csit.infra.dash/app/cdash/report/graphs.py

   1 # Copyright (c) 2024 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 """Implementation of graphs for iterative data.
  15 """
  16
  17
  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, get_hdrh_latencies
  25
  26
  27 def select_iterative_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
  28     """Select the data for graphs and tables from the provided data frame.
  29
  30     :param data: Data frame with data for graphs and tables.
  31     :param itm: Item (in this case job name) which data will be selected from
  32         the input data frame.
  33     :type data: pandas.DataFrame
  34     :type itm: str
  35     :returns: A data frame with selected data.
  36     :rtype: pandas.DataFrame
  37     """
  38
  39     phy = itm["phy"].split("-")
  40     if len(phy) == 4:
  41         topo, arch, nic, drv = phy
  42         if drv == "dpdk":
  43             drv = ""
  44         else:
  45             drv += "-"
  46             drv = drv.replace("_", "-")
  47     else:
  48         return None
  49
  50     if itm["testtype"] in ("ndr", "pdr"):
  51         test_type = "ndrpdr"
  52     elif itm["testtype"] == "mrr":
  53         test_type = "mrr"
  54     elif itm["area"] == "hoststack":
  55         test_type = "hoststack"
  56     df = data.loc[(
  57         (data["release"] == itm["rls"]) &
  58         (data["test_type"] == test_type) &
  59         (data["passed"] == True)
  60     )]
  61
  62     core = str() if itm["dut"] == "trex" else f"{itm['core']}"
  63     ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
  64     regex_test = \
  65         f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$"
  66     df = df[
  67         (df.job.str.endswith(f"{topo}-{arch}")) &
  68         (df.dut_version.str.contains(itm["dutver"].replace(".r", "-r").\
  69             replace("rls", "release"))) &
  70         (df.test_id.str.contains(regex_test, regex=True))
  71     ]
  72
  73     return df
  74
  75
  76 def graph_iterative(data: pd.DataFrame, sel: list, layout: dict,
  77         normalize: bool=False) -> tuple:
  78     """Generate the statistical box graph with iterative data (MRR, NDR and PDR,
  79     for PDR also Latencies).
  80
  81     :param data: Data frame with iterative data.
  82     :param sel: Selected tests.
  83     :param layout: Layout of plot.ly graph.
  84     :param normalize: If True, the data is normalized to CPU frequency
  85         Constants.NORM_FREQUENCY.
  86     :param data: pandas.DataFrame
  87     :param sel: list
  88     :param layout: dict
  89     :param normalize: bool
  90     :returns: Tuple of graphs - throughput and latency.
  91     :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure)
  92     """
  93
  94     def get_y_values(data, y_data_max, param, norm_factor, release=str()):
  95         if param == "result_receive_rate_rate_values":
  96             if release == "rls2402":
  97                 y_vals_raw = data["result_receive_rate_rate_avg"].to_list()
  98             else:
  99                 y_vals_raw = data[param].to_list()[0]
 100         else:
 101             y_vals_raw = data[param].to_list()
 102         y_data = [(y * norm_factor) for y in y_vals_raw]
 103         try:
 104             y_data_max = max(max(y_data), y_data_max)
 105         except TypeError:
 106             y_data_max = 0
 107         return y_data, y_data_max
 108
 109     fig_tput = None
 110     fig_band = None
 111     fig_lat = None
 112
 113     tput_traces = list()
 114     y_tput_max = 0
 115     y_units = set()
 116
 117     lat_traces = list()
 118     y_lat_max = 0
 119     x_lat = list()
 120
 121     band_traces = list()
 122     y_band_max = 0
 123     y_band_units = set()
 124     x_band = list()
 125
 126     for idx, itm in enumerate(sel):
 127
 128         itm_data = select_iterative_data(data, itm)
 129         if itm_data.empty:
 130             continue
 131
 132         phy = itm["phy"].split("-")
 133         topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str()
 134         norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \
 135             if normalize else 1.0
 136
 137         if itm["area"] == "hoststack":
 138             ttype = f"hoststack-{itm['testtype']}"
 139         else:
 140             ttype = itm["testtype"]
 141
 142         y_units.update(itm_data[C.UNIT[ttype]].unique().tolist())
 143
 144         y_data, y_tput_max = get_y_values(
 145             itm_data, y_tput_max, C.VALUE_ITER[ttype], norm_factor, itm["rls"]
 146         )
 147
 148         nr_of_samples = len(y_data)
 149
 150         customdata = list()
 151         metadata = {
 152             "csit release": itm["rls"],
 153             "dut": itm["dut"],
 154             "dut version": itm["dutver"],
 155             "infra": itm["phy"],
 156             "test": (
 157                 f"{itm['area']}-{itm['framesize']}-{itm['core']}-"
 158                 f"{itm['test']}-{itm['testtype']}"
 159             )
 160         }
 161
 162         if itm["testtype"] == "mrr" and itm["rls"] in ("rls2306", "rls2310"):
 163             trial_run = "trial"
 164             metadata["csit-ref"] = (
