--- /dev/null
+# Copyright (c) 2023 Cisco and/or its affiliates.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at:
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""The coverage data tables.
+"""
+
+import hdrh.histogram
+import hdrh.codec
+import pandas as pd
+import dash_bootstrap_components as dbc
+
+from dash import dash_table
+from dash.dash_table.Format import Format, Scheme
+
+from ..utils.constants import Constants as C
+
+
+def select_coverage_data(
+ data: pd.DataFrame,
+ selected: dict,
+ csv: bool=False
+ ) -> list:
+ """Select coverage data for the tables and generate tables as pandas data
+ frames.
+
+ :param data: Coverage data.
+ :param selected: Dictionary with user selection.
+ :param csv: If True, pandas data frame with selected coverage data is
+ returned for "Download Data" feature.
+ :type data: pandas.DataFrame
+ :type selected: dict
+ :type csv: bool
+ :returns: List of tuples with suite name (str) and data (pandas dataframe)
+ or pandas dataframe if csv is True.
+ :rtype: list[tuple[str, pandas.DataFrame], ] or pandas.DataFrame
+ """
+
+ l_data = list()
+
+ # Filter data selected by the user.
+ phy = selected["phy"].split("-")
+ if len(phy) == 4:
+ topo, arch, nic, drv = phy
+ drv = "" if drv == "dpdk" else drv.replace("_", "-")
+ else:
+ return l_data
+
+ df = pd.DataFrame(data.loc[(
+ (data["passed"] == True) &
+ (data["dut_type"] == selected["dut"]) &
+ (data["dut_version"] == selected["dutver"]) &
+ (data["release"] == selected["rls"])
+ )])
+ df = df[
+ (df.job.str.endswith(f"{topo}-{arch}")) &
+ (df.test_id.str.contains(
+ f"^.*\.{selected['area']}\..*{nic}.*{drv}.*$",
+ regex=True
+ ))
+ ]
+ if drv == "dpdk":
+ for driver in C.DRIVERS:
+ df.drop(
+ df[df.test_id.str.contains(f"-{driver}-")].index,
+ inplace=True
+ )
+
+ # Prepare the coverage data
+ def _latency(hdrh_string: str, percentile: float) -> int:
+ """Get latency from HDRH string for given percentile.
+
+ :param hdrh_string: Encoded HDRH string.
+ :param percentile: Given percentile.
+ :type hdrh_string: str
+ :type percentile: float
+ :returns: The latency value for the given percentile from the encoded
+ HDRH string.
+ :rtype: int
+ """
+ try:
+ hdr_lat = hdrh.histogram.HdrHistogram.decode(hdrh_string)
+ return hdr_lat.get_value_at_percentile(percentile)
+ except (hdrh.codec.HdrLengthException, TypeError):
+ return None
+
+ def _get_suite(test_id: str) -> str:
+ """Get the suite name from the test ID.
+ """
+ return test_id.split(".")[-2].replace("2n1l-", "").\
+ replace("1n1l-", "").replace("2n-", "").replace("-ndrpdr", "")
+
+ def _get_test(test_id: str) -> str:
+ """Get the test name from the test ID.
+ """
+ return test_id.split(".")[-1].replace("-ndrpdr", "")
+
+ cov = pd.DataFrame()
+ cov["suite"] = df.apply(lambda row: _get_suite(row["test_id"]), axis=1)
+ cov["Test Name"] = df.apply(lambda row: _get_test(row["test_id"]), axis=1)
+ cov["Throughput_Unit"] = df["result_pdr_lower_rate_unit"]
+ cov["Throughput_NDR"] = df.apply(
+ lambda row: row["result_ndr_lower_rate_value"] / 1e6, axis=1
+ )
+ cov["Throughput_NDR_Mbps"] = df.apply(
+ lambda row: row["result_ndr_lower_bandwidth_value"] /1e9, axis=1
+ )
+ cov["Throughput_PDR"] = \
+ df.apply(lambda row: row["result_pdr_lower_rate_value"] / 1e6, axis=1)
+ cov["Throughput_PDR_Mbps"] = df.apply(
+ lambda row: row["result_pdr_lower_bandwidth_value"] /1e9, axis=1
+ )
+ cov["Latency Forward [us]_10% PDR_P50"] = df.apply(
+ lambda row: _latency(row["result_latency_forward_pdr_10_hdrh"], 50.0),
+ axis=1
+ )
+ cov["Latency Forward [us]_10% PDR_P90"] = df.apply(
+ lambda row: _latency(row["result_latency_forward_pdr_10_hdrh"], 90.0),
+ axis=1
+ )
+ cov["Latency Forward [us]_10% PDR_P99"] = df.apply(
+ lambda row: _latency(row["result_latency_forward_pdr_10_hdrh"], 99.0),
+ axis=1
+ )
+ cov["Latency Forward [us]_50% PDR_P50"] = df.apply(
+ lambda row: _latency(row["result_latency_forward_pdr_50_hdrh"], 50.0),
+ axis=1
+ )
+ cov["Latency Forward [us]_50% PDR_P90"] = df.apply(
+ lambda row: _latency(row["result_latency_forward_pdr_50_hdrh"], 90.0),
