"""
Task 3 - Step 7: 敏感性分析 (Refined)
=====================================

分析以下参数对模型输出的影响 (高分辨率扫描):
1. 合并比例 r_merge: 0.1 ~ 0.9 (步长 0.01)
2. 距离阈值 l_max: 10 ~ 100 miles (步长 1)
3. 容量上限 μ_sum_max: 350 ~ 550 (步长 5)
4. CV阈值: 0.1 ~ 1.0 (步长 0.01)

输出: 
- 07_sensitivity.xlsx (详细数据)
- figures/fig3_sensitivity.png (敏感性曲线图)
"""

import pandas as pd
import numpy as np
from scipy import stats
import warnings
import os
import tempfile
from pathlib import Path

warnings.filterwarnings('ignore')

# Matplotlib cache dirs（避免 home 目录不可写导致的警告）
_mpl_config_dir = Path(tempfile.gettempdir()) / "mm_task3_mplconfig"
_xdg_cache_dir = Path(tempfile.gettempdir()) / "mm_task3_xdg_cache"
_mpl_config_dir.mkdir(parents=True, exist_ok=True)
_xdg_cache_dir.mkdir(parents=True, exist_ok=True)
os.environ.setdefault("MPLCONFIGDIR", str(_mpl_config_dir))
os.environ.setdefault("XDG_CACHE_HOME", str(_xdg_cache_dir))

import matplotlib.pyplot as plt
from cycler import cycler

# 设置绘图风格（低饱和度 Morandi 色系）
MORANDI = {
    "grid": "#D7D1C7",
    "text": "#3F3F3F",
    "muted_blue": "#8FA3A7",
    "muted_blue_dark": "#6E858A",
    "sage": "#AEB8A6",
    "terracotta": "#B07A6A",
    "warm_gray": "#8C857A",
}

FIG_BG = "#FFFFFF"
FIG_GRID = "#DADDE1"

plt.rcParams.update(
    {
        "figure.facecolor": FIG_BG,
        "axes.facecolor": FIG_BG,
        "savefig.facecolor": FIG_BG,
        "axes.edgecolor": FIG_GRID,
        "axes.labelcolor": MORANDI["text"],
        "xtick.color": MORANDI["text"],
        "ytick.color": MORANDI["text"],
        "text.color": MORANDI["text"],
        "grid.color": FIG_GRID,
        "grid.alpha": 0.55,
        "axes.grid": True,
        "axes.prop_cycle": cycler(
            "color",
            [
                MORANDI["muted_blue"],
                MORANDI["sage"],
                MORANDI["terracotta"],
                MORANDI["warm_gray"],
            ],
        ),
        "legend.frameon": True,
        "legend.framealpha": 0.9,
        "legend.facecolor": FIG_BG,
        "legend.edgecolor": FIG_GRID,
        "font.sans-serif": ["Arial Unicode MS", "SimHei", "DejaVu Sans"],
        "axes.unicode_minus": False,
    }
)

# ============================================
# 基础参数和函数 (保持不变)
# ============================================
Q = 400
QUALITY_THRESHOLD = 250
SHORTFALL_THRESHOLD = 0.8

def quality_factor(mu_total):
    return min(1.0, QUALITY_THRESHOLD / mu_total) if mu_total > 0 else 1.0

def expected_service(q, mu, sigma):
    if sigma == 0:
        return min(mu, q)
    z = (q - mu) / sigma
    return mu * stats.norm.cdf(z) - sigma * stats.norm.pdf(z) + q * (1 - stats.norm.cdf(z))

def gini_coefficient(values):
    values = np.array(values)
    values = values[~np.isnan(values)]
    if len(values) == 0:
        return 0
    values = np.sort(values)
    n = len(values)
    cumsum = np.cumsum(values)
    return (2 * np.sum((np.arange(1, n + 1) * values)) - (n + 1) * cumsum[-1]) / (n * cumsum[-1]) if cumsum[-1] > 0 else 0

