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diff --git a/resources/libraries/python/MLRsearch/trimmed_stat.py b/resources/libraries/python/MLRsearch/trimmed_stat.py
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+# 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 TrimmedStat class."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Optional
+
+from .discrete_load import DiscreteLoad
+from .load_stats import LoadStats
+from .target_spec import TargetSpec
+
+
+@dataclass
+class TrimmedStat(LoadStats):
+    """Load stats trimmed to a single target.
+
+    Useful mainly for reporting the overall results.
+    """
+
+    def __post_init__(self) -> None:
+        """Initialize load value and check there is one target to track."""
+        super().__post_init__()
+        if len(self.target_to_stat) != 1:
+            raise ValueError(f"No single target: {self.target_to_stat!r}")
+
+    @staticmethod
+    def for_target(stats: LoadStats, target: TargetSpec) -> TrimmedStat:
+        """Return new instance with only one target in the mapping.
+
+        :param stats: The load stats instance to trim.
+        :param target: The one target which should remain in the mapping.
+        :type stats: LoadStats
+        :type target: TargetSpec
+        :return: Newly created instance.
+        :rtype: TrimmedStat
+        """
+        return TrimmedStat(
+            rounding=stats.rounding,
+            int_load=stats.int_load,
+            target_to_stat={target: stats.target_to_stat[target]},
+        )
+
+    @property
+    def conditional_throughput(self) -> Optional[DiscreteLoad]:
+        """Compute conditional throughput from the load.
+
+        Target stat has dur_rat_sum and good_long.
+        The code here adds intended load and handles the case min load is hit.
+        If min load is not a lower bound, None is returned.
+
+        :return: Conditional throughput assuming self is a relevant lower bound.
+        :rtype: Optional[DiscreteLoad]
+        :raises RuntimeError: If target is unclear or load is spurious.
+        """
+        target = list(self.target_to_stat.keys())[0]
+        _, pes = self.estimates(target)
+        if not pes:
+            if int(self):
+                raise RuntimeError(f"Not a lower bound: {self}")
+            return None
+        # TODO: Verify self is really the clo?
+        stat = self.target_to_stat[target]
+        loss_ratio = stat.dur_rat_sum / stat.good_long
+        ret = self * (1.0 - loss_ratio)
+        return ret