{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "660798fa-cf08-4cfd-b553-c6d750d7e7ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import algo_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff42670b-301d-4ee1-8da2-6a658fc5f286",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'_id': ObjectId('66ebba82a3b7dbe82538e34a'), 'name': 'Alice', 'age': 25, 'city': 'Beijing'}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import subprocess\n",
    "\n",
    "# 使用 subprocess 执行 .py 文件\n",
    "result = subprocess.run(['python', 'algo_test.py'], capture_output=True, text=True)\n",
    "\n",
    "# 打印输出\n",
    "print(result.stdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b47d615a-5953-43c0-9289-387d5c823a7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-26 14:20:47,541 - waitress - INFO - Serving on http://0.0.0.0:10091\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Program starts execution!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-26 14:21:19,377 - evaluation_accuracy log - INFO - {'formulaType': 'DAY_SHORT_ACCURACY', 'cap': '225', 'province': 'E15', 'electricType': 'E2', 'stationCode': 'J00260', 'col_time': 'C_TIME', 'col_rp': '实际功率', 'col_pp': 'predict', 'mongodb_connection': 'mongodb://root:Jydl3377@192.168.1.33:27017', 'mongodb_database': 'algorithm_platform', 'mongodb_read_table': 'J00260NWP_records_predict', 'mongodb_write_table': 'J00260NWP_records_accuracy'}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Program starts execution!\n",
      "args {'formulaType': 'DAY_SHORT_ACCURACY', 'cap': '225', 'province': 'E15', 'electricType': 'E2', 'stationCode': 'J00260', 'col_time': 'C_TIME', 'col_rp': '实际功率', 'col_pp': 'predict', 'mongodb_connection': 'mongodb://root:Jydl3377@192.168.1.33:27017', 'mongodb_database': 'algorithm_platform', 'mongodb_read_table': 'J00260NWP_records_predict', 'mongodb_write_table': 'J00260NWP_records_accuracy'}\n",
      "accuray compute successfully!\n",
      "Collection 'J00260NWP_records_accuracy already exist, deleted successfully!\n",
      "data inserted successfully!\n",
      "Program execution ends!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-26 14:25:11,738 - evaluation_accuracy log - INFO - {'formulaType': 'DAY_SHORT_ACCURACY', 'cap': '225', 'province': 'E15', 'electricType': 'E2', 'stationCode': 'J00260', 'col_time': 'C_TIME', 'col_rp': '实际功率', 'col_pp': 'predict', 'mongodb_connection': 'mongodb://root:Jydl3377@192.168.1.33:27017', 'mongodb_database': 'algorithm_platform', 'mongodb_read_table': 'J00260NWP_records_predict', 'mongodb_write_table': 'J00260NWP_records_accuracy'}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Program starts execution!\n",
      "args {'formulaType': 'DAY_SHORT_ACCURACY', 'cap': '225', 'province': 'E15', 'electricType': 'E2', 'stationCode': 'J00260', 'col_time': 'C_TIME', 'col_rp': '实际功率', 'col_pp': 'predict', 'mongodb_connection': 'mongodb://root:Jydl3377@192.168.1.33:27017', 'mongodb_database': 'algorithm_platform', 'mongodb_read_table': 'J00260NWP_records_predict', 'mongodb_write_table': 'J00260NWP_records_accuracy'}\n",
      "accuray compute successfully!\n",
      "Collection 'J00260NWP_records_accuracy already exist, deleted successfully!\n",
      "data inserted successfully!\n",
      "Program execution ends!\n"
     ]
    }
   ],
   "source": [
    "%run evaluation_accuracy.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "752c3ab3-cdef-47b0-9d9a-805348d30d6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.9.19 (main, May  6 2024, 20:12:36) [MSC v.1916 64 bit (AMD64)]\n"
     ]
    }
   ],
   "source": [
    "'formulaType': 'DAY_SHORT_ACCURACY',\n",
    "    'cap': '225',\n",
    "    'province': 'E15',\n",
    "    'electricType': 'E2',\n",
    "    'stationCode': 'J00260'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "acc6aa2a-0f1d-43f3-985d-cd37c4a5ac6e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output:\n",
      "Program starts execution!\n",
