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=8af9a945d12d92d06dbd6085d4361e7830368175;hb=HEAD;hpb=af940b4664f4778ac4857cbdea6eed16ab2722f7 diff --git a/resources/tools/presentation/generator_plots.py b/resources/tools/presentation/generator_plots.py deleted file mode 100644 index 8af9a945d1..0000000000 --- a/resources/tools/presentation/generator_plots.py +++ /dev/null @@ -1,1489 +0,0 @@ -# Copyright (c) 2019 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.subplots import make_subplots -from plotly.exceptions import PlotlyError - -from pal_utils import mean, stdev - - -COLORS = [u"SkyBlue", u"Olive", u"Purple", u"Coral", u"Indigo", u"Pink", - u"Chocolate", u"Brown", u"Magenta", u"Cyan", u"Orange", u"Black", - u"Violet", u"Blue", u"Yellow", u"BurlyWood", u"CadetBlue", u"Crimson", - u"DarkBlue", u"DarkCyan", u"DarkGreen", u"Green", u"GoldenRod", - u"LightGreen", u"LightSeaGreen", u"LightSkyBlue", u"Maroon", - u"MediumSeaGreen", u"SeaGreen", u"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 - """ - - generator = { - u"plot_nf_reconf_box_name": plot_nf_reconf_box_name, - u"plot_perf_box_name": plot_perf_box_name, - u"plot_lat_err_bars_name": plot_lat_err_bars_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_lat_hdrh_bar_name": plot_lat_hdrh_bar_name, - u"plot_lat_hdrh_percentile": plot_lat_hdrh_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_lat_hdrh_percentile(plot, input_data): - """Generate the plot(s) with algorithm: plot_lat_hdrh_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 - plot_title = plot.get(u"title", u"") - logging.info( - f" Creating the data set for the {plot.get(u'type', u'')} " - f"{plot_title}." - ) - data = input_data.filter_tests_by_name( - plot, params=[u"latency", u"parent", u"tags", u"type"]) - if data is None or len(data[0][0]) == 0: - logging.error(u"No data.") - return - - fig = plgo.Figure() - - # Prepare the data for the plot - directions = [u"W-E", u"E-W"] - for test in data[0][0]: - try: - if test[u"type"] in (u"NDRPDR",): - if u"-pdr" in plot_title.lower(): - ttype = u"PDR" - elif u"-ndr" in plot_title.lower(): - ttype = u"NDR" - else: - 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''). - replace(u'avf-', u'')) - for idx, direction in enumerate( - (u"direction1", u"direction2", )): - try: - hdr_lat = test[u"latency"][ttype][direction][u"hdrh"] - # TODO: Workaround, HDRH data must be aligned to 4 - # bytes, remove when not needed. - hdr_lat += u"=" * (len(hdr_lat) % 4) - xaxis = list() - yaxis = list() - hovertext = list() - decoded = hdrh.histogram.HdrHistogram.decode(hdr_lat) - for item in decoded.get_recorded_iterator(): - percentile = item.percentile_level_iterated_to - if percentile != 100.0: - xaxis.append(100.0 / (100.0 - percentile)) - yaxis.append(item.value_iterated_to) - hovertext.append( - f"Test: {name}
" - f"Direction: {directions[idx]}
" - f"Percentile: {percentile:.5f}
%" - f"Latency: {item.value_iterated_to}uSec" - ) - fig.add_trace( - plgo.Scatter( - x=xaxis, - y=yaxis, - name=name, - mode=u"lines", - hovertext=hovertext, - hoverinfo=u"text" - ) - ) - except hdrh.codec.HdrLengthException as err: - logging.warning( - f"No or invalid data for HDRHistogram for the test " - f"{name}\n{err}" - ) - continue - else: - logging.warning(f"Invalid test type: {test[u'type']}") - continue - except (ValueError, KeyError) as err: - logging.warning(repr(err)) - - layout = deepcopy(plot[u"layout"]) - - layout[u"title"][u"text"] = \ - f"Latency: {plot.get(u'graph-title', u'')}" - fig[u"layout"].update(layout) - - # Create plot - file_type = plot.get(u"output-file-type", u".html") - logging.info(f" Writing file {plot[u'output-file']}{file_type}.") - try: - # Export Plot - ploff.plot(fig, 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)}") - - -def plot_lat_hdrh_bar_name(plot, input_data): - """Generate the plot(s) with algorithm: plot_lat_hdrh_bar_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 the data set for the {plot.get(u'type', u'')} " - f"{plot_title}." - ) - data = input_data.filter_tests_by_name( - plot, params=[u"latency", u"parent", u"tags", u"type"]) - if data is None or len(data[0][0]) == 0: - logging.error(u"No data.") - return - - # Prepare the data for the plot - directions = [u"W-E", u"E-W"] - tests = list() - traces = list() - for idx_row, test in enumerate(data[0][0]): - try: - if test[u"type"] in (u"NDRPDR",): - if u"-pdr" in plot_title.lower(): - ttype = u"PDR" - elif u"-ndr" in plot_title.lower(): - ttype = u"NDR" - else: - 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'')) - histograms = list() - for idx_col, direction in enumerate( - (u"direction1", u"direction2", )): - try: - hdr_lat = test[u"latency"][ttype][direction][u"hdrh"] - # TODO: Workaround, HDRH data must be aligned to 4 - # bytes, remove when not needed. - hdr_lat += u"=" * (len(hdr_lat) % 4) - xaxis = list() - yaxis = list() - hovertext = list() - decoded = hdrh.histogram.HdrHistogram.decode(hdr_lat) - total_count = decoded.get_total_count() - for item in decoded.get_recorded_iterator(): - xaxis.append(item.value_iterated_to) - prob = float(item.count_added_in_this_iter_step) / \ - total_count * 100 - yaxis.append(prob) - hovertext.append( - f"Test: {name}
