REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)-')
# This value depends on latency stream rate (9001 pps) and duration (5s).
-PERCENTILE_MAX = 99.9995
+# Keep it slightly higher to ensure rounding errors to not remove tick mark.
+PERCENTILE_MAX = 99.999501
def generate_plots(spec, data):
u"plot_http_server_perf_box": plot_http_server_perf_box,
u"plot_nf_heatmap": plot_nf_heatmap,
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_hdrh_lat_by_percentile_x_log": plot_hdrh_lat_by_percentile_x_log,
+ u"plot_mrr_error_bars_name": plot_mrr_error_bars_name,
+ u"plot_mrr_box_name": plot_mrr_box_name
}
logging.info(u"Generating the plots ...")
fig = plgo.Figure()
layout = deepcopy(plot[u"layout"])
- xaxis_max = 0
for color, graph in enumerate(graphs):
for idx, direction in enumerate((u"direction1", u"direction2")):
for item in decoded.get_recorded_iterator():
# The real value is "percentile".
- # For 100%, we cut that down to "x_perc" to avoid infinity.
+ # For 100%, we cut that down to "x_perc" to avoid
+ # infinity.
percentile = item.percentile_level_iterated_to
x_perc = min(percentile, PERCENTILE_MAX)
xaxis.append(previous_x)
hoverinfo=u"text"
)
)
- xaxis_max = max(xaxis) if xaxis_max < max(
- xaxis) else xaxis_max
layout[u"title"][u"text"] = f"<b>Latency:</b> {name}"
- layout[u"xaxis"][u"range"] = [0, int(log(xaxis_max, 10)) + 1]
+ x_max = log(100.0 / (100.0 - PERCENTILE_MAX), 10)
+ layout[u"xaxis"][u"range"] = [0, x_max]
fig.update_layout(layout)
# Create plot
df_y = pd.DataFrame(y_vals)
df_y.head()
for i, col in enumerate(df_y.columns):
+
tst_name = re.sub(REGEX_NIC, u"",
- col.lower().replace(u'-ndrpdr', u'').
- replace(u'2n1l-', u''))
+ col.lower().replace(u'-reconf', u'').
+ replace(u'2n1l-', u'').replace(u'2n-', u'').
+ replace(u'-testpmd', u''))
- if u"ipsec" in tst_name:
- show_name = u'-'.join(tst_name.split(u'-')[2:-1])
- else:
- show_name = u'-'.join(tst_name.split(u'-')[3:-2])
traces.append(plgo.Box(
x=[str(i + 1) + u'.'] * len(df_y[col]),
y=df_y[col],
f"({nr_of_samples[i]:02d} "
f"run{u's' if nr_of_samples[i] > 1 else u''}, "
f"packets lost average: {mean(loss[col]):.1f}) "
- f"{show_name}"
+ f"{u'-'.join(tst_name.split(u'-')[2:])}"
),
hoverinfo=u"y+name"
))
return
+def plot_mrr_box_name(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_mrr_box_name
+ specified in the specification file.
+
+ :param plot: Plot to generate.
+ :param input_data: Data to process.
+ :type plot: pandas.Series
+ :type input_data: InputData
+ """
+
+ # Transform the data
+ logging.info(
+ f" Creating data set for the {plot.get(u'type', u'')} "
+ f"{plot.get(u'title', u'')}."
+ )
+ data = input_data.filter_tests_by_name(
+ plot,
+ params=[u"result", u"parent", u"tags", u"type"])
+ if data is None:
+ logging.error(u"No data.")
+ return
+
+ # Prepare the data for the plot
+ data_x = list()
+ data_names = list()
+ data_y = list()
+ data_y_max = list()
+ idx = 1
+ for item in plot.get(u"include", tuple()):
+ reg_ex = re.compile(str(item).lower())
+ for job in data:
+ for build in job:
+ for test_id, test in build.iteritems():
+ if not re.match(reg_ex, str(test_id).lower()):
+ continue
+ try:
+ data_x.append(idx)
+ name = re.sub(REGEX_NIC, u'', test[u'parent'].lower().
+ replace(u'-mrr', u'').
+ replace(u'2n1l-', u''))
+ data_y.append(test[u"result"][u"samples"])
+ data_names.append(
+ f"{idx}."
