T85_code/特征分组建模_lightgbm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import lightgbm as lgb\n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   铭牌容量 (MW)  入炉煤低位热值(kJ/kg)  燃煤挥发份Var(%)  燃煤灰份Aar(%)  longitude  latitude  \\\n0    5.70711        9.818311     3.297687    2.815409   4.807875  3.467769   \n1    5.70711        9.821572     3.297687    2.815409   4.807875  3.467769   \n2    5.70711        9.878580     3.310543    2.769459   4.807875  3.467769   \n3    5.70711        9.883285     3.324316    2.532108   4.807875  3.467769   \n4    5.70711        9.909768     3.255015    2.766319   4.807875  3.467769   \n\n   altitude  发电碳排放因子(kg/kWh)  供热碳排放因子(kg/MJ)  所处地区_上海市  ...  机组类型_供热式  \\\n0  1.386294         0.537574        0.070992       1.0  ...       1.0   \n1  1.386294         0.545516        0.072476       1.0  ...       1.0   \n2  1.386294         0.595849        0.064745       1.0  ...       1.0   \n3  1.386294         0.584432        0.068390       1.0  ...       1.0   \n4  1.386294         0.605369        0.066996       1.0  ...       1.0   \n\n   机组类型_纯凝式  参数分类_亚临界  参数分类_超临界  参数分类_超超临界  参数分类_超高压  参数分类_高压  冷凝器型式_水冷  \\\n0       0.0       1.0       0.0        0.0       0.0      0.0       1.0   \n1       0.0       1.0       0.0        0.0       0.0      0.0       1.0   \n2       0.0       1.0       0.0        0.0       0.0      0.0       1.0   \n3       0.0       1.0       0.0        0.0       0.0      0.0       1.0   \n4       0.0       1.0       0.0        0.0       0.0      0.0       1.0   \n\n   冷凝器型式_直接空冷  冷凝器型式_间接空冷  \n0         0.0         0.0  \n1         0.0         0.0  \n2         0.0         0.0  \n3         0.0         0.0  \n4         0.0         0.0  \n\n[5 rows x 60 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>铭牌容量 (MW)</th>\n      <th>入炉煤低位热值(kJ/kg)</th>\n      <th>燃煤挥发份Var(%)</th>\n      <th>燃煤灰份Aar(%)</th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>altitude</th>\n      <th>发电碳排放因子(kg/kWh)</th>\n      <th>供热碳排放因子(kg/MJ)</th>\n      <th>所处地区_上海市</th>\n      <th>...</th>\n      <th>机组类型_供热式</th>\n      <th>机组类型_纯凝式</th>\n      <th>参数分类_亚临界</th>\n      <th>参数分类_超临界</th>\n      <th>参数分类_超超临界</th>\n      <th>参数分类_超高压</th>\n      <th>参数分类_高压</th>\n      <th>冷凝器型式_水冷</th>\n      <th>冷凝器型式_直接空冷</th>\n      <th>冷凝器型式_间接空冷</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.70711</td>\n      <td>9.818311</td>\n      <td>3.297687</td>\n      <td>2.815409</td>\n      <td>4.807875</td>\n      <td>3.467769</td>\n      <td>1.386294</td>\n      <td>0.537574</td>\n      <td>0.070992</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5.70711</td>\n      <td>9.821572</td>\n      <td>3.297687</td>\n      <td>2.815409</td>\n      <td>4.807875</td>\n      <td>3.467769</td>\n      <td>1.386294</td>\n      <td>0.545516</td>\n      <td>0.072476</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5.70711</td>\n      <td>9.878580</td>\n      <td>3.310543</td>\n      <td>2.769459</td>\n      <td>4.807875</td>\n      <td>3.467769</td>\n      <td>1.386294</td>\n      <td>0.595849</td>\n      <td>0.064745</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5.70711</td>\n      <td>9.883285</td>\n      <td>3.324316</td>\n      <td>2.532108</td>\n      <td>4.807875</td>\n      <td>3.467769</td>\n      <td>1.386294</td>\n      <td>0.584432</td>\n      <td>0.068390</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.70711</td>\n      <td>9.909768</td>\n      <td>3.255015</td>\n      <td>2.766319</td>\n      <td>4.807875</td>\n      <td>3.467769</td>\n      <td>1.386294</td>\n      <td>0.605369</td>\n      <td>0.066996</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 60 columns</p>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_data = pd.read_csv('./train_data_processed.csv')\n",
    "total_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(3080, 60)"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['铭牌容量 (MW)', '入炉煤低位热值(kJ/kg)', '燃煤挥发份Var(%)', '燃煤灰份Aar(%)', 'longitude',\n       'latitude', 'altitude', '发电碳排放因子(kg/kWh)', '供热碳排放因子(kg/MJ)', '所处地区_上海市',\n       '所处地区_云南省', '所处地区_内蒙古', '所处地区_内蒙古自治区', '所处地区_北京市', '所处地区_吉林省',\n       '所处地区_四川省', '所处地区_天津市', '所处地区_宁夏', '所处地区_宁夏回族自治区', '所处地区_安徽省',\n       '所处地区_山东省', '所处地区_山西', '所处地区_山西省', '所处地区_广东省', '所处地区_广西', '所处地区_广西省',\n       '所处地区_新疆', '所处地区_新疆维吾尔自治区', '所处地区_江苏省', '所处地区_江西省', '所处地区_河北',\n       '所处地区_河北省', '所处地区_河南', '所处地区_河南省', '所处地区_浙江省', '所处地区_海南省', '所处地区_湖北',\n       '所处地区_湖北省', '所处地区_湖南', '所处地区_湖南省', '所处地区_甘肃省', '所处地区_福建省', '所处地区_贵州省',\n       '所处地区_辽宁省', '所处地区_重庆市', '所处地区_陕西省', '所处地区_青海省', '所处地区_黑龙江', '所处地区_黑龙江省',\n       '机组类型_供热', '机组类型_供热式', '机组类型_纯凝式', '参数分类_亚临界', '参数分类_超临界', '参数分类_超超临界',\n       '参数分类_超高压', '参数分类_高压', '冷凝器型式_水冷', '冷凝器型式_直接空冷', '冷凝器型式_间接空冷'],\n      dtype='object')"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_data.columns"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "feature_cols = [x for x in total_data.columns if '因子' not in x]\n",
    "target_cols = [x for x in total_data.columns if x not in feature_cols]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "      铭牌容量 (MW)  入炉煤低位热值(kJ/kg)  燃煤挥发份Var(%)  燃煤灰份Aar(%)  longitude  latitude  \\\n0      4.615121        9.527411     3.823629    3.007661   4.834910  3.862442   \n1      4.836282        9.920745     3.625673    3.201526   4.700990  3.563714   \n2      4.836282        9.923023     3.623807    3.231200   4.700990  3.563714   \n3      4.836282        9.932727     3.272227    3.236716   4.700990  3.563714   \n4      4.836282        9.936819     3.278653    3.173460   4.700990  3.563714   \n...         ...             ...          ...         ...        ...       ...   \n3075   6.966967        9.754581     3.100543    3.378270   4.676091  3.667429   \n3076   6.966967        9.755162     3.082827    3.361070   4.676091  3.667429   \n3077   6.966967        9.762903     3.095125    3.288775   4.676091  3.667429   \n3078   6.966967        9.776506     3.096934    3.328268   4.676091  3.667429   \n3079   6.966967        9.792277     3.073156    3.384051   4.676091  3.667429   \n\n      altitude  所处地区_上海市  所处地区_云南省  所处地区_内蒙古  ...  参数分类_亚临界  参数分类_超临界  \\\n0     4.983607       0.0       0.0       0.0  ...       0.0       0.0   \n1     5.981414       0.0       0.0       0.0  ...       0.0       0.0   \n2     5.981414       0.0       0.0       0.0  ...       0.0       0.0   \n3     5.981414       0.0       0.0       0.0  ...       0.0       0.0   \n4     5.981414       0.0       0.0       0.0  ...       0.0       0.0   \n...        ...       ...       ...       ...  ...       ...       ...   \n3075  7.020191       0.0       0.0       0.0  ...       0.0       0.0   \n3076  7.020191       0.0       0.0       0.0  ...       0.0       0.0   \n3077  7.020191       0.0       0.0       0.0  ...       0.0       0.0   \n3078  7.020191       0.0       0.0       0.0  ...       0.0       0.0   \n3079  7.020191       0.0       0.0       0.0  ...       0.0       0.0   \n\n      参数分类_超超临界  参数分类_超高压  参数分类_高压  冷凝器型式_水冷  冷凝器型式_直接空冷  冷凝器型式_间接空冷  \\\n0           0.0       0.0      1.0       1.0         0.0         0.0   \n1           0.0       1.0      0.0       1.0         0.0         0.0   \n2           0.0       1.0      0.0       1.0         0.0         0.0   \n3           0.0       1.0      0.0       1.0         0.0         0.0   \n4           0.0       1.0      0.0       1.0         0.0         0.0   \n...         ...       ...      ...       ...         ...         ...   \n3075        1.0       0.0      0.0       0.0         1.0         0.0   \n3076        1.0       0.0      0.0       0.0         1.0         0.0   \n3077        1.0       0.0      0.0       0.0         1.0         0.0   \n3078        1.0       0.0      0.0       0.0         1.0         0.0   \n3079        1.0       0.0      0.0       0.0         1.0         0.0   \n\n      发电碳排放因子(kg/kWh)  供热碳排放因子(kg/MJ)  \n0            0.483547        0.058613  \n1            0.575553        0.085880  \n2            0.607741        0.084890  \n3            0.595382        0.082342  \n4            0.578838        0.082685  \n...               ...             ...  \n3075         0.426880        0.061722  \n3076         0.456768        0.060739  \n3077         0.455534        0.061277  \n3078         0.450064        0.062032  \n3079         0.468720        0.063016  \n\n[3080 rows x 60 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>铭牌容量 (MW)</th>\n      <th>入炉煤低位热值(kJ/kg)</th>\n      <th>燃煤挥发份Var(%)</th>\n      <th>燃煤灰份Aar(%)</th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>altitude</th>\n      <th>所处地区_上海市</th>\n      <th>所处地区_云南省</th>\n      <th>所处地区_内蒙古</th>\n      <th>...</th>\n      <th>参数分类_亚临界</th>\n      <th>参数分类_超临界</th>\n      <th>参数分类_超超临界</th>\n      <th>参数分类_超高压</th>\n      <th>参数分类_高压</th>\n      <th>冷凝器型式_水冷</th>\n      <th>冷凝器型式_直接空冷</th>\n      <th>冷凝器型式_间接空冷</th>\n      <th>发电碳排放因子(kg/kWh)</th>\n      <th>供热碳排放因子(kg/MJ)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4.615121</td>\n      <td>9.527411</td>\n      <td>3.823629</td>\n      <td>3.007661</td>\n      <td>4.834910</td>\n      <td>3.862442</td>\n      <td>4.983607</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.483547</td>\n      <td>0.058613</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.836282</td>\n      <td>9.920745</td>\n      <td>3.625673</td>\n      <td>3.201526</td>\n      <td>4.700990</td>\n      <td>3.563714</td>\n      <td>5.981414</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.575553</td>\n      <td>0.085880</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.836282</td>\n      <td>9.923023</td>\n      <td>3.623807</td>\n      <td>3.231200</td>\n      <td>4.700990</td>\n      <td>3.563714</td>\n      <td>5.981414</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.607741</td>\n      <td>0.084890</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.836282</td>\n      <td>9.932727</td>\n      <td>3.272227</td>\n      <td>3.236716</td>\n      <td>4.700990</td>\n      <td>3.563714</td>\n      <td>5.981414</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.595382</td>\n      <td>0.082342</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4.836282</td>\n      <td>9.936819</td>\n      <td>3.278653</td>\n      <td>3.173460</td>\n      <td>4.700990</td>\n      <td>3.563714</td>\n      <td>5.981414</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.578838</td>\n      <td>0.082685</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>3075</th>\n      <td>6.966967</td>\n      <td>9.754581</td>\n      <td>3.100543</td>\n      <td>3.378270</td>\n      <td>4.676091</td>\n      <td>3.667429</td>\n      <td>7.020191</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.426880</td>\n      <td>0.061722</td>\n    </tr>\n    <tr>\n      <th>3076</th>\n      <td>6.966967</td>\n      <td>9.755162</td>\n      <td>3.082827</td>\n      <td>3.361070</td>\n      <td>4.676091</td>\n      <td>3.667429</td>\n      <td>7.020191</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.456768</td>\n      <td>0.060739</td>\n    </tr>\n    <tr>\n      <th>3077</th>\n      <td>6.966967</td>\n      <td>9.762903</td>\n      <td>3.095125</td>\n      <td>3.288775</td>\n      <td>4.676091</td>\n      <td>3.667429</td>\n      <td>7.020191</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.455534</td>\n      <td>0.061277</td>\n    </tr>\n    <tr>\n      <th>3078</th>\n      <td>6.966967</td>\n      <td>9.776506</td>\n      <td>3.096934</td>\n      <td>3.328268</td>\n      <td>4.676091</td>\n      <td>3.667429</td>\n      <td>7.020191</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.450064</td>\n      <td>0.062032</td>\n    </tr>\n    <tr>\n      <th>3079</th>\n      <td>6.966967</td>\n      <td>9.792277</td>\n      <td>3.073156</td>\n      <td>3.384051</td>\n      <td>4.676091</td>\n      <td>3.667429</td>\n      <td>7.020191</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.468720</td>\n      <td>0.063016</td>\n    </tr>\n  </tbody>\n</table>\n<p>3080 rows × 60 columns</p>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "use_data = total_data.groupby(feature_cols)[target_cols].mean().reset_index()\n",
    "use_data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "for col in use_data.columns:\n",
    "    use_data[col] = use_data[col].astype(float)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train_data, test_data = train_test_split(use_data.dropna(), test_size=0.1, shuffle=True, random_state=666)\n",
    "train_data, valid_data = train_test_split(train_data.dropna(), test_size=0.2, shuffle=True, random_state=666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X_train, Y_train = train_data[feature_cols], train_data[target_cols[0]]\n",
    "X_valid, Y_valid = valid_data[feature_cols], valid_data[target_cols[0]]\n",
    "X_test, Y_test = test_data[feature_cols], test_data[target_cols[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lgb_train = lgb.Dataset(X_train, Y_train)\n",
    "lgb_eval = lgb.Dataset(X_valid, Y_valid)\n",
    "lgb_test = lgb.Dataset(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "params_gbm = {\n",
    "    'task': 'train',\n",
    "    'boosting_type': 'gbdt',  # 设置提升类型\n",
    "    'objective': 'l1',  # 目标函数\n",
    "    'metric': {'rmse'},  # 评估函数\n",
    "    'max_depth': 12,\n",
    "    'num_leaves': 20,  # 叶子节点数\n",
    "    'learning_rate': 0.05,  # 学习速率\n",
    "    'feature_fraction': 0.9,  # 建树的特征选择比例\n",
    "    'bagging_fraction': 0.9,  # 建树的样本采样比例\n",
    "    'bagging_freq': 10,  # k 意味着每 k 次迭代执行bagging\n",
    "    'verbose': -1  # <0 显示致命的, =0 显示错误 (警告), >0 显示信息\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's rmse: 0.0692875\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[2]\tvalid_0's rmse: 0.06714\n",
      "[3]\tvalid_0's rmse: 0.0646839\n",
      "[4]\tvalid_0's rmse: 0.0623338\n",
      "[5]\tvalid_0's rmse: 0.0600964\n",
      "[6]\tvalid_0's rmse: 0.0580108\n",
      "[7]\tvalid_0's rmse: 0.056067\n",
      "[8]\tvalid_0's rmse: 0.0544344\n",
      "[9]\tvalid_0's rmse: 0.0529408\n",
      "[10]\tvalid_0's rmse: 0.051276\n",
      "[11]\tvalid_0's rmse: 0.0497692\n",
      "[12]\tvalid_0's rmse: 0.0483588\n",
      "[13]\tvalid_0's rmse: 0.0470211\n",
      "[14]\tvalid_0's rmse: 0.0460061\n",
      "[15]\tvalid_0's rmse: 0.0448745\n",
      "[16]\tvalid_0's rmse: 0.043796\n",
      "[17]\tvalid_0's rmse: 0.0428645\n",
      "[18]\tvalid_0's rmse: 0.0419008\n",
      "[19]\tvalid_0's rmse: 0.0409544\n",
      "[20]\tvalid_0's rmse: 0.0400698\n",
      "[21]\tvalid_0's rmse: 0.0392848\n",
      "[22]\tvalid_0's rmse: 0.038578\n",
      "[23]\tvalid_0's rmse: 0.0378727\n",
      "[24]\tvalid_0's rmse: 0.0371929\n",
      "[25]\tvalid_0's rmse: 0.0366533\n",
      "[26]\tvalid_0's rmse: 0.0360842\n",
      "[27]\tvalid_0's rmse: 0.0355757\n",
      "[28]\tvalid_0's rmse: 0.0350562\n",
      "[29]\tvalid_0's rmse: 0.0345382\n",
      "[30]\tvalid_0's rmse: 0.0340975\n",
      "[31]\tvalid_0's rmse: 0.0337632\n",
      "[32]\tvalid_0's rmse: 0.0334232\n",
      "[33]\tvalid_0's rmse: 0.0330998\n",
      "[34]\tvalid_0's rmse: 0.0328678\n",
      "[35]\tvalid_0's rmse: 0.0325827\n",
      "[36]\tvalid_0's rmse: 0.0323483\n",
      "[37]\tvalid_0's rmse: 0.0321363\n",
      "[38]\tvalid_0's rmse: 0.0318823\n",
      "[39]\tvalid_0's rmse: 0.0316983\n",
      "[40]\tvalid_0's rmse: 0.0315094\n",
      "[41]\tvalid_0's rmse: 0.0313339\n",
      "[42]\tvalid_0's rmse: 0.0311663\n",
      "[43]\tvalid_0's rmse: 0.031002\n",
      "[44]\tvalid_0's rmse: 0.0308446\n",
      "[45]\tvalid_0's rmse: 0.0307193\n",
      "[46]\tvalid_0's rmse: 0.03058\n",
      "[47]\tvalid_0's rmse: 0.0304975\n",
      "[48]\tvalid_0's rmse: 0.0303807\n",
      "[49]\tvalid_0's rmse: 0.0302476\n",
      "[50]\tvalid_0's rmse: 0.0301379\n",
      "[51]\tvalid_0's rmse: 0.03\n",
      "[52]\tvalid_0's rmse: 0.0299129\n",
      "[53]\tvalid_0's rmse: 0.0298092\n",
      "[54]\tvalid_0's rmse: 0.0297318\n",
      "[55]\tvalid_0's rmse: 0.0296587\n",
      "[56]\tvalid_0's rmse: 0.0295906\n",
      "[57]\tvalid_0's rmse: 0.0295262\n",
      "[58]\tvalid_0's rmse: 0.0294317\n",
      "[59]\tvalid_0's rmse: 0.0293666\n",
      "[60]\tvalid_0's rmse: 0.029295\n",
      "[61]\tvalid_0's rmse: 0.0292621\n",
      "[62]\tvalid_0's rmse: 0.0291822\n",
      "[63]\tvalid_0's rmse: 0.0291453\n",
      "[64]\tvalid_0's rmse: 0.029071\n",
      "[65]\tvalid_0's rmse: 0.0289955\n",
      "[66]\tvalid_0's rmse: 0.0289425\n",
      "[67]\tvalid_0's rmse: 0.0288803\n",
      "[68]\tvalid_0's rmse: 0.0288438\n",
      "[69]\tvalid_0's rmse: 0.0288004\n",
      "[70]\tvalid_0's rmse: 0.0287685\n",
      "[71]\tvalid_0's rmse: 0.0287379\n",
      "[72]\tvalid_0's rmse: 0.0286942\n",
      "[73]\tvalid_0's rmse: 0.028654\n",
      "[74]\tvalid_0's rmse: 0.0286255\n",
      "[75]\tvalid_0's rmse: 0.0285826\n",
      "[76]\tvalid_0's rmse: 0.0285438\n",
      "[77]\tvalid_0's rmse: 0.0284903\n",
      "[78]\tvalid_0's rmse: 0.0284767\n",
      "[79]\tvalid_0's rmse: 0.0284401\n",
      "[80]\tvalid_0's rmse: 0.0284152\n",
      "[81]\tvalid_0's rmse: 0.0283845\n",
      "[82]\tvalid_0's rmse: 0.028375\n",
      "[83]\tvalid_0's rmse: 0.0283271\n",
      "[84]\tvalid_0's rmse: 0.0283098\n",
      "[85]\tvalid_0's rmse: 0.0282848\n",
      "[86]\tvalid_0's rmse: 0.0282564\n",
      "[87]\tvalid_0's rmse: 0.0282311\n",
      "[88]\tvalid_0's rmse: 0.0281999\n",
      "[89]\tvalid_0's rmse: 0.0281744\n",
      "[90]\tvalid_0's rmse: 0.0281694\n",
      "[91]\tvalid_0's rmse: 0.0281849\n",
      "[92]\tvalid_0's rmse: 0.0281936\n",
      "[93]\tvalid_0's rmse: 0.0281859\n",
      "[94]\tvalid_0's rmse: 0.028193\n",
      "[95]\tvalid_0's rmse: 0.0281768\n",
      "[96]\tvalid_0's rmse: 0.0281729\n",
      "[97]\tvalid_0's rmse: 0.0281829\n",
      "[98]\tvalid_0's rmse: 0.0281698\n",
      "[99]\tvalid_0's rmse: 0.0281678\n",
      "[100]\tvalid_0's rmse: 0.0281451\n",
      "[101]\tvalid_0's rmse: 0.0281243\n",
      "[102]\tvalid_0's rmse: 0.028098\n",
      "[103]\tvalid_0's rmse: 0.028089\n",
      "[104]\tvalid_0's rmse: 0.0280947\n",
      "[105]\tvalid_0's rmse: 0.0280915\n",
      "[106]\tvalid_0's rmse: 0.0280942\n",
      "[107]\tvalid_0's rmse: 0.0280905\n",
      "[108]\tvalid_0's rmse: 0.0280888\n",
      "[109]\tvalid_0's rmse: 0.0280827\n",
      "[110]\tvalid_0's rmse: 0.028075\n",
      "[111]\tvalid_0's rmse: 0.0280506\n",
      "[112]\tvalid_0's rmse: 0.0280414\n",
      "[113]\tvalid_0's rmse: 0.0280254\n",
      "[114]\tvalid_0's rmse: 0.0280016\n",
      "[115]\tvalid_0's rmse: 0.0279858\n",
      "[116]\tvalid_0's rmse: 0.027973\n",
      "[117]\tvalid_0's rmse: 0.027962\n",
      "[118]\tvalid_0's rmse: 0.0279404\n",
      "[119]\tvalid_0's rmse: 0.0279082\n",
      "[120]\tvalid_0's rmse: 0.0279064\n",
      "[121]\tvalid_0's rmse: 0.0279041\n",
      "[122]\tvalid_0's rmse: 0.0278874\n",
      "[123]\tvalid_0's rmse: 0.0278608\n",
      "[124]\tvalid_0's rmse: 0.0278517\n",
      "[125]\tvalid_0's rmse: 0.0278507\n",
      "[126]\tvalid_0's rmse: 0.0278408\n",
      "[127]\tvalid_0's rmse: 0.0278322\n",
      "[128]\tvalid_0's rmse: 0.0278089\n",
      "[129]\tvalid_0's rmse: 0.0278084\n",
      "[130]\tvalid_0's rmse: 0.0277843\n",
      "[131]\tvalid_0's rmse: 0.0277892\n",
      "[132]\tvalid_0's rmse: 0.0277827\n",
      "[133]\tvalid_0's rmse: 0.0277758\n",
      "[134]\tvalid_0's rmse: 0.0277766\n",
      "[135]\tvalid_0's rmse: 0.0277853\n",
      "[136]\tvalid_0's rmse: 0.0277744\n",
      "[137]\tvalid_0's rmse: 0.0277624\n",
      "[138]\tvalid_0's rmse: 0.0277481\n",
      "[139]\tvalid_0's rmse: 0.027733\n",
      "[140]\tvalid_0's rmse: 0.0277201\n",
      "[141]\tvalid_0's rmse: 0.0277112\n",
      "[142]\tvalid_0's rmse: 0.0277081\n",
      "[143]\tvalid_0's rmse: 0.0276965\n",
      "[144]\tvalid_0's rmse: 0.0276911\n",
      "[145]\tvalid_0's rmse: 0.0276786\n",
      "[146]\tvalid_0's rmse: 0.0276798\n",
      "[147]\tvalid_0's rmse: 0.0276724\n",
      "[148]\tvalid_0's rmse: 0.0276479\n",
      "[149]\tvalid_0's rmse: 0.0276436\n",
      "[150]\tvalid_0's rmse: 0.0276115\n",
      "[151]\tvalid_0's rmse: 0.0275966\n",
      "[152]\tvalid_0's rmse: 0.0275874\n",
      "[153]\tvalid_0's rmse: 0.0275693\n",
      "[154]\tvalid_0's rmse: 0.0275769\n",
      "[155]\tvalid_0's rmse: 0.0275677\n",
      "[156]\tvalid_0's rmse: 0.0275517\n",
      "[157]\tvalid_0's rmse: 0.0275422\n",
      "[158]\tvalid_0's rmse: 0.0275326\n",
      "[159]\tvalid_0's rmse: 0.0275205\n",
      "[160]\tvalid_0's rmse: 0.0275234\n",
      "[161]\tvalid_0's rmse: 0.0275164\n",
      "[162]\tvalid_0's rmse: 0.0275097\n",
      "[163]\tvalid_0's rmse: 0.0275092\n",
      "[164]\tvalid_0's rmse: 0.0274879\n",
      "[165]\tvalid_0's rmse: 0.0274696\n",
      "[166]\tvalid_0's rmse: 0.0274685\n",
      "[167]\tvalid_0's rmse: 0.0274698\n",
      "[168]\tvalid_0's rmse: 0.0274655\n",
      "[169]\tvalid_0's rmse: 0.0274796\n",
      "[170]\tvalid_0's rmse: 0.0274609\n",
      "[171]\tvalid_0's rmse: 0.0274455\n",
      "[172]\tvalid_0's rmse: 0.0274493\n",
      "[173]\tvalid_0's rmse: 0.0274369\n",
      "[174]\tvalid_0's rmse: 0.0274299\n",
      "[175]\tvalid_0's rmse: 0.0274234\n",
      "[176]\tvalid_0's rmse: 0.0274104\n",
      "[177]\tvalid_0's rmse: 0.0273984\n",
      "[178]\tvalid_0's rmse: 0.0273957\n",
      "[179]\tvalid_0's rmse: 0.0273894\n",
      "[180]\tvalid_0's rmse: 0.0273696\n",
      "[181]\tvalid_0's rmse: 0.0273432\n",
      "[182]\tvalid_0's rmse: 0.027342\n",
      "[183]\tvalid_0's rmse: 0.0273113\n",
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      "[553]\tvalid_0's rmse: 0.0262031\n",
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      "[555]\tvalid_0's rmse: 0.0261984\n",
      "[556]\tvalid_0's rmse: 0.0261981\n",
      "[557]\tvalid_0's rmse: 0.0261977\n",
      "[558]\tvalid_0's rmse: 0.0262004\n",
      "[559]\tvalid_0's rmse: 0.0261955\n",
      "[560]\tvalid_0's rmse: 0.0261955\n",
      "[561]\tvalid_0's rmse: 0.0261947\n",
      "[562]\tvalid_0's rmse: 0.0261983\n",
      "[563]\tvalid_0's rmse: 0.0261981\n",
      "[564]\tvalid_0's rmse: 0.0261992\n",
      "[565]\tvalid_0's rmse: 0.0261974\n",
      "[566]\tvalid_0's rmse: 0.0261936\n",
      "[567]\tvalid_0's rmse: 0.0261954\n",
      "[568]\tvalid_0's rmse: 0.0261987\n",
      "[569]\tvalid_0's rmse: 0.0261837\n",
      "[570]\tvalid_0's rmse: 0.0261839\n",
      "[571]\tvalid_0's rmse: 0.026185\n",
      "[572]\tvalid_0's rmse: 0.0261849\n",
      "[573]\tvalid_0's rmse: 0.0261842\n",
      "[574]\tvalid_0's rmse: 0.0261826\n",
      "[575]\tvalid_0's rmse: 0.0261834\n",
      "[576]\tvalid_0's rmse: 0.0261825\n",
      "[577]\tvalid_0's rmse: 0.0261717\n",
      "[578]\tvalid_0's rmse: 0.026171\n",
      "[579]\tvalid_0's rmse: 0.0261609\n",
      "[580]\tvalid_0's rmse: 0.02616\n",
      "[581]\tvalid_0's rmse: 0.0261573\n",
      "[582]\tvalid_0's rmse: 0.026159\n",
      "[583]\tvalid_0's rmse: 0.0261576\n",
      "[584]\tvalid_0's rmse: 0.0261557\n",
      "[585]\tvalid_0's rmse: 0.0261582\n",
      "[586]\tvalid_0's rmse: 0.026158\n",
      "[587]\tvalid_0's rmse: 0.0261573\n",
      "[588]\tvalid_0's rmse: 0.0261571\n",
      "[589]\tvalid_0's rmse: 0.0261535\n",
      "[590]\tvalid_0's rmse: 0.0261534\n",
      "[591]\tvalid_0's rmse: 0.0261534\n",
      "[592]\tvalid_0's rmse: 0.0261436\n",
      "[593]\tvalid_0's rmse: 0.0261423\n",
      "[594]\tvalid_0's rmse: 0.0261409\n",
      "[595]\tvalid_0's rmse: 0.0261377\n",
      "[596]\tvalid_0's rmse: 0.0261358\n",
      "[597]\tvalid_0's rmse: 0.0261367\n",
      "[598]\tvalid_0's rmse: 0.026137\n",
      "[599]\tvalid_0's rmse: 0.0261357\n",
      "[600]\tvalid_0's rmse: 0.0261344\n",
      "[601]\tvalid_0's rmse: 0.0261345\n",
      "[602]\tvalid_0's rmse: 0.026133\n",
      "[603]\tvalid_0's rmse: 0.0261313\n",
      "[604]\tvalid_0's rmse: 0.0261344\n",
      "[605]\tvalid_0's rmse: 0.0261339\n",
      "[606]\tvalid_0's rmse: 0.0261321\n",
      "[607]\tvalid_0's rmse: 0.0261288\n",
      "[608]\tvalid_0's rmse: 0.0261285\n",
      "[609]\tvalid_0's rmse: 0.0261298\n",
      "[610]\tvalid_0's rmse: 0.026131\n",
      "[611]\tvalid_0's rmse: 0.0261265\n",
      "[612]\tvalid_0's rmse: 0.0261043\n",
      "[613]\tvalid_0's rmse: 0.0261023\n",
      "[614]\tvalid_0's rmse: 0.0261013\n",
      "[615]\tvalid_0's rmse: 0.0260971\n",
      "[616]\tvalid_0's rmse: 0.0260979\n",
      "[617]\tvalid_0's rmse: 0.0260987\n",
      "[618]\tvalid_0's rmse: 0.0260728\n",
      "[619]\tvalid_0's rmse: 0.026069\n",
      "[620]\tvalid_0's rmse: 0.0260678\n",
      "[621]\tvalid_0's rmse: 0.0260587\n",
      "[622]\tvalid_0's rmse: 0.0260571\n",
      "[623]\tvalid_0's rmse: 0.0260564\n",
      "[624]\tvalid_0's rmse: 0.026054\n",
      "[625]\tvalid_0's rmse: 0.0260544\n",
      "[626]\tvalid_0's rmse: 0.0260502\n",
      "[627]\tvalid_0's rmse: 0.0260444\n",
      "[628]\tvalid_0's rmse: 0.026044\n",
      "[629]\tvalid_0's rmse: 0.02604\n",
      "[630]\tvalid_0's rmse: 0.0260386\n",
      "[631]\tvalid_0's rmse: 0.0260394\n",
      "[632]\tvalid_0's rmse: 0.0260378\n",
      "[633]\tvalid_0's rmse: 0.0260397\n",
      "[634]\tvalid_0's rmse: 0.0260395\n",
      "[635]\tvalid_0's rmse: 0.0260398\n",
      "[636]\tvalid_0's rmse: 0.0260376\n",
      "[637]\tvalid_0's rmse: 0.026039\n",
      "[638]\tvalid_0's rmse: 0.0260362\n",
      "[639]\tvalid_0's rmse: 0.0260345\n",
      "[640]\tvalid_0's rmse: 0.0260342\n",
      "[641]\tvalid_0's rmse: 0.0260336\n",
      "[642]\tvalid_0's rmse: 0.0260337\n",
      "[643]\tvalid_0's rmse: 0.0260325\n",
      "[644]\tvalid_0's rmse: 0.0260305\n",
      "[645]\tvalid_0's rmse: 0.0260308\n",
      "[646]\tvalid_0's rmse: 0.0260319\n",
      "[647]\tvalid_0's rmse: 0.0260334\n",
      "[648]\tvalid_0's rmse: 0.0260338\n",
      "[649]\tvalid_0's rmse: 0.0260325\n",
      "[650]\tvalid_0's rmse: 0.0260265\n",
      "[651]\tvalid_0's rmse: 0.0260269\n",
      "[652]\tvalid_0's rmse: 0.0260251\n",
      "[653]\tvalid_0's rmse: 0.0260252\n",
      "[654]\tvalid_0's rmse: 0.0260251\n",
      "[655]\tvalid_0's rmse: 0.0260257\n",
      "[656]\tvalid_0's rmse: 0.0260234\n",
      "[657]\tvalid_0's rmse: 0.0260219\n",
      "[658]\tvalid_0's rmse: 0.0260211\n",
      "[659]\tvalid_0's rmse: 0.0260209\n",
      "[660]\tvalid_0's rmse: 0.0260217\n",
      "[661]\tvalid_0's rmse: 0.0260234\n",
      "[662]\tvalid_0's rmse: 0.0260244\n",
      "[663]\tvalid_0's rmse: 0.0260219\n",
      "[664]\tvalid_0's rmse: 0.0260216\n",
      "[665]\tvalid_0's rmse: 0.026023\n",
      "[666]\tvalid_0's rmse: 0.026025\n",
      "[667]\tvalid_0's rmse: 0.0260245\n",
      "[668]\tvalid_0's rmse: 0.026022\n",
      "[669]\tvalid_0's rmse: 0.0260216\n",
      "[670]\tvalid_0's rmse: 0.0260231\n",
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      "[672]\tvalid_0's rmse: 0.0260197\n",
      "[673]\tvalid_0's rmse: 0.0260191\n",
      "[674]\tvalid_0's rmse: 0.0260193\n",
      "[675]\tvalid_0's rmse: 0.0260178\n",
      "[676]\tvalid_0's rmse: 0.0260171\n",
      "[677]\tvalid_0's rmse: 0.0260153\n",
      "[678]\tvalid_0's rmse: 0.0260153\n",
      "[679]\tvalid_0's rmse: 0.026013\n",
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      "[681]\tvalid_0's rmse: 0.0260089\n",
      "[682]\tvalid_0's rmse: 0.0260046\n",
      "[683]\tvalid_0's rmse: 0.0260029\n",
      "[684]\tvalid_0's rmse: 0.0260038\n",
      "[685]\tvalid_0's rmse: 0.0260018\n",
      "[686]\tvalid_0's rmse: 0.0260058\n",
      "[687]\tvalid_0's rmse: 0.0260083\n",
      "[688]\tvalid_0's rmse: 0.0260081\n",
      "[689]\tvalid_0's rmse: 0.0260076\n",
      "[690]\tvalid_0's rmse: 0.0260032\n",
      "[691]\tvalid_0's rmse: 0.0260018\n",
      "[692]\tvalid_0's rmse: 0.0260013\n",
      "[693]\tvalid_0's rmse: 0.0260024\n",
      "[694]\tvalid_0's rmse: 0.026003\n",
      "[695]\tvalid_0's rmse: 0.0260023\n",
      "[696]\tvalid_0's rmse: 0.0260022\n",
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      "[698]\tvalid_0's rmse: 0.0260004\n",
      "[699]\tvalid_0's rmse: 0.0259998\n",
      "[700]\tvalid_0's rmse: 0.0259961\n",
      "[701]\tvalid_0's rmse: 0.0259964\n",
      "[702]\tvalid_0's rmse: 0.0259942\n",
      "[703]\tvalid_0's rmse: 0.0259951\n",
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      "[707]\tvalid_0's rmse: 0.0259881\n",
      "[708]\tvalid_0's rmse: 0.0259869\n",
      "[709]\tvalid_0's rmse: 0.0259796\n",
      "[710]\tvalid_0's rmse: 0.0259789\n",
      "[711]\tvalid_0's rmse: 0.0259766\n",
      "[712]\tvalid_0's rmse: 0.0259758\n",
      "[713]\tvalid_0's rmse: 0.0259746\n",
      "[714]\tvalid_0's rmse: 0.0259744\n",
      "[715]\tvalid_0's rmse: 0.0259761\n",
      "[716]\tvalid_0's rmse: 0.0259832\n",
      "[717]\tvalid_0's rmse: 0.0259813\n",
      "[718]\tvalid_0's rmse: 0.0259823\n",
      "[719]\tvalid_0's rmse: 0.0259815\n",
      "[720]\tvalid_0's rmse: 0.0259701\n",
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      "[722]\tvalid_0's rmse: 0.0259679\n",
      "[723]\tvalid_0's rmse: 0.0259668\n",
      "[724]\tvalid_0's rmse: 0.0259646\n",
      "[725]\tvalid_0's rmse: 0.0259639\n",
      "[726]\tvalid_0's rmse: 0.0259672\n",
      "[727]\tvalid_0's rmse: 0.025969\n",
      "[728]\tvalid_0's rmse: 0.0259709\n",
      "[729]\tvalid_0's rmse: 0.0259705\n",
      "[730]\tvalid_0's rmse: 0.0259611\n",
      "[731]\tvalid_0's rmse: 0.0259601\n",
      "[732]\tvalid_0's rmse: 0.0259605\n",
      "[733]\tvalid_0's rmse: 0.02596\n",
      "[734]\tvalid_0's rmse: 0.0259589\n",
      "[735]\tvalid_0's rmse: 0.0259593\n",
      "[736]\tvalid_0's rmse: 0.0259612\n",
      "[737]\tvalid_0's rmse: 0.0259617\n",
      "[738]\tvalid_0's rmse: 0.0259604\n",
      "[739]\tvalid_0's rmse: 0.0259609\n",
      "[740]\tvalid_0's rmse: 0.0259575\n",
      "[741]\tvalid_0's rmse: 0.0259552\n",
      "[742]\tvalid_0's rmse: 0.025958\n",
      "[743]\tvalid_0's rmse: 0.0259575\n",
      "[744]\tvalid_0's rmse: 0.0259551\n",
      "[745]\tvalid_0's rmse: 0.0259555\n",
      "[746]\tvalid_0's rmse: 0.0259564\n",
      "[747]\tvalid_0's rmse: 0.0259554\n",
      "[748]\tvalid_0's rmse: 0.0259536\n",
      "[749]\tvalid_0's rmse: 0.0259524\n",
      "[750]\tvalid_0's rmse: 0.0259526\n",
      "[751]\tvalid_0's rmse: 0.0259521\n",
      "[752]\tvalid_0's rmse: 0.0259515\n",
      "[753]\tvalid_0's rmse: 0.0259512\n",
      "[754]\tvalid_0's rmse: 0.0259504\n",
      "[755]\tvalid_0's rmse: 0.0259508\n",
      "[756]\tvalid_0's rmse: 0.0259495\n",
      "[757]\tvalid_0's rmse: 0.0259432\n",
      "[758]\tvalid_0's rmse: 0.0259428\n",
      "[759]\tvalid_0's rmse: 0.0259422\n",
      "[760]\tvalid_0's rmse: 0.0259443\n",
      "[761]\tvalid_0's rmse: 0.0259459\n",
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      "[763]\tvalid_0's rmse: 0.0259442\n",
      "[764]\tvalid_0's rmse: 0.0259432\n",
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      "[766]\tvalid_0's rmse: 0.0259433\n",
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      "[769]\tvalid_0's rmse: 0.0259404\n",
      "[770]\tvalid_0's rmse: 0.0259398\n",
      "[771]\tvalid_0's rmse: 0.0259375\n",
      "[772]\tvalid_0's rmse: 0.025935\n",
      "[773]\tvalid_0's rmse: 0.0259347\n",
      "[774]\tvalid_0's rmse: 0.0259332\n",
      "[775]\tvalid_0's rmse: 0.0259335\n",
      "[776]\tvalid_0's rmse: 0.0259349\n",
      "[777]\tvalid_0's rmse: 0.0259345\n",
      "[778]\tvalid_0's rmse: 0.0259353\n",
      "[779]\tvalid_0's rmse: 0.0259353\n",
      "[780]\tvalid_0's rmse: 0.0259354\n",
      "[781]\tvalid_0's rmse: 0.025935\n",
      "[782]\tvalid_0's rmse: 0.0259362\n",
      "[783]\tvalid_0's rmse: 0.0259348\n",
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      "[786]\tvalid_0's rmse: 0.0259417\n",
      "[787]\tvalid_0's rmse: 0.0259418\n",
      "[788]\tvalid_0's rmse: 0.0259422\n",
      "[789]\tvalid_0's rmse: 0.0259422\n",
      "[790]\tvalid_0's rmse: 0.0259419\n",
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      "[792]\tvalid_0's rmse: 0.0259409\n",
      "[793]\tvalid_0's rmse: 0.0259433\n",
      "[794]\tvalid_0's rmse: 0.0259438\n",
      "[795]\tvalid_0's rmse: 0.0259415\n",
      "[796]\tvalid_0's rmse: 0.0259423\n",
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      "[799]\tvalid_0's rmse: 0.0259469\n",
      "[800]\tvalid_0's rmse: 0.0259488\n",
      "[801]\tvalid_0's rmse: 0.0259505\n",
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      "[803]\tvalid_0's rmse: 0.0259453\n",
      "[804]\tvalid_0's rmse: 0.0259434\n",
      "[805]\tvalid_0's rmse: 0.0259429\n",
      "[806]\tvalid_0's rmse: 0.0259445\n",
      "[807]\tvalid_0's rmse: 0.0259469\n",
      "[808]\tvalid_0's rmse: 0.0259436\n",
      "[809]\tvalid_0's rmse: 0.0259414\n",
      "[810]\tvalid_0's rmse: 0.0259419\n",
      "[811]\tvalid_0's rmse: 0.0259498\n",
      "[812]\tvalid_0's rmse: 0.0259524\n",
      "[813]\tvalid_0's rmse: 0.025951\n",
      "[814]\tvalid_0's rmse: 0.0259468\n",
      "[815]\tvalid_0's rmse: 0.0259462\n",
      "[816]\tvalid_0's rmse: 0.0259387\n",
      "[817]\tvalid_0's rmse: 0.0259382\n",
      "[818]\tvalid_0's rmse: 0.0259381\n",
      "[819]\tvalid_0's rmse: 0.0259391\n",
      "[820]\tvalid_0's rmse: 0.0259437\n",
      "[821]\tvalid_0's rmse: 0.0259455\n",
      "[822]\tvalid_0's rmse: 0.0259458\n",
      "[823]\tvalid_0's rmse: 0.0259459\n",
      "[824]\tvalid_0's rmse: 0.0259441\n",
      "[825]\tvalid_0's rmse: 0.0259408\n",
      "[826]\tvalid_0's rmse: 0.0259412\n",
      "[827]\tvalid_0's rmse: 0.0259419\n",
      "[828]\tvalid_0's rmse: 0.0259434\n",
      "[829]\tvalid_0's rmse: 0.0259429\n",
      "[830]\tvalid_0's rmse: 0.0259448\n",
      "[831]\tvalid_0's rmse: 0.0259442\n",
      "[832]\tvalid_0's rmse: 0.0259424\n",
      "[833]\tvalid_0's rmse: 0.0259416\n",
      "[834]\tvalid_0's rmse: 0.0259425\n",
      "[835]\tvalid_0's rmse: 0.025941\n",
      "[836]\tvalid_0's rmse: 0.02594\n",
      "[837]\tvalid_0's rmse: 0.0259396\n",
      "[838]\tvalid_0's rmse: 0.0259382\n",
      "[839]\tvalid_0's rmse: 0.0259367\n",
      "[840]\tvalid_0's rmse: 0.0259381\n",
      "[841]\tvalid_0's rmse: 0.0259379\n",
      "[842]\tvalid_0's rmse: 0.0259268\n",
      "[843]\tvalid_0's rmse: 0.0259259\n",
      "[844]\tvalid_0's rmse: 0.0259228\n",
      "[845]\tvalid_0's rmse: 0.0259228\n",
      "[846]\tvalid_0's rmse: 0.0259187\n",
      "[847]\tvalid_0's rmse: 0.0259171\n",
      "[848]\tvalid_0's rmse: 0.0259177\n",
      "[849]\tvalid_0's rmse: 0.0259164\n",
      "[850]\tvalid_0's rmse: 0.0259161\n",
      "[851]\tvalid_0's rmse: 0.0259161\n",
      "[852]\tvalid_0's rmse: 0.0259147\n",
      "[853]\tvalid_0's rmse: 0.0259145\n",
      "[854]\tvalid_0's rmse: 0.0259144\n",
      "[855]\tvalid_0's rmse: 0.0259125\n",
      "[856]\tvalid_0's rmse: 0.0259127\n",
      "[857]\tvalid_0's rmse: 0.0259115\n",
      "[858]\tvalid_0's rmse: 0.0259104\n",
      "[859]\tvalid_0's rmse: 0.0259119\n",
      "[860]\tvalid_0's rmse: 0.0259109\n",
      "[861]\tvalid_0's rmse: 0.02591\n",
      "[862]\tvalid_0's rmse: 0.0259099\n",
      "[863]\tvalid_0's rmse: 0.0259097\n",
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      "[865]\tvalid_0's rmse: 0.0259116\n",
      "[866]\tvalid_0's rmse: 0.0259111\n",
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      "[868]\tvalid_0's rmse: 0.0258982\n",
      "[869]\tvalid_0's rmse: 0.0258979\n",
      "[870]\tvalid_0's rmse: 0.0258956\n",
      "[871]\tvalid_0's rmse: 0.0258967\n",
      "[872]\tvalid_0's rmse: 0.0258972\n",
      "[873]\tvalid_0's rmse: 0.0258971\n",
      "[874]\tvalid_0's rmse: 0.0259015\n",
      "[875]\tvalid_0's rmse: 0.0258999\n",
      "[876]\tvalid_0's rmse: 0.0258987\n",
      "[877]\tvalid_0's rmse: 0.0258987\n",
      "[878]\tvalid_0's rmse: 0.0258985\n",
      "[879]\tvalid_0's rmse: 0.0259\n",
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      "[881]\tvalid_0's rmse: 0.0259018\n",
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      "[883]\tvalid_0's rmse: 0.0259048\n",
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      "[885]\tvalid_0's rmse: 0.0259055\n",
      "[886]\tvalid_0's rmse: 0.0259052\n",
      "[887]\tvalid_0's rmse: 0.0259047\n",
      "[888]\tvalid_0's rmse: 0.0259042\n",
      "[889]\tvalid_0's rmse: 0.0259046\n",
      "[890]\tvalid_0's rmse: 0.0259049\n",
      "[891]\tvalid_0's rmse: 0.0259044\n",
      "[892]\tvalid_0's rmse: 0.0259046\n",
      "[893]\tvalid_0's rmse: 0.0259035\n",
      "[894]\tvalid_0's rmse: 0.0259016\n",
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      "[896]\tvalid_0's rmse: 0.0259025\n",
      "[897]\tvalid_0's rmse: 0.0259047\n",
      "[898]\tvalid_0's rmse: 0.0259051\n",
      "[899]\tvalid_0's rmse: 0.0259101\n",
      "[900]\tvalid_0's rmse: 0.0259099\n",
      "[901]\tvalid_0's rmse: 0.0259106\n",
      "[902]\tvalid_0's rmse: 0.0259101\n",
      "[903]\tvalid_0's rmse: 0.0259044\n",
      "[904]\tvalid_0's rmse: 0.0259034\n",
      "[905]\tvalid_0's rmse: 0.0259038\n",
      "[906]\tvalid_0's rmse: 0.0259047\n",
      "[907]\tvalid_0's rmse: 0.0259061\n",
      "[908]\tvalid_0's rmse: 0.025906\n",
      "[909]\tvalid_0's rmse: 0.025901\n",
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      "[911]\tvalid_0's rmse: 0.0258968\n",
      "[912]\tvalid_0's rmse: 0.0258973\n",
      "[913]\tvalid_0's rmse: 0.0258965\n",
      "[914]\tvalid_0's rmse: 0.025898\n",
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      "[916]\tvalid_0's rmse: 0.0258981\n",
      "[917]\tvalid_0's rmse: 0.0258952\n",
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      "[919]\tvalid_0's rmse: 0.0258947\n",
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      "[921]\tvalid_0's rmse: 0.0258954\n",
      "[922]\tvalid_0's rmse: 0.0258947\n",
      "[923]\tvalid_0's rmse: 0.0258946\n",
      "[924]\tvalid_0's rmse: 0.0258931\n",
      "[925]\tvalid_0's rmse: 0.0258945\n",
      "[926]\tvalid_0's rmse: 0.0258925\n",
      "[927]\tvalid_0's rmse: 0.0258899\n",
      "[928]\tvalid_0's rmse: 0.0258898\n",
      "[929]\tvalid_0's rmse: 0.0258914\n",
      "[930]\tvalid_0's rmse: 0.0258912\n",
      "[931]\tvalid_0's rmse: 0.025892\n",
      "[932]\tvalid_0's rmse: 0.025893\n",
      "[933]\tvalid_0's rmse: 0.0258918\n",
      "[934]\tvalid_0's rmse: 0.0258882\n",
      "[935]\tvalid_0's rmse: 0.0258882\n",
      "[936]\tvalid_0's rmse: 0.0258871\n",
      "[937]\tvalid_0's rmse: 0.0258879\n",
      "[938]\tvalid_0's rmse: 0.0258857\n",
      "[939]\tvalid_0's rmse: 0.0258855\n",
      "[940]\tvalid_0's rmse: 0.0258856\n",
      "[941]\tvalid_0's rmse: 0.0258855\n",
      "[942]\tvalid_0's rmse: 0.0258857\n",
      "[943]\tvalid_0's rmse: 0.0258857\n",
      "[944]\tvalid_0's rmse: 0.0258861\n",
      "[945]\tvalid_0's rmse: 0.0258858\n",
      "[946]\tvalid_0's rmse: 0.0258865\n",
      "[947]\tvalid_0's rmse: 0.0258875\n",
      "[948]\tvalid_0's rmse: 0.0258872\n",
      "[949]\tvalid_0's rmse: 0.0258872\n",
      "[950]\tvalid_0's rmse: 0.0258866\n",
      "[951]\tvalid_0's rmse: 0.0258888\n",
      "[952]\tvalid_0's rmse: 0.0258892\n",
      "[953]\tvalid_0's rmse: 0.0258835\n",
      "[954]\tvalid_0's rmse: 0.0258817\n",
      "[955]\tvalid_0's rmse: 0.0258817\n",
      "[956]\tvalid_0's rmse: 0.0258786\n",
      "[957]\tvalid_0's rmse: 0.0258788\n",
      "[958]\tvalid_0's rmse: 0.0258788\n",
      "[959]\tvalid_0's rmse: 0.0258798\n",
      "[960]\tvalid_0's rmse: 0.0258797\n",
      "[961]\tvalid_0's rmse: 0.0258797\n",
      "[962]\tvalid_0's rmse: 0.0258776\n",
      "[963]\tvalid_0's rmse: 0.0258773\n",
      "[964]\tvalid_0's rmse: 0.025877\n",
      "[965]\tvalid_0's rmse: 0.0258773\n",
      "[966]\tvalid_0's rmse: 0.025879\n",
      "[967]\tvalid_0's rmse: 0.0258802\n",
      "[968]\tvalid_0's rmse: 0.0258794\n",
      "[969]\tvalid_0's rmse: 0.02588\n",
      "[970]\tvalid_0's rmse: 0.0258797\n",
      "[971]\tvalid_0's rmse: 0.0258782\n",
      "[972]\tvalid_0's rmse: 0.0258827\n",
      "[973]\tvalid_0's rmse: 0.0258842\n",
      "[974]\tvalid_0's rmse: 0.0258837\n",
      "[975]\tvalid_0's rmse: 0.0258827\n",
      "[976]\tvalid_0's rmse: 0.0258818\n",
      "[977]\tvalid_0's rmse: 0.0258811\n",
      "[978]\tvalid_0's rmse: 0.0258813\n",
      "[979]\tvalid_0's rmse: 0.0258813\n",
      "[980]\tvalid_0's rmse: 0.0258805\n",
      "[981]\tvalid_0's rmse: 0.0258805\n",
      "[982]\tvalid_0's rmse: 0.0258791\n",
      "[983]\tvalid_0's rmse: 0.0258764\n",
      "[984]\tvalid_0's rmse: 0.0258765\n",
      "[985]\tvalid_0's rmse: 0.0258748\n",
      "[986]\tvalid_0's rmse: 0.025877\n",
      "[987]\tvalid_0's rmse: 0.025878\n",
      "[988]\tvalid_0's rmse: 0.0258776\n",
      "[989]\tvalid_0's rmse: 0.0258761\n",
      "[990]\tvalid_0's rmse: 0.0258762\n",
      "[991]\tvalid_0's rmse: 0.0258591\n",
      "[992]\tvalid_0's rmse: 0.0258595\n",
      "[993]\tvalid_0's rmse: 0.0258594\n",
      "[994]\tvalid_0's rmse: 0.0258605\n",
      "[995]\tvalid_0's rmse: 0.02586\n",
      "[996]\tvalid_0's rmse: 0.0258582\n",
      "[997]\tvalid_0's rmse: 0.0258576\n",
      "[998]\tvalid_0's rmse: 0.0258556\n",
      "[999]\tvalid_0's rmse: 0.0258562\n",
      "[1000]\tvalid_0's rmse: 0.0258543\n",
      "[1001]\tvalid_0's rmse: 0.0258523\n",
      "[1002]\tvalid_0's rmse: 0.0258534\n",
      "[1003]\tvalid_0's rmse: 0.0258537\n",
      "[1004]\tvalid_0's rmse: 0.0258546\n",
      "[1005]\tvalid_0's rmse: 0.0258533\n",
      "[1006]\tvalid_0's rmse: 0.0258519\n",
      "[1007]\tvalid_0's rmse: 0.0258508\n",
      "[1008]\tvalid_0's rmse: 0.0258508\n",
      "[1009]\tvalid_0's rmse: 0.0258509\n",
      "[1010]\tvalid_0's rmse: 0.0258469\n",
      "[1011]\tvalid_0's rmse: 0.025851\n",
      "[1012]\tvalid_0's rmse: 0.0258512\n",
      "[1013]\tvalid_0's rmse: 0.0258474\n",
      "[1014]\tvalid_0's rmse: 0.0258468\n",
      "[1015]\tvalid_0's rmse: 0.0258432\n",
      "[1016]\tvalid_0's rmse: 0.0258409\n",
      "[1017]\tvalid_0's rmse: 0.0258283\n",
      "[1018]\tvalid_0's rmse: 0.0258284\n",
      "[1019]\tvalid_0's rmse: 0.0258254\n",
      "[1020]\tvalid_0's rmse: 0.0258244\n",
      "[1021]\tvalid_0's rmse: 0.0258246\n",
      "[1022]\tvalid_0's rmse: 0.0258249\n",
      "[1023]\tvalid_0's rmse: 0.0258246\n",
      "[1024]\tvalid_0's rmse: 0.0258215\n",
      "[1025]\tvalid_0's rmse: 0.0258211\n",
      "[1026]\tvalid_0's rmse: 0.0258215\n",
      "[1027]\tvalid_0's rmse: 0.0258213\n",
      "[1028]\tvalid_0's rmse: 0.0258215\n",
      "[1029]\tvalid_0's rmse: 0.0258233\n",
      "[1030]\tvalid_0's rmse: 0.0258232\n",
      "[1031]\tvalid_0's rmse: 0.0258233\n",
      "[1032]\tvalid_0's rmse: 0.0258191\n",
      "[1033]\tvalid_0's rmse: 0.0258196\n",
      "[1034]\tvalid_0's rmse: 0.0258169\n",
      "[1035]\tvalid_0's rmse: 0.025816\n",
      "[1036]\tvalid_0's rmse: 0.0258137\n",
      "[1037]\tvalid_0's rmse: 0.0258143\n",
      "[1038]\tvalid_0's rmse: 0.0258121\n",
      "[1039]\tvalid_0's rmse: 0.0258055\n",
      "[1040]\tvalid_0's rmse: 0.0258055\n",
      "[1041]\tvalid_0's rmse: 0.0258079\n",
      "[1042]\tvalid_0's rmse: 0.0258097\n",
      "[1043]\tvalid_0's rmse: 0.0258097\n",
      "[1044]\tvalid_0's rmse: 0.0258109\n",
      "[1045]\tvalid_0's rmse: 0.0258118\n",
      "[1046]\tvalid_0's rmse: 0.0258121\n",
      "[1047]\tvalid_0's rmse: 0.0258112\n",
      "[1048]\tvalid_0's rmse: 0.0258103\n",
      "[1049]\tvalid_0's rmse: 0.0258102\n",
      "[1050]\tvalid_0's rmse: 0.0258113\n",
      "[1051]\tvalid_0's rmse: 0.0258119\n",
      "[1052]\tvalid_0's rmse: 0.0258115\n",
      "[1053]\tvalid_0's rmse: 0.0258116\n",
      "[1054]\tvalid_0's rmse: 0.0258114\n",
      "[1055]\tvalid_0's rmse: 0.0258098\n",
      "[1056]\tvalid_0's rmse: 0.0258097\n",
      "[1057]\tvalid_0's rmse: 0.0258085\n",
      "[1058]\tvalid_0's rmse: 0.0258088\n",
      "[1059]\tvalid_0's rmse: 0.0258058\n",
      "[1060]\tvalid_0's rmse: 0.0258033\n",
      "[1061]\tvalid_0's rmse: 0.0257999\n",
      "[1062]\tvalid_0's rmse: 0.025795\n",
      "[1063]\tvalid_0's rmse: 0.0257936\n",
      "[1064]\tvalid_0's rmse: 0.0257928\n",
      "[1065]\tvalid_0's rmse: 0.025793\n",
      "[1066]\tvalid_0's rmse: 0.0257934\n",
      "[1067]\tvalid_0's rmse: 0.0257928\n",
      "[1068]\tvalid_0's rmse: 0.0257786\n",
      "[1069]\tvalid_0's rmse: 0.0257783\n",
      "[1070]\tvalid_0's rmse: 0.0257778\n",
      "[1071]\tvalid_0's rmse: 0.025777\n",
      "[1072]\tvalid_0's rmse: 0.0257782\n",
      "[1073]\tvalid_0's rmse: 0.0257767\n",
      "[1074]\tvalid_0's rmse: 0.0257763\n",
      "[1075]\tvalid_0's rmse: 0.0257764\n",
      "[1076]\tvalid_0's rmse: 0.025776\n",
      "[1077]\tvalid_0's rmse: 0.0257776\n",
      "[1078]\tvalid_0's rmse: 0.0257782\n",
      "[1079]\tvalid_0's rmse: 0.0257782\n",
      "[1080]\tvalid_0's rmse: 0.0257781\n",
      "[1081]\tvalid_0's rmse: 0.025776\n",
      "[1082]\tvalid_0's rmse: 0.0257761\n",
      "[1083]\tvalid_0's rmse: 0.0257762\n",
      "[1084]\tvalid_0's rmse: 0.0257773\n",
      "[1085]\tvalid_0's rmse: 0.0257783\n",
      "[1086]\tvalid_0's rmse: 0.0257785\n",
      "[1087]\tvalid_0's rmse: 0.0257788\n",
      "[1088]\tvalid_0's rmse: 0.0257792\n",
      "[1089]\tvalid_0's rmse: 0.02578\n",
      "[1090]\tvalid_0's rmse: 0.0257788\n",
      "[1091]\tvalid_0's rmse: 0.0257776\n",
      "[1092]\tvalid_0's rmse: 0.0257795\n",
      "[1093]\tvalid_0's rmse: 0.0257788\n",
      "[1094]\tvalid_0's rmse: 0.0257782\n",
      "[1095]\tvalid_0's rmse: 0.025778\n",
      "[1096]\tvalid_0's rmse: 0.0257811\n",
      "[1097]\tvalid_0's rmse: 0.0257814\n",
      "[1098]\tvalid_0's rmse: 0.0257792\n",
      "[1099]\tvalid_0's rmse: 0.0257788\n",
      "[1100]\tvalid_0's rmse: 0.0257798\n",
      "[1101]\tvalid_0's rmse: 0.0257804\n",
      "[1102]\tvalid_0's rmse: 0.0257804\n",
      "[1103]\tvalid_0's rmse: 0.0257781\n",
      "[1104]\tvalid_0's rmse: 0.0257786\n",
      "[1105]\tvalid_0's rmse: 0.0257794\n",
      "[1106]\tvalid_0's rmse: 0.0257793\n",
      "[1107]\tvalid_0's rmse: 0.0257795\n",
      "[1108]\tvalid_0's rmse: 0.0257792\n",
      "[1109]\tvalid_0's rmse: 0.0257754\n",
      "[1110]\tvalid_0's rmse: 0.0257772\n",
      "[1111]\tvalid_0's rmse: 0.0257766\n",
      "[1112]\tvalid_0's rmse: 0.0257761\n",
      "[1113]\tvalid_0's rmse: 0.0257759\n",
      "[1114]\tvalid_0's rmse: 0.0257754\n",
      "[1115]\tvalid_0's rmse: 0.0257751\n",
      "[1116]\tvalid_0's rmse: 0.0257731\n",
      "[1117]\tvalid_0's rmse: 0.0257728\n",
      "[1118]\tvalid_0's rmse: 0.0257725\n",
      "[1119]\tvalid_0's rmse: 0.025771\n",
      "[1120]\tvalid_0's rmse: 0.0257698\n",
      "[1121]\tvalid_0's rmse: 0.0257699\n",
      "[1122]\tvalid_0's rmse: 0.0257698\n",
      "[1123]\tvalid_0's rmse: 0.0257685\n",
      "[1124]\tvalid_0's rmse: 0.0257678\n",
      "[1125]\tvalid_0's rmse: 0.0257679\n",
      "[1126]\tvalid_0's rmse: 0.0257667\n",
      "[1127]\tvalid_0's rmse: 0.0257669\n",
      "[1128]\tvalid_0's rmse: 0.0257648\n",
      "[1129]\tvalid_0's rmse: 0.0257647\n",
      "[1130]\tvalid_0's rmse: 0.0257651\n",
      "[1131]\tvalid_0's rmse: 0.0257653\n",
      "[1132]\tvalid_0's rmse: 0.0257657\n",
      "[1133]\tvalid_0's rmse: 0.0257652\n",
      "[1134]\tvalid_0's rmse: 0.0257653\n",
      "[1135]\tvalid_0's rmse: 0.0257593\n",
      "[1136]\tvalid_0's rmse: 0.0257585\n",
      "[1137]\tvalid_0's rmse: 0.0257583\n",
      "[1138]\tvalid_0's rmse: 0.0257575\n",
      "[1139]\tvalid_0's rmse: 0.0257571\n",
      "[1140]\tvalid_0's rmse: 0.0257562\n",
      "[1141]\tvalid_0's rmse: 0.0257562\n",
      "[1142]\tvalid_0's rmse: 0.0257561\n",
      "[1143]\tvalid_0's rmse: 0.025755\n",
      "[1144]\tvalid_0's rmse: 0.025754\n",
      "[1145]\tvalid_0's rmse: 0.0257534\n",
      "[1146]\tvalid_0's rmse: 0.0257535\n",
      "[1147]\tvalid_0's rmse: 0.0257503\n",
      "[1148]\tvalid_0's rmse: 0.0257519\n",
      "[1149]\tvalid_0's rmse: 0.0257486\n",
      "[1150]\tvalid_0's rmse: 0.0257485\n",
      "[1151]\tvalid_0's rmse: 0.0257492\n",
      "[1152]\tvalid_0's rmse: 0.0257531\n",
      "[1153]\tvalid_0's rmse: 0.0257529\n",
      "[1154]\tvalid_0's rmse: 0.0257521\n",
      "[1155]\tvalid_0's rmse: 0.0257517\n",
      "[1156]\tvalid_0's rmse: 0.0257545\n",
      "[1157]\tvalid_0's rmse: 0.0257556\n",
      "[1158]\tvalid_0's rmse: 0.0257559\n",
      "[1159]\tvalid_0's rmse: 0.0257578\n",
      "[1160]\tvalid_0's rmse: 0.0257567\n",
      "[1161]\tvalid_0's rmse: 0.0257569\n",
      "[1162]\tvalid_0's rmse: 0.0257559\n",
      "[1163]\tvalid_0's rmse: 0.0257577\n",
      "[1164]\tvalid_0's rmse: 0.0257551\n",
      "[1165]\tvalid_0's rmse: 0.025756\n",
      "[1166]\tvalid_0's rmse: 0.0257558\n",
      "[1167]\tvalid_0's rmse: 0.0257561\n",
      "[1168]\tvalid_0's rmse: 0.0257562\n",
      "[1169]\tvalid_0's rmse: 0.0257558\n",
      "[1170]\tvalid_0's rmse: 0.0257527\n",
      "[1171]\tvalid_0's rmse: 0.0257479\n",
      "[1172]\tvalid_0's rmse: 0.0257481\n",
      "[1173]\tvalid_0's rmse: 0.0257445\n",
      "[1174]\tvalid_0's rmse: 0.0257442\n",
      "[1175]\tvalid_0's rmse: 0.0257454\n",
      "[1176]\tvalid_0's rmse: 0.0257446\n",
      "[1177]\tvalid_0's rmse: 0.0257455\n",
      "[1178]\tvalid_0's rmse: 0.0257465\n",
      "[1179]\tvalid_0's rmse: 0.0257483\n",
      "[1180]\tvalid_0's rmse: 0.0257494\n",
      "[1181]\tvalid_0's rmse: 0.025749\n",
      "[1182]\tvalid_0's rmse: 0.0257492\n",
      "[1183]\tvalid_0's rmse: 0.0257497\n",
      "[1184]\tvalid_0's rmse: 0.02575\n",
      "[1185]\tvalid_0's rmse: 0.0257441\n",
      "[1186]\tvalid_0's rmse: 0.0257412\n",
      "[1187]\tvalid_0's rmse: 0.0257376\n",
      "[1188]\tvalid_0's rmse: 0.025734\n",
      "[1189]\tvalid_0's rmse: 0.0257333\n",
      "[1190]\tvalid_0's rmse: 0.0257326\n",
      "[1191]\tvalid_0's rmse: 0.0257325\n",
      "[1192]\tvalid_0's rmse: 0.0257347\n",
      "[1193]\tvalid_0's rmse: 0.0257189\n",
      "[1194]\tvalid_0's rmse: 0.0257085\n",
      "[1195]\tvalid_0's rmse: 0.0257073\n",
      "[1196]\tvalid_0's rmse: 0.025707\n",
      "[1197]\tvalid_0's rmse: 0.0257055\n",
      "[1198]\tvalid_0's rmse: 0.0257056\n",
      "[1199]\tvalid_0's rmse: 0.0257043\n",
      "[1200]\tvalid_0's rmse: 0.0257063\n",
      "[1201]\tvalid_0's rmse: 0.0257056\n",
      "[1202]\tvalid_0's rmse: 0.0257059\n",
      "[1203]\tvalid_0's rmse: 0.0257041\n",
      "[1204]\tvalid_0's rmse: 0.0257018\n",
      "[1205]\tvalid_0's rmse: 0.025702\n",
      "[1206]\tvalid_0's rmse: 0.0257017\n",
      "[1207]\tvalid_0's rmse: 0.0256966\n",
      "[1208]\tvalid_0's rmse: 0.0256931\n",
      "[1209]\tvalid_0's rmse: 0.0256931\n",
      "[1210]\tvalid_0's rmse: 0.025693\n",
      "[1211]\tvalid_0's rmse: 0.0256934\n",
      "[1212]\tvalid_0's rmse: 0.0256969\n",
      "[1213]\tvalid_0's rmse: 0.0256973\n",
      "[1214]\tvalid_0's rmse: 0.0256982\n",
      "[1215]\tvalid_0's rmse: 0.0256965\n",
      "[1216]\tvalid_0's rmse: 0.0256955\n",
      "[1217]\tvalid_0's rmse: 0.0256956\n",
      "[1218]\tvalid_0's rmse: 0.0256956\n",
      "[1219]\tvalid_0's rmse: 0.0256943\n",
      "[1220]\tvalid_0's rmse: 0.0256932\n",
      "[1221]\tvalid_0's rmse: 0.0256944\n",
      "[1222]\tvalid_0's rmse: 0.0256935\n",
      "[1223]\tvalid_0's rmse: 0.0256947\n",
      "[1224]\tvalid_0's rmse: 0.0256951\n",
      "[1225]\tvalid_0's rmse: 0.0256953\n",
      "[1226]\tvalid_0's rmse: 0.0256967\n",
      "[1227]\tvalid_0's rmse: 0.0256974\n",
      "[1228]\tvalid_0's rmse: 0.0256971\n",
      "[1229]\tvalid_0's rmse: 0.025697\n",
      "[1230]\tvalid_0's rmse: 0.0256973\n",
      "[1231]\tvalid_0's rmse: 0.0256971\n",
      "[1232]\tvalid_0's rmse: 0.0256976\n",
      "[1233]\tvalid_0's rmse: 0.0256976\n",
      "[1234]\tvalid_0's rmse: 0.025696\n",
      "[1235]\tvalid_0's rmse: 0.0256965\n",
      "[1236]\tvalid_0's rmse: 0.0256961\n",
      "[1237]\tvalid_0's rmse: 0.0256962\n",
      "[1238]\tvalid_0's rmse: 0.0256996\n",
      "[1239]\tvalid_0's rmse: 0.0257003\n",
      "[1240]\tvalid_0's rmse: 0.0257023\n",
      "[1241]\tvalid_0's rmse: 0.0257018\n",
      "[1242]\tvalid_0's rmse: 0.0257016\n",
      "[1243]\tvalid_0's rmse: 0.0257023\n",
      "[1244]\tvalid_0's rmse: 0.0257013\n",
      "[1245]\tvalid_0's rmse: 0.0256968\n",
      "[1246]\tvalid_0's rmse: 0.0256967\n",
      "[1247]\tvalid_0's rmse: 0.0256935\n",
      "[1248]\tvalid_0's rmse: 0.0256932\n",
      "[1249]\tvalid_0's rmse: 0.0256959\n",
      "[1250]\tvalid_0's rmse: 0.025695\n",
      "[1251]\tvalid_0's rmse: 0.025695\n",
      "[1252]\tvalid_0's rmse: 0.0256954\n",
      "[1253]\tvalid_0's rmse: 0.0256932\n",
      "[1254]\tvalid_0's rmse: 0.0256933\n",
      "[1255]\tvalid_0's rmse: 0.0256942\n",
      "[1256]\tvalid_0's rmse: 0.0256929\n",
      "[1257]\tvalid_0's rmse: 0.0256918\n",
      "[1258]\tvalid_0's rmse: 0.0256916\n",
      "[1259]\tvalid_0's rmse: 0.0256913\n",
      "[1260]\tvalid_0's rmse: 0.0256924\n",
      "[1261]\tvalid_0's rmse: 0.0256909\n",
      "[1262]\tvalid_0's rmse: 0.0256907\n",
      "[1263]\tvalid_0's rmse: 0.0256914\n",
      "[1264]\tvalid_0's rmse: 0.0256819\n",
      "[1265]\tvalid_0's rmse: 0.0256823\n",
      "[1266]\tvalid_0's rmse: 0.0256822\n",
      "[1267]\tvalid_0's rmse: 0.0256828\n",
      "[1268]\tvalid_0's rmse: 0.025683\n",
      "[1269]\tvalid_0's rmse: 0.0256841\n",
      "[1270]\tvalid_0's rmse: 0.0256839\n",
      "[1271]\tvalid_0's rmse: 0.0256837\n",
      "[1272]\tvalid_0's rmse: 0.0256835\n",
      "[1273]\tvalid_0's rmse: 0.0256819\n",
      "[1274]\tvalid_0's rmse: 0.0256814\n",
      "[1275]\tvalid_0's rmse: 0.0256859\n",
      "[1276]\tvalid_0's rmse: 0.0256845\n",
      "[1277]\tvalid_0's rmse: 0.0256854\n",
      "[1278]\tvalid_0's rmse: 0.0256899\n",
      "[1279]\tvalid_0's rmse: 0.0256912\n",
      "[1280]\tvalid_0's rmse: 0.0256951\n",
      "[1281]\tvalid_0's rmse: 0.0256952\n",
      "[1282]\tvalid_0's rmse: 0.0256956\n",
      "[1283]\tvalid_0's rmse: 0.0256958\n",
      "[1284]\tvalid_0's rmse: 0.0256956\n",
      "[1285]\tvalid_0's rmse: 0.025695\n",
      "[1286]\tvalid_0's rmse: 0.0256955\n",
      "[1287]\tvalid_0's rmse: 0.0256955\n",
      "[1288]\tvalid_0's rmse: 0.0256966\n",
      "[1289]\tvalid_0's rmse: 0.0256969\n",
      "[1290]\tvalid_0's rmse: 0.0256961\n",
      "[1291]\tvalid_0's rmse: 0.0256955\n",
      "[1292]\tvalid_0's rmse: 0.025695\n",
      "[1293]\tvalid_0's rmse: 0.0256959\n",
      "[1294]\tvalid_0's rmse: 0.0256953\n",
      "[1295]\tvalid_0's rmse: 0.0256943\n",
      "[1296]\tvalid_0's rmse: 0.0256935\n",
      "[1297]\tvalid_0's rmse: 0.0256928\n",
      "[1298]\tvalid_0's rmse: 0.0256922\n",
      "[1299]\tvalid_0's rmse: 0.0256921\n",
      "[1300]\tvalid_0's rmse: 0.0256929\n",
      "[1301]\tvalid_0's rmse: 0.0256929\n",
      "[1302]\tvalid_0's rmse: 0.0256922\n",
      "[1303]\tvalid_0's rmse: 0.0256922\n",
      "[1304]\tvalid_0's rmse: 0.0256903\n",
      "[1305]\tvalid_0's rmse: 0.0256902\n",
      "[1306]\tvalid_0's rmse: 0.025689\n",
      "[1307]\tvalid_0's rmse: 0.0256867\n",
      "[1308]\tvalid_0's rmse: 0.025687\n",
      "[1309]\tvalid_0's rmse: 0.0256871\n",
      "[1310]\tvalid_0's rmse: 0.0256871\n",
      "[1311]\tvalid_0's rmse: 0.0256937\n",
      "[1312]\tvalid_0's rmse: 0.0256927\n",
      "[1313]\tvalid_0's rmse: 0.0256883\n",
      "[1314]\tvalid_0's rmse: 0.0256881\n",
      "[1315]\tvalid_0's rmse: 0.0256876\n",
      "[1316]\tvalid_0's rmse: 0.0256871\n",
      "[1317]\tvalid_0's rmse: 0.025685\n",
      "[1318]\tvalid_0's rmse: 0.0256843\n",
      "[1319]\tvalid_0's rmse: 0.0256852\n",
      "[1320]\tvalid_0's rmse: 0.0256852\n",
      "[1321]\tvalid_0's rmse: 0.0256852\n",
      "[1322]\tvalid_0's rmse: 0.0256842\n",
      "[1323]\tvalid_0's rmse: 0.0256825\n",
      "[1324]\tvalid_0's rmse: 0.0256824\n",
      "[1325]\tvalid_0's rmse: 0.0256792\n",
      "[1326]\tvalid_0's rmse: 0.0256781\n",
      "[1327]\tvalid_0's rmse: 0.0256776\n",
      "[1328]\tvalid_0's rmse: 0.0256776\n",
      "[1329]\tvalid_0's rmse: 0.0256782\n",
      "[1330]\tvalid_0's rmse: 0.0256781\n",
      "[1331]\tvalid_0's rmse: 0.0256777\n",
      "[1332]\tvalid_0's rmse: 0.0256777\n",
      "[1333]\tvalid_0's rmse: 0.0256772\n",
      "[1334]\tvalid_0's rmse: 0.025677\n",
      "[1335]\tvalid_0's rmse: 0.0256771\n",
      "[1336]\tvalid_0's rmse: 0.0256768\n",
      "[1337]\tvalid_0's rmse: 0.0256775\n",
      "[1338]\tvalid_0's rmse: 0.0256776\n",
      "[1339]\tvalid_0's rmse: 0.0256774\n",
      "[1340]\tvalid_0's rmse: 0.0256753\n",
      "[1341]\tvalid_0's rmse: 0.0256751\n",
      "[1342]\tvalid_0's rmse: 0.0256747\n",
      "[1343]\tvalid_0's rmse: 0.0256749\n",
      "[1344]\tvalid_0's rmse: 0.0256746\n",
      "[1345]\tvalid_0's rmse: 0.0256722\n",
      "[1346]\tvalid_0's rmse: 0.0256697\n",
      "[1347]\tvalid_0's rmse: 0.0256704\n",
      "[1348]\tvalid_0's rmse: 0.0256681\n",
      "[1349]\tvalid_0's rmse: 0.025668\n",
      "[1350]\tvalid_0's rmse: 0.0256667\n",
      "[1351]\tvalid_0's rmse: 0.0256684\n",
      "[1352]\tvalid_0's rmse: 0.0256685\n",
      "[1353]\tvalid_0's rmse: 0.0256673\n",
      "[1354]\tvalid_0's rmse: 0.0256673\n",
      "[1355]\tvalid_0's rmse: 0.025667\n",
      "[1356]\tvalid_0's rmse: 0.0256675\n",
      "[1357]\tvalid_0's rmse: 0.0256686\n",
      "[1358]\tvalid_0's rmse: 0.0256681\n",
      "[1359]\tvalid_0's rmse: 0.0256681\n",
      "[1360]\tvalid_0's rmse: 0.0256682\n",
      "[1361]\tvalid_0's rmse: 0.025668\n",
      "[1362]\tvalid_0's rmse: 0.0256671\n",
      "[1363]\tvalid_0's rmse: 0.0256675\n",
      "[1364]\tvalid_0's rmse: 0.0256638\n",
      "[1365]\tvalid_0's rmse: 0.0256638\n",
      "[1366]\tvalid_0's rmse: 0.0256526\n",
      "[1367]\tvalid_0's rmse: 0.0256534\n",
      "[1368]\tvalid_0's rmse: 0.0256534\n",
      "[1369]\tvalid_0's rmse: 0.025653\n",
      "[1370]\tvalid_0's rmse: 0.0256528\n",
      "[1371]\tvalid_0's rmse: 0.0256532\n",
      "[1372]\tvalid_0's rmse: 0.025647\n",
      "[1373]\tvalid_0's rmse: 0.0256454\n",
      "[1374]\tvalid_0's rmse: 0.0256457\n",
      "[1375]\tvalid_0's rmse: 0.0256426\n",
      "[1376]\tvalid_0's rmse: 0.0256425\n",
      "[1377]\tvalid_0's rmse: 0.0256441\n",
      "[1378]\tvalid_0's rmse: 0.0256431\n",
      "[1379]\tvalid_0's rmse: 0.0256452\n",
      "[1380]\tvalid_0's rmse: 0.0256455\n",
      "[1381]\tvalid_0's rmse: 0.0256454\n",
      "[1382]\tvalid_0's rmse: 0.0256441\n",
      "[1383]\tvalid_0's rmse: 0.0256446\n",
      "[1384]\tvalid_0's rmse: 0.0256443\n",
      "[1385]\tvalid_0's rmse: 0.0256444\n",
      "[1386]\tvalid_0's rmse: 0.0256445\n",
      "[1387]\tvalid_0's rmse: 0.0256436\n",
      "[1388]\tvalid_0's rmse: 0.0256418\n",
      "[1389]\tvalid_0's rmse: 0.0256422\n",
      "[1390]\tvalid_0's rmse: 0.0256363\n",
      "[1391]\tvalid_0's rmse: 0.0256359\n",
      "[1392]\tvalid_0's rmse: 0.0256348\n",
      "[1393]\tvalid_0's rmse: 0.0256345\n",
      "[1394]\tvalid_0's rmse: 0.0256347\n",
      "[1395]\tvalid_0's rmse: 0.025635\n",
      "[1396]\tvalid_0's rmse: 0.0256333\n",
      "[1397]\tvalid_0's rmse: 0.025633\n",
      "[1398]\tvalid_0's rmse: 0.025633\n",
      "[1399]\tvalid_0's rmse: 0.0256312\n",
      "[1400]\tvalid_0's rmse: 0.025631\n",
      "[1401]\tvalid_0's rmse: 0.025631\n",
      "[1402]\tvalid_0's rmse: 0.0256313\n",
      "[1403]\tvalid_0's rmse: 0.025627\n",
      "[1404]\tvalid_0's rmse: 0.0256275\n",
      "[1405]\tvalid_0's rmse: 0.0256277\n",
      "[1406]\tvalid_0's rmse: 0.0256274\n",
      "[1407]\tvalid_0's rmse: 0.0256277\n",
      "[1408]\tvalid_0's rmse: 0.0256266\n",
      "[1409]\tvalid_0's rmse: 0.025626\n",
      "[1410]\tvalid_0's rmse: 0.0256258\n",
      "[1411]\tvalid_0's rmse: 0.0256246\n",
      "[1412]\tvalid_0's rmse: 0.0256245\n",
      "[1413]\tvalid_0's rmse: 0.0256243\n",
      "[1414]\tvalid_0's rmse: 0.0256237\n",
      "[1415]\tvalid_0's rmse: 0.0256244\n",
      "[1416]\tvalid_0's rmse: 0.0256238\n",
      "[1417]\tvalid_0's rmse: 0.0256171\n",
      "[1418]\tvalid_0's rmse: 0.0256115\n",
      "[1419]\tvalid_0's rmse: 0.0256106\n",
      "[1420]\tvalid_0's rmse: 0.0256105\n",
      "[1421]\tvalid_0's rmse: 0.02561\n",
      "[1422]\tvalid_0's rmse: 0.0256113\n",
      "[1423]\tvalid_0's rmse: 0.0256111\n",
      "[1424]\tvalid_0's rmse: 0.025611\n",
      "[1425]\tvalid_0's rmse: 0.0256113\n",
      "[1426]\tvalid_0's rmse: 0.0256108\n",
      "[1427]\tvalid_0's rmse: 0.0256105\n",
      "[1428]\tvalid_0's rmse: 0.0256095\n",
      "[1429]\tvalid_0's rmse: 0.0256065\n",
      "[1430]\tvalid_0's rmse: 0.0256062\n",
      "[1431]\tvalid_0's rmse: 0.025607\n",
      "[1432]\tvalid_0's rmse: 0.0256074\n",
      "[1433]\tvalid_0's rmse: 0.025607\n",
      "[1434]\tvalid_0's rmse: 0.0256081\n",
      "[1435]\tvalid_0's rmse: 0.0256045\n",
      "[1436]\tvalid_0's rmse: 0.0256057\n",
      "[1437]\tvalid_0's rmse: 0.0256067\n",
      "[1438]\tvalid_0's rmse: 0.0256063\n",
      "[1439]\tvalid_0's rmse: 0.0256066\n",
      "[1440]\tvalid_0's rmse: 0.0256061\n",
      "[1441]\tvalid_0's rmse: 0.025605\n",
      "[1442]\tvalid_0's rmse: 0.0256045\n",
      "[1443]\tvalid_0's rmse: 0.0256032\n",
      "[1444]\tvalid_0's rmse: 0.0256063\n",
      "[1445]\tvalid_0's rmse: 0.0256076\n",
      "[1446]\tvalid_0's rmse: 0.025608\n",
      "[1447]\tvalid_0's rmse: 0.0256077\n",
      "[1448]\tvalid_0's rmse: 0.0256093\n",
      "[1449]\tvalid_0's rmse: 0.0256077\n",
      "[1450]\tvalid_0's rmse: 0.0256074\n",
      "[1451]\tvalid_0's rmse: 0.0256078\n",
      "[1452]\tvalid_0's rmse: 0.025608\n",
      "[1453]\tvalid_0's rmse: 0.0256081\n",
      "[1454]\tvalid_0's rmse: 0.0256081\n",
      "[1455]\tvalid_0's rmse: 0.0256079\n",
      "[1456]\tvalid_0's rmse: 0.0256087\n",
      "[1457]\tvalid_0's rmse: 0.0256062\n",
      "[1458]\tvalid_0's rmse: 0.025602\n",
      "[1459]\tvalid_0's rmse: 0.0256021\n",
      "[1460]\tvalid_0's rmse: 0.0256041\n",
      "[1461]\tvalid_0's rmse: 0.0256042\n",
      "[1462]\tvalid_0's rmse: 0.025605\n",
      "[1463]\tvalid_0's rmse: 0.0256056\n",
      "[1464]\tvalid_0's rmse: 0.0256053\n",
      "[1465]\tvalid_0's rmse: 0.0256077\n",
      "[1466]\tvalid_0's rmse: 0.0256076\n",
      "[1467]\tvalid_0's rmse: 0.0256083\n",
      "[1468]\tvalid_0's rmse: 0.0256082\n",
      "[1469]\tvalid_0's rmse: 0.0256074\n",
      "[1470]\tvalid_0's rmse: 0.0256074\n",
      "[1471]\tvalid_0's rmse: 0.025608\n",
      "[1472]\tvalid_0's rmse: 0.0256081\n",
      "[1473]\tvalid_0's rmse: 0.0256084\n",
      "[1474]\tvalid_0's rmse: 0.0256081\n",
      "[1475]\tvalid_0's rmse: 0.0256084\n",
      "[1476]\tvalid_0's rmse: 0.0256083\n",
      "[1477]\tvalid_0's rmse: 0.0256086\n",
      "[1478]\tvalid_0's rmse: 0.0256084\n",
      "[1479]\tvalid_0's rmse: 0.025608\n",
      "[1480]\tvalid_0's rmse: 0.02561\n",
      "[1481]\tvalid_0's rmse: 0.0256062\n",
      "[1482]\tvalid_0's rmse: 0.0256062\n",
      "[1483]\tvalid_0's rmse: 0.0256062\n",
      "[1484]\tvalid_0's rmse: 0.0256056\n",
      "[1485]\tvalid_0's rmse: 0.0256048\n",
      "[1486]\tvalid_0's rmse: 0.0256054\n",
      "[1487]\tvalid_0's rmse: 0.025605\n",
      "[1488]\tvalid_0's rmse: 0.0256026\n",
      "[1489]\tvalid_0's rmse: 0.0255999\n",
      "[1490]\tvalid_0's rmse: 0.0255993\n",
      "[1491]\tvalid_0's rmse: 0.0255995\n",
      "[1492]\tvalid_0's rmse: 0.0256009\n",
      "[1493]\tvalid_0's rmse: 0.0256006\n",
      "[1494]\tvalid_0's rmse: 0.0256027\n",
      "[1495]\tvalid_0's rmse: 0.0256021\n",
      "[1496]\tvalid_0's rmse: 0.0256017\n",
      "[1497]\tvalid_0's rmse: 0.0256016\n",
      "[1498]\tvalid_0's rmse: 0.0256018\n",
      "[1499]\tvalid_0's rmse: 0.0256011\n",
      "[1500]\tvalid_0's rmse: 0.025602\n",
      "[1501]\tvalid_0's rmse: 0.0256019\n",
      "[1502]\tvalid_0's rmse: 0.025602\n",
      "[1503]\tvalid_0's rmse: 0.0256027\n",
      "[1504]\tvalid_0's rmse: 0.0255921\n",
      "[1505]\tvalid_0's rmse: 0.0255919\n",
      "[1506]\tvalid_0's rmse: 0.025592\n",
      "[1507]\tvalid_0's rmse: 0.0255918\n",
      "[1508]\tvalid_0's rmse: 0.0255914\n",
      "[1509]\tvalid_0's rmse: 0.0255913\n",
      "[1510]\tvalid_0's rmse: 0.0255907\n",
      "[1511]\tvalid_0's rmse: 0.0255905\n",
      "[1512]\tvalid_0's rmse: 0.0255883\n",
      "[1513]\tvalid_0's rmse: 0.0255877\n",
      "[1514]\tvalid_0's rmse: 0.025587\n",
      "[1515]\tvalid_0's rmse: 0.0255873\n",
      "[1516]\tvalid_0's rmse: 0.025587\n",
      "[1517]\tvalid_0's rmse: 0.0255872\n",
      "[1518]\tvalid_0's rmse: 0.0255876\n",
      "[1519]\tvalid_0's rmse: 0.0255883\n",
      "[1520]\tvalid_0's rmse: 0.0255884\n",
      "[1521]\tvalid_0's rmse: 0.0255852\n",
      "[1522]\tvalid_0's rmse: 0.0255853\n",
      "[1523]\tvalid_0's rmse: 0.0255852\n",
      "[1524]\tvalid_0's rmse: 0.0255875\n",
      "[1525]\tvalid_0's rmse: 0.025588\n",
      "[1526]\tvalid_0's rmse: 0.0255894\n",
      "[1527]\tvalid_0's rmse: 0.0255891\n",
      "[1528]\tvalid_0's rmse: 0.0255891\n",
      "[1529]\tvalid_0's rmse: 0.0255892\n",
      "[1530]\tvalid_0's rmse: 0.0255908\n",
      "[1531]\tvalid_0's rmse: 0.0255902\n",
      "[1532]\tvalid_0's rmse: 0.0255903\n",
      "[1533]\tvalid_0's rmse: 0.0255905\n",
      "[1534]\tvalid_0's rmse: 0.0255906\n",
      "[1535]\tvalid_0's rmse: 0.0255913\n",
      "[1536]\tvalid_0's rmse: 0.0255906\n",
      "[1537]\tvalid_0's rmse: 0.0255919\n",
      "[1538]\tvalid_0's rmse: 0.0255919\n",
      "[1539]\tvalid_0's rmse: 0.0255936\n",
      "[1540]\tvalid_0's rmse: 0.025594\n",
      "[1541]\tvalid_0's rmse: 0.0255927\n",
      "[1542]\tvalid_0's rmse: 0.0255924\n",
      "[1543]\tvalid_0's rmse: 0.0255929\n",
      "[1544]\tvalid_0's rmse: 0.0255937\n",
      "[1545]\tvalid_0's rmse: 0.0255927\n",
      "[1546]\tvalid_0's rmse: 0.025592\n",
      "[1547]\tvalid_0's rmse: 0.0255914\n",
      "[1548]\tvalid_0's rmse: 0.0255914\n",
      "[1549]\tvalid_0's rmse: 0.0255913\n",
      "[1550]\tvalid_0's rmse: 0.0255909\n",
      "[1551]\tvalid_0's rmse: 0.0255915\n",
      "[1552]\tvalid_0's rmse: 0.0255916\n",
      "[1553]\tvalid_0's rmse: 0.0255916\n",
      "[1554]\tvalid_0's rmse: 0.0255915\n",
      "[1555]\tvalid_0's rmse: 0.0255921\n",
      "[1556]\tvalid_0's rmse: 0.0255909\n",
      "[1557]\tvalid_0's rmse: 0.0255908\n",
      "[1558]\tvalid_0's rmse: 0.0255916\n",
      "[1559]\tvalid_0's rmse: 0.0255904\n",
      "[1560]\tvalid_0's rmse: 0.0255898\n",
      "[1561]\tvalid_0's rmse: 0.0255908\n",
      "[1562]\tvalid_0's rmse: 0.0255909\n",
      "[1563]\tvalid_0's rmse: 0.0255911\n",
      "[1564]\tvalid_0's rmse: 0.0255908\n",
      "[1565]\tvalid_0's rmse: 0.0255928\n",
      "[1566]\tvalid_0's rmse: 0.0255909\n",
      "[1567]\tvalid_0's rmse: 0.0255908\n",
      "[1568]\tvalid_0's rmse: 0.0255925\n",
      "[1569]\tvalid_0's rmse: 0.0255903\n",
      "[1570]\tvalid_0's rmse: 0.0255904\n",
      "[1571]\tvalid_0's rmse: 0.0255902\n",
      "[1572]\tvalid_0's rmse: 0.0255895\n",
      "[1573]\tvalid_0's rmse: 0.0255941\n",
      "[1574]\tvalid_0's rmse: 0.025596\n",
      "[1575]\tvalid_0's rmse: 0.0255966\n",
      "[1576]\tvalid_0's rmse: 0.0255966\n",
      "[1577]\tvalid_0's rmse: 0.0255965\n",
      "[1578]\tvalid_0's rmse: 0.0255957\n",
      "[1579]\tvalid_0's rmse: 0.0255949\n",
      "[1580]\tvalid_0's rmse: 0.0255931\n",
      "[1581]\tvalid_0's rmse: 0.0255936\n",
      "[1582]\tvalid_0's rmse: 0.0255936\n",
      "[1583]\tvalid_0's rmse: 0.0255941\n",
      "[1584]\tvalid_0's rmse: 0.0255942\n",
      "[1585]\tvalid_0's rmse: 0.0255976\n",
      "[1586]\tvalid_0's rmse: 0.0255974\n",
      "[1587]\tvalid_0's rmse: 0.0255956\n",
      "[1588]\tvalid_0's rmse: 0.025595\n",
      "[1589]\tvalid_0's rmse: 0.0255943\n",
      "[1590]\tvalid_0's rmse: 0.0255946\n",
      "[1591]\tvalid_0's rmse: 0.0255945\n",
      "[1592]\tvalid_0's rmse: 0.0255938\n",
      "[1593]\tvalid_0's rmse: 0.0255907\n",
      "[1594]\tvalid_0's rmse: 0.0255832\n",
      "[1595]\tvalid_0's rmse: 0.0255833\n",
      "[1596]\tvalid_0's rmse: 0.0255824\n",
      "[1597]\tvalid_0's rmse: 0.025583\n",
      "[1598]\tvalid_0's rmse: 0.0255812\n",
      "[1599]\tvalid_0's rmse: 0.0255811\n",
      "[1600]\tvalid_0's rmse: 0.0255808\n",
      "[1601]\tvalid_0's rmse: 0.0255761\n",
      "[1602]\tvalid_0's rmse: 0.0255687\n",
      "[1603]\tvalid_0's rmse: 0.0255698\n",
      "[1604]\tvalid_0's rmse: 0.0255697\n",
      "[1605]\tvalid_0's rmse: 0.0255691\n",
      "[1606]\tvalid_0's rmse: 0.0255697\n",
      "[1607]\tvalid_0's rmse: 0.0255554\n",
      "[1608]\tvalid_0's rmse: 0.0255555\n",
      "[1609]\tvalid_0's rmse: 0.0255572\n",
      "[1610]\tvalid_0's rmse: 0.0255572\n",
      "[1611]\tvalid_0's rmse: 0.0255571\n",
      "[1612]\tvalid_0's rmse: 0.0255571\n",
      "[1613]\tvalid_0's rmse: 0.0255573\n",
      "[1614]\tvalid_0's rmse: 0.0255553\n",
      "[1615]\tvalid_0's rmse: 0.0255563\n",
      "[1616]\tvalid_0's rmse: 0.0255559\n",
      "[1617]\tvalid_0's rmse: 0.0255553\n",
      "[1618]\tvalid_0's rmse: 0.0255544\n",
      "[1619]\tvalid_0's rmse: 0.0255544\n",
      "[1620]\tvalid_0's rmse: 0.0255537\n",
      "[1621]\tvalid_0's rmse: 0.0255486\n",
      "[1622]\tvalid_0's rmse: 0.0255496\n",
      "[1623]\tvalid_0's rmse: 0.0255495\n",
      "[1624]\tvalid_0's rmse: 0.0255509\n",
      "[1625]\tvalid_0's rmse: 0.0255513\n",
      "[1626]\tvalid_0's rmse: 0.0255499\n",
      "[1627]\tvalid_0's rmse: 0.0255497\n",
      "[1628]\tvalid_0's rmse: 0.0255489\n",
      "[1629]\tvalid_0's rmse: 0.0255457\n",
      "[1630]\tvalid_0's rmse: 0.0255384\n",
      "[1631]\tvalid_0's rmse: 0.0255383\n",
      "[1632]\tvalid_0's rmse: 0.0255377\n",
      "[1633]\tvalid_0's rmse: 0.025538\n",
      "[1634]\tvalid_0's rmse: 0.0255383\n",
      "[1635]\tvalid_0's rmse: 0.0255381\n",
      "[1636]\tvalid_0's rmse: 0.0255379\n",
      "[1637]\tvalid_0's rmse: 0.0255386\n",
      "[1638]\tvalid_0's rmse: 0.0255391\n",
      "[1639]\tvalid_0's rmse: 0.0255386\n",
      "[1640]\tvalid_0's rmse: 0.0255322\n",
      "[1641]\tvalid_0's rmse: 0.0255328\n",
      "[1642]\tvalid_0's rmse: 0.0255273\n",
      "[1643]\tvalid_0's rmse: 0.0255264\n",
      "[1644]\tvalid_0's rmse: 0.0255262\n",
      "[1645]\tvalid_0's rmse: 0.0255239\n",
      "[1646]\tvalid_0's rmse: 0.0255234\n",
      "[1647]\tvalid_0's rmse: 0.0255245\n",
      "[1648]\tvalid_0's rmse: 0.0255188\n",
      "[1649]\tvalid_0's rmse: 0.0255174\n",
      "[1650]\tvalid_0's rmse: 0.0255231\n",
      "[1651]\tvalid_0's rmse: 0.0255231\n",
      "[1652]\tvalid_0's rmse: 0.0255237\n",
      "[1653]\tvalid_0's rmse: 0.0255217\n",
      "[1654]\tvalid_0's rmse: 0.025521\n",
      "[1655]\tvalid_0's rmse: 0.0255201\n",
      "[1656]\tvalid_0's rmse: 0.02552\n",
      "[1657]\tvalid_0's rmse: 0.0255204\n",
      "[1658]\tvalid_0's rmse: 0.0255194\n",
      "[1659]\tvalid_0's rmse: 0.0255194\n",
      "[1660]\tvalid_0's rmse: 0.0255194\n",
      "[1661]\tvalid_0's rmse: 0.0255189\n",
      "[1662]\tvalid_0's rmse: 0.0255192\n",
      "[1663]\tvalid_0's rmse: 0.0255183\n",
      "[1664]\tvalid_0's rmse: 0.0255186\n",
      "[1665]\tvalid_0's rmse: 0.0255179\n",
      "[1666]\tvalid_0's rmse: 0.0255182\n",
      "[1667]\tvalid_0's rmse: 0.0255178\n",
      "[1668]\tvalid_0's rmse: 0.0255175\n",
      "[1669]\tvalid_0's rmse: 0.0255181\n",
      "[1670]\tvalid_0's rmse: 0.0255179\n",
      "[1671]\tvalid_0's rmse: 0.025517\n",
      "[1672]\tvalid_0's rmse: 0.0255169\n",
      "[1673]\tvalid_0's rmse: 0.0255012\n",
      "[1674]\tvalid_0's rmse: 0.0255018\n",
      "[1675]\tvalid_0's rmse: 0.0255017\n",
      "[1676]\tvalid_0's rmse: 0.0255032\n",
      "[1677]\tvalid_0's rmse: 0.0255028\n",
      "[1678]\tvalid_0's rmse: 0.0255035\n",
      "[1679]\tvalid_0's rmse: 0.0255038\n",
      "[1680]\tvalid_0's rmse: 0.0255043\n",
      "[1681]\tvalid_0's rmse: 0.0255043\n",
      "[1682]\tvalid_0's rmse: 0.0255052\n",
      "[1683]\tvalid_0's rmse: 0.0255043\n",
      "[1684]\tvalid_0's rmse: 0.0255045\n",
      "[1685]\tvalid_0's rmse: 0.0255044\n",
      "[1686]\tvalid_0's rmse: 0.0255039\n",
      "[1687]\tvalid_0's rmse: 0.0255027\n",
      "[1688]\tvalid_0's rmse: 0.0255026\n",
      "[1689]\tvalid_0's rmse: 0.0255028\n",
      "[1690]\tvalid_0's rmse: 0.0255036\n",
      "[1691]\tvalid_0's rmse: 0.0255024\n",
      "[1692]\tvalid_0's rmse: 0.0255021\n",
      "[1693]\tvalid_0's rmse: 0.0255018\n",
      "[1694]\tvalid_0's rmse: 0.0255018\n",
      "[1695]\tvalid_0's rmse: 0.0255012\n",
      "[1696]\tvalid_0's rmse: 0.0255006\n",
      "[1697]\tvalid_0's rmse: 0.0255006\n",
      "[1698]\tvalid_0's rmse: 0.0255005\n",
      "[1699]\tvalid_0's rmse: 0.0254974\n",
      "[1700]\tvalid_0's rmse: 0.0254964\n",
      "[1701]\tvalid_0's rmse: 0.0254971\n",
      "[1702]\tvalid_0's rmse: 0.0254974\n",
      "[1703]\tvalid_0's rmse: 0.0254974\n",
      "[1704]\tvalid_0's rmse: 0.0254945\n",
      "[1705]\tvalid_0's rmse: 0.0254948\n",
      "[1706]\tvalid_0's rmse: 0.0254947\n",
      "[1707]\tvalid_0's rmse: 0.025495\n",
      "[1708]\tvalid_0's rmse: 0.0254952\n",
      "[1709]\tvalid_0's rmse: 0.025495\n",
      "[1710]\tvalid_0's rmse: 0.0254946\n",
      "[1711]\tvalid_0's rmse: 0.0254946\n",
      "[1712]\tvalid_0's rmse: 0.0254923\n",
      "[1713]\tvalid_0's rmse: 0.0254919\n",
      "[1714]\tvalid_0's rmse: 0.0254932\n",
      "[1715]\tvalid_0's rmse: 0.025493\n",
      "[1716]\tvalid_0's rmse: 0.0254935\n",
      "[1717]\tvalid_0's rmse: 0.025492\n",
      "[1718]\tvalid_0's rmse: 0.0254914\n",
      "[1719]\tvalid_0's rmse: 0.0254918\n",
      "[1720]\tvalid_0's rmse: 0.0254917\n",
      "[1721]\tvalid_0's rmse: 0.0254922\n",
      "[1722]\tvalid_0's rmse: 0.0254925\n",
      "[1723]\tvalid_0's rmse: 0.0254928\n",
      "[1724]\tvalid_0's rmse: 0.0254932\n",
      "[1725]\tvalid_0's rmse: 0.0254931\n",
      "[1726]\tvalid_0's rmse: 0.0254933\n",
      "[1727]\tvalid_0's rmse: 0.0254931\n",
      "[1728]\tvalid_0's rmse: 0.0254962\n",
      "[1729]\tvalid_0's rmse: 0.0254961\n",
      "[1730]\tvalid_0's rmse: 0.0254956\n",
      "[1731]\tvalid_0's rmse: 0.025495\n",
      "[1732]\tvalid_0's rmse: 0.0254947\n",
      "[1733]\tvalid_0's rmse: 0.0254938\n",
      "[1734]\tvalid_0's rmse: 0.0254942\n",
      "[1735]\tvalid_0's rmse: 0.0254946\n",
      "[1736]\tvalid_0's rmse: 0.0254936\n",
      "[1737]\tvalid_0's rmse: 0.0254922\n",
      "[1738]\tvalid_0's rmse: 0.0254917\n",
      "[1739]\tvalid_0's rmse: 0.025492\n",
      "[1740]\tvalid_0's rmse: 0.025492\n",
      "[1741]\tvalid_0's rmse: 0.0254923\n",
      "[1742]\tvalid_0's rmse: 0.0254932\n",
      "[1743]\tvalid_0's rmse: 0.0254933\n",
      "[1744]\tvalid_0's rmse: 0.0254935\n",
      "[1745]\tvalid_0's rmse: 0.0254933\n",
      "[1746]\tvalid_0's rmse: 0.0254937\n",
      "[1747]\tvalid_0's rmse: 0.0254928\n",
      "[1748]\tvalid_0's rmse: 0.0254926\n",
      "[1749]\tvalid_0's rmse: 0.0254945\n",
      "[1750]\tvalid_0's rmse: 0.0254948\n",
      "[1751]\tvalid_0's rmse: 0.025495\n",
      "[1752]\tvalid_0's rmse: 0.025487\n",
      "[1753]\tvalid_0's rmse: 0.0254868\n",
      "[1754]\tvalid_0's rmse: 0.025486\n",
      "[1755]\tvalid_0's rmse: 0.0254842\n",
      "[1756]\tvalid_0's rmse: 0.0254837\n",
      "[1757]\tvalid_0's rmse: 0.025483\n",
      "[1758]\tvalid_0's rmse: 0.0254827\n",
      "[1759]\tvalid_0's rmse: 0.0254805\n",
      "[1760]\tvalid_0's rmse: 0.02548\n",
      "[1761]\tvalid_0's rmse: 0.0254799\n",
      "[1762]\tvalid_0's rmse: 0.0254799\n",
      "[1763]\tvalid_0's rmse: 0.0254794\n",
      "[1764]\tvalid_0's rmse: 0.0254783\n",
      "[1765]\tvalid_0's rmse: 0.0254772\n",
      "[1766]\tvalid_0's rmse: 0.0254773\n",
      "[1767]\tvalid_0's rmse: 0.0254773\n",
      "[1768]\tvalid_0's rmse: 0.0254767\n",
      "[1769]\tvalid_0's rmse: 0.0254775\n",
      "[1770]\tvalid_0's rmse: 0.0254774\n",
      "[1771]\tvalid_0's rmse: 0.0254775\n",
      "[1772]\tvalid_0's rmse: 0.0254769\n",
      "[1773]\tvalid_0's rmse: 0.025477\n",
      "[1774]\tvalid_0's rmse: 0.0254779\n",
      "[1775]\tvalid_0's rmse: 0.025477\n",
      "[1776]\tvalid_0's rmse: 0.0254767\n",
      "[1777]\tvalid_0's rmse: 0.025474\n",
      "[1778]\tvalid_0's rmse: 0.0254756\n",
      "[1779]\tvalid_0's rmse: 0.0254761\n",
      "[1780]\tvalid_0's rmse: 0.025476\n",
      "[1781]\tvalid_0's rmse: 0.0254763\n",
      "[1782]\tvalid_0's rmse: 0.0254763\n",
      "[1783]\tvalid_0's rmse: 0.0254762\n",
      "[1784]\tvalid_0's rmse: 0.0254749\n",
      "[1785]\tvalid_0's rmse: 0.025473\n",
      "[1786]\tvalid_0's rmse: 0.0254723\n",
      "[1787]\tvalid_0's rmse: 0.0254712\n",
      "[1788]\tvalid_0's rmse: 0.0254711\n",
      "[1789]\tvalid_0's rmse: 0.0254718\n",
      "[1790]\tvalid_0's rmse: 0.0254716\n",
      "[1791]\tvalid_0's rmse: 0.0254721\n",
      "[1792]\tvalid_0's rmse: 0.0254709\n",
      "[1793]\tvalid_0's rmse: 0.0254738\n",
      "[1794]\tvalid_0's rmse: 0.0254739\n",
      "[1795]\tvalid_0's rmse: 0.025474\n",
      "[1796]\tvalid_0's rmse: 0.0254719\n",
      "[1797]\tvalid_0's rmse: 0.0254719\n",
      "[1798]\tvalid_0's rmse: 0.0254734\n",
      "[1799]\tvalid_0's rmse: 0.0254738\n",
      "[1800]\tvalid_0's rmse: 0.0254739\n",
      "[1801]\tvalid_0's rmse: 0.0254722\n",
      "[1802]\tvalid_0's rmse: 0.0254725\n",
      "[1803]\tvalid_0's rmse: 0.0254716\n",
      "[1804]\tvalid_0's rmse: 0.0254717\n",
      "[1805]\tvalid_0's rmse: 0.0254718\n",
      "[1806]\tvalid_0's rmse: 0.025471\n",
      "[1807]\tvalid_0's rmse: 0.0254714\n",
      "[1808]\tvalid_0's rmse: 0.0254714\n",
      "[1809]\tvalid_0's rmse: 0.0254713\n",
      "[1810]\tvalid_0's rmse: 0.0254711\n",
      "[1811]\tvalid_0's rmse: 0.0254716\n",
      "[1812]\tvalid_0's rmse: 0.025472\n",
      "[1813]\tvalid_0's rmse: 0.0254719\n",
      "[1814]\tvalid_0's rmse: 0.0254712\n",
      "[1815]\tvalid_0's rmse: 0.0254712\n",
      "[1816]\tvalid_0's rmse: 0.0254708\n",
      "[1817]\tvalid_0's rmse: 0.0254711\n",
      "[1818]\tvalid_0's rmse: 0.0254701\n",
      "[1819]\tvalid_0's rmse: 0.0254683\n",
      "[1820]\tvalid_0's rmse: 0.0254685\n",
      "[1821]\tvalid_0's rmse: 0.0254685\n",
      "[1822]\tvalid_0's rmse: 0.0254687\n",
      "[1823]\tvalid_0's rmse: 0.0254688\n",
      "[1824]\tvalid_0's rmse: 0.0254686\n",
      "[1825]\tvalid_0's rmse: 0.0254686\n",
      "[1826]\tvalid_0's rmse: 0.0254685\n",
      "[1827]\tvalid_0's rmse: 0.0254681\n",
      "[1828]\tvalid_0's rmse: 0.0254681\n",
      "[1829]\tvalid_0's rmse: 0.025468\n",
      "[1830]\tvalid_0's rmse: 0.0254683\n",
      "[1831]\tvalid_0's rmse: 0.025464\n",
      "[1832]\tvalid_0's rmse: 0.0254641\n",
      "[1833]\tvalid_0's rmse: 0.0254636\n",
      "[1834]\tvalid_0's rmse: 0.0254633\n",
      "[1835]\tvalid_0's rmse: 0.0254625\n",
      "[1836]\tvalid_0's rmse: 0.0254622\n",
      "[1837]\tvalid_0's rmse: 0.0254617\n",
      "[1838]\tvalid_0's rmse: 0.0254617\n",
      "[1839]\tvalid_0's rmse: 0.0254609\n",
      "[1840]\tvalid_0's rmse: 0.025452\n",
      "[1841]\tvalid_0's rmse: 0.0254516\n",
      "[1842]\tvalid_0's rmse: 0.0254517\n",
      "[1843]\tvalid_0's rmse: 0.0254523\n",
      "[1844]\tvalid_0's rmse: 0.0254516\n",
      "[1845]\tvalid_0's rmse: 0.0254519\n",
      "[1846]\tvalid_0's rmse: 0.0254519\n",
      "[1847]\tvalid_0's rmse: 0.0254506\n",
      "[1848]\tvalid_0's rmse: 0.0254508\n",
      "[1849]\tvalid_0's rmse: 0.0254503\n",
      "[1850]\tvalid_0's rmse: 0.0254484\n",
      "[1851]\tvalid_0's rmse: 0.0254485\n",
      "[1852]\tvalid_0's rmse: 0.0254486\n",
      "[1853]\tvalid_0's rmse: 0.0254492\n",
      "[1854]\tvalid_0's rmse: 0.0254493\n",
      "[1855]\tvalid_0's rmse: 0.0254488\n",
      "[1856]\tvalid_0's rmse: 0.0254492\n",
      "[1857]\tvalid_0's rmse: 0.0254538\n",
      "[1858]\tvalid_0's rmse: 0.0254541\n",
      "[1859]\tvalid_0's rmse: 0.0254591\n",
      "[1860]\tvalid_0's rmse: 0.0254593\n",
      "[1861]\tvalid_0's rmse: 0.0254593\n",
      "[1862]\tvalid_0's rmse: 0.0254589\n",
      "[1863]\tvalid_0's rmse: 0.0254589\n",
      "[1864]\tvalid_0's rmse: 0.0254596\n",
      "[1865]\tvalid_0's rmse: 0.0254593\n",
      "[1866]\tvalid_0's rmse: 0.02546\n",
      "[1867]\tvalid_0's rmse: 0.0254596\n",
      "[1868]\tvalid_0's rmse: 0.0254609\n",
      "[1869]\tvalid_0's rmse: 0.0254586\n",
      "[1870]\tvalid_0's rmse: 0.0254583\n",
      "[1871]\tvalid_0's rmse: 0.0254584\n",
      "[1872]\tvalid_0's rmse: 0.0254582\n",
      "[1873]\tvalid_0's rmse: 0.025458\n",
      "[1874]\tvalid_0's rmse: 0.0254559\n",
      "[1875]\tvalid_0's rmse: 0.0254556\n",
      "[1876]\tvalid_0's rmse: 0.0254552\n",
      "[1877]\tvalid_0's rmse: 0.0254551\n",
      "[1878]\tvalid_0's rmse: 0.0254557\n",
      "[1879]\tvalid_0's rmse: 0.0254539\n",
      "[1880]\tvalid_0's rmse: 0.0254533\n",
      "[1881]\tvalid_0's rmse: 0.0254524\n",
      "[1882]\tvalid_0's rmse: 0.0254525\n",
      "[1883]\tvalid_0's rmse: 0.0254542\n",
      "[1884]\tvalid_0's rmse: 0.0254548\n",
      "[1885]\tvalid_0's rmse: 0.0254539\n",
      "[1886]\tvalid_0's rmse: 0.0254536\n",
      "[1887]\tvalid_0's rmse: 0.0254537\n",
      "[1888]\tvalid_0's rmse: 0.0254532\n",
      "[1889]\tvalid_0's rmse: 0.0254555\n",
      "[1890]\tvalid_0's rmse: 0.0254548\n",
      "[1891]\tvalid_0's rmse: 0.0254549\n",
      "[1892]\tvalid_0's rmse: 0.0254548\n",
      "[1893]\tvalid_0's rmse: 0.0254545\n",
      "[1894]\tvalid_0's rmse: 0.0254543\n",
      "[1895]\tvalid_0's rmse: 0.0254553\n",
      "[1896]\tvalid_0's rmse: 0.0254551\n",
      "[1897]\tvalid_0's rmse: 0.0254553\n",
      "[1898]\tvalid_0's rmse: 0.0254557\n",
      "[1899]\tvalid_0's rmse: 0.0254553\n",
      "[1900]\tvalid_0's rmse: 0.0254554\n",
      "[1901]\tvalid_0's rmse: 0.025455\n",
      "[1902]\tvalid_0's rmse: 0.0254548\n",
      "[1903]\tvalid_0's rmse: 0.0254559\n",
      "[1904]\tvalid_0's rmse: 0.025455\n",
      "[1905]\tvalid_0's rmse: 0.0254548\n",
      "[1906]\tvalid_0's rmse: 0.0254548\n",
      "[1907]\tvalid_0's rmse: 0.025454\n",
      "[1908]\tvalid_0's rmse: 0.0254535\n",
      "[1909]\tvalid_0's rmse: 0.0254534\n",
      "[1910]\tvalid_0's rmse: 0.0254536\n",
      "[1911]\tvalid_0's rmse: 0.0254536\n",
      "[1912]\tvalid_0's rmse: 0.0254531\n",
      "[1913]\tvalid_0's rmse: 0.0254532\n",
      "[1914]\tvalid_0's rmse: 0.0254535\n",
      "[1915]\tvalid_0's rmse: 0.0254525\n",
      "[1916]\tvalid_0's rmse: 0.025452\n",
      "[1917]\tvalid_0's rmse: 0.0254519\n",
      "[1918]\tvalid_0's rmse: 0.0254518\n",
      "[1919]\tvalid_0's rmse: 0.0254515\n",
      "[1920]\tvalid_0's rmse: 0.0254513\n",
      "[1921]\tvalid_0's rmse: 0.0254524\n",
      "[1922]\tvalid_0's rmse: 0.0254529\n",
      "[1923]\tvalid_0's rmse: 0.0254551\n",
      "[1924]\tvalid_0's rmse: 0.0254534\n",
      "[1925]\tvalid_0's rmse: 0.0254535\n",
      "[1926]\tvalid_0's rmse: 0.0254536\n",
      "[1927]\tvalid_0's rmse: 0.0254536\n",
      "[1928]\tvalid_0's rmse: 0.0254538\n",
      "[1929]\tvalid_0's rmse: 0.0254538\n",
      "[1930]\tvalid_0's rmse: 0.0254529\n",
      "[1931]\tvalid_0's rmse: 0.0254529\n",
      "[1932]\tvalid_0's rmse: 0.0254527\n",
      "[1933]\tvalid_0's rmse: 0.0254525\n",
      "[1934]\tvalid_0's rmse: 0.0254524\n",
      "[1935]\tvalid_0's rmse: 0.0254518\n",
      "[1936]\tvalid_0's rmse: 0.0254518\n",
      "[1937]\tvalid_0's rmse: 0.0254518\n",
      "[1938]\tvalid_0's rmse: 0.0254512\n",
      "[1939]\tvalid_0's rmse: 0.0254511\n",
      "[1940]\tvalid_0's rmse: 0.0254517\n",
      "[1941]\tvalid_0's rmse: 0.0254514\n",
      "[1942]\tvalid_0's rmse: 0.0254517\n",
      "[1943]\tvalid_0's rmse: 0.0254503\n",
      "[1944]\tvalid_0's rmse: 0.0254474\n",
      "[1945]\tvalid_0's rmse: 0.0254471\n",
      "[1946]\tvalid_0's rmse: 0.0254472\n",
      "[1947]\tvalid_0's rmse: 0.0254473\n",
      "[1948]\tvalid_0's rmse: 0.0254469\n",
      "[1949]\tvalid_0's rmse: 0.0254462\n",
      "[1950]\tvalid_0's rmse: 0.0254464\n",
      "[1951]\tvalid_0's rmse: 0.025446\n",
      "[1952]\tvalid_0's rmse: 0.025446\n",
      "[1953]\tvalid_0's rmse: 0.0254422\n",
      "[1954]\tvalid_0's rmse: 0.0254356\n",
      "[1955]\tvalid_0's rmse: 0.0254358\n",
      "[1956]\tvalid_0's rmse: 0.0254357\n",
      "[1957]\tvalid_0's rmse: 0.0254344\n",
      "[1958]\tvalid_0's rmse: 0.0254348\n",
      "[1959]\tvalid_0's rmse: 0.0254348\n",
      "[1960]\tvalid_0's rmse: 0.0254347\n",
      "[1961]\tvalid_0's rmse: 0.0254346\n",
      "[1962]\tvalid_0's rmse: 0.0254346\n",
      "[1963]\tvalid_0's rmse: 0.0254344\n",
      "[1964]\tvalid_0's rmse: 0.0254341\n",
      "[1965]\tvalid_0's rmse: 0.0254337\n",
      "[1966]\tvalid_0's rmse: 0.0254337\n",
      "[1967]\tvalid_0's rmse: 0.0254335\n",
      "[1968]\tvalid_0's rmse: 0.0254336\n",
      "[1969]\tvalid_0's rmse: 0.0254336\n",
      "[1970]\tvalid_0's rmse: 0.0254333\n",
      "[1971]\tvalid_0's rmse: 0.0254335\n",
      "[1972]\tvalid_0's rmse: 0.0254333\n",
      "[1973]\tvalid_0's rmse: 0.0254328\n",
      "[1974]\tvalid_0's rmse: 0.0254329\n",
      "[1975]\tvalid_0's rmse: 0.0254329\n",
      "[1976]\tvalid_0's rmse: 0.0254334\n",
      "[1977]\tvalid_0's rmse: 0.0254333\n",
      "[1978]\tvalid_0's rmse: 0.0254336\n",
      "[1979]\tvalid_0's rmse: 0.0254342\n",
      "[1980]\tvalid_0's rmse: 0.0254343\n",
      "[1981]\tvalid_0's rmse: 0.0254338\n",
      "[1982]\tvalid_0's rmse: 0.0254341\n",
      "[1983]\tvalid_0's rmse: 0.0254341\n",
      "[1984]\tvalid_0's rmse: 0.0254343\n",
      "[1985]\tvalid_0's rmse: 0.0254342\n",
      "[1986]\tvalid_0's rmse: 0.0254341\n",
      "[1987]\tvalid_0's rmse: 0.0254347\n",
      "[1988]\tvalid_0's rmse: 0.025435\n",
      "[1989]\tvalid_0's rmse: 0.0254349\n",
      "[1990]\tvalid_0's rmse: 0.0254338\n",
      "[1991]\tvalid_0's rmse: 0.0254339\n",
      "[1992]\tvalid_0's rmse: 0.0254342\n",
      "[1993]\tvalid_0's rmse: 0.0254341\n",
      "[1994]\tvalid_0's rmse: 0.0254341\n",
      "[1995]\tvalid_0's rmse: 0.0254339\n",
      "[1996]\tvalid_0's rmse: 0.0254349\n",
      "[1997]\tvalid_0's rmse: 0.025434\n",
      "[1998]\tvalid_0's rmse: 0.0254327\n",
      "[1999]\tvalid_0's rmse: 0.0254326\n",
      "[2000]\tvalid_0's rmse: 0.025432\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[2000]\tvalid_0's rmse: 0.025432\n"
     ]
    }
   ],
   "source": [
    "gbm = lgb.train(params_gbm, lgb_train, num_boost_round=2000, valid_sets=lgb_eval, early_stopping_rounds=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y_pred = gbm.predict(X_test)\n",
    "y_true = Y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 3.7E-04\n",
      "RMSE: 0.019\n",
      "MAE: 0.013\n",
      "MAPE: 2.64 %\n",
      "R_2: 0.93\n"
     ]
    }
   ],
   "source": [
    "MSE = mean_squared_error(y_true, y_pred)\n",
    "RMSE = np.sqrt(mean_squared_error(y_true, y_pred))\n",
    "MAE = mean_absolute_error(y_true, y_pred)\n",
    "MAPE = mean_absolute_percentage_error(y_true, y_pred)\n",
    "R_2 = r2_score(y_true, y_pred)\n",
    "print('MSE:', format(MSE, '.1E'))\n",
    "print('RMSE:', round(RMSE, 3))\n",
    "print('MAE:', round(MAE, 3))\n",
    "print('MAPE:', round(MAPE*100, 2), '%')\n",
    "print('R_2:', round(R_2, 3))  #R方为负就说明拟合效果比平均值差a"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [],
   "source": [
    "dtrain = xgb.DMatrix(X_train, Y_train)\n",
    "dvalid = xgb.DMatrix(X_valid, Y_valid)\n",
    "dtest = xgb.DMatrix(X_test, Y_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from xgboost import XGBRegressor\n",
    "from bayes_opt import BayesianOptimization"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "outputs": [],
   "source": [
    "def xgb_cv(max_depth, learning_rate, n_estimators, min_child_weight, subsample, colsample_bytree, reg_alpha, gamma):\n",
    "    val = cross_val_score(estimator=XGBRegressor(max_depth=int(max_depth),\n",
    "                                                 learning_rate=learning_rate,\n",
    "                                                 n_estimators=int(n_estimators),\n",
    "                                                 min_child_weight=min_child_weight,\n",
    "                                                 subsample=max(min(subsample, 1), 0),\n",
    "                                                 colsample_bytree=max(min(colsample_bytree, 1), 0),\n",
    "                                                 reg_alpha=max(reg_alpha, 0), gamma=gamma, objective='reg:squarederror',\n",
    "                                                 booster='gbtree',\n",
    "                                                 seed=666), X=use_data[feature_cols], y=use_data.values[:1], scoring='r2',\n",
    "                          cv=10).max()\n",
    "    return val"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   iter    |  target   | colsam... |   gamma   | learni... | max_depth | min_ch... | n_esti... | reg_alpha | subsample |\n",
      "-------------------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [3080, 1]",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_17148\\1576227182.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m      7\u001B[0m                                                \u001B[1;34m'reg_alpha'\u001B[0m\u001B[1;33m:\u001B[0m \u001B[1;33m(\u001B[0m\u001B[1;36m0.001\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m10\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      8\u001B[0m                                                'gamma': (0.001, 10)})\n\u001B[1;32m----> 9\u001B[1;33m \u001B[0mxgb_bo\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmaximize\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mn_iter\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;36m100\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0minit_points\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;36m10\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[1;32mD:\\miniconda3\\envs\\py37\\lib\\site-packages\\bayes_opt\\bayesian_optimization.py\u001B[0m in \u001B[0;36mmaximize\u001B[1;34m(self, init_points, n_iter, acquisition_function, acq, kappa, kappa_decay, kappa_decay_delay, xi, **gp_params)\u001B[0m\n\u001B[0;32m    309\u001B[0m                 \u001B[0mx_probe\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msuggest\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mutil\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    310\u001B[0m                 \u001B[0miteration\u001B[0m \u001B[1;33m+=\u001B[0m \u001B[1;36m1\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 311\u001B[1;33m             \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mprobe\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mx_probe\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mlazy\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    312\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    313\u001B[0m             \u001B[1;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_bounds_transformer\u001B[0m \u001B[1;32mand\u001B[0m \u001B[0miteration\u001B[0m \u001B[1;33m>\u001B[0m \u001B[1;36m0\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\miniconda3\\envs\\py37\\lib\\site-packages\\bayes_opt\\bayesian_optimization.py\u001B[0m in \u001B[0;36mprobe\u001B[1;34m(self, params, lazy)\u001B[0m\n\u001B[0;32m    206\u001B[0m             \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_queue\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0madd\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mparams\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    207\u001B[0m         \u001B[1;32melse\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 208\u001B[1;33m             \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_space\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mprobe\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mparams\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    209\u001B[0m             \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdispatch\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mEvents\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mOPTIMIZATION_STEP\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    210\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\miniconda3\\envs\\py37\\lib\\site-packages\\bayes_opt\\target_space.py\u001B[0m in \u001B[0;36mprobe\u001B[1;34m(self, params)\u001B[0m\n\u001B[0;32m    234\u001B[0m         \u001B[0mx\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_as_array\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mparams\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    235\u001B[0m         \u001B[0mparams\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mdict\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mzip\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_keys\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 236\u001B[1;33m         \u001B[0mtarget\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mtarget_func\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m**\u001B[0m\u001B[0mparams\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    237\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    238\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_constraint\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_17148\\2288155185.py\u001B[0m in \u001B[0;36mxgb_cv\u001B[1;34m(max_depth, learning_rate, n_estimators, min_child_weight, subsample, colsample_bytree, reg_alpha, gamma)\u001B[0m\n\u001B[0;32m      9\u001B[0m                                                  \u001B[0mbooster\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m'gbtree'\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     10\u001B[0m                                                  seed=666), X=use_data[feature_cols], y=use_data.values[:1], scoring='r2',\n\u001B[1;32m---> 11\u001B[1;33m                           cv=10).max()\n\u001B[0m\u001B[0;32m     12\u001B[0m     \u001B[1;32mreturn\u001B[0m \u001B[0mval\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\miniconda3\\envs\\py37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001B[0m in \u001B[0;36mcross_val_score\u001B[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001B[0m\n\u001B[0;32m    518\u001B[0m         \u001B[0mfit_params\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mfit_params\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    519\u001B[0m         \u001B[0mpre_dispatch\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mpre_dispatch\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 520\u001B[1;33m         \u001B[0merror_score\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0merror_score\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    521\u001B[0m     )\n\u001B[0;32m    522\u001B[0m     \u001B[1;32mreturn\u001B[0m \u001B[0mcv_results\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m\"test_score\"\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\miniconda3\\envs\\py37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001B[0m in \u001B[0;36mcross_validate\u001B[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001B[0m\n\u001B[0;32m    251\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    252\u001B[0m     \"\"\"\n\u001B[1;32m--> 253\u001B[1;33m     \u001B[0mX\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0my\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mgroups\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mindexable\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mX\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0my\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mgroups\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    254\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    255\u001B[0m     \u001B[0mcv\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mcheck_cv\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcv\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0my\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mclassifier\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mis_classifier\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mestimator\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\miniconda3\\envs\\py37\\lib\\site-packages\\sklearn\\utils\\validation.py\u001B[0m in \u001B[0;36mindexable\u001B[1;34m(*iterables)\u001B[0m\n\u001B[0;32m    376\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    377\u001B[0m     \u001B[0mresult\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;33m[\u001B[0m\u001B[0m_make_indexable\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mX\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mfor\u001B[0m \u001B[0mX\u001B[0m \u001B[1;32min\u001B[0m \u001B[0miterables\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 378\u001B[1;33m     \u001B[0mcheck_consistent_length\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0mresult\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    379\u001B[0m     \u001B[1;32mreturn\u001B[0m \u001B[0mresult\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    380\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\miniconda3\\envs\\py37\\lib\\site-packages\\sklearn\\utils\\validation.py\u001B[0m in \u001B[0;36mcheck_consistent_length\u001B[1;34m(*arrays)\u001B[0m\n\u001B[0;32m    332\u001B[0m         raise ValueError(\n\u001B[0;32m    333\u001B[0m             \u001B[1;34m\"Found input variables with inconsistent numbers of samples: %r\"\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 334\u001B[1;33m             \u001B[1;33m%\u001B[0m \u001B[1;33m[\u001B[0m\u001B[0mint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0ml\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mfor\u001B[0m \u001B[0ml\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mlengths\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    335\u001B[0m         )\n\u001B[0;32m    336\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mValueError\u001B[0m: Found input variables with inconsistent numbers of samples: [3080, 1]"
     ]
    }
   ],
   "source": [
    "xgb_bo = BayesianOptimization(xgb_cv, pbounds={'max_depth': (20, 60),\n",
    "                                               'learning_rate': (0.005, 0.1),\n",
    "                                               'n_estimators': (100, 2000),\n",
    "                                               'min_child_weight': (0, 30),\n",
    "                                               'subsample': (0.05, 1),\n",
    "                                               'colsample_bytree': (0.1, 1),\n",
    "                                               'reg_alpha': (0.001, 10),\n",
    "                                               'gamma': (0.001, 10)})\n",
    "xgb_bo.maximize(n_iter=100, init_points=10)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "outputs": [],
   "source": [
    "params_xgb = {'objective': 'reg:squarederror',\n",
    "              'booster': 'gbtree',\n",
    "              'eta': 0.037,\n",
    "              'max_depth': 30,\n",
    "              'subsample': 1.0,\n",
    "              'colsample_bytree': 0.47,\n",
    "              'min_child_weight': 30,\n",
    "              'seed': 42}\n",
    "num_boost_round = 2000\n",
    "\n",
    "dtrain = xgb.DMatrix(X_train, Y_train)\n",
    "dvalid = xgb.DMatrix(X_valid, Y_valid)\n",
    "watchlist = [(dtrain, 'train'), (dvalid, 'eval')]\n",
    "\n",
    "gb_model = xgb.train(params_xgb, dtrain, num_boost_round, evals=watchlist,\n",
    "                early_stopping_rounds=100, verbose_eval=False)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "outputs": [],
   "source": [
    "y_pred_xgb = np.expm1(gb_model.predict(xgb.DMatrix(X_test)))\n",
    "y_true_xgb = np.expm1(Y_test.values)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 1.1E-05\n",
      "RMSE: 0.003\n",
      "MAE: 0.002\n",
      "MAPE: 2.99 %\n",
      "R_2: 0.88\n"
     ]
    }
   ],
   "source": [
    "MSE = mean_squared_error(y_true_xgb, y_pred_xgb)\n",
    "RMSE = np.sqrt(mean_squared_error(y_true_xgb, y_pred_xgb))\n",
    "MAE = mean_absolute_error(y_true_xgb, y_pred_xgb)\n",
    "MAPE = mean_absolute_percentage_error(y_true_xgb, y_pred_xgb)\n",
    "R_2 = r2_score(y_true_xgb, y_pred_xgb)\n",
    "print('MSE:', format(MSE, '.1E'))\n",
    "print('RMSE:', round(RMSE, 3))\n",
    "print('MAE:', round(MAE, 3))\n",
    "print('MAPE:', round(MAPE*100, 2), '%')\n",
    "print('R_2:', round(R_2, 3))  #R方为负就说明拟合效果比平均值差a"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 1.8E-05, RMSE: 0.004, MAE: 0.002, MAPE: 3.47 %, R_2: 0.776\n",
      "MSE: 1.8E-05, RMSE: 0.004, MAE: 0.002, MAPE: 3.19 %, R_2: 0.83\n",
      "MSE: 1.8E-05, RMSE: 0.004, MAE: 0.002, MAPE: 3.87 %, R_2: 0.811\n",
      "MSE: 1.2E-05, RMSE: 0.003, MAE: 0.002, MAPE: 2.96 %, R_2: 0.861\n",
      "MSE: 1.9E-05, RMSE: 0.004, MAE: 0.002, MAPE: 3.65 %, R_2: 0.775\n",
      "MSE: 1.9E-05, RMSE: 0.004, MAE: 0.002, MAPE: 3.56 %, R_2: 0.789\n",
      "MSE: 2.3E-05, RMSE: 0.005, MAE: 0.002, MAPE: 3.05 %, R_2: 0.723\n",
      "MSE: 2.5E-05, RMSE: 0.005, MAE: 0.002, MAPE: 3.94 %, R_2: 0.717\n",
      "MSE: 1.0E-05, RMSE: 0.003, MAE: 0.002, MAPE: 2.9 %, R_2: 0.864\n",
      "MSE: 9.4E-06, RMSE: 0.003, MAE: 0.002, MAPE: 2.89 %, R_2: 0.881\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=10, shuffle=True, random_state=42)\n",
    "eva_list = list()\n",
    "for (train_index, test_index) in kf.split(use_data):\n",
    "    train = use_data.loc[train_index]\n",
    "    test = use_data.loc[test_index]\n",
    "    train, valid = train_test_split(train, test_size=0.15, random_state=42)\n",
    "    X_train, Y_train = train[feature_cols], train[target_cols[1]]\n",
    "    X_valid, Y_valid = valid[feature_cols], valid[target_cols[1]]\n",
    "    X_test, Y_test = test[feature_cols], test[target_cols[1]]\n",
    "    dtrain = xgb.DMatrix(X_train, Y_train)\n",
    "    dvalid = xgb.DMatrix(X_valid, Y_valid)\n",
    "    watchlist = [(dvalid, 'eval')]\n",
    "    gb_model = xgb.train(params_xgb, dtrain, num_boost_round, evals=watchlist,\n",
    "                    early_stopping_rounds=100, verbose_eval=False)\n",
    "    y_pred = gb_model.predict(xgb.DMatrix(X_test))\n",
    "    y_true = Y_test.values\n",
    "    MSE = mean_squared_error(y_true, y_pred)\n",
    "    RMSE = np.sqrt(mean_squared_error(y_true, y_pred))\n",
    "    MAE = mean_absolute_error(y_true, y_pred)\n",
    "    MAPE = mean_absolute_percentage_error(y_true, y_pred)\n",
    "    R_2 = r2_score(y_true, y_pred)\n",
    "    print('MSE:', format(MSE, '.1E'), end=', ')\n",
    "    print('RMSE:', round(RMSE, 3), end=', ')\n",
    "    print('MAE:', round(MAE, 3), end=', ')\n",
    "    print('MAPE:', round(MAPE*100, 2), '%', end=', ')\n",
    "    print('R_2:', round(R_2, 3))  #R方为负就说明拟合效果比平均值差\n",
    "    eva_list.append([MSE, RMSE, MAE, MAPE, R_2])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "outputs": [],
   "source": [
    "record = pd.DataFrame.from_records(eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R2'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "outputs": [
    {
     "data": {
      "text/plain": "        MSE      RMSE       MAE      MAPE        R2\n0  0.000018  0.004221  0.002394  0.034705  0.775560\n1  0.000018  0.004191  0.002405  0.031921  0.829931\n2  0.000018  0.004249  0.002235  0.038677  0.810649\n3  0.000012  0.003395  0.002090  0.029607  0.861337\n4  0.000019  0.004334  0.002302  0.036496  0.775066\n5  0.000019  0.004367  0.002260  0.035588  0.789063\n6  0.000023  0.004806  0.002272  0.030522  0.723082\n7  0.000025  0.004968  0.002401  0.039428  0.717094\n8  0.000010  0.003207  0.002037  0.029033  0.863679\n9  0.000009  0.003072  0.002008  0.028871  0.880821",