" - f"Direction: {directions[idx_col]}
" - f"Latency: {item.value_iterated_to}uSec
" - f"Probability: {prob:.2f}%
" - f"Percentile: " - f"{item.percentile_level_iterated_to:.2f}" - ) - marker_color = [COLORS[idx_row], ] * len(yaxis) - marker_color[xaxis.index( - decoded.get_value_at_percentile(50.0))] = u"red" - marker_color[xaxis.index( - decoded.get_value_at_percentile(90.0))] = u"red" - marker_color[xaxis.index( - decoded.get_value_at_percentile(95.0))] = u"red" - histograms.append( - plgo.Bar( - x=xaxis, - y=yaxis, - showlegend=False, - name=name, - marker={u"color": marker_color}, - hovertext=hovertext, - hoverinfo=u"text" - ) - ) - except hdrh.codec.HdrLengthException as err: - logging.warning( - f"No or invalid data for HDRHistogram for the test " - f"{name}\n{err}" - ) - continue - if len(histograms) == 2: - traces.append(histograms) - tests.append(name) - else: - logging.warning(f"Invalid test type: {test[u'type']}") - continue - except (ValueError, KeyError) as err: - logging.warning(repr(err)) - - if not tests: - logging.warning(f"No data for {plot_title}.") - return - - fig = make_subplots( - rows=len(tests), - cols=2, - specs=[ - [{u"type": u"bar"}, {u"type": u"bar"}] for _ in range(len(tests)) - ] - ) - - layout_axes = dict( - gridcolor=u"rgb(220, 220, 220)", - linecolor=u"rgb(220, 220, 220)", - linewidth=1, - showgrid=True, - showline=True, - showticklabels=True, - tickcolor=u"rgb(220, 220, 220)", - ) - - for idx_row, test in enumerate(tests): - for idx_col in range(2): - fig.add_trace( - traces[idx_row][idx_col], - row=idx_row + 1, - col=idx_col + 1 - ) - fig.update_xaxes( - row=idx_row + 1, - col=idx_col + 1, - **layout_axes - ) - fig.update_yaxes( - row=idx_row + 1, - col=idx_col + 1, - **layout_axes - ) - - layout = deepcopy(plot[u"layout"]) - - layout[u"title"][u"text"] = \ - f"Latency: {plot.get(u'graph-title', u'')}" - layout[u"height"] = 250 * len(tests) + 130 - - layout[u"annotations"][2][u"y"] = 1.06 - 0.008 * len(tests) - layout[u"annotations"][3][u"y"] = 1.06 - 0.008 * len(tests) - - for idx, test in enumerate(tests): - layout[u"annotations"].append({ - u"font": { - u"size": 14 - }, - u"showarrow": False, - u"text": f"{test}", - u"textangle": 0, - u"x": 0.5, - u"xanchor": u"center", - u"xref": u"paper", - u"y": 1.0 - float(idx) * 1.06 / len(tests), - u"yanchor": u"bottom", - u"yref": u"paper" - }) - - fig[u"layout"].update(layout) - - # Create plot - file_type = plot.get(u"output-file-type", u".html") - logging.info(f" Writing file {plot[u'output-file']}{file_type}.") - try: - # Export Plot - ploff.plot(fig, 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)}") - - -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"Implied Time Lost [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"parent", u"tags", u"type"]) - if data is None: - logging.error(u"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[u"parent"], None) is None: - y_vals[test[u"parent"]] = list() - try: - if (test[u"type"] in (u"NDRPDR", ) and - u"-pdr" in plot.get(u"title", u"").lower()): - y_vals[test[u"parent"]].\ - append(test[u"throughput"][u"PDR"][u"LOWER"]) - elif (test[u"type"] in (u"NDRPDR", ) and - u"-ndr" in plot.get(u"title", u"").lower()): - y_vals[test[u"parent"]]. \ - append(test[u"throughput"][u"NDR"][u"LOWER"]) - elif test[u"type"] in (u"SOAK", ): - y_vals[test[u"parent"]].