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# 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,
show_latency: bool=True
) -> 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.
: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("_", "-")
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
)
ttype = df["test_type"].to_list()[0]
# 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)
if ttype == "device":
cov = cov.assign(Result="PASS")
else:
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_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_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
# Split data into tables depending on the test suite.
for suite in cov["Suite"].unique().tolist():
df_suite = pd.DataFrame(cov.loc[(cov["Suite"] == suite)])
if ttype !="device":
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,
show_latency: bool=True
) -> list:
"""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.
:type data: pandas.DataFrame
:type selected: dict
:type show_latency: bool
:returns: Accordion with suite names (titles) and tables.
:rtype: dash_bootstrap_components.Accordion
"""
accordion_items = list()
sel_data = select_coverage_data(data, selected, show_latency=show_latency)
for suite, cov_data in sel_data:
if len(cov_data.columns) == 3: # VPP Device
cols = [
{
"name": col,
"id": col,
"deletable": False,
"selectable": False,
"type": "text"
} for col in cov_data.columns
]
style_cell={"textAlign": "left"}
style_cell_conditional=[
{
"if": {"column_id": "Result"},
"textAlign": "right"
}
]
else: # Performance
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)
})
style_cell={"textAlign": "right"}
style_cell_conditional=[
{
"if": {"column_id": "Test Name"},
"textAlign": "left"
}
]
accordion_items.append(
dbc.AccordionItem(
title=suite,
children=dash_table.DataTable(
columns=cols,
data=cov_data.to_dict("records"),
merge_duplicate_headers=True,
editable=False,
filter_action="none",
sort_action="native",
sort_mode="multi",
selected_columns=[],
selected_rows=[],
page_action="none",
style_cell=style_cell,
style_cell_conditional=style_cell_conditional
)
)
)
return dbc.Accordion(
children=accordion_items,
class_name="gy-1 p-0",
start_collapsed=True,
always_open=True
)
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