X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_plots.py;h=32f146bca84d5e412a1cc7ee6759496b1f0fd896;hp=ac77b3d425570548a89119e192170e2ed7768e71;hb=670a905fcf26395e2064aab79449fe582eec5853;hpb=b1589042d816ce58648153c20906520916feff49 diff --git a/resources/tools/presentation/generator_plots.py b/resources/tools/presentation/generator_plots.py index ac77b3d425..32f146bca8 100644 --- a/resources/tools/presentation/generator_plots.py +++ b/resources/tools/presentation/generator_plots.py @@ -19,11 +19,22 @@ 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. @@ -36,11 +47,14 @@ 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.") @@ -54,10 +68,10 @@ def plot_performance_box(plot, input_data): :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.") @@ -65,57 +79,124 @@ def plot_performance_box(plot, input_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 += "
" + 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"] = "Packet Throughput: {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 - logging.info(" Done.") - -def plot_latency_box(plot, input_data): - """Generate the plot(s) with algorithm: plot_latency_box +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. @@ -124,10 +205,10 @@ def plot_latency_box(plot, input_data): :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.") @@ -135,9 +216,15 @@ def plot_latency_box(plot, input_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 @@ -147,66 +234,174 @@ def plot_latency_box(plot, input_data): 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 - - 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 - - # Add plot traces + 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 += "
" + 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 - - 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"])) + 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}
" + "Test: {test}
" + "Direction: {dir}
".format(test=x_vals[idx], + dir=direction, + nr=nr_of_samples[idx])) + if isinstance(y_maxs[idx], float): + hovertext += "Max: {max:.2f}uSec
".format(max=y_maxs[idx]) + if isinstance(y_vals[idx], float): + hovertext += "Mean: {avg:.2f}uSec
".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"] = "Packet Latency: {0}".\ + format(layout["title"]) + layout["annotations"] = annotations + plpl = plgo.Figure(data=traces, layout=layout) # Export Plot ploff.plot(plpl, @@ -218,11 +413,10 @@ def plot_latency_box(plot, input_data): 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. @@ -231,81 +425,330 @@ def plot_throughput_speedup_analysis(plot, input_data): :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 - 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 += "
" + 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}
" \ + "Mean: {0:.2f}Mpps
".format(val["val"][idx], + val["count"][idx]) + if isinstance(val["diff"][idx], float): + htext += "Diff: {0:.0f}%
".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"] = "Speedup Multi-core: {0}". \ + format(layout["title"]) + layout["annotations"].extend(annotations) + plpl = plgo.Figure(data=traces, layout=layout) # Export Plot ploff.plot(plpl, @@ -317,8 +760,6 @@ def plot_throughput_speedup_analysis(plot, input_data): 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 @@ -330,10 +771,9 @@ def plot_http_server_performance_box(plot, input_data): :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", ""))) data = input_data.filter_data(plot) if data is None: logging.error("No data.") @@ -347,15 +787,17 @@ def plot_http_server_performance_box(plot, input_data): 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))]) @@ -365,13 +807,26 @@ def plot_http_server_performance_box(plot, input_data): 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 += "
" + 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"]) @@ -379,13 +834,10 @@ def plot_http_server_performance_box(plot, input_data): # 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", " "))) return - - logging.info(" Done.")