#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""测试多场景聚类模块"""

import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))

from multi_scenario import MultiScenarioAnalyzer

import numpy as np

def test_multi_scenario_basic():
    """测试基本功能"""
    print("测试多场景聚类基本功能...")
    
    # 生成简单的测试数据（24小时）
    hours = 24
    solar_output = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0,
                   5.0, 4.0, 3.0, 2.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
    wind_output = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0, 1.0, 2.0, 3.0, 4.0, 3.0, 2.0,
                   2.0, 3.0, 4.0, 3.0, 2.0, 1.0, 1.0, 2.0, 3.0, 4.0, 3.0, 2.0]
    load_demand = [3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 18.0,
                  16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0]
    
    # 创建分析器
    analyzer = MultiScenarioAnalyzer(n_clusters=3, random_state=42)
    
    try:
        # 执行聚类分析
        result = analyzer.fit_predict(solar_output, wind_output, load_demand)
        
        print("✅ 基本聚类测试通过")
        print(f"   - 识别场景数: {result.n_scenarios}")
        print(f"   - 轮廓系数: {result.silhouette_score:.3f}")
        print(f"   - 场景名称: {result.scenario_names}")
        
        return True
        
    except Exception as e:
        print(f"❌ 测试失败: {str(e)}")
        import traceback
        traceback.print_exc()
        return False

def test_multi_scenario_yearly():
    """测试年度数据聚类"""
    print("\n测试年度数据聚类...")
    
    # 生成模拟的8760小时数据
    np.random.seed(42)
    
    # 简单的模拟数据
    solar_output = []
    wind_output = []
    load_demand = []
    
    for day in range(365):
        # 光伏：白天有出力，夜间为0
        daily_solar = [0.0] * 6 + list(np.random.uniform(1, 6, 12)) + [0.0] * 6
        solar_output.extend(daily_solar)
        
        # 风电：相对随机
        daily_wind = np.random.exponential(2.5, 24).tolist()
        wind_output.extend(daily_wind)
        
        # 负荷：日内变化，夜间低，白天高
        daily_load = [3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 18.0,
                     16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0]
        load_demand.extend(daily_load)
    
    try:
        analyzer = MultiScenarioAnalyzer(n_clusters=5, random_state=42)
        result = analyzer.fit_predict(solar_output, wind_output, load_demand, find_optimal_k=True)
        
        print("✅ 年度数据聚类测试通过")
        print(f"   - 最优聚类数: {result.n_scenarios}")
        print(f"   - 轮廓系数: {result.silhouette_score:.3f}")
        
        # 输出前3个场景的统计
        for i in range(min(3, result.n_scenarios)):
            stats = result.scenario_stats[f'scenario_{i}']
            print(f"   - 场景{i+1}: 频率{stats['frequency']:.1%}, "
                  f"光伏{stats['solar_mean']:.1f}MW, "
                  f"风电{stats['wind_mean']:.1f}MW, "
                  f"负荷{stats['load_mean']:.1f}MW")
        
        # 测试图表生成
        print("   - 测试图表生成...")
        analyzer.plot_scenario_analysis(result, solar_output, wind_output, load_demand,
                                       save_path="test_scenario_analysis.png")
        print("   ✅ 图表生成成功")
        
        return True
        
    except Exception as e:
        print(f"❌ 年度数据测试失败: {str(e)}")
        import traceback
        traceback.print_exc()
        return False

def test_data_validation():
    """测试数据验证"""
    print("\n测试数据验证...")
    
    # 测试数据长度不一致
    try:
        analyzer = MultiScenarioAnalyzer(n_clusters=2)
        analyzer.fit_predict([1, 2, 3], [1, 2], [1, 2, 3])  # 长度不一致
        print("❌ 应该检测到数据长度不一致")
        return False
    except ValueError as e:
        print("✅ 正确检测到数据长度不一致")
    
    return True

if __name__ == "__main__":
    print("=== 多场景聚类模块测试 ===\n")
    
    success = True
    success &= test_multi_scenario_basic()
    success &= test_multi_scenario_yearly()
    success &= test_data_validation()
    
    print(f"\n{'='*50}")
    if success:
        print("🎉 所有测试通过！多场景聚类模块工作正常。")
    else:
        print("💥 部分测试失败，请检查代码。")
    print(f"{'='*50}")