+ axis=1
+ )
+ cov["Latency Forward [us]_50% PDR_P99"] = df.apply(
+ lambda row: _latency(row["result_latency_forward_pdr_50_hdrh"], 99.0),
+ axis=1
+ )
+ cov["Latency Forward [us]_90% PDR_P50"] = df.apply(
+ lambda row: _latency(row["result_latency_forward_pdr_90_hdrh"], 50.0),
+ axis=1
+ )
+ cov["Latency Forward [us]_90% PDR_P90"] = df.apply(
+ lambda row: _latency(row["result_latency_forward_pdr_90_hdrh"], 90.0),
+ axis=1
+ )
+ cov["Latency Forward [us]_90% PDR_P99"] = df.apply(
+ lambda row: _latency(row["result_latency_forward_pdr_90_hdrh"], 99.0),
+ axis=1
+ )
+ cov["Latency Reverse [us]_10% PDR_P50"] = df.apply(
+ lambda row: _latency(row["result_latency_reverse_pdr_10_hdrh"], 50.0),
+ axis=1
+ )
+ cov["Latency Reverse [us]_10% PDR_P90"] = df.apply(
+ lambda row: _latency(row["result_latency_reverse_pdr_10_hdrh"], 90.0),
+ axis=1
+ )
+ cov["Latency Reverse [us]_10% PDR_P99"] = df.apply(
+ lambda row: _latency(row["result_latency_reverse_pdr_10_hdrh"], 99.0),
+ axis=1
+ )
+ cov["Latency Reverse [us]_50% PDR_P50"] = df.apply(
+ lambda row: _latency(row["result_latency_reverse_pdr_50_hdrh"], 50.0),
+ axis=1
+ )
+ cov["Latency Reverse [us]_50% PDR_P90"] = df.apply(
+ lambda row: _latency(row["result_latency_reverse_pdr_50_hdrh"], 90.0),
+ axis=1
+ )
+ cov["Latency Reverse [us]_50% PDR_P99"] = df.apply(
+ lambda row: _latency(row["result_latency_reverse_pdr_50_hdrh"], 99.0),
+ axis=1
+ )
+ cov["Latency Reverse [us]_90% PDR_P50"] = df.apply(
+ lambda row: _latency(row["result_latency_reverse_pdr_90_hdrh"], 50.0),
+ axis=1
+ )
+ cov["Latency Reverse [us]_90% PDR_P90"] = df.apply(
+ lambda row: _latency(row["result_latency_reverse_pdr_90_hdrh"], 90.0),
+ axis=1
+ )
+ cov["Latency Reverse [us]_90% PDR_P99"] = df.apply(
+ lambda row: _latency(row["result_latency_reverse_pdr_90_hdrh"], 99.0),
+ axis=1
+ )
+
+ if csv:
+ return cov
+
+ # Split data into tabels depending on the test suite.
+ for suite in cov["suite"].unique().tolist():
+ df_suite = pd.DataFrame(cov.loc[(cov["suite"] == suite)])
+ unit = df_suite["Throughput_Unit"].tolist()[0]
+ df_suite.rename(
+ columns={
+ "Throughput_NDR": f"Throughput_NDR_M{unit}",
+ "Throughput_PDR": f"Throughput_PDR_M{unit}"
+ },
+ inplace=True
+ )
+ df_suite.drop(["suite", "Throughput_Unit"], axis=1, inplace=True)
+ l_data.append((suite, df_suite, ))
+ return l_data
+
+
+def coverage_tables(data: pd.DataFrame, selected: dict) -> list:
+ """Generate an accordion with coverage tables.
+
+ :param data: Coverage data.
+ :param selected: Dictionary with user selection.
+ :type data: pandas.DataFrame
+ :type selected: dict
+ :returns: Accordion with suite names (titles) and tables.
+ :rtype: dash_bootstrap_components.Accordion
+ """
+
+ accordion_items = list()
+ for suite, cov_data in select_coverage_data(data, selected):
+ cols = list()
+ for idx, col in enumerate(cov_data.columns):
+ if idx == 0:
+ cols.append({
+ "name": ["", "", col],
+ "id": col,
+ "deletable": False,
+ "selectable": False,
+ "type": "text"
+ })
+ elif idx < 5:
+ cols.append({
+ "name": col.split("_"),
+ "id": col,
+ "deletable": False,
+ "selectable": False,
+ "type": "numeric",
+ "format": Format(precision=2, scheme=Scheme.fixed)
+ })
+ else:
+ cols.append({
+ "name": col.split("_"),
+ "id": col,
+ "deletable": False,
+ "selectable": False,
+ "type": "numeric",
+ "format": Format(precision=0, scheme=Scheme.fixed)
+ })
+
+ accordion_items.append(
+ dbc.AccordionItem(
+ title=suite,
+ children=dash_table.DataTable(
+ columns=cols,
+ data=cov_data.to_dict("records"),
+ merge_duplicate_headers=True,
+ editable=True,
+ filter_action="none",
+ sort_action="native",
+ sort_mode="multi",
+ selected_columns=[],
+ selected_rows=[],
+ page_action="none",
+ style_cell={"textAlign": "right"},
+ style_cell_conditional=[{
+ "if": {"column_id": "Test Name"},
+ "textAlign": "left"
+ }]
+ )
+ )
+ )
+
+ return dbc.Accordion(
+ children=accordion_items,
+ class_name="gy-2 p-0",
+ start_collapsed=True,
+ always_open=True
+ )
Code Review / csit.git / blobdiff