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# Copyright (c) 2023 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.
"""Implementation of graphs for iterative data.
"""
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, get_hdrh_latencies
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
if itm["testtype"] in ("ndr", "pdr"):
test_type = "ndrpdr"
elif itm["testtype"] == "mrr":
test_type = "mrr"
elif itm["area"] == "hoststack":
test_type = "hoststack"
df = data.loc[(
(data["release"] == itm["rls"]) &
(data["test_type"] == test_type) &
(data["passed"] == True)
)]
core = str() if itm["dut"] == "trex" else f"{itm['core']}"
ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
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 frequency
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()
y_units = set()
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["area"] == "hoststack":
ttype = f"hoststack-{itm['testtype']}"
else:
ttype = itm["testtype"]
y_units.update(itm_data[C.UNIT[ttype]].unique().tolist())
if itm["testtype"] == "mrr":
y_data_raw = itm_data[C.VALUE_ITER[ttype]].to_list()[0]
else:
y_data_raw = itm_data[C.VALUE_ITER[ttype]].to_list()
y_data = [(y * norm_factor) for y in y_data_raw]
if y_data:
y_tput_max = max(max(y_data), 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 ttype == "pdr":
customdata = list()
for _, row in itm_data.iterrows():
customdata.append(
get_hdrh_latencies(row, f"{row['job']}/{row['build']}")
)
y_lat_row = itm_data[C.VALUE_ITER["latency"]].to_list()
y_lat = [(y / norm_factor) for y in y_lat_row]
if y_lat:
try:
y_lat_max = max(max(y_lat), y_lat_max)
except TypeError:
continue
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)),
customdata=customdata
)
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))]
pl_tput["yaxis"]["title"] = f"Throughput [{'|'.join(sorted(y_units))}]"
if y_tput_max:
pl_tput["yaxis"]["range"] = [0, (int(y_tput_max / 1e6) + 2) * 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
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