754 lines
28 KiB
Plaintext
754 lines
28 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import requests\n",
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"import json\n",
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"import xlrd\n",
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"import xlwt\n",
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"from datetime import datetime, timedelta \n",
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"import time\n",
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"import pandas as pd\n",
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"pd.set_option('display.max_columns', None)\n",
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"\n",
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"import numpy as np\n",
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"# 变量定义\n",
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"login_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n",
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"search_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos\"\n",
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"upload_url = \"http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList\"\n",
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"\n",
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"\n",
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"login_data = {\n",
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" \"data\": {\n",
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" \"account\": \"api_dev\",\n",
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" \"password\": \"ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=\",\n",
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" \"tenantHashCode\": \"8a4577dbd919675758d57999a1e891fe\",\n",
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" \"terminal\": \"API\"\n",
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" },\n",
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" \"funcModule\": \"API\",\n",
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" \"funcOperation\": \"获取token\"\n",
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"}\n",
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"\n",
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"\n",
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"read_file_path_name = \"定性模型数据项12-11.xls\"\n",
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"one_cols = []\n",
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"two_cols = []\n",
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"\n",
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"\n",
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"def update_e_value(file_path, column_index, threshold):\n",
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" \"\"\"\n",
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" 数据修正需求：2025年1月8日\n",
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" 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",
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"\n",
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" 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",
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"\n",
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" :param file_path: Excel文件路径\n",
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" :param column_index: 需要更新的列索引\n",
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" :param threshold: 变化阈值\n",
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" \"\"\"\n",
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" # 读取Excel文件\n",
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" try:\n",
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" df = pd.read_excel(file_path, engine='openpyxl')\n",
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" except:\n",
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" df = pd.read_excel(file_path, engine='xlrd')\n",
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" # 所有列列统一数据格式为float\n",
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" df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n",
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" \n",
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" print(df.tail())\n",
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" # 填充缺失值\n",
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" df = df.fillna(method='ffill')\n",
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"\n",
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" # 获取最后两行数据\n",
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" df1 = df.tail(2)\n",
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" print(df1)\n",
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" # 获取前一天的指定列值\n",
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" previous_value = df1.iloc[0, column_index]\n",
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" print(previous_value,type(previous_value))\n",
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" # 获取当前的指定列值\n",
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" current_value = df1.iloc[1, column_index]\n",
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" print(current_value,type(current_value))\n",
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" # 判断指定列值的变化是否大于阈值\n",
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" if abs(current_value - previous_value) > threshold:\n",
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" # 如果变化大于阈值，将当前的指定列值改为前一天的值\n",
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" df.iloc[-1, column_index] = previous_value\n",
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" print('修改了')\n",
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" print(df.tail())\n",
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" # 将修改后的数据写回Excel文件\n",
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" df.to_excel(file_path, index=False,engine='openpyxl')\n",
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"\n",
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"def getLogToken():\n",
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" login_res = requests.post(url=login_url, json=login_data, timeout=(3, 5))\n",
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" text = json.loads(login_res.text)\n",
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" if text[\"status\"]:\n",
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" token = text[\"data\"][\"accessToken\"]\n",
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" else:\n",
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" print(\"获取认证失败\")\n",
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" token = None\n",
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" return token\n",
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"\n",
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"def updateExcelData(date='',token=None):\n",
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" workbook = xlrd.open_workbook(read_file_path_name)\n",
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"\n",
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" # 选择第一个表格\n",
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" sheet = workbook.sheet_by_index(0)\n",
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"\n",
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" row_data = sheet.row_values(1)\n",
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" one_cols = row_data\n",
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"\n",
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" cur_time,cur_time2 = getNow(date)\n",
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" search_data = {\n",
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" \"data\": {\n",
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" \"date\": cur_time,\n",
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" \"dataItemNoList\": one_cols[1:]\n",
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" },\n",
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" \"funcModule\": \"数据项\",\n",
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" \"funcOperation\": \"查询\"\n",
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" }\n",
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" headers = {\"Authorization\": token}\n",
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" search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
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" search_value = json.loads(search_res.text)[\"data\"]\n",
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"# datas = search_value\n",
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" if search_value:\n",
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" datas = search_value\n",
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" else :\n",
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" datas = None\n",
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" \n",
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"\n",
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" append_rows = [cur_time2]\n",
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" dataItemNo_dataValue = {}\n",
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"# for data_value in datas:\n",
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"# dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
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" for data_value in datas:\n",
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" if \"dataValue\" not in data_value:\n",
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" print(data_value)\n",
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" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
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" else:\n",
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" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
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" for value in one_cols[1:]:\n",
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" if value in dataItemNo_dataValue:\n",
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" append_rows.append(dataItemNo_dataValue[value])\n",
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" else:\n",
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" append_rows.append(\"\")\n",
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"\n",
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" workbook = xlrd.open_workbook('定性模型数据项12-11.xls')\n",
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"\n",
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" # 获取所有sheet的个数\n",
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" sheet_count = len(workbook.sheet_names())\n",
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"\n",
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" # 获取所有sheet的名称\n",
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" sheet_names = workbook.sheet_names()\n",
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"\n",
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" new_workbook = xlwt.Workbook()\n",
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" for i in range(sheet_count):\n",
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" # 获取当前sheet\n",
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" sheet = workbook.sheet_by_index(i)\n",
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"\n",
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" # 获取sheet的行数和列数\n",
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" row_count = sheet.nrows\n",
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" col_count = sheet.ncols\n",
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" # 获取原有数据\n",
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" data = []\n",
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" for row in range(row_count):\n",
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" row_data = []\n",
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" for col in range(col_count):\n",
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" row_data.append(sheet.cell_value(row, col))\n",
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" data.append(row_data)\n",
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" # 创建xlwt的Workbook对象\n",
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" # 创建sheet\n",
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" new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
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"\n",
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" # 将原有的数据写入新的sheet\n",
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" for row in range(row_count):\n",
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" for col in range(col_count):\n",
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" new_sheet.write(row, col, data[row][col])\n",
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"\n",
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" if i == 0:\n",
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" \n",
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" # 在新的sheet中添加数据\n",
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" for col in range(col_count):\n",
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" new_sheet.write(row_count, col, append_rows[col])\n",
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"\n",
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" # 保存新的xls文件\n",
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" new_workbook.save(\"定性模型数据项12-11.xls\")\n",
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"\n",
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"def qualitativeModel():\n",
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" df = pd.read_excel('定性模型数据项12-11.xls')\n",
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"\n",
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" df=df.fillna(df.ffill())\n",
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" df1 = df[-2:].reset_index()\n",
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" '''\n",
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" # if df1.loc[1,'70号沥青开工率'] > 0.3: \n",
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" 2025年1月8日 修改：\n",
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" 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",
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" '''\n",
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" if df1.loc[1,'70号沥青开工率'] > 30:\n",
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" a = (df1.loc[1,'70号沥青开工率']-0.2)*5/0.1\n",
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" else :\n",
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" a = 0\n",
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" b = df1.loc[1,'资金因素']\n",
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" if df1.loc[1,'昨日计划提货偏差']>0:\n",
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" c = df1.loc[1,'昨日计划提货偏差']*10/2000\n",
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" else :\n",
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" c = df1.loc[1,'昨日计划提货偏差']*10/3000\n",
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" d = df1.loc[1,'生产情况']\n",
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" if df1.loc[1,'基质沥青库存']/265007 >0.8:\n",
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" e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000\n",
