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 """The comparison tables.
19 from numpy import mean, std
20 from copy import deepcopy
22 from ..utils.constants import Constants as C
23 from ..utils.utils import relative_change_stdev
26 def select_comparison_data(
31 """Select data for a comparison table.
33 :param data: Data to be filtered for the comparison table.
34 :param selected: A dictionary with parameters and their values selected by
36 :param normalize: If True, the data is normalized to CPU frequency
37 Constants.NORM_FREQUENCY.
38 :type data: pandas.DataFrame
41 :returns: A data frame with selected data.
42 :rtype: pandas.DataFrame
45 def _calculate_statistics(
46 data_in: pd.DataFrame,
51 """Calculates mean value and standard deviation for provided data.
53 :param data_in: Input data for calculations.
54 :param ttype: The test type.
55 :param drv: The driver.
56 :param norm_factor: The data normalization factor.
57 :type data_in: pandas.DataFrame
60 :type norm_factor: float
61 :returns: A pandas dataframe with: test name, mean value, standard
63 :rtype: pandas.DataFrame
71 for itm in data_in["test_id"].unique().tolist():
72 itm_lst = itm.split(".")
73 test = itm_lst[-1].rsplit("-", 1)[0]
74 if "hoststack" in itm:
75 test_type = f"hoststack-{ttype}"
78 df = data_in.loc[(data_in["test_id"] == itm)]
79 l_df = df[C.VALUE_ITER[test_type]].to_list()
80 if len(l_df) and isinstance(l_df[0], list):
88 except (TypeError, ValueError):
90 d_data["name"].append(f"{test.replace(f'{drv}-', '')}-{ttype}")
91 d_data["mean"].append(int(mean_val * norm_factor))
92 d_data["stdev"].append(int(std_val * norm_factor))
93 d_data["unit"].append(df[C.UNIT[test_type]].to_list()[0])
94 return pd.DataFrame(d_data)
98 if itm["ttype"] in ("NDR", "PDR", "Latency"):
100 elif itm["ttype"] in ("CPS", "RPS", "BPS"):
101 test_type = "hoststack"
103 test_type = itm["ttype"].lower()
105 dutver = itm["dutver"].split("-", 1) # 0 -> release, 1 -> dut version
106 tmp_df = pd.DataFrame(data.loc[(
107 (data["passed"] == True) &
108 (data["dut_type"] == itm["dut"]) &
109 (data["dut_version"] == dutver[1]) &
110 (data["test_type"] == test_type) &
111 (data["release"] == dutver[0])
114 drv = "" if itm["driver"] == "dpdk" else itm["driver"].replace("_", "-")
115 core = str() if itm["dut"] == "trex" else itm["core"].lower()
116 ttype = "ndrpdr" if itm["ttype"] in ("NDR", "PDR", "Latency") \
117 else itm["ttype"].lower()
119 (tmp_df.job.str.endswith(itm["tbed"])) &
120 (tmp_df.test_id.str.contains(
122 f"^.*[.|-]{itm['nic']}.*{itm['frmsize'].lower()}-"
123 f"{core}-{drv}.*-{ttype}$"
128 if itm["driver"] == "dpdk":
129 for drv in C.DRIVERS:
131 tmp_df[tmp_df.test_id.str.contains(f"-{drv}-")].index,
135 # Change the data type from ndrpdr to one of ("NDR", "PDR", "Latency")
136 if test_type == "ndrpdr":
137 tmp_df = tmp_df.assign(test_type=itm["ttype"].lower())
141 if itm["ttype"] == "Latency":
142 norm_factor = C.FREQUENCY[itm["tbed"]] / C.NORM_FREQUENCY
144 norm_factor = C.NORM_FREQUENCY / C.FREQUENCY[itm["tbed"]]
147 tmp_df = _calculate_statistics(
149 itm["ttype"].lower(),
154 lst_df.append(tmp_df)
158 elif len(lst_df) > 1:
170 def comparison_table(
176 """Generate a comparison table.
178 :param data: Iterative data for the comparison table.
179 :param selected: A dictionary with parameters and their values selected by
181 :param normalize: If True, the data is normalized to CPU frequency
182 Constants.NORM_FREQUENCY.
183 :param format: The output format of the table:
184 - html: To be displayed on html page, the values are shown in millions
186 - csv: To be downloaded as a CSV file the values are stored in base
188 :type data: pandas.DataFrame
190 :type normalize: bool
192 :returns: A tuple with the tabe title and the comparison table.
