from xml.etree import ElementTree as ET
from errors import PresentationError
-from utils import mean, stdev, relative_change, remove_outliers, find_outliers
+from utils import mean, stdev, relative_change, remove_outliers, split_outliers
def generate_tables(spec, data):
item = [tbl_dict[tst_name]["name"], ]
if tbl_dict[tst_name]["ref-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
- table["outlier-const"])
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ outlier_const=table["outlier-const"])
+ # TODO: Specify window size.
+ 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]["cmp-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
- table["outlier-const"])
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ outlier_const=table["outlier-const"])
+ # TODO: Specify window size.
+ 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 item[1] is not None and item[3] is not None:
item = [tbl_dict[tst_name]["name"], ]
if tbl_dict[tst_name]["ref-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
- table["outlier-const"])
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ outlier_const=table["outlier-const"])
+ # TODO: Specify window size.
+ 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]["cmp-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
- table["outlier-const"])
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ outlier_const=table["outlier-const"])
+ # TODO: Specify window size.
+ 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 item[1] is not None and item[3] is not None and item[1] != 0:
# Prepare the header of the tables
header = ["Test Case",
"Throughput Trend [Mpps]",
+ "Long Trend Compliance",
"Trend Compliance",
"Top Anomaly [Mpps]",
"Change [%]",
if len(tbl_dict[tst_name]["data"]) > 2:
pd_data = pd.Series(tbl_dict[tst_name]["data"])
- win_size = pd_data.size \
- if pd_data.size < table["window"] else table["window"]
+ win_size = min(pd_data.size, table["window"])
# Test name:
name = tbl_dict[tst_name]["name"]
median = pd_data.rolling(window=win_size, min_periods=2).median()
- trimmed_data, _ = find_outliers(pd_data, outlier_const=1.5)
+ median_idx = pd_data.size - table["long-trend-window"]
+ median_idx = 0 if median_idx < 0 else median_idx
+ max_median = max(median.values[median_idx:])
+ trimmed_data, _ = split_outliers(pd_data, outlier_const=1.5,
+ window=win_size)
stdev_t = pd_data.rolling(window=win_size, min_periods=2).std()
rel_change_lst = [None, ]
else classification
for idx in range(first_idx, len(classification_lst)):
if classification_lst[idx] == tmp_classification:
- index = idx
- break
+ if rel_change_lst[idx]:
+ index = idx
+ break
for idx in range(index+1, len(classification_lst)):
if classification_lst[idx] == tmp_classification:
- if rel_change_lst[idx] > rel_change_lst[index]:
- index = idx
-
- # if "regression" in classification_lst[first_idx:]:
- # classification = "regression"
- # elif "outlier" in classification_lst[first_idx:]:
- # classification = "outlier"
- # elif "progression" in classification_lst[first_idx:]:
- # classification = "progression"
- # elif "normal" in classification_lst[first_idx:]:
- # classification = "normal"
- # else:
- # classification = None
- #
- # nr_outliers = 0
- # consecutive_outliers = 0
- # failure = False
- # for item in classification_lst[first_idx:]:
- # if item == "outlier":
- # nr_outliers += 1
- # consecutive_outliers += 1
- # if consecutive_outliers == 3:
- # failure = True
- # else:
- # consecutive_outliers = 0
- #
- # idx = len(classification_lst) - 1
- # while idx:
- # if classification_lst[idx] == classification:
- # break
- # idx -= 1
- #
- # if failure:
- # classification = "failure"
- # elif classification == "outlier":
- # classification = "normal"
+ if rel_change_lst[idx]:
+ if (abs(rel_change_lst[idx]) >
+ abs(rel_change_lst[index])):
+ index = idx
trend = round(float(median_lst[-1]) / 1000000, 2) \
- if not isnan(median_lst[-1]) else ''
+ if not isnan(median_lst[-1]) else '-'
sample = round(float(sample_lst[index]) / 1000000, 2) \
- if not isnan(sample_lst[index]) else ''
+ if not isnan(sample_lst[index]) else '-'
rel_change = rel_change_lst[index] \
- if rel_change_lst[index] is not None else ''
+ if rel_change_lst[index] is not None else '-'
+ if not isnan(max_median):
+ if not isnan(sample_lst[index]):
+ long_trend_threshold = max_median * \
+ (table["long-trend-threshold"] / 100)
+ if sample_lst[index] < long_trend_threshold:
+ long_trend_classification = "failure"
+ else:
+ long_trend_classification = '-'
+ else:
+ long_trend_classification = "failure"
+ else:
+ long_trend_classification = '-'
tbl_lst.append([name,
trend,
+ long_trend_classification,
classification,
'-' if classification == "normal" else sample,
'-' if classification == "normal" else rel_change,
# Sort the table according to the classification
tbl_sorted = list()
- for classification in ("failure", "regression", "progression", "normal"):
- tbl_tmp = [item for item in tbl_lst if item[2] == classification]
- tbl_tmp.sort(key=lambda rel: rel[0])
- tbl_sorted.extend(tbl_tmp)
+ for long_trend_class in ("failure", '-'):
+ tbl_long = [item for item in tbl_lst if item[2] == long_trend_class]
+ for classification in \
+ ("failure", "regression", "progression", "normal"):
+ tbl_tmp = [item for item in tbl_long if item[3] == classification]
+ tbl_tmp.sort(key=lambda rel: rel[0])
+ tbl_sorted.extend(tbl_tmp)
file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
ref = ET.SubElement(td, "a", attrib=dict(href=url))
ref.text = item
- if c_idx == 2:
+ if c_idx == 3:
if item == "regression":
td.set("bgcolor", "#eca1a6")
elif item == "failure":