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"""Module defining LoadStat class."""

from dataclasses import dataclass, field
from typing import Tuple

from .target_spec import TargetSpec
from .discrete_result import DiscreteResult


@dataclass
class TargetStat:
    """Class for aggregating trial results for a single load and target.

    Reference to the target is included for convenience.

    The main usage is for load classification, done in estimates method.
    If both estimates agree, the load is classified as either a lower bound
    or an upper bound. For additional logic for dealing with loss inversion
    see MeasurementDatabase.

    Besides the duration sums needed for determining upper and lower bound,
    a field useful for computing the conditional throughput is also included.
    The conditional throughput is average of the (relative) forwarding rates
    of good long trials weighted by gool long trial durations.
    As the intended load is stored elsewhere, the one additional field here
    has a peculiar unit, it is a sum of products of seconds and loss ratios.
    """

    target: TargetSpec = field(repr=False)
    """The target for which this instance is aggregating results."""
    good_long: float = 0.0
    """Sum of durations of long enough trials satisfying target loss ratio."""
    bad_long: float = 0.0
    """Sum of durations of long trials not satisfying target loss ratio."""
    good_short: float = 0.0
    """Sum of durations of shorter trials satisfying target loss ratio."""
    bad_short: float = 0.0
    """Sum of durations of shorter trials not satisfying target loss ratio."""
    dur_rat_sum: float = 0.0
    """Sum over good long trials, of duration multiplied by loss ratio."""

    def __str__(self) -> str:
        """Convert into a short human-readable string.

        :returns: The short string.
        :rtype: str
        """
        return (
            f"gl={self.good_long},bl={self.bad_long}"
            f",gs={self.good_short},bs={self.bad_short}"
        )

    def add(self, result: DiscreteResult) -> None:
        """Take into account one more trial result.

        Use intended duration for deciding between long and short trials,
        but use offered duation (with overheads) to increase the duration sums.

        :param result: The trial result to add to the stats.
        :type result: DiscreteResult
        """
        dwo = result.duration_with_overheads
        if result.intended_duration >= self.target.trial_duration:
            if result.loss_ratio > self.target.loss_ratio:
                self.bad_long += dwo
            else:
                self.good_long += dwo
                self.dur_rat_sum += dwo * result.loss_ratio
        else:
            if result.loss_ratio > self.target.loss_ratio:
                self.bad_short += dwo
            else:
                self.good_short += dwo

    def estimates(self) -> Tuple[bool, bool]:
        """Return whether this load can become a lower bound.

        This returns two estimates, hence the weird nonverb name of this method.
        One estimate assumes all following results will satisfy the loss ratio,
        the other assumes all results will not satisfy the loss ratio.
        The sum of durations of the assumed results
        is the minimum to reach target duration sum, or zero if already reached.

        If both estimates are the same, it means the load is a definite bound.
        This may happen even when the sum of durations of already
        measured trials is less than the target, when the missing measurements
        cannot change the classification.

        :returns: Tuple of two estimates whether the load can be a lower bound.
            (True, False) means more trial results are needed.
        :rtype: Tuple[bool, bool]
        """
        coeff = self.target.exceed_ratio
        decrease = self.good_short * coeff / (1.0 - coeff)
        short_excess = self.bad_short - decrease
        effective_excess = self.bad_long + max(0.0, short_excess)
        effective_dursum = max(
            self.good_long + effective_excess,
            self.target.duration_sum,
        )
        limit_dursum = effective_dursum * self.target.exceed_ratio
        optimistic = effective_excess <= limit_dursum
        pessimistic = (effective_dursum - self.good_long) <= limit_dursum
        return optimistic, pessimistic