1 # Copyright (c) 2022 Cisco and/or its affiliates.
2 # Licensed under the Apache License, Version 2.0 (the "License");
3 # you may not use this file except in compliance with the License.
4 # You may obtain a copy of the License at:
6 # http://www.apache.org/licenses/LICENSE-2.0
8 # Unless required by applicable law or agreed to in writing, software
9 # distributed under the License is distributed on an "AS IS" BASIS,
10 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11 # See the License for the specific language governing permissions and
12 # limitations under the License.
18 import plotly.graph_objects as go
21 from copy import deepcopy
23 from ..utils.constants import Constants as C
26 def _get_color(idx: int) -> str:
29 return C.PLOT_COLORS[idx % len(C.PLOT_COLORS)]
32 def get_short_version(version: str, dut_type: str="vpp") -> str:
36 if dut_type in ("trex", "dpdk"):
41 pattern=re.compile(r"^(\d{2}).(\d{2})-(rc0|rc1|rc2|release$)"),
47 f"{groups.group(1)}.{groups.group(2)}.{groups.group(3)}".\
48 replace("release", "rls")
55 def select_iterative_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
59 phy = itm["phy"].split("-")
61 topo, arch, nic, drv = phy
66 drv = drv.replace("_", "-")
70 core = str() if itm["dut"] == "trex" else f"{itm['core']}"
71 ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
72 dut_v100 = "none" if itm["dut"] == "trex" else itm["dut"]
76 (data["release"] == itm["rls"]) &
79 (data["version"] == "1.0.0") &
80 (data["dut_type"].str.lower() == dut_v100)
83 (data["version"] == "1.0.1") &
84 (data["dut_type"].str.lower() == dut_v101)
87 (data["test_type"] == ttype) &
88 (data["passed"] == True)
91 f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$"
93 (df.job.str.endswith(f"{topo}-{arch}")) &
94 (df.dut_version.str.contains(itm["dutver"].replace(".r", "-r").\
95 replace("rls", "release"))) &
96 (df.test_id.str.contains(regex_test, regex=True))
102 def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
103 normalize: bool) -> tuple:
117 for idx, itm in enumerate(sel):
118 itm_data = select_iterative_data(data, itm)
121 phy = itm["phy"].split("-")
122 topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str()
123 norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \
124 if normalize else 1.0
125 if itm["testtype"] == "mrr":
126 y_data_raw = itm_data[C.VALUE_ITER[itm["testtype"]]].to_list()[0]
127 y_data = [(y * norm_factor) for y in y_data_raw]
130 max(y_data) if max(y_data) > y_tput_max else y_tput_max
132 y_data_raw = itm_data[C.VALUE_ITER[itm["testtype"]]].to_list()
133 y_data = [(y * norm_factor) for y in y_data_raw]
136 max(y_data) if max(y_data) > y_tput_max else y_tput_max
137 nr_of_samples = len(y_data)
142 f"({nr_of_samples:02d} "
143 f"run{'s' if nr_of_samples > 1 else ''}) "
149 marker=dict(color=_get_color(idx))
151 tput_traces.append(go.Box(**tput_kwargs))
154 if itm["testtype"] == "pdr":
155 y_lat_row = itm_data[C.VALUE_ITER["pdr-lat"]].to_list()
156 y_lat = [(y / norm_factor) for y in y_lat_row]
158 y_lat_max = max(y_lat) if max(y_lat) > y_lat_max else y_lat_max
159 nr_of_samples = len(y_lat)
164 f"({nr_of_samples:02d} "
165 f"run{u's' if nr_of_samples > 1 else u''}) "
171 marker=dict(color=_get_color(idx))
173 x_lat.append(idx + 1)
174 lat_traces.append(go.Box(**lat_kwargs))
177 lat_traces.append(go.Box())
180 pl_tput = deepcopy(layout["plot-throughput"])
181 pl_tput["xaxis"]["tickvals"] = [i for i in range(len(sel))]
182 pl_tput["xaxis"]["ticktext"] = [str(i + 1) for i in range(len(sel))]
184 pl_tput["yaxis"]["range"] = [0, (int(y_tput_max / 1e6) + 1) * 1e6]
185 fig_tput = go.Figure(data=tput_traces, layout=pl_tput)
188 pl_lat = deepcopy(layout["plot-latency"])
189 pl_lat["xaxis"]["tickvals"] = [i for i in range(len(x_lat))]
190 pl_lat["xaxis"]["ticktext"] = x_lat
192 pl_lat["yaxis"]["range"] = [0, (int(y_lat_max / 10) + 1) * 10]
193 fig_lat = go.Figure(data=lat_traces, layout=pl_lat)
195 return fig_tput, fig_lat
198 def table_comparison(data: pd.DataFrame, sel:dict,
199 normalize: bool) -> pd.DataFrame:
202 table = pd.DataFrame(
205 "64b-2t1c-avf-eth-l2xcbase-eth-2memif-1dcr",
206 "64b-2t1c-avf-eth-l2xcbase-eth-2vhostvr1024-1vm-vppl2xc",
207 "64b-2t1c-avf-ethip4udp-ip4base-iacl50sl-10kflows",
208 "78b-2t1c-avf-ethip6-ip6scale2m-rnd "],
229 "2110.0-9 vs 2110.0-8": [
234 "2202.0-9 vs 2110.0-9": [
242 return pd.DataFrame() #table