-# 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:
"""
+import re
import logging
import pandas as pd
import plotly.offline as ploff
import plotly.graph_objs as plgo
from plotly.exceptions import PlotlyError
+from collections import OrderedDict
+from copy import deepcopy
-from utils import mean
+from utils import mean, stdev
+
+
+COLORS = ["SkyBlue", "Olive", "Purple", "Coral", "Indigo", "Pink",
+ "Chocolate", "Brown", "Magenta", "Cyan", "Orange", "Black",
+ "Violet", "Blue", "Yellow", "BurlyWood", "CadetBlue", "Crimson",
+ "DarkBlue", "DarkCyan", "DarkGreen", "Green", "GoldenRod",
+ "LightGreen", "LightSeaGreen", "LightSkyBlue", "Maroon",
+ "MediumSeaGreen", "SeaGreen", "LightSlateGrey"]
def generate_plots(spec, data):
logging.info("Generating the plots ...")
for index, plot in enumerate(spec.plots):
try:
- logging.info(" Plot nr {0}:".format(index + 1))
+ logging.info(" Plot nr {0}: {1}".format(index + 1,
+ plot.get("title", "")))
+ plot["limits"] = spec.configuration["limits"]
eval(plot["algorithm"])(plot, data)
- except NameError:
- logging.error("The algorithm '{0}' is not defined.".
- format(plot["algorithm"]))
+ logging.info(" Done.")
+ except NameError as err:
+ logging.error("Probably algorithm '{alg}' is not defined: {err}".
+ format(alg=plot["algorithm"], err=repr(err)))
logging.info("Done.")
:type input_data: InputData
"""
- logging.info(" Generating the plot {0} ...".
- format(plot.get("title", "")))
-
# Transform the data
+ plot_title = plot.get("title", "")
logging.info(" Creating the data set for the {0} '{1}'.".
- format(plot.get("type", ""), plot.get("title", "")))
+ format(plot.get("type", ""), plot_title))
data = input_data.filter_data(plot)
if data is None:
logging.error("No data.")
# 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_vals[test["parent"]] = list()
+ y_tags[test["parent"]] = test.get("tags", None)
try:
- y_vals[test["parent"]].append(test["throughput"]["value"])
+ if test["type"] in ("NDRPDR", ):
+ if "-pdr" in plot_title.lower():
+ y_vals[test["parent"]].\
+ append(test["throughput"]["PDR"]["LOWER"])
+ elif "-ndr" in plot_title.lower():
+ y_vals[test["parent"]]. \
+ append(test["throughput"]["NDR"]["LOWER"])
+ else:
+ continue
+ else:
+ continue
except (KeyError, TypeError):
y_vals[test["parent"]].append(None)
+ # 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
+
# Add None to the lists with missing data
max_len = 0
- for val in y_vals.values():
+ nr_of_samples = list()
+ for val in y_sorted.values():
if len(val) > max_len:
max_len = len(val)
- for key, val in y_vals.items():
+ nr_of_samples.append(len(val))
+ for key, val in y_sorted.items():
if len(val) < max_len:
val.extend([None for _ in range(max_len - len(val))])
# Add plot traces
traces = list()
- df = pd.DataFrame(y_vals)
+ df = pd.DataFrame(y_sorted)
df.head()
+ y_max = list()
for i, col in enumerate(df.columns):
- name = "{0}. {1}".format(i + 1, col.lower().replace('-ndrpdrdisc', ''))
+ 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=df[col],
+ y=[y / 1000000 if y else None for y in df[col]],
name=name,
**plot["traces"]))
+ try:
+ val_max = max(df[col])
+ except ValueError as err:
+ logging.error(repr(err))
+ continue
+ if val_max:
+ y_max.append(int(val_max / 1000000) + 1)
try:
# Create plot
- plpl = plgo.Figure(data=traces, layout=plot["layout"])
+ 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, max(y_max)]
+ 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,
+ 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(str(err).replace("\n", " ")))
+ format(repr(err).replace("\n", " ")))
+ 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
- logging.info(" Done.")
+ # 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
-def plot_latency_box(plot, input_data):
- """Generate the plot(s) with algorithm: plot_latency_box
+ 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.
