X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_plots.py;h=28490074736304185bb0b080e43524a40536e14b;hp=b828b3c71d515cd2e9eb8dc078026b798367036c;hb=HEAD;hpb=8a5fee0d46f405a5e7838a99f25862f49b0c8192 diff --git a/resources/tools/presentation/generator_plots.py b/resources/tools/presentation/generator_plots.py deleted file mode 100644 index b828b3c71d..0000000000 --- a/resources/tools/presentation/generator_plots.py +++ /dev/null @@ -1,1270 +0,0 @@ -# Copyright (c) 2020 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 re -import logging - -from collections import OrderedDict -from copy import deepcopy - -import hdrh.histogram -import hdrh.codec -import pandas as pd -import plotly.offline as ploff -import plotly.graph_objs as plgo - -from plotly.exceptions import PlotlyError - -from pal_utils import mean, stdev - - -COLORS = ( - u"#1A1110", - u"#DA2647", - u"#214FC6", - u"#01786F", - u"#BD8260", - u"#FFD12A", - u"#A6E7FF", - u"#738276", - u"#C95A49", - u"#FC5A8D", - u"#CEC8EF", - u"#391285", - u"#6F2DA8", - u"#FF878D", - u"#45A27D", - u"#FFD0B9", - u"#FD5240", - u"#DB91EF", - u"#44D7A8", - u"#4F86F7", - u"#84DE02", - u"#FFCFF1", - u"#614051" -) - -REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)-') - - -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 - """ - - generator = { - u"plot_nf_reconf_box_name": plot_nf_reconf_box_name, - u"plot_perf_box_name": plot_perf_box_name, - u"plot_tsa_name": plot_tsa_name, - u"plot_http_server_perf_box": plot_http_server_perf_box, - u"plot_nf_heatmap": plot_nf_heatmap, - u"plot_hdrh_lat_by_percentile": plot_hdrh_lat_by_percentile - } - - logging.info(u"Generating the plots ...") - for index, plot in enumerate(spec.plots): - try: - logging.info(f" Plot nr {index + 1}: {plot.get(u'title', u'')}") - plot[u"limits"] = spec.configuration[u"limits"] - generator[plot[u"algorithm"]](plot, data) - logging.info(u" Done.") - except NameError as err: - logging.error( - f"Probably algorithm {plot[u'algorithm']} is not defined: " - f"{repr(err)}" - ) - logging.info(u"Done.") - - -def plot_hdrh_lat_by_percentile(plot, input_data): - """Generate the plot(s) with algorithm: plot_hdrh_lat_by_percentile - 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( - f" Creating the data set for the {plot.get(u'type', u'')} " - f"{plot.get(u'title', u'')}." - ) - if plot.get(u"include", None): - data = input_data.filter_tests_by_name( - plot, - params=[u"name", u"latency", u"parent", u"tags", u"type"] - )[0][0] - elif plot.get(u"filter", None): - data = input_data.filter_data( - plot, - params=[u"name", u"latency", u"parent", u"tags", u"type"], - continue_on_error=True - )[0][0] - else: - job = list(plot[u"data"].keys())[0] - build = str(plot[u"data"][job][0]) - data = input_data.tests(job, build) - - if data is None or len(data) == 0: - logging.error(u"No data.") - return - - desc = { - u"LAT0": u"No-load.", - u"PDR10": u"Low-load, 10% PDR.", - u"PDR50": u"Mid-load, 50% PDR.", - u"PDR90": u"High-load, 90% PDR.", - u"PDR": u"Full-load, 100% PDR.", - u"NDR10": u"Low-load, 10% NDR.", - u"NDR50": u"Mid-load, 50% NDR.", - u"NDR90": u"High-load, 90% NDR.", - u"NDR": u"Full-load, 100% NDR." - } - - graphs = [ - u"LAT0", - u"PDR10", - u"PDR50", - u"PDR90" - ] - - file_links = plot.get(u"output-file-links", None) - target_links = plot.get(u"target-links", None) - - for test in data: - try: - if test[u"type"] not in (u"NDRPDR",): - logging.warning(f"Invalid