fix(cdash): Rename

Signed-off-by: pmikus <peter.mikus@protonmail.ch>
Change-Id: Ia6dff2674a28b42ebfbe91230587f1e175ae1137

1 files changed, 275 insertions, 0 deletions

diff --git a/csit.infra.dash/app/cdash/report/graphs.py b/csit.infra.dash/app/cdash/report/graphs.py
new file mode 100644
index 0000000000..36f28d09e8
--- /dev/null
+++ b/csit.infra.dash/app/cdash/report/graphs.py
@@ -0,0 +1,275 @@
+# 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 re
+import plotly.graph_objects as go
+import pandas as pd
+
+from copy import deepcopy
+
+from ..utils.constants import Constants as C
+from ..utils.utils import get_color
+
+
+def get_short_version(version: str, dut_type: str="vpp") -> str:
+    """Returns the short version of DUT without build number.
+
+    :param version: Original version string.
+    :param dut_type: DUT type.
+    :type version: str
+    :type dut_type: str
+    :returns: Short verion string.
+    :rtype: str
+    """
+
+    if dut_type in ("trex", "dpdk"):
+        return version
+
+    s_version = str()
+    groups = re.search(
+        pattern=re.compile(r"^(\d{2}).(\d{2})-(rc0|rc1|rc2|release$)"),
+        string=version
+    )
+    if groups:
+        try:
+            s_version = \
+                f"{groups.group(1)}.{groups.group(2)}.{groups.group(3)}".\
+                    replace("release", "rls")
+        except IndexError:
+            pass
+
+    return s_version
+
+
+def select_iterative_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
+    """Select the data for graphs and tables from the provided data frame.
+
+    :param data: Data frame with data for graphs and tables.
+    :param itm: Item (in this case job name) which data will be selected from
+        the input data frame.
+    :type data: pandas.DataFrame
+    :type itm: str
+    :returns: A data frame with selected data.
+    :rtype: pandas.DataFrame
+    """
+
+    phy = itm["phy"].split("-")
+    if len(phy) == 4:
+        topo, arch, nic, drv = phy
+        if drv == "dpdk":
+            drv = ""
+        else:
+            drv += "-"
+            drv = drv.replace("_", "-")
+    else:
+        return None
+
+    core = str() if itm["dut"] == "trex" else f"{itm['core']}"
+    ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
+    dut_v100 = "none" if itm["dut"] == "trex" else itm["dut"]
+    dut_v101 = itm["dut"]
+
+    df = data.loc[(
+        (data["release"] == itm["rls"]) &
+        (
+            (
+                (data["version"] == "1.0.0") &
+                (data["dut_type"].str.lower() == dut_v100)
+            ) |
+            (
+                (data["version"] == "1.0.1") &
+                (data["dut_type"].str.lower() == dut_v101)
+            )
+        ) &
+        (data["test_type"] == ttype) &
+        (data["passed"] == True)
+    )]
+    regex_test = \
+        f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$"
+    df = df[
+        (df.job.str.endswith(f"{topo}-{arch}")) &
+        (df.dut_version.str.contains(itm["dutver"].replace(".r", "-r").\
+            replace("rls", "release"))) &
+        (df.test_id.str.contains(regex_test, regex=True))
+    ]
+
+    return df
+
+
+def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
+        normalize: bool) -> tuple:
+    """Generate the statistical box graph with iterative data (MRR, NDR and PDR,
+    for PDR also Latencies).
+
+    :param data: Data frame with iterative data.
+    :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.
+    :param data: pandas.DataFrame
+    :param sel: dict
+    :param layout: dict
+    :param normalize: bool
+    :returns: Tuple of graphs - throughput and latency.
+    :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure)
+    """
+
+    fig_tput = None
+    fig_lat = None
+
+    tput_traces = list()
+    y_tput_max = 0
+    lat_traces = list()
+    y_lat_max = 0
+    x_lat = list()
+    show_latency = False
+    show_tput = False
+    for idx, itm in enumerate(sel):
+        itm_data = select_iterative_data(data, itm)
+        if itm_data.empty:
+            continue
+        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 normalize else 1.0
+        if itm["testtype"] == "mrr":
+            y_data_raw = itm_data[C.VALUE_ITER[itm["testtype"]]].to_list()[0]
+            y_data = [(y * norm_factor) for y in y_data_raw]
+            if len(y_data) > 0:
+                y_tput_max = \
+                    max(y_data) if max(y_data) > y_tput_max else y_tput_max