def shortfall_probability(q, mu, sigma, threshold=0.8):
    if sigma == 0:
        return 0 if q >= mu * threshold else 1
    critical_demand = q / threshold
    return 1 - stats.norm.cdf((critical_demand - mu) / sigma)

def optimal_allocation(mu_i, sigma_i, mu_j, sigma_j, Q=400):
    if sigma_i + sigma_j == 0:
        return mu_i
    return (sigma_j * mu_i + sigma_i * (Q - mu_j)) / (sigma_i + sigma_j)

def calc_distance(lat1, lon1, lat2, lon2):
    lat_avg = (lat1 + lat2) / 2
    lat_avg_rad = np.radians(lat_avg)
    delta_lat = lat1 - lat2
    delta_lon = lon1 - lon2
    return 69.0 * np.sqrt(delta_lat**2 + (np.cos(lat_avg_rad) * delta_lon)**2)

def compute_distance_matrix(sites_df: pd.DataFrame) -> np.ndarray:
    lat = sites_df['lat'].to_numpy(dtype=float)
    lon = sites_df['lon'].to_numpy(dtype=float)

    lat_avg = (lat[:, None] + lat[None, :]) / 2.0
    lat_avg_rad = np.radians(lat_avg)
    delta_lat = lat[:, None] - lat[None, :]
    delta_lon = lon[:, None] - lon[None, :]

    dist = 69.0 * np.sqrt(delta_lat**2 + (np.cos(lat_avg_rad) * delta_lon)**2)
    np.fill_diagonal(dist, 0.0)
    return dist

# ============================================
# 完整流水线函数
# ============================================
def run_pipeline(sites_df, distance_matrix, l_max=50, mu_sum_max=450, cv_max=0.5, merge_ratio=0.5):
    """
    运行完整的Task 3流水线，返回评估指标
    """
    sites = sites_df.copy()
    sites['cv'] = sites['sigma'] / sites['mu']
    n = len(sites)

    # Step 2: 配对筛选
    candidates = []

    for i in range(n):
        for j in range(i + 1, n):
            site_i = sites.iloc[i]
            site_j = sites.iloc[j]

            dist = float(distance_matrix[i, j])

            if dist > l_max:
                continue
            mu_sum = site_i['mu'] + site_j['mu']
            if mu_sum > mu_sum_max:
                continue
            if site_i['cv'] > cv_max or site_j['cv'] > cv_max:
                continue

            sigma_sq_sum = site_i['sigma']**2 + site_j['sigma']**2
            value = (1.0 * mu_sum / Q - 0.3 * dist / l_max - 0.5 * sigma_sq_sum / mu_sum**2)

            candidates.append({
                'idx_i': i, 'idx_j': j,
                'site_i_id': site_i['site_id'], 'site_j_id': site_j['site_id'],
                'distance': dist,
                'mu_i': site_i['mu'], 'mu_j': site_j['mu'],
                'sigma_i': site_i['sigma'], 'sigma_j': site_j['sigma'],
                'k_i': site_i['k'], 'k_j': site_j['k'],
                'mu_tilde_i': site_i['mu_tilde'], 'mu_tilde_j': site_j['mu_tilde'],
                'value': value
            })

    if len(candidates) == 0:
        E1 = (sites['k'] * sites['mu']).sum()
        E2 = sum(sites['k'] * sites['mu'].apply(quality_factor) * sites['mu'])
        rates = [row['k'] * row['mu'] / row['mu_tilde'] for _, row in sites.iterrows()]
        F1 = gini_coefficient(rates)
        F2 = min(rates)
        return {
            'num_pairs': 0, 'num_dual_visits': 0,
            'E1': E1, 'E2': E2, 'F1': F1, 'F2': F2, 'R1': 0, 'RS': 0
        }

    # 贪心配对选择
    df_cand = pd.DataFrame(candidates).sort_values('value', ascending=False)
    selected = []
    used = set()
    for _, row in df_cand.iterrows():
        if row['idx_i'] not in used and row['idx_j'] not in used:
            selected.append(row.to_dict())
            used.add(row['idx_i'])
            used.add(row['idx_j'])

    # Step 3: 计算最优分配
    for pair in selected:
        q_star = optimal_allocation(pair['mu_i'], pair['sigma_i'],
                                   pair['mu_j'], pair['sigma_j'], Q)
        pair['q_final'] = q_star
        pair['E_Si'] = expected_service(q_star, pair['mu_i'], pair['sigma_i'])
        pair['E_Sj'] = expected_service(Q - q_star, pair['mu_j'], pair['sigma_j'])
        pair['E_total'] = pair['E_Si'] + pair['E_Sj']

    # Step 4: 重分配访问次数
    sites['k_single'] = sites['k'].copy()
    pair_k = {}
    for pair in selected:
        k_i, k_j = pair['k_i'], pair['k_j']
        k_ij = int(min(k_i, k_j) * merge_ratio)
        if k_ij >= min(k_i, k_j):
            k_ij = min(k_i, k_j) - 1
        if k_ij < 1:
            k_ij = 0

        idx_i, idx_j = pair['idx_i'], pair['idx_j']
        sites.loc[idx_i, 'k_single'] = k_i - k_ij
        sites.loc[idx_j, 'k_single'] = k_j - k_ij
        pair_k[(pair['site_i_id'], pair['site_j_id'])] = k_ij

    # 计算释放槽位并重分配
    total_single = sites['k_single'].sum()
    total_dual = sum(pair_k.values())
    delta_N = 730 - (total_single + total_dual)

    if delta_N > 0:
        total_demand = sites['mu_tilde'].sum()
        sites['k_extra'] = (delta_N * sites['mu_tilde'] / total_demand).apply(np.floor).astype(int)
        remainder = delta_N - sites['k_extra'].sum()
        if remainder > 0:
            fractional = delta_N * sites['mu_tilde'] / total_demand - sites['k_extra']
            top_idx = fractional.nlargest(int(remainder)).index
            sites.loc[top_idx, 'k_extra'] += 1
        sites['k_single_final'] = sites['k_single'] + sites['k_extra']
    else:
        sites['k_single_final'] = sites['k_single']

    # Step 5: 计算指标
    E1 = (sites['k_single_final'] * sites['mu']).sum()
    for pair in selected:
        k_ij = pair_k.get((pair['site_i_id'], pair['site_j_id']), 0)
        E1 += k_ij * pair['E_total']

    E2 = sum(sites['k_single_final'] * sites['mu'].apply(quality_factor) * sites['mu'])
    for pair in selected:
        k_ij = pair_k.get((pair['site_i_id'], pair['site_j_id']), 0)
        mu_sum = pair['mu_i'] + pair['mu_j']
        q_factor = quality_factor(mu_sum)
        E2 += k_ij * q_factor * pair['E_total']

    site_satisfaction = {}
    for idx, row in sites.iterrows():
        r = row['k_single_final'] * row['mu'] / row['mu_tilde'] if row['mu_tilde'] > 0 else 0
        site_satisfaction[row['site_id']] = r

    for pair in selected:
        k_ij = pair_k.get((pair['site_i_id'], pair['site_j_id']), 0)
        r_i = k_ij * pair['E_Si'] / pair['mu_tilde_i'] if pair['mu_tilde_i'] > 0 else 0
        r_j = k_ij * pair['E_Sj'] / pair['mu_tilde_j'] if pair['mu_tilde_j'] > 0 else 0
        site_satisfaction[pair['site_i_id']] += r_i
        site_satisfaction[pair['site_j_id']] += r_j

    rates = list(site_satisfaction.values())
    F1 = gini_coefficient(rates)
    F2 = min(rates) if rates else 0

    shortfall_probs = []
    for pair in selected:
        q = pair['q_final']
        p_i = shortfall_probability(q, pair['mu_i'], pair['sigma_i'])
        p_j = shortfall_probability(Q - q, pair['mu_j'], pair['sigma_j'])
        shortfall_probs.append(1 - (1 - p_i) * (1 - p_j))
    R1 = np.mean(shortfall_probs) if shortfall_probs else 0

