X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=d42c734b9591185618be34a59f7fb95e6a779e49;hp=40eda7b8d96302c88bbb26602124d0900a3c4edc;hb=4b0df8e7baea755e2e1a1c27a7707fb0a3f28b6e;hpb=b8bf181cafb0f4e8a317c308cfe83a3e022ce7c5 diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py index 40eda7b8d9..d42c734b95 100644 --- a/resources/tools/presentation/generator_tables.py +++ b/resources/tools/presentation/generator_tables.py @@ -17,7 +17,6 @@ import logging import csv -import pandas as pd from string import replace from collections import OrderedDict @@ -185,6 +184,8 @@ def table_performance_improvements(table, input_data): """Generate the table(s) with algorithm: table_performance_improvements specified in the specification file. + # FIXME: Not used now. + :param table: Table to generate. :param input_data: Data to process. :type table: pandas.Series @@ -611,7 +612,7 @@ def table_performance_comparison_mrr(table, input_data): "cmp-data": list()} try: tbl_dict[tst_name]["ref-data"].\ - append(tst_data["result"]["throughput"]) + append(tst_data["result"]["receive-rate"].avg) except TypeError: pass # No data in output.xml for this test @@ -620,7 +621,7 @@ def table_performance_comparison_mrr(table, input_data): for tst_name, tst_data in data[job][str(build)].iteritems(): try: tbl_dict[tst_name]["cmp-data"].\ - append(tst_data["result"]["throughput"]) + append(tst_data["result"]["receive-rate"].avg) except KeyError: pass except TypeError: @@ -723,21 +724,21 @@ def table_performance_trending_dashboard(table, input_data): "data": OrderedDict()} try: tbl_dict[tst_name]["data"][str(build)] = \ - tst_data["result"]["throughput"] + tst_data["result"]["receive-rate"] except (TypeError, KeyError): pass # No data in output.xml for this test tbl_lst = list() for tst_name in tbl_dict.keys(): - if len(tbl_dict[tst_name]["data"]) < 2: + data_t = tbl_dict[tst_name]["data"] + if len(data_t) < 2: continue - data_t = pd.Series(tbl_dict[tst_name]["data"]) - classification_lst, avgs = classify_anomalies(data_t) - win_size = min(data_t.size, table["window"]) - long_win_size = min(data_t.size, table["long-trend-window"]) + win_size = min(len(data_t), table["window"]) + long_win_size = min(len(data_t), table["long-trend-window"]) + try: max_long_avg = max( [x for x in avgs[-long_win_size:-win_size]