from __future__ import annotations

import json
from pathlib import Path
from typing import Dict

import numpy as np
import pandas as pd

from .calibration import CONTINUOUS_GENES, RFCalibrationParamsV2, V5Thresholds, V5Weights
from .core import calibrated_surrogate_v4_core
from .materials import attach_material_recommendations
from .metrics import (
    apply_v5_filters_and_scores,
    bdr_eff,
    cooling_proxy_nc,
    cooling_proxy_sc,
    duty_cycle,
    pulse_heating_proxy,
    source_compatibility,
    tolerance_components,
    wall_power_avg_kW,
)
from .sampling import sample_base_designs, sample_family_conditioned_operating_point
from .utils import infer_family, infer_type_from_family, make_candidate_id


def repair_child(child: pd.Series) -> pd.Series:
    family = child.get("family", infer_family(str(child.get("type", ""))))
    if pd.isna(family) or str(family).strip() == "":
        family = infer_family(str(child.get("type", "")))
    if family == "unknown":
        family = "Lband"

    child["family"] = family
    child["type"] = infer_type_from_family(family)

    if family == "Lband":
        child["freq_GHz"] = float(np.clip(child["freq_GHz"], 0.20, 0.80))
        child["R_over_Q_ohm"] = float(np.clip(child["R_over_Q_ohm"], 60.0, 150.0))
        child["Q0"] = float(np.clip(child["Q0"], 1e4, 8e4))
        child["Eacc_MVpm"] = float(np.clip(child["Eacc_MVpm"], 8.0, 20.0))
        child["L_m"] = float(np.clip(child["L_m"], 0.15, 0.60))
        child["beta"] = float(np.clip(child["beta"], 0.7, 2.5))
        child["pulse_length_ns"] = float(np.clip(child["pulse_length_ns"], 800.0, 3000.0))
        child["rep_rate_Hz"] = float(np.clip(child["rep_rate_Hz"], 100.0, 2000.0))
        child["P_aux_kW"] = float(np.clip(child["P_aux_kW"], 0.4, 1.5))
        child["source_power_avail_kW"] = float(np.clip(child["source_power_avail_kW"], 4.0, 8.0))
        child["cooling_capacity_kW"] = float(np.clip(child["cooling_capacity_kW"], 6.0, 12.0))
        child["surf_factor"] = float(np.clip(child["surf_factor"], 0.9, 1.1))
        child["geom_factor"] = float(np.clip(child["geom_factor"], 0.8, 1.1))
        child["freq_factor"] = float(np.clip(child["freq_factor"], 0.9, 1.05))
        child["fab_sigma_um"] = float(np.clip(child["fab_sigma_um"], 10.0, 40.0))
        child["delta_allow_um"] = float(np.clip(child["delta_allow_um"], 40.0, 100.0))
        child["S_f"] = float(np.clip(child["S_f"], 0.003, 0.010))
        child["S_phi"] = float(np.clip(child["S_phi"], 0.002, 0.008))
        child["S_c"] = float(np.clip(child["S_c"], 0.001, 0.006))
        child["cryo_temp_K"] = np.nan

    elif family == "Cband":
        child["freq_GHz"] = float(np.clip(child["freq_GHz"], 5.00, 6.00))
        child["R_over_Q_ohm"] = float(np.clip(child["R_over_Q_ohm"], 100.0, 280.0))
        child["Q0"] = float(np.clip(child["Q0"], 2e4, 8e4))
        child["Eacc_MVpm"] =  float(np.clip(child["Eacc_MVpm"], 12.0, 30.0))
        child["L_m"] = float(np.clip(child["L_m"], 0.20, 0.60))
        child["beta"] = float(np.clip(child["beta"], 0.8, 3.0))
        child["pulse_length_ns"] = float(np.clip(child["pulse_length_ns"], 200.0, 800.0))
        child["rep_rate_Hz"] = float(np.clip(child["rep_rate_Hz"], 50.0, 500.0))
        child["P_aux_kW"] = float(np.clip(child["P_aux_kW"], 0.8, 2.2))
        child["source_power_avail_kW"] = float(np.clip(child["source_power_avail_kW"], 9.0, 15.0))
        child["cooling_capacity_kW"] = float(np.clip(child["cooling_capacity_kW"], 10.0, 20.0))
        child["surf_factor"] = float(np.clip(child["surf_factor"], 0.95, 1.20))
        child["geom_factor"] = float(np.clip(child["geom_factor"], 0.9, 1.2))
        child["freq_factor"] = float(np.clip(child["freq_factor"], 1.0, 1.15))
        child["fab_sigma_um"] = float(np.clip(child["fab_sigma_um"], 5.0, 20.0))
        child["delta_allow_um"] = float(np.clip(child["delta_allow_um"], 15.0, 40.0))
        child["S_f"] = float(np.clip(child["S_f"], 0.010, 0.025))
        child["S_phi"] = float(np.clip(child["S_phi"], 0.008, 0.020))
        child["S_c"] = float(np.clip(child["S_c"], 0.006, 0.015))
        child["cryo_temp_K"] = np.nan

