"""Form cycle detection — identifies peak/trough form periods for racing entities.

This module provides the FormCycleDetector stub for detecting when an
entity (horse/driver/trainer) is in a peak or trough form period based
on recent performance trends.
"""

from __future__ import annotations

from typing import Any

from sqlalchemy.orm import Session


class FormCycleDetector:
    """Detects peak and trough form periods for racing entities.

    Uses rolling performance windows, consistency metrics, and trend
    analysis to classify form periods.
    """

    def __init__(self, session: Session) -> None:
        """Initialize the detector.

        Args:
            session: Database session for loading historical data.
        """
        self._session = session

    def detect_horse_form(
        self,
        horse_id: int,
        window_size: int = 5,
    ) -> dict[str, Any]:
        """Detect form cycle for a horse based on recent starts.

        Args:
            horse_id: The horse entity ID.
            window_size: Number of recent starts to analyze.

        Returns:
            Dict with form classification and supporting metrics:
            {
                "entity_id": int,
                "entity_type": "horse",
                "form_phase": "peak" | "trough" | "neutral" | "improving" | "declining",
                "confidence": float,
                "recent_placings": list[int | None],
                "avg_placing": float | None,
                "trend_slope": float | None,
                "window_size": int,
            }
        """
        raise NotImplementedError

    def detect_driver_form(
        self,
        driver_id: int,
        window_size: int = 5,
    ) -> dict[str, Any]:
        """Detect form cycle for a driver.

        Args:
            driver_id: The driver entity ID.
            window_size: Number of recent drives to analyze.

        Returns:
            Form cycle dict (see detect_horse_form).
        """
        raise NotImplementedError

    def detect_trainer_form(
        self,
        trainer_id: int,
        window_size: int = 8,
    ) -> dict[str, Any]:
        """Detect form cycle for a trainer.

        Args:
            trainer_id: The trainer entity ID.
            window_size: Number of recent runners to analyze.

        Returns:
            Form cycle dict (see detect_horse_form).
        """
        raise NotImplementedError

    def detect_entity_form(
        self,
        entity_type: str,
        entity_id: int,
        window_size: int = 5,
    ) -> dict[str, Any]:
        """Detect form cycle for any entity type.

        Dispatches to the appropriate type-specific detector.

        Args:
            entity_type: "horse", "driver", or "trainer".
            entity_id: Entity ID.
            window_size: Analysis window size.

        Returns:
            Form cycle dict.
        """
        if entity_type == "horse":
            return self.detect_horse_form(entity_id, window_size)
        elif entity_type == "driver":
            return self.detect_driver_form(entity_id, window_size)
        elif entity_type == "trainer":
            return self.detect_trainer_form(entity_id, window_size)
        else:
            raise ValueError(f"Unknown entity type: {entity_type}")

    def batch_detect(
        self,
        entity_type: str,
        entity_ids: list[int],
        window_size: int = 5,
    ) -> list[dict[str, Any]]:
        """Detect form cycles for multiple entities of the same type.

        Args:
            entity_type: Entity type string.
            entity_ids: List of entity IDs.
            window_size: Analysis window size.

        Returns:
            List of form cycle dicts.
        """
        return [
            self.detect_entity_form(entity_type, eid, window_size) for eid in entity_ids
        ]