:type input_data: InputData
"""
- logging.info(" Generating the plot {0} ...".
- format(plot.get("title", "")))
+ # 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.
+
+ :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.get("title", "")))
+ format(plot.get("type", ""), plot_title))
data = input_data.filter_data(plot)
if data is None:
logging.error("No data.")
# Prepare the data for the plot
y_tmp_vals = dict()
+ y_tags = dict()
for job in data:
for build in job:
for test in build:
+ try:
+ logging.debug("test['latency']: {0}\n".
+ format(test["latency"]))
+ except ValueError as err:
+ logging.warning(repr(err))
if y_tmp_vals.get(test["parent"], None) is None:
y_tmp_vals[test["parent"]] = [
list(), # direction1, min
list(), # direction2, avg
list() # direction2, max
]
+ y_tags[test["parent"]] = test.get("tags", None)
try:
- y_tmp_vals[test["parent"]][0].append(
- test["latency"]["direction1"]["50"]["min"])
- y_tmp_vals[test["parent"]][1].append(
- test["latency"]["direction1"]["50"]["avg"])
- y_tmp_vals[test["parent"]][2].append(
- test["latency"]["direction1"]["50"]["max"])
- y_tmp_vals[test["parent"]][3].append(
- test["latency"]["direction2"]["50"]["min"])
- y_tmp_vals[test["parent"]][4].append(
- test["latency"]["direction2"]["50"]["avg"])
- y_tmp_vals[test["parent"]][5].append(
- test["latency"]["direction2"]["50"]["max"])
- except (KeyError, TypeError):
- pass
+ if test["type"] in ("NDRPDR", ):
+ if "-pdr" in plot_title.lower():
+ ttype = "PDR"
+ elif "-ndr" in plot_title.lower():
+ ttype = "NDR"
+ else:
+ logging.warning("Invalid test type: {0}".
+ format(test["type"]))
+ continue
+ y_tmp_vals[test["parent"]][0].append(
+ test["latency"][ttype]["direction1"]["min"])
+ y_tmp_vals[test["parent"]][1].append(
+ test["latency"][ttype]["direction1"]["avg"])
+ y_tmp_vals[test["parent"]][2].append(
+ test["latency"][ttype]["direction1"]["max"])
+ y_tmp_vals[test["parent"]][3].append(
+ test["latency"][ttype]["direction2"]["min"])
+ y_tmp_vals[test["parent"]][4].append(
+ test["latency"][ttype]["direction2"]["avg"])
+ y_tmp_vals[test["parent"]][5].append(
+ test["latency"][ttype]["direction2"]["max"])
+ else:
+ logging.warning("Invalid test type: {0}".
+ format(test["type"]))
+ continue
+ except (KeyError, TypeError) as err:
+ logging.warning(repr(err))
+ logging.debug("y_tmp_vals: {0}\n".format(y_tmp_vals))
- y_vals = dict()
- for key, values in y_tmp_vals.items():
- y_vals[key] = list()
- for val in values:
- if val:
- average = mean(val)
- else:
- average = None
- y_vals[key].append(average)
- y_vals[key].append(average) # Twice for plot.ly
+ # 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_tmp_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_tmp_vals
- # Add plot traces
+ logging.debug("y_sorted: {0}\n".format(y_sorted))
+ x_vals = list()
+ y_vals = list()
+ y_mins = list()
+ y_maxs = list()
+ nr_of_samples = list()
+ for key, val in y_sorted.items():
+ 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(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)
+ nr_of_samples.append(len(val[3]) if val[3] else 0)
+
+ logging.debug("x_vals :{0}\n".format(x_vals))
+ logging.debug("y_vals :{0}\n".format(y_vals))
+ logging.debug("y_mins :{0}\n".format(y_mins))
+ logging.debug("y_maxs :{0}\n".format(y_maxs))
+ logging.debug("nr_of_samples :{0}\n".format(nr_of_samples))
traces = list()
- try:
- df = pd.DataFrame(y_vals)
- df.head()
- except ValueError as err:
- logging.error(" Finished with error: {}".
