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-rw-r--r--resources/tools/dash/app/pal/news/layout.py119
-rw-r--r--resources/tools/dash/app/pal/news/tables.py43
2 files changed, 148 insertions, 14 deletions
diff --git a/resources/tools/dash/app/pal/news/layout.py b/resources/tools/dash/app/pal/news/layout.py
index b8edb7a683..2f66ce5c81 100644
--- a/resources/tools/dash/app/pal/news/layout.py
+++ b/resources/tools/dash/app/pal/news/layout.py
@@ -27,7 +27,8 @@ from yaml import load, FullLoader, YAMLError
from copy import deepcopy
from ..data.data import Data
-from .tables import table_failed
+from ..data.utils import classify_anomalies
+from .tables import table_news
class Layout:
@@ -37,6 +38,9 @@ class Layout:
# The default job displayed when the page is loaded first time.
DEFAULT_JOB = "csit-vpp-perf-mrr-daily-master-2n-icx"
+ # Time period for regressions and progressions.
+ TIME_PERIOD = 21 # [days]
+
def __init__(self, app: Flask, html_layout_file: str, data_spec_file: str,
tooltip_file: str) -> None:
"""Initialization:
@@ -69,7 +73,7 @@ class Layout:
data_stats, data_mrr, data_ndrpdr = Data(
data_spec_file=self._data_spec_file,
debug=True
- ).read_stats(days=10) # To be sure
+ ).read_stats(days=self.TIME_PERIOD)
df_tst_info = pd.concat([data_mrr, data_ndrpdr], ignore_index=True)
@@ -94,6 +98,16 @@ class Layout:
self._default = self._set_job_params(self.DEFAULT_JOB)
# Pre-process the data:
+
+ def _create_test_name(test: str) -> str:
+ lst_tst = test.split(".")
+ suite = lst_tst[-2].replace("2n1l-", "").replace("1n1l-", "").\
+ replace("2n-", "")
+ return f"{suite.split('-')[0]}-{lst_tst[-1]}"
+
+ def _get_rindex(array: list, itm: any) -> int:
+ return len(array) - 1 - array[::-1].index(itm)
+
tst_info = {
"job": list(),
"build": list(),
@@ -101,9 +115,12 @@ class Layout:
"dut_type": list(),
"dut_version": list(),
"hosts": list(),
- "lst_failed": list()
+ "failed": list(),
+ "regressions": list(),
+ "progressions": list()
}
for job in jobs:
+ # Create lists of failed tests:
df_job = df_tst_info.loc[(df_tst_info["job"] == job)]
last_build = max(df_job["build"].unique())
df_build = df_job.loc[(df_job["build"] == last_build)]
@@ -121,13 +138,95 @@ class Layout:
l_failed = list()
try:
for tst in failed_tests:
- lst_tst = tst.split(".")
- suite = lst_tst[-2].replace("2n1l-", "").\
- replace("1n1l-", "").replace("2n-", "")
- l_failed.append(f"{suite.split('-')[0]}-{lst_tst[-1]}")
+ l_failed.append(_create_test_name(tst))
except KeyError:
l_failed = list()
- tst_info["lst_failed"].append(sorted(l_failed))
+ tst_info["failed"].append(sorted(l_failed))
+
+ # Create lists of regressions and progressions:
+ l_reg = list()
+ l_prog = list()
+
+ tests = df_job["test_id"].unique()
+ for test in tests:
+ tst_data = df_job.loc[df_job["test_id"] == test].sort_values(
+ by="start_time", ignore_index=True)
+ x_axis = tst_data["start_time"].tolist()
+ if "-ndrpdr" in test:
+ tst_data = tst_data.dropna(
+ subset=["result_pdr_lower_rate_value", ]
+ )
+ if tst_data.empty:
+ continue
+ try:
+ anomalies, _, _ = classify_anomalies({
+ k: v for k, v in zip(
+ x_axis,
+ tst_data["result_ndr_lower_rate_value"].tolist()
+ )
+ })
+ except ValueError:
+ continue
+ if "progression" in anomalies:
+ l_prog.append((
+ _create_test_name(test).replace("-ndrpdr", "-ndr"),
+ x_axis[_get_rindex(anomalies, "progression")]
+ ))
+ if "regression" in anomalies:
+ l_reg.append((
+ _create_test_name(test).replace("-ndrpdr", "-ndr"),
+ x_axis[_get_rindex(anomalies, "regression")]
+ ))
+ try:
+ anomalies, _, _ = classify_anomalies({
+ k: v for k, v in zip(
+ x_axis,
+ tst_data["result_pdr_lower_rate_value"].tolist()
+ )
+ })
+ except ValueError:
+ continue
+ if "progression" in anomalies:
+ l_prog.append((
+ _create_test_name(test).replace("-ndrpdr", "-pdr"),
