feat(MLRsearch): MLRsearch v7

Replaces MLRv2, suitable for "big bang" upgrade across CSIT.
PyPI metadata updated only partially (full edits will come separately).
Pylint wants less complexity, but the differences are only minor.

+ Use the same (new CSIT) defaults everywhere, also in Python library.
+ Update also PLRsearch to use the new result class.
+ Make upper bound optional in UTI.
+ Fix ASTF approximate duration detection.
+ Do not keep approximated_receive_rate (for MRR) in result structure.

Change-Id: I03406f32d5c93f56b527cb3f93791b61955dfd74
Signed-off-by: Vratko Polak <vrpolak@cisco.com>

1 files changed, 76 insertions, 0 deletions

diff --git a/resources/libraries/python/MLRsearch/strategy/extend_lo.py b/resources/libraries/python/MLRsearch/strategy/extend_lo.py
new file mode 100644
index 0000000000..68d20b6a6a
--- /dev/null
+++ b/resources/libraries/python/MLRsearch/strategy/extend_lo.py
@@ -0,0 +1,76 @@
+# 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.
+
+"""Module defining ExtendLoStrategy class."""
+
+
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+from ..discrete_load import DiscreteLoad
+from ..discrete_width import DiscreteWidth
+from ..relevant_bounds import RelevantBounds
+from .base import StrategyBase
+
+
+@dataclass
+class ExtendLoStrategy(StrategyBase):
+    """This strategy is applied when there is no relevant lower bound.
+
+    Typically this is needed after RefineLoStrategy turned initial lower bound
+    into a current relevant upper bound.
+    """
+
+    def nominate(
+        self, bounds: RelevantBounds
+    ) -> Tuple[Optional[DiscreteLoad], Optional[DiscreteWidth]]:
+        """Nominate current relevant upper bound minus expander width.
+
+        This performs external search in downwards direction,
+        until a valid lower bound for the current target is found,
+        or until min load is hit.
+        Limit handling is used to avoid nominating too close
+        (or below) the min rate.
+
+        Width expansion is only applied if the candidate becomes an upper bound,
+        so that is detected in done method.
+
+        :param bounds: Freshly updated bounds relevant for current target.
+        :type bounds: RelevantBounds
+        :returns: Two nones or candidate intended load and duration.
+        :rtype: Tuple[Optional[DiscreteLoad], Optional[DiscreteWidth]]
+        """
+        if bounds.clo or not bounds.chi or bounds.chi <= self.handler.min_load:
+            return None, None
+        width = self.expander.get_width()
+        load = self.handler.handle(
+            load=bounds.chi - width,
+            width=self.target.discrete_width,
+            clo=bounds.clo,
+            chi=bounds.chi,
+        )
+        if self.not_worth(bounds=bounds, load=load):
+            return None, None
+        self.debug(f"No clo, extending down: {load}")
+        return load, width
+
+    def won(self, bounds: RelevantBounds, load: DiscreteLoad) -> None:
+        """Expand width if the load became new upper bound.
+
+        :param bounds: Freshly updated bounds relevant for current target.
+        :param load: The current load, so strategy does not need to remember.
+        :type bounds: RelevantBounds
+        :type load: DiscreteLoad
+        """
+        if load == bounds.chi:
+            self.expander.expand()