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

   1 # Copyright (c) 2023 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:dict, layout: dict,
  77         normalize: bool) -> 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: dict
  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):
  95         if "receive_rate" in param:
  96             y_vals_raw = data[param].to_list()[0]
  97         else:
  98             y_vals_raw = data[param].to_list()
  99         y_data = [(y * norm_factor) for y in y_vals_raw]
 100         try:
 101             y_data_max = max(max(y_data), y_data_max)
 102         except TypeError:
 103             y_data_max = 0
 104         return y_data, y_data_max
 105
 106     fig_tput = None
 107     fig_band = None
 108     fig_lat = None
 109
 110     tput_traces = list()
 111     y_tput_max = 0
 112     y_units = set()
 113
 114     lat_traces = list()
 115     y_lat_max = 0
 116     x_lat = list()
 117
 118     band_traces = list()
 119     y_band_max = 0
 120     y_band_units = set()
 121     x_band = list()
 122
 123     for idx, itm in enumerate(sel):
 124
 125         itm_data = select_iterative_data(data, itm)
 126         if itm_data.empty:
 127             continue
 128
 129         phy = itm["phy"].split("-")
 130         topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str()
 131         norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \
 132             if normalize else 1.0
 133
 134         if itm["area"] == "hoststack":
 135             ttype = f"hoststack-{itm['testtype']}"
 136         else:
 137             ttype = itm["testtype"]
 138
 139         y_units.update(itm_data[C.UNIT[ttype]].unique().tolist())
 140
 141         y_data, y_tput_max = \
 142             get_y_values(itm_data, y_tput_max, C.VALUE_ITER[ttype], norm_factor)
 143
 144         nr_of_samples = len(y_data)
 145
 146         customdata = list()
 147         metadata = {
 148             "csit release": itm["rls"],
 149             "dut": itm["dut"],
 150             "dut version": itm["dutver"],
 151             "infra": itm["phy"],
 152             "test": (
 153                 f"{itm['area']}-{itm['framesize']}-{itm['core']}-"
 154                 f"{itm['test']}-{itm['testtype']}"
 155             )
 156         }
 157
 158         if itm["testtype"] == "mrr":
 159             metadata["csit-ref"] = (
 160                 f"{itm_data['job'].to_list()[0]}/",
 161                 f"{itm_data['build'].to_list()[0]}"
 162             )
 163             customdata = [{"metadata": metadata}, ] * nr_of_samples
 164         else:
 165             for _, row in itm_data.iterrows():
 166                 metadata["csit-ref"] = f"{row['job']}/{row['build']}"
 167                 customdata.append({"metadata": deepcopy(metadata)})
 168         tput_kwargs = dict(
 169             y=y_data,
 170             name=(
 171                 f"{idx + 1}. "
 172                 f"({nr_of_samples:02d} "
 173                 f"run{'s' if nr_of_samples > 1 else ''}) "
 174                 f"{itm['id']}"
 175             ),
 176             hoverinfo=u"y+name",
 177             boxpoints="all",
 178             jitter=0.3,
 179             marker=dict(color=get_color(idx)),
 180             customdata=customdata
 181         )
 182         tput_traces.append(go.Box(**tput_kwargs))
 183
 184         if ttype in ("ndr", "pdr"):
 185             y_band, y_band_max = get_y_values(
 186                 itm_data,
 187                 y_band_max,
 188                 C.VALUE_ITER[f"{ttype}-bandwidth"],
 189                 norm_factor
 190             )
 191             if not all(pd.isna(y_band)):
 192                 y_band_units.update(
 193                     itm_data[C.UNIT[f"{ttype}-bandwidth"]].unique().\
 194                         dropna().tolist()
 195                 )
 196                 band_kwargs = dict(
 197                     y=y_band,
 198                     name=(
 199                         f"{idx + 1}. "
 200                         f"({nr_of_samples:02d} "
 201                         f"run{'s' if nr_of_samples > 1 else ''}) "
 202                         f"{itm['id']}"
 203                     ),
 204                     hoverinfo=u"y+name",
 205                     boxpoints="all",
 206                     jitter=0.3,
 207                     marker=dict(color=get_color(idx)),
 208                     customdata=customdata
 209                 )
 210                 x_band.append(idx + 1)
 211                 band_traces.append(go.Box(**band_kwargs))
 212
 213         if ttype == "pdr":
 214             y_lat, y_lat_max = get_y_values(
 215                 itm_data,
 216                 y_lat_max,
 217                 C.VALUE_ITER["latency"],
 218                 1 / norm_factor
 219             )
 220             if not all(pd.isna(y_lat)):
 221                 customdata = list()
 222                 for _, row in itm_data.iterrows():
 223                     hdrh = get_hdrh_latencies(
 224                         row,
 225                         f"{metadata['infra']}-{metadata['test']}"
 226                     )
 227                     metadata["csit-ref"] = f"{row['job']}/{row['build']}"
 228                     customdata.append({
 229                         "metadata": deepcopy(metadata),
 230                         "hdrh": hdrh
 231                     })
 232                 nr_of_samples = len(y_lat)
 233                 lat_kwargs = dict(
 234                     y=y_lat,
 235                     name=(
 236                         f"{idx + 1}. "
 237                         f"({nr_of_samples:02d} "
 238                         f"run{u's' if nr_of_samples > 1 else u''}) "
 239                         f"{itm['id']}"
 240                     ),
 241                     hoverinfo="all",
 242                     boxpoints="all",
 243                     jitter=0.3,
 244                     marker=dict(color=get_color(idx)),
 245                     customdata=customdata
 246                 )
 247                 x_lat.append(idx + 1)
 248                 lat_traces.append(go.Box(**lat_kwargs))
 249
 250     if tput_traces:
 251         pl_tput = deepcopy(layout["plot-throughput"])
 252         pl_tput["xaxis"]["tickvals"] = [i for i in range(len(sel))]
 253         pl_tput["xaxis"]["ticktext"] = [str(i + 1) for i in range(len(sel))]
 254         pl_tput["yaxis"]["title"] = f"Throughput [{'|'.join(sorted(y_units))}]"
 255         if y_tput_max:
 256             pl_tput["yaxis"]["range"] = [0, int(y_tput_max) + 2e6]
 257         fig_tput = go.Figure(data=tput_traces, layout=pl_tput)
 258
 259     if band_traces:
 260         pl_band = deepcopy(layout["plot-bandwidth"])
 261         pl_band["xaxis"]["tickvals"] = [i for i in range(len(x_band))]
 262         pl_band["xaxis"]["ticktext"] = x_band
 263         pl_band["yaxis"]["title"] = \
 264             f"Bandwidth [{'|'.join(sorted(y_band_units))}]"
 265         if y_band_max:
 266             pl_band["yaxis"]["range"] = [0, int(y_band_max) + 2e9]
 267         fig_band = go.Figure(data=band_traces, layout=pl_band)
 268
 269     if lat_traces:
 270         pl_lat = deepcopy(layout["plot-latency"])
 271         pl_lat["xaxis"]["tickvals"] = [i for i in range(len(x_lat))]
 272         pl_lat["xaxis"]["ticktext"] = x_lat
 273         if y_lat_max:
 274             pl_lat["yaxis"]["range"] = [0, int(y_lat_max) + 5]
 275         fig_lat = go.Figure(data=lat_traces, layout=pl_lat)
 276
 277     return fig_tput, fig_band, fig_lat