resources/tools/dash/app/pal/report/graphs.py

   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:
   5 #
   6 #     http://www.apache.org/licenses/LICENSE-2.0
   7 #
   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.
  13
  14 """
  15 """
  16
  17 import re
  18 import plotly.graph_objects as go
  19 import pandas as pd
  20
  21 from copy import deepcopy
  22
  23 from ..utils.constants import Constants as C
  24 from ..utils.utils import get_color
  25
  26
  27 def get_short_version(version: str, dut_type: str="vpp") -> str:
  28     """
  29     """
  30
  31     if dut_type in ("trex", "dpdk"):
  32         return version
  33
  34     s_version = str()
  35     groups = re.search(
  36         pattern=re.compile(r"^(\d{2}).(\d{2})-(rc0|rc1|rc2|release$)"),
  37         string=version
  38     )
  39     if groups:
  40         try:
  41             s_version = \
  42                 f"{groups.group(1)}.{groups.group(2)}.{groups.group(3)}".\
  43                     replace("release", "rls")
  44         except IndexError:
  45             pass
  46
  47     return s_version
  48
  49
  50 def select_iterative_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
  51     """
  52     """
  53
  54     phy = itm["phy"].split("-")
  55     if len(phy) == 4:
  56         topo, arch, nic, drv = phy
  57         if drv == "dpdk":
  58             drv = ""
  59         else:
  60             drv += "-"
  61             drv = drv.replace("_", "-")
  62     else:
  63         return None
  64
  65     core = str() if itm["dut"] == "trex" else f"{itm['core']}"
  66     ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
  67     dut_v100 = "none" if itm["dut"] == "trex" else itm["dut"]
  68     dut_v101 = itm["dut"]
  69
  70     df = data.loc[(
  71         (data["release"] == itm["rls"]) &
  72         (
  73             (
  74                 (data["version"] == "1.0.0") &
  75                 (data["dut_type"].str.lower() == dut_v100)
  76             ) |
  77             (
  78                 (data["version"] == "1.0.1") &
  79                 (data["dut_type"].str.lower() == dut_v101)
  80             )
  81         ) &
  82         (data["test_type"] == ttype) &
  83         (data["passed"] == True)
  84     )]
  85     regex_test = \
  86         f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$"
  87     df = df[
  88         (df.job.str.endswith(f"{topo}-{arch}")) &
  89         (df.dut_version.str.contains(itm["dutver"].replace(".r", "-r").\
  90             replace("rls", "release"))) &
  91         (df.test_id.str.contains(regex_test, regex=True))
  92     ]
  93
  94     return df
  95
  96
  97 def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
  98         normalize: bool) -> tuple:
  99     """
 100     """
 101
 102     fig_tput = None
 103     fig_lat = None
 104
 105     tput_traces = list()
 106     y_tput_max = 0
 107     lat_traces = list()
 108     y_lat_max = 0
 109     x_lat = list()
 110     show_latency = False
 111     show_tput = False
 112     for idx, itm in enumerate(sel):
 113         itm_data = select_iterative_data(data, itm)
 114         if itm_data.empty:
 115             continue
 116         phy = itm["phy"].split("-")
 117         topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str()
 118         norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \
 119             if normalize else 1.0
 120         if itm["testtype"] == "mrr":
 121             y_data_raw = itm_data[C.VALUE_ITER[itm["testtype"]]].to_list()[0]
 122             y_data = [(y * norm_factor) for y in y_data_raw]
 123             if len(y_data) > 0:
 124                 y_tput_max = \
 125                     max(y_data) if max(y_data) > y_tput_max else y_tput_max
 126         else:
 127             y_data_raw = itm_data[C.VALUE_ITER[itm["testtype"]]].to_list()
 128             y_data = [(y * norm_factor) for y in y_data_raw]
 129             if y_data:
 130                 y_tput_max = \
 131                     max(y_data) if max(y_data) > y_tput_max else y_tput_max
 132         nr_of_samples = len(y_data)
