-# Copyright (c) 2018 Cisco and/or its affiliates.
+# Copyright (c) 2019 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:
df.head()
y_max = list()
for i, col in enumerate(df.columns):
- name = "{0}. {1} ({2} run{3})".\
- format(i + 1,
- col.lower().replace('-ndrpdr', ''),
- nr_of_samples[i],
- 's' if nr_of_samples[i] > 1 else '')
+ name = "{nr}. ({samples:02d} run{plural}) {name}".\
+ format(nr=(i + 1),
+ samples=nr_of_samples[i],
+ plural='s' if nr_of_samples[i] > 1 else '',
+ name=col.lower().replace('-ndrpdr', ''))
+ if len(name) > 50:
+ name_lst = name.split('-')
+ name = ""
+ split_name = True
+ for segment in name_lst:
+ if (len(name) + len(segment) + 1) > 50 and split_name:
+ name += "<br> "
+ split_name = False
+ name += segment + '-'
+ name = name[:-1]
+
logging.debug(name)
traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]),
y=[y / 1000000 if y else None for y in df[col]],
return
+def plot_soak_bars(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_soak_bars
+ 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
+ plot_title = plot.get("title", "")
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(plot.get("type", ""), plot_title))
+ data = input_data.filter_data(plot)
+ if data is None:
+ logging.error("No data.")
+ return
+
+ # Prepare the data for the plot
+ y_vals = dict()
+ y_tags = dict()
+ for job in data:
+ for build in job:
+ for test in build:
+ if y_vals.get(test["parent"], None) is None:
+ y_tags[test["parent"]] = test.get("tags", None)
+ try:
+ if test["type"] in ("SOAK", ):
+ y_vals[test["parent"]] = test["throughput"]
+ else:
+ continue
+ except (KeyError, TypeError):
+ y_vals[test["parent"]] = dict()
+
+ # Sort the tests
+ order = plot.get("sort", None)
+ if order and y_tags:
+ y_sorted = OrderedDict()
+ y_tags_l = {s: [t.lower() for t in ts] for s, ts in y_tags.items()}
+ for tag in order:
+ logging.debug(tag)
+ for suite, tags in y_tags_l.items():
+ if "not " in tag:
+ tag = tag.split(" ")[-1]
+ if tag.lower() in tags:
+ continue
+ else:
+ if tag.lower() not in tags:
+ continue
+ try:
+ y_sorted[suite] = y_vals.pop(suite)
+ y_tags_l.pop(suite)
+ logging.debug(suite)
+ except KeyError as err:
+ logging.error("Not found: {0}".format(repr(err)))
+ finally:
+ break
+ else:
+ y_sorted = y_vals
+
+ idx = 0
+ y_max = 0
+ traces = list()
+ for test_name, test_data in y_sorted.items():
+ idx += 1
+ name = "{nr}. {name}".\
+ format(nr=idx, name=test_name.lower().replace('-soak', ''))
+ if len(name) > 50:
+ name_lst = name.split('-')
+ name = ""
+ split_name = True
+ for segment in name_lst:
+ if (len(name) + len(segment) + 1) > 50 and split_name:
+ name += "<br> "
+ split_name = False
+ name += segment + '-'
+ name = name[:-1]
+
+ y_val = test_data.get("LOWER", None)
+ if y_val:
+ y_val /= 1000000
+ if y_val > y_max:
+ y_max = y_val
+
+ time = "No Information"
+ result = "No Information"
+ hovertext = ("{name}<br>"
+ "Packet Throughput: {val:.2f}Mpps<br>"
+ "Final Duration: {time}<br>"
+ "Result: {result}".format(name=name,
+ val=y_val,
+ time=time,
+ result=result))
+ traces.append(plgo.Bar(x=[str(idx) + '.', ],
+ y=[y_val, ],
+ name=name,
+ text=hovertext,
+ hoverinfo="text"))
+ try:
+ # Create plot
+ layout = deepcopy(plot["layout"])
+ if layout.get("title", None):
+ layout["title"] = "<b>Packet Throughput:</b> {0}". \
+ format(layout["title"])
+ if y_max:
+ layout["yaxis"]["range"] = [0, y_max + 1]
+ plpl = plgo.Figure(data=traces, layout=layout)
+ # Export Plot
+ logging.info(" Writing file '{0}{1}'.".
+ format(plot["output-file"], plot["output-file-type"]))
+ ploff.plot(plpl, show_link=False, auto_open=False,
+ filename='{0}{1}'.format(plot["output-file"],
+ plot["output-file-type"]))
+ except PlotlyError as err:
+ logging.error(" Finished with error: {}".