    RS = total_dual / 730

    return {
        'num_pairs': len(selected),
        'num_dual_visits': total_dual,
        'E1': E1, 'E2': E2, 'F1': F1, 'F2': F2, 'R1': R1, 'RS': RS
    }

# ============================================
# 主程序
# ============================================
print("=" * 60)
print("Task 3 - Step 7: 敏感性分析 (High Res)")
print("=" * 60)

# 读取基础数据
BASE_DIR = Path(__file__).resolve().parent
SITES_PATH = BASE_DIR.parent / 'task1' / '03_allocate.xlsx'
OUTPUT_XLSX = BASE_DIR / '07_sensitivity.xlsx'
OUTPUT_FIG = BASE_DIR / 'figures' / 'fig3_sensitivity.png'
OUTPUT_FIG.parent.mkdir(parents=True, exist_ok=True)

sites_df = pd.read_excel(SITES_PATH)
print(f"站点数据: {len(sites_df)} 个")

# 预计算距离矩阵（高分辨率扫描时显著加速）
distance_matrix = compute_distance_matrix(sites_df)

# 基准参数
BASE_L_MAX = 50
BASE_MU_SUM_MAX = 450
BASE_CV_MAX = 0.5
BASE_MERGE_RATIO = 0.5

# 计算基准结果
base_result = run_pipeline(sites_df, distance_matrix, BASE_L_MAX, BASE_MU_SUM_MAX, BASE_CV_MAX, BASE_MERGE_RATIO)
print(f"基准结果: E1={base_result['E1']:.0f}, R1={base_result['R1']:.4f}")

# 定义扫描参数
params_config = {
    'merge_ratio': np.round(np.arange(0.10, 0.90 + 1e-9, 0.01), 2),
    'l_max': np.arange(10, 100 + 1e-9, 1, dtype=float),
    'mu_sum_max': np.arange(350, 550 + 1e-9, 5, dtype=float),
    'cv_max': np.round(np.arange(0.10, 1.00 + 1e-9, 0.01), 2),
}

all_results = []

# 执行扫描
print("\n开始参数扫描...")

# 1. Merge Ratio
print("Scanning Merge Ratio...")
for val in params_config['merge_ratio']:
    res = run_pipeline(sites_df, distance_matrix, BASE_L_MAX, BASE_MU_SUM_MAX, BASE_CV_MAX, val)
    res.update({'param': 'merge_ratio', 'value': val})
    all_results.append(res)

# 2. Distance Threshold
print("Scanning Distance Threshold...")
for val in params_config['l_max']:
    res = run_pipeline(sites_df, distance_matrix, val, BASE_MU_SUM_MAX, BASE_CV_MAX, BASE_MERGE_RATIO)
    res.update({'param': 'l_max', 'value': val})
    all_results.append(res)

# 3. Capacity Cap
print("Scanning Capacity Cap...")
for val in params_config['mu_sum_max']:
    res = run_pipeline(sites_df, distance_matrix, BASE_L_MAX, val, BASE_CV_MAX, BASE_MERGE_RATIO)
    res.update({'param': 'mu_sum_max', 'value': val})
    all_results.append(res)

# 4. CV Threshold
print("Scanning CV Threshold...")
for val in params_config['cv_max']:
    res = run_pipeline(sites_df, distance_matrix, BASE_L_MAX, BASE_MU_SUM_MAX, val, BASE_MERGE_RATIO)
    res.update({'param': 'cv_max', 'value': val})
    all_results.append(res)

