# -*- coding:utf-8 -*-
# !/usr/bin/env python
"""
Date: 2025/12/1 22:00
Desc: 百度股市通-经济数据
https://gushitong.baidu.com/calendar
"""
import math
import re

import pandas as pd
from curl_cffi import requests


def _get_baidu_cookie(headers: dict) -> str:
    """
    安全获取百度股市通所需的Cookie
    :param headers: 基础请求头
    :return: 格式化的Cookie字符串
    :raises ValueError: 当无法获取必要Cookie时
    :raises ConnectionError: 网络请求失败时
    """
    try:
        # 使用Session保持Cookie上下文
        with requests.Session() as session:
            session.headers.update(headers)

            # 第一步：获取基础Cookie (BAIDUID系列)
            resp1 = session.get(
                "https://gushitong.baidu.com/calendar",
                impersonate="chrome110",
                timeout=10
            )
            resp1.raise_for_status()

            # 验证必要Cookie
            baiduid = resp1.cookies.get("BAIDUID")
            baiduid_bfess = resp1.cookies.get("BAIDUID_BFESS")
            if not all([baiduid, baiduid_bfess]):
                raise ValueError("Missing BAIDUID cookies in first response")

            # 第二步：提取并请求hm.js
            hm_pattern = r'https://hm\.baidu\.com/hm\.js\?\w+'
            hm_match = re.search(hm_pattern, resp1.text)
            if not hm_match:
                # 尝试备用正则模式
                hm_match = re.search(r'//hm\.baidu\.com/hm\.js\?\w+', resp1.text)
                if not hm_match:
                    raise ValueError("Failed to extract hm.js URL from response")

            hm_url = "https:" + hm_match.group() if hm_match.group().startswith("//") else hm_match.group()

            # 第二步请求 (自动携带第一步的Cookie)
            resp2 = session.get(hm_url, impersonate="chrome110", timeout=10)
            resp2.raise_for_status()

            # 验证必要Cookie
            hmac_count = resp2.cookies.get("HMACCOUNT")
            hmac_count_bfess = resp2.cookies.get("HMACCOUNT_BFESS")
            if not all([hmac_count, hmac_count_bfess]):
                raise ValueError("Missing HMACCOUNT cookies in second response")

            # 安全拼接Cookie
            return (
                f"BAIDUID={baiduid}; "
                f"BAIDUID_BFESS={baiduid_bfess}; "
                f"HMACCOUNT={hmac_count}; "
                f"HMACCOUNT_BFESS={hmac_count_bfess}"
            )

    except requests.exceptions.RequestException as e:
        raise ConnectionError(f"Network request failed: {str(e)}") from e
    except re.error as e:
        raise ValueError(f"Regex pattern error: {str(e)}") from e


def _baidu_finance_calendar(
    date: str,
    cate: str,
    process_func,
    cookie: str = None
) -> pd.DataFrame:
    """
    百度股市通日历数据基础函数（支持分页）
    :param date: 查询日期 (格式: YYYYMMDD)
    :param cate: 数据类别 ("economic_data" 或 "notify_suspend")
    :param process_func: 数据处理函数
    :param cookie: cookie
    :return: 处理后的DataFrame
    """
    # 日期格式转换
    formatted_date = "-".join([date[:4], date[4:6], date[6:]])

    # 构建请求参数
    base_params = {
        "start_date": formatted_date,
        "end_date": formatted_date,
        "pn": "0",
        "rn": "100",  # 每页100条
        "cate": cate,
        "finClientType": "pc",
    }

    # 构建请求头
    headers = {
        "accept": "application/vnd.finance-web.v1+json",
        "accept-encoding": "gzip, deflate, br, zstd",
        "accept-language": "en,zh-CN;q=0.9,zh;q=0.8",
        "cache-control": "no-cache",
        "origin": "https://gushitong.baidu.com",
        "pragma": "no-cache",
        "priority": "u=1, i",
        "referer": "https://gushitong.baidu.com/",
        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
                      "Chrome/142.0.0.0 Safari/537.36"
    }

    # 在_baidu_finance_calendar函数中替换原代码块
    if cookie is None:
        try:
            cookie = _get_baidu_cookie(headers.copy())  # 保护原始headers
        except Exception as e:
            # 可降级处理或保留原始行为
            raise RuntimeError(f"Failed to obtain Baidu cookies: {str(e)}") from e
    headers["cookie"] = cookie

    url = "https://finance.pae.baidu.com/sapi/v1/financecalendar"
    big_df = pd.DataFrame()

    # 获取指定日期的总记录数
    target_date = formatted_date
    total_records = 0

    # 第一次请求
    params = base_params.copy()
    response = requests.get(url=url, params=params, headers=headers)
    response.raise_for_status()
    data_json = response.json()