+ format(repr(err).replace("\n", " ")))
+ return
+
+
+def plot_soak_boxes(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_soak_boxes
+ 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
+ plot_title = plot.get("title", "")
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(plot.get("type", ""), plot_title))
+ data = input_data.filter_data(plot)
+ if data is None:
+ logging.error("No data.")
+ return
+
+ # Prepare the data for the plot
+ y_vals = dict()
+ y_tags = dict()
+ for job in data:
+ for build in job:
+ for test in build:
+ if y_vals.get(test["parent"], None) is None:
+ y_tags[test["parent"]] = test.get("tags", None)
+ try:
+ if test["type"] in ("SOAK", ):
+ y_vals[test["parent"]] = test["throughput"]
+ else:
+ continue
+ except (KeyError, TypeError):
+ y_vals[test["parent"]] = dict()
+
+ # Sort the tests
+ order = plot.get("sort", None)
+ if order and y_tags:
+ y_sorted = OrderedDict()
+ y_tags_l = {s: [t.lower() for t in ts] for s, ts in y_tags.items()}
+ for tag in order:
+ logging.debug(tag)
+ for suite, tags in y_tags_l.items():
+ if "not " in tag:
+ tag = tag.split(" ")[-1]
+ if tag.lower() in tags:
+ continue
+ else:
+ if tag.lower() not in tags:
+ continue
+ try:
+ y_sorted[suite] = y_vals.pop(suite)
+ y_tags_l.pop(suite)
+ logging.debug(suite)
+ except KeyError as err:
+ logging.error("Not found: {0}".format(repr(err)))
+ finally:
+ break
+ else:
+ y_sorted = y_vals
+
+ idx = 0
+ y_max = 0
+ traces = list()
+ for test_name, test_data in y_sorted.items():
+ idx += 1
+ name = "{nr}. {name}".\
+ format(nr=idx, name=test_name.lower().replace('-soak', ''))
+ if len(name) > 50:
+ name_lst = name.split('-')
+ name = ""
+ split_name = True
+ for segment in name_lst:
+ if (len(name) + len(segment) + 1) > 50 and split_name:
+ name += "<br> "
+ split_name = False
+ name += segment + '-'
+ name = name[:-1]
+
+ y_val = test_data.get("UPPER", None)
+ if y_val:
+ y_val /= 1000000
+ if y_val > y_max:
+ y_max = y_val
+
+ y_base = test_data.get("LOWER", None)
+ if y_base:
+ y_base /= 1000000
+
+ hovertext = ("{name}<br>"
+ "Upper bound: {upper:.2f}Mpps<br>"
+ "Lower bound: {lower:.2f}Mpps".format(name=name,
+ upper=y_val,
+ lower=y_base))
+ traces.append(plgo.Bar(x=[str(idx) + '.', ],
+ # +0.05 to see the value in case lower == upper
+ y=[y_val - y_base + 0.05, ],
+ base=y_base,
+ name=name,
+ text=hovertext,
+ hoverinfo="text"))
+ try:
+ # Create plot
+ layout = deepcopy(plot["layout"])
+ if layout.get("title", None):
+ layout["title"] = "<b>Soak Tests:</b> {0}". \
+ format(layout["title"])
+ if y_max:
+ layout["yaxis"]["range"] = [0, y_max + 1]
+ plpl = plgo.Figure(data=traces, layout=layout)
+ # Export Plot
+ logging.info(" Writing file '{0}{1}'.".
+ format(plot["output-file"], plot["output-file-type"]))
+ ploff.plot(plpl, show_link=False, auto_open=False,
+ filename='{0}{1}'.format(plot["output-file"],
+ plot["output-file-type"]))
+ except PlotlyError as err:
+ logging.error(" Finished with error: {}".