193 :rtype: tuple[str, pandas.DataFrame]
196 def _create_selection(sel: dict) -> list:
197 """Transform the complex dictionary with user selection to list
200 :param sel: A complex dictionary with user selection.
202 :returns: A list of simple items.
205 l_infra = sel["infra"].split("-")
207 for core in sel["core"]:
208 for fsize in sel["frmsize"]:
209 for ttype in sel["ttype"]:
212 "dutver": sel["dutver"],
213 "tbed": f"{l_infra[0]}-{l_infra[1]}",
215 "driver": l_infra[-1].replace("_", "-"),
222 r_sel = deepcopy(selected["reference"]["selection"])
223 c_params = selected["compare"]
224 r_selection = _create_selection(r_sel)
226 if format == "html" and "Latency" not in r_sel["ttype"]:
227 unit_factor, s_unit_factor = (1e6, "M")
229 unit_factor, s_unit_factor = (1, str())
231 # Create Table title and titles of columns with data
233 params.remove(c_params["parameter"])
237 if isinstance(value, list):
238 lst_title.append("|".join(value))
240 lst_title.append(value)
241 title = "Comparison for: " + "-".join(lst_title)
242 r_name = r_sel[c_params["parameter"]]
243 if isinstance(r_name, list):
244 r_name = "|".join(r_name)
245 c_name = c_params["value"]
247 # Select reference data
248 r_data = select_comparison_data(data, r_selection, normalize)
250 # Select compare data
251 c_sel = deepcopy(selected["reference"]["selection"])
252 if c_params["parameter"] in ("core", "frmsize", "ttype"):
253 c_sel[c_params["parameter"]] = [c_params["value"], ]
255 c_sel[c_params["parameter"]] = c_params["value"]
257 c_selection = _create_selection(c_sel)
258 c_data = select_comparison_data(data, c_selection, normalize)
260 if r_data.empty or c_data.empty:
261 return str(), pd.DataFrame()
263 l_name, l_r_mean, l_r_std, l_c_mean, l_c_std, l_rc_mean, l_rc_std, unit = \
264 list(), list(), list(), list(), list(), list(), list(), set()
265 for _, row in r_data.iterrows():
266 if c_params["parameter"] in ("core", "frmsize", "ttype"):
267 l_cmp = row["name"].split("-")
268 if c_params["parameter"] == "core":
270 (c_data.name.str.contains(l_cmp[0])) &
271 (c_data.name.str.contains("-".join(l_cmp[2:])))
273 elif c_params["parameter"] == "frmsize":
274 c_row = c_data[c_data.name.str.contains("-".join(l_cmp[1:]))]
275 elif c_params["parameter"] == "ttype":
276 regex = r"^" + f"{'-'.join(l_cmp[:-1])}" + r"-.{3}$"
277 c_row = c_data[c_data.name.str.contains(regex, regex=True)]
279 c_row = c_data[c_data["name"] == row["name"]]
281 unit.add(f"{s_unit_factor}{row['unit']}")
284 c_mean = c_row["mean"].values[0]
285 c_std = c_row["stdev"].values[0]
286 l_name.append(row["name"])
287 l_r_mean.append(r_mean / unit_factor)
288 l_r_std.append(r_std / unit_factor)
289 l_c_mean.append(c_mean / unit_factor)
290 l_c_std.append(c_std / unit_factor)
291 delta, d_stdev = relative_change_stdev(r_mean, c_mean, r_std, c_std)
292 l_rc_mean.append(delta)
293 l_rc_std.append(d_stdev)
295 s_unit = "|".join(unit)
296 df_cmp = pd.DataFrame.from_dict({
298 f"{r_name} Mean [{s_unit}]": l_r_mean,
299 f"{r_name} Stdev [{s_unit}]": l_r_std,
300 f"{c_name} Mean [{s_unit}]": l_c_mean,
301 f"{c_name} Stdev [{s_unit}]": l_c_std,
302 "Relative Change Mean [%]": l_rc_mean,
303 "Relative Change Stdev [%]": l_rc_std
306 by="Relative Change Mean [%]",
311 return (title, df_cmp)