"LightGreen", "LightSeaGreen", "LightSkyBlue", "Maroon",
"MediumSeaGreen", "SeaGreen", "LightSlateGrey"]
+REGEX_NIC = re.compile(r'\d*ge\dp\d\D*\d*-')
+
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
- """
+ :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))
- logging.info("Generating the plots ...")
- for index, plot in enumerate(spec.plots):
+ # 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
+
+ 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
+ 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(nic_limit)
+
+ 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(lnk_limit)
+
+ pci_limit /= 1000000.0
+ if (pci_limit < threshold and
+ (pci_limit < lnk_limit * 0.95 or lnk_limit > lnk_limit * 1.05)):
+ 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(pci_limit)
+
+ # Perfect and measured:
+ cidx = 0
+ for name, val in vals.iteritems():
+ hovertext = list()
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.")
+ 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
+ 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>Speedup Multi-core:</b> {0}". \
+ format(layout["title"])
+ layout["yaxis"]["range"] = [0, int(max(y_max) * 1.1)]
+ 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"], file_type))
+ except PlotlyError as err:
+ logging.error(" Finished with error: {}".
+ format(repr(err).replace("\n", " ")))
+ return
def plot_performance_box(plot, input_data):
"""Generate the plot(s) with algorithm: plot_performance_box
specified in the specification file.
+ TODO: Remove when not needed.
+
:param plot: Plot to generate.
:param input_data: Data to process.
:type plot: pandas.Series
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):
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=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]
+ name=tst_name)
logging.debug(name)
traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]),
logging.error(repr(err))
continue
if val_max:
- y_max.append(int(val_max / 1000000) + 1)
+ y_max.append(int(val_max / 1000000) + 2)
try:
# Create plot
layout = deepcopy(plot["layout"])
if layout.get("title", None):
- layout["title"] = "<b>Packet Throughput:</b> {0}". \
+ layout["title"] = "<b>Throughput:</b> {0}". \
format(layout["title"])
if y_max:
layout["yaxis"]["range"] = [0, max(y_max)]
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:
+ format(nr=idx, name=test_name.lower().replace('-soak', '').
+ replace('2n1l-', ''))
+ if len(name) > 55:
name_lst = name.split('-')
name = ""
split_name = True
for segment in name_lst:
- if (len(name) + len(segment) + 1) > 50 and split_name:
+ if (len(name) + len(segment) + 1) > 55 and split_name:
name += "<br> "
split_name = False
name += segment + '-'
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,
+ hovertext = ("Upper bound: {upper:.2f}<br>"
+ "Lower bound: {lower:.2f}".format(upper=y_val,
lower=y_base))
traces.append(plgo.Bar(x=[str(idx) + '.', ],
# +0.05 to see the value in case lower == upper
# Create plot
layout = deepcopy(plot["layout"])
if layout.get("title", None):
- layout["title"] = "<b>Soak Tests:</b> {0}". \
+ layout["title"] = "<b>Throughput:</b> {0}". \
format(layout["title"])
if y_max:
layout["yaxis"]["range"] = [0, y_max + 1]
"""Generate the plot(s) with algorithm: plot_latency_error_bars
specified in the specification file.
+ TODO: Remove when not needed.
+
:param plot: Plot to generate.
:param input_data: Data to process.
:type plot: pandas.Series
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]
+ 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)
format(plot["output-file"], plot["output-file-type"]))
layout = deepcopy(plot["layout"])
if layout.get("title", None):
- layout["title"] = "<b>Packet Latency:</b> {0}".\
+ layout["title"] = "<b>Latency:</b> {0}".\
format(layout["title"])
layout["annotations"] = annotations
plpl = plgo.Figure(data=traces, layout=layout)
plot_throughput_speedup_analysis
specified in the specification file.
+ TODO: Remove when not needed.
+
:param plot: Plot to generate.
:param input_data: Data to process.
:type plot: pandas.Series
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]
-
+ name = re.sub(REGEX_NIC, "", test_name.replace('-ndrpdr', '').
+ replace('2n1l-', ''))
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] \
test_vals["4"][1]]
try:
- val_max = max(max(vals[name]["val"], vals[name]["ideal"]))
+ # val_max = max(max(vals[name]["val"], vals[name]["ideal"]))
+ val_max = max(vals[name]["val"])
except ValueError as err:
logging.error(err)
continue
if val_max:
- y_max.append(int((val_max / 10) + 1) * 10)
+ # y_max.append(int((val_max / 10) + 1) * 10)
+ y_max.append(val_max)
if y_val_2:
vals[name]["rel"][1] = round(y_val_2 / y_val_1, 2)
for tag in order:
for test, tags in y_tags_l.items():
if tag.lower() in tags:
- name = "-".join(test.split('-')[1:-1])
+ name = re.sub(REGEX_NIC, "",
+ test.replace('-ndrpdr', '').
