abs(rel_change_lst[index])):
index = idx
- logging.info("{}".format(name))
- logging.info("sample_lst: {} - {}".format(len(sample_lst), sample_lst))
- logging.info("median_lst: {} - {}".format(len(median_lst), median_lst))
- logging.info("rel_change: {} - {}".format(len(rel_change_lst), rel_change_lst))
- logging.info("classn_lst: {} - {}".format(len(classification_lst), classification_lst))
- logging.info("index: {}".format(index))
- logging.info("classifica: {}".format(classification))
+ 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 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)
+ 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 = '-'
+ long_trend_classification = 'normal'
else:
long_trend_classification = "failure"
else:
long_trend_classification,
classification,
'-' if classification == "normal" else sample,
- '-' if classification == "normal" else rel_change,
+ '-' if classification == "normal" else
+ rel_change,
nr_outliers])
except IndexError as err:
logging.error("{}".format(err))
result_lst.append(y)
return result_lst
- # input_series = pd.Series()
- # for index, value in enumerate(input_list):
- # item_pd = pd.Series([value, ], index=[index, ])
- # input_series.append(item_pd)
- # output_series, _ = split_outliers(input_series, outlier_const=outlier_const,
- # window=window)
- # output_list = [y for x, y in output_series.items() if not np.isnan(y)]
- #
- # return output_list
-
def split_outliers(input_series, outlier_const=1.5, window=14):
"""Go through the input data and generate two pandas series: