import requests
import pandas as pd
from io import BytesIO
import logging

# 配置日志
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

def download_unzip_csv(url, **kwargs):
    """
    从URL下载文件，解压并返回CSV内容作为pandas DataFrame
    
    参数:
        url: 文件下载URL
        **kwargs: 传递给pandas.read_csv的额外参数
        
    返回:
        pandas.DataFrame: 解压后的CSV内容
    """
    logger.info(f"Downloading file from {url}")
    
    try:
        # 下载文件
        response = requests.get(url)
        response.raise_for_status()  # 检查下载是否成功
        logger.info(f"Successfully downloaded file from {url}")
        
        # 使用BytesIO处理下载的内容
        zip_data = BytesIO(response.content)
        
        # 读取并解压CSV文件
        logger.info("Reading and decompressing CSV file")
        df = pd.read_csv(zip_data, compression='zip', **kwargs)
        
        # 智能判定首行是否为标题行
        def is_header_row(row):
            """智能检测行是否为标题行"""
            # 检查条件
            conditions = [
                # 1. 首行包含常见的时间相关列名
                any(keyword in str(cell).lower() for keyword in ['time', 'date', 'datetime', 'timestamp'] for cell in row),
                # 2. 首行包含常见的价格相关列名
                any(keyword in str(cell).lower() for keyword in ['open', 'high', 'low', 'close', 'volume'] for cell in row),
                # 3. 首行包含常见的交易相关列名
                any(keyword in str(cell).lower() for keyword in ['taker', 'quote', 'count', 'ignore'] for cell in row),
                # 4. 首行全为字符串，而第二行包含数值
                len(df) > 1 and all(isinstance(str(cell), str) and not str(cell).replace('.', '').isdigit() for cell in row) and \
                any(str(cell).replace('.', '').isdigit() for cell in df.iloc[1] if pd.notna(cell)),
                # 5. 首行包含'_'字符（常见于编程命名的列名）
                any('_' in str(cell) for cell in row)
            ]
            return any(conditions)
        
        if len(df) > 0 and is_header_row(df.iloc[0]):
            df = df[1:].reset_index(drop=True)
            logger.info("Skipped header row")
        
        # 转换数值列
        numeric_columns = ['open', 'high', 'low', 'close', 'volume',
                          'close_time', 'quote_asset_volume', 'number_of_trades',
                          'taker_buy_volume', 'taker_buy_quote_volume', 'taker_buy_base_asset_volume',
                          'taker_buy_quote_asset_volume', 'ignore']
        
        # 只转换存在的列
        for col in numeric_columns:
            if col in df.columns:
                try:
                    if col in ['number_of_trades', 'count']:
                        df[col] = df[col].astype(int)
                    else:
                        df[col] = df[col].astype(float)
                except ValueError:
                    logger.warning(f"Could not convert column {col} to numeric type")
        
        logger.info(f"Successfully parsed CSV file with {len(df)} rows")
        return df
    except requests.exceptions.RequestException as e:
        logger.error(f"Failed to download file: {e}")
        raise
    except Exception as e:
        logger.error(f"Failed to process file: {e}")
        raise

if __name__ == "__main__":
    # 测试函数
    test_url = "https://data.binance.vision/data/futures/um/monthly/klines/BTCUSDT/1d/BTCUSDT-1d-2024-01.zip"
    
    # CSV列名
    columns = [
        "open_time", "open", "high", "low", "close", "volume",
        "close_time", "quote_asset_volume", "number_of_trades",
        "taker_buy_base_asset_volume", "taker_buy_quote_asset_volume", "ignore"
    ]
    
    try:
        df = download_unzip_csv(test_url, header=None, names=columns)
        print(f"DataFrame shape: {df.shape}")
        print("DataFrame head:")
        print(df.head())
        print("DataFrame dtypes:")
        print(df.dtypes)
    except Exception as e:
        print(f"Error: {e}")