 133         tput_kwargs = dict(
 134             y=y_data,
 135             name=(
 136                 f"{idx + 1}. "
 137                 f"({nr_of_samples:02d} "
 138                 f"run{'s' if nr_of_samples > 1 else ''}) "
 139                 f"{itm['id']}"
 140             ),
 141             hoverinfo=u"y+name",
 142             boxpoints="all",
 143             jitter=0.3,
 144             marker=dict(color=get_color(idx))
 145         )
 146         tput_traces.append(go.Box(**tput_kwargs))
 147         show_tput = True
 148
 149         if itm["testtype"] == "pdr":
 150             y_lat_row = itm_data[C.VALUE_ITER["pdr-lat"]].to_list()
 151             y_lat = [(y / norm_factor) for y in y_lat_row]
 152             if y_lat:
 153                 y_lat_max = max(y_lat) if max(y_lat) > y_lat_max else y_lat_max
 154             nr_of_samples = len(y_lat)
 155             lat_kwargs = dict(
 156                 y=y_lat,
 157                 name=(
 158                     f"{idx + 1}. "
 159                     f"({nr_of_samples:02d} "
 160                     f"run{u's' if nr_of_samples > 1 else u''}) "
 161                     f"{itm['id']}"
 162                 ),
 163                 hoverinfo="all",
 164                 boxpoints="all",
 165                 jitter=0.3,
 166                 marker=dict(color=get_color(idx))
 167             )
 168             x_lat.append(idx + 1)
 169             lat_traces.append(go.Box(**lat_kwargs))
 170             show_latency = True
 171         else:
 172             lat_traces.append(go.Box())
 173
 174     if show_tput:
 175         pl_tput = deepcopy(layout["plot-throughput"])
 176         pl_tput["xaxis"]["tickvals"] = [i for i in range(len(sel))]
 177         pl_tput["xaxis"]["ticktext"] = [str(i + 1) for i in range(len(sel))]
 178         if y_tput_max:
 179             pl_tput["yaxis"]["range"] = [0, (int(y_tput_max / 1e6) + 1) * 1e6]
 180         fig_tput = go.Figure(data=tput_traces, layout=pl_tput)
 181
 182     if show_latency:
 183         pl_lat = deepcopy(layout["plot-latency"])
 184         pl_lat["xaxis"]["tickvals"] = [i for i in range(len(x_lat))]
 185         pl_lat["xaxis"]["ticktext"] = x_lat
 186         if y_lat_max:
 187             pl_lat["yaxis"]["range"] = [0, (int(y_lat_max / 10) + 1) * 10]
 188         fig_lat = go.Figure(data=lat_traces, layout=pl_lat)
 189
 190     return fig_tput, fig_lat
 191
 192
 193 def table_comparison(data: pd.DataFrame, sel:dict,
 194         normalize: bool) -> pd.DataFrame:
 195     """
 196     """
 197     table = pd.DataFrame(
 198         {
 199             "Test Case": [
 200                 "64b-2t1c-avf-eth-l2xcbase-eth-2memif-1dcr",
 201                 "64b-2t1c-avf-eth-l2xcbase-eth-2vhostvr1024-1vm-vppl2xc",
 202                 "64b-2t1c-avf-ethip4udp-ip4base-iacl50sl-10kflows",
 203                 "78b-2t1c-avf-ethip6-ip6scale2m-rnd "],
 204             "2106.0-8": [
 205                 "14.45 +- 0.08",
 206                 "9.63 +- 0.05",
 207                 "9.7 +- 0.02",
 208                 "8.95 +- 0.06"],
 209             "2110.0-8": [
 210                 "14.45 +- 0.08",
 211                 "9.63 +- 0.05",
 212                 "9.7 +- 0.02",
 213                 "8.95 +- 0.06"],
 214             "2110.0-9": [
 215                 "14.45 +- 0.08",
 216                 "9.63 +- 0.05",
 217                 "9.7 +- 0.02",
 218                 "8.95 +- 0.06"],
 219             "2202.0-9": [
 220                 "14.45 +- 0.08",
 221                 "9.63 +- 0.05",
 222                 "9.7 +- 0.02",
 223                 "8.95 +- 0.06"],
 224             "2110.0-9 vs 2110.0-8": [
 225                 "-0.23 +-  0.62",
 226                 "-1.37 +-   1.3",
 227                 "+0.08 +-   0.2",
 228                 "-2.16 +-  0.83"],
 229             "2202.0-9 vs 2110.0-9": [
 230                 "+6.95 +-  0.72",
 231                 "+5.35 +-  1.26",
 232                 "+4.48 +-  1.48",
 233                 "+4.09 +-  0.95"]
 234         }
 235     )
 236
 237     return pd.DataFrame()  #table