{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "#新增加的两行\n",
    "from pylab import mpl\n",
    "# 设置显示中文字体\n",
    "mpl.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "\n",
    "mpl.rcParams[\"axes.unicode_minus\"] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "          日期        企业名称         地址   省份            经度            纬度  烟囱高度（m）  \\\n0 2018-10-01  浙江秀舟热电有限公司  嘉兴市南湖区凤桥镇  浙江省  120°51′5.54″  30°39′14.76″       80   \n1 2018-10-02  浙江秀舟热电有限公司  嘉兴市南湖区凤桥镇  浙江省  120°51′5.54″  30°39′14.76″       80   \n2 2018-10-03  浙江秀舟热电有限公司  嘉兴市南湖区凤桥镇  浙江省  120°51′5.54″  30°39′14.76″       80   \n3 2018-10-04  浙江秀舟热电有限公司  嘉兴市南湖区凤桥镇  浙江省  120°51′5.54″  30°39′14.76″       80   \n4 2018-10-05  浙江秀舟热电有限公司  嘉兴市南湖区凤桥镇  浙江省  120°51′5.54″  30°39′14.76″       80   \n\n       脱硝工艺 脱硝剂名称  脱硝设备数量  ...  供热量（吉焦） 产渣量（吨） 机组运行时间（小时）  硫分（%） 脱硫副产品产量（吨）  \\\n0  SNCR SCR    氨水       3  ...  6536.83    NaN       24.0   0.51        NaN   \n1  SNCR SCR    氨水       3  ...  2484.64    NaN       24.0   0.51        NaN   \n2  SNCR SCR    氨水       3  ...  3020.83    NaN       24.0   0.51        NaN   \n3  SNCR SCR    氨水       3  ...  5599.23    NaN       24.0   0.51      72.52   \n4  SNCR SCR    氨水       3  ...  4702.65    NaN       24.0   0.51        NaN   \n\n   脱硫剂使用量（吨）  脱硫设施运行时间（小时）  脱硝还原剂消耗量（吨）  脱硝运行时间（小时）  燃料消耗量（吨）  \n0       5.06          24.0         2.98        24.0       323  \n1       5.04          24.0         2.97        24.0       218  \n2       5.04          24.0         2.95        24.0       212  \n3       5.03          24.0         2.98        24.0       223  \n4       5.06          24.0         3.01        24.0       243  \n\n[5 rows x 44 columns]",
      "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>日期</th>\n      <th>企业名称</th>\n      <th>地址</th>\n      <th>省份</th>\n      <th>经度</th>\n      <th>纬度</th>\n      <th>烟囱高度（m）</th>\n      <th>脱硝工艺</th>\n      <th>脱硝剂名称</th>\n      <th>脱硝设备数量</th>\n      <th>...</th>\n      <th>供热量（吉焦）</th>\n      <th>产渣量（吨）</th>\n      <th>机组运行时间（小时）</th>\n      <th>硫分（%）</th>\n      <th>脱硫副产品产量（吨）</th>\n      <th>脱硫剂使用量（吨）</th>\n      <th>脱硫设施运行时间（小时）</th>\n      <th>脱硝还原剂消耗量（吨）</th>\n      <th>脱硝运行时间（小时）</th>\n      <th>燃料消耗量（吨）</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2018-10-01</td>\n      <td>浙江秀舟热电有限公司</td>\n      <td>嘉兴市南湖区凤桥镇</td>\n      <td>浙江省</td>\n      <td>120°51′5.54″</td>\n      <td>30°39′14.76″</td>\n      <td>80</td>\n      <td>SNCR SCR</td>\n      <td>氨水</td>\n      <td>3</td>\n      <td>...</td>\n      <td>6536.83</td>\n      <td>NaN</td>\n      <td>24.0</td>\n      <td>0.51</td>\n      <td>NaN</td>\n      <td>5.06</td>\n      <td>24.0</td>\n      <td>2.98</td>\n      <td>24.0</td>\n      <td>323</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2018-10-02</td>\n      <td>浙江秀舟热电有限公司</td>\n      <td>嘉兴市南湖区凤桥镇</td>\n      <td>浙江省</td>\n      <td>120°51′5.54″</td>\n      <td>30°39′14.76″</td>\n      <td>80</td>\n      <td>SNCR SCR</td>\n      <td>氨水</td>\n      <td>3</td>\n      <td>...</td>\n      <td>2484.64</td>\n      <td>NaN</td>\n      <td>24.0</td>\n      <td>0.51</td>\n      <td>NaN</td>\n      <td>5.04</td>\n      <td>24.0</td>\n      <td>2.97</td>\n      <td>24.0</td>\n      <td>218</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2018-10-03</td>\n      <td>浙江秀舟热电有限公司</td>\n      <td>嘉兴市南湖区凤桥镇</td>\n      <td>浙江省</td>\n      <td>120°51′5.54″</td>\n      <td>30°39′14.76″</td>\n      <td>80</td>\n      <td>SNCR SCR</td>\n      <td>氨水</td>\n      <td>3</td>\n      <td>...</td>\n      <td>3020.83</td>\n      <td>NaN</td>\n      <td>24.0</td>\n      <td>0.51</td>\n      <td>NaN</td>\n      <td>5.04</td>\n      <td>24.0</td>\n      <td>2.95</td>\n      <td>24.0</td>\n      <td>212</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2018-10-04</td>\n      <td>浙江秀舟热电有限公司</td>\n      <td>嘉兴市南湖区凤桥镇</td>\n      <td>浙江省</td>\n      <td>120°51′5.54″</td>\n      <td>30°39′14.76″</td>\n      <td>80</td>\n      <td>SNCR SCR</td>\n      <td>氨水</td>\n      <td>3</td>\n      <td>...</td>\n      <td>5599.23</td>\n      <td>NaN</td>\n      <td>24.0</td>\n      <td>0.51</td>\n      <td>72.52</td>\n      <td>5.03</td>\n      <td>24.0</td>\n      <td>2.98</td>\n      <td>24.0</td>\n      <td>223</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2018-10-05</td>\n      <td>浙江秀舟热电有限公司</td>\n      <td>嘉兴市南湖区凤桥镇</td>\n      <td>浙江省</td>\n      <td>120°51′5.54″</td>\n      <td>30°39′14.76″</td>\n      <td>80</td>\n      <td>SNCR SCR</td>\n      <td>氨水</td>\n      <td>3</td>\n      <td>...</td>\n      <td>4702.65</td>\n      <td>NaN</td>\n      <td>24.0</td>\n      <td>0.51</td>\n      <td>NaN</td>\n      <td>5.06</td>\n      <td>24.0</td>\n      <td>3.01</td>\n      <td>24.0</td>\n      <td>243</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 44 columns</p>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_data = pd.read_excel('./data/机器学习样表.xlsx',sheet_name=0, header=[0, 1])\n",
    "old_cols = daily_data.columns\n",
    "new_cols = [x[0].strip() if 'Unnamed' in x[1] else x[0]+'_'+x[1] for x in old_cols]\n",
    "daily_data.columns = new_cols\n",
    "daily_data.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "daily_data.rename(columns={\"日期\": \"days\"}, inplace=True)\n",
    "daily_data.days = daily_data.days.astype(str)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "(1178, 44)"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_data.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "                     date       flow     rNOx     rO2    temp    rSO2  rsmoke\n0     2018-03-23 00:00:00  244136.19  28.6370   7.700  51.400  0.8900  1.2000\n1     2018-03-23 01:00:00  234599.89  29.9710   7.800  51.300  0.7600  1.1700\n2     2018-03-23 02:00:00  249264.88  20.9960   7.300  54.900  2.1800  1.3600\n3     2018-03-23 03:00:00  229360.17  24.3590   7.500  52.700  1.9600  1.3500\n4     2018-03-23 04:00:00  236416.45  18.3680   7.200  55.100  1.6500  1.3500\n...                   ...        ...      ...     ...     ...     ...     ...\n33714 2022-01-26 19:00:00  255639.10   2.1000  15.719  36.720  1.7939  1.0533\n33715 2022-01-26 20:00:00  253412.80   1.6378  15.580  36.812  1.7928  1.0543\n33716 2022-01-26 21:00:00  261648.90   2.0940  15.595  36.948  1.8048  1.0547\n33717 2022-01-26 22:00:00  271429.70   1.9489  15.532  37.160  1.7887  1.0566\n33718 2022-01-26 23:00:00  272750.00   1.5552  15.435  37.279  1.7655  1.0570\n\n[33715 rows x 7 columns]",
      "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>date</th>\n      <th>flow</th>\n      <th>rNOx</th>\n      <th>rO2</th>\n      <th>temp</th>\n      <th>rSO2</th>\n      <th>rsmoke</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2018-03-23 00:00:00</td>\n      <td>244136.19</td>\n      <td>28.6370</td>\n      <td>7.700</td>\n      <td>51.400</td>\n      <td>0.8900</td>\n      <td>1.2000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2018-03-23 01:00:00</td>\n      <td>234599.89</td>\n      <td>29.9710</td>\n      <td>7.800</td>\n      <td>51.300</td>\n      <td>0.7600</td>\n      <td>1.1700</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2018-03-23 02:00:00</td>\n      <td>249264.88</td>\n      <td>20.9960</td>\n      <td>7.300</td>\n      <td>54.900</td>\n      <td>2.1800</td>\n      <td>1.3600</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2018-03-23 03:00:00</td>\n      <td>229360.17</td>\n      <td>24.3590</td>\n      <td>7.500</td>\n      <td>52.700</td>\n      <td>1.9600</td>\n      <td>1.3500</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2018-03-23 04:00:00</td>\n      <td>236416.45</td>\n      <td>18.3680</td>\n      <td>7.200</td>\n      <td>55.100</td>\n      <td>1.6500</td>\n      <td>1.3500</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>33714</th>\n      <td>2022-01-26 19:00:00</td>\n      <td>255639.10</td>\n      <td>2.1000</td>\n      <td>15.719</td>\n      <td>36.720</td>\n      <td>1.7939</td>\n      <td>1.0533</td>\n    </tr>\n    <tr>\n      <th>33715</th>\n      <td>2022-01-26 20:00:00</td>\n      <td>253412.80</td>\n      <td>1.6378</td>\n      <td>15.580</td>\n      <td>36.812</td>\n      <td>1.7928</td>\n      <td>1.0543</td>\n    </tr>\n    <tr>\n      <th>33716</th>\n      <td>2022-01-26 21:00:00</td>\n      <td>261648.90</td>\n      <td>2.0940</td>\n      <td>15.595</td>\n      <td>36.948</td>\n      <td>1.8048</td>\n      <td>1.0547</td>\n    </tr>\n    <tr>\n      <th>33717</th>\n      <td>2022-01-26 22:00:00</td>\n      <td>271429.70</td>\n      <td>1.9489</td>\n      <td>15.532</td>\n      <td>37.160</td>\n      <td>1.7887</td>\n      <td>1.0566</td>\n    </tr>\n    <tr>\n      <th>33718</th>\n      <td>2022-01-26 23:00:00</td>\n      <td>272750.00</td>\n      <td>1.5552</td>\n      <td>15.435</td>\n      <td>37.279</td>\n      <td>1.7655</td>\n      <td>1.0570</td>\n    </tr>\n  </tbody>\n</table>\n<p>33715 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly_data = pd.read_excel('./data/机器学习样表.xlsx',sheet_name=1).drop_duplicates()\n",
    "hourly_data.columns = ['date', 'flow', 'rNOx', 'rO2', 'temp', 'rSO2', 'rsmoke']\n",
    "hourly_data.date = hourly_data.date.astype(\"datetime64\")\n",
    "ori_hourly_data = hourly_data.copy()\n",
    "hourly_data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "hourly_data['rNOx'] = hourly_data.apply((lambda x: x['rNOx'] * 101800 * 273 / (101325 * (273 + x['temp']))), axis=1)\n",
    "hourly_data['rSO2'] = hourly_data.apply((lambda x: x['rSO2'] * 101800 * 273 / (101325 * (273 + x['temp']))), axis=1)\n",
    "hourly_data['rsmoke'] = hourly_data.apply((lambda x: x['rsmoke'] * 101800 * 273 / (101325 * (273 + x['temp']))), axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "我们将每天24个小时的数据作为特征，因此对数据不足24小时的，要先填充Nan，以备后续处理"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "(33744, 33715)"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hour_range = pd.date_range(hourly_data.date.min(), hourly_data.date.max(), freq='H')\n",
    "hour_range.shape[0], hourly_data.shape[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "可见少了约30条数据，因此进行index对齐"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "hourly_data = hourly_data.set_index(\"date\").reindex(hour_range)\n",
    "hourly_data['days'] = hourly_data.index.astype(str).to_series().apply(lambda x:x.split(' ')[0]).values"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 异常值处理\n",
    "对于出现的负值，若一天之中出现的较多，这一天的数据视为脏数据，可以统一处理"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "flow        49432.59\nrNOx       -0.338727\nrO2             19.1\ntemp            34.7\nrSO2        0.124794\nrsmoke      0.490263\ndays      2020-12-01\nName: 2020-12-01 12:00:00, dtype: object"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly_data[hourly_data.rNOx < 0].iloc[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "                         flow  rNOx   rO2  temp      rSO2    rsmoke  \\\n2020-12-01 12:00:00  49432.59   NaN  19.1  34.7  0.124794  0.490263   \n\n                           days  \n2020-12-01 12:00:00  2020-12-01  ",
      "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>flow</th>\n      <th>rNOx</th>\n      <th>rO2</th>\n      <th>temp</th>\n      <th>rSO2</th>\n      <th>rsmoke</th>\n      <th>days</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-12-01 12:00:00</th>\n      <td>49432.59</td>\n      <td>NaN</td>\n      <td>19.1</td>\n      <td>34.7</td>\n      <td>0.124794</td>\n      <td>0.490263</td>\n      <td>2020-12-01</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly_data[hourly_data._get_numeric_data() < 0] = np.nan\n",
