-# Copyright (c) 2020 Cisco and/or its affiliates.
+# Copyright (c) 2021 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:
"""Generation of Continuous Performance Trending and Analysis.
"""
+import re
import logging
import csv
# Command to build the html format of the report
-HTML_BUILDER = u'sphinx-build -v -c conf_cpta -a ' \
+HTML_BUILDER = u'sphinx-build -v -c sphinx_conf/trending -a ' \
u'-b html -E ' \
u'-t html ' \
u'-D version="{date}" ' \
css_file:
css_file.write(THEME_OVERRIDES)
- if spec.configuration.get(u"archive-inputs", True):
+ if spec.environment.get(u"archive-inputs", False):
archive_input_data(spec)
logging.info(u"Done.")
def _generate_trending_traces(in_data, job_name, build_info,
- name=u"", color=u"", incl_tests=u"MRR"):
+ name=u"", color=u"", incl_tests=u"mrr"):
"""Generate the trending traces:
- samples,
- outliers, regress, progress
:param build_info: Information about the builds.
:param name: Name of the plot
:param color: Name of the color for the plot.
- :param incl_tests: Included tests, accepted values: MRR, NDR, PDR
+ :param incl_tests: Included tests, accepted values: mrr, ndr, pdr
:type in_data: OrderedDict
:type job_name: str
:type build_info: dict
:rtype: tuple(traces, result)
"""
- if incl_tests not in (u"MRR", u"NDR", u"PDR"):
+ if incl_tests not in (u"mrr", u"ndr", u"pdr", u"pdr-lat"):
return list(), None
data_x = list(in_data.keys())
data_y_pps = list()
data_y_mpps = list()
data_y_stdev = list()
- for item in in_data.values():
- data_y_pps.append(float(item[u"receive-rate"]))
- data_y_stdev.append(float(item[u"receive-stdev"]) / 1e6)
- data_y_mpps.append(float(item[u"receive-rate"]) / 1e6)
-
+ if incl_tests == u"pdr-lat":
+ for item in in_data.values():
+ data_y_pps.append(float(item.get(u"lat_1", u"nan")) / 1e6)
+ data_y_stdev.append(float(u"nan"))
+ data_y_mpps.append(float(item.get(u"lat_1", u"nan")) / 1e6)
+ multi = 1.0
+ else:
+ for item in in_data.values():
+ data_y_pps.append(float(item[u"receive-rate"]))
+ data_y_stdev.append(float(item[u"receive-stdev"]) / 1e6)
+ data_y_mpps.append(float(item[u"receive-rate"]) / 1e6)
+ multi = 1e6
hover_text = list()
xaxis = list()
for index, key in enumerate(data_x):
str_key = str(key)
date = build_info[job_name][str_key][0]
hover_str = (u"date: {date}<br>"
- u"{property} [Mpps]: {value:.3f}<br>"
+ u"{property} [Mpps]: <val><br>"
u"<stdev>"
u"{sut}-ref: {build}<br>"
u"csit-ref: {test}-{period}-build-{build_nr}<br>"
u"testbed: {testbed}")
- if incl_tests == u"MRR":
+ if incl_tests == u"mrr":
hover_str = hover_str.replace(
u"<stdev>", f"stdev [Mpps]: {data_y_stdev[index]:.3f}<br>"
)
else:
hover_str = hover_str.replace(u"<stdev>", u"")
+ if incl_tests == u"pdr-lat":
+ hover_str = hover_str.replace(u"<val>", u"{value:.1e}")
+ else:
+ hover_str = hover_str.replace(u"<val>", u"{value:.3f}")
if u"-cps" in name:
- hover_str = hover_str.replace(u"[Mpps]", u"[Mcps]")
+ hover_str = hover_str.replace(u"[Mpps]", u"[Mcps]").\
+ replace(u"throughput", u"connection rate")
if u"dpdk" in job_name:
- hover_text.append(hover_str.format(
+ hover_str = hover_str.format(
date=date,
- property=u"average" if incl_tests == u"MRR" else u"throughput",
+ property=u"average" if incl_tests == u"mrr" else u"throughput",
value=data_y_mpps[index],
sut=u"dpdk",
build=build_info[job_name][str_key][1].rsplit(u'~', 1)[0],
- test=incl_tests.lower(),
+ test=incl_tests,
period=u"weekly",
build_nr=str_key,
