+++ /dev/null
-# Copyright (c) 2022 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.
-
-"""
-"""
-
-import plotly.graph_objects as go
-import pandas as pd
-
-import hdrh.histogram
-import hdrh.codec
-
-from datetime import datetime
-
-from ..utils.constants import Constants as C
-from ..utils.utils import classify_anomalies, get_color
-
-
-def _get_hdrh_latencies(row: pd.Series, name: str) -> dict:
- """Get the HDRH latencies from the test data.
-
- :param row: A row fron the data frame with test data.
- :param name: The test name to be displayed as the graph title.
- :type row: pandas.Series
- :type name: str
- :returns: Dictionary with HDRH latencies.
- :rtype: dict
- """
-
- latencies = {"name": name}
- for key in C.LAT_HDRH:
- try:
- latencies[key] = row[key]
- except KeyError:
- return None
-
- return latencies
-
-
-def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
- """Select the data for graphs from the provided data frame.
-
- :param data: Data frame with data for graphs.
- :param itm: Item (in this case job name) which data will be selected from
- the input data frame.
- :type data: pandas.DataFrame
- :type itm: str
- :returns: A data frame with selected data.
- :rtype: pandas.DataFrame
- """
-
- phy = itm["phy"].split("-")
- if len(phy) == 4:
- topo, arch, nic, drv = phy
- if drv == "dpdk":
- drv = ""
- else:
- drv += "-"
- drv = drv.replace("_", "-")
- else:
- return None
-
- core = str() if itm["dut"] == "trex" else f"{itm['core']}"
- ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
- dut_v100 = "none" if itm["dut"] == "trex" else itm["dut"]
- dut_v101 = itm["dut"]
-
- df = data.loc[(
- (
- (
- (data["version"] == "1.0.0") &
- (data["dut_type"].str.lower() == dut_v100)
- ) |
- (
- (data["version"] == "1.0.1") &
- (data["dut_type"].str.lower() == dut_v101)
- )
- ) &
- (data["test_type"] == ttype) &
- (data["passed"] == True)
- )]
- df = df[df.job.str.endswith(f"{topo}-{arch}")]
- df = df[df.test_id.str.contains(
- f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$",
- regex=True
- )].sort_values(by="start_time", ignore_index=True)
-
- return df
-
-
-def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
- color: str, norm_factor: float) -> list:
- """Generate the trending traces for the trending graph.
-
- :param ttype: Test type (MRR, NDR, PDR).
- :param name: The test name to be displayed as the graph title.
- :param df: Data frame with test data.
- :param color: The color of the trace (samples and trend line).
- :param norm_factor: The factor used for normalization of the results to CPU
- frequency set to Constants.NORM_FREQUENCY.
- :type ttype: str
- :type name: str
- :type df: pandas.DataFrame
- :type color: str
- :type norm_factor: float
- :returns: Traces (samples, trending line, anomalies)
- :rtype: list
- """
-
- df = df.dropna(subset=[C.VALUE[ttype], ])
- if df.empty:
- return list()
- if df.empty:
- return list()
-
- x_axis = df["start_time"].tolist()
- if ttype == "pdr-lat":
- y_data = [(itm / norm_factor) for itm in df[C.VALUE[ttype]].tolist()]
- else:
- y_data = [(itm * norm_factor) for itm in df[C.VALUE[ttype]].tolist()]
-
- anomalies, trend_avg, trend_stdev = classify_anomalies(
- {k: v for k, v in zip(x_axis, y_data)}
- )
-
- hover = list()
- customdata = list()
- for idx, (_, row) in enumerate(df.iterrows()):
- d_type = "trex" if row["dut_type"] == "none" else row["dut_type"]
