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# 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:
#
# 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 search data tables.
"""
import pandas as pd
from ..utils.constants import Constants as C
def select_search_data(data: pd.DataFrame, selection: list) -> pd.DataFrame:
"""Return the searched data based on the user's "selection".
:param data: Input data to be searched through.
:param selection: User selection.
:type data: pandas.DataFrame
:type selection: list[dict]
:returns: A dataframe with selected tests.
:trype: pandas.DataFrame
"""
sel_data = data[selection["datatype"]]
if selection["datatype"] == "trending":
df = pd.DataFrame(sel_data.loc[
sel_data["dut_type"] == selection["dut"]
])
else:
df = pd.DataFrame(sel_data.loc[(
(sel_data["dut_type"] == selection["dut"]) &
(sel_data["release"] == selection["release"])
)])
try:
df = df[
df.full_id.str.contains(
selection["regexp"].replace(" ", ".*"),
regex=True
)
]
except Exception:
return pd.DataFrame()
return df
def search_table(data: pd.DataFrame, selection: list) -> pd.DataFrame:
"""Generate a table listing tests based on user's selection.
:param data: Input data (all tests).
:param selection: User selection.
:type data: pandas.DataFrame
:type selection: list[dict]
:returns: A dataframe with selected tests/
:rtype: pandas.DataFrame
"""
sel = select_search_data(data, selection)
if sel.empty:
return pd.DataFrame()
l_tb, l_nic, l_drv, l_test, = list(), list(), list(), list()
if selection["datatype"] == "trending":
cols = ["job", "test_id"]
else:
l_dutver = list()
cols = ["job", "test_id", "dut_version"]
for _, row in sel[cols].drop_duplicates().iterrows():
l_id = row["test_id"].split(".")
suite = l_id[-2].replace("2n1l-", "").replace("1n1l-", "").\
replace("2n-", "")
l_tb.append("-".join(row["job"].split("-")[-2:]))
l_nic.append(suite.split("-")[0])
if selection["datatype"] != "trending":
l_dutver.append(row["dut_version"])
for driver in C.DRIVERS:
if driver in suite:
l_drv.append(driver)
break
else:
l_drv.append("dpdk")
l_test.append(l_id[-1])
if selection["datatype"] == "trending":
selected = pd.DataFrame.from_dict({
"Test Bed": l_tb,
"NIC": l_nic,
"Driver": l_drv,
"Test": l_test
})
selected.sort_values(
by=["Test Bed", "NIC", "Driver", "Test"],
ascending=True,
inplace=True
)
else:
selected = pd.DataFrame.from_dict({
"DUT Version": l_dutver,
"Test Bed": l_tb,
"NIC": l_nic,
"Driver": l_drv,
"Test": l_test
})
selected.sort_values(
by=["DUT Version", "Test Bed", "NIC", "Driver", "Test"],
ascending=True,
inplace=True
)
return selected
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