# - result_latency_value
- start_time
- passed
- - telemetry
- test_id
- version
- data_type: coverage
from dash import Input, Output, State
from ..utils.constants import Constants as C
-from ..utils.utils import classify_anomalies, gen_new_url
+from ..utils.utils import gen_new_url
+from ..utils.anomalies import classify_anomalies
from ..utils.url_processing import url_decode
from .tables import table_summary
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()
+ tst_data = df_job.loc[(
+ (df_job["test_id"] == test) &
+ (df_job["passed"] == True)
+ )].sort_values(by="start_time", ignore_index=True)
if "-ndrpdr" in test:
tst_data = tst_data.dropna(
subset=["result_pdr_lower_rate_value", ]
)
if tst_data.empty:
continue
+ x_axis = tst_data["start_time"].tolist()
try:
anomalies, _, _ = classify_anomalies({
k: v for k, v in zip(
)
if tst_data.empty:
continue
+ x_axis = tst_data["start_time"].tolist()
try:
anomalies, _, _ = classify_anomalies({
k: v for k, v in zip(
import pandas as pd
from ..utils.constants import Constants as C
-from ..utils.utils import classify_anomalies, get_color, get_hdrh_latencies
+from ..utils.utils import get_color, get_hdrh_latencies
+from ..utils.anomalies import classify_anomalies
def select_trending_data(data: pd.DataFrame, itm: dict) -> pd.DataFrame:
--- /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.
+
+"""Functions used by Dash applications to detect anomalies.
+"""
+
+from numpy import isnan
+
+from ..jumpavg import classify
+
+
+def classify_anomalies(data):
+ """Process the data and return anomalies and trending values.
+
+ Gather data into groups with average as trend value.
+ Decorate values within groups to be normal,
+ the first value of changed average as a regression, or a progression.
+
+ :param data: Full data set with unavailable samples replaced by nan.
+ :type data: OrderedDict
+ :returns: Classification and trend values
+ :rtype: 3-tuple, list of strings, list of floats and list of floats
+ """
+ # NaN means something went wrong.
+ # Use 0.0 to cause that being reported as a severe regression.
+ bare_data = [0.0 if isnan(sample) else sample for sample in data.values()]
+ # TODO: Make BitCountingGroupList a subclass of list again?
+ group_list = classify(bare_data).group_list
+ group_list.reverse() # Just to use .pop() for FIFO.
+ classification = list()
+ avgs = list()
+ stdevs = list()
+ active_group = None
+ values_left = 0
+ avg = 0.0
+ stdv = 0.0
+ for sample in data.values():
+ if isnan(sample):
+ classification.append("outlier")
+ avgs.append(sample)
+ stdevs.append(sample)
+ continue
+ if values_left < 1 or active_group is None:
+ values_left = 0
+ while values_left < 1: # Ignore empty groups (should not happen).
+ active_group = group_list.pop()
+ values_left = len(active_group.run_list)
+ avg = active_group.stats.avg
+ stdv = active_group.stats.stdev
+ classification.append(active_group.comment)
+ avgs.append(avg)
+ stdevs.append(stdv)
+ values_left -= 1
+ continue
+ classification.append("normal")
+ avgs.append(avg)
+ stdevs.append(stdv)
+ values_left -= 1
+ return classification, avgs, stdevs
# See the License for the specific language governing permissions and
# limitations under the License.
-"""Function used by Dash applications.
+"""Functions used by Dash applications.
"""
import pandas as pd
import hdrh.codec
from math import sqrt
-from numpy import isnan
from dash import dcc
from datetime import datetime
-from ..jumpavg import classify
from ..utils.constants import Constants as C
from ..utils.url_processing import url_encode
-def classify_anomalies(data):
- """Process the data and return anomalies and trending values.
-
- Gather data into groups with average as trend value.
- Decorate values within groups to be normal,
- the first value of changed average as a regression, or a progression.
-
- :param data: Full data set with unavailable samples replaced by nan.
- :type data: OrderedDict
- :returns: Classification and trend values
- :rtype: 3-tuple, list of strings, list of floats and list of floats
- """
- # NaN means something went wrong.
- # Use 0.0 to cause that being reported as a severe regression.
- bare_data = [0.0 if isnan(sample) else sample for sample in data.values()]
- # TODO: Make BitCountingGroupList a subclass of list again?
- group_list = classify(bare_data).group_list
- group_list.reverse() # Just to use .pop() for FIFO.
- classification = list()
- avgs = list()
- stdevs = list()
- active_group = None
- values_left = 0
- avg = 0.0
- stdv = 0.0
- for sample in data.values():
- if isnan(sample):
- classification.append("outlier")
- avgs.append(sample)
- stdevs.append(sample)
- continue
- if values_left < 1 or active_group is None:
- values_left = 0
- while values_left < 1: # Ignore empty groups (should not happen).
- active_group = group_list.pop()
- values_left = len(active_group.run_list)
- avg = active_group.stats.avg
- stdv = active_group.stats.stdev
- classification.append(active_group.comment)
- avgs.append(avg)
- stdevs.append(stdv)
- values_left -= 1
- continue
- classification.append("normal")
- avgs.append(avg)
- stdevs.append(stdv)
- values_left -= 1
- return classification, avgs, stdevs
-
-
def get_color(idx: int) -> str:
"""Returns a color from the list defined in Constants.PLOT_COLORS defined by
its index.
Code Review / csit.git / commitdiff