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authorTibor Frank <tifrank@cisco.com>2018-05-24 13:37:53 +0200
committerTibor Frank <tifrank@cisco.com>2018-05-24 13:48:11 +0200
commit3a90d6c0ba09e47d576c92aab21b2ed9b2dd75ce (patch)
treedda8699219206be6fcf609dd496b43a57b682682
parent372eab0eac428149d547b2d6eb2ce43cd0d750f6 (diff)
FIX: Trending dashboard
Change-Id: I7634a4074647ef226cd6fb3ac1b5e0ee5376c4d4 Signed-off-by: Tibor Frank <tifrank@cisco.com>
-rw-r--r--resources/tools/presentation/generator_tables.py44
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,