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authorTibor Frank <tifrank@cisco.com>2018-03-26 14:14:43 +0200
committerTibor Frank <tifrank@cisco.com>2018-03-26 14:14:43 +0200
commit8e75584f75d0e675f1fb050f25b80ee5caa5935d (patch)
treee4997c6a5b907604cb0feda350cb9864ff38d8dc
parent6ef96e8c0a95dc9ccfaf51fe51b60b7934ed6e89 (diff)
Report: Detect outliers for the comparative tables
Change-Id: I87f7f239560544261e21d36d020c56dda0e601fd Signed-off-by: Tibor Frank <tifrank@cisco.com>
-rw-r--r--resources/tools/presentation/generator_tables.py22
1 files changed, 9 insertions, 13 deletions
diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py
index f4fe1be174..a022c65e09 100644
--- a/resources/tools/presentation/generator_tables.py
+++ b/resources/tools/presentation/generator_tables.py
@@ -22,7 +22,7 @@ import prettytable
from string import replace
from errors import PresentationError
-from utils import mean, stdev, relative_change
+from utils import mean, stdev, relative_change, find_outliers
def generate_tables(spec, data):
@@ -401,21 +401,17 @@ def table_performance_comparison(table, input_data):
for tst_name in tbl_dict.keys():
item = [tbl_dict[tst_name]["name"], ]
if tbl_dict[tst_name]["ref-data"]:
- item.append(round(mean(remove_outliers(
- tbl_dict[tst_name]["ref-data"],
- table["outlier-const"])) / 1000000, 2))
- item.append(round(stdev(remove_outliers(
- tbl_dict[tst_name]["ref-data"],
- table["outlier-const"])) / 1000000, 2))
+ data_t, _ = find_outliers(tbl_dict[tst_name]["ref-data"],
+ table["outlier-const"])
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
else:
item.extend([None, None])
if tbl_dict[tst_name]["cmp-data"]:
- item.append(round(mean(remove_outliers(
- tbl_dict[tst_name]["cmp-data"],
- table["outlier-const"])) / 1000000, 2))
- item.append(round(stdev(remove_outliers(
- tbl_dict[tst_name]["cmp-data"],
- table["outlier-const"])) / 1000000, 2))
+ data_t, _ = find_outliers(tbl_dict[tst_name]["cmp-data"],
+ table["outlier-const"])
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
else:
item.extend([None, None])
if item[1] is not None and item[3] is not None: