import plotly.offline as ploff
import plotly.graph_objs as plgo
import plotly.exceptions as plerr
-import pandas as pd
from collections import OrderedDict
from datetime import datetime
-from utils import split_outliers, archive_input_data, execute_command,\
+from utils import archive_input_data, execute_command, \
classify_anomalies, Worker
return ret_code
-def _generate_trending_traces(in_data, job_name, build_info, moving_win_size=10,
+def _generate_trending_traces(in_data, job_name, build_info,
show_trend_line=True, name="", color=""):
"""Generate the trending traces:
- samples,
- - trimmed moving median (trending line)
- outliers, regress, progress
+ - average of normal samples (trending line)
:param in_data: Full data set.
:param job_name: The name of job which generated the data.
:param build_info: Information about the builds.
- :param moving_win_size: Window size.
:param show_trend_line: Show moving median (trending plot).
:param name: Name of the plot
:param color: Name of the color for the plot.
:type in_data: OrderedDict
:type job_name: str
:type build_info: dict
- :type moving_win_size: int
:type show_trend_line: bool
:type name: str
:type color: str
hover_text = list()
xaxis = list()
for idx in data_x:
+ date = build_info[job_name][str(idx)][0]
+ hover_str = ("date: {0}<br>"
+ "value: {1:,}<br>"
+ "{2}-ref: {3}<br>"
+ "csit-ref: mrr-{4}-build-{5}")
if "dpdk" in job_name:
- hover_text.append("dpdk-ref: {0}<br>csit-ref: mrr-weekly-build-{1}".
- format(build_info[job_name][str(idx)][1].
- rsplit('~', 1)[0], idx))
+ hover_text.append(hover_str.format(
+ date,
+ int(in_data[idx].avg),
+ "dpdk",
+ build_info[job_name][str(idx)][1].
+ rsplit('~', 1)[0],
+ "weekly",
+ idx))
elif "vpp" in job_name:
- hover_text.append("vpp-ref: {0}<br>csit-ref: mrr-daily-build-{1}".
- format(build_info[job_name][str(idx)][1].
- rsplit('~', 1)[0], idx))
- date = build_info[job_name][str(idx)][0]
+ hover_text.append(hover_str.format(
+ date,
+ int(in_data[idx].avg),
+ "vpp",
+ build_info[job_name][str(idx)][1].
+ rsplit('~', 1)[0],
+ "daily",
+ idx))
+
xaxis.append(datetime(int(date[0:4]), int(date[4:6]), int(date[6:8]),
int(date[9:11]), int(date[12:])))
- data_pd = pd.Series(data_y, index=xaxis)
+ data_pd = OrderedDict()
+ for key, value in zip(xaxis, data_y):
+ data_pd[key] = value
- t_data, outliers = split_outliers(data_pd, outlier_const=1.5,
- window=moving_win_size)
- anomaly_classification = classify_anomalies(t_data, window=moving_win_size)
+ anomaly_classification, avgs = classify_anomalies(data_pd)
- anomalies = pd.Series()
+ anomalies = OrderedDict()
anomalies_colors = list()
+ anomalies_avgs = list()
anomaly_color = {
- "outlier": 0.0,
- "regression": 0.33,
- "normal": 0.66,
+ "regression": 0.0,
+ "normal": 0.5,
"progression": 1.0
}
if anomaly_classification:
- for idx, item in enumerate(data_pd.items()):
+ for idx, (key, value) in enumerate(data_pd.iteritems()):
if anomaly_classification[idx] in \
("outlier", "regression", "progression"):
- anomalies = anomalies.append(pd.Series([item[1], ],
- index=[item[0], ]))
+ anomalies[key] = value
anomalies_colors.append(
anomaly_color[anomaly_classification[idx]])
- anomalies_colors.extend([0.0, 0.33, 0.66, 1.0])
+ anomalies_avgs.append(avgs[idx])
+ anomalies_colors.extend([0.0, 0.5, 1.0])
# Create traces
trace_samples = plgo.Scatter(
x=xaxis,
- y=data_y,
+ y=[y.avg for y in data_y],
mode='markers',
line={
"width": 1
"symbol": "circle",
},
text=hover_text,
- hoverinfo="x+y+text+name"
+ hoverinfo="text"
)
traces = [trace_samples, ]
+ if show_trend_line:
+ trace_trend = plgo.Scatter(
+ x=xaxis,
+ y=avgs,
+ mode='lines',
+ line={
+ "shape": "linear",
+ "width": 1,
+ "color": color,
+ },
+ showlegend=False,
+ legendgroup=name,
+ name='{name}'.format(name=name),
+ text=["trend: {0:,}".format(int(avg)) for avg in avgs],
+ hoverinfo="text+name"
+ )
+ traces.append(trace_trend)
+
trace_anomalies = plgo.Scatter(
x=anomalies.keys(),
- y=anomalies.values,
+ y=anomalies_avgs,
mode='markers',
hoverinfo="none",
showlegend=False,
"size": 15,
"symbol": "circle-open",
"color": anomalies_colors,
- "colorscale": [[0.00, "grey"],
- [0.25, "grey"],
- [0.25, "red"],
- [0.50, "red"],
- [0.50, "white"],
- [0.75, "white"],
- [0.75, "green"],
+ "colorscale": [[0.00, "red"],
+ [0.33, "red"],
+ [0.33, "white"],
+ [0.66, "white"],
+ [0.66, "green"],
[1.00, "green"]],
"showscale": True,
"line": {
"size": 14
},
"tickmode": 'array',
- "tickvals": [0.125, 0.375, 0.625, 0.875],
- "ticktext": ["Outlier", "Regression", "Normal", "Progression"],
+ "tickvals": [0.167, 0.500, 0.833],
+ "ticktext": ["Regression", "Normal", "Progression"],
"ticks": "",
"ticklen": 0,
"tickangle": -90,
)
traces.append(trace_anomalies)
- if show_trend_line:
- data_trend = t_data.rolling(window=moving_win_size,
- min_periods=2).median()
- trace_trend = plgo.Scatter(
- x=data_trend.keys(),
- y=data_trend.tolist(),
- mode='lines',
- line={
- "shape": "spline",
- "width": 1,
- "color": color,
- },
- showlegend=False,
- legendgroup=name,
- name='{name}-trend'.format(name=name)
- )
- traces.append(trace_trend)
-
if anomaly_classification:
return traces, anomaly_classification[-1]
else:
chart_data[test_name] = OrderedDict()
try:
chart_data[test_name][int(index)] = \
- test["result"]["throughput"]
+ test["result"]["receive-rate"]
except (KeyError, TypeError):
pass
tst_lst = list()
for bld in builds_dict[job_name]:
itm = tst_data.get(int(bld), '')
+ if not isinstance(itm, str):
+ itm = itm.avg
tst_lst.append(str(itm))
csv_tbl.append("{0},".format(tst_name) + ",".join(tst_lst) + '\n')
# Generate traces:
traces = list()
- win_size = 14
index = 0
for test_name, test_data in chart_data.items():
if not test_data:
test_data,
job_name=job_name,
build_info=build_info,
- moving_win_size=win_size,
- name='-'.join(test_name.split('-')[3:-1]),
+ name='-'.join(test_name.split('-')[2:-1]),
color=COLORS[index])
traces.extend(trace)
res.append(rslt)
Code Review / csit.git / blobdiff