test type: {test[u'type']}") - continue - name = re.sub(REGEX_NIC, u"", test[u"parent"]. - replace(u'-ndrpdr', u'').replace(u'2n1l-', u'')) - try: - nic = re.search(REGEX_NIC, test[u"parent"]).group(1) - except (IndexError, AttributeError, KeyError, ValueError): - nic = u"" - name_link = f"{nic}-{test[u'name']}".replace(u'-ndrpdr', u'') - - logging.info(f" Generating the graph: {name_link}") - - fig = plgo.Figure() - layout = deepcopy(plot[u"layout"]) - - for color, graph in enumerate(graphs): - for idx, direction in enumerate((u"direction1", u"direction2")): - xaxis = [0.0, ] - yaxis = [0.0, ] - hovertext = [ - f"{desc[graph]}
" - f"Direction: {(u'W-E', u'E-W')[idx % 2]}
" - f"Percentile: 0.0%
" - f"Latency: 0.0uSec" - ] - try: - decoded = hdrh.histogram.HdrHistogram.decode( - test[u"latency"][graph][direction][u"hdrh"] - ) - except hdrh.codec.HdrLengthException: - logging.warning( - f"No data for direction {(u'W-E', u'E-W')[idx % 2]}" - ) - continue - - for item in decoded.get_recorded_iterator(): - percentile = item.percentile_level_iterated_to - if percentile > 99.9: - continue - xaxis.append(percentile) - yaxis.append(item.value_iterated_to) - hovertext.append( - f"{desc[graph]}
" - f"Direction: {(u'W-E', u'E-W')[idx % 2]}
" - f"Percentile: {percentile:.5f}%
" - f"Latency: {item.value_iterated_to}uSec" - ) - fig.add_trace( - plgo.Scatter( - x=xaxis, - y=yaxis, - name=desc[graph], - mode=u"lines", - legendgroup=desc[graph], - showlegend=bool(idx), - line=dict( - color=COLORS[color], - dash=u"dash" if idx % 2 else u"solid" - ), - hovertext=hovertext, - hoverinfo=u"text" - ) - ) - - layout[u"title"][u"text"] = f"Latency: {name}" - fig.update_layout(layout) - - # Create plot - file_name = f"{plot[u'output-file']}-{name_link}.html" - logging.info(f" Writing file {file_name}") - - try: - # Export Plot - ploff.plot(fig, show_link=False, auto_open=False, - filename=file_name) - # Add link to the file: - if file_links and target_links: - with open(file_links, u"a") as file_handler: - file_handler.write( - f"- `{name_link} " - f"<{target_links}/{file_name.split(u'/')[-1]}>`_\n" - ) - except FileNotFoundError as err: - logging.error( - f"Not possible to write the link to the file " - f"{file_links}\n{err}" - ) - except PlotlyError as err: - logging.error(f" Finished with error: {repr(err)}") - - except hdrh.codec.HdrLengthException as err: - logging.warning(repr(err)) - continue - - except (ValueError, KeyError) as err: - logging.warning(repr(err)) - continue - - -def plot_nf_reconf_box_name(plot, input_data): - """Generate the plot(s) with algorithm: plot_nf_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 - logging.info( - f" Creating the data set for the {plot.get(u'type', u'')} " - f"{plot.get(u'title', u'')}." - ) - data = input_data.filter_tests_by_name( - plot, params=[u"result", u"parent", u"tags", u"type"] - ) - if data is None: - logging.error(u"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[u"parent"], None) is None: - y_vals[test[u"parent"]] = list() - loss[test[u"parent"]] = list() - try: - y_vals[test[u"parent"]].append(test[u"result"][u"time"]) - loss[test[u"parent"]].append(test[u"result"][u"loss"]) - except (KeyError, TypeError): - y_vals[test[u"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 val in y_vals.values(): - if len(val) < max_len: - val.extend([None for _ in range(max_len - len(val))]) - - # Add plot traces - traces = list() - df_y = pd.DataFrame(y_vals) - df_y.head() - for i, col in enumerate(df_y.columns): - tst_name = re.sub(REGEX_NIC, u"", - col.lower().replace(u'-ndrpdr', u''). - replace(u'2n1l-', u'')) - - traces.append(plgo.Box( - x=[str(i + 1) + u'.'] * len(df_y[col]), - y=[y if y else None for y in df_y[col]], - name=( - f"{i + 1}. " - f"({nr_of_samples[i]:02d} " - f"run{u's' if nr_of_samples[i] > 1 else u''}, " - f"packets lost average: {mean(loss[col]):.1f}) " - f"{u'-'.join(tst_name.split(u'-')[3:-2])}" - ), - hoverinfo=u"y+name" - )) - try: - # Create plot - layout = deepcopy(plot[u"layout"]) - layout[u"title"] = f"Time Lost: {layout[u'title']}" - layout[u"yaxis"][u"title"] = u"Effective Blocked Time [s]" - layout[u"legend"][u"font"][u"size"] = 14 - layout[u"yaxis"].pop(u"range") - plpl = plgo.Figure(data=traces, layout=layout) - - # Export Plot - file_type = plot.get(u"output-file-type", u".html") - logging.info(f" Writing file {plot[u'output-file']}{file_type}.") - ploff.plot( - plpl, - show_link=False, - auto_open=False, - filename=f"{plot[u'output-file']}{file_type}" - ) - except PlotlyError as err: - logging.error( - f" Finished with error: {repr(err)}".replace(u"\n", u" ") - ) - return - - -def plot_perf_box_name(plot, input_data): - """Generate the plot(s) with algorithm: plot_perf_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 - logging.info( - f" Creating data set for the {plot.get(u'type', u'')} " - f"{plot.get(u'title', u'')}." - ) - data = input_data.filter_tests_by_name( - plot, - params=[u"throughput", u"gbps", u"result", u"parent", u"tags", u"type"]) - if data is None: - logging.error(u"No data.") - return - - # Prepare the data for the plot - plot_title = plot.get(u"title", u"").lower() - - if u"-gbps" in plot_title: - value = u"gbps" - multiplier = 1e6 - else: - value = u"throughput" - multiplier = 1.0 - y_vals = OrderedDict() - test_type = u"" - for job in data: - for build in job: - for test in build: - if y_vals.get(test[u"parent"], None) is None: - y_vals[test[u"parent"]] = list() - try: - if test[u"type"] in (u"NDRPDR", ): - test_type = u"NDRPDR" - - if u"-pdr" in plot_title: - ttype = u"PDR" - elif u"-ndr" in plot_title: - ttype = u"NDR" - else: - raise RuntimeError( - u"Wrong title. No information about test type. " - u"Add '-ndr' or '-pdr' to the test title." - ) - - y_vals[test[u"parent"]].append( - test[value][ttype][u"LOWER"] * multiplier - ) - - elif test[u"type"] in (u"SOAK", ): - y_vals[test[u"parent"]].\ - append(test[u"throughput"][u"LOWER"]) - test_type = u"SOAK" - - elif test[u"type"] in (u"HOSTSTACK", ): - if u"LDPRELOAD" in test[u"tags"]: - y_vals[test[u"parent"]].append( - float(test[u"result"][u"bits_per_second"]) / 1e3 - ) - elif u"VPPECHO" in test[u"tags"]: - y_vals[test[u"parent"]].append( - (float(test[u"result"][u"client"][u"tx_data"]) - * 8 / 1e3) / - ((float(test[u"result"][u"client"][u"time"]) + - float(test[u"result"][u"server"][u"time"])) / - 2) - ) - test_type = u"HOSTSTACK" - - else: - continue - - except (KeyError, TypeError): - y_vals[test[u"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 val in y_vals.values(): - if len(val) < max_len: - val.extend([None for _ in range(max_len - len(val))]) - - # Add plot traces - traces = list() - df_y = pd.DataFrame(y_vals) - df_y.head() - y_max = list() - for i, col in enumerate(df_y.columns): - tst_name = re.sub(REGEX_NIC, u"", - col.lower().replace(u'-ndrpdr', u''). - replace(u'2n1l-', u'')) - kwargs = dict( - x=[str(i + 1) + u'.'] * len(df_y[col]), - y=[y / 1e6 if y else None for y in df_y[col]], - name=( - f"{i + 1}. " - f"({nr_of_samples[i]:02d} " - f"run{u's' if nr_of_samples[i] > 1 else u''}) " - f"{tst_name}" - ), - hoverinfo=u"y+name" - ) - if test_type in (u"SOAK", ): - kwargs[u"boxpoints"] = u"all" - - traces.append(plgo.Box(**kwargs)) - - try: - val_max = max(df_y[col]) - if val_max: - y_max.append(int(val_max / 1e6) + 2) - except (ValueError, TypeError) as err: - logging.error(repr(err)) - continue - - try: - # Create plot - layout = deepcopy(plot[u"layout"]) - if layout.get(u"title", None): - if test_type in (u"HOSTSTACK", ): - layout[u"title"] = f"Bandwidth: {layout[u'title']}" - else: - layout[u"title"] = f"Throughput: {layout[u'title']}" - if y_max: - layout[u"yaxis"][u"range"] = [0, max(y_max)] - plpl = plgo.Figure(data=traces, layout=layout) - - # Export Plot - logging.info(f" Writing file {plot[u'output-file']}.html.") - ploff.plot( - plpl, - show_link=False, - auto_open=False, - filename=f"{plot[u'output-file']}.html" - ) - except PlotlyError as err: - logging.error( - f" Finished with error: {repr(err)}".replace(u"\n", u" ") - ) - return - - -def plot_tsa_name(plot, input_data): - """Generate the plot(s) with algorithm: - plot_tsa_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(u"title", u"") - logging.info( - f" Creating data set for the {plot.get(u'type', u'')} {plot_title}." - ) - data = input_data.filter_tests_by_name( - plot, - params=[u"throughput", u"gbps", u"parent", u"tags", u"type"] - ) - if data is None: - logging.error(u"No data.") - return - - plot_title = plot_title.lower() - - if u"-gbps" in plot_title: - value = u"gbps" - h_unit = u"Gbps" - multiplier = 1e6 - else: - value = u"throughput" - h_unit = u"Mpps" - multiplier = 1.0 - - y_vals = OrderedDict() - for job in data: - for build in job: - for test in build: - if y_vals.get(test[u"parent"], None) is None: - y_vals[test[u"parent"]] = { - u"1": list(), - u"2": list(), - u"4": list() - } - try: - if test[u"type"] not in (u"NDRPDR",): - continue - - if u"-pdr" in plot_title: - ttype = u"PDR" - elif u"-ndr" in plot_title: - ttype = u"NDR" - else: - continue - - if u"1C" in test[u"tags"]: - y_vals[test[u"parent"]][u"1"]. \ - append(test[value][ttype][u"LOWER"] * multiplier) - elif u"2C" in test[u"tags"]: - y_vals[test[u"parent"]][u"2"]. \ - append(test[value][ttype][u"LOWER"] * multiplier) - elif u"4C" in test[u"tags"]: - y_vals[test[u"parent"]][u"4"]. \ - append(test[value][ttype][u"LOWER"] * multiplier) - except (KeyError, TypeError): - pass - - if not y_vals: - logging.warning(f"No data for the plot {plot.get(u'title', u'')}") - 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) * 1e6) - 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[u"limits"][u"pci"][u"pci-g3-x8"] - for test_name, test_vals in y_vals.items(): - try: - if test_vals[u"1"][1]: - name = re.sub( - REGEX_NIC, - u"", - test_name.replace(u'-ndrpdr', u'').replace(u'2n1l-', u'') - ) - vals[name] = OrderedDict() - y_val_1 = test_vals[u"1"][0] / 1e6 - y_val_2 = test_vals[u"2"][0] / 1e6 if test_vals[u"2"][0] \ - else None - y_val_4 = test_vals[u"4"][0] / 1e6 if test_vals[u"4"][0] \ - else None - - vals[name][u"val"] = [y_val_1, y_val_2, y_val_4] - vals[name][u"rel"] = [1.0, None, None] - vals[name][u"ideal"] = [ - y_1c_max[test_name], - y_1c_max[test_name] * 