"""
Task 3: k-min allocation with ordered two-stop pairing.

- Uses 2019 data to allocate visit frequencies.
- Pairs are drawn from ordered_pairs_allocation_k6_cap250.csv (ordered i->j).
- Total annual trips (paired + single) are fixed to N_TARGET via fixed-point adjustment.
"""

from __future__ import annotations

import argparse
import os
import math
from typing import Dict, List, Tuple

import numpy as np
import pandas as pd

try:
    import matplotlib

    matplotlib.use("Agg")
    import matplotlib.pyplot as plt

    _HAS_MPL = True
except ModuleNotFoundError:
    plt = None
    _HAS_MPL = False


INPUT_XLSX = "prob/MFP Regular Sites 2019.xlsx"
INPUT_PAIRS = "data/ordered_pairs_allocation_k6_cap250.csv"
OUTPUT_DIR = "data"

C_OPT = 250
N_TARGET = 730
ALPHA = 0.6
BETA = 0.2
N_SIMS = 2000
RANDOM_SEED = 606


def gini_coefficient(values: np.ndarray) -> float:
    x = np.asarray(values, dtype=float)
    x = x[np.isfinite(x)]
    if x.size == 0:
        return 0.0
    x = np.clip(x, 0, None)
    total = x.sum()
    if total <= 0:
        return 0.0

    x_sorted = np.sort(x)
    n = x_sorted.size
    idx = np.arange(1, n + 1, dtype=float)
    return float((2.0 * (idx * x_sorted).sum()) / (n * total) - (n + 1.0) / n)


def _norm_pdf(z):
    return np.exp(-0.5 * z * z) / np.sqrt(2.0 * np.pi)


def _norm_cdf(z):
    z = np.asarray(z, dtype=float)
    erf_vec = np.vectorize(math.erf, otypes=[float])
    return 0.5 * (1.0 + erf_vec(z / np.sqrt(2.0)))


def expected_clipped_normal(mu, sigma, lower=0.0, upper=1.0):
    mu = np.asarray(mu, dtype=float)
    sigma = np.asarray(sigma, dtype=float)
    lower = float(lower)
    upper = float(upper)

    if lower > upper:
        raise ValueError("lower must be <= upper")

    out = np.empty_like(mu, dtype=float)
    mask = sigma > 0
    out[~mask] = np.clip(mu[~mask], lower, upper)

    if np.any(mask):
        m = mu[mask]
        s = sigma[mask]

        z_u = (upper - m) / s
        z_l = (lower - m) / s

        Phi_u = _norm_cdf(z_u)
        Phi_l = _norm_cdf(z_l)
        phi_u = _norm_pdf(z_u)
        phi_l = _norm_pdf(z_l)

        ex_le_u = m * Phi_u - s * phi_u
        ex_le_l = m * Phi_l - s * phi_l

        p_le_l = Phi_l
        p_gt_u = 1.0 - Phi_u

        out[mask] = lower * p_le_l + (ex_le_u - ex_le_l) + upper * p_gt_u

    return out


def _find_col(df: pd.DataFrame, candidates: List[str]) -> str:
    for name in candidates:
        if name in df.columns:
            return name
    lower_map = {c.lower(): c for c in df.columns}
    for name in candidates:
        key = name.lower()
        if key in lower_map:
            return lower_map[key]
    raise ValueError(f"Missing required column. Tried: {candidates}")


def load_sites(path: str) -> pd.DataFrame:
    df = pd.read_excel(path)

    col_site = _find_col(df, ["Site Name", "site name", "site"])
    col_mu = _find_col(df, ["Average Demand per Visit", "average demand per visit", "avg demand"])
    col_sigma = _find_col(df, ["StDev(Demand per Visit)", "stdev(demand per visit)", "stdev", "std"])
    col_visits = _find_col(df, ["Number of Visits in 2019", "number of visits in 2019", "visits"])

    out = df[[col_site, col_mu, col_sigma, col_visits]].copy()
    out.columns = ["site_name", "mu", "sigma", "visits_2019"]
    out["mu"] = pd.to_numeric(out["mu"], errors="coerce")
    out["sigma"] = pd.to_numeric(out["sigma"], errors="coerce").fillna(0.0)
    out["visits_2019"] = pd.to_numeric(out["visits_2019"], errors="coerce")
    if out[["mu", "visits_2019"]].isna().any().any():
        missing = out[out[["mu", "visits_2019"]].isna().any(axis=1)]
        raise ValueError(f"Missing mu/visits_2019 for {len(missing)} rows.")

