diff --git a/aisenzhecode/沥青/定性模型数据项12-11.xlsx b/aisenzhecode/沥青/定性模型数据项12-11.xlsx index 05e1d28..9894340 100644 Binary files a/aisenzhecode/沥青/定性模型数据项12-11.xlsx and b/aisenzhecode/沥青/定性模型数据项12-11.xlsx differ diff --git a/aisenzhecode/沥青/日度价格预测_最佳模型.pkl b/aisenzhecode/沥青/日度价格预测_最佳模型.pkl index 8fb5923..5349529 100644 Binary files a/aisenzhecode/沥青/日度价格预测_最佳模型.pkl and b/aisenzhecode/沥青/日度价格预测_最佳模型.pkl differ diff --git a/aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb b/aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb index e331ed1..f33e9c0 100644 --- a/aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb +++ b/aisenzhecode/沥青/沥青定性模型每日推送-ytj.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -18,6 +18,7 @@ "import numpy as np\n", "# 变量定义\n", "login_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n", + "login_push_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", @@ -46,11 +47,43 @@ " \"funcOperation\": \"获取token\"\n", "}\n", "\n", + "login_push_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", + "def get_head_auth():\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", + " return token\n", + " else:\n", + " print(\"获取认证失败\")\n", + " return None\n", + "\n", + "\n", + "def get_head_push_auth():\n", + " login_res = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5))\n", + " text = json.loads(login_res.text)\n", + " if text[\"status\"]:\n", + " token = text[\"data\"][\"accessToken\"]\n", + " return token\n", + " else:\n", + " print(\"获取认证失败\")\n", + " return None\n", + "\n", "\n", "def update_e_value(file_path, column_index, threshold):\n", " \"\"\"\n", @@ -1068,12 +1101,28 @@ " data_df[\"dataDate\"] = data_df[\"dataDate\"].dt.strftime('%Y-%m-%d')\n", " save_queryDataListItemNos_xls(data_df,dataItemNoList)\n", "\n", + "def main(start_date=None,token=None,token_push=None):\n", + " if start_date is None:\n", + " start_date = datetime.now()\n", + " if token is None:\n", + " token = get_head_auth()\n", + " if token_push is None:\n", + " token_push = get_head_push_auth()\n", + " date = start_date.strftime('%Y%m%d')\n", + " print(date)\n", + " updateExcelData(date,token)\n", + " queryDataListItemNos(token=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" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -1113,69 +1162,24 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# if __name__ == \"__main__\":\n", - " # def main(date='',token=None):\n", - " # updateExcelData(date,token)\n", - " # queryDataListItemNos(token=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": 8, + "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "20250401\n" + "运行中...\n", + "20250408\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_1412\\43143909.py:66: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_25972\\2961115944.py:99: 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_1412\\43143909.py:70: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_25972\\2961115944.py:103: 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" ] }, @@ -1201,37 +1205,79 @@ "1 3630 2972.5098 1 4982.8366 5522.676 \n", "昨日计划提货偏差改之前 478.28\n", "昨日计划提货偏差改之后 539.8394000000008\n", - "**************************************************预测结果： 3567.73\n" + "**************************************************预测结果： 3567.73\n", + "更新前一天数据\n", + "更新数据前\n", + " 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n", + "1401 2025-04-08 3450 36.784 1 478.28 6000 107945.963078 3630 \n", + "\n", + " 即期成本 订单结构 计划产量 京博产量 \n", + "1401 3096.5238 1 4982.8366 5522.676 \n", + "日期存在，即将更新\n", + "新数据 [3500.0, 36.784, '', 478.28, '', 107945.9630779, '', 2972.5098, '', 4982.8366, 5522.676]\n", + "更新数据后\n", + " 日期 京博指导价 70号沥青开工率 资金因素 昨日计划提货偏差 生产情况 基质沥青库存 下游客户价格预期 \\\n", + "1401 2025-04-08 3450 36.784 1 478.28 6000 107945.963078 3630 \n", + "\n", + " 即期成本 订单结构 计划产量 京博产量 \n", + "1401 3096.5238 1 4982.8366 5522.676 \n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[45], line 15\u001b[0m\n\u001b[0;32m 13\u001b[0m token \u001b[38;5;241m=\u001b[39m getLogToken()\n\u001b[0;32m 14\u001b[0m updateYesterdayExcelData(token\u001b[38;5;241m=\u001b[39mtoken)\n\u001b[1;32m---> 15\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 16\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 17\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m执行失败: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ - "# 重新上传定性数据\n", - "\n", - "def main(date='',token=None):\n", - " updateExcelData(date,token)\n", - " queryDataListItemNos(token=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", + "if __name__ == \"__main__\":\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", + " main()\n", + " elif current_time == \"20:00:00\":\n", + " print(\"更新前一天数据\")\n", + " token = getLogToken()\n", + " updateYesterdayExcelData(token=token)\n", + " time.sleep(1)\n", + " except Exception as e:\n", + " print(f\"执行失败: {e}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # 重新上传定性数据\n", "\n", "\n", - "start_date = datetime(2025, 4, 1)\n", - "end_date = datetime(2025, 4, 2)\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", + "# start_date = datetime(2025, 4, 1)\n", + "# end_date = datetime(2025, 4, 2)\n", + "# token = getLogToken()\n", + "# while start_date < end_date:\n", + "# main(start_date,token)\n", + "# start_date += timedelta(days=1)\n", + "# time.sleep(5)\n", " \n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1244,7 +1290,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1271,7 +1317,7 @@ ], "metadata": { "kernelspec": { - "display_name": "base", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/aisenzhecode/沥青/沥青定量价格预测每日推送-ytj.ipynb b/aisenzhecode/沥青/沥青定量价格预测每日推送-ytj.ipynb index 9b79cf5..b2858b4 100644 --- a/aisenzhecode/沥青/沥青定量价格预测每日推送-ytj.ipynb +++ b/aisenzhecode/沥青/沥青定量价格预测每日推送-ytj.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -169,26 +169,6 @@ "\n", "# xls文件处理\n", "\n", - "\n", - "\n", - "\n", - "\n", - "def write_xls(data,date):\n", - " # 创建一个Workbook对象\n", - " workbook = xlwt.Workbook()\n", - "\n", - " # 创建一个Sheet对象，可指定名称\n", - " sheet = workbook.load('Sheet1')\n", - "\n", - " # 写入数据行\n", - " for row_index, row_data in enumerate(data):\n", - " for col_index, cell_data in enumerate(row_data):\n", - " sheet.write(row_index, col_index, cell_data)\n", - "\n", - " # 保存Workbook到文件\n", - " workbook.save(get_cur_time(date)[0] + '.xls')\n", - "\n", - "\n", "def get_cur_time(date = ''):\n", " if date == '':\n", " now = datetime.now()\n", @@ -219,8 +199,6 @@ " print(\"获取认证失败\")\n", " return None\n", "\n", - "\n", - "\n", "def upload_data_to_system(token_push,date):\n", " data = {\n", " \"funcModule\": \"数据表信息列表\",\n", @@ -455,14 +433,6 @@ " with open(Pkl_Filename, 'wb') as file: \n", " pickle.dump(grid_search_XGB, file)\n", "\n", - "\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", "def read_xls_data():\n", " global one_cols, two_cols\n", " # 打开 XLS 文件\n", @@ -490,59 +460,16 @@ " # 关闭 XLS 文件\n", " # workbook.close()\n", "\n", - "\n", - "\n", - "\n", - "def start():\n", + "def start_3(date=None,token=None,token_push=None):\n", " '''预测上传数据'''\n", " read_xls_data()\n", "\n", - " token = get_head_auth()\n", - " if not token:\n", - " return\n", - " token_push = get_head_push_auth()\n", - " if not token_push:\n", - " return\n", - "\n", - " datas = get_data_value(token, one_cols[1:])\n", - " if not datas:\n", - " return\n", - "\n", - " # data_list = [two_cols, one_cols]\n", - " append_rows = [get_cur_time()[1]]\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", - " \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", - " save_xls(append_rows)\n", - " \n", - " # 获取当月的数据写入到指定文件\n", - " queryDataListItemNos(token)\n", - " optimize_Model()\n", - " upload_data_to_system(token_push)\n", - " # data_list.append(three_cols)\n", - " # write_xls(data_list)\n", - "\n", - "\n", - "def start_3(date,token,token_push):\n", - " '''预测上传数据'''\n", - " read_xls_data()\n", - "\n", - " # token = get_head_auth()\n", - " # if not token:\n", - " # return\n", - " # token_push = get_head_push_auth()\n", - " # if not token_push:\n", - " # return\n", + " if date==None:\n", + " date = datetime.now()\n", + " if token==None:\n", + " token = get_head_auth()\n", + " if token_push==None:\n", + " token = get_head_auth()\n", "\n", " datas = get_data_value(token, one_cols[1:],date)\n", " if not datas:\n", @@ -571,126 +498,6 @@ " # data_list.append(three_cols)\n", " # write_xls(data_list)\n", "\n", - "\n", - "\n", - "def start_1():\n", - " '''更新数据'''\n", - " print(\"更新当天数据\")\n", - " read_xls_data()\n", - "\n", - " token = get_head_auth()\n", - " if not token:\n", - " return\n", - " \n", - "\n", - " datas = get_data_value(token, one_cols[1:])\n", - " if not datas:\n", - " return\n", - "\n", - " # data_list = [two_cols, one_cols]\n", - " append_rows = [get_cur_time()[1]]\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", - " \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", - " print(\"当天数据为：\",append_rows)\n", - " save_xls_1(append_rows)\n", - "\n", - " \n", - " # data_list.append(three_cols)\n", - " # write_xls(data_list)\n", - "\n", - "\n", - "def start_2(date,token):\n", - " '''更新数据'''\n", - " read_xls_data()\n", - "\n", - " # token = get_head_auth()\n", - " # if not token:\n", - " # return\n", - " \n", - "\n", - " datas = get_data_value(token, one_cols[1:],date)\n", - " if not datas:\n", - " return\n", - "\n", - " # data_list = [two_cols, one_cols]\n", - " append_rows = [get_cur_time(date=date)[1]]\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", - " \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", - " print('新增数据：',append_rows)\n", - " save_xls_1(append_rows)\n", - "\n", - " \n", - " # data_list.append(three_cols)\n", - " # write_xls(data_list)\n", - " \n", - "def save_xls_1(append_rows):\n", - "\n", - " # 打开xls文件\n", - " workbook = xlrd.open_workbook('沥青数据项.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(\"沥青数据项.xlsx\") \n", - "\n", - " \n", - " \n", - " \n", "def check_data(dataItemNo):\n", " token = get_head_auth()\n", " if not token:\n", @@ -700,7 +507,6 @@ " if not datas:\n", " return\n", "\n", - "\n", "def save_xls(append_rows):\n", "\n", " # 打开xls文件\n", @@ -744,10 +550,6 @@ " # 保存新的xls文件\n", " new_workbook.save(\"沥青数据项.xlsx\")\n", "\n", - "\n", - "\n", - "\n", - "\n", "def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEnd):\n", "\n", " search_data = {\n", @@ -768,8 +570,6 @@ " else:\n", " return None\n", "\n", - "\n", - "\n", "def save_queryDataListItemNos_xls(data_df,dataItemNoList):\n", "\n", " current_year_month = datetime.now().strftime('%Y-%m')\n", @@ -837,9 +637,6 @@ " # 保存新的xls文件\n", " new_workbook.save(\"沥青数据项.xlsx\")\n", "\n", - "\n", - "\n", - "\n", "def queryDataListItemNos(token=None):\n", " df = pd.read_excel('沥青数据项.xlsx')\n", " dataItemNoList = df.iloc[0].tolist()[1:]\n", @@ -894,7 +691,17 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "运行中ing...\n", + "18:13:56任务失败\n", + "18:13:59任务失败\n" + ] + } + ], "source": [ "\n", "if __name__ == \"__main__\":\n", @@ -903,16 +710,19 @@ " while True:\n", " # 获取当前时间\n", " current_time = time.strftime(\"%H:%M:%S\", time.localtime())\n", + " # print(current_time)\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", - " if current_time == \"15:36:00\":\n", - " print(\"执行定时任务\")\n", - " main()\n", - " time.sleep(1)\n", - " elif current_time == \"15:40:00\":\n", - " start_1()\n", + " try:\n", + " if current_time == \"17:05:00\":\n", + " print(\"执行定时任务\")\n", + " main()\n", + " elif current_time == \"17:10:00\":\n", + " print('更新数据')\n", + " start_3()\n", " time.sleep(1)\n", + " except :\n", + " print(f\"{current_time}任务失败\")\n", "\n", "\n", "# # 检测数据准确性, 需要检测放开\n", @@ -924,16 +734,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "更新当天数据\n", - "当天数据为： ['2025-04-08', '', '', 0.05, 0.25, 3680.0, 0.7, 0.0, 0.0, 3450.0, 7.9, 0.15, 0.2, 3400.0, 10.27, '', 3450.0, 65.88, 64.36, '', '', '', '', '', '', '', '', '', 3096.5238, '', '', '', '', '']\n" - ] - } - ], + "outputs": [], "source": [ "# start_date = datetime(2025, 4, 8)\n", "# end_date = datetime(2025, 4, 9)\n", @@ -941,18 +742,201 @@ "\n", "# token_push = get_head_push_auth()\n", "\n", - "\n", - "\n", - "\n", "# while start_date < end_date:\n", " # main(start_date,token,token_push)\n", "# start_date += timedelta(days=1)\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### 代码备份：\n", + "\n", + "\n", + "class codeBackup:\n", + " def save_xls_1(append_rows):\n", + "\n", + " # 打开xls文件\n", + " workbook = xlrd.open_workbook('沥青数据项.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(\"沥青数据项.xlsx\") \n", + "\n", + " \n", + "\n", + " def start_2(date,token):\n", + " '''更新数据'''\n", + " read_xls_data()\n", + "\n", + " # token = get_head_auth()\n", + " # if not token:\n", + " # return\n", + " \n", + "\n", + " datas = get_data_value(token, one_cols[1:],date)\n", + " if not datas:\n", + " return\n", + "\n", + " # data_list = [two_cols, one_cols]\n", + " append_rows = [get_cur_time(date=date)[1]]\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", + " \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", + " print('新增数据：',append_rows)\n", + " save_xls_1(append_rows)\n", + "\n", + " \n", + " # data_list.append(three_cols)\n", + " # write_xls(data_list)\n", + " \n", + "\n", + " def write_xls(data,date):\n", + " # 创建一个Workbook对象\n", + " workbook = xlwt.Workbook()\n", + "\n", + " # 创建一个Sheet对象，可指定名称\n", + " sheet = workbook.load('Sheet1')\n", + "\n", + " # 写入数据行\n", + " for row_index, row_data in enumerate(data):\n", + " for col_index, cell_data in enumerate(row_data):\n", + " sheet.write(row_index, col_index, cell_data)\n", + "\n", + " # 保存Workbook到文件\n", + " workbook.save(get_cur_time(date)[0] + '.xls')\n", + "\n", + "\n", + " def start():\n", + " '''预测上传数据'''\n", + " read_xls_data()\n", + "\n", + " token = get_head_auth()\n", + " if not token:\n", + " return\n", + " token_push = get_head_push_auth()\n", + " if not token_push:\n", + " return\n", + "\n", + " datas = get_data_value(token, one_cols[1:])\n", + " if not datas:\n", + " return\n", + "\n", + " # data_list = [two_cols, one_cols]\n", + " append_rows = [get_cur_time()[1]]\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", + " \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", + " save_xls(append_rows)\n", + " \n", + " # 获取当月的数据写入到指定文件\n", + " queryDataListItemNos(token)\n", + " optimize_Model()\n", + " upload_data_to_system(token_push)\n", + " # data_list.append(three_cols)\n", + " # write_xls(data_list)\n", + "\n", + "\n", + "\n", + "\n", + " def start_1():\n", + " '''更新数据'''\n", + " print(\"更新当天数据\")\n", + " read_xls_data()\n", + "\n", + " token = get_head_auth()\n", + " if not token:\n", + " return\n", + " \n", + "\n", + " datas = get_data_value(token, one_cols[1:])\n", + " if not datas:\n", + " return\n", + "\n", + " # data_list = [two_cols, one_cols]\n", + " append_rows = [get_cur_time()[1]]\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", + " \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", + " print(\"当天数据为：\",append_rows)\n", + " save_xls_1(append_rows)\n", + "\n", + " \n", + " # data_list.append(three_cols)\n", + " # write_xls(data_list)\n" + ] } ], "metadata": { "kernelspec": { - "display_name": "base", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/aisenzhecode/液化石油气/日度价格预测_液化气最佳模型.pkl b/aisenzhecode/液化石油气/日度价格预测_液化气最佳模型.pkl index 1491959..dd6a932 100644 Binary files a/aisenzhecode/液化石油气/日度价格预测_液化气最佳模型.pkl and b/aisenzhecode/液化石油气/日度价格预测_液化气最佳模型.pkl differ diff --git a/aisenzhecode/液化石油气/液化气价格预测ytj.ipynb b/aisenzhecode/液化石油气/液化气价格预测ytj.ipynb index fb29845..434942a 100644 --- a/aisenzhecode/液化石油气/液化气价格预测ytj.ipynb +++ b/aisenzhecode/液化石油气/液化气价格预测ytj.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -69,7 +69,7 @@ " \"funcOperation\": \"获取token\"\n", "}\n", "\n", - "read_file_path_name = \"液化气数据.xls\"\n", + "read_file_path_name = \"液化气数据.xlsx\"\n", "one_cols = []\n", "two_cols = []\n", "\n", @@ -281,7 +281,7 @@ " \n", "def forecast_price():\n", " # df_test = pd.read_csv('定价模型数据收集0212.csv')\n", - " df_test = pd.read_excel('液化气数据.xls')\n", + " df_test = pd.read_excel('液化气数据.xlsx')\n", " df_test.drop([0],inplace=True)\n", " try:\n", " df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True)\n", @@ -333,7 +333,7 @@ "\n", " pd.set_option('display.max_rows',40) \n", " pd.set_option('display.max_columns',40) \n", - " df_test = pd.read_excel('液化气数据.xls')\n", + " df_test = pd.read_excel('液化气数据.xlsx')\n", " df_test.drop([0],inplace=True)\n", " try:\n", " df_test['Date']=pd.to_datetime(df_test['Date'], format='%m/%d/%Y',infer_datetime_format=True)\n", @@ -561,7 +561,7 @@ " print('更新数据后')\n", " print(df.tail(1))\n", " # 使用pandas保存Excel文件\n", - " df.to_excel(\"液化气数据.xls\", index=False, engine='openpyxl')\n", + " df.to_excel(\"液化气数据.xlsx\", index=False, engine='openpyxl')\n", "\n", "\n", "def read_xls_data_bak():\n", @@ -627,7 +627,7 @@ " append_rows.append(dataItemNo_dataValue[value])\n", " else:\n", " append_rows.append(\"\")\n", - " save_xls(append_rows)\n", + " save_xlsx(append_rows)\n", " \n", " \n", " # 获取当月的数据写入到指定文件\n", @@ -706,7 +706,7 @@ "def save_xls_1(append_rows):\n", "\n", " # 打开xls文件\n", - " workbook = xlrd.open_workbook('液化气数据.xls')\n", + " workbook = xlrd.open_workbook('液化气数据.xlsx')\n", "\n", " # 