2, - y_1c_max[test_name] * 4 - ] - vals[name][u"diff"] = [ - (y_val_1 - y_1c_max[test_name]) * 100 / y_val_1, None, None - ] - vals[name][u"count"] = [ - test_vals[u"1"][1], - test_vals[u"2"][1], - test_vals[u"4"][1] - ] - - try: - val_max = max(vals[name][u"val"]) - except ValueError as err: - logging.error(repr(err)) - continue - if val_max: - y_max.append(val_max) - - if y_val_2: - vals[name][u"rel"][1] = round(y_val_2 / y_val_1, 2) - vals[name][u"diff"][1] = \ - (y_val_2 - vals[name][u"ideal"][1]) * 100 / y_val_2 - if y_val_4: - vals[name][u"rel"][2] = round(y_val_4 / y_val_1, 2) - vals[name][u"diff"][2] = \ - (y_val_4 - vals[name][u"ideal"][2]) * 100 / y_val_4 - except IndexError as err: - logging.warning(f"No data for {test_name}") - logging.warning(repr(err)) - - # Limits: - if u"x520" in test_name: - limit = plot[u"limits"][u"nic"][u"x520"] - elif u"x710" in test_name: - limit = plot[u"limits"][u"nic"][u"x710"] - elif u"xxv710" in test_name: - limit = plot[u"limits"][u"nic"][u"xxv710"] - elif u"xl710" in test_name: - limit = plot[u"limits"][u"nic"][u"xl710"] - elif u"x553" in test_name: - limit = plot[u"limits"][u"nic"][u"x553"] - elif u"cx556a" in test_name: - limit = plot[u"limits"][u"nic"][u"cx556a"] - else: - limit = 0 - if limit > nic_limit: - nic_limit = limit - - mul = 2 if u"ge2p" in test_name else 1 - if u"10ge" in test_name: - limit = plot[u"limits"][u"link"][u"10ge"] * mul - elif u"25ge" in test_name: - limit = plot[u"limits"][u"link"][u"25ge"] * mul - elif u"40ge" in test_name: - limit = plot[u"limits"][u"link"][u"40ge"] * mul - elif u"100ge" in test_name: - limit = plot[u"limits"][u"link"][u"100ge"] * mul - else: - limit = 0 - if limit > lnk_limit: - lnk_limit = limit - - traces = list() - annotations = list() - x_vals = [1, 2, 4] - - # Limits: - if u"-gbps" not in plot_title: - try: - threshold = 1.1 * max(y_max) # 10% - except ValueError as err: - logging.error(err) - return - nic_limit /= 1e6 - traces.append(plgo.Scatter( - x=x_vals, - y=[nic_limit, ] * len(x_vals), - name=f"NIC: {nic_limit:.2f}Mpps", - showlegend=False, - mode=u"lines", - line=dict( - dash=u"dot", - color=COLORS[-1], - width=1), - hoverinfo=u"none" - )) - annotations.append(dict( - x=1, - y=nic_limit, - xref=u"x", - yref=u"y", - xanchor=u"left", - yanchor=u"bottom", - text=f"NIC: {nic_limit:.2f}Mpps", - font=dict( - size=14, - color=COLORS[-1], - ), - align=u"left", - showarrow=False - )) - y_max.append(nic_limit) - - lnk_limit /= 1e6 - if lnk_limit < threshold: - traces.append(plgo.Scatter( - x=x_vals, - y=[lnk_limit, ] * len(x_vals), - name=f"Link: {lnk_limit:.2f}Mpps", - showlegend=False, - mode=u"lines", - line=dict( - dash=u"dot", - color=COLORS[-1], - width=1), - hoverinfo=u"none" - )) - annotations.append(dict( - x=1, - y=lnk_limit, - xref=u"x", - yref=u"y", - xanchor=u"left", - yanchor=u"bottom", - text=f"Link: {lnk_limit:.2f}Mpps", - font=dict( - size=14, - color=COLORS[-1], - ), - align=u"left", - showarrow=False - )) - y_max.append(lnk_limit) - - pci_limit /= 1e6 - 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=f"PCIe: {pci_limit:.2f}Mpps", - showlegend=False, - mode=u"lines", - line=dict( - dash=u"dot", - color=COLORS[-1], - width=1), - hoverinfo=u"none" - )) - annotations.append(dict( - x=1, - y=pci_limit, - xref=u"x", - yref=u"y", - xanchor=u"left", - yanchor=u"bottom", - text=f"PCIe: {pci_limit:.2f}Mpps", - font=dict( - size=14, - color=COLORS[-1], - ), - align=u"left", - showarrow=False - )) - y_max.append(pci_limit) - - # Perfect and measured: - cidx = 0 - for name, val in vals.items(): - hovertext = list() - try: - for idx in range(len(val[u"val"])): - htext = "" - if isinstance(val[u"val"][idx], float): - htext += ( - f"No. of Runs: {val[u'count'][idx]}