+        else:
+            y_data_raw = itm_data[C.VALUE_ITER[itm["testtype"]]].to_list()
+            y_data = [(y * norm_factor) for y in y_data_raw]
+            if y_data:
+                y_tput_max = \
+                    max(y_data) if max(y_data) > y_tput_max else y_tput_max
+        nr_of_samples = len(y_data)
+        tput_kwargs = dict(
+            y=y_data,
+            name=(
+                f"{idx + 1}. "
+                f"({nr_of_samples:02d} "
+                f"run{'s' if nr_of_samples > 1 else ''}) "
+                f"{itm['id']}"
+            ),
+            hoverinfo=u"y+name",
+            boxpoints="all",
+            jitter=0.3,
+            marker=dict(color=get_color(idx))
+        )
+        tput_traces.append(go.Box(**tput_kwargs))
+        show_tput = True
+
+        if itm["testtype"] == "pdr":
+            y_lat_row = itm_data[C.VALUE_ITER["pdr-lat"]].to_list()
+            y_lat = [(y / norm_factor) for y in y_lat_row]
+            if y_lat:
+                y_lat_max = max(y_lat) if max(y_lat) > y_lat_max else y_lat_max
+            nr_of_samples = len(y_lat)
+            lat_kwargs = dict(
+                y=y_lat,
+                name=(
+                    f"{idx + 1}. "
+                    f"({nr_of_samples:02d} "
+                    f"run{u's' if nr_of_samples > 1 else u''}) "
+                    f"{itm['id']}"
+                ),
+                hoverinfo="all",
+                boxpoints="all",
+                jitter=0.3,
+                marker=dict(color=get_color(idx))
+            )
+            x_lat.append(idx + 1)
+            lat_traces.append(go.Box(**lat_kwargs))
+            show_latency = True
+        else:
+            lat_traces.append(go.Box())
+
+    if show_tput:
+        pl_tput = deepcopy(layout["plot-throughput"])
+        pl_tput["xaxis"]["tickvals"] = [i for i in range(len(sel))]
+        pl_tput["xaxis"]["ticktext"] = [str(i + 1) for i in range(len(sel))]
+        if y_tput_max:
+            pl_tput["yaxis"]["range"] = [0, (int(y_tput_max / 1e6) + 1) * 1e6]
+        fig_tput = go.Figure(data=tput_traces, layout=pl_tput)
+
+    if show_latency:
+        pl_lat = deepcopy(layout["plot-latency"])
+        pl_lat["xaxis"]["tickvals"] = [i for i in range(len(x_lat))]
+        pl_lat["xaxis"]["ticktext"] = x_lat
+        if y_lat_max:
+            pl_lat["yaxis"]["range"] = [0, (int(y_lat_max / 10) + 1) * 10]
+        fig_lat = go.Figure(data=lat_traces, layout=pl_lat)
+
+    return fig_tput, fig_lat
+
+
+def table_comparison(data: pd.DataFrame, sel:dict,
+        normalize: bool) -> pd.DataFrame:
+    """Generate the comparison table with selected tests.
+
+    :param data: Data frame with iterative data.
+    :param sel: Selected tests.
+    :param normalize: If True, the data is normalized to CPU frquency
+        Constants.NORM_FREQUENCY.
+    :param data: pandas.DataFrame
+    :param sel: dict
+    :param normalize: bool
+    :returns: Comparison table.
+    :rtype: pandas.DataFrame
+    """
+    table = pd.DataFrame(
+        # {
+        #     "Test Case": [
+        #         "64b-2t1c-avf-eth-l2xcbase-eth-2memif-1dcr",
+        #         "64b-2t1c-avf-eth-l2xcbase-eth-2vhostvr1024-1vm-vppl2xc",
+        #         "64b-2t1c-avf-ethip4udp-ip4base-iacl50sl-10kflows",
+        #         "78b-2t1c-avf-ethip6-ip6scale2m-rnd "],
+        #     "2106.0-8": [
+        #         "14.45 +- 0.08",
+        #         "9.63 +- 0.05",
+        #         "9.7 +- 0.02",
+        #         "8.95 +- 0.06"],
+        #     "2110.0-8": [
+        #         "14.45 +- 0.08",
+        #         "9.63 +- 0.05",
+        #         "9.7 +- 0.02",
+        #         "8.95 +- 0.06"],
+        #     "2110.0-9": [
+        #         "14.45 +- 0.08",
+        #         "9.63 +- 0.05",
+        #         "9.7 +- 0.02",
+        #         "8.95 +- 0.06"],
+        #     "2202.0-9": [
+        #         "14.45 +- 0.08",
+        #         "9.63 +- 0.05",
+        #         "9.7 +- 0.02",
+        #         "8.95 +- 0.06"],
+        #     "2110.0-9 vs 2110.0-8": [
+        #         "-0.23 +-  0.62",
+        #         "-1.37 +-   1.3",
+        #         "+0.08 +-   0.2",
+        #         "-2.16 +-  0.83"],
+        #     "2202.0-9 vs 2110.0-9": [
+        #         "+6.95 +-  0.72",
+        #         "+5.35 +-  1.26",
+        #         "+4.48 +-  1.48",
+        #         "+4.09 +-  0.95"]
+        # }
+    )
+
+    return table