{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "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", "# query_data_list_item_nos_url\n", "search_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos\" #jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos\n", "upload_url = \"http://10.200.32.39/jingbo-api/api/dw/dataValue/pushDataValueList\"\n", "\n", "\n", "query_data_list_item_nos_data = {\n", " \"funcModule\": \"数据项\",\n", " \"funcOperation\": \"查询\",\n", " \"data\": {\n", " \"dateStart\": \"20200101\",\n", " \"dateEnd\": \"20241231\",\n", " \"dataItemNoList\": [\"Brentzdj\", \"Brentzgj\"] # 数据项编码,代表 brent最低价和最高价\n", " }\n", "}\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.xlsx\"\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", " \n", " df = pd.read_excel(file_path)\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('前一天的',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('现在的',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 updateExcelDatabak(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.xlsx')\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.xlsx\")\n", "\n", "def updateYesterdayExcelData(date='', token=None):\n", " # 使用pandas读取Excel文件\n", " df = pd.read_excel(read_file_path_name, engine='openpyxl')\n", "\n", " # 获取第二行的数据作为列名\n", " one_cols = df.iloc[0,:].tolist()\n", "\n", " # 获取当前日期的前一天\n", " if date == '':\n", " previous_date = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d')\n", " else:\n", " # 字符串转日期\n", " previous_date = (datetime.strptime(date, \"%Y-%m-%d\")-timedelta(days=1)).strftime('%Y-%m-%d')\n", " \n", "\n", " cur_time, cur_time2 = getNow(previous_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", " if search_value:\n", " datas = search_value\n", " else:\n", " datas = None\n", "\n", " append_rows = [cur_time2]\n", " dataItemNo_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", " print('更新数据前')\n", " print(df.tail(1))\n", " # 检查日期是否已存在于数据中\n", " if previous_date not in df['日期'].values:\n", " # 将新的数据添加到DataFrame中\n", " new_row = pd.DataFrame([append_rows], columns=df.columns.tolist())\n", " df = pd.concat([df, new_row], ignore_index=True)\n", " else:\n", " # 更新现有数据\n", " print('日期存在，即将更新')\n", " print('新数据',append_rows[1:])\n", " df.loc[df['日期'] == previous_date, df.columns.tolist()[1:]] = append_rows[1:]\n", "\n", " print('更新数据后')\n", " print(df.tail(1))\n", " # 使用pandas保存Excel文件\n", " df.to_excel(\"定性模型数据项12-11.xlsx\", index=False, engine='openpyxl')\n", "\n", "\n", "def updateExcelData(date='', token=None):\n", " # 使用pandas读取Excel文件\n", " df = pd.read_excel(read_file_path_name, engine='openpyxl')\n", "\n", " # 获取第一行的数据作为列名\n", " # one_cols = df.columns.tolist()\n", " \n", " # 获取第二行的数据作为列名\n", " one_cols = df.iloc[0,:].tolist()\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", " if search_value:\n", " datas = search_value\n", " else:\n", " datas = None\n", "\n", " append_rows = [cur_time2]\n", " dataItemNo_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", " # 将新的数据添加到DataFrame中\n", " new_row = pd.DataFrame([append_rows], columns=df.columns.tolist())\n", " df = pd.concat([df, new_row], ignore_index=True)\n", " # df = df.append(pd.Series(append_rows), ignore_index=True)\n", "\n", " # 使用pandas保存Excel文件\n", " df.to_excel(\"定性模型数据项12-11.xlsx\", index=False, engine='openpyxl')\n", "\n", "\n", "def qualitativeModel():\n", " df = pd.read_excel('定性模型数据项12-11.xlsx')\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'] / 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", " \n", " print('昨日计划提货偏差改之前',df1.loc[1,'昨日计划提货偏差'])\n", " # 昨日计划提货偏差 = 京博产量 - 计划产量\n", " df1.loc[1,'昨日计划提货偏差'] = df1.loc[1,'京博产量'] - df1.loc[1,'计划产量']\n", " \n", " print('昨日计划提货偏差改之后',df1.loc[1,'昨日计划提货偏差'])\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", " \n", " # 生产情况 = (京博产量 - 计划产量)/500*5\n", " d = (df1.loc[1,'京博产量'] - df1.loc[1,'计划产量']) / 500 * 5\n", " \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", " f = 1 # 2025年1月23日修改：价格预期都按1计算\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", " try:\n", " date = datetime.strptime(date, \"%Y-%m-%d\")\n", " except:\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.xlsx')\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.xlsx\")\n", "\n", " update_e_value('定性模型数据项12-11.xlsx', 8, 1000)\n", "\n", " df = pd.read_excel('定性模型数据项12-11.xlsx')\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", " f = 1 # 2025年1月23日修改：价格预期都按1计算\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.xlsx')\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.xlsx\")\n", " \n", " update_e_value('定性模型数据项12-11.xlsx', 8, 1000)\n", "\n", " df = pd.read_excel('定性模型数据项12-11.xlsx')\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", " f = 1 # 2025年1月23日修改：价格预期都按1计算\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_test():\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.xlsx')\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.xlsx\")\n", " \n", " update_e_value('定性模型数据项12-11.xlsx', 8, 1000)\n", "\n", " df = pd.read_excel('定性模型数据项12-11.xlsx')\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", " f = 1 # 2025年1月23日修改：价格预期都按1计算\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", "\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.xlsx')\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.xlsx\")\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "### 原始代码备份\n", "\n", "# 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": 14, "metadata": {}, "outputs": [], "source": [ "# if __name__ == \"__main__\":\n", "# def main():\n", "# # 获取当前日期\n", "# date = datetime.now().date()\n", "# date = date.strftime('%Y%m%d')\n", "# # 获取登录token\n", "# token = getLogToken()\n", "# updateExcelData(date,token)\n", "# update_e_value('定性模型数据项12-11.xlsx', 8, 1000)\n", "# x = qualitativeModel()\n", "# print('预测结果：',x)\n", "# cur_time,cur_time2 = getNow(date)\n", "# pushData(cur_time,x,token)\n", "\n", "# print(\"运行中...\")\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", "# try:\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", "# try:\n", "# main()\n", "# except Exception as e:\n", "# print(f\"12点执行失败: {e}\")\n", "# # 等待到12点30分再次执行\n", "# while current_time != \"12:30:00\":\n", "# current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n", "# time.sleep(1)\n", "# try:\n", "# main()\n", "# except Exception as e:\n", "# print(f\"12点30分也执行失败: {e}\")\n", "# elif current_time == \"20:00:00\":\n", "# print(\"更新前一天数据\")\n", "# token = getLogToken()\n", "# updateYesterdayExcelData(token=token)\n", " \n", "# time.sleep(1)\n", "# except Exception as e:\n", "# print(f\"执行失败: {e}\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20250304\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:53: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", " df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n", "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:57: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " df = df.fillna(method='ffill')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "前一天的 3543.2385 \n", "现在的 3414.5246 \n", "昨日计划提货偏差改之前 -571.14\n", "昨日计划提货偏差改之后 339.1062000000002\n", "**************************************************预测结果： 3585.63\n", "20250305\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:53: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", " df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n", "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:57: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " df = df.fillna(method='ffill')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "前一天的 3414.5246 \n", "现在的 3409.5135 \n", "昨日计划提货偏差改之前 3173.74\n", "昨日计划提货偏差改之后 436.6761999999999\n", "**************************************************预测结果： 3781.57\n", "20250306\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:53: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", " df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n", "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:57: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " df = df.fillna(method='ffill')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "前一天的 3409.5135 \n", "现在的 3304.2291 \n", "昨日计划提货偏差改之前 2324.63\n", "昨日计划提货偏差改之后 608.3411999999998\n", "**************************************************预测结果： 3760.78\n", "20250307\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:53: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", " df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n", "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:57: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " df = df.fillna(method='ffill')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "前一天的 3304.2291 \n", "现在的 3312.3543 \n", "昨日计划提货偏差改之前 3426.43\n", "昨日计划提货偏差改之后 232.5551999999998\n", "**************************************************预测结果： 3781.31\n", "20250308\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:53: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", " df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n", "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:57: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " df = df.fillna(method='ffill')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "前一天的 3312.3543 \n", "现在的 3375.9949 \n", "昨日计划提货偏差改之前 2314.8\n", "昨日计划提货偏差改之后 95.26919999999973\n", "**************************************************预测结果： 3781.04\n", "20250309\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:53: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", " df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n", "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:57: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " df = df.fillna(method='ffill')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "前一天的 3375.9949 \n", "现在的 3367.7575 \n", "昨日计划提货偏差改之前 4676.24\n", "昨日计划提货偏差改之后 -324.57479999999987\n", "**************************************************预测结果： 3780.47\n", "20250310\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:53: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", " df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n", "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:57: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " df = df.fillna(method='ffill')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "前一天的 3367.7575 \n", "现在的 3347.97 \n", "昨日计划提货偏差改之前 -1038.55\n", "昨日计划提货偏差改之后 -176.92599999999993\n", "**************************************************预测结果： 3780.72\n", "20250311\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:53: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", " df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n", "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:57: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " df = df.fillna(method='ffill')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "前一天的 