- # Trend list:
- trend_lst = list(pd_data.rolling(window=win_size, min_periods=2).
- median())
- # Stdevs list:
- t_data, _ = find_outliers(pd_data)
- t_data_lst = list(t_data)
- stdev_lst = list(t_data.rolling(window=win_size, min_periods=2).
- std())
-
- rel_change_lst = [None, ]
- classification_lst = [None, ]
- for idx in range(1, len(trend_lst)):
- # Relative changes list:
- if not isnan(sample_lst[idx]) \
- and not isnan(trend_lst[idx])\
- and trend_lst[idx] != 0:
- rel_change_lst.append(
- int(relative_change(float(trend_lst[idx]),
- float(sample_lst[idx]))))
- else:
- rel_change_lst.append(None)
- # Classification list:
- if isnan(t_data_lst[idx]) or isnan(stdev_lst[idx]):
+ logging.info("{}".format(name))
+ logging.info("pd_data : {}".format(pd_data))
+ logging.info("data_t : {}".format(data_t))
+ logging.info("median_t : {}".format(median_t))
+ logging.info("last_median_t : {}".format(last_median_t))
+ logging.info("median_t_14 : {}".format(median_t_14))
+ logging.info("max_median : {}".format(max_median))
+
+ # Classification list:
+ classification_lst = list()
+ for build_nr, value in pd_data.iteritems():
+
+ if isnan(data_t[build_nr]) \
+ or isnan(median_t[build_nr]) \
+ or isnan(stdev_t[build_nr]) \
+ or isnan(value):