- hover_itm = (
- f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
- f"<prop> [{row[C.UNIT[ttype]]}]: {y_data[idx]:,.0f}<br>"
- f"<stdev>"
- f"{d_type}-ref: {row['dut_version']}<br>"
- f"csit-ref: {row['job']}/{row['build']}<br>"
- f"hosts: {', '.join(row['hosts'])}"
- )
- if ttype == "mrr":
- stdev = (
- f"stdev [{row['result_receive_rate_rate_unit']}]: "
- f"{row['result_receive_rate_rate_stdev']:,.0f}<br>"
- )
- else:
- stdev = ""
- hover_itm = hover_itm.replace(
- "<prop>", "latency" if ttype == "pdr-lat" else "average"
- ).replace("<stdev>", stdev)
- hover.append(hover_itm)
- if ttype == "pdr-lat":
- customdata.append(_get_hdrh_latencies(row, name))
-
- hover_trend = list()
- for avg, stdev, (_, row) in zip(trend_avg, trend_stdev, df.iterrows()):
- d_type = "trex" if row["dut_type"] == "none" else row["dut_type"]
- hover_itm = (
- f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
- f"trend [pps]: {avg:,.0f}<br>"
- f"stdev [pps]: {stdev:,.0f}<br>"
- f"{d_type}-ref: {row['dut_version']}<br>"
- f"csit-ref: {row['job']}/{row['build']}<br>"
- f"hosts: {', '.join(row['hosts'])}"
- )
- if ttype == "pdr-lat":
- hover_itm = hover_itm.replace("[pps]", "[us]")
- hover_trend.append(hover_itm)
-
- traces = [
- go.Scatter( # Samples
- x=x_axis,
- y=y_data,
- name=name,
- mode="markers",
- marker={
- "size": 5,
- "color": color,
- "symbol": "circle",
- },
- text=hover,
- hoverinfo="text+name",
- showlegend=True,
- legendgroup=name,
- customdata=customdata
- ),
- go.Scatter( # Trend line
- x=x_axis,
- y=trend_avg,
- name=name,
- mode="lines",
- line={
- "shape": "linear",
- "width": 1,
- "color": color,
- },
- text=hover_trend,
- hoverinfo="text+name",
- showlegend=False,
- legendgroup=name,
- )
- ]
-
- if anomalies:
- anomaly_x = list()
- anomaly_y = list()
- anomaly_color = list()
- hover = list()
- for idx, anomaly in enumerate(anomalies):
- if anomaly in ("regression", "progression"):
- anomaly_x.append(x_axis[idx])
- anomaly_y.append(trend_avg[idx])
- anomaly_color.append(C.ANOMALY_COLOR[anomaly])
- hover_itm = (
- f"date: {x_axis[idx].strftime('%Y-%m-%d %H:%M:%S')}<br>"
- f"trend [pps]: {trend_avg[idx]:,.0f}<br>"
- f"classification: {anomaly}"
- )
- if ttype == "pdr-lat":
- hover_itm = hover_itm.replace("[pps]", "[us]")
- hover.append(hover_itm)
- anomaly_color.extend([0.0, 0.5, 1.0])
- traces.append(
- go.Scatter(
- x=anomaly_x,
- y=anomaly_y,
- mode="markers",
- text=hover,
- hoverinfo="text+name",
- showlegend=False,
- legendgroup=name,
- name=name,
- marker={
- "size": 15,
- "symbol": "circle-open",
- "color": anomaly_color,
- "colorscale": C.COLORSCALE_LAT \
- if ttype == "pdr-lat" else C.COLORSCALE_TPUT,
- "showscale": True,
- "line": {
- "width": 2
- },
- "colorbar": {
- "y": 0.5,
- "len": 0.8,
- "title": "Circles Marking Data Classification",
- "titleside": "right",
- "tickmode": "array",
- "tickvals": [0.167, 0.500, 0.833],
- "ticktext": C.TICK_TEXT_LAT \
- if ttype == "pdr-lat" else C.TICK_TEXT_TPUT,
- "ticks": "",
- "ticklen": 0,
- "tickangle": -90,
- "thickness": 10
- }
- }
- )
- )
-
- return traces
-
-
-def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
- normalize: bool) -> tuple:
- """Generate the trending graph(s) - MRR, NDR, PDR and for PDR also Latences
- (result_latency_forward_pdr_50_avg).
-
- :param data: Data frame with test results.
- :param sel: Selected tests.
