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:
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.
14 """Implementation of graphs for iterative data.
18 import plotly.graph_objects as go
21 from copy import deepcopy
23 from ..utils.constants import Constants as C
24 from ..utils.utils import get_color, get_hdrh_latencies
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.
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
33 :type data: pandas.DataFrame
35 :returns: A data frame with selected data.
36 :rtype: pandas.DataFrame
39 phy = itm["phy"].split("-")
41 topo, arch, nic, drv = phy
46 drv = drv.replace("_", "-")
50 if itm["testtype"] in ("ndr", "pdr"):
52 elif itm["testtype"] == "mrr":
54 elif itm["area"] == "hoststack":
55 test_type = "hoststack"
57 (data["release"] == itm["rls"]) &
58 (data["test_type"] == test_type) &
59 (data["passed"] == True)
62 core = str() if itm["dut"] == "trex" else f"{itm['core']}"
63 ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
65 f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$"
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))
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).
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
89 :param normalize: bool
90 :returns: Tuple of graphs - throughput and latency.
91 :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure)
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()
99 y_vals_raw = data[param].to_list()[0]
101 y_vals_raw = data[param].to_list()
102 y_data = [(y * norm_factor) for y in y_vals_raw]
104 y_data_max = max(max(y_data), y_data_max)
107 return y_data, y_data_max
126 for idx, itm in enumerate(sel):
128 itm_data = select_iterative_data(data, itm)
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
137 if itm["area"] == "hoststack":
138 ttype = f"hoststack-{itm['testtype']}"
140 ttype = itm["testtype"]
142 y_units.update(itm_data[C.UNIT[ttype]].unique().tolist())
144 y_data, y_tput_max = get_y_values(
145 itm_data, y_tput_max, C.VALUE_ITER[ttype], norm_factor, itm["rls"]
148 nr_of_samples = len(y_data)
152 "csit release": itm["rls"],
154 "dut version": itm["dutver"],
157 f"{itm['area']}-{itm['framesize']}-{itm['core']}-"
158 f"{itm['test']}-{itm['testtype']}"
162 if itm["testtype"] == "mrr" and itm["rls"] in ("rls2306", "rls2310"):
164 metadata["csit-ref"] = (
165 f"{itm_data['job'].to_list()[0]}/",
166 f"{itm_data['build'].to_list()[0]}"
168 customdata = [{"metadata": metadata}, ] * nr_of_samples
171 for _, row in itm_data.iterrows():
172 metadata["csit-ref"] = f"{row['job']}/{row['build']}"
173 customdata.append({"metadata": deepcopy(metadata)})
178 f"({nr_of_samples:02d} "
179 f"{trial_run}{'s' if nr_of_samples > 1 else ''}) "
185 marker=dict(color=get_color(idx)),
186 customdata=customdata
188 tput_traces.append(go.Box(**tput_kwargs))
190 if ttype in ("ndr", "pdr", "mrr"):
191 y_band, y_band_max = get_y_values(
194 C.VALUE_ITER[f"{ttype}-bandwidth"],
197 if not all(pd.isna(y_band)):
199 itm_data[C.UNIT[f"{ttype}-bandwidth"]].unique().\
206 f"({nr_of_samples:02d} "
207 f"run{'s' if nr_of_samples > 1 else ''}) "
213 marker=dict(color=get_color(idx)),
214 customdata=customdata
216 x_band.append(idx + 1)
217 band_traces.append(go.Box(**band_kwargs))
220 y_lat, y_lat_max = get_y_values(
223 C.VALUE_ITER["latency"],
226 if not all(pd.isna(y_lat)):
228 for _, row in itm_data.iterrows():
229 hdrh = get_hdrh_latencies(
231 f"{metadata['infra']}-{metadata['test']}"
233 metadata["csit-ref"] = f"{row['job']}/{row['build']}"
235 "metadata": deepcopy(metadata),
238 nr_of_samples = len(y_lat)
243 f"({nr_of_samples:02d} "
244 f"run{u's' if nr_of_samples > 1 else u''}) "
250 marker=dict(color=get_color(idx)),
251 customdata=customdata
253 x_lat.append(idx + 1)
254 lat_traces.append(go.Box(**lat_kwargs))
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))}]"
262 pl_tput["yaxis"]["range"] = [0, int(y_tput_max) + 2e6]
263 fig_tput = go.Figure(data=tput_traces, layout=pl_tput)
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))}]"
272 pl_band["yaxis"]["range"] = [0, int(y_band_max) + 2e9]
273 fig_band = go.Figure(data=band_traces, layout=pl_band)
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
280 pl_lat["yaxis"]["range"] = [0, int(y_lat_max) + 5]
281 fig_lat = go.Figure(data=lat_traces, layout=pl_lat)
283 return fig_tput, fig_band, fig_lat