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 import plotly.graph_objects as go
  18 import pandas as pd
  19
  20 from copy import deepcopy
  21 from numpy import percentile
  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, remove_outliers: 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 remove_outliers: If True the outliers are removed before
  87         generating the table.
  88     :type data: pandas.DataFrame
  89     :type sel: list
  90     :type layout: dict
  91     :type normalize: bool
  92     :type remove_outliers: bool
  93     :returns: Tuple of graphs - throughput and latency.
  94     :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure)
  95     """
  96
  97     def get_y_values(data, y_data_max, param, norm_factor, release=str(),
  98                      remove_outliers=False):
  99         if param == "result_receive_rate_rate_values":
 100             if release == "rls2402":
 101                 y_vals_raw = data["result_receive_rate_rate_avg"].to_list()
 102             else:
 103                 y_vals_raw = data[param].to_list()[0]
 104         else:
 105             y_vals_raw = data[param].to_list()
 106         y_data = [(y * norm_factor) for y in y_vals_raw]
 107
 108         if remove_outliers:
 109             try:
 110                 q1 = percentile(y_data, 25, method=C.COMP_PERCENTILE_METHOD)
 111                 q3 = percentile(y_data, 75, method=C.COMP_PERCENTILE_METHOD)
 112                 irq = q3 - q1
 113                 lif = q1 - C.COMP_OUTLIER_TYPE * irq
 114                 uif = q3 + C.COMP_OUTLIER_TYPE * irq
 115                 y_data = [i for i in y_data if i >= lif and i <= uif]
 116             except TypeError:
 117                 pass
 118         try:
 119             y_data_max = max(max(y_data), y_data_max)
 120         except TypeError:
 121             y_data_max = 0
 122         return y_data, y_data_max
 123
 124     fig_tput = None
 125     fig_band = None
 126     fig_lat = None
 127
 128     tput_traces = list()
 129     y_tput_max = 0
 130     y_units = set()
 131
 132     lat_traces = list()
 133     y_lat_max = 0
 134     x_lat = list()
 135
 136     band_traces = list()
 137     y_band_max = 0
 138     y_band_units = set()
 139     x_band = list()
 140
 141     for idx, itm in enumerate(sel):
 142
 143         itm_data = select_iterative_data(data, itm)
 144         if itm_data.empty:
 145             continue
 146
 147         phy = itm["phy"].split("-")
 148         topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str()
 149         norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \
 150             if normalize else 1.0
 151
 152         if itm["area"] == "hoststack":
 153             ttype = f"hoststack-{itm['testtype']}"
 154         else:
 155             ttype = itm["testtype"]
 156
 157         y_units.update(itm_data[C.UNIT[ttype]].unique().tolist())
 158
 159         y_data, y_tput_max = get_y_values(
 160             itm_data,
 161             y_tput_max,
 162             C.VALUE_ITER[ttype],
 163             norm_factor,
 164             itm["rls"],
 165             remove_outliers
 166         )
 167
 168         nr_of_samples = len(y_data)
 169
 170         customdata = list()
 171         metadata = {
 172             "csit release": itm["rls"],
 173             "dut": itm["dut"],
 174             "dut version": itm["dutver"],
 175             "infra": itm["phy"],
 176             "test": (
 177                 f"{itm['area']}-{itm['framesize']}-{itm['core']}-"
 178                 f"{itm['test']}-{itm['testtype']}"
 179             )
 180         }
 181
 182         if itm["testtype"] == "mrr" and itm["rls"] in ("rls2306", "rls2310"):
 183             trial_run = "trial"
 184             metadata["csit-ref"] = (
 185                 f"{itm_data['job'].to_list()[0]}/",
 186                 f"{itm_data['build'].to_list()[0]}"
 187             )
 188             customdata = [{"metadata": metadata}, ] * nr_of_samples
 189         else:
 190             trial_run = "run"
 191             for _, row in itm_data.iterrows():
 192                 metadata["csit-ref"] = f"{row['job']}/{row['build']}"
 193                 customdata.append({"metadata": deepcopy(metadata)})
 194         tput_kwargs = dict(
 195             y=y_data,
 196             name=(
 197                 f"{idx + 1}. "
 198                 f"({nr_of_samples:02d} "
 199                 f"{trial_run}{'s' if nr_of_samples > 1 else ''}) "
 200                 f"{itm['id']}"
 201             ),
