+ As a side effect, PAL is now part of "resource" package tree.
- Testable only with 23558 (the rest of PAL Python 3 migration).
Change-Id: Icbd90fd71458c07bced86f4bab9fa4e68282c38c
Signed-off-by: Vratko Polak <vrpolak@cisco.com>
--- /dev/null
+# Copyright (c) 2019 Cisco and/or its affiliates.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at:
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+__init__ file for directory presentation
+
+This makes the presentation a part of the great CSIT resources package.
+"""
if "dpdk" in job_name:
hover_text.append(hover_str.format(
date=date,
- value=int(in_data[idx].avg),
+ value=int(in_data[idx]),
sut="dpdk",
build=build_info[job_name][str(idx)][1].rsplit('~', 1)[0],
period="weekly",
elif "vpp" in job_name:
hover_text.append(hover_str.format(
date=date,
- value=int(in_data[idx].avg),
+ value=int(in_data[idx]),
sut="vpp",
build=build_info[job_name][str(idx)][1].rsplit('~', 1)[0],
period="daily",
trace_samples = plgo.Scatter(
x=xaxis,
- y=[y.avg for y in data_y],
+ y=[y for y in data_y], # Was: y.avg
mode='markers',
line={
"width": 1
tst_lst = list()
for bld in builds_dict[job_name]:
itm = tst_data.get(int(bld), '')
- if not isinstance(itm, str):
- itm = itm.avg
+ # CSIT-1180: Itm will be list, compute stats.
tst_lst.append(str(itm))
csv_tbl.append("{0},".format(tst_name) + ",".join(tst_lst) + '\n')
stdev=None)
try:
if plot["include-tests"] == "MRR":
- result = test["result"]["receive-rate"].avg
+ result = test["result"]["receive-rate"] # .avg
elif plot["include-tests"] == "PDR":
result = test["throughput"]["PDR"]["LOWER"]
elif plot["include-tests"] == "NDR":
stdev_c=None)
try:
if plot["include-tests"] == "MRR":
- result = test["result"]["receive-rate"].avg
+ result = test["result"]["receive-rate"] # .avg
elif plot["include-tests"] == "PDR":
result = test["throughput"]["PDR"]["LOWER"]
elif plot["include-tests"] == "NDR":
def _tpc_insert_data(target, src, include_tests):
try:
if include_tests == "MRR":
- target.append(src["result"]["receive-rate"].avg)
+ target.append(src["result"]["receive-rate"]) # .avg)
elif include_tests == "PDR":
target.append(src["throughput"]["PDR"]["LOWER"])
elif include_tests == "NDR":
"cmp-data": list()}
try:
if table["include-tests"] == "MRR":
- result = tst_data["result"]["receive-rate"].avg
+ result = tst_data["result"]["receive-rate"] # .avg
elif table["include-tests"] == "PDR":
result = tst_data["throughput"]["PDR"]["LOWER"]
elif table["include-tests"] == "NDR":
try:
if tst_data["type"] in ("NDRPDR", "MRR", "BMRR"):
if table["include-tests"] == "MRR":
- result = tst_data["result"]["receive-rate"].avg
+ result = tst_data["result"]["receive-rate"]
elif table["include-tests"] == "PDR":
result = tst_data["throughput"]["PDR"]["LOWER"]
elif table["include-tests"] == "NDR":
from datetime import datetime as dt
from datetime import timedelta
from json import loads
-from jumpavg.AvgStdevMetadataFactory import AvgStdevMetadataFactory
+from resources.libraries.python import jumpavg
from input_data_files import download_and_unzip_data_file
"type": "MRR" | "BMRR",
"status": "PASS" | "FAIL",
"result": {
- "receive-rate": AvgStdevMetadata,
+ "receive-rate": float,
+ # Average of a list, computed using AvgStdevStats.
+ # In CSIT-1180, replace with List[float].
}
}
items_str = groups.group(1)
items_float = [float(item.strip()) for item
in items_str.split(",")]
- metadata = AvgStdevMetadataFactory.from_data(items_float)
- # Next two lines have been introduced in CSIT-1179,
- # to be removed in CSIT-1180.
- metadata.size = 1
- metadata.stdev = 0.0
- test_result["result"]["receive-rate"] = metadata
+ # Use whole list in CSIT-1180.
+ stats = jumpavg.AvgStdevStats.for_runs(items_float)
+ test_result["result"]["receive-rate"] = stats.avg
else:
groups = re.search(self.REGEX_MRR, test.message)
test_result["result"]["receive-rate"] = \
- AvgStdevMetadataFactory.from_data([
- float(groups.group(3)) / float(groups.group(1)), ])
+ float(groups.group(3)) / float(groups.group(1))
elif test_result["type"] == "RECONF":
test_result["result"] = None
-jumpavg==0.1.3
Sphinx==1.7.6
sphinx-rtd-theme==0.4.0
robotframework==2.9.2
from pprint import pformat
from errors import PresentationError
-from utils import get_last_successful_build_number
-from utils import get_last_completed_build_number
+from utils import (
+ get_last_successful_build_number, get_last_completed_build_number)
class Specification:
-# Copyright (c) 2018 Cisco and/or its affiliates.
+# Copyright (c) 2019 Cisco and/or its affiliates.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at:
from datetime import datetime
from pandas import Series
+from resources.libraries.python import jumpavg
from errors import PresentationError
-from jumpavg.BitCountingClassifier import BitCountingClassifier
def mean(items):
:returns: Classification and trend values
:rtype: 2-tuple, list of strings and list of floats
"""
- # Nan mean something went wrong.
+ # Nan means something went wrong.
# Use 0.0 to cause that being reported as a severe regression.
- bare_data = [0.0 if np.isnan(sample.avg) else sample
- for _, sample in data.iteritems()]
- # TODO: Put analogous iterator into jumpavg library.
- groups = BitCountingClassifier().classify(bare_data)
- groups.reverse() # Just to use .pop() for FIFO.
+ bare_data = [0.0 if np.isnan(sample) else sample
+ for sample in data.itervalues()]
+ # TODO: Make BitCountingGroupList a subclass of list again?
+ group_list = jumpavg.classify(bare_data).group_list
+ group_list.reverse() # Just to use .pop() for FIFO.
classification = []
avgs = []
active_group = None
values_left = 0
avg = 0.0
- for _, sample in data.iteritems():
- if np.isnan(sample.avg):
+ for sample in data.itervalues():
+ if np.isnan(sample):
classification.append("outlier")
- avgs.append(sample.avg)
+ avgs.append(sample)
continue
if values_left < 1 or active_group is None:
values_left = 0
while values_left < 1: # Ignore empty groups (should not happen).
- active_group = groups.pop()
- values_left = len(active_group.values)
- avg = active_group.metadata.avg
- classification.append(active_group.metadata.classification)
+ active_group = group_list.pop()
+ values_left = len(active_group.run_list)
+ avg = active_group.stats.avg
+ classification.append(active_group.comment)
avgs.append(avg)
values_left -= 1
continue
Code Review / csit.git / commitdiff