" - f"Mean: {val[u'val'][idx]:.2f}{h_unit}
" - ) - if isinstance(val[u"diff"][idx], float): - htext += f"Diff: {round(val[u'diff'][idx]):.0f}%
" - if isinstance(val[u"rel"][idx], float): - htext += f"Speedup: {val[u'rel'][idx]:.2f}" - hovertext.append(htext) - traces.append( - plgo.Scatter( - x=x_vals, - y=val[u"val"], - name=name, - legendgroup=name, - mode=u"lines+markers", - line=dict( - color=COLORS[cidx], - width=2), - marker=dict( - symbol=u"circle", - size=10 - ), - text=hovertext, - hoverinfo=u"text+name" - ) - ) - traces.append( - plgo.Scatter( - x=x_vals, - y=val[u"ideal"], - name=f"{name} perfect", - legendgroup=name, - showlegend=False, - mode=u"lines", - line=dict( - color=COLORS[cidx], - width=2, - dash=u"dash"), - text=[f"Perfect: {y:.2f}Mpps" for y in val[u"ideal"]], - hoverinfo=u"text" - ) - ) - cidx += 1 - except (IndexError, ValueError, KeyError) as err: - logging.warning(f"No data for {name}\n{repr(err)}") - - try: - # Create plot - file_type = plot.get(u"output-file-type", u".html") - logging.info(f" Writing file {plot[u'output-file']}{file_type}.") - layout = deepcopy(plot[u"layout"]) - if layout.get(u"title", None): - layout[u"title"] = f"Speedup Multi-core: {layout[u'title']}" - layout[u"yaxis"][u"range"] = [0, int(max(y_max) * 1.1)] - layout[u"annotations"].extend(annotations) - plpl = plgo.Figure(data=traces, layout=layout) - - # Export Plot - ploff.plot( - plpl, - show_link=False, - auto_open=False, - filename=f"{plot[u'output-file']}{file_type}" - ) - except PlotlyError as err: - logging.error( - f" Finished with error: {repr(err)}".replace(u"\n", u" ") - ) - return - - -def plot_http_server_perf_box(plot, input_data): - """Generate the plot(s) with algorithm: plot_http_server_perf_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( - f" Creating the data set for the {plot.get(u'type', u'')} " - f"{plot.get(u'title', u'')}." - ) - data = input_data.filter_data(plot) - if data is None: - logging.error(u"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[u"name"], None) is None: - y_vals[test[u"name"]] = list() - try: - y_vals[test[u"name"]].append(test[u"result"]) - except (KeyError, TypeError): - y_vals[test[u"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 val in y_vals.values(): - if len(val) < max_len: - val.extend([None for _ in range(max_len - len(val))]) - - # Add plot traces - traces = list() - df_y = pd.DataFrame(y_vals) - df_y.head() - for i, col in enumerate(df_y.columns): - name = \ - f"{i + 1}. " \ - f"({nr_of_samples[i]:02d} " \ - f"run{u's' if nr_of_samples[i] > 1 else u''}) " \ - f"{col.lower().replace(u'-ndrpdr', u'')}" - if len(name) > 50: - name_lst = name.split(u'-') - name = u"" - split_name = True - for segment in name_lst: - if (len(name) + len(segment) + 1) > 50 and split_name: - name += u"
