X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_plots.py;h=2fb4159a1f2150f2714158a0e651fc02ec74c5b4;hb=c81eac1e36b37c78f144a4218c231da846a908f6;hp=dda519600835121b1ee05f74b9f4b560a1d7a8ad;hpb=cbfa26dc0f5334bcd367c161b4eaad342355bbde;p=csit.git
diff --git a/resources/tools/presentation/generator_plots.py b/resources/tools/presentation/generator_plots.py
index dda5196008..2fb4159a1f 100644
--- a/resources/tools/presentation/generator_plots.py
+++ b/resources/tools/presentation/generator_plots.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019 Cisco and/or its affiliates.
+# 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:
@@ -21,10 +21,13 @@ 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
@@ -37,7 +40,7 @@ COLORS = [u"SkyBlue", u"Olive", u"Purple", u"Coral", u"Indigo", u"Pink",
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*-')
+REGEX_NIC = re.compile(r'(\d*ge\dp\d\D*\d*[a-z]*)-')
def generate_plots(spec, data):
@@ -55,7 +58,10 @@ def generate_plots(spec, data):
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_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,
+ u"plot_hdrh_lat_by_percentile": plot_hdrh_lat_by_percentile
}
logging.info(u"Generating the plots ...")
@@ -73,6 +79,439 @@ def generate_plots(spec, data):
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 color, 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''))
+ 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",
+ legendgroup=name,
+ showlegend=bool(idx),
+ line=dict(
+ color=COLORS[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
+ 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_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"
+ ]
+ decoded = hdrh.histogram.HdrHistogram.decode(
+ test[u"latency"][graph][direction][u"hdrh"]
+ )
+ 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"solid" if idx % 2 else u"dash"
+ ),
+ 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 fw:
+ fw.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_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.
@@ -891,7 +1330,7 @@ def plot_nf_heatmap(plot, input_data):
regex_cn = re.compile(r'^(\d*)R(\d*)C$')
regex_test_name = re.compile(r'^.*-(\d+ch|\d+pl)-'
r'(\d+mif|\d+vh)-'
- r'(\d+vm\d+t|\d+dcr\d+t).*$')
+ r'(\d+vm\d+t|\d+dcr\d+t|\d+dcr\d+c).*$')
vals = dict()
# Transform the data
@@ -1190,15 +1629,12 @@ def plot_nf_heatmap(plot, input_data):
plpl = plgo.Figure(data=traces, layout=layout)
# Export Plot
- logging.info(
- f" Writing file {plot[u'output-file']}"
- f"{plot[u'output-file-type']}."
- )
+ 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']}{plot[u'output-file-type']}"
+ filename=f"{plot[u'output-file']}.html"
)
except PlotlyError as err:
logging.error(