    out = out.reset_index(drop=True)
    out["site_idx"] = np.arange(1, len(out) + 1, dtype=int)
    out["TotalDemand"] = out["mu"] * out["visits_2019"]
    return out


def allocate_visits(df: pd.DataFrame, k_min_real: float, n_total: int) -> np.ndarray:
    df_sorted = df.sort_values("TotalDemand").reset_index(drop=False)
    n = len(df_sorted)

    k_floor = int(np.floor(k_min_real))
    k_ceil = int(np.ceil(k_min_real))
    frac = k_min_real - k_floor

    n_ceil = int(round(n * frac))
    n_floor = n - n_ceil

    k_base = np.array([k_floor] * n_floor + [k_ceil] * n_ceil, dtype=int)

    n_reserved = int(k_base.sum())
    n_free = int(n_total - n_reserved)
    if n_free < 0:
        return None

    weights = df_sorted["TotalDemand"] / df_sorted["TotalDemand"].sum()
    allocated = (k_base + n_free * weights.values).round().astype(int)
    allocated = np.maximum(allocated, k_base)

    diff = int(n_total - allocated.sum())
    if diff != 0:
        sorted_idx = weights.sort_values(ascending=(diff < 0)).index.tolist()
        for idx in sorted_idx[:abs(diff)]:
            allocated[idx] += int(np.sign(diff))

    alloc_sorted = df_sorted[["site_idx"]].copy()
    alloc_sorted["AllocatedVisits"] = allocated
    alloc = alloc_sorted.sort_values("site_idx")["AllocatedVisits"].to_numpy(dtype=int)
    return alloc


def assign_pairs(pairs_df: pd.DataFrame, visits: np.ndarray) -> Tuple[pd.DataFrame, np.ndarray]:
    remaining = visits.astype(int).copy()
    pair_counts = np.zeros(len(pairs_df), dtype=int)

    for idx, row in pairs_df.iterrows():
        i = int(row["site_i_idx"]) - 1
        j = int(row["site_j_idx"]) - 1
        if remaining[i] <= 0 or remaining[j] <= 0:
            continue
        t = int(min(remaining[i], remaining[j]))
        if t <= 0:
            continue
        pair_counts[idx] = t
        remaining[i] -= t
        remaining[j] -= t

    out = pairs_df.copy()
    out["pair_count"] = pair_counts
    return out, remaining


def _compute_metrics(
    sites: pd.DataFrame,
    visits: np.ndarray,
    pairs_with_counts: pd.DataFrame,
    singles: np.ndarray,
    *,
    alpha: float,
    beta: float,
    capacity: float,
) -> Dict[str, float]:
    n = len(sites)
    mu = sites["mu"].to_numpy(dtype=float)
    sigma = sites["sigma"].to_numpy(dtype=float)
    demand = sites["TotalDemand"].to_numpy(dtype=float)

    eff_single = expected_clipped_normal(mu, sigma, lower=0.0, upper=capacity)
    served_single = singles * eff_single
    cap_single = singles * capacity

    pair_first = np.zeros(n, dtype=float)
    pair_second = np.zeros(n, dtype=float)
    served_first = np.zeros(n, dtype=float)
    served_second = np.zeros(n, dtype=float)
    cap_first = np.zeros(n, dtype=float)
    cap_second = np.zeros(n, dtype=float)

    for _, row in pairs_with_counts.iterrows():
        count = int(row["pair_count"])
        if count <= 0:
            continue
        i = int(row["site_i_idx"]) - 1
        j = int(row["site_j_idx"]) - 1
        q_opt = float(row["q_opt"])
        served_i = float(row["served_i_mean"])
        served_j = float(row["served_j_mean"])