+ replace('2n1l-', ''))
try:
y_sorted[name] = vals.pop(name)
y_tags_l.pop(test)
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)
+ # 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)
+ y_max.append(nic_limit)
lnk_limit /= 1000000.0
if lnk_limit < threshold:
align="left",
showarrow=False
))
- y_max.append(int((lnk_limit / 10) + 1) * 10)
+ # y_max.append(int((lnk_limit / 10) + 1) * 10)
+ y_max.append(lnk_limit)
pci_limit /= 1000000.0
- if pci_limit < threshold:
+ if (pci_limit < threshold and
+ (pci_limit < lnk_limit * 0.95 or lnk_limit > lnk_limit * 1.05)):
traces.append(plgo.Scatter(
x=x_vals,
y=[pci_limit, ] * len(x_vals),
align="left",
showarrow=False
))
- y_max.append(int((pci_limit / 10) + 1) * 10)
+ # y_max.append(int((pci_limit / 10) + 1) * 10)
+ y_max.append(pci_limit)
# Perfect and measured:
cidx = 0
if layout.get("title", None):
layout["title"] = "<b>Speedup Multi-core:</b> {0}". \
format(layout["title"])
+ # layout["yaxis"]["range"] = [0, int((max(y_max) / 10) + 1) * 10]
+ layout["yaxis"]["range"] = [0, int(max(y_max) * 1.1)]
layout["annotations"].extend(annotations)
plpl = plgo.Figure(data=traces, layout=layout)
:type input_data: InputData
"""
- REGEX_CN = re.compile(r'^(\d*)C(\d*)N$')
+ REGEX_CN = re.compile(r'^(\d*)R(\d*)C$')
+ REGEX_TEST_NAME = re.compile(r'^.*-(\d+ch|\d+pl)-'
+ r'(\d+mif|\d+vh)-'
+ r'(\d+vm\d+t|\d+dcr\d+t).*$')
txt_chains = list()
txt_nodes = list()
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:
+ if data is None or data.empty:
logging.error("No data.")
return
break
else:
continue
+ groups = re.search(REGEX_TEST_NAME, test["name"])
+ if groups and len(groups.groups()) == 3:
+ hover_name = "{chain}-{vhost}-{vm}".format(
+ chain=str(groups.group(1)),
+ vhost=str(groups.group(2)),
+ vm=str(groups.group(3)))
+ else:
+ hover_name = ""
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[c][n] = dict(name=hover_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:
+ try:
+ 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
+ except TypeError:
result = None
if result:
vals[c][n]["vals"].append(result)
+ if not vals:
+ logging.error("No data.")
+ return
+
for key_c in vals.keys():
txt_chains.append(key_c)
for key_n in vals[key_c].keys():
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)
+ round(mean(vals[key_c][key_n]["vals"]) / 1000000, 1)
vals[key_c][key_n]["stdev"] = \
- round(stdev(vals[key_c][key_n]["vals"]) / 1000000, 2)
+ round(stdev(vals[key_c][key_n]["vals"]) / 1000000, 1)
txt_nodes = list(set(txt_nodes))
txt_chains = sorted(txt_chains, key=lambda chain: int(chain))
val = None
data[c - 1].append(val)
+ # Colorscales:
+ my_green = [[0.0, 'rgb(235, 249, 242)'],
+ [1.0, 'rgb(45, 134, 89)']]
+
+ my_blue = [[0.0, 'rgb(236, 242, 248)'],
+ [1.0, 'rgb(57, 115, 172)']]
+
+ my_grey = [[0.0, 'rgb(230, 230, 230)'],
+ [1.0, 'rgb(102, 102, 102)']]
+
hovertext = list()
annotations = list()
- text = ("{name}<br>"
- "No. of Samples: {nr}<br>"
- "Throughput: {val}<br>"
- "Stdev: {stdev}")
+ text = ("Test: {name}<br>"
+ "Runs: {nr}<br>"
+ "Thput: {val}<br>"
+ "StDev: {stdev}")
for c in range(len(txt_chains)):
hover_line = list()
y=chains,
z=data,
colorbar=dict(
- title="Packet Throughput [Mpps]",
+ title=plot.get("z-axis", ""),