+ f"({len(data_y[-1]):02d} "
+ f"run{u's' if len(data_y[-1]) > 1 else u''}) "
+ f"{name}"
+ )
+ data_y_max.append(max(test[u"result"][u"samples"]))
+ idx += 1
+ except (KeyError, TypeError):
+ pass
+
+ # Add plot traces
+ traces = list()
+ for idx in range(len(data_x)):
+ traces.append(
+ plgo.Box(
+ x=[data_x[idx], ] * len(data_y[idx]),
+ y=data_y[idx],
+ name=data_names[idx],
+ hoverinfo=u"y+name"
+ )
+ )
+
+ try:
+ # Create plot
+ layout = deepcopy(plot[u"layout"])
+ if layout.get(u"title", None):
+ layout[u"title"] = f"<b>Throughput:</b> {layout[u'title']}"
+ if data_y_max:
+ layout[u"yaxis"][u"range"] = [0, max(data_y_max) + 1]
+ plpl = plgo.Figure(data=traces, layout=layout)
+
+ # Export Plot
+ logging.info(f" Writing file {plot[u'output-file']}.html.")
+ ploff.plot(
+ plpl,
+ show_link=False,
+ auto_open=False,
+ filename=f"{plot[u'output-file']}.html"
+ )
+ except PlotlyError as err:
+ logging.error(
+ f" Finished with error: {repr(err)}".replace(u"\n", u" ")
+ )
+ return
+
+
+def plot_mrr_error_bars_name(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_mrr_error_bars_name
+ specified in the specification file.
+
+ :param plot: Plot to generate.
+ :param input_data: Data to process.
+ :type plot: pandas.Series
+ :type input_data: InputData
+ """
+
+ # Transform the data
+ logging.info(
+ f" Creating data set for the {plot.get(u'type', u'')} "
+ f"{plot.get(u'title', u'')}."
+ )
+ data = input_data.filter_tests_by_name(
+ plot,
+ params=[u"result", u"parent", u"tags", u"type"])
+ if data is None:
+ logging.error(u"No data.")
+ return
+
+ # Prepare the data for the plot
+ data_x = list()
+ data_names = list()
+ data_y_avg = list()
+ data_y_stdev = list()
+ data_y_max = 0
+ hover_info = list()
+ idx = 1
+ for item in plot.get(u"include", tuple()):
+ reg_ex = re.compile(str(item).lower())
+ for job in data:
+ for build in job:
+ for test_id, test in build.iteritems():
+ if not re.match(reg_ex, str(test_id).lower()):
+ continue
+ try:
+ data_x.append(idx)
+ name = re.sub(REGEX_NIC, u'', test[u'parent'].lower().
+ replace(u'-mrr', u'').
+ replace(u'2n1l-', u''))
+ data_names.append(f"{idx}. {name}")
+ data_y_avg.append(
+ round(test[u"result"][u"receive-rate"], 3)
+ )
+ data_y_stdev.append(
+ round(test[u"result"][u"receive-stdev"], 3)
+ )
+ hover_info.append(
+ f"{data_names[-1]}<br>"
+ f"average [Gbps]: {data_y_avg[-1]}<br>"
+ f"stdev [Gbps]: {data_y_stdev[-1]}"
+ )
+ if data_y_avg[-1] + data_y_stdev[-1] > data_y_max:
+ data_y_max = data_y_avg[-1] + data_y_stdev[-1]
+ idx += 1
+ except (KeyError, TypeError):
+ pass
+
+ # Add plot traces
+ traces = list()
+ for idx in range(len(data_x)):
+ traces.append(
+ plgo.Scatter(
+ x=[data_x[idx], ],
+ y=[data_y_avg[idx], ],
+ error_y=dict(
+ type=u"data",
+ array=[data_y_stdev[idx], ],
+ visible=True
+ ),
+ name=data_names[idx],
+ mode=u"markers",
+ text=hover_info[idx],
+ hoverinfo=u"text"
+ )
+ )
+
+ try:
+ # Create plot
+ layout = deepcopy(plot[u"layout"])
+ if layout.get(u"title", None):
+ layout[u"title"] = f"<b>Throughput:</b> {layout[u'title']}"
+ if data_y_max:
+ layout[u"yaxis"][u"range"] = [0, int(data_y_max) + 1]
+ plpl = plgo.Figure(data=traces, layout=layout)
+
+ # Export Plot
+ logging.info(f" Writing file {plot[u'output-file']}.html.")
+ ploff.plot(
+ plpl,
+ show_link=False,
+ auto_open=False,
+ filename=f"{plot[u'output-file']}.html"
+ )
+ except PlotlyError as err:
+ logging.error(
+ f" Finished with error: {repr(err)}".replace(u"\n", u" ")
+ )
+ return
+
+
def plot_tsa_name(plot, input_data):
"""Generate the plot(s) with algorithm:
plot_tsa_name
Code Review / csit.git / blobdiff