"""Tier-3 physical systems: held out of training to support a generalisation
claim ("converges on systems it never trained on")."""

from __future__ import annotations

import numpy as np

from physix.systems.base import PhysicalSystem, SystemTier


class ProjectileWithDrag(PhysicalSystem):
    """2-D projectile with quadratic air drag.

    Equations of motion::

        d2x/dt2 = -k * |v| * vx
        d2y/dt2 = -g - k * |v| * vy

    where ``|v| = sqrt(vx**2 + vy**2)``.
    """

    system_id: str = "projectile_drag"
    tier: SystemTier = SystemTier.TIER_3
    state_variables: tuple[str, ...] = ("x", "y", "vx", "vy")
    duration: float = 5.0  # typical flight time for the parameter ranges below
    hint_template: str = (
        "Projectile launched at angle {angle_deg:.0f} degrees with initial "
        "speed {v0:.1f} m/s. Air drag is non-negligible."
    )

    def sample_parameters(self, rng: np.random.Generator) -> dict[str, float]:
        return {
            "g": 9.81,
            "k": float(rng.uniform(0.005, 0.02)),
        }

    def sample_initial_conditions(self, rng: np.random.Generator) -> dict[str, float]:
        speed = float(rng.uniform(15.0, 30.0))
        angle = float(rng.uniform(np.deg2rad(30.0), np.deg2rad(70.0)))
        return {
            "x": 0.0,
            "y": 0.0,
            "vx": float(speed * np.cos(angle)),
            "vy": float(speed * np.sin(angle)),
        }

    def rhs(
        self,
        t: float,
        state: np.ndarray,
        params: dict[str, float],
    ) -> np.ndarray:
        _x, _y, vx, vy = state
        speed = float(np.sqrt(vx * vx + vy * vy))
        return np.array(
            [
                vx,
                vy,
                -params["k"] * speed * vx,
                -params["g"] - params["k"] * speed * vy,
            ],
            dtype=float,
        )

    def ground_truth_equation(self) -> str:
        # Two-equation system rendered in a single string so the parser can
        # split on ';' or '\n'. Verifier handles both delimiters.
        return (
            "d2x/dt2 = -k*sqrt(vx**2 + vy**2)*vx; "
            "d2y/dt2 = -g - k*sqrt(vx**2 + vy**2)*vy"
        )

    def hint(self, parameters: dict[str, float]) -> str:
        ic = self.initial_conditions
        if not ic:
            return self.hint_template
        v0 = float(np.sqrt(ic["vx"] ** 2 + ic["vy"] ** 2))
        angle_deg = float(np.rad2deg(np.arctan2(ic["vy"], ic["vx"])))
        return self.hint_template.format(angle_deg=angle_deg, v0=v0)


class ChargedInBField(PhysicalSystem):
    """Charged particle in a uniform magnetic field along z (circular motion).

    Equations of motion (assuming B = B_z * ẑ and v in xy-plane)::

        d2x/dt2 = (q*B/m) * vy
        d2y/dt2 = -(q*B/m) * vx
    """

    system_id: str = "charged_b_field"
    tier: SystemTier = SystemTier.TIER_3
    state_variables: tuple[str, ...] = ("x", "y", "vx", "vy")
    hint_template: str = (
        "Charged particle in a uniform magnetic field. Charge-to-mass ratio "
        "q/m = {qm:.2f}, field strength {B:.2f} T."
    )

    def sample_parameters(self, rng: np.random.Generator) -> dict[str, float]:
        return {
            "q": float(rng.choice([-1.0, 1.0])),
            "m": float(rng.uniform(0.5, 2.0)),
            "B": float(rng.uniform(0.5, 2.0)),
        }

    def sample_initial_conditions(self, rng: np.random.Generator) -> dict[str, float]:
        return {
            "x": 0.0,
            "y": 0.0,
            "vx": float(rng.uniform(0.5, 2.0)),
            "vy": float(rng.uniform(-2.0, 2.0)),
        }

    def rhs(
        self,
        t: float,
        state: np.ndarray,
        params: dict[str, float],
    ) -> np.ndarray:
        _x, _y, vx, vy = state
        omega = params["q"] * params["B"] / params["m"]
        return np.array([vx, vy, omega * vy, -omega * vx], dtype=float)

    def ground_truth_equation(self) -> str:
        return (
            "d2x/dt2 = (q*B/m)*vy; "
            "d2y/dt2 = -(q*B/m)*vx"
        )

    def hint(self, parameters: dict[str, float]) -> str:
        qm = parameters["q"] / parameters["m"]
        return self.hint_template.format(qm=qm, B=parameters["B"])