+ x_axis[_get_rindex(anomalies, "progression")]
+ ))
+ if "regression" in anomalies:
+ l_reg.append((
+ _create_test_name(test).replace("-ndrpdr", "-pdr"),
+ x_axis[_get_rindex(anomalies, "regression")]
+ ))
+ else: # mrr
+ tst_data = tst_data.dropna(
+ subset=["result_receive_rate_rate_avg", ]
+ )
+ if tst_data.empty:
+ continue
+ try:
+ anomalies, _, _ = classify_anomalies({
+ k: v for k, v in zip(
+ x_axis,
+ tst_data["result_receive_rate_rate_avg"].\
+ tolist()
+ )
+ })
+ except ValueError:
+ continue
+ if "progression" in anomalies:
+ l_prog.append((
+ _create_test_name(test),
+ x_axis[_get_rindex(anomalies, "progression")]
+ ))
+ if "regression" in anomalies:
+ l_reg.append((
+ _create_test_name(test),
+ x_axis[_get_rindex(anomalies, "regression")]
+ ))
+
+ tst_info["regressions"].append(
+ sorted(l_reg, key=lambda k: k[1], reverse=True))
+ tst_info["progressions"].append(
+ sorted(l_prog, key=lambda k: k[1], reverse=True))
self._data = pd.DataFrame.from_dict(tst_info)
@@ -156,7 +255,7 @@ class Layout:
f"{self._tooltip_file}\n{err}"
)
- self._default_tab_failed = table_failed(self.data, self._default["job"])
+ self._default_tab_failed = table_news(self.data, self._default["job"])
# Callbacks:
if self._app is not None and hasattr(self, 'callbacks'):
@@ -659,7 +758,7 @@ class Layout:
ctrl_panel.get("dd-tbeds-value")
)
ctrl_panel.set({"al-job-children": job})
- tab_failed = table_failed(self.data, job)
+ tab_failed = table_news(self.data, job)
ret_val = [
ctrl_panel.panel,
diff --git a/resources/tools/dash/app/pal/news/tables.py b/resources/tools/dash/app/pal/news/tables.py
index c8f851b030..53b24608d5 100644
--- a/resources/tools/dash/app/pal/news/tables.py
+++ b/resources/tools/dash/app/pal/news/tables.py
@@ -18,17 +18,30 @@ import pandas as pd
import dash_bootstrap_components as dbc
-def table_failed(data: pd.DataFrame, job: str) -> list:
+# Time period for regressions and progressions.
+TIME_PERIOD = 21 # [days]
+
+
+def table_news(data: pd.DataFrame, job: str) -> list:
"""
"""
job_data = data.loc[(data["job"] == job)]
- failed = job_data["lst_failed"].to_list()[0]
+ failed = job_data["failed"].to_list()[0]
+ regressions = {"Test Name": list(), "Last Regression": list()}
+ for itm in job_data["regressions"].to_list()[0]:
+ regressions["Test Name"].append(itm[0])
+ regressions["Last Regression"].append(itm[1].strftime('%Y-%m-%d %H:%M'))
+ progressions = {"Test Name": list(), "Last Progression": list()}
+ for itm in job_data["progressions"].to_list()[0]:
+ progressions["Test Name"].append(itm[0])
+ progressions["Last Progression"].append(
+ itm[1].strftime('%Y-%m-%d %H:%M'))
return [
dbc.Table.from_dataframe(pd.DataFrame.from_dict({
"Job": job_data["job"],
- "Build": job_data["build"],
+ "Last Build": job_data["build"],
"Date": job_data["start"],
"DUT": job_data["dut_type"],
"DUT Version": job_data["dut_version"],
@@ -39,5 +52,27 @@ def table_failed(data: pd.DataFrame, job: str) -> list:
f"Last Failed Tests on "
f"{job_data['start'].values[0]} ({len(failed)})"
): failed
- }), bordered=True, striped=True, hover=True, size="sm", color="light")
+ }), bordered=True, striped=True, hover=True, size="sm", color="light"),
+ dbc.Label(
+ class_name="p-0",
+ size="lg",
+ children=(
+ f"Regressions during the last {TIME_PERIOD} days "
+ f"({len(regressions['Test Name'])})"
+ )
+ ),
+ dbc.Table.from_dataframe(
+ pd.DataFrame.from_dict(regressions),
+ bordered=True, striped=True, hover=True, size="sm", color="light"),
+ dbc.Label(
+ class_name="p-0",
+ size="lg",
+ children=(
+ f"Progressions during the last {TIME_PERIOD} days "
+ f"({len(progressions['Test Name'])})"
+ )
+ ),
+ dbc.Table.from_dataframe(
+ pd.DataFrame.from_dict(progressions),
+ bordered=True, striped=True, hover=True, size="sm", color="light")
]