-# Copyright (c) 2019 Cisco and/or its affiliates.
+# Copyright (c) 2020 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:
"""
data_x = list(in_data.keys())
- data_y = [float(item) / 1e6 for item in in_data.values()]
+ data_y_pps = list(in_data.values())
+ data_y_mpps = [float(item) / 1e6 for item in data_y_pps]
hover_text = list()
xaxis = list()
- for idx in data_x:
- date = build_info[job_name][str(idx)][0]
+ 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"value: {value:,}<br>"
+ u"value [Mpps]: {value:.3f}<br>"
u"{sut}-ref: {build}<br>"
u"csit-ref: mrr-{period}-build-{build_nr}<br>"
u"testbed: {testbed}")
if u"dpdk" in job_name:
hover_text.append(hover_str.format(
date=date,
- value=int(in_data[idx]),
+ value=data_y_mpps[index],
sut=u"dpdk",
- build=build_info[job_name][str(idx)][1].rsplit(u'~', 1)[0],
+ build=build_info[job_name][str_key][1].rsplit(u'~', 1)[0],
period=u"weekly",
- build_nr=idx,
- testbed=build_info[job_name][str(idx)][2]))
+ build_nr=str_key,
+ testbed=build_info[job_name][str_key][2]))
elif u"vpp" in job_name:
hover_text.append(hover_str.format(
date=date,
- value=int(in_data[idx]),
+ value=data_y_mpps[index],
sut=u"vpp",
- build=build_info[job_name][str(idx)][1].rsplit(u'~', 1)[0],
+ build=build_info[job_name][str_key][1].rsplit(u'~', 1)[0],
period=u"daily",
- build_nr=idx,
- testbed=build_info[job_name][str(idx)][2]))
+ build_nr=str_key,
+ testbed=build_info[job_name][str_key][2]))
xaxis.append(datetime(int(date[0:4]), int(date[4:6]), int(date[6:8]),
int(date[9:11]), int(date[12:])))
data_pd = OrderedDict()
- for key, value in zip(xaxis, data_y):
+ for key, value in zip(xaxis, data_y_pps):
data_pd[key] = value
- anomaly_classification, avgs = classify_anomalies(data_pd)
+ anomaly_classification, avgs_pps = classify_anomalies(data_pd)
+ avgs_mpps = [avg_pps / 1e6 for avg_pps in avgs_pps]
anomalies = OrderedDict()
anomalies_colors = list()
u"progression": 1.0
}
if anomaly_classification:
- for idx, (key, value) in enumerate(data_pd.items()):
- if anomaly_classification[idx] in \
+ for index, (key, value) in enumerate(data_pd.items()):
+ if anomaly_classification[index] in \
(u"outlier", u"regression", u"progression"):
- anomalies[key] = value
+ anomalies[key] = value / 1e6
anomalies_colors.append(
- anomaly_color[anomaly_classification[idx]])
- anomalies_avgs.append(avgs[idx])
+ anomaly_color[anomaly_classification[index]])
+ anomalies_avgs.append(avgs_mpps[index])
anomalies_colors.extend([0.0, 0.5, 1.0])
# Create traces
trace_samples = plgo.Scatter(
x=xaxis,
- y=data_y,
+ y=data_y_mpps,
mode=u"markers",
line={
u"width": 1
u"symbol": u"circle",
},
text=hover_text,
- hoverinfo=u"text"
+ hoverinfo=u"text+name"
)
traces = [trace_samples, ]
if show_trend_line:
trace_trend = plgo.Scatter(
x=xaxis,
- y=avgs,
+ y=avgs_mpps,
mode=u"lines",
line={
u"shape": u"linear",
showlegend=False,
legendgroup=name,
name=f"{name}",
- text=[f"trend: {int(avg):,}" for avg in avgs],
+ text=[f"trend [Mpps]: {avg:.3f}" for avg in avgs_mpps],
hoverinfo=u"text+name"
)
traces.append(trace_trend)
:rtype: dict
"""
- logs = list()
-
- logs.append(
- (u"INFO", f" Generating the chart {graph.get(u'title', u'')} ...")