    elif family == "Xband":
        child["freq_GHz"] = float(np.clip(child["freq_GHz"], 9.00, 12.00))
        child["R_over_Q_ohm"] = float(np.clip(child["R_over_Q_ohm"], 80.0, 200.0))
        child["Q0"] = float(np.clip(child["Q0"], 1e4, 6e4))
        child["Eacc_MVpm"] = float(np.clip(child["Eacc_MVpm"], 20.0, 45.0))
        child["L_m"] = float(np.clip(child["L_m"], 0.15, 0.45))
        child["beta"] = float(np.clip(child["beta"], 0.8, 3.0))
        child["pulse_length_ns"] = float(np.clip(child["pulse_length_ns"], 50.0, 300.0))
        child["rep_rate_Hz"] = float(np.clip(child["rep_rate_Hz"], 10.0, 200.0))
        child["P_aux_kW"] = float(np.clip(child["P_aux_kW"], 1.0, 3.0))
        child["source_power_avail_kW"] = float(np.clip(child["source_power_avail_kW"], 12.0, 20.0))
        child["cooling_capacity_kW"] = float(np.clip(child["cooling_capacity_kW"], 12.0, 24.0))
        child["surf_factor"] = float(np.clip(child["surf_factor"], 1.0, 1.35))
        child["geom_factor"] = float(np.clip(child["geom_factor"], 1.0, 1.3))
        child["freq_factor"] = float(np.clip(child["freq_factor"], 1.1, 1.3))
        child["fab_sigma_um"] = float(np.clip(child["fab_sigma_um"], 2.0, 10.0))
        child["delta_allow_um"] = float(np.clip(child["delta_allow_um"], 5.0, 18.0))
        child["S_f"] = float(np.clip(child["S_f"], 0.020, 0.060))
        child["S_phi"] = float(np.clip(child["S_phi"], 0.015, 0.050))
        child["S_c"] = float(np.clip(child["S_c"], 0.010, 0.035))
        child["cryo_temp_K"] = np.nan

    else:  # SC
        child["freq_GHz"] = float(np.clip(child["freq_GHz"], 0.80, 1.60))
        child["R_over_Q_ohm"] = float(np.clip(child["R_over_Q_ohm"], 200.0, 400.0))
        child["Q0"] = float(np.clip(child["Q0"], 5e9, 2e10))
        child["Eacc_MVpm"] = float(np.clip(child["Eacc_MVpm"], 10.0, 25.0))
        child["L_m"] = float(np.clip(child["L_m"], 0.60, 1.20))
        child["beta"] = float(np.clip(child["beta"], 0.8, 2.0))
        child["pulse_length_ns"] = float(np.clip(child["pulse_length_ns"], 5000.0, 100000.0))
        child["rep_rate_Hz"] = float(np.clip(child["rep_rate_Hz"], 1.0, 100.0))
        child["P_aux_kW"] = float(np.clip(child["P_aux_kW"], 4.0, 15.0))
        child["source_power_avail_kW"] = float(np.clip(child["source_power_avail_kW"], 8.0, 20.0))
        child["cooling_capacity_kW"] = float(np.clip(child["cooling_capacity_kW"], 15.0, 40.0))
        child["surf_factor"] = float(np.clip(child["surf_factor"], 0.9, 1.1))
        child["geom_factor"] = float(np.clip(child["geom_factor"], 0.8, 1.0))
        child["freq_factor"] = float(np.clip(child["freq_factor"], 0.9, 1.05))
        child["fab_sigma_um"] = float(np.clip(child["fab_sigma_um"], 10.0, 30.0))
        child["delta_allow_um"] = float(np.clip(child["delta_allow_um"], 30.0, 80.0))
        child["S_f"] = float(np.clip(child["S_f"], 0.004, 0.012))
        child["S_phi"] = float(np.clip(child["S_phi"], 0.003, 0.010))
        child["S_c"] = float(np.clip(child["S_c"], 0.002, 0.008))
        cryo = child.get("cryo_temp_K", 2.0)
        if pd.isna(cryo):
            cryo = 2.0
        child["cryo_temp_K"] = float(np.clip(cryo, 1.5, 5.0))

    child["freq_Hz"] = child["freq_GHz"] * 1e9
    child["Vc_MV"] = child["Eacc_MVpm"] * child["L_m"]
    child["Q_loaded"] = child["Q0"] / (1.0 + child["beta"])
    return child