      "args: Namespace(formulaType='DAY_SHORT_ACCURACY', cap='225', province='E15', electricType='E2', stationCode='J00260', col_time='C_TIME', col_rp='实际功率', col_pp='predict', mongodb_connection='mongodb://root:Jydl3377@192.168.1.33:27017', mongodb_database='algorithm_platform', mongodb_read_table='J00260NWP_records_predict', mongodb_write_table='J00260NWP_records_accuracy')\n",
      "accuray compute successfully!\n",
      "Collection 'J00260NWP_records_accuracy already exist, deleted successfully!\n",
      "data inserted successfully!\n",
      "Program execution ends!\n",
      "\n",
      "Error:\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import subprocess\n",
    "\n",
    "# 构建命令\n",
    "command = [\n",
    "    \"python\", \"accuracy.py\",\n",
    "    \"--formulaType\", \"DAY_SHORT_ACCURACY\",\n",
    "    \"--cap\",\"225\",\n",
    "    \"--province\",\"E15\",\n",
    "    \"--electricType\",\"E2\",\n",
    "    \"--stationCode\",\"J00260\",\n",
    "    \"--col_time\",\"C_TIME\",\n",
    "    \"--col_rp\",\"实际功率\",\n",
    "    \"--col_pp\",\"predict\",\n",
    "    \"--mongodb_connection\", \"mongodb://root:Jydl3377@192.168.1.33:27017\",\n",
    "    \"--mongodb_database\", \"algorithm_platform\",\n",
    "    \"--mongodb_read_table\", \"J00260NWP_records_predict\",\n",
    "    \"--mongodb_write_table\", \"J00260NWP_records_accuracy\",\n",
    "]\n",
    "\n",
    "# 使用 subprocess.run 来执行命令\n",
    "result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)\n",
    "\n",
    "# 输出结果和错误信息\n",
    "print(\"Output:\")\n",
    "print(result.stdout)\n",
    "print(\"Error:\")\n",
    "print(result.stderr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cb1c745-0673-4dfa-968f-8985e788852a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"--electricType\",\"E2\",\n",
    "\"--stationCode\",\"J00260\",\n",
    "\"--col_time\",\"C_TIME\",\n",
    "\"--col_rp\",\"实际功率\",\n",
    "\"--col_pp\",\"predict\",\n",
    "\"--mongodb_connection\", \"mongodb://root:Jydl3377@192.168.1.33:27017\",\n",
    "\"--mongodb_database\", \"algorithm_platform\",\n",
    "\"--mongodb_read_table\", \"J00260NWP_records_predict\",\n",
    "\"--mongodb_write_table\", \"J00260NWP_records_accuracy\","
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14a9feb4-5625-45d7-8e0f-6a8dea8c9f24",
   "metadata": {},
   "outputs": [],
   "source": [
    "docker run evaluation_accuracy  --formulaType \"DAY_SHORT_ACCURACY\"     --cap \"225\"   --province \"E15\"     --electricType \"E2\"    --stationCode \"J00260\"     --col_time \"C_TIME\"     --col_rp \"实际功率\"   --col_pp \"predict\"  --mongodb_connection \"mongodb://root:Jydl3377@192.168.1.33:27017\"   --mongodb_database \"algorithm_platform\"   --mongodb_read_table \"J00260NWP_records_predict\"  --mongodb_write_table \"J00260NWP_records_accuracy\"        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0d791f8-12d8-4ab9-b561-b474e2a739f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "docker run evaluation_accuracy.py --numerical_features ','.join(['NWP预测总辐射', 'NWP-10米预测风速', 'NWP-10米预测风向', 'NWP-30米预测风速', 'NWP-30米预测风向','NWP-50米预测风速', 'NWP-50米预测风向', 'NWP-70米预测风速', 'NWP-70米预测风向','NWP-90米预测风速', 'NWP-90米预测风向', 'NWP-110米预测风速', 'NWP-110米预测风向','NWP-150米预测风速', 'NWP-150米预测风向', 'NWP-170米预测风速', 'NWP-170米预测风向','NWP-低云量', 'NWP-中云量', 'NWP-高云量', 'NWP-总云量', 'NWP-晴空总辐射', 'NWP-法相直辐射','NWP-总降水强度', 'NWP-温度', 'NWP-湿度', 'NWP-气压'])  --categorical_features  ','.join([])  --label  \"实际功率\" --learning_rate  \"0.01\" --num_leaves \"31\"  --min_data_in_leaf  \"21\"  --mongodb_connection  \"mongodb://root:Jydl3377@192.168.1.33:27017\" --mongodb_database  \"algorithm_platform\" --mongodb_read_table  \"J00260NWP_records\" --mongodb_write_table  \"models\" --model_name \"lightgbm_model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9ca17599-e453-479c-a9b3-b1d15f2d54de",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "import argparse\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "from pymongo import MongoClient\n",
    "import pickle\n",
    "client = MongoClient(\"mongodb://root:Jydl3377@192.168.1.33:27017\")\n",
    "db = client[\"algorithm_platform\"]\n",
    "# collection = db[\"models\"]\n",
    "# model_data = collection.find_one({\"model_name\": \"lightgbm_model\"})\n",
    "# if model_data is not None:\n",
    "#     # 假设模型存储在字段 'model' 中\n",
    "#     model_binary = model_data['model']  # 确保这个字段是存储模型的二进制数据\n",
    "\n",
    "#     # 反序列化模型\n",
    "#     model = pickle.loads(model_binary)\n",
    "# model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ff940fec-55fd-4cbd-99ef-5564c6632efb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.50265625"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collection = db[\"J00260NWP_records_accuracy\"]  # 集合名称\n",
    "data_from_db = collection.find()  # 这会返回一个游标（cursor）\n",
    "# 将游标转换为列表，并创建 pandas DataFrame\n",
    "df = pd.DataFrame(list(data_from_db))\n",
    "df['accuracy'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8c55f915-4db7-4859-b449-07b2891635bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_id</th>\n",
       "      <th>date</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66f24cdb7783ad7f1a7a270c</td>\n",
       "      <td>2024-05-05</td>\n",
       "      <td>92.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66f24cdb7783ad7f1a7a270d</td>\n",
       "      <td>2024-05-06</td>\n",
       "      <td>92.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>66f24cdb7783ad7f1a7a270e</td>\n",
       "      <td>2024-05-07</td>\n",
       "      <td>90.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>66f24cdb7783ad7f1a7a270f</td>\n",
       "      <td>2024-05-08</td>\n",
       "      <td>94.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>66f24cdb7783ad7f1a7a2710</td>\n",
       "      <td>2024-05-09</td>\n",
       "      <td>84.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>66f24cdb7783ad7f1a7a2784</td>\n",
       "      <td>2024-09-07</td>\n",
       "      <td>91.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>66f24cdb7783ad7f1a7a2785</td>\n",
       "      <td>2024-09-08</td>\n",
       "      <td>83.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>66f24cdb7783ad7f1a7a2786</td>\n",
       "      <td>2024-09-10</td>\n",
       "      <td>91.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>66f24cdb7783ad7f1a7a2787</td>\n",
       "      <td>2024-09-11</td>\n",
       "      <td>88.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>66f24cdb7783ad7f1a7a2788</td>\n",
       "      <td>2024-09-12</td>\n",
       "      <td>86.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          _id        date  accuracy\n",
       "3    66f24cdb7783ad7f1a7a270c  2024-05-05     92.48\n",
       "4    66f24cdb7783ad7f1a7a270d  2024-05-06     92.05\n",
       "5    66f24cdb7783ad7f1a7a270e  2024-05-07     90.81\n",
       "6    66f24cdb7783ad7f1a7a270f  2024-05-08     94.91\n",
       "7    66f24cdb7783ad7f1a7a2710  2024-05-09     84.49\n",
       "..                        ...         ...       ...\n",
       "123  66f24cdb7783ad7f1a7a2784  2024-09-07     91.89\n",
       "124  66f24cdb7783ad7f1a7a2785  2024-09-08     83.68\n",
       "125  66f24cdb7783ad7f1a7a2786  2024-09-10     91.89\n",
       "126  66f24cdb7783ad7f1a7a2787  2024-09-11     88.76\n",
       "127  66f24cdb7783ad7f1a7a2788  2024-09-12     86.05\n",
       "\n",
       "[125 rows x 3 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['date']>='2024-05-05']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7feaff6d-e41a-4bd7-b6fb-a63fc6900cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "import importlib\n",
    "import acc\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "collection = db[\"J00260NWP_records_predict\"]  # 集合名称\n",
    "data_from_db = collection.find()  # 这会返回一个游标（cursor）\n",
    "# 将游标转换为列表，并创建 pandas DataFrame\n",
    "df = pd.DataFrame(list(data_from_db))\n",
    "df.to_csv('J00260_predict.csv',encoding='gbk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8a22f3e9-feff-438e-81e2-b5ceaa15ac48",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.50265625000002"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import argparse\n",
    "import importlib\n",
    "import acc\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "collection = db[\"J00260NWP_records_predict\"]  # 集合名称\n",
    "data_from_db = collection.find()  # 这会返回一个游标（cursor）\n",
    "# 将游标转换为列表，并创建 pandas DataFrame\n",
    "df = pd.DataFrame(list(data_from_db))\n",
    "\n",
    "# 重新加载模块\n",
    "importlib.reload(acc)\n",
    "from acc import calculate_acc\n",
    "\n",
    "df_tmp = df[['C_TIME','实际功率','predict']].rename(columns={'实际功率':'realValue','predict':'forecastAbleValue'})\n",
    "df_tmp = df_tmp[~np.isnan(df_tmp['realValue'])]\n",
    "df_tmp['ableValue'] = df_tmp['realValue']\n",
    "df_tmp['C_TIME'] = df_tmp['C_TIME'].apply(lambda x: datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\"))\n",
    "\n",
    "cc = {\n",
    "    'formulaType': 'DAY_SHORT_ACCURACY',\n",
    "    'cap': '225',\n",
    "    'province': 'E15',\n",
    "    'electricType': 'E2',\n",
    "    'stationCode': 'J00260' }\n",
    "            \n",
    "opt = argparse.Namespace(**cc)\n",
    "\n",
    "calculate_acc(df_tmp, opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "abccb68a-cc63-4474-8167-ab1e01363ad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_id</th>\n",
       "      <th>C_TIME</th>\n",
       "      <th>理论功率</th>\n",
       "      <th>第1个设备轮毂风速</th>\n",
       "      <th>实际功率</th>\n",
       "      <th>实时环境数据</th>\n",
       "      <th>预测功率</th>\n",
       "      <th>NWP预测总辐射</th>\n",
       "      <th>NWP-10米预测风速</th>\n",
       "      <th>NWP-10米预测风向</th>\n",
       "      <th>...</th>\n",
       "      <th>NWP-中云量</th>\n",
       "      <th>NWP-高云量</th>\n",
       "      <th>NWP-总云量</th>\n",
       "      <th>NWP-晴空总辐射</th>\n",
       "      <th>NWP-法相直辐射</th>\n",
       "      <th>NWP-总降水强度</th>\n",
       "      <th>NWP-温度</th>\n",
       "      <th>NWP-湿度</th>\n",
       "      <th>NWP-气压</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>66ecc86f1075358d129a1e72</td>\n",
       "      <td>2024-05-05 21:30:00</td>\n",
       "      <td>128.71</td>\n",
       "      <td>8.98</td>\n",
       "      <td>108.10</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>93.61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6149</td>\n",
       "      <td>58.2727</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0536</td>\n",
       "      <td>44.5345</td>\n",
       "      <td>46.8773</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0082</td>\n",
       "      <td>14.4073</td>\n",
       "      <td>22.4573</td>\n",
       "      <td>890.2109</td>\n",
       "      <td>76.442233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>66ecc86f1075358d129a1e73</td>\n",
       "      <td>2024-05-05 21:45:00</td>\n",
       "      <td>129.84</td>\n",
       "      <td>8.98</td>\n",
       "      <td>108.34</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>92.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5280</td>\n",
       "      <td>56.5700</td>\n",
       "      <td>...</td>\n",
       "      <td>8.3291</td>\n",
       "      <td>43.2064</td>\n",
       "      <td>45.5273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0091</td>\n",
       "      <td>14.1027</td>\n",
       "      <td>23.0645</td>\n",
       "      <td>890.3364</td>\n",
       "      <td>75.284865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>66ecc86f1075358d129a1e74</td>\n",
       "      <td>2024-05-05 22:00:00</td>\n",
       "      <td>136.81</td>\n",
       "      <td>8.98</td>\n",
       "      <td>111.86</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>91.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5088</td>\n",
       "      <td>55.0418</td>\n",
       "      <td>...</td>\n",
       "      <td>7.3591</td>\n",
       "      <td>41.9909</td>\n",
       "      <td>44.1800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>13.8064</td>\n",
       "      <td>23.6636</td>\n",
       "      <td>890.4555</td>\n",
       "      <td>75.199564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 35 columns</p>\n",
       "</div>"
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      "text/plain": [
       "                         _id               C_TIME    理论功率  第1个设备轮毂风速    实际功率  \\\n",
       "51  66ecc86f1075358d129a1e72  2024-05-05 21:30:00  128.71       8.98  108.10   \n",
       "52  66ecc86f1075358d129a1e73  2024-05-05 21:45:00  129.84       8.98  108.34   \n",
       "53  66ecc86f1075358d129a1e74  2024-05-05 22:00:00  136.81       8.98  111.86   \n",
       "\n",
       "    实时环境数据   预测功率  NWP预测总辐射  NWP-10米预测风速  NWP-10米预测风向  ...  NWP-中云量  NWP-高云量  \\\n",
       "51   -0.99  93.61       0.0       3.6149      58.2727  ...   9.0536  44.5345   \n",
       "52   -0.99  92.70       0.0       3.5280      56.5700  ...   8.3291  43.2064   \n",
       "53   -0.99  91.75       0.0       3.5088      55.0418  ...   7.3591  41.9909   \n",
       "\n",
       "    NWP-总云量  NWP-晴空总辐射  NWP-法相直辐射  NWP-总降水强度   NWP-温度   NWP-湿度    NWP-气压  \\\n",
       "51  46.8773        0.0        0.0     0.0082  14.4073  22.4573  890.2109   \n",
       "52  45.5273        0.0        0.0     0.0091  14.1027  23.0645  890.3364   \n",
       "53  44.1800        0.0        0.0     0.0100  13.8064  23.6636  890.4555   \n",
       "\n",
       "      predict  \n",
       "51  76.442233  \n",
       "52  75.284865  \n",
       "53  75.199564  \n",
       "\n",
       "[3 rows x 35 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['C_TIME'].str.contains('2024-05-05')]"
   ]
  },
  {
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   "execution_count": 56,
   "id": "abf7687b-faf2-4b78-a9f2-44a2fea9daaa",
   "metadata": {},
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    {
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       "      <th>51</th>\n",
       "      <td>66ecc86f1075358d129a1e72</td>\n",
       "      <td>2024-05-05 21:30:00</td>\n",
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       "      <td>890.2109</td>\n",
       "      <td>76.442233</td>\n",
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       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>66ecc86f1075358d129a1e73</td>\n",
       "      <td>2024-05-05 21:45:00</td>\n",
       "      <td>129.84</td>\n",
       "      <td>8.98</td>\n",
       "      <td>108.34</td>\n",
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       "      <th>53</th>\n",
       "      <td>66ecc86f1075358d129a1e74</td>\n",
       "      <td>2024-05-05 22:00:00</td>\n",
       "      <td>136.81</td>\n",
       "      <td>8.98</td>\n",
       "      <td>111.86</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>91.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5088</td>\n",
       "      <td>55.0418</td>\n",
       "      <td>...</td>\n",
       "      <td>7.3591</td>\n",
       "      <td>41.9909</td>\n",
       "      <td>44.1800</td>\n",
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       "                         _id               C_TIME    理论功率  第1个设备轮毂风速    实际功率  \\\n",
       "51  66ecc86f1075358d129a1e72  2024-05-05 21:30:00  128.71       8.98  108.10   \n",
       "52  66ecc86f1075358d129a1e73  2024-05-05 21:45:00  129.84       8.98  108.34   \n",
       "53  66ecc86f1075358d129a1e74  2024-05-05 22:00:00  136.81       8.98  111.86   \n",
       "\n",
       "    实时环境数据   预测功率  NWP预测总辐射  NWP-10米预测风速  NWP-10米预测风向  ...  NWP-中云量  NWP-高云量  \\\n",
       "51   -0.99  93.61       0.0       3.6149      58.2727  ...   9.0536  44.5345   \n",
       "52   -0.99  92.70       0.0       3.5280      56.5700  ...   8.3291  43.2064   \n",
       "53   -0.99  91.75       0.0       3.5088      55.0418  ...   7.3591  41.9909   \n",
       "\n",
       "    NWP-总云量  NWP-晴空总辐射  NWP-法相直辐射  NWP-总降水强度   NWP-温度   NWP-湿度    NWP-气压  \\\n",
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       "53  44.1800        0.0        0.0     0.0100  13.8064  23.6636  890.4555   \n",
       "\n",
       "      predict  \n",
       "51  76.442233  \n",
       "52  75.284865  \n",
       "53  75.199564  \n",
       "\n",
       "[3 rows x 35 columns]"
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     "execution_count": 56,
     "metadata": {},
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    "df[df['C_TIME'].str.contains('2024-05-05')]"
   ]
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