diff options
Diffstat (limited to 'csit.infra.dash')
| -rw-r--r-- | csit.infra.dash/app/cdash/data/data.yaml | 1 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/news/layout.py | 12 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/trending/graphs.py | 3 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/utils/anomalies.py | 69 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/utils/utils.py | 54 |
5 files changed, 80 insertions, 59 deletions
diff --git a/csit.infra.dash/app/cdash/data/data.yaml b/csit.infra.dash/app/cdash/data/data.yaml index 8beee0bacc..720109b552 100644 --- a/csit.infra.dash/app/cdash/data/data.yaml +++ b/csit.infra.dash/app/cdash/data/data.yaml @@ -240,7 +240,6 @@ # - result_latency_value - start_time - passed - - telemetry - test_id - version - data_type: coverage diff --git a/csit.infra.dash/app/cdash/news/layout.py b/csit.infra.dash/app/cdash/news/layout.py index da36b1430c..d8ad92a1db 100644 --- a/csit.infra.dash/app/cdash/news/layout.py +++ b/csit.infra.dash/app/cdash/news/layout.py @@ -24,7 +24,8 @@ from dash import callback_context 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 @@ -132,15 +133,17 @@ class Layout: 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( @@ -185,6 +188,7 @@ class Layout: ) if tst_data.empty: continue + x_axis = tst_data["start_time"].tolist() try: anomalies, _, _ = classify_anomalies({ k: v for k, v in zip( diff --git a/csit.infra.dash/app/cdash/trending/graphs.py b/csit.infra.dash/app/cdash/trending/graphs.py index fc26f8bd79..ba94eefeed 100644 --- a/csit.infra.dash/app/cdash/trending/graphs.py +++ b/csit.infra.dash/app/cdash/trending/graphs.py @@ -18,7 +18,8 @@ import plotly.graph_objects as go 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: diff --git a/csit.infra.dash/app/cdash/utils/anomalies.py b/csit.infra.dash/app/cdash/utils/anomalies.py new file mode 100644 index 0000000000..9a7b232fda --- /dev/null +++ b/csit.infra.dash/app/cdash/utils/anomalies.py @@ -0,0 +1,69 @@ +# 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 diff --git a/csit.infra.dash/app/cdash/utils/utils.py b/csit.infra.dash/app/cdash/utils/utils.py index d9347b1c13..29bee3d039 100644 --- a/csit.infra.dash/app/cdash/utils/utils.py +++ b/csit.infra.dash/app/cdash/utils/utils.py @@ -11,7 +11,7 @@ # 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 @@ -22,65 +22,13 @@ import hdrh.histogram 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. |