{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "import xlrd\n",
    "import xlwt\n",
    "from datetime import datetime, timedelta \n",
    "import time\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "import numpy as np\n",
    "# 变量定义\n",
    "login_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n",
    "search_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos\"\n",
    "upload_url = \"http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList\"\n",
    "\n",
    "\n",
    "login_data = {\n",
    "    \"data\": {\n",
    "        \"account\": \"api_dev\",\n",
    "        \"password\": \"ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=\",\n",
    "        \"tenantHashCode\": \"8a4577dbd919675758d57999a1e891fe\",\n",
    "        \"terminal\": \"API\"\n",
    "    },\n",
    "    \"funcModule\": \"API\",\n",
    "    \"funcOperation\": \"获取token\"\n",
    "}\n",
    "\n",
    "\n",
    "read_file_path_name = \"定性模型数据项12-11.xls\"\n",
    "one_cols = []\n",
    "two_cols = []\n",
    "\n",
    "\n",
    "def update_e_value(file_path, column_index, threshold):\n",
    "    \"\"\"\n",
    "    数据修正需求：2025年1月8日\n",
    "        如果如果今天的成本即期价跟昨天的成本价差正负1000以上，就按照昨天的成本价计算\n",
    "\n",
    "    更新Excel文件中指定列的值，如果新值与前一天的值变化大于阈值，则将新值改为前一天的值。\n",
    "\n",
    "    :param file_path: Excel文件路径\n",
    "    :param column_index: 需要更新的列索引\n",
    "    :param threshold: 变化阈值\n",
    "    \"\"\"\n",
    "    # 读取Excel文件\n",
    "    try:\n",
    "        df = pd.read_excel(file_path, engine='openpyxl')\n",
    "    except:\n",
    "        df = pd.read_excel(file_path, engine='xlrd')\n",
    "    # 所有列列统一数据格式为float\n",
    "    df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n",
    "    \n",
    "    print(df.tail())\n",
    "    # 填充缺失值\n",
    "    df = df.fillna(method='ffill')\n",
    "\n",
    "    # 获取最后两行数据\n",
    "    df1 = df.tail(2)\n",
    "    print(df1)\n",
    "    # 获取前一天的指定列值\n",
    "    previous_value = df1.iloc[0, column_index]\n",
    "    print(previous_value,type(previous_value))\n",
    "    # 获取当前的指定列值\n",
    "    current_value = df1.iloc[1, column_index]\n",
    "    print(current_value,type(current_value))\n",
    "    # 判断指定列值的变化是否大于阈值\n",
    "    if abs(current_value - previous_value) > threshold:\n",
    "        # 如果变化大于阈值，将当前的指定列值改为前一天的值\n",
    "        df.iloc[-1, column_index] = previous_value\n",
    "        print('修改了')\n",
    "    print(df.tail())\n",
    "    # 将修改后的数据写回Excel文件\n",
    "    df.to_excel(file_path, index=False,engine='openpyxl')\n",
    "\n",
    "def getLogToken():\n",
    "    login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))\n",
    "    text = json.loads(login_res.text)\n",
    "    if text[\"status\"]:\n",
    "        token = text[\"data\"][\"accessToken\"]\n",
    "    else:\n",
    "        print(\"获取认证失败\")\n",
    "        token =  None\n",
    "    return token\n",
    "\n",
    "def updateExcelData(date='',token=None):\n",
    "    workbook = xlrd.open_workbook(read_file_path_name)\n",
    "\n",
    "    # 选择第一个表格\n",
    "    sheet = workbook.sheet_by_index(0)\n",
    "\n",
    "    row_data = sheet.row_values(1)\n",
    "    one_cols = row_data\n",
    "\n",
    "    cur_time,cur_time2 = getNow(date)\n",
    "    search_data = {\n",
    "        \"data\": {\n",
    "            \"date\": cur_time,\n",
    "            \"dataItemNoList\": one_cols[1:]\n",
    "        },\n",
    "        \"funcModule\": \"数据项\",\n",
    "        \"funcOperation\": \"查询\"\n",
    "    }\n",
    "    headers = {\"Authorization\": token}\n",
    "    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
    "    search_value = json.loads(search_res.text)[\"data\"]\n",
    "#     datas = search_value\n",
    "    if search_value:\n",
    "        datas = search_value\n",
    "    else :\n",
    "        datas = None\n",
    "   \n",
    "\n",
    "    append_rows = [cur_time2]\n",
    "    dataItemNo_dataValue = {}\n",
    "#     for data_value in datas:\n",
    "#         dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
    "    for data_value in datas:\n",
    "        if \"dataValue\" not in data_value:\n",
    "            print(data_value)\n",
    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
    "        else:\n",
    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
    "    for value in one_cols[1:]:\n",
    "        if value in dataItemNo_dataValue:\n",
    "            append_rows.append(dataItemNo_dataValue[value])\n",
    "        else:\n",
    "            append_rows.append(\"\")\n",
    "\n",
    "    workbook = xlrd.open_workbook('定性模型数据项12-11.xls')\n",
    "\n",
    "    # 获取所有sheet的个数\n",
    "    sheet_count = len(workbook.sheet_names())\n",
    "\n",
    "    # 获取所有sheet的名称\n",
    "    sheet_names = workbook.sheet_names()\n",
    "\n",
    "    new_workbook = xlwt.Workbook()\n",
    "    for i in range(sheet_count):\n",
    "        # 获取当前sheet\n",
    "        sheet = workbook.sheet_by_index(i)\n",
    "\n",
    "        # 获取sheet的行数和列数\n",
    "        row_count = sheet.nrows\n",
    "        col_count = sheet.ncols\n",
    "        # 获取原有数据\n",
    "        data = []\n",
    "        for row in range(row_count):\n",
    "            row_data = []\n",
    "            for col in range(col_count):\n",
    "                row_data.append(sheet.cell_value(row, col))\n",
    "            data.append(row_data)\n",
    "        # 创建xlwt的Workbook对象\n",
    "        # 创建sheet\n",
    "        new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
    "\n",
    "        # 将原有的数据写入新的sheet\n",
    "        for row in range(row_count):\n",
    "            for col in range(col_count):\n",
    "                new_sheet.write(row, col, data[row][col])\n",
    "\n",
    "        if i == 0:\n",
    "            \n",
    "            # 在新的sheet中添加数据\n",
    "            for col in range(col_count):\n",
    "                new_sheet.write(row_count, col, append_rows[col])\n",
    "\n",
    "    # 保存新的xls文件\n",
    "    new_workbook.save(\"定性模型数据项12-11.xls\")\n",
    "\n",
    "def qualitativeModel():\n",
    "    df = pd.read_excel('定性模型数据项12-11.xls')\n",
    "\n",
    "    df=df.fillna(df.ffill())\n",
    "    df1 = df[-2:].reset_index()\n",
    "    '''\n",
    "    # if df1.loc[1,'70号沥青开工率'] > 0.3:   \n",
    "    2025年1月8日 修改：\n",
    "        复盘分析后发现2024-7月开始，开工率数据从0.28 变为了28 ，改为下面的判断规则\n",
    "    '''\n",
    "    if df1.loc[1,'70号沥青开工率'] > 30:\n",
    "        a = (df1.loc[1,'70号沥青开工率']-0.2)*5/0.1\n",
    "    else :\n",
    "        a = 0\n",
    "    b = df1.loc[1,'资金因素']\n",
    "    if df1.loc[1,'昨日计划提货偏差']>0:\n",
    "        c = df1.loc[1,'昨日计划提货偏差']*10/2000\n",
    "    else :\n",
    "        c = df1.loc[1,'昨日计划提货偏差']*10/3000\n",
    "    d = df1.loc[1,'生产情况']\n",
    "    if df1.loc[1,'基质沥青库存']/265007 >0.8:\n",
    "        e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000\n",
    "    else : \n",
    "        e = 0\n",
    "    f = df1.loc[1,'下游客户价格预期']\n",
    "    if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:\n",
    "        g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100\n",
    "    else :\n",
    "        g = 0\n",
    "    h = df1.loc[1,'订单结构']\n",
    "    x = round(0.08*a+0*b+0.15*c+0.08*d +0.03*e +0.08*f +0.4*g+0.18*h+df1.loc[0,'京博指导价'],2)\n",
    "    return x\n",
    "\n",
    "\n",
    "def getNow(date='',offset=0):\n",
    "    if date == '':\n",
    "        now = datetime.now() - timedelta(days=offset)\n",
    "    else:\n",
    "        date = datetime.strptime(date, \"%Y-%m-%d\")\n",
    "        now = date\n",
    "\n",
    "    year = now.year\n",
    "    month = now.month\n",
    "    day = now.day\n",
    "\n",
    "    if month < 10:\n",
    "        month = \"0\" + str(month)\n",
    "    if day < 10:\n",
    "        day = \"0\" + str(day)\n",
    "    cur_time = str(year) + str(month) + str(day)\n",
    "    cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n",
    "    return cur_time,cur_time2\n",
    "\n",
    "def pushData(cur_time,x,token_push):\n",
    "    data1 = {\n",
    "        \"funcModule\": \"数据表信息列表\",\n",
    "        \"funcOperation\": \"新增\",\n",
    "        \"data\": [\n",
    "            {\"dataItemNo\": \"C01100036|Forecast_Price|DX|ACN\",\n",
    "             \"dataDate\": cur_time,\n",
    "             \"dataStatus\": \"add\",\n",
    "             \"dataValue\": x\n",
    "             }\n",
    "        ]\n",
    "    }\n",
    "    headers1 = {\"Authorization\": token_push}\n",
    "    res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))\n",
    "    \n",
    "def start_2(date='',token=None):\n",
    "    workbook = xlrd.open_workbook(read_file_path_name)\n",
    "\n",
    "    # 选择第一个表格\n",
    "    sheet = workbook.sheet_by_index(0)\n",
    "\n",
    "    # 获取行数和列数\n",
    "    num_rows = sheet.nrows\n",
    "    \n",
    "    row_data = sheet.row_values(1)\n",
    "    one_cols = row_data\n",
    "\n",
    "    cur_time,cur_time2 = getNow(date)\n",
    "    \n",
    "    \n",
    "    \n",
    "    search_data = {\n",
    "        \"data\": {\n",
    "            \"date\": cur_time,\n",
    "            \"dataItemNoList\": one_cols[1:]\n",
    "        },\n",
    "        \"funcModule\": \"数据项\",\n",
    "        \"funcOperation\": \"查询\"\n",
    "    }\n",
    "    headers = {\"Authorization\": token}\n",
    "    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
    "    search_value = json.loads(search_res.text)[\"data\"]\n",
    "#     datas = search_value\n",
    "    if search_value:\n",
    "        datas = search_value\n",
    "    else :\n",
    "        datas = None\n",
    "   \n",
    "\n",
    "    append_rows = [cur_time2]\n",
    "    dataItemNo_dataValue = {}\n",
    "#     for data_value in datas:\n",
    "#         dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
    "    for data_value in datas:\n",
    "        if \"dataValue\" not in data_value:\n",
    "            print(data_value)\n",
    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
    "        else:\n",
    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
    "    for value in one_cols[1:]:\n",
    "        if value in dataItemNo_dataValue:\n",
    "            append_rows.append(dataItemNo_dataValue[value])\n",
    "        else:\n",
    "            append_rows.append(\"\")\n",
    "\n",
    "    workbook = xlrd.open_workbook('定性模型数据项12-11.xls')\n",
    "\n",
    "    # 获取所有sheet的个数\n",
    "    sheet_count = len(workbook.sheet_names())\n",
    "\n",
    "    # 获取所有sheet的名称\n",
    "    sheet_names = workbook.sheet_names()\n",
    "\n",
    "    new_workbook = xlwt.Workbook()\n",
    "    for i in range(sheet_count):\n",
    "        # 获取当前sheet\n",
    "        sheet = workbook.sheet_by_index(i)\n",
    "\n",
    "        # 获取sheet的行数和列数\n",
    "        row_count = sheet.nrows\n",
    "        col_count = sheet.ncols\n",
    "        # 获取原有数据\n",
    "        data = []\n",
    "        for row in range(row_count):\n",
    "            row_data = []\n",
    "            for col in range(col_count):\n",
    "                row_data.append(sheet.cell_value(row, col))\n",
    "            data.append(row_data)\n",
    "        # 创建xlwt的Workbook对象\n",
    "        # 创建sheet\n",
    "        new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
    "\n",
    "        # 将原有的数据写入新的sheet\n",
    "        for row in range(row_count):\n",
    "            for col in range(col_count):\n",
    "                new_sheet.write(row, col, data[row][col])\n",
    "\n",
    "        if i == 0:\n",
    "            \n",
    "            # 在新的sheet中添加数据\n",
    "            for col in range(col_count):\n",
    "                new_sheet.write(row_count, col, append_rows[col])\n",
    "\n",
    "    # 保存新的xls文件\n",
    "    new_workbook.save(\"定性模型数据项12-11.xls\")\n",
    "\n",
    "    update_e_value('定性模型数据项12-11.xls', 8, 1000)\n",
    "\n",
