from datetime import datetime
import pandas as pd
from Common.CEnum import AUTYPE, DATA_FIELD, KL_TYPE
from Common.CTime import CTime
from Common.func_util import kltype_lt_day, str2float
from KLine.KLine_Unit import CKLine_Unit
from .CommonStockAPI import CCommonStockApi


class WebDataAPI(CCommonStockApi):
    """Web应用的数据API适配器，用于将web应用的数据转换为chan.py格式"""
    
    # 类变量用于存储数据
    _data_cache = {}
    
    def __init__(self, code, k_type=KL_TYPE.K_DAY, begin_date=None, end_date=None, autype=AUTYPE.QFQ):
        super().__init__(code, k_type, begin_date, end_date, autype)
    
    @classmethod
    def set_data(cls, code, df_data):
        """设置指定代码的数据"""
        cls._data_cache[code] = df_data
    
    def get_kl_data(self):
        """将DataFrame数据转换为CKLine_Unit迭代器"""
        df_data = self._data_cache.get(self.code)
        if df_data is None or len(df_data) == 0:
            return
        
        # 确保数据按时间排序并重置索引
        df_data = df_data.sort_values('date').reset_index(drop=True)
        print(f"WebDataAPI: 处理 {len(df_data)} 条K线数据，K线级别: {self.k_type}")
        
        # 检查并处理重复时间
        if 'timestamp' in df_data.columns:
            df_data = df_data.drop_duplicates(subset=['timestamp'], keep='last')
            df_data = df_data.sort_values('timestamp').reset_index(drop=True)
        
        prev_timestamp = None
        for idx, row in df_data.iterrows():
            # 转换时间格式
            if isinstance(row['date'], str):
                time_obj = datetime.fromisoformat(row['date'].replace('Z', '+00:00'))
            else:
                time_obj = row['date']
            
            # 检查时间戳确保单调递增
            if 'timestamp' in row:
                current_timestamp = row['timestamp']
                if prev_timestamp is not None and current_timestamp <= prev_timestamp:
                    print(f"跳过重复或倒序时间戳: {current_timestamp}, 上个时间戳: {prev_timestamp}")
                    continue
                prev_timestamp = current_timestamp
            
            # 创建CTime对象 - 根据K线级别智能决定auto参数
            # 对于日线及以上级别，且时分秒为0的情况，使用auto=True
            # 对于分钟级别或有具体时分的数据，使用auto=False确保精确时间
            use_auto = False  # 默认不使用auto，确保时间精确
            
            # 只有在日线级别且时分秒都为0时才考虑使用auto
            if self.k_type in [KL_TYPE.K_DAY, KL_TYPE.K_WEEK, KL_TYPE.K_MON]:
                if time_obj.hour == 0 and time_obj.minute == 0 and time_obj.second == 0:
                    use_auto = True
            
            ctime = CTime(
                time_obj.year, 
                time_obj.month, 
                time_obj.day, 
                time_obj.hour, 
                time_obj.minute,
                time_obj.second,
                auto=use_auto
            )
            
            # 输出详细调试信息（只输出前几条）
            if idx < 3:
                print(f"第{idx+1}条数据: 原始时间={time_obj}, CTime={ctime}, auto={use_auto}, timestamp={ctime.ts}")
            
            # 创建数据字典
            data_dict = {
                DATA_FIELD.FIELD_TIME: ctime,
                DATA_FIELD.FIELD_OPEN: float(row['open']),
                DATA_FIELD.FIELD_HIGH: float(row['high']),
                DATA_FIELD.FIELD_LOW: float(row['low']),
                DATA_FIELD.FIELD_CLOSE: float(row['close']),
                DATA_FIELD.FIELD_VOLUME: float(row['volume']) if 'volume' in row and pd.notna(row['volume']) else 0.0
            }
            
            yield CKLine_Unit(data_dict, autofix=True)
    
    def SetBasciInfo(self):
        pass
    
    @classmethod
    def do_init(cls):
        pass
    
    @classmethod
    def do_close(cls):
        pass