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 088799a..b12aa15 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 1958113..c616dca 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 5dec613..b0ff1ee 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": 25, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -967,7 +967,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -1000,7 +1000,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -1050,70 +1050,737 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "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, 1, 15)\n", - "# end_date = datetime(2025, 1, 24)\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": 29, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "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 即期成本 \\\n", - "1326 2025-01-23 3650 6 6 NaN NaN NaN 3650 3846.3643 \n", - "\n", - " 订单结构 计划产量 京博产量 \n", - "1326 1 4505.365 NaN \n", - "日期存在，即将更新\n", - "新数据 [3650.0, '', '', '', '', '', 3650.0, 3846.3643, 1.0, 4505.365, '']\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", - "1326 2025-01-23 3650.0 3650.0 \n", - "\n", - " 即期成本 订单结构 计划产量 京博产量 \n", - "1326 3846.3643 1.0 4505.365 \n", - "更新完了\n" + "20250201\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3587.7887 \n", + "现在的 3619.0713 \n", + "昨日计划提货偏差改之前 162.28\n", + "昨日计划提货偏差改之后 520.4207999999999\n", + "**************************************************预测结果： 3651.07\n", + "20250202\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3619.0713 \n", + "现在的 3624.724 \n", + "昨日计划提货偏差改之前 162.28\n", + "昨日计划提货偏差改之后 -75.01419999999962\n", + "**************************************************预测结果： 3650.16\n", + "20250203\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3624.724 \n", + "现在的 3698.7029 \n", + "昨日计划提货偏差改之前 162.28\n", + "昨日计划提货偏差改之后 -68.52419999999984\n", + "**************************************************预测结果： 3650.17\n", + "20250204\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3698.7029 \n", + "现在的 3671.4625 \n", + "昨日计划提货偏差改之前 162.28\n", + "昨日计划提货偏差改之后 -117.01919999999973\n", + "**************************************************预测结果： 3650.11\n", + "20250205\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3671.4625 \n", + "现在的 3622.508 \n", + "昨日计划提货偏差改之前 -817.76\n", + "昨日计划提货偏差改之后 -81.05919999999969\n", + "**************************************************预测结果： 3650.15\n", + "20250206\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3622.508 \n", + "现在的 3513.5033 \n", + "昨日计划提货偏差改之前 578.78\n", + "昨日计划提货偏差改之后 -33.19920000000002\n", + "**************************************************预测结果： 3678.42\n", + "20250207\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3513.5033 \n", + "现在的 3631.0462 \n", + "昨日计划提货偏差改之前 1303\n", + "昨日计划提货偏差改之后 -53.10919999999987\n", + "**************************************************预测结果： 3773.7\n", + "20250208\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3631.0462 \n", + "现在的 3664.8865 \n", + "昨日计划提货偏差改之前 215.92\n", + "昨日计划提货偏差改之后 -71.42919999999958\n", + "**************************************************预测结果： 3750.17\n", + "20250209\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3664.8865 \n", + "现在的 3680.6229 \n", + "昨日计划提货偏差改之前 -337.98\n", + "昨日计划提货偏差改之后 -116.51919999999973\n", + "**************************************************预测结果： 3750.11\n", + "20250210\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3680.6229 \n", + "现在的 3719.1501 \n", + "昨日计划提货偏差改之前 1105.3\n", + "昨日计划提货偏差改之后 -118.21919999999955\n", + "**************************************************预测结果： 3750.11\n", + "20250211\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3719.1501 \n", + "现在的 3806.0186 \n", + "昨日计划提货偏差改之前 776.6\n", + "昨日计划提货偏差改之后 -111.46919999999955\n", + "**************************************************预测结果： 3750.12\n", + "20250212\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3806.0186 \n", + "现在的 3870.8876 \n", + "昨日计划提货偏差改之前 -543.94\n", + "昨日计划提货偏差改之后 -77.78920000000016\n", + "**************************************************预测结果： 3800.16\n", + "20250213\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3870.8876 \n", + "现在的 3669.5691 \n", + "昨日计划提货偏差改之前 319.6\n", + "昨日计划提货偏差改之后 9.210799999999836\n", + "**************************************************预测结果： 3760.01\n", + "20250214\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3669.5691 \n", + "现在的 3727.5257 \n", + "昨日计划提货偏差改之前 811.66\n", + "昨日计划提货偏差改之后 14.500800000000709\n", + "**************************************************预测结果： 3750.28\n", + "20250215\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3727.5257 \n", + "现在的 3653.1038 \n", + "昨日计划提货偏差改之前 3093.54\n", + "昨日计划提货偏差改之后 79.19080000000031\n", + "**************************************************预测结果： 