" - split_name = False - name += segment + u'-' - name = name[:-1] - - traces.append(plgo.Box(x=[str(i + 1) + u'.'] * len(df_y[col]), - y=df_y[col], - name=name, - **plot[u"traces"])) - try: - # Create plot - plpl = plgo.Figure(data=traces, layout=plot[u"layout"]) - - # Export Plot - logging.info( - f" Writing file {plot[u'output-file']}" - f"{plot[u'output-file-type']}." - ) - ploff.plot( - plpl, - show_link=False, - auto_open=False, - filename=f"{plot[u'output-file']}{plot[u'output-file-type']}" - ) - except PlotlyError as err: - logging.error( - f" Finished with error: {repr(err)}".replace(u"\n", u" ") - ) - return - - -def plot_nf_heatmap(plot, input_data): - """Generate the plot(s) with algorithm: plot_nf_heatmap - specified in the specification file. - - :param plot: Plot to generate. - :param input_data: Data to process. - :type plot: pandas.Series - :type input_data: InputData - """ - - regex_cn = re.compile(r'^(\d*)R(\d*)C$') - regex_test_name = re.compile(r'^.*-(\d+ch|\d+pl)-' - r'(\d+mif|\d+vh)-' - r'(\d+vm\d+t|\d+dcr\d+t|\d+dcr\d+c).*$') - vals = dict() - - # Transform the data - logging.info( - f" Creating the data set for the {plot.get(u'type', u'')} " - f"{plot.get(u'title', u'')}." - ) - data = input_data.filter_data(plot, continue_on_error=True) - if data is None or data.empty: - logging.error(u"No data.") - return - - for job in data: - for build in job: - for test in build: - for tag in test[u"tags"]: - groups = re.search(regex_cn, tag) - if groups: - chain = str(groups.group(1)) - node = str(groups.group(2)) - break - else: - continue - groups = re.search(regex_test_name, test[u"name"]) - if groups and len(groups.groups()) == 3: - hover_name = ( - f"{str(groups.group(1))}-" - f"{str(groups.group(2))}-" - f"{str(groups.group(3))}" - ) - else: - hover_name = u"" - if vals.get(chain, None) is None: - vals[chain] = dict() - if vals[chain].get(node, None) is None: - vals[chain][node] = dict( - name=hover_name, - vals=list(), - nr=None, - mean=None, - stdev=None - ) - try: - if plot[u"include-tests"] == u"MRR": - result = test[u"result"][u"receive-rate"] - elif plot[u"include-tests"] == u"PDR": - result = test[u"throughput"][u"PDR"][u"LOWER"] - elif plot[u"include-tests"] == u"NDR": - result = test[u"throughput"][u"NDR"][u"LOWER"] - else: - result = None - except TypeError: - result = None - - if result: - vals[chain][node][u"vals"].append(result) - - if not vals: - logging.error(u"No data.") - return - - txt_chains = list() - txt_nodes = list() - for key_c in vals: - txt_chains.append(key_c) - for key_n in vals[key_c].keys(): - txt_nodes.append(key_n) - if vals[key_c][key_n][u"vals"]: - vals[key_c][key_n][u"nr"] = len(vals[key_c][key_n][u"vals"]) - vals[key_c][key_n][u"mean"] = \ - round(mean(vals[key_c][key_n][u"vals"]) / 1000000, 1) - vals[key_c][key_n][u"stdev"] = \ - round(stdev(vals[key_c][key_n][u"vals"]) / 1000000, 1) - txt_nodes = list(set(txt_nodes)) - - def sort_by_int(value): - """Makes possible to sort a list of strings which represent integers. - - :param value: Integer as a string. - :type value: str - :returns: Integer representation of input parameter 'value'. - :rtype: int - """ - return int(value) - - txt_chains = sorted(txt_chains, key=sort_by_int) - txt_nodes = sorted(txt_nodes, key=sort_by_int) - - chains = [i + 1 for i in range(len(txt_chains))] - nodes = [i + 1 for i in range(len(txt_nodes))] - - data = [list() for _ in range(len(chains))] - for chain in chains: - for node in nodes: - try: - val = vals[txt_chains[chain - 1]][txt_nodes[node - 1]][u"mean"] - except (KeyError, IndexError): - val = None - data[chain - 1].append(val) - - # Color scales: - my_green = [[0.0, u"rgb(235, 249, 242)"], - [1.0, u"rgb(45, 134, 89)"]] - - my_blue = [[0.0, u"rgb(236, 242, 248)"], - [1.0, u"rgb(57, 115, 172)"]] - - my_grey = [[0.0, u"rgb(230, 230, 230)"], - [1.0, u"rgb(102, 102, 102)"]] - - hovertext = list() - annotations = list() - - text = (u"Test: {name}