- testbed=build_info[job_name][str_key][2]))
+ testbed=build_info[job_name][str_key][2])
elif u"vpp" in job_name:
hover_str = hover_str.format(
date=date,
- property=u"average" if incl_tests == u"MRR" else u"throughput",
+ property=u"average" if incl_tests == u"mrr" else u"throughput",
value=data_y_mpps[index],
sut=u"vpp",
build=build_info[job_name][str_key][1].rsplit(u'~', 1)[0],
- test=incl_tests.lower(),
- period=u"daily" if incl_tests == u"MRR" else u"weekly",
+ test=incl_tests,
+ period=u"daily" if incl_tests == u"mrr" else u"weekly",
build_nr=str_key,
testbed=build_info[job_name][str_key][2])
- if u"-cps" in name:
- hover_str = hover_str.replace(u"throughput", u"connection rate")
- hover_text.append(hover_str)
-
+ if incl_tests == u"pdr-lat":
+ hover_str = hover_str.replace(
+ u"throughput [Mpps]", u"latency [s]"
+ )
+ hover_text.append(hover_str)
xaxis.append(datetime(int(date[0:4]), int(date[4:6]), int(date[6:8]),
int(date[9:11]), int(date[12:])))
for key, value in zip(xaxis, data_y_pps):
data_pd[key] = value
- anomaly_classification, avgs_pps, stdevs_pps = classify_anomalies(data_pd)
- avgs_mpps = [avg_pps / 1e6 for avg_pps in avgs_pps]
- stdevs_mpps = [stdev_pps / 1e6 for stdev_pps in stdevs_pps]
+ try:
+ anomaly_classification, avgs_pps, stdevs_pps = \
+ classify_anomalies(data_pd)
+ except ValueError as err:
+ logging.info(f"{err} Skipping")
+ return list(), None
+ avgs_mpps = [avg_pps / multi for avg_pps in avgs_pps]
+ stdevs_mpps = [stdev_pps / multi for stdev_pps in stdevs_pps]
anomalies = OrderedDict()
anomalies_colors = list()
if anomaly_classification:
for index, (key, value) in enumerate(data_pd.items()):
if anomaly_classification[index] in (u"regression", u"progression"):
- anomalies[key] = value / 1e6
+ anomalies[key] = value / multi
anomalies_colors.append(
anomaly_color[anomaly_classification[index]])
anomalies_avgs.append(avgs_mpps[index])
trend_hover_text = list()
for idx in range(len(data_x)):
- trend_hover_str = (
- f"trend [Mpps]: {avgs_mpps[idx]:.3f}<br>"
- f"stdev [Mpps]: {stdevs_mpps[idx]:.3f}"
- )
+ if incl_tests == u"pdr-lat":
+ trend_hover_str = (
+ f"trend [s]: {avgs_mpps[idx]:.1e}<br>"
+ )
+ else:
+ trend_hover_str = (
+ f"trend [Mpps]: {avgs_mpps[idx]:.3f}<br>"
+ f"stdev [Mpps]: {stdevs_mpps[idx]:.3f}"
+ )
trend_hover_text.append(trend_hover_str)
trace_trend = plgo.Scatter(
)
traces.append(trace_trend)
+ if incl_tests == u"pdr-lat":
+ colorscale = [
+ [0.00, u"green"],
+ [0.33, u"green"],
+ [0.33, u"white"],
+ [0.66, u"white"],
+ [0.66, u"red"],
+ [1.00, u"red"]
+ ]
+ ticktext = [u"Progression", u"Normal", u"Regression"]
+ else:
+ colorscale = [
+ [0.00, u"red"],
+ [0.33, u"red"],
+ [0.33, u"white"],
+ [0.66, u"white"],
+ [0.66, u"green"],
+ [1.00, u"green"]
+ ]
+ ticktext = [u"Regression", u"Normal", u"Progression"]
trace_anomalies = plgo.Scatter(
x=list(anomalies.keys()),
y=anomalies_avgs,
u"size": 15,
u"symbol": u"circle-open",
u"color": anomalies_colors,
- u"colorscale": [
- [0.00, u"red"],
- [0.33, u"red"],
- [0.33, u"white"],
- [0.66, u"white"],
- [0.66, u"green"],
- [1.00, u"green"]
- ],
+ u"colorscale": colorscale,
u"showscale": True,
u"line": {
u"width": 2
},
u"tickmode": u"array",
u"tickvals": [0.167, 0.500, 0.833],
- u"ticktext": [u"Regression", u"Normal", u"Progression"],
+ u"ticktext": ticktext,
u"ticks": u"",
u"ticklen": 0,
u"tickangle": -90,
logging.info(f" Generating the chart {graph.get(u'title', u'')} ...")