      "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>MSE</th>\n      <th>RMSE</th>\n      <th>MAE</th>\n      <th>MAPE</th>\n      <th>R2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.000018</td>\n      <td>0.004221</td>\n      <td>0.002394</td>\n      <td>0.034705</td>\n      <td>0.775560</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.000018</td>\n      <td>0.004191</td>\n      <td>0.002405</td>\n      <td>0.031921</td>\n      <td>0.829931</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.000018</td>\n      <td>0.004249</td>\n      <td>0.002235</td>\n      <td>0.038677</td>\n      <td>0.810649</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.000012</td>\n      <td>0.003395</td>\n      <td>0.002090</td>\n      <td>0.029607</td>\n      <td>0.861337</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.000019</td>\n      <td>0.004334</td>\n      <td>0.002302</td>\n      <td>0.036496</td>\n      <td>0.775066</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.000019</td>\n      <td>0.004367</td>\n      <td>0.002260</td>\n      <td>0.035588</td>\n      <td>0.789063</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.000023</td>\n      <td>0.004806</td>\n      <td>0.002272</td>\n      <td>0.030522</td>\n      <td>0.723082</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0.000025</td>\n      <td>0.004968</td>\n      <td>0.002401</td>\n      <td>0.039428</td>\n      <td>0.717094</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0.000010</td>\n      <td>0.003207</td>\n      <td>0.002037</td>\n      <td>0.029033</td>\n      <td>0.863679</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0.000009</td>\n      <td>0.003072</td>\n      <td>0.002008</td>\n      <td>0.028871</td>\n      <td>0.880821</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "outputs": [
    {
     "data": {
      "text/plain": "       MSE      RMSE       MAE      MAPE        R2\n8  0.00001  0.003207  0.002037  0.029033  0.863679",
      "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>MSE</th>\n      <th>RMSE</th>\n      <th>MAE</th>\n      <th>MAPE</th>\n      <th>R2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8</th>\n      <td>0.00001</td>\n      <td>0.003207</td>\n      <td>0.002037</td>\n      <td>0.029033</td>\n      <td>0.863679</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "outputs": [],
   "source": [
    "index = [0, 1, 2, 3, 4, 5, 6, 8]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "outputs": [
    {
     "data": {
      "text/plain": "MSE     0.000017\nRMSE    0.004096\nMAE     0.002249\nMAPE    0.033319\nR2      0.803546\ndtype: float64"
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record.loc[index].mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "data": {
      "text/plain": "MSE     0.000552\nRMSE    0.022978\nMAE     0.014251\nMAPE    0.034105\nR2      0.896138\ndtype: float64"
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record.mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#新增加的两行\n",
    "from pylab import mpl\n",
    "# 设置显示中文字体\n",
    "mpl.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "\n",
    "mpl.rcParams[\"axes.unicode_minus\"] = False"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x1000 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 10))\n",
    "plt.plot(range(len(y_true)), y_true, 'o-', label='真实值')\n",
    "plt.plot(range(len(y_pred)), y_pred, '*-', label='预测值')\n",
    "plt.legend(loc='best')\n",
    "plt.title('预测结果')\n",
    "plt.savefig('./figure/CO2排放强度预测结果.png')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [],
   "source": [
    "pd.DataFrame.from_records([y_pred, y_true]).T.to_csv('pred.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [],
   "source": [
    "rst = pd.DataFrame.from_records(([y_true_xgb, y_pred_xgb])).T\n",
    "rst.columns = ['y_true', 'y_pred']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [],
   "source": [
    "rst['mAP'] = abs(rst.y_pred - rst.y_true) / rst.y_true"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "data": {
      "text/plain": "      y_true    y_pred       mAP\n23  0.233161  0.228589  0.019609\n46  0.242031  0.260373  0.075782\n42  0.233845  0.215675  0.077700\n1   0.233773  0.237715  0.016864\n58  0.258407  0.259042  0.002460\n41  0.233404  0.246465  0.055956\n15  0.249245  0.248289  0.003837\n63  0.237670  0.284324  0.196296\n59  0.244008  0.242001  0.008228\n37  0.252681  0.251169  0.005983",
      "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>y_true</th>\n      <th>y_pred</th>\n      <th>mAP</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>23</th>\n      <td>0.233161</td>\n      <td>0.228589</td>\n      <td>0.019609</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>0.242031</td>\n      <td>0.260373</td>\n      <td>0.075782</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>0.233845</td>\n      <td>0.215675</td>\n      <td>0.077700</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.233773</td>\n      <td>0.237715</td>\n      <td>0.016864</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>0.258407</td>\n      <td>0.259042</td>\n      <td>0.002460</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>0.233404</td>\n      <td>0.246465</td>\n      <td>0.055956</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>0.249245</td>\n      <td>0.248289</td>\n      <td>0.003837</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>0.237670</td>\n      <td>0.284324</td>\n      <td>0.196296</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>0.244008</td>\n      <td>0.242001</td>\n      <td>0.008228</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>0.252681</td>\n      <td>0.251169</td>\n      <td>0.005983</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rst.sort_values(by='mAP').sample(10)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x1000 with 1 Axes>",
      "image/png": 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pVnXocTmU7XFFtwWpRAQAALC7jELEL774QjfffLP69u2r6dOnp7XI9oIFCzRs2DBNnjxZV111lTZs2FBjn2AwqOHDh2v9+vWZnA4AAABOsEBViOhzu+RzRz8q0s4MAACAtEPEQCCgwsJCde/eXa+//rp27typN954I+Uxu3bt0vz587V8+XItW7ZMY8aM0axZs2rsN2/ePH3xxReZnz0AAABOqFhg6HU75XNXVSLSzgwAAGB7aYeIa9euVXl5uYqKitSpUyc99NBDWrJkScpjwuGwpk6dqrZt20qSunXrpkOHDiXs8/XXX2vBggXq0KFDPU4fAAAAJ1IsMPS5nfJ5oh8VK2lnBgAAsD13ujtu27ZNvXr1UnZ2tiSpa9eu2rlzZ8pjunTpoi5dukiSKioqtHDhQl177bUJ+0yaNEn33nuv1q1bl+m5m8Lhxvnb8dh1NdbrQ+PG/Qsr4/6FlR3v/Xs0EJIUrUT0upxV24L8f8Apw89gWBn3L6yM+9e+0v03TztELC8vV8eOHc3vHQ6HnE6nDh06pNzc3JTHrlmzRuPHj1fHjh1VWFhobn/99ddVXl6uMWPGHFeIWFxcXO9jraCxXx8aN+5fWBn3L6ysvvfv1hK/JCkSDChSVYD4+fYdyj78fyfq1IC08DMYVsb9Cyvj/kVt0g4RXS6XvF5vwjafz6fKyso6Q8T+/ftr7ty5evTRR/XEE09owoQJ2r9/v5566inNmzdPbnfap5FUXl6eXC7Xcb3G6SgcDqu4uLjRXh8aN+5fWBn3L6zseO/fA9v3SWs3KrdZk2g784GD6tDpXOV3b3cSzhaoiZ/BsDLuX1gZ9699xf7t65J2epebm6svv/wyYVtFRYU8Hk/db+J2q0+fPpo4caIKCws1YcIEPfbYY7rlllv0ox/9KN1TqJXL5WrUN3hjvz40bty/sDLuX1hZfe/fUMSQJPk8LmVVrYkYjBj8X8Apx89gWBn3L6yM+xe1SXuwSl5enj755BPz+z179igQCKSsQly2bJkWLFhgfh9/I7711lt65ZVX1KdPH/Xp00cbN25UYWGh5syZU5/rAAAAwAkQm87sS5jOzGAVAAAAu0u7ErFv374qKyvT0qVLdbz9I6oAACAASURBVOONN2rOnDnq16+fXC6XysvL5fP5alQldu7cWZMmTdLZZ5+tH/3oR5o9e7auu+46SdK7776bsO9DDz2kO++8U1dcccUJuCwAAADUR2KI6EzYBgAAAPtKO0R0u92aOnWqfv3rX2vGjBkKh8NauHChJOmGG27QhAkTNHjw4IRjunfvrilTpmjatGkqKyvTddddp6KiIklKGNIiRddXbN26tXJyco73mgAAAFBPscDQGx8iBpnSCAAAYHcZTTQZPHiwVq1apeLiYvXu3VstW7aUJL333nu1HjNixAiNGDGiztd+5ZVXMjkVAAAAnASxwNDndtHODAAAAFPGY5HbtWundu2YzgcAANAYBcJx7cwe2pkBAAAQlfZgFQAAADR+/mCSduYQ7cwAAAB2R4gIAAAAU/Vglbh25iCViAAAAHZHiAgAAABTIBYiepjODAAAgGqEiAAAADDFWpe9rvg1EWlnBgAAsDtCRAAAAJgSKxGZzgwAAIAoQkQAAACYYoGh1xXXzsyaiAAAALZHiAgAAABTrHXZ53HRzgwAAAATISIAAABMZjuzm3ZmAAAAVCNEBAAAgMmfECIynRkAAABRhIgAAAAw+ZNVIgZpZwYAALA7QkQAAACYqtuZq9dEDFCJCAAAYHuEiAAAADDFhqh4aWcGAABAHEJEAAAAmJIPVqGdGQAAwO4IEQEAAGCKVR0mVCIGqUQEAACwO0JEAAAAmPxJ1kSknRkAAACEiAAAADAla2cOhCOKRIyGPC0AAAA0MEJEAAAAmJINVpGiQSIAAADsixARAAAAkqRIxFAwHK049B0TIrIuIgAAgL0RIgIAAEBSYrWhz+OS2+WUy+mQxIRmAAAAuyNEBAAAgKTEakOvK/ox0ZzQzHAVAAAAWyNEBAAAgKTqakOHQ/K4ohWI1SEilYgAAAB2RogIAAAASdXVhj63Uw5HLESMTmiuZE1EAAAAWyNEBAAAgKTqEDHWyixJPg/tzAAAACBEBAAAQJVArBLR4zK30c4MAAAAiRARAAAAVWJBYXwlYlZVoEglIgAAgL0RIgIAAEBS3JqInrh25lglImsiAgAA2BohIgAAACTFtTO749uZY5WItDMDAADYGSEiAAAAJMUNVnEnqUSknRkAAMDWCBEBAAAgqbra0OdmOjMAAAASESICAABAUnw7c3wlYlU7c5B2ZgAAADsjRAQAAICkuMEqtDMDAADgGISIAAAAkFTbYJXYdGYqEQEAAOyMEBEAAACSqtdETBis4olNZ6YSEQAAwM4IEQEAACBJ8gdpZwYAAEByhIgAAACQJAXCqUJE2pkBAADsjBARAAAAkqqrDb1JpzNTiQgAAGBnhIgAAACQVD08JWGwiod2ZgAAABAiAgAAoArtzAAAAKgNISIAAAAkVbcsJ21nphIRAADA1ggRAQAAIKk6KExaiciaiAAAALZGiAgAAABJcSGiJ9maiLQzAwAA2BkhIgAAACRVB4VeF+3MAAAASESICAAAAElSwKxETDZYhRARAADAzggRAQAAIKk6KExaiRiknRkAAMDOCBEBAAAgqa41EalEBAAAsDNCRAAAAEiSAlVrIiadzkyICAAAYGuEiAAAAJAU187sTjZYhXZmAAAAOyNEBAAAgKS4duYklYjBsKFwxGiQ8wIAAEDDI0QEAACApLjpzO6aayLGPw8AAAD7IUQEAACApOqW5fhKxPhJzbQ0AwAA2BchIgAAAGQYRlwlYvVHRLfLKbfTIYnhKgAAAHZGiAgAAACFIoZiSx7GtzNHv6+a0BwkRAQAALArQkQAAAAkVBnGT2eWJJ+HCc0AAAB2R4gIAACAhKEpNULEWCUi7cwAAAC2RYgIAAAAs8rQ7XTIVbUGYkx1iEglIgAAgF0RIgIAAMBc79DnrvnxMLZGImsiAgAA2BchIgAAABQIV4WIHleN53we2pkBAADsjhARAAAAZpWh15WsEpF2ZgAAALsjRAQAAIAZEMaqDuOZ7cxUIgIAANgWISIAAADM6czJ10SsqkRkTUQAAADbIkQEAACAWWXoTRYiemhnBgAAsDtCRAAAAJghYqx1OR7tzAAAACBEBAAAgFllmHqwCiEiAACAXREiAgAAoLoSMelgldiaiLQzAwAA2BUhIgAAAFIPVvHQzgwAAGB3hIgAAACIG6ySbE1E2pkBAADsjhARAAAA5pqIySoRs8xKRNqZAQAA7IoQEQAAAKnbmc01EalEBAAAsCtCRAAAAMS1MzOdGQAAADURIgIAACCuEjHZmoi0MwMAANgdISIAAABSrono80S3VdLODAAAYFuEiAAAADDXO0zdzkwlIgAAgF0RIgIAAECBcKrBKrF2ZioRAQAA7IoQEQAAAGYlItOZAQAAkAwhIgAAAOLWREwyWMVDOzMAAIDdESICAACgup3ZQzszAAAAaiJEBAAAQPVgFVeqwSqEiAAAAHZFiAgAAAAzIExZiRiknRkAAMCuCBEBAACgQCxETLkmIpWIAAAAdkWICAAAAHNoijfFdOZQxFAoTJAIAABgR4SIAAAAiKtErL2dWaoewAIAAAB7IUQEAACA2aqcrBIxfltsAAsAAADshRARAAAA1YNVkqyJ6HI65HE5EvYDAACAvRAiAgAAIGU7c3R71YTmEBOaAQAA7IgQEQAAwOYiEcNc6zBZO7NUHS5SiQgAAGBPhIgAAAA2Fz8spfZKxKoQkTURAQAAbIkQEQAAwObiqwuTrYkoST4P7cwAAAB2RogIAABgc/HBYGyAyrFoZwYAALA3QkQAAACbix+q4nDUFSJmWIn47SbppWHRRwAAAFgWISIAAIDN+euYzBx9rqqdOdM1ET95Tfp6nfTp4nqfHwAAABqe+1S8SUlJib799ltdeOGFatas2al4SwAAAKQpFgx6a1kPUZJ8ngzamQ9+Ix35lySH9Nkb0W1bXpd63S7JkJq0klp0Os6zBgAAwKmUUSXiF198oZtvvll9+/bV9OnTZRhGnccsWLBAw4YN0+TJk3XVVVdpw4YN5nOLFy/W5Zdfru7du2vMmDEqLS3N/AoAAABwXGLTmVNXImbQzvx0njRngDTnKqliX3RbxQ/R7+cMiD4PAAAAS0k7RAwEAiosLFT37t31+uuva+fOnXrjjTdSHrNr1y7Nnz9fy5cv17JlyzRmzBjNmjVLkvTRRx9p5syZmjFjht599135/X5Nnz79+K4GAAAAGfMHo8FgWu3M6VQijpwrOY9teKn65bPTHX0eAAAAlpJ2iLh27VqVl5erqKhInTp10kMPPaQlS5akPCYcDmvq1Klq27atJKlbt246dOiQpGjAOGXKFPXr109nnnmmRo4cqS1bthzHpQAAAKA+YsGgN51KxHTWROx5q3TPu8mfu+fd6PMAAACwlLTXRNy2bZt69eql7OxsSVLXrl21c+fOlMd06dJFXbp0kSRVVFRo4cKFuvbaayVJBQUFCfvu2rVL55xzTkYnHxMOZzgl0CJi19VYrw+NG/cvrIz7F1ZWn/u3MhCSFA0KazvO44pObT4aCKX32pGI4ldYNOSQQ4bCkYjE/y2kwM9gWBn3L6yM+9e+0v03TztELC8vV8eOHc3vHQ6HnE6nDh06pNzc3JTHrlmzRuPHj1fHjh1VWFhY4/kDBw5o8eLFeuKJJ9I9nQTFxcX1Os4qGvv1oXHj/oWVcf/CyjK5f7f/X6UkKVB5RJs3b066T9nBw5Kkb779Tps3l9X5mp6j+9TD6ZUzElBlk7MU9jSXt7JUn39dqmBJ8vcA4vEzGFbG/Qsr4/5FbdIOEV0ul7xeb8I2n8+nysrKOkPE/v37a+7cuXr00Uf1xBNPaMKECQnP/+EPf9DFF1+sAQMGpH/mcfLy8uRy1T5N0KrC4bCKi4sb7fWhceP+hZVx/8LK6nP/fq3vpA8PqlWLHOXn5yfdp8P326Udu9SiVRvl53dL63UdX/WTvvq7fDltFLnnPSkcUHe3L+1rgT3xMxhWxv0LK+P+ta/Yv31d0g4Rc3Nz9eWXXyZsq6iokMfjqftN3G716dNHEydOVGFhYUKIuGTJEn300UdaunRpuqdSg8vlatQ3eGO/PjRu3L+wMu5fWFkm928wHB16kuWp/Zhsj8vcN+3/F/5ySZIjdFQut1typ/3RE+BnMCyN+xdWxv2L2qQ9WCUvL0+ffPKJ+f2ePXsUCARSViEuW7ZMCxYsML8/9kb89NNP9R//8R966qmn1Lp160zPHQAAACdAWoNVPBlMZ46pjA7UU/Bovc8NAAAAp4e0Q8S+ffuqrKzMrBicM2eO+vXrJ5fLpfLycgWDwRrHdO7cWbNnz9Y777yjPXv2aPbs2bruuuskST/88IMKCwt17733qnv37qqoqFBFRcUJuiwAAACkK1AVDPrctVcdmNOZCREBAABsKe0Q0e12a+rUqZo8ebL69eunv/3tb/r1r38tSbrhhhu0Zs2aGsd0795dU6ZM0bRp03TTTTfprLPOUlFRkSTprbfe0r/+9S89/fTT6t27t/kHAAAAp5Y/FJ3I50tViWiGiBlMbPRHh7EQIgIAAFhfRgvTDB48WKtWrVJxcbF69+6tli1bSpLee++9Wo8ZMWKERowYUWP76NGjNXr06MzOFgAAACdcWu3MVVWK/mCalYjBSikUnfqs0FHJMCSH47jOEwAAAA0n49Wt27Vrp3bt2p2McwEAAEADqG5nTrUmYobtzLEqREkyIlI4IDGZGQAAwLLSbmcGAABA45ReJWKG7cyx9RBjgkfqdW4AAAA4PRAiAgAA2Fz1moipBqtkOJ25RohYWa9zAwAAwOmBEBEAAMDm/Om0M8cqEdNdE7HyYOL3VCICAABYGiEiAACAzaXVzuw53nZmJjQDAABYGSEiAACAzVUPVjmR7cyHE78P0c4MAABgZYSIAAAANpdRO3O910SknRkAAMDKCBEBAABszh+MtiinamfO8kQrESuD9W1nphIRAADAyggRAQAAbC4QphIRAAAAqREiAgAA2Fxs4nLKwSpVayKGI4ZC4TSCRAarAAAANCqEiAAAADYXm7iccrCKp/pjY1rViMeGiCFCRAAAACsjRAQAALA5s53ZU/tHQ6+rniGiOzv6SCUiAACApREiAgAA2JzZzuyq/aOh0+kwn49VLqZ+0cPRx+ZnRh9ZExEAAMDSCBEBAABsLlaJmJWiElGKG64SzKASsXn76CPTmQEAACyNEBEAAMDmYqFgqjURpep254zamZu3iz7SzgwAAGBphIgAAAA2F2tPTjWdWaoOGetsZw4FqtuXY5WIDFYBAACwNEJEAAAAGwuFI4oY0a99dYaIaVYixtZDlKSmbaKPVCICAABYGiEiAACAjcUHgnW1M3vTXRMx1srsbS75mke/ZrAKAACApREiAgAA2Fh8iFhnO7MnzXbmyoPRx6xcyZMd/ZrBKgAAAJZGiAgAAGBjgaoQ0e10yOV0pNw37Xbmyqp25qxcyZ0V/ZpKRAAAAEsjRAQAALCxdIeqSPEhYl2ViFXtzFm5kqdJ9GvWRAQAALA0QkQAAAAbi1UV1jVUJbpPVTtzumsixrczh2hnBgAAsDJCRAAAABsLmCFi6qEqkuTzpNvOHAsRc+LWRKSdGQAAwMoIEQEAAGzs5LczM1gFAACgMSBEBAAAsLGT3s7sphIRAACgMSBEBAAAsDEzRPRkUolYjzURGawCAABgaYSIAAAANharKvS60ggRPWm2M/sPRx/jpzOH/VKkjvARAAAApy1CRAAAABsLhDMYrBJrZ86oEjGrenuIakQAAACrIkQEAACwMX+wHoNV6rMmokRLMwAAgIURIgIAANhYZoNVMpzO7MuRnE7JXVWNSIgIAABgWYSIAAAANhYwB6uk0c7sqUc7s0SICAAA0AgQIgIAANhYLBBMa7BKOtOZwyEpUB79OqtF9DE2XIU1EQEAACyLEBEAAMDGqisRT1A7c2wysyRl5UQfPVXrIlKJCAAAYFmEiAAAADYWCwTTWxOxqp051WCVWCuzp6nk8lR9HQsRj9T7PAEAANCwCBEBAABszGxnTidE9KTRznzseohSXIhYWa9zBAAAQMMjRAQAALAxs53ZncZglXTamZOFiOZgFSoRAQAArIoQEQAAwMbq1c6cViViTvW22GAV1kQEAACwLEJEAAAAG/OblYgZDFZJZ03EZO3MIdqZAQAArIoQEQAAwMYCGYSIWZ56tjMzWAUAAMDyCBEBAABsLKPBKhm1MzNYBQAAoDEhRAQAALCx6jURMxmskiJE9B+OPiaEiLE1EalEBAAAsCpCRAAAABvLpJ05FjSGI4ZC4VqCxJTTmRmsAgAAYFWEiAAAADaWUTuzp3qfWqsRUw5WIUQEAACwKkJEAAAAG6uuRKy7ndnryiBE9OVUbzPXRCREBAAAsCpCRAAAABuLhYHxVYa1cTodZpBY64TmlINVCBEBAACsihARAADAxvzBaBgYX2WYijlcJVhXO3OL6m3mYBVCRAAAAKsiRAQAALCxQDj9SsT4/WpvZ04ynZnBKgAAAJZHiAgAAGBjsYrCdNZEjN+vMpiknTkSkfxJQsRYJSKDVQAAACyLEBEAAMDGMpnOLMW1MyerRPQflmREv85isAoAAEBjQogIAABgU4ZhVLczpxsieqKViEkHq8TWQ3RnS25f9XYzRDxS73MFAABAwyJEBAAAsKn4asKMKxGTDVYxh6rkJG43Q8TKjM8RAAAApwdCRAAAAJuKVSFKGVQipmpnNkPE3MTtDFYBAACwPEJEAAAAm4qvJvS6TmA787EhYmywCu3MAAAAlkWICAAAYFOxINDrdsrhcKR1TN2DVZQkRKxqZzbCUjhYr3MFAABAwyJEBAAAsKlAKLOhKvH7+oOZVCJmV39NNSIAAIAlESICAADYlN8MEV1pHxPbN6M1EV1eyVH1sZPhKgAAAJZEiAgAAGBT/vpUInrSGKziO2Y6s8PBuogAAAAWR4gIAABgU8fVzpzJYBWJCc0AAAAWR4gIAABgU/GDVdJltjMHM2hnlqorEUOEiAAAAFZEiAgAAGBTsSCwfpWImYaIVCICAABYGSEiAACATQXC9Ris4kmnnblFzediE5oJEQEAACyJEBEAAMCmjqudOeNKxNhgFUJEAAAAKyJEBAAAsKnjGqyS6ZqIDFYBAACwNEJEAAAAm4pVE8ZalNNR63TmSETyH45+nZVT80AGqwAAAFgaISIAAIBNxaoJva4MQkRPLe3MgXLJqNqWtJ2ZNREBAACsjBARAADApuo1WKW26cyxVmaXt7p1OZ45nflIxucJAACAhkeICAAAYFP+YLQl+YS0M8evh+hw1DzQHKxSmfF5AgAAoOERIgIAANhUrJowo3bm2HTmYwermOshJmllluIGq1CJCAAAYEWEiAAAADZVr8EqnjramWsLEc1KRNZEBAAAsCJCRAAAAJuqrkSsz5qIKdqZk4kNVgnRzgwAAGBFhIgAAAA2FahPJaK7lunMsRDRl5P8QHM6M+3MAAAAVkSICAAAYFOxasJYdWE6zErEY9dETLcSkcEqAAAAlkSICAAAYFNmO3MmIaKnup3ZMIzqJ9JeE5FKRAAAACsiRAQAALAps53ZncmaiNF9I4YUisSHiAejj3VOZ2awCgAAgBURIgIAANjU8bQzR4+Pa2muPBx9rHOwCiEiAACAFREiAgAA2FS92pnjQ8Rg3IRms525RfIDzTURCREBAACsiBARAADApqrbmdP/SOhwOMzQMbESMd3BKoSIAAAAVkSICAAAYFP1qUSU4iY0Jw0Rc5IfZA5WIUQEAACwIkJEAAAAm6rPYJX4/WNrKkqquxKRwSoAAACWRogIAABgU/UZrBK/vz9YVYloGGm0M1dVIoaORvcHAACApRAiAgAA2JS/HmsiSpLPc0w7c6BCMqqqEutaE1GSQpUZvR8AAAAaHiEiAACATZ2wdmb/4eij011dcXis+BCRlmYAAADLIUQEAACwKbMS0XOc7czxrcwOR/KDnC7J5Y1+TYgIAABgOYSIAAAANhQKRxSORNcm9LqOczpzXeshxrirqhEJEQEAACyHEBEAAMCGAuGI+XXGlYieY9qZYyGiLyf1gbGW5uCRjN4PAAAADY8QEQAAwIbMVmSdwkrEWIjIYBUAAADLIUQEAACwoVgA6HI65K5viBg8phIx3RCRSkQAAADLIUQEAACwoerJzJl/HIxNZ640KxEPRh/TDhGpRAQAALAaQkQAAAAbiq1n6K1PiOg5djrz4ehjnSFik+gjlYgAAACWQ4gIAABgQ/7jqkSMrYl4bDtzi9QHurOij0xnBgAAsBxCRAAAABuqDhFdGR8bO6bmYJU0pzOHCBEBAACshhARAADAho6rnbnWSsR010QkRAQAALCaUxIilpSUaNOmTSovLz8VbwcAAIA6HM9glSxPVSVi8NhKREJEAACAxiqjT41ffPGFbr75ZvXt21fTp0+XYRh1HrNgwQINGzZMkydP1lVXXaUNGzaYz23YsEFDhw7VT37yE7344ouZnz0AAADq5cSsiZhpiBgbrEKICAAAYDVpf2oMBAIqLCxU9+7d9frrr2vnzp164403Uh6za9cuzZ8/X8uXL9eyZcs0ZswYzZo1S5K0f/9+3X///br++uu1ePFiLVu2TB9++OHxXQ0AAADSEgsAj2s6c6btzAxWAQAAsKy0PzWuXbtW5eXlKioqUqdOnfTQQw9pyZIlKY8Jh8OaOnWq2rZtK0nq1q2bDh2Kfsh888031aZNG/37v/+7zj33XI0dO7bO1wMAAMCJEThRg1UMQ/Ifjj6RbiUig1UAAAAsx53ujtu2bVOvXr2UnR1dy6Zr167auXNnymO6dOmiLl26SJIqKiq0cOFCXXvttZKk7du369JLL5XD4ZAk9ezZU0899VS9LiIcDtfruNNd7Loa6/WhceP+hZVx/8LK0r1/jwaCkiSvy5HxvV5ViKjKYFhhf4Vc4UD0PT3NpBSv5XD75JQUCRyRwf8v1IKfwbAy7l9YGfevfaX7b552iFheXq6OHTua3zscDjmdTh06dEi5ual/67xmzRqNHz9eHTt2VGFhofl6559/vrlPs2bNVFJSku7pJCguLq7XcVbR2K8PjRv3L6yM+xdWVtf9u2t3hSTpSPlhbd68OaPX/u7bSknSgUNl2rLxn+olyZBTm7fukKp+QZxM6+9/0DmSDu3bq68yfM/TRZOD29Vh6wv69qJ/05EWXRv6dBo1fgbDyrh/YWXcv6hN2iGiy+WS1+tN2Obz+VRZWVlniNi/f3/NnTtXjz76qJ544glNmDChxuvFXqs+8vLy5HJl3opzuguHwyouLm6014fGjfsXVsb9CytL9/5df/grSWVq17ql8vN7ZvQeh5vuk/65UW5vtnqc30p6R1JWjvIvvjjlcQ7H51Kx1KKZT/n5+Rm95+nCsfI1Of+1Wc38H8vIv62hT6dR4mcwrIz7F1bG/WtfsX/7uqQdIubm5urLL79M2FZRUSGPx1P3m7jd6tOnjyZOnKjCwkJNmDBBubm52r9/f8avlYzL5WrUN3hjvz40bty/sDLuX1hZXfdvsKprxedxZ3yfZ3ujn9n84YhcwXJJkiMrt+7X8UbXRHQEK631f+vgN9KRf0lySFv/IklyfvYXKf8OSYbUpJXUolODnmJjxM9gWBn3L6yM+xe1STtEzMvLSxh8smfPHgUCgZRViMuWLdO+ffs0ZswYSYk3Yl5enpYvX27u+/nnn6tdu3YZXwAAAAAyF6ha+8ZXn+nM7rjpzOlOZpaqB6sEj2T8ng3q6bya2yr2SXOuqv5+yqFTdz4AAAANIO1PjX379lVZWZmWLl0qSZozZ4769esnl8ul8vJyBYPBGsd07txZs2fP1jvvvKM9e/Zo9uzZuu666yRJgwYN0saNG/Xhhx8qFAppwYIFuvzyy0/QZQEAACAVf7BqOrOnPiGiq/o1MgoRowP6FKrfEjYNZuRcyVnL796d7ujzAID/z969B8lV33ef/5y+zkUzI41Gd0BIILCRxEU2xFZkm/CQFCnHl+C1K6k4CeXCKVz7pJKYSqVcdhaIa53CfuLgxGvX4lQ5ybq2zAYTstiJ8bPAgx3HDubBgMTFCDFCSDPSaEZz7Zm+n/3jnN/pnlHPTJ/T9z7vV5Wre7qne86Y1nT3tz/f7xdAl6v6VWMsFtPnP/953XPPPTp8+LAef/xx3X333ZKkD37wg3r66acvus3+/ft177336i//8i/1m7/5m9q5c6c+85nPSJKGh4f1Z3/2Z7rzzjt15MgRHT9+XJ/61Kfq9GsBAABgLZm8W0SMBigixk0SMWARsdOSiNd+TLrzicrX3fmEcz0AAECXq7qdWZJuvfVW/eAHP9DRo0d16NAhDQ8PS5KefPLJVW/zoQ99SB/60IcqXvc7v/M7OnLkiE6cOKGbbrpJGzZs8HM4AAAACChriohx/zOPKrczb1z/hl4RscOSiAAAAPBXRJSkbdu21XV24e7du7V79+663R8AAADWl8nXMhPRbWfOF2WnZ2VJUs/g+jf0ZiIu+f6ZLde/RdqwVVq8IBXzTtE0lnAuBwAACAH/rxoBAADQ8Uw7cyJIEdFtZ7Ztqbg041xYTTtzrMc57bR2Zkka2iX90VHnl5akXe+U/viYczkAAEAIUEQEAAAIIa+duYbtzJLPIqJpZy7mpELe989tuXxasp0Ep3IpKZZs7fEAAAA0EUVEAACAEKoliZgoW8ZiLwVYrCJJ+Q5saV6cKp3PLLTuOAAAAFqAIiIAAEAIlZKI/herWJZVSiOm55xTP+3MUmfORSwvImbnW3ccAAAALUAREQAAIIRqWayy7HYZH0lEy+rs5SqpydJ5kogAACBkKCICAACEUC3tzJKUjDsJxkjGRxJRKluu0oFFxGVJRIqIAAAgXCgiAgAAhFAt7czO7ZyXkRGTREwOVndDk0TsyJmIZUnExYuxPAAAIABJREFUfLozl8MAAAAERBERAAAghDI1bGc2t0sqq0gh41xQbRLRLFfp9CSixFxEAAAQKhQRAQAAQsjMRAzczhyLakCmEGj5SCKadubFQD+3pVIriojMRQQAACFCEREAACCEak4ixiMatFLuF4NSpMr78RarpAP93Ja6KIlIEREAAIQHRUQAAIAQ8oqI8eAzEQfkpgmrbWWWOnyxyuTyr0kiAgCAEIm1+gAAAADQXLZte4tVEtHg7cxxK0AR0UsidmA7MzMRAQBAiJFEBAAACJlsoeidT8aDL1YZDJJENItV8h3YzmxmIvaNOKckEQEAQIhQRAQAAAgZ08os1ZBEjEdLMxF7qlyqIpVtZ+6wJGI+U0oebtrtnDITEQAAhAhFRAAAgJDJlhURAy9WqTWJ2GmLVUwrsxWVBnc550kiAgCAEKGICAAAEDImiZiIRWRZVqD7SMbKtjMHKiJ2WBIx5S5V6Rt2tlFLzEQEAAChQhERAAAgZDK5giQpGbCVWXIWqwRKIsZMEbHDtjMvls1DTG5wzpNEBAAAIUIREQAAIGTMYpWgS1XMbQcCbWc2i1U6tIjYPyIl3CJiNtW64wEAAGgyiogAAAAhk8m5RcRYNPB91D4TsUOLiH3DpSQii1UAAECIUEQEAAAImfKZiEElY1ENmiRiMsh25g4rInozEcuSiBlmIgIAgPCgiAgAABAyZjtz0M3M5raDCrJYpc857bQiopdE3CwlB5zzJBEBAECIUEQEAAAImUzeWaxSUxIxHiklEX0tVulxTjuuiOgmEctnIrJYBQAAhAhFRAAAgJCpTxIx4HZmk0TsuMUqF5zTvs3MRAQAAKFEEREAACBkMvnaF6v0RArqszLuFyFYrOLNRNwsJdx2ZpKIAAAgRGKtPgAAAAA0Vz3amfvtVOkLX4tVOrWduWwmYizpnM+yWAUAAIQHRUQAAICQqUc7c59bRFxUr/qiPl5SduJilWKxVETsH5Fs2zlPEhEAAIQI7cwAAAAhk6lHEbHgFNAWrD5/N+zExSqZWcl20pvLZiIWc1I+07rjAgAAaCKKiAAAACFjioi1tDP3ukXEefX7u6GXRFwsJfraXcpNISYGnFZms51ZIo0IAABCgyIiAABAyNRjsUrSLSLO+S4iuotVZEuFbOCf31SL7lKV/s3OaSRaKoYyFxEAAIQERUQAAICQMYtVamln7ik4xbM5u3ed71whXvb9ucXAP7+pypeqGCaNSBIRAACEBEVEAACAkMnWoZ05kXeKiDNFnzMRo3Ep4i5iyaUD//ymSrlJxL6R0mUJN4GZpYgIAADCgSIiAABAyNSjnTmec4pn08U+2X5nG5bPRewElZKISZKIAAAgXCgiAgAAhEwmV3sSMZadkyTNqU/ZQtHnjTtsQ7MpIvaXtzMPOKfMRAQAACFBEREAACBkTNGvlpmIUbeIOG/3ecnGqpm5iPkOaWdeK4mYTTX/eAAAAFqAIiIAAEDIZHLuYpV48JeCkeysJGc7s0k2Vs0UETulnbniTETamQEAQLhQRAQAAAgZkxxMRIO/FLTSbjuz3edte66aV0TssHbmiklE2pkBAEA4UEQEAAAIGbOdORkPvlhFaZNEDNLObBardEoR0U0i9pcnEd2ZiCQRAQBASFBEBAAACBmTHKxlJqJXRLQDtDN33GKVC85pxSQiRUQAABAOFBEBAABCxmtnrqmIWNrOHLyduQNmIubSpUJheRGRmYgAACBkKCICAACEjNfOHLSIWMh7swDnAm1ndtuZO2E7s2lljsSknqHS5SQRAQBAyFBEBAAACJmMV0QMOBMxM+ednQ80E9G0M3dAErF8qYpllS73ZiKyWAUAAIQDRUQAAICQqTmJ6M5DTCupvGLK5Py2M5vFKh2QREy5ScS+keWXk0QEAAAhQxERAAAgZGperOIWERcjG9z76+LFKt5SleHllzMTEQAAhAxFRAAAgJCpebGKW0RcigYsInpJxE5oZ3aTiP0kEQEAQLhRRAQAAAiZbK0zES8qIgbcztwRi1XKZiKW82YiUkQEAADhQBERAAAgRPKFovJFW1IN7czuYpVM1CmkZXJ+k4huEbETkojrzkScl2y7uccEAADQAhQRAQAAQiRbKBX8am1nzsaCtjObImInzERcLYnY75zaxc74PQAAAGpEEREAACBEsmUFv1oXq5SKiAHbmTuh+GaKiP0riojx/tJ55iICAIAQoIgIAAAQIiY1GI1YikVrKyLm4oPL7rNqsQ4sIq5MIkYiZRua55t7TAAAAC1AEREAACBETBIxEbSAKHlFxHzCLSIGnYnYCYtVVpuJKJWKiCQRAQBACFBEBAAACBHTepyM115ELJgiYuB25jZfrFIsSksXnPP9FYqI3nKVVPOOCQAAoEUoIgIAAIRIOlePJKKznbmYDNjO3CkzEdMzzuIUSeodvvh6r52ZJCIAAOh+FBEBAABCxGxnrkcSMXgRsc85bfciomllTg5JscTF1ycHnNMsMxEBAED3o4gIAAAQImZ+YTIWDX4nbhFRySH3Pn22M8d6nNN2LyJ6S1UqpBAlkogAACBUKCICAACEiJlfWI/FKupxi4hBk4iFjFT0WYBspkU3iVhpHqJUNhORIiIAAOh+FBEBAABCxGxnDtzOXCxKGWcmotVriogBF6tI7b2h2Usibq58PUlEAAAQIhQRAQAAQsSkBpOxgC8DM3OSbElStHfjsvusmmlnltq7pdnMROxbL4nITEQAAND9KCICAACEiCn4JYLORDStzLEexZNOotDMWaxaJNIZcxEXLzinq85EdBerkEQEAAAhQBERAAAgRLJ1SSJK6hny7sN3O7PUIUVEZiICAAAYFBEBAABCxBT8AhcRy5aqJONR9z59JhGl0nKV3GKw42gGZiICAAB4KCICAACESNZrZ66xiJgcLEsiBikiustV2nmxStUzESkiAgCA7kcREQAAIERKi1VqnIlY1s6czgVoZzZFxLZOIrozEVdrZ/ZmIrJYBQAAdL9Yqw8AAAAAzdM+7cymiNgBMxFXXazS75ySRASAUCkUbT0zekET82ltHejRTXuGFY1YrT4soOEoIgIAAIRIzYtVKiQRs/mibNuWZfl4A9XuRcTsYikluV47MzMRASA0vn9sXPc99rLGZ0vjOHYM9eieD1yj2w7saOGRAY1HOzMAAECIZBpQRCy/36rF2ryIaJaqROJScqDy9ySYiQgAYfL9Y+P61LeeW1ZAlKSzs2l96lvP6fvHxlt0ZEBzUEQEAAAIkUyu1sUqc85pz9CyuYq+i4jtvljFtDL3j0irJSxNcTG7IBUDtHQDADpGoWjrvsdell3hOnPZfY+9rEKx0ncA3YEiIgAAQIhkC7UuVplxTnuGFI9aXn3NzFqsWrsvVjFJxL7Nq3+PSSJK7ft7AADq4pnRCxclEMvZksZn03pm9ELzDgpoMoqIAAAAIeItVonX3s5sWZbX0mwSjlVr95mIqSqKiPFeyXL/f6SlGQC62sR8dcn5ar8P6EQUEQEAAELELFZJRGsvIkqlRKP/duY+57Rdi4jVJBEtS0q4Lc0sVwGArrZ1oKeu3wd0IoqIAAAAIeItVqlDElEqLWjx3c4cc99ktW0RsWwm4lrMhubsfGOPBwDQUjftGdaOoR6tMiVXlpwtzTftGW7mYWE9Z56T/v43nFPUjCIiAABAiHiLVaJBZyKuKCLGTRExaDtzm84SrCaJKJXmIpJEBICuFo1YuucD11S8zhQW7/nANYpGViszoiVe+LZ08kfSiw+1+ki6AkVEAACAEMl4i1UCvAy0bSlT2s7s3I/bzux7JqLbztyu25lTbhJxvSKil0SkiAgA3e62Azv09Y8f0oZkbNnl24d69PWPH9JtB3a06MiwzMwpaezn0tjz0kuPOJcd+47z9djPnesRSGz9bwEAAEC3yORqWKySXZBst1joFhF74gHbmePt3s7sbtdcr52ZJCIAhMptB3boP0cv6Js/PilJOrBzUP/yX4+QQGwnDxy8+LLUpPTg+0pf3zvbvOPpIiQRAQAAQqSmxSqmlTma8GYadu9ilWqTiO5iFWYiAkBozC7lvPPzmTwFxHZz+zekyMrMnO2cRGLO9QiEIiIAAECIlBarBJiJaIqIyUFnM7HKF6v4LCK2/WIVMxORJCIAYLmZxVIRcWxmScWi3cKjwUWu/Zh05xOVr7vzCed6BEIREQAAIES8ImKQmYgrlqqU349pk66al0Rsw8UqxUKpnZmZiACAFWYWs975XMHWxHymhUeD6lD+qgf+XwQAAAiRrDu7MFG3ImLQdmZ3O3M7LlZZmpbX9tQ3vPb3kkQEgNApTyJK0unpNvxALOz6t5S6HgZ2SDuvkzZsdS5HYBQRAQAAQqTuScR4wHZmU0RsxySiaWXuGZKi8bW/10siMhMRAMJi2k0ibu5PSJJOT7fpaI4wG9ol7X6Pcz4Slz75lPTHx5zLERhFRAAAgJCwbbusiFjDTMRK7cy+tzObImIbvvFKmaUq68xDlEgiAkDIFIu2t1jlwC7n+ZAkYpvKzrmn884s51iytcfTBSgiAgAAhESuUBr8Hqyd2X0xXqmdORc0idiG7czeUpV15iFKpSIiMxEBIBTm0jmZPSoHdg1KIonYtpZmnFM+6KsbiogAAAAhUZ4WDNbO7L4Y7xm86H78b2du53ZmN4nYX0USMUkSEQDCZNqdh9ifiGrPiPMcQBGxTZkOimJOyrP8ph4oIgIAAIREeaEvEa33TMSA7cx2QSrk1v7eZvOSiOssVZGkxIBzykxEAAgFMw9xY19Cl2xynstoZ25T5sNPScrwPF0PFBEBAABCIusWERPRiCIRy/8deEXEjd5FNW9nltovjZgyRUSSiACA5WbcIuKm/rhXRDwzs6Ri0V7rZmi2XFrKl41MoYhYFxQRAQAAQqKmzczS2otV/M5EjCYkyz2OdluuYpKI1bQzezMRU407HgBA25hOOen5Q7GT2vnox3R99A3lCrYm5mmXbSvlKUSJ2cV1QhERAAAgJEzLcaClKlJ9tzNblhTvc863XRHRbGeuYrFKksUqABAmpp35lsyTirz5I/1Oz08k0dLcdpZWFBHpGKgLiogAAAAhkW1EEjEesJ1ZkmI9zmnbFRF9tDObmYi5Ranos5AKAOgsM6eUnHhR+61RvXPhKUnSr9k/1n5rVPOjz0ozp1p8gPCQRGyIWKsPAAAAAM3htTO7hT//dzDnnFZMIgYoIrZrEtGbiegjiSg5b1DK/r8BAHSZBw7qdyX9blKy885Fg8UZfS/5WelpOf+7d7aFBwjPRUlEZiLWA0lEAACAkChfrOKbbZeSiMlB72JvsUouQArPLFfJt1kR0ZuJWEURMZaUInHnPK1SANDdbv+GCnKe98x6MnNaUFS6/RstOSxUQBKxISgiAgAAhISZW5iMB3gJmFuUim7som5JxDZsZ86mSkXNapKIEnMRASAsrv2Y/nzLVypedc+2v5Gu/ViTDwirYiZiQ1BEBAAACAmzQTnQTESTQrSiUqLfu9gUJLumnTnlLlWJJkubl9dj5iLyBgUAut5cOr/sa9vNIk7MsZ25rZBEbAiKiAAAACGRLbjtzLUUEXuGnM3KLq+d2e92Zqk9F6ssls1DLPs91+QlEZm3BADd7q1Mn9J23Pu60L9NE/aQXplLqli0W3hkWCa9YjYlMxHrgiIiAABASJSSiAEWq1TYzOzcV2TZffviJREX/d+2UfzMQzRMYpEkIgB0vdeWhjRq7/C+jlz+y3pf7m/1VmGTzi+QRmwbpp3ZvNYgiVgXvoqIr732mj7ykY/oxhtv1P333y/bXr/K/tBDD+nIkSPav3+/PvGJT2hiYsK77u/+7u90+PBhHTp0SH/4h3+o6elp/78BAAAAqmLSgoEWq6xWRKypndksVkn7v22jLPrYzGwwExEAQiGdKyiTy2mPNe5dFpl+Q8ODzliL09Nt9KFY2Jl25qFLnFM+6KuLql9BZrNZ3XXXXdq/f7++853v6MSJE3rkkUfWvM2zzz6rr3zlK/riF7+oJ554QplMRvfff78k6Wc/+5keffRRfetb39I///M/L7sOAAAA9WcKfYEWq6TnnNOLkog1tDN7i1Xa6E2XmYnYN1L9bbwkIq1SANDNZhZz2mlNqsfKlS6cOqFLNjrPZ6en22g8R9gtrSgi8kFfXVT9CvKHP/yhFhYW9JnPfEaXXXaZPv3pT+vhhx9e8zajo6O69957dfjwYW3fvl233367jh07Jkl68cUX9d73vld79+7V7t279f73v18nT56s6ZcBAADA6rwiYqCZiO6L8Z7BZRfXtp25DRereO3MAYqIvEEBgK42vZjVldYZ54vNV0qypMyc3j7oJOopIraRi5KIfNBXD7Fqv/HVV1/Vddddp95ep+3k6quv1okTJ9a8zUc/+tFlX4+Ojmr37t2SpH379unb3/62fuu3fkv9/f16+OGHdfjwYb/HL0kqFAJ88t0BzO/Vrb8fuhuPX3QyHr/oZGs9fjM5Z6NkPGL5fnxbSzOKSComB2WX3dbUI7P5ovL5vKxql5FIsqI9zn1mF5fdZytZqfPOMfVsqvqYrES/c5v0fNv8Hp2Mv8HoZDx+u9vUQlpXWmOSpOK2A7IKOVkzb+rt8XOSBnVqKtXR/+276fEbWZqWJak4sFMRSXZmXsUu+L0apdr/5lUXERcWFnTJJZd4X1uWpUgkotnZWQ0NDa1xS8f09LQeeughfelLX5Ikvfe979Xu3bv1q7/6q5KkgwcP6g/+4A+qPZxljh49Guh2naLbfz90Nx6/6GQ8ftHJKj1+T51xPoWfnZ7S888/7+v+dp06ru2Szs9ldbrstktlC1V+9tzzSkSrLyLumJrRTkmT42/pLZ/H0yh7x9/QJklvXVjSZJXHtHM6pR2Szp8ZXfb/DWrD32B0Mh6/3en502kviXg2P6j++DYN6U1tnDkm6bBefWvC9/NrO+qGx+/1i9OKSjo1W9TlkjJzk3qpC/7btFrVRcRoNKpEIrHssmQyqXQ6XVUR8b777tMNN9ygm2++WZL0r//6rxobG9O//du/aXh4WPfff7/+9E//VH/7t3/r7zeQU4CMRgNsGWxzhUJBR48e7drfD92Nxy86GY9fdLK1Hr+PnXlFUkqX7tyu66+/ytf9WqeTkqQtl16pkeuv9y7PFYrSoz+QJL3tmgMa7I1Xf5+Le6XXpJGN/dpcdp+tFHneSWtectV1uuSa6o7JWrhCel3aMtiz7P8bBMPfYHQyHr/d7eXsKV0ZcZKI2/e/RzrTI51/RgcGnTbm2XxM13fw80DXPH4LWUUfc1rMLz1wWHpeSlr5jv5v02jmv/16qi4iDg0N6fjx48suS6VSisfXf6H48MMP69lnn9Wjjz7qXfa9731Pv/3bv629e/dKkj772c/qHe94h+bm5jQ4OLjaXVUUjUY7+wG+jm7//dDdePyik/H4RSer9PjNFWxJUjIe4LGdcRarRPo2SWW3jUQiilhS0ZZytvzdb8KZiRjJp5fdZ0stXpAkRQe2Vn9M7pzISC7VPr9HF+BvMDoZj9/uNLeU95KIkW1vlzKzkqTh9FuSpLGZtCwrokik+lR+O+r4x+9Saf5hdOOlkiQru9DZv1ObqHqq9sGDB/XCCy94X58+fVrZbHbdFOKLL76oL3zhC/ryl7+skZHSgOpCoaDJyUnv64mJCe9yAAAA1F9psUqAF9GZytuZLcsqbWjO+VyuEndmbbfXYhWznXlz9bdJmu3MLFYBgG6WmT2njVZKtixnscrmKyVJPXOjikYsZQtFnV/ItPgo4W1mTg6VFsJlF6RigCVwWKbqJOKNN96o+fl5Pfroo/rwhz+sBx98UIcPH1Y0GtXCwoKSyeRFqcTJyUnddddd+uQnP6n9+/crlUpJkvr7+3Xo0CF985vf1Pbt29XT06N/+Id/0A033KBNmzbV9zcEAACApFIRMRFoO7OTtlhZRJSkZDyipVzB/4bmdtvOXMiX3nj0BdnOnKr/MQEA2kZyxlkuO9ezU0PxXq+IaE2PatdATKdmczo9vahtgz2tPEyY1yy9Q6XnaEnKpaTkQGuOqUtU/QoyFovp85//vO655x4dPnxYjz/+uO6++25J0gc/+EE9/fTTF93mu9/9rqampvTAAw/o0KFD3v8k6fd///f1/ve/X1/72tf053/+5xoYGNAXv/jFOv1aAAAAWCmbdzo+krUUEZMXj50x95fO+ewoiblvstqliLg0Lclp+Vavjw+2TRIxSxIRALrZwLxTRFwYuMK5YHCXFOuVinndMOgk9k9Pt8lzWpil3Q8EezY6XQ+W+7qHjoGaVZ1ElKRbb71VP/jBD3T06FEdOnRIw8PDkqQnn3yy4vffcccduuOOOypel0wm9bnPfU6f+9zn/B0xAAAAAim1M9c5iWjamX0nEdusndm0MvdukqI+XiYn3FRDZn7t7wMAdLThpVFJUnajW0SMRKTNV0jnjulgz6T+RZdSRGwHpqugd6NkWc7zdGaWD/vqwFcRUZK2bdumbdu2NeJYAAAA0EBmZqHvdmbbXqeI6NxfJu8ziWjamfNt8oZrcco59TMPUSKJCAAhsT17SpJUHLmqdOHmK6Vzx7QvOi5RRGwP5UlEyXmezszyYV8dBPgYGgAAAJ0oWwi4WCWflgpZ5/wqMxGlIEnENmtnTpmlKj7mIUqleUu0SQFAV7uk4Gxhjm17e+lCdy7iJUVna/Pp6cWmHxdWKE8iSqU5iHzYVzOKiAAAACFhkoKm6Fc1k0K0IssHlLuCb2dus8UqJonY77OIaJKIhYxUyNX3mAAAbaG4NKftcp4n+naWFRFH9jknGafAeIYkYuutTCLyYV/dUEQEAAAIiayZiRj1W0R0hsUrOejMf1ohcDtzuy1W8dqZh/3dLlG26ZFWKQDoSotjr0qSzttDGhzeUrrCTSL2L5yUJJ2eWVKxaDf78FDOJBFN9wRjR+qGIiIAAEBIeItVgiYRK7QyS+VFxIBJxPySM3ex1bwios8kYjRWKojyBgUAutLS+CuSpFHtWj4WZLOzZCWWOqsBa0nZfFGTC5lWHCKM9Ip2Zi+JyAd9taKICAAAEBKm3dj3TESviDhY8eqatzNLztzFVvNmIvpcrCLRKgUAXa4w4SQRz8QuXX5F7ybvw6d3DkxLkt6ipbm1llYuVnE7Bigi1owiIgAAQEiYxSq+tzOvnC20grdYJed3O3NZEbEdWpqDzkSUaJUCgC4XnTouSTqX3H3xlW5L8/V95yWxXKXlVksi8hxdM4qIAAAAIWGKfEnfRcQGtTNHolI04ZzPtcEbrsV6JBFJOQBAN+qZfV2SNNO35+IrR5wi4tXxCUnSaZKIreW9btnknCbpFqgXiogAAAAhYYp8/pOI6xURA7YzS6U0Yq4N2pkXLzintRQRSTkAQPfJZ9WfOiVJWhi44uLr3STibntMEkXElltaLYnIB321oogIAAAQAoWirby7LTL4TMT1kog+25klKWaKiC1OItp2bTMRSTkAQPeaHlXELmjB7pE1uPPi6zfvkyRty70liXbmlirkS8XCi2Yi8hxdK4qIAAAAIZAtSwn6bmfOzDmnqxURvZmINSQRW71YJZuSCu42zSAzEUkiAkD3Ov8LSdIJe6c29Scuvt5NIg4unpJk6wxJxNYxH3xKpdctPEfXTazVBwAAAIDGKy8iBm5nTlbeztxTl3bmFqc2zDzEWI8U7/N/+yQzEQGga006RcTX7Z3a2FehiDi8R7IiiuUWtEUzOjMTlW3bsiyryQcKb6lKYkCKuiUvugXqhiQiAABACJhW44glxSI+39Ss184cr6Gd2Ssitji1kXI3M/eNSEHe9CXcVilSDgDQfc6/Jkk6UdylTf3xi6+PJaWNl0mSroicVSZf1PmFTDOPEMbKeYgSScQ6oogIAAAQAiYlmIxF/ScjGrpYxU39tbqIuOgWEfsDzEOUSimHbKo+xwMAaB/rJRElr6X5hj4n2c5ylRZJTzun5a9ZvJmIdAvUiiIiAABACATezCxVv1glyEzEWI9z2vIiYg1LVaRSyoFWKQDoLsWiNHlckvS6vUubVi0iOstVrklOSKKI2DImidhDErERKCICAACEgGk19r1URWpSO3OrZyKWtTMH4SURSTkAQFeZOyPlFpW1ozplb9WmvgrtzJK0+QpJ0h5rXBIbmlsmXaGdme3MdUMREQAAIAQam0SsQztzq7czp2pNIvIGBQC6ktvK/Ka9XXnFVm9nHnGSiDvzpyWRRGyZSklE80FfISMVcs0/pi5CEREAACAEst5MRJ8v//KZUoFvvXbmQEXEdmlnrtdMRIqIANBV3KUqr9s7FY1YGuyJVf4+dybixsyYYspTRGwV88HnssUqA6XzzEWsCUVEAACAEChfrOJLeq50PjlQ8Vu8JGIuSDtzmy1WYSYiAKCct1Rll4Z646svJxvYKcX7FLHzutQ6Tztzq6QrJBGjsdIMZj7sqwlFRAAAgBAwBT7f7czmE/3koBSpXIA0MxGzQZKIbbNYpV4zEXlzAgBdxSQRizu1cbV5iJIUiUjDpbmIZ6aXZNt2M44Q5ZYqzESUyj7sI4lYC4qIAAAAIZAtBGxnXmceYvl91jQTsdWLVeo2E5E3JwDQVcqSiKtuZjbc5SpXRMaVyRd1fiHT6KPDSpWSiFLpwz46BmpCEREAACAEMjm3iBj3285sXoyvVUQ0i1Vq2M7c6sUq3kzEOiQRSZ4AQHdITXnPD2/YO1bfzGy4y1X2JyYksVylJVZNIrof9mX5sK8WFBEBAABCwCQRE9EGJhFztSxWaWESsZArFUtrnYlYzDvLaAAAnW/SaWWeT27XknpW38xsuMtV9kXPSqKI2BIkERuKIiIAAEAImJmIZn5h1aopIsbr0c7cwjdaixfcM5bUuynYfST6S+eZiwgA3cFtZZ5I7pbuZRSDAAAgAElEQVSk9ZOIm50k4iX2mCSxXKUVllZ53ZJgdnE9UEQEAAAIgdJ2Zp8v/zLuduYq2pmzhaKKRZ+tvKadOdfCdmZvqcrwqstj1hWJlgqizEUEgO7gLlU5E7tMkqpIIjozEYfyU+rXks6QRGyuYkHKuEXEle3MJBHrgiIiAABACGSDFhHLtzOvovw+Tdt01WKmiNjCtMZijUtVDFIOANBd3CTiSWuXJK2/WKV3o9S/RZKzoZl25iYzr1mki9uZvedoPuirBUVEAACAECglEf0uVql+JqIUYC5iOyxW8ZKIAZeqGKQcAKC7uEnE14qmiLhOO7PkzUXca52lnbnZzDzEeJ8UW1HwTbqLVXiOrglFRAAAgBAwm5MTQZOIaxQRY9GIohFr2c+pWrwNkogpk0Qcru1+SCICQPfILkqzpyRJL2W3SaqinVkqFREjYzo9vSTb9jnmA8F5r1k2Xnwdz9F1QRERAAAgBGpuZ16jiFh+v76Xq3hFxDZYrNJfaxLRpBxolQKAjjd13DntHdabaWfm7aZ+f0nETL6oyYVso44QKy25ScSV8xAlugXqhCIiAABACARerOK7iOg3iWi2M7eynZmZiACAFdxWZnvkas0sOoXAdWciSl4R8arYWUlsaG4q085cKYloPujjObomFBEBAABCwCQRG9HOLJVmLab9zkSM9TinLV2sUueZiNlUbfcDAGg9d6lKbvhKFd2O5I3VzEQc2SdJ2q1xSTbLVZpprSRigm6BeqCICAAAEALBF6vMOafrFRHjNbYzF3NSIe/vtvWSqnMSkVYpAOh8550i4sLAFZKkvkS0uufQTZdLVkS99pK2aoYiYjOtmUSkW6AeKCICAACEgGkzDt7OPLjmt9XczixJ+Ra90fJmItZYRPRapUg5AEDHm3Tamaf790iqspVZkmJJaeNuSdLeyDjtzM20ZhKRD/rqgSIiAABACGSCtDMXclLObc2t9Kl+GZPO8J1EjCUlOZudW7Zcpd4zEXmDAgCdrZCXpk5IkiYSl0mShnqraGU23LmIe6xxkojN5CURK3RPeItV+KCvFhQRAQAAQiBQO7NpZZakZJVJRL8zES2rtRuabbsBMxEpIgJAR5s+6YzZiPfprOU8N1S1mdlw5yLutUgiNtXSGu3MCRar1ANFRAAAgBAItJ3ZfKKf2CBFY2t+a2kmos92ZqlsuUoLioiZeangbN0kiQgAkOQtVdHmKzW96Dyvbay2nVmSNjtzFE0S0bbteh8hKkmv0c5c/kEf/z0Co4gIAAAQAoG2M1e5mVmqoZ1ZKs1FbMWGZtPKHO+TEn1rf+96mIkIAN3BXaqiLVdrZtH5oGlTNZuZjc1OEvEKa1yZfFGTC9l6HyEqWTOJ6BYR7WJrXm90CYqIAAAAIRBosYqvIqJpZw6QRDTtzPm0/9vWyixVqbWVWSKJCADdwl2qopGrNb2Yk+RjsYrkzUS8NDKhmPK0NDeLed1ScbFKv7wZzDxPB0YREQAAIATMrEJfScSMOxPRTxExUBLRtDO34E1WyixVGa79vpiJCADdwUsiXqVpN4noq515YIcU71NMRV1mTejMDMtVmiK9RhLRskof9vE8HRhFRAAAgBDIFoIsVnE/0V9nqUr5/dbWztyCN1lmqUo/SUQAgJx5eZPHnfMjV2nGSyL6aGeORC6ai4gGKxbXTiJKbGiuA4qIAAAAIWDajM0ClKr4aWeO16GdOdeKdmaTRKxxqYpEwgEAusH8uDPb1opKw1d4SURf7cyS19LMhuYmyc478w6lyklEiefpOqCICAAAEAImiZiINngmYpAkYswUEVuxWMVNItZjJiKbHwGg85lW5uE9UizhJRE3+kkiSt5yFZKITWKWqsR6SmNSVkrSMVAriogAAABdzrZtr7jXsCRiTe3MLVyskjJFxDrMRGTzIwB0vrKlKpJqTyJGzlJEbIa15iEaJBFrRhERAACgy+UKtheMCzQT0VcSsZZ25hYmEesyE5HNjwDQ8cqWqmTyBS1mnec130XEkeXtzDYJ9cYyScTV5iFKpRnPzEQMjCIiAABAlzOtzFKp2FeVQDMRa0gitmSxipmJWIciIpsfAaDzeUnE0lKViCUN9MT83c+ws1hlqzWjWG5BU6lsPY8SK3lJxDVesyR5jq4VRUQAAIAuV77sxN9MxDnntKfR25lbWUQ07cx1WKwisfkRADpdWTtzaR5iQpGI5e9+ejdK/VskSXssWpobbslHOzPdAoFRRAQAAOhyprAXj1r+3gQ1q5051sIiYqqO7cxSWRIxVZ/7AwA0z9KMtHDOOT+yz5uH6HupirFsuQqzchsqXU07Mx/01YoiIgAAQJfLmqUqfuYhSsHamTspiVjISRn3d6x3EpFWKQDoPCaFOLBT6hnUTNClKsZmp6X5iggbmhuuqiTigHOapYgYFEVEAACALudtZvYzD1EqKyKu8YLc5bUzB5qJ2Oec5pv8Bsu0MluRqn7HqiRIOQBAxypbqiJJ06aduTdgEnGEJGLTmNcsVSUR+aAvKIqIAAAAXc4kERN+ioiFfOmT+oZvZ+5xTpudREy5S1V6h6VInV4WJ03KgTcoANBxJt0i4sjVklTWzhw0iehsaHaKiCQRGyrtYyYiz9GBUUQEAADocqaw5yuJmJkrnU82erGKm0RsdhFxsc7zECWGtgNAJztvlqo4CUKzWGVTzTMRz+r0BZKIDbXkZyYiz9FBUUQEAADocpkgMxFNETHeJ8XWT2DUNBMx1qIk4qKbRKzXPESJmYgA0MnMTMQtbhIx5c5E7A+YRNx0uWwrqg1WWtmZMdm2XY+jRCVVJRGZiVgriogAAABdLlA7s5ktVEUKUSprZ84FaWc2ScQmpzQWLzin9SwiMhMRADpTLi3NvOmc99qZ3ZmIQZOIsYTsjbslSbsKp3XBLUqiAUgiNgVFRAAAgC4XqJ3Zx2ZmSeqJ19LO7G5nzqf937YWqUYkEZmJCAAdaep1yS46z3sbtkpS7duZJUVGzFzEs8xFbCRmIjYFRUQAAIAu57UzxxtXRCwtVglSRGxVOzMzEQEArvKlKpYlqXyxSsAkouQtV9lrjVFEbBTbLiUR13rdQhKxZhQRAQAAupwp7CWijSwimiRiJ7UzmyRiHYuIzEQEgM5klqpsucq7qLRYJXgSsbSh+axOT7NcpSGyC5Ltvv5Yq53ZzETML0mFfOOPqwtRRAQAAOhygRarBEwi5gq2CkWfg+NNO3Ouye3MJolY15mI/c4pKQcA6CxeEtEpItq2rZml+hURSSI2kEkhRuKlDyYrMR/0SXzYFxBFRAAAgC5nlp0EWqxSbRGxrFU667elOWaKiItOS1KzpEw7cz2LiGx+BICONHncOXWXqsyl896HYjW1M4/skyRdap3X+IXZmg4Rq0iXLVVxW9EriiWlqFsQpogYCEVEAACALpctmCSinyLinHNaZRGxvFXad0uzSSLKlgpN3FzZiCQi85YAoPMUC6UiotvObJaq9Maj3vKwQAZ2qBDrU8wqqjB1ssYDRUXeB59rtDIbzC6uCUVEAACALpfJ1bJYZbCqb49FI4pFnE//fS9X8YqIat5cRNsuKyI2YLEKCQcA6Bwzb0qFjBRNSht3S5KmvXmINaQQJcmylN+0V5LUOzcqu5mJ+7BYKksirofZxTWhiAgAANDlTBIxEW3cTESpbENzzmcRMRqXIjHnfLM2NGfmpKLzBpEkIgCEnFmqMrJPijjPlTPeZuYa5iG6YlucluZdxTO6kGpi4j4sTDtzVUlEd+xIZq5xx9PFKCICAAB0udqSiD6KiPF6bGhuUhEx5W5mTmyQ4j31u1/z5iSXkoo+i6kAgNbwlqrs8y7yNjP315hElBR1W6T3WuMsV2mEIElEPuwLhCIiAABAlzNFPX8zEWtIIvptZ5akmFvIa1YR0WtlHq7v/bL5EQA6j5dEvNq7aLqOSURvQ3OEImJD+Eoi0s5cC4qIAAAAXc5sS/a1nXlhwjmdP1f1TUpFxCBJRLOhudlFxDrOQ5ScYqjlto1nU/W9bwBAY0y6RUQ3MSiVZiJu7K09iajNV0gyScQmzf4NE5KITUMREQAAoMuZZGAy5mcm4rRzOvrDqm9i7t/3TESp1M6cb3I7cz3nIUqSZTG0HQA6iW2XtTOXkohmJuKmOiYRt1izOj95vvb7w3JeErGK7gkzdiQ737jj6WKxVh8AwqNQtPXM6AVNzKe1daBHN+0ZVtTd4ggAABrHJAPXTSLOnHISerakgjv4/fX/Lo09L8l2Cm4bL1v15mbmYqB25niL2pn765xElJw3KOlZKcMbFABoewsTzt9sK+IV+6SyJGKt25klqWdIS4nN6s1OqTh5XNK7ar9PlCz5aGcmiVgTiohoiu8fG9d9j72s8dm0d9mOoR7d84FrdNuBHS08MgAAul/WSyKuU0R84ODFly1ekB58X+nre2dXvXlt7cxNXqyy2KAkokQSEQA6iUkhbty9bNFWXZOIkrIb96p3YkqJmTfqcn8ok/bRzsxMxJrQzoyG+/6xcX3qW88tKyBK0tnZtD71ref0/WPjLToyAADCIVNtEfH2b0iRlZ8x285JJOZcvwavnbkjFqtccE4bUURMkHIAgI5x3rQyX7XsYrNYpR7bmSUp4m5+Hlx8U7Zt1+U+4SKJ2DQUEdFQhaKt+x57WZX+RJrL7nvsZRWK/BEFAKBRqk4iXvsx6c4nKl935xPO9WvwkoiBZiKaxSpNGjhvZiI2op2ZJCIAdI4KS1UkaTpl2pnrk0Ts3eHMW7y0eMZrlUadpN0uCV9JREaOBEEREQ31zOiFixKI5WxJ47NpPTN6oXkHBQBAyARarOKp/uViaSZiDe3M+dVfN9SVt525kUlE3qAAQNszRcSypSpS/duZY26Rco91lg3N9WTbZYtVqkkiuotVSCIGQhERDTUxX90bgWq/DwAA+GeKeusmESWpf4sU73fOX/ouaed10oatzuXrqKmd2Vus0qQ3Vt5MxEYkEc3mR96gAEDbO2+SiKUiYjZfVCrrPHduqsdiFUly25n3WOM6fYEiYt3klkrL4JiJ2HAUEdFQWwd61v8mH98HAAD8M+3M625nlqShXdL+Dzvnr7hF+uRT0h8fcy5fR2mxSpAiYrMXqzATEQBCLz0nzY8558tmIpoUYsSSBnvqVETcuFsFRdRvZXTh3Jv1uU+UUohWtPT8uxaSiDWhiIiGumnPsHYM9cha5XpLzpbmm/YMN/OwAAAIFd/tzAvnnNPBnZJlSbFkVTcrzUQM0s5sZiI2oTshn5Eyc875/kYUEd0kJykHIHzGfq59//FpaeznrT4SVGPyuHO6YduyFJuZWTjUG1ckstq7WZ9iCc317JQk5c69Vp/7RGmpSu9G5zXLephbXBOKiGioaMTSPR+4puJ15p/3PR+4RtF6/WEGAAAX8YqI8Spf+s25qYzBnb5+TjJey3bmJi5WMfMQraiUHKr//SeZiQiElfXiQxqcel7W0YdafSioxuQ6m5nrNA/RWBzYK0mKXjhR1/sNNT/zECUpYZKIPEcHQRERDXfbgR36+scPXfShwPCGhL7+8UO67cCO1hwYAAAh4bUzR6stIp5xTgfXb2EuV1s7s1tEbMZilfKlKpEGvBxOMBMRCJWZU07y8NQzsl74vyVJ1rFHpLHnnctnTrX4ALGq85WLiDOLZjNznVqZXfbwFZKkDQujdb3fUDNJxJ4qPxQs/6DPthtzTF0s1uoDQDj80p7N3r/PAzsHdWxsTh975yUUEAEAaAJvsUo1ScRsSkrPOucH/T1Pl4qItbQzNyGJmDJLVRrQyiyVvUGhiAiEwgMHvbNebmJxSnrwfaXvuXe2qYeEKpl25i2N3cxsJLdfLf1C2px5S7Zty6qm/RZrS5e1M1fDzE20C84Hl+b1B6pCEhFNMTqVkiRtH+zRJ47skST9j19MtvKQAAAIhWLRVq7gfJJXVRJxbtw5TWyQkoO+fpa3nTlXQxKxGYtVTBKxvwGbmSU2PwJhc/s3pMjyfI4lN0ERiTnXoz2t2s5skoj1LSIOXfJ2SdJl9pj3M1CjJb/tzGXLV/iwzzeKiGiKk5NOEXHPSL/ed9UWWZb0yvicxmebtIERAICQyhZKBT0zs3BNXivzzuoGlJcxScd0TUnEJhYRSSICqIdrPybd+UTl6+58wrke7SeflS64bcWrJhHr286c2Ob8nEut8zozOVPX+w4tv0nESESKmwVozEX0iyIimmLULSJePtKvzRuSuv5S5x/4//jF+VYeFgAAXa88FWjajdcUcKlK+f0HSiLGuqiI6M1E5M0JEFa2aFNtexdOOC2tiQFpYPn4DrNYpd4zETWwXUtWj2JWUdOnj9f3vsPKjGCpNoko8WFfDSgioilMEXHviFPxv+XqrZKkJ1+daNkxAQAQBpmCkwq0LCkWqeJNbcClKlJZO3Mti1WaUUQ0MxEb1c5s3pxkU425fwDtp3+LFC1rfd24W9qw1bkc7clbqrLvouR9o9qZZVmaTFwqSUqffbW+9x1WSz6TiBJjR2pAERFNUZ5ElKRfeZtTRPzx65PBhq8DAICqmFRgMhapboB7PZKIgdqZ+5zTfDckEUk4AKEztEvadsD70t5/u/THx5zL0Z4mX3NOV7QyS41brCJJCxsud85MvV73+w6ltM+ZiBJJxBpQRETD2ba9bCaiJO3fOaitA0ktZgv6zzcutPLwAADoaiYVaFKC66qliBg3RcQgScQe57Qb2pmTbjtzfkkq5BvzMwC0n6Vp76w19ZoUS7bwYLAuU0RcsVRFKiUR6z0TUZLym66QJPXMjdb9vkMpUBKRsSNBUUREw52fzyiVLShiSZcNOykDy7L0K7Q0AwDQcFm3oJeoZh6iJM27RcSBIEnEWrYzu0nE3KL/2/rVrCSiRKsUECaLZeEIU6BC+zLtzGskEeveziwpttUpWm5aOlX3+w4lkohNRRERDWdamXdt6l32Bsa0ND/1iwnZtt2SYwMAoNuZ1uKqlqpIrWtnjpkkYtr/bf1q9EzEWKI0G40iIhAOhZyUmS19feENZ/sv2lOxKE26i01GlhcRbdvWjEki9tc/iTiw622SpB3507wPrgdmIjYVRUQ03KjXyrxh2eVH9o0oHrX05tSi3phk8DgAAI1Qameu4mVfPiOlzjvnm75YxU0iFjJSsYHzkm278UlEibmIQNi4rcy2LBWivbKKeaeQiPY0+5YzciISlzZdvuyq+Uxe+aJT3GvETMSR3dc4p9asZqan6n7/oeMlEYeqvw1JxMAoIqLhRqfcIuLmvmWXb0jG9Et7nBfvT9HSDABAQ5TamauYiTg/7pxGk1LfsO+fVdtMxN7S+XwD04jpGcl2i5SNLCImSTkAoWJamXs3amngcuf8ebbvti3Tbr75CikaW3bVTMpJIfbEI+qJVzlP2IeeDZs0KSc1N/Xmy3W//1DJpUuvGXy1MzMTMSiKiGi40fPLl6qUK29pBgAA9ecriVjeylzNJucVvHbmXA3tzFJjl6uk3NRHYqCxSw/M0PYMb1CAUDAJ597NSg9c5pw3M/fQfsx/m4pLVRq3mdk4G79UkpQae6VhPyMUTApRlpQcrP523nM0H/T5RRERDXfSTSJeXqGIeItbRHxm9IIWMmwvBACg3nwtVvGKiP5bmaUa25kjkbK5iA1crmLe6Pc3MIUokUQEwmbJTSL2bVJ6w27nPEnE9mWSiBWWqkw3cKmKMdvnPEYK54837GeEwlJZK3PER3mL5+jAKCKioYpFWyennDcCe1fMRJScdOKekX7lCrb+/fj5Zh8eOsGZ56S//w3nFADgm6/FKl4RcUegn2V+Rr5oK1+ooaW5kctVFt2lKn0NWqpiJNwPT0k5AOFQlkQstTOTRGxbpog4Umkzs7tUpa/+S1WM7NAeSVJi5kTDfkYopN1lRn6Wqkhlc4vpFvCLIiIaamx2Sdl8UfGopZ0beyp+z81Xb5EkPclcRFTywrelkz+SXnyo1UcCAB2p1M5cxVynGjYzS6WZiJKUDVJEjJkiYhOSiI2chyix+REIG/dvi923SekBN4k4dVwq0G3VlkyBd8vF7cwzTWhntkb2SZIGF99s2M8IBW+pis8iIotVAqOIiIY6Oem8CbhsuE+xaOWH2y3eXMTzKhZZcQ9JM6eksZ9LY89LLz3iXHbsO87XYz93rgcAVCXraybiGec0YDtzouy5PpOrJYnYyJmIbhKxv8FJxCQzEYFQMYtV+jYr27tNdqxXKmSl6ZMtPSxUkJostZ9vvvKiq6fdJOLGBiYR+3a+TZK0NXtaKgZ4voTDtDP7TiKyWCWo2PrfAgQ3OulU9istVTFu2jOsvkRU5+czemlsTgcv8bGaHd3pgYMXX5aalB58X+nre2ebdzwA0MGCtTMHSyLGohHFIpbyRbu2Dc35BhYRvSSi/+3TvpBERDc785z03/836Vf/Qtp1qNVH0x687cybJSviLOw4+4IzF3Hk4kIVWuiVx5zT/m2l0RNlmpFE3HzJVcrbEfVYGdnzY7KGLmnYz+pqJBGbzlcS8bXXXtNHPvIR3Xjjjbr//vtl2+unxh566CEdOXJE+/fv1yc+8QlNTFzcsvonf/In+vznP+/nUNAhRt0k4lpFxGQsqiNXOmkAWpohSbr9G1Jk5Wcc7t+bSMy5HgBQFZMILG81XlWNRUSpbENzPsCG5mYkEb0iYqOTiLxBQRdj3MzFyharSJJtFnawXKX9HPuOcxqrXCRsRhJx1+ZBnbKdjrzUGLMzAwucROSDvqCqLiJms1nddddd2r9/v77zne/oxIkTeuSRR9a8zbPPPquvfOUr+uIXv6gnnnhCmUxG999//7Lv+dGPfqSf/vSn+qM/+qNgvwHamkkiVtrMXK7U0kwREZKu/Zh05xOVr7vzCed6AEBVzGzCxCpjRTyFvLRw1jkfsJ1ZkpLxGjY0N7WIyExEwJfycTNH/x/nMsbNlJiZiL3u3xazsMMs8EBrlT9+Tz/rXJaarPj4bcZ25p54VGeiznPt3OlXGvZzuh5JxKarup35hz/8oRYWFvSZz3xGvb29+vSnP6377rtPH/nIR1a9zejoqO69914dPnxYknT77bfrwQcf9K5Pp9O67777dPfdd2twcLCGXwPtymxm3rN57SLir7hFxBdOz2hqIaPNG5INPzZ0GkteGhEAUDVvsUp8ncUqqQnJLjqJ7/4tgX+el0QMMhMxxkxEoG1VHDdznnEzhjcTcVhKk0RsO5Uev/mlio/fZmxnlqQLPZdJS/9T2XMUmgMzScQenyPRzEzEXMqZSRlhXUi1qi4ivvrqq7ruuuvU2+u8uLv66qt14sTa68g/+tGPLvt6dHRUu3fv9r7+2te+pnQ6rVgspp/85Cd617veJcuy/By/JKlQCNAu0wHM79Wpv1+uUNSpC2axSu+av8dIf1z7dwzqpfE5PfnqOd1+Q/AEBNpDzY/f5JAickqHkmRv2CrZtoq9w1KH/ptA5+j0v78It5WP33TO2Qwai6zzmJ55S1FJ9oZtKtoK/Lc24RYRF7M53/+GrFiPIpKK2ZTsBv37iyxOyZJU6NnY0OcTK9aniCQ7M68if0t84W9we7I+/H/K+n//V1nFi7cN25GY7A/+Hw37d9sJvL8tyY2SMioMX+n8TT3/mor5nDMnES3j5/FrkoiDPdGG/h1aHNgjLUmRC6+3zd+7Tvv7G1maliWpmBzy9/cn1ivz0WohPSslCbVV+9+86iLiwsKCLrmkNOzTsixFIhHNzs5qaGj9qu/09LQeeughfelLX5IkjY2N6Zvf/KauvfZajY2N6R//8R+1Y8cOffWrX/VdSDx69Kiv7+80nfr7jc3nVSjaSkSl8Tde0bl1/ru+bWNBL41L//zT49prnW/SUaLRgj5+e+dO6JqyrxfiW3T83f9N9hvnJfH4QHN06t9fQCo9fsfOOsmK6fMTev75xVW/f+PYf+gKSanokH7x/POBf66dd958vfzqa4pN++ss2D2f1oiksTdP6Fw8+DGs5fr5CUUlvfLmeWUmG/MzJGnj+Hnn/8/piZr+/wwz/ga3m33q/eWv6pof3XXRNa/88le1VNgnhfWxXizoHW5b5Ssnz0rJTTr61rxuiMQVyS/p5Z88rmzfjhYfZNhV//idmk9Lks6+eULPX2jcLtopy1nw1TN7Qs+32b+dTvn7e9XkaQ1IOnluRtN+/j+0bR2yIrLsol567j+V6w3egRE2Vf+LiEajSiSWzwRIJpNKp9NVFRHvu+8+3XDDDbr55pslSY888ohGRkb0zW9+U4lEQr/3e7+nW265RT/+8Y915MgRX7/EwYMHFY2u06LTgQqFgo4ePdqxv9/0qxOSJrV3y4AO3XDDut9fHJ7Wd175Tx2dzGv/wWsVX292E9parY9f6/ljkiR76FJZs29pw9xxXXfdtVKsp96HClyk0//+ItxWPn43HH9R0pJ2X7pL11+/Z9XbWZmfSpL6tu/T9ddfH/jnb/yPn+jU7Kwu3b1H17vjSqplje2UTks7t2zSjhqOYVX5tKKPOa3Sb3/HEf/tT34MzkjPSv1xu6b/P8OIv8FtbNySflT60pbTNXL11VdLO65r1VG1XmpS+p5z9u2HDuvoS6/o4HXXy/rZPmniZV2zJSrt4+9Ay7mPX/O4tWXJkr3s8ZvNF7X0Tz+QJL37Hdc2dEPz6FxeOieNFM5py4t/ruKtfyHtXP99cyN12t/fyE+dZOnut12n3Xt9/hv7/wak9Kz279vtbFMPOfPffj1VFxGHhoZ0/PjxZZelUinF4+vPCXj44Yf17LPP6tFHH/UuO3funN71rnd5hckNGzZo9+7dOn36dLWH5IlGox3xAA+qU3+/N6edT3D2jPRXdfyHdm/WcH9CF1JZPX96Tu/a2+CB52iKwI/fsy9Kkqz9H5ZeeEhWakLRsy9Iuw/X+QiB1XXq319AKj1+c535HNIAACAASURBVEVnnmxPfJ3H88K4JCkydIlUw+O+x529mCvK/7+fhDNDOVJI13QMq1pwZydF4or2bZICjNGpWq9ToLSyC/wdCYi/wW1oYKvTlms7M08tSerfqujAtsb8m+0UmdJyh2jcSWBHo1FZW94mTbys6NRr0tt+vYUHCEnSwDapb0TWojMb19pxvTR/Ztnjd96dh2hZ0qb+HkUjjXue2LJzjxbsHm2w0tKbP1b02D9Jl76zYT/Pj475+5uZkyRF+4b9/w1KOEXEaH4x3H+/fKo66nXw4EG98MIL3tenT59WNptdN4X44osv6gtf+IK+/OUva2SkNMB6+/btymQy3tfFYlFnz57Vzp07/Rw/2tjJyZQkp4hYjWjE0vuucmLET73KlubQG/u5c7rzBmn3u53zb/5H644HADqUWXCy7mKVuTHndLC212LJuLtYJch2ZpM2z6VrOoZVuW8c1be5sQVEqbSdmc2P6Ca5JXcBU6K0DfW3viUNhXyeeflSlXJb3uacnv9Fc48HlQ3tkn7nn5zzfZulP3hK+uNjyx6/ZqnKUG+8oQVEzZzS3vzrGrc3lS5j27l/ZrFKr8/tzBIbmgOquoh44403an5+3ksTPvjggzp8+LCi0agWFhaUy+Uuus3k5KTuuusuffKTn9T+/fuVSqWUSjmFpV//9V/XU089pccff1xnz57VX/3VXymbzerQoUN1+tXQaqNuEfHyKouIUmlL81O/oIgYavmsdNZpZ3aKiL/snD/1k9YdEwB0qEzeGZRttiavql5FRLOdOR9gKHvcbGdefXZjTRannNO+JnQ7mDcnWd6coIuM/tA5veyXpJ1u6+DEK607nnax2t8WNjS3H3d2pTZscz5Mii2f3Tudcub6NrKNWZL0wEFd+k+/rn2Rce8i22w7f/DmytuksVwh52xXlkofaviRdDc08zztS9VFxFgsps9//vO65557dPjwYT3++OO6++67JUkf/OAH9fTTT190m+9+97uamprSAw88oEOHDnn/k6S9e/fqr//6r/X1r39dv/Zrv6ann35aX/va17Rhw4Y6/WpoNVNE3OujiPi+fVsUjVh67dyCTk836A0E2t/5V6RCxplVtWmPdJmbRDz1n1KxMzaFAUC7yLqJwETTiohO4jFQEjHe55zmG5RETLlv9PubUEQ0ScRC1vlwDOgGJ92BiJe/R9p+rXN+/MXWHU+7WHKTiL1rJBFtu7nHhMoW3LBKf+VFGtNuEnFj3/pj22py+zdUtJZ3CJjcY9GKSrd/o7E/vxuYFKIUbMax1zEwX5/jCQlfq4ZuvfVW/eAHP9DRo0d16NAhDQ87fySffPLJit9/xx136I477lj1/m6++WZv0Qq6SzpX0NisM7jcTxJxqC+ud1y2Sc+cvKCnXp3Q77778gYdIdramLtZa8f1zieE2/ZLyUFn5sXZo6VPvgEA6zLFPFPcq6hYlObdNES9koi5IEVE0868VNMxVFIo2jp16pT2SJosDmhT0W5sq1qi7IPx7IIUG179e4FOYNvSqFtE3POe0gcPZykirppEHN4rRWLO34C5M9LQJc0/NixniogbtlW8emaxOUnE70feo6+m/0LfTX72ous+mP4L/dfIe3RbQ4+gC5hUaXJIigSYaZikiBiE7/W327Zt06233uoVEIFKTl1YlG1LAz0xbe739wf45rc5nwo9yVzE8Cqfhyg5TwqX/pJznpZmAPClVERc42Xf4pSTmJMlbdhe088rzUQM0s7sJhHr3M78/WPjOnL/k3rsp06x43snsjpy/5P6/rHxdW5Zg2hMirnt2bxBQZsoFG395MSU/uX5M/rJiSkVij7ScROvOHNFY73SrneWtjGfPUaniFdEXPEeOZaQhq9wztPS3B4WzjmnG7ZWvHpmqfFJxELR1n2PvayV//qKdumDrfsee9nfv88w8uYhBkghSs5iFYl2Zp98FxGBarxxvrRUxfI5uPwWdy7if5yY0lI25C9IwmplEVEqbWVmuQoA+JJ1i3lrtjPPnXFON2x13vTWoLZ2ZjMTsX7tzN8/