# 转换为DataFrame
df_results = pd.DataFrame(all_results)

# ============================================
# 绘图
# ============================================
print("\n绘制敏感性曲线...")

fig, axes = plt.subplots(2, 2, figsize=(15, 12))
fig.suptitle('Task 3 Sensitivity Analysis', fontsize=16, fontweight='bold')

# 参数对应的中文标签和单位
param_labels = {
    'merge_ratio': ('Merge Ratio', 'Ratio'),
    'l_max': ('Distance Threshold (l_max)', 'Miles'),
    'mu_sum_max': ('Capacity Cap (μ_sum)', 'lbs (proxy)'),
    'cv_max': ('CV Threshold', 'CV')
}

# 绘图循环
params_list = ['merge_ratio', 'l_max', 'mu_sum_max', 'cv_max']
for i, param in enumerate(params_list):
    ax = axes[i // 2, i % 2]
    data = df_results[df_results['param'] == param].sort_values('value')
    
    # 绘制 E1/E2 - 左轴
    line1, = ax.plot(
        data['value'],
        data['E1'],
        linestyle='-',
        color=MORANDI["muted_blue_dark"],
        linewidth=1.8,
        label='Expected Service (E1)',
    )
    line2, = ax.plot(
        data['value'],
        data['E2'],
        linestyle='-',
        color=MORANDI["sage"],
        linewidth=1.8,
        alpha=0.95,
        label='Quality-weighted (E2)',
    )
    ax.set_xlabel(param_labels[param][0])
    ax.set_ylabel('Service (E1/E2)')
    
    # 绘制 R1 (风险) - 右轴
    ax2 = ax.twinx()
    line3, = ax2.plot(
        data['value'],
        data['R1'],
        linestyle='--',
        color=MORANDI["terracotta"],
        linewidth=1.8,
        label='Shortfall Risk (R1)',
    )
    ax2.set_ylabel('Risk Probability (R1)', color=MORANDI["terracotta"])
    ax2.tick_params(axis='y', labelcolor=MORANDI["terracotta"])
    
    # 添加基准线
    if param == 'merge_ratio':
        base_val = BASE_MERGE_RATIO
    elif param == 'l_max':
        base_val = BASE_L_MAX
    elif param == 'mu_sum_max':
        base_val = BASE_MU_SUM_MAX
    elif param == 'cv_max':
        base_val = BASE_CV_MAX
        
    ax.axvline(x=base_val, color=MORANDI["warm_gray"], linestyle=':', alpha=0.65)
    
    # 图例
    lines = [line1, line2, line3]
    labels = [l.get_label() for l in lines]
    ax.legend(lines, labels, loc='best', frameon=True)
    
    ax.set_title(f'Effect of {param_labels[param][0]}')
    ax.grid(True, alpha=0.55)

plt.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.savefig(OUTPUT_FIG, dpi=300, facecolor=FIG_BG)
print(f"图表已保存至: {OUTPUT_FIG}")

# ============================================
# 保存详细数据
# ============================================
with pd.ExcelWriter(OUTPUT_XLSX, engine='openpyxl') as writer:
    df_results.to_excel(writer, sheet_name='detailed_data', index=False)
    
    # 摘要统计
    summary = []
    for param in params_list:
        sub = df_results[df_results['param'] == param]
        summary.append({
            'Parameter': param,
            'E1_Range': f"{sub['E1'].min():.0f} - {sub['E1'].max():.0f}",
            'E1_Var%': (sub['E1'].max() - sub['E1'].min()) / base_result['E1'] * 100,
            'E2_Range': f"{sub['E2'].min():.0f} - {sub['E2'].max():.0f}",
            'E2_Var%': (sub['E2'].max() - sub['E2'].min()) / base_result['E2'] * 100,
            'R1_Range': f"{sub['R1'].min():.4f} - {sub['R1'].max():.4f}"
        })
    pd.DataFrame(summary).to_excel(writer, sheet_name='summary', index=False)

print(f"数据已保存至: {OUTPUT_XLSX}")
print("完成!")