titleside="right",
titlefont=dict(
- size=14
+ size=16
+ ),
+ tickfont=dict(
+ size=16,
),
+ tickformat=".1f",
+ yanchor="bottom",
+ y=-0.02,
+ len=0.925,
),
showscale=True,
- colorscale="Reds",
+ colorscale=my_green,
text=hovertext,
hoverinfo="text")
]
for idx, item in enumerate(txt_nodes):
+ # X-axis, numbers:
annotations.append(dict(
x=idx+1,
- y=0,
+ y=0.05,
xref="x",
yref="y",
xanchor="center",
showarrow=False
))
for idx, item in enumerate(txt_chains):
+ # Y-axis, numbers:
annotations.append(dict(
- x=0.3,
+ x=0.35,
y=idx+1,
xref="x",
yref="y",
align="center",
showarrow=False
))
- # X-axis:
+ # X-axis, title:
annotations.append(dict(
x=0.55,
- y=1.05,
+ y=-0.15,
xref="paper",
- yref="paper",
+ yref="y",
xanchor="center",
- yanchor="middle",
- text="<b>No. of Network Functions per Service Instance</b>",
+ yanchor="bottom",
+ text=plot.get("x-axis", ""),
font=dict(
size=16,
),
align="center",
showarrow=False
))
- # Y-axis:
+ # Y-axis, title:
annotations.append(dict(
- x=-0.04,
+ x=-0.1,
y=0.5,
- xref="paper",
+ xref="x",
yref="paper",
xanchor="center",
yanchor="middle",
- text="<b>No. of Service Instances</b>",
+ text=plot.get("y-axis", ""),
font=dict(
size=16,
),
direction='up',
buttons=list([
dict(
- args=[{"colorscale": "Reds", "reversescale": False}],
- label="Red",
+ args=[{"colorscale": [my_green, ], "reversescale": False}],
+ label="Green",
method="update"
),
dict(
- args=[{"colorscale": "Blues", "reversescale": True}],
+ args=[{"colorscale": [my_blue, ], "reversescale": False}],
label="Blue",
method="update"
),
dict(
- args=[{"colorscale": "Greys", "reversescale": True}],
+ args=[{"colorscale": [my_grey, ], "reversescale": False}],
label="Grey",
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
+
+
+def plot_service_density_heatmap_compare(plot, input_data):
+ """Generate the plot(s) with algorithm: plot_service_density_heatmap_compare
+ 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$')
+ REGEX_TEST_NAME = re.compile(r'^.*-(\d+ch|\d+pl)-'
+ r'(\d+mif|\d+vh)-'
+ r'(\d+vm\d+t|\d+dcr\d+t).*$')
+ REGEX_THREADS = re.compile(r'^(\d+)(VM|DCR)(\d+)T$')
+
+ 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 or data.empty:
+ 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
+ groups = re.search(REGEX_TEST_NAME, test["name"])
+ if groups and len(groups.groups()) == 3:
+ hover_name = "{chain}-{vhost}-{vm}".format(
+ chain=str(groups.group(1)),
+ vhost=str(groups.group(2)),
+ vm=str(groups.group(3)))
+ else:
+ hover_name = ""
+ if vals.get(c, None) is None:
+ vals[c] = dict()
+ if vals[c].get(n, None) is None:
+ vals[c][n] = dict(name=hover_name,
+ vals_r=list(),
+ vals_c=list(),
+ nr_r=None,
+ nr_c=None,
+ mean_r=None,
+ mean_c=None,
+ stdev_r=None,
+ stdev_c=None)
+ try:
+ 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
+ except TypeError:
+ result = None
+
+ if result:
+ for tag in test['tags']:
+ groups = re.search(REGEX_THREADS, tag)
+ if groups and len(groups.groups()) == 3:
+ if str(groups.group(3)) == \
+ plot["reference"]["include"]:
+ vals[c][n]["vals_r"].append(result)
+ elif str(groups.group(3)) == \
+ plot["compare"]["include"]:
+ vals[c][n]["vals_c"].append(result)
+ break
+ if not vals:
+ logging.error("No data.")
