X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;f=resources%2Flibraries%2Fpython%2Fjumpavg%2FAvgStdevStats.py;fp=resources%2Flibraries%2Fpython%2Fjumpavg%2FAvgStdevStats.py;h=0000000000000000000000000000000000000000;hb=079c390e0903a98182781ff5c2af2bba9902b4ed;hp=d40b316bf1b7b9a7e4a5c66024d9db01a6b2e28c;hpb=2c9b2a4298cac5cc4d6ca60cb1da8bd72ec23c37;p=csit.git

diff --git a/resources/libraries/python/jumpavg/AvgStdevStats.py b/resources/libraries/python/jumpavg/AvgStdevStats.py
deleted file mode 100644
index d40b316bf1..0000000000
--- a/resources/libraries/python/jumpavg/AvgStdevStats.py
+++ /dev/null
@@ -1,89 +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.
-
-"""Module holding AvgStdevStats class."""
-
-import dataclasses
-import math
-import typing
-
-
-@dataclasses.dataclass
-class AvgStdevStats:
-    """Class for statistics which include average and stdev of a group.
-
-    Contrary to other stats types, adding values to the group
-    is computationally light without any caching.
-
-    Instances are only statistics, the data itself is stored elsewhere.
-    """
-
-    size: int = 0
-    """Number of scalar values (samples) participating in this group."""
-    avg: float = 0.0
-    """Population average of the participating sample values."""
-    stdev: float = 0.0
-    """Population standard deviation of the sample values."""
-
-    @classmethod
-    def for_runs(
-        cls,
-        runs: typing.Iterable[typing.Union[float, "AvgStdevStats"]],
-    ) -> "AvgStdevStats":
-        """Return new stats instance describing the sequence of runs.
-
-        If you want to append data to existing stats object,
-        you can simply use the stats object as the first run.
-
-        Instead of a verb, "for" is used to start this method name,
-        to signify the result contains less information than the input data.
-
-        Here, run is a hypothetical abstract class, an union of float and cls.
-        Defining that as a real abstract class in Python is too much hassle.
-
-        :param runs: Sequence of data to describe by the new metadata.
-        :type runs: Iterable[Union[float, cls]]
-        :returns: The new stats instance.
-        :rtype: cls
-        """
-        # Using Welford method to be more resistant to rounding errors.
-        # Adapted from code for sample standard deviation at:
-        # https://www.johndcook.com/blog/standard_deviation/
-        # The logic of plus operator is taken from
-        # https://www.johndcook.com/blog/skewness_kurtosis/
-        total_size = 0
-        total_avg = 0.0
-        moment_2 = 0.0
-        for run in runs:
-            if isinstance(run, (float, int)):
-                run_size = 1
-                run_avg = run
-                run_stdev = 0.0
-            else:
-                run_size = run.size
-                run_avg = run.avg
-                run_stdev = run.stdev
-            old_total_size = total_size
-            delta = run_avg - total_avg
-            total_size += run_size
-            total_avg += delta * run_size / total_size
-            moment_2 += run_stdev * run_stdev * run_size
-            moment_2 += delta * delta * old_total_size * run_size / total_size
-        if total_size < 1:
-            # Avoid division by zero.
-            return cls(size=0)
-        # TODO: Is it worth tracking moment_2 instead, and compute and cache
-        # stdev on demand, just to possibly save some sqrt calls?
-        total_stdev = math.sqrt(moment_2 / total_size)
-        ret_obj = cls(size=total_size, avg=total_avg, stdev=total_stdev)
-        return ret_obj