3750.38\n", + "20250216\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3653.1038 \n", + "现在的 3690.2938 \n", + "昨日计划提货偏差改之前 448.52\n", + "昨日计划提货偏差改之后 -50.14919999999984\n", + "**************************************************预测结果： 3750.19\n", + "20250217\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3690.2938 \n", + "现在的 3666.4264 \n", + "昨日计划提货偏差改之前 -1512.9\n", + "昨日计划提货偏差改之后 -138.7491999999993\n", + "**************************************************预测结果： 3750.08\n", + "20250218\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3666.4264 \n", + "现在的 3710.5143 \n", + "昨日计划提货偏差改之前 1736.44\n", + "昨日计划提货偏差改之后 -120.28920000000016\n", + "**************************************************预测结果： 3800.53\n", + "20250219\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3710.5143 \n", + "现在的 3786.9784 \n", + "昨日计划提货偏差改之前 449.02\n", + "昨日计划提货偏差改之后 -205.32920000000013\n", + "**************************************************预测结果： 3800.41\n", + "20250220\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3786.9784 \n", + "现在的 3766.1403 \n", + "昨日计划提货偏差改之前 1284.6\n", + "昨日计划提货偏差改之后 -186.1891999999998\n", + "**************************************************预测结果： 3830.46\n", + "20250221\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3766.1403 \n", + "现在的 3747.6395 \n", + "昨日计划提货偏差改之前 2301.56\n", + "昨日计划提货偏差改之后 -186.66919999999936\n", + "**************************************************预测结果： 3850.48\n", + "20250222\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3747.6395 \n", + "现在的 3564.5329 \n", + "昨日计划提货偏差改之前 3639.18\n", + "昨日计划提货偏差改之后 -204.83919999999944\n", + "**************************************************预测结果： 3813.85\n", + "20250223\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3564.5329 \n", + "现在的 3585.6018 \n", + "昨日计划提货偏差改之前 2779.86\n", + "昨日计划提货偏差改之后 -191.76919999999973\n", + "**************************************************预测结果： 3850.49\n", + "20250224\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3585.6018 \n", + "现在的 3668.5404 \n", + "昨日计划提货偏差改之前 787.18\n", + "昨日计划提货偏差改之后 -169.46919999999955\n", + "**************************************************预测结果： 3850.04\n", + "20250225\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3668.5404 \n", + "现在的 3791.3805 \n", + "昨日计划提货偏差改之前 -2220.81\n", + "昨日计划提货偏差改之后 284.0117\n", + "**************************************************预测结果： 3825.27\n", + "20250226\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3791.3805 \n", + "现在的 3553.9243 \n", + "昨日计划提货偏差改之前 1969.52\n", + "昨日计划提货偏差改之后 347.3217000000004\n", + "**************************************************预测结果： 3753.31\n", + "20250227\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3553.9243 \n", + "现在的 3576.4854 \n", + "昨日计划提货偏差改之前 4503.35\n", + "昨日计划提货偏差改之后 302.53169999999955\n", + "**************************************************预测结果： 3800.73\n", + "20250228\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3576.4854 \n", + "现在的 3645.4569 \n", + "昨日计划提货偏差改之前 4517.04\n", + "昨日计划提货偏差改之后 -363.1983\n", + "**************************************************预测结果： 3799.79\n", + "20250301\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3645.4569 \n", + "现在的 3615.0332 \n", + "昨日计划提货偏差改之前 993.87\n", + "昨日计划提货偏差改之后 1276.8767999999995\n", + "**************************************************预测结果： 3802.24\n", + "20250302\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3615.0332 \n", + "现在的 3615.0995 \n", + "昨日计划提货偏差改之前 1499.46\n", + "昨日计划提货偏差改之后 663.4968000000003\n", + "**************************************************预测结果： 3801.29\n", + "20250303\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_12764\\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_12764\\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 3615.0995 \n", + "现在的 3657.0767 \n", + "昨日计划提货偏差改之前 1499.46\n", + "昨日计划提货偏差改之后 663.4968000000003\n", + "**************************************************预测结果： 3801.29\n" ] } ], "source": [ - "# 调试更新数据\n", - "date = '2025-01-24'\n", + "# 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, 2, 1)\n", + "end_date = datetime(2025, 3, 4)\n", "token = getLogToken()\n", - "updateYesterdayExcelData(date=date,token=token)\n", - "print('更新完了')" + "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": 30, + "execution_count": 5, + "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": 6, "metadata": {}, "outputs": [], "source": [ 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 35957ad..610b88f 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 @@ -2,9 +2,17 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From C:\\Users\\EDY\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n", + "\n" + ] + }, { "data": { "text/html": [ @@ -548,16 +556,16 @@ " # write_xls(data_list)\n", "\n", "\n", - "def start_3(date):\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", + " # 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", @@ -579,8 +587,8 @@ " else:\n", " append_rows.append(\"\")\n", " save_xls(append_rows)\n", - " # optimize_Model()\n", - " # upload_data_to_system(token_push,date)\n", + " optimize_Model()\n", + " upload_data_to_system(token_push,date)\n", " # data_list.append(three_cols)\n", " # write_xls(data_list)\n", "\n", @@ -621,13 +629,13 @@ " # write_xls(data_list)\n", "\n", "\n", - "def start_2(date):\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", + " # token = get_head_auth()\n", + " # if not token:\n", + " # return\n", " \n", "\n", " datas = get_data_value(token, one_cols[1:],date)\n", @@ -788,43 +796,138 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "20241231\n" + "20250102\n", + "新增数据： ['2025-01-02', 8057.0, 6784.0, 0.05, 0.0, 3650.0, 0.75, 0.0, 0.0, 3520.0, 7.9, 0.2, 0.2, 3540.0, 1.15, '', 3600.0, 73.36, '', '', 3669.0, 25.1642, '', '', '', '', 229522.1, 6197.58, 3551.9952, '', '', 75999.0902808, 6931.295, '']\n", + "20250103\n", + "新增数据： ['2025-01-03', 8121.0, 6829.0, 0.05, 0.0, 3650.0, 0.7, 0.0, 0.0, 3520.0, 7.9, 0.2, 0.2, 3540.0, 1.15, '', 3600.0, 73.36, 76.03, '', 3678.0, 29.9291, '', '', '', '', 229522.1, 8038.22, 897.5595, '', 50180.0, 73015.8650188, 6693.26, 3602.01]\n", + "20250104\n", + "新增数据： ['2025-01-04', 8156.0, 6856.0, 0.05, 0.0, 3650.0, 0.7, 0.0, 0.0, 3520.0, 7.9, 0.2, 0.2, 3540.0, 1.15, '', 3600.0, 73.36, 76.69, '', '', 32.2931, '', '', '', '', 229522.1, 7900.62, 3667.3859, '', '', 72148.9646528, 6804.94, '']\n", + "20250105\n", + "新增数据： ['2025-01-05', 8192.0, 6856.0, 0.05, 0.0, 3650.0, 0.65, 0.0, 0.0, 3520.0, 7.9, 0.15, 0.2, 3540.0, 1.2, '', 3600.0, 76.69, '', '', '', 30.8747, '', '', '', '', 229522.1, 6417.5, 3657.2132, '', '', 75308.7978357, 6814.96, '']\n", + "20250106\n", + "新增数据： ['2025-01-06', 8226.0, 6856.0, 0.05, 0.0, 3650.0, 0.6, 0.0, 0.0, 3500.0, 7.9, 0.12, 0.2, 3510.0, 1.28, '', 3600.0, 76.69, '', '', 3610.0, 31.8203, '', '', '', '', 229522.1, 5489.2, 3679.4214, '', '', 74486.3079283, 6814.99, '']\n", + "20250107\n", + "新增数据： ['2025-01-07', 8226.0, 6904.0, 0.03, 0.0, 3600.0, 0.57, 0.0, 0.0, 3480.0, 7.9, 0.3, 0.2, 3510.0, 1.26, '', 3550.0, 76.69, 76.26, '', 3603.0, 33.0024, '', '', '', 12.82003192, 229522.1, 5358.32, 3634.1004, '', '', 76588.1941448, 6795.11, '']\n", + "20250108\n", + "新增数据： ['2025-01-08', 8318.0, 6939.0, 0.03, 0.25, 3600.0, 0.57, 0.0, 0.0, 3480.0, 7.9, 0.3, 0.2, 3470.0, 1.16, '', 3530.0, 76.69, 77.25, '', 3615.0, 34.5154, '', '', '', '', 229522.1, 6866.76, 3720.2633, '', '', 75902.1162471, 6756.81, '']\n", + "20250109\n", + "新增数据： ['2025-01-09', 8363.0, 6984.0, 0.03, 0.25, 3600.0, 0.79, 0.0, 0.0, 3480.0, 7.9, 0.2, 0.2, 3470.0, 1.06, '', 3530.0, 76.69, 76.2, '', 3649.0, 31.6785, '', '', '', '', 229522.1, 8478.06, 3571.7593, '', '', 73757.9160547, 6772.99, '']\n", + "20250110\n", + "新增数据： ['2025-01-10', 8467.0, 7031.0, 0.03, 0.25, 3600.0, 1.0, 0.0, 0.0, 3500.0, 7.9, 0.2, 0.2, 3490.0, 1.06, '', 3530.0, 76.69, 77.21, '', 3691.0, 34.5154, '', '', '', '', 229522.1, 6857.04, 3750.5711, '', '', 73779.8843598, 6569.2, '']\n", + "20250111\n", + "新增数据： ['2025-01-11', 9050.0, 7567.0, '', '', '', '', '', '', '', '', '', '', '', '', '', 3700.0, 76.69, 79.59, '', '', 34.5154, '', '', '', '', 229522.1, 8679.56, 3907.1448, '', 140.0, 72204.2847252, 6466.43, 3900.0]\n", + "20250112\n", + "新增数据： ['2025-01-12', 9323.0, 7675.0, 0.15, 0.25, 3600.0, 1.22, 0.0, 0.0, 3630.0, 7.9, 0.1, 0.2, 3650.0, 1.26, '', 3700.0, 79.59, '', '', '', 32.6241, '', '', '', '', 229522.1, 2818.9, 3687.137, '', '', 76933.4067215, 6479.1, '']\n", + "20250113\n", + "新增数据： ['2025-01-13', 9323.0, 7675.0, 0.15, 0.25, 3600.0, 1.32, 0.0, 0.0, 3630.0, 7.9, 0.1, 0.2, 3650.0, 1.36, '', 3800.0, 79.59, '', '', 3807.0, 34.9882, '', '', '', '', 229522.1, 2971.16, 3777.5616, '', 210.0, 79773.9416121, 5746.0, 3800.0]\n", + "20250114\n", + "新增数据： ['2025-01-14', 9047.0, 7454.0, 0.15, 0.25, 3600.0, 1.42, 0.0, 0.0, 3620.0, 7.9, 0.15, 0.2, 3620.0, 1.46, '', 3800.0, 79.59, 80.86, '', 3748.0, 34.9882, '', '', '', 12.95294256, 229522.1, 6394.5, 3781.754, '', '', 79492.6684067, 5645.07, '']\n", + "20250115\n", + "新增数据： ['2025-01-15', 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229522.1, '', 3585.9993, '', '', 130781.5671614, 4620.688, '']\n", + "20250131\n", + "新增数据： ['2025-01-31', 8857.0, 6939.0, '', '', '', '', '', '', '', '', '', '', '', '', '', 3650.0, 74.88, 76.15, '', '', 25.5319, 68.6, 225.29, '', '', 229522.1, '', 3587.7887, '', '', 134131.216744, 4025.178, '']\n", + "20250201\n", + "新增数据： ['2025-02-01', 8857.0, 