    "hourly_data[hourly_data.index=='2020-12-01 12:00:00']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 缺失值分析"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "['flow', 'rNOx', 'rO2', 'temp', 'rSO2', 'rsmoke']"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征列\n",
    "num_cols = [x for x in hourly_data.columns if not x.startswith('da')]\n",
    "num_cols"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "# 写一个逻辑，首先统计出每天有缺失数据的比例，对任一数据缺失记录高于4条的，判断该天在daily_data是否有生产记录，若无，则为脏数据，需要删去\n",
    "del_date = list()\n",
    "for col in num_cols:\n",
    "    na_counts = hourly_data[hourly_data[col].isna()].days.value_counts().to_dict()\n",
    "    for date in na_counts:\n",
    "        if na_counts.get(date) < 4:\n",
    "            continue\n",
    "        try:\n",
    "            if date in del_date:\n",
    "                continue\n",
    "            if daily_data[daily_data.days==date].shape[0] == 0:\n",
    "                del_date.append(date)\n",
    "                continue\n",
    "            if daily_data[daily_data.days==date]['发电量（千瓦时）'].values[0] > 100000 or  daily_data[daily_data.days==date]['供热量（吉焦）'].values[0] > 2500:\n",
    "                # 取缺失率高且有较大发电量的天作为删除值\n",
    "                del_date.append(date)\n",
    "        except:\n",
    "            print(date)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "101"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(del_date)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "((31320, 7), (1108, 44))"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删掉不要的脏数据\n",
    "hourly_data = hourly_data[~hourly_data.days.isin(del_date)].copy()\n",
    "daily_data = daily_data[~daily_data.days.isin(del_date)].copy()\n",
    "hourly_data.shape, daily_data.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "最后看一下有无哪一天仍有很多缺失数据"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "2019-04-08    22\n2019-07-17     3\n2018-12-19     3\n2021-02-18     3\n2019-01-16     3\n              ..\n2019-03-12     1\n2019-03-17     1\n2019-03-27     1\n2019-04-04     1\n2019-05-24     1\nName: days, Length: 220, dtype: int64"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly_data[hourly_data[num_cols].isnull().T.any()].days.value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "于是再去掉2019-04-08的数据"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "(31296, 7)"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly_data = hourly_data[hourly_data.days!='2019-04-08'].copy()\n",
    "hourly_data.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 缺失值补充\n",
    "1. 对Nan值 取其上下两个时刻的均值作为填充值\n",
    "2. 对于多个连续缺失的值，实际上应该用窗口法填充，但是难度太大，因此仍用均值填充\n",
    "3. 使用ffill和bfill，然后合并取均值。"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "                          flow       rNOx     rO2    temp      rSO2    rsmoke  \\\n2018-03-23 00:00:00  244136.19  24.212548   7.700  51.400  0.752494  1.014598   \n2018-03-23 01:00:00  234599.89  25.348257   7.800  51.300  0.642777  0.989539   \n2018-03-23 02:00:00  249264.88  17.562606   7.300  54.900  1.823513  1.137605   \n2018-03-23 03:00:00  229360.17  20.513299   7.500  52.700  1.650563  1.136867   \n2018-03-23 04:00:00  236416.45  15.354987   7.200  55.100  1.379341  1.128551   \n...                        ...        ...     ...     ...       ...       ...   \n2022-01-26 19:00:00  255639.10   1.859704  15.719  36.720  1.588630  0.932774   \n2022-01-26 20:00:00  253412.80   1.449961  15.580  36.812  1.587185  0.933383   \n2022-01-26 21:00:00  261648.90   1.853027  15.595  36.948  1.597107  0.933327   \n2022-01-26 22:00:00  271429.70   1.723446  15.532  37.160  1.581778  0.934369   \n2022-01-26 23:00:00  272750.00   1.374762  15.435  37.279  1.560663  0.934365   \n\n                           days  \n2018-03-23 00:00:00  2018-03-23  \n2018-03-23 01:00:00  2018-03-23  \n2018-03-23 02:00:00  2018-03-23  \n2018-03-23 03:00:00  2018-03-23  \n2018-03-23 04:00:00  2018-03-23  \n...                         ...  \n2022-01-26 19:00:00  2022-01-26  \n2022-01-26 20:00:00  2022-01-26  \n2022-01-26 21:00:00  2022-01-26  \n2022-01-26 22:00:00  2022-01-26  \n2022-01-26 23:00:00  2022-01-26  \n\n[31296 rows x 7 columns]",
      "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>flow</th>\n      <th>rNOx</th>\n      <th>rO2</th>\n      <th>temp</th>\n      <th>rSO2</th>\n      <th>rsmoke</th>\n      <th>days</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2018-03-23 00:00:00</th>\n      <td>244136.19</td>\n      <td>24.212548</td>\n      <td>7.700</td>\n      <td>51.400</td>\n      <td>0.752494</td>\n      <td>1.014598</td>\n      <td>2018-03-23</td>\n    </tr>\n    <tr>\n      <th>2018-03-23 01:00:00</th>\n      <td>234599.89</td>\n      <td>25.348257</td>\n      <td>7.800</td>\n      <td>51.300</td>\n      <td>0.642777</td>\n      <td>0.989539</td>\n      <td>2018-03-23</td>\n    </tr>\n    <tr>\n      <th>2018-03-23 02:00:00</th>\n      <td>249264.88</td>\n      <td>17.562606</td>\n      <td>7.300</td>\n      <td>54.900</td>\n      <td>1.823513</td>\n      <td>1.137605</td>\n      <td>2018-03-23</td>\n    </tr>\n    <tr>\n      <th>2018-03-23 03:00:00</th>\n      <td>229360.17</td>\n      <td>20.513299</td>\n      <td>7.500</td>\n      <td>52.700</td>\n      <td>1.650563</td>\n      <td>1.136867</td>\n      <td>2018-03-23</td>\n    </tr>\n    <tr>\n      <th>2018-03-23 04:00:00</th>\n      <td>236416.45</td>\n      <td>15.354987</td>\n      <td>7.200</td>\n      <td>55.100</td>\n      <td>1.379341</td>\n      <td>1.128551</td>\n      <td>2018-03-23</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2022-01-26 19:00:00</th>\n      <td>255639.10</td>\n      <td>1.859704</td>\n      <td>15.719</td>\n      <td>36.720</td>\n      <td>1.588630</td>\n      <td>0.932774</td>\n      <td>2022-01-26</td>\n    </tr>\n    <tr>\n      <th>2022-01-26 20:00:00</th>\n      <td>253412.80</td>\n      <td>1.449961</td>\n      <td>15.580</td>\n      <td>36.812</td>\n      <td>1.587185</td>\n      <td>0.933383</td>\n      <td>2022-01-26</td>\n    </tr>\n    <tr>\n      <th>2022-01-26 21:00:00</th>\n      <td>261648.90</td>\n      <td>1.853027</td>\n      <td>15.595</td>\n      <td>36.948</td>\n      <td>1.597107</td>\n      <td>0.933327</td>\n      <td>2022-01-26</td>\n    </tr>\n    <tr>\n      <th>2022-01-26 22:00:00</th>\n      <td>271429.70</td>\n      <td>1.723446</td>\n      <td>15.532</td>\n      <td>37.160</td>\n      <td>1.581778</td>\n      <td>0.934369</td>\n      <td>2022-01-26</td>\n    </tr>\n    <tr>\n      <th>2022-01-26 23:00:00</th>\n      <td>272750.00</td>\n      <td>1.374762</td>\n      <td>15.435</td>\n      <td>37.279</td>\n      <td>1.560663</td>\n      <td>0.934365</td>\n      <td>2022-01-26</td>\n    </tr>\n  </tbody>\n</table>\n<p>31296 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly_data_ffill = hourly_data.ffill()\n",
    "hourly_data_ffill"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "                          flow       rNOx     rO2    temp      rSO2    rsmoke  \\\n2018-03-23 00:00:00  244136.19  24.212548   7.700  51.400  0.752494  1.014598   \n2018-03-23 01:00:00  234599.89  25.348257   7.800  51.300  0.642777  0.989539   \n2018-03-23 02:00:00  249264.88  17.562606   7.300  54.900  1.823513  1.137605   \n2018-03-23 03:00:00  229360.17  20.513299   7.500  52.700  1.650563  1.136867   \n2018-03-23 04:00:00  236416.45  15.354987   7.200  55.100  1.379341  1.128551   \n...                        ...        ...     ...     ...       ...       ...   \n2022-01-26 19:00:00  255639.10   1.859704  15.719  36.720  1.588630  0.932774   \n2022-01-26 20:00:00  253412.80   1.449961  15.580  36.812  1.587185  0.933383   \n2022-01-26 21:00:00  261648.90   1.853027  15.595  36.948  1.597107  0.933327   \n2022-01-26 22:00:00  271429.70   1.723446  15.532  37.160  1.581778  0.934369   \n2022-01-26 23:00:00  272750.00   1.374762  15.435  37.279  1.560663  0.934365   \n\n                           days  \n2018-03-23 00:00:00  2018-03-23  \n2018-03-23 01:00:00  2018-03-23  \n2018-03-23 02:00:00  2018-03-23  \n2018-03-23 03:00:00  2018-03-23  \n2018-03-23 04:00:00  2018-03-23  \n...                         ...  \n2022-01-26 19:00:00  2022-01-26  \n2022-01-26 20:00:00  2022-01-26  \n2022-01-26 21:00:00  2022-01-26  \n2022-01-26 22:00:00  2022-01-26  \n2022-01-26 23:00:00  2022-01-26  \n\n[31296 rows x 7 columns]",
      "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>flow</th>\n      <th>rNOx</th>\n      <th>rO2</th>\n      <th>temp</th>\n      <th>rSO2</th>\n      <th>rsmoke</th>\n      <th>days</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2018-03-23 00:00:00</th>\n      <td>244136.19</td>\n      <td>24.212548</td>\n      <td>7.700</td>\n      <td>51.400</td>\n      <td>0.752494</td>\n      <td>1.014598</td>\n      <td>2018-03-23</td>\n    </tr>\n    <tr>\n      <th>2018-03-23 01:00:00</th>\n      <td>234599.89</td>\n      <td>25.348257</td>\n      <td>7.800</td>\n      <td>51.300</td>\n      <td>0.642777</td>\n      <td>0.989539</td>\n      <td>2018-03-23</td>\n    </tr>\n    <tr>\n      <th>2018-03-23 02:00:00</th>\n      <td>249264.88</td>\n      <td>17.562606</td>\n      <td>7.300</td>\n      <td>54.900</td>\n      <td>1.823513</td>\n      <td>1.137605</td>\n      <td>2018-03-23</td>\n    </tr>\n    <tr>\n      <th>2018-03-23 03:00:00</th>\n      <td>229360.17</td>\n      <td>20.513299</td>\n      <td>7.500</td>\n      <td>52.700</td>\n      <td>1.650563</td>\n      <td>1.136867</td>\n      <td>2018-03-23</td>\n    </tr>\n    <tr>\n      <th>2018-03-23 04:00:00</th>\n      <td>236416.45</td>\n      <td>15.354987</td>\n      <td>7.200</td>\n      <td>55.100</td>\n      <td>1.379341</td>\n      <td>1.128551</td>\n      <td>2018-03-23</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2022-01-26 19:00:00</th>\n      <td>255639.10</td>\n      <td>1.859704</td>\n      <td>15.719</td>\n      <td>36.720</td>\n      <td>1.588630</td>\n      <td>0.932774</td>\n      <td>2022-01-26</td>\n    </tr>\n    <tr>\n      <th>2022-01-26 20:00:00</th>\n      <td>253412.80</td>\n      <td>1.449961</td>\n      <td>15.580</td>\n      <td>36.812</td>\n      <td>1.587185</td>\n      <td>0.933383</td>\n      <td>2022-01-26</td>\n    </tr>\n    <tr>\n      <th>2022-01-26 21:00:00</th>\n      <td>261648.90</td>\n      <td>1.853027</td>\n      <td>15.595</td>\n      <td>36.948</td>\n      <td>1.597107</td>\n      <td>0.933327</td>\n      <td>2022-01-26</td>\n    </tr>\n    <tr>\n      <th>2022-01-26 22:00:00</th>\n      <td>271429.70</td>\n      <td>1.723446</td>\n      <td>15.532</td>\n      <td>37.160</td>\n      <td>1.581778</td>\n      <td>0.934369</td>\n      <td>2022-01-26</td>\n    </tr>\n    <tr>\n      <th>2022-01-26 23:00:00</th>\n      <td>272750.00</td>\n      <td>1.374762</td>\n      <td>15.435</td>\n      <td>37.279</td>\n      <td>1.560663</td>\n      <td>0.934365</td>\n      <td>2022-01-26</td>\n    </tr>\n  </tbody>\n</table>\n<p>31296 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly_data_bfill = hourly_data.bfill()\n",