    # 从JSON中提取指定日期的总记录数
    if "Result" in data_json and "calendarInfo" in data_json["Result"]:
        calendar_info = data_json["Result"]["calendarInfo"]

        # 查找目标日期的记录
        for item in calendar_info:
            if item.get("date") == target_date:
                total_records = item.get("total", 0)
                break

    # 计算总页数 (每页100条)
    total_pages = math.ceil(total_records / 100) if total_records > 0 else 1

    # 处理所有页码
    for page in range(total_pages):
        if page > 0:  # 第一页已在前面获取
            params = base_params.copy()
            params["pn"] = str(page)
            response = requests.get(url=url, params=params, headers=headers)
            response.raise_for_status()
            data_json = response.json()

        # 提取并处理指定日期的数据
        if "Result" in data_json and "calendarInfo" in data_json["Result"]:
            for item in data_json["Result"]["calendarInfo"]:
                if item.get("date") == target_date and item.get("list"):
                    processed_df = process_func(item["list"])
                    big_df = pd.concat([big_df, processed_df], ignore_index=True)

    return big_df


def _process_economic_data(data_list: list) -> pd.DataFrame:
    """处理经济数据"""
    if not data_list:
        return pd.DataFrame()
    temp_df = pd.DataFrame(data_list)
    rename_dict = {
        "date": "日期",
        "time": "时间",
        "title": "事件",
        "star": "重要性",
        "formerVal": "前值",
        "pubVal": "公布",
        "region": "地区",
        "indicateVal": "预期",
        "country": "国家",
        "timePeriod": "统计周期"
    }
    temp_df.rename(columns=rename_dict, inplace=True)
    required_cols = ["公布", "预期", "前值", "重要性"]
    for col in required_cols:
        if col not in temp_df.columns:
            temp_df[col] = None
    available_cols = []
    for col in ["日期", "时间", "国家", "地区", "事件", "统计周期", "公布", "预期", "前值", "重要性"]:
        if col in temp_df.columns:
            available_cols.append(col)
    if available_cols:
        temp_df = temp_df[available_cols]
    for col in ["公布", "预期", "前值", "重要性"]:
        if col in temp_df.columns:
            temp_df[col] = pd.to_numeric(temp_df[col], errors="coerce")
    if "日期" in temp_df.columns:
        temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date

    return temp_df


def _process_suspend_data(data_list: list) -> pd.DataFrame:
    """处理停复牌数据 - 根据实际JSON结构精确修正"""
    if not data_list:
        return pd.DataFrame()
    temp_df = pd.DataFrame(data_list)
    rename_dict = {
        "code": "股票代码",
        "name": "股票简称",
        "exchange": "交易所代码",
        "start": "停牌时间",
        "reason": "停牌事项说明",
        "marketValue": "市值",
        "date": "公告日期",
        "time": "公告时间",
        "type": "证券类型",
        "market": "市场类型",
        "isSkip": "是否跳过",
        "end": "复牌时间"
    }
    temp_df.rename(columns=rename_dict, inplace=True)
    if "复牌时间" not in temp_df.columns:
        temp_df['复牌时间'] = '-'
    temp_df = temp_df[[
        "股票代码",
        "股票简称",
        "交易所代码",
        "停牌时间",
        "复牌时间",
        "停牌事项说明",
        "市值",
        "公告日期",
        "公告时间",
        "证券类型",
        "市场类型",
        "是否跳过"
    ]]
    return temp_df


def news_economic_baidu(date: str = "20251126", cookie: str = None) -> pd.DataFrame:
    """
    百度股市通-经济数据
    https://gushitong.baidu.com/calendar
    :param date: 查询日期 (格式: YYYYMMDD)
    :param cookie: cookie
    :return: 经济数据DataFrame
    """
    return _baidu_finance_calendar(
        date=date,
        cate="economic_data",
        process_func=_process_economic_data,
        cookie=cookie
    )


def news_trade_notify_suspend_baidu(date: str = "20251126", cookie: str = None) -> pd.DataFrame:
    """
    百度股市通-交易提醒-停复牌
    https://gushitong.baidu.com/calendar
    :param date: 查询日期 (格式: YYYYMMDD)
    :param cookie: cookie
    :return: 停复牌数据DataFrame
    """
    return _baidu_finance_calendar(
        date=date,
        cate="notify_suspend",
        process_func=_process_suspend_data,
        cookie=cookie
    )


def _process_dividend_data(data_list: list) -> pd.DataFrame:
    """处理分红派息数据"""
    if not data_list:
        return pd.DataFrame()

    temp_df = pd.DataFrame(data_list)

