"""Adjustable-potential assessment skill v1 (docs/05 §2.2 可调潜力辨识).

For each resource: adjustable capacity = certified capacity × rolling
fulfillment rate (delivered / planned over the evidence window), with
confidence from the profile's own confidence, the reliability score and the
amount of evidence. A resource with no execution history keeps its certified
figure at the profile's confidence — the "画像时效性" risk (docs/13 §0) is
made visible through `evidence_days` rather than hidden.

The de-rating exponent and evidence saturation are placeholders: OPEN-QUESTION
B10 (可靠性/潜力辨识口径). The target accuracy (≥ 90%) is judged by the eval
harness on real execution feedback (docs/12 L2).
"""

from __future__ import annotations

from decimal import Decimal

import numpy as np

from vpp_contracts.potential_assessment_request import PotentialAssessmentRequest
from vpp_contracts.potential_assessment_result import PotentialAssessmentResult

from . import SKILL_VERSIONS
from .numeric import INTERVALS, SCALE_MW, curve, quantize

SKILL = "potential-assessment"

EVIDENCE_WINDOW_DAYS = 14  # OPEN-QUESTION B10
EVIDENCE_SATURATION_DAYS = 7  # OPEN-QUESTION B10: confidence saturates after this many days


def assess_potential(req: PotentialAssessmentRequest) -> PotentialAssessmentResult:
    assessments = []
    total = np.zeros(INTERVALS)
    for r in req.resources:
        profile = r.profile
        certified = float(Decimal(profile.certified_adjustable_mw))
        recent = sorted(r.fulfillment, key=lambda f: f.market_date)[-EVIDENCE_WINDOW_DAYS:]
        planned = sum(Decimal(f.planned_mwh) for f in recent)
        delivered = sum(Decimal(f.delivered_mwh) for f in recent)
        if planned > 0:
            rate = min(Decimal(1), delivered / planned)
            evidence_days = len(recent)
        else:
            rate = None
            evidence_days = 0

        derate = float(rate) if rate is not None else 1.0
        adjustable = np.full(INTERVALS, certified * derate)
        evidence_factor = min(1.0, evidence_days / EVIDENCE_SATURATION_DAYS)
        base_conf = float(Decimal(profile.confidence))
        reliability = float(Decimal(profile.reliability_score))
        confidence = base_conf * (0.5 + 0.5 * evidence_factor) * (0.5 + 0.5 * reliability)

        total += adjustable
        assessments.append(
            {
                "resource_id": profile.resource_id,
                "adjustable_mw": curve(adjustable, req.market_date, SCALE_MW),
                "confidence": quantize(confidence, 3),
                "fulfillment_rate": quantize(rate, 4) if rate is not None else None,
                "evidence_days": evidence_days,
            }
        )
    return PotentialAssessmentResult.model_validate(
        {
            "market_date": req.market_date,
            "assessments": assessments,
            "total_adjustable_mw": curve(total, req.market_date, SCALE_MW),
            "skill_version": SKILL_VERSIONS[SKILL],
        }
    )