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
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
}
logging.info(u"Generating the plots ...")
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}<br>"
+ f"Direction: {directions[idx]}<br>"
+ f"Percentile: {percentile:.5f}<br>%"
+ 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"<b>Latency:</b> {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}<br>"
+ f"Direction: {directions[idx_col]}<br>"
+ f"Latency: {item.value_iterated_to}uSec<br>"
+ f"Probability: {prob:.2f}%<br>"
+ 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"<b>Latency:</b> {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"<b>{test}</b>",
+ 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.
Code Review / csit.git / blobdiff