\ - append(test[u"throughput"][u"LOWER"]) - 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'')) - traces.append( - plgo.Box( - x=[str(i + 1) + u'.'] * len(df_y[col]), - y=[y / 1000000 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" - ) - ) - try: - val_max = max(df_y[col]) - if val_max: - y_max.append(int(val_max / 1000000) + 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): - 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_lat_err_bars_name(plot, input_data): - """Generate the plot(s) with algorithm: plot_lat_err_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(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"latency", u"parent", u"tags", u"type"]) - if data is None: - logging.error(u"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(f"test[u'latency']: {test[u'latency']}\n") - except ValueError as err: - logging.warning(repr(err)) - if y_tmp_vals.get(test[u"parent"], None) is None: - y_tmp_vals[test[u"parent"]] = [ - list(), # direction1, min - list(), # direction1, avg - list(), # direction1, max - list(), # direction2, min - list(), # direction2, avg - list() # direction2, max - ] - try: - if test[u"type"] not in (u"NDRPDR", ): - logging.warning(f"Invalid test type: {test[u'type']}") - continue - if u"-pdr" in plot_title.lower(): - ttype = u"PDR" - elif u"-ndr" in plot_title.lower(): - ttype = u"NDR" - else: - logging.warning( - f"Invalid test type: {test[u'type']}" - ) - continue - y_tmp_vals[test[u"parent"]][0].append( - test[u"latency"][ttype][u"direction1"][u"min"]) - y_tmp_vals[test[u"parent"]][1].append( - test[u"latency"][ttype][u"direction1"][u"avg"]) - y_tmp_vals[test[u"parent"]][2].append( - test[u"latency"][ttype][u"direction1"][u"max"]) - y_tmp_vals[test[u"parent"]][3].append( - test[u"latency"][ttype][u"direction2"][u"min"]) - y_tmp_vals[test[u"parent"]][4].append( - test[u"latency"][ttype][u"direction2"][u"avg"]) - y_tmp_vals[test[u"parent"]][5].append( - test[u"latency"][ttype][u"direction2"][u"max"]) - 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, u"", key.replace(u'-ndrpdr', u''). - replace(u'2n1l-', u'')) - 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 enumerate(x_vals): - if not bool(int(idx % 2)): - direction = u"West-East" - else: - direction = u"East-West" - hovertext = ( - f"No. of Runs: {nr_of_samples[idx]}
" - f"Test: {x_vals[idx]}
" - f"Direction: {direction}
" - ) - if isinstance(y_maxs[idx], float): - hovertext += f"Max: {y_maxs[idx]:.2f}uSec
" - if isinstance(y_vals[idx], float): - hovertext += f"Mean: {y_vals[idx]:.2f}uSec
" - if isinstance(y_mins[idx], float): - hovertext += f"Min: {y_mins[idx]:.2f}uSec" - - 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=u"markers", - error_y=dict( - type=u"data", - symmetric=False, - array=array, - arrayminus=arrayminus, - color=COLORS[int(idx / 2)] - ), - marker=dict( - size=10, - color=COLORS[int(idx / 2)], - ), - text=hovertext, - hoverinfo=u"text", - )) - annotations.append(dict( - x=idx, - y=0, - xref=u"x", - yref=u"y", - xanchor=u"center", - yanchor=u"top", - text=u"E-W" if bool(int(idx % 2)) else u"W-E", - font=dict( - size=16, - ), - align=u"center", - showarrow=False - )) - - 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"Latency: {layout[u'title']}" - layout[u"annotations"] = 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_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"parent", u"tags", u"type"]) - if data is None: - logging.error(u"No data.") - return - - 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.lower(): - ttype = u"PDR" - elif u"-ndr" in plot_title.lower(): - ttype = u"NDR" - else: - continue - - if u"1C" in test[u"tags"]: - y_vals[test[u"parent"]][u"1"]. \ - append(test[u"throughput"][ttype][u"LOWER"]) - elif u"2C" in test[u"tags"]: - y_vals[test[u"parent"]][u"2"]. \ - append(test[u"throughput"][ttype][u"LOWER"]) - elif u"4C" in test[u"tags"]: - y_vals[test[u"parent"]][u"4"]. \ - append(test[u"throughput"][ttype][u"LOWER"]) - 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) * 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[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] / 1000000.0 - y_val_2 = test_vals[u"2"][0] / 1000000.0 if test_vals[u"2"][0] \ - else None - y_val_4 = test_vals[u"4"][0] / 1000000.0 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"] - 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: - 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=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 /= 1000000.0 - 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[-2], - 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[-2], - ), - align=u"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=f"PCIe: {pci_limit:.2f}Mpps", - showlegend=False, - mode=u"lines", - line=dict( - dash=u"dot", - color=COLORS[-3], - 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[-3], - ), - 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}Mpps
" - ) - 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$') - regex_test_name = re.compile(r'^.*-(\d+ch|\d+pl)-' - r'(\d+mif|\d+vh)-' - r'(\d+vm\d+t|\d+dcr\d+t).*$') - 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']}" - 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