+ return
+
+ 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_r"]:
+ vals[key_c][key_n]["nr_r"] = len(vals[key_c][key_n]["vals_r"])
+ vals[key_c][key_n]["mean_r"] = \
+ mean(vals[key_c][key_n]["vals_r"])
+ vals[key_c][key_n]["stdev_r"] = \
+ round(stdev(vals[key_c][key_n]["vals_r"]) / 1000000, 1)
+ if vals[key_c][key_n]["vals_c"]:
+ vals[key_c][key_n]["nr_c"] = len(vals[key_c][key_n]["vals_c"])
+ vals[key_c][key_n]["mean_c"] = \
+ mean(vals[key_c][key_n]["vals_c"])
+ vals[key_c][key_n]["stdev_c"] = \
+ round(stdev(vals[key_c][key_n]["vals_c"]) / 1000000, 1)
+
+ 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_r = [list() for _ in range(len(chains))]
+ data_c = [list() for _ in range(len(chains))]
+ diff = [list() for _ in range(len(chains))]
+ for c in chains:
+ for n in nodes:
+ try:
+ val_r = vals[txt_chains[c - 1]][txt_nodes[n - 1]]["mean_r"]
+ except (KeyError, IndexError):
+ val_r = None
+ try:
+ val_c = vals[txt_chains[c - 1]][txt_nodes[n - 1]]["mean_c"]
+ except (KeyError, IndexError):
+ val_c = None
+ if val_c is not None and val_r:
+ val_d = (val_c - val_r) * 100 / val_r
+ else:
+ val_d = None
+
+ if val_r is not None:
+ val_r = round(val_r / 1000000, 1)
+ data_r[c - 1].append(val_r)
+ if val_c is not None:
+ val_c = round(val_c / 1000000, 1)
+ data_c[c - 1].append(val_c)
+ if val_d is not None:
+ val_d = int(round(val_d, 0))
+ diff[c - 1].append(val_d)
+
+ # Colorscales:
+ my_green = [[0.0, 'rgb(235, 249, 242)'],
+ [1.0, 'rgb(45, 134, 89)']]
+
+ my_blue = [[0.0, 'rgb(236, 242, 248)'],
+ [1.0, 'rgb(57, 115, 172)']]
+
+ my_grey = [[0.0, 'rgb(230, 230, 230)'],
+ [1.0, 'rgb(102, 102, 102)']]
+
+ hovertext = list()
+
+ annotations = list()
+ annotations_r = list()
+ annotations_c = list()
+ annotations_diff = list()
+
+ text = ("Test: {name}"
+ "<br>{title_r}: {text_r}"
+ "<br>{title_c}: {text_c}{text_diff}")
+ text_r = "Thput: {val_r}; StDev: {stdev_r}; Runs: {nr_r}"
+ text_c = "Thput: {val_c}; StDev: {stdev_c}; Runs: {nr_c}"
+ text_diff = "<br>Relative Difference {title_c} vs. {title_r}: {diff}%"
+
+ for c in range(len(txt_chains)):
+ hover_line = list()
+ for n in range(len(txt_nodes)):
+ point = dict(
+ x=n + 1,
+ y=c + 1,
+ xref="x",
+ yref="y",
+ xanchor="center",
+ yanchor="middle",
+ text="",
+ font=dict(
+ size=14,
),
+ align="center",
+ showarrow=False
+ )
+
+ point_text_r = "Not present"
+ point_text_c = "Not present"
+ point_text_diff = ""
+ try:
+ point_r = data_r[c][n]
+ if point_r is not None:
+ point_text_r = text_r.format(
+ val_r=point_r,
+ stdev_r=vals[txt_chains[c]][txt_nodes[n]]["stdev_r"],
+ nr_r=vals[txt_chains[c]][txt_nodes[n]]["nr_r"])
+ except KeyError:
+ point_r = None
+ point["text"] = "" if point_r is None else point_r
+ annotations_r.append(deepcopy(point))
+
+ try:
+ point_c = data_c[c][n]
+ if point_c is not None:
+ point_text_c = text_c.format(
+ val_c=point_c,
+ stdev_c=vals[txt_chains[c]][txt_nodes[n]]["stdev_c"],
+ nr_c=vals[txt_chains[c]][txt_nodes[n]]["nr_c"])
+ except KeyError:
+ point_c = None
+ point["text"] = "" if point_c is None else point_c
+ annotations_c.append(deepcopy(point))
+
+ try:
+ point_d = diff[c][n]
+ if point_d is not None:
+ point_text_diff = text_diff.format(
+ title_r=plot["reference"]["name"],
+ title_c=plot["compare"]["name"],
+ diff=point_d)
+ except KeyError:
+ point_d = None
+ point["text"] = "" if point_d is None else point_d
+ annotations_diff.append(deepcopy(point))
+
+ try:
+ name = vals[txt_chains[c]][txt_nodes[n]]["name"]
+ except KeyError:
+ continue
+
+ hover_line.append(text.format(
+ name=name,
+ title_r=plot["reference"]["name"],
+ text_r=point_text_r,
+ title_c=plot["compare"]["name"],
+ text_c=point_text_c,
+ text_diff=point_text_diff
+ ))
+
+ hovertext.append(hover_line)
+
+ traces = [
+ plgo.Heatmap(x=nodes,
+ y=chains,
+ z=data_r,
+ visible=True,
+ colorbar=dict(
+ title=plot.get("z-axis", ""),
+ titleside="right",
+ titlefont=dict(
+ size=16
+ ),
+ tickfont=dict(
+ size=16,
+ ),
+ tickformat=".1f",
+ yanchor="bottom",
+ y=-0.02,
+ len=0.925,
+ ),
+ showscale=True,
+ colorscale=my_green,
+ reversescale=False,
+ text=hovertext,
+ hoverinfo="text"),
+ plgo.Heatmap(x=nodes,
+ y=chains,
+ z=data_c,
+ visible=False,
+ colorbar=dict(
+ title=plot.get("z-axis", ""),
+ titleside="right",
+ titlefont=dict(
+ size=16
+ ),
+ tickfont=dict(
+ size=16,
+ ),
+ tickformat=".1f",
+ yanchor="bottom",
+ y=-0.02,
+ len=0.925,
+ ),
+ showscale=True,
+ colorscale=my_blue,
+ reversescale=False,
+ text=hovertext,
+ hoverinfo="text"),
+ plgo.Heatmap(x=nodes,
+ y=chains,
+ z=diff,
+ name="Diff",
+ visible=False,
+ colorbar=dict(
+ title="Relative Difference {name_c} vs. {name_r} [%]".