- )
+ logging.info(f" Generating the chart {graph.get(u'title', u'')} ...")
job_name = list(graph[u"data"].keys())[0]
res = dict()
# Transform the data
- logs.append(
- (u"INFO",
+ 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, continue_on_error=True
+ graph,
+ params=[u"type", u"result", u"tags"],
+ continue_on_error=True
)
else:
- data = input_data.filter_data(graph, continue_on_error=True)
+ data = input_data.filter_data(
+ graph,
+ params=[u"type", u"result", u"tags"],
+ continue_on_error=True)
if data is None or data.empty:
logging.error(u"No data.")
for tag in group:
for tst_name, test_data in chart_data.items():
if not test_data:
- logs.append(
- (u"WARNING", f"No data for the test {tst_name}")
- )
+ logging.warning(f"No data for the test {tst_name}")
continue
if tag not in chart_tags[tst_name]:
continue
- message = f"index: {index}, test: {tst_name}"
try:
trace, rslt = _generate_trending_traces(
test_data,
split(u'-')[2:-1]),
color=COLORS[index])
except IndexError:
- logs.append(
- (u"ERROR", f"Out of colors: {message}")
- )
- logging.error(f"Out of colors: {message}")
+ logging.error(f"Out of colors: index: "
+ f"{index}, test: {tst_name}")
index += 1
continue
traces.extend(trace)
else:
for tst_name, test_data in chart_data.items():
if not test_data:
- logs.append(
- (u"WARNING", f"No data for the test {tst_name}")
- )
+ logging.warning(f"No data for the test {tst_name}")
continue
- message = f"index: {index}, test: {tst_name}"
try:
trace, rslt = _generate_trending_traces(
test_data,
tst_name.split(u'.')[-1].split(u'-')[2:-1]),
color=COLORS[index])
except IndexError:
- logs.append((u"ERROR", f"Out of colors: {message}"))
- logging.error(f"Out of colors: {message}")
+ logging.error(
+ f"Out of colors: index: {index}, test: {tst_name}"
+ )
index += 1
continue
traces.extend(trace)
f"{spec.cpta[u'output-file']}/{graph[u'output-file-name']}"
f"{spec.cpta[u'output-file-type']}")
- logs.append((u"INFO", f" Writing the file {name_file} ..."))
+ 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:
- logs.append((u"WARNING", u"No data for the plot. Skipped."))
-
- for level, line in logs:
- if level == u"INFO":
- logging.info(line)
- elif level == u"ERROR":
- logging.error(line)
- elif level == u"DEBUG":
- logging.debug(line)
- elif level == u"CRITICAL":
- logging.critical(line)
- elif level == u"WARNING":
- logging.warning(line)
+ logging.warning(u"No data for the plot. Skipped.")
return {u"job_name": job_name, u"csv_table": csv_tbl, u"results": res}
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"):
+ if status not in (u"failed", u"not found", u"removed", None):
builds_dict[job].append(str(build[u"build"]))
# Create "build ID": "date" dict:
# Write the tables:
for job_name, csv_table in csv_tables.items():
file_name = f"{spec.cpta[u'output-file']}/{job_name}-trending"
- with open(f"{file_name}.csv", u"w") as file_handler:
+ with open(f"{file_name}.csv", u"wt") as file_handler:
file_handler.writelines(csv_table)
txt_table = None
)
line_nr += 1
txt_table.align[u"Build Number:"] = u"l"
- with open(f"{file_name}.txt", u"w") as txt_file:
+ with open(f"{file_name}.txt", u"wt") as txt_file:
txt_file.write(str(txt_table))
# Evaluate result:
Code Review / csit.git / blobdiff