- :param layout: Layout of plot.ly graph.
- :param normalize: If True, the data is normalized to CPU frquency
- Constants.NORM_FREQUENCY.
- :type data: pandas.DataFrame
- :type sel: dict
- :type layout: dict
- :type normalize: bool
- :returns: Trending graph(s)
- :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure)
- """
-
- if not sel:
- return None, None
-
- fig_tput = None
- fig_lat = None
- for idx, itm in enumerate(sel):
-
- df = select_trending_data(data, itm)
- if df is None or df.empty:
- continue
-
- name = "-".join((itm["dut"], itm["phy"], itm["framesize"], itm["core"],
- itm["test"], itm["testtype"], ))
- if normalize:
- phy = itm["phy"].split("-")
- topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str()
- norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \
- if topo_arch else 1.0
- else:
- norm_factor = 1.0
- traces = _generate_trending_traces(
- itm["testtype"], name, df, get_color(idx), norm_factor
- )
- if traces:
- if not fig_tput:
- fig_tput = go.Figure()
- fig_tput.add_traces(traces)
-
- if itm["testtype"] == "pdr":
- traces = _generate_trending_traces(
- "pdr-lat", name, df, get_color(idx), norm_factor
- )
- if traces:
- if not fig_lat:
- fig_lat = go.Figure()
- fig_lat.add_traces(traces)
-
- if fig_tput:
- fig_tput.update_layout(layout.get("plot-trending-tput", dict()))
- if fig_lat:
- fig_lat.update_layout(layout.get("plot-trending-lat", dict()))
-
- return fig_tput, fig_lat
-
-
-def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure:
- """Generate HDR Latency histogram graphs.
-
- :param data: HDRH data.
- :param layout: Layout of plot.ly graph.
- :type data: dict
- :type layout: dict
- :returns: HDR latency Histogram.
- :rtype: plotly.graph_objects.Figure
- """
-
- fig = None
-
- traces = list()
- for idx, (lat_name, lat_hdrh) in enumerate(data.items()):
- try:
- decoded = hdrh.histogram.HdrHistogram.decode(lat_hdrh)
- except (hdrh.codec.HdrLengthException, TypeError) as err:
- continue
- previous_x = 0.0
- prev_perc = 0.0
- xaxis = list()
- yaxis = list()
- hovertext = list()
- for item in decoded.get_recorded_iterator():
- # The real value is "percentile".
- # For 100%, we cut that down to "x_perc" to avoid
- # infinity.
- percentile = item.percentile_level_iterated_to
- x_perc = min(percentile, C.PERCENTILE_MAX)
- xaxis.append(previous_x)
- yaxis.append(item.value_iterated_to)
- hovertext.append(
- f"<b>{C.GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>"
- f"Direction: {('W-E', 'E-W')[idx % 2]}<br>"
- f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
- f"Latency: {item.value_iterated_to}uSec"
- )
- next_x = 100.0 / (100.0 - x_perc)
- xaxis.append(next_x)
- yaxis.append(item.value_iterated_to)
- hovertext.append(
- f"<b>{C.GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>"
- f"Direction: {('W-E', 'E-W')[idx % 2]}<br>"
- f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
- f"Latency: {item.value_iterated_to}uSec"
- )
- previous_x = next_x
- prev_perc = percentile
-
- traces.append(
- go.Scatter(
- x=xaxis,
- y=yaxis,
- name=C.GRAPH_LAT_HDRH_DESC[lat_name],
- mode="lines",
- legendgroup=C.GRAPH_LAT_HDRH_DESC[lat_name],
- showlegend=bool(idx % 2),
- line=dict(
- color=get_color(int(idx/2)),
- dash="solid",
- width=1 if idx % 2 else 2
- ),
- hovertext=hovertext,
- hoverinfo="text"
- )
- )
- if traces:
- fig = go.Figure()
- fig.add_traces(traces)
- layout_hdrh = layout.get("plot-hdrh-latency", None)
- if lat_hdrh:
- fig.update_layout(layout_hdrh)
-
- return fig
Code Review / csit.git / blobdiff