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diff --git a/csit.infra.dash/app/cdash/report/graphs.py b/csit.infra.dash/app/cdash/report/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 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