3347.97 \n", "现在的 3282.8269 \n", "昨日计划提货偏差改之前 2467.05\n", "昨日计划提货偏差改之后 348.5867999999996\n", "**************************************************预测结果： 3701.49\n", "20250312\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:53: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", " df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n", "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:57: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " df = df.fillna(method='ffill')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "前一天的 3282.8269 \n", "现在的 3362.2709 \n", "昨日计划提货偏差改之前 -198.73\n", "昨日计划提货偏差改之后 338.9767999999999\n", "**************************************************预测结果： 3621.48\n", "20250313\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:53: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", " df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n", "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:57: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " df = df.fillna(method='ffill')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "前一天的 3362.2709 \n", "现在的 3476.9658 \n", "昨日计划提货偏差改之前 2356.71\n", "昨日计划提货偏差改之后 703.9968000000003\n", "**************************************************预测结果： 3644.94\n", "20250314\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:53: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", " df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n", "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:57: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " df = df.fillna(method='ffill')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "前一天的 3476.9658 \n", "现在的 3426.2475 \n", "昨日计划提货偏差改之前 2295.03\n", "昨日计划提货偏差改之后 299.41520000000037\n", "**************************************************预测结果： 3621.29\n", "20250315\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:53: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", " df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n", "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:57: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " df = df.fillna(method='ffill')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "前一天的 3426.2475 \n", "现在的 3471.3062 \n", "昨日计划提货偏差改之前 3608.02\n", "昨日计划提货偏差改之后 326.89519999999993\n", "**************************************************预测结果： 3651.37\n", "20250316\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:53: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", " df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n", "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:57: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " df = df.fillna(method='ffill')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "前一天的 3471.3062 \n", "现在的 3484.929 \n", "昨日计划提货偏差改之前 3044.14\n", "昨日计划提货偏差改之后 309.15520000000015\n", "**************************************************预测结果： 3651.31\n", "20250317\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:53: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", " df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n", "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:57: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " df = df.fillna(method='ffill')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "前一天的 3484.929 \n", "现在的 3553.0486 \n", "昨日计划提货偏差改之前 653.09\n", "昨日计划提货偏差改之后 304.6226999999999\n", "**************************************************预测结果： 3651.3\n", "20250318\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:53: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", " df = df.applymap(lambda x: float(x) if isinstance(x, (int, float)) else x)\n", "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_10124\\1472055096.py:57: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " df = df.fillna(method='ffill')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "前一天的 3553.0486 \n", "现在的 3543.2385 \n", "昨日计划提货偏差改之前 3654.03\n", "昨日计划提货偏差改之后 31.435199999999895\n", "**************************************************预测结果： 3610.87\n" ] } ], "source": [ "# 重新上传定性数据\n", "\n", "def main(date='',token=None):\n", " updateExcelData(date,token)\n", " update_e_value('定性模型数据项12-11.xlsx', 8, 1000)\n", " x = qualitativeModel()\n", " print('**************************************************预测结果：',x)\n", " # cur_time,cur_time2 = getNow(date)\n", " # pushData(cur_time,x,token)\n", "\n", "\n", "start_date = datetime(2025, 3, 4)\n", "end_date = datetime(2025, 3, 19)\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(5)\n", " \n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# # 调试更新数据\n", "# date = '2025-01-24'\n", "# token = getLogToken()\n", "# updateYesterdayExcelData(date=date,token=token)\n", "# print('更新完了')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# # 快速调试线上\n", "# def main():\n", "# # 获取当前日期\n", "# date = datetime.now().date()\n", "# date = date.strftime('%Y%m%d')\n", "# # 获取登录token\n", "# token = getLogToken()\n", "# updateExcelData(date,token)\n", "# update_e_value('定性模型数据项12-11.xlsx', 8, 1000)\n", "# x = qualitativeModel()\n", "# print('预测结果：',x)\n", "# cur_time,cur_time2 = getNow(date)\n", "# pushData(cur_time,x,token)\n", "\n", "# current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n", "# main()\n", "# token = getLogToken()\n", "# updateYesterdayExcelData(token=token)" ] } ], "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 }