        pair_first[i] += count
        pair_second[j] += count
        served_first[i] += count * served_i
        served_second[j] += count * served_j
        cap_first[i] += count * q_opt
        cap_second[j] += count * (capacity - q_opt)

    annual_eff = served_single + served_first + served_second
    cap_total = cap_single + cap_first + cap_second

    with np.errstate(divide="ignore", invalid="ignore"):
        base = np.where(demand > 0, annual_eff / demand, 0.0)
        unmet = np.where(demand > 0, np.maximum(0.0, demand - annual_eff) / demand, 0.0)
        waste = np.where(cap_total > 0, np.maximum(0.0, cap_total - annual_eff) / cap_total, 0.0)

    score = np.clip(base - alpha * unmet - beta * waste, 0.0, 1.0)
    bottom_n = max(1, int(np.ceil(n * 0.10)))

    total_served = float(annual_eff.sum())
    total_demand = float(demand.sum())
    total_unmet = float(np.maximum(0.0, demand - annual_eff).sum())
    total_waste = float(np.maximum(0.0, cap_total - annual_eff).sum())

    return {
        "effectiveness": float(score.mean()),
        "min_eff": float(score.min()),
        "bottom10_eff": float(np.sort(score)[:bottom_n].mean()),
        "gini_eff": float(gini_coefficient(score)),
        "std_eff": float(score.std()),
        "total_unmet": total_unmet,
        "total_waste": total_waste,
        "total_served": total_served,
        "total_demand": total_demand,
        "serve_ratio": float(total_served / total_demand) if total_demand > 0 else 0.0,
        "score_per_site": score,
        "annual_eff": annual_eff,
        "pair_first": pair_first,
        "pair_second": pair_second,
    }


def allocate_with_pairs(
    sites: pd.DataFrame,
    pairs_df: pd.DataFrame,
    k_min: float,
    *,
    n_target: int,
    capacity: float,
    max_iter: int = 30,
) -> Tuple[np.ndarray, np.ndarray, pd.DataFrame, int]:
    n_total_guess = int(n_target)

    for _ in range(max_iter):
        visits = allocate_visits(sites, k_min, n_total_guess)
        if visits is None:
            return None, None, None, None

        pairs_with_counts, singles = assign_pairs(pairs_df, visits)
        pair_total = int(pairs_with_counts["pair_count"].sum())
        new_guess = int(n_target + pair_total)
        if new_guess == n_total_guess:
            return visits, singles, pairs_with_counts, n_total_guess
        n_total_guess = new_guess

    return visits, singles, pairs_with_counts, n_total_guess


def select_kmin(results: pd.DataFrame) -> float:
    gini_candidates = results.loc[results["gini_eff"] < 0.2, "k_min"]
    if len(gini_candidates) > 0:
        return float(gini_candidates.iloc[0])
    idx = results["effectiveness"].idxmax()
    return float(results.loc[idx, "k_min"])


def plot_results(results: pd.DataFrame, output_dir: str) -> float:
    if not _HAS_MPL:
        raise RuntimeError("缺少依赖: matplotlib（无法绘图）。请先安装 matplotlib 再运行绘图部分。")

    fig, axes = plt.subplots(4, 2, figsize=(12, 13))

    selected_k = select_kmin(results)
    selected_idx = (results["k_min"] - selected_k).abs().idxmin()
    selected_eff = float(results.loc[selected_idx, "effectiveness"])
    selected_label = f"Selected k_min={selected_k:.1f}"

    ax = axes[0, 0]
    ax.plot(results["k_min"], results["effectiveness"], "b-", lw=2)
    ax.axvline(selected_k, color="r", ls="--", label=selected_label)
    ax.scatter([selected_k], [selected_eff], c="r", s=100, zorder=5)
    ax.set_xlabel("k_min")
    ax.set_ylabel("Mean Effectiveness")
    ax.set_title("Mean Effectiveness vs k_min")
    ax.legend()
    ax.grid(True, alpha=0.3)

