"""Adaptive gain tuner -- Sprint 8.

Watches the steady-state heading error and adjusts the outer-loop Kp/Ki
within the bounds defined by ``PidConfig.adaptive_max_deviation_pct``.

The strategy is a simple integral-error gradient scheme:
- If |mean_error| > dead_band for a sustained window, nudge Kp up by step_pct.
- If the response is oscillating (error sign changes frequently), nudge Kp down.
- Ki is adjusted proportionally to maintain the ZN ratio Ki = 2*Kp / Tu_est.
- Kd is not adapted (derivative magnifies noise; ZN auto-tune sets it once).

All changes are bounded to ±adaptive_max_deviation_pct of the base gains
(brief section 6: "never outside ±50 %").

Usage::

    tuner = AdaptiveTuner(pid_config, base_gains)
    # On each outer-loop tick (10 Hz):
    new_gains = tuner.step(heading_error_deg, dt_s=0.1)
    if new_gains is not None:
        apply_outer_gains(new_gains)
"""

from __future__ import annotations

import math
from dataclasses import dataclass, field

from arautopilot.core.pid_config import PidConfig, PidGains


@dataclass
class AdaptiveTuner:
    """Gradient-descent adaptive gain tuner for the outer PID loop.

    Parameters
    ----------
    config:
        PidConfig that owns the base gains and adaptive bounds.
    base_gains:
        The current base gains (set by commissioning or default).
    dead_band_deg:
        Error below which no adaptation occurs.
    window_steps:
        Number of 10 Hz steps over which the error statistics are computed.
    step_pct:
        Fractional Kp adjustment per adaptation event (default 2 %).
    """

    config: PidConfig
    base_gains: PidGains
    dead_band_deg: float = 1.0
    window_steps: int = 100       # 10 seconds
    step_pct: float = 0.02        # 2 % per step

    _error_buffer: list[float] = field(default_factory=list)
    _current_kp: float = field(init=False)
    _current_ki: float = field(init=False)
    _current_kd: float = field(init=False)

    def __post_init__(self) -> None:
        self._current_kp = self.base_gains.kp
        self._current_ki = self.base_gains.ki
        self._current_kd = self.base_gains.kd

    # ------------------------------------------------------------------

    @property
    def current_gains(self) -> PidGains:
        return PidGains(kp=self._current_kp, ki=self._current_ki, kd=self._current_kd)

    def step(self, error_deg: float, dt_s: float = 0.1) -> PidGains | None:
        """Feed one heading error sample; return updated gains if adapted.

        Returns ``None`` if no adaptation occurred this step.
        """
        if not self.config.adaptive_enabled:
            return None
        if not math.isfinite(error_deg):
            return None

        self._error_buffer.append(error_deg)
        if len(self._error_buffer) > self.window_steps:
            self._error_buffer.pop(0)

        if len(self._error_buffer) < self.window_steps:
            return None  # buffer not yet full

        mean_abs = sum(abs(e) for e in self._error_buffer) / len(self._error_buffer)

        # Count sign changes (oscillation indicator)
        sign_changes = sum(
            1 for i in range(1, len(self._error_buffer))
            if (self._error_buffer[i] >= 0) != (self._error_buffer[i - 1] >= 0)
        )
        oscillating = sign_changes > self.window_steps * 0.3  # > 30 % sign flips

        # Decision
        if oscillating:
            # Reduce Kp to damp oscillation.
            return self._adjust_kp(-self.step_pct)
        elif mean_abs > self.dead_band_deg:
            # Increase Kp to reduce steady-state error.
            return self._adjust_kp(+self.step_pct)

        return None

    def _adjust_kp(self, delta_frac: float) -> PidGains | None:
        """Adjust Kp by ``delta_frac`` fraction (signed), clamped to bounds."""
        new_kp = self._current_kp * (1.0 + delta_frac)

        # Clamp to ±adaptive_max_deviation_pct of base.
        limit = self.config.adaptive_max_deviation_pct / 100.0
        lo = self.base_gains.kp * (1.0 - limit)
        hi = self.base_gains.kp * (1.0 + limit)
        new_kp = max(lo, min(hi, new_kp))
        if new_kp == self._current_kp:
            return None

        # Adjust Ki proportionally (maintain integral-to-proportional ratio).
        if self.base_gains.kp > 0:
            ki_ratio = self.base_gains.ki / self.base_gains.kp
        else:
            ki_ratio = 0.0
        new_ki = new_kp * ki_ratio

        # Clamp Ki as well.
        ki_lo = self.base_gains.ki * (1.0 - limit)
        ki_hi = self.base_gains.ki * (1.0 + limit)
        new_ki = max(ki_lo, min(ki_hi, new_ki))

        self._current_kp = new_kp
        self._current_ki = new_ki
        # Clear buffer after each adaptation to avoid consecutive nudges.
        self._error_buffer.clear()
        return self.current_gains

    def reset(self) -> None:
        """Reset to base gains."""
        self._current_kp = self.base_gains.kp
        self._current_ki = self.base_gains.ki
        self._current_kd = self.base_gains.kd
        self._error_buffer.clear()