import json
import numpy as np
import os
import sys
import re

def load_xrd_data(file_path):
    """
    读取 XRD 导出的 txt 文件（如 background_result.txt），跳过头部的 Metadata。
    """
    if not os.path.exists(file_path):
        raise FileNotFoundError(f"文件不存在: {file_path}")
    
    data = []
    with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
        for line in f:
            line = line.strip()
            if not line:
                continue
            parts = line.split()
            try:
                values = [float(x) for x in parts]
                if len(values) >= 2:
                    data.append(values[:2])
            except ValueError:
                continue
    
    if not data:
        raise ValueError(f"在文件中未找到有效的数值数据: {file_path}")
    
    return np.array(data)

def load_peak_report(file_path):
    """
    专门解析 JADE Peak Search Report (.pid)
    提取表格部分的数值数据
    """
    if not os.path.exists(file_path):
        raise FileNotFoundError(f"文件不存在: {file_path}")
    
    peaks = []
    metadata = {}
    
    with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
        lines = f.readlines()
        
    # 查找表格开始的位置（在含有 "2-Theta" 的行之后）
    table_started = False
    for line in lines:
        line = line.strip()
        if not line:
            continue
            
        # 提取元数据示例: JADE: Peak Search Report (72 Peaks, Max P/N = 19.3)
        if "Peak Search Report" in line:
            match = re.search(r"\((\d+) Peaks", line)
            if match:
                metadata['reported_peak_count'] = int(match.group(1))
        
        # 识别表头
        if "2-Theta" in line and "d(" in line:
            table_started = True
            continue
            
        if table_started:
            parts = line.split()
            # Peak 报告通常每行有 8-9 个字段
            if len(parts) >= 6:
                try:
                    # 尝试将前几个字段转为 float
                    # 0: 2-Theta, 1: d, 2: BG, 3: Height, 4: I%, 5: Area, 6: I%, 7: FWHM
                    peak_data = [float(p) for p in parts[:8]]
                    peaks.append(peak_data)
                except ValueError:
                    # 如果转换失败，可能是说明文字或空行，跳过
                    continue
                    
    if not peaks:
        raise ValueError(f"未能从 Peak 报告中解析出有效数据: {file_path}")
        
    return np.array(peaks), metadata

def evaluate(gt_path, agent_path, tolerance=1e-4, mode="xrd_data"):
    """
    对比 Ground Truth 和 Agent Output。
    支持两种模式:
    - xrd_data: 对比 (2-Theta, Intensity) 原始数据点
    - peak_report: 对比 Peak 搜索结果列表
    """
    try:
        if mode == "peak_report":
            gt_data, gt_meta = load_peak_report(gt_path)
            agent_data, agent_meta = load_peak_report(agent_path)
            
            # 对于 Peak 报告，我们主要关注 2-Theta 位置和 Height
            # 这里对比全表，但放宽容差，因为 Peak Search 的算法可能在不同环境下有极细微差异
            if gt_data.shape != agent_data.shape:
                # 如果数量不匹配，直接判定失败
                return 0, f"失败: Peak 数量不匹配。GT {len(gt_data)}, Agent {len(agent_data)}"
        else:
            gt_data = load_xrd_data(gt_path)
            agent_data = load_xrd_data(agent_path)
            
        if gt_data.shape != agent_data.shape:
            return 0, f"失败: 数据维度不匹配。GT 形状 {gt_data.shape}, Agent 形状 {agent_data.shape}"

        diff = np.abs(gt_data - agent_data)
        max_error = np.max(diff)
        
        if max_error < tolerance:
            return 1, f"成功: 最大绝对误差 {max_error:.2e} < 阈值 {tolerance}"
        else:
            return 0, f"失败: 最大绝对误差 {max_error:.2e} 超过阈值 {tolerance}"
            
    except Exception as e:
        return 0, f"错误: {str(e)}"

def evaluate_by_config(config_path):
    """
    根据任务配置文件进行评测。
    """
    # 兼容性处理：如果传入的是任务 ID 路径
    if not os.path.isabs(config_path) and not config_path.startswith('.'):
        # 尝试补全路径，例如 instructions/smoothing_001.json
        if not config_path.endswith('.json'):
            config_path = config_path + ".json"
        if not os.path.exists(config_path):
            # 尝试在任务目录下找
            pass

    base_dir = os.path.dirname(os.path.dirname(os.path.abspath(config_path)))
    
    with open(config_path, 'r', encoding='utf-8') as f:
        config = json.load(f)
    
    eval_cfg = config.get('evaluation', {})
    # 处理相对路径
    gt_path = os.path.join(base_dir, eval_cfg['ground_truth'])
    agent_path = os.path.join(base_dir, eval_cfg['target_output'])
    tolerance = eval_cfg.get('tolerance', 1e-4)
    
    # 自动识别模式
    mode = "xrd_data"
    if gt_path.lower().endswith('.pid') or eval_cfg.get('type') == 'peak_report':
        mode = "peak_report"
        # Peak 报告的默认容差放宽一些，因为算法可能受环境微小影响
        if 'tolerance' not in eval_cfg:
            tolerance = 1e-2 
    
    print(f"--- 正在执行评测: {config.get('id', 'unknown')} ---")
    print(f"指令: {config.get('instruction')}")
    print(f"模式: {mode}")
    
    score, message = evaluate(gt_path, agent_path, tolerance, mode=mode)
    return score, message

if __name__ == "__main__":
    if len(sys.argv) == 2 and sys.argv[1].endswith('.json'):
        # 模式 A: Config 模式
        try:
            score, message = evaluate_by_config(sys.argv[1])
        except Exception as e:
            print(f"配置文件解析失败: {e}")
            sys.exit(1)
    elif len(sys.argv) >= 3:
        # 模式 B: 直接对比模式
        score, message = evaluate(sys.argv[1], sys.argv[2])
    else:
        print("用法:")
        print("  python scripts/evaluator.py instructions/smoothing_001.json")
        print("  python scripts/evaluator.py <gt_file_path> <agent_file_path>")
        sys.exit(1)

    print("-" * 30)
    print(f"Score: {score}")
    print(f"Reason: {message}")
    print("-" * 30)
    sys.exit(0 if score == 1 else 1)