    "    df = pd.read_excel('定性模型数据项12-11.xls')\n",
    "\n",
    "    df=df.fillna(df.ffill())\n",
    "    df1 = df[-2:].reset_index()\n",
    "    '''\n",
    "    # if df1.loc[1,'70号沥青开工率'] > 0.3:   \n",
    "    2025年1月8日 修改：\n",
    "        复盘分析后发现2024-7月开始，开工率数据从0.28 变为了28 ，改为下面的判断规则\n",
    "    '''\n",
    "    if df1.loc[1,'70号沥青开工率'] > 30:\n",
    "        a = (df1.loc[1,'70号沥青开工率']-0.2)*5/0.1\n",
    "    else :\n",
    "        a = 0\n",
    "    b = df1.loc[1,'资金因素']\n",
    "    if df1.loc[1,'昨日计划提货偏差']>0:\n",
    "        c = df1.loc[1,'昨日计划提货偏差']*10/2000\n",
    "    else :\n",
    "        c = df1.loc[1,'昨日计划提货偏差']*10/3000\n",
    "    d = df1.loc[1,'生产情况']\n",
    "    if df1.loc[1,'基质沥青库存']/265007 >0.8:\n",
    "        e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000\n",
    "    else : \n",
    "        e = 0\n",
    "    f = df1.loc[1,'下游客户价格预期']\n",
    "    if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:\n",
    "        g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100\n",
    "    else :\n",
    "        g = 0\n",
    "    h = df1.loc[1,'订单结构']\n",
    "    x = round(0.08*a+0*b+0.15*c+0.08*d +0.03*e +0.08*f +0.4*g+0.18*h+df1.loc[0,'京博指导价'],2)\n",
    "\n",
    "    login_res1 = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5))\n",
    "    text1 = json.loads(login_res1.text)\n",
    "    token_push = text1[\"data\"][\"accessToken\"]\n",
    "\n",
    "\n",
    "    data1 = {\n",
    "        \"funcModule\": \"数据表信息列表\",\n",
    "        \"funcOperation\": \"新增\",\n",
    "        \"data\": [\n",
    "            {\"dataItemNo\": \"C01100036|Forecast_Price|DX|ACN\",\n",
    "             \"dataDate\": cur_time,\n",
    "             \"dataStatus\": \"add\",\n",
    "             \"dataValue\": x\n",
    "             }\n",
    "\n",
    "        ]\n",
    "    }\n",
    "    headers1 = {\"Authorization\": token_push}\n",
    "    # res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))\n",
    "    \n",
    "\n",
    "def start():\n",
    "    workbook = xlrd.open_workbook(read_file_path_name)\n",
    "\n",
    "\n",
    "\n",
    "    # 选择第一个表格\n",
    "    sheet = workbook.sheet_by_index(0)\n",
    "\n",
    "    # 获取行数和列数\n",
    "    num_rows = sheet.nrows\n",
    "\n",
    "\n",
    "\n",
    "    row_data = sheet.row_values(1)\n",
    "    one_cols = row_data\n",
    "\n",
    "\n",
    "    login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))\n",
    "    text = json.loads(login_res.text)\n",
    "    if text[\"status\"]:\n",
    "        token = text[\"data\"][\"accessToken\"]\n",
    "    else:\n",
    "        print(\"获取认证失败\")\n",
    "        token =  None\n",
    "\n",
    "\n",
    "    now = datetime.now()\n",
    "    year = now.year\n",
    "    month = now.month\n",
    "    day = now.day\n",
    "\n",
    "    if month < 10:\n",
    "        month = \"0\" + str(month)\n",
    "    if day < 10:\n",
    "        day = \"0\" + str(day)\n",
    "    cur_time = str(year) + str(month) + str(day)\n",
    "    cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n",
    "    search_data = {\n",
    "        \"data\": {\n",
    "            \"date\": cur_time,\n",
    "            \"dataItemNoList\": one_cols[1:]\n",
    "        },\n",
    "        \"funcModule\": \"数据项\",\n",
    "        \"funcOperation\": \"查询\"\n",
    "    }\n",
    "    headers = {\"Authorization\": token}\n",
    "    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
    "    search_value = json.loads(search_res.text)[\"data\"]\n",
    "#     datas = search_value\n",
    "    if search_value:\n",
    "        datas = search_value\n",
    "    else :\n",
    "        datas = None\n",
    "   \n",
    "\n",
    "    append_rows = [cur_time2]\n",
    "    dataItemNo_dataValue = {}\n",
    "#     for data_value in datas:\n",
    "#         dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
    "    for data_value in datas:\n",
    "        if \"dataValue\" not in data_value:\n",
