设置原油配置，判断周一的时候发送预警信息
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workpc
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185
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "9048d286-415f-4a33-be3a-9d7099aa85de",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from config_jingbo import *"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "07e338e7-1dd1-417f-b4e2-65d0efc983d6",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv(os.path.join(dataset,'last_update_times.csv'))\n",
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"df = df.sort_values(by=['warning_date'], ascending=[False])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"id": "659eec10-f0b8-4d65-b70e-6f07f45291bc",
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"metadata": {},
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"outputs": [],
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"source": [
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"y_last_update_time = df[df['feature']=='y']['warning_date'].values[0]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 49,
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"id": "31035e6f-36e8-4b17-a14b-f7dd79ae085a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>last_update_time</th>\n",
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" <th>feature</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>500</th>\n",
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" <td>2024-11-14</td>\n",
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" <td>lmcads03 lme comdty</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>251</th>\n",
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" <td>2024-11-14</td>\n",
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" <td>美国MEH轻原油现货价格（美湾）</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>231</th>\n",
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" <td>2024-11-14</td>\n",
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" <td>WTI原油Cushing-C1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>264</th>\n",
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" <td>2024-11-14</td>\n",
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" <td>LLS原油现货价格</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>82</th>\n",
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" <td>2024-11-14</td>\n",
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" <td>OMAN原油：中质：远期现货DES价：山东（日）</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>83</th>\n",
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" <td>2023-01-03</td>\n",
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" <td>PVM Dubai EFS/Swap vs ICE First Month Brent</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>91</th>\n",
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" <td>2022-11-16</td>\n",
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" <td>Brent/迪拜原油升贴水</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>94</th>\n",
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" <td>2022-01-31</td>\n",
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" <td>W-B活跃合约价差</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>95</th>\n",
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" <td>2022-01-31</td>\n",
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" <td>B-W活跃合约价差</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>31</th>\n",
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" <td>2018-10-24</td>\n",
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" <td>西北欧即期Brent 211利润</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>73 rows × 2 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" last_update_time feature\n",
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"500 2024-11-14 lmcads03 lme comdty\n",
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"251 2024-11-14 美国MEH轻原油现货价格（美湾）\n",
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"231 2024-11-14 WTI原油Cushing-C1\n",
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"264 2024-11-14 LLS原油现货价格\n",
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"82 2024-11-14 OMAN原油：中质：远期现货DES价：山东（日）\n",
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".. ... ...\n",
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"83 2023-01-03 PVM Dubai EFS/Swap vs ICE First Month Brent\n",
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"91 2022-11-16 Brent/迪拜原油升贴水\n",
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"94 2022-01-31 W-B活跃合约价差\n",
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"95 2022-01-31 B-W活跃合约价差\n",
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"31 2018-10-24 西北欧即期Brent 211利润\n",
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"\n",
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"[73 rows x 2 columns]"
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]
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},
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"execution_count": 49,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df[df['warning_date']<y_last_update_time][['last_update_time','feature']]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "937a9adb-1652-4d6b-a5c7-828d7c17769d",
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"metadata": {},
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"outputs": [],
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"source": [
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"df2 = pd.read_csv(os.path.join(dataset,''))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -175,7 +175,7 @@ warning_data = {
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# is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
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# is_update_eta = True # 预测结果上传到eta
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# is_update_report = True # 是否上传报告
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# is_update_warning_data = False # 是否上传预警数据
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# is_update_warning_data = True if datetime.datetime.now().weekday() == 1 else False # 是否上传预警数据
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### 开关
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@ -188,7 +188,7 @@ is_edbcode = False # 特征使用edbcoding列表中的
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is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
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is_update_eta = False # 预测结果上传到eta
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is_update_report = False # 是否上传报告
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is_update_warning_data = True # 是否上传预警数据
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is_update_warning_data = False # 是否上传预警数据
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# 数据截取日期
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end_time = '' # 数据截取日期
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@ -504,21 +504,27 @@ def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y'
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logger.info('特征停更信息保存到文件：last_update_times.csv')
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logger.info(f'删除预警的特征前数据量：{df.shape}')
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y_last_update_time = last_update_times['y']
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y_last_update_time = last_update_times_df[last_update_times_df['feature']=='y']['warning_date'].values[0]
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columns_to_drop = last_update_times_df[last_update_times_df['warning_date'] < y_last_update_time ]['feature'].values.tolist()
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df = df.drop(columns = columns_to_drop)
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logger.info(f'删除预警的特征后数据量：{df.shape}')
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if is_update_warning_data:
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logger.info(f'上传预警信息')
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try:
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warning_data_df = last_update_times_df[last_update_times_df['feature'].isin(columns_to_drop)][['last_update_time','feature']]
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warning_data_df.sort_values(by='last_update_time',ascending=False,inplace=True)
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warning_data_df = last_update_times_df[last_update_times_df['warning_date']<y_last_update_time][['last_update_time','feature']]
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if len(warning_data_df) > 0:
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content = '原油特征指标预警信息：\n\n'
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warning_data_df = warning_data_df.sort_values(by='last_update_time',ascending=False)
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for ds, df in warning_data_df.groupby('last_update_time'):
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content += f'{ds} \n {df["feature"].to_string(index=False).replace(" ", "")}\n\n'
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else:
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logger.info(f'没有需要上传的预警信息')
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content = '没有需要维护的特征指标'
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warning_data['data']['WARNING_DATE'] = y_last_update_time
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warning_data['data']['WARNING_CONTENT'] = warning_data_df.to_string(index=False)
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warning_data['data']['WARNING_CONTENT'] = content
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token = get_head_auth_report()
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upload_warning_data(token, warning_data)
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logger.info(f'上传预警信息成功')
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