- format(str(err).replace("\n", " ")))
- return
+ annotations = list()
- for i, col in enumerate(df.columns):
- name = "{0}. {1}".format(i + 1, col.lower().replace('-ndrpdrdisc', ''))
- traces.append(plgo.Box(x=['TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint1-to-SUT1-to-SUT2-to-TGint2',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1',
- 'TGint2-to-SUT2-to-SUT1-to-TGint1'],
- y=df[col],
- name=name,
- **plot["traces"]))
+ for idx in range(len(x_vals)):
+ if not bool(int(idx % 2)):
+ direction = "West-East"
+ else:
+ 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 += "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])
+
+ if isinstance(y_maxs[idx], float) and isinstance(y_vals[idx], float):
+ array = [y_maxs[idx] - y_vals[idx], ]
+ else:
+ array = [None, ]
+ if isinstance(y_mins[idx], float) and isinstance(y_vals[idx], float):
+ arrayminus = [y_vals[idx] - y_mins[idx], ]
+ else:
+ arrayminus = [None, ]
+ logging.debug("y_vals[{1}] :{0}\n".format(y_vals[idx], idx))
+ logging.debug("array :{0}\n".format(array))
+ logging.debug("arrayminus :{0}\n".format(arrayminus))
+ traces.append(plgo.Scatter(
+ x=[idx, ],
+ y=[y_vals[idx], ],
+ name=x_vals[idx],
+ legendgroup=x_vals[idx],
+ showlegend=bool(int(idx % 2)),
+ mode="markers",
+ error_y=dict(
+ type='data',
+ symmetric=False,
+ array=array,
+ arrayminus=arrayminus,
+ color=COLORS[int(idx / 2)]
+ ),
+ marker=dict(
+ size=10,
+ color=COLORS[int(idx / 2)],
+ ),
+ text=hovertext,
+ hoverinfo="text",
+ ))
+ annotations.append(dict(
+ x=idx,
+ y=0,
+ xref="x",
+ yref="y",
+ xanchor="center",
+ yanchor="top",
+ text="E-W" if bool(int(idx % 2)) else "W-E",
+ font=dict(
+ size=16,
+ ),
+ align="center",
+ showarrow=False
+ ))
try:
# Create plot
logging.info(" Writing file '{0}{1}'.".
format(plot["output-file"], plot["output-file-type"]))
- plpl = plgo.Figure(data=traces, layout=plot["layout"])
+ layout = deepcopy(plot["layout"])
+ if layout.get("title", None):
+ layout["title"] = "<b>Packet Latency:</b> {0}".\
+ format(layout["title"])
+ layout["annotations"] = annotations
+ plpl = plgo.Figure(data=traces, layout=layout)
# Export Plot
ploff.plot(plpl,
format(str(err).replace("\n", " ")))
return
- logging.info(" Done.")
-
def plot_throughput_speedup_analysis(plot, input_data):
- """Generate the plot(s) with algorithm: plot_throughput_speedup_analysis
+ """Generate the plot(s) with algorithm:
+ plot_throughput_speedup_analysis
specified in the specification file.
:param plot: Plot to generate.
:type input_data: InputData
"""
- logging.info(" Generating the plot {0} ...".
- format(plot.get("title", "")))
-
# Transform the data
+ plot_title = plot.get("title", "")
logging.info(" Creating the data set for the {0} '{1}'.".
- format(plot.get("type", ""), plot.get("title", "")))
+ format(plot.get("type", ""), plot_title))
data = input_data.filter_data(plot)
if data is None:
logging.error("No data.")
return
- throughput = dict()
+ y_vals = dict()
+ y_tags = dict()
for job in data:
for build in job:
for test in build:
- if throughput.get(test["parent"], None) is None:
- throughput[test["parent"]] = {"1": list(),
- "2": list(),
- "4": list()}
+ if y_vals.get(test["parent"], None) is None:
+ y_vals[test["parent"]] = {"1": list(),
+ "2": list(),
+ "4": list()}
+ y_tags[test["parent"]] = test.get("tags", None)
try:
- if "1T1C" in test["tags"]:
- throughput[test["parent"]]["1"].\
- append(test["throughput"]["value"])
- elif "2T2C" in test["tags"]:
- throughput[test["parent"]]["2"]. \
- append(test["throughput"]["value"])
- elif "4T4C" in test["tags"]:
- throughput[test["parent"]]["4"]. \
- append(test["throughput"]["value"])
+ if test["type"] in ("NDRPDR",):
+ if "-pdr" in plot_title.lower():
+ ttype = "PDR"
+ elif "-ndr" in plot_title.lower():
+ ttype = "NDR"
+ else:
+ continue
+ if "1C" in test["tags"]:
+ y_vals[test["parent"]]["1"]. \
+ append(test["throughput"][ttype]["LOWER"])
+ elif "2C" in test["tags"]:
+ y_vals[test["parent"]]["2"]. \
+ append(test["throughput"][ttype]["LOWER"])
+ elif "4C" in test["tags"]:
+ y_vals[test["parent"]]["4"]. \
+ append(test["throughput"][ttype]["LOWER"])
except (KeyError, TypeError):
pass
- if not throughput:
+ if not y_vals:
logging.warning("No data for the plot '{}'".
format(plot.get("title", "")))
return
- for test_name, test_vals in throughput.items():
+ y_1c_max = dict()
+ for test_name, test_vals in y_vals.items():
for key, test_val in test_vals.items():
if test_val:
- throughput[test_name][key] = sum(test_val) / len(test_val)
+ avg_val = sum(test_val) / len(test_val)
+ y_vals[test_name][key] = (avg_val, len(test_val))
+ ideal = avg_val / (int(key) * 1000000.0)
+ if test_name not in y_1c_max or ideal > y_1c_max[test_name]:
+ y_1c_max[test_name] = ideal
+
+ vals = dict()
+ y_max = list()
+ nic_limit = 0
+ lnk_limit = 0
+ pci_limit = plot["limits"]["pci"]["pci-g3-x8"]
+ for test_name, test_vals in y_vals.items():
+ 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
+ y_val_2 = test_vals["2"][0] / 1000000.0 if test_vals["2"][0] \
+ else None
+ y_val_4 = test_vals["4"][0] / 1000000.0 if test_vals["4"][0] \
+ else None
+
+ vals[name]["val"] = [y_val_1, y_val_2, y_val_4]
+ vals[name]["rel"] = [1.0, None, None]
+ vals[name]["ideal"] = [y_1c_max[test_name],
+ y_1c_max[test_name] * 2,
+ y_1c_max[test_name] * 4]
+ vals[name]["diff"] = [(y_val_1 - y_1c_max[test_name]) * 100 /
+ y_val_1, None, None]
+ vals[name]["count"] = [test_vals["1"][1],
+ test_vals["2"][1],
+ test_vals["4"][1]]
+
+ try:
+ val_max = max(max(vals[name]["val"], vals[name]["ideal"]))
+ except ValueError as err:
+ logging.error(err)
+ continue
+ if val_max:
+ y_max.append(int((val_max / 10) + 1) * 10)
+
+ if y_val_2:
+ vals[name]["rel"][1] = round(y_val_2 / y_val_1, 2)
+ vals[name]["diff"][1] = \
+ (y_val_2 - vals[name]["ideal"][1]) * 100 / y_val_2
+ if y_val_4:
+ vals[name]["rel"][2] = round(y_val_4 / y_val_1, 2)
+ vals[name]["diff"][2] = \
+ (y_val_4 - vals[name]["ideal"][2]) * 100 / y_val_4
+ except IndexError as err:
+ logging.warning("No data for '{0}'".format(test_name))
+ logging.warning(repr(err))
+
+ # Limits:
+ if "x520" in test_name:
+ limit = plot["limits"]["nic"]["x520"]
+ elif "x710" in test_name:
+ limit = plot["limits"]["nic"]["x710"]