    "hourly_data_bfill"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "hourly_data_fixed = (hourly_data_ffill[num_cols] + hourly_data_ffill[num_cols]) / 2"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "                         flow     rNOx   rO2  temp      rSO2    rsmoke  \\\n2020-12-01 12:00:00  49432.59  1.35584  19.1  34.7  0.124794  0.490263   \n\n                           days  \n2020-12-01 12:00:00  2020-12-01  ",
      "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>flow</th>\n      <th>rNOx</th>\n      <th>rO2</th>\n      <th>temp</th>\n      <th>rSO2</th>\n      <th>rsmoke</th>\n      <th>days</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-12-01 12:00:00</th>\n      <td>49432.59</td>\n      <td>1.35584</td>\n      <td>19.1</td>\n      <td>34.7</td>\n      <td>0.124794</td>\n      <td>0.490263</td>\n      <td>2020-12-01</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly_data_fixed['days'] = hourly_data_fixed.index.astype(str).to_series().apply(lambda x:x.split(' ')[0]).values\n",
    "hourly_data_fixed[hourly_data_fixed.index=='2020-12-01 12:00:00']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 特征工程，将每天每小时的数据平铺开作为当天的24*6个特征"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "['0_flow',\n '0_rNOx',\n '0_rO2',\n '0_temp',\n '0_rSO2',\n '0_rsmoke',\n '1_flow',\n '1_rNOx',\n '1_rO2',\n '1_temp',\n '1_rSO2',\n '1_rsmoke',\n '2_flow',\n '2_rNOx',\n '2_rO2',\n '2_temp',\n '2_rSO2',\n '2_rsmoke',\n '3_flow',\n '3_rNOx',\n '3_rO2',\n '3_temp',\n '3_rSO2',\n '3_rsmoke',\n '4_flow',\n '4_rNOx',\n '4_rO2',\n '4_temp',\n '4_rSO2',\n '4_rsmoke',\n '5_flow',\n '5_rNOx',\n '5_rO2',\n '5_temp',\n '5_rSO2',\n '5_rsmoke',\n '6_flow',\n '6_rNOx',\n '6_rO2',\n '6_temp',\n '6_rSO2',\n '6_rsmoke',\n '7_flow',\n '7_rNOx',\n '7_rO2',\n '7_temp',\n '7_rSO2',\n '7_rsmoke',\n '8_flow',\n '8_rNOx',\n '8_rO2',\n '8_temp',\n '8_rSO2',\n '8_rsmoke',\n '9_flow',\n '9_rNOx',\n '9_rO2',\n '9_temp',\n '9_rSO2',\n '9_rsmoke',\n '10_flow',\n '10_rNOx',\n '10_rO2',\n '10_temp',\n '10_rSO2',\n '10_rsmoke',\n '11_flow',\n '11_rNOx',\n '11_rO2',\n '11_temp',\n '11_rSO2',\n '11_rsmoke',\n '12_flow',\n '12_rNOx',\n '12_rO2',\n '12_temp',\n '12_rSO2',\n '12_rsmoke',\n '13_flow',\n '13_rNOx',\n '13_rO2',\n '13_temp',\n '13_rSO2',\n '13_rsmoke',\n '14_flow',\n '14_rNOx',\n '14_rO2',\n '14_temp',\n '14_rSO2',\n '14_rsmoke',\n '15_flow',\n '15_rNOx',\n '15_rO2',\n '15_temp',\n '15_rSO2',\n '15_rsmoke',\n '16_flow',\n '16_rNOx',\n '16_rO2',\n '16_temp',\n '16_rSO2',\n '16_rsmoke',\n '17_flow',\n '17_rNOx',\n '17_rO2',\n '17_temp',\n '17_rSO2',\n '17_rsmoke',\n '18_flow',\n '18_rNOx',\n '18_rO2',\n '18_temp',\n '18_rSO2',\n '18_rsmoke',\n '19_flow',\n '19_rNOx',\n '19_rO2',\n '19_temp',\n '19_rSO2',\n '19_rsmoke',\n '20_flow',\n '20_rNOx',\n '20_rO2',\n '20_temp',\n '20_rSO2',\n '20_rsmoke',\n '21_flow',\n '21_rNOx',\n '21_rO2',\n '21_temp',\n '21_rSO2',\n '21_rsmoke',\n '22_flow',\n '22_rNOx',\n '22_rO2',\n '22_temp',\n '22_rSO2',\n '22_rsmoke',\n '23_flow',\n '23_rNOx',\n '23_rO2',\n '23_temp',\n '23_rSO2',\n '23_rsmoke']"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_cols = [f\"{x}_{y}\" for x in range(24) for y in num_cols]\n",
    "feature_cols"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "                                                         flow  \\\ndays                                                            \n2018-03-23  [244136.19, 234599.89, 249264.88, 229360.17, 2...   \n2018-03-24  [234070.07, 235778.62, 231371.79, 234002.61, 2...   \n2018-03-25  [228939.37, 232613.69, 229586.17, 235035.84, 2...   \n2018-03-26  [231112.06, 225984.7, 224547.59, 221822.01, 21...   \n2018-03-27  [226140.95, 219510.17, 215491.43, 205450.35, 2...   \n...                                                       ...   \n2022-01-22  [217544.3, 223416.0, 221987.6, 216571.4, 21647...   \n2022-01-23  [204086.4, 213480.5, 207928.1, 210432.4, 20739...   \n2022-01-24  [196331.5, 204396.6, 209247.7, 208345.4, 20784...   \n2022-01-25  [241509.4, 251172.7, 222005.2, 216005.8, 21869...   \n2022-01-26  [263819.8, 263171.3, 260461.9, 257509.9, 25479...   \n\n                                                         rNOx  \\\ndays                                                            \n2018-03-23  [24.212547548922526, 25.348256763471422, 17.56...   \n2018-03-24  [21.236174270928696, 21.840986084108295, 21.63...   \n2018-03-25  [17.854300390828886, 18.93070026209273, 18.963...   \n2018-03-26  [25.934359751728444, 21.81253685318773, 21.535...   \n2018-03-27  [11.968345200357508, 21.178438475476018, 26.25...   \n...                                                       ...   \n2022-01-22  [3.045354822347651, 2.8669384829826425, 3.2684...   \n2022-01-23  [0.5965432853216215, 0.7573690670940121, 0.493...   \n2022-01-24  [11.031038246691466, 11.518845457518038, 13.18...   \n2022-01-25  [9.760638960096678, 9.68924708625155, 13.61718...   \n2022-01-26  [3.085553795959118, 2.6547312605083198, 2.1764...   \n\n                                                          rO2  \\\ndays                                                            \n2018-03-23  [7.7, 7.8, 7.3, 7.5, 7.2, 7.7, 7.3, 7.0, 6.2, ...   \n2018-03-24  [7.4, 7.5, 7.4, 7.3, 7.5, 7.6, 7.5, 6.8, 5.7, ...   \n2018-03-25  [7.4, 7.6, 7.8, 8.1, 8.0, 7.6, 7.4, 6.9, 5.9, ...   \n2018-03-26  [7.5, 7.4, 7.5, 7.5, 7.5, 7.1, 7.0, 7.0, 5.7, ...   \n2018-03-27  [6.9, 7.4, 7.5, 7.6, 7.6, 7.2, 7.3, 6.6, 5.7, ...   \n...                                                       ...   \n2022-01-22  [7.756, 7.856, 7.885, 8.05, 8.674, 8.493, 8.32...   \n2022-01-23  [7.43, 8.015, 7.812, 7.805, 7.938, 8.12, 8.161...   \n2022-01-24  [9.172, 9.131, 8.842, 8.987, 9.201, 9.489, 9.1...   \n2022-01-25  [12.177, 11.365, 10.113, 10.497, 10.501, 10.84...   \n2022-01-26  [15.38, 15.623, 15.897, 15.944, 15.919, 15.777...   \n\n                                                         temp  \\\ndays                                                            \n2018-03-23  [51.4, 51.3, 54.9, 52.7, 55.1, 51.9, 51.7, 52....   \n2018-03-24  [51.3, 51.6, 51.7, 52.0, 52.0, 51.9, 51.7, 52....   \n2018-03-25  [52.6, 52.4, 52.3, 52.2, 52.0, 53.3, 55.5, 53....   \n2018-03-26  [52.4, 52.3, 52.2, 52.6, 52.4, 54.1, 55.2, 53....   \n2018-03-27  [55.7, 53.0, 52.0, 52.0, 51.8, 52.2, 52.3, 53....   \n...                                                       ...   \n2022-01-22  [55.539, 55.741, 55.812, 55.564, 55.491, 54.76...   \n2022-01-23  [55.836, 55.577, 55.237, 55.573, 55.512, 54.88...   \n2022-01-24  [52.855, 53.409, 53.889, 54.006, 53.944, 53.07...   \n2022-01-25  [49.297, 48.991, 50.58, 50.893, 50.853, 50.416...   \n2022-01-26  [37.445, 37.231, 36.495, 35.936, 35.819, 35.78...   \n\n                                                         rSO2  \\\ndays                                                            \n2018-03-23  [0.7524938826881673, 0.642777189290924, 1.8235...   \n2018-03-24  [0.2791006216657959, 0.287292450812319, 0.2956...   \n2018-03-25  [5.332282211556822, 3.009154456372548, 4.68796...   \n2018-03-26  [2.0988220157892563, 1.7874981882009273, 1.796...   \n2018-03-27  [0.21695389751746164, 0.3281261324263327, 0.39...   \n...                                                       ...   \n2022-01-22  [0.6289737165296122, 0.6122342875306045, 0.625...   \n2022-01-23  [0.5333190389186867, 0.5314856001529347, 0.561...   \n2022-01-24  [0.8258148706118134, 0.8324800807378938, 0.886...   \n2022-01-25  [0.819102568494695, 0.8377693046259155, 0.9752...   \n2022-01-26  [0.5047465592805648, 0.5081007278163424, 0.497...   \n\n                                                       rsmoke  \ndays                                                           \n2018-03-23  [1.0145984935121357, 0.9895385677241856, 1.137...  \n2018-03-24  [0.92187781095672, 0.8956764642972298, 0.92074...  \n2018-03-25  [0.8255350027370751, 0.8260423997885425, 0.809...  \n2018-03-26  [0.9271904487422418, 0.944338665464641, 0.9108...  \n2018-03-27  [1.1014582489348053, 1.0853402841794082, 0.978...  \n...                                                       ...  \n2022-01-22  [1.0424734269053981, 1.0191389782210867, 1.039...  \n2022-01-23  [1.0451184794574826, 1.022569279389579, 1.0344...  \n2022-01-24  [1.018401194529847, 0.9991105440570864, 1.0109...  \n2022-01-25  [0.981306152448034, 0.9660540603723952, 0.9886...  \n2022-01-26  [0.9335116655624799, 0.9270826921667246, 0.929...  \n\n[1304 rows x 6 columns]",
      "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>flow</th>\n      <th>rNOx</th>\n      <th>rO2</th>\n      <th>temp</th>\n      <th>rSO2</th>\n      <th>rsmoke</th>\n    </tr>\n    <tr>\n      <th>days</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2018-03-23</th>\n      <td>[244136.19, 234599.89, 249264.88, 229360.17, 2...</td>\n      <td>[24.212547548922526, 25.348256763471422, 17.56...</td>\n      <td>[7.7, 7.8, 7.3, 7.5, 7.2, 7.7, 7.3, 7.0, 6.2, ...</td>\n      <td>[51.4, 51.3, 54.9, 52.7, 55.1, 51.9, 51.7, 52....</td>\n      <td>[0.7524938826881673, 0.642777189290924, 1.8235...</td>\n      <td>[1.0145984935121357, 0.9895385677241856, 1.137...</td>\n    </tr>\n    <tr>\n      <th>2018-03-24</th>\n      <td>[234070.07, 235778.62, 231371.79, 234002.61, 2...</td>\n      <td>[21.236174270928696, 21.840986084108295, 21.63...</td>\n      <td>[7.4, 7.5, 7.4, 7.3, 7.5, 7.6, 7.5, 6.8, 5.7, ...</td>\n      <td>[51.3, 51.6, 51.7, 52.0, 52.0, 51.9, 51.7, 52....</td>\n      <td>[0.2791006216657959, 0.287292450812319, 0.2956...</td>\n      <td>[0.92187781095672, 0.8956764642972298, 0.92074...</td>\n    </tr>\n    <tr>\n      <th>2018-03-25</th>\n      <td>[228939.37, 232613.69, 229586.17, 235035.84, 2...</td>\n      <td>[17.854300390828886, 18.93070026209273, 18.963...</td>\n      <td>[7.4, 7.6, 7.8, 8.1, 8.0, 7.6, 7.4, 6.9, 5.9, ...</td>\n      <td>[52.6, 52.4, 52.3, 52.2, 52.0, 53.3, 55.5, 53....</td>\n      <td>[5.332282211556822, 3.009154456372548, 4.68796...</td>\n      <td>[0.8255350027370751, 0.8260423997885425, 0.809...</td>\n    </tr>\n    <tr>\n      <th>2018-03-26</th>\n      <td>[231112.06, 225984.7, 224547.59, 221822.01, 21...</td>\n      <td>[25.934359751728444, 21.81253685318773, 21.535...</td>\n      <td>[7.5, 7.4, 7.5, 7.5, 7.5, 7.1, 7.0, 7.0, 5.7, ...</td>\n      <td>[52.4, 52.3, 52.2, 52.6, 52.4, 54.1, 55.2, 53....</td>\n      <td>[2.0988220157892563, 1.7874981882009273, 1.796...</td>\n      <td>[0.9271904487422418, 0.944338665464641, 0.9108...</td>\n    </tr>\n    <tr>\n      <th>2018-03-27</th>\n      <td>[226140.95, 219510.17, 215491.43, 205450.35, 2...</td>\n      <td>[11.968345200357508, 21.178438475476018, 26.25...</td>\n      <td>[6.9, 7.4, 7.5, 7.6, 7.6, 7.2, 7.3, 6.6, 5.7, ...</td>\n      <td>[55.7, 53.0, 52.0, 52.0, 51.8, 52.2, 52.3, 53....</td>\n      <td>[0.21695389751746164, 0.3281261324263327, 0.39...</td>\n      <td>[1.1014582489348053, 1.0853402841794082, 0.978...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2022-01-22</th>\n      <td>[217544.3, 223416.0, 221987.6, 216571.4, 21647...</td>\n      <td>[3.045354822347651, 2.8669384829826425, 3.2684...</td>\n      <td>[7.756, 7.856, 7.885, 8.05, 8.674, 8.493, 8.32...</td>\n      <td>[55.539, 55.741, 55.812, 55.564, 55.491, 54.76...</td>\n      <td>[0.6289737165296122, 0.6122342875306045, 0.625...</td>\n      <td>[1.0424734269053981, 1.0191389782210867, 1.039...</td>\n    </tr>\n    <tr>\n      <th>2022-01-23</th>\n      <td>[204086.4, 213480.5, 207928.1, 210432.4, 20739...</td>\n      <td>[0.5965432853216215, 0.7573690670940121, 0.493...</td>\n      <td>[7.43, 8.015, 7.812, 7.805, 7.938, 8.12, 8.161...</td>\n      <td>[55.836, 55.577, 55.237, 55.573, 55.512, 54.88...</td>\n      <td>[0.5333190389186867, 0.5314856001529347, 0.561...</td>\n      <td>[1.0451184794574826, 1.022569279389579, 1.0344...</td>\n    </tr>\n    <tr>\n      <th>2022-01-24</th>\n      <td>[196331.5, 204396.6, 209247.7, 208345.4, 20784...</td>\n      <td>[11.031038246691466, 11.518845457518038, 13.18...</td>\n      <td>[9.172, 9.131, 8.842, 8.987, 9.201, 9.489, 9.1...</td>\n      <td>[52.855, 53.409, 53.889, 54.006, 53.944, 53.07...</td>\n      <td>[0.8258148706118134, 0.8324800807378938, 0.886...</td>\n      <td>[1.018401194529847, 0.9991105440570864, 1.0109...</td>\n    </tr>\n    <tr>\n      <th>2022-01-25</th>\n      <td>[241509.4, 251172.7, 222005.2, 216005.8, 21869...</td>\n      <td>[9.760638960096678, 9.68924708625155, 13.61718...</td>\n      <td>[12.177, 11.365, 10.113, 10.497, 10.501, 10.84...</td>\n      <td>[49.297, 48.991, 50.58, 50.893, 50.853, 50.416...</td>\n      <td>[0.819102568494695, 0.8377693046259155, 0.9752...</td>\n      <td>[0.981306152448034, 0.9660540603723952, 0.9886...</td>\n    </tr>\n    <tr>\n      <th>2022-01-26</th>\n      <td>[263819.8, 263171.3, 260461.9, 257509.9, 25479...</td>\n      <td>[3.085553795959118, 2.6547312605083198, 2.1764...</td>\n      <td>[15.38, 15.623, 15.897, 15.944, 15.919, 15.777...</td>\n      <td>[37.445, 37.231, 36.495, 35.936, 35.819, 35.78...</td>\n      <td>[0.5047465592805648, 0.5081007278163424, 0.497...</td>\n      <td>[0.9335116655624799, 0.9270826921667246, 0.929...</td>\n    </tr>\n  </tbody>\n</table>\n<p>1304 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_emiss_data = hourly_data_fixed.groupby('days').agg(list)\n",
    "daily_emiss_data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "def merge(x1, x2, x3, x4, x5, x6):\n",
    "    return sum([x1, x2, x3, x4, x5, x6], [])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['flow', 'rNOx', 'rO2', 'temp', 'rSO2', 'rsmoke'], dtype='object')"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_emiss_data.columns"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "               0_flow     0_rNOx      0_rO2     0_temp     0_rSO2   0_rsmoke  \\\ndays                                                                           \n2018-03-23  244136.19  234599.89  249264.88  229360.17  236416.45  236113.88   \n2018-03-24  234070.07  235778.62  231371.79  234002.61  224972.48  212372.97   \n2018-03-25  228939.37  232613.69  229586.17  235035.84  227862.00  233114.53   \n2018-03-26  231112.06  225984.70  224547.59  221822.01  219699.59  216640.45   \n2018-03-27  226140.95  219510.17  215491.43  205450.35  209391.39  202650.57   \n...               ...        ...        ...        ...        ...        ...   \n2022-01-22  217544.30  223416.00  221987.60  216571.40  216474.30  217356.80   \n2022-01-23  204086.40  213480.50  207928.10  210432.40  207398.30  205233.10   \n2022-01-24  196331.50  204396.60  209247.70  208345.40  207840.00  203811.20   \n2022-01-25  241509.40  251172.70  222005.20  216005.80  218697.30  217854.50   \n2022-01-26  263819.80  263171.30  260461.90  257509.90  254797.00  255295.30   \n\n               1_flow     1_rNOx      1_rO2     1_temp  ...    22_rO2  \\\ndays                                                    ...             \n2018-03-23  243835.88  254941.06  263172.44  265048.62  ...  1.108454   \n2018-03-24  227885.84  252032.30  257109.81  252191.47  ...  1.139022   \n2018-03-25  224467.42  252500.03  240797.89  247235.60  ...  0.924819   \n2018-03-26  228010.51  255410.68  260558.74  253688.93  ...  0.849763   \n2018-03-27  207802.10  247740.44  254831.56  251766.47  ...  1.292986   \n...               ...        ...        ...        ...  ...       ...   \n2022-01-22  215403.80  216200.90  224753.10  226410.50  ...  1.036403   \n2022-01-23  202121.80  203224.70  218894.90  238739.10  ...  1.024024   \n2022-01-24  204427.60  208614.90  204340.80  206945.40  ...  1.007186   \n2022-01-25  198460.70  199237.90  203870.10  224508.80  ...  0.936690   \n2022-01-26  255801.90  261359.50  267883.50  271961.30  ...  0.929660   \n\n             22_temp   22_rSO2  22_rsmoke   23_flow   23_rNOx    23_rO2  \\\ndays                                                                      \n2018-03-23  1.149077  1.116597   1.070484  0.965953  1.005916  1.013989   \n2018-03-24  1.155038  1.161774   1.112287  0.960906  0.885317  0.892926   \n2018-03-25  0.975402  1.093083   0.968145  0.934758  0.900796  0.934184   \n2018-03-26  0.929620  0.984937   0.971792  0.953290  0.882604  0.943758   \n2018-03-27  1.344673  1.348917   1.185938  1.146697  1.155129  1.088680   \n...              ...       ...        ...       ...       ...       ...   \n2022-01-22  1.037442  1.039126   1.045166  1.045001  1.037889  1.040583   \n2022-01-23  1.032411  1.035073   1.033276  1.035860  1.033211  1.031058   \n2022-01-24  1.008622  1.012684   1.021570  1.022493  1.015565  1.008908   \n2022-01-25  0.932645  0.935892   0.934892  0.933680  0.932380  0.928861   \n2022-01-26  0.929660  0.929660   0.929507  0.931257  0.932774  0.933383   \n\n             23_temp   23_rSO2  23_rsmoke  \ndays                                       \n2018-03-23  0.994012  0.978667   0.945210  \n2018-03-24  0.885317  0.870327   0.800264  \n2018-03-25  0.954531  0.960316   0.926621  \n2018-03-26  0.960906  0.995540   1.021165  \n2018-03-27  1.045839  0.937636   0.894574  \n...              ...       ...        ...  \n2022-01-22  1.039620  1.038964   1.041814  \n2022-01-23  1.025498  1.023998   1.022248  \n2022-01-24  0.994918  0.986704   0.987378  \n2022-01-25  0.928384  0.929663   0.932935  \n2022-01-26  0.933327  0.934369   0.934365  \n\n[1304 rows x 144 columns]",
      "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>0_flow</th>\n      <th>0_rNOx</th>\n      <th>0_rO2</th>\n      <th>0_temp</th>\n      <th>0_rSO2</th>\n      <th>0_rsmoke</th>\n      <th>1_flow</th>\n      <th>1_rNOx</th>\n      <th>1_rO2</th>\n      <th>1_temp</th>\n      <th>...</th>\n      <th>22_rO2</th>\n      <th>22_temp</th>\n      <th>22_rSO2</th>\n      <th>22_rsmoke</th>\n      <th>23_flow</th>\n      <th>23_rNOx</th>\n      <th>23_rO2</th>\n      <th>23_temp</th>\n      <th>23_rSO2</th>\n      <th>23_rsmoke</th>\n    </tr>\n    <tr>\n      <th>days</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2018-03-23</th>\n      <td>244136.19</td>\n      <td>234599.89</td>\n      <td>249264.88</td>\n      <td>229360.17</td>\n      <td>236416.45</td>\n      <td>236113.88</td>\n      <td>243835.88</td>\n      <td>254941.06</td>\n      <td>263172.44</td>\n      <td>265048.62</td>\n      <td>...</td>\n      <td>1.108454</td>\n      <td>1.149077</td>\n      <td>1.116597</td>\n      <td>1.070484</td>\n      <td>0.965953</td>\n      <td>1.005916</td>\n      <td>1.013989</td>\n      <td>0.994012</td>\n      <td>0.978667</td>\n      <td>0.945210</td>\n    </tr>\n    <tr>\n      <th>2018-03-24</th>\n      <td>234070.07</td>\n      <td>235778.62</td>\n      <td>231371.79</td>\n      <td>234002.61</td>\n      <td>224972.48</td>\n      <td>212372.97</td>\n      <td>227885.84</td>\n      <td>252032.30</td>\n      <td>257109.81</td>\n      <td>252191.47</td>\n      <td>...</td>\n      <td>1.139022</td>\n      <td>1.155038</td>\n      <td>1.161774</td>\n      <td>1.112287</td>\n      <td>0.960906</td>\n      <td>0.885317</td>\n      <td>0.892926</td>\n      <td>0.885317</td>\n      <td>0.870327</td>\n      <td>0.800264</td>\n    </tr>\n    <tr>\n      <th>2018-03-25</th>\n      <td>228939.37</td>\n      <td>232613.69</td>\n      <td>229586.17</td>\n      <td>235035.84</td>\n      <td>227862.00</td>\n      <td>233114.53</td>\n      <td>224467.42</td>\n      <td>252500.03</td>\n      <td>240797.89</td>\n      <td>247235.60</td>\n      <td>...</td>\n      <td>0.924819</td>\n      <td>0.975402</td>\n      <td>1.093083</td>\n      <td>0.968145</td>\n      <td>0.934758</td>\n      <td>0.900796</td>\n      <td>0.934184</td>\n      <td>0.954531</td>\n      <td>0.960316</td>\n      <td>0.926621</td>\n    </tr>\n    <tr>\n      <th>2018-03-26</th>\n      <td>231112.06</td>\n      <td>225984.70</td>\n      <td>224547.59</td>\n      <td>221822.01</td>\n      <td>219699.59</td>\n      <td>216640.45</td>\n      <td>228010.51</td>\n      <td>255410.68</td>\n      <td>260558.74</td>\n      <td>253688.93</td>\n      <td>...</td>\n      <td>0.849763</td>\n      <td>0.929620</td>\n      <td>0.984937</td>\n      <td>0.971792</td>\n      <td>0.953290</td>\n      <td>0.882604</td>\n      <td>0.943758</td>\n      <td>0.960906</td>\n      <td>0.995540</td>\n      <td>1.021165</td>\n    </tr>\n    <tr>\n      <th>2018-03-27</th>\n      <td>226140.95</td>\n      <td>219510.17</td>\n      <td>215491.43</td>\n      <td>205450.35</td>\n      <td>209391.39</td>\n      <td>202650.57</td>\n      <td>207802.10</td>\n      <td>247740.44</td>\n      <td>254831.56</td>\n      <td>251766.47</td>\n      <td>...</td>\n      <td>1.292986</td>\n      <td>1.344673</td>\n      <td>1.348917</td>\n      <td>1.185938</td>\n      <td>1.146697</td>\n      <td>1.155129</td>\n      <td>1.088680</td>\n      <td>1.045839</td>\n      <td>0.937636</td>\n      <td>0.894574</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2022-01-22</th>\n      <td>217544.30</td>\n      <td>223416.00</td>\n      <td>221987.60</td>\n      <td>216571.40</td>\n      <td>216474.30</td>\n      <td>217356.80</td>\n      <td>215403.80</td>\n      <td>216200.90</td>\n      <td>224753.10</td>\n      <td>226410.50</td>\n      <td>...</td>\n      <td>1.036403</td>\n      <td>1.037442</td>\n      <td>1.039126</td>\n      <td>1.045166</td>\n      <td>1.045001</td>\n      <td>1.037889</td>\n      <td>1.040583</td>\n      <td>1.039620</td>\n      <td>1.038964</td>\n      <td>1.041814</td>\n    </tr>\n    <tr>\n      <th>2022-01-23</th>\n      <td>204086.40</td>\n      <td>213480.50</td>\n      <td>207928.10</td>\n      <td>210432.40</td>\n      <td>207398.30</td>\n      <td>205233.10</td>\n      <td>202121.80</td>\n      <td>203224.70</td>\n      <td>218894.90</td>\n      <td>238739.10</td>\n      <td>...</td>\n      <td>1.024024</td>\n      <td>1.032411</td>\n      <td>1.035073</td>\n      <td>1.033276</td>\n      <td>1.035860</td>\n      <td>1.033211</td>\n      <td>1.031058</td>\n      <td>1.025498</td>\n      <td>1.023998</td>\n      <td>1.022248</td>\n    </tr>\n    <tr>\n      <th>2022-01-24</th>\n      <td>196331.50</td>\n      <td>204396.60</td>\n      <td>209247.70</td>\n      <td>208345.40</td>\n      <td>207840.00</td>\n      <td>203811.20</td>\n      <td>204427.60</td>\n      <td>208614.90</td>\n      <td>204340.80</td>\n      <td>206945.40</td>\n      <td>...</td>\n      <td>1.007186</td>\n      <td>1.008622</td>\n      <td>1.012684</td>\n      <td>1.021570</td>\n      <td>1.022493</td>\n      <td>1.015565</td>\n      <td>1.008908</td>\n      <td>0.994918</td>\n      <td>0.986704</td>\n      <td>0.987378</td>\n    </tr>\n    <tr>\n      <th>2022-01-25</th>\n      <td>241509.40</td>\n      <td>251172.70</td>\n      <td>222005.20</td>\n      <td>216005.80</td>\n      <td>218697.30</td>\n      <td>217854.50</td>\n      <td>198460.70</td>\n      <td>199237.90</td>\n      <td>203870.10</td>\n      <td>224508.80</td>\n      <td>...</td>\n      <td>0.936690</td>\n      <td>0.932645</td>\n      <td>0.935892</td>\n      <td>0.934892</td>\n      <td>0.933680</td>\n      <td>0.932380</td>\n      <td>0.928861</td>\n      <td>0.928384</td>\n      <td>0.929663</td>\n      <td>0.932935</td>\n    </tr>\n    <tr>\n      <th>2022-01-26</th>\n      <td>263819.80</td>\n      <td>263171.30</td>\n      <td>260461.90</td>\n      <td>257509.90</td>\n      <td>254797.00</td>\n      <td>255295.30</td>\n      <td>255801.90</td>\n      <td>261359.50</td>\n      <td>267883.50</td>\n      <td>271961.30</td>\n      <td>...</td>\n      <td>0.929660</td>\n      <td>0.929660</td>\n      <td>0.929660</td>\n      <td>0.929507</td>\n      <td>0.931257</td>\n      <td>0.932774</td>\n      <td>0.933383</td>\n      <td>0.933327</td>\n      <td>0.934369</td>\n      <td>0.934365</td>\n    </tr>\n  </tbody>\n</table>\n<p>1304 rows × 144 columns</p>\n</div>"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_emiss_data['feature_cols'] = daily_emiss_data.apply(lambda row: merge(row['flow'], row['rNOx'], row['rO2'], row['temp'], row['rSO2'], row['rsmoke']), axis=1)\n",
    "train_hourly_data = pd.DataFrame.from_records(np.array(daily_emiss_data['feature_cols'].values))\n",
    "train_hourly_data.set_index(daily_emiss_data.index, inplace=True)\n",
    "train_hourly_data.columns = feature_cols\n",
    "train_hourly_data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "hourly_data_fixed['cNOx'] = hourly_data_fixed.flow * hourly_data_fixed.rNOx\n",
    "hourly_data_fixed['cO2'] = hourly_data_fixed.flow * hourly_data_fixed.rO2\n",
    "hourly_data_fixed['cSO2'] = hourly_data_fixed.flow * hourly_data_fixed.rSO2\n",
    "hourly_data_fixed['csmoke'] = hourly_data_fixed.flow * hourly_data_fixed.rsmoke"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "(1304, 11)"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp = hourly_data_fixed.groupby('days')\n",
    "new_data = grp.agg({'cNOx': sum, 'cSO2':sum, 'cO2':sum, 'csmoke':sum,\n",
    "                    'flow':np.mean, 'rNOx':np.mean, 'rO2':np.mean, 'temp':np.mean,'rSO2':np.mean, 'rsmoke':np.mean}).reset_index()\n",
    "new_data.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "(1304, 155)"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_hourly_data = train_hourly_data.reset_index().merge(new_data, how='left', on='days')\n",
    "final_hourly_data.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "(1108, 44)"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_data.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from scipy.stats import norm"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhaojh\\Miniconda3\\envs\\py38\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='燃料消耗量（吨）', ylabel='Density'>"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(daily_data['燃料消耗量（吨）'], fit=norm)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 4000x4000 with 43 Axes>",
      "image/png": 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mxmtPT081adJES5YsUfbs2eO9xjdhwgQdO3ZMVatWlRSbu2rWrKnAwEB16dJFly9f1sSJE9W9e3d5eHgkeAzIYMCTzcP+z1IwAGme3W5X/fr11aRJEw0fPtxY/sUXX8T74FrhwoVVunRpp+WdO3dW0aJFNWrUKKd1W7du1dixY/XVV18pS5YsKW7rd999p44dOyo4OFhVqlSRJM2cOVOff/65vL29HUJIdHS0JkyYoHLlyiW4v0WLFmn69OnavHmzihQpkuB2q1ev1vvvv5+iNgcFBenAgQPGa4vFoi+//FL169eXn5+f7t27pyZNmmjs2LFq2LChRo0apcKFC+uNN95IcJ/9+/fX77//Lj8/vwS3iYiIUI0aNTR27NgUtftRff311ypTpoyyZ8/+n3weAAB4PGRAMmBqIAMCAOA+GjRooCpVqmjChAmSpNOnT6tfv34ym83y9Pz/8ZZsNpvq1Kmjd955J8F9Xbp0SS1atFCnTp00YMCABLeLiopS2bJlU9zmJUuWqEaNGgoPD9ebb76pxo0bq2vXrvLw8JDFYlHt2rXVs2dPdenSxeF9VqtV0dHR8vX1NZaFhISoWbNmj/T5Q4cO1ZtvvpnoNp07d5a3t7cWLlz4SPsGAABILamV89JjxpOkP/74Q4GBgQoICDCWPffcc+rVq5dee+01Y9nt27fl7e2tjBkzprjt8ZkxY4a2bNmiGTNmqGzZslq/fn2811rjtGrVKtlFubNnz9aiRYt0+PBh45piTEyMIiMj5e/vb/Tvtm3btHTpUgUEBGjWrFny9/d//C8GAACQiKioKFWtWlUffPCBXnzxRYd1wcHB+uCDD/TKK6/oww8/TPY+mzdvrgoVKmjMmDGJbnf37l01aNBALVq00PDhw+Xt7a06deokOJCgJH311VfGjBWSFB4ergwZMhj3qW02m+7duyez2awaNWqoe/fuqlWrlkqXLm3kVJvNZmSy+/fvq2/fvrp79646duyoFi1aJPt7Akg/vFzdAACPbv/+/bpz54569OhhjBxiNps1ceJEFSlSREWLFjW2/eKLL9SsWbN4H6jz8vJS7ty54/2Mxo0ba+fOnbp582aCD9Rdu3ZNrVu31tq1ax1CyNdff62+ffs6bBt3YczT01Pr169XpUqV5Ovrq8GDB6tJkyZq1KiRIiMj9dRTTxnvuX//vtMUYG3atNEPP/yg0NDQeNvu5+cnT09PtWzZUk2aNJHZbDbCUOvWrdWzZ081atRIknThwgX16NFDn376qfG5MTExTqOlnD59WoMGDdKaNWtUoUIF/fHHH7p586YxjViNGjU0ZMgQ5c6d26EK9mGRkZFq2bKlevXqFe96Sfrwww8fe+Tp5IqIiNDMmTO1atUqffvtt+rUqZNOnz79n3w2AABIGVdnQJvNpk8++UTr16/X7du3lT9/fr377ruqWbOmJDJgfMiAAADgv1arVi3dvXtXUmzG2bJlizFr17Bhw/Tuu+/K19dX27dv17fffqvx48crKirKIfvFjSD3sGzZshk5NCIiwulzvb29ZTab5ePjo0OHDsnX19d44Gvu3Lk6f/68Jk2aZGzfr18/lS1bVt26dZMUW0AcFRWlTJkySZIyZsyohg0basqUKTp58qQmTZqkvXv36s6dO5owYYLxAOHDcuTIocOHDxuv4x6emzRpksqUKZPksYuOjla2bNmS3M7LyyvRotnkistjxYoV07Zt2yTF5vj+/furSpUqCg4O1vDhw7V582ZJkoeHh/LkyaMmTZqof//+Tg8XAgCAJ9vj5rwnJeNJ0muvvab33nvP4UE2b29veXk5Pv7Su3dvVapUSYMGDXJYbrFYdOvWrQSOtLNs2bLJbDYbr3v16qW//vpL7du315IlS2Q2m5UvXz7Nnz/f6b0LFy50utYpSdu3b9fgwYMT/Mzy5cs7Lfv8889VuHBhSdLPP/+sjRs36rvvvtP+/fv1/PPPG9tduXJFhw4d0rVr11SgQAFVrVo1wevBAAAAyXX48GFlyZLFIXdIsZlv1apVqlu3rnbs2KGOHTsa91OT4u/vL5PJlOR2AQEBWrBggXr06KELFy5o4cKF8vHx0fDhw1WnTh2HbSMjI9W6dWunfWTMmFFNmjTR+fPn4/2MGTNmaMaMGQ7LmjVrpmnTpkmSfvnlF7Vt21aNGjXSmDFjHLKozWbTwYMHdfLkSfn4+Kh48eKqXr26Q8EzgPSBogognbHb7Zo7d648PDzUpEkTRUdHq1+/furSpYu8vLzUrFkzh2Dw+++/O1zkeZinp6d8fX1ltVoVFRUlf39/NWjQQJcvXza22bdvn/H3999/X+3atZMUW73Zv39/h6m1Ht5v5syZdejQIW3evFmNGjVSpkyZdPnyZTVq1EhFixZVsWLFFBMTo9DQUDVv3lwlS5Z02k+DBg0SvKC1d+/eeJdv375dRYsWlZ+fn3FzMyIiQtHR0dqyZYtDWClcuLC+/PJLSbEX2hIKMt9//73KlCmjChUqSJKOHz+uQoUKGYUkzZo10/HjxzVt2jTVr18/3uPt4eGh5cuX67PPPov3MyTp1q1batiwYYLrExMWFqZly5apc+fOypw5c5LbL1q0SB07dpSPj0+KPg8AAPy30kIGnDNnjhYvXqwOHTqocOHCWrZsmfr06aPdu3crMDCQDEgGBAAAaYDdbtfs2bPl6empoKAgo/C2Y8eOypgxo1EQu3HjRtWsWTPeh7WmT5+uRYsWJfgZM2fOdFrWtWtXDRs2TJIUGBgoKfZm4p07d9SzZ0+nB9zmzJkjKTabent7S5LTCMLdu3dX7ty59eWXX8rLy0uLFy9Wu3btnEZattvtstlsiomJcVieIUMGSVLu3LmNh8/ivPXWW2rdunWCxbFx+33w4IHMZrNMJpPDbGvJudmbXOfOnZPFYpHZbI634DVv3ryaMWOGoqOjderUKc2YMUMRERH/2UxnAAAgbXjcnPekZDxJ8vX1Tdb1LbPZbLTjYb/++qs6dOiQ5PvjrFq1Ss8++6zx2tvbWxMnTlT27Nn1zDPP6OrVqzKZTPFen/Px8VFkZKTTci8vL2XKlMkoGDl37pzmzZun0aNHGzl2y5Yt+u233zRs2DBZLBaH/ZcqVUpTp05VdHS0evfuLSn2mM2YMUMLFy5UlixZFBgYqNDQUN2+fVvt2rXTiBEjUjXHAgAA97Jhwwa1bdtWJpNJw4YNU7Vq1dSqVSvt3LlTly5d0sKFC7VkyRINHDhQ69evT9ZsYd7e3k5ZyWq1KjQ0VLly5XJYXqFCBS1ZskTHjx+XFJs5/fz8nDJYQvfI4z5v6NChev311yVJCxYsUM6cOY0CiZiYGPXu3Vs9e/bUM88843A9sGzZsho7dqz+/PNPh2z4xx9/qF+/frp48aIKFiwom82mCxcuKFeuXJo+fXq8gyACSLsoqgDSmQ0bNuj8+fPau3ev0+jBdrtdp06dcngI7vbt206j7r777rtq3ry57Ha7zGazgoODdfToUc2ZM0fe3t7q37+/U8VmmzZtjItOt27d0htvvBHvRShJCgoK0muvvaYNGzZo3LhxypMnj6pVq6bcuXNr3759+v333/XVV18pLCxM0dHR2r17t/bs2aMyZcroueeeM/ZjNps1evRoI8gkJiQkRM2aNXO6eCdJy5cv1+zZs+Xj4+MQdmw2mzHqyrx58xw+O05YWJjWr1+vRo0aKSQkRJJ05MgRFSlSxHgtSS1btlSDBg10/vx5+fn5KX/+/A77iY6OVuXKlVWvXr0Ev8P27dudRohJrrCwMM2aNUutWrVK8oG60NBQffPNN+rTp0+KPgsAAPz3XJ0B7927p0WLFmny5MnGg2dVqlRRo0aNdODAAb388stkQDIgAABIA1q3bi1fX1/17dtXTZo0MR68nzFjhnx8fIyH5Y4ePaoSJUpo5syZ8vLyUs+ePY19mM1mlSpVSps2bUrWZ7722mvxXie8fv266tevLx8fH6eHt+7fvy+r1aqmTZs6jQD3sBdffFEvvPCC9uzZo1OnTmny5Mm6fv26fHx8jOJYu90ui8WiPHnyOLz34Qz4T8eOHTNmMkvItWvXVLdu3QTX79y5M97lixYtUq1atRLd98Oio6P1559/qkSJEjp16pTTerPZbMy0UbFiRVksFk2ePFmjRo1K9CYxAAB4sjxuzntSMp6UeM5Ljrjv9dVXXxkDqMTn8uXLatCggVPmCgkJUeHChTV06FBJsQ/0XbhwIcEM2KpVK6dlnp6e8vT0NL5LTEyMPv/8c40bN84oGAkNDdXZs2fl7+/vdP3z5ZdfVkxMjMPyKVOm6LPPPtPChQtVrVo1Y/mBAwfUv39/+fn5JTo7BgAAQEKOHDmi7777Th9++KFCQkK0detWvfrqq7p586Y++OADderUSXnz5lW/fv20d+9e9ejRQ7NmzVKWLFkUHh6uCxcuON2zzZs3ryTpwoULDp+1YMECrV69WitXrjSy2pUrV5QtWzY988wzeuaZZyTFZsL33ntP7733XrK/h4eHh7y9vWWz2WQ2m3XixAk99dRTRv7y8fHR0aNH1b17d/n5+Tncb8+QIYMmTZrkkMGuXbumDh066Pnnn9fatWuNGdrCwsI0atQodenSRbt27VL27Nkf9ZADcBGKKoB05NSpU/rwww/Vu3dvZciQQTExMYqMjDQqO4sUKaJjx47p+++/V0REhLJkySIfHx/lyJHDYT8HDx5UixYtZLPZ9PPPP2v79u3q1q2bMWpI5syZnao9TSaTcYHpjz/+UOXKldWxY0c1btzYqZ3FixeXh4eH2rZtK5vNpv79+0uKvVi3ceNGnT17VitWrNCrr76qbt26KTo6Wt98843CwsIcHmpr2bKl8eCa2WyO9wJZ3AW18PBwderUSf7+/sa6y5cvy8PDQ61atTIuVmXMmFHXrl3Tzp07tWPHDlWqVEnt2rVTgQIFtGDBAnXu3NnhwtiBAwf0559/asGCBQoODpaHh4ciIiLk7++vI0eOOLXHarWqbt26mj59uiTpwYMH8vHxUffu3XXz5s0E+1aKvfiVO3du2Ww2RUdH/2sjCM+aNUu9e/dmijEAANKJtJABfXx8FBwc7DCSRlxxR1xBABmQDAgAAFyvf//+6t69u+7cuaNdu3Zp165dkqR+/fqpQ4cOmjt3rl588UU1a9ZMUuwNyT179jgUVZQuXVoBAQG6fPmyrFZrgqPZxsTEyGKxqEmTJnrqqaeM5Xfu3NG9e/fk5eVlzDRmMpnk5+en3bt367PPPpPValXbtm3VqFEjbd68WaVKlVKxYsWMfSxcuFABAQFq0aKF7Ha7xo8fr7Zt2+r69et64403nB64i46O1s6dO42bsUnx8PBIcpTeHDlyaPfu3cYN37jP69ixo8qUKWM8RBfn+++/18CBA52KoJNSoUIFnTp1SiVKlNDp06dVrly5RLcvVKiQrFar/vrrLz399NOP9FkAACD9etyc5w4ZL7kSGjgwIQ9fNzx37py6dOmicuXKafTo0QoKCpLValXBggX1xRdfOL130qRJun37drz79fDwUHBwsCZPnmw8mFe9enVjvc1mk9VqVenSpRUTE6O1a9c6zEDycEHFhQsXtGTJEq1du9ZpNOQ6depoyJAh2r9/v+x2+2MXpQAAAPdy9+5dDRgwQFmzZtXkyZP122+/6bnnnlPZsmXVo0cPeXh46K233pIkZc6cWXPnzlXnzp31yiuvaOTIkfLx8dEbb7zhtN+4AQN//vlnhYaGKjAwUBs2bNCMGTPUqVMnh/vWK1as0NatW/XOO+8Y939tNpumTp2qF154wWG/ERERqlixYoLfx9PTUzVq1FBUVJSRjVatWmWst9vteuutt2Sz2VSjRg2n2d4ezmBTp05VhQoV9P777ztskzlzZn300Udq06aNTpw4kejALQDSFooqgHTE29tbzZo107JlyzR16lT5+/vL09NT33//vaTYkXal2FE1Bg0apN27d8e7n9u3bytnzpwKDw/Xxo0b1bdv30casbZSpUqqUqWKLl++HO/606dPq1u3bmrWrJkaNGig2rVrq1u3bipXrpxy5swpHx8fZc2aVUOGDNGGDRtUpUoV3b9/32mUj4EDB+rq1auqW7euTCaTcXEvOjpanp6eMplMstvtslqt8YaY3r176+LFi/L09JTdbldERITmz5+v48ePa968efLz81OXLl0UHR2tV155RR4eHqpWrZox8pskNWnSROXKlVO+fPnk4eGh/fv3a8CAATpw4IBRXZoQi8WiypUry9vbWxkyZFCGDBmSvEgVGRmpsLAwlStXTkuWLEl0W0natGmTRowYYbxu0KCBpNiRo4ODg522P3funC5fvqzatWs7rQsNDdXo0aN19OhRBQYGavTo0apTp06SbQAAAP+utJABzWaz0824AwcOSJJxUYoMGIsMCAAAXMViseidd97R33//rYkTJ6pBgwb66quvNHfuXL388suSYkdba9++vSwWiywWiwICAoxcF6dhw4aSpC5duujo0aPGQ2c2m00xMTFGfot7fejQIQUGBhrv37Rpk2bMmCGTySRPT0/dv39fjRs31jvvvKMRI0bIbDarb9++qlevnsaOHavt27erd+/eDg/c+fj4aOnSpZo2bZqmT5+ubdu2KSoqSlmyZNFvv/32yMcmIiLCGIkuLpvFtT/u71arVX5+fsZ7vLy8HB4kjHPz5k0VKlTI4TtL//9wXkBAwCO1rVixYjp9+rTCw8N15coV1ahRw2mEvofduHFDkowi6hs3bujDDz/UN998I7vdrurVq+u9995TtmzZHqkdAAAg7UqNnPckZTyLxWIM6PKwESNGOFwzk6SyZcs6bfeoRQUPb1+wYEFt375dI0aM0L59+9S2bVtZLBadO3dOxYsXj/f98c1UYbPZ5OXlpc6dO+uNN97Q77//rtatW+vo0aPKkCGDJGnOnDk6cuSIgoODZbVaE233tm3bVLFiRadruHE6dOigDh06PMrXBgAAkBR7ratatWrKli2bTCaTTp8+rTFjxqhHjx765ptvVLZsWVWtWtXhPe3bt9fPP/+sxYsXa968eTpy5IgyZswos9msRYsWadmyZcaMZDabTTNnzlS+fPk0a9YsTZw4US1atHDY37Bhw1SxYkUtWLBADRs2VKZMmWSxWDRo0CANGjQo2d/FbrfLZDLp22+/ldlsVo8ePVSwYEENGzbM2OaZZ57R4sWLVbVqVWNwwfhERkZq9+7d+vTTT+Nd7+Pjoy1btiS7bQDSBooqgHSkcOHCGj9+vOx2uxo0aKDRo0erXr16kmIflIobBffSpUsymUz64YcfjPcWK1ZMmTNn1p07d/TgwQNlzZpVd+/eVb9+/dS7d29jO6vVqujoaEVERDh8tt1ul91ul6QkR7e9d++eXnrpJdWvX1+DBw+W1WqV2WzW/PnznbYdOXKk5syZIyn+C1i5cuXSyZMnHUZLad26terVq6e+ffs6tPuftm7davx98uTJ2r9/v2rVqqWbN2+qaNGiqlmzptq1ayc/Pz/16dNHrVu3dhqVxdvbW/nz5zdeBwcHq2nTpvL09HQ6RpLk6+tr7CNumjAp9kJWeHh4/AfsITly5FCjRo2S3C5OvXr1tGHDBoWGhqpnz56aO3euAgMDjYtt/zRt2jQNHDgw3nXdu3dX9erV1blzZ3366acaNmyYDh8+zGjGAAC4WFrJgA+Ljo7WnDlzVKtWLePGKBmQDAgAAFzrwYMHypQpk+bOnavx48frww8/lMVi0dy5c+Xr6+uw7ebNm3Xv3j29/fbbCT6ctXDhQoeR11asWKHFixcbIxNLineU465du6pr166SpJMnT6pdu3bq1KmTMcLw2LFjNXbsWC1btkwNGjTQl19+6VSg0LFjR3Xo0EF79uxRhQoV5O3trVq1aun+/fvxtnXChAlq3bp1gsdm0KBB+vrrrx2WjRw5UiNHjjReV69eXUuXLk1wH5J07do1hYWFqWDBgk7r4nJi5syZE93HPxUrVkxfffWVTp06pSxZsigoKCjeooro6GidPn1aCxcuVN26dY2Z64YOHarz589r8uTJMplMmjhxoj7++GONHz/+kdoBAADSrtTMeU9Cxtu+fbvTNcv27durU6dOatq0qcPyhK6XPY6AgADj2qYkNWvWTLVq1Upw+4cLd+NERkbK29vb6Iu465wPHjwwtomOjpbNZpOkJGdZO3nypMMsFgAAAKlpxowZkqSPP/5YNWvWVIUKFdS5c2c9++yz6ty5s+x2u1GkGzdQnre3tyIjI+Xv7y9/f39jX3v37nWYueG5557Thg0blClTJi1atEiVK1eOtw2NGjVyuJ+6bt06IyvF559ZVPr/DPZwQXF898jj9pvYDGfnzp3TgwcPyGDAE4aiCiCduXPnjjw8PGS32xUZGam7d+/K09NTCxYs0I8//qjChQvr1q1biomJ0eLFi2W327V3716tWLFClStX1pkzZ+Tl5SVfX19duXJFNWvWdNh/eHi4Jk6cqIkTJzp9tsViSVYbK1WqpEqVKunixYvKnj27oqOjdenSJXXp0kWjRo0ytosbCS6+EPOwHTt2qGrVqgoKCkpwm4QuJNlsNs2aNUsrVqzQqlWrZDKZjItSgwcPNqbw8vPzS/LBsW+++UYHDx6UFDsayz9lzpxZe/bsiXc0utmzZ6tMmTLGlK23b9/W5MmTNWbMGCOo7dmzR/fu3XukB+qyZs2qrFmzGrOGFCtWTPny5Yt322PHjsnb2zvBEUoqVapkVN5myJBBr776qkJDQxM97gAA4L+R1jLg7NmzdfHiRU2bNs1YRgYkAwIAANcKCAjQBx98IIvFomLFiunYsWPKnTu3hgwZon79+qlNmzbGttHR0cZMBwn57bffjFm4EpLYw10///yzevfurfbt26tixYrGzchSpUpp8eLF6tGjh8MN13/y9PRU48aNjdd79uyR2Wx2ym+1atWK92G5uAftvL29NXr0aA0fPtx476uvvqru3bsb+48bKTgpcQXM8d0sDQ8Pl4eHR5Izm/1TsWLFNGfOHJ0+fdphJOc4/xz1uEaNGho3bpzxumnTpipZsqQx81r16tV1+PDhR2oDAABI21Iz56X3jLdr1y55eno6XUszmUzKnDmzcuXKZSy7efOmtm7dqubNmz9y4WtSzp8/rwIFCsjT01NmsznJbG232x2KXKKiohzaFBkZKUlO123jm2kjPtevX0+0TwEAAB7XpUuXFBwcrEWLFkmS6tat61AcEcfHx8f4+z+LEs6dO6effvrJYdaxggULyt/fX9euXUsyz/z5558qVKiQbDZbimZpjYyMdLin++DBAwUHBys4ONhhu+Q8I3n9+nX5+/s7fF8A6R9FFUA606dPH/3222968OCBhg8fLpvNpueff15+fn5q1KiRhgwZovXr1ysmJkZz5sxRTEyMSpUqZYSUKlWq6Pjx4zp79qyio6ON6eKl2EAQGhqqKVOm6MUXX3T43KtXrxqjnyXlr7/+0uLFi7V582Z17dpVPXr00I0bNzRhwgQNHjxYvXr1kiSFhYVJUqIPbEVHR+v999/Xiy++qLFjxxrLb9++rZCQEON1ZGSknnrqKaONkZGR2rNnjxYsWKC//vpLc+fOValSpfTRRx/p66+/NoLVoEGDVKRIEY0bN07Tpk1TzZo19fbbbxujqsQJDw/X+++/r0yZMqlgwYJas2aNcUHRbrerffv2qlevXrwP00mxIfHgwYM6duyYpP8fbWThwoXGBbS7d+/qmWeeSdYxTonp06c73HD9p4enfI07PolNYwYAAP47aSkD/vDDD5o/f74GDhzo8HAXGdAZGRAAAPyXLBaLtm7dqoULFypXrlz67LPPFBgYqLVr1+rjjz9W4cKFjW3DwsJUpEiRRPe3adMmffHFF9q7d6/xQFt0dLRDHouOjlaGDBkcZvn69ddftXr1am3ZskXt27fXO++8oz179mjjxo2SYgs5ixUrpnXr1mn48OF67rnnVLNmTb300ktq0qSJQxs2b96sb775RhMmTFD27Nnjbaenp2e8D/7duXNHkpQpUyanAlQPDw9lz55d3t7eCg4O1tChQxOcseNhW7ZsUenSpR0e1osTFhamTJkyPfKMX8WKFdPNmzd16NCheIsq8ubNq9mzZ8vDw0N58uRxeiCwWbNm2rJli+bNm6djx47pxo0bypMnzyO1AQAApG2pmfPSe8bbs2ePvLy8kjVAyfXr1zVu3DjVq1cv1YsqFixYIF9fX7311luqU6dOktt/+OGHeuWVV4zX9+7dU2BgoOx2u27evKkSJUrop59+ive9Dx48iHe2i4f5+/vrr7/+erQvAQAAkExnz57VwIEDlT17dv3444/atGmT2rZtK7vdrtdeey3e9yxevNipYHTWrFkqVqyYqlSp4rB82LBhatasmRYvXmzMjvZPV69eVevWrfX5559rzpw5Wr9+faJtzp07t9PMtREREQoMDFR4eLiio6M1d+7cBK8JxsTEJDoIi7+/vyIiIhQWFpbqWROA61BUAaQzK1askCTVr19fo0ePVr169SRJY8aMMbbZtm2bTp06pU2bNql58+ZO+/D09NTRo0eVIUMGDRgwQG+//bb69Omjixcvymaz6amnnnJ6T3w3ChPy9ddfa//+/bJYLFq1apVWrVolKXYUlfXr1+vAgQOSpNOnTytnzpzGA3VxD5k9bM+ePYqIiFDHjh0dlm/cuFE7duyQFPtAW1RUlBYvXqxnn31WUuzocsuXL9fTTz+tKVOmqECBApKkPHnyqHr16mrbtq2xXaNGjfTss89q8+bNunTpktPDdFarVUOGDFFERIS2bNmi7t27a968ecaDgStXrtTVq1fVqVOnBI+JxWJR7dq1nUYp7tatm9Moxf+GvXv3qlixYg4XP/8p7hgBAIC0J61kwNDQUA0YMEA1a9ZUt27dHNaRAZ2RAQEAwH/pxo0bmj17tmJiYnT27FmHh7bGjx+vKlWqGNnrzJkz6ty5s6T485jFYtEXX3yh1q1bO4wQfOPGDbVv3954HR0d7VQI+/333+unn37S1KlTVa9ePdntdhUtWlRms1lTpkwxclfu3Lk1b948HTx4UJs3b3Yo2JViR++dPXu2KlasmOgNzEyZMik8PNxp+dWrVyUpwQf1pNh8tnTpUuXNm1evv/56gttJ0rfffqsDBw5o0qRJCe4roWLbxGTJkkU5c+bU3r17NXbsWF2/ft1hvdlsVsmSJeN9b3h4uFq1aiVfX181b95c7du318WLFzV//vxHbgcAAEi7UivnPQkZz2QyJbuINe76W0KzZqSUzWbT/v37NWTIEGNQm+DgYN28eVMLFizQvHnz9N577ylfvnzq3r27Onbs6PRdr169qpw5c+revXt67rnn5OXl5TR7hc1mU1hYmHr06KGBAwcm2qYSJUro4MGDTjNixLl06ZK6deum6dOnJ5gtAQAAEvLgwQOdPXtWgYGBOnr0qHLkyKF8+fLp2rVrkqSlS5cqZ86cxvbNmjVzymBfffWVduzYoXnz5jntPygoSCNGjNDo0aOVLVs2tWzZ0mmbffv2KUeOHMqTJ4+8vb1Vr149jRs3Tr169VLnzp2VK1cudejQQQcOHNCBAwecPufu3bu6f/++AgMDNXPmTK1bt06RkZHy9fV1aGtERIT8/Pz0xRdfJHpdsVixYvL09NTBgwf1wgsvxLtN3GxzD+dqAGkbRRVAOrd3716VKFHCuDgyf/58nT592hg92N/fP973bd26Va+++qrKly+vIUOGKDQ0VCVLlpSvr6/Txa1H1b59e7Vs2VIVKlTQtm3blC1bNu3bt0+jR49WxowZZbfbFRAQoG3btql58+ay2WzxTolqt9u1dOlS+fj4OF386datm/r27ZtgG/z9/bVy5Up5e3urUqVKTg+q/XParj59+sS7v+joaI0YMULffvutli1bpnz58unjjz9Whw4d9PTTT8vf318zZ87UsmXL5Ovrm2B7+vfvr4iICOPi5cP/G/f3evXqpWhqMun/p06LiopyWme1WjV//nzNnTs30X0kNpUvAABIW1yRAe/fv6+3335bPj4++uijj5zyGRnQGRkQAAD8l/LkyaOvvvpKQ4YMUenSpfXmm29KkqpWrWo8NJchQwYdO3ZMVqtVVatW1R9//BHvrAYbNmzQnTt3nEbEzZ07t/bu3ZtoO7p27aouXbpo4sSJGjBggMO6L774QoMHDzZe582bV3v37o33xuOsWbMUFham4cOHJ/p5uXLl0oULF5yWnzp1SoGBgYlmrVKlSqlly5aaPn26WrZsmeBMvVevXtWQIUNUtmxZvfTSS/Fuc+PGjXjzbXIUK1ZM169fV/HixZ2KKhJz9OhRXbx4UXv27DEKab/55psUtQEAAKRdqZXznoSMlxZ8//33unPnjho0aKCYmBhJsYWycaMZBwYGysfHR35+fgoMDJSXl5fTNbgLFy7omWeeUebMmXXixAndvHlTL730kiZMmKC6detKkkaNGqUTJ06od+/eSbapVatWWrZsmVauXOlULBwdHa13331XGTJkUIkSJVLpKAAAAHdStmxZHTp0SIGBgQ7LQ0NDJUn58+ePd6bYOKdOndLQoUPVsmVLI+v80yuvvKLz589r2LBhOn78uAYOHOhwre7zzz83ZkDz9PSU2Ww2slbmzJmVNWtWSbEDrGTKlCne/GUymRQYGKgRI0ZoxIgR+uSTT3T06FEtX75cXl5eCgkJ0auvvqoxY8YkWlAhxQ4sWK9ePX3yySeqWbOmsmTJ4rD+yJEjWrVqlWbOnJnofgCkLRRVAOlYcHCwfv75Z33wwQey2+2KjIzUpUuXNHfuXFWsWFHLly+Pd1TavXv36uTJk5o8ebKKFCmiO3fuKH/+/JozZ45q166dKqN1xPdwVtxIHS+88IJy5cqlAQMGaMOGDercubNq1Kihnj17Omy/du1aHTt2TK1bt1aHDh303nvvqVmzZsluw9GjR7Vy5Upt3rxZ2bJl0/Hjx5UlSxYVLFhQknTw4EF5e3urfPny8vb21qhRo1S2bFljWrKLFy9qxIgRunDhgpYuXaqyZctKir3ZOmHCBA0dOlReXl6aMWNGohegvv76a3300Ucym81GYIy7wLZ8+XJjmc1mU1RUlEqVKvVIM4NIUmBgoPLkyaPly5frpZde0vnz51W+fHkVKVJEmzZtUr169YzwCAAA0jdXZcDx48fr5MmTGjdunC5duqRLly5JkrJmzWpcJCMD/j8yIAAAcIWECibjRsbdt2+f2rdvr3feeUdr167VZ599psWLFztse+PGDU2bNk3Vq1fXli1bdOPGDQ0dOjTZbbDZbOrcubM6deqk/v3768aNGzp37pxq1KghSfrrr7+0f/9+vfLKK4qJidHevXu1bt06zZ0718hI3377rRYtWqQRI0YkWYBapkwZHTt2TBaLRdOnT9fbb7+tgIAA7du3TxUqVEiyvb169dJrr72WYEHFn3/+qe7du8tqtWrKlCnxHuPo6Gj99ttvRnZ8VMWKFdPhw4dVpEgRHTx4MNnvi8t6mzdvVrVq1bR7926tXLnS6QY3AABI/x435z0pGc9utye7zUm5ceNGorNl3LhxI97lq1evVrVq1ZQpUybdvXs3WZ/18OwaMTEx+vHHH9WuXTtjWfbs2TV27FgNHTpUn376qb777jvt27dPa9asSda9+5IlS6pbt2768MMPFRISotdff13Zs2fXqVOnNHPmTIWEhGjlypXxzmIBAACQHH5+fjpx4oSOHTum7777TvXr19czzzyT5PsOHDigAQMG6Omnn9bo0aMT3XbIkCHKmjWrpkyZos2bN2vmzJmqWbOmQkJC9N1332nIkCGSlKxM889tjh49qhIlSjhkq549e+rgwYMaPny4+vfvr169eqldu3bJvjc9YsQItWvXTm3atFH//v1VtWpV3b9/Xzt37tScOXPUrl07NWjQIFn7ApA2UFQBpEMhISG6evWqMmbMqM8++0z58+fXt99+qwwZMmjGjBnGdgUKFNC5c+ck/f8otqGhoRo7dqyef/55FSlSRJLUrl07HT16VD/++GOqTQ1vs9kkyagutdvtRkXm3r179f7772vGjBkKCgpSmzZtNHr0aF29elXvvvuuTCaTvvnmG02aNEmvvfaaxo0bp/3792vYsGFauHChrl69qosXL+rXX3+Vv7+/PD09ZbFYFBERoQcPHqhWrVqSpFWrVikiIsJ4qHDmzJkqWbKkRo0aJSm2gvXmzZtatmyZcbymTJmi559/XpkyZdKtW7fk6emp9evXK3fu3JJiR/zdv3+/FixYoIwZM+revXvatGmTsmbNqjJlysR7LKpWraqNGzcqICDAuOB5+fJlNWjQQJs2bTJGkIl7oC6urx7VtGnT9P7772vjxo3Kli2b5s2bp8jISK1bt85pVGYAAJD+uDoD7ty5U3a73chScVq1aqWJEydKIgM+jAwIAABcxWazaerUqZo+fbokyWKxKCYmRteuXdPgwYNVqVIlNW/eXJcuXdLGjRvVpk0bLVy4UPnz59fdu3fVq1cv+fj4aNq0abLb7erfv78aNGig/PnzKzIyUt9//70CAgLk5eUlq9WqBw8e6Pbt2ypdurSyZ8+uvXv36ueff9b48eOVMWNGffHFF5o8ebKOHj0qT09PXb58WZMmTVKjRo2UN29eFS9eXIcOHTJmNAsJCVH//v1VqFAhhwfNEtKiRQu1bdtWa9as0aJFi9SuXTuFhYXpyJEjmjBhgs6dO6eYmBiHB9lsNptCQ0MVEhIiScqcObPOnj0rb29vPf3005JiH3ZbtWqVpk6dqoCAAC1ZskQFChRw+OxJkybp77//1smTJ3XhwgX16dMnRX1WvHhx5c2bN8HCjoQ8++yz6t+/v1auXKkVK1aofPnyGjZsmCZNmqRLly7FW2wNAADSr5TmvMyZMz8RGS8gIEAWi0VbtmzRhg0bnN4TN+JwUuJmkG3Tpk2yjrv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jRmjevHnq3r27WrZsqd9++63A5/DDDz9o4sSJmjRpkm655RZj+eOPP67HH3883+2zR4hbvXq16tatq++++05jxoxRenq6jh07JldXV5nNZplMJplMJmVmZiolJUUnTpyQp6enatSooU2bNmnu3Ln65JNP7GYyy+1BYnaSb2GTKmw2m8aOHavg4GC72BbkHAvy8NXV1VX33Xef8WD6ueeek6urq06fPq2MjAy5urrKZDLJxcVFSUlJkrKSgtPS0nT99dcbsXzssceUlJSkkSNHatWqVbk+gAYAACisY8eO6Z9//jESEdzd3Y0X+vbt26ewsDBVqVJFI0eO1HPPPWfXPsw2ZcoUZWZm6vnnn7db7uvrq6lTp2r48OGqV6+eRowYUaQ6Tp8+XePGjbNLqJCkhIQEPf300/Lw8FBCQoJsNpvi4uIcJvi6u7tr7ty56tevn/r16ycfH58i1QUAACCbowE3XF1ddfz4cbm6uuqNN97QyZMn9emnn6pKlSqqWrWqfvvtN7Vs2VJ169bVypUrtXPnTo0dO1bLly+XyWRSYmKixo4dq/Pnz2vYsGEKDw9XixYtJGX1Y2VkZBgv8nfp0kUBAQH6/PPPHdavZcuWatOmjRYtWqTvvvtOw4cPd7jevffeq9tuu01ubm7au3evatasqaZNm9qts2bNGt16661q0qSJ0Q/n6NyrVKmSZ0JDbjNI1K1bV9u2bZOHh4dOnjyp+Ph43X333Xbr7Nq1S+np6brrrruUlpamOnXqONz3E088YTeDxdWSk5PzLM/L0aNHNXXqVC1YsCDHjBsXLlwwBrvZsWOHtmzZolGjRtklWkjSQw89ZPc5LCzMLomZfkAAAMq/0k6+Xb9+vQ4cOGC0V1u1aiWr1arXX3/daFM2bdpUd911l9zc3FSjRg1VqVIlR7vj77//1u+//65///vfOnbsmDw8PIw2V/ZgxefOnZMkYzA/b29veXt7G/twdXVVSEiIhg0bZix77LHHdOedd9otmzdvnr7++muH8UxNTVXv3r3t1r/a9OnTlZiYmGt5fnIbhO+3337TqFGj7NZ9/fXX1bx5cyNebm5udoM5p6enKyMjw0gCMZlMeuuttzR06FC5urrq3//+d5HrCcAxkioAFFiVKlWMB47PPfecEhMTtXHjRruGUHR0tC5duqTNmzfbLTeZTBo8eLAGDBigKlWq6L777pMkjR07VmfPntW3336rNm3aSMoaZTm3h5EWi0U333yz+vbtm2s9d+3apVOnThXpHOPi4rRt2zZ99tlnOcqyz6dp06aqW7eu3N3d1axZM+3du9cY4fjdd99VfHy8kQWcnp7usEPQ19dXM2fO1NixY9WtW7cCjU4MAAAql6CgILvPrVu3lpQ1dXt2EsGVrnyAZ7PZtHz5cr300ksymUz673//q+rVq2vOnDmFftA3ZMgQPfLII4Xe7u+//9awYcP06KOPqn///tqxY4c6duxY4Gnspf97oFyrVi3VrVvXGFklJiZGw4cPN0Zny+50S0tLU3x8vJYtW6YBAwZo0qRJMpvNOnbsmJEkkZKSkmNE5Oxj5TVVbX6+/PJLnThxQu+8806e6x0/fjzHS3zZbDab/vzzT910000O6+fp6Wk8TM4eDXDkyJH65ZdfjDi4uLgY5xccHKz09HR9++23djN7PPPMM9q5c6eioqI0YMCAop4yAACAnc2bN+u2226ze/B34MABvfnmm1q4cKHRxrnzzjs1dOhQrVy5UjfccIOxbnR0tDZt2qR58+Y57Bu85557NGbMGM2ePVsJCQmaPHmy3bHy8+uvv+rs2bPq2bNnjrLMzExjJjar1aqdO3fqrrvuynVfjRs31u23367169dr8ODBBa4DAABAYSxYsEBbtmyxSy6oXbu2RowYIZvNpq+++spY/tNPP+mOO+4w+gyvu+46SdKoUaPk7++vtm3b6tChQ5o/f75ee+21HO2o48ePKzw8PEdf0XfffaeuXbuqTZs2cnFxybOP0MvLS15eXrJYLJo1a5ZuuOEGrVixwijPzMzUBx98oK+//lqrVq3KdT9WqzXf2Djq35Oy+tCy25jTp0/Xnj171LVrV7u+se3btxvLswcIvFJ6erqkrIFhrnyR72pTpkwx1i2sOXPm6KmnnsqRUCFlPZP28fHRLbfcoj/++ENms1mvvvqqZs6cacS/VatWioqKUtOmTWWz2ZSSkuIwaYJ+QAAAyrfSTr6tUaOGGjZsaDyLvf766yVlPavdvn27XnjhBdWsWVMTJ05U8+bNNWrUKIfPsT/88EN5enqqW7duxgxh2e0ai8WijIwMI7EjIyND6enpmjhxol37xWw2O3yee/UyFxeXXN/DM5lMWrZsmT755JNcY37+/HkFBwfnWp6XvAbhy8jI0LFjx7Ru3TpJUu/evfXll1+qX79+OfazdOlS4/9vvPFGu/cYb7zxRi1cuFCPPPKI2rZta7xDAMA5eIsXQJGMGTNGTz31lD766CNjlOEjR45o//79eu211+w6bTIzM3X8+HE1atTIaHTZbDZFRERo7969WrVqle68806Fhobqo48+Up8+fTR16lR16tTJ2EdKSoo8PDz08MMP6/jx43nWrWvXrqpevbpsNpvS0tIK9eLeypUr1adPH4cPbh2NIOzm5qbDhw/r888/18iRI+Xq6ioXFxd5eHjo6NGj2rJli55++mmHx7rjjjvk6+urL774olBTfQEAgMph586duu6669StWzcFBARo/vz5SktLM0buyG00NinrhTRXV1d17dpVx48f1+7du+1mokhPT1dycrLx2WKx5NiHxWKRzWaTm5ubvL29tWLFCn300UfGy/tXto1sNpsyMzOVkZGhYcOGqXv37qpZs6YmT56s+++/X7t379awYcM0duxYDRw4UGlpaXJ3d3d4Dunp6UpPT5evr2+u59ihQwfFxsbmWN63b1916dJFL7zwgjGFbXa7NLvzrHfv3jpy5EiObe+//35FREQ4PF5BrFy5Uo8//nieI75ZLBb16tVLAwYMcDgt7sKFC7VgwQLt2LEjx4NtR7EwmUxauXJljnbqF198oZCQELvZ0q7e7qmnntJbb73Fw1QAAOAUaWlpWrFihc6cOWPM4Prll1/qmWeekcVi0fPPP6+kpCQlJSXJ09NTVapU0dChQ7VmzRr5+Pho165dmjBhgh544AE1b95cJ0+edNj+6dmzp12/Ynh4eJ7JD1fat2+fOnXq5PChaq1atfTnn3/q6NGj6tixoz7//HO98soree6vc+fO2r17d4GODQAAUBD33nuvpP9LKnB3d1dwcLCmT59ut15UVJTeffddu2WbN2/OMYPEu+++K5PJpDlz5sjV1VWNGjVSUlKSxowZozfeeMOuveXu7i6z2axJkybZ7ePll18u9OBw7u7uGjFihMaPH68vv/xSnTt3liTt2bNHFy5cyHdQksTERG3YsEEbNmzIdR1H/ZlXCwsL0/33368FCxYYfXFJSUn65ptv9Oyzz6pmzZp266enp8tms8nLy0tvvPFGvvt/9dVXddNNNxmJFQWd+fbMmTP6+uuvNXXqVIflV/f1ubi4yMvLS/Pnz1ebNm10xx13GMfz8PBQVFSUGjRooNtuu83hvugHBACg4imp5Nu7775bX3/9tebOnatevXrpoYceUkpKihYsWKDp06cb77tFR0fnOnhdSkqKPvzwQ/n6+srd3V0///yzXfm7776r3bt364MPPsjznPN6Nl7QddPT0xUUFKQuXbrkuu2nn35a5MTZvAbhy36GfOXsvXfddZeeeuopubm5yWQyacKECfL19VVYWJikrDavo7o0atRIY8aM0auvvqrVq1cXqa4AHCOpAkCBWSwWJSUlSZKaN2+uvn37qnnz5jp//rykrBfJ6tatqw4dOuj8+fPKzMyUm5ub9u3bp2HDhumJJ57QCy+8oMOHD2v27Nm6cOGCli1bZtdhNXv2bC1evFjPP/+8brnlFr399tuqXbu27r//fl28eFFVq1aVl5eXwwSHq+t66dIl1axZM8dUqHn59ddfHb7glhuTyaT4+Hh9+OGHGjlypF3ZiRMnNGfOHA0dOjTX7e+++27t27ePpAoAAJCDn5+fYmJijLaWi4uL0cmVmZmZ56hwW7duVUJCgrp27apLly6pffv2du2N9957T++9957dNtkji2R76623tGDBAklZM5ZFRUWpYcOGcnNz059//qmpU6dq2rRpatiwoZFUkZ6erqZNmxr76NGjhywWiyZPnqx77rlHQ4YM0YcffqjJkyfnee5Vq1bVzz//bLT5unXrZlee3RF25MgRNW7c2OE+cmsvenh4aOLEiRo0aJCx7OWXX1ZGRkaedcrPr7/+mu+Ld7t379bly5dznE+2vn37av78+VqwYEGOtqUkrV+/3m463qCgIJlMJiUnJys5OVl16tQpcH3vuusuvfDCC0pNTS1UEjIAAIAjb7/9ts6cOaOwsDD1799fd9xxhzw9PY2Zw+rVq6caNWqoRo0acnNzU3p6ukJCQnTs2DHVq1dPL774ojp16qSmTZsaLxPm5sEHH9TKlSs1efJku7Znfk6ePOlwJGIpqx327LPPqkaNGho4cKCSk5PzfVDr5+dX5NlyAQAAHNm6dasaNGigrl275rvulX1fO3bs0JEjRzRq1CiNGjVKHTt21ODBg/Xmm2+qQYMG6tatm5KSkmSxWOTt7a20tDS99tprds9E09LSlJ6err59+9od5+TJkw5Hz3UkJSVFKSkpkqROnTrpgQceUJ06dYz+zVWrVqlVq1a68cYbdf78eVmtVnl6euYYXGT06NE5Rva92pUzT1wpPT1diYmJkrJeGHz88cd12223GXXIHpW4Z8+eOn/+vGw2m0wmk2rUqKH169dr2rRp8vDw0HXXXZfn4ClS1kAziYmJSk5O1pQpU9S7d++8A/T//f7772rZsqWqV69eoPWzffTRR6pWrZqRVHHl8g4dOjhMqpDoBwQAoCIozeTb9u3bKzMzU6tXr1ZERISqVKmiQYMGFfhdt8WLFyspKUlVq1aVJG3YsMGuHZr9nDkgIMBY1r17d82dO9duPyaTSfPnz1dkZKSxLD09XbGxsXbLrFarWrZsabdt9mDOQ4cO1d9//51nffv166d69eoZz75zSxZxJK9B+Bw9u/bw8NDPP/+sgwcP6umnn5bZbJbZbJaHh4d+++03/fLLL3bPtK/Uq1cvzZkzR/v377eLHYBrQ1IFgALbtGlTjoSDqKioHOt17NjR+P+QkBCNHDlSCxYs0OTJk5WUlCRvb2/deuuteu6551SlShVZLBY9+uijqlGjhkwmk4YMGaKuXbvq559/Vu3atSVlZXJK0ueff64zZ87kW1dPT88Cd1xdKa+Hq7kxm82qVq2aw+VmsznPBJA6derop59+KnQ9AQBA5fDxxx9Lks6ePasePXpo8eLFuv7662WxWPJ8qPfcc88pNTVVPj4+GjBggMaPH29XPnz4cLsHkx9//LHdTBaSNGTIED366KP6/fffNWnSJN1000266aabJGVN6XrjjTfq4Ycflslk0q+//upwqnpJmjZtmo4cOWJ0fPXq1Utdu3Y1Rr+TsqZ8XbNmjdavX6/09HQjwSG7w2716tWqW7euvvvuO40ZM0Ymk0m7du3SM888ow0bNhTqZTqTyZRjpg6r1Zpv0m5eLl++rH/++SffpIbNmzerYcOGateuncPyGjVqaMiQIXr//ff15JNP2iUfu7i46L777tO4ceMkZV3j7Do//fTTqlevXo7Oxby4u7urevXqOnXqlG688cYCbwcAAHC1kydP6v3331ejRo1kNpuNdqqLi4vMZrOioqI0b948JScny2w2680335SPj48WLlxo7OPDDz9Uo0aNZLVaNXDgQJ0+fVq9evXSokWLdOuttxrrPfzww7r++uvVqlUrh/2SeXF3d7drA16pQ4cO+vLLL1W1alW99dZb6tu3r3r16iUpa+Q/R/2FSUlJBR6NGAAAoCgyMzOVmpqqc+fO2S1PTEw0XqhLTEzUa6+9Jilr5tvNmzdrz549ateunaZOnapGjRqpTp06qlGjhnx8fGQymfTzzz/rf//7n10fY4sWLYyEg6v5+voWqL4LFy7U22+/bbfs008/zbFe+/btjf+fNm2a+vfvb1de2GSDKzl68WzZsmU51rvyJcAHH3xQr7/+uh555BE98sgjOnv2rHbs2FGg4wUFBRWqb1KSTp06VajBUbLl9kza1dU1zwF46AcEAKD8K83kW1dXV/n5+cnHx0e+vr4KCgrSggULdOTIEQ0bNkxNmjTJtS4JCQlasGCBHn74Ye3cuVNSVtvkuuuu03fffScp50wVL7/8ssNZyWw2m5577jm7Z9x9+/ZVly5d7JbNnTvXbnZZi8WioKAgubm5qVq1aqpWrVq+z4VTU1N16dIltW7dWosXL85z3SsVZBC+K5lMJu3bt0+ff/65nn76abuyP/74Q4sWLco1qcLNzU3t27fXvn37SKoAnIikCgAFZjabVbVq1QJ3ImVmZhodce3bt1d0dLTS09Pl7e0tKWuEkn/++cdoqGSPumuxWLRt2zb16dMnxz5XrFghk8mkHj16GMvGjRunESNGGA83f/zxR+3evbtISRVubm65PlzNTfYIJkWRnJzMw1cAAOBQUlKS1q9fr/r16xsP2UaPHq3ly5crOTnZ4QO0bNkJDmFhYXrssceMZIjCyB5J+OTJk3ZtnczMTEVFRSkkJMRIqBg4cKCWLl2qf/3rX3b7WLJkiZEY4uqa9fUzu7PqSh4eHnJxcTHaiVceS5Jq1aqlunXr2j3Abd++vZo2bar58+drzpw5hTq3mTNnaubMmXbLHLU9C8rV1VUmk0mXL1/ONdklKSlJW7Zs0ZAhQ/Lc11NPPaWlS5fq/ffft+uwzMzMlKenpzGN75VtyKeeekqhoaEaPnx4ga+1zWbT5cuXaYsCAIBrVr9+fc2YMcN4MHqlixcv6vDhw7LZbGrXrp3Wrl2r06dP55glrHnz5naff/rpJ5lMJrVs2dKu7fjXX3+pfv36Rapn48aN80zEqFmzpi5evCiz2axt27YZdVq/fr1CQkJyrB8XF8dLaQAAoFi5uLjoq6++0ubNm43+p/T0dFWtWlW1atWSlJUwYLPZJGX1F7m6usrFxUVVq1bVH3/8IUlq166dMZDJ448/rtmzZ+eYxXbo0KFKT0/PtS4TJkzIt76urq5q0qSJVqxYUaDzs1qtxojFktSyZcscdahWrZp++uknRUZGOuwDvOuuu4zZdiUZyQWfffaZfHx88q2DzWbLMUPZyZMnNXnyZE2ZMsXo69u+fbvOnDmjxx9/3Fhv1qxZGjt2bKGTKoryPFrK6h8syjNp+gEBAKh4Sir59rXXXtPmzZtlsVg0YMAAbdiwQV5eXnrxxRc1d+5cPfjgg3k+p01JSVH79u3Vvn17o+8w+/lvUc65KNzd3fXbb79JypolIykpKd9tatWqVeCZOLIVdBC+q+U3mHNemEkXcD6SKgAU2KVLl+Tt7Z3rdKr5OXz4sDw8PIyX5apUqaKFCxfq9ttvtztGUFBQri+jubm56ccff9TRo0ftlq9evdroCMrvJcO8NG7cWHFxcWrRokWu61gsFqWlpclms8lms8lqterixYuaNm2afvrpJ/31118KCQnRX3/9ZXRi5iYuLk7NmjUrUl0BAEDFtnjxYnl5ealjx446deqUxo8fr3Xr1iktLU1//fWXMaNXblatWqUffvhB69ev159//qkLFy4YSQ+XL1+262QrSOdRtjVr1qhKlSrGyL2tWrXS448/rtGjR2vDhg1GO+z777/XjBkzNGHCBE2bNq2wpy/p/6awvVr2w87HH39cU6dO1fDhwwv1QtvEiRPtRvV4+eWXcz1WQbi7u6t+/fqKi4vTbbfd5nCd6OhopaSk5Jv46+XlpQEDBuiDDz5QSEiI8fDXUUdhdluze/fuql27tt577z3Nnj27QHU+duyYTCaT6tWrV6D1AQAA8tKnTx+HSRWXL19Wo0aNZDKZVKVKFVWpUkUuLi45Xly72o4dO3TzzTfbvQh3+fJlXbp0yUgyLaxOnTpp4sSJOnr0qBo1auRwnXXr1qlfv35Gn6XNZlNMTEyO9TIyMhQdHa2JEycWqS4AAABXyk4k6Natm93yCRMmaMKECerdu7f69eunatWqafny5XaJoo8++qj8/f31/PPP59hvfHy8GjRooE8++USLFy/Whg0b9OuvvzrsZ4qNjdXo0aPVokUL7dq1S5s3bzb69AYNGuRwtOCrXbp0Sb6+vkV+luzm5qYZM2YYI+3GxMQYM7O6u7srMDBQs2bNMtZfsWKFzpw5Y7ePxMRESVLDhg2NQV6KUg9Jev/9940khosXLyo9PV3z58831ktKSsr3RTdHGjdurIMHD+a5TmZmptLS0uySkTMzM7Vx40b9+uuvkqRXX31Vbm5uOnDggO64445c90U/IAAAFU9JJd/27NlTnTt3VrVq1RQVFWW0Cb/88kvdfPPNeuyxx9SyZUtt2rTJYT2bNWumefPm6fPPPzeWWSwW/fPPP/L397db98rP3bt3z7GvjIwMpaen2yWn2my2HMuuHszlSu+8844CAwONmdMuXLig2bNn50imTUxMLHRSRUEG4XOkqImzUlZ7NL93BgAUDkkVAArs/PnzqlmzZpG3X7hwoTIyMvTmm29KyprCavDgwYVqGKSnp6t169Y5Zqp49NFHc8xUURSdO3fW2rVr8xyp+MpGU2ZmpurWratu3bopPT1dbm5u8vb21r/+9S+dPn1aMTExyszMdPig+OLFi9q+fbueeeaZItUVAABUXEeOHNHChQv14osv6siRI5Kkm266SWPGjDFG+m3YsGGu2+/fv1+vvPKK3Nzc1KFDB3l6eiogIMAYJW7RokVatGiR3TbXX399vvXauXOnZsyYoTvvvFOvvfaaLly4oL///lunTp3SiRMnNHfuXGPUun/961+aNWuWunbtWuSkiuyOvr/++kuurq66cOGCpP9LMLj//vtltVoLVPdsuY1ikl8ybH6y25GOkiosFosiIyPVsWPHPK9btoEDB2rBggWKjo7WU089ZdQ7JSVFp0+flpTVLs4+FxcXF/33v/8t1Kh4a9euVceOHYv04BcAAKAgMjMzdfjw4UIPKBIfH6+PP/7YbtYuScYLc0V9Gax27drq2bOnZs6cafcyXDar1apjx44Zybp5tQ8XLVqkKlWqqEuXLkWqCwAAwJX++usvSVkvp3l7e+uBBx6wKz9z5ozq16+vixcv5ti2Vq1auvPOOx3uNyUlRY0bN1ZGRoaqVKkiKasfyVF/kKurqy5evKi//vpLiYmJysjIMOpls9nk6uoqm82W5wjB1/os2cXFRfXq1TNmYs3uE5SyRur19PS0m6XV19fXqGO2v//+W9WrVy9yQoUk42XBZ555Jt+ZKooyUMutt96qxMRE7d6923ih72pRUVFG8swtt9wiKWtWjoyMDOPcmjVrpvr16+vo0aN51oN+QAAAyq/STr7NTnbdvXu3VqxYoTFjxkiS/vjjDyUmJmro0KH5nsPV78w98MADslqtCgoKUp06dTR16lSlp6crPDw8120kKTU1Ve+9916OGdf279+fY1nr1q0d1sXNzU1ff/21fvnlF0n/N8Df1cm02eddGAUZhE+S0tLSjP/Pbl8fO3ZM4eHh+vnnnyVlDVx94sSJfJ9fHzx4MM9jASg8kioAFNj+/fvVsmXLIm2blpamr776ym7Kr8zMTC1ZssThTBW5NQqefPJJnT17NkfHkNVqNZbdeuutdvssjH79+um9997T1q1bde+999qVZTccN27cqDp16qhXr17KzMxUx44d1bFjR0nS22+/rYMHD2ro0KE6e/asbr755lzP5Y033tCtt96qm2++uUh1BQAAFZe3t7fuvvtuDRo0KEdCwp49e9S4ceM8R3wLCAjQhAkT1LRpU/n7++dYd/jw4XrhhReMzx9//LGR+JoXHx8fVa1aVenp6bJarbrlllt0/fXXq27duvr555+1dOlShYaGqmrVqjKbzerdu7cuXbpUyLP/P9ntu0cffdThch8fH7sHmQWRmpqq8PBwu445KStB41o88cQT6tOnjx5//PEcHXVnzpxRo0aNNGTIkALtq27dupozZ446depkLMvMzNRnn32mzz77zFgWFBRk/P+VScf5OXr0qFauXJmjgxEAAOBaZGZm6o033tCbb76pqlWr6vLly9qxY0ee7czU1FR5eHgYDy3j4+M1dOhQNWvWLEcbMDvZ+FpG2B09erR69+6td999V8OGDbMr2759u4KDgyVJ9evX13fffSdJOZJCvvnmG82bN0+LFi3ixTQAAOAUcXFx8vb2Nto5Vw5IFxsbq/PnzysgIKDAg8plZmYaL2c1bdpUsbGxBRrkbsuWLdq9e7f+/vtvnTlzRkuXLpX0f6P9pqWl5Tljxf79+9WzZ88C1dERR3XMfs5a0EH6ruV5drYGDRoYfbLZ/ZCZmZmy2Wx2z6hHjRqV6wt7eXF3d9egQYP0+uuv68MPP8wxkrHNZtNDDz2kCRMmaMOGDVq1apUkacqUKcY6H3zwgfr376/mzZvrxhtvzLWvmH5AAADKt7KQfCtl9eFVr17daLeYzWa7dfNrJ17JbDbrxx9/1LJly7R48WIlJCTo9ttvl4eHR57bnTt3ThMnTtSgQYOMZX379lWXLl3snnvPnTs313azxWLRnXfemWOmiquTabNnPyusvAbhk7Lalq1atTI+22w23XjjjbrjjjtktVrl6uoqHx8ftWvXTtWqVdNPP/2U67Hi4+MVExOjDh06FKmuABwjqQJAgZw/f167du3Sq6++WqTtt27dKldXVyP5QJKqVaum0NDQHOvWqlXL4VRcsbGxeuWVV+wetGZbv369MRWrzWZTWlqaGjZsqDZt2hSqntWrV9f48eM1fvx4NW7cWM2bNzfKsht/Xl5e8vb21k033ZRng65OnTrq37+/tm7dqrZt2xrTq0lZI4Js2LBB69atK1T9AABA5VCjRg2HL59ZrVatWLFCDz74oMPt9u/fr127dmnw4MF64oknJElnz57V7t27lZycbLwkVlAXL17Uxx9/rEuXLmnFihV6/PHHtWvXLoejg7Ro0UL9+/c3Ot6cITupdceOHWrQoIG+//57/fe//y3UCHDZ69psNplMJn3yyScOR9TLHuGtsA9qs91444165plnNHLkSK1cuVJ169Y1yho2bKgPPvigUPu7OknCarWqT58+mjlzpiTp+eefV7Vq1Qq1Tynrmr744ovq2bOn2rVrV+jtAQAAcpOWlqbQ0FA9/fTTkqQFCxaoSZMmxoNCq9Uqm81mPHA9duyYHnjgAf3www+yWq1au3at3n77bdWtW1eRkZFGX9/OnTv1008/afPmzWrSpImuu+66ItexVq1a+t///qchQ4bo0qVL+u9//2uU7d+/XyNGjJCU9UB26NChMplMxgh8UtZgK+PGjdOUKVNoSwEAAKf5/vvvcx0Nd+XKlWrRokWOxNIffvhBX3/9tV544QWjr+vuu++Wi4uLOnTooG+//Va1atVS48aNFRsbm2O/GRkZyszMtHuhf+zYsbr99tu1ceNGLV++XEuWLJEk43lrSEiIQkJCHNYzLi5OBw4csHvxv7BsNptOnTql+Ph4SdKpU6eM57M2m00pKSlGmSRjVttsFotFn332mdEvWlSvv/66fvjhB6M9mn2s9PR0LVy40FiWnp6uFi1a6K233ir0Mf7zn/9o69atmjp1qsLDw+36Ii0Wi9zc3OTt7S0/P798Z+nt3Lmzjhw5ou+++07t2rUz2tv0AwIAUP6VleTbI0eOqHHjxrmWDx06VF5eXgWqgyRNnTpVI0aM0IABA3Tq1CmH7w9e6dKlSzp37pyaNm1a4GM48tJLLyk5Odl4fnzlf7P/v0uXLnkObpiXvAbhy8zMlNls1p49eyRlDZ5ntVr14IMPGs/+//vf/8rX11fPPPOMjhw5ol9//dXhcWw2m2bNmqWHHnqoyHUF4BhJFQAKJCIiQt7e3jlmbygIm82mhQsX6t5775Wrq6uSkpKUkZGhDz/8MM9tzp8/Lx8fH6Pjp0mTJlq7dq18fHzsOrH8/f01f/58IwEiO6miqNO69u7dW8eOHdPAgQM1f/58YwRgd3d3Pfzww/L09JQku06zbGazWfHx8frzzz9VtWpVpaSkaOLEiRowYIBGjhwpSVq0aJHeeustvfPOO7rxxhuLVEcAAFB5XNmJM3fuXJ09e1YDBw40yq98Kc1isRjTk27cuFEJCQmqUqWKmjdvrttvv11//PGHUlNTdeHCBbsHkOfOnVN6erpiY2Pl6+srPz8/bdiwQdOnT9fNN9+sqVOnasmSJVq1apW6d++ugIAA+fr6GgkU6enpSklJ0aVLl5SSkqJevXoZ+85vWtK81vH09FSLFi2Mh7u33XabPv/88xzrpaamSspKBL664y85Odmoo7u7u9GWu5rVatXOnTuNTseiJIc8//zzSkhI0COPPKJFixbl27GXlpam1NTUArVbGzVqpDp16hif33nnnRzrWCwWmc1mnThxwmEH6JkzZ/Tvf/9b119/vSZMmFCAMwIAACi46dOnGwOQbNu2TWvXrtWyZcskZT00tFgsSktL01133aVPPvlEH330kf7973/L3d1dCxcu1BtvvKEnn3xSL7zwgqpWrWrst3r16lq1apXatm2b7wPWgmjZsqVWrVqlH3/80W55dt+dlDVz2IYNG3JsW6tWLb399tu6++67r7keAAAAUla/1tatW/X000/r7Nmz+vbbb/X333/LxcVFX3zxhT766CMtWrRIUtbowWfOnFFsbKx27Nihjz/+WCNHjlRSUpIk6auvvlKNGjWUlpamAQMG6Pnnn5f0fy/PZe9DkubNm6czZ85oxowZkuRwwLsrOSq/MiFj1qxZCggIKPSAd1fKzMzUqFGj7JZ5e3tLyuq7i4mJyTEQyZWDyCxevFgXL17Uww8/XOQ6SFnJJe7u7nYvBr799ts6ePCgXQJFRkaG0tPTi3SMKlWqaP78+Xrqqac0YsQIzZkzx+gjvPXWW42+veDgYIcD5WS/kJcdn6ioKC1ZskS7d+9WtWrV6AcEAKCCKAvJt+np6Vq/fr3uueeeXOuZXwKnzWazex5sNps1Y8YMdevWTWlpafrtt98UGBjocGA/SdqzZ4/c3d1166235nmcvHz55Zd67bXX5O7ubrS1stu4y5YtM5ZlZmYqLS1NLVq0sBtEryDyGoQvO1k4u/3WrFmzPGfBbdy4sRo3bqxPP/1Ud911l7GdJL311luKjY01BuMD4DwkVQDI1y+//KLVq1dr2rRpOaYfLYiffvpJBw4c0NixYyVJ7777rj788MM8GwbZiRFbtmzR9ddfr3nz5mn58uXy8fHJ8YLYddddp+eff95ufzabTcnJyWrXrp0iIiIKXecXX3xR9erVs8vmrF69uqZPn57ndh07dtTSpUt1//33S8rqmGzUqJEGDBhgrFOvXj0tW7bMbjovAACA3GRPl3r+/Hl99dVXevXVV+3aKNWqVdPtt9+uSZMmyWazaejQoWrVqpVcXFzUpUsXI4nz4MGD6tu3r9zc3HTo0CF98skndsex2WwaMGCAnnnmGQ0fPlypqalq3769XnvtNbm6uqpPnz5av369vvrqK23YsEHnzp1TampqjlkjhgwZYpdUcfVIH1f67bfftGXLFn3xxRcO25ktWrRQVFRUvjGaMmWKoqKi5ObmlqPTLjupwmKx5NmWNZvNevfdd7V//3498MADatiwYb7HvZrJZNKMGTO0ePFiVa9ePdf1bDab+vfvr9jYWNlstlw7Q680derUfNc5deqUevbsqfT0dIdTvXp5eemhhx7SkCFD7JKUAQAAnKFmzZrG/wcHB+v22283Hva5uLgYD2lbtGiR48Wwp59+Wvfff7/q16+fY7+tWrUyRnBzliZNmqhJkyaF3u722293aj0AAAD++OMPValSRX379lVKSor++9//qm3btjp9+rTGjBmjYcOGqX379pKkf/3rXzKbzXrooYfk7u6ucePGScp6MWvHjh2qXr26bDabRo0apcDAQPXs2VOSlJKSorS0NElZs6Nu2rRJsbGxeu6554x6WCwWDR482O45bHafldVqNV4CO3bsmL788ktlZGSoe/fukrJm8/r222+1YMGCa4pFenq6VqxYYfTv/fDDD3rppZeMsttuu81uNtglS5bowIEDkqSTJ0/q7bff1jPPPGPXLi0Mq9Wqdu3aycfHJ8eAK5cvX5bVatV9992Xo84XL17U8uXLdfPNNxfqeA0bNtSqVau0ceNGu0FX+vbtm++29913n2bMmGE8u65atar+85//GDPb0g8IAED5VxaSbzMyMjR+/Hj9/fffGjRokFG3jIyMHM9+jxw5ooSEBCUkJORof1gsFrtk1B9//FHh4eEKCgrS3Xffrblz52rBggW69957FRgYqHr16qlevXrGjF3r1q3T3XffnaONdmX9bTabMjIy9Ndffzls/9x+++1at26d3QDPCQkJ6tatm6Kioox2VHZSRfbgMYWV2yB8vr6+dsm/n376aY5tzWaz9u/fr+PHj8vNzU1//fWXRo8eralTp6p///6SpGnTpmnDhg1asmQJs1QAxcBkK8iwoQAqvR07dqhbt25F3j4+Pl6NGzfOM5ECAAAAecvIyCjybFyl5fTp07r77ru1evXqHKOHWCwWdevWTddff72GDBlSpFnRJOno0aM6dOiQWrdurVq1ahW5rqdOnVLNmjWLlEhcWJ9//rkSEhLUoUOHa56q9krbt29XnTp11KJFC9reAAAAAAAA5cCVL20lJSXJy8tLGRkZ2rBhg/r06VPo/V28eFE+Pj6F2iYxMVGenp4F6ntMSUmxmw02MzNTO3fuVOfOnQtbVaf68ssvddddd+U6wjEAAEB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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(40, 40))\n",
    "for index, col in enumerate(daily_data.columns):\n",
    "    if col != 'days':\n",
    "        # u = x_data[col].mean()\n",
    "        # std = x_data[col].std()\n",
    "        try:\n",
    "            plt.subplot(9,5,index+1)\n",
    "            plt.title(col)\n",
    "            plt.hist(daily_data[col])\n",
    "        except:\n",
    "            print(col)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [],
   "source": [
    "use_cols = ['days']\n",
    "for col in daily_data.columns:\n",
    "    if col != 'days':\n",
    "        if daily_data[col].value_counts().shape[0] > 1 and daily_data[col].isna().sum() / daily_data.shape[0] <= 0.01:\n",
    "            use_cols.append(col)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "['days',\n '发电量（千瓦时）',\n '供热量（吉焦）',\n '机组运行时间（小时）',\n '硫分（%）',\n '脱硫剂使用量（吨）',\n '脱硫设施运行时间（小时）',\n '脱硝还原剂消耗量（吨）',\n '脱硝运行时间（小时）',\n '燃料消耗量（吨）']"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "use_cols"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "(1108, 10)"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_data = daily_data[use_cols].copy()\n",
    "tmp_data.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [],
   "source": [
    "train_data = tmp_data.merge(final_hourly_data, on='days', how='left')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "(1108, 164)"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "24.0    1103\n21.0       2\n0.0        1\n15.5       1\n19.0       1\nName: 机组运行时间（小时）, dtype: int64"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['机组运行时间（小时）'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "(1087, 164)"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_data = train_data[~((train_data['机组运行时间（小时）']==0)|(train_data['燃料消耗量（吨）']<=200)|(train_data.flow==0)|(train_data.flow.isna()))].copy()\n",
    "valid_data.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [],
   "source": [
    "import datetime as dt"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [],
   "source": [
    "def cal_timedelta(x):\n",
    "    date = dt.datetime.strptime(x, '%Y-%m-%d')\n",
    "    date = dt.date(date.year, date.month, date.day)\n",
    "    start_date = dt.date(date.year, 1, 1)\n",
    "    time_delta = (date - start_date).days\n",
    "    return time_delta"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "            days   发电量（千瓦时）   供热量（吉焦）  机组运行时间（小时）  硫分（%）  脱硫剂使用量（吨）  \\\n0     2018-10-01  156796.00   6536.83        24.0   0.51       5.06   \n1     2018-10-02  133984.00   2484.64        24.0   0.51       5.04   \n2     2018-10-03  134023.00   3020.83        24.0   0.51       5.04   \n3     2018-10-04  124765.00   5599.23        24.0   0.51       5.03   \n4     2018-10-05  134414.00   4702.65        24.0   0.51       5.06   \n...          ...        ...       ...         ...    ...        ...   \n1103  2022-01-22      52.24  12472.00        24.0   0.59       8.46   \n1104  2022-01-23      51.36  12051.00        24.0   0.59       8.46   \n1105  2022-01-24      51.12  11276.00        24.0   0.59       8.43   \n1106  2022-01-25      49.32  11007.00        24.0   0.59       8.43   \n1107  2022-01-26      29.64   8132.00        24.0   0.59       8.44   \n\n      脱硫设施运行时间（小时）  脱硝还原剂消耗量（吨）  脱硝运行时间（小时）  燃料消耗量（吨）  ...          cSO2  \\\n0             24.0         2.98        24.0       323  ...  1.810937e+07   \n1             24.0         2.97        24.0       218  ...  1.337057e+07   \n2             24.0         2.95        24.0       212  ...  2.404455e+07   \n3             24.0         2.98        24.0       223  ...  8.668474e+06   \n4             24.0         3.01        24.0       243  ...  1.579668e+06   \n...            ...          ...         ...       ...  ...           ...   \n1103          24.0         4.56        24.0       822  ...  3.028139e+06   \n1104          24.0         4.58        24.0       790  ...  3.412421e+06   \n1105          24.0         4.57        24.0       751  ...  4.146250e+06   \n1106          24.0         4.56        24.0       672  ...  3.971702e+06   \n1107          24.0         4.57        24.0       484  ...  5.050733e+06   \n\n               cO2        csmoke           flow       rNOx        rO2  \\\n0     3.745944e+07  5.495410e+05  162345.192917  24.417792   9.900000   \n1     2.832146e+07  3.078217e+05  140175.330833  18.705945   9.400000   \n2     3.174159e+07  4.348207e+05  154686.184167  20.891791   8.550000   \n3     2.511504e+07  1.946970e+06  120345.545833  18.457892  10.202083   \n4     4.106346e+07  5.390776e+06  162533.103542  22.017321  11.497917   \n...            ...           ...            ...        ...        ...   \n1103  4.149625e+07  5.438282e+06  218349.604167   2.174717   7.921417   \n1104  4.422277e+07  5.194162e+06  210121.608333   5.565075   8.756333   \n1105  4.655727e+07  5.133802e+06  211378.329167  10.326585   9.110167   \n1106  7.959093e+07  5.497492e+06  240801.208333   4.874698  13.636042   \n1107  9.866431e+07  5.879454e+06  263197.579167   1.481812  15.621583   \n\n           temp      rSO2    rsmoke  day_of_year  \n0     51.250000  4.705029  0.182338          273  \n1     50.679167  3.675542  0.166718          274  \n2     52.808333  6.440365  0.117143          275  \n3     48.854167  2.364306  0.761071          276  \n4     45.783333  0.339330  1.858999          277  \n...         ...       ...       ...          ...  \n1103  55.441542  0.576979  1.037822           21  \n1104  54.574333  0.678481  1.030052           22  \n1105  53.031042  0.818827  1.012412           23  \n1106  42.908458  0.698443  0.953106           24  \n1107  36.412917  0.801224  0.930781           25  \n\n[1087 rows x 165 columns]",
      "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>days</th>\n      <th>发电量（千瓦时）</th>\n      <th>供热量（吉焦）</th>\n      <th>机组运行时间（小时）</th>\n      <th>硫分（%）</th>\n      <th>脱硫剂使用量（吨）</th>\n      <th>脱硫设施运行时间（小时）</th>\n      <th>脱硝还原剂消耗量（吨）</th>\n      <th>脱硝运行时间（小时）</th>\n      <th>燃料消耗量（吨）</th>\n      <th>...</th>\n      <th>cSO2</th>\n      <th>cO2</th>\n      <th>csmoke</th>\n      <th>flow</th>\n      <th>rNOx</th>\n      <th>rO2</th>\n      <th>temp</th>\n      <th>rSO2</th>\n      <th>rsmoke</th>\n      <th>day_of_year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2018-10-01</td>\n      <td>156796.00</td>\n      <td>6536.83</td>\n      <td>24.0</td>\n      <td>0.51</td>\n      <td>5.06</td>\n      <td>24.0</td>\n      <td>2.98</td>\n      <td>24.0</td>\n      <td>323</td>\n      <td>...</td>\n      <td>1.810937e+07</td>\n      <td>3.745944e+07</td>\n      <td>5.495410e+05</td>\n      <td>162345.192917</td>\n      <td>24.417792</td>\n      <td>9.900000</td>\n      <td>51.250000</td>\n      <td>4.705029</td>\n      <td>0.182338</td>\n      <td>273</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2018-10-02</td>\n      <td>133984.00</td>\n      <td>2484.64</td>\n      <td>24.0</td>\n      <td>0.51</td>\n      <td>5.04</td>\n      <td>24.0</td>\n      <td>2.97</td>\n      <td>24.0</td>\n      <td>218</td>\n      <td>...</td>\n      <td>1.337057e+07</td>\n      <td>2.832146e+07</td>\n      <td>3.078217e+05</td>\n      <td>140175.330833</td>\n      <td>18.705945</td>\n      <td>9.400000</td>\n      <td>50.679167</td>\n      <td>3.675542</td>\n      <td>0.166718</td>\n      <td>274</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2018-10-03</td>\n      <td>134023.00</td>\n      <td>3020.83</td>\n      <td>24.0</td>\n      <td>0.51</td>\n      <td>5.04</td>\n      <td>24.0</td>\n      <td>2.95</td>\n      <td>24.0</td>\n      <td>212</td>\n      <td>...</td>\n      <td>2.404455e+07</td>\n      <td>3.174159e+07</td>\n      <td>4.348207e+05</td>\n      <td>154686.184167</td>\n      <td>20.891791</td>\n      <td>8.550000</td>\n      <td>52.808333</td>\n      <td>6.440365</td>\n      <td>0.117143</td>\n      <td>275</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2018-10-04</td>\n      <td>124765.00</td>\n      <td>5599.23</td>\n      <td>24.0</td>\n      <td>0.51</td>\n      <td>5.03</td>\n      <td>24.0</td>\n      <td>2.98</td>\n      <td>24.0</td>\n      <td>223</td>\n      <td>...</td>\n      <td>8.668474e+06</td>\n      <td>2.511504e+07</td>\n      <td>1.946970e+06</td>\n      <td>120345.545833</td>\n      <td>18.457892</td>\n      <td>10.202083</td>\n      <td>48.854167</td>\n      <td>2.364306</td>\n      <td>0.761071</td>\n      <td>276</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2018-10-05</td>\n      <td>134414.00</td>\n      <td>4702.65</td>\n      <td>24.0</td>\n      <td>0.51</td>\n      <td>5.06</td>\n      <td>24.0</td>\n      <td>3.01</td>\n      <td>24.0</td>\n      <td>243</td>\n      <td>...</td>\n      <td>1.579668e+06</td>\n      <td>4.106346e+07</td>\n      <td>5.390776e+06</td>\n      <td>162533.103542</td>\n      <td>22.017321</td>\n      <td>11.497917</td>\n      <td>45.783333</td>\n      <td>0.339330</td>\n      <td>1.858999</td>\n      <td>277</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1103</th>\n      <td>2022-01-22</td>\n      <td>52.24</td>\n      <td>12472.00</td>\n      <td>24.0</td>\n      <td>0.59</td>\n      <td>8.46</td>\n      <td>24.0</td>\n      <td>4.56</td>\n      <td>24.0</td>\n      <td>822</td>\n      <td>...</td>\n      <td>3.028139e+06</td>\n      <td>4.149625e+07</td>\n      <td>5.438282e+06</td>\n      <td>218349.604167</td>\n      <td>2.174717</td>\n      <td>7.921417</td>\n      <td>55.441542</td>\n      <td>0.576979</td>\n      <td>1.037822</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>1104</th>\n      <td>2022-01-23</td>\n      <td>51.36</td>\n      <td>12051.00</td>\n      <td>24.0</td>\n      <td>0.59</td>\n      <td>8.46</td>\n      <td>24.0</td>\n      <td>4.58</td>\n      <td>24.0</td>\n      <td>790</td>\n      <td>...</td>\n      <td>3.412421e+06</td>\n      <td>4.422277e+07</td>\n      <td>5.194162e+06</td>\n      <td>210121.608333</td>\n      <td>5.565075</td>\n      <td>8.756333</td>\n      <td>54.574333</td>\n      <td>0.678481</td>\n      <td>1.030052</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>1105</th>\n      <td>2022-01-24</td>\n      <td>51.12</td>\n      <td>11276.00</td>\n      <td>24.0</td>\n      <td>0.59</td>\n      <td>8.43</td>\n      <td>24.0</td>\n      <td>4.57</td>\n      <td>24.0</td>\n      <td>751</td>\n      <td>...</td>\n      <td>4.146250e+06</td>\n      <td>4.655727e+07</td>\n      <td>5.133802e+06</td>\n      <td>211378.329167</td>\n      <td>10.326585</td>\n      <td>9.110167</td>\n      <td>53.031042</td>\n      <td>0.818827</td>\n      <td>1.012412</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>1106</th>\n      <td>2022-01-25</td>\n      <td>49.32</td>\n      <td>11007.00</td>\n      <td>24.0</td>\n      <td>0.59</td>\n      <td>8.43</td>\n      <td>24.0</td>\n      <td>4.56</td>\n      <td>24.0</td>\n      <td>672</td>\n      <td>...</td>\n      <td>3.971702e+06</td>\n      <td>7.959093e+07</td>\n      <td>5.497492e+06</td>\n      <td>240801.208333</td>\n      <td>4.874698</td>\n      <td>13.636042</td>\n      <td>42.908458</td>\n      <td>0.698443</td>\n      <td>0.953106</td>\n      <td>24</td>\n    </tr>\n    <tr>\n      <th>1107</th>\n      <td>2022-01-26</td>\n      <td>29.64</td>\n      <td>8132.00</td>\n      <td>24.0</td>\n      <td>0.59</td>\n      <td>8.44</td>\n      <td>24.0</td>\n      <td>4.57</td>\n      <td>24.0</td>\n      <td>484</td>\n      <td>...</td>\n      <td>5.050733e+06</td>\n      <td>9.866431e+07</td>\n      <td>5.879454e+06</td>\n      <td>263197.579167</td>\n      <td>1.481812</td>\n      <td>15.621583</td>\n      <td>36.412917</td>\n      <td>0.801224</td>\n      <td>0.930781</td>\n      <td>25</td>\n    </tr>\n  </tbody>\n</table>\n<p>1087 rows × 165 columns</p>\n</div>"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_data['day_of_year'] = valid_data.days.apply(cal_timedelta)\n",
    "valid_data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [],
   "source": [
    "valid_data.to_csv(\n",
    "    './train_data.csv', encoding='utf-8-sig', index=False\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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