+ format(repr(err).replace("\n", " ")))
+ return
+
+
def plot_latency_error_bars(plot, input_data):
"""Generate the plot(s) with algorithm: plot_latency_error_bars
specified in the specification file.
y_maxs = list()
nr_of_samples = list()
for key, val in y_sorted.items():
- key = "-".join(key.split("-")[1:-1])
- x_vals.append(key) # dir 1
+ name = "-".join(key.split("-")[1:-1])
+ if len(name) > 50:
+ name_lst = name.split('-')
+ name = ""
+ split_name = True
+ for segment in name_lst:
+ if (len(name) + len(segment) + 1) > 50 and split_name:
+ name += "<br>"
+ split_name = False
+ name += segment + '-'
+ name = name[:-1]
+ x_vals.append(name) # dir 1
y_vals.append(mean(val[1]) if val[1] else None)
y_mins.append(mean(val[0]) if val[0] else None)
y_maxs.append(mean(val[2]) if val[2] else None)
nr_of_samples.append(len(val[1]) if val[1] else 0)
- x_vals.append(key) # dir 2
+ x_vals.append(name) # dir 2
y_vals.append(mean(val[4]) if val[4] else None)
y_mins.append(mean(val[3]) if val[3] else None)
y_maxs.append(mean(val[5]) if val[5] else None)
for idx in range(len(x_vals)):
if not bool(int(idx % 2)):
- direction = "West - East"
+ direction = "West-East"
else:
- direction = "East - West"
- hovertext = ("Test: {test}<br>"
- "Direction: {dir}<br>"
- "No. of Runs: {nr}<br>".format(test=x_vals[idx],
- dir=direction,
- nr=nr_of_samples[idx]))
+ direction = "East-West"
+ hovertext = ("No. of Runs: {nr}<br>"
+ "Test: {test}<br>"
+ "Direction: {dir}<br>".format(test=x_vals[idx],
+ dir=direction,
+ nr=nr_of_samples[idx]))
if isinstance(y_maxs[idx], float):
hovertext += "Max: {max:.2f}uSec<br>".format(max=y_maxs[idx])
if isinstance(y_vals[idx], float):
- hovertext += "Avg: {avg:.2f}uSec<br>".format(avg=y_vals[idx])
+ hovertext += "Mean: {avg:.2f}uSec<br>".format(avg=y_vals[idx])
if isinstance(y_mins[idx], float):
hovertext += "Min: {min:.2f}uSec".format(min=y_mins[idx])
try:
if test_vals["1"][1]:
name = "-".join(test_name.split('-')[1:-1])
+ if len(name) > 50:
+ name_lst = name.split('-')
+ name = ""
+ split_name = True
+ for segment in name_lst:
+ if (len(name) + len(segment) + 1) > 50 and split_name:
+ name += "<br>"
+ split_name = False
+ name += segment + '-'
+ name = name[:-1]
vals[name] = dict()
y_val_1 = test_vals["1"][0] / 1000000.0
limit = plot["limits"]["nic"]["xxv710"]
elif "xl710" in test_name:
limit = plot["limits"]["nic"]["xl710"]
+ elif "x553" in test_name:
+ limit = plot["limits"]["nic"]["x553"]
else:
limit = 0
if limit > nic_limit:
for idx in range(len(val["val"])):
htext = ""
if isinstance(val["val"][idx], float):
- htext += "Value: {0:.2f}Mpps<br>" \
- "No. of Runs: {1}<br>".format(val["val"][idx],
- val["count"][idx])
+ htext += "No. of Runs: {1}<br>" \
+ "Mean: {0:.2f}Mpps<br>".format(val["val"][idx],
+ val["count"][idx])
if isinstance(val["diff"][idx], float):
htext += "Diff: {0:.0f}%<br>".format(round(val["diff"][idx]))
if isinstance(val["rel"][idx], float):
color=COLORS[cidx],
width=2,
dash="dash"),
- text=["perfect: {0:.2f}Mpps".format(y)
+ text=["Perfect: {0:.2f}Mpps".format(y)
for y in val["ideal"]],
hoverinfo="text"
))
df = pd.DataFrame(y_vals)
df.head()
for i, col in enumerate(df.columns):
- name = "{0}. {1} ({2} run{3})".\
- format(i + 1,
- col.lower().replace('-cps', '').replace('-rps', ''),
- nr_of_samples[i],
- 's' if nr_of_samples[i] > 1 else '')
+ name = "{nr}. ({samples:02d} run{plural}) {name}".\
+ format(nr=(i + 1),
+ samples=nr_of_samples[i],
+ plural='s' if nr_of_samples[i] > 1 else '',
+ name=col.lower().replace('-ndrpdr', ''))
+ if len(name) > 50:
+ name_lst = name.split('-')
+ name = ""
+ split_name = True
+ for segment in name_lst:
+ if (len(name) + len(segment) + 1) > 50 and split_name:
+ name += "<br> "
+ split_name = False
+ name += segment + '-'
+ name = name[:-1]
+
traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]),
y=df[col],
name=name,
Code Review / csit.git / blobdiff