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authorpmikus <peter.mikus@protonmail.ch>2022-09-19 08:49:01 +0200
committerPeter Mikus <peter.mikus@protonmail.ch>2022-09-19 06:54:43 +0000
commitd6a60b5043c6f7c3dfc45853feb68d0aca5a4a5f (patch)
tree022343584d4e00a0da8ef12eb4e713d67344fc95 /csit.infra.dash/app/pal/trending/graphs.py
parentd2ef7bc01df66f6a27f25d061db064cf4a463267 (diff)
feat(uti): Move directory
Signed-off-by: pmikus <peter.mikus@protonmail.ch> Change-Id: I7300ecfe756baaf3fbeedb020070f882cfaca445
Diffstat (limited to 'csit.infra.dash/app/pal/trending/graphs.py')
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diff --git a/csit.infra.dash/app/pal/trending/graphs.py b/csit.infra.dash/app/pal/trending/graphs.py
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+# 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