import logging
import pandas as pd
import requests
import math
import time
from typing import List, Dict
from akshare.utils.tqdm import get_tqdm
from loguru import logger
def __stock_zh_main_spot_em() -> pd.DataFrame:
    """
    东方财富网-行情中心-沪深重要指数
    https://quote.eastmoney.com/center/hszs.html
    :return: 指数的实时行情数据
    :rtype: pandas.DataFrame
    """
    url = "https://33.push2.eastmoney.com/api/qt/clist/get"
    params = {
        "pn": "1",
        "pz": "100",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "dect": "1",
        "wbp2u": "|0|0|0|web",
        "fid": "",
        "fs": "b:MK0010",
        "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,"
        "f23,f24,f25,f26,f22,f11,f62,f128,f136,f115,f152",
    }
    r = requests.get(url, params=params)
    data_json = r.json()
    temp_df = pd.DataFrame(data_json["data"]["diff"])
    temp_df.reset_index(inplace=True)
    temp_df["index"] = temp_df["index"].astype(int) + 1
    temp_df.rename(
        columns={
            "index": "序号",
            "f2": "最新价",
            "f3": "涨跌幅",
            "f4": "涨跌额",
            "f5": "成交量",
            "f6": "成交额",
            "f7": "振幅",
            "f10": "量比",
            "f12": "代码",
            "f14": "名称",
            "f15": "最高",
            "f16": "最低",
            "f17": "今开",
            "f18": "昨收",
        },
        inplace=True,
    )
    temp_df = temp_df[
        [
            "序号",
            "代码",
            "名称",
            "最新价",
            "涨跌幅",
            "涨跌额",
            "成交量",
            "成交额",
            "振幅",
            "最高",
            "最低",
            "今开",
            "昨收",
            "量比",
        ]
    ]
    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
    temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
    temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
    temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
    return temp_df


def fetch_paginated_data(url: str, base_params: Dict, timeout: int = 15*2):
    """
    东方财富-分页获取数据并合并结果
    https://quote.eastmoney.com/f1.html?newcode=0.000001
    :param url: 股票代码
    :type url: str
    :param base_params: 基础请求参数
    :type base_params: dict
    :param timeout: 请求超时时间
    :type timeout: str
    :return: 合并后的数据
    :rtype: pandas.DataFrame
    """
    # 复制参数以避免修改原始参数
    params = base_params.copy()
    # 获取第一页数据，用于确定分页信息
    r = requests.get(url, params=params, timeout=timeout)
    data_json = r.json()
    # 计算分页信息
    per_page_num = len(data_json["data"]["diff"])
    total_page = math.ceil(data_json["data"]["total"] / per_page_num)
    # 存储所有页面数据
    temp_list = []
    # 添加第一页数据
    temp_list.append(pd.DataFrame(data_json["data"]["diff"]))
    # 获取进度条
    tqdm = get_tqdm()
    # 获取剩余页面数据
    for page in range(2, total_page + 1):
        logger.info(f"获取第 {page}/{total_page} 页数据")
        params.update({"pn": page})
        r = requests.get(url, params=params, timeout=timeout)
        data_json = r.json()
        inner_temp_df = pd.DataFrame(data_json["data"]["diff"])
        temp_list.append(inner_temp_df)
        time.sleep(300)
    # 合并所有数据
    temp_df = pd.concat(temp_list, ignore_index=True)
    temp_df["f3"] = pd.to_numeric(temp_df["f3"], errors="coerce")
    temp_df.sort_values(by=["f3"], ascending=False, inplace=True, ignore_index=True)
    temp_df.reset_index(inplace=True)
    temp_df["index"] = temp_df["index"].astype(int) + 1
    return temp_df

def stock_zh_index_spot_em(symbol: str = "上证系列指数") -> pd.DataFrame:
    """
    东方财富网-行情中心-沪深京指数
    https://quote.eastmoney.com/center/gridlist.html#index_sz
    :param symbol: "上证系列指数"; choice of {"沪深重要指数", "上证系列指数", "深证系列指数", "指数成份", "中证系列指数"}
    :type symbol: str
    :return: 指数的实时行情数据
    :rtype: pandas.DataFrame
    """
    if symbol == "沪深重要指数":
        return __stock_zh_main_spot_em()

    url = "https://48.push2.eastmoney.com/api/qt/clist/get"
    symbol_map = {
        "上证系列指数": "m:1+t:1",
        "深证系列指数": "m:0 t:5",
        "指数成份": "m:1+s:3,m:0+t:5",
        "中证系列指数": "m:2",
    }
    params = {
        "pn": "1",
        "pz": "100",
        "po": "1",
        "np": "1",
        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
        "fltt": "2",
        "invt": "2",
        "wbp2u": "|0|0|0|web",
        "fid": "f12",
        "fs": symbol_map[symbol],
        "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,"
        "f26,f22,f33,f11,f62,f128,f136,f115,f152",
    }
    temp_df = fetch_paginated_data(url, params)
    temp_df.rename(
        columns={
            "index": "序号",
            "f2": "最新价",
            "f3": "涨跌幅",
            "f4": "涨跌额",
            "f5": "成交量",
            "f6": "成交额",
            "f7": "振幅",
            "f10": "量比",
            "f12": "代码",
            "f14": "名称",
            "f15": "最高",
            "f16": "最低",
            "f17": "今开",
            "f18": "昨收",
        },
        inplace=True,
    )
    temp_df = temp_df[
        [
            "序号",
            "代码",
            "名称",
            "最新价",
            "涨跌幅",
            "涨跌额",
            "成交量",
            "成交额",
            "振幅",
            "最高",
            "最低",
            "今开",
            "昨收",
            "量比",
        ]
    ]
    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
    temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
    temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
    temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
    temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
    return temp_df