 165                 f"{itm_data['job'].to_list()[0]}/",
 166                 f"{itm_data['build'].to_list()[0]}"
 167             )
 168             customdata = [{"metadata": metadata}, ] * nr_of_samples
 169         else:
 170             trial_run = "run"
 171             for _, row in itm_data.iterrows():
 172                 metadata["csit-ref"] = f"{row['job']}/{row['build']}"
 173                 customdata.append({"metadata": deepcopy(metadata)})
 174         tput_kwargs = dict(
 175             y=y_data,
 176             name=(
 177                 f"{idx + 1}. "
 178                 f"({nr_of_samples:02d} "
 179                 f"{trial_run}{'s' if nr_of_samples > 1 else ''}) "
 180                 f"{itm['id']}"
 181             ),
 182             hoverinfo=u"y+name",
 183             boxpoints="all",
 184             jitter=0.3,
 185             marker=dict(color=get_color(idx)),
 186             customdata=customdata
 187         )
 188         tput_traces.append(go.Box(**tput_kwargs))
 189
 190         if ttype in ("ndr", "pdr", "mrr"):
 191             y_band, y_band_max = get_y_values(
 192                 itm_data,
 193                 y_band_max,
 194                 C.VALUE_ITER[f"{ttype}-bandwidth"],
 195                 norm_factor
 196             )
 197             if not all(pd.isna(y_band)):
 198                 y_band_units.update(
 199                     itm_data[C.UNIT[f"{ttype}-bandwidth"]].unique().\
 200                         dropna().tolist()
 201                 )
 202                 band_kwargs = dict(
 203                     y=y_band,
 204                     name=(
 205                         f"{idx + 1}. "
 206                         f"({nr_of_samples:02d} "
 207                         f"run{'s' if nr_of_samples > 1 else ''}) "
 208                         f"{itm['id']}"
 209                     ),
 210                     hoverinfo=u"y+name",
 211                     boxpoints="all",
 212                     jitter=0.3,
 213                     marker=dict(color=get_color(idx)),
 214                     customdata=customdata
 215                 )
 216                 x_band.append(idx + 1)
 217                 band_traces.append(go.Box(**band_kwargs))
 218
 219         if ttype == "pdr":
 220             y_lat, y_lat_max = get_y_values(
 221                 itm_data,
 222                 y_lat_max,
 223                 C.VALUE_ITER["latency"],
 224                 1 / norm_factor
 225             )
 226             if not all(pd.isna(y_lat)):
 227                 customdata = list()
 228                 for _, row in itm_data.iterrows():
 229                     hdrh = get_hdrh_latencies(
 230                         row,
 231                         f"{metadata['infra']}-{metadata['test']}"
 232                     )
 233                     metadata["csit-ref"] = f"{row['job']}/{row['build']}"
 234                     customdata.append({
 235                         "metadata": deepcopy(metadata),
 236                         "hdrh": hdrh
 237                     })
 238                 nr_of_samples = len(y_lat)
 239                 lat_kwargs = dict(
 240                     y=y_lat,
 241                     name=(
 242                         f"{idx + 1}. "
 243                         f"({nr_of_samples:02d} "
 244                         f"run{u's' if nr_of_samples > 1 else u''}) "
 245                         f"{itm['id']}"
 246                     ),
 247                     hoverinfo="all",
 248                     boxpoints="all",
 249                     jitter=0.3,
 250                     marker=dict(color=get_color(idx)),
 251                     customdata=customdata
 252                 )
 253                 x_lat.append(idx + 1)
 254                 lat_traces.append(go.Box(**lat_kwargs))
 255
 256     if tput_traces:
 257         pl_tput = deepcopy(layout["plot-throughput"])
 258         pl_tput["xaxis"]["tickvals"] = [i for i in range(len(sel))]
 259         pl_tput["xaxis"]["ticktext"] = [str(i + 1) for i in range(len(sel))]
 260         pl_tput["yaxis"]["title"] = f"Throughput [{'|'.join(sorted(y_units))}]"
 261         if y_tput_max:
 262             pl_tput["yaxis"]["range"] = [0, int(y_tput_max) + 2e6]
 263         fig_tput = go.Figure(data=tput_traces, layout=pl_tput)
 264
 265     if band_traces:
 266         pl_band = deepcopy(layout["plot-bandwidth"])
 267         pl_band["xaxis"]["tickvals"] = [i for i in range(len(x_band))]
 268         pl_band["xaxis"]["ticktext"] = x_band
 269         pl_band["yaxis"]["title"] = \
 270             f"Bandwidth [{'|'.join(sorted(y_band_units))}]"
 271         if y_band_max:
 272             pl_band["yaxis"]["range"] = [0, int(y_band_max) + 2e9]
 273         fig_band = go.Figure(data=band_traces, layout=pl_band)
 274
 275     if lat_traces:
 276         pl_lat = deepcopy(layout["plot-latency"])
 277         pl_lat["xaxis"]["tickvals"] = [i for i in range(len(x_lat))]
 278         pl_lat["xaxis"]["ticktext"] = x_lat
 279         if y_lat_max:
 280             pl_lat["yaxis"]["range"] = [0, int(y_lat_max) + 5]
 281         fig_lat = go.Figure(data=lat_traces, layout=pl_lat)
 282
 283     return fig_tput, fig_band, fig_lat