last_key = pd_data.keys()[-1]
win_size = min(pd_data.size, table["window"])
win_first_idx = pd_data.size - win_size
- key_14 = pd_data.keys()[-win_first_idx]
+ key_14 = pd_data.keys()[win_first_idx]
long_win_size = min(pd_data.size, table["long-trend-window"])
data_t, _ = split_outliers(pd_data, outlier_const=1.5,
rel_change_last = nan
else:
rel_change_last = round(
- (last_median_t - median_t_14) / median_t_14, 2)
+ ((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, 2)
+ ((last_median_t - max_median) / max_median) * 100, 2)
logging.info("rel_change_last : {}".format(rel_change_last))
logging.info("rel_change_long : {}".format(rel_change_long))