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()
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 max_median is not None:
if not isnan(sample_lst[index]):
long_trend_threshold = \
max_median * (table["long-trend-threshold"] / 100)
# 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"):