+ format(name_c=plot["compare"]["name"],
+ name_r=plot["reference"]["name"]),
+ titleside="right",
+ titlefont=dict(
+ size=16
+ ),
+ tickfont=dict(
+ size=16,
+ ),
+ tickformat=".1f",
+ yanchor="bottom",
+ y=-0.02,
+ len=0.925,
+ ),
+ showscale=True,
+ colorscale=my_grey,
+ reversescale=False,
+ text=hovertext,
+ hoverinfo="text")
+ ]
+
+ for idx, item in enumerate(txt_nodes):
+ # X-axis, numbers:
+ annotations.append(dict(
+ x=idx+1,
+ y=0.05,
+ 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):
+ # Y-axis, numbers:
+ annotations.append(dict(
+ x=0.35,
+ y=idx+1,
+ xref="x",
+ yref="y",
+ xanchor="right",
+ yanchor="middle",
+ text=item,
+ font=dict(
+ size=16,
+ ),
+ align="center",
+ showarrow=False
+ ))
+ # X-axis, title:
+ annotations.append(dict(
+ x=0.55,
+ y=-0.15,
+ xref="paper",
+ yref="y",
+ xanchor="center",
+ yanchor="bottom",
+ text=plot.get("x-axis", ""),
+ font=dict(
+ size=16,
+ ),
+ align="center",
+ showarrow=False
+ ))
+ # Y-axis, title:
+ annotations.append(dict(
+ x=-0.1,
+ y=0.5,
+ xref="x",
+ yref="paper",
+ xanchor="center",
+ yanchor="middle",
+ text=plot.get("y-axis", ""),
+ font=dict(
+ size=16,
+ ),
+ align="center",
+ textangle=270,
+ showarrow=False
+ ))
+ updatemenus = list([
+ dict(
+ active=0,
+ x=1.0,
+ y=0.0,
+ xanchor='right',
+ yanchor='bottom',
+ direction='up',
+ buttons=list([
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"
+ label=plot["reference"]["name"],
+ method="update",
+ args=[
+ {
+ "visible": [True, False, False]
+ },
+ {
+ "colorscale": [my_green, ],
+ "reversescale": False,
+ "annotations": annotations + annotations_r,
+ },
+ ]
),
dict(
- args=[{"colorscale": "Viridis", "reversescale": True}],
- label="Viridis",
- method="update"
+ label=plot["compare"]["name"],
+ method="update",
+ args=[
+ {
+ "visible": [False, True, False]
+ },
+ {
+ "colorscale": [my_blue, ],
+ "reversescale": False,
+ "annotations": annotations + annotations_c,
+ },
+ ]
),
dict(
- args=[{"colorscale": "Cividis", "reversescale": True}],
- label="Cividis",
- method="update"
+ label="Diff",
+ method="update",
+ args=[
+ {
+ "visible": [False, False, True]
+ },
+ {
+ "colorscale": [my_grey, ],
+ "reversescale": False,
+ "annotations": annotations + annotations_diff,
+ },
+ ]
),
])
)
logging.error(repr(err))
return
- layout["annotations"] = annotations
+ layout["annotations"] = annotations + annotations_r
layout['updatemenus'] = updatemenus
try:
Code Review / csit.git / blobdiff