median = pd_data.rolling(window=win_size, min_periods=2).median()
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:])
+ try:
+ max_median = max([x for x in median.values[median_idx:]
+ if not isnan(x)])
+ except ValueError:
+ max_median = None
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()
else:
tmp_classification = "outlier" if classification == "failure" \
else classification
+ index = None
for idx in range(first_idx, len(classification_lst)):
if classification_lst[idx] == tmp_classification:
if rel_change_lst[idx]:
index = idx
break
+ if index is None:
+ continue
for idx in range(index+1, len(classification_lst)):
if classification_lst[idx] == tmp_classification:
if 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 '-'
- sample = round(float(sample_lst[index]) / 1000000, 2) \
- if not isnan(sample_lst[index]) else '-'
- rel_change = rel_change_lst[index] \
- 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"
+ logging.debug("{}".format(name))
+ logging.debug("sample_lst: {} - {}".
+ format(len(sample_lst), sample_lst))
+ logging.debug("median_lst: {} - {}".
+ format(len(median_lst), median_lst))
+ logging.debug("rel_change: {} - {}".
+ format(len(rel_change_lst), rel_change_lst))
+ logging.debug("classn_lst: {} - {}".
+ format(len(classification_lst), classification_lst))
+ logging.debug("index: {}".format(index))
+ logging.debug("classifica: {}".format(classification))
+
+ try:
+ trend = round(float(median_lst[-1]) / 1000000, 2) \
+ if not isnan(median_lst[-1]) else '-'
+ sample = round(float(sample_lst[index]) / 1000000, 2) \
+ if not isnan(sample_lst[index]) else '-'
+ rel_change = rel_change_lst[index] \
+ if rel_change_lst[index] is not None else '-'
+ if max_median is not None:
+ 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 = 'normal'
else:
- long_trend_classification = '-'
+ long_trend_classification = "failure"
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,
- nr_outliers])
+ long_trend_classification = '-'
+ tbl_lst.append([name,
+ trend,
+ long_trend_classification,
+ classification,
+ '-' if classification == "normal" else sample,
+ '-' if classification == "normal" else
+ rel_change,
+ nr_outliers])
+ except IndexError as err:
+ logging.error("{}".format(err))
+ continue
# Sort the table according to the classification
tbl_sorted = list()
- for long_trend_class in ("failure", '-'):
+ for long_trend_class in ("failure", 'normal', '-'):
tbl_long = [item for item in tbl_lst if item[2] == long_trend_class]
for classification in \
("failure", "regression", "progression", "normal"):