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))
+ 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])
+ 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)
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"
stdev_t = data_t.rolling(window=win_size, min_periods=2).std()
median_first_idx = pd_data.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:
# 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():
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")
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: