AQI-Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GPU: NVIDIA GeForce MX450\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt   ##绘图库\n",
    "import seaborn as sns ##图片风格扩展\n",
    "from datetime import timedelta  #时间\n",
    "from sklearn.preprocessing import LabelEncoder  ##label编码\n",
    "from matplotlib import font_manager  ##解决中文字符显示\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 指定默认字体为新宋体。\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决保存图像时'-'显示为方块的问题。\n",
    "from matplotlib import colors\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from sklearn.model_selection import train_test_split   ## 划分\n",
    "from sklearn.linear_model import LinearRegression  ##多元线性回归\n",
    "from sklearn.metrics import mean_squared_error  ##方差\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "\n",
    "import torch\n",
    "## 代替keras库\n",
    "from torch import nn\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 检查是否有可用的 GPU\n",
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")  # 使用 \n",
    "    print(\"Using GPU:\", torch.cuda.get_device_name(0))\n",
    "else:\n",
    "    device = torch.device(\"cpu\")  # 使用 CPU\n",
    "    print(\"Using CPU\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据处理，清洗天气数据，对列名重命名，求每日均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>气温</th>\n",
       "      <th>气压</th>\n",
       "      <th>平均海平面大气压</th>\n",
       "      <th>气压趋势</th>\n",
       "      <th>湿度</th>\n",
       "      <th>风向</th>\n",
       "      <th>平均风速</th>\n",
       "      <th>最大阵风</th>\n",
       "      <th>天气情况</th>\n",
       "      <th>最低气温</th>\n",
       "      <th>最高气温</th>\n",
       "      <th>露点温度</th>\n",
       "      <th>降水时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-12-02</td>\n",
       "      <td>3.1625</td>\n",
       "      <td>759.7125</td>\n",
       "      <td>764.4625</td>\n",
       "      <td>-0.0125</td>\n",
       "      <td>45.875</td>\n",
       "      <td>7.375</td>\n",
       "      <td>1.375</td>\n",
       "      <td>7.176996</td>\n",
       "      <td>5.500</td>\n",
       "      <td>6.896037</td>\n",
       "      <td>18.301323</td>\n",
       "      <td>-8.5875</td>\n",
       "      <td>10.716845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-12-03</td>\n",
       "      <td>5.4875</td>\n",
       "      <td>761.7250</td>\n",
       "      <td>766.4250</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>39.000</td>\n",
       "      <td>8.000</td>\n",
       "      <td>1.625</td>\n",
       "      <td>7.176996</td>\n",
       "      <td>4.750</td>\n",
       "      <td>7.208537</td>\n",
       "      <td>18.313823</td>\n",
       "      <td>-8.9000</td>\n",
       "      <td>10.716845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-12-04</td>\n",
       "      <td>5.2500</td>\n",
       "      <td>760.3000</td>\n",
       "      <td>764.9875</td>\n",
       "      <td>-0.1375</td>\n",
       "      <td>45.375</td>\n",
       "      <td>9.375</td>\n",
       "      <td>1.250</td>\n",
       "      <td>7.176996</td>\n",
       "      <td>1.750</td>\n",
       "      <td>7.133537</td>\n",
       "      <td>18.713823</td>\n",
       "      <td>-6.6750</td>\n",
       "      <td>10.716845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-12-05</td>\n",
       "      <td>6.1500</td>\n",
       "      <td>763.2750</td>\n",
       "      <td>767.9750</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>30.000</td>\n",
       "      <td>6.875</td>\n",
       "      <td>2.250</td>\n",
       "      <td>7.176996</td>\n",
       "      <td>3.875</td>\n",
       "      <td>7.758537</td>\n",
       "      <td>18.551323</td>\n",
       "      <td>-10.9125</td>\n",
       "      <td>10.716845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-12-06</td>\n",
       "      <td>2.9250</td>\n",
       "      <td>760.3250</td>\n",
       "      <td>765.0750</td>\n",
       "      <td>-0.2750</td>\n",
       "      <td>52.750</td>\n",
       "      <td>4.875</td>\n",
       "      <td>1.250</td>\n",
       "      <td>7.176996</td>\n",
       "      <td>1.000</td>\n",
       "      <td>7.121037</td>\n",
       "      <td>18.238823</td>\n",
       "      <td>-6.3500</td>\n",
       "      <td>10.716845</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3676</th>\n",
       "      <td>2023-12-26</td>\n",
       "      <td>-2.0500</td>\n",
       "      <td>770.6375</td>\n",
       "      <td>773.9375</td>\n",
       "      <td>0.9250</td>\n",
       "      <td>54.125</td>\n",
       "      <td>8.250</td>\n",
       "      <td>1.500</td>\n",
       "      <td>7.147125</td>\n",
       "      <td>0.500</td>\n",
       "      <td>-9.475000</td>\n",
       "      <td>2.587500</td>\n",
       "      <td>-10.8625</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3677</th>\n",
       "      <td>2023-12-27</td>\n",
       "      <td>-3.8875</td>\n",
       "      <td>771.5375</td>\n",
       "      <td>774.8500</td>\n",
       "      <td>-0.5375</td>\n",
       "      <td>67.750</td>\n",
       "      <td>7.250</td>\n",
       "      <td>1.125</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-8.900000</td>\n",
       "      <td>5.348403</td>\n",
       "      <td>-9.4500</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3678</th>\n",
       "      <td>2023-12-28</td>\n",
       "      <td>-3.0125</td>\n",
       "      <td>769.1375</td>\n",
       "      <td>772.4375</td>\n",
       "      <td>-0.0375</td>\n",
       "      <td>69.875</td>\n",
       "      <td>6.625</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>0.375</td>\n",
       "      <td>-9.100000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>-8.2875</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3679</th>\n",
       "      <td>2023-12-29</td>\n",
       "      <td>-2.8000</td>\n",
       "      <td>765.1125</td>\n",
       "      <td>768.4000</td>\n",
       "      <td>-0.9375</td>\n",
       "      <td>78.125</td>\n",
       "      <td>5.625</td>\n",
       "      <td>1.125</td>\n",
       "      <td>4.147125</td>\n",
       "      <td>2.000</td>\n",
       "      <td>-6.302352</td>\n",
       "      <td>3.975000</td>\n",
       "      <td>-6.3000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3680</th>\n",
       "      <td>2023-12-30</td>\n",
       "      <td>-1.2375</td>\n",
       "      <td>760.2500</td>\n",
       "      <td>763.5125</td>\n",
       "      <td>0.2250</td>\n",
       "      <td>75.125</td>\n",
       "      <td>2.625</td>\n",
       "      <td>1.125</td>\n",
       "      <td>3.875000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>-6.562500</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>-5.6250</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3681 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              日期      气温        气压  平均海平面大气压    气压趋势      湿度     风向   平均风速  \\\n",
       "0     2013-12-02  3.1625  759.7125  764.4625 -0.0125  45.875  7.375  1.375   \n",
       "1     2013-12-03  5.4875  761.7250  766.4250  0.1000  39.000  8.000  1.625   \n",
       "2     2013-12-04  5.2500  760.3000  764.9875 -0.1375  45.375  9.375  1.250   \n",
       "3     2013-12-05  6.1500  763.2750  767.9750  0.2500  30.000  6.875  2.250   \n",
       "4     2013-12-06  2.9250  760.3250  765.0750 -0.2750  52.750  4.875  1.250   \n",
       "...          ...     ...       ...       ...     ...     ...    ...    ...   \n",
       "3676  2023-12-26 -2.0500  770.6375  773.9375  0.9250  54.125  8.250  1.500   \n",
       "3677  2023-12-27 -3.8875  771.5375  774.8500 -0.5375  67.750  7.250  1.125   \n",
       "3678  2023-12-28 -3.0125  769.1375  772.4375 -0.0375  69.875  6.625  1.000   \n",
       "3679  2023-12-29 -2.8000  765.1125  768.4000 -0.9375  78.125  5.625  1.125   \n",
       "3680  2023-12-30 -1.2375  760.2500  763.5125  0.2250  75.125  2.625  1.125   \n",
       "\n",
       "          最大阵风   天气情况      最低气温       最高气温     露点温度       降水时间  \n",
       "0     7.176996  5.500  6.896037  18.301323  -8.5875  10.716845  \n",
       "1     7.176996  4.750  7.208537  18.313823  -8.9000  10.716845  \n",
       "2     7.176996  1.750  7.133537  18.713823  -6.6750  10.716845  \n",
       "3     7.176996  3.875  7.758537  18.551323 -10.9125  10.716845  \n",
       "4     7.176996  1.000  7.121037  18.238823  -6.3500  10.716845  \n",
       "...        ...    ...       ...        ...      ...        ...  \n",
       "3676  7.147125  0.500 -9.475000   2.587500 -10.8625  12.000000  \n",
       "3677  4.250000  0.000 -8.900000   5.348403  -9.4500  12.000000  \n",
       "3678  3.750000  0.375 -9.100000   3.750000  -8.2875  12.000000  \n",
       "3679  4.147125  2.000 -6.302352   3.975000  -6.3000  12.000000  \n",
       "3680  3.875000  2.000 -6.562500   2.950000  -5.6250  12.000000  \n",
       "\n",
       "[3681 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加载 csv文件\n",
    "weather_data = pd.read_csv('weatherdata.csv')\n",
    "\n",
    "# 将时间列转化为日期时间格式\n",
    "weather_data['当地时间 北京'] = pd.to_datetime(weather_data['当地时间 北京'], format='%d.%m.%Y %H:%M')  # 处理中文版本csv文件 \n",
    "\n",
    "# 遍历数据集中的每一个列\n",
    "for col in weather_data.columns:\n",
    "    # 如果数据类型是非数值型并且列名不是'当地时间 北京'\n",
    "    if pd.api.types.is_string_dtype(weather_data[col]) and col != '当地时间 北京':  # 对中文版csv文件操作\n",
    "        # 使用factorize对非数值型的列进行编码\n",
    "        weather_data[col] = pd.factorize(weather_data[col])[0]\n",
    "\n",
    "# 下面的步骤只会对数值类型的列进行操作\n",
    "numeric_columns = weather_data.columns[weather_data.dtypes != 'object']\n",
    "\n",
    "# 使用每列的平均值填充对应列的缺失值\n",
    "weather_data[numeric_columns] = weather_data[numeric_columns].fillna(weather_data[numeric_columns].mean())\n",
    "# 删除含有 NaN 的列\n",
    "weather_data = weather_data.dropna(axis=1)\n",
    "\n",
    "# 定义新的列名\n",
    "column_dict = {'当地时间 北京':'日期','T': '气温', 'Po': '气压', 'P': '平均海平面大气压', 'Pa': '气压趋势', 'U': '湿度', 'DD': '风向', 'Ff': '平均风速', 'ff3': '最大阵风', 'WW': '天气情况', 'Tn': '最低气温', 'Tx': '最高气温', 'Td': '露点温度', 'tR': '降水时间'}\n",
    "\n",
    "# 更改列名\n",
    "weather_data = weather_data.rename(columns=column_dict)\n",
    "\n",
    "# 从'日期'列中提取日期\n",
    "weather_data['日期'] = pd.to_datetime(weather_data['日期']).dt.date\n",
    "\n",
    "# 计算每日平均值\n",
    "daily_avg_data = weather_data.groupby('日期').mean().reset_index()\n",
    "\n",
    "# 检查结果\n",
    "daily_avg_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "合并AQI数据与清洗好的天气数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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>气温</th>\n",
       "      <th>气压</th>\n",
       "      <th>平均海平面大气压</th>\n",
       "      <th>气压趋势</th>\n",
       "      <th>湿度</th>\n",
       "      <th>风向</th>\n",
       "      <th>平均风速</th>\n",
       "      <th>最大阵风</th>\n",
       "      <th>天气情况</th>\n",
       "      <th>...</th>\n",
       "      <th>降水时间</th>\n",
       "      <th>季节</th>\n",
       "      <th>AQI</th>\n",
       "      <th>质量等级</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3_8h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-12-02</td>\n",
       "      <td>3.1625</td>\n",
       "      <td>759.7125</td>\n",
       "      <td>764.4625</td>\n",
       "      <td>-0.0125</td>\n",
       "      <td>45.875</td>\n",
       "      <td>7.375</td>\n",
       "      <td>1.375</td>\n",
       "      <td>7.176996</td>\n",
       "      <td>5.500</td>\n",
       "      <td>...</td>\n",
       "      <td>10.716845</td>\n",
       "      <td>冬</td>\n",
       "      <td>142</td>\n",
       "      <td>轻度污染</td>\n",
       "      <td>109</td>\n",
       "      <td>138</td>\n",
       "      <td>88</td>\n",
       "      <td>2.6</td>\n",
       "      <td>61</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-12-03</td>\n",
       "      <td>5.4875</td>\n",
       "      <td>761.7250</td>\n",
       "      <td>766.4250</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>39.000</td>\n",
       "      <td>8.000</td>\n",
       "      <td>1.625</td>\n",
       "      <td>7.176996</td>\n",
       "      <td>4.750</td>\n",
       "      <td>...</td>\n",
       "      <td>10.716845</td>\n",
       "      <td>冬</td>\n",
       "      <td>86</td>\n",
       "      <td>良</td>\n",
       "      <td>64</td>\n",
       "      <td>86</td>\n",
       "      <td>54</td>\n",
       "      <td>1.6</td>\n",
       "      <td>38</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-12-04</td>\n",
       "      <td>5.2500</td>\n",
       "      <td>760.3000</td>\n",
       "      <td>764.9875</td>\n",
       "      <td>-0.1375</td>\n",
       "      <td>45.375</td>\n",
       "      <td>9.375</td>\n",
       "      <td>1.250</td>\n",
       "      <td>7.176996</td>\n",
       "      <td>1.750</td>\n",
       "      <td>...</td>\n",
       "      <td>10.716845</td>\n",
       "      <td>冬</td>\n",
       "      <td>109</td>\n",
       "      <td>轻度污染</td>\n",
       "      <td>82</td>\n",
       "      <td>101</td>\n",
       "      <td>62</td>\n",
       "      <td>2.0</td>\n",
       "      <td>42</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-12-05</td>\n",
       "      <td>6.1500</td>\n",
       "      <td>763.2750</td>\n",
       "      <td>767.9750</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>30.000</td>\n",
       "      <td>6.875</td>\n",
       "      <td>2.250</td>\n",
       "      <td>7.176996</td>\n",
       "      <td>3.875</td>\n",
       "      <td>...</td>\n",
       "      <td>10.716845</td>\n",
       "      <td>冬</td>\n",
       "      <td>56</td>\n",
       "      <td>良</td>\n",
       "      <td>39</td>\n",
       "      <td>56</td>\n",
       "      <td>38</td>\n",
       "      <td>1.2</td>\n",
       "      <td>30</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-12-06</td>\n",
       "      <td>2.9250</td>\n",
       "      <td>760.3250</td>\n",
       "      <td>765.0750</td>\n",
       "      <td>-0.2750</td>\n",
       "      <td>52.750</td>\n",
       "      <td>4.875</td>\n",
       "      <td>1.250</td>\n",
       "      <td>7.176996</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>10.716845</td>\n",
       "      <td>冬</td>\n",
       "      <td>169</td>\n",
       "      <td>中度污染</td>\n",
       "      <td>128</td>\n",
       "      <td>162</td>\n",
       "      <td>78</td>\n",
       "      <td>2.5</td>\n",
       "      <td>48</td>\n",
       "      <td>15</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>3676</th>\n",
       "      <td>2023-12-26</td>\n",
       "      <td>-2.0500</td>\n",
       "      <td>770.6375</td>\n",
       "      <td>773.9375</td>\n",
       "      <td>0.9250</td>\n",
       "      <td>54.125</td>\n",
       "      <td>8.250</td>\n",
       "      <td>1.500</td>\n",
       "      <td>7.147125</td>\n",
       "      <td>0.500</td>\n",
       "      <td>...</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>冬</td>\n",
       "      <td>55</td>\n",
       "      <td>良</td>\n",
       "      <td>26</td>\n",
       "      <td>46</td>\n",
       "      <td>44</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3677</th>\n",
       "      <td>2023-12-27</td>\n",
       "      <td>-3.8875</td>\n",
       "      <td>771.5375</td>\n",
       "      <td>774.8500</td>\n",
       "      <td>-0.5375</td>\n",
       "      <td>67.750</td>\n",
       "      <td>7.250</td>\n",
       "      <td>1.125</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>冬</td>\n",
       "      <td>64</td>\n",
       "      <td>良</td>\n",
       "      <td>45</td>\n",
       "      <td>71</td>\n",
       "      <td>51</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3678</th>\n",
       "      <td>2023-12-28</td>\n",
       "      <td>-3.0125</td>\n",
       "      <td>769.1375</td>\n",
       "      <td>772.4375</td>\n",
       "      <td>-0.0375</td>\n",
       "      <td>69.875</td>\n",
       "      <td>6.625</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>0.375</td>\n",
       "      <td>...</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>冬</td>\n",
       "      <td>129</td>\n",
       "      <td>轻度污染</td>\n",
       "      <td>98</td>\n",
       "      <td>132</td>\n",
       "      <td>69</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3679</th>\n",
       "      <td>2023-12-29</td>\n",
       "      <td>-2.8000</td>\n",
       "      <td>765.1125</td>\n",
       "      <td>768.4000</td>\n",
       "      <td>-0.9375</td>\n",
       "      <td>78.125</td>\n",
       "      <td>5.625</td>\n",
       "      <td>1.125</td>\n",
       "      <td>4.147125</td>\n",
       "      <td>2.000</td>\n",
       "      <td>...</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>冬</td>\n",
       "      <td>150</td>\n",
       "      <td>轻度污染</td>\n",
       "      <td>115</td>\n",
       "      <td>145</td>\n",
       "      <td>62</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3680</th>\n",
       "      <td>2023-12-30</td>\n",
       "      <td>-1.2375</td>\n",
       "      <td>760.2500</td>\n",
       "      <td>763.5125</td>\n",
       "      <td>0.2250</td>\n",
       "      <td>75.125</td>\n",
       "      <td>2.625</td>\n",
       "      <td>1.125</td>\n",
       "      <td>3.875000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>...</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>冬</td>\n",
       "      <td>32</td>\n",
       "      <td>优</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3681 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             日期      气温        气压  平均海平面大气压    气压趋势      湿度     风向   平均风速  \\\n",
       "0    2013-12-02  3.1625  759.7125  764.4625 -0.0125  45.875  7.375  1.375   \n",
       "1    2013-12-03  5.4875  761.7250  766.4250  0.1000  39.000  8.000  1.625   \n",
       "2    2013-12-04  5.2500  760.3000  764.9875 -0.1375  45.375  9.375  1.250   \n",
       "3    2013-12-05  6.1500  763.2750  767.9750  0.2500  30.000  6.875  2.250   \n",
       "4    2013-12-06  2.9250  760.3250  765.0750 -0.2750  52.750  4.875  1.250   \n",
       "...         ...     ...       ...       ...     ...     ...    ...    ...   \n",
       "3676 2023-12-26 -2.0500  770.6375  773.9375  0.9250  54.125  8.250  1.500   \n",
       "3677 2023-12-27 -3.8875  771.5375  774.8500 -0.5375  67.750  7.250  1.125   \n",
       "3678 2023-12-28 -3.0125  769.1375  772.4375 -0.0375  69.875  6.625  1.000   \n",
       "3679 2023-12-29 -2.8000  765.1125  768.4000 -0.9375  78.125  5.625  1.125   \n",
       "3680 2023-12-30 -1.2375  760.2500  763.5125  0.2250  75.125  2.625  1.125   \n",
       "\n",
       "          最大阵风   天气情况  ...       降水时间  季节  AQI  质量等级 PM2.5  PM10 NO2   CO  \\\n",
       "0     7.176996  5.500  ...  10.716845   冬  142  轻度污染   109   138  88  2.6   \n",
       "1     7.176996  4.750  ...  10.716845   冬   86     良    64    86  54  1.6   \n",
       "2     7.176996  1.750  ...  10.716845   冬  109  轻度污染    82   101  62  2.0   \n",
       "3     7.176996  3.875  ...  10.716845   冬   56     良    39    56  38  1.2   \n",
       "4     7.176996  1.000  ...  10.716845   冬  169  中度污染   128   162  78  2.5   \n",
       "...        ...    ...  ...        ...  ..  ...   ...   ...   ...  ..  ...   \n",
       "3676  7.147125  0.500  ...  12.000000   冬   55     良    26    46  44  0.7   \n",
       "3677  4.250000  0.000  ...  12.000000   冬   64     良    45    71  51  0.8   \n",
       "3678  3.750000  0.375  ...  12.000000   冬  129  轻度污染    98   132  69  1.2   \n",
       "3679  4.147125  2.000  ...  12.000000   冬  150  轻度污染   115   145  62  1.2   \n",
       "3680  3.875000  2.000  ...  12.000000   冬   32     优    12    29  21  0.3   \n",
       "\n",
       "      SO2  O3_8h  \n",
       "0      61     11  \n",
       "1      38     45  \n",
       "2      42     23  \n",
       "3      30     52  \n",
       "4      48     15  \n",
       "...   ...    ...  \n",
       "3676    3      0  \n",
       "3677    3     34  \n",
       "3678    3     21  \n",
       "3679    3     45  \n",
       "3680    2     64  \n",
       "\n",
       "[3681 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 确保两个 DataFrame 的日期列都是日期格式\n",
    "aqi_data = pd.read_csv('SeasonAdded.csv')\n",
    "\n",
    "aqi_data['日期'] = pd.to_datetime(aqi_data['日期'])\n",
    "daily_avg_data['日期'] = pd.to_datetime(daily_avg_data['日期'])\n",
    "\n",
    "# 合并两个 DataFrame\n",
    "merged_data = pd.merge(daily_avg_data, aqi_data, on='日期', how='inner')\n",
    "\n",
    "merged_data.to_csv('merged_data.csv', index=False)\n",
    "\n",
    "merged_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5       1.000000\n",
      "湿度          0.254683\n",
      "风向          0.181264\n",
      "平均海平面大气压    0.105069\n",
      "气压          0.075169\n",
      "天气情况        0.015611\n",
      "露点温度       -0.000350\n",
      "季节         -0.035743\n",
      "降水时间       -0.048534\n",
      "最高气温       -0.088200\n",
      "最低气温       -0.100938\n",
      "最大阵风       -0.146053\n",
      "气压趋势       -0.154671\n",
      "气温         -0.156376\n",
      "平均风速       -0.288130\n",
      "Name: PM2.5, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_22176\\1955616724.py:29: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=pm25_correlation.values, y=pm25_correlation.index, palette=sns.color_palette(\"coolwarm\", len(pm25_correlation)))\n"
     ]
    },
    {
     "data": {
      "image/png": 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aTCadP39ekydPVmpqqubPn69x48bJ5XLp+eeflyQVL15cTzzxhMaMGaNevXplWEPhwoVVrFgxnTp1St27d1dQUJDCwsIk3XgH6aVLlzRq1Cjlzp1bERER7neP/tXq1av18MMPpztuMpk0fPhwdyCdNWuWOnbsqKefftojcP/dhx9+qAoVKmjfvn2aOnWq5s6dm+XvCADIHKEUAIA7zGKx6KeffspwGWfaktnMpC3BTZOQkKB+/fqpT58++s9//qMWLVqoaNGiWrNmjc6cOaMyZcrovffe09WrV+/kLbgdPXpUrVq10smTJ5WYmJhhG39/fw0aNEht2rTx2E1XkgoVKqRt27apSZMm+vjjjzVgwABJ0syZM90zjWlhPCUlJdM6tm3bphYtWuj8+fPuDZ3SHDhwQJMnT1a1atW0d+/edH2vXr2qJUuWqGXLlpmOn7aMt0uXLnrvvfc0ffp0j3eTOhwOj/Z9+vTR4MGDtXv3btWtW1ft2rVLN+bN/q4BADcQSgEAyAFpQctut3sEmrQ/V6xYUSaTyeNz6tQpdyg9ceKEPvzwQ5UpU0Y//vijVq1a5X5PZ1r/33//XbVq1dLw4cNVoEABNW3aNF1wdLlc7g2UYmJidO7cOV27dk0xMTHujZZiY2MVExOjS5cu6cyZM+6+v/76q/t9nb/88ot7+avT6ZTL5XI/6xoZGanVq1fr119/VY0aNTzGkKSHHnpIISEhOnnypD7++GNFRkZq4cKFio6OVrNmzXTw4EF9+umnevvttxUVFeWx4ZMkTZ8+XQ0aNFCTJk3066+/Knfu3B7hvUOHDtq7d68CAwNVq1YtzZgxw6P/qFGjFBIS4hEc0+5Bknbu3KkGDRpo4sSJ+u677zLcRMput+v8+fM6evSojh49qtjYWNWrV0+TJk3SihUr1KNHD/e5o0ePymazpfsHBgBAxnimFACAHJQ2+2e1WmWxWGS1WiVJK1asUOnSpT3a1q1bVzabTZI0YsQILVy4UG+++ab69eun4ODgdGOn7f66YMECjRs3TjVr1kz3PKXVanUH4XHjxmnYsGHpxilYsKD7z2k7+Kampur9999Xq1at3Jv/JCcn691339XJkyfldDoVEhLi7tegQQOtWbNG77//vkJDQyVJp0+f1hdffKF58+Ypf/78Wrx4serUqSOn06kLFy6oYsWKatCggTZs2KBcuXLJ29tbb731lrZs2aKFCxfKy8tLq1atUteuXTVixAhFRkbK5XLprbfe0urVqyXJfb/FixfX+vXr1bNnTw0YMEBt27ZVSEiIDh06pC+++EKffPKJx9LjtOdPT5w4ofr166tkyZL6/fffVa5cuQz/Hh0Oh3r06JHhucwQSgEge0yuv/9zJAAAuGMSEhJ05coVFStWTCaTSXa7XdHR0SpSpIjH8tC/S0pKksPhyDCMZuavs5dpnn32Wdntdi1YsEBWq9X9XGhGz6+mpqbKZrO5g15akP6rNm3a6OrVq2rRooUiIyPTXe+vnE6n3n77bTVv3tz9mpg03377rSwWi9q3b+9xfN26dQoKClK1atXcxw4ePOh+z6gktWvXThs2bNCbb76pIUOGpLvu0aNHVbJkSY/vxYsXT/d6nTQ7d+5UhQoVsvz78PLy0uLFi9WqVatM2/xVyZIl9cYbb7ifVQUAZI5QCgAA7ip2u11m87+72Ovo0aMqXLjwLe92DAC4OUIpAAAAAMAwbHQEAAAAADAMoRQAAAAAYBhCKQAAAADAMLwSBneU0+nUuXPnFBwcnOWOjAAAAADubS6XS/Hx8SpcuHCGu76nIZTijjp37pzCw8ONLgMAAADA/4gzZ86oaNGimZ4nlOKOSnuf3pkzZzxeqg4AAADg/hIXF6fw8PCbvnObUIo7Km3JbkhICKEUAAAAwE0f62OjIwAAAACAYQilAAAAAADDsHwXAO6wLgO2GF0CAAC4j00fU9voEm4JM6UAAAAAAMMQSgEAAAAAhiGUAgAAAAAMQygFAAAAABiGUAoAAAAAMAyhFAAAAABgGEIpAAAAAMAwhFIAAAAAgGEIpQAAAAAAwxBKAQAAAACGIZQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCE0rvA2rVrZTKZZDKZ5OPjo0qVKmnFihX64IMPZDKZ9NNPP0mSvvzyS5lMJk2fPl2StGjRIj344IMym82qUqWKDh48mK3rtW7d2n09k8mkpk2b5tStAQAAALjPEUrvEiEhIbp+/bpOnz6tPn36qH379jp37pwk6cCBA5Kk/fv3u9sfO3ZML7/8skaNGqWzZ8+qdOnS6tatW7autX37du3du1fXr1/X9evXtWjRojt/QwAAAAAgQuldw2QyKSwsTIUKFVL37t1VvHhxrVu3Tt7e3u4wum/fPnl7e0uSDh48qFGjRumZZ55RgQIF1KtXL+3cufOm1zl79qxcLpcqVKigsLAwhYWFKTAwMEfvDQAAAMD9i1B6lzKbzUpNTVX16tXdofTQoUOqVq2aJKlVq1bq0aOHu/3hw4dVqlSpm467detWORwOFS1aVIGBgXruued0/fr1nLkJAAAAAPc9QuldaNWqVTp06JDq1KmjUqVK6eTJkzp37pzCwsLk7++frn1qaqrGjBmjnj173nTsQ4cOqXLlylqyZIl+++03nThxQpGRkZm2t1qtiouL8/gAAAAAQHYRSu8SsbGxCgsLk5+fnzp06KAJEyaoZMmS8vb2VvHixfXTTz+pUqVKGfZ9//33FRgYmK1nSiMjI7Vq1SpVrlxZFStW1Keffqrvv/8+0/ZRUVEKDQ11f8LDw2/7HgEAAADcfwild4ng4GDt2rVLx44dU0xMjLp27eo+V6lSJc2fPz/DULpmzRpNnDhR3377rXx8fG75uvnz59fVq1dltVozPB8ZGanY2Fj358yZM7d8DQAAAAD3L0LpXcLLy0sREREqUqSITCaTx7lKlSppzZo16ULpiRMn9Pzzz2vixIkqV65ctq7z7LPPauPGje7vW7ZsUYECBWSxWDJsb7FYFBIS4vEBAAAAgOwilN4D0sLoX0NpcnKyWrVqpaeeekpt27ZVQkKCEhIS5HK5JElxcXGy2WzpxqpYsaL69eunjRs3auHChYqMjFSvXr3+nRsBAAAAcN8hlN4DKleurJCQEEVERLiPrVy5UgcOHNB//vMfBQcHuz+nTp2SdCPALlmyJN1Y77zzjipVqqTHH39cvXr10muvvabBgwf/W7cCAAAA4D5jcqVNnQF3QFxcnEJDQxUbG8tSXty3ugzYYnQJAADgPjZ9TG2jS5CU/WzATCkAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYhlAIAAAAADEMoBQAAAAAYhlAKAAAAADAMoRQAAAAAYBhCKQAAAADAMIRSAAAAAIBhCKUAAAAAAMMQSgEAAAAAhiGUAgAAAAAMQygFAAAAABjGbHQBAHCvmT6mttElAAAA3DWYKQUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYhlAIAAAAADEMoBQAAAAAYxmx0AQBwr3lr9BGjS8BdYvRbpY0uAQAAwzFTCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYhlAIAAAAADEMoBQAAAAAYhlAKAAAAADAMoRQAAAAAYBhCKQAAAADAMIRSAAAAAIBhCKUAAAAAAMMQSgEAAAAAhiGU3gOuXr0ql8v1j8fZt2+f1q9ffwcqAgAAAIDsIZTeAyIjI9WmTRvFxcXJbrfLZDIpb9686T4+Pj5avXp1puOsWbNG3bt3/xcrBwAAAHC/MxtdAP65yZMna/DgwXrssce0efNmeXt768qVK+naNWzYUL6+vpKkLVu2qFWrVgoODnafdzgcio+PV0REhCTJbrcrISFBixcvVv369f+VewEAAABwfyGU3gO8vLwUFRWlixcvymQyycvLS3nz5k3XLjY2Vl5eXu4+RYoU0Z49eyRJixcvVtWqVVW0aFFJ0ty5c1WrVi13QAUAAACAnMDy3bvc6dOn9cknn0iSChQoIEkymUy6cuVKuk+dOnU8+ppMJlmtVtntdo0YMUJHjhxxn/vss8905MgRpaamym63/3s3BAAAAOC+wkzpXe7ChQv69NNPdfz4cX355Zfy8vKSw+HItL3T6ZQkuVwueXt765133tHq1at14sQJ9ejRQ0FBQZKkP//8U7169ZLFYtH48ePVrFmzDMezWq2yWq3u73FxcXfw7gAAAADc6wild7kaNWpo586dateunXbs2KGqVasqKChIYWFhGbZPC6UpKSny8fHRuHHj3OO8+eabqlmzpiTp6aef1pgxY9S4ceMsrx8VFaVhw4bdsfsBAAAAcH9h+e49oGjRotq0aZMeeeQRXb58WRcuXNDmzZv1zDPPKCYmRl27dtWcOXMUExOjYsWKSZISExOVJ08e9xjt2rXTli1bNG7cOI0bN0516tRxLwfOSmRkpGJjY92fM2fO5Nh9AgAAALj3MFN6j+jVq5defvlljRgxQm3btlWlSpW0YcMGSdLBgwdVtWpVxcXFqWrVqtq5c6cuXLigwoUL66efftIbb7whl8ul0NBQmUwmSTdmUg8ePKjly5fLx8cn0+taLBZZLJZ/5R4BAAAA3HuYKb0H2Gw2/fzzzypdurTefPNNff7557JYLPL29pZ0Y6ddX19fffbZZ3r88ccVERGho0ePqmjRonryySd14sQJPfXUU3riiSe0a9curVmzRna7XcOGDcsykAIAAADAP8VM6T1g8eLFqlWrlvLly6fmzZvr8uXLOnDggEebI0eOaPz48dq1a5ckac2aNfrggw/c56OiotSgQQOZzWYtX75c/fv3V926df/FuwAAAABwP2Km9B4wbtw4derUSdKNWdHt27erfPnyHm1KlSqldevWqVixYtq9e7cOHz6sBg0aSLqxE+/hw4dVpUoVjRgxQmfOnJHJZNKuXbt05coVuVyuf/2eAAAAANwfmCm9yx04cED79u3TE088obi4OFWoUEGBgYFyOBy6cuWKKlSooEuXLunAgQOyWCxKSEjQwIED1b9/f509e1YdO3bU+fPnVaZMGT3zzDP65JNPtGvXLi1cuFDTpk3Tvn37VK5cOe3YsUNeXvwbBgAAAIA7i1B6lytXrpyOHDkiPz8/+fn56fTp07fUf/78+XrggQfcGxxJUqNGjdSoUSNJkt1uV3x8PIEUAAAAQI4glN4D8uXLd9t9014Rkxmz2axcuXLd9vgAAAAAkBWmvwAAAAAAhiGUAgAAAAAMQygFAAAAABiGUAoAAAAAMAyhFAAAAABgGEIpAAAAAMAwhFIAAAAAgGEIpQAAAAAAwxBKAQAAAACGIZQCAAAAAAxDKAUAAAAAGMZsdAEAcK8Z/VZpo0sAAAC4azBTCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYhlAIAAAAADEMoBQAAAAAYhlAKAAAAADAMoRQAAAAAYBiz0QUAwL1m1KwLRpeA/29Qp4JGlwAAAG6CmVIAAAAAgGEIpQAAAAAAwxBKAQAAAACGIZQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYhlAIAAAAADEMoBQAAAAAYhlAKpaam6sqVK0aXAQAAAOA+RCi9R9SvX19msznTj8lkUmJiYoZ9t27dqqJFi+rSpUv/ctUAAAAA7neE0nuEn5+f1qxZI7vdnu6TkJAgSQoICMiw74oVK9S6dWvlz5//3ywZAAAAAAil9wovLy+ZTCYdP37cfSwpKUnDhg2TzWZzz5am+eCDD+Tl5SWz2ayoqCj98MMPGc6wenl56eWXXzbilgAAAADcBwil9wiHwyEvLy+99tprGjFihCRp5cqVWrx4scxms7y9vT3a+/v766WXXspwZvWvn/fee09+fn5G3BIAAACA+4DZ6AJwZ9jtdvn4+GjWrFmqXbu2atasqVmzZmnQoEEym81yOp0e7f8eUrOSVVur1Sqr1er+HhcXd+vFAwAAALhvMVN6j0hMTJSfn5/y5cunGTNmqFOnTtq/f7/at28vb29v2Wy2TPs+//zzyp07tyIiIhQREaGgoCANHDgwW9eNiopSaGio+xMeHn6nbgkAAADAfYBQeo+IiYlRSEiIJKlOnToqVqyYHnzwQXl5ecnLyyvLYGqxWPTuu+/q5MmTOnnypDp06CCLxZKt60ZGRio2Ntb9OXPmzB27JwAAAAD3PkLpPcDlcuncuXMqXLiwJOmLL76Q2WzW4cOH9csvv0iSChYsqKtXr97xa1ssFoWEhHh8AAAAACC7CKX3gEOHDqlw4cLy9fXVjBkzNGbMGC1YsEAfffSRBgwYIJfLpeLFi+vIkSMZ9k9JSdFHH33kXr77/fffezwnCgAAAAA5hY2O7gErVqxQvXr1tHHjRg0dOlRr1qxRwYIF9cwzzygqKkqbN29WqVKltHHjRm3dulXPPvusR/+5c+caVDkAAACA+x2h9C5nt9s1ZcoUjR8/XnXr1tWuXbuUO3duSZLJZNKGDRsUHBys8+fPa+DAgbp27Zpq1aql5OTkLMe9du2aoqOjdeLECfd4AAAAAHCnsXz3Lrdjxw75+/uradOmkpQuQAYHB0uS2rVrp/DwcJUoUUJ16tSRt7e3AgICMh3X4XCofv36+uOPP9ShQ4ecuwEAAAAA9zWTy+VyGV0E/pnr168rV65cN23ndDrlcDjk4+OTY7XExcUpNDRUsbGxbHqE+9aoWReMLgH/36BOBY0uAQCA+1Z2swHLd+8B2QmkktyvhwEAAACA/xUkFAAAAACAYQilAAAAAADDEEoBAAAAAIYhlAIAAAAADEMoBQAAAAAYhlAKAAAAADAMoRQAAAAAYBhCKQAAAADAMIRSAAAAAIBhCKUAAAAAAMMQSgEAAAAAhiGUAgAAAAAMYza6AAC41wzqVNDoEgAAAO4azJQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYhlAIAAAAADGM2ugAAuNdMWxprdAk5rlvLUKNLAAAA9whmSgEAAAAAhiGUAgAAAAAMQygFAAAAABiGUAoAAAAAMAyhFAAAAABgGEIpAAAAAMAwhFIAAAAAgGEIpQAAAAAAwxBKAQAAAACGIZQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCGhtLU1NQszzudTlmt1puOc/XqVTmdzkzPu1yudMeWL1+upKSkLMe12+2y2+231MZqtcpms92k4htSU1Oz3fZmEhISNHv27Gz9XrfC6XTe9O8JAAAAAG6XYaE0NjZWoaGhCg0NVVhYmMLCwmSxWGSxWNzfw8LCVKNGDY9+kydPVuPGjT2OdevWTUOGDMn0Ws2aNdOQIUPcge3atWtq3769Lly4kGWNkyZN8qglLCxMJpNJwcHBHsfGjh3r7tO3b1/lzZvX4+Pv7y9/f/90x/PkyaO5c+dmev0FCxaoatWqcjgcWdYpSdOnT9e77757SyG3cOHC8vb2ltlszvLz2muvZXtMAAAAALgVZqMuHBoaquTkZFmtVlksFkk3Ap0kjRs3TpJ0+vRpFSlSxKOfn5+f/P393d+tVqvWrVunzz//PNNrTZ8+Xd26ddMjjzyibdu2afLkyTKZTOnCbUpKil566SV98sknkqQ+ffqoR48e7vokKSwsTGvXrlWVKlUkSSdOnFDx4sXd5ydNmqRJkyZ5jPvUU0+pTZs2evnll7Pxy/wfb29vxcbGytvbO8t2MTEx+vDDD2UymVShQoV0510ul+Lj4/XZZ5+pS5cu7uPBwcGaOXOmmjZt6j52+fJl+fn5KTg42H3sZrPFAAAAAHC7DAulkvT5559r+/btmjlzZobnO3bsqLp16+qjjz7yWIJrMpnkcrnkcrn0008/yeFwqFGjRu7zcXFxqly5slatWiVJKlq0qJYtW6Zt27bp+vXrmjBhgvbu3esRJjPicrlUp04dffzxx2rSpEm680ePHlX58uW1a9culS1bNsMxUlNTtWHDBv33v/+96e/xd15eXgoLC7tpu9dff12lS5fWhg0bZDKZsj3+X8N9mhdffFFt2rTxmB01mw39zwQAAADAPczQtNGsWTO9/fbb6tChg1q3bu1xbtOmTfrtt9/01VdfSZJ+//131a5dW15eXnK5XPL29tbUqVP19ddfa+rUqXr22WfdfSdPnqylS5dKknbs2KELFy7oiSee0MMPP6ymTZsqPDzcI8Sm6dq1q4YOHer+bjKZ1LJlS73yyivav3+/goKCPNp//PHHevTRR9MF0tdee819fbvdrri4OFWrVi3d9YYOHaquXbtKkq5cuaJr164pKChIXl5e7mNms9m9zNjlcik5OVlJSUkqXry4AgMD9emnn2r16tX68ccfVblyZfn7+3sE07RZ0o8++kht2rTJ4m/jBl9fXwUEBNy0HQAAAADcCYaG0rJly+qtt95Snz59PJaQOhwO9enTR71791apUqUkSY888ohSUlI0Z84cff/991q4cKEWLVqkixcvymKxKDIyUlFRUZJuzE6mzQLGxMSoZ8+eqlevnj7//HO9+eabOnXqlLy8vNSiRQvNmzdPQ4cO1ezZs7V///50NQ4ZMkTffPONhg0bpk8//dR9fPv27ZoxY4Z+//33dH1iY2M1aNAg9ezZUw0bNtTy5ctVoUIFTZs2TUePHtWoUaPUpUsXJScnu/vMnz9fAwcOlI+Pj/tYamqqrFarypQp4z5mt9tltVq1ceNG1axZU23atFHz5s1VuXJl7dmz57b+HqxWq8xmc4bLhG02m0dNGfX96+ZKcXFxt1UDAAAAgPuT4a+E6d+/v0JDQ3Xt2jX3sc2bN+vChQv64IMP3Me8vb09nu00m83Knz+/pkyZorp162r+/PnuTYP+GkqbNGmiPXv2yOVyKTo6Wu3atVNcXJyCg4NVokQJbdiwQQkJCR59/srX11eRkZG6fv26x/HJkyerW7duevjhh9P1+XuIa9u2rSpUqKD333/f4/hfl8W+9tprSkxMVExMjPszcOBABQQE6MKFC+5jCQkJstlsqlmzpiSpVKlS6tSpkwoVKqSIiIgMP3nz5lVCQkKmfweVK1eW2WyWyWTSkiVL9PLLL8tkMslkMsnX1zfTfpIUFRXl3rAqNDRU4eHhWbYHAAAAgL8yZKbU5XIpNTVVvr6+yp07t3bs2OER5OrVq6c9e/YoNDRUDodDNptNFosl3fOS9erVc//5hx9+UGJion799dd0ATNXrlyaMmWKQkJC1LBhQ+3cuVO+vr768MMPJUkVKlRQQkKC7Ha7fvvtNy1ZskQ2m00mk0lms1kvv/yyunXr5nHtiRMnul+VYrPZ5HQ6PULzX/34448eM6XZ9dNPPyk4OFgzZszQq6++mmm7rGZIY2NjFRYWluGSXIfDIYvFol9++UUmk0leXl7q1KmTWrVqpWeffdb995SVyMhI9e/f3/09Li6OYAoAAAAg2wwJpUeOHFGVKlXShbi05azTp093H7Pb7bLZbDp9+rQKFCjgPu5yuZSUlKTAwEBJ0v79+zVw4ECNHTs2w1nPt99+W7Vr19ayZcvUu3dvderUSbly5VJsbKwaNGigUaNGKVeuXOrcubMk6YMPPtC4cePSzXrGxsaqXr16HktdU1NT1bhxY/3888///Mf5/1avXq2LFy9q4cKFat26tdq3b6+8efPe8jhpr5PJaAOkq1evKiQkxGOHY4vFotDQUC1atEjnzp3TsGHDshw/7TU+AAAAAHA7DFm++9BDDyk5OdljqWpMTIx69eqlXr16eRxLSEiQ1WpVgQIFFB8fr0OHDumPP/5QRESEli9froMHD+qxxx7ThAkTtH79ej3zzDNKTU2Vn5+f+3oJCQmaO3eu6tevL39/f12/fl0hISFKSkrSW2+9JenG62CCgoLc/UaOHJluOW1MTIxCQ0O1YcMGj2NJSUlZBtLMlu9mJiEhQa+99pqGDx+uGjVqqH379urZs6fHDsTZFRwcLJPJpFOnTikxMdF9PD4+XhcvXlRERESG/R566CF99NFHt/2cKgAAAABkh+HPlGbXt99+q9DQUK1du1Zvv/22Nm7cqPbt22vs2LGqUqWK1q9frxIlSki6sfnOX2dKZ82apZo1a7pfAXP9+nUFBwfr0Ucf1ZNPPimr1aqUlBT5+vrmyDs5f/zxR+3bt++ms47SjZnYJ598UpUqVdIrr7wiSfrkk0906NAh9e7dW06n85au7ePjo+LFi2vbtm0aMGCA5syZI0n67bffVKxYMYWGhmbYr2HDhurRo4dsNtstXQ8AAAAAbsX/1Aso05aaZqRVq1bavXu3Klas6HF88uTJ7leobNiwQbGxsVq5cqV69uwp6cby39GjR3vsnBsQEKDmzZu7v//3v//V5cuXFRAQIB8fnyxfneJwOLKsU5KcTqciIyM1YsQIXb58WU2bNpXZbFZSUpLsdru++eYbXb9+3b1ZUZqff/5Zb7zxhooVK6YZM2a4jwcGBmrRokWqX7++jh07pq+//lqFCxfOsoa/euaZZzRs2DCdO3fO/eqcb775Ri1atFBcXJx7U6O0+7NarUpISNDHH38sp9Opa9euKXfu3Nm+HgAAAABk1//UTGlycrJSUlIyPBcSEpIukEpyB1LpRtDq0aOHSpcu7Q5fP/zwg0wmk5566il3u6VLl+rkyZPuz8SJE1WwYEFt2rTppu/yzKrGNFarVVFRUYqOjpbVatWFCxcUHR2ta9euKS4uTtHR0Xr66afdmwg5HA41adJErVu3Vps2bbRy5Ur3s7Jp0nYKvnDhgh566CEdOXIkyxr+6u2331ZoaKhatGihRo0a6dChQ/r222/1wgsvqF27dgoJCVFwcLCCg4O1fPly9ezZ0/09NDRU9evXz/a1AAAAAOBWmFy386DiXebs2bMem/n8Xf/+/ZU7d24NGTLkX6zK0/r162WxWNLNnv6d1WrV8uXLPUL2rbp69ar+85//aNCgQbJarfLx8fEI93/ldDrTPaOblbi4OIWGhio2NlYhISG3XSNwN5u2NNboEnJct5YZL/0HAABIk91scF+EUvx7CKUAoRQAAEDKfjb4n1q+CwAAAAC4vxBKAQAAAACGIZQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYhlAIAAAAADGM2ugAAuNd0axlqdAkAAAB3DWZKAQAAAACGIZQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYxG10AANxrFm5JMLqEm2pTO8joEgAAACQxUwoAAAAAMBChFAAAAABgGEIpAAAAAMAwhFIAAAAAgGEIpQAAAAAAwxBKAQAAAACGIZQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYhlN5D9u3bpzVr1tzRMe12u+x2+x0dEwAAAADSEErvIR988IEmTpyYrbZHjhyRyWSS2WzO8uPj46O5c+fmcOUAAAAA7ldmowvAnbFp0yYtXLhQBQsWVERERLrzDodDsbGx2rVrlx588EEFBwdLklJSUmQ2/99/BtHR0cqfP798fX0lSS6XS06n81+5BwAAAAD3H0LpPSAxMVGvvPKK3n77bX300UfZ6uPv75/h8QcffFC7du1SuXLlJEkmk0ne3t53rFYAAAAA+CtC6V3O4XDohRdeUIECBVSlShVVqVJFfn5+6drExsZq/fr1KliwYJbj+fr6KiAgICdLBgAAAAA3QuldztvbW3379lWlSpWUJ08ePfPMM7fU3+VyKSUlRRaLRSaTKd15m80mHx+fTPtbrVZZrVb397i4uFu6PgAAAID7G6H0HlC6dGlFREQoNDTU4/nQNA6HQ6VLl9Yvv/yS7tzFixcVHh7ucax48eLuPzdo0EBr167N9NpRUVEaNmzY7RcPAAAA4L5GKL0HFClSRPHx8ZmeX7RokcaMGeNxzOFwyNvbWwULFtTJkyfl6+srk8mkEiVKaO3atQoPD5fL5brptSMjI9W/f3/397i4uHQhFwAAAAAyQyi9DzgcjnRLc69evarg4GCZzWYVK1bMfdxkMilfvnx666231KVLFzVt2jTLsS0WiywWS47UDQAAAODex3tK7wMhISE6deqUpBs79UrSn3/+meGrY9KULFlS3bp1U1JS0r9RIgAAAID7FKH0PlC6dGmdPn1aly5dUtOmTXXgwAFt3rxZDz/8cKZ9BgwYoDp16rhDLAAAAADkBELpfeCBBx5QzZo11aFDB+3fv19FihTRnDlz1KJFC127dk3x8fFKSEhQQkKCJCkpKUkmk0lTpkyRr69vls+rAgAAAMA/QSi9T0yYMEFnzpzRqFGjNH/+fF27dk0tW7ZU/vz5FRISouDgYAUHBysxMVHly5d3fw8LC1NkZKTR5QMAAAC4R7HR0X2iWrVqOnHihCTp4MGD+uyzzxQYGKiEhAT5+vrKyyvjf59wOBxyOBz/ZqkAAAAA7iOE0vtQ2bJlVbZsWUmSn59flm29vb3l7e39b5QFAAAA4D7E8l0AAAAAgGEIpQAAAAAAwxBKAQAAAACGIZQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYxG10AANxr2tQOMroEAACAuwYzpQAAAAAAwxBKAQAAAACGIZQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYxG10AANxr1u+Ny/J8/Yoh/1IlAAAA//uYKQUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYhlAIAAAAADEMoBQAAAAAYhlAKAAAAADAMoRQAAAAAYBhCKQAAAADAMIRSAAAAAIBhCKUAAAAAAMMQSnNIUlKSbDZbpudTU1OVkpKS7vjmzZu1ffv2nCzNfX0AAAAAMBqhNIc8+uij8vX1lclkyvBjsVjUsWPHdP3efPNNzZkz55au1apVK02bNk2SZLVa5XQ6JUkvvPCCxo0bJ0lyOBzukHz58mWVKlVKu3fvliTNmTNHfn5+ioiI8Pj4+flp0qRJt/sTAAAAAMBNmY0u4F61bt06+fj4yMvrRu7v16+f8uTJoyFDhki6ER5NJpNHn9mzZ2v37t26ePGiFixYkG5Mm82muLg4xcTEyNvb2308NDRUFotFktS8eXNdvnxZXl5eOnv2rNatW6dp06bJbrerbdu2ioqKUr58+dSqVSs9//zz2rFjhywWi6pXr66NGzd6XK9u3brucQEAAAAgJxBKc0hoaKjHd29vb5nNZvn5+UmS+3/TnD9/Xv369dOECRPUo0ePbF3D6XRq+vTpMpvNOnz4sL7++mu99dZb2rJli3x9fbVw4UIVLVpU1apVU0pKinr27OnuO3r0aD300EP68ccfFRAQkOk10kI1AAAAAOQEQmkOsNlsMpvN6WZC/87hcMhut8tms6lt27Zq0qSJbDabHn744XQzlHa7XQkJCTp06JD72JkzZzRixAjVr19f/v7+evfdd7V06VI1adJEgYGB2rZtm8qXL6/HH39cSUlJCggIkMvlkslkkr+/vzZt2qTw8HD98MMPmdbocrmyvAer1Sqr1er+HhcXl2V7AAAAAPgrQmkOaNGihX755ZcMzw0bNszje+XKlbVz504NHTpUTZo0kZ+fn3r37p2t6+zZs0c1atSQJBUrVky//PKLWrduLV9fX/cM56lTp7RkyRI5nU7Z7XZt2rRJ+fLlkySFh4dLkvsZ1Iw4HI4sa4iKikp3TwAAAACQXYTSHLBgwQKZTCZ3MHS5XCpVqpT8/Py0e/du9/OgLpdLDodDJpNJuXLlUsGCBRUSEpLhklmbzaZmzZpp+vTp7mN79+5V7dq1tXPnTklSuXLltHLlSv33v/9N1z8gIEDvvvuux7HU1FSdPXtWZrNZ0dHR8vb2VuHChRUXFyeTyaSwsDD5+vpmea+RkZHq37+/+3tcXJw77AIAAADAzRBKc8DfnydduXKlgoODFRQUpCVLluiFF15I1+fRRx9VTExMpmOOHz9eO3bs8DiWFjK7dOniPnb27FktWrRIX3zxhfvYtWvX9Prrr6cLpV9++aUWLFigDRs26KmnnpKfn5+2bdumcePGyc/PTx988MFN79VisbAZEgAAAIDbRijNYQ6HQ0OHDtVrr72mBx98UL1791azZs3cS2hvZZybPaMq3dhA6fjx43r99dfdx+x2e7oZz9jYWI0cOVJz586VdGOjJUnKmzfvLdUFAAAAAP8EW6vmsAEDBig2Nlbdu3fXU089pUceeUTPPfecEhMTb2mckJAQnTp1SpKy7GsymfTwww9r37597s/atWvTtfvwww9Vo0YNNWnSRJL0/fffq0aNGjKb+XcKAAAAAP8eQmkOuXr1qp555hnNmDFDCxYsUFBQkCRp+vTpun79umrWrKn9+/dne7yHHnpIe/bsUXx8vGrWrKlLly5l2nbnzp2qUKGC+9OoUSOP83v27NGXX36pTz/9VJJ0+fJlffbZZ3r11VfTjbVlyxZNmTIl23UCAAAAwK0glN5h165d06effqry5cvr8OHD2r59u8qXL+8+Hxoaqg0bNqhatWqqWLGiGjdurKNHj9503Nq1aysoKEhPPPGEkpKSPJbZJicnu3fJdTqd6WZK169f7zHWL7/8oldeeUXlypXTwYMH1aRJE1WoUEEvvviiJMlsNuvy5cvutvPmzfvHvwsAAAAAZIRQeoc5HA4tWrRIgwcP1rZt21SiRIl0bQIDAzVjxgxt2rRJjRs3VsmSJW86rtls1uTJk3Xy5EmNGzfOY4fetHePSjcC6t+9+OKLatWqlft7v379NGHCBM2bN0/ly5dX1apV9f3337vHePzxx7V8+XLly5dP06ZN0zvvvHPLvwMAAAAAZIfJ5XK5jC4CxrBardq9e7f7Xad3QlxcnEJDQxUbG6uQkJA7Ni5wN1m/Ny7L8/Ur8n8bAADg3pfdbMBM6X3MYrHc0UAKAAAAALeKUAoAAAAAMAyhFAAAAABgGEIpAAAAAMAwhFIAAAAAgGEIpQAAAAAAwxBKAQAAAACGIZQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDBmowsAgHtN/YohRpcAAABw12CmFAAAAABgGEIpAAAAAMAwhFIAAAAAgGEIpQAAAAAAwxBKAQAAAACGIZQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikA3EG7/7xkdAkAAAB3FUIpAAAAAMAwhFIAAAAAgGEIpQAAAAAAwxBKAQAAAACGIZQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYhlAIAAAAADEMoBQAAAAAYhlCaQ1JTU+V0OjM973A4lJqa+o+vc/Xq1X88RpoVK1bo2LFjd2w8AAAAALgZs9EF3Kveeustbdu2Td7e3pKkM2fOyGKxKH/+/JIkm82mRx99VGPHjs2w/6FDh1S2bFklJyfLz8/PfdzpdMpkMslkMkmSOnfurNy5c2vSpElavHixXnjhhQzHmzVrljp27JhlzVOmTFHevHk1derUW75fAAAAALgdhNIc8vnnn3t8HzFihIoWLaouXbpkq39aEC1YsKD7mMvlUlJSkpYvX64mTZpIkn788Ue99dZbmjJliooVK6b27dtr+vTpHmP17Nkz3fiffvqpRo8eLX9/f/cxq9Uqm82mlStXSrox2xsXF6eEhIRs1QwAAAAAt4pQmgPq1aun6OhoBQYGuo9dvnxZPj4+Gj16tPvYtWvX1LlzZ0VFRWU61pUrV2Q2Z/zXZLfbZbPZNH78eEk3AurixYtVtGhRj3ZJSUl6/PHHPY45nU49++yz+vzzz5Wamqpvv/1WHTt2lNlsltVq1X/+8x+9+uqr8vHxueX7BwAAAIDsIpTmgAoVKmjYsGHuGceGDRtq+/btKlq0qJKSknT69GkVLlxYRYsW1aVLl277OgsWLNCkSZO0bNky+fv7y2QyqV27dpo7d65Hu8xmZ00mk5KTk+VwOPTyyy/rpZdeknTjedc+ffqoe/fuSklJ8Vg+DAAAAAB3EqE0B0yaNEnx8fHq37+/JKlw4cKSboTATZs2affu3apatareeOMNj34DBgzQF198oYCAAElSaGio8ubN6z4fGxurvXv3qkKFCpKk1q1ba+rUqXr11Vc1c+ZMORyObNfocrnk7e2tJk2aKC4uTpJUtWpV9zlJeuSRR2S1WrVt2zaFhIRkOI7VapXVanV/TxsLAAAAALKDUJoD4uPjtWnTJo0YMUKSlDt3bj366KPq1q2batasqdmzZ2fYz8fHRy+88EK6Z0LTBAUFecxa+vv7a+HChRo0aJDsdrtMJpNWr17tEWTTtGrVyuN7SkqKfHx8tHnzZiUlJSkwMFDff/+9pBvPkpYvX167du2Sl1fWGzRHRUVp2LBhWbYBAAAAgMwQSnPAsWPH1KdPHz3xxBOKi4vTn3/+qS+//FIzZsxQ0aJFNXPmTJ06dUqPP/64Zs2a5e53swAoyb3rbprg4GCNHDlSZrNZ7dq1U7t27bJVY2JiovLly+ces3fv3ho3bpz7fO/evWW32+Xr65vlOJGRke4ZYenGTGl4eHi2agAAAAAAQmkOCQwMVEREhK5du6YjR45o0KBBGj16tBYuXKinn35as2bN0hdffOHRx2q1au7cufr5558zHDMxMVEpKSnpjj300EM6c+aM8ufPL6fTKavVKh8fHzmdTjkcDvn7+ys1NVVJSUnufhcuXFDlypUVGRmp+fPny+l0KjQ01H0+ISFB/v7++vTTT7O8T4vFIovFcqs/DwAAAABIkm4+NYdbVqJECY0aNUp79uzR5cuX9dlnnykqKkqBgYFKSkrSjz/+KLPZrOHDh+vLL7+UdON50ccff1xJSUm6cuWKLl68qCtXrujChQs6deqUrly5IpfL5X7PaZqlS5eqevXq8vX1lY+Pj/bs2aNOnTrp559/1pdffqlOnTrpwoUL6YLj0aNHVbRoUUVFRenw4cPKly+fRo0apV27dmny5Mmy2+0aNGjQv/abAQAAALg/EUpzwOnTpzVnzhyVLl1aCxYs0IgRI/T9999r+PDhqlChgpo0aaK6devq5MmT7g2EZsyYoV69esnlcqlLly7u50oHDhyowYMHS5IWLlyounXremwsNGPGDLVv315S1st//3ouLi5Ou3btUrVq1SRJZrNZM2fO1Kuvvqrx48frhRde0Pz585UnT547+rsAAAAAwN8RSnOAzWZTYmKiJKlz587q1auX3nrrLVWtWlX16tVT06ZNVbFiRVmtVnXs2FGpqan64osvNGjQIHl7e6t+/fqaMGGCJKlHjx6aMmWKTpw4odatWyswMFAfffSRJOnw4cNauXKlWrdufdOa/vos6vTp01W3bl0FBwdLurFs+MyZMypWrJj69eun/Pnz6/jx4zp8+DC76QIAAADIUTxTmgMmT56sixcv6tSpUypXrpzsdrv8/f1lt9s1duxYBQcHKyUlRQ8++KAk6fjx44qIiFDnzp0lSW3bttX69etltVpVtmxZvfnmm4qJiVHx4sX10Ucf6YcffpAk7d69W4899phOnz6tUqVKyeVyqX79+rJarVq+fLn7mdLVq1fLZrMpNDRUly5dUkJCgvr06aPvvvtOw4cPV3x8vGrVqqWBAwfq559/1rJly7R06VK99957Onr0qHr37p3u+VcAAAAAuBNMrrSXUuKuZLPZ5OPjc1t9rVarrl696n6PakaSkpLkcrkUGBiYrTHj4uIUGhqq2NjYTN9tCtzLdv95SZVL5b95QwAAgHtcdrMBM6V3udsNpNKNnXOzCqSSFBAQcNvjAwAAAMDN8EwpAAAAAMAwhFIAAAAAgGEIpQAAAAAAwxBKAQAAAACGIZQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoB4A6qXCq/0SUAAADcVQilAAAAAADDEEoBAAAAAIYhlAIAAAAADEMoBQAAAAAYhlAKAAAAADAMoRQAAAAAYBhCKQAAAADAMIRSAAAAAIBhCKUAAAAAAMOYjS4AAIx2/NixOzregyVK3NHxAAAA7mXMlAIAAAAADEMoBQAAAAAYhlAKAAAAADAMoRQAAAAAYBhCKQAAAADAMIRSAAAAAIBhCKUAAAAAAMMQSgEAAAAAhrmjodTlcunEiRN3ckgAAAAAwD0sW6E0Li5OjzzyiPu70+nUq6++mq5dSkqKypUrd+eqAwAAAADc07IVSn18fHTw4MH/6+TlpVmzZqVrZ7FY5OPjc+eqAwAAAADc07IVSr28vOTl5dnUz88vw3Zms/nOVAYAAAAAuOdlO0FarVa999577u/Jycke3wEAAAAAuFXZDqUul0tnzpxxf3/uuec8vgMAAAAAcKuyHUr9/Pz09ddf37Rd7ty5/1FBuD0ul0t2u112u11JSUmKjY3VtWvXdObMGR0/flx79uzR6NGjlS9fPqNLBQAAAAC3bIVSp9Mpp9PpcaxBgwYymUzpnjWFMV588UVt3LhRISEhCgkJUXR0tEqXLq0aNWooT548atKkiZKSkjLsW7JkSU2YMEGPP/64JKlgwYKaNWuWmjVr9m/eAgAAAID7ULZCqc1mU8mSJT2Ode/eXb6+vvL29nYfczgc6tGjx52tENny7bffenzv27evqlevro4dO2bYtnv37u6dkuPj49WhQwf3JlVxcXFq27atzGazHA6HcufOrVOnTuX8TQAAAAC475hcLpfrZo02btyounXrSpJmzZql2rVrpwup0o3Nj/LkyZPpjBxyRoUKFZScnCyLxSIvLy8lJibq2rVrCgwMlJ+fnwICAuRyuXTt2jU1bNhQ06ZNk9VqVa5cuWQymVSmTBlNmjRJjRo1kiQVKlRI33zzjZo0aSKHwyGbzZbhbssZiYuLU2hoqGJjYxUSEpKTtw3cMcePHbuj4z1YosQdHQ8AAOBulN1scNOZ0j/++EMNGjTQoEGDNGLECP3yyy8aOnSohg4dKpPJ5NHWbrcrKirqn1ePW7Jv3z73nz/99FNNmDBBLVu2VJMmTTRu3Dh1795db775pkefwMBA95+tVqtsNpv7u81mk9VqlSR5e3t7zIYDAAAAwJ1001BatWpVrVu3Tl27dtWuXbs0d+5cbdmyRS+99JJ8fHz0yCOPKG2yNTU1VampqekCEHLe9evX1b9/f23atEnr1q3T6NGj5e3trSVLlqh+/fpasWKF+vXrp4YNG8rHx0cpKSkym80ym82aNWuWypQp4x5rwYIFKl++fLaua7Va3QFWuvGvIQAAAACQXdnapahu3bratm2bkpOTVadOHTVs2FDr16+XyWRS4cKFNW/ePP34449asmSJVq1aldM14y/Onz+vN954Q8WKFZPVatW2bdsUERGhOnXqqFSpUipWrJi2bt2qoKAgNW/eXPny5dPVq1dVokQJ5cqVS2FhYWrVqpUKFCig4OBghYWF6amnnlLJkiUVEhIik8mk2NjYTK8fFRWl0NBQ9yc8PPxfvHsAAAAAd7tsvxImJCREP/30kzZs2KAffvhBzz77rNauXatff/1Vvr6+OVkjspAnTx4dPXpUc+bM0bVr1/Tggw8qKCjIvbQ6ISFBFStW1K+//qrdu3dr3bp1ypMnj86ePesxTsmSJfXNN9+oVq1a7mN79uxR1apVs1z/HRkZqf79+7u/x8XFEUwBAAAAZFu2NjqSpGHDhmnw4MEym80qWLCgLly4ILvdrvz58+vatWs5XSdu0bFjx9StWzdVq1ZNb7zxhh544IEs22cUSrds2aInn3xSV65cyfZ12egIdyM2OgIAALjz7thGR2m+/vprvf/++5IkPz8/ffLJJ3K5XEpNTdUnn3wiSTKZTGrSpImqVq36D8vHrWjWrJkOHDjgfsWLJKWkpOj69es6ceKEvv/+e0k33jebP39+bd++PVvjXr16Vfnz58+RmgEAAABAuoVQ6uX1f4+fulwupaSkuDc4Sk5OlnRjqeiLL76ogwcP3uEykZWMnuPduHGj+vbtm+0A+le//vqrhgwZor1796pv3753oEIAAAAAyFi2QmndunV14cIFNW7c2P2+y/fee0+SNHHiRPcMqiTNmDFDLpcr3eti8O+5du2ajh07dkvP+tpsNjmdTklSrVq11KNHDz388MOqVKlSTpUJAAAAANkLpR9++KE6deqkUaNGyel0qkOHDunabNiwQblz59aZM2cIpAZ77bXXtHnzZg0aNCjbfWJjY90z3v7+/urcuXNOlQcAAAAAbtkKpY0bN5aPj4+qVasmb29vj6W8DodD69evV+fOnfX1119n+/2WyDlz58695T4xMTF3vhAAAAAAuIlsP1PapUsXOZ1OeXt7KyUlRTabTS6XSyVLltSjjz6qQ4cOyWKx5GStAAAAAIB7TLZD6QcffCBJevHFF3X+/Hl5e3trxIgRGjlypMxms8zmbA8FAAAAAICkbIbShQsXKleuXJKkH374QT179pTL5VJ8fLyioqLcG+q4XC5ZrVY1b9485yoGAAAAANwzshVKP/vsMwUGBkq68QzpRx995D73xx9/6N1331VwcLCcTqdSUlIIpQAAAACAbMlWKF2/fr02bdqkgIAANWnSRMuWLXOfGzBggEqUKKHXXnstx4oEAAAAANybvG7e5IZffvlFHTt2VEJCgho3bqxp06ZJktq3b6/vvvsuxwoEAAAAANy7TC6Xy3UrHVJSUrRt2zZZLBbVqFFDdrtdly9fVqFChXKqRtxF4uLiFBoaqtjYWIWEhBhdDpAtx48du6PjPViixB0dDwAA4G6U3Wxwy1vm+vn5qV69ev83gNlMIAUAAAAA3JZsL98FAAAAAOBOI5QCAAAAAAxDKAUAAAAAGIZQCgAAAAAwzC1vdAQA9xp2ywUAADAOM6UAAAAAAMMQSgEAAAAAhiGUAgAAAAAMQygFAAAAABiGUAoAAAAAMAyhFAAAAABgGEIpAAAAAMAwhFIAAAAAgGEIpQAAAAAAwxBKAQAAAACGMRtdAAD8Gy7s3/avXatg+Uf+tWsBAADc7ZgpBQAAAAAYhlAKAAAAADAMoRQAAAAAYBhCKQAAAADAMIRSAAAAAIBhCKUAAAAAAMMQSgEAAAAAhiGUAgAAAAAMQygFAAAAABiGUAoAAAAAMAyhFAAAAABgGEIpAAAAAMAwhFIAAAAAgGEIpf+iFStWaNmyZUaX4fbCCy9o48aNkqTU1FTZbDa5XC6DqwIAAABwPyGU5oAiRYooJCREQUFBKliwoBwOh1wul/r376+jR49me5wqVaro66+//sf1NGjQQBMmTEh3vFy5curYsaNiYmL02muvKTQ0VGFhYR4fk8mkffv2/eMaAAAAACAjhNIc4Ovrqz179mj06NF6+umn5e3trblz5+rkyZOaMmWKKlSooCJFiqhIkSKKj4/PdJygoCCZzeZMz2/YsEFeXl4eAXLv3r0Z1uPr65vu+MCBA1WjRg1dv35d06ZNU1JSkmJjYxUTE+P+BAYGys/P7/Z+CAAAAAC4icwTD25bWgBcuXKl+vTpo3Pnzql///4qUKCAPvnkEzVu3Fh169ZVZGSkgoOD5XK55HQ65e3t7TGO2WzW9evX3d+PHDmiqVOnatSoUTKbzfLx8VHhwoUVHR0t6UaIjY6O1g8//CCz2ewe78yZM1qxYoXOnz+vfv366fLlyypfvryuXbum+fPn3/R+sgrGAAAAAPBPMFOaA0wmk5KSkrRjxw7Vq1dPmzZt0sCBA7V69Wr16NFDjz76qB5//HG1b99ekrR//353iDSbzTKbzVqwYIEkadWqVZJuBMsnnnhCiYmJ7uc+MwqLoaGhKleunMqXL+/+35CQEBUtWlTly5eXj4+PgoKCZLVa5e/vrwMHDigiIiLd59dff/2Xfi0AAAAA9zNCaQ7ZvHmzwsPDZTab1bx5c1WuXFkTJ06Uw+HQ9evXtWnTJn311VfaunWrHnjgAV29elWpqamKjY2Vj4+PatWqJenG5kgTJ05U/fr11b9/f02aNEk+Pj6SJC8vL507d869fDcxMVGFChXSk08+qTJlyqh169Z68sknlT9/fj388MPq0KGD/P393TOoJpPJXe/Jkyfdn4IFC2Z7wyOr1aq4uDiPDwAAAABkF6E0hzRp0kSBgYH6/vvv1apVK3344Yd64IEH9Ouvv8pkMum///2vvLy8NG/ePKWmpip37tzy9vbW8uXLVaZMGRUpUkTSjR1yX3/9dX3xxRfq1auXxzW8vLxUuHBhj+c/TSaTZs+erdatW8vhcNy0Ti+vjP8TyOz430VFRSk0NNT9CQ8Pz1Y/AAAAAJAIpTnGZDLpk08+0bBhw7RhwwYdP35cY8eOVfPmzXXmzBk1atRI77//vipXrqy8efO6+40bN05du3Z1f2/VqpXq1KmjkydPpruG0+nM8NqdO3dWamqqvvnmm2zVGh0d7bF0d+fOndm+z8jISMXGxro/Z86cyXZfAAAAAGAHmxySkJCgAwcO6PTp0zp+/Li2bNmiIkWKKDY2VmXKlNHJkydlt9s9lskuWLBAx44d8wilkvSf//xHtWvXVrly5dS4cWP38YxmQl0ul8xms/r27avU1NRs1Vq0aFGP0Ju2dDg7LBaLLBZLttsDAAAAwF8xU5pDVq1apRdeeEGvvPKK3nvvPf3www8ez3BK8tghd//+/XrllVc0ceJE+fv7e7QrW7asZsyYodatW2vSpEnuGVKHw6Hz588rb968yps3rxITE2W32yVJffv2Vffu3W9aZ2bPjmY2CwsAAAAAdxKhNAc4nU61aNFCBw4c0NixYxUbG6vRo0crIiJClSpV0uXLlxUREaHw8HDly5dP69atU926ddW9e3e1bds2wzGfeuopffvttxo8eLAeffRRJSQkyG63q1ChQrpy5YquXLmiwMBA2Wy2W6o1LZT+dfnuhQsX0gVoAAAAAMgJLN/NAXa7XX5+foqIiJAk/fTTT+5zMTEx7uW7aVJTUzVu3Di99NJLHuPEx8d7bDjUunVr7d69W5cvX1ZQUJC8vb2VP39+9/k9e/aoaNGi6eqJi4vzCJlJSUnuOsuVK5fh86pp/axWqwICArJ97wAAAABwK5gpzQFWqzXTc0lJSenO+/r6qnPnzu7gePz4ceXNm1cnTpxQ5cqVPdqGh4eratWqkqTatWvrjz/+cJ978MEH5evr6/6+a9cu5c2bV3/88YeqVavmPp6QkCBJSklJybTOJUuWqGDBgnrsscc8gi8AAAAA3EkmV3ZfSIl/1caNG1W1atV/PEu5fft2lStX7pbHsVqtSkxMVO7cuW+pX1xcnEJDQxUbG6uQkJBb6gvkpAv7t/1r1ypY/pF/7VoAAAD/q7KbDVi++z+qbt26d2Sc6tWr31Y/dtUFAAAA8G9g+S4AAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYhlAIAAAAADEMoBQAAAAAYhlAKAAAAADAMoRQAAAAAYBhCKQAAAADAMIRSAAAAAIBhCKUAAAAAAMMQSgEAAAAAhjEbXQAA/BsKln/E6BIAAACQAWZKAQAAAACGIZQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYxG10AAPzV1d+WGl3CP5anVkujSwAAALhrMFMKAAAAADAMoRQAAAAAYBhCKQAAAADAMIRSAAAAAIBhCKUAAAAAAMMQSgEAAAAAhiGUAgAAAAAMQygFAAAAABiGUAoAAAAAMAyhFAAAAABgGEIpAAAAAMAwhFIAAAAAgGEIpQAAAAAAwxBKAQAAAACGIZT+j7Jarfr111+VkJCQrfYrV67UoUOHsj3+1atXb7c0AAAAALhjCKU5ZPPmzQoODlatWrXSfWrWrCmTyaSkpCRJktPpVGpqqkd/p9OpJ598UqtWrUp33Gq1ehyzWq16+eWX07XNjMPhUNOmTfXOO+/IbrcrKipKJpMpw8/GjRv/wa8AAAAAAFkzG13Avcrf31/58uVTly5d0p2z2+3aunWrAgICJElbtmxRu3bt5O/v79Eub9686tevn/r16+c+5nQ6Vb58eS1btsx9bPTo0bp69apGjx6tMWPGpLue1WpVSEiIDh8+LEny9vbW+vXr1bVrVy1evFi+vr7q37+/hg0b5tGvRYsWt33/AAAAAJAdhNIcZDKZZDZn/hO7XC6ZTCbVqVNHFy9evK1r7N27VyNHjtSiRYv02GOPZatPYmKiAgICNH/+fEnS2LFjNXHiRH311Vce7bK7dBgAAAAAbhehNIfYbDZZrVZNmzYt3TmXy6U8efIoJSXFY3b09OnTKl68uMqWLZuuz8WLF9W3b18NHjzYfezChQtq166d3nzzTa1fv16RkZHy9fX16JeamqqwsDCtWbPGfWzChAnatWuXZs+eLS8vL5lMJvXt21ejRo3y6NuwYcPbvX0AAAAAyBZC6R3mcrmUkpKiGjVqKDo6Osu2DodDVqtVFotFkuTn5ydfX1+NGDEiXdtZs2bJx8fH41jevHk1evRotW7dWiaTSSNHjsxWjV27dlXjxo31/vvva/jw4XI4HNm8u/SsVqvHM65xcXG3PRYAAACA+w+h9A67cuWKateurStXrig0NFTBwcGKj49XfHy8Chcu7NHWbrerTJkyWrhwoSQpICBAY8eO1YULF9KN26xZM9WoUcPjmNlsVlJSknLnzq2QkBCZTKZ0/VJTU9WtWzd9+OGH7mN58+bVsmXLNHbsWPc406ZNy3BW9+9B+O+ioqLSPYsKAAAAANllcrlcLqOLuBd16dJFRYsWVfXq1fX777/r8uXLatu2raxWq7y8bmx6HBERoSpVqkiSWrVqpZMnT2Zr7N69e6tXr17ZatuvXz8FBwd7hFLpxoxuXFycQkNDs31PGclopjQ8PFyxsbEKCQn5R2Pj/nT1t6VGl/CP5anV0ugSAAAADJeWN26WDZgpzUGJiYmKiYlRYmKiJGnbtm367bff9Nxzz2nmzJnq2LGjO5SePn1a06ZNU61atbIc8/XXX1d8fHy2a3A4HBnOoB45ckTt27fXxo0blStXLoWGhioxMVH+/v5KTU2VyWSSj4+PihYtqgMHDmQ6vsVicS8/BgAAAIBbxXtKc9DKlSs1bdo0rV69WpI0cOBAXb9+XS+++KIuXbqktm3butumzZ5KUuHChVWtWjVVr15d1atX1wMPPODxWpi/tr2ZkJAQnTp1SpLc4ViSFi9erAYNGriX58bExKhBgwbas2ePBg0apMGDB2vr1q0ETgAAAAA5ilCag0aMGKGZM2eqU6dO2rZtm7Zt26Y2bdqoUaNGatGihXLlypVhv+DgYG3atEnbt2/X9u3b1b9//5s+25mZhx56SNu3b9fZs2dVp04dpaamSpJmzpyp9u3b3zTg3koABgAAAIBbxfLdHPDKK69o5cqVOnDggKpXr66UlBQVL15cjzzyiA4ePKjk5GRt3rxZ3333nVq2bKnAwEA5nU53/+LFi6tu3boeYz7//POSJKfTmeFy3My0bNlSvXr10vPPP6/Q0FD5+vpq9erVOn/+vBo0aOAOqZm5lWsBAAAAwK0ilOaAYcOGadKkSe6lr7Nnz9ayZcs0YsQIJSQk6LffftPZs2f18ccfKzo6Wv369fPYLGj58uUZjjt06FCtWbNGnTp1ynYtefLk0bhx4zR8+HB99913kqR9+/apffv2Wrx4sbp166aQkBBFREQoMTFR9evXdz9TOnXqVMXFxalkyZI6evToP/hFAAAAACBj7L77L3K5XP9o5vGv7zT9p2w2220vCc5KdnfYAjLD7rsAAAD3huxmAx4Y/Bf906Wwd3LToZwIpAAAAABwqwilAAAAAADDEEoBAAAAAIYhlAIAAAAADEMoBQAAAAAYhlAKAAAAADAMoRQAAAAAYBhCKQAAAADAMIRSAAAAAIBhCKUAAAAAAMMQSgEAAAAAhiGUAgAAAAAMYza6AAD4qzy1WhpdAgAAAP5FzJQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYhlAIAAAAADGM2ugAA958rS6cbXUKOytuyi9ElAAAA3DWYKQUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYhlAIAAAAADEMoBQAAAAAYhlAKAAAAADAMoRQAAAAAYBhCKQAAAADAMIRSAAAAAIBhCKUAAAAAAMMQSv/HnD59+rb6rVy5UocOHcp2+6tXr97WdQAAAADgTiKU5pDNmzcrODhYtWrVSvepWbOmTCaTkpKSPPqcPXtWJUuW1Pnz5z2Ou1wu/fnnn5oyZYrmzZuX7lpWq1Uvv/yyVq1ala3aHA6HmjZtqnfeeUd2u11RUVEymUwZfjZu3Hj7PwIAAAAA3ITZ6ALuVf7+/sqXL5+6dOmS7pzdbtfWrVsVEBDgcXzJkiVq0aKFvLy81KJFC8XHx+vixYuKjo5WsWLFVKlSJdWoUSPdeKNHj9bVq1c1evRojRkzJt15q9WqkJAQHT58WJLk7e2t9evXq2vXrlq8eLF8fX3Vv39/DRs2zKNfixYt/sEvAAAAAAA3RyjNQSaTSWZz5j+xy+WSyWRyf//qq6/06quvyuFwaO/evfrtt9+UO3dudenSRZ06ddKTTz6Zboy9e/dq5MiRWrRokR577LFs1ZWYmKiAgADNnz9fkjR27FhNnDhRX331lUe7hISEbI0HAAAAALeLUJpDbDabrFarpk2blu6cy+VSnjx5lJKSIn9/f0nS77//rq1bt2rgwIHy8vKS2WxW0aJFJUl+fn5KTk5ON86FCxfUrl07vfnmm1q/fr0iIyPl6+vr0SY1NVVhYWFas2aN+9iECRO0a9cuzZ49W15eXjKZTOrbt69GjRrl0bdhw4b/9GcAAAAAgCwRSu8wl8ullJQU1ahRQ9HR0Vm2dTgcslqtslgsGjJkSLrlvGmCgoKUmJgo6UbIdLlcslgsyps3r0aPHq3WrVvLZDJp5MiR2aqxa9euaty4sd5//30NHz5cDofj1m7yL6xWq6xWq/t7XFzcbY8FAAAA4P5DKL3Drly5otq1a+vKlSsKDQ1VcHCw4uPjFR8fr8KFC3u0tdvtKlOmjJ555hlduHBBTZo0cZ+Ljo5WRESEpBtB79tvv9WwYcNks9nUvXt3ffDBBzKbzUpKSlLu3LkVEhLisRQ4TWpqqrp166YPP/zQfSxv3rxatmyZxo4dK0kym82aNm1ahrO6Pj4+Wd5vVFRUumdRAQAAACC7TC6Xy2V0EfeiLl26qGjRoqpevbp+//13Xb58WW3btpXVapWX141NjyMiIlSlShVduXJF58+f19ChQ9WxY0fVrVtXtWrV0smTJyVJM2fO1MaNGzV16tRbrqNfv34KDg72CKXSjRnduLg4hYaG/qP7zGimNDw8XLGxsQoJCflHY+PedWXpdKNLyFF5W3YxugQAAADDpeWNm2UDXgmTgxITExUTE+Neertt2zZNnTpVMTEx+vzzz/XHH39IujFzWbFixUzHqVy5snbu3HlbNTgcjgxnUI8cOaI6deooJiZGJpNJYWFh8vHxUUhIiPz8/OTv76+QkBCVK1cuy/EtFotCQkI8PgAAAACQXYTSHLRy5UpNmzZNq1evliQNHDhQ169f14svvqhLly6pbdu2Wfa32Wz64osvVLFiRZ0+fVoXL1685RpCQkJ06tQpSXKHY0lavHixGjRo4F6eGxMTowYNGmjPnj0aNGiQBg8erK1bt8pisdzyNQEAAAAguwilOWjEiBGaOXOmOnXqpG3btmnbtm1q06aNGjVqpBYtWihXrlwZ9nO5XEpOTtZjjz2mffv2ycvLSx06dHA/A/rBBx8oPj4+WzU89NBD2r59u86ePas6deooNTVV0o0lwe3bt3cvJc7Mzc4DAAAAwD9B4sgBr7zyilauXKmoqCiNHj1af/75p4oXL65HHnlEYWFhSk5O1ubNm/Xdd995zF7abDbZ7XYdPnxY169f17PPPqt9+/Zp06ZNGjRokKZNm6Y5c+Zo3Lhx2Z7BbNmypU6ePKnnn39eoaGh8vX11erVq3X+/Hk1aNDgpv0zWvoLAAAAAHcKu+/mgGHDhmnSpEnu4Dh79mwtW7ZMI0aMUEJCgn777TedPXtWH3/8saKjo9WvXz9JNzYNSklJUf369fXbb7+patWqKl++vD777DMtWLBA06dPV5cuXdSrV6907yPNTJ48eTRu3DgNHz5c3333nSRp3759at++vRYvXqxu3bopJCREERERSkxMVP369ZWamiqTyaSpU6cqLi5OJUuW1NGjR3PmxwIAAABwX2P33X+Ry+X6n5l5tNlsN33dy+3I7g5buL+x+y4AAMC9j913/wf9rwRS6ebvHwUAAACAfwOhFAAAAABgGEIpAAAAAMAwhFIAAAAAgGEIpQAAAAAAwxBKAQAAAACGIZQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYcxGFwDg/pO3ZRejSwAAAMD/CGZKAQAAAACGIZQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYhlAIAAAAADGM2ugAA/3vOzfjE6BLuaoU7v210CQAAAHcNZkoBAAAAAIYhlAIAAAAADEMoBQAAAAAYhlAKAAAAADAMoRQAAAAAYBhCKQAAAADAMIRSAAAAAIBhCKUAAAAAAMMQSgEAAAAAhiGUAgAAAAAMQygFAAAAABiGUAoAAAAAMAyhFAAAAABgGELpPWrDhg3q3r27rFar0aUAAAAAQKYIpTlk8+bNCg4OVq1atdJ9atasKZPJpKSkpCzHaNiwof7zn/9kev7MmTPy8vJSRESEgoKC9PTTT7vP7dixQzt27JDFYsmw7/Hjx2UymWQ2mzP8eHt7e4wHAAAAADnBbHQB9yp/f3/ly5dPXbp0SXfObrdr69atCggIyHKMmJgY+fn5ZXreYrEoICBAJ0+eVNOmTdWmTRslJibK19dXO3fuVLt27dxtbTabHA6Hezw/Pz8VL15cx48fz3DsyZMna8uWLdm4UwAAAAC4fYTSHJQ2E5kZl8slk8mU6XkvLy+FhYVlet7X19c9zu7du9WiRQtVrlxZly5dUkJCgnLnzq1x48ZJklJTU9W+fXt9/fXX7rHTrn3s2DGVKFFCkrR06VL3uGn/CwAAAAA5hVCaQ2w2m6xWq6ZNm5bunMvlUp48eZSSkiJ/f39J0sGDBxUQEOCx3DYlJUUpKSm6cOGCpBszrImJifLx8dGDDz7obnfgwAGVLFlSuXPn1tGjRzVnzhzNnTtXixYtyrQ+h8MhLy8vnThxQnXq1NGaNWtUrlw5TZ06VY0bN5bFYpG3t/ed+jkAAAAAIEOE0jvM5XIpJSVFNWrUUHR0dJZtHQ6HrFarLBaLmjRpooSEBHl5/d9jvnFxcerSpYt8fHzcY6empqpRo0ZaunSpu9369evVqFEjd5vPPvtM48eP15dffqnDhw9r/Pjx6a5tt9vl4+Oj4sWLa8KECWrfvr2WLVumHTt2aM6cOfrmm2/kdDpver9Wq9VjM6W4uLib9gEAAACANITSO+zKlSuqXbu2rly5otDQUAUHBys+Pl7x8fEqXLiwR1u73a4yZcpo4cKFOnfunMc5q9Wq4OBgRUZGasiQIVles2rVqpoxY4bsdrvGjh2r4OBgPfroo9q8ebNiY2PlcrlktVrl4+Pjnv1MTEx0P1/aoUMHrVq1SrVr11avXr3k7+8vb29v2Wy2m95vVFSUhg0bdis/EQAAAAC4EUrvsHz58uno0aPq0qWLihYtqurVq+v333/X5cuX1bZtW1mtVvdsaEREhKpUqZLhOCtWrFDevHk1bdo0vf3221k+31mzZk2VK1dOU6ZM0cqVK7V27VqFhITI4XDIZDJp7dq1slqtmjdvnurXry/pxiZKISEh7jHefPNNTZ06VWXKlJF043nS1NTUm95vZGSk+vfv7/4eFxen8PDwm/YDAAAAAIlXwuSoxMRExcTEKDExUZK0bds2TZ06VTExMfr888/1xx9/ZNr3k08+0dChQ1WlShVFRUXd9FotW7bU3LlzNXfuXCUkJCguLk7Dhg1Thw4ddPLkSZ0/f94dSCXp9OnT7vBotVrVvXt3derUSe+++66sVqsKFiyoq1ev3vS6FotFISEhHh8AAAAAyC5CaQ5auXKlpk2bptWrV0uSBg4cqOvXr+vFF1/UpUuX1LZt2wz7TZ06VRcvXtQrr7yicePGafz48dqxY0em19m8ebOmTp2q0NBQBQUFuTdP+iun0+nx7OfOnTtVtmxZpaSkqEOHDipXrpxmzpypUqVKaeLEiSpevLiOHDnyD38BAAAAAMgaoTQHjRgxQjNnzlSnTp20bds2bdu2TW3atFGjRo3UokUL5cqVK12f77//Xv3799fcuXNlsVgUERGhsWPHqmXLltq9e7dHW4fDIenG617Kli2rkSNHKjQ0VGFhYQoLC9PQoUP17bffur9XqlTJ3XfFihWqV6+eXn/9dYWGhmry5MnumqdOnarw8HCdO3dOe/bs0ciRI+VyuXLwlwIAAABwv+KZ0hzwyiuvaOXKlTpw4ICqV6+ulJQUFS9eXI888ogOHjyo5ORkbd68Wd99951atmypwMBAXblyRUOHDtX06dM1e/ZsVatWzT1e586ddfDgQdWrV09ffPGFOnfuLEnujYgaNmyohg0bSrrxGpk0o0eP1r59+zR9+nSP+jZt2qQrV66oTp06qlSpkoKDg93PuVarVk3btm2Tr6+vmjZtqpdeekkJCQkaPHhwDv5iAAAAAO5XhNIcMGzYME2aNMn9ztHZs2dr2bJlGjFihBISEvTbb7/p7Nmz+vjjjxUdHa1SpUrpueeeU6FChbRu3TrVqFEj3ZijRo1S7ty51bVrV23atElTp0696e64qampSk5OTnf822+/1YABA+Tl5aXQ0NB054ODgyVJH374oRo2bKjhw4ffzs8AAAAAADdlcrEu81/jcrlkMpnSHbfb7Zo2bZo6d+6c4fOgf/XHH38oNDRUJUqUuO06EhISZLFY3O8/zUrae1SzKy4uTqGhoYqNjWXTo7vYuRmfGF3CXa1w57eNLgEAAMBw2c0GzJT+izIKpJJkNpvVs2fPbI1RtWrVf1xHUFBQttveSiAFAAAAgFvFRkcAAAAAAMMQSgEAAAAAhiGUAgAAAAAMQygFAAAAABiGUAoAAAAAMAyhFAAAAABgGEIpAAAAAMAwhFIAAAAAgGEIpQAAAAAAwxBKAQAAAACGIZQCAAAAAAxDKAUAAAAAGMZsdAEA/vcU7vy20SUAAADgPsFMKQAAAADAMIRSAAAAAIBhCKUAAAAAAMMQSgEAAAAAhiGUAgAAAAAMQygFAAAAABiGUAoAAAAAMAyhFAAAAABgGEIpAAAAAMAwZqMLAGCsPz9+y+gS7jml3hltdAkAAAB3DWZKAQAAAACGIZQCAAAAAAxDKAUAAAAAGIZQCgAAAAAwDKEUAAAAAGAYQikAAAAAwDCEUgAAAACAYQilAAAAAADDEEoBAAAAAIYhlAIAAAAADEMoBQAAAAAYhlAKAAAAADAMoRQAAAAAYBhCKQAAAADAMITSHLRv3z6tWbPmjo5pt9tlt9uz1dZms93y+ElJSbfcBwAAAABuF6E0B33wwQeaOHFittoeOXJEJpNJZrM5y4+Pj4/mzp2brn9ERIR27drl/p6UlKTw8HBt3bo102sePXpU+fPn9zj22muv6YsvvpDT6VRKSkr2bhQAAAAAbpPZ6ALuVZs2bdLChQtVsGBBRUREpDvvcDgUGxurXbt26cEHH1RwcLAkKSUlRWbz//21REdHK3/+/PL19ZUkuVwuOZ1O9/nExERZLBb5+fnJx8dHycnJ8vf314QJE5SSkqLNmzdr8+bN8vLyUvfu3eXv76/x48fro48+ktls1vXr11WgQAFNnz5dAwYM0NWrV/Xrr79qypQpOnLkiFJTU3P2hwIAAABwXzO5XC6X0UXcaxITE1W1alW1b99eH330Ubb6xMTEKFeuXLLZbB6h1NfXV7t27VK5cuUy7FepUiU5HA4dP35c4eHhKlmypCZNmqTKlStrxIgRKliwoKZMmaIqVaro008/9eh78uRJNW3aVFu3blVAQIBcLpc6deqkjh076oEHHlDr1q0VHR19S/ceFxen0NBQxcbGKiQk5Jb6whh/fvyW0SXcc0q9M9roEgAAAAyX3WzATOkd5nA49MILL6hAgQKqUqWKqlSpIj8/v3RtYmNjtX79ehUsWDDL8Xx9fRUQEJDp+T179kiSKlSooIULF6pkyZLq06ePmjZtqlq1aun48eM6cOCAhg0bpujoaBUtWlSnTp3Szp07tWrVKp08eVIVK1bUhg0b1KxZM8XHx+vtt99WUlKScufO/c9/EAAAAADIAqH0DvP29lbfvn1VqVIl5cmTR88888wt9Xe5XEpJSZHFYpHJZEp33mazycfHR5L0/fff64MPPpC/v79OnTql1q1by+FwaPr06bpw4YL+/PNPmUwmjRkzRh9//LHKli2rUaNGaciQITpz5ozq1aunYsWK6dixY5KkwYMHq2vXrnrrrbf04YcfKjk5WbGxsQoODpaXV8aPH1utVlmtVvf3uLi4W7pfAAAAAPc3QmkOKF26tCIiIhQaGuqxFDeNw+FQ6dKl9csvv6Q7d/HiRYWHh3scK168uPvPDRo00Nq1ayVJHTp0UIcOHSRJffv2VZkyZdSzZ88Ma9q1a5f7udRZs2ZJurHR0Zw5cyRJ+/fv19ChQzVz5kx5e3vrkUceUXR0tCZOnKjt27frhx9+yHDcqKgoDRs2LKufAwAAAAAyRSjNAUWKFFF8fHym5xctWqQxY8Z4HHM4HPL29lbBggV18uRJ+fr6ymQyqUSJElq7dq3Cw8OV0eO/S5cuVZ48edIdnzZtmk6cOCGz2SyTyaSNGzeqadOm7vPz58/XiBEjVKhQISUmJuqNN97QsGHD1KlTJ3ebt956S4MHD9Z7772X6b1ERkaqf//+7u9xcXHpQjUAAAAAZIZQagCHw5Fuae7Vq1cVHBwss9msYsWKuY+bTCbly5dPb731lrp06eIRLCVp7ty5evjhhyVJI0eO1NSpU/XHH38oJCREefPmlbe3t0wmU7rnUitVqqQPP/xQzZo1U2BgoFq1aqXz5897tBk+fLhatGihqlWrZnovFotFFovltn4HAAAAACCUGiAkJESnTp2SdGOn3sDAQP35558ZvjomTcmSJdWtWzcdOHDAI2AeP37cvTPv4MGD3ct3//4s69mzZz1eJdO8eXO5XC55e3tLki5cuKCQkBB99dVXkm4823r69GlduHAh3UZNAAAAAHCnEEoNULp0aZ0+fVqXLl3SU089pa+++kqbN292z3hmZMCAAfrzzz+VmJjoDqUOh0P79+9XUFCQzp07pzJlykiSjh07pqpVq8rf318+Pj7y8vJSUlKSx/Omp0+fdv/5jz/+UNOmTbVv3z7lz59fknTmzBmVKlXK/R0AAAAAcgKh1AAPPPCAatasqQ4dOmj//v0qUqSI5syZo48//ljXrl2Tj4+Px/LepKQk5c2bV1OmTJHD4VB8fLyCg4O1fPlylShRQj///LPee+89TZgwQdeuXdPjjz+uw4cPK1++fPLy8lJqaqri4+N1/vx5paSkuGc+HQ6H5syZowEDBujTT/9fe3ceHEWdv3H8mdyEMDNAgAgJBOUQQYxLUOSQQnRF1gMFYREFhMghZmFF9MchKiCwtShyuBwKAgoiqJxL4YGccolGkEOQIwFkkXAkExIyCZn+/eEySwhHGJl8CXm/qroq3TM9/en5VE/Nk293zz/zBdDvvvtONWvWvOgdgAEAAADgWrn473zA7yZOnKhDhw5p9OjRmjdvnk6ePKnWrVurYsWKstvtKlOmjMqUKaPMzEzVrVvXO+90OjVw4EBJUlJSkrp27aqgoCCNHDlS//73v3X27Fm99NJLatCggcLDwxUQEKCwsDBVqFBBrVu3VmhoqH755Rd17txZlStX1sSJEzVnzhx1795dkvTJJ5+oZcuWSkhI0JNPPmnyLQIAAABQAtisi93SFUVq165d2rBhg7p166bs7GyFhIRc8ndB8/LylJeX5/15F7fbfdkbDeXk5Mjj8SggICDfCOyECRPUrFkzxcXF5Xv+iRMntHz5ct1+++2qX7/+Ve+Ly+WSw+FQenq67Hb7Va+PovfLP14yXcINp+YrY0yXAAAAYFxhswGhFNcUobT4IZRee4RSAACAwmcDTt8FAAAAABhDKAUAAAAAGEMoBQAAAAAYQygFAAAAABhDKAUAAAAAGEMoBQAAAAAYQygFAAAAABhDKAUAAAAAGEMoBQAAAAAYQygFAAAAABhDKAUAAAAAGBNkugAAZtV8ZYzpEgAAAFCCMVIKAAAAADCGUAoAAAAAMIZQCgAAAAAwhlAKAAAAADCGUAoAAAAAMIZQCgAAAAAwhlAKAAAAADCGUAoAAAAAMIZQCgAAAAAwJsh0AQDM2ZLYxXQJN6T4CTNNlwAAAFBsMFIKAAAAADCGUAoAAAAAMIZQCgAAAAAwhlAKAAAAADCGUAoAAAAAMIZQCgAAAAAwhlAKAAAAADCGUAoAAAAAMIZQCgAAAAAwhlAKAAAAADCGUAoAAAAAMIZQCgAAAAAwhlAKAAAAADCGUAoAAAAAMIZQegPZvn27vvnmm0I99+TJk7Isy88VAQAAAMDlEUpvIK+//rrefffdQj136NCheuihh5SamqoNGzbIZrNddBoxYoSfqwYAAABQkhFKbxDffvutFi5cqE2bNik2NrbAFBMTI7vdrv3790uSJkyYoBYtWujNN99USEiIGjZsqIyMjHzTwIEDDe8VAAAAgBtdkOkC8MdlZmaqW7duevnllzVy5MhCreNyufTKK69IkpKSkvTDDz8oOjo633Oys7M1ZMiQa14vAAAAAJxDKC3m8vLy9NRTT6lSpUqKi4tTXFycwsLCCjwnPT1da9asUVRUlDZt2qTu3btr1apVioyMlM1mU3x8vDZu3Jhvvddff70I9wQAAABASUQoLeYCAwPVr18/1a9fX+XLl1f79u2vuE7Dhg0VFxen9u3b68svv1ReXp7P23e73XK73d55l8vl82sBAAAAKHkIpTeAWrVqKTY2Vg6HQ0FBBVual5enWrVqacWKFZKkgIAAzZgxQ/369VNWVpYsy9LevXsVGRlZYN1BgwZddtujRo3SG2+8cW12BAAAAECJY7P4XZAb3qJFi/TWW29pzZo1+ZanpaXJ6XT+ode+2EhpTEyM0tPTZbfb/9Brw/+2JHYxXcINKX7CTNMlAAAAGOdyueRwOK6YDbj7bgmQl5cnm81WYPmdd96pI0eOKC4uThERESpVqpTCw8MVERGh4OBgOZ1OBQQE6NixY5d87dDQUNnt9nwTAAAAABQWp++WAHa7XSkpKZJ+v1Nv6dKl9d133yk8PFyVK1dWcHCwli5dqnXr1ik6OlqxsbEaMWKEvv76a0VFRSk0NNTwHgAAAAC4UTFSWgLUqlVLBw8e1LFjx3T//fdr586dmjlzptq2bSvp92tML+dKjwMAAACArxgpLQGqVq2qu+++W+3atdOOHTsUERGhWbNmFbjG9FIuduovAAAAAFwLhNISYuLEiWrXrp1Gjx6t5ORk1a5dW9HR0bLb7QoMDNTTTz/tvfY0ICBAbrdbsbGxys7OVnR0tDZt2qTatWub3g0AAAAANxhCaQnRoEEDHThwwDu/fv16BQcH87uiAAAAAIziYsESKjg42HQJAAAAAEAoBQAAAACYQygFAAAAABhDKAUAAAAAGEMoBQAAAAAYQygFAAAAABhDKAUAAAAAGEMoBQAAAAAYQygFAAAAABhDKAUAAAAAGEMoBQAAAAAYQygFAAAAABgTZLoAAObET5hpugQAAACUcIyUAgAAAACMIZQCAAAAAIwhlAIAAAAAjCGUAgAAAACMIZQCAAAAAIwhlAIAAAAAjCGUAgAAAACMIZQCAAAAAIwhlAIAAAAAjCGUAgAAAACMCTJdAOBPyx9pbboElECtliwzXQIAAECxwUgpAAAAAMAYQikAAAAAwBhCKQAAAADAGEIpAAAAAMAYQikAAAAAwBhCKQAAAADAGEIpAAAAAMAYQikAAAAAwBhCKQAAAADAGEIpAAAAAMAYQikAAAAAwBhCKQAAAADAGEIpAAAAAMAYQqmf5OTkXPZxj8cjt9t9xdc5ceKEPB7PtSoLAAAAAK4rhFI/SE9Pl8PhkMPhkNPplNPpVGhoqEJDQ73zTqdTd911V771Jk+erPvuuy/fsoSEBA0ZMuSS2/J4PLLZbAoKCrroFBgYqIYNGxZYb/LkyUpPT1deXp6ys7O9y+fOnavOnTt753NycpSbm+vrWwEAAAAAlxVkuoAbkcPh0JkzZ+R2uxUaGipJ6tevnyTpnXfekSQdPHhQVapUybdeWFiYSpUq5Z13u91avXq1xo8ff8ltBQQEKCQk5JKjrsuXL9eoUaMKLF+2bJmOHDmiVq1aqW3btt46s7KydObMGcXGxkqScnNzNXz4cHXr1q1Q+w4AAAAAV4NQ6ifjx4/Xli1bNGvWrIs+/vTTT6tp06YaOXKkLMvyLrfZbLIsS5ZlacmSJcrLy1OLFi28j7tcLt1xxx366quvvMsCAwMlSfv27dMtt9wiSdq8ebP2798vp9OpkJCQAtt/6qmnNHz4cA0bNkz/+c9/vMsXLlyopUuX6v333/9jbwAAAAAAFAKn7/rJAw88oHnz5mnx4sUFHvv222+1ceNGPfvss5KkTZs2KSAgQN27d9eyZcsUGBio6dOna+zYsZo6dar27t3rnYYNG5ZvNPUcl8ule++9VytXrpQkzZkzR5s3b5bNZvOG1vO1adNGP/zwgySpa9euioyMVGxsrHr06KFPPvlEsbGxio6OVmRk5LV8WwAAAAAgH0Kpn9SpU0cvvfSSEhMTlZWV5V2el5enxMRE9enTRzVr1pQkNWzYUNnZ2Zo2bZpat26tnJwclS1bVr/99ptCQ0M1cOBA7/o5OTkFQqnNZpPdbtf8+fPVuXNnpaSkaMGCBerfv78CAgIK3Chp0aJFWrBggTfAhoSEaMyYMUpOTtbUqVPVoUMHJScna9WqVd7Tei/F7XbL5XLlmwAAAACgsAilfvTiiy/K4XDo5MmT3mXr16/X0aNH9frrr3uXBQYG5gt/QUFBqlixoqZMmaKmTZtq3rx5mjt3rqSCoTQrK8u7buPGjfX888+rUaNGatmypapUqaLAwMACNyo6evSoVq1apUGDBnlfc8iQIapXr5769u2rzz//XPXq1fMG5MsZNWqU96ZODodDMTExvr1ZAAAAAEokQuk1ZlmW3G63LMtSuXLl9P333ys6Otr7eLNmzbRt2zY5HA7vnW/Pv6b0/Oe1bNlSkZGR+vzzz1W1alWtXLmyQChNS0uT3W73zvfq1UunTp3SrbfeKun3UdALg2XPnj3VsWNHBQcHS5JmzJihw4cPq127dnK5XHriiSfUp08fbd++XampqZfd34EDByo9Pd07HTp06OrfNAAAAAAlFqH0GtuzZ4+cTqfKli0rp9OpChUqyOl0atKkSZo0aZKcTqdq1Kghp9PpHV08duxYvtewLEuZmZne+R07dujJJ59UampqgVB68OBB7+ikZVnq0aOHnnjiCY0ZM0bHjx9XVFSUTpw4cdFabTab9+/FixdrxowZGj16tLKzs7V48WK1bNnyiqE0NDRUdrs93wQAAAAAhUUovcZq166tM2fOKC0tLd/Uu3dv9e7dO9+y06dPy+12q1KlSsrIyNDPP/+sH374QbGxsVq+fLl27dqlBx98UBMnTtSaNWvUvn175eTkKCwszLu9pKQk1alTRx6PR7169VJGRoY+/PBDtW3bVsOGDVPVqlV18ODBS/7W6NmzZ/XOO+8oISFBn332mSIjIxUSEqKlS5eqbt26io+P1759+4rq7QMAAABQwhBKrwNz5syRw+HQqlWr9PLLL2vdunVq27atxo4dq7i4OK1Zs8b7Uy9utzvfSOkXX3yhZs2aaeTIkdq7d68+//xzBQYG6tVXX9X8+fOVm5urqKgobd68WQMHDlR2drZ33VOnTmn//v3atGmTvvnmGzVo0EC5ubnKy8tTYGCgJk+erDfffFNVq1Yt8vcEAAAAQMlgsy52QSOuucTEREnShAkTCjzmcrmUkpKi22+/Pd9yj8ejgIDf/2+wdu1apaena+DAgerVq5f69Omj5ORk1a9fX4cOHfL+Fun5gTUjI0NlypTRCy+8oHXr1mnv3r06fvy49uzZo7/+9a/yeDzKzMxUp06d1KRJE1WvXl1paWlKTU1VXFycsrKydPLkSR0/flyPP/54ofbT5XLJ4XAoPT39ujiVd/kjrU2XgBKo1ZJlpksAAAAwrrDZIKgIayrRzpw5k+8azvPZ7fYCgVSSN5BK0kcffaQlS5bonnvuUYcOHSRJ8+fP17PPPiuHw3HR1y1Tpowk6ZVXXtFnn32m5557TmFhYRo3bpx69+6txMREHT58WB988IEmTZqkvXv3Kj09XdnZ2Tp79qz3BkyPPvpooUMpAAAAAFwNRkqLsby8PGVmZhZqRDInJ0fBwcGy2Wz5RmCvNUZKAUZKAQAAJEZKS4TAwMBCB79zp/dK8lsgBQAAAICrRToBAAAAABhDKAUAAAAAGEMoBQAAAAAYQygFAAAAABhDKAUAAAAAGEMoBQAAAAAYQygFAAAAABhDKAUAAAAAGEMoBQAAAAAYQygFAAAAABhDKAUAAAAAGEMoBQAAAAAYE2S6AMCfWi1ZZroEAAAAAJfBSCkAAAAAwBhCKQAAAADAGEIpAAAAAMAYrinFNWVZliTJ5XIZrgQAAACASecywbmMcCmEUlxTGRkZkqSYmBjDlQAAAAC4HmRkZMjhcFzycZt1pdgKXAWPx6MjR46oTJkystlspssp1lwul2JiYnTo0CHZ7XbT5aCQ6FvxQ8+KH3pWPNG34oeeFU/XU98sy1JGRoYqV66sgIBLXznKSCmuqYCAAEVHR5su44Zit9uNf6Dg6tG34oeeFT/0rHiib8UPPSuerpe+XW6E9BxudAQAAAAAMIZQCgAAAAAwhlAKXKdCQ0P12muvKTQ01HQpuAr0rfihZ8UPPSue6FvxQ8+Kp+LYN250BAAAAAAwhpFSAAAAAIAxhFIAAAAAgDGEUgAAAACAMYRSAAAAAIAxhFLgOrB9+3Y1bNhQZcuW1YABA1TY+4+98cYbKleunEJDQ/X4448rIyPDz5XCl159+umnqlatmipXrqyPP/64CKrE+XzpGceWWb5+JkpSWlqabrrpJiUnJ/uvQFyUr33zeDxq3Lix3nrrLT9XiAtdbc8sy1Lv3r1Vrlw5OZ1Ode3aVWfOnCmianHO8ePHVb169UJ/zq1evVp16tRRZGSk3n77bf8W5yNCKWCY2+3WI488ogYNGmjLli3auXOnZsyYccX1Zs+erdmzZ2v58uXasWOHdu3apdGjR/u/4BLMl15t375dnTp10quvvqovvvhCQ4cO1e7du4umYPjUM44ts3z9TDxnwIABOnr0qP8KxEX9kb5NnjxZ6enp+tvf/ubfIpGPLz378MMPtXv3biUlJWnt2rXasWOHRo0aVTQFQ9LvgfThhx8udCBNTU3Vo48+qo4dO2rDhg2aPXu2Vq5c6d8ifWEBMGrBggVW2bJlrczMTMuyLOvHH3+0mjRpcsX1Ro0aZa1fv947P3ToUOuhhx7yW53wrVd9+/a1HnzwQe/8O++8Yw0ePNivdeJ/fOkZx5ZZvn4mWpZlrV692qpYsaJVvnx568CBA36sEhfytW+//vqr5XA4rBUrVvi7RFzAl5716dPHevfdd73zI0aMsDp27OjXOpFfy5YtrXHjxlmSCvU5N3bsWOvWW2+1PB6PZVmWtXDhQqtTp05+rvLqMVIKGLZ161Y1atRI4eHhkqT69etr586dV1zv//7v/3TPPfd453fv3q2aNWv6rU741qutW7fqvvvu887fdddd+v777/1aJ/7Hl55xbJnl62ei2+1Wz549NX78eEVERPi7TFzA177169dP1apV06FDh7R+/Xp/l4nz+NKzunXr6qOPPtJvv/2mlJQUzZ07Vw888EBRlIv/eu+9967qrIKtW7eqRYsWstlskq7f7yGEUqCItGnTRk6ns8A0fvx4Va9e3fs8m82mwMBAnTp1qtCvvWfPHi1YsEA9evTwR+n4L5fLddW9unAdu92uI0eO+LVO/I8vPTsfx1bR87VnI0eOVK1atdShQwd/l4iL8KVvGzZs0Pz58xUdHa19+/apS5cueuGFF4qiXMi3niUkJOj06dOKiopSbGysqlevri5duhRFufiv83tWGMXle0iQ6QKAkmLKlCkXvRnAuHHjvP+9OicsLExZWVkqW7bsFV/X4/GoW7duSkhIUN26da9ZvSgoKChIoaGh+ZZdqVcXrnPu+SgavvTsHI4tM3zp2a5duzR58mQlJSUVRYm4CF/69t577+nuu+/W0qVLZbPZ9Nxzz6latWpKTExU7dq1i6LsEs2Xno0bN05Op1MpKSmy2Wzq2bOnBgwYwE2qrmPF5XsII6VAEalUqZJiY2MLTFFRUUpNTc333IyMDIWEhBTqdYcPH66TJ0/qn//8pz/KxnnKlSt31b26cJ2r6S3+OF96dg7HlhlX2zPLstSjRw+NGDFClStXLooScRG+HGuHDx9W69atvf+YjYmJUYUKFbRv3z6/1orf+dKz2bNna8CAAapatapiYmI0atQoTZs2zd+l4g8oLt9DCKWAYQ0bNtSGDRu88wcOHJDb7Va5cuWuuO6SJUv09ttv67PPPvNeEwL/8aVXF66TlJSkKlWq+LVO/I+vxxfHljlX27ODBw9q3bp1GjBggPeyiIMHD6p+/fqaM2dOUZVd4vlyrEVHR+c7g+j06dM6efIkn5FFxJeeeTweHTt2zDt/9OhR5eXl+bVO/DHF5nuI6TstASVdbm6uVaFCBWv69OmWZVlWQkKC9fDDD3sfP3XqlHX27NkC6+3cudMqXbq0NXPmTCsjI8PKyMjw3kEP/nG5Xl2qTz/++KNVunRpa9u2bVZGRoYVFxdnjRkzpkjrLsl86RnHlllX27Pc3FzrwIED+aYqVapYa9eutTIyMoq8/pLKl2Ptyy+/tMqXL299/fXXVnJysvXMM89Y9erV894lFP7lS8/69Olj1ahRw/rggw+sKVOmWDfffLP11FNPFWnd+J0uuPtuenq6lZOTU+B5qampVlhYmPXVV19ZOTk5VqtWrawXXnihCCstHEIpcB1YtGiRFR4ebpUvX96qUKGCtWPHDu9jkqykpKQC6/Tr18+SlG+qVq1a0RVdQl2qV5fqk2VZ1qBBg6yQkBDLbrdbDRo0sLKysoqwYlxtzzi2zPPlODtftWrV+EkYA3zp2/vvv2/VrFnTCgsLsxo1amT9/PPPRVgxrrZnp06dsp555hmrQoUKVlhYmPXYY49ZqampRVw1LKtgKK1WrZq1YMGCiz530qRJVnBwsFW2bFmrevXq1tGjR4umyKtgsyzLKvrxWQAXOnr0qL7//ns1atRI5cuXN10OLsOXXu3cuVO//vqrmjdvfl1ey3Gj4/gqfuhZ8UTfih96VjIcOHBAP//8s5o1a3Zd/mwWoRQAAAAAYAw3OgIAAAAAGEMoBQAAAAAYQygFAAAAABhDKAUAAAAAGEMoBQAAxpw8eTLffE5Ojk6fPm2oGgCACYRSAADglZeXp+zsbKWnp+vgwYPatm2bvvzyS02ZMkX9+/dX06ZN1ahRI2VlZUmSpk6dqn379mnWrFlq06aNsrOz9eKLL0qSnn/+eU2bNu2S28rOztYtt9yiRYsWeZetWbNGFSpU0JkzZ/y7owCA60aQ6QIAAMD1YefOnWrcuLHsdrtKly6t4OBg7dy5U88884xq1Kih+vXr68EHH1TZsmWVm5srSSpXrpw6dOigv//97woJCdEnn3yitLQ0SdLq1av12GOPXXJ7ixcvVsWKFXXixAnFxMQoMDBQ2dnZys3NVZ06dSRJZ8+e1aBBg/T888/7ff8BAGbwO6UAAKAAj8ejhIQEbd26VXPmzFHt2rW1fPlyVa5cWfXr15ckud1uLV68WHv27FFGRobWr1+v5s2bKyIiQq1atdJdd92ltLQ0lSpVSh6PR7m5uQoNDZUkWZalO+64Qz169FCvXr0kSUFBQfroo480ceJEbdy4UZKUm5srm82moCD+jw4ANypO3wUAAF7Z2dn6+uuv9cADDygqKkqbNm1SpUqV1L17d7344otav369zv0/Ozc3V0uXLtV3332nf/zjHzpy5IiOHTum3bt368MPP1RwcLDq1Kmjm266SeHh4UpMTPRuZ/r06frpp58UFRWloKAgvfbaa4qPj9fgwYO1a9cuxcfHKz4+XosWLSKQAsANjlAKAAC80tLS1LZtW+3bt08LFy5UeHi4GjdurIceekg//fSTunfvLpvNJkmKiIjQiBEjdPjwYbVp00a33Xab9u7dq5EjR+rHH39Uv379lJycrOnTp+v+++/X1KlTJUnJycnq37+/KlWq5N3uoUOHlJCQoJSUFKWnp2vLli2Kj4+Xy+Uy8j4AAIoOoRQAAHhFRUXpl19+0eDBg1W3bl29//772r59u9q1a6dFixbpz3/+s7Zu3SpJ2r59u5o2baohQ4aoY8eOKl26tHr27KlXXnlFq1at0r59+yRJR44cUeXKlb3bWLhwoR5++GHFx8d7lwUEBGjo0KGqUaOGd/r4448VEMBXFQC40XE+DAAA8Lrjjju0bds2hYWF6Z577tHkyZM1ZswYZWZm6uzZs8rNzVWLFi302muvKTExUVu2bFFERITq1KmjNWvWqGrVqnK5XIqOjtacOXMkSfv371etWrW82+jbt6+ysrLUoUOHfNseNmyY9/pSSfn+BgDcuAilAADAa9q0aQoICJDT6VR4eLhSUlLUvn177d69W2FhYbIsS7t27dKnn36qtWvXqkOHDsrOzpZlWbr33nvlcrnUqFEjLVu2TCtWrFBSUpLWrVunUaNGebdhs9lUunTpAtsePny4Jk6c6J0/cuSIGjVqVCT7DQAwh3NiAACA12233aYBAwZo06ZNioqKksPhkM1mU1hYmCZNmqS//OUvqlixooYOHarmzZvr6NGjuv3227V48WIlJyerWbNm6ty5sySpa9eu6t+/v3bv3p3vVN1LefXVV7V9+3bv1L59e3/vLgDgOkAoBQAAkqTTp0/rySefVOnSpfXII48UeLxTp04qU6aM7rzzTm3evFmS9OuvvyokJET9+vVTp06dtHHjRj366KOSpM6dO2vjxo3q2LGjQkJCrrj94cOHq169et5p3rx513YHAQDXJUIpAABQbm6umjdvrooVK2rBggWKiIjQypUr9emnn6pUqVKSJLvdrrlz56pdu3YaPHiwJKlKlSpasWKFhgwZohUrVqhly5Zq0qSJMjMz1bt3b/3pT3/SrFmz9O233xbYZl5ennJycpSbmyvp0iOlbre7iN4FAIAJNuvcj40BAIASLSUlRVWrVvX+5EuXLl20a9cu9enTR126dMn3XLfbrbNnz2rOnDmaPXu2wsLC9K9//Us333yzZs6cqeHDh6t169YaO3asPv74Y/Xs2VMjR45U3759va/RsmVLNW3aVFOmTFFoaKh3u+fzeDwKCgrS/v37/bvzAABjCKUAAMBnY8eOVdOmTdWwYUPvsnnz5qlKlSpq0qSJd9mGDRsUGRmpmjVrmigTAHAdI5QCAAAAAIzhmlIAAAAAgDGEUgAAAACAMYRSAAAAAIAxhFIAAAAAgDGEUgAAAACAMYRSAAAAAIAxhFIAAAAAgDGEUgAAAACAMf8PjXV0YGA5TtQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 定义你需要的列\n",
    "columns = ['气温', '气压', '平均海平面大气压', '气压趋势', '湿度', '风向', '平均风速', '最大阵风', '天气情况', '最低气温', '最高气温', '露点温度', '降水时间', '季节', 'PM2.5']\n",
    "\n",
    "# 初始化编码器\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "# 对\"季节\"进行标签编码\n",
    "merged_data['季节'] = encoder.fit_transform(merged_data['季节'])\n",
    "\n",
    "\n",
    "# 只选择这些列进行相关性分析\n",
    "selected_data = merged_data[columns]\n",
    "correlation = selected_data.corr()\n",
    "\n",
    "# 找到与'PM2.5'相关的列并按降序排列\n",
    "pm25_correlation = correlation['PM2.5'].sort_values(ascending=False)\n",
    "\n",
    "# 打印结果\n",
    "print(pm25_correlation)\n",
    "\n",
    "\n",
    "# 创建一个新的图像和设置图像的大小\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "# 创建颜色映射，使用颜色渐变。取决对系数值的绝对值创建颜色映射，以表示相关性强度\n",
    "color_map = colors.LinearSegmentedColormap.from_list(\"\", [\"green\",\"yellow\",\"red\"])\n",
    "\n",
    "# 创建条形图，并用颜色映射来设置每个条的颜色\n",
    "sns.barplot(x=pm25_correlation.values, y=pm25_correlation.index, palette=sns.color_palette(\"coolwarm\", len(pm25_correlation)))\n",
    "\n",
    "# 添加标题和标签\n",
    "plt.title('相关性系数条形图')\n",
    "plt.xlabel('系数值')\n",
    "plt.ylabel('特征')\n",
    "\n",
    "# 显示图像\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "PM2.5的相关性分析结果：\n",
      "\n",
      "PM2.5       1.000000\n",
      "平均风速        0.288130\n",
      "湿度          0.254683\n",
      "风向          0.181264\n",
      "气温          0.156376\n",
      "气压趋势        0.154671\n",
      "最大阵风        0.146053\n",
      "平均海平面大气压    0.105069\n",
      "最低气温        0.100938\n",
      "最高气温        0.088200\n",
      "气压          0.075169\n",
      "降水时间        0.048534\n",
      "季节          0.035743\n",
      "天气情况        0.015611\n",
      "露点温度        0.000350\n",
      "Name: PM2.5, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_22176\\1151200639.py:37: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=target_correlation_abs.values, y=target_correlation_abs.index, palette=sns.color_palette(\"coolwarm\", len(target_correlation_abs)))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "PM10的相关性分析结果：\n",
      "\n",
      "PM10        1.000000\n",
      "平均风速        0.183021\n",
      "气压趋势        0.160883\n",
      "气温          0.124652\n",
      "风向          0.116126\n",
      "最低气温        0.104168\n",
      "最大阵风        0.087428\n",
      "露点温度        0.071516\n",
      "最高气温        0.056745\n",
      "平均海平面大气压    0.052923\n",
      "湿度          0.049332\n",
      "天气情况        0.035941\n",
      "降水时间        0.033461\n",
      "气压          0.027194\n",
      "季节          0.016593\n",
      "Name: PM10, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_22176\\1151200639.py:37: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=target_correlation_abs.values, y=target_correlation_abs.index, palette=sns.color_palette(\"coolwarm\", len(target_correlation_abs)))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_22176\\1151200639.py:37: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=target_correlation_abs.values, y=target_correlation_abs.index, palette=sns.color_palette(\"coolwarm\", len(target_correlation_abs)))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "NO2的相关性分析结果：\n",
      "\n",
      "NO2         1.000000\n",
      "气温          0.313375\n",
      "平均风速        0.305271\n",
      "平均海平面大气压    0.297696\n",
      "气压          0.253523\n",
      "最低气温        0.227686\n",
      "露点温度        0.223513\n",
      "最高气温        0.197562\n",
      "气压趋势        0.167166\n",
      "风向          0.118285\n",
      "最大阵风        0.115523\n",
      "降水时间        0.100654\n",
      "天气情况        0.099558\n",
      "湿度          0.011815\n",
      "季节          0.011596\n",
      "Name: NO2, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CO的相关性分析结果：\n",
      "\n",
      "CO          1.000000\n",
      "平均风速        0.290830\n",
      "气温          0.247104\n",
      "湿度          0.232125\n",
      "风向          0.213774\n",
      "平均海平面大气压    0.178089\n",
      "季节          0.156474\n",
      "气压          0.142584\n",
      "最大阵风        0.119047\n",
      "气压趋势        0.102731\n",
      "天气情况        0.085846\n",
      "最高气温        0.080422\n",
      "露点温度        0.077971\n",
      "降水时间        0.074262\n",
      "最低气温        0.073583\n",
      "Name: CO, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_22176\\1151200639.py:37: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=target_correlation_abs.values, y=target_correlation_abs.index, palette=sns.color_palette(\"coolwarm\", len(target_correlation_abs)))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SO2的相关性分析结果：\n",
      "\n",
      "SO2         1.000000\n",
      "气温          0.306539\n",
      "露点温度        0.254694\n",
      "平均海平面大气压    0.233614\n",
      "季节          0.227524\n",
      "气压          0.198480\n",
      "风向          0.131312\n",
      "平均风速        0.075902\n",
      "湿度          0.075378\n",
      "最低气温        0.073289\n",
      "最高气温        0.072346\n",
      "气压趋势        0.066586\n",
      "降水时间        0.013597\n",
      "天气情况        0.011971\n",
      "最大阵风        0.003603\n",
      "Name: SO2, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_22176\\1151200639.py:37: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=target_correlation_abs.values, y=target_correlation_abs.index, palette=sns.color_palette(\"coolwarm\", len(target_correlation_abs)))\n"
     ]
    },
    {
     "data": {
      "image/png": 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A/H+NGjXSgw8+qMjISL377rt6/PHHJcljtk66OGNnt9s92lJTU9WhQwfdd999GjhwYLaxH3jgAS1YsMD9u0iRIu4/t2nTRsWKFdO4ceM0fvx4paWlZfusSlRUlNq1a6fdu3fLx8dHU6dOVWRkpJxOp+bPn+9eofWv/vpdy3r16mnnzp3y8fFxr44rSQMHDtTMmTPVuXNnRUVFudsvrZybL18+SRcfWS5ZsqSio6M9zhEYGKju3bvrzz//lLe3t5KTk/Xrr79q7ty5cjqdat++vT7//HP38SdPntSDDz4oSbJYLNqwYYNKlSqllJQU93jBwcEKDAxU5cqVtWLFCj333HOaNWuWvLy8VK9ePSUmJiojI0NWq1XNmjWTYRhKSUnRpk2bVLlyZXXr1k2///671q9f7/HNUEkKDg7W22+/rRo1amjYsGHZ7pkkffnll9nazp075559vvR3ExMToxo1angc5+XlpT/++EPPPfecHA6Hli5dqtq1a+v8+fOy2+3ZPuvi7++vefPmqWXLlvr888/VuHFj/fnnn3rllVc0cuRIzZo167Kf9gGAvIKZUgDAbe3HH39U1apV3e8hWiwWjRo1Sna7XQcOHFDBggV19OhRjz7Hjx/XHXfc4dHWu3dvnThxQrNmzcrxHVIfHx+PGcq/8vLyUpcuXTR37ly5XC6lpKR4PH67YMECLV26VK+++qq7rUKFCvruu+9ks9n02GOP6dy5cx7bxx9/7HGOgIAAlSpVSsWKFXO//+l0OvXNN9+oa9eumj59ujZu3OjeV6xYMZUuXVqhoaFXvYfr1q1TYmKi4uPj9cgjj6hv376Kj49XamqqRyCVLn4jdc2aNdq9e7dOnz6tgwcPKiEhQcOGDVP9+vWVmJioU6dO6cCBA/rzzz/16aef6uGHH3YH6XPnzun48ePy9/fXli1blJSUpE8++UR33HGHatSoIZvNpnbt2mncuHH69ttv9dBDD2Wrt1KlSjp48KCio6M97lmzZs3UoUMH9++zZ8/qxIkTOnDggDuc/9WBAweyvW8sXfwcT8WKFbVlyxbVrl1bSUlJatGihVq0aKGsrCyPY/Pnz6+1a9dq4MCBKlSokObNm6fixYvr6aeflmEY+vjjj3P8Rw4AyEsIpQCA21poaKj+/PNPbdu2zd2WnJystLQ0lSpVSg8++KB+/vlnd5hIS0vTmjVr1KBBA/fxUVFRmjZtmmbOnOnx/uK16N69u37//Xd5e3srKSnJYybWx8dHvXr1UvXq1XPs6+vr6xF4w8LCFBQUdMXzxcbGqnnz5qpevbr+97//adCgQXriiSfc703eTHXr1tVHH32kdevWKTAwMFuIP3bsmFq0aKEjR46oR48eev755z32R0VFyWazafHixdqwYYNGjhypwYMHu/f369dPs2bNUmZmplq3bp3t/H5+fipTpoyKFSumV155Rb///rvCwsLk6+srm82msLAwjR07VocOHVKJEiVUtmzZbDXu2bNHp06d0r333ptt/HLlymn+/PkKDw9XXFycHnroIR06dEhDhgzxmKX+K4fDoaJFi2rQoEGqWbOmduzY4bFYFgDkZYRSAMBtrVatWqpataq6deumefPmadmyZXryySeVP39+tWvXTgMGDNCBAwfUoUMHLV26VE888YRSU1PVu3dvSdLatWv15ptv6sknn1TBggW1ZcsWbdmyRTt27PA4T1pamo4ePere/u7SDKUkXbhwwSOUNm/eXB9++GGO9f995u2vMjMzc2xfvXq1HnjgAfn6+mrRokXu2eEXX3xRvXv3Vp06dbRy5Ur3arM32quvvqrPP/9cZ8+ezXF/enq6oqOjde+992r+/PnZ9g8fPlzTpk3TyZMn1aBBAx08eFDr16/Xvn37JF2815dWAY6Li8vxHC6XS88//7zmzp2rmJgYj31Op9P96O8777wjl8uVrf+0adNUvnx5j0ek/+7PP/9UrVq1FBcXp19//dUjZP59zKCgIP3www+688479eabb6pixYpaunSpxzFX+rsGgFsZoRQAcFuzWq368ccfdd9996lXr156+umn5eXlpVWrVilfvnyqUaOGvvvuO/3555967LHHdOLECf3444+KiIiQJH377bfKysrSnDlzVKNGDff22GOPuc+RlZWl3377TaVLl3ZvLpfrsiFjz5492T4FcmmGLTMz0yNsulwuzZs3L9siRV26dPE4LiMjQwsXLlSrVq3UqFEjNWrUSOvWrXM/lmqxWDRhwgRNnDhRO3bsUOPGjVW8eHHNmDHDo45LQfXSf48dO6Zz584pPj5e8fHxyszMlNPpVHx8vOLi4nTy5Ek5HA5JF9+77dSpk/73v//pp59+Us+ePd3jZmZmuhePKleunDZu3KimTZuqdevWGj16tEcNQUFBqlixoo4cOaI777xTS5YskcVi0bZt2/TZZ5+pUaNGeueddxQYGKgGDRpkW2wpJiZGDz30kBYsWKCffvpJnTp1ctcgXfwUzy+//KIxY8Zo9OjReuihh3TmzBl3/9OnT+vTTz91/8PEpb/jS/ckLS1N7733nmrXrq3y5ctr06ZNKlu2rEcNl+7TwYMHdfDgQfeCUT169NCqVas0evRolSxZ0r3/zJkzl/1HBgC45Zn0fVQAAG4bb7zxhtGgQQOPNm9vb2Px4sXu3ytWrDD69etntGvXzvDx8THefffdHMcqU6aMMXjwYPfvrl27Gs2aNTOOHDnisb3//vtGoUKF3Mft2LHDCAkJMRo1amSsXbv2ivVGR0cb/fv3NyIiIoyzZ8967Dtw4IAhydi7d69hGIYh6arbxx9/bBiGYSxYsMAoWrSosWnTJvd48+fPN3r27GmEh4cbLVu29DiXy+UyunfvbsyaNcvd9tVXXxmPPvqoERQUZLz22mtGSkqKYRiGsXXrVqNu3bpGaGio8fXXXxuGYRgnT540KlasaAQFBRmrVq1yj/HAAw8YlSpVMg4ePGgYhmEsW7bMeO655wxfX1/j5Zdf9qhh7dq1RqFChYyRI0e62zp16mQUL17cSE5Odrf99NNPRnBwsGEYhtG7d2/Dx8fHGD58uOFyuXK8xyNHjszVvfvrVrBgwcv9lQHALc1iGDfp2RwAACBJ+uGHH3Ts2DG99NJL7rZPP/1UzZs3d8+4HjhwQDVq1FDlypX15JNPqlevXjm+fxgdHa3AwED3DOf58+dlGIYKFix41TrOnDmjwoUL57puI4dP3+zZs0d33323tm/frnvuuUdJSUmy2Ww5rhB7aQVfb29v96qzf19ZeP369erUqZNq1qypQYMGXfbTKZcsX75cmzZtUrdu3VSoUCF3e2JiokaOHKl+/fp5XGNqaqqmTZumHj16uNuio6MVGhrqfu92+/btql69umrWrKkvv/xS5cqV8zjniRMnVKhQIfdnelwul44cOZJt9vOStLQ07d+/X5UrV77sdQwbNkwLFy7Uli1brni9l8ycOVMvv/zyZR9HBoBbGaEUAADc9lwuV46rJt8scXFxSklJUYkSJf61cwLAfxWhFAAAAABgGhY6AgAAAACYhlAKAAAAADANoRQAAAAAYBqr2QUgb8nKytKpU6cUHBycbcVGAAAAALcPwzCUmJioYsWK5bii/CWEUtxQp06dUnh4uNllAAAAAPiPOHHixBVXGyeU4oYKDg6WdPF/PLvdbnI1AAAAAMzicDgUHh7uzgiXQyjFDXXpkV273U4oBQAAAHDV1/pY6AgAAAAAYBpCKQAAAADANDy+i5uix+ub5GsLNLsMAAAA4Lbx5dj7zS7hujBTCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADANoRQAAAAAYBpCKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMQyjN4zZu3Kjq1asrODhYTZo00cmTJyVJ33//vcqUKSN/f3+1bt1aFy5ccPcZOnSo8ufPL5vNptatWysxMdGs8gEAAADkcYTSPCwlJUWPP/64XnrpJe3evVvBwcHq06ePDh48qKeffloDBgzQ9u3bdfbsWfXp00eS9NVXX+mrr77SkiVLtGvXLu3Zs0ejR482+UoAAAAA5FWE0jxsz549io+PV5cuXRQeHq63335bLpdLs2fPVq1atdSzZ0+VK1dOUVFRmjdvnpxOp06cOKFp06apZs2aKlu2rJ566in98ccfZl8KAAAAgDyKUJqHhYeHy8vLSyNGjFBmZqbuvfdeLViwQDt27FDVqlXdx1WqVElpaWk6fPiwBg8erPvvv9+9b9++fSpXrpwJ1QMAAAC4HRBK87BChQppxowZGjNmjMqWLasZM2ZIki5cuKCQkBD3cZf+/Nf3SiVp//79+v7779W9e/fLnsPpdMrhcHhsAAAAAJBbhNI8rl27djp27Jg6d+6s7t27a+DAgdmOMQwjW1tWVpaef/55devWTRUrVrzs+FFRUQoJCXFv4eHhN7R+AAAAAHkboTQPO3XqlA4dOqSQkBANGTJEixcv1tixY+VyuTxmRRMSEiRJ+fPnd7cNHz5ccXFxev/99694jsjISCUkJLi3EydO3JyLAQAAAJAnWc0uADfP119/rYULF2rVqlWSpPr168tqtaphw4ZatmyZ+7gdO3YoICBAd9xxhyTphx9+0Lhx47Rx40YFBARc8Rw2m002m+3mXQQAAACAPI2Z0jysSZMm2rBhg2bPnq2TJ09qyJAhKlq0qJ566ilt3rxZn376qfbv36/IyEg98cQT8vX11Z49e9S+fXt9/PHHCg8PV1JSklJSUsy+FAAAAAB5FKE0D7vnnnv0xRdf6J133lGFChW0atUqLViwQBUqVNDcuXM1duxYValSRUWLFtUHH3wgSZoyZYqSk5PVqVMnBQcHKzg4WHfffbe5FwIAAAAgz7IYOa1yA1wnh8OhkJAQte+9TL62QLPLAQAAAG4bX469/+oH/YsuZYOEhATZ7fbLHsdMKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMQygFAAAAAJiGUAoAAAAAMA2hFAAAAABgGkIpAAAAAMA0hFIAAAAAgGkIpQAAAAAA0xBKAQAAAACmIZQCAAAAAExDKAUAAAAAmMZqdgHImyaNqim73W52GQAAAAD+45gpBQAAAACYhlAKAAAAADANoRQAAAAAYBpCKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMQygFAAAAAJjGanYByJve+OigbH5BZpcB3LbGDChvdgkAAAC5wkwpAAAAAMA0hFIAAAAAgGkIpQAAAAAA0xBKAQAAAACmIZQCAAAAAExDKAUAAAAAmIZQCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlCah+zcuVMrV67M1bFxcXEyDOMmVwQAAAAAV0YozUOGDBmiTz75JFfHvv3222rWrJnOnTunX3/9VRaLJcdtxIgRN7lqAAAAALczQmkesX79es2fP1+//fabIiIism3h4eGy2+06fPiwJOnjjz/Wgw8+qJEjR8rX11c1atRQYmKixxYZGWnyVQEAAADI66xmF4B/Ljk5Wc8//7wGDRqkUaNG5aqPw+HQa6+9Jkn6448/tHXrVpUoUcLjmLS0NL355ps3vF4AAAAAuIRQeotzuVx65plnVLhwYVWtWlVVq1aVn59ftmMSEhK0Zs0aFSlSRL/99pu6du2q1atXKywsTBaLRffdd582btzo0W/IkCH/4pUAAAAAuB0RSm9x3t7e6tu3rypXrqwCBQroySefvGqfGjVqqGrVqnryySe1dOlSuVyu6z6/0+mU0+l0/3Y4HNc9FgAAAIDbD6E0DyhfvrwiIiIUEhIiqzX7X6nL5VL58uW1YsUKSZKXl5e+/PJL9e3bVykpKTIMQwcPHlRYWFi2vq+//voVzx0VFaWhQ4femAsBAAAAcNuxGHwXJM9bsGCBxo4dqzVr1ni0x8fHKzQ09B+NndNMaXh4uF4a/rtsfkH/aGwA12/MgPJmlwAAAG5zDodDISEhSkhIkN1uv+xxrL57G3C5XLJYLNna7733Xp06dUpVq1ZVUFCQ/P39FRAQoKCgIPn4+Cg0NFReXl46e/bsZce22Wyy2+0eGwAAAADkFo/v3gbsdruOHTsm6eJKvYGBgdq8ebMCAgJUrFgx+fj4aNGiRVq3bp1KlCihiIgIjRgxQsuXL1eRIkVks9lMvgIAAAAAeRUzpbeB8uXL6/jx4zp79qyaNGmi3bt3a9q0aWrbtq2ki++YXsnV9gMAAADA9WKm9DZQsmRJ1apVS+3atdOuXbsUFBSk6dOnZ3vH9HJyevQXAAAAAG4EQultYsKECWrXrp1Gjx6to0ePqkKFCipRooTsdru8vb3VoUMH97unXl5ecjqdioiIUFpamkqUKKHffvtNFSpUMPsyAAAAAOQxhNLbRPXq1XXkyBH37w0bNsjHx4fvigIAAAAwFS8L3qZ8fHzMLgEAAAAACKUAAAAAAPMQSgEAAAAApiGUAgAAAABMQygFAAAAAJiGUAoAAAAAMA2hFAAAAABgGkIpAAAAAMA0hFIAAAAAgGkIpQAAAAAA0xBKAQAAAACmIZQCAAAAAExjNbsA5E0jXy4ru91udhkAAAAA/uOYKQUAAAAAmIZQCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADCN1ewCkDeNm3NGfv4pZpcB3HSDOxYxuwQAAIBbGjOlAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADANoRQAAAAAYBpCKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMQygFAAAAAJiGUAoAAAAAMA2h9CZJT09XVlbWZfe7XC6lp6f/4/PExsb+4zEu+fnnn3Xo0KEbNh4AAAAAXI3V7ALyqgEDBmjz5s3y9vaWJJ04cUI2m02FChWSJGVkZOiBBx7Q+PHjc+y/d+9e3XXXXUpNTZWfn5+7PSsrSxaLRRaLRZLUqVMn5c+fXxMnTtTChQv1zDPP5DjejBkz1KFDhyvWPHnyZIWFhWnKlCnXfL0AAAAAcD0IpTfJRx995PF7xIgRKlGihDp37pyr/peCaJEiRdxthmEoJSVFS5YsUePGjSVJ33//vQYMGKDJkyerVKlSatu2rb788kuPsXr06JFt/Pfff19jxoyRv7+/u83pdCojI0NLly6VdHG21+FwKCkpKVc1AwAAAMC1IpTeBPXq1VN0dLQCAwPdbefOnZOPj4/GjBnjbouLi1OnTp0UFRV12bHOnz8vqzXnv6bMzExlZGToww8/lHQxoC5cuFAlSpTwOC4lJUVNmzb1aMvKytJTTz2ljz76SOnp6Zo1a5Y6dOggq9Uqp9Op//3vf3rxxRfl4+NzzdcPAAAAALlFKL0JKlWqpKFDh7pnHBs2bKgtW7aoRIkSSklJ0fHjx1WsWDGVKFFCZ8+eve7zzJs3TxMnTtTixYvl7+8vi8WiNm3aaM6cOR7HXW521mKxKDU1VS6XS126dNFzzz0n6eL7rn369NELL7ygtLQ0j8eHAQAAAOBGIpTeBBMnTlRiYqL69+8vSSpWrJikiyFw/fr1+vPPP1WtWjW9/PLLHv1effVVffzxxwoICJAkhYSEKCwszL0/ISFBO3bsUKVKlSRJLVu21JQpU/Tiiy9q+vTpcrlcua7RMAx5e3urcePGcjgckqRq1aq590lSjRo15HQ6tXnzZtnt9hzHcTqdcjqd7t+XxgIAAACA3CCU3gSJiYlav369RowYIUnKnz+/HnjgAXXr1k21atXSV199lWM/Hx8fPfPMM9neCb0kKCjIY9bS399f8+fP1+DBg5WZmSmLxaLly5d7BNlLWrRo4fE7LS1NPj4+2rBhg1JSUhQYGKhvv/1W0sV3SStWrKht27bJy+vKCzRHRUVp6NChVzwGAAAAAC6HUHoTHDp0SH369NGjjz4qh8OhAwcO6NNPP9W0adNUokQJTZ8+XceOHVPTpk01Y8YMd7+rBUBJ7lV3LwkODtbIkSNltVrVpk0btWnTJlc1Jicnq2DBgu4xe/furQ8++MC9v3fv3srMzJSvr+8Vx4mMjHTPCEsXZ0rDw8NzVQMAAAAAEEpvksDAQEVERCguLk779+/X4MGDNWbMGM2fP19PPPGEZsyYoY8//tijj9Pp1Jw5c7Ro0aIcx0xOTlZaWlq2tgoVKujEiRMqVKiQsrKy5HQ65ePjo6ysLLlcLvn7+ys9PV0pKSnufjExMapSpYoiIyM1d+5cZWVlKSQkxL0/KSlJ/v7+ev/99694nTabTTab7VpvDwAAAABIkq4+NYdrVqZMGY0ePVrbt2/XuXPnNG7cOEVFRSkwMFApKSn6/vvvZbVaNXz4cH366aeSLr4v2rRpU6WkpOj8+fM6c+aMzp8/r5iYGB07dkznz5+XYRju75xe8tNPP+m+++6Tr6+vfHx8tH37dnXs2FGLFi3Sp59+qo4dOyomJiZbcDx48KBKlCihqKgo7du3TwULFtTo0aO1bds2TZo0SZmZmRo8ePC/ds8AAAAA3J4IpTfB8ePHNXv2bJUvX17z5s3TiBEj9O2332r48OGqVKmSGjdurLp16+ro0aPuBYSmTZumnj17yjAMde7c2f1e6cCBA/XGG29IkubPn6+6det6LCw0bdo0tW3bVtKVH//96z6Hw6Ft27apevXqkiSr1arp06frxRdf1IcffqhnnnlGc+fOVYECBW7ofQEAAACAvyOU3gQZGRlKTk6WJHXq1Ek9e/bUgAEDVK1aNdWrV09NmjTRPffcI6fTqQ4dOig9PV0ff/yxBg8eLG9vb9WvX18TJkyQJHXv3l2TJ0/WkSNH1LJlSwUGBmrUqFGSpH379mnp0qVq2bLlVWv667uoX375perWravg4GBJFx8bPnHihEqVKqV+/fqpUKFCOnz4sPbt28dqugAAAABuKt4pvQkmTZqkM2fO6NixY7r77ruVmZkpf39/ZWZmavz48QoODlZaWpruuOMOSdLhw4cVERGhTp06SZJat26tNWvWyOl06q677tIrr7yi+Ph4lS5dWqNGjdJ3330nSfrzzz/1yCOP6Pjx4ypXrpwMw1D9+vXldDq1ZMkS9zuly5cvV0ZGhkJCQnT27FklJSWpT58++uabbzR8+HAlJiaqdu3aGjhwoBYtWqTFixfrp59+0ttvv62DBw+qd+/e2d5/BQAAAIAbwWJc+iglbkkZGRny8fG5rr5Op1OxsbHu76jmJCUlRYZhKDAwMFdjOhwOhYSE6J3J++XnH3xddQG3ksEdi5hdAgAAwH/SpWyQkJDgfm0xJ8yU3uKuN5BKF1fOvVIglaSAgIDrHh8AAAAAroZ3SgEAAAAApiGUAgAAAABMQygFAAAAAJiGUAoAAAAAMA2hFAAAAABgGkIpAAAAAMA0hFIAAAAAgGkIpQAAAAAA0xBKAQAAAACmIZQCAAAAAExDKAUAAAAAmIZQCgAAAAAwjdXsApA39X+6sOx2u9llAAAAAPiPY6YUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADANoRQAAAAAYBqr2QUgb5q2NEH+AYbZZQBX1a15iNklAAAA3NaYKQUAAAAAmIZQCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADANoRQAAAAAYBpCKQAAAADANIRSAAAAAIBpCKUAAAAAANOYGkrT09OvuD8rK0tOp/Oq48TGxiorK+uy+w3DyNa2ZMkSpaSkXHHczMxMZWZmXtMxTqdTGRkZV6n4ovT09FwfezVJSUn66quvcnW/rkVWVtZV/54AAAAA4HqZFkoTEhIUEhKikJAQhYaGKjQ0VDabTTabzf07NDRUNWvW9Og3adIkNWrUyKOtW7duevPNNy97roceekhvvvmmO7DFxcWpbdu2iomJuWKNEydO9KglNDRUFotFwcHBHm3jx4939+nbt6/CwsI8Nn9/f/n7+2drL1CggObMmXPZ88+bN0/VqlWTy+W6Yp2S9OWXX+r111+/ppBbrFgxeXt7y2q1XnHr1atXrscEAAAAgGthNevEISEhSk1NldPplM1mk3Qx0EnSBx98IEk6fvy4ihcv7tHPz89P/v7+7t9Op1O//PKLPvroo8ue68svv1S3bt1Uo0YNbd68WZMmTZLFYskWbtPS0vTcc8/pvffekyT16dNH3bt3d9cnSaGhoVq9erWqVq0qSTpy5IhKly7t3j9x4kRNnDjRY9zHH39crVq1UpcuXXJxZ/6Pt7e3EhIS5O3tfcXj4uPjNWzYMFksFlWqVCnbfsMwlJiYqHHjxqlz587u9uDgYE2fPl1NmjRxt507d05+fn4KDg52t11tthgAAAAArpdpoVSSPvroI23ZskXTp0/PcX+HDh1Ut25djRo1yuMRXIvFIsMwZBiGfvjhB7lcLj344IPu/Q6HQ1WqVNGyZcskSSVKlNDixYu1efNmXbhwQRMmTNCOHTs8wmRODMNQnTp19O6776px48bZ9h88eFAVK1bUtm3bdNddd+U4Rnp6utauXavPP//8qvfj77y8vBQaGnrV41566SWVL19ea9eulcViyfX4fw33lzz77LNq1aqVx+yo1Wrq/yYAAAAA8jBT08ZDDz2kQYMGqV27dmrZsqXHvvXr12vjxo367LPPJEm//fab7r//fnl5eckwDHl7e2vKlCn64osvNGXKFD311FPuvpMmTdJPP/0kSfr9998VExOjRx99VPfee6+aNGmi8PBwjxB7SdeuXfXWW2+5f1ssFjVv3lzPP/+8du3apaCgII/j3333XT3wwAPZAmmvXr3c58/MzJTD4VD16tWzne+tt95S165dJUnnz59XXFycgoKC5OXl5W6zWq3ux4wNw1BqaqpSUlJUunRpBQYG6v3339fy5cv1/fffq0qVKvL39/cIppdmSUeNGqVWrVpd4W/jIl9fXwUEBFz1OAAAAAC4EUwNpXfddZcGDBigPn36eDxC6nK51KdPH/Xu3VvlypWTJNWoUUNpaWmaPXu2vv32W82fP18LFizQmTNnZLPZFBkZqaioKEkXZycvzQLGx8erR48eqlevnj766CO98sorOnbsmLy8vNSsWTN9/fXXeuutt/TVV19p165d2Wp88803NXPmTA0dOlTvv/++u33Lli2aNm2afvvtt2x9EhISNHjwYPXo0UMNGzbUkiVLVKlSJU2dOlUHDx7U6NGj1blzZ6Wmprr7zJ07VwMHDpSPj4+7LT09XU6nU3feeae7LTMzU06nU+vWrVOtWrXUqlUrPfzww6pSpYq2b99+XX8PTqdTVqs1x8eEMzIyPGrKqe9fF1dyOBzXVQMAAACA25Ppn4Tp37+/QkJCFBcX527bsGGDYmJiNGTIEHebt7e3x7udVqtVhQoV0uTJk1W3bl3NnTvXvWjQX0Np48aNtX37dhmGoejoaLVp00YOh0PBwcEqU6aM1q5dq6SkJI8+f+Xr66vIyEhduHDBo33SpEnq1q2b7r333mx9/h7iWrdurUqVKumdd97xaP/rY7G9evVScnKy4uPj3dvAgQMVEBCgmJgYd1tSUpIyMjJUq1YtSVK5cuXUsWNHFS1aVBERETluYWFhSkpKuuzfQZUqVWS1WmWxWPTjjz+qS5cuslgsslgs8vX1vWw/SYqKinIvWBUSEqLw8PArHg8AAAAAf2XKTKlhGEpPT5evr6/y58+v33//3SPI1atXT9u3b1dISIhcLpcyMjJks9myvS9Zr14995+/++47JScna9WqVdkCZr58+TR58mTZ7XY1bNhQf/zxh3x9fTVs2DBJUqVKlZSUlKTMzExt3LhRP/74ozIyMmSxWGS1WtWlSxd169bN49yffPKJ+1MpGRkZysrK8gjNf/X99997zJTm1g8//KDg4GBNmzZNL7744mWPu9IMaUJCgkJDQ3N8JNflcslms2nFihWyWCzy8vJSx44d1aJFCz311FPuv6criYyMVP/+/d2/HQ4HwRQAAABArpkSSvfv36+qVatmC3GXHmf98ssv3W2ZmZnKyMjQ8ePHVbhwYXe7YRhKSUlRYGCgJGnXrl0aOHCgxo8fn+Os56BBg3T//fdr8eLF6t27tzp27Kh8+fIpISFBDRo00OjRo5UvXz516tRJkjRkyBB98MEH2WY9ExISVK9ePY9HXdPT09WoUSMtWrTon9+c/2/58uU6c+aM5s+fr5YtW6pt27YKCwu75nEufU4mpwWQYmNjZbfbPVY4ttlsCgkJ0YIFC3Tq1CkNHTr0iuNf+owPAAAAAFwPUx7frVChglJTUz0eVY2Pj1fPnj3Vs2dPj7akpCQ5nU4VLlxYiYmJ2rt3r7Zu3aqIiAgtWbJEe/bs0SOPPKIJEyZozZo1evLJJ5Weni4/Pz/3+ZKSkjRnzhzVr19f/v7+unDhgux2u1JSUjRgwABJFz8HExQU5O43cuTIbI/TxsfHKyQkRGvXrvVoS0lJuWIgvdzju5eTlJSkXr16afjw4apZs6batm2rHj16eKxAnFvBwcGyWCw6duyYkpOT3e2JiYk6c+aMIiIicuxXoUIFjRo16rrfUwUAAACA3DD9ndLcmjVrlkJCQrR69WoNGjRI69atU9u2bTV+/HhVrVpVa9asUZkyZSRdXHznrzOlM2bMUK1atdyfgLlw4YKCg4P1wAMP6LHHHpPT6VRaWpp8fX1vyjc5v//+e+3cufOqs47SxZnYxx57TJUrV9bzzz8vSXrvvfe0d+9e9e7dW1lZWdd0bh8fH5UuXVqbN2/Wq6++qtmzZ0uSNm7cqFKlSikkJCTHfg0bNlT37t2VkZFxTecDAAAAgGvxn/oA5aVHTXPSokUL/fnnn7rnnns82idNmuT+hMratWuVkJCgpUuXqkePHpIuPv47ZswYj5VzAwIC9PDDD7t/f/755zp37pwCAgLk4+NzxU+nuFyuK9YpSVlZWYqMjNSIESN07tw5NWnSRFarVSkpKcrMzNTMmTN14cIF92JFlyxatEgvv/yySpUqpWnTprnbAwMDtWDBAtWvX1+HDh3SF198oWLFil2xhr968sknNXToUJ06dcr96ZyZM2eqWbNmcjgc7kWNLl2f0+lUUlKS3n33XWVlZSkuLk758+fP9fkAAAAAILf+UzOlqampSktLy3Gf3W7PFkgluQOpdDFode/eXeXLl3eHr++++04Wi0WPP/64+7iffvpJR48edW+ffPKJihQpovXr11/1W55XqvESp9OpqKgoRUdHy+l0KiYmRtHR0YqLi5PD4VB0dLSeeOIJ9yJCLpdLjRs3VsuWLdWqVSstXbrU/a7sJZdWCo6JiVGFChW0f//+K9bwV4MGDVJISIiaNWumBx98UHv37tWsWbP0zDPPqE2bNrLb7QoODlZwcLCWLFmiHj16uH+HhISofv36uT4XAAAAAFwLi3E9LyreYk6ePOmxmM/f9e/fX/nz59ebb775L1blac2aNbLZbNlmT//O6XRqyZIlHiH7WsXGxup///ufBg8eLKfTKR8fH49w/1dZWVnZ3tG9EofDoZCQEH30zXH5B9ivu0bg39Ktec6PsAMAAOCfuZQNEhISZLdfPhvcFqEU/x5CKW41hFIAAICbI7eh9D/1+C4AAAAA4PZCKAUAAAAAmIZQCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADCN1ewCkDd1ejhEdrvd7DIAAAAA/McxUwoAAAAAMA2hFAAAAABgGkIpAAAAAMA0hFIAAAAAgGkIpQAAAAAA0xBKAQAAAACmIZQCAAAAAExDKAUAAAAAmIZQCgAAAAAwjdXsApA3LdqUpIBA/s0D/z2t7g8yuwQAAAD8BakBAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADANoRQAAAAAYBpCKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMYzW7ANwYhmEoMzNTmZmZSklJUUJCguLi4nTixAkdPnxY27dv15gxY1SwYEGzSwUAAAAAN2ZK84hnn31WZcqUUY0aNfTYY4+pYcOGGjx4sDZv3ixJaty4sVJSUnLsW7ZsWS1ZssT9u0iRIlq2bNm/UjcAAACA25vFMAzD7CJw4/Xt21f33XefOnTokG3frFmz9MILL8jHx0eSlJiYKH9/f1mtFyfOHQ6HAgICZLVa5XK5lD9/fh07dixX53U4HAoJCdFXy04qINB+4y4IuEFa3R9kdgkAAAC3hUvZICEhQXb75bMBoTQPqFSpklJTU2Wz2eTl5aXk5GTFxcUpMDBQfn5+CggIkGEYiouLU8OGDTV16lQ5nU7ly5dPFotFd955pyZOnKgHH3xQklS0aFHNnDlTjRs3lsvlUkZGhvz8/HJVC6EU/3WEUgAAgH9HbkMpj+/mATt37tShQ4e0e/duderUSVlZWWrevLmGDRumgIAAvfDCC9q1a5dOnz6t2bNnKzAwUPnz55fFYpEkOZ1OZWRkuMfLyMiQ0+mUJHl7e+c6kAIAAADAtSKU5hEXLlxQly5d9L///U+//PKLChQoIG9vb/34448aN26cmjdvrmXLlrnDZ1pamjIzMyVJM2bMULVq1dxjzZs3TzVr1szVeZ1OpxwOh8cGAAAAALlFKL3FnT59Wi+//LJKlSolp9OpzZs3KyIiQnXq1FG5cuVUqlQpbdq0SUFBQXr44YdVsGBBxcbGqkyZMsqXL59CQ0PVokULFS5cWMHBwQoNDdXjjz+usmXLym63y2KxKCEh4bLnj4qKUkhIiHsLDw//F68eAAAAwK2OT8Lc4goUKKCDBw9q9uzZiouL0x133KGgoCD3o7lJSUm65557tGrVKv3555/uWdSTJ096jFO2bFnNnDlTtWvXdrdt375d1apVu+Lz35GRkerfv7/7t8PhIJgCAAAAyDVC6S3O19dXP/30k/t3x44dJUmHDh1St27dVL16db388suSpCpVqqhKlSq5Hjs5OVmhoaHugJsTm80mm812ndUDAAAAuN0RSvOAhx56SLt373Z/4kW6+M7ohQsXdOTIEX377beSpKysLBUqVEhbtmzJ1bixsbEqVKjQTakZAAAAACRCaZ6wbNmybG3r1q1T3759cx1A/2rVqlV68803tWPHDvXt2/cGVAgAAAAAOSOU5kFxcXE6dOiQfH19c90nIyNDWVlZkqTatWure/fuuvfee1W5cuWbVSYAAAAAEErzol69emnDhg0aPHhwrvskJCQoNTVVkuTv769OnTrdrPIAAAAAwI1QmgfNmTPnmvvEx8ff+EIAAAAA4Cr4TikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADANoRQAAAAAYBpCKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSgEAAAAAprGaXQDyphY1g2S3B5ldBgAAAID/OGZKAQAAAACmIZQCAAAAAExDKAUAAAAAmIZQCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATGM1uwDkTet3ORQYZHYVuJXVv8dudgkAAAD4FzBTCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADANoRQAAAAAYBpCKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSvOQnTt3auXKlTd0zMzMTGVmZt7QMQEAAADgEkJpHjJkyBB98sknuTp2//79slgsslqtV9x8fHw0Z86cm1w5AAAAgNuV1ewCcGOsX79e8+fPV5EiRRQREZFtv8vlUkJCgrZt26Y77rhDwcHBkqS0tDRZrf/3v0F0dLQKFSokX19fSZJhGMrKyvpXrgEAAADA7YdQmgckJyfr+eef16BBgzRq1Khc9fH398+x/Y477tC2bdt09913S5IsFou8vb1vWK0AAAAA8FeE0lucy+XSM888o8KFC6tq1aqqWrWq/Pz8sh2TkJCgNWvWqEiRIlccz9fXVwEBATezZAAAAABwI5Te4ry9vdW3b19VrlxZBQoU0JNPPnlN/Q3DUFpammw2mywWS7b9GRkZ8vHxuWx/p9Mpp9Pp/u1wOK7p/AAAAABub4TSPKB8+fKKiIhQSEiIx/uhl7hcLpUvX14rVqzItu/MmTMKDw/3aCtdurT7zw0aNNDq1asve+6oqCgNHTr0+osHAAAAcFsjlOYBxYsXV2Ji4mX3L1iwQGPHjvVoc7lc8vb2VpEiRXT06FH5+vrKYrGoTJkyWr16tcLDw2UYxlXPHRkZqf79+7t/OxyObCEXAAAAAC6HUHobcLlc2R7NjY2NVXBwsKxWq0qVKuVut1gsKliwoAYMGKDOnTurSZMmVxzbZrPJZrPdlLoBAAAA5H18p/Q2YLfbdezYMUkXV+qVpAMHDuT46ZhLypYtq27duiklJeXfKBEAAADAbYpQehsoX768jh8/rrNnz6pJkybavXu3NmzYoHvvvfeyfV599VXVqVPHHWIBAAAA4GYglN4GSpYsqVq1aqldu3batWuXihcvrtmzZ6tZs2aKi4tTYmKikpKSlJSUJElKSUmRxWLR5MmT5evre8X3VQEAAADgnyCU3iYmTJigEydOaPTo0Zo7d67i4uLUvHlzFSpUSHa7XcHBwQoODlZycrIqVqzo/h0aGqrIyEizywcAAACQR7HQ0W2ievXqOnLkiCRpz549GjdunAIDA5WUlCRfX195eeX87xMul0sul+vfLBUAAADAbYRQehu66667dNddd0mS/Pz8rnist7e3vL29/42yAAAAANyGeHwXAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADANoRQAAAAAYBpCKQAAAADANIRSAAAAAIBpCKUAAAAAANNYzS4AeVOdinbZ7XazywAAAADwH8dMKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMQygFAAAAAJiGUAoAAAAAMA2hFAAAAABgGkIpAAAAAMA0VrMLQN6049A5BQWlmV3GbatKuUJmlwAAAADkCjOlAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADANoRQAAAAAYBpCKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMQygFAAAAAJiGUAoAAAAAMA2hFAAAAABgGkIplJ6ervPnz5tdBgAAAIDbEKE0j6hfv76sVutlN4vFouTk5Bz7btq0SSVKlNDZs2f/5aoBAAAA3O4IpXmEn5+fVq5cqczMzGxbUlKSJCkgICDHvj///LNatmypQoUK/ZslAwAAAAChNK/w8vKSxWLR4cOH3W0pKSkaOnSoMjIy3LOllwwZMkReXl6yWq2KiorSd999l+MMq5eXl7p06WLGJQEAAAC4DRBK8wiXyyUvLy/16tVLI0aMkCQtXbpUCxculNVqlbe3t8fx/v7+eu6553KcWf3r9vbbb8vPz8+MSwIAAABwG7CaXQBujMzMTPn4+GjGjBm6//77VatWLc2YMUODBw+W1WpVVlaWx/F/D6lXcqVjnU6nnE6n+7fD4bj24gEAAADctpgpzSOSk5Pl5+enggULatq0aerYsaN27dqltm3bytvbWxkZGZft2759e+XPn18RERGKiIhQUFCQBg4cmKvzRkVFKSQkxL2Fh4ffqEsCAAAAcBsglOYR8fHxstvtkqQ6deqoVKlSuuOOO+Tl5SUvL68rBlObzabXX39dR48e1dGjR9WuXTvZbLZcnTcyMlIJCQnu7cSJEzfsmgAAAADkfYTSPMAwDJ06dUrFihWTJH388ceyWq3at2+fVqxYIUkqUqSIYmNjb/i5bTab7Ha7xwYAAAAAuUUozQP27t2rYsWKydfXV9OmTdPYsWM1b948jRo1Sq+++qoMw1Dp0qW1f//+HPunpaVp1KhR7sd3v/32W4/3RAEAAADgZmGhozzg559/Vr169bRu3Tq99dZbWrlypYoUKaInn3xSUVFR2rBhg8qVK6d169Zp06ZNeuqppzz6z5kzx6TKAQAAANzuCKW3uMzMTE2ePFkffvih6tatq23btil//vySJIvForVr1yo4OFinT5/WwIEDFRcXp9q1ays1NfWK48bFxSk6OlpHjhxxjwcAAAAANxqP797ifv/9d/n7+6tJkyaSlC1ABgcHS5LatGmj8PBwlSlTRnXq1JG3t7cCAgIuO67L5VL9+vW1detWtWvX7uZdAAAAAIDbmsUwDMPsIvDPXLhwQfny5bvqcVlZWXK5XPLx8blptTgcDoWEhGjd1oMKCgq+aefBlVUpV8jsEgAAAHCbu5QNEhISrrggKo/v5gG5CaSS3J+HAQAAAID/ChIKAAAAAMA0hFIAAAAAgGkIpQAAAAAA0xBKAQAAAACmIZQCAAAAAExDKAUAAAAAmIZQCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTWM0uAHnTPWUKym63m10GAAAAgP84ZkoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADANoRQAAAAAYBpCKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSgEAAAAAprGaXQDypiOHDys4ONjsMm6KO8qUMbsEAAAAIM9gphQAAAAAYBpCKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMQygFAAAAAJiGUAoAAAAAMM0NDaWGYejIkSM3ckgAAAAAQB6Wq1DqcDhUo0YN9++srCy9+OKL2Y5LS0vT3XfffeOqAwAAAADkabkKpT4+PtqzZ8//dfLy0owZM7IdZ7PZ5OPjc+OqAwAAAADkabkKpV5eXvLy8jzUz88vx+OsVuuNqQwAAAAAkOflOkE6nU69/fbb7t+pqakevwEAAAAAuFa5DqWGYejEiRPu308//bTHbwAAAAAArlWuQ6mfn5+++OKLqx6XP3/+f1RQXpGeni5fX9/L7s/KylJGRoZsNtsVx4mNjVW+fPmyPT4NAAAAAHlBrkJpVlaWsrKyPNoaNGggi8VCWMpBQkKCihQpIl9fX1ksFkkXH3eWJH9/f0kX72np0qX1559/uvtNmjRJc+fO1cqVK91t3bp101133aVRo0bleK6srCx5e3vL29s7x/2GYahatWravHmzR/ukSZPUvn17BQUFKSMjw/2O8Jw5c/TTTz9p+vTpki6Ga4vFwgJWAAAAAG6KXIXSjIwMlS1b1qPthRdekK+vr0cYcrlc6t69+42t8BYUEhKi1NRUOZ1O90xo3759JUkffPCBJOn48eMqXry4Rz8/Pz93aJUuvsf7yy+/6KOPPrrsuby8vOTr6yun05nj/iVLligqKipb+08//aRTp06padOmatu2rbvOlJQUpaamKiIiQtLFv/vhw4fr+eefz9W1AwAAAMC1yFUo3b59u7Zt2yZJmjFjhu6//3516NAh23GpqalKT0+/oQXeqj766CNt2bLFPeP4dx06dFDdunU1atQoGYbhbrdYLDIMQ4Zh6IcffpDL5dKDDz7o3u9wOFSlShUtW7bM3XbpHwYOHTqkMmXKSJI2bdqkw4cPKzQ0NMfHiJ955hkNHz5cw4YN0+nTp93t8+fP16JFizR16tR/dgMAAAAAIBeuGkq3bt2qBg0aaPDgwRoxYoRWrFiht956S2+99Zb70dRLMjMzc5yVux099NBDGjRokNq1a6eWLVt67Fu/fr02btyozz77TJL022+/6f7775eXl5cMw5C3t7emTJmiL774QlOmTNFTTz3l7jtp0iT99NNP2c7ncDhUv359zZw5Uw8++KBmzZolLy8vPfLIIzk+2tuqVSu1bt1aktS5c2ctWrRIQUFB7pnS5cuXKzMzU2lpaTp//vyNvDUAAAAA4HbVUFqtWjX98ssv6tq1q7Zt26Y5c+bo119/1XPPPScfHx/VqFHDPdOXnp6u9PR0vfLKKze98P+6u+66SwMGDFCfPn3UpEkTd7vL5VKfPn3Uu3dvlStXTpJUo0YNpaWlafbs2fr22281f/58LViwQGfOnJHNZlNkZKQ77Kenp3s84itdnF212+365ptv9NRTT2ndunX6/vvvtWHDBu3evTvb+8ALFixQSkqK8uXLp6ZNm8rX11djxoxR586dPWZKDx48qAYNGlzxOp1Op8ejww6H4x/dNwAAAAC3l1w9vlu3bl1t3rxZrVq1Up06dbRlyxatWbNGTZo0UbFixTRu3LgrrjR7u+rfv78WLlyouLg4d9uGDRsUExOjIUOGuNv+vlCR1WpVoUKFNHnyZFWpUkWvvvqqqlSpoqeffjpbKE1JSXG/D/rAAw+oV69eql27tpo1a6bixYtr3759ysjI8KgrJiZGW7du1ebNm9W0aVOlp6frzTff1JgxY5SYmKjExERt3LjR/Y8MVxIVFaWhQ4f+k9sEAAAA4DaW66Vz7Xa7fvjhB7333nv67rvvVK5cOa1evVr33nsvgfQvDMOQ0+mUYRjKnz+/fv/9d5UoUcK9v169etq+fbtCQkLkcrmUlpbm8U7pX49r3LixwsLC9N1336lkyZJatWpVtlAaHx8vu93u/t2jRw9duHBBd955pyTJ19c3W7B88cUX1b59e/eKul9++aWio6PVrl07ORwOtWnTRr1799bOnTt17ty5K15vZGSkEhIS3BvfrgUAAABwLXIdSocOHSqbzaamTZuqb9++slgsKlmypAYOHHgz67vl7N+/X6GhocqXL59CQ0NVsGBBhYaGauLEiZo4caJCQ0NVtmxZhYaGKiQkRCEhITp79qzHGIZhKDk52f17165deuKJJ3Tu3LlsofT48eMKDw939+vevbvatGmjMWPG6Pz58ypSpIhiY2NzrPWv7wQvXLhQX375pUaPHq20tDQtXLhQjRs3vmootdlsstvtHhsAAAAA5FauQ+kXX3whq/Xi075+fn567733NHbsWKWnp+u9997Te++9p/fff19bt269acXeCipUqKDU1FTFx8d7bD179lTPnj092pKSkuR0OlW4cGElJiZq79692rp1qyIiIrRkyRLt2bNHjzzyiCZMmKA1a9boySefVHp6uvubopL0xx9/6K677lJWVpZ69OihxMREzZgxQ23bttWwYcNUsmRJHT9+PNsjvJdkZmbqgw8+ULdu3TRv3jyFhYXJ19dXixYtUsWKFXXffffp0KFD/9btAwAAAHCbyXUo9fL6v0MNw1BaWprS0tIkXfwUTGpqqs6ePatnn332xleZx82aNUshISFavXq1Bg0apHXr1qlt27YaP368qlatqjVr1rg/9eJ0Oj1mSn/++WfVq1dPo0aN0sGDB/Xdd9/J29tbb731lr755htlZGSoSJEi2rRpkyIjI91/Z5J04cIFHT58WL/99ptWrlyp6tWrKyMjQy6XS97e3po0aZJGjhypkiVL/uv3BAAAAMDtwWLk9ELj39StW1dbt25V7dq1ZRiGtmzZosTERElSoUKFPB4/LVSokM6cOZPtczG3uz59+kiSPv7442z7HA6Hjh07pnvuucejPSsry/2PAWvXrlVCQoIiIyPVo0cP9e7dW0ePHlXlypV14sQJ93u9fw2siYmJCg4O1ksvvaR169bp4MGDOn/+vPbv36+nn35aWVlZSk5O1rPPPqs6deqodOnSio+P17lz51S1alWlpKQoLi5O58+fd38+5mocDodCQkK07Y8/FBwcfF336r/ujv//DwQAAAAALu9SNkhISLjia365Wn132LBh6tixo0aPHq2srCy1a9cu2zFr165V/vz5deLECQJpDlJTUy97X+x2e7ZAKnnOTs+cOVM//PCD7r//fvd3S7/55ht16dJFISEhOY57KRS+9tprmjdvnl544QX5+fnpww8/VM+ePdWnTx9FR0friy++0MSJE3Xw4EElJCQoLS1NmZmZ7gWYWrZsmetQCgAAAADXIlczpZIUERGhQ4cOydvb2/2eoiQVKFBA33//vTp16qQvvvhCDRs2vJn14i9cLpeSk5NztbhQenq6fHx8ZLFYPGZgbzRmSgEAAABIN3imVJI6d+6srKwseXt7Ky0tTRkZGTIMQ2XLltUDDzygvXv3ur+XiX+Ht7d3rle7/etne25WIAUAAACAa5XrUDpkyBBJ0rPPPqvTp0/L29tbI0aM0MiRI2W1Wt0r8wIAAAAAkFu5SpLz589Xvnz5JEnfffedevToIcMwlJiYqKioKPcsnGEYcjqdevjhh29exQAAAACAPCNXoXTcuHEKDAyUdPE9xlGjRrn3bd26Va+//rqCg4OVlZWltLQ0QikAAAAAIFdyFUrXrFmj9evXKyAgQI0bN9bixYvd+1599VWVKVNGvXr1umlFAgAAAADyplyveLNixQp16NBBSUlJatSokaZOnSpJatu2rb755pubViAAAAAAIO/K9SdhLklLS9PmzZtls9lUs2ZNZWZm6ty5cypatOjNqhG3ED4JAwAAAEC6CZ+EucTPz0/16tX7vwGsVgIpAAAAAOC68MFKAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMc80LHQG5UfqOO664whYAAAAASMyUAgAAAABMRCgFAAAAAJiGUAoAAAAAMA2hFAAAAABgGkIpAAAAAMA0hFIAAAAAgGkIpQAAAAAA0xBKAQAAAACmIZQCAAAAAExDKAUAAAAAmMZqdgHIm87s+V0pQUFml3HDFalYw+wSAAAAgDyFmVIAAAAAgGkIpQAAAAAA0xBKAQAAAACmIZQCAAAAAExDKAUAAAAAmIZQCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADANoTQPiI2NlWEY/3icnTt3as2aNTegIgAAAADIHUJpHhAZGalWrVrJ4XAoMzNTFotFYWFh2TYfHx8tX778suOsXLlSL7zwwr9YOQAAAIDbndXsAvDPTZo0SW+88YYeeeQRbdiwQd7e3jp//ny24xo2bChfX19J0q+//qoWLVooODjYvd/lcikxMVERERGSpMzMTCUlJWnhwoWqX7/+v3ItAAAAAG4vhNI8wMvLS1FRUTpz5owsFou8vLwUFhaW7biEhAR5eXm5+xQvXlzbt2+XJC1cuFDVqlVTiRIlJElz5sxR7dq13QEVAAAAAG4GHt+9xR0/flzvvfeeJKlw4cKSJIvFovPnz2fb6tSp49HXYrHI6XQqMzNTI0aM0P79+937xo0bp/379ys9PV2ZmZmXPb/T6ZTD4fDYAAAAACC3CKW3uJiYGL3//vvq0aOHsrKyJF18DPdyLh1jGIa8vb312muvqWrVqtq1a5e6d++uqlWrun/37NlTVatW1apVqy47XlRUlEJCQtxbeHj4jb1AAAAAAHkaj+/e4mrWrKk//vhDbdq00e+//65q1aopKChIoaGhOR5/KZSmpaXJx8dHH3zwgXucV155RbVq1ZIkPfHEExo7dqwaNWp0xfNHRkaqf//+7t8Oh4NgCgAAACDXCKV5QIkSJbR+/Xr5+PgoJiZGMTExOnz4sD744ANNmTJFr776qpo0aaJmzZrpyJEjkqTk5GQVKFDAPUabNm3066+/6tdff5Uk1alTx/048JXYbDbZbLabc2EAAAAA8jxCaR7Rs2dPdenSRSNGjFDr1q1VuXJlrV27VpK0Z88eVatWTQ6HQ9WqVdMff/yhmJgYFStWTD/88INefvllGYahkJAQWSwWSRdnUvfs2aMlS5bIx8fHzEsDAAAAkIfxTmkekJGRoUWLFql8+fJ65ZVX9NFHH8lms8nb21vSxZV2fX19NW7cODVt2lQRERE6ePCgSpQooccee0xHjhzR448/rkcffVTbtm3TypUrlZmZqaFDhxJIAQAAANxUzJTmAQsXLlTt2rVVsGBBPfzwwzp37px2797tccz+/fv14Ycfatu2bZKklStXasiQIe79UVFRatCggaxWq5YsWaL+/furbt26/+JVAAAAALgdMVOaB3zwwQfq2LGjpIuzolu2bFHFihU9jilXrpx++eUXlSpVSn/++af27dunBg0aSLq4Eu++fftUtWpVjRgxQidOnJDFYtG2bdt0/vx5GYbxr18TAAAAgNuDxSBx3NJ2796tOnXq6PTp00pPT1elSpUUGBgol8ul8+fPq1ixYjp79qyCgoJks9mUlJSkgQMHKj4+Xu3bt1eHDh10+vRp3XnnnXryySfVtm1bbdu2TfPnz9e6deu0c+dO3X333fr999/l5XX1f8NwOBwKCQnR/o0rFRwU9C/cgX9XkYo1zC4BAAAAuCVcygYJCQmy2+2XPY5QmgecO3dOBQsWvK6+x44dU8mSJd0LHP1dZmamEhMTlS9fvlyNRygFAAAAIOU+lPJOaR5wvYFUkkqVKnXF/VarNdeBFAAAAACuFe+UAgAAAABMQygFAAAAAJiGUAoAAAAAMA2hFAAAAABgGkIpAAAAAMA0hFIAAAAAgGkIpQAAAAAA0xBKAQAAAACmIZQCAAAAAExDKAUAAAAAmIZQCgAAAAAwjdXsApA3Fb6ruux2u9llAAAAAPiPY6YUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADANoRQAAAAAYBqr2QUgb4rbvFQZgQFml3FDFajd3OwSAAAAgDyHmVIAAAAAgGkIpQAAAAAA0xBKAQAAAACmIZQCAAAAAExDKAUAAAAAmIZQCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADANofQ/5vjx49fVb+nSpdq7d2+uj4+Njb2u8wAAAADAjUQovUk2bNig4OBg1a5dO9tWq1YtWSwWpaSkePQ5efKkypYtq9OnT3u0G4ahAwcOaPLkyfr666+zncvpdKpLly5atmxZrmpzuVxq0qSJXnvtNWVmZioqKkoWiyXHbd26ddd/EwAAAADgKqxmF5BX+fv7q2DBgurcuXO2fZmZmdq0aZMCAgI82n/88Uc1a9ZMXl5eatasmRITE3XmzBlFR0erVKlSqly5smrWrJltvDFjxig2NlZjxozR2LFjs+13Op2y2+3at2+fJMnb21tr1qxR165dtXDhQvn6+qp///4aOnSoR79mzZr9gzsAAAAAAFdHKL2JLBaLrNbL32LDMGSxWNy/P/vsM7344otyuVzasWOHNm7cqPz586tz587q2LGjHnvssWxj7NixQyNHjtSCBQv0yCOP5Kqu5ORkBQQEaO7cuZKk8ePH65NPPtFnn33mcVxSUlKuxgMAAACA60UovUkyMjLkdDo1derUbPsMw1CBAgWUlpYmf39/SdJvv/2mTZs2aeDAgfLy8pLValWJEiUkSX5+fkpNTc02TkxMjNq0aaNXXnlFa9asUWRkpHx9fT2OSU9PV2hoqFauXOlumzBhgrZt26avvvpKXl5eslgs6tu3r0aPHu3Rt2HDhv/0NgAAAADAFRFKbzDDMJSWlqaaNWsqOjr6ise6XC45nU7ZbDa9+eab2R7nvSQoKEjJycmSLoZMwzBks9kUFhamMWPGqGXLlrJYLBo5cmSuauzatasaNWqkd955R8OHD5fL5bq2i/wLp9Mpp9Pp/u1wOK57LAAAAAC3H0LpDXb+/Hndf//9On/+vEJCQhQcHKzExEQlJiaqWLFiHsdmZmbqzjvv1JNPPqmYmBg1btzYvS86OloRERGSLga9WbNmaejQocrIyNALL7ygIUOGyGq1KiUlRfnz55fdbvd4FPiS9PR0devWTcOGDXO3hYWFafHixRo/frwkyWq1aurUqTnO6vr4+FzxeqOiorK9iwoAAAAAuWUxDMMwu4i8qHPnzipRooTuu+8+/fbbbzp37pxat24tp9MpL6+Lix5HRESoatWqOn/+vE6fPq233npLHTp0UN26dVW7dm0dPXpUkjR9+nStW7dOU6ZMueY6+vXrp+DgYI9QKl2c0XU4HAoJCflH15nTTGl4eLiOLP9GwYE5z/zeqgrUbm52CQAAAMAt41LeSEhIkN1uv+xxfBLmJkpOTlZ8fLz70dvNmzdrypQpio+P10cffaStW7dKujhzec8991x2nCpVquiPP/64rhpcLleOM6j79+9XnTp1FB8fL4vFotDQUPn4+Mhut8vPz0/+/v6y2+26++67rzi+zWaT3W732AAAAAAgtwilN9HSpUs1depULV++XJI0cOBAXbhwQc8++6zOnj2r1q1bX7F/RkaGPv74Y91zzz06fvy4zpw5c8012O12HTt2TJLc4ViSFi5cqAYNGrgfz42Pj1eDBg20fft2DR48WG+88YY2bdokm812zecEAAAAgNwilN5EI0aM0PTp09WxY0dt3rxZmzdvVqtWrfTggw+qWbNmypcvX479DMNQamqqHnnkEe3cuVNeXl5q166d+x3QIUOGKDExMVc1VKhQQVu2bNHJkydVp04dpaenS7r4SHDbtm3djxJfztX2AwAAAMA/QeK4CZ5//nktXbpUUVFRGjNmjA4cOKDSpUurRo0aCg0NVWpqqjZs2KBvvvnGY/YyIyNDmZmZ2rdvny5cuKCnnnpKO3fu1Pr16zV48GBNnTpVs2fP1gcffJDrGczmzZvr6NGjat++vUJCQuTr66vly5fr9OnTatCgwVX75/ToLwAAAADcKKy+exMMHTpUEydOdAfHr776SosXL9aIESOUlJSkjRs36uTJk3r33XcVHR2tfv36Sbq4aFBaWprq16+vjRs3qlq1aqpYsaLGjRunefPm6csvv1Tnzp3Vs2fPbN8jvZwCBQrogw8+0PDhw/XNN99Iknbu3Km2bdtq4cKF6tatm+x2uyIiIpScnKz69esrPT1dFotFU6ZMkcPhUNmyZXXw4MGbc7MAAAAA3NZYffdfZBjGf2bmMSMj46qfe7kel1bYYvVdAAAA4PbG6rv/Qf+VQCpd/fujAAAAAPBvIJQCAAAAAExDKAUAAAAAmIZQCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADCN1ewCkDflr/Gw7Ha72WUAAAAA+I9jphQAAAAAYBpCKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMQygFAAAAAJiGUAoAAAAAMA2hFAAAAABgGqvZBSBvil36ldID/M0u44YJa97Z7BIAAACAPImZUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADANoRQAAAAAYBpCKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMQygFAAAAAJiGUAoAAAAAMA2h9D/K6XRq1apVSkpKytXxS5cu1d69e3M9fmxs7PWWBgAAAAA3DKH0JtmwYYOCg4NVu3btbFutWrVksViUkpIiScrKylJ6erpH/6ysLD322GNatmxZtnan0+nR5nQ61aVLl2zHXo7L5VKTJk302muvKTMzU1FRUbJYLDlu69at+wd3AQAAAACuzGp2AXmVv7+/ChYsqM6dO2fbl5mZqU2bNikgIECS9Ouvv6pNmzby9/f3OC4sLEz9+vVTv3793G1ZWVmqWLGiFi9e7G4bM2aMYmNjNWbMGI0dOzbb+ZxOp+x2u/bt2ydJ8vb21po1a9S1a1ctXLhQvr6+6t+/v4YOHerRr1mzZtd9/QAAAACQG4TSm8hischqvfwtNgxDFotFderU0ZkzZ67rHDt27NDIkSO1YMECPfLII7nqk5ycrICAAM2dO1eSNH78eH3yySf67LPPPI7L7aPDAAAAAHC9CKU3SUZGhpxOp6ZOnZptn2EYKlCggNLS0jxmR48fP67SpUvrrrvuytbnzJkz6tu3r9544w13W0xMjNq0aaNXXnlFa9asUWRkpHx9fT36paenKzQ0VCtXrnS3TZgwQdu2bdNXX30lLy8vWSwW9e3bV6NHj/bo27Bhw+u9fAAAAADIFULpDWYYhtLS0lSzZk1FR0df8ViXyyWn0ymbzSZJ8vPzk6+vr0aMGJHt2BkzZsjHx8ejLSwsTGPGjFHLli1lsVg0cuTIXNXYtWtXNWrUSO+8846GDx8ul8uVy6vLzul0erzj6nA4rnssAAAAALcfQukNdv78ed1///06f/68QkJCFBwcrMTERCUmJqpYsWIex2ZmZurOO+/U/PnzJUkBAQEaP368YmJiso370EMPqWbNmh5tVqtVKSkpyp8/v+x2uywWS7Z+6enp6tatm4YNG+ZuCwsL0+LFizV+/Hj3OFOnTs1xVvfvQfjvoqKisr2LCgAAAAC5ZTEMwzC7iLyoc+fOKlGihO677z799ttvOnfunFq3bi2n0ykvr4uLHkdERKhq1aqSpBYtWujo0aO5Grt3797q2bNnro7t16+fgoODPUKpdHFG1+FwKCQkJNfXlJOcZkrDw8N1+JtPFRzgf4Wet5aw5p3NLgEAAAC4pVzKGwkJCbLb7Zc9jpnSmyg5OVnx8fFKTk6WJG3evFkbN27U008/renTp6tDhw7uUHr8+HFNnTpVtWvXvuKYL730khITE3Ndg8vlynEGdf/+/Wrbtq3WrVunfPnyKSQkRMnJyfL391d6erosFot8fHxUokQJ7d69+7Lj22w29+PHAAAAAHCt+E7pTbR06VJNnTpVy5cvlyQNHDhQFy5c0LPPPquzZ8+qdevW7mMvzZ5KUrFixVS9enXdd999uu+++1SyZEmPz8L89dirsdvtOnbsmCS5w7EkLVy4UA0aNHA/nhsfH68GDRpo+/btGjx4sN544w1t2rSJwAkAAADgpiKU3kQjRozQ9OnT1bFjR23evFmbN29Wq1at9OCDD6pZs2bKly9fjv2Cg4O1fv16bdmyRVu2bFH//v2v+m7n5VSoUEFbtmzRyZMnVadOHaWnp0uSpk+frrZt21414F5LAAYAAACAa8XjuzfB888/r6VLl2r37t267777lJaWptKlS6tGjRras2ePUlNTtWHDBn3zzTdq3ry5AgMDlZWV5e5funRp1a1b12PM9u3bS5KysrJyfBz3cpo3b66ePXuqffv2CgkJka+vr5YvX67Tp0+rQYMG7pB6OddyLgAAAAC4VoTSm2Do0KGaOHGi+9HXr776SosXL9aIESOUlJSkjRs36uTJk3r33XcVHR2tfv36eSwWtGTJkhzHfeutt7Ry5Up17Ngx17UUKFBAH3zwgYYPH65vvvlGkrRz5061bdtWCxcuVLdu3WS32xUREaHk5GTVr1/f/U7plClT5HA4VLZsWR08ePAf3BEAAAAAyBmr7/6LDMP4RzOPf/2m6T+VkZFx3Y8EX8mlFbZYfRcAAAC4veV29V1eGPwX/dNHYW/kokM3I5ACAAAAwLUilAIAAAAATEMoBQAAAACYhlAKAAAAADANoRQAAAAAYBpCKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMQygFAAAAAJiGUAoAAAAAMI3V7AKQNxV4+FnZ7XazywAAAADwH8dMKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMQygFAAAAAJiGUAoAAAAAMA2hFAAAAABgGkIpAAAAAMA0hFIAAAAAgGmsZheAvOn0rA+U5O9ndhnXrVinQWaXAAAAANwWmCkFAAAAAJiGUAoAAAAAMA2hFAAAAABgGkIpAAAAAMA0hFIAAAAAgGkIpQAAAAAA0xBKAQAAAACmIZQCAAAAAExDKAUAAAAAmIZQCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilN9HOnTu1cuXKGzpmZmamMjMzc3VsRkbGNY+fkpJyzX0AAAAA4HoRSm+iIUOG6JNPPsnVsfv375fFYpHVar3i5uPjozlz5mTrHxERoW3btrl/p6SkKDw8XJs2bbrsOQ8ePKhChQp5tPXq1Usff/yxsrKylJaWlrsLBQAAAIDrZDW7gLxq/fr1mj9/vooUKaKIiIhs+10ulxISErRt2zbdcccdCg4OliSlpaXJav2/v5bo6GgVKlRIvr6+kiTDMJSVleXen5ycLJvNJj8/P/n4+Cg1NVX+/v6aMGGC0tLStGHDBm3YsEFeXl564YUX5O/vrw8//FCjRo2S1WrVhQsXVLhwYX355Zd69dVXFRsbq1WrVmny5Mnav3+/0tPTb+6NAgAAAHBbsxiGYZhdRF6TnJysatWqqW3btho1alSu+sTHxytfvnzKyMjwCKW+vr7atm2b7r777hz7Va5cWS6XS4cPH1Z4eLjKli2riRMnqkqVKhoxYoSKFCmiyZMnq2rVqnr//fc9+h49elRNmjTRpk2bFBAQIMMw1LFjR3Xo0EElS5ZUy5YtFR0dfU3X7nA4FBISor0ThyrY3++a+v6XFOs0yOwSAAAAgFvapWyQkJAgu91+2eOYKb3BXC6XnnnmGRUuXFhVq1ZV1apV5efnl+2YhIQErVmzRkWKFLnieL6+vgoICLjs/u3bt0uSKlWqpPnz56ts2bLq06ePmjRpotq1a+vw4cPavXu3hg4dqujoaJUoUULHjh3TH3/8oWXLluno0aO65557tHbtWj300ENKTEzUoEGDlJKSovz58//zGwIAAAAAV0AovcG8vb3Vt29fVa5cWQUKFNCTTz55Tf0Nw1BaWppsNpssFku2/RkZGfLx8ZEkffvttxoyZIj8/f117NgxtWzZUi6XS19++aViYmJ04MABWSwWjR07Vu+++67uuusujR49Wm+++aZOnDihevXqqVSpUjp06JAk6Y033lDXrl01YMAADRs2TKmpqUpISFBwcLC8vHJ+/djpdMrpdLp/OxyOa7peAAAAALc3QulNUL58eUVERCgkJMTjUdxLXC6XypcvrxUrVmTbd+bMGYWHh3u0lS5d2v3nBg0aaPXq1ZKkdu3aqV27dpKkvn376s4771SPHj1yrGnbtm3u91JnzJgh6eJCR7Nnz5Yk7dq1S2+99ZamT58ub29v1ahRQ9HR0frkk0+0ZcsWfffddzmOGxUVpaFDh17pdgAAAADAZRFKb4LixYsrMTHxsvsXLFigsWPHerS5XC55e3urSJEiOnr0qHx9fWWxWFSmTBmtXr1a4eHhyun1359++kkFChTI1j516lQdOXJEVqtVFotF69atU5MmTdz7586dqxEjRqho0aJKTk7Wyy+/rKFDh6pjx47uYwYMGKA33nhDb7/99mWvJTIyUv3793f/djgc2UI1AAAAAFwOodQELpcr26O5sbGxCg4OltVqValSpdztFotFBQsW1IABA9S5c2ePYClJc+bM0b333itJGjlypKZMmaKtW7fKbrcrLCxM3t7eslgs2d5LrVy5soYNG6aHHnpIgYGBatGihU6fPu1xzPDhw9WsWTNVq1btstdis9lks9mu6z4AAAAAAKHUBHa7XceOHZN0caXewMBAHThwIMdPx1xStmxZdevWTbt37/YImIcPH3avzPvGG2+4H9/9+7usJ0+e9PiUzMMPPyzDMOTt7S1JiomJkd1u12effSbp4rutx48fV0xMTLaFmgAAAADgRiGUmqB8+fI6fvy4zp49q8cff1yfffaZNmzY4J7xzMmrr76qAwcOKDk52R1KXS6Xdu3apaCgIJ06dUp33nmnJOnQoUOqVq2a/P395ePjIy8vL6WkpHi8b3r8+HH3n7du3aomTZpo586dKlSokCTpxIkTKleunPs3AAAAANwMhFITlCxZUrVq1VK7du20a9cuFS9eXLNnz9a7776ruLg4+fj4eDzem5KSorCwME2ePFkul0uJiYkKDg7WkiVLVKZMGS1atEhvv/22JkyYoLi4ODVt2lT79u1TwYIF5eXlpfT0dCUmJur06dNKS0tzz3y6XC7Nnj1br776qt5//32PALp582aVK1cuxxWAAQAAAOBGyfk7H7jpJkyYoBMnTmj06NGaO3eu4uLi1Lx5cxUqVEh2u13BwcEKDg5WcnKyKlas6P4dGhqqyMhISdIff/yhzp07y2q1atSoUfrxxx+VmZmpAQMGqHr16goICJCXl5f8/PxUsGBBNW/eXDabTQcOHNBzzz2nYsWKacKECZo1a5a6du0qSfr666/VuHFjdevWTU888YSZtwgAAADAbcBi5LSkK/5Ve/bs0a+//qrnn39eaWlp8vX1vex3QV0ul1wul/vzLk6n84oLDaWnpysrK0teXl4eM7Aff/yx6tWrp6pVq3ocHxsbqyVLluiee+5R5cqVr/laHA6HQkJCtHfiUAX737rvohbrNMjsEgAAAIBb2qVskJCQILvdftnjCKW4oQilAAAAAKTch1Ie3wUAAAAAmIZQCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADANoRQAAAAAYBpCKQAAAADANFazC0DeVPSZvrLb7WaXAQAAAOA/jplSAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMQygFAAAAAJiGUAoAAAAAMA2hFAAAAABgGkIpAAAAAMA0hFIAAAAAgGmsZheAvOnQ+DcU5Ge74jHlXhvzL1UDAAAA4L+KmVIAAAAAgGkIpQAAAAAA0xBKAQAAAACmIZQCAAAAAExDKAUAAAAAmIZQCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlD6L/r555+1ePFis8twe+aZZ7Ru3TpJUnp6ujIyMmQYhslVAQAAALidEEpvguLFi8tutysoKEhFihSRy+WSYRjq37+/Dh48mOtxqlatqi+++OIf19OgQQNNmDAhW/vdd9+tDh06KD4+Xr169VJISIhCQ0M9NovFop07d/7jGgAAAAAgJ4TSm8DX11fbt2/XmDFj9MQTT8jb21tz5szR0aNHNXnyZFWqVEnFixdX8eLFlZiYeNlxgoKCZLVaL7t/7dq18vLy8giQO3bsyLEeX1/fbO0DBw5UzZo1deHCBU2dOlUpKSlKSEhQfHy8ewsMDJSfn9/13QgAAAAAuIrLJx5ct0sBcOnSperTp49OnTql/v37q3DhwnrvvffUqFEj1a1bV5GRkQoODpZhGMrKypK3t7fHOFarVRcuXHD/3r9/v6ZMmaLRo0fLarXKx8dHxYoVU3R0tKSLITY6OlrfffedrFare7wTJ07o559/1unTp9WvXz+dO3dOFStWVFxcnObOnXvV67lSMAYAAACAf4KZ0pvAYrEoJSVFv//+u+rVq6f169dr4MCBWr58ubp3764HHnhATZs2Vdu2bSVJu3btcodIq9Uqq9WqefPmSZKWLVsm6WKwfPTRR5WcnOx+7zOnsBgSEqK7775bFStWdP/XbrerRIkSqlixonx8fBQUFCSn0yl/f3/t3r1bERER2bZVq1b9S3cLAAAAwO2MUHqTbNiwQeHh4bJarXr44YdVpUoVffLJJ3K5XLpw4YLWr1+vzz77TJs2bVLJkiUVGxur9PR0JSQkyMfHR7Vr15Z0cXGkTz75RPXr11f//v01ceJE+fj4SJK8vLx06tQp9+O7ycnJKlq0qB577DHdeeedatmypR577DEVKlRI9957r9q1ayd/f3/3DKrFYnHXe/ToUfdWpEgRFjwCAAAA8K8glN4kjRs3VmBgoL799lu1aNFCw4YNU8mSJbVq1SpZLBZ9/vnn+n/t3XtcVXW+//H3BmRvEdmI4BhKauak2OikeNdK0UzH0Tw6lXkKH5Za5hxvx9NJ08a0aMpJTfOCVNqEl0ZHbSbHLKfMxBovhClEJxWvmZjB5iKbvWH9/vDB+sUgisRmgb2ej8d62Lp912fvxyfk7br5+flpw4YNKioqUlhYmPz9/bV9+3a1bdtWzZo1k3T5CbmTJk3SkiVL9MQTT5Q5hp+fnyIjI8vc/2mz2ZSUlKShQ4equLj4mnX6+V25BSpa/u/cbrdcLleZCQAAAAAqi1DqIzabTS+99JLmzp2r3bt369ixY1q4cKHuuecenTp1Sn379tWzzz6rjh07Kjw83Nxv0aJFevTRR835IUOGqFevXsrMzCx3jJKSkiseOy4uTkVFRXr77bcrVevp06fLXLqbkpJS6c8ZHx8vp9NpTlFRUZXeFwAAAAB4go2P5OXlKS0tTSdPntSxY8e0d+9eNWvWTDk5OWrbtq0yMzPl9XrLXCa7adMmHT16tEwolaRVq1apR48eio6OVr9+/czlVzoTahiGAgICNGXKFBUVFVWq1ubNm5cJvaWXDlfG008/rWnTppnzLpeLYAoAAACg0jhT6iMffPCBHnroIY0dO1Zz5szRX//61zL3cEoq84TcI0eOaOzYsXrttddUv379Mtu1a9dOa9as0dChQ7V8+XLzDGlxcbG+/fZbhYeHKzw8XPn5+fJ6vZKkKVOmaNy4cdess6J7Rys6C/vv7Ha7QkJCykwAAAAAUFmEUh8oKSnRoEGDlJaWpoULFyonJ0cLFixQy5Yt1aFDB2VlZally5aKiopSRESEdu3apd69e2vcuHEaPnz4FcccNmyY1q5dq1mzZqlnz57Ky8uT1+vVTTfdpAsXLujChQtq0KCBPB7PddVaGkp/fPnuuXPnygVoAAAAAPAFLt/1Aa/XK4fDoZYtW0qS/va3v5nrsrOzzct3SxUVFWnRokV65JFHyoyTm5tb5oFDQ4cOVWpqqrKyshQcHCx/f381adLEXH/o0CE1b968XD0ul6tMyCwoKDDrjI6OvuL9qqX7ud1uBQUFVfqzAwAAAMD14EypD7jd7grXFRQUlFsfGBiouLg4MzgeO3ZM4eHhOn78uDp27Fhm26ioKHXq1EmS1KNHDx08eNBcd8sttygwMNCc/+KLLxQeHq6DBw+qc+fO5vK8vDxJUmFhYYV1vvfee2ratKkGDhxYJvgCAAAAQHWyGbyQslb69NNP1alTp598lnL//v2Kjo6+7nHcbrfy8/MVFhZ2Xfu5XC45nU4d/MMkBTvsV922zVMLrmtsAAAAAHVHaTbIycm56rNnuHy3lurdu3e1jBMTE1Ol/ex2u+z2q4dKAAAAAPipuHwXAAAAAGAZQikAAAAAwDKEUgAAAACAZQilAAAAAADLEEoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALAMoRQAAAAAYBlCKQAAAADAMoRSAAAAAIBlCKUAAAAAAMsEWF0Abkytpz6vkJAQq8sAAAAAUMtxphQAAAAAYBlCKQAAAADAMoRSAAAAAIBlCKUAAAAAAMsQSgEAAAAAliGUAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgmQCrC8CN6eCMCQoODKxwfcySNTVYDQAAAIDaijOlAAAAAADLEEoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALAMoRQAAAAAYBlCKQAAAADAMoRSAAAAAIBlCKUAAAAAAMsQSgEAAAAAliGUAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgGUKpjxQUFMjj8VS4vqioSIWFheWWJycna//+/b4szTw+AAAAAFiNUOojPXv2VGBgoGw22xUnu92u//zP/yy33+TJk7Vu3brrOtaQIUOUmJgoSXK73SopKZEkPfTQQ1q0aJEkqbi42AzJWVlZatOmjVJTUyVJ69atk8PhUMuWLctMDodDy5cvr+pXAAAAAADXFGB1ATeqXbt2qV69evLzu5z7p06dqsaNG+uZZ56RdDk82my2MvskJSUpNTVV3333nTZt2lRuTI/HI5fLpezsbPn7+5vLnU6n7Ha7JOmee+5RVlaW/Pz8dObMGe3atUuJiYnyer0aPny44uPjFRERoSFDhmjUqFE6cOCA7Ha7YmJi9Omnn5Y5Xu/evc1xAQAAAMAXCKU+4nQ6y8z7+/srICBADodDksw/S3377beaOnWqli5dqvHjx1fqGCUlJVq9erUCAgKUkZGhN998U//93/+tvXv3KjAwUFu2bFHz5s3VuXNnFRYW6vHHHzf3XbBggW677TZt3rxZQUFBFR6jNFQDAAAAgC8QSn3A4/EoICCg3JnQf1dcXCyv1yuPx6Phw4crNjZWHo9Hd9xxR7kzlF6vV3l5efrqq6/MZadOndL8+fN15513qn79+po5c6a2bdum2NhYNWjQQPv27VP79u117733qqCgQEFBQTIMQzabTfXr19eePXsUFRWlv/71rxXWaBjGT/syAAAAAOAqCKU+MGjQIO3cufOK6+bOnVtmvmPHjkpJSdHs2bMVGxsrh8OhJ598slLHOXTokLp27SpJatGihXbu3KmhQ4cqMDDQPMN54sQJvffeeyopKZHX69WePXsUEREhSYqKipIk8x7UKykuLr5qDW63W26325x3uVyVqh0AAAAAJEKpT2zatEk2m80MhoZhqE2bNnI4HEpNTTXvBzUMQ8XFxbLZbGrUqJGaNm2qkJCQK14y6/F4NGDAAK1evdpc9uWXX6pHjx5KSUmRJEVHR2vHjh164403yu0fFBSkmTNnlllWVFSkM2fOKCAgQKdPn5a/v78iIyPlcrlks9kUGhqqwMDAq37W+Pj4ckEbAAAAACqLUOoD/34/6Y4dO9SwYUMFBwfrvffe00MPPVRun549eyo7O7vCMRcvXqwDBw6UWVYaMseMGWMuO3PmjLZu3aolS5aYyy5evKhJkyaVC6XLli3Tpk2btHv3bg0bNkwOh0P79u3TokWL5HA49Ic//OGan/Xpp5/WtGnTzHmXy2WegQUAAACAayGU+lhxcbFmz56tiRMn6pZbbtGTTz6pAQMGmJfQXs8417pHVbr8AKVjx45p0qRJ5jKv11vujGdOTo6ef/55rV+/XtLlBy1JUnh4+HXVZbfbeUIvAAAAgCrj0ao+Nn36dOXk5GjcuHEaNmyYunTpogcffFD5+fnXNU5ISIhOnDghSVfd12az6Y477tDhw4fN6eOPPy633XPPPaeuXbsqNjZWkrRx40Z17dpVAQH8OwUAAACAmkMo9ZHvv/9e999/v9asWaNNmzYpODhYkrR69Wr98MMP6tatm44cOVLp8W677TYdOnRIubm56tatm86fP1/htikpKbr99tvNqW/fvmXWHzp0SMuWLdPLL78sScrKytIrr7yiCRMmlBtr7969WrlyZaXrBAAAAIDrQSitZhcvXtTLL7+s9u3bKyMjQ/v371f79u3N9U6nU7t371bnzp31q1/9Sv369dM333xzzXF79Oih4OBg/eY3v1FBQUGZy2wvXbpkPiW3pKSk3JnSTz75pMxYO3fu1NixYxUdHa309HTFxsbq9ttv1+jRoyVJAQEBysrKMrfdsGHDT/5eAAAAAOBKCKXVrLi4WFu3btWsWbO0b98+tW7dutw2DRo00Jo1a7Rnzx7169dPt9566zXHDQgI0IoVK5SZmalFixaVeUJv6btHpcsB9d+NHj1aQ4YMMeenTp2qpUuXasOGDWrfvr06deqkjRs3mmPce++92r59uyIiIpSYmKinnnrqur8HAAAAAKgMm2EYhtVFwBput1upqanmu06rg8vlktPp1EfjH1TwVV4nE7NkTbUdEwAAAEDtU5oNcnJyFBISUuF2nCn9GbPb7dUaSAEAAADgehFKAQAAAACWIZQCAAAAACxDKAUAAAAAWIZQCgAAAACwDKEUAAAAAGAZQikAAAAAwDKEUgAAAACAZQilAAAAAADLEEoBAAAAAJYhlAIAAAAALEMoBQAAAABYJsDqAnBj6vTySoWEhFhdBgAAAIBajjOlAAAAAADLEEoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALAMoRQAAAAAYBlCKQAAAADAMoRSAAAAAIBlCKUAAAAAAMsQSgEAAAAAliGUwic+uH+E1SUAAAAAqAMIpQAAAAAAyxBKAQAAAACWIZQCAAAAACxDKAUAAAAAWIZQCgAAAACwDKEUAAAAAGAZQikAAAAAwDKEUgAAAACAZQilAAAAAADLEEoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALAMofQGtXv3bo0bN05ut9vqUgAAAACgQoRSH0lOTlbDhg3VvXv3clO3bt1ks9lUUFBw1THuvvturVq1qsL1p06dkp+fn1q2bKng4GD97ne/M9cdOHBABw4ckN1uv+K+x44dk81mU0BAwBUnf3//MuMBAAAAgC8EWF3Ajap+/fqKiIjQmDFjyq3zer3617/+paCgoKuOkZ2dLYfDUeF6u92uoKAgZWZmqn///rrvvvuUn5+vwMBApaSk6D/+4z/MbT0ej4qLi83xHA6HWrVqpWPHjl1x7BUrVmjv3r2V+KQAAAAAUHWEUh8qPRNZEcMwZLPZKlzv5+en0NDQCtcHBgaa46SmpmrQoEHq2LGjzp8/r7y8PIWFhWnRokWSpKKiIo0YMUJvvvmmOXbpsY8eParWrVtLkrZt22aOW/onAAAAAPgKodRHPB6P3G63EhMTy60zDEONGzdWYWGh6tevL0lKT09XUFBQmcttCwsLVVhYqHPnzkm6fIY1Pz9f9erV0y233GJul5aWpltvvVVhYWH65ptvtG7dOq1fv15bt26tsL7i4mL5+fnp+PHj6tWrl/75z38qOjpaCQkJ6tevn+x2u/z9/avr6wAAAACAKyKUVjPDMFRYWKiuXbvq9OnTV922uLhYbrdbdrtdsbGxysvLk5/f/7/N1+VyacyYMapXr545dlFRkfr27att27aZ233yySfq27evuc0rr7yixYsXa9myZcrIyNDixYvLHdvr9apevXpq1aqVli5dqhEjRugf//iHDhw4oHXr1untt99WSUnJNT+v2+0u8zAll8t1zX0AAAAAoBShtJpduHBBPXr00IULF+R0OtWwYUPl5uYqNzdXkZGRZbb1er1q27attmzZorNnz5ZZ53a71bBhQz399NN65plnrnrMTp06ac2aNfJ6vVq4cKEaNmyonj17Kjk5WTk5OTIMQ263W/Xq1TPPfubn55v3l44cOVIffPCBevTooSeeeEL169eXv7+/PB7PNT9vfHy85s6dez1fEQAAAACYCKXVLCIiQt98843GjBmj5s2bKyYmRp9//rmysrI0fPhwud1u82xoy5Yt9etf//qK47z//vsKDw9XYmKi/ud//ueq93d269ZN0dHRWrlypXbs2KGPP/5YISEhKi4uls1m08cffyy3260NGzbozjvvlHT5IUohISHmGJMnT1ZCQoLatm0r6fL9pEVFRdf8vE8//bSmTZtmzrtcLkVFRV1zPwAAAACQeCWMT+Xn5ys7O1v5+fmSpH379ikhIUHZ2dl69dVXdfDgwQr3femllzR79mz9+te/Vnx8/DWPNXjwYK1fv17r169XXl6eXC6X5s6dq5EjRyozM1PffvutGUgl6eTJk2Z4dLvdGjdunB5++GHNnDlTbrdbTZs21ffff3/N49rtdoWEhJSZAAAAAKCyCKU+tGPHDiUmJurDDz+UJM2YMUM//PCDRo8erfPnz2v48OFX3C8hIUHfffedxo4dq0WLFmnx4sU6cOBAhcdJTk5WQkKCnE6ngoODzYcn/VhJSUmZez9TUlLUrl07FRYWauTIkYqOjtZbb72lNm3a6LXXXlOrVq309ddf/8RvAAAAAACujlDqQ/Pnz9dbb72lhx9+WPv27dO+fft03333qW/fvho0aJAaNWpUbp+NGzdq2rRpWr9+vex2u1q2bKmFCxdq8ODBSk1NLbNtcXGxpMuve2nXrp2ef/55OZ1OhYaGKjQ0VLNnz9batWvN+Q4dOpj7vv/+++rTp48mTZokp9OpFStWmDUnJCQoKipKZ8+e1aFDh/T888/LMAwfflMAAAAAfq64p9QHxo4dqx07digtLU0xMTEqLCxUq1at1KVLF6Wnp+vSpUtKTk7WX/7yFw0ePFgNGjTQhQsXNHv2bK1evVpJSUnq3LmzOV5cXJzS09PVp08fLVmyRHFxcZJkPojo7rvv1t133y3p8mtkSi1YsECHDx/W6tWry9S3Z88eXbhwQb169VKHDh3UsGFD8z7Xzp07a9++fQoMDFT//v31yCOPKC8vT7NmzfLhNwYAAADg54pQ6gNz587V8uXLzXeOJiUl6R//+Ifmz5+vvLw8ffbZZzpz5oz++Mc/6vTp02rTpo0efPBB3XTTTdq1a5e6du1abswXX3xRYWFhevTRR7Vnzx4lJCRc8+m4RUVFunTpUrnla9eu1fTp0+Xn5yen01lufcOGDSVJzz33nO6++27NmzevKl8DAAAAAFyTzeC6zBpjGIZsNlu55V6vV4mJiYqLi7vi/aA/dvDgQTmdTrVu3brKdeTl5clut5vvP72a0veoVpbL5ZLT6dTGgf01YvsHVa4RAAAAQN1Wmg1ycnKu+kBUzpTWoCsFUkkKCAjQ448/XqkxOnXq9JPrCA4OrvS21xNIAQAAAOB68aAjAAAAAIBlCKUAAAAAAMsQSgEAAAAAliGUAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgGUIpAAAAAMAyhFIAAAAAgGUIpQAAAAAAyxBKAQAAAACWIZQCAAAAACxDKIVPDHhnk9UlAAAAAKgDCKUAAAAAAMsQSgEAAAAAliGUAgAAAAAsE2B1AbixGIYhSXK5XBZXAgAAAMBKpZmgNCNUhFCKavX9999LkqKioiyuBAAAAEBtkJubK6fTWeF6QimqVVhYmCTp5MmTV2084FpcLpeioqJ06tQphYSEWF0O6jj6CdWJfkJ1oZdQnWpjPxmGodzcXEVGRl51O0IpqpWf3+XblJ1OZ635nwF1W0hICL2EakM/oTrRT6gu9BKqU23rp8qcqOJBRwAAAAAAyxBKAQAAAACWIZSiWtntdj377LOy2+1Wl4I6jl5CdaKfUJ3oJ1QXegnVqS73k8241vN5AQAAAADwEc6UAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgGUIprsvhw4fVpUsXNWrUSDNmzFBlnpO1ceNGtWjRQpGRkVq3bl0NVIm6oCq9NHfuXIWFhclut2v48OHKzc2tgUpRF1Sln0plZ2frpptuUmZmpu8KRJ1S1X4qKSlRz5499ac//cnHFaKuuN5eMgxDTzzxhMLCwhQaGqoxY8bo0qVLNVQt6oILFy6oVatWlf47a9euXWrXrp3Cw8P1yiuv+La4n4BQikpzu9367W9/q86dO2v//v1KS0vT6tWrr7rP4cOHNXr0aM2ePVvvv/++5syZo4yMjJopGLVWVXopKSlJSUlJ2r59u44cOaL09HS9+OKLNVMwarWq9NOPzZgxQ+fOnfNdgahTfko/rVixQjk5Ofqv//ov3xaJOqEqvfTnP/9ZGRkZSklJ0e7du3XkyBHFx8fXTMGo9S5cuKAhQ4ZUOpBmZWVp6NChGjVqlPbu3aukpCR99NFHvi2yqgygkjZv3mw0atTIyM/PNwzDML744gujV69eV91n8uTJxsCBA835RYsWGbNmzfJpnaj9qtJL8fHxRnJysjk/Z84cY9CgQT6tE3VDVfqp1K5du4wmTZoYjRs3No4fP+7DKlFXVLWfzpw5YzidTmPnzp2+LhF1RFV66cknnzRee+01c37+/PnGqFGjfFon6o7Y2Fhj8eLFhqRK/Z21cOFCo23btkZJSYlhGIaxZcsWY/To0T6usmo4U4pKS01NVffu3RUUFCRJ6tChg9LS0q65T79+/cz5rl276sCBAz6tE7VfVXrpf//3f9WjRw9zPiMjQ23atPFpnagbqtJP0uWzGBMmTNCrr76q4OBgX5eJOqKq/TRlyhS1aNFCp06dUnJysq/LRB1QlV5q37693n77bX333Xc6ceKE1q9frwEDBtREuagDVq1adV1XYqSmpqpv376y2WySavfv4YRSVJrL5VKrVq3MeZvNJn9/f/3www+V3ickJERnz571aZ2o/arSSz/29ddfa/PmzRo/fryvSkQdUtV+euGFF/TLX/5SDzzwgK9LRB1SlX7au3ev/vKXv6h58+Y6evSo4uLiNGnSpJooF7VYVXrpscceU15enpo2baqWLVuqVatWiouLq4lyUQf8uJ8qoy79Hk4oRaUFBATIbreXWeZwOFRQUFDpfa61PX4eqtJLpUpKSjR27Fg99thjat++va9KRB1SlX5KT0/XihUrtHz5cl+XhzqmKv20atUqdevWTX//+9/13HPP6Z///KeWLVvGMxR+5qrSS4sXL1ZoaKhOnDihkydPyuv1asaMGb4uFTeouvR7OKEUlRYWFqasrKwyy3JzcxUYGFjpfa61PX4eqtJLpebNm6eLFy/q5Zdf9lV5qGOut58Mw9D48eM1f/58RUZG1kSJqEOq8vPp9OnTGjx4sHmJXFRUlCIiInT06FGf1orarSq9lJSUpBkzZujmm29WVFSU4uPj9frrr/u6VNyg6tLv4YRSVFqXLl20d+9ec/748eNyu90KCwur9D4pKSlq1qyZT+tE7VeVXpKkv/3tb3rllVe0adMm8x4d4Hr76eTJk/r00081Y8YMhYaGKjQ0VCdPnlSHDh20du3amiobtVRVfj41b968zGs78vLydPHiRf6++5mrSi+VlJTo/Pnz5vy5c+dUXFzs0zpx46pLv4cTSlFpd955p1wul958801Jl+/H6t+/v/z9/ZWdnX3FH5ojRozQ+vXr9eWXXyovL0+vvvqqBg4cWNOlo5apSi+lp6dr1KhRWrJkiaKiopSXl1drL0FBzbrefmrWrJmOHz+uL774wpwiIyO1bds2DR061IqPgFqkKj+fRo0apVWrVmnnzp06ceKEJk6cqLZt26pDhw41XT5qkar0Up8+ffTiiy9q9erVSkhI0MSJE/m5hGtyuVzyeDzllg8dOlR79uzRhx9+KI/Ho5deeqn2/h5u9eN/Ubds3brVCAoKMho3bmxEREQYR44cMQzDMCQZKSkpV9xn5syZRmBgoBESEmJ07tzZKCgoqMGKUVtdby9NmTLFkFRmatGiRc0WjVqrKj+bfqxFixa8EgamqvRTYmKi0aZNG8PhcBjdu3c3vvrqqxqsGLXV9fbSDz/8YDz88MNGRESE4XA4jGHDhhlZWVk1XDVqO/3bK2FatGhhbN68+YrbLl++3KhXr57RqFEjo1WrVsa5c+dqpsjrZDMMw7AqEKNuOnfunA4cOKDu3burcePGldonLS1NZ86c0V133VVrr2VHzatKLwEVoZ9QnegnVBd6CVY7fvy4vvrqK/Xp06fWvgKNUAoAAAAAsAz3lAIAAAAALEMoBQAAAABYhlAKAAAAALAMoRQAAAAAYBlCKQAAsMzFixfLzBcVFSkvL8+iagAAViCUAgAAU3FxsQoLC5WTk6OTJ0/q0KFD2rFjh1auXKnp06erd+/e6t69uwoKCiRJCQkJOnr0qN566y3dd999Kiws1LRp0yRJEydO1Ouvv17hsQoLC9W6dWtt3brVXPbJJ58oIiJCly5d8u0HBQDUGgFWFwAAAGqHtLQ09ezZUyEhIWrQoIHq1auntLQ0Pfzww7r11lvVoUMHDRw4UI0aNZLH45EkhYWF6YEHHtDUqVMVGBioDRs2KDs7W5K0a9cuDRs2rMLjvfvuu2rSpIm+//57RUVFyd/fX4WFhfJ4PGrXrp0kyev1aubMmZo4caLPPz8AwBq8pxQAAJRTUlKixx57TKmpqVq7dq1uu+02bd++XZGRkerQoYMkye12691339XXX3+t3NxcJScn66677lJwcLDuvfdede3aVdnZ2apfv75KSkrk8Xhkt9slSYZhqGPHjho/frwef/xxSVJAQIDefvttLV26VJ999pkkyePxyGazKSCAf0cHgBsVl+8CAABTYWGhPvzwQw0YMEBNmzbV559/rl/84hd69NFHNW3aNCUnJ6v037M9Ho/+/ve/a9++ffrjH/+os2fP6vz588rIyNCf//xn1atXT+3atdNNN92koKAg/f73vzeP88Ybb+jLL79U06ZNFRAQoGeffVYxMTGaNWuW0tPTFRMTo5iYGG3dupVACgA3OEIpAAAwZWdna8SIETp69Ki2bNmioKAg9ezZU4MGDdKXX36pRx99VDabTZIUHBys+fPn6/Tp07rvvvsUHR2tb775Ri+88IK++OILTZkyRZmZmXrjjTfUv39/JSQkSJIyMzM1ffp0/eIXvzCPe+rUKT322GM6ceKEcnJytH//fsXExMjlclnyPQAAag6hFAAAmJo2bar/+7//06xZs9S+fXslJibq8OHDGjlypLZu3ap77rlHqampkqTDhw+rd+/eeuaZZzRq1Cg1aNBAEyZM0FNPPaWPP/5YR48elSSdPXtWkZGR5jG2bNmiIUOGKCYmxlzm5+enOXPm6NZbbzWndevWyc+PX1UA4EbH9TAAAMDUsWNHHTp0SA6HQz169NCKFSu0YMEC5efny+v1yuPxqG/fvnr22Wf1+9//Xvv371dwcLDatWunTz75RDfffLNcLpeaN2+utWvXSpKOHTumX/7yl+YxJk+erIKCAj3wwANljv3cc8+Z95dKKvPfAIAbF6EUAACYXn/9dfn5+Sk0NFRBQUE6ceKE7r//fmVkZMjhcMgwDKWnp2vjxo3avXu3HnjgARUWFsowDN15551yuVzq3r27tm3bpp07dyolJUWffvqp4uPjzWPYbDY1aNCg3LHnzZunpUuXmvNnz55V9+7da+RzAwCswzUxAADAFB0drRkzZujzzz9X06ZN5XQ6ZbPZ5HA4tHz5cv3mN79RkyZNNGfOHN111106d+6cfvWrX+ndd99VZmam+vTpo0ceeUSSNGbMGE2fPl0ZGRllLtWtyOzZs3X48GFzuv/++339cQEAtQChFAAASJLy8vL0u9/9Tg0aNNBvf/vbcutHjx6thg0b6o477tC//vUvSdKZM2cUGBioKVOmaPTo0frss880dOhQSdIjjzyizz77TKNGjVJgYOA1jz9v3jzdfvvt5vTOO+9U7wcEANRKhFIAACCPx6O77rpLTZo00ebNmxUcHKyPPvpIGzduVP369SVJISEhWr9+vUaOHKlZs2ZJkpo1a6adO3fqmWee0c6dOxUbG6tevXopPz9fTzzxhDp16qS33npLe/bsKXfM4uJiFRUVyePxSKr4TKnb7a6hbwEAYAWbUfqyMQAA8LN24sQJ3XzzzeYrX+Li4pSenq4nn3xScXFxZbZ1u93yer1au3atkpKS5HA4tGzZMt1yyy1as2aN5s2bp8GDB2vhwoVat26dJkyYoBdeeEGTJ082x4iNjVXv3r21cuVK2e1287g/VlJSooCAAB07dsy3Hx4AYBlCKQAAqLKFCxeqd+/e6tKli7nsnXfeUbNmzdSrVy9z2d69exUeHq42bdpYUSYAoBYjlAIAAAAALMM9pQAAAAAAyxBKAQAAAACWIZQCAAAAACxDKAUAAAAAWIZQCgAAAACwDKEUAAAAAGAZQikAAAAAwDKEUgAAAACAZf4fzHReR/bSXMgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "O3_8h的相关性分析结果：\n",
      "\n",
      "O3_8h       1.000000\n",
      "气温          0.740916\n",
      "平均海平面大气压    0.685108\n",
      "气压          0.682996\n",
      "露点温度        0.616646\n",
      "最高气温        0.500138\n",
      "最低气温        0.460674\n",
      "降水时间        0.140947\n",
      "湿度          0.113398\n",
      "季节          0.060095\n",
      "平均风速        0.059269\n",
      "气压趋势        0.026291\n",
      "天气情况        0.007104\n",
      "最大阵风        0.004723\n",
      "风向          0.001603\n",
      "Name: O3_8h, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_22176\\1151200639.py:37: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=target_correlation_abs.values, y=target_correlation_abs.index, palette=sns.color_palette(\"coolwarm\", len(target_correlation_abs)))\n"
     ]
    },
    {
     "data": {
      "image/png": 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v317h4eEaNmyYPB6Pfv31V91///2qXLmy5s6de96Vc5cvX64FCxboq6++UmBgoGbPnq0jR47o5Zdf1quvvqq7777bvE0bAK5VhFIAQIHlcDgknQ1WOSuiSmffm5lzTDo7s/Xee++pTZs2io2N1YwZM/TUU09ddPxJkyapUKFCmjt3rvl84S233KLGjRurR48e5vN+7du3V0hIiNmvWLFiXrfXStKoUaP0xBNPSJK+/PJLDR061DzWo0cPNWrUSH/88Ydq1Kih9PR0FS1a1Dy+f/9+TZ8+XW+//bZee+013XLLLerbt68ZRB999NE867/vvvvMn9977z0NHTrUfK+pdHZGMjY2VpmZmZo/f75iY2PN8zMzM+V2u81Z6L+HuxynT5/WHXfc4XWL7OOPPy7pbFhevHix6tevbx4bO3asBg8eLOnsrdZvvPGGevToIbfbLbvdrtDQUIWGhio4OFhlypTRc889p/DwcDVv3lw///yzWrRoIT8/P6Wnp6tQoUKqXLmyMjIyVKFCBW3evFnTp083n1ldvXq14uLivOpt2bKlectuXho0aJCr7f7779eCBQvM/ZCQEHPV4pzVdnPkvFZnwIABaty4sWbOnKlChQrp0KFDZqg91759+7R9+3a98847Kly4sB599FE9/fTTWrBggXbs2KFp06YpMjIyz1oB4FrB7bsAgAKrXLlykmQGBElyuVxKSEhQ2bJltWfPHmVnZ5vHbrnlFpUuXVpbt27N1/jbt2/XTTfd5LXgTUxMjKT/e5713DouJCQkxHwG8twAK519xvHGG2/U559/LulsWMx5FYskvfzyy6pcubK5iJMk9evXz3wf5rRp03TixAmv7c477/T6jOLFiysyMlI33HCDSpQooRIlSmjWrFny9fVVixYt1L17d9ntdvNYVFSUypcvf9FbSCtVqqT09HQlJiZq165dkqS1a9fK5XIpIyPDK5BKUrdu3fTLL79o9+7d2rt3rw4ePKi0tDSVK1dOH3/8sU6fPq19+/Zp69at2rZtmzp27KgKFSpIkh544AFlZWXpnXfeUa1atczboe+8804zCD/44IPav3+/+vbtK4/HY64efK4333xTe/bs0eHDh83vK6f2BQsWmG05z4tOmDAh1xg7duxQSEiI1x8PpLMr5hYpUkQ9evTQ/PnzVahQIS1btkyVKlXSp59+mmucNm3aaM6cOXrooYdUt25dTZgwQWvWrNFTTz2lxx9/XPPnz/d6lhcArkWEUgBAgVWnTh35+fnp22+/NdsWL14s6eyMV/Xq1b3eJ5qQkKATJ07keqfn+YSHh+v333/3uhU257POnfX6p++PtNlsmjNnjoYMGSLp7MxvzkyvYRgqVKiQxo0bd96A6HA4zMCbs/39PZh/N2/ePPXr10/x8fGaPHmyJKl27dr5DuyXq3jx4ipevLh69eql5ORkBQYG5rquCRMmqFevXrLb7Ro/frzXK2bS0tI0dOhQBQUFacqUKZo+fbp+++03de3aVZIUGhqqTp06adiwYerQoUOeC0YVK1ZM0dHRSk9PV+vWreXj46MiRYpIOvsO0uzsbPXt21eBgYEqU6aMSpYsmWuMJUuWmH+g+LvOnTsrPj5ePj4+Wrx4sZo1a6a6deuqZcuW5/1ePv30U5UtW1ZLlizR22+/reXLl/NMKYACg1AKACiwnE6nunTpogEDBmjs2LH68ssv9cILL6hZs2aqWrWqnn/+eT3//PMaP368fvjhBz3xxBOKiIjItdrt+Tz44IPasWOH6tWrp7i4OLVt21bPPfecYmJi8lzd9kJOnjypvXv3au/evearZs518803mzOyZ86cMUOpzWbTlClTzNfB/N25M8F/d+67OXNkZGRo8ODBeuyxx9S3b18999xzCg0N1Q8//CC73a6YmBh169ZNBw8evKTry6+TJ0+qSZMm2rp1q1wuV57nBAQE6KOPPlL9+vVzvVc1KChIK1euVJs2bTR58mQ99dRTCggI0OTJk5Wamirp/17hc6FX/+zatUsNGzbU3r17vRbFkqS//vpLX331lapXr57nq3UyMzP1+eefm++RPZ+RI0fq/vvvN5/nPffZ2r8/89uqVSu9//77OnLkiJo2bao6dep4rfgrXfh3DQD/ZTxTCgAo0N59913Z7Xb1799f6enpat68ucaMGSNJGjp0qAIDAzVw4EAlJyfr7rvv1tKlS/O9+u7tt9+u7777TnFxcRoxYoTsdrsaNGigUaNGXdLsaHZ2tnr16qVevXqZbVFRUXmem5mZqZ07d3o9I5sTVnNC5rlh0+Px6MEHH8xzrLp165o/Hzt2TNOmTdP48eN16tQpTZ061etZ1KioKK1cuVJdu3bV6NGjNWbMGN16662aM2eO16xwzrtMpbMzuomJiQoKCpKPj48ZMpOSkpSQkKDMzEylpaUpKipKNptNGzduVMuWLVWiRAnNnz/f6/eQlZVlzgy2a9dONWvWVJMmTVSzZk19//33qlGjhnludHS0ihcvru3bt+uVV15RrVq19Mknn6hly5bq06ePVqxYoY8++kgvvfSSDMPQyJEjvWaOv/vuO7Vp00bVq1fXV199paJFi3r9oaBevXraunWr2rVrp0aNGmnw4MHq3bu3Wd+ECRN04sQJdezY0eyTnZ1tfi87d+5Ujx49tHDhQo0YMUIvvvhirt9NVlaWTp48qZ07d5ptsbGxio2NVc+ePbVt2zbt37/fPJaWlpbnHxkA4JpgAACAXDwej5GZmXnezePxXLHPKlWqlPHJJ5+Y+1OnTjWKFy/udc7QoUONrl27GjVr1jTsdrtx8ODBXOMsX77ckGRs27bNbPP19TUmTZpk7Nmzx2u7/fbbjd69e5vnvf3220ZwcLDRo0cP49ixYxesd8mSJUbTpk2Np556Ktexvn37GrVr1zYMwzA++eQTQ9JFt6SkJMMwDOPxxx83mjZtaqSkpBiGYRjZ2dnG66+/brRp08aw2WzG7NmzvT5r9+7dxr333mscOnTIMAzDOHXqlNG/f3+jUqVKRoUKFYx58+aZ544ZM8YoVqyYUbt2bWP//v2GYRjGjBkzjMDAQKNWrVpGamqqYRiGsWXLFsPX19fo2rWrkZmZaRiGYQwaNMioU6eOIcn47bffzDFz6gsICDB2795tGIZhnDhxwihatKjxyiuveNX6xBNPGF27djXOnDljREZGGmXLljVWrFhx3u+4QoUK+fruzt169ep1wd8bAPxXEUoBAMjDm2++ecEA8NBDD12xz+rXr5+xZs0ac/+vv/4yPv7441znREVFGY899pixevXqPMdJS0sz9uzZY2RkZJhte/bsMQPXhXg8HuP48eOXVHd2dnautt69exuxsbGGYRhGRkaGkZKSYmRlZeXZPyMjwwykOTWcW7thGEbXrl2NWrVqGa+99pqRlpZ20ZoGDBhgfPnll7k+c9GiRcb48eNz/TFh48aNxqJFi7zatmzZ4rX/yiuvGA6Hw+jatWuef4zYsWOH1/6hQ4cMl8t13hq3bNlyweOGYRhly5Y1Ro0adcFzztWoUaNcQRgArhU2w/j/95IAAADT0aNHdfTo0fMedzqd5jssUbB5PJ5/vFjVpdq3b5/CwsK8btMGgIKKUAoAAAAAsAyr7wIAAAAALEMoBQAAAABYhlAKAAAAALAM7ynFFZWdna3Dhw8rNDTUfF8bAAAAgOuPYRhKSkpSyZIlzXdq54VQiivq8OHD533hOwAAAIDrz4EDBxQZGXne44RSXFGhoaGSzv6D53A4LK4GAAAAgFVcLpeioqLMjHA+hFJcUTm37DocDkIpAAAAgIs+1sdCRwAAAAAAyxBKAQAAAACW4fZdXBWdX18r/4BCVpcBAAAAXDcmv3eH1SVcFmZKAQAAAACWIZQCAAAAACxDKAUAAAAAWIZQCgAAAACwDKEUAAAAAGAZQikAAAAAwDKEUgAAAACAZQilAAAAAADLEEoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALAMoRQAAAAAYBlCKQAAAADAMoTSAuDYsWNau3atUlJSrC4FAAAAAC4JofQasWLFClWvXl2BgYG6++67tW/fPknSyJEjValSJbVv316RkZFavnx5vsYbOHCgihcvrpCQEDVv3lwnT56UJE2ePFkNGjS4WpcBAAAAAF4IpdeAhIQENW/eXA8//LC2bNmiIkWK6KmnntLOnTs1dOhQbd68WVu2bNHLL7+s/v37X3S8ZcuW6YsvvtCyZcu0YcMGeTwe9ejR41+4EgAAAADwRii9BsyfP18Oh0MDBgzQjTfeqBEjRmjlypU6dOiQJkyYoFKlSkmSYmNjderUqYuOt3btWjVr1kyVKlVS+fLl1bp1a+3cufNqXwYAAAAA5EIovQb8+eefuvnmm2Wz2SRJpUuXVmhoqFJTU9W8eXNJUkpKisaMGaOHH374ouNVrVpVc+bM0e7du3X8+HFNmjRJ99xzj9c5H3/8sYoXL67ixYtr9uzZV/6iAAAAAECE0mvCmTNn5HQ6vdqcTqfOnDkjSfr22291ww036PDhw/m6fbdp06YqV66cypUrp+LFiys5OVl9+vQxj2/atEmzZ8/WypUr9cwzz6h79+7nHcvtdsvlcnltAAAAAJBfhNJrlGEY5s/33nuv5s+fL0mKi4u7aN8vv/xS+/fv15YtW3T8+HFVrVpVbdq0MY+npKTo008/Vfny5dWhQwcdOHDgvGPFx8fL6XSaW1RU1D+4KgAAAADXG0LpNaBo0aLmrGiOxMREFSlSRJJkt9tVv359ffjhh5o0adJFx5s2bZq6dOmiKlWqqFixYho5cqRmz56thIQESVKVKlUUEREhSfL397/gWHFxcUpMTDS3CwVYAAAAAPg7Quk14Oabb9bGjRvN2dG9e/cqOTlZx44d03vvvWee5+/vL19f34uOl52drePHj5v7R48elSR5PB5JksPhyHdtAQEBcjgcXhsAAAAA5Jfd6gJwcQ888IBeeukl9e/fXx06dNCrr76q+vXrq0aNGqpXr55uvPFG3XLLLRowYIAeffTRi45Xr149vfvuuypVqpSCgoI0cuRI3XnnnSpatOi/cDUAAAAA8H+YKb0GOBwOzZs3T19//bWqVKmihIQETZ06VTExMRo/frx69OihW265RWXKlPGaOT2fbt266YknntDAgQP13HPPyel06rPPPvsXrgQAAAAAvNmMc1fMAf4hl8slp9OpJ19cLP+AQlaXAwAAAFw3Jr93h9UleMnJBomJiRd8zI+Z0gJo//79CgsLy3N74oknrC4PAAAAAEw8U1oAlSxZUhs2bMjzWHBw8L9bDAAAAABcAKG0ALLb7YqOjra6DAAAAAC4KG7fBQAAAABYhlAKAAAAALAMoRQAAAAAYBlCKQAAAADAMoRSAAAAAIBlCKUAAAAAAMsQSgEAAAAAliGUAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgGbvVBaBgGjfkNjkcDqvLAAAAAPAfx0wpAAAAAMAyhFIAAAAAgGUIpQAAAAAAyxBKAQAAAACWIZQCAAAAACxDKAUAAAAAWIZQCgAAAACwDKEUAAAAAGAZQikAAAAAwDJ2qwtAwdT3w50KCAyxugwAAHAdGt6zotUlALgEzJQCAAAAACxDKAUAAAAAWIZQCgAAAACwDKEUAAAAAGAZQikAAAAAwDKEUgAAAACAZQilAAAAAADLEEoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALAMoRQAAAAAYBlCKQAAAADAMoRSAAAAAIBlCKUFyKZNm7R06dJ8nXv69GkZhnGVKwIAAACACyOUFiBvvfWWxowZk69z33jjDTVt2lQnTpzQL7/8IpvNluc2aNCgq1w1AAAAgOsZobSAWLlypebOnas1a9YoOjo61xYVFSWHw6Hdu3dLkkaNGqW7775bgwcPlr+/v2rVqqWkpCSvLS4uzuKrAgAAAFDQ2a0uAP9cSkqKOnTooN69e2vIkCH56uNyufTaa69Jkn7//Xf99ttvioyM9DonPT1d/fr1u+L1AgAAAEAOQuk1zuPxqHXr1ipevLhiYmIUExOjwMDAXOckJiZq2bJlKlGihNasWaOOHTvqp59+Unh4uGw2m2699VatXr3aq99bb731L14JAAAAgOsRofQa5+vrq+7du+vmm29W0aJF9dhjj120T61atRQTE6PHHntMixYtksfjuezPd7vdcrvd5r7L5brssQAAAABcfwilBUDFihUVHR0tp9Mpuz33r9Tj8ahixYr64YcfJEk+Pj6aPHmyunfvrtTUVBmGoZ07dyo8PDxX39dff/2Cnx0fH68BAwZcmQsBAAAAcN2xGbwXpMD7+uuv9d5772nZsmVe7QkJCQoLC/tHY+c1UxoVFaWuA39VQGDIPxobAADgcgzvWdHqEgDobDZwOp1KTEyUw+E473msvnsd8Hg8stlsudpvueUWHT58WDExMQoJCVFQUJCCg4MVEhIiPz8/hYWFycfHR8ePHz/v2AEBAXI4HF4bAAAAAOQXt+9eBxwOh/bt2yfp7Eq9hQoV0rp16xQcHKySJUvKz89PCxYs0IoVKxQZGano6GgNGjRIS5YsUYkSJRQQEGDxFQAAAAAoqJgpvQ5UrFhR+/fv1/Hjx9W4cWNt2bJFn376qVq1aiXp7DOmF3Kx4wAAAABwuZgpvQ6ULl1at99+ux555BFt3rxZISEhmjJlSq5nTM8nr1t/AQAAAOBKIJReJ0aPHq1HHnlEQ4cO1d69e1WpUiVFRkbK4XDI19dXbdq0MZ899fHxkdvtVnR0tNLT0xUZGak1a9aoUqVKVl8GAAAAgAKGUHqdqFmzpvbs2WPur1q1Sn5+frxXFAAAAICleFjwOuXn52d1CQAAAABAKAUAAAAAWIdQCgAAAACwDKEUAAAAAGAZQikAAAAAwDKEUgAAAACAZQilAAAAAADLEEoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALAMoRQAAAAAYBm71QWgYBr8Unk5HA6rywAAAADwH8dMKQAAAADAMoRSAAAAAIBlCKUAAAAAAMsQSgEAAAAAliGUAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgGUIpAAAAAMAyhFIAAAAAgGXsVheAgun9GccUGJRqdRkAAOAK6tO2hNUlACiAmCkFAAAAAFiGUAoAAAAAsAyhFAAAAABgGUIpAAAAAMAyhFIAAAAAgGUIpQAAAAAAyxBKAQAAAACWIZQCAAAAACxDKAUAAAAAWIZQCgAAAACwDKEUAAAAAGAZQikAAAAAwDKEUgAAAACAZSwNpRkZGRc8np2dLbfbfdFxTp06pezs7PMeNwwjV9vChQuVmpp6wXGzsrKUlZV1See43W5lZmZepOKzMjIy8n3uxSQnJ2vatGn5+r4uRXZ29kV/TwAAAABwuSwLpYmJiXI6nXI6nQoLC1NYWJgCAgIUEBBg7oeFhem2227z6jdu3Dg1bNjQq61Tp07q16/feT/rnnvuUb9+/czAdvr0abVq1UpHjx69YI1jx471qiUsLEw2m02hoaFebSNGjDD7dO/eXeHh4V5bUFCQgoKCcrUXLVpUM2bMOO/nf/XVV4qNjZXH47lgnZI0efJkvf7665cUckuWLClfX1/Z7fYLbi+88EK+xwQAAACAS2G36oOdTqfS0tLkdrsVEBAg6Wygk6SRI0dKkvbv369SpUp59QsMDFRQUJC573a79fPPP+vDDz8872dNnjxZnTp1Uq1atbRu3TqNGzdONpstV7hNT0/X008/rXfeeUeS1K1bNz333HNmfZIUFhamn376STExMZKkPXv2qGzZsubxsWPHauzYsV7jPvTQQ2rRooWeeeaZfHwz/8fX11eJiYny9fW94HkJCQl6++23ZbPZVK1atVzHDcNQUlKS3n//fbVv395sDw0N1ZQpU9S4cWOz7cSJEwoMDFRoaKjZdrHZYgAAAAC4XJaFUkn68MMPtX79ek2ZMiXP423atFHdunU1ZMgQr1twbTabDMOQYRiaP3++PB6P7r77bvO4y+VSjRo1tHjxYklSZGSkvvvuO61bt05nzpzR6NGj9eeff3qFybwYhqE6depo2LBhatSoUa7jO3fuVNWqVbVhwwZVqVIlzzEyMjK0fPly/e9//7vo9/F3Pj4+CgsLu+h5Xbt2VcWKFbV8+XLZbLZ8j39uuM/x1FNPqUWLFl6zo3a7pf+YAAAAACjALE0b99xzj3r37q1HHnlEzZs39zq2cuVKrV69WpMmTZIkrVmzRnfccYd8fHxkGIZ8fX01YcIEffLJJ5owYYIef/xxs++4ceP07bffSpJ+/fVXHT16VPfff79uueUWNW7cWFFRUV4hNkfHjh3Vv39/c99ms6lZs2bq0KGDNm/erJCQEK/zhw0bpjvvvDNXIH3hhRfMz8/KypLL5VLNmjVzfV7//v3VsWNHSdLJkyd1+vRphYSEyMfHx2yz2+3mbcaGYSgtLU2pqakqW7asChUqpHfffVdLlizRnDlzVKNGDQUFBXkF05xZ0iFDhqhFixYX+G2c5e/vr+Dg4IueBwAAAABXgqWhtEqVKurZs6e6devmdQupx+NRt27d9OKLL6pChQqSpFq1aik9PV2ff/65vvzyS82dO1dff/21jh07poCAAMXFxSk+Pl7S2dnJnFnAhIQEde7cWfXq1dOHH36ol19+Wfv27ZOPj4+aNm2qL774Qv3799e0adO0efPmXDX269dPn332mQYMGKB3333XbF+/fr0+/fRTrVmzJlefxMRE9enTR507d1aDBg20cOFCVatWTRMnTtTOnTs1dOhQtW/fXmlpaWafmTNnqlevXvLz8zPbMjIy5Ha7VblyZbMtKytLbrdbK1as0O23364WLVro3nvvVY0aNbRx48bL+j243W7Z7fY8bxPOzMz0qimvvucuruRyuS6rBgAAAADXJ8tfCdOjRw85nU6dPn3abFu1apWOHj2qt956y2zz9fX1erbTbrcrIiJC48ePV926dTVz5kxz0aBzQ2mjRo20ceNGGYahgwcPqmXLlnK5XAoNDVW5cuW0fPlyJScne/U5l7+/v+Li4nTmzBmv9nHjxqlTp0665ZZbcvX5e4h7+OGHVa1aNb355pte7efeFvvCCy8oJSVFCQkJ5tarVy8FBwfr6NGjZltycrIyMzN1++23S5IqVKigtm3b6oYbblB0dHSeW3h4uJKTk8/7O6hRo4bsdrtsNpu++eYbPfPMM7LZbLLZbPL39z9vP0mKj483F6xyOp2Kioq64PkAAAAAcC5LZkoNw1BGRob8/f1VpEgR/frrr15Brl69etq4caOcTqc8Ho8yMzMVEBCQ63nJevXqmT/Pnj1bKSkp+vHHH3MFzMKFC2v8+PFyOBxq0KCBfv/9d/n7++vtt9+WJFWrVk3JycnKysrS6tWr9c033ygzM1M2m012u13PPPOMOnXq5PXZY8aMMV+VkpmZqezsbK/QfK45c+Z4zZTm1/z58xUaGqpPP/1Uzz///HnPu9AMaWJiosLCwvK8Jdfj8SggIEA//PCDbDabfHx81LZtWz3wwAN6/PHHzd/ThcTFxalHjx7mvsvlIpgCAAAAyDdLQun27dsVExOTK8Tl3M46efJksy0rK0uZmZnav3+/ihcvbrYbhqHU1FQVKlRIkrR582b16tVLI0aMyHPWs3fv3rrjjjv03Xff6cUXX1Tbtm1VuHBhJSYmqn79+ho6dKgKFy6sdu3aSZLeeustjRw5MtesZ2JiourVq+d1q2tGRoYaNmyoBQsW/PMv5/9bsmSJjh07prlz56p58+Zq1aqVwsPDL3mcnNfJ5LUA0qlTp+RwOLxWOA4ICJDT6dTXX3+tw4cPa8CAARccP+c1PgAAAABwOSy5fbdSpUpKS0vzulU1ISFBXbp0UZcuXbzakpOT5Xa7Vbx4cSUlJWnbtm367bffFB0drYULF2rr1q267777NHr0aC1btkyPPfaYMjIyFBgYaH5ecnKyZsyYobvuuktBQUE6c+aMHA6HUlNT1bNnT0lnXwcTEhJi9hs8eHCu22kTEhLkdDq1fPlyr7bU1NQLBtLz3b57PsnJyXrhhRc0cOBA3XbbbWrVqpU6d+7stQJxfoWGhspms2nfvn1KSUkx25OSknTs2DFFR0fn2a9SpUoaMmTIZT+nCgAAAAD5Yfkzpfk1ffp0OZ1O/fTTT+rdu7dWrFihVq1aacSIEYqJidGyZctUrlw5SWcX3zl3pnTq1Km6/fbbzVfAnDlzRqGhobrzzjv14IMPyu12Kz09Xf7+/lflnZxz5szRpk2bLjrrKJ2diX3wwQd18803q0OHDpKkd955R9u2bdOLL76o7OzsS/psPz8/lS1bVuvWrdOrr76qzz//XJK0evVqlSlTRk6nM89+DRo00HPPPafMzMxL+jwAAAAAuBT/qRdQ5txqmpcHHnhAf/zxh6pXr+7VPm7cOPMVKsuXL1diYqIWLVqkzp07Szp7++/w4cO9Vs4NDg7Wvffea+7/73//04kTJxQcHCw/P78LvjrF4/FcsE5Jys7OVlxcnAYNGqQTJ06ocePGstvtSk1NVVZWlj777DOdOXPGXKwox4IFC/TSSy+pTJky+vTTT832QoUK6euvv9Zdd92lXbt26ZNPPlHJkiUvWMO5HnvsMQ0YMECHDx82X53z2WefqWnTpnK5XOaiRjnX53a7lZycrGHDhik7O1unT59WkSJF8v15AAAAAJBf/6mZ0rS0NKWnp+d5zOFw5AqkksxAKp0NWs8995wqVqxohq/Zs2fLZrPpoYceMs/79ttvtXfvXnMbM2aMSpQooZUrV170XZ4XqjGH2+1WfHy8Dh48KLfbraNHj+rgwYM6ffq0XC6XDh48qEcffdRcRMjj8ahRo0Zq3ry5WrRooUWLFpnPyubIWSn46NGjqlSpkrZv337BGs7Vu3dvOZ1ONW3aVHfffbe2bdum6dOnq3Xr1mrZsqUcDodCQ0MVGhqqhQsXqnPnzua+0+nUXXfdle/PAgAAAIBLYTMu50HFa8yhQ4e8FvP5ux49eqhIkSLq16/fv1iVt2XLlikgICDX7Onfud1uLVy40CtkX6pTp07p448/Vp8+feR2u+Xn5+cV7s+VnZ2d6xndC3G5XHI6nXpz/HYFBoVedo0AAOC/p0/bElaXAOAakpMNEhMT5XA4znvedRFK8e8hlAIAUHARSgFcivyG0v/U7bsAAAAAgOsLoRQAAAAAYBlCKQAAAADAMoRSAAAAAIBlCKUAAAAAAMsQSgEAAAAAliGUAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgGUIpAAAAAMAyhFIAAAAAgGXsVheAgqnHE8XlcDisLgMAAADAfxwzpQAAAAAAyxBKAQAAAACWIZQCAAAAACxDKAUAAAAAWIZQCgAAAACwDKEUAAAAAGAZQikAAAAAwDKEUgAAAACAZQilAAAAAADL2K0uAAXTp4sSFRRsWF0GAAC4Qjo1c1pdAoACiplSAAAAAIBlCKUAAAAAAMsQSgEAAAAAliGUAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgGUIpAAAAAMAyhFIAAAAAgGUIpQAAAAAAyxBKAQAAAACWIZQCAAAAACxDKAUAAAAAWIZQCgAAAACwDKG0ANm0aZOWLl16RcfMyspSVlbWFR0TAAAAAHIQSguQt956S2PGjMnXudu3b5fNZpPdbr/g5ufnpxkzZlzlygEAAABcr+xWF4ArY+XKlZo7d65KlCih6OjoXMc9Ho8SExO1YcMG3XjjjQoNDZUkpaeny27/v38MDh48qIiICPn7+0uSDMNQdnb2v3INAAAAAK4/hNICICUlRR06dFDv3r01ZMiQfPUJCgrKs/3GG2/Uhg0bdNNNN0mSbDabfH19r1itAAAAAHAuQuk1zuPxqHXr1ipevLhiYmIUExOjwMDAXOckJiZq2bJlKlGixAXH8/f3V3Bw8NUsGQAAAABMhNJrnK+vr7p3766bb75ZRYsW1WOPPXZJ/Q3DUHp6ugICAmSz2XIdz8zMlJ+f33n7u91uud1uc9/lcl3S5wMAAAC4vhFKC4CKFSsqOjpaTqfT6/nQHB6PRxUrVtQPP/yQ69ixY8cUFRXl1Va2bFnz5/r16+unn34672fHx8drwIABl188AAAAgOsaobQAKFWqlJKSks57/Ouvv9Z7773n1ebxeOTr66sSJUpo79698vf3l81mU7ly5fTTTz8pKipKhmFc9LPj4uLUo0cPc9/lcuUKuQAAAABwPoTS64DH48l1a+6pU6cUGhoqu92uMmXKmO02m03FihVTz5491b59ezVu3PiCYwcEBCggIOCq1A0AAACg4OM9pdcBh8Ohffv2STq7Uq8k7dixI89Xx+QoX768OnXqpNTU1H+jRAAAAADXKULpdaBixYrav3+/jh8/rsaNG2vLli1atWqVbrnllvP2efXVV1WnTh0zxAIAAADA1UAovQ6ULl1at99+ux555BFt3rxZpUqV0ueff66mTZvq9OnTSkpKUnJyspKTkyVJqampstlsGj9+vPz9/S/4vCoAAAAA/BOE0uvE6NGjdeDAAQ0dOlQzZ87U6dOn1axZM0VERMjhcCg0NFShoaFKSUlR1apVzf2wsDDFxcVZXT4AAACAAoqFjq4TNWvW1J49eyRJW7du1fvvv69ChQopOTlZ/v7+8vHJ++8THo9HHo/n3ywVAAAAwHWEUHodqlKliqpUqSJJCgwMvOC5vr6+8vX1/TfKAgAAAHAd4vZdAAAAAIBlCKUAAAAAAMsQSgEAAAAAliGUAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgGUIpAAAAAMAyhFIAAAAAgGUIpQAAAAAAyxBKAQAAAACWsVtdAAqmdvc65XA4rC4DAAAAwH8cM6UAAAAAAMsQSgEAAAAAliGUAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgGUIpAAAAAMAyhFIAAAAAgGUIpQAAAAAAy9itLgAF04K1yQouxN88AACQpBZ3hFhdAgD8Z5EaAAAAAACWIZQCAAAAACxDKAUAAAAAWIZQCgAAAACwDKEUAAAAAGAZQikAAAAAwDKEUgAAAACAZQilAAAAAADLEEoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALAMoRQAAAAAYBlCKQAAAADAMoTSqyQjI0PZ2dnnPe7xeJSRkfGPP+fUqVP/eIwc33//vXbt2nXFxgMAAACAi7FbXUBB1bNnT61bt06+vr6SpAMHDiggIEARERGSpMzMTN15550aMWJEnv23bdumKlWqKC0tTYGBgWZ7dna2bDabbDabJKldu3YqUqSIxo4dq3nz5ql169Z5jjd16lS1adPmgjWPHz9e4eHhmjBhwiVfLwAAAABcDkLpVfLhhx967Q8aNEiRkZFq3759vvrnBNESJUqYbYZhKDU1VQsXLlSjRo0kSXPmzFHPnj01fvx4lSlTRq1atdLkyZO9xurcuXOu8d99910NHz5cQUFBZpvb7VZmZqYWLVok6exsr8vlUnJycr5qBgAAAIBLRSi9CurVq6eDBw+qUKFCZtuJEyfk5+en4cOHm22nT59Wu3btFB8ff96xTp48Kbs9719TVlaWMjMz9cEHH0g6G1DnzZunyMhIr/NSU1PVpEkTr7bs7Gw9/vjj+vDDD5WRkaHp06erTZs2stvtcrvd+vjjj/X888/Lz8/vkq8fAAAAAPKLUHoVVKtWTQMGDDBnHBs0aKD169crMjJSqamp2r9/v0qWLKnIyEgdP378sj/nq6++0tixY/Xdd98pKChINptNLVu21IwZM7zOO9/srM1mU1pamjwej5555hk9/fTTks4+79qtWzc9++yzSk9P97p9GAAAAACuJELpVTB27FglJSWpR48ekqSSJUtKOhsCV65cqT/++EOxsbF66aWXvPq9+uqrGjVqlIKDgyVJTqdT4eHh5vHExET9+eefqlatmiSpefPmmjBhgp5//nlNmTJFHo8n3zUahiFfX181atRILpdLkhQbG2sek6RatWrJ7XZr3bp1cjgceY7jdrvldrvN/ZyxAAAAACA/CKVXQVJSklauXKlBgwZJkooUKaI777xTnTp10u23365p06bl2c/Pz0+tW7fO9UxojpCQEK9Zy6CgIM2dO1d9+vRRVlaWbDablixZ4hVkczzwwANe++np6fLz89OqVauUmpqqQoUK6csvv5R09lnSqlWrasOGDfLxufACzfHx8RowYMAFzwEAAACA8yGUXgW7du1St27ddP/998vlcmnHjh366KOP9OmnnyoyMlJTpkzRvn371KRJE02dOtXsd7EAKMlcdTdHaGioBg8eLLvdrpYtW6ply5b5qjElJUXFihUzx3zxxRc1cuRI8/iLL76orKws+fv7X3CcuLg4c0ZYOjtTGhUVla8aAAAAAIBQepUUKlRI0dHROn36tLZv364+ffpo+PDhmjt3rh599FFNnTpVo0aN8urjdrs1Y8YMLViwIM8xU1JSlJ6enqutUqVKOnDggCIiIpSdnS232y0/Pz9lZ2fL4/EoKChIGRkZSk1NNfsdPXpUNWrUUFxcnGbOnKns7Gw5nU7zeHJysoKCgvTuu+9e8DoDAgIUEBBwqV8PAAAAAEiSLj41h0tWrlw5DR06VBs3btSJEyf0/vvvKz4+XoUKFVJqaqrmzJkju92ugQMH6qOPPpJ09nnRJk2aKDU1VSdPntSxY8d08uRJHT16VPv27dPJkydlGIb5ntMc3377rW699Vb5+/vLz89PGzduVNu2bbVgwQJ99NFHatu2rY4ePZorOO7cuVORkZGKj4/XX3/9pWLFimno0KHasGGDxo0bp6ysLPXp0+df+84AAAAAXJ8IpVfB/v379fnnn6tixYr66quvNGjQIH355ZcaOHCgqlWrpkaNGqlu3brau3evuYDQp59+qi5dusgwDLVv3958rrRXr17q27evJGnu3LmqW7eu18JCn376qVq1aiXpwrf/nnvM5XJpw4YNqlmzpiTJbrdrypQpev755/XBBx+odevWmjlzpooWLXpFvxcAAAAA+DtC6VWQmZmplJQUSVK7du3UpUsX9ezZU7GxsapXr54aN26s6tWry+12q02bNsrIyNCoUaPUp08f+fr66q677tLo0aMlSc8995zGjx+vPXv2qHnz5ipUqJCGDBkiSfrrr7+0aNEiNW/e/KI1nfss6uTJk1W3bl2FhoZKOnvb8IEDB1SmTBm98sorioiI0O7du/XXX3+xmi4AAACAq4pnSq+CcePG6dixY9q3b59uuukmZWVlKSgoSFlZWRoxYoRCQ0OVnp6uG2+8UZK0e/duRUdHq127dpKkhx9+WMuWLZPb7VaVKlX08ssvKyEhQWXLltWQIUM0e/ZsSdIff/yh++67T/v371eFChVkGIbuuusuud1uLVy40HymdMmSJcrMzJTT6dTx48eVnJysbt26adasWRo4cKCSkpJUu3Zt9erVSwsWLNB3332nb7/9Vm+88YZ27typF198MdfzrwAAAABwJdiMnJdS4pqUmZkpPz+/y+rrdrt16tQp8z2qeUlNTZVhGCpUqFC+xnS5XHI6nZq2+JCCC+X9blMAAK43Le4IsboEAPjX5WSDxMRE87HFvDBTeo273EAqnV0590KBVJKCg4Mve3wAAAAAuBieKQUAAAAAWIZQCgAAAACwDKEUAAAAAGAZQikAAAAAwDKEUgAAAACAZQilAAAAAADLEEoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALAMoRQAAAAAYBm71QWgYHrgthA5HCFWlwEAAADgP46ZUgAAAACAZQilAAAAAADLEEoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALAMoRQAAAAAYBlCKQAAAADAMoRSAAAAAIBlCKUAAAAAAMvYrS4ABdPKzS4VCrG6CgDApbqrusPqEgAA1xlmSgEAAAAAliGUAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgGUIpAAAAAMAyhFIAAAAAgGUIpQAAAAAAyxBKAQAAAACWIZQCAAAAACxDKAUAAAAAWIZQCgAAAACwDKEUAAAAAGAZQul/lNvt1o8//qjk5OR8nb9o0SJt27Yt3+OfOnXqcksDAAAAgCuGUHqVrFq1SqGhoapdu3au7fbbb5fNZlNqaqokKTs7WxkZGV79s7Oz9eCDD2rx4sW52t1ut1eb2+3WM888k+vc8/F4PGrcuLFee+01ZWVlKT4+XjabLc9txYoV/+BbAAAAAIALs1tdQEEVFBSkYsWKqX379rmOZWVlae3atQoODpYk/fLLL2rZsqWCgoK8zgsPD9crr7yiV155xWzLzs5W1apV9d1335ltw4cP16lTpzR8+HC99957uT7P7XbL4XDor7/+kiT5+vpq2bJl6tixo+bNmyd/f3/16NFDAwYM8OrXtGnTy75+AAAAAMgPQulVZLPZZLef/ys2DEM2m0116tTRsWPHLusz/vzzTw0ePFhff/217rvvvnz1SUlJUXBwsGbOnClJGjFihMaMGaNJkyZ5nZffW4cBAAAA4HIRSq+SzMxMud1uTZw4MdcxwzBUtGhRpaene82O7t+/X2XLllWVKlVy9Tl27Ji6d++uvn37mm1Hjx5Vy5Yt9fLLL2vZsmWKi4uTv7+/V7+MjAyFhYVp6dKlZtvo0aO1YcMGTZs2TT4+PrLZbOrevbuGDh3q1bdBgwaXe/kAAAAAkC+E0ivMMAylp6frtttu08GDBy94rsfjkdvtVkBAgCQpMDBQ/v7+GjRoUK5zp06dKj8/P6+28PBwDR8+XM2bN5fNZtPgwYPzVWPHjh3VsGFDvfnmmxo4cKA8Hk8+ry43t9vt9Yyry+W67LEAAAAAXH8IpVfYyZMndccdd+jkyZNyOp0KDQ1VUlKSkpKSVLJkSa9zs7KyVLlyZc2dO1eSFBwcrBEjRujo0aO5xr3nnnt02223ebXZ7XalpqaqSJEicjgcstlsufplZGSoU6dOevvtt8228PBwfffddxoxYoQ5zsSJE/Oc1f17EP67+Pj4XM+iAgAAAEB+2QzDMKwuoiBq3769IiMjdeutt2rNmjU6ceKEHn74Ybndbvn4nF30ODo6WjExMZKkBx54QHv37s3X2C+++KK6dOmSr3NfeeUVhYaGeoVS6eyMrsvlktPpzPc15SWvmdKoqCh9u+qACoU4/tHYAIB/313V+Xc3AODKyMkbiYmJcjjO/98XZkqvopSUFCUkJCglJUWStG7dOq1evVpPPPGEpkyZojZt2pihdP/+/Zo4caJq1659wTG7du2qpKSkfNfg8XjynEHdvn27WrVqpRUrVqhw4cJyOp1KSUlRUFCQMjIyZLPZ5Ofnp8jISG3ZsuW84wcEBJi3HwMAAADApeI9pVfRokWLNHHiRC1ZskSS1KtXL505c0ZPPfWUjh8/rocfftg8N2f2VJJKliypmjVr6tZbb9Wtt96q0qVLe70W5txzL8bhcGjfvn2SZIZjSZo3b57q169v3p6bkJCg+vXra+PGjerTp4/69u2rtWvXEjgBAAAAXFWE0qto0KBBmjJlitq2bat169Zp3bp1atGihe6++241bdpUhQsXzrNfaGioVq5cqfXr12v9+vXq0aPHRZ/tPJ9KlSpp/fr1OnTokOrUqaOMjAxJ0pQpU9SqVauLBtxLCcAAAAAAcKm4ffcq6NChgxYtWqQtW7bo1ltvVXp6usqWLatatWpp69atSktL06pVqzRr1iw1a9ZMhQoVUnZ2ttm/bNmyqlu3rteYTz75pCQpOzs7z9txz6dZs2bq0qWLnnzySTmdTvn7+2vJkiU6cuSI6tevb4bU87mUzwIAAACAS0UovQoGDBigsWPHmre+Tps2Td99950GDRqk5ORkrV69WocOHdKwYcN08OBBvfLKK16LBS1cuDDPcfv376+lS5eqbdu2+a6laNGiGjlypAYOHKhZs2ZJkjZt2qRWrVpp3rx56tSpkxwOh6Kjo5WSkqK77rrLfKZ0woQJcrlcKl++vHbu3PkPvhEAAAAAyBur7/6LDMP4RzOP577T9J/KzMy87FuCLyRnhS1W3wWAaxOr7wIArpT8rr7LA4P/on96K+yVXHToagRSAAAAALhUhFIAAAAAgGUIpQAAAAAAyxBKAQAAAACWIZQCAAAAACxDKAUAAAAAWIZQCgAAAACwDKEUAAAAAGAZQikAAAAAwDKEUgAAAACAZQilAAAAAADLEEoBAAAAAJYhlAIAAAAALGO3ugAUTHWqOuRwOKwuAwAAAMB/HDOlAAAAAADLEEoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALAMoRQAAAAAYBlCKQAAAADAMoRSAAAAAIBlCKUAAAAAAMvYrS4ABdOfu04oJCTd6jIA4JpWo0KE1SUAAHDVMVMKAAAAALAMoRQAAAAAYBlCKQAAAADAMoRSAAAAAIBlCKUAAAAAAMsQSgEAAAAAliGUAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgGUIpAAAAAMAyhFIAAAAAgGUIpQAAAAAAyxBKAQAAAACWIZT+x+zfv/+y+i1atEjbtm3L9/mnTp26rM8BAAAAgCuJUHqVrFq1SqGhoapdu3au7fbbb5fNZlNqaqpXn0OHDql8+fI6cuSIV7thGNqxY4fGjx+vL774Itdnud1uPfPMM1q8eHG+avN4PGrcuLFee+01ZWVlKT4+XjabLc9txYoVl/8lAAAAAMBF2K0uoKAKCgpSsWLF1L59+1zHsrKytHbtWgUHB3u1f/PNN2ratKl8fHzUtGlTJSUl6dixYzp48KDKlCmjm2++Wbfddluu8YYPH65Tp05p+PDheu+993Idd7vdcjgc+uuvvyRJvr6+WrZsmTp27Kh58+bJ399fPXr00IABA7z6NW3a9B98AwAAAABwcYTSq8hms8luP/9XbBiGbDabuT9p0iQ9//zz8ng8+vPPP7V69WoVKVJE7du3V9u2bfXggw/mGuPPP//U4MGD9fXXX+u+++7LV10pKSkKDg7WzJkzJUkjRozQmDFjNGnSJK/zkpOT8zUeAAAAAFwuQulVkpmZKbfbrYkTJ+Y6ZhiGihYtqvT0dAUFBUmS1qxZo7Vr16pXr17y8fGR3W5XZGSkJCkwMFBpaWm5xjl69Khatmypl19+WcuWLVNcXJz8/f29zsnIyFBYWJiWLl1qto0ePVobNmzQtGnT5OPjI5vNpu7du2vo0KFefRs0aPBPvwYAAAAAuCBC6RVmGIbS09N122236eDBgxc81+PxyO12KyAgQP369ct1O2+OkJAQpaSkSDobMg3DUEBAgMLDwzV8+HA1b95cNptNgwcPzleNHTt2VMOGDfXmm29q4MCB8ng8l3aR53C73XK73ea+y+W67LEAAAAAXH8IpVfYyZMndccdd+jkyZNyOp0KDQ1VUlKSkpKSVLJkSa9zs7KyVLlyZT322GM6evSoGjVqZB47ePCgoqOjJZ0NetOnT9eAAQOUmZmpZ599Vm+99ZbsdrtSU1NVpEgRORwOr1uBc2RkZKhTp056++23zbbw8HB99913GjFihCTJbrdr4sSJec7q+vn5XfB64+Pjcz2LCgAAAAD5ZTMMw7C6iIKoffv2ioyM1K233qo1a9boxIkTevjhh+V2u+Xjc3bR4+joaMXExOjkyZM6cuSI+vfvrzZt2qhu3bqqXbu29u7dK0maMmWKVqxYoQkTJlxyHa+88opCQ0O9Qql0dkbX5XLJ6XT+o+vMa6Y0KipKK37bqZCQ0H80NgBc72pUiLC6BAAALltO3khMTJTD4TjvebwS5ipKSUlRQkKCeevtunXrNGHCBCUkJOjDDz/Ub7/9JunszGX16tXPO06NGjX0+++/X1YNHo8nzxnU7du3q06dOkpISJDNZlNYWJj8/PzkcDgUGBiooKAgORwO3XTTTRccPyAgQA6Hw2sDAAAAgPwilF5FixYt0sSJE7VkyRJJUq9evXTmzBk99dRTOn78uB5++OEL9s/MzNSoUaNUvXp17d+/X8eOHbvkGhwOh/bt2ydJZjiWpHnz5ql+/frm7bkJCQmqX7++Nm7cqD59+qhv375au3atAgICLvkzAQAAACC/CKVX0aBBgzRlyhS1bdtW69at07p169SiRQvdfffdatq0qQoXLpxnP8MwlJaWpvvuu0+bNm2Sj4+PHnnkEfMZ0LfeektJSUn5qqFSpUpav369Dh06pDp16igjI0PS2VuCW7VqZd5KfD4XOw4AAAAA/wSJ4yro0KGDFi1apPj4eA0fPlw7duxQ2bJlVatWLYWFhSktLU2rVq3SrFmzvGYvMzMzlZWVpb/++ktnzpzR448/rk2bNmnlypXq06ePJk6cqM8//1wjR47M9wxms2bNtHfvXj355JNyOp3y9/fXkiVLdOTIEdWvX/+i/fO69RcAAAAArhRW370KBgwYoLFjx5rBcdq0afruu+80aNAgJScna/Xq1Tp06JCGDRumgwcP6pVXXpF0dtGg9PR03XXXXVq9erViY2NVtWpVvf/++/rqq680efJktW/fXl26dMn1PtLzKVq0qEaOHKmBAwdq1qxZkqRNmzapVatWmjdvnjp16iSHw6Ho6GilpKTorrvuUkZGhmw2myZMmCCXy6Xy5ctr586dV+fLAgAAAHBdY/Xdf5FhGP+ZmcfMzMyLvu7lcuSssMXquwDwz7H6LgDgWsbqu/9B/5VAKl38/aMAAAAA8G8glAIAAAAALEMoBQAAAABYhlAKAAAAALAMoRQAAAAAYBlCKQAAAADAMoRSAAAAAIBlCKUAAAAAAMsQSgEAAAAAliGUAgAAAAAsQygFAAAAAFiGUAoAAAAAsIzd6gJQMFUvV0wOh8PqMgAAAAD8xzFTCgAAAACwDKEUAAAAAGAZQikAAAAAwDKEUgAAAACAZQilAAAAAADLEEoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALCM3eoCUDDt2b1boaGhVpeBPNxYrpzVJQAAAAAmZkoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALAMoRQAAAAAYBlCKQAAAADAMoRSAAAAAIBlCKUAAAAAAMtc0VBqGIb27NlzJYcEAAAAABRg+QqlLpdLtWrVMvezs7P1/PPP5zovPT1dN91005WrDgAAAABQoOUrlPr5+Wnr1q3/18nHR1OnTs11XkBAgPz8/K5cdQAAAACAAi1fodTHx0c+Pt6nBgYG5nme3W6/MpUBAAAAAAq8fCdIt9utN954w9xPS0vz2gcAAAAA4FLlO5QahqEDBw6Y+0888YTXPi7u+++/V3Z2tpo2bWp1KZKk1q1b64UXXlDdunWVkZEhm80mu90um81mdWkAAAAArhP5DqWBgYH65JNPLnpekSJF/lFBBUGpUqWUlJSk7OxshYSE6NChQ/Lx8VGPHj3UuXPnfI8TExOjl19+Wc8888w/qqd+/fp69NFH1bVrV6/2m266SW3atNGGDRvUs2dPTZ8+XX5+fl6hNDExUX/++aeqVav2j2oAAAAAgLzkK5RmZ2crOzvbq61+/fqy2Wy5njWF5O/vr40bN2rhwoXavHmzfH199fnnn2vv3r0aP368xo8frzNnzkiStm3bptDQ0DzHCQkJueAzusuXL1f9+vXlcDgknQ2QGzduVPXq1XPV4+/vn6t/r169tHHjRp05c0YTJ07UxIkT86whr+eHAQAAAOBKyFcozczMVPny5b3ann32Wfn7+8vX19ds83g8eu65565shdegnAC4aNEidevWTYcPH1aPHj1UvHhxvfPOO2rYsKHq1q2ruLg4hYaGyjAMZWdne32XkmS3283wKknbt2/XhAkTNHToUNntdvn5+alkyZI6ePCgpLMB8uDBg5o9e7bsdrs53oEDB/T999/ryJEjeuWVV3TixAlVrVpVp0+f1syZMy96PSxeBQAAAOBqyVfa2LhxozZs2CBJmjp1qu644w61adMm13lpaWnKyMi4ogVei2w2m1JTU/Xrr7+qXr16mjNnjnr16qUWLVrorrvuUkREhJo1a6ZWrVpJkjZv3qzq1avLx8fHvHX2iy++kCQtXrxYL730kg4cOKD7779fjRs3lmEYkvIOi06nUzfddJP8/PzM8RwOhyIjI1W1alX5+fkpJCREbrdbQUFB2rJli5o1a5ZrnE8++UR333331fqKAAAAAEBSPkLpb7/9pvr166tPnz4aNGiQfvjhB/Xv31/9+/fPtSBOVlaW4uPjr1qx15JVq1YpKipKdrtd9957r9avX68xY8bI4/HozJkzWrlypSZNmqTq1aurcuXKOnXqlJxOp9LT0xUeHq7atWtr1KhR+v777zVmzBgNHz5cvXv3VpcuXczP8PHx0eHDhxUWFiZJSklJ0Q033KDY2Fjt3btXlStXliSNHz9et9xyix555BHzPElev7+9e/eaP9euXdsMvgAAAABwNV00lMbGxurnn39Wx44dtWHDBs2YMUO//PKLnn76afn5+alWrVpmgMnIyFBGRoZefvnlq174f12jRo00a9Ysffnll/rggw/k4+Ojli1b6scff1SzZs30v//9Tz/99JO++OILxcXFKTw8XJK0cOFCVa5cWaVKlZJ0doXcrl27av78+XrggQe8PsPHxyfX7bs2m03Tpk3TsGHDtHXr1ly3BP/d+Z4Jzu+zwm63W26329x3uVz56gcAAAAAkpSv5FG3bl2tW7dOaWlpqlOnjho0aKBly5bJZrOpZMmS+uKLLzRnzhx98803Wrx48dWu+Zpgs9n0zjvvaMCAAVq+fLl2796tESNG6N5779WBAwd09913680331SNGjXMQCpJI0eOVMeOHc39Bx54QHXq1PGayczx98WncrRr104ZGRn67LPP8lXrwYMHFR0dbW6///57vq8zPj5eTqfT3KKiovLdFwAAAADyvYKNw+HQ/PnztXz5cs2ePVuPP/64fvrpJ/344495rux6vUtOTtaWLVu0f/9+7d69W7/88otKlSqlxMREVa5cWXv37lVWVpbXbbJfffWVdu3a5RVKJenjjz/WHXfcoZtuukkNGzY02z0eT67PNQxDdrtd3bt3z/fzvZGRkblu382vuLg49ejRw9x3uVwEUwAAAAD5lu/3uQwYMEABAQFq0qSJunfvLpvNptKlS6tXr15Xs75r1uLFi9W6dWt16NBBb7zxhmbPnp3rGdxzV8jdvHmzOnTooDFjxigoKMjrvCpVqujTTz9V8+bNNXbsWHOG1OPx6MiRIwoPD1d4eLhSUlKUlZUlSerevbueffbZi9Z5vmdHzzcL+3cBAQFyOBxeGwAAAADkV75nSj/55BO9+eabkqTAwEC98847MgxDGRkZeueddySdvWW1UaNGio2NvTrVXiOys7PVtGlTNWnSRFWqVNGDDz6o4cOH6/3331d2drZOnDih6OhoeTwepaamavbs2WrRooWeffZZPfzww3mO+dBDD2n69Olq3769Pv30Uy1ZskRZWVm64YYbvJ4pzczMvKRac0JpdHS0V/vfAzQAAAAAXA35DqXnLnxjGIbS09PNQJOWlibp7C2rTz31lLZu3XqFy7y2ZGVlKTAw0Ax68+fPN48lJCSYt+/myMjI0MiRI/X00097jZOUlOT1vTdv3lx//PGHTpw4oZCQEPn6+ioiIsI8vnHjRkVGRuaqx+VyeYXM1NRUs86bbropz+dVc/q53W4FBwfn+9oBAAAA4FLYjHy8+6Nu3br67bffzFeFrF+/XklJSZKkiIgIHT9+3Dw3IiJCx44du65n2kqVKqWVK1fmmn2UpMOHD6tq1ao6c+bMefvv3r1bt912m7Kzs7Vu3TqVK1fusurYsGGDGjdurPT0dK1evVrVqlWTJG3ZskVVq1ZVUlKSQkJC8uz7zTff6NFHH1XDhg21YMGCfH+my+WS0+nUht9/V2ho6GXVjavrxsv85wkAAAC4FDnZIDEx8YKP+eUrlC5dulRt27bVnDlzlJ2drUceecS8ZTQnlC5fvlxFihRR+fLlFRAQcOWu5Dq1YsUKxcbG/uNZyvXr1+umm2665HHcbrdSUlJUpEiRS+pHKP3vI5QCAADg35DfUJqvhY4aNmwoPz8/1axZU7Vr1/a6pdTj8WjZsmV6+umndeLECQLpFVK3bt0rctvsrbfeelnjBAQEXHIgBQAAAIBLle9nStu3b6/s7Gz5+voqPT1dmZmZMgxD5cuX15133qlt27YRSAEAAAAAlyTfofStt96SJD311FM6cuSIfH19NWjQIA0ePFh2u112e76HAgAAAABAUj5D6dy5c1W4cGFJ0uzZs9W5c2cZhqGkpCTFx8fL399f0tlVed1ut+69996rVzEAAAAAoMDIVyh9//33VahQIUlnnyEdMmSIeey3337T66+/rtDQUGVnZys9PZ1QCgAAAADIl3yF0mXLlmnlypUKDg5Wo0aN9N1335nHXn31VZUrV04vvPDCVSsSAAAAAFAw5Wv1XUn64Ycf1KZNGyUnJ6thw4aaOHGiJKlVq1aaNWvWVSsQAAAAAFBw5es9pedKT0/XunXrFBAQoNtuu01ZWVk6ceKEbrjhhqtVI64hvKf0v4/3lAIAAODfkN/3lF7ykrmBgYGqV6/e/w1gtxNIAQAAAACXJd+37wIAAAAAcKURSgEAAAAAliGUAgAAAAAsQygFAAAAAFjmkhc6AvKj7I03XnCFLQAAAACQmCkFAAAAAFiIUAoAAAAAsAyhFAAAAABgGUIpAAAAAMAyhFIAAAAAgGUIpQAAAAAAyxBKAQAAAACWIZQCAAAAACxDKAUAAAAAWIZQCgAAAACwjN3qAlAwHdv6q1JDQqwu45pXomotq0sAAAAAripmSgEAAAAAliGUAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgGUIpAAAAAMAyhFIAAAAAgGUIpQAAAAAAyxBKAQAAAACWIZQCAAAAACxDKAUAAAAAWIZQCgAAAACwDKEUAAAAAGAZQikAAAAAwDKE0gLg1KlTMgzjH4+zadMmLVu27ApUBAAAAAD5QygtAOLi4tSiRQu5XC5lZWXJZrMpPDw81+bn56clS5acd5ylS5fq2Wef/RcrBwAAAHC9s1tdAP65cePGqW/fvrrvvvu0atUq+fr66uTJk7nOa9Cggfz9/SVJv/zyix544AGFhoaaxz0ej5KSkhQdHS1JysrKUnJysubNm6e77rrrX7kWAAAAANcXQmkB4OPjo/j4eB07dkw2m00+Pj4KDw/PdV5iYqJ8fHzMPqVKldLGjRslSfPmzVNsbKwiIyMlSTNmzFDt2rXNgAoAAAAAVwO3717j9u/fr3feeUeSVLx4cUmSzWbTyZMnc2116tTx6muz2eR2u5WVlaVBgwZp+/bt5rH3339f27dvV0ZGhrKyss77+W63Wy6Xy2sDAAAAgPwilF7jjh49qnfffVedO3dWdna2pLO34Z5PzjmGYcjX11evvfaaYmJitHnzZj333HOKiYkx97t06aKYmBj9+OOP5x0vPj5eTqfT3KKioq7sBQIAAAAo0Lh99xp322236ffff1fLli3166+/KjY2ViEhIQoLC8vz/JxQmp6eLj8/P40cOdIc5+WXX9btt98uSXr00Uf13nvvqWHDhhf8/Li4OPXo0cPcd7lcBFMAAAAA+UYoLQAiIyO1cuVK+fn56ejRozp69Kh2796tkSNHasKECXr11VfVuHFjNW3aVHv27JEkpaSkqGjRouYYLVu21C+//KJffvlFklSnTh3zduALCQgIUEBAwNW5MAAAAAAFHqG0gOjSpYueeeYZDRo0SA8//LBuvvlmLV++XJK0detWxcbGyuVyKTY2Vr///ruOHj2qkiVLav78+XrppZdkGIacTqdsNpukszOpW7du1cKFC+Xn52flpQEAAAAowHimtADIzMzUggULVLFiRb388sv68MMPFRAQIF9fX0lnV9r19/fX+++/ryZNmig6Olo7d+5UZGSkHnzwQe3Zs0cPPfSQ7r//fm3YsEFLly5VVlaWBgwYQCAFAAAAcFUxU1oAzJs3T7Vr11axYsV077336sSJE9qyZYvXOdu3b9cHH3ygDRs2SJKWLl2qt956yzweHx+v+vXry263a+HCherRo4fq1q37L14FAAAAgOsRM6UFwMiRI9W2bVtJZ2dF169fr6pVq3qdU6FCBf38888qU6aM/vjjD/3111+qX7++pLMr8f7111+KiYnRoEGDdODAAdlsNm3YsEEnT56UYRj/+jUBAAAAuD7YDBLHNW3Lli2qU6eOjhw5ooyMDFWrVk2FChWSx+PRyZMnVbJkSR0/flwhISEKCAhQcnKyevXqpYSEBD355JNq06aNjhw5osqVK+uxxx5Tq1attGHDBs2dO1crVqzQpk2bdNNNN+nXX3+Vj8/F/4bhcrnkdDq1ffVShYaE/AvfQMFWomotq0sAAAAALktONkhMTJTD4TjveYTSAuDEiRMqVqzYZfXdt2+fSpcubS5w9HdZWVlKSkpS4cKF8zUeofTKIpQCAADgWpXfUMozpQXA5QZSSSpTpswFj9vt9nwHUgAAAAC4VDxTCgAAAACwDKEUAAAAAGAZQikAAAAAwDKEUgAAAACAZQilAAAAAADLEEoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALAMoRQAAAAAYBlCKQAAAADAMnarC0DBVLxKTTkcDqvLAAAAAPAfx0wpAAAAAMAyhFIAAAAAgGUIpQAAAAAAyxBKAQAAAACWIZQCAAAAACxDKAUAAAAAWIZQCgAAAACwDKEUAAAAAGAZQikAAAAAwDJ2qwtAwXR63SJlFgq2uox8KVq7mdUlAAAAANctZkoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALAMoRQAAAAAYBlCKQAAAADAMoRSAAAAAIBlCKUAAAAAAMsQSgEAAAAAliGUAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgGUIpAAAAAMAydqsLwJVhGIaysrKUlZWl1NRUJSYm6vTp0zpw4IB2796tjRs3avjw4SpWrJjVpQIAAACAiZnSAuKpp55SuXLlVKtWLT344INq0KCB+vTpo3Xr1kmSGjVqpNTU1Dz7li9fXgsXLjT3S5QoocWLF/8rdQMAAAC4vtkMwzCsLgJXXvfu3XXrrbeqTZs2uY5Nnz5dzz77rPz8/CRJSUlJCgoKkt1+duLc5XIpODhYdrtdHo9HRYoU0b59+/L1uS6XS06nU3uWzFJooeArd0FXUdHazawuAQAAAChwcrJBYmKiHA7Hec8jlBYA1apVU1pamgICAuTj46OUlBSdPn1ahQoVUmBgoIKDg2UYhk6fPq0GDRpo4sSJcrvdKly4sGw2mypXrqyxY8fq7rvvliTdcMMN+uyzz9SoUSN5PB5lZmYqMDAwX7UQSgEAAABI+Q+l3L5bAGzatEm7du3Sli1b1K5dO2VnZ6tZs2Z6++23FRwcrGeffVabN2/WkSNH9Pnnn6tQoUIqUqSIbDabJMntdiszM9McLzMzU263W5Lk6+ub70AKAAAAAJeKUFpAnDlzRs8884w+/vhj/fzzzypatKh8fX31zTff6P3331ezZs20ePFiM3ymp6crKytLkjR16lTFxsaaY3311Ve67bbb8vW5brdbLpfLawMAAACA/CKUXuOOHDmil156SWXKlJHb7da6desUHR2tOnXqqEKFCipTpozWrl2rkJAQ3XvvvSpWrJhOnTqlcuXKqXDhwgoLC9MDDzyg4sWLKzQ0VGFhYXrooYdUvnx5ORwO2Ww2JSYmnvfz4+Pj5XQ6zS0qKupfvHoAAAAA1zpeCXONK1q0qHbu3KnPP/9cp0+f1o033qiQkBDz1tzk5GRVr15dP/74o/744w9zFvXQoUNe45QvX16fffaZateubbZt3LhRsbGxF7z/Oy4uTj169DD3XS4XwRQAAABAvhFKr3H+/v769ttvzf22bdtKknbt2qVOnTqpZs2aeumllyRJNWrUUI0aNfI9dkpKisLCwsyAm5eAgAAFBARcZvUAAAAArneE0gLgnnvu0ZYtW8xXvEhnnxk9c+aM9uzZoy+//FKSlJ2drYiICK1fvz5f4546dUoRERFXpWYAAAAAkAilBcLixYtzta1YsULdu3fPdwA9148//qh+/frpzz//VPfu3a9AhQAAAACQN0JpAXT69Gnt2rVL/v7++e6TmZmp7OxsSVLt2rX13HPP6ZZbbtHNN998tcoEAAAAAEJpQfTCCy9o1apV6tOnT777JCYmKi0tTZIUFBSkdu3aXa3yAAAAAMBEKC2AZsyYccl9EhISrnwhAAAAAHARvKcUAAAAAGAZQikAAAAAwDKEUgAAAACAZQilAAAAAADLEEoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALAMoRQAAAAAYBlCKQAAAADAMoRSAAAAAIBlCKUAAAAAAMvYrS4ABVORWvfK4XBYXQYAAACA/zhmSgEAAAAAliGUAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgGUIpAAAAAMAyhFIAAAAAgGUIpQAAAAAAyxBKAQAAAACWsVtdAAqmU4umKSM4yOoyLiq8WXurSwAAAACua8yUAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgGUIpAAAAAMAyhFIAAAAAgGUIpQAAAAAAyxBKAQAAAACWIZQCAAAAACxDKAUAAAAAWIZQCgAAAACwDKEUAAAAAGAZQikAAAAAwDKEUgAAAACAZQilV0lGRsYFj2dnZ8vtdl90nFOnTik7O/tKlQUAAAAA/ymE0qsgMTFRTqdTTqdTYWFhCgsLU0BAgAICAsz9sLAw3XbbbV79xo0bp4YNG3q1derUSf369TvvZ2VnZ8tms8lut+e5+fr6qlatWrn6jRs3TomJifJ4PEpPTzfbZ8yYoaefftrcz8jIUGZm5uV+FQAAAABwQXarCyiInE6n0tLS5Ha7FRAQIEnq3r27JGnkyJGSpP3796tUqVJe/QIDAxUUFGTuu91u/fzzz/rwww/P+1k+Pj7y9/c/76zrwoULFR8fn6v922+/1eHDh9WkSRO1atXKrDM1NVVpaWmKjo6WJGVmZmrgwIHq0KFDvq4dAAAAAC4FofQq+fDDD7V+/XpNmTIlz+Nt2rRR3bp1NWTIEBmGYbbbbDYZhiHDMDR//nx5PB7dfffd5nGXy6UaNWpo8eLFZpuvr68kadeuXSpXrpwkae3atdq9e7fCwsLk7++f6/Nbt26tgQMH6u2339aRI0fM9rlz52rBggWaOHHiP/sCAAAAACAfuH33Krnnnns0c+ZMzZs3L9exlStXavXq1XrmmWckSWvWrJGPj486duyob7/9Vr6+vvrf//6nESNGaMKECdq5c6e5vf32216zqTlcLpfuuusu/fjjj5Kk6dOna+3atbLZbGZoPVeLFi3022+/SZLat2+v8PBwRUdH67nnntMXX3yh6OhoRUZGKjw8/Ep+LQAAAADghVB6lVSpUkU9e/ZUt27dlJqaarZ7PB5169ZNL774oipUqCBJqlWrltLT0zVp0iQ1a9ZMGRkZKly4sI4dO6aAgADFxcWZ/TMyMnKFUpvNJofDoVmzZunpp5/Wvn37NGfOHL366qvy8fHJtVDS119/rTlz5pgB1t/fX8OHD9fevXs1YcIEPf7449q7d69++ukn87be83G73XK5XF4bAAAAAOQXofQq6tGjh5xOp06fPm22rVq1SkePHtVbb71ltvn6+nqFP7vdroiICI0fP15169bVzJkzNWPGDEm5Q2lqaqrZ984779QLL7yg2rVrq1GjRipVqpR8fX1zLVR09OhR/fTTT3r99dfNMfv166dq1arp5Zdf1uzZs1WtWjUzIF9IfHy8uaiT0+lUVFTU5X1ZAAAAAK5LhNIrzDAMud1uGYahIkWK6Ndff1VkZKR5vF69etq4caOcTqe58u25z5See16jRo0UHh6u2bNnq3Tp0vrxxx9zhdKEhAQ5HA5zv3Pnzjpz5owqV64s6ews6N+D5fPPP68nn3xSfn5+kqTJkyfr4MGDeuSRR+RyudSyZUu9+OKL2rRpk06cOHHB642Li1NiYqK5HThw4NK/NAAAAADXLULpFbZ9+3aFhYWpcOHCCgsLU7FixRQWFqaxY8dq7NixCgsLU/ny5RUWFmbOLh4/ftxrDMMwlJKSYu5v3rxZjz76qE6cOJErlO7fv9+cnTQMQ88995xatmyp4cOH6+TJkypRooROnTqVZ602m838ed68eZo8ebKGDh2q9PR0zZs3T40aNbpoKA0ICJDD4fDaAAAAACC/CKVXWKVKlZSWlqaEhASvrUuXLurSpYtXW3Jystxut4oXL66kpCRt27ZNv/32m6Kjo7Vw4UJt3bpV9913n0aPHq1ly5bpscceU0ZGhgIDA83P+/3331WlShVlZ2erc+fOSkpK0tSpU9WqVSu9/fbbKl26tPbv33/ed41mZWVp5MiR6tSpk7766iuFh4fL399fCxYsUNWqVXXrrbdq165d/9bXBwAAAOA6Qyj9D5g+fbqcTqd++ukn9e7dWytWrFCrVq00YsQIxcTEaNmyZearXtxut9dM6ffff6969eppyJAh2rlzp2bPni1fX1/1799fs2bNUmZmpkqUKKG1a9cqLi5O6enpZt8zZ85o9+7dWrNmjZYuXaqaNWsqMzNTHo9Hvr6+GjdunAYPHqzSpUv/698JAAAAgOuDzcjrgUZccd26dZMkjRo1Ktcxl8ulffv2qXr16l7t2dnZ8vE5+3eD5cuXKzExUXFxcercubNefPFF7d27VzfffLMOHDhgvov03MCalJSk0NBQde3aVStWrNDOnTt18uRJbd++XU888YSys7OVkpKip556SnXq1FHZsmWVkJCgEydOKCYmRqmpqTp9+rROnjyphx9+OF/X6XK55HQ6tXvWRwoNzv3qmv+a8GbtrS4BAAAAKJByskFiYuIFH/Oz/4s1XdfS0tK8nuE8l8PhyBVIJZmBVJI+++wzzZ8/X3fccYcef/xxSdKsWbP0zDPPyOl05jluaGioJOm1117TV199pWeffVaBgYH64IMP1KVLF3Xr1k0HDx7UJ598orFjx2rnzp1KTExUenq6srKyzAWYmjdvnu9QCgAAAACXgpnSa5jH41FKSkq+FhfKyMiQn5+fbDab1wzslcZMKQAAAACJmdLrgq+vb75Xu825vVfSVQukAAAAAHCpSCcAAAAAAMsQSgEAAAAAliGUAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgGUIpAAAAAMAyhFIAAAAAgGUIpQAAAAAAyxBKAQAAAACWIZQCAAAAACxjt7oAFExF731KDofD6jIAAAAA/McxUwoAAAAAsAyhFAAAAABgGUIpAAAAAMAyhFIAAAAAgGUIpQAAAAAAyxBKAQAAAACWIZQCAAAAACxDKAUAAAAAWIZQCgAAAACwDKEUAAAAAGAZu9UFoGA6Mn2kkoMC//E4Jdv1vgLVAAAAAPivYqYUAAAAAGAZQikAAAAAwDKEUgAAAACAZQilAAAAAADLEEoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALAMoRQAAAAAYBlCKQAAAADAMoRSAAAAAIBlCKUAAAAAAMsQSgEAAAAAliGUXkWbNm3S0qVLr+iYWVlZysrKyte5mZmZlzx+amrqJfcBAAAAgMtFKL2K3nrrLY0ZMyZf527fvl02m012u/2Cm5+fn2bMmJGrf3R0tDZs2GDup6amKioqSmvXrj3vZ+7cuVMRERFebS+88IJGjRql7Oxspaen5+9CAQAAAOAy2a0uoKBauXKl5s6dqxIlSig6OjrXcY/Ho8TERG3YsEE33nijQkNDJUnp6emy2//v13Lw4EFFRETI399fkmQYhrKzs83jKSkpCggIUGBgoPz8/JSWlqagoCCNHj1a6enpWrVqlVatWiUfHx89++yzCgoK0gcffKAhQ4bIbrfrzJkzKl68uCZPnqxXX31Vp06d0o8//qjx48dr+/btysjIuLpfFAAAAIDrms0wDMPqIgqalJQUxcbGqlWrVhoyZEi++iQkJKhw4cLKzMz0CqX+/v7asGGDbrrppjz73XzzzfJ4PNq9e7eioqJUvnx5jR07VjVq1NCgQYNUokQJjR8/XjExMXr33Xe9+u7du1eNGzfW2rVrFRwcLMMw1LZtW7Vp00alS5dW8+bNdfDgwUu6dpfLJafTqW1jByg0KPCS+ualZLve/3gMAAAAAP++nGyQmJgoh8Nx3vOYKb3CPB6PWrdureLFiysmJkYxMTEKDAzMdU5iYqKWLVumEiVKXHA8f39/BQcHn/f4xo0bJUnVqlXT3LlzVb58eXXr1k2NGzdW7dq1tXv3bm3ZskUDBgzQwYMHFRkZqX379un333/X4sWLtXfvXlWvXl3Lly/XPffco6SkJPXu3VupqakqUqTIP/9CAAAAAOACCKVXmK+vr7p3766bb75ZRYsW1WOPPXZJ/Q3DUHp6ugICAmSz2XIdz8zMlJ+fnyTpyy+/1FtvvaWgoCDt27dPzZs3l8fj0eTJk3X06FHt2LFDNptN7733noYNG6YqVapo6NCh6tevnw4cOKB69eqpTJky2rVrlySpb9++6tixo3r27Km3335baWlpSkxMVGhoqHx88n782O12y+12m/sul+uSrhcAAADA9Y1QehVUrFhR0dHRcjqdXrfi5vB4PKpYsaJ++OGHXMeOHTumqKgor7ayZcuaP9evX18//fSTJOmRRx7RI488Iknq3r27KleurM6dO+dZ04YNG8znUqdOnSrp7EJHn3/+uSRp8+bN6t+/v6ZMmSJfX1/VqlVLBw8e1JgxY7R+/XrNnj07z3Hj4+M1YMCAC30dAAAAAHBehNKroFSpUkpKSjrv8a+//lrvvfeeV5vH45Gvr69KlCihvXv3yt/fXzabTeXKldNPP/2kqKgo5fX477fffquiRYvmap84caL27Nkju90um82mFStWqHHjxubxmTNnatCgQbrhhhuUkpKil156SQMGDFDbtm3Nc3r27Km+ffvqjTfeOO+1xMXFqUePHua+y+XKFaoBAAAA4HwIpRbweDy5bs09deqUQkNDZbfbVaZMGbPdZrOpWLFi6tmzp9q3b+8VLCVpxowZuuWWWyRJgwcP1oQJE/Tbb7/J4XAoPDxcvr6+stlsuZ5Lvfnmm/X222/rnnvuUaFChfTAAw/oyJEjXucMHDhQTZs2VWxs7HmvJSAgQAEBAZf1PQAAAAAAodQCDodD+/btk3R2pd5ChQppx44deb46Jkf58uXVqVMnbdmyxStg7t6921yZt2/fvubtu39/lvXQoUNer5K59957ZRiGfH19JUlHjx6Vw+HQpEmTJJ19tnX//v06evRoroWaAAAAAOBKIZRaoGLFitq/f7+OHz+uhx56SJMmTdKqVavMGc+8vPrqq9qxY4dSUlLMUOrxeLR582aFhITo8OHDqly5siRp165dio2NVVBQkPz8/OTj46PU1FSv5033799v/vzbb7+pcePG2rRpkyIiIiRJBw4cUIUKFcx9AAAAALgaCKUWKF26tG6//XY98sgj2rx5s0qVKqXPP/9cw4YN0+nTp+Xn5+d1e29qaqrCw8M1fvx4eTweJSUlKTQ0VAsXLlS5cuW0YMECvfHGGxo9erROnz6tJk2a6K+//lKxYsXk4+OjjIwMJSUl6ciRI0pPTzdnPj0ejz7//HO9+uqrevfdd70C6Lp161ShQoU8VwAGAAAAgCsl7/d84KobPXq0Dhw4oKFDh2rmzJk6ffq0mjVrpoiICDkcDoWGhio0NFQpKSmqWrWquR8WFqa4uDhJ0u+//6727dvLbrdryJAh+uabb5SVlaWePXuqZs2aCg4Olo+PjwIDA1WsWDE1a9ZMAQEB2rFjh55++mmVLFlSo0eP1vTp09WxY0dJ0hdffKFGjRqpU6dOevTRR638igAAAABcB2xGXku64l+1detW/fLLL+rQoYPS09Pl7+9/3veCejweeTwe8/Uubrf7ggsNZWRkKDs7Wz4+Pl4zsKNGjVK9evUUExPjdf6pU6e0cOFCVa9eXTfffPMlX4vL5ZLT6dS2sQMUGvTPn0Ut2a73Px4DAAAAwL8vJxskJibK4XCc9zxCKa4oQikAAAAAKf+hlNt3AQAAAACWIZQCAAAAACxDKAUAAAAAWIZQCgAAAACwDKEUAAAAAGAZQikAAAAAwDKEUgAAAACAZQilAAAAAADLEEoBAAAAAJYhlAIAAAAALEMoBQAAAABYhlAKAAAAALCM3eoCUDDd0Lq7HA6H1WUAAAAA+I9jphQAAAAAYBlCKQAAAADAMoRSAAAAAIBlCKUAAAAAAMsQSgEAAAAAliGUAgAAAAAsQygFAAAAAFiGUAoAAAAAsAyhFAAAAABgGbvVBaBg2jWir0ICA8z9Cq8Nt7AaAAAAAP9VzJQCAAAAACxDKAUAAAAAWIZQCgAAAPy/9u49Lqo6/+P4e2BgBgQGMVwiScnYUlvbFO+XMmxLM8vVzVzXdLtoma2py7ZpWqZJbW1qul6ISiuVdnXVdtdNNypTsVIiTCXaVLxmXmEEZJgZzu8PH84vFlEkhgP6ej4e52Hn9v1+zjy+wbw5NwCmIZQCAAAAAExDKAUAAAAAmIZQCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADANodRPSkpK5Ha7q1xfVlam0tLSSsszMzO1detWf5bm6x8AAAAAzEYo9ZOuXbsqODhYFovlnJPNZtNvfvObSvuNHTtWy5Ytu6i++vXrp7S0NEmSy+VSeXm5JOnXv/61Zs2aJUnyer2+kHz06FElJCQoJydHkrRs2TLZ7Xa1aNGiwmS32zV//vyafgQAAAAAcEFWswu4VK1fv15BQUEKCDiT+8eNG6cmTZro6aeflnQmPFoslgr7LFmyRDk5Ofr++++1YsWKSm263W45nU4VFBQoMDDQt9zhcMhms0mSfvGLX+jo0aMKCAjQwYMHtX79eqWlpcnj8WjAgAFKSUlRdHS0+vXrpyFDhigrK0s2m02JiYnauHFjhf66d+/uaxcAAAAA/IFQ6icOh6PCfGBgoKxWq+x2uyT5/j3ru+++07hx4zR37lyNHDmyWn2Ul5dr0aJFslqtysvL05tvvqnf//732rx5s4KDg7Vq1So1a9ZM7du3V2lpqR555BHfvi+//LKuu+46rVy5UqGhoVX2cTZUAwAAAIA/EEr9wO12y2q1VjoT+r+8Xq88Ho/cbrcGDBigpKQkud1u3XTTTZXOUHo8HhUVFenrr7/2Ldu/f7+mT5+unj17KiQkRBMnTtSaNWuUlJSkRo0aacuWLWrTpo3uuOMOlZSUKDQ0VIZhyGKxKCQkRJs2bVJcXJz+/ve/V1mjYRg/7sMAAAAAgPMglPpBnz59lJGRcc51U6dOrTB/4403Kjs7W5MnT1ZSUpLsdrsee+yxavWzbds2dezYUZLUvHlzZWRkqH///goODvad4dy7d6/+9a9/qby8XB6PR5s2bVJ0dLQkKS4uTpJ896Cei9frPW8NLpdLLpfLN+90OqtVOwAAAABIhFK/WLFihSwWiy8YGoahhIQE2e125eTk+O4HNQxDXq9XFotFjRs3VkxMjCIiIs55yazb7dZtt92mRYsW+ZZ99dVX6tKli7KzsyVJrVu31rp16/TGG29U2j80NFQTJ06ssKysrEwHDx6U1WrVgQMHFBgYqNjYWDmdTlksFkVGRio4OPi8x5qSklIpaAMAAABAdRFK/eB/7yddt26dwsPDFRYWpn/961/69a9/XWmfrl27qqCgoMo2Z8+eraysrArLzobMESNG+JYdPHhQq1ev1pw5c3zLTpw4oTFjxlQKpfPmzdOKFSu0YcMG3X333bLb7dqyZYtmzZolu92uZ5999oLH+tRTT2n8+PG+eafT6TsDCwAAAAAXQij1M6/Xq8mTJ2v06NG65ppr9Nhjj+m2227zXUJ7Me1c6B5V6cwDlHbv3q0xY8b4lnk8nkpnPAsLC/X8888rPT1d0pkHLUnSFVdccVF12Ww2ntALAAAAoMZ4tKqfTZgwQYWFhXr44Yd19913q0OHDrrvvvtUXFx8Ue1ERERo7969knTefS0Wi2666SZt377dN3388ceVtnvuuefUsWNHJSUlSZKWL1+ujh07ymrl7xQAAAAA6g6h1E+OHz+ue++9V4sXL9aKFSsUFhYmSVq0aJFOnjypTp06aceOHdVu77rrrtO2bdt06tQpderUSUeOHKly2+zsbN1www2+qVevXhXWb9u2TfPmzdNLL70kSTp69KheeeUVjRo1qlJbmzdv1sKFC6tdJwAAAABcDEJpLTtx4oReeukltWnTRnl5edq6davatGnjW+9wOLRhwwa1b99eP/vZz3Trrbfq22+/vWC7Xbp0UVhYmO68806VlJRUuMz29OnTvqfklpeXVzpT+sknn1RoKyMjQw888IBat26t3NxcJSUl6YYbbtDQoUMlSVarVUePHvVt++677/7ozwUAAAAAzoVQWsu8Xq9Wr16tSZMmacuWLWrZsmWlbRo1aqTFixdr06ZNuvXWW3XttddesF2r1aoFCxYoPz9fs2bNqvCE3rPvHpXOBNT/NXToUPXr1883P27cOM2dO1fvvvuu2rRpo3bt2mn58uW+Nu644w69//77io6OVlpamp588smL/hwAAAAAoDoshmEYZhcBc7hcLuXk5PjedVobnE6nHA6Hvnh2jMLs//8ApIQnX661PgAAAADUf2ezQWFhoSIiIqrcjjOllzGbzVargRQAAAAALhahFAAAAABgGkIpAAAAAMA0hFIAAAAAgGkIpQAAAAAA0xBKAQAAAACmIZQCAAAAAExDKAUAAAAAmIZQCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaaxmF4BLU8txzysiIsLsMgAAAADUc5wpBQAAAACYhlAKAAAAADANoRQAAAAAYBpCKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMQygFAAAAAJiGUAq/+CJ5lLY+PtzsMgAAAADUc4RSAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMQygFAAAAAJiGUAoAAAAAMA2hFAAAAABgGkIpAAAAAMA0hFIAAAAAgGkIpQAAAAAA0xBKAQAAAACmIZQCAAAAAExDKAUAAAAAmIZQCgAAAAAwDaH0ErVhwwY9/PDDcrlcZpcCAAAAAFUilPpJZmamwsPD1blz50pTp06dZLFYVFJSct42brnlFr322mtVrt+/f78CAgLUokULhYWF6Ve/+pVvXVZWlrKysmSz2c657+7du2WxWGS1Ws85BQYGVmgPAAAAAPzBanYBl6qQkBBFR0drxIgRldZ5PB59/vnnCg0NPW8bBQUFstvtVa632WwKDQ1Vfn6+evfurXvuuUfFxcUKDg5Wdna2fvnLX/q2dbvd8nq9vvbsdrvi4+O1e/fuc7a9YMECbd68uRpHCgAAAAA1Ryj1o7NnIqtiGIYsFkuV6wMCAhQZGVnl+uDgYF87OTk56tOnj2688UYdOXJERUVFioqK0qxZsyRJZWVlGjhwoN58801f22f73rVrl1q2bClJWrNmja/ds/8CAAAAgL8QSv3E7XbL5XIpLS2t0jrDMNSkSROVlpYqJCREkpSbm6vQ0NAKl9uWlpaqtLRUhw8flnTmDGtxcbGCgoJ0zTXX+LbbuXOnrr32WkVFRenbb7/VsmXLlJ6ertWrV1dZn9frVUBAgPbs2aNu3brpww8/VOvWrZWamqpbb71VNptNgYGBtfVxAAAAAMA5EUprmWEYKi0tVceOHXXgwIHzbuv1euVyuWSz2ZSUlKSioiIFBPz/bb5Op1MjRoxQUFCQr+2ysjL16tVLa9as8W33ySefqFevXr5tXnnlFc2ePVvz5s1TXl6eZs+eXalvj8ejoKAgxcfHa+7cuRo4cKD+/e9/KysrS8uWLdM777yj8vLyCx6vy+Wq8DAlp9N5wX0AAAAA4CxCaS07duyYunTpomPHjsnhcCg8PFynTp3SqVOnFBsbW2Fbj8ej66+/XqtWrdKhQ4cqrHO5XAoPD9dTTz2lp59++rx9tmvXTosXL5bH49HMmTMVHh6url27KjMzU4WFhTIMQy6XS0FBQb6zn8XFxb77SwcNGqT//Oc/6tKlix599FGFhIQoMDBQbrf7gsebkpKiqVOnXsxHBAAAAAA+hNJaFh0drW+//VYjRoxQs2bNlJiYqM8++0xHjx7VgAED5HK5fGdDW7RooZ///OfnbGft2rW64oorlJaWpj/84Q/nvb+zU6dOat26tRYuXKh169bp448/VkREhLxerywWiz7++GO5XC69++676tmzp6QzD1GKiIjwtTF27Filpqbq+uuvl3TmftKysrILHu9TTz2l8ePH++adTqfi4uIuuB8AAAAASLwSxq+Ki4tVUFCg4uJiSdKWLVuUmpqqgoICvfrqq/riiy+q3PdPf/qTJk+erJ///OdKSUm5YF99+/ZVenq60tPTVVRUJKfTqalTp2rQoEHKz8/Xd9995wukkrRv3z5feHS5XHr44Yc1bNgwTZw4US6XSzExMTp+/PgF+7XZbIqIiKgwAQAAAEB1EUr9aN26dUpLS9MHH3wgSUpOTtbJkyc1dOhQHTlyRAMGDDjnfqmpqfr+++/1wAMPaNasWZo9e7aysrKq7CczM1OpqalyOBwKCwvzPTzph8rLyyvc+5mdna1WrVqptLRUgwYNUuvWrfXWW28pISFBf/nLXxQfH69vvvnmR34CAAAAAHB+hFI/mj59ut566y0NGzZMW7Zs0ZYtW3TPPfeoV69e6tOnjxo3blxpn+XLl2v8+PFKT0+XzWZTixYtNHPmTPXt21c5OTkVtvV6vZLOvO6lVatWev755+VwOBQZGanIyEhNnjxZS5cu9c23bdvWt+/atWvVo0cPjRkzRg6HQwsWLPDVnJqaqri4OB06dEjbtm3T888/L8Mw/PhJAQAAALhccU+pHzzwwANat26ddu7cqcTERJWWlio+Pl4dOnRQbm6uTp8+rczMTP3tb39T37591ahRIx07dkyTJ0/WokWLtGTJErVv397X3vDhw5Wbm6sePXpozpw5Gj58uCT5HkR0yy236JZbbpF05jUyZ7388svavn27Fi1aVKG+TZs26dixY+rWrZvatm2r8PBw332u7du315YtWxQcHKzevXvr/vvvV1FRkSZNmuTHTwwAAADA5YpQ6gdTp07V/Pnzfe8cXbJkif79739r+vTpKioq0qeffqqDBw/qxRdf1IEDB5SQkKD77rtPV155pdavX6+OHTtWavOFF15QVFSUHnzwQW3atEmpqakXfDpuWVmZTp8+XWn50qVLNWHCBAUEBMjhcFRaHx4eLkl67rnndMstt2jatGk1+RgAAAAA4IIsBtdl1hnDMGSxWCot93g8SktL0/Dhw895P+gPffHFF3I4HGrZsmWN6ygqKpLNZvO9//R8zr5HtbqcTqccDoc+GnmfwoKDlThncY3rBAAAANBwnc0GhYWF530gKmdK69C5AqkkWa1WPfLII9Vqo127dj+6jrCwsGpvezGBFAAAAAAuFg86AgAAAACYhlAKAAAAADANoRQAAAAAYBpCKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMQygFAAAAAJiGUAoAAAAAMA2hFAAAAABgGqvZBeDS1O6lhYqIiDC7DAAAAAD1HGdKAQAAAACmIZQCAAAAAExDKAUAAAAAmIZQCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYhlAKAAAAADANoRQAAAAAYBpCKQAAAADANIRSAAAAAIBpCKUAAAAAANMQSgEAAAAApiGUAgAAAABMQygFAAAAAJiGUAoAAAAAMA2hFAAAAABgGkIpAAAAAMA0hFIAAAAAgGkIpQAAAAAA0xBKobKyMh07dszsMgAAAABchgill4iePXvKarVWOVksFhUXF59z388//1zNmjXTkSNH6rhqAAAAAJc7Quklwm6368MPP5TH46k0FRUVSZJCQ0PPue/atWvVv39/NW3atC5LBgAAAABC6aUiICBAFotFu3fv9i0rKSnR1KlT5Xa7fWdLz3r22WcVEBAgq9WqlJQU/f3vfz/nGdaAgAD99re/NeOQAAAAAFwGCKWXCK/Xq4CAAI0ePVrTp0+XJK1bt07vvfeerFarAgMDK2wfEhKi+++//5xnVn84TZkyRXa73YxDAgAAAHAZsJpdAGqHx+NRUFCQ3n77bXXp0kWdOnXS22+/rT/+8Y+yWq0qLy+vsP3/htTzOd+2LpdLLpfLN+90Oi++eAAAAACXLc6UXiKKi4tlt9sVHR2txYsXa9iwYdqxY4cGDhyowMBAud3uKvcdMmSIoqKi1KJFC7Vo0UJhYWFKTk6uVr8pKSlyOBy+KS4urrYOCQAAAMBlgFB6iSgoKFBERIQkqVu3bmrevLmuueYaBQQEKCAg4LzB1GazaeLEicrPz1d+fr4GDRokm81WrX6feuopFRYW+qb9+/fX2jEBAAAAuPQRSi8BhmHo0KFDio2NlSTNmTNHVqtVeXl5ysjIkCTFxMTo+PHjtd63zWZTREREhQkAAAAAqotQegn4+uuvFRsbq+DgYC1evFh//vOftWLFCs2YMUMTJkyQYRiKj4/XN998c879S0tLNWPGDN/lu8uXL69wnygAAAAA+AsPOroErF27Vj169NDGjRs1efJkffjhh4qJidG9996rlJQUZWZmKiEhQRs3btTnn3+uwYMHV9g/PT3dpMoBAAAAXO4IpQ2cx+PRwoULNXv2bHXv3l1ffvmloqKiJEkWi0UbNmxQeHi4vvvuOyUnJ+vEiRPq3LmzTp8+fd52T5w4oQMHDmjPnj2+9gAAAACgtnH5bgOXlZWlkJAQ9e7dW5IqBcjw8HBJ0i9/+UvFxcWpZcuW6tatmwIDAxUaGlplu16vVz179tQXX3yhQYMG+e8AAAAAAFzWLIZhGGYXgR/n5MmTaty48QW3Ky8vl9frVVBQkN9qcTqdcjgcKiws5KFHAAAAwGWsutmAy3cvAdUJpJJ8r4cBAAAAgPqChAIAAAAAMA2hFAAAAABgGkIpAAAAAMA0hFIAAAAAgGkIpQAAAAAA0xBKAQAAAACmIZQCAAAAAExDKAUAAAAAmIZQCgAAAAAwDaEUAAAAAGAaQikAAAAAwDSEUgAAAACAaQilAAAAAADTEEoBAAAAAKYhlAIAAAAATEMoBQAAAACYxmp2Abi0GIYhSXI6nSZXAgAAAMBMZzPB2YxQFUIpatXx48clSXFxcSZXAgAAAKA+OHXqlBwOR5XrCaWoVVFRUZKkffv2nXfgARfidDoVFxen/fv3KyIiwuxy0MAxnlCbGE+oLYwl1Kb6OJ4Mw9CpU6cUGxt73u0IpahVAQFnblN2OBz15n8GNGwRERGMJdQaxhNqE+MJtYWxhNpU38ZTdU5U8aAjAAAAAIBpCKUAAAAAANMQSlGrbDabnnnmGdlsNrNLQQPHWEJtYjyhNjGeUFsYS6hNDXk8WYwLPZ8XAAAAAAA/4UwpAAAAAMA0hFIAAAAAgGkIpQAAAAAA0xBKAQAAAACmIZTiomzfvl0dOnRQ48aNlZycrOo8J2v58uVq3ry5YmNjtWzZsjqoEg1BTcbS1KlTFRUVJZvNpgEDBujUqVN1UCkagpqMp7MKCgp05ZVXKj8/338FokGp6XgqLy9X165d9ec//9nPFaKhuNixZBiGHn30UUVFRSkyMlIjRozQ6dOn66haNATHjh1TfHx8tX9nrV+/Xq1atdIVV1yhV155xb/F/QiEUlSby+XSXXfdpfbt22vr1q3auXOnFi1adN59tm/frqFDh2ry5Mlau3atpkyZory8vLopGPVWTcbSkiVLtGTJEr3//vvasWOHcnNz9cILL9RNwajXajKefig5OVmHDx/2X4FoUH7MeFqwYIEKCwv1u9/9zr9FokGoyVh6++23lZeXp+zsbG3YsEE7duxQSkpK3RSMeu/YsWPq169ftQPp0aNH1b9/fw0ZMkSbN2/WkiVL9NFHH/m3yJoygGpauXKl0bhxY6O4uNgwDMP48ssvjW7dup13n7Fjxxq33367b37WrFnGpEmT/Fon6r+ajKWUlBQjMzPTNz9lyhSjT58+fq0TDUNNxtNZ69evN5o2bWo0adLE2LNnjx+rRENR0/F08OBBw+FwGBkZGf4uEQ1ETcbSY489ZvzlL3/xzU+fPt0YMmSIX+tEw5GUlGTMnj3bkFSt31kzZ840rr/+eqO8vNwwDMNYtWqVMXToUD9XWTOcKUW15eTkqHPnzgoNDZUktW3bVjt37rzgPrfeeqtvvmPHjsrKyvJrnaj/ajKW/vjHP6pLly6++by8PCUkJPi1TjQMNRlP0pmzGKNGjdKrr76qsLAwf5eJBqKm4+mJJ55Q8+bNtX//fmVmZvq7TDQANRlLbdq00TvvvKPvv/9ee/fuVXp6um677ba6KBcNwGuvvXZRV2Lk5OSoV69eslgskur393BCKarN6XQqPj7eN2+xWBQYGKiTJ09We5+IiAgdOnTIr3Wi/qvJWPqhb775RitXrtTIkSP9VSIakJqOpxkzZuinP/2pBg8e7O8S0YDUZDxt3rxZf/vb39SsWTPt2rVLw4cP15gxY+qiXNRjNRlLDz30kIqKihQTE6MWLVooPj5ew4cPr4ty0QD8cDxVR0P6Hk4oRbVZrVbZbLYKy+x2u0pKSqq9z4W2x+WhJmPprPLycj3wwAN66KGH1KZNG3+ViAakJuMpNzdXCxYs0Pz58/1dHhqYmoyn1157TZ06ddI///lPPffcc/rwww81b948nqFwmavJWJo9e7YiIyO1d+9e7du3Tx6PR8nJyf4uFZeohvQ9nFCKaouKitLRo0crLDt16pSCg4Orvc+FtsfloSZj6axp06bpxIkTeumll/xVHhqYix1PhmFo5MiRmj59umJjY+uiRDQgNfn5dODAAfXt29d3iVxcXJyio6O1a9cuv9aK+q0mY2nJkiVKTk7W1Vdfrbi4OKWkpOj111/3d6m4RDWk7+GEUlRbhw4dtHnzZt/8nj175HK5FBUVVe19srOzddVVV/m1TtR/NRlLkvSPf/xDr7zyilasWOG7Rwe42PG0b98+bdy4UcnJyYqMjFRkZKT27duntm3baunSpXVVNuqpmvx8atasWYXXdhQVFenEiRP8vrvM1WQslZeX68iRI775w4cPy+v1+rVOXLoa0vdwQimqrWfPnnI6nXrzzTclnbkfq3fv3goMDFRBQcE5f2gOHDhQ6enp+uqrr1RUVKRXX31Vt99+e12XjnqmJmMpNzdXQ4YM0Zw5cxQXF6eioqJ6ewkK6tbFjqerrrpKe/bs0ZdffumbYmNjtWbNGvXv39+MQ0A9UpOfT0OGDNFrr72mjIwM7d27V6NHj9b111+vtm3b1nX5qEdqMpZ69OihF154QYsWLVJqaqpGjx7NzyVckNPplNvtrrS8f//+2rRpkz744AO53W796U9/qr/fw81+/C8altWrVxuhoaFGkyZNjOjoaGPHjh2GYRiGJCM7O/uc+0ycONEIDg42IiIijPbt2xslJSV1WDHqq4sdS0888YQhqcLUvHnzui0a9VZNfjb9UPPmzXklDHxqMp7S0tKMhIQEw263G507dza+/vrrOqwY9dXFjqWTJ08aw4YNM6Kjow273W7cfffdxtGjR+u4atR3+p9XwjRv3txYuXLlObedP3++ERQUZDRu3NiIj483Dh8+XDdFXiSLYRiGWYEYDdPhw4eVlZWlzp07q0mTJtXaZ+fOnTp48KBuvvnmenstO+peTcYSUBXGE2oT4wm1hbEEs+3Zs0dff/21evToUW9fgUYoBQAAAACYhntKAQAAAACmIZQCAAAAAExDKAUAAAAAmIZQCgAAAAAwDaEUAACY5sSJExXmy8rKVFRUZFI1AAAzEEoBAICP1+tVaWmpCgsLtW/fPm3btk3r1q3TwoULNWHCBHXv3l2dO3dWSUmJJCk1NVW7du3SW2+9pXvuuUelpaUaP368JGn06NF6/fXXq+yrtLRULVu21OrVq33LPvnkE0VHR+v06dP+PVAAQL1hNbsAAABQP+zcuVNdu3ZVRESEGjVqpKCgIO3cuVPDhg3Ttddeq7Zt2+r2229X48aN5Xa7JUlRUVEaPHiwxo0bp+DgYL377rsqKCiQJK1fv1533313lf299957atq0qY4fP664uDgFBgaqtLRUbrdbrVq1kiR5PB5NnDhRo0eP9vvxAwDMwXtKAQBAJeXl5XrooYeUk5OjpUuX6rrrrtP777+v2NhYtW3bVpLkcrn03nvv6ZtvvtGpU6eUmZmpm2++WWFhYbrjjjvUsWNHFRQUKCQkROXl5XK73bLZbJIkwzB04403auTIkXrkkUckSVarVe+8847mzp2rTz/9VJLkdrtlsVhktfJ3dAC4VHH5LgAA8CktLdUHH3yg2267TTExMfrss8/0k5/8RA8++KDGjx+vzMxMnf17ttvt1j//+U9t2bJFL774og4dOqQjR44oLy9Pb7/9toKCgtSqVStdeeWVCg0N1eOPP+7r54033tBXX32lmJgYWa1WPfPMM0pMTNSkSZOUm5urxMREJSYmavXq1QRSALjEEUoBAIBPQUGBBg4cqF27dmnVqlUKDQ1V165d1adPH3311Vd68MEHZbFYJElhYWGaPn26Dhw4oHvuuUetW7fWt99+qxkzZujLL7/UE088ofz8fL3xxhvq3bu3UlNTJUn5+fmaMGGCfvKTn/j63b9/vx566CHt3btXhYWF2rp1qxITE+V0Ok35HAAAdYdQCgAAfGJiYvTf//5XkyZNUps2bZSWlqbt27dr0KBBWr16tX7xi18oJydHkrR9+3Z1795dTz/9tIYMGaJGjRpp1KhRevLJJ/Xxxx9r165dkqRDhw4pNjbW18eqVavUr18/JSYm+pYFBARoypQpuvbaa33TsmXLFBDAVxUAuNRxPQwAAPC58cYbtW3bNtntdnXp0kULFizQyy+/rOLiYnk8HrndbvXq1UvPPPOMHn/8cW3dulVhYWFq1aqVPvnkE1199dVyOp1q1qyZli5dKknavXu3fvrTn/r6GDt2rEpKSjR48OAKfT/33HO++0slVfhvAMCli1AKAAB8Xn/9dQUEBCgyMlKhoaHau3ev7r33XuXl5clut8swDOXm5mr58uXasGGDBg8erNLSUhmGoZ49e8rpdKpz585as2aNMjIylJ2drY0bNyolJcXXh8ViUaNGjSr1PW3aNM2dO9c3f+jQIXXu3LlOjhsAYB6uiQEAAD6tW7dWcnKyPvvsM8XExMjhcMhischut2v+/Pm688471bRpU02ZMkU333yzDh8+rJ/97Gd67733lJ+frx49euj++++XJI0YMUITJkxQXl5ehUt1qzJ58mRt377dN917773+PlwAQD1AKAUAAJKkoqIi/epXv1KjRo101113VVo/dOhQhYeH66abbtLnn38uSTp48KCCg4P1xBNPaOjQofr000/Vv39/SdL999+vTz/9VEOGDFFwcPAF+582bZpuuOEG3/TXv/61dg8QAFAvEUoBAIDcbrduvvlmNW3aVCtXrlRYWJg++ugjLV++XCEhIZKkiIgIpaena9CgQZo0aZIk6aqrrlJGRoaefvppZWRkKCkpSd26dVNxcbEeffRRtWvXTm+99ZY2bdpUqU+v16uysjK53W5JVZ8pdblcdfQpAADMYDHOvmwMAABc1vbu3aurr77a98qX4cOHKzc3V4899piGDx9eYVuXyyWPx6OlS5dqyZIlstvtmjdvnq655hotXrxY06ZNU9++fTVz5kwtW7ZMo0aN0owZMzR27FhfG0lJSerevbsWLlwom83m6/eHysvLZbVatXv3bv8ePADANIRSAABQYzNnzlT37t3VoUMH37K//vWvuuqqq9StWzffss2bN+uKK65QQkKCGWUCAOoxQikAAAAAwDTcUwoAAAAAMA2hFAAAAABgGkIpAAAAAMA0hFIAAAAAgGkIpQAAAAAA0xBKAQAAAACmIZQCAAAAAExDKAUAAAAAmOb/AK4KGXGb1cMDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 定义你需要的列\n",
    "columns = ['气温', '气压', '平均海平面大气压', '气压趋势', '湿度', '风向', '平均风速', '最大阵风', '天气情况', '最低气温', '最高气温', '露点温度', '降水时间', '季节', 'PM2.5', 'PM10', 'NO2', 'CO', 'SO2', 'O3_8h']\n",
    "\n",
    "# 初始化编码器\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "# 对\"季节\"进行标签编码\n",
    "merged_data['季节'] = encoder.fit_transform(merged_data['季节'])\n",
    "\n",
    "# 目标列列表\n",
    "targets = ['PM2.5', 'PM10', 'NO2', 'CO', 'SO2', 'O3_8h']\n",
    "\n",
    "# 对每个目标列进行相关性分析\n",
    "for target in targets:\n",
    "\n",
    "    # 暂时移除其他目标列\n",
    "    temp_columns = [c for c in columns if c not in targets or c == target]\n",
    "\n",
    "    # 选择这些列进行相关性分析\n",
    "    selected_data = merged_data[temp_columns]\n",
    "    correlation = selected_data.corr()\n",
    "\n",
    "    # 找到与目标列相关的列并按相关性的绝对值进行降序排列\n",
    "    target_correlation_abs = correlation[target].abs().sort_values(ascending=False)\n",
    "\n",
    "    # 打印结果\n",
    "    print(f'\\n{target}的相关性分析结果：\\n')\n",
    "    print(target_correlation_abs)\n",
    "\n",
    "    # 创建一个新的图像并设置其大小\n",
    "    plt.figure(figsize=(10,10))\n",
    "\n",
    "    # 创建颜色映射，使用颜色渐变。取决对系数值的绝对值创建颜色映射，以表示相关性强度\n",
    "    color_map = colors.LinearSegmentedColormap.from_list(\"\", [\"green\",\"yellow\",\"red\"])\n",
    "\n",
    "    # 创建条形图，并用颜色映射来设置每个条的颜色\n",
    "    sns.barplot(x=target_correlation_abs.values, y=target_correlation_abs.index, palette=sns.color_palette(\"coolwarm\", len(target_correlation_abs)))\n",
    "\n",
    "    # 添加标题和标签\n",
    "    plt.title(f'{target}的相关性系数条形图')\n",
    "    plt.xlabel('系数值')\n",
    "    plt.ylabel('特征')\n",
    "\n",
    "    # 显示图像\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "筛选出合适的特征进行 回归预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PM2.5的 Mean Squared Error: 645.6640196630362\n",
      "PM10的 Mean Squared Error: 2733.5883683273946\n",
      "NO2的 Mean Squared Error: 144.949104703928\n",
      "CO的 Mean Squared Error: 0.038400425436887285\n",
      "SO2的 Mean Squared Error: 31.604987155637602\n",
      "O3_8h的 Mean Squared Error: 124.59340398244373\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted PM2.5</th>\n",
       "      <th>Actual PM2.5</th>\n",
       "      <th>Predicted PM10</th>\n",
       "      <th>Actual PM10</th>\n",
       "      <th>Predicted NO2</th>\n",
       "      <th>Actual NO2</th>\n",
       "      <th>Predicted CO</th>\n",
       "      <th>Actual CO</th>\n",
       "      <th>Predicted SO2</th>\n",
       "      <th>Actual SO2</th>\n",
       "      <th>Predicted O3_8h</th>\n",
       "      <th>Actual O3_8h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>136.204708</td>\n",
       "      <td>252</td>\n",
       "      <td>99.238236</td>\n",
       "      <td>284</td>\n",
       "      <td>84.370285</td>\n",
       "      <td>104</td>\n",
       "      <td>3.509186</td>\n",
       "      <td>3.6</td>\n",
       "      <td>17.942564</td>\n",
       "      <td>18</td>\n",
       "      <td>20.538190</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.138365</td>\n",
       "      <td>16</td>\n",
       "      <td>45.862187</td>\n",
       "      <td>26</td>\n",
       "      <td>14.847015</td>\n",
       "      <td>12</td>\n",
       "      <td>0.558785</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.047075</td>\n",
       "      <td>3</td>\n",
       "      <td>81.353162</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.779034</td>\n",
       "      <td>22</td>\n",
       "      <td>39.611637</td>\n",
       "      <td>40</td>\n",
       "      <td>13.203669</td>\n",
       "      <td>18</td>\n",
       "      <td>0.576160</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.061661</td>\n",
       "      <td>2</td>\n",
       "      <td>154.787793</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.209446</td>\n",
       "      <td>14</td>\n",
       "      <td>64.626582</td>\n",
       "      <td>18</td>\n",
       "      <td>21.516255</td>\n",
       "      <td>17</td>\n",
       "      <td>0.514261</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.046056</td>\n",
       "      <td>3</td>\n",
       "      <td>62.275692</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42.300012</td>\n",
       "      <td>43</td>\n",
       "      <td>72.107803</td>\n",
       "      <td>47</td>\n",
       "      <td>25.482705</td>\n",
       "      <td>28</td>\n",
       "      <td>0.518999</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.057245</td>\n",
       "      <td>2</td>\n",
       "      <td>59.315973</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>84.550307</td>\n",
       "      <td>109</td>\n",
       "      <td>66.076487</td>\n",
       "      <td>76</td>\n",
       "      <td>37.584055</td>\n",
       "      <td>30</td>\n",
       "      <td>1.341557</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.919507</td>\n",
       "      <td>7</td>\n",
       "      <td>59.430684</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17.232072</td>\n",
       "      <td>10</td>\n",
       "      <td>43.898624</td>\n",
       "      <td>26</td>\n",
       "      <td>18.062922</td>\n",
       "      <td>16</td>\n",
       "      <td>0.391220</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.357530</td>\n",
       "      <td>2</td>\n",
       "      <td>113.254984</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>55.938362</td>\n",
       "      <td>60</td>\n",
       "      <td>71.385442</td>\n",
       "      <td>88</td>\n",
       "      <td>35.605282</td>\n",
       "      <td>39</td>\n",
       "      <td>0.670406</td>\n",
       "      <td>0.7</td>\n",
       "      <td>7.013440</td>\n",
       "      <td>7</td>\n",
       "      <td>154.703421</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>109.026258</td>\n",
       "      <td>141</td>\n",
       "      <td>55.332168</td>\n",
       "      <td>77</td>\n",
       "      <td>37.416517</td>\n",
       "      <td>46</td>\n",
       "      <td>1.666535</td>\n",
       "      <td>1.7</td>\n",
       "      <td>16.563972</td>\n",
       "      <td>16</td>\n",
       "      <td>128.550789</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>115.942164</td>\n",
       "      <td>188</td>\n",
       "      <td>49.870597</td>\n",
       "      <td>263</td>\n",
       "      <td>63.854739</td>\n",
       "      <td>37</td>\n",
       "      <td>1.000082</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44.917389</td>\n",
       "      <td>53</td>\n",
       "      <td>74.938373</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.382137</td>\n",
       "      <td>5</td>\n",
       "      <td>45.592761</td>\n",
       "      <td>17</td>\n",
       "      <td>18.638060</td>\n",
       "      <td>19</td>\n",
       "      <td>0.243669</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.365923</td>\n",
       "      <td>2</td>\n",
       "      <td>77.822358</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18.131688</td>\n",
       "      <td>14</td>\n",
       "      <td>57.348767</td>\n",
       "      <td>23</td>\n",
       "      <td>28.098741</td>\n",
       "      <td>20</td>\n",
       "      <td>0.425632</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.383762</td>\n",
       "      <td>2</td>\n",
       "      <td>59.363149</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>141.583125</td>\n",
       "      <td>175</td>\n",
       "      <td>92.674546</td>\n",
       "      <td>168</td>\n",
       "      <td>81.749806</td>\n",
       "      <td>86</td>\n",
       "      <td>2.888166</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.742670</td>\n",
       "      <td>6</td>\n",
       "      <td>17.539746</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>99.583177</td>\n",
       "      <td>120</td>\n",
       "      <td>63.096262</td>\n",
       "      <td>139</td>\n",
       "      <td>39.290175</td>\n",
       "      <td>30</td>\n",
       "      <td>1.177287</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.177481</td>\n",
       "      <td>2</td>\n",
       "      <td>196.791482</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>115.075557</td>\n",
       "      <td>117</td>\n",
       "      <td>86.290208</td>\n",
       "      <td>109</td>\n",
       "      <td>43.195550</td>\n",
       "      <td>56</td>\n",
       "      <td>0.815462</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.922541</td>\n",
       "      <td>3</td>\n",
       "      <td>65.957292</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>80.671063</td>\n",
       "      <td>76</td>\n",
       "      <td>63.367795</td>\n",
       "      <td>77</td>\n",
       "      <td>43.891170</td>\n",
       "      <td>49</td>\n",
       "      <td>0.860686</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.005942</td>\n",
       "      <td>3</td>\n",
       "      <td>108.191188</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>83.563721</td>\n",
       "      <td>82</td>\n",
       "      <td>64.397900</td>\n",
       "      <td>102</td>\n",
       "      <td>36.114164</td>\n",
       "      <td>33</td>\n",
       "      <td>0.797910</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.111290</td>\n",
       "      <td>3</td>\n",
       "      <td>170.279601</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>25.855081</td>\n",
       "      <td>34</td>\n",
       "      <td>38.001372</td>\n",
       "      <td>65</td>\n",
       "      <td>27.682934</td>\n",
       "      <td>49</td>\n",
       "      <td>0.812632</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8.836892</td>\n",
       "      <td>9</td>\n",
       "      <td>39.012495</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>115.312765</td>\n",
       "      <td>124</td>\n",
       "      <td>84.483489</td>\n",
       "      <td>179</td>\n",
       "      <td>75.484141</td>\n",
       "      <td>80</td>\n",
       "      <td>1.150776</td>\n",
       "      <td>1.2</td>\n",
       "      <td>21.679115</td>\n",
       "      <td>21</td>\n",
       "      <td>117.447000</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40.202392</td>\n",
       "      <td>47</td>\n",
       "      <td>45.485755</td>\n",
       "      <td>98</td>\n",
       "      <td>43.728049</td>\n",
       "      <td>52</td>\n",
       "      <td>1.384214</td>\n",
       "      <td>1.3</td>\n",
       "      <td>29.255922</td>\n",
       "      <td>29</td>\n",
       "      <td>56.398190</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Predicted PM2.5  Actual PM2.5  Predicted PM10  Actual PM10  Predicted NO2  \\\n",
       "0        136.204708           252       99.238236          284      84.370285   \n",
       "1         24.138365            16       45.862187           26      14.847015   \n",
       "2         23.779034            22       39.611637           40      13.203669   \n",
       "3         22.209446            14       64.626582           18      21.516255   \n",
       "4         42.300012            43       72.107803           47      25.482705   \n",
       "5         84.550307           109       66.076487           76      37.584055   \n",
       "6         17.232072            10       43.898624           26      18.062922   \n",
       "7         55.938362            60       71.385442           88      35.605282   \n",
       "8        109.026258           141       55.332168           77      37.416517   \n",
       "9        115.942164           188       49.870597          263      63.854739   \n",
       "10         7.382137             5       45.592761           17      18.638060   \n",
       "11        18.131688            14       57.348767           23      28.098741   \n",
       "12       141.583125           175       92.674546          168      81.749806   \n",
       "13        99.583177           120       63.096262          139      39.290175   \n",
       "14       115.075557           117       86.290208          109      43.195550   \n",
       "15        80.671063            76       63.367795           77      43.891170   \n",
       "16        83.563721            82       64.397900          102      36.114164   \n",
       "17        25.855081            34       38.001372           65      27.682934   \n",
       "18       115.312765           124       84.483489          179      75.484141   \n",
       "19        40.202392            47       45.485755           98      43.728049   \n",
       "\n",
       "    Actual NO2  Predicted CO  Actual CO  Predicted SO2  Actual SO2  \\\n",
       "0          104      3.509186        3.6      17.942564          18   \n",
       "1           12      0.558785        0.6       3.047075           3   \n",
       "2           18      0.576160        0.6       2.061661           2   \n",
       "3           17      0.514261        0.5       3.046056           3   \n",
       "4           28      0.518999        0.6       2.057245           2   \n",
       "5           30      1.341557        1.4       6.919507           7   \n",
       "6           16      0.391220        0.4       2.357530           2   \n",
       "7           39      0.670406        0.7       7.013440           7   \n",
       "8           46      1.666535        1.7      16.563972          16   \n",
       "9           37      1.000082        1.0      44.917389          53   \n",
       "10          19      0.243669        0.2       2.365923           2   \n",
       "11          20      0.425632        0.4       2.383762           2   \n",
       "12          86      2.888166        2.9       5.742670           6   \n",
       "13          30      1.177287        1.3       2.177481           2   \n",
       "14          56      0.815462        0.9       2.922541           3   \n",
       "15          49      0.860686        0.9       3.005942           3   \n",
       "16          33      0.797910        0.8       3.111290           3   \n",
       "17          49      0.812632        0.8       8.836892           9   \n",
       "18          80      1.150776        1.2      21.679115          21   \n",
       "19          52      1.384214        1.3      29.255922          29   \n",
       "\n",
       "    Predicted O3_8h  Actual O3_8h  \n",
       "0         20.538190            17  \n",
       "1         81.353162            71  \n",
       "2        154.787793           159  \n",
       "3         62.275692            55  \n",
       "4         59.315973            59  \n",
       "5         59.430684            66  \n",
       "6        113.254984           111  \n",
       "7        154.703421           157  \n",
       "8        128.550789           130  \n",
       "9         74.938373            74  \n",
       "10        77.822358            80  \n",
       "11        59.363149            59  \n",
       "12        17.539746            11  \n",
       "13       196.791482           217  \n",
       "14        65.957292            77  \n",
       "15       108.191188           110  \n",
       "16       170.279601           168  \n",
       "17        39.012495            39  \n",
       "18       117.447000           116  \n",
       "19        56.398190            56  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义每个目标的特征\n",
    "feature_dict = {\n",
    "    'PM2.5': ['平均风速', '湿度', '风向', '气温', '气压趋势', '最大阵风', '平均海平面大气压', '最低气温','PM2.5'],\n",
    "    'PM10': ['平均风速', '气压趋势', '气温', '风向', '最低气温'],\n",
    "    'NO2': ['气温', '平均风速', '平均海平面大气压', '气压', '最低气温', '露点温度', '最高气温', '气压趋势', '风向', '最大阵风', '降水时间','PM10'],\n",
    "    'CO': ['平均风速', '气温', '湿度', '风向', '平均海平面大气压', '季节', '气压', '最大阵风', '气压趋势','CO'],\n",
    "    'SO2': ['气温', '露点温度', '平均海平面大气压', '季节', '气压', '风向','SO2'],\n",
    "    'O3_8h': ['气温', '平均海平面大气压', '气压', '露点温度', '最高气温', '最低气温', '降水时间', '湿度','O3_8h']\n",
    "}\n",
    "# 创建一个空的DataFrame来存储预测结果\n",
    "result = pd.DataFrame()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# 在每个目标上\n",
    "for target in feature_dict:\n",
    "  \n",
    "    # 设定数据集\n",
    "    X = merged_data[feature_dict[target]]\n",
    "    y = merged_data[target]\n",
    "\n",
    "    # 切分数据集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "    # 特征缩放（对于SVR，这一步非常重要）\n",
    "    scaler = StandardScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train)\n",
    "    X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "    # 创建SVR模型，你也可以更改内核函数例如使用'poly'或者'sigmoid'\n",
    "    model = SVR(kernel='rbf', C=1.0, epsilon=0.1)\n",
    "\n",
    "    # 训练模型\n",
    "    model.fit(X_train_scaled, y_train)\n",
    "\n",
    "    # 对测试集进行预测\n",
    "    predictions = model.predict(X_test_scaled)\n",
    "\n",
    "    # 计算MSE\n",
    "    mse = mean_squared_error(y_test, predictions)\n",
    "\n",
    "    # 打印MSE的值\n",
    "    print(f'{target}的 Mean Squared Error:', mse)\n",
    "    \n",
    "    # 将预测结果加入到总的结果中\n",
    "    result[f\"Predicted {target}\"] = predictions\n",
    "    result[f\"Actual {target}\"] = y_test.values\n",
    "    \n",
    "\n",
    "# 打印所有预测值和实际值\n",
    "result.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Software\\Python\\Lib\\site-packages\\xgboost\\core.py:160: UserWarning: [08:51:57] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\common\\error_msg.cc:58: Falling back to prediction using DMatrix due to mismatched devices. This might lead to higher memory usage and slower performance. XGBoost is running on: cuda:0, while the input data is on: cpu.\n",
      "Potential solutions:\n",
      "- Use a data structure that matches the device ordinal in the booster.\n",
      "- Set the device for booster before call to inplace_predict.\n",
      "\n",
      "This warning will only be shown once.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[8], line 41\u001b[0m\n\u001b[0;32m     39\u001b[0m     model \u001b[38;5;241m=\u001b[39m XGBRegressor(tree_method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhist\u001b[39m\u001b[38;5;124m'\u001b[39m, device\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;66;03m# 在此处进行设定\u001b[39;00m\n\u001b[0;32m     40\u001b[0m     grid_search \u001b[38;5;241m=\u001b[39m GridSearchCV(model, xgb_parameters, cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m, scoring\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneg_mean_squared_error\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 41\u001b[0m     \u001b[43mgrid_search\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train_scaled\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     42\u001b[0m     model \u001b[38;5;241m=\u001b[39m grid_search\u001b[38;5;241m.\u001b[39mbest_estimator_    \n\u001b[0;32m     43\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m target \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCO\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\sklearn\\base.py:1474\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1467\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m   1469\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1470\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1471\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1472\u001b[0m     )\n\u001b[0;32m   1473\u001b[0m ):\n\u001b[1;32m-> 1474\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:970\u001b[0m, in \u001b[0;36mBaseSearchCV.fit\u001b[1;34m(self, X, y, **params)\u001b[0m\n\u001b[0;32m    964\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_results(\n\u001b[0;32m    965\u001b[0m         all_candidate_params, n_splits, all_out, all_more_results\n\u001b[0;32m    966\u001b[0m     )\n\u001b[0;32m    968\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m results\n\u001b[1;32m--> 970\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_search\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevaluate_candidates\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    972\u001b[0m \u001b[38;5;66;03m# multimetric is determined here because in the case of a callable\u001b[39;00m\n\u001b[0;32m    973\u001b[0m \u001b[38;5;66;03m# self.scoring the return type is only known after calling\u001b[39;00m\n\u001b[0;32m    974\u001b[0m first_test_score \u001b[38;5;241m=\u001b[39m all_out[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_scores\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1527\u001b[0m, in \u001b[0;36mGridSearchCV._run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1525\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_run_search\u001b[39m(\u001b[38;5;28mself\u001b[39m, evaluate_candidates):\n\u001b[0;32m   1526\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Search all candidates in param_grid\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1527\u001b[0m     \u001b[43mevaluate_candidates\u001b[49m\u001b[43m(\u001b[49m\u001b[43mParameterGrid\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparam_grid\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:916\u001b[0m, in \u001b[0;36mBaseSearchCV.fit.<locals>.evaluate_candidates\u001b[1;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[0;32m    908\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    909\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\n\u001b[0;32m    910\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFitting \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m folds for each of \u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[38;5;124m candidates,\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    911\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m totalling \u001b[39m\u001b[38;5;132;01m{2}\u001b[39;00m\u001b[38;5;124m fits\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m    912\u001b[0m             n_splits, n_candidates, n_candidates \u001b[38;5;241m*\u001b[39m n_splits\n\u001b[0;32m    913\u001b[0m         )\n\u001b[0;32m    914\u001b[0m     )\n\u001b[1;32m--> 916\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mparallel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    917\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_fit_and_score\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    918\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbase_estimator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    919\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    920\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    921\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    922\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtest\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    923\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    924\u001b[0m \u001b[43m        \u001b[49m\u001b[43msplit_progress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msplit_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_splits\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    925\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcandidate_progress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcand_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_candidates\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    926\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_and_score_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    927\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    928\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mcand_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mproduct\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    929\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcandidate_params\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    930\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrouted_params\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplitter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    931\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    932\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    934\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(out) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m    935\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    936\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo fits were performed. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    937\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWas the CV iterator empty? \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    938\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWere there no candidates?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    939\u001b[0m     )\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\sklearn\\utils\\parallel.py:67\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m     62\u001b[0m config \u001b[38;5;241m=\u001b[39m get_config()\n\u001b[0;32m     63\u001b[0m iterable_with_config \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m     64\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n\u001b[0;32m     65\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[0;32m     66\u001b[0m )\n\u001b[1;32m---> 67\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterable_with_config\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\joblib\\parallel.py:1918\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1916\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_sequential_output(iterable)\n\u001b[0;32m   1917\u001b[0m     \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[1;32m-> 1918\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1920\u001b[0m \u001b[38;5;66;03m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[0;32m   1921\u001b[0m \u001b[38;5;66;03m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[0;32m   1922\u001b[0m \u001b[38;5;66;03m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[0;32m   1923\u001b[0m \u001b[38;5;66;03m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[0;32m   1924\u001b[0m \u001b[38;5;66;03m# callback.\u001b[39;00m\n\u001b[0;32m   1925\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\joblib\\parallel.py:1847\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1845\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_batches \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m-> 1847\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1848\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_completed_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1849\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_progress()\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\sklearn\\utils\\parallel.py:129\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    127\u001b[0m     config \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m    128\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig):\n\u001b[1;32m--> 129\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:895\u001b[0m, in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, score_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[0;32m    893\u001b[0m         estimator\u001b[38;5;241m.\u001b[39mfit(X_train, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\n\u001b[0;32m    894\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 895\u001b[0m         \u001b[43mestimator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    897\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m    898\u001b[0m     \u001b[38;5;66;03m# Note fit time as time until error\u001b[39;00m\n\u001b[0;32m    899\u001b[0m     fit_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m start_time\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\xgboost\\core.py:730\u001b[0m, in \u001b[0;36mrequire_keyword_args.<locals>.throw_if.<locals>.inner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    728\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig\u001b[38;5;241m.\u001b[39mparameters, args):\n\u001b[0;32m    729\u001b[0m     kwargs[k] \u001b[38;5;241m=\u001b[39m arg\n\u001b[1;32m--> 730\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\xgboost\\sklearn.py:1090\u001b[0m, in \u001b[0;36mXGBModel.fit\u001b[1;34m(self, X, y, sample_weight, base_margin, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, base_margin_eval_set, feature_weights, callbacks)\u001b[0m\n\u001b[0;32m   1079\u001b[0m     obj \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1081\u001b[0m (\n\u001b[0;32m   1082\u001b[0m     model,\n\u001b[0;32m   1083\u001b[0m     metric,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1088\u001b[0m     xgb_model, eval_metric, params, early_stopping_rounds, callbacks\n\u001b[0;32m   1089\u001b[0m )\n\u001b[1;32m-> 1090\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_Booster \u001b[38;5;241m=\u001b[39m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1091\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1092\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrain_dmatrix\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1093\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_num_boosting_rounds\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1094\u001b[0m \u001b[43m    \u001b[49m\u001b[43mevals\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mevals\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1095\u001b[0m \u001b[43m    \u001b[49m\u001b[43mearly_stopping_rounds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mearly_stopping_rounds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1096\u001b[0m \u001b[43m    \u001b[49m\u001b[43mevals_result\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mevals_result\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1097\u001b[0m \u001b[43m    \u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1098\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcustom_metric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetric\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1099\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1100\u001b[0m \u001b[43m    \u001b[49m\u001b[43mxgb_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1101\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1102\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1104\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_evaluation_result(evals_result)\n\u001b[0;32m   1105\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\xgboost\\core.py:730\u001b[0m, in \u001b[0;36mrequire_keyword_args.<locals>.throw_if.<locals>.inner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    728\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig\u001b[38;5;241m.\u001b[39mparameters, args):\n\u001b[0;32m    729\u001b[0m     kwargs[k] \u001b[38;5;241m=\u001b[39m arg\n\u001b[1;32m--> 730\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\xgboost\\training.py:181\u001b[0m, in \u001b[0;36mtrain\u001b[1;34m(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, custom_metric)\u001b[0m\n\u001b[0;32m    179\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cb_container\u001b[38;5;241m.\u001b[39mbefore_iteration(bst, i, dtrain, evals):\n\u001b[0;32m    180\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m--> 181\u001b[0m \u001b[43mbst\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cb_container\u001b[38;5;241m.\u001b[39mafter_iteration(bst, i, dtrain, evals):\n\u001b[0;32m    183\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\xgboost\\core.py:2051\u001b[0m, in \u001b[0;36mBooster.update\u001b[1;34m(self, dtrain, iteration, fobj)\u001b[0m\n\u001b[0;32m   2047\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_assign_dmatrix_features(dtrain)\n\u001b[0;32m   2049\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fobj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2050\u001b[0m     _check_call(\n\u001b[1;32m-> 2051\u001b[0m         \u001b[43m_LIB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mXGBoosterUpdateOneIter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2052\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mctypes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mc_int\u001b[49m\u001b[43m(\u001b[49m\u001b[43miteration\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtrain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle\u001b[49m\n\u001b[0;32m   2053\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2054\u001b[0m     )\n\u001b[0;32m   2055\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   2056\u001b[0m     pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredict(dtrain, output_margin\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, training\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# 定义每个目标的特征\n",
    "feature_dict = {\n",
    "    'PM2.5': ['平均风速', '湿度', '风向', '气温', '气压趋势', '最大阵风', '平均海平面大气压', '最低气温'],\n",
    "    'PM10': ['平均风速', '气压趋势', '气温', '风向', '最低气温'],\n",
    "    'NO2': ['气温', '平均风速', '平均海平面大气压', '气压', '最低气温', '露点温度', '最高气温', '气压趋势', '风向', '最大阵风', '降水时间'],\n",
    "    'CO': ['平均风速', '气温', '湿度', '风向', '平均海平面大气压', '季节', '气压', '最大阵风', '气压趋势'],\n",
    "    'SO2': ['气温', '露点温度', '平均海平面大气压', '季节', '气压', '风向'],\n",
    "    'O3_8h': ['气温', '平均海平面大气压', '气压', '露点温度', '最高气温', '最低气温', '降水时间', '湿度']\n",
    "}\n",
    "# 创建一个空的DataFrame来存储预测结果\n",
    "result = pd.DataFrame()\n",
    "\n",
    "xgb_parameters = {\n",
    "    'n_estimators': [100, 500, 1000],\n",
    "    'learning_rate': [0.01, 0.05, 0.1],\n",
    "    'max_depth': [3, 5, 7, 10],\n",
    "    'subsample': [0.5, 0.75, 1],\n",
    "    'gamma': [0, 1, 5]\n",
    "}\n",
    "\n",
    "# 在每个目标上\n",
    "for target in feature_dict:\n",
    "  \n",
    "    # 设定数据集\n",
    "    X = merged_data[feature_dict[target]]\n",
    "    y = merged_data[target]\n",
    "\n",
    "    # 切分数据集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "    # 特征缩放\n",
    "    scaler = StandardScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train)\n",
    "    X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "    # 选择模型\n",
    "    if target == 'PM2.5':\n",
    "        # 为PM2.5使用网格搜索找到最优的XGBoost参数\n",
    "        model = XGBRegressor(tree_method='hist', device='cuda') # 在此处进行设定\n",
    "        grid_search = GridSearchCV(model, xgb_parameters, cv=5, scoring='neg_mean_squared_error')\n",
    "        grid_search.fit(X_train_scaled, y_train)\n",
    "        model = grid_search.best_estimator_    \n",
    "    elif target == 'CO':\n",
    "        model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "    else:\n",
    "        model = LinearRegression()\n",
    "\n",
    "    # 训练模型\n",
    "    model.fit(X_train_scaled, y_train)\n",
    "\n",
    "    # 对测试集进行预测\n",
    "    predictions = model.predict(X_test_scaled)\n",
    "\n",
    "    # 计算MSE\n",
    "    mse = mean_squared_error(y_test, predictions)\n",
    "\n",
    "\n",
    "    # 打印MSE的值\n",
    "    print(f'{target}的 Mean Squared Error:', mse)\n",
    "    \n",
    "    # 将预测结果加入到总的结果中\n",
    "    result[f\"Predicted {target}\"] = predictions\n",
    "    result[f\"Actual {target}\"] = y_test.values\n",
    "    \n",
    "\n",
    "# 打印所有预测值和实际值\n",
    "print(result.head(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "时间序列预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[9], line 35\u001b[0m\n\u001b[0;32m     33\u001b[0m data \u001b[38;5;241m=\u001b[39m scaler\u001b[38;5;241m.\u001b[39mfit_transform(data\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m     34\u001b[0m model \u001b[38;5;241m=\u001b[39m ARIMA(data, order\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m))   \u001b[38;5;66;03m# order 的参数由 AIC 或 BIC 准则确定\u001b[39;00m\n\u001b[1;32m---> 35\u001b[0m model_fit \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     36\u001b[0m feature_forecast \u001b[38;5;241m=\u001b[39m model_fit\u001b[38;5;241m.\u001b[39mforecast(steps\u001b[38;5;241m=\u001b[39mforecast_steps)\n\u001b[0;32m     38\u001b[0m \u001b[38;5;66;03m# 将特征的预测值存到结果DataFrame\u001b[39;00m\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\statsmodels\\tsa\\arima\\model.py:395\u001b[0m, in \u001b[0;36mARIMA.fit\u001b[1;34m(self, start_params, transformed, includes_fixed, method, method_kwargs, gls, gls_kwargs, cov_type, cov_kwds, return_params, low_memory)\u001b[0m\n\u001b[0;32m    392\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    393\u001b[0m     method_kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdisp\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m--> 395\u001b[0m     res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    396\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlow_memory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlow_memory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    397\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcov_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcov_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcov_kwds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcov_kwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmethod_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    398\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m return_params:\n\u001b[0;32m    399\u001b[0m         res\u001b[38;5;241m.\u001b[39mfit_details \u001b[38;5;241m=\u001b[39m res\u001b[38;5;241m.\u001b[39mmlefit\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\mlemodel.py:703\u001b[0m, in \u001b[0;36mMLEModel.fit\u001b[1;34m(self, start_params, transformed, includes_fixed, cov_type, cov_kwds, method, maxiter, full_output, disp, callback, return_params, optim_score, optim_complex_step, optim_hessian, flags, low_memory, **kwargs)\u001b[0m\n\u001b[0;32m    701\u001b[0m         flags[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhessian_method\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m optim_hessian\n\u001b[0;32m    702\u001b[0m     fargs \u001b[38;5;241m=\u001b[39m (flags,)\n\u001b[1;32m--> 703\u001b[0m     mlefit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    704\u001b[0m \u001b[43m                         \u001b[49m\u001b[43mfargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    705\u001b[0m \u001b[43m                         \u001b[49m\u001b[43mmaxiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaxiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    706\u001b[0m \u001b[43m                         \u001b[49m\u001b[43mfull_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfull_output\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    707\u001b[0m \u001b[43m                         \u001b[49m\u001b[43mdisp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdisp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    708\u001b[0m \u001b[43m                         \u001b[49m\u001b[43mskip_hessian\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    710\u001b[0m \u001b[38;5;66;03m# Just return the fitted parameters if requested\u001b[39;00m\n\u001b[0;32m    711\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_params:\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\statsmodels\\base\\model.py:566\u001b[0m, in \u001b[0;36mLikelihoodModel.fit\u001b[1;34m(self, start_params, method, maxiter, full_output, disp, fargs, callback, retall, skip_hessian, **kwargs)\u001b[0m\n\u001b[0;32m    563\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muse_t\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    565\u001b[0m optimizer \u001b[38;5;241m=\u001b[39m Optimizer()\n\u001b[1;32m--> 566\u001b[0m xopt, retvals, optim_settings \u001b[38;5;241m=\u001b[39m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscore\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    567\u001b[0m \u001b[43m                                               \u001b[49m\u001b[43mfargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    568\u001b[0m \u001b[43m                                               \u001b[49m\u001b[43mhessian\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhess\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    569\u001b[0m \u001b[43m                                               \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    570\u001b[0m \u001b[43m                                               \u001b[49m\u001b[43mdisp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdisp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    571\u001b[0m \u001b[43m                                               \u001b[49m\u001b[43mmaxiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaxiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    572\u001b[0m \u001b[43m                                               \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    573\u001b[0m \u001b[43m                                               \u001b[49m\u001b[43mretall\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretall\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    574\u001b[0m \u001b[43m                                               \u001b[49m\u001b[43mfull_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfull_output\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    575\u001b[0m \u001b[38;5;66;03m# Restore cov_type, cov_kwds and use_t\u001b[39;00m\n\u001b[0;32m    576\u001b[0m optim_settings\u001b[38;5;241m.\u001b[39mupdate(kwds)\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\statsmodels\\base\\optimizer.py:243\u001b[0m, in \u001b[0;36mOptimizer._fit\u001b[1;34m(self, objective, gradient, start_params, fargs, kwargs, hessian, method, maxiter, full_output, disp, callback, retall)\u001b[0m\n\u001b[0;32m    240\u001b[0m     fit_funcs\u001b[38;5;241m.\u001b[39mupdate(extra_fit_funcs)\n\u001b[0;32m    242\u001b[0m func \u001b[38;5;241m=\u001b[39m fit_funcs[method]\n\u001b[1;32m--> 243\u001b[0m xopt, retvals \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobjective\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    244\u001b[0m \u001b[43m                     \u001b[49m\u001b[43mdisp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdisp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmaxiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaxiter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    245\u001b[0m \u001b[43m                     \u001b[49m\u001b[43mretall\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretall\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfull_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfull_output\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    246\u001b[0m \u001b[43m                     \u001b[49m\u001b[43mhess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhessian\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    248\u001b[0m optim_settings \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moptimizer\u001b[39m\u001b[38;5;124m'\u001b[39m: method, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstart_params\u001b[39m\u001b[38;5;124m'\u001b[39m: start_params,\n\u001b[0;32m    249\u001b[0m                   \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmaxiter\u001b[39m\u001b[38;5;124m'\u001b[39m: maxiter, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfull_output\u001b[39m\u001b[38;5;124m'\u001b[39m: full_output,\n\u001b[0;32m    250\u001b[0m                   \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdisp\u001b[39m\u001b[38;5;124m'\u001b[39m: disp, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfargs\u001b[39m\u001b[38;5;124m'\u001b[39m: fargs, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcallback\u001b[39m\u001b[38;5;124m'\u001b[39m: callback,\n\u001b[0;32m    251\u001b[0m                   \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mretall\u001b[39m\u001b[38;5;124m'\u001b[39m: retall, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mextra_fit_funcs\u001b[39m\u001b[38;5;124m\"\u001b[39m: extra_fit_funcs}\n\u001b[0;32m    252\u001b[0m optim_settings\u001b[38;5;241m.\u001b[39mupdate(kwargs)\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\statsmodels\\base\\optimizer.py:660\u001b[0m, in \u001b[0;36m_fit_lbfgs\u001b[1;34m(f, score, start_params, fargs, kwargs, disp, maxiter, callback, retall, full_output, hess)\u001b[0m\n\u001b[0;32m    657\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m approx_grad:\n\u001b[0;32m    658\u001b[0m     func \u001b[38;5;241m=\u001b[39m f\n\u001b[1;32m--> 660\u001b[0m retvals \u001b[38;5;241m=\u001b[39m \u001b[43moptimize\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfmin_l_bfgs_b\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmaxiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaxiter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    661\u001b[0m \u001b[43m                                 \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    662\u001b[0m \u001b[43m                                 \u001b[49m\u001b[43mbounds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbounds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdisp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdisp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    663\u001b[0m \u001b[43m                                 \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mextra_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    665\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m full_output:\n\u001b[0;32m    666\u001b[0m     xopt, fopt, d \u001b[38;5;241m=\u001b[39m retvals\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\scipy\\optimize\\_lbfgsb_py.py:237\u001b[0m, in \u001b[0;36mfmin_l_bfgs_b\u001b[1;34m(func, x0, fprime, args, approx_grad, bounds, m, factr, pgtol, epsilon, iprint, maxfun, maxiter, disp, callback, maxls)\u001b[0m\n\u001b[0;32m    225\u001b[0m callback \u001b[38;5;241m=\u001b[39m _wrap_callback(callback)\n\u001b[0;32m    226\u001b[0m opts \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdisp\u001b[39m\u001b[38;5;124m'\u001b[39m: disp,\n\u001b[0;32m    227\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124miprint\u001b[39m\u001b[38;5;124m'\u001b[39m: iprint,\n\u001b[0;32m    228\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmaxcor\u001b[39m\u001b[38;5;124m'\u001b[39m: m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    234\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcallback\u001b[39m\u001b[38;5;124m'\u001b[39m: callback,\n\u001b[0;32m    235\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmaxls\u001b[39m\u001b[38;5;124m'\u001b[39m: maxls}\n\u001b[1;32m--> 237\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43m_minimize_lbfgsb\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjac\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjac\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbounds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbounds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    238\u001b[0m \u001b[43m                       \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mopts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    239\u001b[0m d \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgrad\u001b[39m\u001b[38;5;124m'\u001b[39m: res[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjac\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[0;32m    240\u001b[0m      \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtask\u001b[39m\u001b[38;5;124m'\u001b[39m: res[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmessage\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[0;32m    241\u001b[0m      \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfuncalls\u001b[39m\u001b[38;5;124m'\u001b[39m: res[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnfev\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[0;32m    242\u001b[0m      \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnit\u001b[39m\u001b[38;5;124m'\u001b[39m: res[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnit\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[0;32m    243\u001b[0m      \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwarnflag\u001b[39m\u001b[38;5;124m'\u001b[39m: res[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstatus\u001b[39m\u001b[38;5;124m'\u001b[39m]}\n\u001b[0;32m    244\u001b[0m f \u001b[38;5;241m=\u001b[39m res[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfun\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\scipy\\optimize\\_lbfgsb_py.py:407\u001b[0m, in \u001b[0;36m_minimize_lbfgsb\u001b[1;34m(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, finite_diff_rel_step, **unknown_options)\u001b[0m\n\u001b[0;32m    401\u001b[0m task_str \u001b[38;5;241m=\u001b[39m task\u001b[38;5;241m.\u001b[39mtobytes()\n\u001b[0;32m    402\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m task_str\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFG\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m    403\u001b[0m     \u001b[38;5;66;03m# The minimization routine wants f and g at the current x.\u001b[39;00m\n\u001b[0;32m    404\u001b[0m     \u001b[38;5;66;03m# Note that interruptions due to maxfun are postponed\u001b[39;00m\n\u001b[0;32m    405\u001b[0m     \u001b[38;5;66;03m# until the completion of the current minimization iteration.\u001b[39;00m\n\u001b[0;32m    406\u001b[0m     \u001b[38;5;66;03m# Overwrite f and g:\u001b[39;00m\n\u001b[1;32m--> 407\u001b[0m     f, g \u001b[38;5;241m=\u001b[39m \u001b[43mfunc_and_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    408\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m task_str\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNEW_X\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m    409\u001b[0m     \u001b[38;5;66;03m# new iteration\u001b[39;00m\n\u001b[0;32m    410\u001b[0m     n_iterations \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\scipy\\optimize\\_differentiable_functions.py:297\u001b[0m, in \u001b[0;36mScalarFunction.fun_and_grad\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    295\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_x_impl(x)\n\u001b[0;32m    296\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_fun()\n\u001b[1;32m--> 297\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    298\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mf, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mg\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\scipy\\optimize\\_differentiable_functions.py:267\u001b[0m, in \u001b[0;36mScalarFunction._update_grad\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    265\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_update_grad\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    266\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mg_updated:\n\u001b[1;32m--> 267\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_grad_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    268\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mg_updated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\scipy\\optimize\\_differentiable_functions.py:181\u001b[0m, in \u001b[0;36mScalarFunction.__init__.<locals>.update_grad\u001b[1;34m()\u001b[0m\n\u001b[0;32m    179\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_fun()\n\u001b[0;32m    180\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mngev \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m--> 181\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mg \u001b[38;5;241m=\u001b[39m \u001b[43mapprox_derivative\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun_wrapped\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    182\u001b[0m \u001b[43m                           \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfinite_diff_options\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\scipy\\optimize\\_numdiff.py:519\u001b[0m, in \u001b[0;36mapprox_derivative\u001b[1;34m(fun, x0, method, rel_step, abs_step, f0, bounds, sparsity, as_linear_operator, args, kwargs)\u001b[0m\n\u001b[0;32m    516\u001b[0m     use_one_sided \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m    518\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sparsity \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 519\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_dense_difference\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun_wrapped\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    520\u001b[0m \u001b[43m                             \u001b[49m\u001b[43muse_one_sided\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    521\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    522\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m issparse(sparsity) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(sparsity) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\scipy\\optimize\\_numdiff.py:590\u001b[0m, in \u001b[0;36m_dense_difference\u001b[1;34m(fun, x0, f0, h, use_one_sided, method)\u001b[0m\n\u001b[0;32m    588\u001b[0m     x \u001b[38;5;241m=\u001b[39m x0 \u001b[38;5;241m+\u001b[39m h_vecs[i]\n\u001b[0;32m    589\u001b[0m     dx \u001b[38;5;241m=\u001b[39m x[i] \u001b[38;5;241m-\u001b[39m x0[i]  \u001b[38;5;66;03m# Recompute dx as exactly representable number.\u001b[39;00m\n\u001b[1;32m--> 590\u001b[0m     df \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m-\u001b[39m f0\n\u001b[0;32m    591\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m3-point\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m use_one_sided[i]:\n\u001b[0;32m    592\u001b[0m     x1 \u001b[38;5;241m=\u001b[39m x0 \u001b[38;5;241m+\u001b[39m h_vecs[i]\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\scipy\\optimize\\_numdiff.py:470\u001b[0m, in \u001b[0;36mapprox_derivative.<locals>.fun_wrapped\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m    467\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m xp\u001b[38;5;241m.\u001b[39misdtype(x\u001b[38;5;241m.\u001b[39mdtype, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreal floating\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m    468\u001b[0m     x \u001b[38;5;241m=\u001b[39m xp\u001b[38;5;241m.\u001b[39mastype(x, x0\u001b[38;5;241m.\u001b[39mdtype)\n\u001b[1;32m--> 470\u001b[0m f \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39matleast_1d(\u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m    471\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m f\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m    472\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`fun` return value has \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    473\u001b[0m                        \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmore than 1 dimension.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\scipy\\optimize\\_differentiable_functions.py:145\u001b[0m, in \u001b[0;36mScalarFunction.__init__.<locals>.fun_wrapped\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m    141\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnfev \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    142\u001b[0m \u001b[38;5;66;03m# Send a copy because the user may overwrite it.\u001b[39;00m\n\u001b[0;32m    143\u001b[0m \u001b[38;5;66;03m# Overwriting results in undefined behaviour because\u001b[39;00m\n\u001b[0;32m    144\u001b[0m \u001b[38;5;66;03m# fun(self.x) will change self.x, with the two no longer linked.\u001b[39;00m\n\u001b[1;32m--> 145\u001b[0m fx \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    146\u001b[0m \u001b[38;5;66;03m# Make sure the function returns a true scalar\u001b[39;00m\n\u001b[0;32m    147\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misscalar(fx):\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\statsmodels\\base\\model.py:534\u001b[0m, in \u001b[0;36mLikelihoodModel.fit.<locals>.f\u001b[1;34m(params, *args)\u001b[0m\n\u001b[0;32m    533\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mf\u001b[39m(params, \u001b[38;5;241m*\u001b[39margs):\n\u001b[1;32m--> 534\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m-\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloglike\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m/\u001b[39m nobs\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\mlemodel.py:938\u001b[0m, in \u001b[0;36mMLEModel.loglike\u001b[1;34m(self, params, *args, **kwargs)\u001b[0m\n\u001b[0;32m    935\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m complex_step:\n\u001b[0;32m    936\u001b[0m     kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minversion_method\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m INVERT_UNIVARIATE \u001b[38;5;241m|\u001b[39m SOLVE_LU\n\u001b[1;32m--> 938\u001b[0m loglike \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mssm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloglike\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcomplex_step\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcomplex_step\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    940\u001b[0m \u001b[38;5;66;03m# Koopman, Shephard, and Doornik recommend maximizing the average\u001b[39;00m\n\u001b[0;32m    941\u001b[0m \u001b[38;5;66;03m# likelihood to avoid scale issues, but the averaging is done\u001b[39;00m\n\u001b[0;32m    942\u001b[0m \u001b[38;5;66;03m# automatically in the base model `fit` method\u001b[39;00m\n\u001b[0;32m    943\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loglike\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\kalman_filter.py:1005\u001b[0m, in \u001b[0;36mKalmanFilter.loglike\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m   1002\u001b[0m loglikelihood_burn \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloglikelihood_burn\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m   1003\u001b[0m                                 \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloglikelihood_burn)\n\u001b[0;32m   1004\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mconserve_memory\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m&\u001b[39m MEMORY_NO_LIKELIHOOD):\n\u001b[1;32m-> 1005\u001b[0m     loglike \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkfilter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloglikelihood\u001b[49m\u001b[43m[\u001b[49m\u001b[43mloglikelihood_burn\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1006\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1007\u001b[0m     loglike \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msum(kfilter\u001b[38;5;241m.\u001b[39mloglikelihood)\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:2313\u001b[0m, in \u001b[0;36msum\u001b[1;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[0;32m   2310\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m out\n\u001b[0;32m   2311\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m res\n\u001b[1;32m-> 2313\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapreduction\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msum\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2314\u001b[0m \u001b[43m                      \u001b[49m\u001b[43minitial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Software\\Python\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:88\u001b[0m, in \u001b[0;36m_wrapreduction\u001b[1;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[0;32m     85\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     86\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m reduction(axis\u001b[38;5;241m=\u001b[39maxis, out\u001b[38;5;241m=\u001b[39mout, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpasskwargs)\n\u001b[1;32m---> 88\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mufunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpasskwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# 定义每个目标的特征\n",
    "feature_dict = {\n",
    "    'PM2.5': ['平均风速', '湿度', '风向', '气温', '气压趋势', '最大阵风', '平均海平面大气压', '最低气温'],\n",
    "    'PM10': ['平均风速', '气压趋势', '气温', '风向', '最低气温'],\n",
    "    'NO2': ['气温', '平均风速', '平均海平面大气压', '气压', '最低气温', '露点温度', '最高气温', '气压趋势', '风向', '最大阵风', '降水时间'],\n",
    "    'CO': ['平均风速', '气温', '湿度', '风向', '平均海平面大气压', '季节', '气压', '最大阵风', '气压趋势'],\n",
    "    'SO2': ['气温', '露点温度', '平均海平面大气压', '季节', '气压', '风向'],\n",
    "    'O3_8h': ['气温', '平均海平面大气压', '气压', '露点温度', '最高气温', '最低气温', '降水时间', '湿度']\n",
    "}\n",
    "# 定义用于预测的时间步数\n",
    "forecast_steps = 5\n",
    "\n",
    "# 创建一个空的DataFrame用于存储预测结果\n",
    "all_forecasts = pd.DataFrame()\n",
    "\n",
    "# 标准化器\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "for target, features in feature_dict.items():\n",
    "    # 进行目标变量的预测\n",
    "    data = merged_data[target]\n",
    "    data = scaler.fit_transform(data.values.reshape(-1, 1))\n",
    "    model = ARIMA(data, order=(5, 1, 0))   # order 的参数由 AIC 或 BIC 准则确定\n",
    "    model_fit = model.fit()\n",
    "    target_forecast = model_fit.forecast(steps=forecast_steps)\n",
    "\n",
    "    # 将目标变量预测值存到结果DataFrame\n",
    "    all_forecasts[target] = target_forecast\n",
    "    \n",
    "    # 对每个特征进行预测\n",
    "    for feature in features:\n",
    "        data = merged_data[feature]\n",
    "        data = scaler.fit_transform(data.values.reshape(-1, 1))\n",
    "        model = ARIMA(data, order=(5, 1, 0))   # order 的参数由 AIC 或 BIC 准则确定\n",
    "        model_fit = model.fit()\n",
    "        feature_forecast = model_fit.forecast(steps=forecast_steps)\n",
    "        \n",
    "        # 将特征的预测值存到结果DataFrame\n",
    "        all_forecasts[feature] = feature_forecast\n",
    "\n",
    "    # 查看预测结果 DataFrame\n",
    "    print(all_forecasts.head())\n",
    "    # 创建一个空的DataFrame用于存储预测结果\n",
    "    prediction_result = pd.DataFrame()\n",
    "\n",
    "    # 训练模型\n",
    "    model = SVR(kernel='rbf', C=1.0, epsilon=0.1)\n",
    "    model.fit(merged_data[feature_dict[target]], merged_data[target])\n",
    "\n",
    "    # 预测未来N个时间点\n",
    "    future_forecast = model.predict(all_forecasts[feature_dict[target]])\n",
    "\n",
    "    # 将预测结果加入到总的结果DataFrame\n",
    "    prediction_result['Predicted_' + target] = future_forecast\n",
    "\n",
    "    # 打印预测值\n",
    "    print(f'Predicted future {target}:', future_forecast)\n",
    "\n",
    "    # 打印预测结果 DataFrame\n",
    "    print(prediction_result.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "时间序列预测模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 \tLoss: 0.11275748908519745\n",
      "Epoch: 10 \tLoss: 0.032901208847761154\n",
      "Epoch: 20 \tLoss: 0.02033434435725212\n",
      "Epoch: 30 \tLoss: 0.01662355847656727\n",
      "Epoch: 40 \tLoss: 0.01426304504275322\n",
      "Epoch: 50 \tLoss: 0.013597064651548862\n",
      "Epoch: 60 \tLoss: 0.012909411452710629\n",
      "Epoch: 70 \tLoss: 0.012348520569503307\n",
      "Epoch: 80 \tLoss: 0.011907948181033134\n",
      "Epoch: 90 \tLoss: 0.011476278305053711\n",
      "Train loss: 0.011\n",
      "Test loss: 0.010\n",
      "Train RMSE: 0.105\n",
      "Test RMSE: 0.098\n",
      "Train R^2: 0.702\n",
      "Test R^2: 0.680\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>O3_8h_pred</th>\n",
       "      <th>O3_8h_real</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.037033</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.720921</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41.972225</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.578987</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55.863922</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>19.057470</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>21.677528</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>27.526384</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>24.517090</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>27.209795</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>735 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     O3_8h_pred  O3_8h_real\n",
       "0     37.037033        32.0\n",
       "1     40.720921        22.0\n",
       "2     41.972225        65.0\n",
       "3     50.578987        67.0\n",
       "4     55.863922        30.0\n",
       "..          ...         ...\n",
       "730   19.057470         0.0\n",
       "731   21.677528        34.0\n",
       "732   27.526384        21.0\n",
       "733   24.517090        45.0\n",
       "734   27.209795        64.0\n",
       "\n",
       "[735 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征和目标变量选择\n",
    "feature_cols = ['气温', '平均海平面大气压', '气压', '露点温度', '最高气温', '最低气温', '降水时间', '湿度','O3_8h'] \n",
    "target_cols = ['O3_8h']\n",
    "\n",
    "X = merged_data[feature_cols].values\n",
    "y = merged_data[target_cols].values\n",
    "\n",
    "# 数据规范化\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "X = scaler.fit_transform(X)\n",
    "y = scaler.fit_transform(y)\n",
    "\n",
    "def create_dataset(X, y, window_size):\n",
    "    data_seq = []\n",
    "    label_seq = []\n",
    "    length = len(X)\n",
    "    for i in range(length - window_size):\n",
    "        window_data = X[i : i + window_size, :]\n",
    "        window_label = y[i + window_size]\n",
    "        data_seq.append(window_data)\n",
    "        label_seq.append(window_label)\n",
    "    return np.array(data_seq), np.array(label_seq)\n",
    "\n",
    "window_size = 7  \n",
    "X_dataset, y_dataset = create_dataset(X, y, window_size)\n",
    "\n",
    "# 划分训练集和测试集\n",
    "train_size = int(len(X_dataset) * 0.8)\n",
    "test_size = len(X_dataset) - train_size\n",
    "\n",
    "\n",
    "# 获取训练集和测试集的X，y\n",
    "X_train, y_train = X_dataset[:train_size], y_dataset[:train_size]\n",
    "X_test, y_test = X_dataset[train_size:], y_dataset[train_size:]\n",
    "\n",
    "# 设置模型\n",
    "class LSTMModel(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, output_dim, n_layers):\n",
    "        super(LSTMModel, self).__init__()\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.n_layers = n_layers\n",
    "        self.lstm = nn.LSTM(input_dim, hidden_dim, n_layers, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.Tensor(x)\n",
    "        h0 = torch.zeros(self.n_layers, x.size(0), self.hidden_dim).detach()\n",
    "        c0 = torch.zeros(self.n_layers, x.size(0), self.hidden_dim).detach()\n",
    "        out, _ = self.lstm(x, (h0, c0)) \n",
    "        out = self.fc(out[:, -1, :])  \n",
    "        return out\n",
    "\n",
    "model = LSTMModel(X_train.shape[2], 128, 1, 2)\n",
    "criterion = torch.nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
    "\n",
    "# 进行训练\n",
    "epochs = 100\n",
    "for epoch in range(epochs):\n",
    "    optimizer.zero_grad()\n",
    "    outputs = model(X_train)\n",
    "    optimizer.zero_grad()\n",
    "    loss = criterion(outputs, torch.Tensor(y_train))\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    if epoch % 10 == 0: \n",
    "        print(f'Epoch: {epoch} \\tLoss: {loss.item()}')\n",
    "        \n",
    "# 评估模型\n",
    "with torch.no_grad():\n",
    "    y_train_pred = model(X_train)\n",
    "    y_test_pred = model(X_test)\n",
    "train_loss = criterion(y_train_pred, torch.Tensor(y_train)).item() \n",
    "test_loss = criterion(y_test_pred, torch.Tensor(y_test)).item()\n",
    "\n",
    "# 训练集和测试集上的损失\n",
    "print(\"Train loss: %.3f\" % train_loss)\n",
    "print(\"Test loss: %.3f\" % test_loss)\n",
    "\n",
    "# 对预测结果进行反归一化\n",
    "y_train_pred = y_train_pred.detach().numpy()\n",
    "y_test_pred = y_test_pred.detach().numpy()\n",
    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
    "y_test_pred = scaler.inverse_transform(y_test_pred)\n",
    "\n",
    "# 对真实结果进行反归一化\n",
    "y_train_real = scaler.inverse_transform(y_train)\n",
    "y_test_real = scaler.inverse_transform(y_test)\n",
    "\n",
    "# 为预测结果和真实结果创建DataFrame\n",
    "train_dict = {}\n",
    "test_dict = {}\n",
    "for idx, col in enumerate(target_cols):\n",
    "    train_dict[col+'_pred'] = y_train_pred[:, idx]\n",
    "    train_dict[col+'_real'] = y_train_real[:, idx]\n",
    "    test_dict[col+'_pred'] = y_test_pred[:, idx]\n",
    "    test_dict[col+'_real'] = y_test_real[:, idx]\n",
    "train_result = pd.DataFrame(train_dict)\n",
    "test_result = pd.DataFrame(test_dict)\n",
    "\n",
    "# 计算RMSE\n",
    "train_rmse = np.sqrt(train_loss)\n",
    "test_rmse = np.sqrt(test_loss)\n",
    "\n",
    "print(\"Train RMSE: %.3f\" % train_rmse)\n",
    "print(\"Test RMSE: %.3f\" % test_rmse)\n",
    "\n",
    "# 训练集的R2 score\n",
    "r2_train = r2_score(y_train_real, y_train_pred)\n",
    "print(\"Train R^2: %.3f\" % r2_train)\n",
    "\n",
    "# 测试集的R2 score\n",
    "r2_test = r2_score(y_test_real, y_test_pred)\n",
    "print(\"Test R^2: %.3f\" % r2_test)\n",
    "\n",
    "test_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 \tLoss: 0.10242171585559845\n",
      "Epoch: 10 \tLoss: 0.031894534826278687\n",
      "Epoch: 20 \tLoss: 0.018624432384967804\n",
      "Epoch: 30 \tLoss: 0.017154980450868607\n",
      "Epoch: 40 \tLoss: 0.014707257971167564\n",
      "Epoch: 50 \tLoss: 0.013401344418525696\n",
      "Epoch: 60 \tLoss: 0.01241430826485157\n",
      "Epoch: 70 \tLoss: 0.011808994226157665\n",
      "Epoch: 80 \tLoss: 0.01127883605659008\n",
      "Epoch: 90 \tLoss: 0.011018553748726845\n",
      "Train loss: 0.011\n",
      "Test loss: 0.009\n",
      "Train RMSE: 0.104\n",
      "Test RMSE: 0.096\n",
      "Train R^2: 0.708\n",
      "Test R^2: 0.689\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>O3_8h_pred</th>\n",
       "      <th>O3_8h_real</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41.350193</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43.997211</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42.813171</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.989227</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55.922283</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>19.375181</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>21.112106</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>26.289581</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>23.028431</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>29.353914</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>735 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     O3_8h_pred  O3_8h_real\n",
       "0     41.350193        32.0\n",
       "1     43.997211        22.0\n",
       "2     42.813171        65.0\n",
       "3     50.989227        67.0\n",
       "4     55.922283        30.0\n",
       "..          ...         ...\n",
       "730   19.375181         0.0\n",
       "731   21.112106        34.0\n",
       "732   26.289581        21.0\n",
       "733   23.028431        45.0\n",
       "734   29.353914        64.0\n",
       "\n",
       "[735 rows x 2 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征和目标变量选择\n",
    "feature_cols = ['气温', '平均海平面大气压', '气压', '露点温度', '最高气温', '最低气温', '降水时间', '湿度','O3_8h'] \n",
    "target_cols = ['O3_8h']\n",
    "\n",
    "X = merged_data[feature_cols].values\n",
    "y = merged_data[target_cols].values\n",
    "\n",
    "# 数据规范化\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "X = scaler.fit_transform(X)\n",
    "y = scaler.fit_transform(y)\n",
    "\n",
    "def create_dataset(X, y, window_size):\n",
    "    data_seq = []\n",
    "    label_seq = []\n",
    "    length = len(X)\n",
    "    for i in range(length - window_size):\n",
    "        window_data = X[i : i + window_size, :]\n",
    "        window_label = y[i + window_size]\n",
    "        data_seq.append(window_data)\n",
    "        label_seq.append(window_label)\n",
    "    return np.array(data_seq), np.array(label_seq)\n",
    "\n",
    "window_size = 7  \n",
    "X_dataset, y_dataset = create_dataset(X, y, window_size)\n",
    "\n",
    "# 划分训练集和测试集\n",
    "train_size = int(len(X_dataset) * 0.8)\n",
    "test_size = len(X_dataset) - train_size\n",
    "\n",
    "\n",
    "# 获取训练集和测试集的X，y\n",
    "X_train, y_train = X_dataset[:train_size], y_dataset[:train_size]\n",
    "X_test, y_test = X_dataset[train_size:], y_dataset[train_size:]\n",
    "\n",
    "# 设置模型\n",
    "class LSTMModel(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, output_dim, n_layers):\n",
    "        super(LSTMModel, self).__init__()\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.n_layers = n_layers\n",
    "        self.lstm = nn.LSTM(input_dim, hidden_dim, n_layers, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.Tensor(x)\n",
    "        h0 = torch.zeros(self.n_layers, x.size(0), self.hidden_dim).detach()\n",
    "        c0 = torch.zeros(self.n_layers, x.size(0), self.hidden_dim).detach()\n",
    "        out, _ = self.lstm(x, (h0, c0)) \n",
    "        out = self.fc(out[:, -1, :])  \n",
    "        return out\n",
    "\n",
    "model = LSTMModel(X_train.shape[2], 128, 1, 2)\n",
    "criterion = torch.nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
    "\n",
    "# 进行训练\n",
    "epochs = 100\n",
    "for epoch in range(epochs):\n",
    "    optimizer.zero_grad()\n",
    "    outputs = model(X_train)\n",
    "    optimizer.zero_grad()\n",
    "    loss = criterion(outputs, torch.Tensor(y_train))\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    if epoch % 10 == 0: \n",
    "        print(f'Epoch: {epoch} \\tLoss: {loss.item()}')\n",
    "        \n",
    "# 评估模型\n",
    "with torch.no_grad():\n",
    "    y_train_pred = model(X_train)\n",
    "    y_test_pred = model(X_test)\n",
    "train_loss = criterion(y_train_pred, torch.Tensor(y_train)).item() \n",
    "test_loss = criterion(y_test_pred, torch.Tensor(y_test)).item()\n",
    "\n",
    "# 训练集和测试集上的损失\n",
    "print(\"Train loss: %.3f\" % train_loss)\n",
    "print(\"Test loss: %.3f\" % test_loss)\n",
    "\n",
    "# 对预测结果进行反归一化\n",
    "y_train_pred = y_train_pred.detach().numpy()\n",
    "y_test_pred = y_test_pred.detach().numpy()\n",
    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
    "y_test_pred = scaler.inverse_transform(y_test_pred)\n",
    "\n",
    "# 对真实结果进行反归一化\n",
    "y_train_real = scaler.inverse_transform(y_train)\n",
    "y_test_real = scaler.inverse_transform(y_test)\n",
    "\n",
    "# 为预测结果和真实结果创建DataFrame\n",
    "train_dict = {}\n",
    "test_dict = {}\n",
    "for idx, col in enumerate(target_cols):\n",
    "    train_dict[col+'_pred'] = y_train_pred[:, idx]\n",
    "    train_dict[col+'_real'] = y_train_real[:, idx]\n",
    "    test_dict[col+'_pred'] = y_test_pred[:, idx]\n",
    "    test_dict[col+'_real'] = y_test_real[:, idx]\n",
    "train_result = pd.DataFrame(train_dict)\n",
    "test_result = pd.DataFrame(test_dict)\n",
    "\n",
    "# 计算RMSE\n",
    "train_rmse = np.sqrt(train_loss)\n",
    "test_rmse = np.sqrt(test_loss)\n",
    "\n",
    "print(\"Train RMSE: %.3f\" % train_rmse)\n",
    "print(\"Test RMSE: %.3f\" % test_rmse)\n",
    "\n",
    "# 训练集的R2 score\n",
    "r2_train = r2_score(y_train_real, y_train_pred)\n",
    "print(\"Train R^2: %.3f\" % r2_train)\n",
    "\n",
    "# 测试集的R2 score\n",
    "r2_test = r2_score(y_test_real, y_test_pred)\n",
    "print(\"Test R^2: %.3f\" % r2_test)\n",
    "\n",
    "test_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 \tLoss: 0.038332197815179825\n",
      "Epoch: 10 \tLoss: 0.013394604437053204\n",
      "Epoch: 20 \tLoss: 0.012989003211259842\n",
      "Epoch: 30 \tLoss: 0.01104271411895752\n",
      "Epoch: 40 \tLoss: 0.009559047408401966\n",
      "Epoch: 50 \tLoss: 0.008376723155379295\n",
      "Epoch: 60 \tLoss: 0.0076932464726269245\n",
      "Epoch: 70 \tLoss: 0.007406032644212246\n",
      "Epoch: 80 \tLoss: 0.007272147107869387\n",
      "Epoch: 90 \tLoss: 0.007204096298664808\n",
      "Train RMSE: 0.084\n",
      "Test RMSE: 0.050\n",
      "Train R^2: 0.474\n",
      "Test R^2: 0.323\n",
      "     PM2.5_pred  PM2.5_real\n",
      "0     29.939812        23.0\n",
      "1     53.429356        35.0\n",
      "2     51.339649        11.0\n",
      "3     18.069838        11.0\n",
      "4     22.001661        21.0\n",
      "..          ...         ...\n",
      "730   33.081287        26.0\n",
      "731   35.878094        45.0\n",
      "732   46.975395        98.0\n",
      "733   88.688248       115.0\n",
      "734   86.755402        12.0\n",
      "\n",
      "[735 rows x 2 columns]\n",
      "Epoch: 0 \tLoss: 0.0276825912296772\n",
      "Epoch: 10 \tLoss: 0.008819203823804855\n",
      "Epoch: 20 \tLoss: 0.008460551500320435\n",
      "Epoch: 30 \tLoss: 0.007980696856975555\n",
      "Epoch: 40 \tLoss: 0.007210148964077234\n",
      "Epoch: 50 \tLoss: 0.006471459753811359\n",
      "Epoch: 60 \tLoss: 0.005773603916168213\n",
      "Epoch: 70 \tLoss: 0.005450368393212557\n",
      "Epoch: 80 \tLoss: 0.0052936021238565445\n",
      "Epoch: 90 \tLoss: 0.005197025369852781\n",
      "Train RMSE: 0.072\n",
      "Test RMSE: 0.079\n",
      "Train R^2: 0.382\n",
      "Test R^2: 0.264\n",
      "      PM10_pred  PM10_real\n",
      "0     48.470886       52.0\n",
      "1     78.577484       62.0\n",
      "2     79.828201       42.0\n",
      "3     57.693367       36.0\n",
      "4     49.296501       47.0\n",
      "..          ...        ...\n",
      "730   66.147354       46.0\n",
      "731   55.353477       71.0\n",
      "732   71.744499      132.0\n",
      "733  108.027122      145.0\n",
      "734  111.526230       29.0\n",
      "\n",
      "[735 rows x 2 columns]\n",
      "Epoch: 0 \tLoss: 0.10327405482530594\n",
      "Epoch: 10 \tLoss: 0.024309895932674408\n",
      "Epoch: 20 \tLoss: 0.02010936290025711\n",
      "Epoch: 30 \tLoss: 0.015772154554724693\n",
      "Epoch: 40 \tLoss: 0.014256160706281662\n",
      "Epoch: 50 \tLoss: 0.013079098425805569\n",
      "Epoch: 60 \tLoss: 0.012010330334305763\n",
      "Epoch: 70 \tLoss: 0.011090717278420925\n",
      "Epoch: 80 \tLoss: 0.010307328775525093\n",
      "Epoch: 90 \tLoss: 0.010990338400006294\n",
      "Train RMSE: 0.101\n",
      "Test RMSE: 0.069\n",
      "Train R^2: 0.506\n",
      "Test R^2: 0.346\n",
      "      NO2_pred   NO2_real\n",
      "0    40.676979  45.000000\n",
      "1    51.651253  52.000000\n",
      "2    48.094719  20.000000\n",
      "3    26.385971  21.000000\n",
      "4    34.131870  49.000000\n",
      "..         ...        ...\n",
      "730  39.756973  44.000000\n",
      "731  43.824638  51.000004\n",
      "732  48.080944  69.000000\n",
      "733  56.469315  62.000000\n",
      "734  51.293671  21.000000\n",
      "\n",
      "[735 rows x 2 columns]\n",
      "Epoch: 0 \tLoss: 0.023521937429904938\n",
      "Epoch: 10 \tLoss: 0.009285378269851208\n",
      "Epoch: 20 \tLoss: 0.008315124548971653\n",
      "Epoch: 30 \tLoss: 0.007089103106409311\n",
      "Epoch: 40 \tLoss: 0.006083785090595484\n",
      "Epoch: 50 \tLoss: 0.005214491859078407\n",
      "Epoch: 60 \tLoss: 0.005044665187597275\n",
      "Epoch: 70 \tLoss: 0.004549460485577583\n",
      "Epoch: 80 \tLoss: 0.004410407040268183\n",
      "Epoch: 90 \tLoss: 0.004321360029280186\n",
      "Train RMSE: 0.065\n",
      "Test RMSE: 0.028\n",
      "Train R^2: 0.543\n",
      "Test R^2: 0.187\n",
      "      CO_pred  CO_real\n",
      "0    0.593191      0.6\n",
      "1    0.962523      0.9\n",
      "2    0.958574      0.3\n",
      "3    0.426874      0.3\n",
      "4    0.612366      0.7\n",
      "..        ...      ...\n",
      "730  0.617327      0.7\n",
      "731  0.842627      0.8\n",
      "732  0.754389      1.2\n",
      "733  1.072474      1.2\n",
      "734  0.916278      0.3\n",
      "\n",
      "[735 rows x 2 columns]\n",
      "Epoch: 0 \tLoss: 0.009236253798007965\n",
      "Epoch: 10 \tLoss: 0.008620194159448147\n",
      "Epoch: 20 \tLoss: 0.006835170090198517\n",
      "Epoch: 30 \tLoss: 0.004529519937932491\n",
      "Epoch: 40 \tLoss: 0.0038283802568912506\n",
      "Epoch: 50 \tLoss: 0.0035609041806310415\n",
      "Epoch: 60 \tLoss: 0.003344860626384616\n",
      "Epoch: 70 \tLoss: 0.003176676342263818\n",
      "Epoch: 80 \tLoss: 0.0030336095951497555\n",
      "Epoch: 90 \tLoss: 0.002908485708758235\n",
      "Train RMSE: 0.053\n",
      "Test RMSE: 0.013\n",
      "Train R^2: 0.683\n",
      "Test R^2: -3.463\n",
      "     SO2_pred  SO2_real\n",
      "0    3.199011       3.0\n",
      "1    5.707390       4.0\n",
      "2    5.692593       2.0\n",
      "3    4.556930       3.0\n",
      "4    5.602013       4.0\n",
      "..        ...       ...\n",
      "730  3.893078       3.0\n",
      "731  3.882786       3.0\n",
      "732  3.176748       3.0\n",
      "733  2.352838       3.0\n",
      "734  2.774427       2.0\n",
      "\n",
      "[735 rows x 2 columns]\n",
      "Epoch: 0 \tLoss: 0.08212408423423767\n",
      "Epoch: 10 \tLoss: 0.03007514216005802\n",
      "Epoch: 20 \tLoss: 0.017283599823713303\n",
      "Epoch: 30 \tLoss: 0.014910390600562096\n",
      "Epoch: 40 \tLoss: 0.01328679546713829\n",
      "Epoch: 50 \tLoss: 0.012130343355238438\n",
      "Epoch: 60 \tLoss: 0.01153157651424408\n",
      "Epoch: 70 \tLoss: 0.011099041439592838\n",
      "Epoch: 80 \tLoss: 0.010888062417507172\n",
      "Epoch: 90 \tLoss: 0.010783622041344643\n",
      "Train RMSE: 0.104\n",
      "Test RMSE: 0.096\n",
      "Train R^2: 0.712\n",
      "Test R^2: 0.692\n",
      "     O3_8h_pred  O3_8h_real\n",
      "0     39.659840        32.0\n",
      "1     40.831543        22.0\n",
      "2     42.041767        65.0\n",
      "3     54.258698        67.0\n",
      "4     57.592682        30.0\n",
      "..          ...         ...\n",
      "730   18.419725         0.0\n",
      "731   20.050058        34.0\n",
      "732   27.408510        21.0\n",
      "733   24.853092        45.0\n",
      "734   28.469599        64.0\n",
      "\n",
      "[735 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "class LSTMModel(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, output_dim, n_layers):\n",
    "        super(LSTMModel, self).__init__()\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.n_layers = n_layers\n",
    "        self.lstm = nn.LSTM(input_dim, hidden_dim, n_layers, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.Tensor(x).to(device)\n",
    "        h0 = torch.zeros(self.n_layers, x.size(0), self.hidden_dim).detach().to(device)\n",
    "        c0 = torch.zeros(self.n_layers, x.size(0), self.hidden_dim).detach().to(device)\n",
    "        out, _ = self.lstm(x, (h0, c0)) \n",
    "        out = self.fc(out[:, -1, :])  \n",
    "        return out\n",
    "\n",
    "def create_dataset(X, y, window_size):\n",
    "    data_seq = []\n",
    "    label_seq = []\n",
    "    length = len(X)\n",
    "    for i in range(length - window_size):\n",
    "        window_data = X[i : i + window_size, :]\n",
    "        window_label = y[i + window_size]\n",
    "        data_seq.append(window_data)\n",
    "        label_seq.append(window_label)\n",
    "    return np.array(data_seq), np.array(label_seq)\n",
    "\n",
    "def process_data(df, feature_cols, target_cols, window_size=7):\n",
    "\n",
    "    # Normalize the data\n",
    "    scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "    X = df[feature_cols].values\n",
    "    y = df[target_cols].values\n",
    "\n",
    "    X = scaler.fit_transform(X)\n",
    "    y = scaler.fit_transform(y)\n",
    "\n",
    "    # Create the dataset\n",
    "    X_dataset, y_dataset = create_dataset(X, y, window_size)\n",
    "\n",
    "    # Split the dataset into training and testing sets\n",
    "    train_size = int(len(X_dataset) * 0.8)\n",
    "    test_size = len(X_dataset) - train_size\n",
    "\n",
    "    X_train, y_train = X_dataset[:train_size], y_dataset[:train_size]\n",
    "    X_test, y_test = X_dataset[train_size:], y_dataset[train_size:]\n",
    "\n",
    "    return X_train, y_train, X_test, y_test, scaler\n",
    "\n",
    "def train_model(X_train, y_train, X_test, y_test, scaler, feature_cols, target_cols, epochs=100):\n",
    "\n",
    "    model = LSTMModel(X_train.shape[2], 128, 1, 2).to(device)  # move model to device\n",
    "    criterion = torch.nn.MSELoss()\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
    "\n",
    "    # Move data to device\n",
    "    X_train = torch.Tensor(X_train).to(device)\n",
    "    y_train = torch.Tensor(y_train).to(device)\n",
    "    \n",
    "    for epoch in range(epochs):\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(X_train)\n",
    "        optimizer.zero_grad()\n",
    "        loss = criterion(outputs, y_train)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if epoch % 10 == 0: \n",
    "            print(f'Epoch: {epoch} \\tLoss: {loss.item()}')\n",
    "        \n",
    "    # Evaluate the model\n",
    "    with torch.no_grad():\n",
    "        # Move data to device\n",
    "        X_test = torch.Tensor(X_test).to(device)\n",
    "        y_test = torch.Tensor(y_test).to(device)\n",
    "        \n",
    "        y_train_pred = model(X_train)\n",
    "        y_test_pred = model(X_test)\n",
    "        \n",
    "    train_loss = criterion(y_train_pred, y_train).item() \n",
    "    test_loss = criterion(y_test_pred, y_test).item()\n",
    "\n",
    "    y_train_pred = y_train_pred.detach().cpu().numpy()   # 将预测结果移动到CPU\n",
    "    y_test_pred = y_test_pred.detach().cpu().numpy()     # 将预测结果移动到CPU\n",
    "    y_train_pred = scaler.inverse_transform(y_train_pred)\n",
    "    y_test_pred = scaler.inverse_transform(y_test_pred)\n",
    "\n",
    "    y_train_real = y_train.cpu().numpy()    # 将真实值移动到CPU\n",
    "    y_test_real = y_test.cpu().numpy()      # 将真实值移动到CPU\n",
    "    y_train_real = scaler.inverse_transform(y_train_real)\n",
    "    y_test_real = scaler.inverse_transform(y_test_real)\n",
    "\n",
    "    train_dict = {}\n",
    "    test_dict = {}\n",
    "    for idx, col in enumerate(target_cols):\n",
    "        train_dict[col+'_pred'] = y_train_pred[:, idx]\n",
    "        train_dict[col+'_real'] = y_train_real[:, idx]\n",
    "        test_dict[col+'_pred'] = y_test_pred[:, idx]\n",
    "        test_dict[col+'_real'] = y_test_real[:, idx]\n",
    "    train_result = pd.DataFrame(train_dict)\n",
    "    test_result = pd.DataFrame(test_dict)\n",
    "\n",
    "\n",
    "    # 计算RMSE\n",
    "    train_rmse = np.sqrt(train_loss)\n",
    "    test_rmse = np.sqrt(test_loss)\n",
    "\n",
    "    print(\"Train RMSE: %.3f\" % train_rmse)\n",
    "    print(\"Test RMSE: %.3f\" % test_rmse)\n",
    "\n",
    "    # 训练集的R2 score\n",
    "    r2_train = r2_score(y_train_real, y_train_pred)\n",
    "    print(\"Train R^2: %.3f\" % r2_train)\n",
    "\n",
    "    # 测试集的R2 score\n",
    "    r2_test = r2_score(y_test_real, y_test_pred)\n",
    "    print(\"Test R^2: %.3f\" % r2_test)\n",
    "\n",
    "    return train_result, test_result\n",
    "\n",
    "feature_dict = {\n",
    "    'PM2.5': ['平均风速', '湿度', '风向', '气温', '气压趋势', '最大阵风', '平均海平面大气压', '最低气温','PM2.5'],\n",
    "    'PM10': ['平均风速', '气压趋势', '气温', '风向', '最低气温','PM10'],\n",
    "    'NO2': ['气温', '平均风速', '平均海平面大气压', '气压', '最低气温', '露点温度', '最高气温', '气压趋势', '风向', '最大阵风', '降水时间','NO2'],\n",
    "    'CO': ['平均风速', '气温', '湿度', '风向', '平均海平面大气压', '季节', '气压', '最大阵风', '气压趋势','CO'],\n",
    "    'SO2': ['气温', '露点温度', '平均海平面大气压', '季节', '气压', '风向','SO2'],\n",
    "    'O3_8h': ['气温', '平均海平面大气压', '气压', '露点温度', '最高气温', '最低气温', '降水时间', '湿度','O3_8h']\n",
    "}\n",
    "\n",
    "# Loop over the dictionary to process data and train a model for each target\n",
    "for target, features in feature_dict.items():\n",
    "    # add target to features list\n",
    "    features.append(target)\n",
    "    X_train, y_train, X_test, y_test, scaler = process_data(merged_data, features, [target])\n",
    "    train_result, test_result = train_model(X_train, y_train, X_test, y_test, scaler, features, [target])\n",
    "    print(test_result)\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 \tLoss: 0.042480383068323135\n",
      "Epoch: 1 \tLoss: 0.021265478804707527\n",
      "Epoch: 2 \tLoss: 0.014904214069247246\n",
      "Epoch: 3 \tLoss: 0.018077246844768524\n",
      "Epoch: 4 \tLoss: 0.017358742654323578\n",
      "Epoch: 5 \tLoss: 0.013818809762597084\n",
      "Epoch: 6 \tLoss: 0.011710159480571747\n",
      "Epoch: 7 \tLoss: 0.011738265864551067\n",
      "Epoch: 8 \tLoss: 0.012186129577457905\n",
      "Epoch: 9 \tLoss: 0.011386876925826073\n",
      "Epoch: 10 \tLoss: 0.009364156052470207\n",
      "Epoch: 11 \tLoss: 0.0075406767427921295\n",
      "Epoch: 12 \tLoss: 0.006920245010405779\n",
      "Epoch: 13 \tLoss: 0.006944223307073116\n",
      "Epoch: 14 \tLoss: 0.006724396254867315\n",
      "Epoch: 15 \tLoss: 0.006187254097312689\n",
      "Epoch: 16 \tLoss: 0.005476147402077913\n",
      "Epoch: 17 \tLoss: 0.004901761654764414\n",
      "Epoch: 18 \tLoss: 0.004731078632175922\n",
      "Epoch: 19 \tLoss: 0.004727597814053297\n",
      "Epoch: 20 \tLoss: 0.004656194243580103\n",
      "Epoch: 21 \tLoss: 0.004456078168004751\n",
      "Epoch: 22 \tLoss: 0.004093891475349665\n",
      "Epoch: 23 \tLoss: 0.0037602498196065426\n",
      "Epoch: 24 \tLoss: 0.0036472950596362352\n",
      "Epoch: 25 \tLoss: 0.0036651059053838253\n",
      "Epoch: 26 \tLoss: 0.003659923793748021\n",
      "Epoch: 27 \tLoss: 0.0036178852897137403\n",
      "Epoch: 28 \tLoss: 0.0035509616136550903\n",
      "Epoch: 29 \tLoss: 0.003398971166461706\n",
      "Epoch: 30 \tLoss: 0.003158021718263626\n",
      "Epoch: 31 \tLoss: 0.0029500785749405622\n",
      "Epoch: 32 \tLoss: 0.0028627479914575815\n",
      "Epoch: 33 \tLoss: 0.0028258555103093386\n",
      "Epoch: 34 \tLoss: 0.0027288165874779224\n",
      "Epoch: 35 \tLoss: 0.00257757562212646\n",
      "Epoch: 36 \tLoss: 0.0024454237427562475\n",
      "Epoch: 37 \tLoss: 0.002355690812692046\n",
      "Epoch: 38 \tLoss: 0.0022922889329493046\n",
      "Epoch: 39 \tLoss: 0.0022505649831146\n",
      "Epoch: 40 \tLoss: 0.0022126841358840466\n",
      "Epoch: 41 \tLoss: 0.002132951281964779\n",
      "Epoch: 42 \tLoss: 0.002003540052101016\n",
      "Epoch: 43 \tLoss: 0.0018827107269316912\n",
      "Epoch: 44 \tLoss: 0.0018112888792529702\n",
      "Epoch: 45 \tLoss: 0.0017613256350159645\n",
      "Epoch: 46 \tLoss: 0.0017002247041091323\n",
      "Epoch: 47 \tLoss: 0.0016309131169691682\n",
      "Epoch: 48 \tLoss: 0.0015540359308943152\n",
      "Epoch: 49 \tLoss: 0.0014707701047882438\n",
      "Epoch: 50 \tLoss: 0.0014054953353479505\n",
      "Epoch: 51 \tLoss: 0.0013618641532957554\n",
      "Epoch: 52 \tLoss: 0.0013150047743692994\n",
      "Epoch: 53 \tLoss: 0.0012622106587514281\n",
      "Epoch: 54 \tLoss: 0.0012164266081526875\n",
      "Epoch: 55 \tLoss: 0.001176781370304525\n",
      "Epoch: 56 \tLoss: 0.0011461807880550623\n",
      "Epoch: 57 \tLoss: 0.001131968921981752\n",
      "Epoch: 58 \tLoss: 0.0011109737679362297\n",
      "Epoch: 59 \tLoss: 0.0010801288299262524\n",
      "Epoch: 60 \tLoss: 0.0010523071978241205\n",
      "Epoch: 61 \tLoss: 0.0010253464570268989\n",
      "Epoch: 62 \tLoss: 0.0010026254458352923\n",
      "Epoch: 63 \tLoss: 0.0009821505518630147\n",
      "Epoch: 64 \tLoss: 0.0009546112269163132\n",
      "Epoch: 65 \tLoss: 0.0009299125522375107\n",
      "Epoch: 66 \tLoss: 0.00090684654423967\n",
      "Epoch: 67 \tLoss: 0.0008833414176478982\n",
      "Epoch: 68 \tLoss: 0.0008633697289042175\n",
      "Epoch: 69 \tLoss: 0.0008425366831943393\n",
      "Epoch: 70 \tLoss: 0.0008203228353522718\n",
      "Epoch: 71 \tLoss: 0.0008010310120880604\n",
      "Epoch: 72 \tLoss: 0.0007813702686689794\n",
      "Epoch: 73 \tLoss: 0.0007624216377735138\n",
      "Epoch: 74 \tLoss: 0.0007422915077768266\n",
      "Epoch: 75 \tLoss: 0.0007200367981567979\n",
      "Epoch: 76 \tLoss: 0.0006996144074946642\n",
      "Epoch: 77 \tLoss: 0.0006808745092712343\n",
      "Epoch: 78 \tLoss: 0.000661952537484467\n",
      "Epoch: 79 \tLoss: 0.0006421267753466964\n",
      "Epoch: 80 \tLoss: 0.0006223786622285843\n",
      "Epoch: 81 \tLoss: 0.0006037646671757102\n",
      "Epoch: 82 \tLoss: 0.0005847355932928622\n",
      "Epoch: 83 \tLoss: 0.0005645147175528109\n",
      "Epoch: 84 \tLoss: 0.0005444920971058309\n",
      "Epoch: 85 \tLoss: 0.0005257937591522932\n",
      "Epoch: 86 \tLoss: 0.0005070777260698378\n",
      "Epoch: 87 \tLoss: 0.0004880052583757788\n",
      "Epoch: 88 \tLoss: 0.000468946440378204\n",
      "Epoch: 89 \tLoss: 0.0004510479047894478\n",
      "Epoch: 90 \tLoss: 0.0004333108081482351\n",
      "Epoch: 91 \tLoss: 0.00041544102714397013\n",
      "Epoch: 92 \tLoss: 0.0003984369395766407\n",
      "Epoch: 93 \tLoss: 0.00038244581082835793\n",
      "Epoch: 94 \tLoss: 0.00036676001036539674\n",
      "Epoch: 95 \tLoss: 0.00035114644560962915\n",
      "Epoch: 96 \tLoss: 0.00033650544355623424\n",
      "Epoch: 97 \tLoss: 0.00032273289980366826\n",
      "Epoch: 98 \tLoss: 0.00030899661942385137\n",
      "Epoch: 99 \tLoss: 0.00029609669581986964\n",
      "Train loss: 0.000\n",
      "Test loss: 0.000\n",
      "Train RMSE: 0.017\n",
      "Test RMSE: 0.017\n",
      "Train R^2: 0.977\n",
      "Test R^2: 0.974\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# 特征和目标变量选择\n",
    "feature_cols = ['气温', '平均风速', '平均海平面大气压', '气压', '最低气温', '露点温度', '最高气温', '气压趋势', '风向', '最大阵风', '降水时间','PM2.5', 'PM10', 'NO2', 'CO', 'SO2', 'O3_8h'] \n",
    "target_cols = ['PM2.5', 'PM10', 'NO2', 'CO', 'SO2', 'O3_8h']\n",
    "\n",
    "X = merged_data[feature_cols].values\n",
    "y = merged_data[target_cols].values\n",
    "\n",
    "# 数据规范化\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "X = scaler.fit_transform(X)\n",
    "y = scaler.fit_transform(y)\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 转换输入形状为(samples, time steps, features)\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))\n",
    "\n",
    "class LSTMModel(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
    "        super(LSTMModel, self).__init__()\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.output_dim = output_dim\n",
    "        self.lstm = nn.LSTM(input_dim, hidden_dim, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.Tensor(x)\n",
    "        output, (hn, cn) = self.lstm(x)\n",
    "        hn = hn.view(-1, self.hidden_dim)\n",
    "        out = self.fc(hn)\n",
    "        return out\n",
    "    \n",
    "model = LSTMModel(X_train.shape[2], 128, len(target_cols))\n",
    "criterion = torch.nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=1e-8) # 加入了权重衰减项\n",
    "\n",
    "epochs = 100\n",
    "for epoch in range(epochs):\n",
    "    optimizer.zero_grad()\n",
    "    outputs = model.forward(X_train)\n",
    "    optimizer.zero_grad()\n",
    "    loss = criterion(outputs, torch.Tensor(y_train))\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    if epoch % 1 == 0:  # print loss every 1 epochs\n",
    "        print(f'Epoch: {epoch} \\tLoss: {loss.item()}')\n",
    "\n",
    "with torch.no_grad():\n",
    "    y_train_pred = model.forward(X_train)\n",
    "    y_test_pred = model.forward(X_test)\n",
    "train_loss = criterion(y_train_pred, torch.Tensor(y_train)).item() \n",
    "test_loss = criterion(y_test_pred, torch.Tensor(y_test)).item()\n",
    "\n",
    "# 训练集和测试集上的损失\n",
    "print(\"Train loss: %.3f\" % train_loss)\n",
    "print(\"Test loss: %.3f\" % test_loss)\n",
    "\n",
    "\n",
    "# 获取预测结果，并进行反归一化\n",
    "y_train_pred = model(X_train).detach().numpy()\n",
    "y_test_pred = model(X_test).detach().numpy()\n",
    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
    "y_test_pred = scaler.inverse_transform(y_test_pred)\n",
    "\n",
    "# 对真实结果进行反归一化\n",
    "y_train_real = scaler.inverse_transform(y_train)\n",
    "y_test_real = scaler.inverse_transform(y_test)\n",
    "\n",
    "# 为预测结果和真实结果创建DataFrame\n",
    "train_dict = {}\n",
    "test_dict = {}\n",
    "for idx, col in enumerate(target_cols):\n",
    "    train_dict[col+'_pred'] = y_train_pred[:, idx]\n",
    "    train_dict[col+'_real'] = y_train_real[:, idx]\n",
    "    test_dict[col+'_pred'] = y_test_pred[:, idx]\n",
    "    test_dict[col+'_real'] = y_test_real[:, idx]\n",
    "train_result = pd.DataFrame(train_dict)\n",
    "test_result = pd.DataFrame(test_dict)\n",
    "\n",
    "# 计算RMSE\n",
    "train_rmse = np.sqrt(train_loss)\n",
    "test_rmse = np.sqrt(test_loss)\n",
    "\n",
    "print(\"Train RMSE: %.3f\" % train_rmse)\n",
    "print(\"Test RMSE: %.3f\" % test_rmse)\n",
    "\n",
    "# 训练集的R2 score\n",
    "r2_train = r2_score(y_train_real, y_train_pred)\n",
    "print(\"Train R^2: %.3f\" % r2_train)\n",
    "\n",
    "# 测试集的R2 score\n",
    "r2_test = r2_score(y_test_real, y_test_pred)\n",
    "print(\"Test R^2: %.3f\" % r2_test)\n",
    "\n",
    "# 显示结果\n",
    "test_result.to_csv('测试集结果.csv')\n",
    "\n",
    "recent_data = X[-1:, :] \n",
    "predictions = []\n",
    "\n",
    "for _ in range(300): # 对未来300天进行预测\n",
    "    recent_data = recent_data.reshape((1, *recent_data.shape)) # 改变数据形状以匹配模型输入\n",
    "    pred = model(recent_data).detach().numpy()\n",
    "    predictions.append(pred)\n",
    "    # 使用预测值创建新的输入数据\n",
    "    next_input = np.concatenate((recent_data.reshape(-1)[len(target_cols):], pred), axis=None)\n",
    "    recent_data = next_input\n",
    "\n",
    "predictions = np.array(predictions)\n",
    "predictions = predictions.reshape(predictions.shape[0], predictions.shape[2]) # 改变数据形状以匹配模型输入\n",
    "predictions = scaler.inverse_transform(predictions) # 将预测数据进行反归一化\n",
    "predictions_df = pd.DataFrame(predictions, columns=target_cols)\n",
    "predictions_df.to_csv('pred.csv')"
   ]
  }
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