"""
数据处理工具模块
"""
from typing import List, Dict, Tuple, Optional
import pandas as pd
import numpy as np


def deduplicate_dataframe(
    df: pd.DataFrame,
    existing_df: pd.DataFrame,
    core_columns: List[str] = None,
    tolerance: float = 0.0001
) -> pd.DataFrame:
    """
    去重DataFrame
    
    Args:
        df: 新数据
        existing_df: 已存在的数据
        core_columns: 核心比较列
        tolerance: 差异容忍度
    
    Returns:
        需要去重的数据
    """
    if core_columns is None:
        core_columns = ["open", "high", "low", "close", "volume"]
    
    if existing_df.empty:
        return df
    
    # 获取已存在的索引
    existing_indices = set(existing_df.index)
    
    to_keep = []
    for idx, row in df.iterrows():
        if idx not in existing_indices:
            to_keep.append(idx)
        else:
            # 比较核心字段
            existing_row = existing_df.loc[idx]
            is_same = all(
                abs(row[col] - existing_row[col]) < tolerance
                for col in core_columns
                if col in row and col in existing_row
            )
            if not is_same:
                to_keep.append(idx)
    
    return df.loc[to_keep]


def compare_kline_data(
    new_data: Dict,
    existing_data: Dict,
    tolerance: float = 0.0001
) -> bool:
    """
    比较K线数据是否相同
    
    Args:
        new_data: 新数据
        existing_data: 已存在数据
        tolerance: 差异容忍度
    
    Returns:
        是否相同
    """
    core_fields = ["open", "high", "low", "close", "volume"]
    
    for field in core_fields:
        if field not in new_data or field not in existing_data:
            return False
        if abs(new_data[field] - existing_data[field]) >= tolerance:
            return False
    
    return True


def dataframe_to_dict_list(df: pd.DataFrame) -> List[Dict]:
    """将DataFrame转换为字典列表"""
    if df.empty:
        return []
    
    # 重置索引以便包含日期信息
    df_reset = df.reset_index()
    
    # 转换列名
    df_reset.columns = [str(col).lower() for col in df_reset.columns]
    
    # 转换为字典列表
    records = df_reset.to_dict("records")
    
    # 处理数值类型
    for record in records:
        for key, value in record.items():
            if isinstance(value, (np.integer, np.floating)):
                record[key] = float(value) if isinstance(value, np.floating) else int(value)
            elif pd.isna(value):
                record[key] = None
    
    return records


def merge_kline_data(
    cached_data: pd.DataFrame,
    sdk_data: pd.DataFrame
) -> pd.DataFrame:
    """合并K线数据（SDK数据优先）"""
    if cached_data.empty:
        return sdk_data
    
    if sdk_data.empty:
        return cached_data
    
    # 合并数据，SDK数据优先
    combined = pd.concat([cached_data, sdk_data])
    combined = combined[~combined.index.duplicated(keep="last")]
    combined = combined.sort_index()
    
    return combined


def calculate_data_completeness(
    actual_count: int,
    expected_count: int
) -> float:
    """计算数据完整度"""
    if expected_count == 0:
        return 1.0
    return actual_count / expected_count


def detect_missing_periods(
    existing_dates: List,
    expected_dates: List
) -> List[Tuple]:
    """检测缺失的时间段"""
    missing = set(expected_dates) - set(existing_dates)
    return sorted(list(missing))