+ elif "xxv710" in test_name:
+ 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:
+ nic_limit = limit
+
+ mul = 2 if "ge2p" in test_name else 1
+ if "10ge" in test_name:
+ limit = plot["limits"]["link"]["10ge"] * mul
+ elif "25ge" in test_name:
+ limit = plot["limits"]["link"]["25ge"] * mul
+ elif "40ge" in test_name:
+ limit = plot["limits"]["link"]["40ge"] * mul
+ elif "100ge" in test_name:
+ limit = plot["limits"]["link"]["100ge"] * mul
+ else:
+ limit = 0
+ if limit > lnk_limit:
+ lnk_limit = limit
+
+ # 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:
+ for test, tags in y_tags_l.items():
+ if tag.lower() in tags:
+ name = "-".join(test.split('-')[1:-1])
+ try:
+ y_sorted[name] = vals.pop(name)
+ y_tags_l.pop(test)
+ except KeyError as err:
+ logging.error("Not found: {0}".format(err))
+ finally:
+ break
+ else:
+ y_sorted = vals
- names = ['1 core', '2 cores', '4 cores']
- x_vals = list()
- y_vals_1 = list()
- y_vals_2 = list()
- y_vals_4 = list()
-
- for test_name, test_vals in throughput.items():
- if test_vals["1"]:
- x_vals.append("-".join(test_name.split('-')[1:-1]))
- y_vals_1.append(1)
- if test_vals["2"]:
- y_vals_2.append(
- round(float(test_vals["2"]) / float(test_vals["1"]), 2))
- else:
- y_vals_2.append(None)
- if test_vals["4"]:
- y_vals_4.append(
- round(float(test_vals["4"]) / float(test_vals["1"]), 2))
- else:
- y_vals_4.append(None)
-
- y_vals = [y_vals_1, y_vals_2, y_vals_4]
-
- y_vals_zipped = zip(names, y_vals)
traces = list()
- for val in y_vals_zipped:
- traces.append(plgo.Bar(x=x_vals,
- y=val[1],
- name=val[0]))
+ annotations = list()
+ x_vals = [1, 2, 4]
+
+ # Limits:
+ try:
+ threshold = 1.1 * max(y_max) # 10%
+ except ValueError as err:
+ logging.error(err)
+ return
+ nic_limit /= 1000000.0
+ if nic_limit < threshold:
+ traces.append(plgo.Scatter(
+ x=x_vals,
+ y=[nic_limit, ] * len(x_vals),
+ name="NIC: {0:.2f}Mpps".format(nic_limit),
+ showlegend=False,
+ mode="lines",
+ line=dict(
+ dash="dot",
+ color=COLORS[-1],
+ width=1),
+ hoverinfo="none"
+ ))
+ annotations.append(dict(
+ x=1,
+ y=nic_limit,
+ xref="x",
+ yref="y",
+ xanchor="left",
+ yanchor="bottom",
+ text="NIC: {0:.2f}Mpps".format(nic_limit),
+ font=dict(
+ size=14,
+ color=COLORS[-1],
+ ),
+ align="left",
+ showarrow=False
+ ))
+ y_max.append(int((nic_limit / 10) + 1) * 10)
+
+ lnk_limit /= 1000000.0
+ if lnk_limit < threshold:
+ traces.append(plgo.Scatter(
+ x=x_vals,
+ y=[lnk_limit, ] * len(x_vals),
+ name="Link: {0:.2f}Mpps".format(lnk_limit),
+ showlegend=False,
+ mode="lines",
+ line=dict(
+ dash="dot",
+ color=COLORS[-2],
+ width=1),
+ hoverinfo="none"
+ ))
+ annotations.append(dict(
+ x=1,
+ y=lnk_limit,
+ xref="x",
+ yref="y",
+ xanchor="left",
+ yanchor="bottom",
+ text="Link: {0:.2f}Mpps".format(lnk_limit),
+ font=dict(
+ size=14,
+ color=COLORS[-2],
+ ),
+ align="left",
+ showarrow=False
+ ))
+ y_max.append(int((lnk_limit / 10) + 1) * 10)
+
+ pci_limit /= 1000000.0
+ if pci_limit < threshold:
+ traces.append(plgo.Scatter(
+ x=x_vals,
+ y=[pci_limit, ] * len(x_vals),
+ name="PCIe: {0:.2f}Mpps".format(pci_limit),
+ showlegend=False,
+ mode="lines",
+ line=dict(
+ dash="dot",
+ color=COLORS[-3],
+ width=1),
+ hoverinfo="none"
+ ))
+ annotations.append(dict(
+ x=1,
+ y=pci_limit,
+ xref="x",
+ yref="y",
+ xanchor="left",