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" else : \n",
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" e = 0\n",
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" f = df1.loc[1,'下游客户价格预期']\n",
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" if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:\n",
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" g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100\n",
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" else :\n",
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" g = 0\n",
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" h = df1.loc[1,'订单结构']\n",
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" 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",
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" return x\n",
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"\n",
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"\n",
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"def getNow(date='',offset=0):\n",
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" if date == '':\n",
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" now = datetime.now() - timedelta(days=offset)\n",
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" else:\n",
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" date = datetime.strptime(date, \"%Y-%m-%d\")\n",
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" now = date\n",
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"\n",
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" year = now.year\n",
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" month = now.month\n",
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" day = now.day\n",
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"\n",
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" if month < 10:\n",
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" month = \"0\" + str(month)\n",
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" if day < 10:\n",
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" day = \"0\" + str(day)\n",
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" cur_time = str(year) + str(month) + str(day)\n",
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" cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n",
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" return cur_time,cur_time2\n",
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"\n",
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"def pushData(cur_time,x,token_push):\n",
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" data1 = {\n",
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" \"funcModule\": \"数据表信息列表\",\n",
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" \"funcOperation\": \"新增\",\n",
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" \"data\": [\n",
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" {\"dataItemNo\": \"C01100036|Forecast_Price|DX|ACN\",\n",
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" \"dataDate\": cur_time,\n",
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" \"dataStatus\": \"add\",\n",
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" \"dataValue\": x\n",
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" }\n",
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" ]\n",
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" }\n",
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" headers1 = {\"Authorization\": token_push}\n",
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" res = requests.post(url=upload_url, headers=headers1, json=data1, timeout=(3, 5))\n",
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" \n",
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"def start_2(date='',token=None):\n",
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" workbook = xlrd.open_workbook(read_file_path_name)\n",
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"\n",
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" # 选择第一个表格\n",
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" sheet = workbook.sheet_by_index(0)\n",
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"\n",
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" # 获取行数和列数\n",
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" num_rows = sheet.nrows\n",
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" \n",
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" row_data = sheet.row_values(1)\n",
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" one_cols = row_data\n",
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"\n",
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" cur_time,cur_time2 = getNow(date)\n",
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" \n",
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" \n",
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" \n",
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" search_data = {\n",
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" \"data\": {\n",
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" \"date\": cur_time,\n",
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" \"dataItemNoList\": one_cols[1:]\n",
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" },\n",
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" \"funcModule\": \"数据项\",\n",
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" \"funcOperation\": \"查询\"\n",
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" }\n",
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" headers = {\"Authorization\": token}\n",
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" search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
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" search_value = json.loads(search_res.text)[\"data\"]\n",
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"# datas = search_value\n",
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" if search_value:\n",
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" datas = search_value\n",
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" else :\n",
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" datas = None\n",
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" \n",
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"\n",
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" append_rows = [cur_time2]\n",
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" dataItemNo_dataValue = {}\n",
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"# for data_value in datas:\n",
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"# dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
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" for data_value in datas:\n",
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" if \"dataValue\" not in data_value:\n",
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" print(data_value)\n",
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" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
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" else:\n",
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" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
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" for value in one_cols[1:]:\n",
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" if value in dataItemNo_dataValue:\n",
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" append_rows.append(dataItemNo_dataValue[value])\n",
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" else:\n",
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" append_rows.append(\"\")\n",
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"\n",
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" workbook = xlrd.open_workbook('定性模型数据项12-11.xls')\n",
