X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;f=resources%2Ftools%2Fdash%2Fapp%2Fpal%2Fdata%2Futils.py;fp=resources%2Ftools%2Fdash%2Fapp%2Fpal%2Fdata%2Futils.py;h=0000000000000000000000000000000000000000;hb=ae1fe880286d7b0414664bce2b2c7c91c3f543f3;hp=63c9c1aaa44336a9ae59d3942bb96d4e29db54f5;hpb=739e01de7a65045dc42e6c16406a6d054da72f7b;p=csit.git

diff --git a/resources/tools/dash/app/pal/data/utils.py b/resources/tools/dash/app/pal/data/utils.py
deleted file mode 100644
index 63c9c1aaa4..0000000000
--- a/resources/tools/dash/app/pal/data/utils.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# Copyright (c) 2022 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.
-
-"""
-"""
-
-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