- incl_tests = graph.get(u"include-tests", u"MRR")
-
job_name = list(graph[u"data"].keys())[0]
- csv_tbl = list()
- res = dict()
-
# Transform the data
logging.info(
f" Creating the data set for the {graph.get(u'type', u'')} "
f"{graph.get(u'title', u'')}."
)
- if graph.get(u"include", None):
- data = input_data.filter_tests_by_name(
- graph,
- params=[u"type", u"result", u"throughput", u"tags"],
- continue_on_error=True
- )
- else:
- data = input_data.filter_data(
- graph,
- params=[u"type", u"result", u"throughput", u"tags"],
- continue_on_error=True)
+ data = input_data.filter_tests_by_name(
+ graph,
+ params=[u"type", u"result", u"throughput", u"latency", u"tags"],
+ continue_on_error=True
+ )
if data is None or data.empty:
logging.error(u"No data.")
return dict()
- chart_data = dict()
- chart_tags = dict()
- for job, job_data in data.items():
- if job != job_name:
- continue
- for index, bld in job_data.items():
- for test_name, test in bld.items():
- if chart_data.get(test_name, None) is None:
- chart_data[test_name] = OrderedDict()
- try:
- if incl_tests == u"MRR":
- rate = test[u"result"][u"receive-rate"]
- stdev = test[u"result"][u"receive-stdev"]
- elif incl_tests == u"NDR":
- rate = test[u"throughput"][u"NDR"][u"LOWER"]
- stdev = float(u"nan")
- elif incl_tests == u"PDR":
- rate = test[u"throughput"][u"PDR"][u"LOWER"]
- stdev = float(u"nan")
- else:
+ return_lst = list()
+
+ for ttype in graph.get(u"test-type", (u"mrr", )):
+ for core in graph.get(u"core", tuple()):
+ csv_tbl = list()
+ csv_tbl_lat_1 = list()
+ csv_tbl_lat_2 = list()
+ res = dict()
+ chart_data = dict()
+ chart_tags = dict()
+ for item in graph.get(u"include", tuple()):
+ reg_ex = re.compile(str(item.format(core=core)).lower())
+ for job, job_data in data.items():
+ if job != job_name:
continue
- chart_data[test_name][int(index)] = {
- u"receive-rate": rate,
- u"receive-stdev": stdev
- }
- chart_tags[test_name] = test.get(u"tags", None)
- except (KeyError, TypeError):
- pass
-
- # Add items to the csv table:
- for tst_name, tst_data in chart_data.items():
- tst_lst = list()
- for bld in builds_dict[job_name]:
- itm = tst_data.get(int(bld), dict())
- # CSIT-1180: Itm will be list, compute stats.
- try:
- tst_lst.append(str(itm.get(u"receive-rate", u"")))
- except AttributeError:
- tst_lst.append(u"")
- csv_tbl.append(f"{tst_name}," + u",".join(tst_lst) + u'\n')
-
- # Generate traces:
- traces = list()
- index = 0
- groups = graph.get(u"groups", None)
- visibility = list()
-
- if groups:
- for group in groups:
- visible = list()
- for tag in group:
+ for index, bld in job_data.items():
+ for test_id, test in bld.items():
+ if not re.match(reg_ex, str(test_id).lower()):
+ continue
+ if chart_data.get(test_id, None) is None:
+ chart_data[test_id] = OrderedDict()
+ try:
+ lat_1 = u""
+ lat_2 = u""
+ if ttype == u"mrr":
+ rate = test[u"result"][u"receive-rate"]
+ stdev = \
+ test[u"result"][u"receive-stdev"]
+ elif ttype == u"ndr":
+ rate = \
+ test["throughput"][u"NDR"][u"LOWER"]
+ stdev = float(u"nan")
+ elif ttype == u"pdr":
+ rate = \
+ test["throughput"][u"PDR"][u"LOWER"]
+ stdev = float(u"nan")
+ lat_1 = test[u"latency"][u"PDR50"]\
+ [u"direction1"][u"avg"]
+ lat_2 = test[u"latency"][u"PDR50"]\
+ [u"direction2"][u"avg"]
+ else:
+ continue
+ chart_data[test_id][int(index)] = {
+ u"receive-rate": rate,
+ u"receive-stdev": stdev
+ }
+ if ttype == u"pdr":
+ chart_data[test_id][int(index)].update(
+ {
+ u"lat_1": lat_1,
+ u"lat_2": lat_2
+ }
+ )
+ chart_tags[test_id] = \
+ test.get(u"tags", None)
+ except (KeyError, TypeError):
+ pass
+
+ # Add items to the csv table:
+ for tst_name, tst_data in chart_data.items():
+ tst_lst = list()
+ tst_lst_lat_1 = list()
+ tst_lst_lat_2 = list()
+ for bld in builds_dict[job_name]:
+ itm = tst_data.get(int(bld), dict())
+ # CSIT-1180: Itm will be list, compute stats.
