--- /dev/null
+# Copyright (c) 2018 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:
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Algorithms to generate plots.
+"""
+
+
+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
+
+
+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):
+ """Generate all plots specified in the specification file.
+
+ :param spec: Specification read from the specification file.
+ :param data: Data to process.
+ :type spec: Specification
+ :type data: InputData
+ """
+
+ logging.info("Generating the plots ...")
+ for index, plot in enumerate(spec.plots):
+ try:
+ logging.info(" Plot nr {0}: {1}".format(index + 1,
+ plot.get("title", "")))
+ plot["limits"] = spec.configuration["limits"]
+ eval(plot["algorithm"])(plot, data)
+ 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.")
+
+
+def plot_performance_box(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_performance_box
+ 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_vals[test["parent"]] = list()
+ y_tags[test["parent"]] = test.get("tags", None)
+ try:
+ 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
+ nr_of_samples = list()
+ for val in y_sorted.values():
+ if len(val) > max_len:
+ max_len = len(val)
+ 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_sorted)
+ df.head()
+ y_max = list()
+ for i, col in enumerate(df.columns):
+ 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]],
+ 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
+ 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,
+ 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_title))
+ data = input_data.filter_data(plot)
+ if data is None:
+ logging.error("No data.")
+ return
+
+ # 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(), # direction1, avg
+ list(), # direction1, max
+ list(), # direction2, min
+ list(), # direction2, avg
+ list() # direction2, max
+ ]
+ y_tags[test["parent"]] = test.get("tags", None)
+ try:
+ 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))
+
+ # 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
+
+ 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()
+ annotations = list()
+
+ 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"]))
+ 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,
+ 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
+
+
+def plot_throughput_speedup_analysis(plot, input_data):
+ """Generate the plot(s) with algorithm:
+ plot_throughput_speedup_analysis
+ 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
+
+ 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"]] = {"1": list(),
+ "2": list(),
+ "4": list()}
+ y_tags[test["parent"]] = test.get("tags", None)
+ try:
+ 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 y_vals:
+ logging.warning("No data for the plot '{}'".
+ format(plot.get("title", "")))
+ return
+
+ y_1c_max = dict()
+ for test_name, test_vals in y_vals.items():
+ for key, test_val in test_vals.items():
+ if 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
+
+ traces = list()
+ 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"]))
+ 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,
+ 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
+
+
+def plot_http_server_performance_box(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_http_server_performance_box
+ 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(" Creating the data set for the {0} '{1}'.".
+ format(plot.get("type", ""), plot.get("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()
+ for job in data:
+ for build in job:
+ for test in build:
+ if y_vals.get(test["name"], None) is None:
+ y_vals[test["name"]] = list()
+ try:
+ 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))])
+
+ # Add plot traces
+ traces = list()
+ df = pd.DataFrame(y_vals)
+ df.head()
+ for i, col in enumerate(df.columns):
+ 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,
+ **plot["traces"]))
+ try:
+ # Create plot
+ plpl = plgo.Figure(data=traces, layout=plot["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