def recalculate_physics_and_score(
    df: pd.DataFrame,
    thresholds: V5Thresholds,
    weights: V5Weights,
    bdr_preset="nominal",
) -> pd.DataFrame:
    params = RFCalibrationParamsV2()
    scored = df.copy()

    scored["family"] = scored["family"].fillna(scored["type"].map(infer_family))
    scored["type"] = np.where(
        scored["family"].eq("SC"),
        "SC_elliptical_1p3GHz",
        np.where(
            scored["family"].eq("Lband"),
            "NC_pillbox_Lband",
            np.where(
                scored["family"].eq("Cband"),
                "NC_Cband_TW",
                np.where(scored["family"].eq("Xband"), "NC_Xband_SW", scored["type"])
            ),
        ),
    )

    scored["freq_Hz"] = scored["freq_GHz"] * 1e9
    scored["Vc_MV"] = scored["Eacc_MVpm"] * scored["L_m"]
    scored["Q_loaded"] = scored["Q0"] / (1.0 + scored["beta"])

    core = calibrated_surrogate_v4_core(scored, params=params)
    for k, v in core.items():
        scored[k] = v

    scored["duty_cycle"] = duty_cycle(
        scored["rep_rate_Hz"].values,
        scored["pulse_length_ns"].values,
    )
    scored["P_wall_avg_kW"] = wall_power_avg_kW(
        scored["P_wall_peak_kW"].values,
        scored["duty_cycle"].values,
        scored["P_aux_kW"].values,
    )

    scored["deltaT_pulse"] = pulse_heating_proxy(
        scored["Eacc_MVpm"].values,
        scored["pulse_length_ns"].values,
        scored["geom_factor"].values,
        scored["family"].values,
    )

    scored["BDR_eff"] = bdr_eff(
        scored["Eacc_MVpm"].values,
        scored["pulse_length_ns"].values,
        scored["surf_factor"].values,
        scored["freq_factor"].values,
        preset=bdr_preset,
    )

    scored["C_src"] = source_compatibility(
        scored["P_wall_peak_kW"].values,
        scored["source_power_avail_kW"].values,
        scored["pulse_length_ns"].values,
        scored["rep_rate_Hz"].values,
        scored["family"].values,
    )

    is_sc = scored["family"].eq("SC").values
    c_cool = np.empty(len(scored), dtype=float)

    if np.any(~is_sc):
        c_cool[~is_sc] = cooling_proxy_nc(
            scored.loc[~is_sc, "P_wall_avg_kW"].values,
            scored.loc[~is_sc, "cooling_capacity_kW"].values,
        )

    if np.any(is_sc):
        p_dyn = (
            scored.loc[is_sc, "P_wall_peak_kW"].values
            * scored.loc[is_sc, "duty_cycle"].values
        )
        p_static = scored.loc[is_sc, "P_aux_kW"].values
        q_cool = scored.loc[is_sc, "cooling_capacity_kW"].values
        c_cool[is_sc] = cooling_proxy_sc(
            p_dyn,
            p_static,
            q_cool,
        )

    scored["C_cool"] = c_cool

    R_f, R_phi, R_c, R_tol = tolerance_components(
        scored["fab_sigma_um"].values,
        scored["S_f"].values,
        scored["S_phi"].values,
        scored["S_c"].values,
        scored["delta_allow_um"].values,
    )
    scored["R_f"] = R_f
    scored["R_phi"] = R_phi
    scored["R_c"] = R_c
    scored["R_tol"] = R_tol

    scored["candidate_id"] = scored.apply(make_candidate_id, axis=1)
    scored = attach_material_recommendations(scored)

    return apply_v5_filters_and_scores(scored, thresholds, weights)


def crossover(p1: pd.Series, p2: pd.Series, rng: np.random.Generator) -> pd.Series:
    child = p1.copy()
    for gene in CONTINUOUS_GENES:
        if gene in p1.index and  gene in p2.index:
            if not pd.isna(p1[gene]) and not pd.isna(p2[gene]):
                if rng.random() > 0.5:
                    child[gene] = p2[gene]
    return child


def mutate(
    child: pd.Series,
    rng: np.random.Generator,
    mutation_rate=0.20,
    mutation_scale=0.05,
) -> pd.Series:
    for gene in CONTINUOUS_GENES:
        if gene in child.index and not pd.isna(child[gene]) and rng.random() < mutation_rate:
            magnitude = max(abs(float(child[gene])), 1e-9)
            noise = rng.normal(0.0, mutation_scale * magnitude)
            child[gene] = float(child[gene]) + noise