    ax = axes[0, 1]
    ax.plot(results["k_min"], results["bottom10_eff"], "m-", lw=2)
    ax.axvline(selected_k, color="r", ls="--")
    ax.set_xlabel("k_min")
    ax.set_ylabel("Bottom 10% Mean Effectiveness")
    ax.set_title("Bottom 10% Mean Effectiveness vs k_min")
    ax.grid(True, alpha=0.3)

    ax = axes[1, 0]
    ax.plot(results["k_min"], results["total_served"] / 1000, "c-", lw=2)
    ax.axhline(results["total_demand"].iloc[0] / 1000, color="gray", ls=":", label="Total Demand")
    ax.axvline(selected_k, color="r", ls="--")
    ax.set_xlabel("k_min")
    ax.set_ylabel("Served Families (×1000)")
    ax.set_title("Total Served vs k_min")
    ax.legend()
    ax.grid(True, alpha=0.3)

    ax = axes[1, 1]
    ax.plot(results["k_min"], results["min_eff"], "g-", lw=2)
    ax.axvline(selected_k, color="r", ls="--")
    ax.set_xlabel("k_min")
    ax.set_ylabel("Min Effectiveness")
    ax.set_title("Worst Site Effectiveness vs k_min")
    ax.grid(True, alpha=0.3)

    ax = axes[2, 0]
    ax.plot(results["k_min"], results["unmet"] / 1000, "r-", lw=2, label="Unmet")
    ax.plot(results["k_min"], results["waste"] / 1000, "b-", lw=2, label="Waste")
    ax.axvline(selected_k, color="gray", ls="--")
    ax.set_xlabel("k_min")
    ax.set_ylabel("Families (×1000)")
    ax.set_title("Unmet Demand vs Wasted Capacity")
    ax.legend()
    ax.grid(True, alpha=0.3)

    ax = axes[2, 1]
    ax.plot(results["k_min"], results["std_eff"], color="tab:orange", lw=2)
    ax.axvline(selected_k, color="gray", ls="--")
    ax.set_xlabel("k_min")
    ax.set_ylabel("Std Effectiveness")
    ax.set_title("Effectiveness Std vs k_min")
    ax.grid(True, alpha=0.3)

    ax = axes[3, 0]
    ax.plot(results["k_min"], results["gini_eff"], color="tab:purple", lw=2)
    ax.axhline(0.2, color="gray", ls=":", lw=1)
    ax.axvline(selected_k, color="r", ls="--")
    ax.set_xlabel("k_min")
    ax.set_ylabel("Gini Coefficient")
    ax.set_title("Gini (Effectiveness) vs k_min")
    ax.grid(True, alpha=0.3)

    axes[3, 1].axis("off")

    plt.tight_layout()
    os.makedirs(output_dir, exist_ok=True)
    plt.savefig(os.path.join(output_dir, "p3_kmin_effectiveness.png"), dpi=150)
    plt.close(fig)

    return selected_k


def main() -> None:
    parser = argparse.ArgumentParser(description="Task 3 k-min allocation with two-stop pairing.")
    parser.add_argument("--input-xlsx", default=INPUT_XLSX)
    parser.add_argument("--input-pairs", default=INPUT_PAIRS)
    parser.add_argument("--output-dir", default=OUTPUT_DIR)
    parser.add_argument("--kmin-start", type=float, default=1.0)
    parser.add_argument("--kmin-end", type=float, default=10.0)
    parser.add_argument("--kmin-step", type=float, default=0.1)
    parser.add_argument("--capacity", type=float, default=C_OPT)
    parser.add_argument("--n-target", type=int, default=N_TARGET)
    parser.add_argument("--alpha", type=float, default=ALPHA)
    parser.add_argument("--beta", type=float, default=BETA)
    args = parser.parse_args()

    sites = load_sites(args.input_xlsx)
    pairs = pd.read_csv(args.input_pairs)
    required_cols = {
        "site_i_idx",
        "site_j_idx",
        "score_mean",
        "q_opt",
        "served_i_mean",
        "served_j_mean",
        "distance_miles",
    }
    missing = required_cols.difference(pairs.columns)
    if missing:
        raise ValueError(f"Missing columns in pairs CSV: {sorted(missing)}")