+ yanchor="bottom",
+ text="PCIe: {0:.2f}Mpps".format(pci_limit),
+ font=dict(
+ size=14,
+ color=COLORS[-3],
+ ),
+ align="left",
+ showarrow=False
+ ))
+ y_max.append(int((pci_limit / 10) + 1) * 10)
+
+ # Perfect and measured:
+ cidx = 0
+ for name, val in y_sorted.iteritems():
+ hovertext = list()
+ try:
+ for idx in range(len(val["val"])):
+ htext = ""
+ if isinstance(val["val"][idx], float):
+ 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):
+ htext += "Speedup: {0:.2f}".format(val["rel"][idx])
+ hovertext.append(htext)
+ traces.append(plgo.Scatter(x=x_vals,
+ y=val["val"],
+ name=name,
+ legendgroup=name,
+ mode="lines+markers",
+ line=dict(
+ color=COLORS[cidx],
+ width=2),
+ marker=dict(
+ symbol="circle",
+ size=10
+ ),
+ text=hovertext,
+ hoverinfo="text+name"
+ ))
+ traces.append(plgo.Scatter(x=x_vals,
+ y=val["ideal"],
+ name="{0} perfect".format(name),
+ legendgroup=name,
+ showlegend=False,
+ mode="lines",
+ line=dict(
+ color=COLORS[cidx],
+ width=2,
+ dash="dash"),
+ text=["Perfect: {0:.2f}Mpps".format(y)
+ for y in val["ideal"]],
+ hoverinfo="text"
+ ))
+ cidx += 1
+ except (IndexError, ValueError, KeyError) as err:
+ logging.warning("No data for '{0}'".format(name))
+ logging.warning(repr(err))
try:
# Create plot
logging.info(" Writing file '{0}{1}'.".
format(plot["output-file"], plot["output-file-type"]))
- plpl = plgo.Figure(data=traces, layout=plot["layout"])
+ layout = deepcopy(plot["layout"])
+ if layout.get("title", None):
+ layout["title"] = "<b>Speedup Multi-core:</b> {0}". \
+ format(layout["title"])
+ layout["annotations"].extend(annotations)
+ plpl = plgo.Figure(data=traces, layout=layout)
# Export Plot
ploff.plot(plpl,
format(str(err).replace("\n", " ")))
return
- logging.info(" Done.")
-
def plot_http_server_performance_box(plot, input_data):
"""Generate the plot(s) with algorithm: plot_http_server_performance_box
:type input_data: InputData
"""
- logging.info(" Generating the plot {0} ...".
- format(plot.get("title", "")))
-
# Transform the data
logging.info(" Creating the data set for the {0} '{1}'.".
format(plot.get("type", ""), plot.get("title", "")))
if y_vals.get(test["name"], None) is None:
y_vals[test["name"]] = list()
try:
- y_vals[test["name"]].append(test["result"]["value"])
+ y_vals[test["name"]].append(test["result"])
except (KeyError, TypeError):
y_vals[test["name"]].append(None)
# Add None to the lists with missing data
max_len = 0
+ nr_of_samples = list()
for val in y_vals.values():
if len(val) > max_len:
max_len = len(val)
+ nr_of_samples.append(len(val))
for key, val in y_vals.items():
if len(val) < max_len:
val.extend([None for _ in range(max_len - len(val))])
df = pd.DataFrame(y_vals)
df.head()
for i, col in enumerate(df.columns):
- name = "{0}. {1}".format(i + 1, col.lower().replace('-cps', '').
- replace('-rps', ''))
+ 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,
# 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,
+ ploff.plot(plpl, show_link=False, auto_open=False,
filename='{0}{1}'.format(plot["output-file"],
plot["output-file-type"]))
except PlotlyError as err:
format(str(err).replace("\n", " ")))
return
- logging.info(" Done.")
+
+def plot_service_density_heatmap(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_service_density_heatmap
+ specified in the specification file.
