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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
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