    child = repair_child(child)
    return child


def run_evolution(
    pop_size: int,
    generations: int,
    target_class: str,
    bdr_preset: str,
    class_config: Dict[str, V5Thresholds],
    seed: int = 7,
    out_dir: Path | None = None,
) -> pd.DataFrame:
    rng = np.random.default_rng(seed)
    print(f"\nLaunching evolution ({generations} generations, population {pop_size}) target: {target_class}")

    thresholds = class_config[target_class]
    weights = V5Weights()

    df = sample_base_designs(n=pop_size, seed=seed)
    df = sample_family_conditioned_operating_point(df, seed=seed + 101)
    current_pop = recalculate_physics_and_score(df, thresholds, weights, bdr_preset)
    current_pop["fitness"] = np.where(current_pop["hard_pass"], current_pop["balanced_v5"], 0.0)

    history = []

    for gen in range(1, generations + 1):
        current_pop = current_pop.sort_values("fitness", ascending=False).reset_index(drop=True)

        best_fitness = float(current_pop["fitness"].max())
        best_yield = float(current_pop.iloc[0]["delivered"]) if best_fitness > 0 else 0.0
        best_family = current_pop.iloc[0]["family"]
        best_type = current_pop.iloc[0]["type"]

        print(
            f"Generation {gen}/{generations} | Max balanced score: {best_fitness:.4f} "
            f"| Max yield: {best_yield:.2e} | Best: {best_family} / {best_type}"
        )

        history.append({
            "generation": gen,
            "best_fitness": best_fitness,
            "best_yield": best_yield,
            "best_family": best_family,
            "best_type": best_type,
            "qualified_count": int(current_pop["hard_pass"].sum()),
        })

        elite_size = max(1, int(pop_size * 0.10))
        parent_pool_size = max(2, int(pop_size * 0.40))

        elite = current_pop.head(elite_size).copy()
        parents = current_pop.head(parent_pool_size).copy()

        offspring_list = []
        for _ in range(pop_size - elite_size):
            families = parents["family"].dropna().unique()
            if len(families) == 0:
                families = np.array(["Lband"])

            family = rng.choice(families)
            fam_parents = parents[parents["family"] == family]

            if len(fam_parents) >= 2:
                pair_idx = rng.choice(fam_parents.index.to_numpy(), size=2, replace=True)
                p1 = fam_parents.loc[pair_idx[0]]
                p2 = fam_parents.loc[pair_idx[1]]
            else:
                pair_idx = rng.choice(parents.index.to_numpy(), size=2, replace=True)
                p1 = parents.loc[pair_idx[0]]
                p2 = parents.loc[pair_idx[1]]

            child = crossover(p1, p2, rng)
            child["family"] = family
            child["type"] = infer_type_from_family(family)
            child = mutate(child, rng, mutation_rate=0.25, mutation_scale=0.08)
            offspring_list.append(child)

        next_gen_base = pd.concat([elite, pd.DataFrame(offspring_list)], ignore_index=True)
        current_pop = recalculate_physics_and_score(next_gen_base, thresholds, weights, bdr_preset)
        current_pop["fitness"] = np.where(current_pop["hard_pass"], current_pop["balanced_v5"], 0.0)

    print("\nEvolution completed. Best evolved RF design:")
    winner = current_pop.sort_values("fitness", ascending=False).iloc[0]
    print(f"Type: {winner['type']}  | Frequency: {winner['freq_GHz']:.3f} GHz | Eacc: {winner['Eacc_MVpm']:.2f} MV/m")
    print(f"Pulse: {winner['pulse_length_ns']:.1f} ns | Rep rate: {winner['rep_rate_Hz']:.1f} Hz")
    print(f"Yield: {winner['delivered']:.2e} | Pavg: {winner['P_wall_avg_kW']:.2f} kW | ΔT: {winner['deltaT_pulse']:.3f} K")
    print(f"Balanced v5: {winner['balanced_v5']:.4f}")
    print(f"Suggested material: {winner['suggested_primary_material']}")

    if out_dir is not None:
        out_dir.mkdir(parents=True, exist_ok=True)
        current_pop.sort_values(["fitness", "delivered"], ascending=False).to_csv(
            out_dir / "rf_mode_pack_v6_0_evolution_population.csv",
            index=False,
        )
        pd.DataFrame(history).to_csv(
            out_dir / "rf_mode_pack_v6_0_evolution_history.csv",
            index=False,
        )
        winner.to_frame().T.to_csv(
            out_dir / "rf_mode_pack_v6_0_evolution_winner.csv",
            index=False,
        )
        (out_dir / "rf_mode_pack_v6_0_evolution_meta.json").write_text(
            json.dumps({
                "pop_size": pop_size,
                "generations": generations,
                "target_class": target_class,
                "bdr_preset": bdr_preset,
                "seed": seed,
            }, ensure_ascii=False, indent=2),
            encoding="utf-8",
        )

    return current_pop