+
+ :param plot: Plot to generate.
+ :param input_data: Data to process.
+ :type plot: pandas.Series
+ :type input_data: InputData
+ """
+
+ REGEX_CN = re.compile(r'^(\d*)R(\d*)C$')
+
+ txt_chains = list()
+ txt_nodes = list()
+ vals = dict()
+
+ # Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(plot.get("type", ""), plot.get("title", "")))
+ data = input_data.filter_data(plot, continue_on_error=True)
+ if data is None:
+ logging.error("No data.")
+ return
+
+ for job in data:
+ for build in job:
+ for test in build:
+ for tag in test['tags']:
+ groups = re.search(REGEX_CN, tag)
+ if groups:
+ c = str(groups.group(1))
+ n = str(groups.group(2))
+ break
+ else:
+ continue
+ if vals.get(c, None) is None:
+ vals[c] = dict()
+ if vals[c].get(n, None) is None:
+ vals[c][n] = dict(name=test["name"],
+ vals=list(),
+ nr=None,
+ mean=None,
+ stdev=None)
+ if plot["include-tests"] == "MRR":
+ result = test["result"]["receive-rate"].avg
+ elif plot["include-tests"] == "PDR":
+ result = test["throughput"]["PDR"]["LOWER"]
+ elif plot["include-tests"] == "NDR":
+ result = test["throughput"]["NDR"]["LOWER"]
+ else:
+ result = None
+
+ if result:
+ vals[c][n]["vals"].append(result)
+
+ for key_c in vals.keys():
+ txt_chains.append(key_c)
+ for key_n in vals[key_c].keys():
+ txt_nodes.append(key_n)
+ if vals[key_c][key_n]["vals"]:
+ vals[key_c][key_n]["nr"] = len(vals[key_c][key_n]["vals"])
+ vals[key_c][key_n]["mean"] = \
+ round(mean(vals[key_c][key_n]["vals"]) / 1000000, 2)
+ vals[key_c][key_n]["stdev"] = \
+ round(stdev(vals[key_c][key_n]["vals"]) / 1000000, 2)
+ txt_nodes = list(set(txt_nodes))
+
+ txt_chains = sorted(txt_chains, key=lambda chain: int(chain))
+ txt_nodes = sorted(txt_nodes, key=lambda node: int(node))
+
+ chains = [i + 1 for i in range(len(txt_chains))]
+ nodes = [i + 1 for i in range(len(txt_nodes))]
+
+ data = [list() for _ in range(len(chains))]
+ for c in chains:
+ for n in nodes:
+ try:
+ val = vals[txt_chains[c - 1]][txt_nodes[n - 1]]["mean"]
+ except (KeyError, IndexError):
+ val = None
+ data[c - 1].append(val)
+
+ hovertext = list()
+ annotations = list()
+
+ text = ("{name}<br>"
+ "No. of Samples: {nr}<br>"
+ "Throughput: {val}<br>"
+ "Stdev: {stdev}")
+
+ for c in range(len(txt_chains)):
+ hover_line = list()
+ for n in range(len(txt_nodes)):
+ if data[c][n] is not None:
+ annotations.append(dict(
+ x=n+1,
+ y=c+1,
+ xref="x",
+ yref="y",
+ xanchor="center",
+ yanchor="middle",
+ text=str(data[c][n]),
+ font=dict(
+ size=14,
+ ),
+ align="center",
+ showarrow=False
+ ))
+ hover_line.append(text.format(
+ name=vals[txt_chains[c]][txt_nodes[n]]["name"],
+ nr=vals[txt_chains[c]][txt_nodes[n]]["nr"],
+ val=data[c][n],
+ stdev=vals[txt_chains[c]][txt_nodes[n]]["stdev"]))
+ hovertext.append(hover_line)
+
+ traces = [
+ plgo.Heatmap(x=nodes,
+ y=chains,
+ z=data,
+ colorbar=dict(
+ title="Packet Throughput [Mpps]",
+ titleside="right",
+ titlefont=dict(
+ size=14
+ ),
+ ),
+ showscale=True,
+ colorscale="Reds",
+ text=hovertext,
+ hoverinfo="text")
+ ]
+
+ for idx, item in enumerate(txt_nodes):
