diff options
| -rw-r--r-- | resources/tools/presentation/generator_tables.py | 44 |
1 files changed, 18 insertions, 26 deletions
diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index 38439bac5c..b2e60be478 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -21,9 +21,8 @@ import prettytable import pandas as pd from string import replace -from math import isnan from collections import OrderedDict -from numpy import nan +from numpy import nan, isnan from xml.etree import ElementTree as ET from errors import PresentationError @@ -730,13 +729,13 @@ def table_performance_trending_dashboard(table, input_data): data = input_data.filter_data(table, continue_on_error=True) # Prepare the header of the tables - header = [" Test Case", + header = ["Test Case", "Trend [Mpps]", - " Short-Term Change [%]", - " Long-Term Change [%]", - " Regressions [#]", - " Progressions [#]", - " Outliers [#]" + "Short-Term Change [%]", + "Long-Term Change [%]", + "Regressions [#]", + "Progressions [#]", + "Outliers [#]" ] header_str = ",".join(header) + "\n" @@ -752,7 +751,7 @@ def table_performance_trending_dashboard(table, input_data): "-".join(tst_data["name"]. split("-")[1:])) tbl_dict[tst_name] = {"name": name, - "data": dict()} + "data": OrderedDict()} try: tbl_dict[tst_name]["data"][str(build)] = \ tst_data["result"]["throughput"] @@ -764,18 +763,16 @@ def table_performance_trending_dashboard(table, input_data): if len(tbl_dict[tst_name]["data"]) > 2: pd_data = pd.Series(tbl_dict[tst_name]["data"]) - 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] - long_win_size = min(pd_data.size, table["long-trend-window"]) - data_t, _ = split_outliers(pd_data, outlier_const=1.5, - window=win_size) - + window=table["window"]) + last_key = data_t.keys()[-1] + win_size = min(data_t.size, table["window"]) + win_first_idx = data_t.size - win_size + key_14 = data_t.keys()[win_first_idx] + long_win_size = min(data_t.size, table["long-trend-window"]) 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_first_idx = pd_data.size - long_win_size + median_first_idx = median_t.size - long_win_size try: max_median = max( [x for x in median_t.values[median_first_idx:-win_size] @@ -791,15 +788,10 @@ def table_performance_trending_dashboard(table, input_data): except KeyError: median_t_14 = nan - # Test name: - name = tbl_dict[tst_name]["name"] - # 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]) \ + for build_nr, value in data_t.iteritems(): + if isnan(median_t[build_nr]) \ or isnan(stdev_t[build_nr]) \ or isnan(value): classification_lst.append("outlier") @@ -823,7 +815,7 @@ def table_performance_trending_dashboard(table, input_data): ((last_median_t - max_median) / max_median) * 100, 2) tbl_lst.append( - [name, + [tbl_dict[tst_name]["name"], '-' if isnan(last_median_t) else round(last_median_t / 1000000, 2), '-' if isnan(rel_change_last) else rel_change_last, |