+ try:
+ tst_lst.append(str(itm.get(u"receive-rate", u"")))
+ if ttype == u"pdr":
+ tst_lst_lat_1.append(
+ str(itm.get(u"lat_1", u""))
+ )
+ tst_lst_lat_2.append(
+ str(itm.get(u"lat_2", u""))
+ )
+ except AttributeError:
+ tst_lst.append(u"")
+ if ttype == u"pdr":
+ tst_lst_lat_1.append(u"")
+ tst_lst_lat_2.append(u"")
+ csv_tbl.append(f"{tst_name}," + u",".join(tst_lst) + u'\n')
+ csv_tbl_lat_1.append(
+ f"{tst_name}," + u",".join(tst_lst_lat_1) + u"\n"
+ )
+ csv_tbl_lat_2.append(
+ f"{tst_name}," + u",".join(tst_lst_lat_2) + u"\n"
+ )
+
+ # Generate traces:
+ traces = list()
+ traces_lat = list()
+ index = 0
+ groups = graph.get(u"groups", None)
+ visibility = list()
+
+ if groups:
+ for group in groups:
+ visible = list()
+ for tag in group:
+ for tst_name, test_data in chart_data.items():
+ if not test_data:
+ logging.warning(
+ f"No data for the test {tst_name}"
+ )
+ continue
+ if tag not in chart_tags[tst_name]:
+ continue
+ try:
+ trace, rslt = _generate_trending_traces(
+ test_data,
+ job_name=job_name,
+ build_info=build_info,
+ name=u'-'.join(tst_name.split(u'.')[-1].
+ split(u'-')[2:-1]),
+ color=COLORS[index],
+ incl_tests=ttype
+ )
+ except IndexError:
+ logging.error(f"Out of colors: index: "
+ f"{index}, test: {tst_name}")
+ index += 1
+ continue
+ traces.extend(trace)
+ visible.extend(
+ [True for _ in range(len(trace))]
+ )
+ res[tst_name] = rslt
+ index += 1
+ break
+ visibility.append(visible)
+ else:
for tst_name, test_data in chart_data.items():
if not test_data:
logging.warning(f"No data for the test {tst_name}")
continue
- if tag not in chart_tags[tst_name]:
- continue
try:
trace, rslt = _generate_trending_traces(
test_data,
job_name=job_name,
build_info=build_info,
- name=u'-'.join(tst_name.split(u'.')[-1].
- split(u'-')[2:-1]),
+ name=u'-'.join(
+ tst_name.split(u'.')[-1].split(u'-')[2:-1]),
color=COLORS[index],
- incl_tests=incl_tests
+ incl_tests=ttype
)
+ if ttype == u"pdr":
+ trace_lat, _ = _generate_trending_traces(
+ test_data,
+ job_name=job_name,
+ build_info=build_info,
+ name=u'-'.join(
+ tst_name.split(u'.')[-1].split(
+ u'-')[2:-1]),
+ color=COLORS[index],
+ incl_tests=u"pdr-lat"
+ )
+ traces_lat.extend(trace_lat)
except IndexError:
- logging.error(f"Out of colors: index: "
- f"{index}, test: {tst_name}")
+ logging.error(
+ f"Out of colors: index: "
+ f"{index}, test: {tst_name}"
+ )
index += 1
continue
traces.extend(trace)
- visible.extend([True for _ in range(len(trace))])
res[tst_name] = rslt
index += 1
- break
- visibility.append(visible)
- else:
- for tst_name, test_data in chart_data.items():
- if not test_data:
- logging.warning(f"No data for the test {tst_name}")
- continue
- try:
- trace, rslt = _generate_trending_traces(
- test_data,
- job_name=job_name,
- build_info=build_info,
- name=u'-'.join(
- tst_name.split(u'.')[-1].split(u'-')[2:-1]),
- color=COLORS[index],
- incl_tests=incl_tests
- )
- except IndexError:
- logging.error(
- f"Out of colors: index: {index}, test: {tst_name}"
+
+ if traces:
+ # Generate the chart:
+ try:
+ layout = deepcopy(graph[u"layout"])
+ except KeyError as err:
+ logging.error(u"Finished with error: No layout defined")
+ logging.error(repr(err))
+ return dict()
+ if groups:
+ show = list()
+ for i in range(len(visibility)):
+ visible = list()
+ for vis_idx, _ in enumerate(visibility):
+ for _ in range(len(visibility[vis_idx])):
+ visible.append(i == vis_idx)
+ show.append(visible)
+
+ buttons = list()
+ buttons.append(dict(
+ label=u"All",
+ method=u"update",
+ args=[{u"visible":
+ [True for _ in range(len(show[0]))]}, ]
+ ))
+ for i in range(len(groups)):
+ try:
+ label = graph[u"group-names"][i]
+ except (IndexError, KeyError):
+ label = f"Group {i + 1}"
+ buttons.append(dict(
+ label=label,
+ method=u"update",
+ args=[{u"visible": show[i]}, ]
+ ))
+
+ layout[u"updatemenus"] = list([
+ dict(
+ active=0,
+ type=u"dropdown",
+ direction=u"down",
+ xanchor=u"left",
+ yanchor=u"bottom",
+ x=-0.12,
+ y=1.0,
+ buttons=buttons
+ )
+ ])
+
+ name_file = (
+ f"{spec.cpta[u'output-file']}/"
+ f"{graph[u'output-file-name']}.html"
)
- index += 1
- continue
- traces.extend(trace)
- res[tst_name] = rslt
- index += 1
-
- if traces:
- # Generate the chart:
- try:
- layout = deepcopy(graph[u"layout"])
- except KeyError as err:
- logging.error(u"Finished with error: No layout defined")
- logging.error(repr(err))
- return dict()
- if groups:
- show = list()
- for i in range(len(visibility)):
- visible = list()
- for vis_idx, _ in enumerate(visibility):
- for _ in range(len(visibility[vis_idx])):
- visible.append(i == vis_idx)
- show.append(visible)
-
- buttons = list()
- buttons.append(dict(
- label=u"All",
- method=u"update",
- args=[{u"visible": [True for _ in range(len(show[0]))]}, ]
- ))
- for i in range(len(groups)):
+ name_file = name_file.format(core=core, test_type=ttype)
+
+ logging.info(f" Writing the file {name_file}")
+ plpl = plgo.Figure(data=traces, layout=layout)
try:
- label = graph[u"group-names"][i]
- except (IndexError, KeyError):
- label = f"Group {i + 1}"
- buttons.append(dict(
- label=label,
- method=u"update",
- args=[{u"visible": show[i]}, ]
- ))
-
- layout[u"updatemenus"] = list([
- dict(
- active=0,
- type=u"dropdown",
- direction=u"down",
- xanchor=u"left",
- yanchor=u"bottom",
- x=-0.12,
- y=1.0,
- buttons=buttons
+ ploff.plot(
+ plpl,
+ show_link=False,
+ auto_open=False,
+ filename=name_file
+ )
+ except plerr.PlotlyEmptyDataError:
+ logging.warning(u"No data for the plot. Skipped.")
+
+ if traces_lat:
+ try:
+ layout = deepcopy(graph[u"layout"])
+ layout[u"yaxis"][u"title"] = u"Latency [s]"
+ layout[u"yaxis"][u"tickformat"] = u".3s"
+ except KeyError as err:
+ logging.error(u"Finished with error: No layout defined")
+ logging.error(repr(err))
+ return dict()
+ name_file = (
+ f"{spec.cpta[u'output-file']}/"
+ f"{graph[u'output-file-name']}-lat.html"
)
- ])
+ name_file = name_file.format(core=core, test_type=ttype)
- name_file = (
- f"{spec.cpta[u'output-file']}/{graph[u'output-file-name']}"
- f"{spec.cpta[u'output-file-type']}")
+ logging.info(f" Writing the file {name_file}")
+ plpl = plgo.Figure(data=traces_lat, layout=layout)
+ try:
+ ploff.plot(
+ plpl,
+ show_link=False,
+ auto_open=False,
+ filename=name_file
+ )
+ except plerr.PlotlyEmptyDataError:
+ logging.warning(u"No data for the plot. Skipped.")
- logging.info(f" Writing the file {name_file} ...")