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"\n",
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" # 获取所有sheet的个数\n",
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" sheet_count = len(workbook.sheet_names())\n",
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"\n",
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" # 获取所有sheet的名称\n",
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" sheet_names = workbook.sheet_names()\n",
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"\n",
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" new_workbook = xlwt.Workbook()\n",
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" for i in range(sheet_count):\n",
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" # 获取当前sheet\n",
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" sheet = workbook.sheet_by_index(i)\n",
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"\n",
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" # 获取sheet的行数和列数\n",
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" row_count = sheet.nrows\n",
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" col_count = sheet.ncols\n",
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" # 获取原有数据\n",
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" data = []\n",
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" for row in range(row_count):\n",
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" row_data = []\n",
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" for col in range(col_count):\n",
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" row_data.append(sheet.cell_value(row, col))\n",
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" data.append(row_data)\n",
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" # 创建xlwt的Workbook对象\n",
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" # 创建sheet\n",
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" new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
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"\n",
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" # 将原有的数据写入新的sheet\n",
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" for row in range(row_count):\n",
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" for col in range(col_count):\n",
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" new_sheet.write(row, col, data[row][col])\n",
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"\n",
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" if i == 0:\n",
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" \n",
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" # 在新的sheet中添加数据\n",
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" for col in range(col_count):\n",
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" new_sheet.write(row_count, col, append_rows[col])\n",
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"\n",
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" # 保存新的xls文件\n",
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" new_workbook.save(\"定性模型数据项12-11.xls\")\n",
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"\n",
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" update_e_value('定性模型数据项12-11.xls', 8, 1000)\n",
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"\n",
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" df = pd.read_excel('定性模型数据项12-11.xls')\n",
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"\n",
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" df=df.fillna(df.ffill())\n",
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" df1 = df[-2:].reset_index()\n",
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" '''\n",
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" # if df1.loc[1,'70号沥青开工率'] > 0.3: \n",
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" 2025年1月8日 修改：\n",
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" 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",
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" '''\n",
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" if df1.loc[1,'70号沥青开工率'] > 30:\n",
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" a = (df1.loc[1,'70号沥青开工率']-0.2)*5/0.1\n",
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" else :\n",
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" a = 0\n",
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" b = df1.loc[1,'资金因素']\n",
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" if df1.loc[1,'昨日计划提货偏差']>0:\n",
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" c = df1.loc[1,'昨日计划提货偏差']*10/2000\n",
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" else :\n",
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" c = df1.loc[1,'昨日计划提货偏差']*10/3000\n",
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" d = df1.loc[1,'生产情况']\n",
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" if df1.loc[1,'基质沥青库存']/265007 >0.8:\n",
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" e = (df1.loc[1,'基质沥青库存'] - df1.loc[0,'基质沥青库存'])*10/-5000\n",
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" else : \n",
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" e = 0\n",
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" f = df1.loc[1,'下游客户价格预期']\n",
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" if abs(df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])>=100:\n",
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" g = (df1.loc[1,'即期成本'] - df1.loc[0,'即期成本'])*50/100\n",
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" else :\n",
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" g = 0\n",
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" h = df1.loc[1,'订单结构']\n",
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" 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",
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"\n",
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" 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'] / 100 > 0.3:\n",
|
||
" a = (df1.loc[1,'70号沥青开工率'] / 100 -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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# if __name__ == \"__main__\":\n",
|
||
"\n",
|
||
" # 需要单独运行放开\n",
|
||
"# start_1()\n",
|
||
"\n",
|
||
" # # 每天定时12点运行\n",
|
||
" # while True:\n",
|
||
" # # 获取当前时间\n",
|
||
" # current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n",
|
||
" # current_time_1 = time.strftime(\"%H:%M:%S\", time.localtime())\n",
|
||
"\n",
|
||
" # # 判断当前时间是否为执行任务的时间点\n",
|
||
" # if current_time == \"12:00:00\":\n",
|
||
" # print(\"执行定时任务\")\n",
|
||
" # start()\n",
|
||
"\n",
|
||
" # # 休眠1秒钟，避免过多占用CPU资源\n",
|
||
" # time.sleep(1)\n",
|
||
" \n",
|
||
" # elif current_time_1 == \"20:00:00\":\n",
|
||
" # print(\"更新数据\")\n",
|
||
" # start_1()\n",
|
||
" # time.sleep(1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 重新上传定性数据\n",
|
||
"\n",
|
||
"from datetime import datetime, timedelta\n",
|
||
"\n",
|
||
"def main(date='',token=None):\n",
|
||
" updateExcelData(date,token)\n",
|
||
" update_e_value('定性模型数据项12-11.xls', 8, 1000)\n",
|
||
" x = qualitativeModel()\n",
|
||
" cur_time,cur_time2 = getNow(date)\n",
|
||
" pushData(cur_time,x,token)\n",
|
||
"\n",
|
||
"\n",
|
||
"start_date = datetime(2024, 7, 1)\n",
|
||
"end_date = datetime(2025, 1, 1)\n",
|
||
"token = getLogToken()\n",
|
||
"while start_date < end_date:\n",
|
||
" print(start_date.strftime('%Y%m%d'))\n",
|
||
" main(start_date.strftime('%Y%m%d'),token)\n",
|
||
" start_date += timedelta(days=1)\n",
|
||
" time.sleep(1)\n",
|
||
" \n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "base",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.11.7"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|