    pairs = pairs.sort_values(
        ["score_mean", "distance_miles"], ascending=[False, True]
    ).reset_index(drop=True)

    k_range = np.arange(args.kmin_start, args.kmin_end + 1e-9, args.kmin_step)
    results = []

    for k_min in k_range:
        visits, singles, pairs_with_counts, n_total_guess = allocate_with_pairs(
            sites,
            pairs,
            float(k_min),
            n_target=args.n_target,
            capacity=args.capacity,
        )
        if visits is None:
            continue

        metrics = _compute_metrics(
            sites,
            visits,
            pairs_with_counts,
            singles,
            alpha=args.alpha,
            beta=args.beta,
            capacity=args.capacity,
        )

        pair_total = int(pairs_with_counts["pair_count"].sum())
        total_trips = int(visits.sum() - pair_total)
        row = {
            "k_min": float(k_min),
            "effectiveness": metrics["effectiveness"],
            "min_eff": metrics["min_eff"],
            "bottom10_eff": metrics["bottom10_eff"],
            "gini_eff": metrics["gini_eff"],
            "std_eff": metrics["std_eff"],
            "unmet": metrics["total_unmet"],
            "waste": metrics["total_waste"],
            "total_served": metrics["total_served"],
            "total_demand": metrics["total_demand"],
            "serve_ratio": metrics["serve_ratio"],
            "total_visits_allocated": int(visits.sum()),
            "pair_trips": pair_total,
            "total_trips": total_trips,
            "n_total_guess": int(n_total_guess),
        }
        results.append(row)

    results_df = pd.DataFrame(results)
    if len(results_df) == 0:
        raise RuntimeError("No feasible k_min values found.")

    best_k = select_kmin(results_df)
    best_idx = (results_df["k_min"] - best_k).abs().idxmin()

    visits, singles, pairs_with_counts, n_total_guess = allocate_with_pairs(
        sites,
        pairs,
        float(best_k),
        n_target=args.n_target,
        capacity=args.capacity,
    )
    metrics = _compute_metrics(
        sites,
        visits,
        pairs_with_counts,
        singles,
        alpha=args.alpha,
        beta=args.beta,
        capacity=args.capacity,
    )

    pair_total = int(pairs_with_counts["pair_count"].sum())
    total_trips = int(visits.sum() - pair_total)

    site_rows = pd.DataFrame(
        {
            "site_idx": sites["site_idx"],
            "site_name": sites["site_name"],
            "total_visits_allocated": visits,
            "single_visits": singles,
            "paired_first": metrics["pair_first"].astype(int),
            "paired_second": metrics["pair_second"].astype(int),
            "paired_total": (metrics["pair_first"] + metrics["pair_second"]).astype(int),
        }
    )

    pairs_out = pairs_with_counts.loc[pairs_with_counts["pair_count"] > 0].copy()
    pairs_out = pairs_out.sort_values(["pair_count", "score_mean"], ascending=[False, False])

    os.makedirs(args.output_dir, exist_ok=True)
    results_df.to_csv(os.path.join(args.output_dir, "p3_kmin_data.csv"), index=False)
    site_rows.to_csv(os.path.join(args.output_dir, "p3_kmin_sites.csv"), index=False)
    pairs_out.to_csv(os.path.join(args.output_dir, "p3_kmin_pairs.csv"), index=False)

    if _HAS_MPL:
        plot_results(results_df, args.output_dir)
    else:
        print("未检测到 matplotlib，跳过绘图（仍会保存CSV结果）。")

    print(f"Best k_min={best_k:.1f} (total_trips={total_trips}, pair_trips={pair_total})")
    print(
        "Saved: data/p3_kmin_data.csv, data/p3_kmin_sites.csv, "
        "data/p3_kmin_pairs.csv, data/p3_kmin_effectiveness.png"
    )


if __name__ == "__main__":
    main()