from xml.etree import ElementTree as ET
from errors import PresentationError
-from utils import mean, stdev, relative_change, remove_outliers, split_outliers
+from utils import mean, stdev, relative_change, remove_outliers,\
+ split_outliers, classify_anomalies
def generate_tables(spec, data):
tbl_lst = list()
for tst_name in tbl_dict.keys():
- if len(tbl_dict[tst_name]["data"]) > 2:
-
- pd_data = pd.Series(tbl_dict[tst_name]["data"])
- data_t, _ = split_outliers(pd_data, outlier_const=1.5,
- 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 = median_t.size - long_win_size
- try:
- 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:
- last_median_t = median_t[last_key]
- except KeyError:
- last_median_t = nan
- try:
- median_t_14 = median_t[key_14]
- except KeyError:
- median_t_14 = nan
-
- # Classification list:
- classification_lst = list()
- 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] - 3 * stdev_t[build_nr]):
- classification_lst.append("regression")
- elif value > (median_t[build_nr] + 3 * stdev_t[build_nr]):
- classification_lst.append("progression")
- else:
- classification_lst.append("normal")
+ if len(tbl_dict[tst_name]["data"]) < 3:
+ continue
+
+ pd_data = pd.Series(tbl_dict[tst_name]["data"])
+ data_t, _ = split_outliers(pd_data, outlier_const=1.5,
+ 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()
+ median_first_idx = median_t.size - long_win_size
+ try:
+ 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:
+ last_median_t = median_t[last_key]
+ except KeyError:
+ last_median_t = nan
+ try:
+ median_t_14 = median_t[key_14]
+ except KeyError:
+ median_t_14 = nan
- 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) * 100, 2)
+ 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) * 100, 2)
- 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) * 100, 2)
+ 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) * 100, 2)
+ # Classification list:
+ classification_lst = classify_anomalies(data_t, window=14)
+
+ if classification_lst:
tbl_lst.append(
[tbl_dict[tst_name]["name"],
'-' if isnan(last_median_t) else
if "memif" in item:
file_name = "container_memif.html"
+ elif "srv6" in item:
+ file_name = "srv6.html"
+
elif "vhost" in item:
if "l2xcbase" in item or "l2bdbasemaclrn" in item:
file_name = "vm_vhost_l2.html"
if "64b" in item:
anchor += "64b-"
elif "78b" in item:
- anchor += "78b"
+ anchor += "78b-"
elif "imix" in item:
anchor += "imix-"
elif "9000b" in item: