feat(uti): Move directory

Signed-off-by: pmikus <peter.mikus@protonmail.ch>
Change-Id: I7300ecfe756baaf3fbeedb020070f882cfaca445

1 files changed, 408 insertions, 0 deletions

diff --git a/csit.infra.dash/app/pal/trending/graphs.py b/csit.infra.dash/app/pal/trending/graphs.py
new file mode 100644
index 0000000000..1eff4aa889
--- /dev/null
+++ b/csit.infra.dash/app/pal/trending/graphs.py
@@ -0,0 +1,408 @@
+# 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