6904.0, '', '', '', '', '', '', '', '', '', '', '', '', '', 3650.0, 74.88, 76.48, '', '', 25.5319, '', '', '', '', 229522.1, '', 3619.0713, '', '', 141390.8400342, 5245.69, '']\n", + "20250202\n", + "新增数据： ['2025-02-02', 8857.0, 6883.0, '', '', '', '', '', '', '', '', '', '', '', '', '', 3650.0, 76.48, '', '', '', 25.5319, '', '', '', '', 229522.1, '', 3624.724, '', '', 146444.993536, 4650.255, '']\n", + "20250203\n", + "新增数据： ['2025-02-03', 8857.0, 6883.0, '', '', '', '', '', '', '', '', '', '', '', '', '', 3650.0, 76.48, '', '', '', 25.5319, '', '', '', '', 229522.1, '', 3698.7029, '', '', 151802.9709409, 4656.745, '']\n", + "20250204\n", + "新增数据： ['2025-02-04', 8905.0, 6948.0, 0.0, 0.0, 3600.0, 0.0, 0.0, 0.0, 3600.0, 7.9, 0.0, 0.2, 3610.0, 4.0, '', 3650.0, 76.48, 75.4, '', '', 26.4775, '', '', '', 17.21173912, 229522.1, 32.7, 3671.4625, '', '', 156872.340789, 4608.25, '']\n", + "20250205\n", + "新增数据： ['2025-02-05', 8956.0, 7022.0, 0.0, 0.25, 3600.0, 0.5, 0.0, 0.0, 3600.0, 7.9, 0.0, 0.2, 3610.0, 4.0, '', 3700.0, 76.48, 76.06, '', 3797.0, 27.4232, '', '', '', '', 229522.1, 857.76, 3622.508, '', '', 160664.5999171, 4644.21, '']\n", + "20250206\n", + "新增数据： ['2025-02-06', 8956.0, 7066.0, 0.0, 0.25, 3600.0, 0.75, 0.0, 0.0, 3600.0, 7.9, 0.0, 0.2, 3610.0, 4.2, '', 3750.0, 76.48, 74.68, '', 3779.0, 27.7541, '', '', '', '', 229522.1, 1217.12, 3513.5033, '', '', 164388.6227375, 4692.07, '']\n", + "20250207\n", + "新增数据： ['2025-02-07', 8921.0, 7031.0, 0.1, 0.25, 3650.0, 0.8, 0.0, 0.0, 3600.0, 7.9, 0.0, 0.2, 3610.0, 4.4, '', 3750.0, 76.48, 74.23, '', 3812.0, 27.7541, '', '', '', '', 229522.1, 942.0, 3631.0462, '', '', 168393.8166163, 4672.16, '']\n", + "20250208\n", + "新增数据： ['2025-02-08', 8866.0, 7004.0, 0.15, 0.25, 3650.0, 0.8, 0.0, 0.0, 3650.0, 7.9, 0.0, 0.2, 3900.0, 4.6, '', 3750.0, 76.48, 74.64, '', '', 27.7541, '', '', '', '', 229522.1, 1089.08, 3664.8865, '', 1000.0, 172610.1660848, 4653.84, 4300.0]\n", + "20250209\n", + "新增数据： ['2025-02-09', 8875.0, 7013.0, '', '', '', '', '', '', '', '', '', '', '', '', '', 3750.0, 74.64, '', '', '', 27.7541, '', '', '', '', 229522.1, 1952.98, 3680.6229, '', '', 175400.4021806, 4608.75, '']\n", + "20250210\n", + "新增数据： ['2025-02-10', 8875.0, 7013.0, 0.15, 0.25, 3650.0, 0.9, 0.0, 0.0, 3650.0, 7.9, 0.0, 0.2, 3900.0, 4.8, '', 3750.0, 74.64, '', '', 3778.0, 28.608, '', '', '', '', 229522.1, 2114.7, 3719.1501, '', 2000.0, 177876.7630674, 4607.05, 4350.0]\n", + "20250211\n", + "新增数据： ['2025-02-11', 8875.0, 7031.0, 0.15, 0.25, 3650.0, 1.0, 0.0, 0.0, 3650.0, 7.9, 0.0, 0.2, 3900.0, 4.8, '', 3800.0, 74.64, 76.01, '', 3776.0, 26.9001, '', '', '', 18.76416033, 229522.1, 1951.18, 3806.0186, '', '', 180448.1199647, 4613.8, '']\n", + "20250212\n" ] }, { - "ename": "PermissionError", - "evalue": "[Errno 13] Permission denied: '沥青数据项.xls'", + "ename": "KeyboardInterrupt", + "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mPermissionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[6], line 8\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m start_date \u001b[38;5;241m<\u001b[39m end_date:\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28mprint\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 start_3(start_date)\n\u001b[0;32m 9\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 10\u001b[0m start_2(start_date)\n", - "Cell \u001b[1;32mIn[5], line 548\u001b[0m, in \u001b[0;36mstart_3\u001b[1;34m(date)\u001b[0m\n\u001b[0;32m 546\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 547\u001b[0m append_rows\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 548\u001b[0m save_xls(append_rows)\n", - "Cell \u001b[1;32mIn[5], line 723\u001b[0m, in \u001b[0;36msave_xls\u001b[1;34m(append_rows)\u001b[0m\n\u001b[0;32m 720\u001b[0m new_sheet\u001b[38;5;241m.\u001b[39mwrite(row_count, col, append_rows[col])\n\u001b[0;32m 722\u001b[0m \u001b[38;5;66;03m# 保存新的xls文件\u001b[39;00m\n\u001b[1;32m--> 723\u001b[0m new_workbook\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m沥青数据项.xls\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\xlwt\\Workbook.py:710\u001b[0m, in \u001b[0;36mWorkbook.save\u001b[1;34m(self, filename_or_stream)\u001b[0m\n\u001b[0;32m 707\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CompoundDoc\n\u001b[0;32m 709\u001b[0m doc \u001b[38;5;241m=\u001b[39m CompoundDoc\u001b[38;5;241m.\u001b[39mXlsDoc()\n\u001b[1;32m--> 710\u001b[0m doc\u001b[38;5;241m.\u001b[39msave(filename_or_stream, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_biff_data())\n", - "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\xlwt\\CompoundDoc.py:262\u001b[0m, in \u001b[0;36mXlsDoc.save\u001b[1;34m(self, file_name_or_filelike_obj, stream)\u001b[0m\n\u001b[0;32m 260\u001b[0m we_own_it \u001b[38;5;241m=\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(f, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwrite\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 261\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m we_own_it:\n\u001b[1;32m--> 262\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(file_name_or_filelike_obj, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw+b\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 263\u001b[0m f\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mheader)\n\u001b[0;32m 264\u001b[0m f\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpacked_MSAT_1st)\n", - "\u001b[1;31mPermissionError\u001b[0m: [Errno 13] Permission denied: '沥青数据项.xls'" + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[2], line 13\u001b[0m\n\u001b[0;32m 11\u001b[0m start_3(start_date,token,token_push)\n\u001b[0;32m 12\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m---> 13\u001b[0m start_2(start_date,token)\n\u001b[0;32m 14\u001b[0m start_date \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m timedelta(days\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "Cell \u001b[1;32mIn[1], line 600\u001b[0m, in \u001b[0;36mstart_2\u001b[1;34m(date, token)\u001b[0m\n\u001b[0;32m 593\u001b[0m read_xls_data()\n\u001b[0;32m 595\u001b[0m \u001b[38;5;66;03m# token = get_head_auth()\u001b[39;00m\n\u001b[0;32m 596\u001b[0m \u001b[38;5;66;03m# if not token:\u001b[39;00m\n\u001b[0;32m 597\u001b[0m \u001b[38;5;66;03m# return\u001b[39;00m\n\u001b[1;32m--> 600\u001b[0m datas \u001b[38;5;241m=\u001b[39m get_data_value(token, one_cols[\u001b[38;5;241m1\u001b[39m:],date)\n\u001b[0;32m 601\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m datas:\n\u001b[0;32m 602\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n", + "Cell \u001b[1;32mIn[1], line 126\u001b[0m, in \u001b[0;36mget_data_value\u001b[1;34m(token, dataItemNoList, date)\u001b[0m\n\u001b[0;32m 117\u001b[0m search_data \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 118\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\n\u001b[0;32m 119\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m\"\u001b[39m: get_cur_time(date)[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 123\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfuncOperation\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m查询\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 124\u001b[0m }\n\u001b[0;32m 125\u001b[0m headers \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAuthorization\u001b[39m\u001b[38;5;124m\"\u001b[39m: token}\n\u001b[1;32m--> 126\u001b[0m search_res \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mpost(url\u001b[38;5;241m=\u001b[39msearch_url, headers\u001b[38;5;241m=\u001b[39mheaders, json\u001b[38;5;241m=\u001b[39msearch_data, timeout\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m5\u001b[39m))\n\u001b[0;32m 127\u001b[0m search_value \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mloads(search_res\u001b[38;5;241m.\u001b[39mtext)[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m search_value:\n", + "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\requests\\api.py:115\u001b[0m, in \u001b[0;36mpost\u001b[1;34m(url, data, json, **kwargs)\u001b[0m\n\u001b[0;32m 103\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(url, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, json\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 104\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a POST request.\u001b[39;00m\n\u001b[0;32m 105\u001b[0m \n\u001b[0;32m 106\u001b[0m \u001b[38;5;124;03m :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 112\u001b[0m \u001b[38;5;124;03m :rtype: requests.Response\u001b[39;00m\n\u001b[0;32m 113\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m request(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url, data\u001b[38;5;241m=\u001b[39mdata, json\u001b[38;5;241m=\u001b[39mjson, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\requests\\api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m 55\u001b[0m \u001b[38;5;66;03m# By using the 'with' statement we are sure the session is closed, thus we\u001b[39;00m\n\u001b[0;32m 56\u001b[0m \u001b[38;5;66;03m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001b[39;00m\n\u001b[0;32m 57\u001b[0m \u001b[38;5;66;03m# cases, and look like a memory leak in others.\u001b[39;00m\n\u001b[0;32m 58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[1;32m---> 59\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m session\u001b[38;5;241m.\u001b[39mrequest(method\u001b[38;5;241m=\u001b[39mmethod, url\u001b[38;5;241m=\u001b[39murl, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\requests\\sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m 584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 585\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[0;32m 586\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[0;32m 587\u001b[0m }\n\u001b[0;32m 588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[1;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(prep, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msend_kwargs)\n\u001b[0;32m 591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n", + "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\requests\\sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m 700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[0;32m 702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[1;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m adapter\u001b[38;5;241m.\u001b[39msend(request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[0;32m 706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n", + "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\requests\\adapters.py:486\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m 483\u001b[0m timeout \u001b[38;5;241m=\u001b[39m TimeoutSauce(connect\u001b[38;5;241m=\u001b[39mtimeout, read\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[0;32m 485\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 486\u001b[0m resp \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39murlopen(\n\u001b[0;32m 487\u001b[0m method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[0;32m 488\u001b[0m url\u001b[38;5;241m=\u001b[39murl,\n\u001b[0;32m 489\u001b[0m body\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mbody,\n\u001b[0;32m 490\u001b[0m headers\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mheaders,\n\u001b[0;32m 491\u001b[0m redirect\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 492\u001b[0m assert_same_host\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 493\u001b[0m preload_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 494\u001b[0m decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 495\u001b[0m retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_retries,\n\u001b[0;32m 496\u001b[0m timeout\u001b[38;5;241m=\u001b[39mtimeout,\n\u001b[0;32m 497\u001b[0m chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m 498\u001b[0m )\n\u001b[0;32m 500\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m 501\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n", + "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\connectionpool.py:791\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[0;32m 788\u001b[0m response_conn \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m release_conn \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 790\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[1;32m--> 791\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_request(\n\u001b[0;32m 792\u001b[0m conn,\n\u001b[0;32m 793\u001b[0m method,\n\u001b[0;32m 794\u001b[0m url,\n\u001b[0;32m 795\u001b[0m timeout\u001b[38;5;241m=\u001b[39mtimeout_obj,\n\u001b[0;32m 796\u001b[0m body\u001b[38;5;241m=\u001b[39mbody,\n\u001b[0;32m 797\u001b[0m headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[0;32m 798\u001b[0m chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m 799\u001b[0m retries\u001b[38;5;241m=\u001b[39mretries,\n\u001b[0;32m 800\u001b[0m response_conn\u001b[38;5;241m=\u001b[39mresponse_conn,\n\u001b[0;32m 801\u001b[0m preload_content\u001b[38;5;241m=\u001b[39mpreload_content,\n\u001b[0;32m 802\u001b[0m decode_content\u001b[38;5;241m=\u001b[39mdecode_content,\n\u001b[0;32m 803\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mresponse_kw,\n\u001b[0;32m 804\u001b[0m )\n\u001b[0;32m 806\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n\u001b[0;32m 807\u001b[0m clean_exit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\connectionpool.py:537\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[1;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[0;32m 535\u001b[0m \u001b[38;5;66;03m# Receive the response from the server\u001b[39;00m\n\u001b[0;32m 536\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 537\u001b[0m response \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39mgetresponse()\n\u001b[0;32m 538\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (BaseSSLError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 539\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_timeout(err\u001b[38;5;241m=\u001b[39me, url\u001b[38;5;241m=\u001b[39murl, timeout_value\u001b[38;5;241m=\u001b[39mread_timeout)\n", + "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\urllib3\\connection.py:461\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 458\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mresponse\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HTTPResponse\n\u001b[0;32m 460\u001b[0m \u001b[38;5;66;03m# Get the response from http.client.HTTPConnection\u001b[39;00m\n\u001b[1;32m--> 461\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mgetresponse()\n\u001b[0;32m 463\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 464\u001b[0m assert_header_parsing(httplib_response\u001b[38;5;241m.\u001b[39mmsg)\n", + "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\http\\client.py:1386\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1384\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1385\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1386\u001b[0m response\u001b[38;5;241m.\u001b[39mbegin()\n\u001b[0;32m 1387\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n\u001b[0;32m 1388\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n", + "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\http\\client.py:325\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 323\u001b[0m \u001b[38;5;66;03m# read until we get a non-100 response\u001b[39;00m\n\u001b[0;32m 324\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 325\u001b[0m version, status, reason \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_read_status()\n\u001b[0;32m 326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m CONTINUE:\n\u001b[0;32m 327\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", + "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\http\\client.py:286\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_read_status\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 286\u001b[0m line \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp\u001b[38;5;241m.\u001b[39mreadline(_MAXLINE \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miso-8859-1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 287\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(line) \u001b[38;5;241m>\u001b[39m _MAXLINE:\n\u001b[0;32m 288\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m LineTooLong(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstatus line\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\socket.py:706\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m 704\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m 705\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 706\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sock\u001b[38;5;241m.\u001b[39mrecv_into(b)\n\u001b[0;32m 707\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[0;32m 708\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "from datetime import datetime, timedelta\n", "\n", - "start_date = datetime(2024, 12, 31)\n", - "end_date = datetime(2025, 1, 2)\n", + "start_date = datetime(2025, 2, 1)\n", + "end_date = datetime(2025, 3, 1)\n", + "token = get_head_auth()\n", + "\n", + "token_push = get_head_push_auth()\n", "\n", "while start_date < end_date:\n", " print(start_date.strftime('%Y%m%d'))\n", - " start_3(start_date)\n", + " start_3(start_date,token,token_push)\n", " time.sleep(1)\n", - " start_2(start_date)\n", + " start_2(start_date,token)\n", " start_date += timedelta(days=1)" ] }, diff --git a/aisenzhecode/沥青/沥青数据项.xls b/aisenzhecode/沥青/沥青数据项.xls index 4439064..4f0dee4 100644 Binary files a/aisenzhecode/沥青/沥青数据项.xls and b/aisenzhecode/沥青/沥青数据项.xls differ diff --git a/config_jingbo_yuedu.py b/config_jingbo_yuedu.py index ae90922..04c7e92 100644 --- a/config_jingbo_yuedu.py +++ b/config_jingbo_yuedu.py @@ -159,9 +159,9 @@ table_name = 'v_tbl_crude_oil_warning' ### 开关 -is_train = True # 是否训练 +is_train = False # 是否训练 is_debug = False # 是否调试 -is_eta = True # 是否使用eta接口 +is_eta = False # 是否使用eta接口 is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效 is_timefurture = True # 是否使用时间特征 is_fivemodels = False # 是否使用之前保存的最佳的5个模型 @@ -182,7 +182,7 @@ print("数据库连接成功",host,dbname,dbusername) # 数据截取日期 -start_year = 2020 # 数据开始年份 +start_year = 1993 # 数据开始年份 end_time = '' # 数据截取日期 freq = 'M' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日 delweekenday = True if freq == 'B' else False # 是否删除周末数据 diff --git a/lib/dataread.py b/lib/dataread.py index 7d82d18..1ce2fc9 100644 --- a/lib/dataread.py +++ b/lib/dataread.py @@ -42,7 +42,8 @@ plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 # from config_jingbo_pro import * # from config_jingbo import * -from config_jingbo_zhoudu import * +# from config_jingbo_zhoudu import * +from config_jingbo_yuedu import * # from config_yongan import * # from config_juxiting import * # from config_juxiting_zhoudu import * diff --git a/main_yuanyou_yuedu.py b/main_yuanyou_yuedu.py index 