CSIT-1110: Integrate anomaly detection into PAL

+ Keep the original detection,
+ add the new one as subdirectory
  (both in source and in rendered tree).
- The new detection is not rebased over "Add dpdk mrr tests to trending".

New detection features:
+ Do not remove (nor detect) outliers.
+ Trend line shows the constant average within a group.
+ Anomaly circles are placed at the changed average.
+ Small bias against too similar averages.
+ Should be ready for moving the detection library out to pip.

Change-Id: I7ab1a92b79eeeed53ba65a071b1305e927816a89
Signed-off-by: Vratko Polak <vrpolak@cisco.com>

1 files changed, 68 insertions, 0 deletions

diff --git a/resources/tools/presentation/new/jumpavg/ClassifiedBitCountingMetadata.py b/resources/tools/presentation/new/jumpavg/ClassifiedBitCountingMetadata.py
new file mode 100644
index 0000000000..9a7277bc3e
--- /dev/null
+++ b/resources/tools/presentation/new/jumpavg/ClassifiedBitCountingMetadata.py
@@ -0,0 +1,68 @@
+# Copyright (c) 2018 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 BitCountingMetadata import BitCountingMetadata
+
+
+class ClassifiedBitCountingMetadata(BitCountingMetadata):
+    """Class for metadata which includes classification."""
+
+    def __init__(
+            self, max_value, size=0, avg=0.0, stdev=0.0, prev_avg=None,
+            classification=None):
+        """Delegate to ancestor constructors and set classification.
+
+        :param max_value: Maximal expected value.
+        :param size: Number of values participating in this group.
+        :param avg: Population average of the participating sample values.
+        :param stdev: Population standard deviation of the sample values.
+        :param prev_avg: Population average of the previous group.
+            If None, no previous average is taken into account.
+            If not None, the given previous average is used to discourage
+            consecutive groups with similar averages
+            (opposite triangle distribution is assumed).
+        :param classification: Arbitrary object classifying this group.
+        :type max_value: float
+        :type size: int
+        :type avg: float
+        :type stdev: float
+        :type prev_avg: float
+        :type classification: object
+        """
+        super(ClassifiedBitCountingMetadata, self).__init__(
+            max_value, size, avg, stdev, prev_avg)
+        self.classification = classification
+
+    def __str__(self):
+        """Return string with human readable description of the group.
+
+        :returns: Readable description.
+        :rtype: str
+        """
+        # str(super(...)) describes the proxy, not the proxied object.
+        super_str = super(ClassifiedBitCountingMetadata, self).__str__()
+        return super_str + " classification={classification}".format(
+            classification=self.classification)
+
+    def __repr__(self):
+        """Return string executable as Python constructor call.
+
+        :returns: Executable constructor call.
+        :rtype: str
+        """
+        return ("ClassifiedBitCountingMetadata(max_value={max_value}," +
+                "size={size},avg={avg},stdev={stdev},prev_avg={prev_avg}," +
+                "classification={cls})").format(
+                    max_value=self.max_value, size=self.size, avg=self.avg,
+                    stdev=self.stdev, prev_avg=self.prev_avg,
+                    cls=self.classification)