+ :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_service_density_reconf_box_name(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_service_density_reconf_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
+ 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_tests_by_name(
+ plot, params=["result", "parent", "tags", "type"])
+ if data is None:
+ logging.error("No data.")
+ return
+
+ # Prepare the data for the plot
+ y_vals = OrderedDict()
+ loss = 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()
+ loss[test["parent"]] = list()
+ try:
+ y_vals[test["parent"]].append(test["result"]["time"])
+ loss[test["parent"]].append(test["result"]["loss"])
+ except (KeyError, TypeError):
+ y_vals[test["parent"]].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()
+ y_max = list()
+ for i, col in enumerate(df.columns):
+ tst_name = re.sub(REGEX_NIC, "",
+ col.lower().replace('-ndrpdr', '').
+ replace('2n1l-', ''))
+ tst_name = "-".join(tst_name.split("-")[3:-2])
+ name = "{nr}. ({samples:02d} run{plural}, avg pkt loss: {loss:.1f}, " \
+ "stdev: {stdev:.2f}) {name}".format(
+ nr=(i + 1),
+ samples=nr_of_samples[i],
+ plural='s' if nr_of_samples[i] > 1 else '',
+ name=tst_name,
+ loss=mean(loss[col]) / 1000000,
+ stdev=stdev(loss[col]) / 1000000)
+
+ traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]),
+ y=[y if y else None for y in df[col]],
+ name=name,
+ hoverinfo="x+y",
+ boxpoints="outliers",
+ whiskerwidth=0))
+ try:
+ val_max = max(df[col])
+ except ValueError as err:
+ logging.error(repr(err))
+ continue
+ if val_max:
+ y_max.append(int(val_max) + 1)
+
+ try:
+ # Create plot
+ layout = deepcopy(plot["layout"])
+ layout["title"] = "<b>Time Lost:</b> {0}".format(layout["title"])
+ layout["yaxis"]["title"] = "<b>Implied Time Lost [s]</b>"
+ layout["legend"]["font"]["size"] = 14
+ if y_max:
+ layout["yaxis"]["range"] = [0, max(y_max)]
+ plpl = plgo.Figure(data=traces, layout=layout)
+
+ # Export Plot
+ file_type = plot.get("output-file-type", ".html")
+ logging.info(" Writing file '{0}{1}'.".
+ format(plot["output-file"], file_type))
+ ploff.plot(plpl, show_link=False, auto_open=False,
+ filename='{0}{1}'.format(plot["output-file"], file_type))
+ except PlotlyError as err:
+ logging.error(" Finished with error: {}".
+ format(repr(err).replace("\n", " ")))
+ return
+
+
+def plot_performance_box_name(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_performance_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
+ 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_tests_by_name(
+ plot, params=["throughput", "parent", "tags", "type"])
+ if data is None:
+ logging.error("No data.")
+ return
+
+ # Prepare the data for the plot
+ y_vals = OrderedDict()
+ 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()
+ 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
+ elif test["type"] in ("SOAK", ):
+ y_vals[test["parent"]].\
+ append(test["throughput"]["LOWER"])
+ else:
+ continue
+ except (KeyError, TypeError):
+ y_vals[test["parent"]].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()
+ y_max = list()
+ for i, col in enumerate(df.columns):
+ tst_name = re.sub(REGEX_NIC, "",
+ col.lower().replace('-ndrpdr', '').
+ replace('2n1l-', ''))
+ 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=tst_name)
+
+ 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,
+ hoverinfo="x+y",
+ boxpoints="outliers",
+ whiskerwidth=0))
+ 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) + 2)
+
+ try:
+ # Create plot
+ layout = deepcopy(plot["layout"])
+ if layout.get("title", None):
+ layout["title"] = "<b>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
+ file_type = plot.get("output-file-type", ".html")
+ logging.info(" Writing file '{0}{1}'.".
+ format(plot["output-file"], file_type))
+ ploff.plot(plpl, show_link=False, auto_open=False,
+ filename='{0}{1}'.format(plot["output-file"], file_type))
+ except PlotlyError as err:
+ logging.error(" Finished with error: {}".
+ format(repr(err).replace("\n", " ")))
+ return
+
+
+def plot_latency_error_bars_name(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_latency_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
+ 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_tests_by_name(
+ plot, params=["latency", "parent", "tags", "type"])
+ if data is None:
+ logging.error("No data.")
+ return
+
+ # Prepare the data for the plot
+ y_tmp_vals = OrderedDict()
+ 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
+ ]
+ 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))
+
+ x_vals = list()
+ y_vals = list()
+ y_mins = list()
+ y_maxs = list()
+ nr_of_samples = list()
+ for key, val in y_tmp_vals.items():
+ name = re.sub(REGEX_NIC, "", key.replace('-ndrpdr', '').
+ replace('2n1l-', ''))
+ 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)
+
+ 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, ]
+ 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
+ file_type = plot.get("output-file-type", ".html")
+ logging.info(" Writing file '{0}{1}'.".
+ format(plot["output-file"], file_type))
+ layout = deepcopy(plot["layout"])
+ if layout.get("title", None):
+ layout["title"] = "<b>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"], file_type))
+ except PlotlyError as err:
+ logging.error(" Finished with error: {}".
+ format(str(err).replace("\n", " ")))
+ return
+
+
+def plot_throughput_speedup_analysis_name(plot, input_data):
+ """Generate the plot(s) with algorithm:
+ plot_throughput_speedup_analysis_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
+ 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_tests_by_name(
+ plot, params=["throughput", "parent", "tags", "type"])
+ if data is None:
+ logging.error("No data.")
+ return
+
+ y_vals = OrderedDict()
+ 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()}
+ 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 = OrderedDict()
+ 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 = re.sub(REGEX_NIC, "", test_name.replace('-ndrpdr', '').
+ replace('2n1l-', ''))
+ vals[name] = OrderedDict()
+ 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(vals[name]["val"])
+ except ValueError as err:
+ logging.error(repr(err))
+ continue
+ if val_max:
+ y_max.append(val_max)
+
+ 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))