" - u"Runs: {nr}
" - u"Thput: {val}
" - u"StDev: {stdev}") - - for chain, _ in enumerate(txt_chains): - hover_line = list() - for node, _ in enumerate(txt_nodes): - if data[chain][node] is not None: - annotations.append( - dict( - x=node+1, - y=chain+1, - xref=u"x", - yref=u"y", - xanchor=u"center", - yanchor=u"middle", - text=str(data[chain][node]), - font=dict( - size=14, - ), - align=u"center", - showarrow=False - ) - ) - hover_line.append(text.format( - name=vals[txt_chains[chain]][txt_nodes[node]][u"name"], - nr=vals[txt_chains[chain]][txt_nodes[node]][u"nr"], - val=data[chain][node], - stdev=vals[txt_chains[chain]][txt_nodes[node]][u"stdev"])) - hovertext.append(hover_line) - - traces = [ - plgo.Heatmap( - x=nodes, - y=chains, - z=data, - colorbar=dict( - title=plot.get(u"z-axis", u""), - titleside=u"right", - titlefont=dict( - size=16 - ), - tickfont=dict( - size=16, - ), - tickformat=u".1f", - yanchor=u"bottom", - y=-0.02, - len=0.925, - ), - showscale=True, - colorscale=my_green, - text=hovertext, - hoverinfo=u"text" - ) - ] - - for idx, item in enumerate(txt_nodes): - # X-axis, numbers: - annotations.append( - dict( - x=idx+1, - y=0.05, - xref=u"x", - yref=u"y", - xanchor=u"center", - yanchor=u"top", - text=item, - font=dict( - size=16, - ), - align=u"center", - showarrow=False - ) - ) - for idx, item in enumerate(txt_chains): - # Y-axis, numbers: - annotations.append( - dict( - x=0.35, - y=idx+1, - xref=u"x", - yref=u"y", - xanchor=u"right", - yanchor=u"middle", - text=item, - font=dict( - size=16, - ), - align=u"center", - showarrow=False - ) - ) - # X-axis, title: - annotations.append( - dict( - x=0.55, - y=-0.15, - xref=u"paper", - yref=u"y", - xanchor=u"center", - yanchor=u"bottom", - text=plot.get(u"x-axis", u""), - font=dict( - size=16, - ), - align=u"center", - showarrow=False - ) - ) - # Y-axis, title: - annotations.append( - dict( - x=-0.1, - y=0.5, - xref=u"x", - yref=u"paper", - xanchor=u"center", - yanchor=u"middle", - text=plot.get(u"y-axis", u""), - font=dict( - size=16, - ), - align=u"center", - textangle=270, - showarrow=False - ) - ) - updatemenus = list([ - dict( - x=1.0, - y=0.0, - xanchor=u"right", - yanchor=u"bottom", - direction=u"up", - buttons=list([ - dict( - args=[ - { - u"colorscale": [my_green, ], - u"reversescale": False - } - ], - label=u"Green", - method=u"update" - ), - dict( - args=[ - { - u"colorscale": [my_blue, ], - u"reversescale": False - } - ], - label=u"Blue", - method=u"update" - ), - dict( - args=[ - { - u"colorscale": [my_grey, ], - u"reversescale": False - } - ], - label=u"Grey", - method=u"update" - ) - ]) - ) - ]) - - try: - layout = deepcopy(plot[u"layout"]) - except KeyError as err: - logging.error(f"Finished with error: No layout defined\n{repr(err)}") - return - - layout[u"annotations"] = annotations - layout[u'updatemenus'] = updatemenus - - try: - # Create plot - plpl = plgo.Figure(data=traces, layout=layout) - - # Export Plot - logging.info(f" Writing file {plot[u'output-file']}.html") - ploff.plot( - plpl, - show_link=False, - auto_open=False, - filename=f"{plot[u'output-file']}.html" - ) - except PlotlyError as err: - logging.error( - f" Finished with error: {repr(err)}".replace(u"\n", u" ") - ) - return