import logging
from collections import OrderedDict
+from datetime import datetime
from copy import deepcopy
from math import log
import pandas as pd
import plotly.offline as ploff
import plotly.graph_objs as plgo
+import plotly.exceptions as plerr
from plotly.exceptions import PlotlyError
u"plot_hdrh_lat_by_percentile": plot_hdrh_lat_by_percentile,
u"plot_hdrh_lat_by_percentile_x_log": plot_hdrh_lat_by_percentile_x_log,
u"plot_mrr_box_name": plot_mrr_box_name,
- u"plot_ndrpdr_box_name": plot_ndrpdr_box_name
+ u"plot_ndrpdr_box_name": plot_ndrpdr_box_name,
+ u"plot_statistics": plot_statistics
}
logging.info(u"Generating the plots ...")
logging.info(u"Done.")
+def plot_statistics(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_statistics
+ specified in the specification file.
+
+ :param plot: Plot to generate.
+ :param input_data: Data to process.
+ :type plot: pandas.Series
+ :type input_data: InputData
+ """
+
+ data_x = list()
+ data_y_pass = list()
+ data_y_fail = list()
+ data_y_duration = list()
+ hover_text = list()
+ hover_str = (
+ u"date: {date}<br>"
+ u"passed: {passed}<br>"
+ u"failed: {failed}<br>"
+ u"duration: {duration}<br>"
+ u"{sut}-ref: {build}<br>"
+ u"csit-ref: {test}-{period}-build-{build_nr}<br>"
+ u"testbed: {testbed}"
+ )
+ for job, builds in plot[u"data"].items():
+ for build_nr in builds:
+ try:
+ meta = input_data.metadata(job, str(build_nr))
+ generated = meta[u"generated"]
+ date = datetime(
+ int(generated[0:4]),
+ int(generated[4:6]),
+ int(generated[6:8]),
+ int(generated[9:11]),
+ int(generated[12:])
+ )
+ d_y_pass = meta[u"tests_passed"]
+ d_y_fail = meta[u"tests_failed"]
+ minutes = meta[u"elapsedtime"] // 60000
+ duration = f"{(minutes // 60):02d}:{(minutes % 60):02d}"
+ version = meta[u"version"]
+ except (KeyError, IndexError, ValueError, AttributeError):
+ continue
+ data_x.append(date)
+ data_y_pass.append(d_y_pass)
+ data_y_fail.append(d_y_fail)
+ data_y_duration.append(minutes)
+ hover_text.append(hover_str.format(
+ date=date,
+ passed=d_y_pass,
+ failed=d_y_fail,
+ duration=duration,
+ sut=u"vpp" if u"vpp" in job else u"dpdk",
+ build=version,
+ test=u"mrr" if u"mrr" in job else u"ndrpdr",
+ period=u"daily" if u"daily" in job else u"weekly",
+ build_nr=build_nr,
+ testbed=meta[u"testbed"]
+ ))
+
+ traces = [
+ plgo.Bar(
+ x=data_x,
+ y=data_y_pass,
+ name=u"Passed",
+ text=hover_text,
+ hoverinfo=u"text"
+ ),
+ plgo.Bar(
+ x=data_x,
+ y=data_y_fail,
+ name=u"Failed",
+ text=hover_text,
+ hoverinfo=u"text"),
+ plgo.Scatter(
+ x=data_x,
+ y=data_y_duration,
+ name=u"Duration",
+ yaxis=u"y2",
+ text=hover_text,
+ hoverinfo=u"text"
+ )
+ ]
+
+ name_file = f"{plot[u'output-file']}.html"
+
+ logging.info(f" Writing the file {name_file}")
+ plpl = plgo.Figure(data=traces, layout=plot[u"layout"])
+ tickvals = [0, (max(data_y_duration) // 60) * 60]
+ step = tickvals[1] / 5
+ for i in range(5):
+ tickvals.append(int(tickvals[0] + step * (i + 1)))
+ plpl.update_layout(
+ yaxis2=dict(
+ title=u"Duration [hh:mm]",
+ anchor=u"x",
+ overlaying=u"y",
+ side=u"right",
+ rangemode="tozero",
+ tickmode=u"array",
+ tickvals=tickvals,
+ ticktext=[f"{(val // 60):02d}:{(val % 60):02d}" for val in tickvals]
+ )
+ )
+ plpl.update_layout(barmode=u"stack")
+ 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.")
+
+
def plot_hdrh_lat_by_percentile(plot, input_data):
"""Generate the plot(s) with algorithm: plot_hdrh_lat_by_percentile
specified in the specification file.
if u"TCP_CPS" in test[u"tags"]:
test_type = u"VSAP_CPS"
y_vals[test[u"parent"]].append(
- test[u"result"][u"cps"] / 1e6
+ test[u"result"][u"cps"]
)
elif u"TCP_RPS" in test[u"tags"]:
test_type = u"VSAP_RPS"
y_vals[test[u"parent"]].append(
- test[u"result"][u"rps"] / 1e6
+ test[u"result"][u"rps"]
)
else:
continue
tst_name = re.sub(REGEX_NIC, u"",
col.lower().replace(u'-ndrpdr', u'').
replace(u'2n1l-', u''))
+ if test_type in (u"VSAP_CPS", u"VSAP_RPS"):
+ data_y = [y if y else None for y in df_y[col]]
+ else:
+ data_y = [y / 1e6 if y else None for y in df_y[col]]
kwargs = dict(
x=[str(i + 1) + u'.'] * len(df_y[col]),
- y=[y / 1e6 if y else None for y in df_y[col]],
+ y=data_y,
name=(
f"{i + 1}. "
f"({nr_of_samples[i]:02d} "
layout[u"title"] = f"<b>Bandwidth:</b> {layout[u'title']}"
elif test_type == u"VSAP_CPS":
layout[u"title"] = f"<b>CPS:</b> {layout[u'title']}"
- layout[u"yaxis"][u"title"] = u"<b>Connection Rate [Mcps]</b>"
+ layout[u"yaxis"][u"title"] = u"<b>Connection Rate [cps]</b>"
elif test_type == u"VSAP_RPS":
layout[u"title"] = f"<b>RPS:</b> {layout[u'title']}"
- layout[u"yaxis"][u"title"] = u"<b>Connection Rate [Mrps]</b>"
+ layout[u"yaxis"][u"title"] = u"<b>Connection Rate [rps]</b>"
else:
layout[u"title"] = f"<b>Tput:</b> {layout[u'title']}"
if y_max and max(y_max) > 1:
Code Review / csit.git / blobdiff