last_median_t) or max_median == 0:
+ if isnan(max_median) or isnan(last_median_t) or max_median == 0.0:
rel_change_long = nan
else:
rel_change_long = round(
- (last_median_t - max_median) / max_median, 2)
+ ((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,
- '-' if isnan(last_median_t) else
- round(last_median_t / 1000000, 2),
- '-' if isnan(rel_change_last) else rel_change_last,
- '-' if isnan(rel_change_long) else rel_change_long,
- classification_lst[win_size:].count("regression"),
- classification_lst[win_size:].count("progression"),
- classification_lst[win_size:].count("outlier")])
+ tbl_lst.append(
+ [name,
+ '-' if isnan(last_median_t) else
+ round(last_median_t / 1000000, 2),
+ '-' if isnan(rel_change_last) else rel_change_last,
+ '-' if isnan(rel_change_long) else rel_change_long,
+ classification_lst[win_first_idx:].count("regression"),
+ classification_lst[win_first_idx:].count("progression"),
+ classification_lst[win_first_idx:].count("outlier")])
tbl_lst.sort(key=lambda rel: rel[0])
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