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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 LoadStats class."""
from __future__ import annotations
from dataclasses import dataclass
from typing import Dict, Tuple
from .target_spec import TargetSpec
from .target_stat import TargetStat
from .discrete_load import DiscreteLoad
from .discrete_result import DiscreteResult
# The eq=False part is needed to make sure comparison is inherited properly.
@dataclass(eq=False)
class LoadStats(DiscreteLoad):
"""An offered load together with stats for all possible targets.
As LoadStats is frequently passed instead of plan DiscreteLoad,
equality and ordering is dictated by the float load.
"""
target_to_stat: Dict[TargetSpec, TargetStat] = None
"""Mapping from target specification to its current stat for this load."""
def __post_init__(self) -> None:
"""Initialize load value and check there are targets to track."""
super().__post_init__()
if not self.target_to_stat:
raise ValueError(f"No targets: {self.target_to_stat!r}")
def __str__(self) -> str:
"""Convert into a short human-readable string.
This works well only for trimmed stats,
as only the stat for the first target present is shown.
:returns: The short string.
:rtype: str
"""
return (
f"fl={self.float_load}"
f",s=({next(iter(self.target_to_stat.values()))})"
)
def __hash__(self) -> int:
"""Raise as stats are mutable by definition.
:returns: Hash value for this instance if possible.
:rtype: int
:raises TypeError: Not immutable.
"""
raise TypeError("Loadstats are mutable so constant hash is impossible.")
def add(self, result: DiscreteResult) -> None:
"""Take into account one more trial measurement result.
:param result: The result to take into account.
:type result: DiscreteResult
:raises RuntimeError: If result load does is not equal to the self load.
"""
if result.intended_load != float(self):
raise RuntimeError(
f"Attempting to add load {result.intended_load}"
f" to result set for {float(self)}"
)
for stat in self.target_to_stat.values():
stat.add(result)
@staticmethod
def new_empty(load: DiscreteLoad, targets: Tuple[TargetSpec]) -> LoadStats:
"""Factory method to initialize mapping for given targets.
:param load: The intended load value for the new instance.
:param targets: The target specifications to track stats for.
:type load: DiscreteLoad
:type targets: Tuple[TargetSpec]
:returns: New instance with empty stats initialized.
:rtype: LoadStats
:raise ValueError: Is the load is not rounded.
"""
if not load.is_round:
raise ValueError(f"Not round: {load!r}")
return LoadStats(
rounding=load.rounding,
int_load=int(load),
target_to_stat={target: TargetStat(target) for target in targets},
)
def estimates(self, target: TargetSpec) -> Tuple[bool, bool]:
"""Classify this load according to given target.
:param target: According to which target this should be classified.
:type target: TargetSpec
:returns: Tuple of two estimates whether load can be lower bound.
(True, False) means target is not reached yet.
:rtype: Tuple[bool, bool]
"""
return self.target_to_stat[target].estimates()
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