+ annotations.append(dict(
+ x=idx+1,
+ y=0,
+ xref="x",
+ yref="y",
+ xanchor="center",
+ yanchor="top",
+ text=item,
+ font=dict(
+ size=16,
+ ),
+ align="center",
+ showarrow=False
+ ))
+ for idx, item in enumerate(txt_chains):
+ annotations.append(dict(
+ x=0.3,
+ y=idx+1,
+ xref="x",
+ yref="y",
+ xanchor="right",
+ yanchor="middle",
+ text=item,
+ font=dict(
+ size=16,
+ ),
+ align="center",
+ showarrow=False
+ ))
+ # X-axis:
+ annotations.append(dict(
+ x=0.55,
+ y=1.05,
+ xref="paper",
+ yref="paper",
+ xanchor="center",
+ yanchor="middle",
+ text="<b>No. of Network Functions per Service Instance</b>",
+ font=dict(
+ size=16,
+ ),
+ align="center",
+ showarrow=False
+ ))
+ # Y-axis:
+ annotations.append(dict(
+ x=-0.04,
+ y=0.5,
+ xref="paper",
+ yref="paper",
+ xanchor="center",
+ yanchor="middle",
+ text="<b>No. of Service Instances</b>",
+ font=dict(
+ size=16,
+ ),
+ align="center",
+ textangle=270,
+ showarrow=False
+ ))
+ updatemenus = list([
+ dict(
+ x=1.0,
+ y=0.0,
+ xanchor='right',
+ yanchor='bottom',
+ direction='up',
+ buttons=list([
+ dict(
+ args=[{"colorscale": "Reds", "reversescale": False}],
+ label="Red",
+ method="update"
+ ),
+ dict(
+ args=[{"colorscale": "Blues", "reversescale": True}],
+ label="Blue",
+ method="update"
+ ),
+ dict(
+ args=[{"colorscale": "Greys", "reversescale": True}],
+ label="Grey",
+ method="update"
+ ),
+ dict(
+ args=[{"colorscale": "Greens", "reversescale": True}],
+ label="Green",
+ method="update"
+ ),
+ dict(
+ args=[{"colorscale": "RdBu", "reversescale": False}],
+ label="RedBlue",
+ method="update"
+ ),
+ dict(
+ args=[{"colorscale": "Picnic", "reversescale": False}],
+ label="Picnic",
+ method="update"
+ ),
+ dict(
+ args=[{"colorscale": "Rainbow", "reversescale": False}],
+ label="Rainbow",
+ method="update"
+ ),
+ dict(
+ args=[{"colorscale": "Portland", "reversescale": False}],
+ label="Portland",
+ method="update"
+ ),
+ dict(
+ args=[{"colorscale": "Jet", "reversescale": False}],
+ label="Jet",
+ method="update"
+ ),
+ dict(
+ args=[{"colorscale": "Hot", "reversescale": True}],
+ label="Hot",
+ method="update"
+ ),
+ dict(
+ args=[{"colorscale": "Blackbody", "reversescale": True}],
+ label="Blackbody",
+ method="update"
+ ),
+ dict(
+ args=[{"colorscale": "Earth", "reversescale": True}],
+ label="Earth",
+ method="update"
+ ),
+ dict(
+ args=[{"colorscale": "Electric", "reversescale": True}],
+ label="Electric",
+ method="update"
+ ),
+ dict(
+ args=[{"colorscale": "Viridis", "reversescale": True}],
+ label="Viridis",
+ method="update"
+ ),
+ dict(
+ args=[{"colorscale": "Cividis", "reversescale": True}],
+ label="Cividis",
+ method="update"
+ ),
+ ])
+ )
+ ])
+
+ try:
+ layout = deepcopy(plot["layout"])
+ except KeyError as err:
+ logging.error("Finished with error: No layout defined")
+ logging.error(repr(err))
+ return
+
+ layout["annotations"] = annotations
+ layout['updatemenus'] = updatemenus
+
+ try:
+ # Create plot
+ 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(str(err).replace("\n", " ")))
+ return
Code Review / csit.git / blobdiff