8e02741..ad62f74 100644 --- a/main_yuanyou_yuedu.py +++ b/main_yuanyou_yuedu.py @@ -255,15 +255,15 @@ def predict_main(): logger.info('训练数据绘图end') # 模型报告 - logger.info('制作报告ing') - title = f'{settings}--{end_time}-预测报告' # 报告标题 - reportname = f'Brent原油大模型月度预测--{end_time}.pdf' # 报告文件名 - reportname = reportname.replace(':', '-') # 替换冒号 - brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time, - reportname=reportname,sqlitedb=sqlitedb), + # logger.info('制作报告ing') + # title = f'{settings}--{end_time}-预测报告' # 报告标题 + # reportname = f'Brent原油大模型月度预测--{end_time}.pdf' # 报告文件名 + # reportname = reportname.replace(':', '-') # 替换冒号 + # brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time, + # reportname=reportname,sqlitedb=sqlitedb), - logger.info('制作报告end') - logger.info('模型训练完成') + # logger.info('制作报告end') + # logger.info('模型训练完成') # # LSTM 单变量模型 # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset) @@ -291,7 +291,7 @@ if __name__ == '__main__': global end_time is_on = True # 遍历2024-11-25 到 2024-12-3 之间的工作日日期 - for i_time in pd.date_range('2024-12-1', '2025-2-1', freq='ME'): + for i_time in pd.date_range('2022-6-1', '2025-3-1', freq='ME'): end_time = i_time.strftime('%Y-%m-%d') predict_main() diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py index 6857fe8..3c4e5e9 100644 --- a/models/nerulforcastmodels.py +++ b/models/nerulforcastmodels.py @@ -837,13 +837,23 @@ def model_losss_yongan(sqlitedb,end_time,table_name_prefix): def model_losss(sqlitedb,end_time): global dataset global rote - most_model = [sqlitedb.select_data('most_model',columns=['most_common_model'],order_by='ds desc',limit=1).values[0][0]] - most_model_name = most_model[0] + # 从数据库取最佳模型，如果没有表，先自定义空，后面根据模型评估取第一个 + try: + most_model = [sqlitedb.select_data('most_model',columns=['most_common_model'],order_by='ds desc',limit=1).values[0][0]] + most_model_name = most_model[0] + except: + most_model_name = '' # 预测数据处理 predict - # df_combined = loadcsv(os.path.join(dataset,"cross_validation.csv")) - # df_combined = dateConvert(df_combined) - df_combined = sqlitedb.select_data('accuracy',where_condition=f"created_dt <= '{end_time}'") + + try: + df_combined = sqlitedb.select_data('accuracy',where_condition=f"created_dt <= '{end_time}'") + if len(df_combined) < 10: + len(df_combined) + '' + except: + df_combined = loadcsv(os.path.join(dataset,"cross_validation.csv")) + df_combined = dateConvert(df_combined) + df_combined['CREAT_DATE'] = df_combined['cutoff'] df_combined4 = df_combined.copy() # 备份df_combined,后面画图需要 # 删除缺失值大于80%的列 logger.info(df_combined.shape) @@ -860,11 +870,13 @@ def model_losss(sqlitedb,end_time): # 然后筛选出那些 cutoff 等于 max_cutoff 的行，这样就得到了每个分组中 cutoff 最大的行，并保留了其他列 df_combined = df_combined[df_combined['CREAT_DATE'] == df_combined['max_cutoff']] # 删除模型生成的cutoff列 - df_combined.drop(columns=['CREAT_DATE', 'max_cutoff','created_dt','min_within_quantile','max_within_quantile','id','min_price','max_price','LOW_PRICE','HIGH_PRICE','mean'], inplace=True,errors='ignore') + df_combined.drop(columns=['CREAT_DATE', 'max_cutoff','created_dt','min_within_quantile','max_within_quantile','id','min_price','max_price','LOW_PRICE','HIGH_PRICE','mean','cutoff'], inplace=True,errors='ignore') # 获取模型名称 modelnames = df_combined.columns.to_list()[1:] if 'y' in modelnames: modelnames.remove('y') + if 'cutoff' in modelnames: + modelnames.remove('cutoff') df_combined3 = df_combined.copy() # 备份df_combined,后面画图需要 @@ -886,6 +898,7 @@ def model_losss(sqlitedb,end_time): model_results3 = model_results3.sort_values(by='平均平方误差(MSE)', ascending=True) model_results3.to_csv(os.path.join(dataset,"model_evaluation.csv"),index=False) modelnames = model_results3['模型(Model)'].tolist() + most_model_name = modelnames[0] allmodelnames = modelnames.copy() # 保存5个最佳模型的名称 if len(modelnames) > 5: @@ -1158,8 +1171,9 @@ def model_losss(sqlitedb,end_time): for i, j in zip(df['ds'], df['y']): plt.text(i, j, str(j), ha='center', va='bottom') - for model in most_model: - plt.plot(df['ds'], df[model], label=model,marker='o') + # for model in most_model: + # plt.plot(df['ds'], df[model], label=model,marker='o') + plt.plot(df['ds'], df[most_model_name], label=model,marker='o') # 当前日期画竖虚线 plt.axvline(x=df['ds'].iloc[-horizon], color='r', linestyle='--') plt.legend() @@ -1247,10 +1261,10 @@ def model_losss(sqlitedb,end_time): plt.savefig(os.path.join(dataset,'模型评估.png'), bbox_inches='tight') plt.close() - _plt_predict_ture(df_combined3) - _plt_modeltopten_predict_ture(df_combined4) - _plt_predict_table(df_combined3) - _plt_model_results3() + # _plt_predict_ture(df_combined3) + # _plt_modeltopten_predict_ture(df_combined4) + # _plt_predict_table(df_combined3) + # _plt_model_results3() return model_results3