- plpl = plgo.Figure(data=traces, layout=layout)
- try:
- ploff.plot(plpl, show_link=False, auto_open=False,
- filename=name_file)
- except plerr.PlotlyEmptyDataError:
- logging.warning(u"No data for the plot. Skipped.")
+ return_lst.append(
+ {
+ u"job_name": job_name,
+ u"csv_table": csv_tbl,
+ u"csv_lat_1": csv_tbl_lat_1,
+ u"csv_lat_2": csv_tbl_lat_2,
+ u"results": res
+ }
+ )
- return {u"job_name": job_name, u"csv_table": csv_tbl, u"results": res}
+ return return_lst
builds_dict = dict()
- for job in spec.input[u"builds"].keys():
+ for job, builds in spec.input.items():
if builds_dict.get(job, None) is None:
builds_dict[job] = list()
- for build in spec.input[u"builds"][job]:
- status = build[u"status"]
- if status not in (u"failed", u"not found", u"removed", None):
+ for build in builds:
+ if build[u"status"] not in (u"failed", u"not found", u"removed",
+ None):
builds_dict[job].append(str(build[u"build"]))
# Create "build ID": "date" dict:
# Create the table header:
csv_tables = dict()
+ csv_tables_l1 = dict()
+ csv_tables_l2 = dict()
for job_name in builds_dict:
if csv_tables.get(job_name, None) is None:
csv_tables[job_name] = list()
+ if csv_tables_l1.get(job_name, None) is None:
+ csv_tables_l1[job_name] = list()
+ if csv_tables_l2.get(job_name, None) is None:
+ csv_tables_l2[job_name] = list()
header = f"Build Number:,{u','.join(builds_dict[job_name])}\n"
csv_tables[job_name].append(header)
+ csv_tables_l1[job_name].append(header)
+ csv_tables_l2[job_name].append(header)
build_dates = [x[0] for x in build_info[job_name].values()]
header = f"Build Date:,{u','.join(build_dates)}\n"
csv_tables[job_name].append(header)
+ csv_tables_l1[job_name].append(header)
+ csv_tables_l2[job_name].append(header)
versions = [x[1] for x in build_info[job_name].values()]
header = f"Version:,{u','.join(versions)}\n"
csv_tables[job_name].append(header)
+ csv_tables_l1[job_name].append(header)
+ csv_tables_l2[job_name].append(header)
+ testbed = [x[2] for x in build_info[job_name].values()]
+ header = f"Test bed:,{u','.join(testbed)}\n"
+ csv_tables[job_name].append(header)
+ csv_tables_l1[job_name].append(header)
+ csv_tables_l2[job_name].append(header)
for chart in spec.cpta[u"plots"]:
- result = _generate_chart(chart)
- if not result:
+ results = _generate_chart(chart)
+ if not results:
continue
- csv_tables[result[u"job_name"]].extend(result[u"csv_table"])
+ for result in results:
+ csv_tables[result[u"job_name"]].extend(result[u"csv_table"])
+ csv_tables_l1[result[u"job_name"]].extend(result[u"csv_lat_1"])
+ csv_tables_l2[result[u"job_name"]].extend(result[u"csv_lat_2"])
- if anomaly_classifications.get(result[u"job_name"], None) is None:
- anomaly_classifications[result[u"job_name"]] = dict()
- anomaly_classifications[result[u"job_name"]].update(result[u"results"])
+ if anomaly_classifications.get(result[u"job_name"], None) is None:
+ anomaly_classifications[result[u"job_name"]] = dict()
+ anomaly_classifications[result[u"job_name"]].\
+ update(result[u"results"])
# Write the tables:
for job_name, csv_table in csv_tables.items():
with open(f"{file_name}.txt", u"wt") as txt_file:
txt_file.write(str(txt_table))
+ for job_name, csv_table in csv_tables_l1.items():
+ file_name = f"{spec.cpta[u'output-file']}/{job_name}-lat-P50-50-d1"
+ with open(f"{file_name}.csv", u"wt") as file_handler:
+ file_handler.writelines(csv_table)
+ for job_name, csv_table in csv_tables_l2.items():
+ file_name = f"{spec.cpta[u'output-file']}/{job_name}-lat-P50-50-d2"
+ with open(f"{file_name}.csv", u"wt") as file_handler:
+ file_handler.writelines(csv_table)
+
# Evaluate result:
if anomaly_classifications:
result = u"PASS"
Code Review / csit.git / blobdiff