    # 字段映射
    rename_dict = {
        "code": "股票代码",
        "market": "-",  # 这个字段在最终结果中会被删除
        "exchange": "交易所",
        "name": "股票简称",
        "diviDate": "除权日",
        "date": "报告期",
        "diviCash": "分红",
        "shareDivide": "送股",
        "transfer": "转增",
        "physical": "实物",
    }
    temp_df.rename(columns=rename_dict, inplace=True)

    # 确保必要列存在
    if "实物" not in temp_df.columns:
        temp_df["实物"] = "-"
    if "送股" not in temp_df.columns:
        temp_df["送股"] = "-"
    if "转增" not in temp_df.columns:
        temp_df["转增"] = "-"

    # 选择需要的列
    temp_df = temp_df[
        [
            "股票代码",
            "除权日",
            "分红",
            "送股",
            "转增",
            "实物",
            "交易所",
            "股票简称",
            "报告期",
        ]
    ]

    # 日期格式转换
    if "除权日" in temp_df.columns:
        temp_df["除权日"] = pd.to_datetime(temp_df["除权日"], errors="coerce").dt.date
    if "报告期" in temp_df.columns:
        temp_df["报告期"] = pd.to_datetime(temp_df["报告期"], errors="coerce").dt.date

    return temp_df


def news_trade_notify_dividend_baidu(date: str = "20251126", cookie: str = None) -> pd.DataFrame:
    """
    百度股市通-交易提醒-分红派息
    https://gushitong.baidu.com/calendar
    :param date: 查询日期 (格式: YYYYMMDD)
    :param cookie: cookie
    :return: 交易提醒-分红派息DataFrame
    """
    return _baidu_finance_calendar(
        date=date,
        cate="notify_divide",
        process_func=_process_dividend_data,
        cookie=cookie
    )


def _process_report_data(data_list: list) -> pd.DataFrame:
    """处理财报发行数据 - 根据实际JSON结构精确修正"""
    if not data_list:
        return pd.DataFrame()

    # 创建DataFrame
    temp_df = pd.DataFrame(data_list)

    # 精确字段映射 (根据提供的JSON结构)
    rename_dict = {
        "code": "股票代码",
        "name": "股票简称",
        "exchange": "交易所",
        "reportType": "财报类型",
        "time": "发布时间",
        "marketValue": "市值",
        "capitalization": "总市值",
        "date": "发布日期"
    }
    temp_df.rename(columns=rename_dict, inplace=True)

    # 确保必要列存在
    if "财报类型" not in temp_df.columns:
        temp_df["财报类型"] = "-"
    if "发布时间" not in temp_df.columns:
        temp_df["发布时间"] = "-"
    if "市值" not in temp_df.columns and "总市值" in temp_df.columns:
        temp_df["市值"] = temp_df["总市值"]

    # 选择并排序列
    available_cols = []
    for col in ["股票代码", "股票简称", "交易所", "财报类型", "发布时间", "市值", "发布日期"]:
        if col in temp_df.columns:
            available_cols.append(col)

    if available_cols:
        temp_df = temp_df[available_cols]
    else:
        # 如果没有匹配的列，返回空DataFrame
        return pd.DataFrame()

    # 类型转换
    if "市值" in temp_df.columns:
        temp_df["市值"] = pd.to_numeric(temp_df["市值"], errors="coerce")

    if "发布日期" in temp_df.columns:
        temp_df["发布日期"] = pd.to_datetime(temp_df["发布日期"], errors="coerce").dt.date

    return temp_df


def news_report_time_baidu(date: str = "20251126", cookie: str = None) -> pd.DataFrame:
    """
    百度股市通-财报发行
    https://gushitong.baidu.com/calendar
    :param date: 查询日期 (格式: YYYYMMDD)
    :param cookie: cookie
    :return: 财报发行DataFrame
    """
    return _baidu_finance_calendar(
        date=date,
        cate="report_time",
        process_func=_process_report_data,
        cookie=cookie
    )


if __name__ == "__main__":
    news_economic_baidu_df = news_economic_baidu(date="20251126")
    print(news_economic_baidu_df)

    news_trade_notify_suspend_baidu_df = news_trade_notify_suspend_baidu(
        date="20251126"
    )
    print(news_trade_notify_suspend_baidu_df)

    news_trade_notify_dividend_baidu_df = news_trade_notify_dividend_baidu(
        date="20251126"
    )
    print(news_trade_notify_dividend_baidu_df)

    news_report_time_baidu_df = news_report_time_baidu(date="20251126")
    print(news_report_time_baidu_df)
