+++ /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 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
Code Review / csit.git / blobdiff