获取所有sheet的个数\n", " sheet_count = len(workbook.sheet_names())\n", @@ -744,12 +744,12 @@ " new_sheet.write(row_count, col, append_rows[col])\n", "\n", " # 保存新的xls文件\n", - " new_workbook.save(\"液化气数据.xls\") \n", + " new_workbook.save(\"液化气数据.xlsx\") \n", "\n", "def save_xls_2(append_rows):\n", " try:\n", " # 读取现有数据（假设第一行为列名）\n", - " df = pd.read_excel('液化气数据.xls', sheet_name=0)\n", + " df = pd.read_excel('液化气数据.xlsx', sheet_name=0)\n", " print(df.columns)\n", " append_rows = pd.DataFrame(append_rows, index=df.columns)\n", " # 创建新数据行\n", @@ -772,11 +772,11 @@ " print(f\"插入 {new_date} 新数据\")\n", " \n", " # 保存更新后的数据\n", - " df.to_excel('液化气数据.xls', index=False, engine='openpyxl')\n", + " df.to_excel('液化气数据.xlsx', index=False, engine='openpyxl')\n", " \n", " except FileNotFoundError:\n", " # 如果文件不存在则创建新文件\n", - " pd.DataFrame([append_rows]).to_excel('液化气数据.xls', index=False, engine='openpyxl')\n", + " pd.DataFrame([append_rows]).to_excel('液化气数据.xlsx', index=False, engine='openpyxl')\n", " except Exception as e:\n", " print(f\"保存数据时发生错误: {str(e)}\")\n", " \n", @@ -790,17 +790,43 @@ " if not datas:\n", " return\n", "\n", + "def save_xlsx(append_rows):\n", + " from openpyxl import load_workbook\n", + "\n", + " # 加载现有工作簿\n", + " workbook = load_workbook('液化气数据.xlsx')\n", + " \n", + " # 创建新工作簿并复制所有工作表结构\n", + " new_workbook = load_workbook('液化气数据.xlsx')\n", + " \n", + " # 遍历所有工作表\n", + " for sheetname in workbook.sheetnames:\n", + " sheet = workbook[sheetname]\n", + " new_sheet = new_workbook[sheetname]\n", + " \n", + " # 获取原始数据行数\n", + " row_count = sheet.max_row\n", + " \n", + " # 在第一个工作表中追加数据\n", + " if sheetname == workbook.sheetnames[0]:\n", + " # 追加新行数据\n", + " new_row = (row_count + 1, )\n", + " for col_idx, value in enumerate(append_rows, start=1):\n", + " new_sheet.cell(row=row_count+1, column=col_idx, value=value)\n", + "\n", + " # 保存修改后的工作簿\n", + " new_workbook.save(\"液化气数据.xlsx\")\n", "\n", "def save_xls(append_rows):\n", "\n", " # 修改文件打开方式\n", " try:\n", " # 尝试用xlrd打开旧版xls文件\n", - " workbook = xlrd.open_workbook('液化气数据.xls')\n", + " workbook = xlrd.open_workbook('液化气数据.xlsx')\n", " except:\n", " # 使用openpyxl打开新版xlsx文件\n", " from openpyxl import load_workbook\n", - " workbook = load_workbook('液化气数据.xls')\n", + " workbook = load_workbook('液化气数据.xlsx')\n", "\n", " # 获取所有sheet的个数\n", " sheet_count = len(workbook.sheet_names())\n", @@ -838,7 +864,7 @@ " new_sheet.write(row_count, col, append_rows[col])\n", "\n", " # 保存新的xls文件\n", - " new_workbook.save(\"液化气数据.xls\")\n", + " new_workbook.save(\"液化气数据.xlsx\")\n", "\n", "\n", "\n", @@ -867,79 +893,111 @@ " else:\n", " return None\n", "\n", - "\n", - "\n", "def save_queryDataListItemNos_xls(data_df,dataItemNoList):\n", - "\n", " current_year_month = datetime.now().strftime('%Y-%m')\n", " grouped = data_df.groupby(\"dataDate\")\n", "\n", - " # 打开xls文件\n", - " workbook = xlrd.open_workbook('液化气数据.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", - "\n", - " # 创建xlwt的Workbook对象\n", - " # 创建sheet\n", - " new_sheet = new_workbook.add_sheet(sheet_names[i])\n", - "\n", + " # 使用openpyxl打开xlsx文件\n", + " from openpyxl import load_workbook\n", + " workbook = load_workbook('液化气数据.xlsx')\n", "\n", + " # 创建新工作簿\n", + " new_workbook = load_workbook('液化气数据.xlsx')\n", + " \n", + " for sheetname in workbook.sheetnames:\n", + " sheet = workbook[sheetname]\n", + " new_sheet = new_workbook[sheetname]\n", + " \n", " current_year_month_row = 0\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", - " col0 = data[row][0]\n", - " # print(\"col0\",col0[:7])\n", - " if col0[:7] == current_year_month:\n", - " current_year_month_row += 1\n", - " break\n", - " new_sheet.write(row, col, data[row][col])\n", + " # 查找当前月份数据起始行\n", + " for row_idx, row in enumerate(sheet.iter_rows(values_only=True), 1):\n", + " if str(row[0]).startswith(current_year_month):\n", + " current_year_month_row += 1\n", "\n", - "\n", - " # print(\"current_year_month_row\",current_year_month_row)\n", - " if i == 0:\n", - " rowFlag = 0\n", - " # 查看每组数据\n", - " for date, group in grouped:\n", - " new_sheet.write(row_count + rowFlag - current_year_month_row, 0, date)\n", - " for j in range(len(dataItemNoList)):\n", - " dataItemNo = dataItemNoList[j]\n", - "\n", - " # for dataItemNo in dataItemNoList:\n", + " # 追加新数据\n", + " if sheetname == workbook.sheetnames[0]:\n", + " start_row = sheet.max_row - current_year_month_row + 1\n", + " for row_idx, (date, group) in enumerate(grouped, start=start_row):\n", + " new_sheet.cell(row=row_idx, column=1, value=date)\n", + " for j, dataItemNo in enumerate(dataItemNoList, start=2):\n", " if group[group[\"dataItemNo\"] == dataItemNo][\"dataValue\"].values:\n", + " new_sheet.cell(row=row_idx, column=j, \n", + " value=group[group[\"dataItemNo\"] == dataItemNo][\"dataValue\"].values[0])\n", "\n", - " new_sheet.write(row_count + rowFlag - current_year_month_row, j + 1, group[group[\"dataItemNo\"] == dataItemNo][\"dataValue\"].values[0])\n", + " # 保存修改后的xlsx文件\n", + " new_workbook.save(\"液化气数据.xlsx\")\n", "\n", - " rowFlag += 1\n", + "# def save_queryDataListItemNos_xls(data_df,dataItemNoList):\n", "\n", - " # 保存新的xls文件\n", - " new_workbook.save(\"液化气数据.xls\")\n", + "# current_year_month = datetime.now().strftime('%Y-%m')\n", + "# grouped = data_df.groupby(\"dataDate\")\n", + "\n", + "# # 打开xls文件\n", + "# workbook = xlrd.open_workbook('液化气数据.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", + "\n", + "# # 创建xlwt的Workbook对象\n", + "# # 创建sheet\n", + "# new_sheet = new_workbook.add_sheet(sheet_names[i])\n", + "\n", + "\n", + "# current_year_month_row = 0\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", + "# col0 = data[row][0]\n", + "# # print(\"col0\",col0[:7])\n", + "# if col0[:7] == current_year_month:\n", + "# current_year_month_row += 1\n", + "# break\n", + "# new_sheet.write(row, col, data[row][col])\n", + "\n", + "\n", + "# # print(\"current_year_month_row\",current_year_month_row)\n", + "# if i == 0:\n", + "# rowFlag = 0\n", + "# # 查看每组数据\n", + "# for date, group in grouped:\n", + "# new_sheet.write(row_count + rowFlag - current_year_month_row, 0, date)\n", + "# for j in range(len(dataItemNoList)):\n", + "# dataItemNo = dataItemNoList[j]\n", + "\n", + "# # for dataItemNo in dataItemNoList:\n", + "# if group[group[\"dataItemNo\"] == dataItemNo][\"dataValue\"].values:\n", + "\n", + "# new_sheet.write(row_count + rowFlag - current_year_month_row, j + 1, group[group[\"dataItemNo\"] == dataItemNo][\"dataValue\"].values[0])\n", + "\n", + "# rowFlag += 1\n", + "\n", + "# # 保存新的xls文件\n", + "# new_workbook.save(\"液化气数据.xlsx\")\n", "\n", "\n", "\n", "\n", "def queryDataListItemNos(date=None,token=None):\n", - " df = pd.read_excel('液化气数据.xls')\n", + " df = pd.read_excel('液化气数据.xlsx')\n", " dataItemNoList = df.iloc[0].tolist()[1:]\n", " \n", " if token is None:\n", @@ -974,6 +1032,59 @@ "\n", "\n", "\n", + "def start_3(date,token,token_push):\n", + " '''预测上传数据'''\n", + " read_xls_data()\n", + "\n", + " # token = get_head_auth()\n", + " # if not token:\n", + " # return\n", + " # token_push = get_head_push_auth()\n", + " # if not token_push:\n", + " # return\n", + "\n", + " datas = get_data_value(token, one_cols[1:],date)\n", + " if not datas:\n", + " return\n", + "\n", + " # data_list = [two_cols, one_cols]\n", + " append_rows = [get_cur_time(date)[1]]\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", + " \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", + " save_xlsx(append_rows)\n", + "\n", + " # 获取当月的数据写入到指定文件\n", + " # optimize_Model()\n", + " # upload_data_to_system(token_push,date)\n", + " # data_list.append(three_cols)\n", + " # write_xls(data_list)\n", + "\n", + "def main(start_date=None,token=None,token_push=None):\n", + " if start_date is None:\n", + " start_date = datetime.now()\n", + " if token is None:\n", + " token = get_head_auth()\n", + " if token_push is None:\n", + " token_push = get_head_push_auth()\n", + " date = start_date.strftime('%Y%m%d')\n", + " print(date)\n", + " # 更新当月数据\n", + " queryDataListItemNos(start_date,token)\n", + " # 训练模型\n", + " optimize_Model()\n", + " # 预测&上传预测结果\n", + " upload_data_to_system(token_push,start_date)\n", "\n", "\n", "# if __name__ == \"__main__\":\n", @@ -984,7 +1095,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 16, "metadata": { "scrolled": true }, @@ -1033,48 +1144,147 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0NDEyODI3NywianRpIjoiMjk0MTk4NTU0MWIxNGY2NmJjMTgyY2Y5ZDY1MWVjYzUifQ.2uFevYbViecixxMPgJNCvWO_q2hjzJM35_FavDmEXnI\n" + "获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0NDE0NDA1MSwianRpIjoiY2MwZTVjOWRkNjFjNDE5YTlkYTc1MDY1ZDNlOGFlN2QifQ.OIWm02tY_o0PjCdNQeQUvHwGNJ6u1SQ5wNGDnDThIx4\n", + "20250408\n", + " dataDate dataItemNo dataValue\n", + "0 2025-04-01 100028046|LISTING_PRICE 8208.0\n", + "1 2025-04-02 100028046|LISTING_PRICE 8244.0\n", + "2 2025-04-03 100028046|LISTING_PRICE 8244.0\n", + "3 2025-04-04 100028046|LISTING_PRICE 8165.0\n", + "4 2025-04-05 100028046|LISTING_PRICE 8114.0\n", + ".. ... ... ...\n", + "181 2025-04-07 YHQMXBB|C01100008|STRIKE_PRICE 5180.0\n", + "182 2025-04-02 YHQMXBB|C01100008|STRIKE_PRICE 5310.0\n", + "183 2025-04-01 YHQMXBB|C01100008|STRIKE_PRICE 5260.0\n", + "184 2025-04-04 YHQMXBB|C01100008|STRIKE_PRICE 5230.0\n", + "185 2025-04-05 YHQMXBB|C01100008|STRIKE_PRICE 5180.0\n", + "\n", + "[186 rows x 3 columns]\n" ] }, { - "ename": "ValueError", - "evalue": "Unknown engine: xlwt", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[62], line 8\u001b[0m\n\u001b[0;32m 6\u001b[0m date \u001b[38;5;241m=\u001b[39m start_date\u001b[38;5;241m.\u001b[39mstrftime(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mm\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 7\u001b[0m date2 \u001b[38;5;241m=\u001b[39m start_date\u001b[38;5;241m.\u001b[39mstrftime(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY-\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mm-\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 8\u001b[0m updateYesterdayExcelData(date\u001b[38;5;241m=\u001b[39mdate2,token\u001b[38;5;241m=\u001b[39mtoken)\n\u001b[0;32m 9\u001b[0m \u001b[38;5;66;03m# queryDataListItemNos(date=start_date,token=token)\u001b[39;00m\n\u001b[0;32m 10\u001b[0m start(date)\n", - "Cell \u001b[1;32mIn[60], line 471\u001b[0m, in \u001b[0;36mupdateYesterdayExcelData\u001b[1;34m(date, token)\u001b[0m\n\u001b[0;32m 469\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mupdateYesterdayExcelData\u001b[39m(date\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m, token\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m 470\u001b[0m \u001b[38;5;66;03m# 使用pandas读取Excel文件\u001b[39;00m\n\u001b[1;32m--> 471\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_excel(read_file_path_name, engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxlwt\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 473\u001b[0m \u001b[38;5;66;03m# 获取第二行的数据作为列名\u001b[39;00m\n\u001b[0;32m 474\u001b[0m one_cols \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m0\u001b[39m,:]\u001b[38;5;241m.\u001b[39mtolist()\n", - "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\excel\\_base.py:504\u001b[0m, in \u001b[0;36mread_excel\u001b[1;34m(io, sheet_name, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, date_format, thousands, decimal, comment, skipfooter, storage_options, dtype_backend, engine_kwargs)\u001b[0m\n\u001b[0;32m 502\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(io, ExcelFile):\n\u001b[0;32m 503\u001b[0m should_close \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 504\u001b[0m io \u001b[38;5;241m=\u001b[39m ExcelFile(\n\u001b[0;32m 505\u001b[0m io,\n\u001b[0;32m 506\u001b[0m storage_options\u001b[38;5;241m=\u001b[39mstorage_options,\n\u001b[0;32m 507\u001b[0m engine\u001b[38;5;241m=\u001b[39mengine,\n\u001b[0;32m 508\u001b[0m engine_kwargs\u001b[38;5;241m=\u001b[39mengine_kwargs,\n\u001b[0;32m 509\u001b[0m )\n\u001b[0;32m 510\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m engine \u001b[38;5;129;01mand\u001b[39;00m engine \u001b[38;5;241m!=\u001b[39m io\u001b[38;5;241m.\u001b[39mengine:\n\u001b[0;32m 511\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 512\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEngine should not be specified when passing \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 513\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124man ExcelFile - ExcelFile already has the engine set\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 514\u001b[0m )\n", - "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\excel\\_base.py:1531\u001b[0m, in \u001b[0;36mExcelFile.__init__\u001b[1;34m(self, path_or_buffer, engine, storage_options, engine_kwargs)\u001b[0m\n\u001b[0;32m 1528\u001b[0m engine_kwargs \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m 1530\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m engine \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m engine \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engines:\n\u001b[1;32m-> 1531\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnknown engine: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mengine\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 1533\u001b[0m \u001b[38;5;66;03m# First argument can also be bytes, so create a buffer\u001b[39;00m\n\u001b[0;32m 1534\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(path_or_buffer, \u001b[38;5;28mbytes\u001b[39m):\n", - "\u001b[1;31mValueError\u001b[0m: Unknown engine: xlwt" + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_19260\\847692049.py:890: DeprecationWarning:\n", + "\n", + "The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "当月数据更新完成\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_19260\\847692049.py:306: UserWarning:\n", + "\n", + "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", + "\n", + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_19260\\847692049.py:308: UserWarning:\n", + "\n", + "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", + "\n", + "d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\magics\\pylab.py:162: UserWarning:\n", + "\n", + "pylab import has clobbered these variables: ['random', '__version__', 'plot', 'datetime']\n", + "`%matplotlib` prevents importing * from pylab and numpy\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using matplotlib backend: QtAgg\n", + "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n", + "Populating the interactive namespace from numpy and matplotlib\n", + "Fitting 3 folds for each of 180 candidates, totalling 540 fits\n", + "Date\n", + "2025-04-08 5181.777832\n", + "Name: 日度预测价格, dtype: float32\n", + "预测值： 5181.78\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_19260\\847692049.py:254: UserWarning:\n", + "\n", + "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", + "\n", + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_19260\\847692049.py:256: UserWarning:\n", + "\n", + "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n", + "\n", + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_19260\\847692049.py:288: FutureWarning:\n", + "\n", + "Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + "\n" ] } ], "source": [ - "start_date = datetime(2025, 4, 2)\n", - "end_date = datetime(2025, 4, 3)\n", - "token = get_head_auth()\n", "\n", - "while start_date < end_date:\n", - " date = start_date.strftime('%Y%m%d')\n", - " date2 = start_date.strftime('%Y-%m-%d')\n", - " updateYesterdayExcelData(date=date2,token=token)\n", - " # queryDataListItemNos(date=start_date,token=token)\n", - " start(date)\n", - " # # time.sleep(1)\n", - " # start_1(start_date)\n", - " start_date += timedelta(days=1)\n", - " time.sleep(5)\n", + "if __name__ == \"__main__\":\n", + " print(\"运行中ing...\")\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", + " # print(\"执行定时任务\")\n", + " # main()\n", + " # elif current_time == \"17:45:00\":\n", + " # start_1()\n", + " # time.sleep(1)\n", + " # except:\n", + " # print(f\"{current_time}执行失败\")\n", "\n", - "# print(price_list)" + " start_1()\n", + " # 检测数据准确性, 需要检测放开\n", + " # check_data(\"100028098|LISTING_PRICE\")\n", + " # check_data(\"9137070016544622XB|DAY_Yield\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# start_date = datetime(2025, 4, 2)\n", + "# end_date = datetime(2025, 4, 3)\n", + "# token = get_head_auth()\n", + "\n", + "# while start_date < end_date:\n", + "# date = start_date.strftime('%Y%m%d')\n", + "# date2 = start_date.strftime('%Y-%m-%d')\n", + "# queryDataListItemNos(date=start_date,token=token)\n", + "# updateYesterdayExcelData(date=date2,token=token)\n", + "# start(date)\n", + "# # # time.sleep(1)\n", + "# # start_1(start_date)\n", + "# start_date += timedelta(days=1)\n", + "# time.sleep(5)\n", + "\n", + "# # print(price_list)" ] }, { @@ -1087,7 +1297,7 @@ ], "metadata": { "kernelspec": { - "display_name": "base", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/aisenzhecode/液化石油气/液化气数据 - 副本.xls b/aisenzhecode/液化石油气/液化气数据 - 副本.xls new file mode 100644 index 0000000..2ba8b49 Binary files /dev/null and b/aisenzhecode/液化石油气/液化气数据 - 副本.xls differ diff --git a/aisenzhecode/液化石油气/液化气数据.xlsx b/aisenzhecode/液化石油气/液化气数据.xlsx new file mode 100644 index 0000000..6db1ce6 Binary files /dev/null and b/aisenzhecode/液化石油气/液化气数据.xlsx differ