    "            print(data_value)\n",
    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
    "        else:\n",
    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
    "    for value in one_cols[1:]:\n",
    "        if value in dataItemNo_dataValue:\n",
    "            append_rows.append(dataItemNo_dataValue[value])\n",
    "        else:\n",
    "            append_rows.append(\"\")\n",
    "\n",
    "    workbook = xlrd.open_workbook('定性模型数据项12-11.xls')\n",
    "\n",
    "    # 获取所有sheet的个数\n",
    "    sheet_count = len(workbook.sheet_names())\n",
    "\n",
    "    # 获取所有sheet的名称\n",
    "    sheet_names = workbook.sheet_names()\n",
    "\n",
    "    new_workbook = xlwt.Workbook()\n",
    "    for i in range(sheet_count):\n",
    "        # 获取当前sheet\n",
    "        sheet = workbook.sheet_by_index(i)\n",
    "\n",
    "        # 获取sheet的行数和列数\n",
    "        row_count = sheet.nrows\n",
    "        col_count = sheet.ncols\n",
    "        # 获取原有数据\n",
    "        data = []\n",
    "        for row in range(row_count):\n",
    "            row_data = []\n",
    "            for col in range(col_count):\n",
    "                row_data.append(sheet.cell_value(row, col))\n",
    "            data.append(row_data)\n",
    "        # 创建xlwt的Workbook对象\n",
    "        # 创建sheet\n",
    "        new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
    "\n",
    "        # 将原有的数据写入新的sheet\n",
    "        for row in range(row_count):\n",
    "            for col in range(col_count):\n",
    "                new_sheet.write(row, col, data[row][col])\n",
    "\n",
    "        if i == 0:\n",
    "            # 在新的sheet中添加数据\n",
    "            for col in range(col_count):\n",
    "                new_sheet.write(row_count, col, append_rows[col])\n",
    "\n",
    "    # 保存新的xls文件\n",
    "    new_workbook.save(\"定性模型数据项12-11.xls\")\n",
    "    \n",
    "    update_e_value('定性模型数据项12-11.xls', 8, 1000)\n",
    "\n",
    "    df = pd.read_excel('定性模型数据项12-11.xls')\n",
    "    df=df.fillna(df.ffill())\n",
    "    df1 = df[-2:].reset_index()\n",
    "    # if df1.loc[1,'70号沥青开工率'] > 0.3:   -- 2025年1月9日 发版更改\n",
    "    if df1.loc[1,'70号沥青开工率'] > 30:\n",
    "        a = (df1.loc[1,'70号沥青开工率']-0.2)*5/0.1\n",
    "    else :\n",
    "        a = 0\n",
    "    b = df1.loc[1,'资金因素']\n",
    "    if df1.loc[1,'昨日计划提货偏差']>0:\n",
    "        c = df1.loc[1,'昨日计划提货偏差']*10/2000\n",
    "    else :\n",
    "        c = df1.loc[1,'昨日计划提货偏差']*10/3000\n",
    "    d = df1.loc[1,'生产情况']\n",
    "    if df1.loc[1,'基质沥青库存']/265007 >0.8:\n",
    "        e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000\n",
    "    else : \n",
    "        e = 0\n",
    "    f = df1.loc[1,'下游客户价格预期']\n",
    "    if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:\n",
    "        g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100\n",
    "    else :\n",
    "        g = 0\n",
    "    h = df1.loc[1,'订单结构']\n",
    "    x = round(0.08*a+0*b+0.15*c+0.08*d +0.03*e +0.08*f +0.4*g+0.18*h+df1.loc[0,'京博指导价'],2)\n",
    "\n",
    "\n",
    "    login_res1 = requests.post(url=login_url, json=login_data, timeout=(3, 30))\n",
    "    text1 = json.loads(login_res1.text)\n",
    "    token_push = text1[\"data\"][\"accessToken\"]\n",
    "\n",
    "\n",
    "    data1 = {\n",
    "        \"funcModule\": \"数据表信息列表\",\n",
    "        \"funcOperation\": \"新增\",\n",
    "        \"data\": [\n",
    "            {\"dataItemNo\": \"C01100036|Forecast_Price|DX|ACN\",\n",
    "             \"dataDate\": cur_time,\n",
    "             \"dataStatus\": \"add\",\n",
    "             \"dataValue\": x\n",
    "             }\n",
    "\n",
    "        ]\n",
    "    }\n",
    "    headers1 = {\"Authorization\": token_push}\n",
    "    res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))\n",
    "    \n",
    "    \n",
    "\n",
    "\n",
    "def start_1():\n",
    "    workbook = xlrd.open_workbook(read_file_path_name)\n",
