diff options
Diffstat (limited to 'resources/tools/presentation/generator_tables.py')
| -rw-r--r-- | resources/tools/presentation/generator_tables.py | 39 |
1 files changed, 26 insertions, 13 deletions
diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index c2007a1a49..0c189426c6 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -722,7 +722,8 @@ def table_performance_trending_dashboard(table, input_data): pd_data = pd.Series(tbl_dict[tst_name]["data"]) last_key = pd_data.keys()[-1] win_size = min(pd_data.size, table["window"]) - key_14 = pd_data.keys()[-(pd_data.size - win_size)] + win_first_idx = pd_data.size - win_size + 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, @@ -730,9 +731,9 @@ def table_performance_trending_dashboard(table, input_data): median_t = data_t.rolling(window=win_size, min_periods=2).median() stdev_t = data_t.rolling(window=win_size, min_periods=2).std() - median_idx = pd_data.size - long_win_size + median_first_idx = pd_data.size - long_win_size try: - max_median = max([x for x in median_t.values[median_idx:] + max_median = max([x for x in median_t.values[median_first_idx:] if not isnan(x)]) except ValueError: max_median = nan @@ -748,6 +749,14 @@ def table_performance_trending_dashboard(table, input_data): # Test name: name = tbl_dict[tst_name]["name"] + 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(): @@ -764,26 +773,30 @@ def table_performance_trending_dashboard(table, input_data): else: classification_lst.append("normal") - if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0: + if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0.0: rel_change_last = nan else: rel_change_last = round( (last_median_t - median_t_14) / median_t_14, 2) - if isnan(max_median) or isnan(last_median_t) or max_median == 0: + 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) - tbl_lst.append([name, - '-' if isnan(last_median_t) else - round(last_median_t / 1000000, 2), - '-' if isnan(rel_change_last) else rel_change_last, - '-' if isnan(rel_change_long) else rel_change_long, - classification_lst[win_size:].count("regression"), - classification_lst[win_size:].count("progression"), - classification_lst[win_size:].count("outlier")]) + logging.info("rel_change_last : {}".format(rel_change_last)) + logging.info("rel_change_long : {}".format(rel_change_long)) + + tbl_lst.append( + [name, + '-' if isnan(last_median_t) else + round(last_median_t / 1000000, 2), + '-' if isnan(rel_change_last) else rel_change_last, + '-' if isnan(rel_change_long) else rel_change_long, + classification_lst[win_first_idx:].count("regression"), + classification_lst[win_first_idx:].count("progression"), + classification_lst[win_first_idx:].count("outlier")]) tbl_lst.sort(key=lambda rel: rel[0]) |