 202             hoverinfo=u"y+name",
 203             boxpoints="all",
 204             jitter=0.3,
 205             marker=dict(color=get_color(idx)),
 206             customdata=customdata
 207         )
 208         tput_traces.append(go.Box(**tput_kwargs))
 209
 210         if ttype in ("ndr", "pdr", "mrr"):
 211             y_band, y_band_max = get_y_values(
 212                 itm_data,
 213                 y_band_max,
 214                 C.VALUE_ITER[f"{ttype}-bandwidth"],
 215                 norm_factor,
 216                 remove_outliers=remove_outliers
 217             )
 218             if not all(pd.isna(y_band)):
 219                 y_band_units.update(
 220                     itm_data[C.UNIT[f"{ttype}-bandwidth"]].unique().\
 221                         dropna().tolist()
 222                 )
 223                 band_kwargs = dict(
 224                     y=y_band,
 225                     name=(
 226                         f"{idx + 1}. "
 227                         f"({nr_of_samples:02d} "
 228                         f"run{'s' if nr_of_samples > 1 else ''}) "
 229                         f"{itm['id']}"
 230                     ),
 231                     hoverinfo=u"y+name",
 232                     boxpoints="all",
 233                     jitter=0.3,
 234                     marker=dict(color=get_color(idx)),
 235                     customdata=customdata
 236                 )
 237                 x_band.append(idx + 1)
 238                 band_traces.append(go.Box(**band_kwargs))
 239
 240         if ttype == "pdr":
 241             y_lat, y_lat_max = get_y_values(
 242                 itm_data,
 243                 y_lat_max,
 244                 C.VALUE_ITER["latency"],
 245                 1 / norm_factor,
 246                 remove_outliers=remove_outliers
 247             )
 248             if not all(pd.isna(y_lat)):
 249                 customdata = list()
 250                 for _, row in itm_data.iterrows():
 251                     hdrh = get_hdrh_latencies(
 252                         row,
 253                         f"{metadata['infra']}-{metadata['test']}"
 254                     )
 255                     metadata["csit-ref"] = f"{row['job']}/{row['build']}"
 256                     customdata.append({
 257                         "metadata": deepcopy(metadata),
 258                         "hdrh": hdrh
 259                     })
 260                 nr_of_samples = len(y_lat)
 261                 lat_kwargs = dict(
 262                     y=y_lat,
 263                     name=(
 264                         f"{idx + 1}. "
 265                         f"({nr_of_samples:02d} "
 266                         f"run{u's' if nr_of_samples > 1 else u''}) "
 267                         f"{itm['id']}"
 268                     ),
 269                     hoverinfo="all",
 270                     boxpoints="all",
 271                     jitter=0.3,
 272                     marker=dict(color=get_color(idx)),
 273                     customdata=customdata
 274                 )
 275                 x_lat.append(idx + 1)
 276                 lat_traces.append(go.Box(**lat_kwargs))
 277
 278     if tput_traces:
 279         pl_tput = deepcopy(layout["plot-throughput"])
 280         pl_tput["xaxis"]["tickvals"] = [i for i in range(len(sel))]
 281         pl_tput["xaxis"]["ticktext"] = [str(i + 1) for i in range(len(sel))]
 282         pl_tput["yaxis"]["title"] = f"Throughput [{'|'.join(sorted(y_units))}]"
 283         if y_tput_max:
 284             pl_tput["yaxis"]["range"] = [0, int(y_tput_max) + 2e6]
 285         fig_tput = go.Figure(data=tput_traces, layout=pl_tput)
 286
 287     if band_traces:
 288         pl_band = deepcopy(layout["plot-bandwidth"])
 289         pl_band["xaxis"]["tickvals"] = [i for i in range(len(x_band))]
 290         pl_band["xaxis"]["ticktext"] = x_band
 291         pl_band["yaxis"]["title"] = \
 292             f"Bandwidth [{'|'.join(sorted(y_band_units))}]"
 293         if y_band_max:
 294             pl_band["yaxis"]["range"] = [0, int(y_band_max) + 2e9]
 295         fig_band = go.Figure(data=band_traces, layout=pl_band)
 296
 297     if lat_traces:
 298         pl_lat = deepcopy(layout["plot-latency"])
 299         pl_lat["xaxis"]["tickvals"] = [i for i in range(len(x_lat))]
 300         pl_lat["xaxis"]["ticktext"] = x_lat
 301         if y_lat_max:
 302             pl_lat["yaxis"]["range"] = [0, int(y_lat_max) + 5]
 303         fig_lat = go.Figure(data=lat_traces, layout=pl_lat)
 304
 305     return fig_tput, fig_band, fig_lat