-# Copyright (c) 2023 Cisco and/or its affiliates.
+# Copyright (c) 2024 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:
"""The coverage data tables.
"""
+
import hdrh.histogram
import hdrh.codec
import pandas as pd
def select_coverage_data(
data: pd.DataFrame,
selected: dict,
- csv: bool=False
+ csv: bool=False,
+ show_latency: bool=True
) -> list:
"""Select coverage data for the tables and generate tables as pandas data
frames.
:param selected: Dictionary with user selection.
:param csv: If True, pandas data frame with selected coverage data is
returned for "Download Data" feature.
+ :param show_latency: If True, latency is displayed in the tables.
:type data: pandas.DataFrame
:type selected: dict
:type csv: bool
+ :type show_latency: 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("_", "-")
+ phy = selected["phy"].rsplit("-", maxsplit=2)
+ if len(phy) == 3:
+ topo_arch, nic, drv = phy
+ drv_str = "" if drv == "dpdk" else drv.replace("_", "-")
else:
- return l_data
+ return l_data, None
df = pd.DataFrame(data.loc[(
(data["passed"] == True) &
(data["release"] == selected["rls"])
)])
df = df[
- (df.job.str.endswith(f"{topo}-{arch}")) &
+ (df.job.str.endswith(topo_arch)) &
(df.test_id.str.contains(
- f"^.*\.{selected['area']}\..*{nic}.*{drv}.*$",
+ f"^.*\.{selected['area']}\..*{nic}.*{drv_str}.*$",
regex=True
))
]
df[df.test_id.str.contains(f"-{driver}-")].index,
inplace=True
)
-
- ttype = df["test_type"].to_list()[0]
+ try:
+ ttype = df["test_type"].to_list()[0]
+ except IndexError:
+ return l_data, None
# Prepare the coverage data
- def _laten(hdrh_string: str, percentile: float) -> int:
+ def _latency(hdrh_string: str, percentile: float) -> int:
"""Get latency from HDRH string for given percentile.
:param hdrh_string: Encoded HDRH string.
if ttype == "device":
cov = cov.assign(Result="PASS")
- else:
+ elif ttype == "mrr":
+ cov["Throughput_Unit"] = df["result_receive_rate_rate_unit"]
+ cov["Throughput_AVG"] = df.apply(
+ lambda row: row["result_receive_rate_rate_avg"] / 1e9, axis=1
+ )
+ cov["Throughput_STDEV"] = df.apply(
+ lambda row: row["result_receive_rate_rate_stdev"] / 1e9, axis=1
+ )
+ else: # NDRPDR
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_NDR_Gbps"] = 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: _laten(row["result_latency_forward_pdr_10_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Forward [us]_10% PDR_P90"] = df.apply(
- lambda row: _laten(row["result_latency_forward_pdr_10_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Forward [us]_10% PDR_P99"] = df.apply(
- lambda row: _laten(row["result_latency_forward_pdr_10_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Forward [us]_50% PDR_P50"] = df.apply(
- lambda row: _laten(row["result_latency_forward_pdr_50_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Forward [us]_50% PDR_P90"] = df.apply(
- lambda row: _laten(row["result_latency_forward_pdr_50_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Forward [us]_50% PDR_P99"] = df.apply(
- lambda row: _laten(row["result_latency_forward_pdr_50_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Forward [us]_90% PDR_P50"] = df.apply(
- lambda row: _laten(row["result_latency_forward_pdr_90_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Forward [us]_90% PDR_P90"] = df.apply(
- lambda row: _laten(row["result_latency_forward_pdr_90_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Forward [us]_90% PDR_P99"] = df.apply(
- lambda row: _laten(row["result_latency_forward_pdr_90_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Reverse [us]_10% PDR_P50"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_10_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Reverse [us]_10% PDR_P90"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_10_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Reverse [us]_10% PDR_P99"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_10_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Reverse [us]_50% PDR_P50"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_50_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Reverse [us]_50% PDR_P90"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_50_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Reverse [us]_50% PDR_P99"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_50_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Reverse [us]_90% PDR_P50"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_90_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Reverse [us]_90% PDR_P90"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_90_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Reverse [us]_90% PDR_P99"] = df.apply(
- lambda row: _laten(row["result_latency_reverse_pdr_90_hdrh"], 99.0),
- axis=1
+ cov["Throughput_PDR_Gbps"] = df.apply(
+ lambda row: row["result_pdr_lower_bandwidth_value"] / 1e9, axis=1
)
+ if show_latency:
+ for way in ("Forward", "Reverse"):
+ for pdr in (10, 50, 90):
+ for perc in (50, 90, 99):
+ latency = f"result_latency_{way.lower()}_pdr_{pdr}_hdrh"
+ cov[f"Latency {way} [us]_{pdr}% PDR_P{perc}"] = \
+ df.apply(
+ lambda row: _latency(row[latency], perc),
+ axis=1
+ )
if csv:
return cov
df_suite.rename(
columns={
"Throughput_NDR": f"Throughput_NDR_M{unit}",
- "Throughput_PDR": f"Throughput_PDR_M{unit}"
+ "Throughput_PDR": f"Throughput_PDR_M{unit}",
+ "Throughput_AVG": f"Throughput_G{unit}_AVG",
+ "Throughput_STDEV": f"Throughput_G{unit}_STDEV"
},
inplace=True
)
l_data.append((suite, df_suite, ))
- return l_data
+ return l_data, ttype
-def coverage_tables(data: pd.DataFrame, selected: dict) -> list:
+def coverage_tables(
+ data: pd.DataFrame,
+ selected: dict,
+ show_latency: bool=True,
+ start_collapsed: bool=True
+ ) -> dbc.Accordion:
"""Generate an accordion with coverage tables.
:param data: Coverage data.
:param selected: Dictionary with user selection.
+ :param show_latency: If True, latency is displayed in the tables.
+ :param start_collapsed: If True, the accordion with tables is collapsed when
+ displayed.
:type data: pandas.DataFrame
:type selected: dict
+ :type show_latency: bool
+ :type start_collapsed: bool
: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):
- if len(cov_data.columns) == 3: # VPP Device
+ sel_data, ttype = \
+ select_coverage_data(data, selected, show_latency=show_latency)
+ for suite, cov_data in sel_data:
+ if ttype == "device": # VPP Device
cols = [
{
"name": col,
"textAlign": "right"
}
]
- else: # Performance
+ elif ttype == "mrr": # MRR
+ 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"
+ })
+ else:
+ cols.append({
+ "name": col.split("_"),
+ "id": col,
+ "deletable": False,
+ "selectable": False,
+ "type": "numeric",
+ "format": Format(precision=2, scheme=Scheme.fixed)
+ })
+ style_cell={"textAlign": "right"}
+ style_cell_conditional=[
+ {
+ "if": {"column_id": "Test Name"},
+ "textAlign": "left"
+ }
+ ]
+ else: # Performance NDRPDR
cols = list()
for idx, col in enumerate(cov_data.columns):
if idx == 0:
)
)
)
+ if not accordion_items:
+ accordion_items.append(dbc.AccordionItem(
+ title="No data.",
+ children="No data."
+ ))
+ start_collapsed = True
return dbc.Accordion(
children=accordion_items,
class_name="gy-1 p-0",
- start_collapsed=True,
+ start_collapsed=start_collapsed,
always_open=True
)
Code Review / csit.git / blobdiff