-# Copyright (c) 2022 Cisco and/or its affiliates.
+# 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:
# See the License for the specific language governing permissions and
# limitations under the License.
-"""
+"""Implementation of graphs for iterative data.
"""
-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$)"),
- string=version
- )
- if groups:
- try:
- s_version = \
- f"{groups.group(1)}.{groups.group(2)}.{groups.group(3)}".\
- replace("release", "rls")
- except IndexError:
- pass
-
- return s_version
+from ..utils.utils import get_color, get_hdrh_latencies
def select_iterative_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
else:
return None
- core = str() if itm["dut"] == "trex" else f"{itm['core']}"
- ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
- dut_v100 = "none" if itm["dut"] == "trex" else itm["dut"]
- dut_v101 = itm["dut"]
-
+ if itm["testtype"] in ("ndr", "pdr"):
+ test_type = "ndrpdr"
+ elif itm["testtype"] == "mrr":
+ test_type = "mrr"
+ elif itm["area"] == "hoststack":
+ test_type = "hoststack"
df = data.loc[(
(data["release"] == itm["rls"]) &
- (
- (
- (data["version"] == "1.0.0") &
- (data["dut_type"].str.lower() == dut_v100)
- ) |
- (
- (data["version"] == "1.0.1") &
- (data["dut_type"].str.lower() == dut_v101)
- )
- ) &
- (data["test_type"] == ttype) &
+ (data["test_type"] == test_type) &
(data["passed"] == True)
)]
+
+ core = str() if itm["dut"] == "trex" else f"{itm['core']}"
+ ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
regex_test = \
f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$"
df = df[
: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
+ :param normalize: If True, the data is normalized to CPU frequency
Constants.NORM_FREQUENCY.
:param data: pandas.DataFrame
:param sel: dict
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[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
+ y_data_raw = itm_data[C.VALUE_ITER[ttype]].to_list()[0]
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
+ 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)
+
+ if itm["testtype"] == "mrr":
+ c_data = [
+ (
+ f"{itm_data['job'].to_list()[0]}/",
+ f"{itm_data['build'].to_list()[0]}"
+ ),
+ ] * nr_of_samples
+ else:
+ c_data = list()
+ for _, row in itm_data.iterrows():
+ c_data.append(f"{row['job']}/{row['build']}")
tput_kwargs = dict(
y=y_data,
name=(
hoverinfo=u"y+name",
boxpoints="all",
jitter=0.3,
- marker=dict(color=get_color(idx))
+ marker=dict(color=get_color(idx)),
+ customdata=c_data
)
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()
+ 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:
- y_lat_max = max(y_lat) if max(y_lat) > y_lat_max else y_lat_max
+ 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,
hoverinfo="all",
boxpoints="all",
jitter=0.3,
- marker=dict(color=get_color(idx))
+ marker=dict(color=get_color(idx)),
+ customdata=customdata
)
x_lat.append(idx + 1)
lat_traces.append(go.Box(**lat_kwargs))
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) + 1) * 1e6]
+ 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:
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