    "\n",
    "\n",
    "\n",
    "    # 选择第一个表格\n",
    "    sheet = workbook.sheet_by_index(0)\n",
    "\n",
    "    # 获取行数和列数\n",
    "    num_rows = sheet.nrows\n",
    "\n",
    "\n",
    "\n",
    "    row_data = sheet.row_values(1)\n",
    "    one_cols = row_data\n",
    "\n",
    "\n",
    "    login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))\n",
    "    text = json.loads(login_res.text)\n",
    "    if text[\"status\"]:\n",
    "        token = text[\"data\"][\"accessToken\"]\n",
    "    else:\n",
    "        print(\"获取认证失败\")\n",
    "        token =  None\n",
    "\n",
    "\n",
    "    now = datetime.now()  - timedelta(days=1)  \n",
    "    year = now.year\n",
    "    month = now.month\n",
    "    day = now.day\n",
    "\n",
    "    if month < 10:\n",
    "        month = \"0\" + str(month)\n",
    "    if day < 10:\n",
    "        day = \"0\" + str(day)\n",
    "    cur_time = str(year) + str(month) + str(day)\n",
    "    cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n",
    "    search_data = {\n",
    "        \"data\": {\n",
    "            \"date\": cur_time,\n",
    "            \"dataItemNoList\": one_cols[1:]\n",
    "        },\n",
    "        \"funcModule\": \"数据项\",\n",
    "        \"funcOperation\": \"查询\"\n",
    "    }\n",
    "    headers = {\"Authorization\": token}\n",
    "    search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
    "    search_value = json.loads(search_res.text)[\"data\"]\n",
    "#     datas = search_value\n",
    "    if search_value:\n",
    "        datas = search_value\n",
    "    else :\n",
    "        datas = None\n",
    "    \n",
    "   \n",
    "\n",
    "    append_rows = [cur_time2]\n",
    "    dataItemNo_dataValue = {}\n",
    "#     for data_value in datas:\n",
    "#         dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
    "    for data_value in datas:\n",
    "        if \"dataValue\" not in data_value:\n",
    "            print(data_value)\n",
    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
    "        else:\n",
    "            dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
    "    for value in one_cols[1:]:\n",
    "        if value in dataItemNo_dataValue:\n",
    "            append_rows.append(dataItemNo_dataValue[value])\n",
    "        else:\n",
    "            append_rows.append(\"\")\n",
    "\n",
    "    workbook = xlrd.open_workbook('定性模型数据项12-11.xls')\n",
    "\n",
    "    # 获取所有sheet的个数\n",
    "    sheet_count = len(workbook.sheet_names())\n",
    "\n",
    "    # 获取所有sheet的名称\n",
    "    sheet_names = workbook.sheet_names()\n",
    "\n",
    "    new_workbook = xlwt.Workbook()\n",
    "    for i in range(sheet_count):\n",
    "        # 获取当前sheet\n",
    "        sheet = workbook.sheet_by_index(i)\n",
    "\n",
    "        # 获取sheet的行数和列数\n",
    "        row_count = sheet.nrows - 1\n",
    "        col_count = sheet.ncols\n",
    "        # 获取原有数据\n",
    "        data = []\n",
    "        for row in range(row_count):\n",
    "            row_data = []\n",
    "            for col in range(col_count):\n",
    "                row_data.append(sheet.cell_value(row, col))\n",
    "            data.append(row_data)\n",
    "        # 创建xlwt的Workbook对象\n",
    "        # 创建sheet\n",
    "        new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
    "\n",
    "        # 将原有的数据写入新的sheet\n",
    "        for row in range(row_count):\n",
    "            for col in range(col_count):\n",
    "                new_sheet.write(row, col, data[row][col])\n",
    "\n",
    "        if i == 0:\n",
    "            # 在新的sheet中添加数据\n",
    "            for col in range(col_count):\n",
    "                new_sheet.write(row_count, col, append_rows[col])\n",
    "\n",
    "    # 保存新的xls文件\n",
    "    new_workbook.save(\"定性模型数据项12-11.xls\")\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  }
 ],
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