Nq5Pfes5jc+mNSynmDetAZ2dTetT33qusYVEd9s0b1DQDkwx/be/8VP90bef129/46f+iummlfmyd5WKY/F+Kb8kTR5v3IF3gsVp53RlEVEqm4v4WvOOB6vzkoiVi4jTTUgiPjN6QeOzaU3Zg5qwh5SznefnE/YOTdhDmrQHNT6b1jOjFxp2DF3BJBGDtDJLLFYJiCIiGuLklLtUZXP1rczG1dsGtHOoR5l8UT95Y7Leh4Z2l89I515yzq9WRGSmDIAuVFNCaA1VJRHrNA+x/OcE285c38UqK9Mew5ZTRJyyB73LGpr24A0K2kR5Mb2cr2K6Waqy5z3OaSQibXeXP4S9pXmtpU3eXESSiG0htU47c8pJIm5qYBJxYt75d3hWm3Uk8zf69+IBSdL/VbhVRzJ/o7PavOz7sAqTRAyyVEUqjR3hgz5fKCKiIUbLkoh+WZblbWmmpTmEzr0kFXNS7yZp42Wly3feIEWTThvN1OutOz4AaICaE0JrqGqxikkiDu6q+eeVZiLWsp25PjMRTdrDMEXEadtpYbKlxqY9vFYp2pnROqu1TkqqvpheLEon/905f/l7S5fvMMtVXqjHoXau1RarSGVJxF8073iwunUXqzhJxI0NTCJuHejxzmcV11F7ryTpoHVSWcUrfh8qMEnE3oBFRD7oC4QiIhpidCp4EVEqtTQ/9ep52aTOwmXcXaqy8wZnqYoRS0qX3Oicp6UZQBepS0JoDVUtVqnTUhVJSsbr0M6cr08RcWWKY1hzkqQpDa75fXXDGxS0gZXF9JWqKqafO+q8YU8MLO8U2U4RUdI6SURTRHyVbpp2sO5iFZNEbFwR8aY9w9ox1ONtYz5W3CNJOhAZleRsad4x1KOb9rCHYk01JxH5oC8IiohoiJOTtRURD18xomQsojMzSzo+wQvvUKk0D9HY/W7nlCIigC5Rl4TQOsyCk6ramQd2BP45Rk3tzHVOIq5McWyxnDccI5pd8/vqhlYptIFqi+Rrfp/Zyrz73c7SIMMkEc++GO4C2aJbgK00E3HzlZIVcYqwC3RZtVQhVyr4rlJELCURG9fOHI1YuucD10hyCoZHi04ScZ91Rj1yfv49H7hG0Yi/3QKhQxKxJSgiou4WMnlNzGckSZcHLCL2JqJ69xXOJ3m0NIfMWkXEy9wi4imKiAC6Q10SQmuwbdtrZ167iNiAduZA25nru1ilPO1hqaghOR9y/nLkmKQmpD14g4I2UG2RfM3vM0tVLn/P8su3vF2KxKX0rDRzKuARdrhiQVoyi1UqJBHjvdKmy53zzEVsrdSkJNsp6lYq+EqaXmz8dmZJuu3ADn3944e0fahH4xrWpD2omFXUkYFxff3jh3Tbgdo/1Ot6aWYitgJFRNSdSSFu7k9oqHfFH98zz0l//xvO6Tp+5WrmIoZOLi1NvOKcr1REvPQm50l/5pQ0e6a5xwYADVCXhNAa8kVbJsS4ZjtzXReruO3MuRoXq9Qh1RSNWPrLW4Z0wHpDv2S9IhPquDX6nA5YozpgvaG/vGWocWkP7w0KrVJonZWtkyutW0wv5EtdIHtWFBFjCWnr253zYW1pXpqRlx3v3VT5e7zlKsxFbKlU2TzEyMXPibZta6YJ25mN2w7s0L//2S36b//LdV5L89dviVJArNYSScRWoIiIuht1i4gVU4gvfNv5JPPFh9a9HzMX8X++Oa1Z9xMhdLlzL0nFvNQ3UjkNkxyQdlznnD/1k+YeGwA0QF0SQmsobylOxld52Wfb9S0ixuvQzmwXnLazOrj53/6LHkt+Tt9O/u/eZcOa03eTn9Vjyc/p5n/7L3X5ORUl3XlLvEFBC5W3Tq5kCotrtk6OvyBl5qSeodIMxHLlLc1hZJaqJIek6CrptfK5iGgdb6nK1spXZ/LKu5+8NaOIKDn/Pm8/dImOR6+QJM298bOm/NyukHZHkzATsakoIqLuLpqHOHPKaVEde94pIkrSse84X4/9fNXWh0uH+3Tl1g0qFG398Pj5Zhw6Wm3MTaiuXKpS7rLDzumbP27OMQFAA9WcEFpHtqyQl4iu8rIvPeMk/6Q6JRFraWfuK52v03IV3f4NKRJbdpFXK4nEnOsbhVYptInbDuzQn932tosu3z7Us37r5MkfOqe7j1RMb2nH9c7peEiLiN5SlTX+TpNEbA8L55zTDZWLiGapSjIWUW9ijfR+nUUilrJb3aCEWTKJ9ZFEbAmKiKi70ZVFxAcOSg/eLD34PinjflqQmnS+fvBm5/pVlLY009IcCmNlm5lX4y1XIYkIoPPVnBBahynkxaOWIqvdh0kh9g6XkoA18NqZgyQRo3FnbIVUt+Uquvb/Z++8A+Oq7nz/vXeqem9WtWy5yhUwNrhhQnAIJMSb5UHqJoFNIJslm8bu22R5vE02D9II+xISCLt5GxZIIyEkYEIA9wouWC6SrN67NNJIU+99f5xz7p2Rps+dopnz+WfGM6OZK1maued3vuUuyPf+xfd9974BrL9Lm9fxBVugOKyxew0OJ0SKsomyir0TLCvJwpGH9gS3TrJSlflWZka6NzR7lKq4JRkn2sdwuHsOJ9rH1FIsrkRMDoI0M0/E0co8n/xl15LLmTbtPv9SHSUT0U+MQDD4Rl9E8CEiR3M6xqiduYgOEX0oAJTckCAKAJaLeKBlJKpmSs4iIVCpCoOVq4xcVk/aOBwOZxHDwtXnKwVDUggFgSkR/aoQAcAyQC41UCECHkrESDIRBUFVIzJ1pAZMzbm8/i3JZIwyF4laMhzYAsXOrVKcxNM6TBbKN9FN+u7xWTjdQf5OXQ6g+wS5Pr9UhVG2FoAAzAymZ/swVSIOu7Ox/dE38dFnTuPxk1P46DOnsf3RN7G/aQAoXkEfOwpYxxJ4sGmOMkQs8Xl3vEpVfLF61RqMyrnQQYI82BT311+UMCWiOS+yr2eRI24H4LJrc0xpAB8icjRngRJx/V3Ard/2/eAgCoBr6wqQY9Zj3OrA+d5JrQ+Vk0w4ZtXd2SUb/T8uqxgopru5PBeRw+GkCHsbK1CZr+Ye3rG+IjSFUBCYGtBkCFSqwpqZNRoiGqKwMwOAnv4cNGpoBoBBVw7mZLIolFe+H1fEZRiW83B2dP4mp8awBQpXOXCSgJYhMszes6oUhVlGON0yLg1YAn9R/xnAaSWtw6W+VdMwZQNFy8n1dLQ000zEI31uDEx5v28NTtlw/7NnsL/FAuTXkBtHuaU5YVgDKxHjWaoyn8bKfFySSbnKSOvJuL/+osPtUrMMI7Uzs40+gFuaw4APETmaMmF1KFkSdcVUSSDLwNn/iuj5DDoROxvITtEBrS3NYTRFc+LAUBMJ0s8uA3KCLJoVS/Ox2B8Xh8PhxAFZljFoUXfB7S5Jk8ZgpgZk6kCfaFiqQl4rCjsz4KFE1M7O1e3KxwW6OBMa9+HZdf+J7fYn8OfeGA8RjTxviZM8tA6R38MVZTnYVE0W3ee6g2zSMytz3XZADPA+UpG+eW4SVRaOyzkL7mM+qkdevgS5mFuaE04wO7OVDhGz4q9ENOpFjOaSpvOZ9rfj/vqLDlaqAkSuRNTpAT2NceHlKiHDh4gcTWFW5vJcMzKN9MS86ygweIFcL2sEGm4l10U9UZUFgVku3mzWeIgYRlM0Jw54Wpn9laowWLkKVyJyOJwUwTLnwpxTVe51jWlj5XW4yXMaAw4RmRKxUpPXZANLmzNCJSLLZdSqWAXAwJQNlQK1EObXYueKUjhgwKGWGBe3mXjeEic5mLG70DdJ/qZWlGVjIx0inu0JMkRkpSr+rMyMNG5oHh0iGzG+hogAGSQOTNkwYKwlN/BylcTBilWygtmZ469EBABdJYl0Mo1cSMjrLypYHqIx238reijwcpWw4UNEjqawZmZFhQgAh75DLjf/DfC5I8CHfgLoMwHJBQxfDvqcu1aQN/mmPguGLVFamzyboi++SG4LoSmaEwdCyUNk1NIh4sB5/obP4XBSggELWdyzPZTOMSskDbKAF6cSkdmZtRsiDk5YUA6ao5tfgxuWF0EnCmgftaJnXLvsxQVwJSInSWilVuaSHBPyM43YWEOViD0T/r/IaQN6TpHrS3cGfgGlXCX9hohuqkSchO8hImPIxIaIXImYMIIoEafmyBCxIAGZiABQtoq4rcrsHZpGeqQkSqlKhFZmBi9XCRs+RORoSqeSh0j/GHtOA+0HiOpw55fJ6iizELju0+T+w98L+pwlOSZsqCIS5beiVSN6NkVbqfogxKZoTowJpZmZkV8N5FWTQXTv6dgeF4fD4cQBlqO1ojQHelGA3SVhMNqNM3hkIuoDZSJqPERUMhGjtTNrN9yzj3ZDJ8hwiWYguxS5ZgOuqSFtjgdjqUZUMhG5TYqTWFQrMzlH31CdD0EAesbnMDrjp1Cg9zTgspGBCysG8QezM090eNsM04AsN/l+/SkRGfoyminJlYiJwWVXB0/ZpT4fksh2ZgBYu2o1xuQc6OHGWDuP3AoIK1WJNA+RwZWIYcOHiBxNaVeGiHQBwFSIG+5Ww4QBYNvfATojsaOGkGunWJqjzUWMoimaE0PsM2rIdEWAUhVPWEsztzRzOJwUYJAOEasKMlBVQOy8bGMuGtggL6CdeZoNEbW1M7slGa5gza++YHZmDVUY8mQXAGAuq1KRe+5cQSJVYjpENNKSOfsMyYjmcBIEK1VpKCWDrlyzActKyOLZby5iJ8tD3BE8aiazkGzwAmqMUZqQI5Of7SSyfd4vAKjIM2PN+mvJDdMD6gCEEz+YClE0+FWvJdrOnJNhRKexAQDQf5mvcQKimRKRb/aFCx8icjSlc8xDiThwHmh9DRBEYPuXvB+YWwFs/Ci5fui7QZ93Dx0iHmkdjbztESBN0H/ziu/7gjRFc2LI4AVAloCcJUCOb3vBAni5CofDSSGYErE8z4y6YjJ46tQgF5F9Zvq1M9tnVNVQsFKrEPFUPUakRmQh5xoqEU3TvQAAKbdauW3XCnJucbxtDI5IVZPBYDYp2U0UXRxOgmgZVktVGCwX8Zy/XERWqrI0SB4iI00tzcIssTNP+FAistHrw3esgS4zn5zrAsBoS5yOjqOgNDOX+i0JUtuZE2NnBgBrYSMAwNlzNmHHsCjgSsSEwYeIHM2QZRkdIx5KRDYcbPwroGjZwi+48UFA0AFtb6h5eH5oXJKH4mwTrA43TncEyG4JhfMvRPf1HO0JJw+RUXsjuew9Dbgc2h8Th8OJCW5JxvG2Mbx0rg/H28bg1iD3LxUYnCL5fxV5ZtQVsSGidkpEv0PE6QFyacwBzLlRvx7grXqMaIioKBG1yUR0SzJy7URtqS+qU25fuyQXRVlGzNhdONMd5bmFP4weyiS+QOEkEJaJyOzMALCpJsAQ0TGrRsYEK1VhMEtzOpWrSBIwR94//nHfNojzBJvleWY8+bHN2NtIN2lKeENzwmBKRD+lKoBqZ06UEhEAsuqIYjVv8mLCjmFRwDMREwYfInI0Y2TGDqvDDVEAatxdwOU/kDt2fMX3FxQuBdZ9mFw//P2Azy2KAm5aSd7wo8pFnOwBzj1HrudVqbdnFgf8QOHEmEiGiMUrgMwiouwYOBeb4+JwOJqyv2kA2x99E/c8fQIPvnAO9zx9AtsffRP7mwYSfWgJR1UiZmApVSJ2aGBndgSzMyvNzNrkIQKAThRg0JGVdETuAVasolE78+iMHUtAzh3MpfXK7aIoYEcDsTTHrKVZFAEDtTRzqxQnQVhsTuU9psGHEvF8z+TCIqeeE4DkJDEHhfUICdbQPHA+6mNeNNgmiZsGwPYNK+H5YyzMMuDIQ3vUASIAlKwilzwXMf4EKVUBgEkrszMnTolYs44USNa4umCZ4Z8bfuFKxITBh4gczWAqxMqCDBiPPU5uXP0BoHSV/y/a/g/k8vLLQT9MmaX5rWhyEV//F8BtI3l6D14AqraQ23d+DcjTJguKEwFsCLgkxDxEgGTz1HBLM4ezWNjfNID7nz2jLGQZg1M23P/smbQfJLJMxIo8M2qLSK5wl6ZKRD/FKhqXqjCUhmZnJEpEVqyizRCxf3IO1QIZEuoKar3u27mCbCAeao1luQpfoHASCytVKcs1IS9DHY6sLMtBhkGHabsLbSPzfj8VK/PO4HmIDGZnHmnWtF09qZmlre+mXIzMkgmiThQgABi3OjFuneeW4UrExDHjYWf2gdMtYdruApC4YhUAKKlcjknkwCC4cfXCqYQdR9KjKBHzonsenokYNnyIyNEMZru6PncSaPotuXGnHxUio3Q1sOp2ADJw5PGAD72xoRg6gZS3PHO4PXwbXOdR4OKLJKPxfY8RdcDK95H7rr4e+vNwtMVmAUZbyfVQS1UYtWSnjpercDjJjVuS8cjLl+DrHZvd9sjLl9La2jzokYnIlIhdY7ML1UFhEroSUduNNGafTgY788CUDVV0iIh5Q8QdDWSI2NRnwci0n4baaOFWKU6CUa3M3pl9ep2IdVVkAX52vqXZs1QlVHKXEJeI7AaGL0V8vIuKOTpEzCjAoIW8j5flmlCZQzZS3u2d93NVlIg8EzHuzAyRSz9DxElaqiII8Bq2xx1BwFAW+T0ZazmZuONIduY0sjPzjb6w4UNEjmawZuaPOH5DZP0Nt6rZKIHYQUtX3v0lMNHl92HHro5CR0Nw//VPl8OzwUlu4NWHyPXNn1TtFmyI2HGIv3EkisF3Acik0S87TEu5Z0OzFKNQfA6HEzWnOsYXKBA9kUEGPac6xuN3UEnEtM2pqB/Kc82ozM+AXhRgd0kYsERXxhG0WMVCP0M1VyKyIWIEdma9tkPEobEJlAl0sZHvPUQsyTFh7RKSBXnkaozUiHyBwkkwLVSJyJqZPdlELc1nPRua7dNA3xlyPdRSFYBMX9i5f7pYmmmpCjKLMDTFhohmLC8kQ6jzvVPej2dKxKlu/p4Qb6yB7cysVCXXbIBufrhlnHGXk78jcTBN/o4iwaaRnZlv9IUNHyJyNKNz1IoqYQQbJ14jN+z8amhfWHkNUH8T2bU89oTPhzAbnMPtPSgK2QZ35r+AoQtE7rznG+rtJauAgjrAbQfa3wrteDnaouQhhqlCBIhtxphNmkXTZcebw1mEDE97D8LWCe14zvBNrBPaAz4uXRiig8Jcsx5ZJj30OhE1hcTS2xllLmLodmZtmpkZJoPO6/XDQmMl4txIJzkWMRPIKFhwP7M0H2yO0RCRW6U4CaZ1eGGpCsNnQ3PXcXJenl8L5NeE92Lp1tDM7MyZhYoSsTzXhGV0iLhAiZhZqOaw84bm+BKkWGWCKhET2czMKGwgkVvls1ci24xLB7gSMWHwISJHMzpGrfis7mWIsguo3w1UXxf6F+/4Mrk88wtgesjrrqhtcHMTwJv/Sq7v/p9AVpF6nyAAK6gasXl/6MfL0Y5+mocYrpUZAHR6oIr+nnFLM4eTtJTmmL3+vU93GDfoLmGf7nDAx6ULA0oeYoZyG8tFjLahOeF25mgyETUqVpGoy8GaWeUz220XHSIebh2N2j7uE75A4SSYFmpnbijzoUSsIYP15kELZh1EEY3OQ+Ry6c7wX4y5fdKlodlDiajamc1YXsCGiFOQ5XnvK7xcJTEodmbfSsRkaGZmlK28HgDQgB5c7I5hZu9iRisloom4EfhGX+jwISJHEyRJxtxYL+7SHSA3hKpCZNRtJyUnbjtw4kded0VtgzvwKPmAL1kFXPeZhfczS3PLfmJ75sSXSJqZPam9kVx2HdXmeDgcjuZsWVqITbkWrBPasVbowB06MvS/Q3cca4UOrBPasSnXgi1LCxN8pIlhwCMPkVFHcxGjVyIGszPHqlglCjsza2fWSImot/SQp8up8nn/5poCZBl1GLM6cLHfoslreqFYpaIvypmPW5JxvG0ML53rCz8rmpMWTM05MWQheZ8NPpSI5XlmlOeaIclk4AXAu1QlXNim8NBFwO2K5JAXF0omYqGXnbku3wC9KGDc6kDvxLz3Ml6ukhhm6DAuiJ05GZSIQn4tZsQcGAU3Oi6eTvThJCc2+n4VrRKRfUbb+RAxVPSJPgBOajBgseFv8DJMggty9VYIbLATKoJA1IjP/w/g9DOktZlajkK1t/l83PAV4NRT5PrebwM6Hx8KtTcApjxgdhToeweo3hLesXMiZ24SGG8j1yMeIrKG5uOALIfeIMjhcOKGThTwO8fnABP5NxNlFMGCP5n+mfzDAUC8JyHHl2g8m5kZrFylY3Q2qudmSkCTwccQ0Wkjn31ADJSI0diZWTtzdN87I2u2FwAgFtb5vN+oF3HD8mK8fmkIh1pHlKIJzTDFJm9pf9MAHnn5ktdGa0WeGQ/fsQZ7G7W1p3MWL6xUpSLPjFyz7+HIxup87L84iHM9k9haIaoqwnBKVRgFS4mF3zFN7LplayI99MWBpxKxR7UzG2UBK8tzcLHfgnd7p1BNIyoAcCViInDMqkozPxnsqp058UpECAIm89cie/wErJ1vA7gj0UeUXEgSKecENFAicrdAuHAlIkcTenu68BHdGwAAYdfXIhvkrLgVKGskJ9mnnlZuDtXetuBxsgzs/0eS6bLy/cCyPb6/UGcAGt5Drje/Ev5xR0laqwhY6HZ+LcmIiYTKawDRAMwMAhMd2h0bh8PRln1PQxLIYIl9RLBLSdAB+57284Wpj5Kj5TFErC2iSsRo7cw0S9io83HKN03zhPVmn1mB0cCGlpEVqzAlYvQZmU63hELnIADAXFLn93FKLmJLDGxjMVA5sKzo+U6NkLOiOWmDUqriw8rM2FRDcxG7J4GuY6QgsWh5ZFmpogiUN5Lr6WBp9shEZIrPslzyHra+kmxILGxo5krEuMNKVfRm1b46j2SyMwOAoWozACBr7EJsojYWM/YpKMFmWikRebFKyPAhIkcTss48hQzBgQ7jSv/DILDhKwAAIABJREFUumAIAlEgAsCJHyu7AVuWFqIiz4xAY8lMow7X1M5bADW/QspSdEbg1m8Gfu0E5SLubxrA9kffxD1Pn8CDL5wLr3E6FRigeYiRqhABEsBfeQ253sVzETmcpGX9XWi67Xc+7xLvexNYf1ecDyh58KlEpEPE7rHZqDaXVCWij2IVTyuzxiru6DIRtStWGbLYUCWQwWBW2TK/j9vVQIaIZ7omMG1zRv26XphYsYo2C5Sos6I5aQXLQ1xRutDKzGDlKmd7JqKzMjPSqVyFDhHljEIMTJH3rLJcIrtfT1XN5xcMEakScaJTs9gGThBYqUp2qd/Pu0lr8hSrAEARLVdZIbWhbYQPuLxgpSqGTEAf5dCXfUZzJWLI8CEiJ3pmx9HQ9TwA4FTNZ6JbiKz9EFBYT8pQ3vk5AGKDe/gOYoXw98yzDje+8PwZ2JxU8eC0Aa/9T3J929+R5wxEw3sAQQeMXAbG46Nm4yoCRNfM7IliaT4W3fNwOJyY8k7XhNe/52fNpytqJqJarLIk3wyDToDDLSkL00hgSkRTICWixlZmQCM7swbFKgNT6hBRLKjz+7iaokwsLc6CS5JxrG0s6tf1wqitVSrqrGhOWqE2M/tXIq6ryoNOFDBkscPZdpDcGImVmVGxgVwyx0kqQ+3MVl0ebHTTpJwpEekQ8ULvlPdQP6uEqr9lYLQ1roebtrBSlaxSvw+ZnKNKxKzkUCLqq4jIYqXQg7fbBhN8NEmGTaNmZsBbichPTEOCDxE50XPypzBJs7gk1cK1/NbonkvUqWrEY/8OuIgtYG9jBZ782GYvqxdAVBv37VgKo07EaxeH8LGfnSShuCd+RHb3cirU5udAZBSQbESAFKzEGK4ioERbqsKoof933XyIyOEkM290S7DLahyzA3o4zMVkQZXGDNIhoacSUa8TlQytzihyEZViFV+ZiKyZOUf7/LxkKVYZGh1FkUBtxPk1AR+7s6EYQAwszRpnIkaVFc1JO1Q7s38lYqZRj5VlOSiABYbRS+TGqIaIrKH5QuovymmxyqhE1ON5GQaYqfJ7eUkWMgw6WB1utHsqyQSB5yLGG0WJ6LtUBfDMREwOJSLya2HT58IouNHfcibRR5NcMCWiWYMMY/YZDTkmBWipCB8icqLDZgFOPgkA+L+uD2Jpsf8TlJBZfzdRRcwMAueeU27e21iBIw/twfP3bcUP796I5+/biiMP7cE/v38N/uszW5Bj1uPtrgl87sd/hHTou+SL3vOIxxtDEFhLcxxyEbmKAMT+MdFJrrMd60ip3gJAAMbbgWm+U8fhJCMDU3M4PSxA8tCU6+DGH3b+EcjTXgm3WLA53crCZf5GWR21NHdEkYuo2Jl9tTPHqJkZ8MhEjMjOrF2xyuxQOwDAKuYCZt85WIxdK8kw+1DLCGQtBx8aZyJGnBXNSTsmZx0YmWbNzP6ViACwsSYfW8XL5B8lq/2WT4REySoSJ2SfUs/1UhFZVuzMA07yfs1UiADZDGqsJO8751nzNYPnIsYXZYjo//dabWdODiUiBAFzxesAAK7eswk+mCSDKRGjLVUByDmHQM+ReC5iSPAhIic6Tv8MsE3hqrwE+6UtqKNtklGhNwI3fIFcP/o44HYpd+lEAduWFeGDGyuxbVkRdCJZjG6tL8KvP7cN5blm3DX1DETnLObKrgkvY2vFXnLZdUzd3YgRXEUA1eJSWB99oH9GvhrizS3NHE5ScqB5BJvFVmQITiC7DA7RDL0gY2q4J9GHllBYHmKWUYcck97rPjZE7BqNfIioFKv4HCJSJWLS2ZlZJqIGxSpjnQCA6Yzgasut9UUw6kT0TsyhPYqf+QI0zkTcsrQQJdkmv/cLIKrWLUsjLCzjpAxMhViZn4Hsee8v89lUnY9tIlUhRpOHCJDSwlLaypzKlmbbFClwBNBnI+9bZfM2g9ZXkSHHgnKVYjpEHOVKxLhgDV2JmJ8sSkQAWXUk973a1oz+SZ6fqTCnoZ1ZEDSPHUl1+BCREzkOK3D8/wIAfuT8IIwGvdfuW1Rs/gSQWUR2Ly/6DuKfz6ryXLx8pxH7dEcgyQI+NfRhnAxHyVe0jHygSy7g6l8iO+4Q4SoCqFbmiijzEBmKpZmXq3A4yciB5mHcKDaRf9TvhiWjGgDgGm1L3EElAWoeohnCvEzhpcXUzqyJEjFIsYrGRGVn1tMhomsOkCIYQnogTnWT48iuDvrYTKMe1y0lm1qHtLQ0G+kGq0aLE7ckI9Pk4/8Tanb0w3esUTZaOekLK1UJZGVmbKpRh4ju2u3Rv7hiaU7hchWahwhjNvqtLA/Re8Cvlqv4UyLyIWJc8CxW8YEsy8mnRARgrCYNzY1iB96elyud1mipRAQ8chG1cQykOnyIyImcd34OzI5hLqsaf5BuQF1RFkStTliNWcDWB8j1w98LbREhSSg58i8AgLcybsEJWy0+/h+n8OqFMApKVlI1YoxzEYM1TqeFikCrPESGUq7Ch4gcTrLhcEk4enUM28UL5Ib63XDmLQUAGKbiU2aVrAxaWB5ixoL7mLq/IwpVnJKJGHc7swZKRABwRadGzLD2AgDk/NqQHr+TtjRrmoto1DYT8Qd/aUHX2CwyjTqU5piwTmjHc4ZvYp3QjvI8M5782GbsbdQ+55Kz+GgdCl6qwqg3W9Eg9kGSBbSYo4yZAdKjoZlamZFZiEEL3RCaJ6jYQJWIl/stcHi+H7JMxLE2wOWI+aGmPUGKVawON5xuEmORTEpEJrZYKfTgTNtQgg8midBSiQio8WdciRgSfIjIiQynDTj6BADg7ZpPwQ0dlmphZfbkunsBUy5pTG55Nfjjzz8H9J8BjDm48bNP4JY1ZXC4JDzw3Bn84nhnaK+58jZy2fpnwO2M9MiDwhqnAyUupbyKoP8cudRqiMiUiENNMbejczic8Hi7axyifRLrRTowrN8NXclyAEC2tStxB5YEeCoR58PszD3jcxEXbbFF6wI7s9ulLqpiYmeOJhNRuyFino1sJJqK60J6/M4VZIh4on0MNmcEKkpfMDuzBouTY1dH8ZODRL37/bs24Pg/3YwfNzbjBt0l7NMdxn/fez0fIHIUlFKV0uBKRLHrCADgslyDd7SYoadDQzMtVUFGIYboe/l8O3NtUSbyMgxwuCU0D3qonHKXAMYcYoceT29FflwIUqwyYSWDXKNeRIbBt9I7IRTUwWHIg0lwYbSd5yIq2KiyV3MlIh8ihgIfInIi49yzpPgktwpvGPYAgDZ5iJ5k5JNBIkDUiIFCzm0W4C+PkOu7vgZzQQWe/OhmfOT6Gsgy8I2XLuI7r10JHpRedR2xUdumgO4T2nwfftjbWIEPb/a9cLt7S3VqLwKsYwC1mEVdqsLIKQMKlwGQgZ6T2jwnh8PRhIPNI9gmXoIImcRG5C5BZnkDAKDM2a/dsGYRwjIRlWbmvjPAz28H+s5gSX4GjDoRDrcUcRYSUwIusDPPDAGyBIj6mLRjR2VnFnWklAGIqlzF5nSjTCKD0uzyZSF9zaryHJTmmGBzSni7UyPrmOfiJIrClgmrA//wq3OQZeD+jQbsLRyEbvA8qvuJe+IO3XFcOXOYKP0nu7U4cs4ip3U4dCUiOg4BAI5Ja3GuR4PN2LK1pKzAOpy6pXfMzpxZpCgRK+YNEQVB8LA0T3rewctV4oUsB7UzT3o0M8+PFkkogqBEA+RMXMTUXOxELosKG1ciJhI+ROSEj8sBHHmcXN/+RbRNkJ0bzZWIALE0681A3ztAx0H/jzv0GDlJKVoOXP85AKQR7Vt3NuJLt6wAAPzorTZ87TfvwumW4JZkHG8bw0vn+nC8bUxVeIg6oOFWcr05BPVjlFwaICd3n76xDj+8eyM+uY3YrY55HlMqMkB30ooagrZlhoViaeblKhxOMvFW8zC2e+QhAkBWBXlvrhMG0TuRvmHhC5SI518AOg8D7/4SOlFAdSFR5UWai6gOEeed8jErc04FIGp/OhiVnRnQpFxlcMqGKoFIqrLLQhsiCoKgqBEPtgxH/NpesMUJZJInHQGyLOOh376LIYsd9SVZeOjKXwNP7Qae2gVYyfdYCAtuO343uf3xdZocOmfxMm51YHSGnKMvD0GJiM7DAIDj0hqc7dZggG7MIud5QOpamj3szEN0iFjmIx+eDREXlKswSzPPRYwt9mmSsQv4HSJOJGEeIkPJRRQ6cIbnIhLmtM5EZAVoPBMxFPgQkRM+7/4SmOohcvBNH0P7CDkhjskQMbsE2PxJcv3w93w/ZvQqcOIn5Pqt3ybtzhRBEPD3Nzfg/+xbB1EAfv1OLz70o6O44f+8gXuePoEHXziHe54+ge2Pvon9TTQ7keUiNr8SlWIgGM2D07g0YIFBJ+ALexrwwY2VeOh9q5CfaUDX2Cxeu5iiu7aARx6iRqUqDF6uwuEkHf2Tc2gZmsEOjzxEABCKiJ25ShhB32j6RhAMTtlQiRGscF8lMQ8XXyR3NP0W6D+H3Tl9qMQIOiPMRfRrZ1aambXPQwQ8lYgRDhFZuUoUSsSh4UHkCuTrhfyakL9uFx0iHmoZjfi1vTBkEkUWELFV6rlT3fjzpSEYdAKeuHsTsO9poiL1gCWgyKKe3M9Ja1ipSlVBBrKCNDNjqhcYb4csiDgtrULbiFUbxZNSrpKilmaqRHSbC5WBra+SSbWh2V+5ClcixhS60QJjtlp0NQ82REyqPEQGzUVsFDtwujOM0tBURnMlIosd4UPEUOBDRE54uF3Ake+T6zd8ATYY0T9FdnZiMkSkrwNRT2wWPacX3v/aPwGSE2h4L7DivT6f4u4tNXjq49fCoBPQ1G/BkMXudf/glA33P3uGDBKX7SE2qokOYLQlFt8RAODFsyTs/aaVpSjIIoPPTKMen9hK1Ig/PdgW3H69WNE6D5FRS4eIfWcAZ/oqmzicZOJA8wiqhBHUiUOAoAPqaOtndhlsghk6QcZk/9XEHmQCGZiy4aj5QVz3531eqjJYR4GnduEb/Q/gqPlBdI6FP0yTJBkOdxAlYsyHiBFa1RUlYuTv5TMDJGdsUiwAjJkhf9325cUQBKB5aBoDUxp8lgiCammOwCp1dXga//pH0pr70N5VaKzMA9bfBdz7hs/HX37/78j9nLSmdZj8roVmZSYqRKFiIwoKiwH4UM1FQqrnItJMxBkdcdUYdSIKsxYq2TZWk0FHy9A0Zh0u9Q6uRIwPSqmK/+gO1c6cfEpEJrpYJXTjbAcvVwGgvRKR25nDgg8ROeFx8UVgvB3IKASu+RS6x2chy0COSY8iHx+ampBfDWy4m1yfr0Zs+TMpQRENRIUYgJtWlSLX7Ht3iY3qHnn5EtyGbKBuB7mh+ZUoDtw/bknG788SFci+zVVe933ihjqY9CLO907hZEeK7jZp3czMKKgj1jzJCfS+re1zcziciHireRg3MCtz1bVqhIEgYNJcDQCwD6fnENHhkjA6Y8eDjgeIeswL8skkCTo86HggIiWi062qAP0rEbUvVQHUDMaIilUAot4DVAtaBNjHSJHPlCm8jOGCLKPSqHpYKzWikosYnsrB7nLjC8+fg80pYUdDMT5941K/j5VApIhnutNX2ctRYc3MDWWhW5mxdKcy8Dqrxe9Rqjc0UyXiFMigtjTX5DNPryzXjLJcEyQZaOqzqHcwJeJoKxFqpDoemb9xJUipCuCpREzCIWLBUrhNpFxltvdiWudIKyhKxDxtno8Xq4QFHyJyQkeSgEPfJde3fR4wZaODLmrqirNiG0J74z8AEEhL8yBdjLocRIUIAFs/BxQvD/gUpzrGMUabt3whgyhCTnWMAyvfR25s3h/9sfvgeNsYhix25GUYcNMq712x4mwT/vpaMlj86cEUbGubGaaLV0E9udQKQQBqaC4itzRzOAnH7nLj2NXRBXmIjLkcorwWx9vje2BJAsvQelXcCdz7F5+POb/3RbwkbUdHBJmInlbiBcUq0zTCI1ZKREOUdmYDtQRGoyqfIM3fc1lVQR64EDUXUYuaWkSscnhsfzMuD1hQlGXE9+7aAFH0ONfKLPR67GzGEgzLeTjQm6IuBk5YMDvzitLQlYhYugObasgQUZNylXKazTnZBcylYJYbzUQclcjP2JeVmaFamj1+rnnVZMNEchIHVKrjkfkbV4KUqgDexSpJhyBAXEJUvavQjqa+qSBfkOJIktrOrHmxCrczhwIfInJCo+8M8OQNwGgzYMoDttwHAMoQMWZWZkbxcmDtneT6kR+Qy1M/BcauAlmlwM6vBX2K4enQwtmHp23ACpqL2HOSWMo0hlmZb19fsXBhB+De7fUQBeCt5hFcGbQsuH9Rw6zMxSs8wuY1hFmaeblKWuK3NImTEN7unMCsw4kduovkhvrdXvfLhaTsImOmM67HlSx4tnkKmL8RR/69JJ8sSnvGZ+FyhzeQYwM8QQAMunnPn/R2ZqpEjCIT0ThDPmvdeaHnITJ2rSCWziNXR7V5H1FUDqEPgw80D+OZI2Sw8NiH16M0Z96Awj0vs27FrdhufwJv9usxNcsbPNOd1qEQ7cwTncBUN4kOqt6qKBHP9UxGH6uTWQiwPNLBC9E9VzJCh4iDLvJ+VZbnf4i4QWlo9hgAiSJQTMtnUjUXcbKbOJB8ZP7GrUme2ZkDDBGTuVgFIFEDALBOaMepdM9FdEwDMj0f0rxYhSsRQ4EPETmhcf55YOQyuX79ZxXpcKeHEjHmbP8SuWz6LfCTncBb1L78nodDavhdcPId6HH51XT3VCZ2aQ2Zdbiwv4mUpsy3MjPqirPwvkZiv3rqUIopdGJlZWawIWLPqZS1hvBBmW/2Nw1g+6Nv+i9N4sSdA83DWC10owAWMkSpus7rfnMpUZAX2HoTcXgJR2lmzjWTrCbB47QsuxTILkVxWRWMOhFOt6w8PlSUUhWduNAtwOzMObEaIkbZzqxnSsTI25lz5sj3qC+sDftrN1TlI9esx9ScE+e1yIYzhWeVGpm24yu/Jjlyn9xWi5tX+7DhDV30+mf2+EXUlBZAkoGjbdpvgHIWD2MzdoxZHRCEEJqZOw6Ry8prAFM21izJhVEnYtzqQPd45EN8hVS2NFM7c7+DDBHDViICqZ+L+Pi6BU3yLPM3bk3y1uB2ZqZETMpiFUDJRWwUO/B2ZwqqesOB5SHqTGp+crTwTMSw4ENEjn88d47Ov0BvFEheIN05aqdDxPp4DBEr1pPyFMik5c1pBZZsBjZ8JKQv37K0kKo9fCOAqEG2LKX2oBXM0qxtLuJrFwcx63CjrigTm2v875787c56AMAfzvWjfzKFSkJiPUQsWU2k7U5rSrYB8kGZb/Y3DeD+Z88sGLJ4lSZx4s6B5hHcyKzMtTcCOu+T8/xqsniqkvoxY0/NoX8gBmlpR0WemWyGyR4Dt+IVwBeboMuvQk0RWaB2hJmLyFSAC0pVJAmwxNjOzJSIEWciRt/OXOQiG3ZZZcvC/lq9TsT2BqJGPNisgaXZGHrzoyzL+OpvzmN0xoGVZTn4p9tW+34gGyJWbSGXA+9i53Ka5diqkQ2bsyhpoSrE6oJMZBgXOl686FDzEAGyAbBmCdmc18TSzMpVBlNsiCjLSrFK96wJQLAhIhFgdI3NYnLWI15JaWhO0SGijyZ5JY0+Xk3yIdmZk1uJyBqaVws9ONc5DCmdBQQ2jUtVAJ6JGCZ8iMjxj+fOkZ1ZamXgv+5Qdo7ipkRkA83VH/C+/br7yKAoBCm8ThTw8B1rAMDvIPHhO9ZAx/KGWC7i1TejUkLM58UzRBlx56ZKVRniI2h4Q3U+ttUXwSXJ+I8jKZSTMhCjZmaGKAI1W8n1rtTKReSDMt+4JRmPvHwJvk6nvEqT0vmEKwH0TsyidXgGO3TUwla/e8FjMsvJ4qlSGEXvaPqVQShKxLwMdQHJ1IgD50lpGIC6IvIZ2xlmLiJTAZoM84YIs6MkgwsCkFMe2cEHwWzQyM7siuzz12pzYolMBmn5S8IfIgLALpqLeEiLgVwYSsSfH+vEgeYRGPUinrhnE8zz//8Yw3SIuOYDZAHkmsPeMjKkPNQyGr0VlbNoaR2meYjBSlVkWS1VYaWCQIzKVVJsY9duASSy+dVGh4iB7Mz5mUbU0g2hdz0tzYoSMUXtzAGa5HHvG/FpklfamQPZmWkmYlaSKhEL6yGbcmESnCi3d6JlOI2z+7TOQwS4EjFM+BCR4x+fO0cUUQ/bHT/B8LQdALC0KMZDRDbQ/MPfed/+0v1hSeH3NlbgyY9tRrmPD/kHb27A3kaPBseKjUB2OVG0dR6J/Ng9GLLYcPQqsRjt2+RhZfYTNPzZXUSN+Pyp7tTIN7IMkDB/QVTDtmMBszSnULkKH5T551THeECbp1dpEiduHGgegQkOXC+2kBuW3bTwQdmlmEUGdIKMsZ6W+B5gEjA4pWYiYvgSubH2RkCfQRaoY6S1emlxZEpETzuzF8zKnF22QB2qFVHbmaMsVhke7EWmYIckC8gu9d9oHAhWrnK+Z9JbORQJxtAWKJcHLPj2q2SY8PX3r8bK8gB5dkyJWL5OGdRs0HXAqBPRNzmnuEU46UeL0swcJA9xrI2cl+mMQPUW5WZWrnJWSyXiaAvg0MAenSzQPEQYstBjIedegZSIgB9LMxsijrYAUrq07sawjNMXM3QjaLG2MwOAIECgf0uNYgdOp7OleS4WSkSWiZjGw9kw4ENEjn+C7BxdLb8NAFCYZURerPMjggw0w5HC722swJGH9uD5+7bih3dvxHvXkA+UBSdKogispAUrLa9GctQLeOlcHyQZuLa2ADW6UdUu3vRb8oB5QcO7VpRgVXkOrA43nj3ZpckxJBSmQixZBRgzY/c6NR7lKlKEC9gkgw/K/BNWaRInbhxoHsZmsRUm2MmJO1soeSIIGDNWAgBmB1rjfISJR1UimoFhqkIpX0fiOwCgn6jTa+lGXddYeAtwVYk4f4gY21IVwLNYJdIhYnTFKpP9ZAA7JhYBelNEz1GRl4EVZdmQZFKwEhUhKBFtTjf+/vmzcLgk3LyqFB/fGiDL0WEFxqlLoXStou43DV/AtXUFAIDDWjVLcxYdLUqpSrA8xIPksmqLV7bYpmryO3S53xK5mpiRU04yX2VJ3SxJBegQUc4sUEqygg0RfZar5NeSbDeXLT4lI4kgq0TdSAGArGJiLc4qif1ry7JHJqLv13O5JUzbiKo0ae3MgJKLuF5ox+k0PN9XYHZmrkRMGHyIyAkT9VeG2api3swMaC6F14kCti0rwgc3VuLr718DUQAOtvhoQlZyEfeTD6EoYVbmD22u9LaLz9LFiXXEK2hYEARFjfifRzthcy7yHcpY5yEyKjYQJc/cONnZTQH4oMw/YZUmceKC3eXG0atjah5i/W5SEeyDmWwyKJGo6i4h+IiUiAdeSkRWXlayihQcAEDfOwDUz9nOcJWItM2ZqQIV4jJEJK/pluSwW6UBeGQiRvaeNjdCBmzjxujs2jsbyKIz6lzEEDIRv/Wny2gdnkFJjgmPfXj9wjIcT4avAJCJPS+7RFlcYuAcdtBjPtzKy1XSEVmW0cqUiKVBlIid3nmIjOrCDBRmGeFwS7jUb/HxhWEgCKlpaaalKm5zoaL6Ls0NvGGxodqHElGn92hoTtFcxLxKYO0+9d9LNgNfbCK3xxrbJOCmSnI/dubJOdXtlZeRpHZmQMlFJOUqaTxEjIkSkWcihgMfInICk1kERXK+9fPAkg3KzlHHCM1DjLWVeQHivMvoqCnK9N+EXL+LDKMsvcDghahe51K/BVcGp2HUibh93ZIg6kqdoq68ff0SLMkzY3TGjt+d7YvqGBJOvIaIeiNQTVtgu4/F9rXixPwB2DqhHc8Zvol1QnvAx6UDYZcmcWLOqY5xzDnduMlA7Zb1u/0+1pVPrKZGS2fMj8svfiIlYonLLSlDf6JEpEPE0tVkgQUoQ02WO9w9PhvWQI6VmhjnF6soQ8TYLeA81Y8RqRH10RWrSOOdAABrRnTf466Vai5iVBmDRnqu5GeB8vqlIfziBHEcfO+vN6AoO4h6kuUhlpGsZ7a49CxXOd4+pgw3OOnD6IwDE7NOiMGamWVZjetZusPrLkEQtM1FZJbmVBoi0lIVm4GoCwsyDf7zSylrl+RCFIAhix1DFo8NEqVcJUVzEQFgwiPffbI7YoV42LBSFXOeGpMxDxZXkWvWq9n4yQhdP60WejA8NYO+VCreDIeYKBHphovLBrjTr+gvXPgQkROY2VEAMmDIBm55BLjvLWXnqIMqEetL4jREzCohA8wlG4Dbf+A10IwWzybkgSmPN2RDhprj1Rydpfl3Z3sBADevLiX27/V3Abf/0PeD9Rnkww6AQSfi09vJIvvpQ+2Lt41LlolVG4j9EBHwtjSnAPMHZft0h3GD7hL26YiKIJ0HZZ6lSfNhPy+v0iROzDnQPIJczGC13EZuqN/t97H6ElJ6kTsbZxsXK+zqPwdcfJHcNi9SIpaMzNghyYBeFFAszpFcMoAqEekQcfAC4HKgItcMo16ES5LDWjQodma/Q8QKxArPHMaIhoiKEjGyRZLe0kO+PKc6oq9nXFdXCLNBxJDFjuahKLKS5lml3JKM421jeOlcH165MICv/pp8Pt63Y6mSxRgQlodY1kgui5Yr5Sqr9YMozjZi1uHGO11pnJuVpjAVYk1hZuCh1sgV4oDRZ6jqZw820SGiNg3NVImYSg3NVIlo1ZHz9bIgVmYAyDTqsYLmVJ7v8ZGLmKpKRIDkbzImuzVxeIVEWKUqSWxlBoCCpQAtV1kh9KavpZkpEelaWRM87fY8FzEofIjICUzrX8jlst0kfF0QlJ0jFvAeNyViXiUZYN73FnDtp70GmtGyoTof1y8thEuS8Z9HO73vZC3NUeQiutwSfn+OLNo+tIker30GeOtb8x5JhxyOGeC5u4A3vwVIbty9pQa5Zj3aR614/fJQxMeRUCz9JJNE0AFla2P/erXbyGVT/4pVAAAgAElEQVSKNDTrRAHf3pOHRqEda4UO3KEj39cduuNYK3SgUWjHt/fkpe2gbG9jBb5x+8JBYnmeGU9+bLN3aRIn5hxoHsY28RJEyEDxyoC22ZwlRIFR5uyLb5usZ6SElVpVraNekRKxhOUhluWaIY5S9UluFWDOBQrryQ672w4MX4QoCqgtJBmBnWHkIqp2Zj/FKjFUIoqioAwSI8pUY4oRV2RDxMw58j2KhQFyBUPAbNDh+qVFAIBD0WQMelil9jcNYPujb+Kep0/gwRfO4YH/PoPJOReqCzLwlVtXhvZ8bIhYSt/3RFFRe4mD57F9eTEA4LAWzdKcRUXIpSodh8hlzfU+VWEbazQcIjI789AlwJ0CRYGAkok4BfJz9lXa6Iv1NBfRu6E5xZWI9hlgul/9t9OqDGFjDlMiBipVsSZ5qQrD432elKuk6RDRFgM7s95ICqYAnosYAnyIyAnMVTpEXP6eBXexbKa64hgWZMxHb1JztTwGmlrAsgefO9kNi83jBKfhVnLZf5a0C0fA0bYxjEzbUZBpwO6VdCfs9X8hH6iCSE6ubv8ByTTKKgU2foQ85tBjwLN/hWzXFD6+jSyEfnKwLb4Lba1gVubSNV7h3TGj6jpiF7f0pkxQ9e5Xb8bLpq/jT6Z/RhFIRlEhLPiT6Z/xsunr2P3qzQk+wsQy/wT+i+9pwJGH9vABYpzpGZ9F24gVO3UeeYgBKK4hQ5AKjGJqOo4nbj4jJeh7a5iFXZHgMw+xdDW5FARVjTjP0hxOLqKd5uj6VyLGLhPR83VtzkiUiKxYJbIhYqGdfF6bS+oj+npPdlFl4KGWKDIGqVVqamoC9z97xmdRVs/EHN66Mhz8uWTZQ4nosSnHLM39PBcxnWkZDrVUhQ4R5+UhMliTcPf4LMZm7NEdVMFSkgvqtqeO2o4OwcZkOkQMQYkIqD/X8565iMV0iDjaEj+FXjwZp9E7GYVADv3cmYhTYeRM4FIVAJhkSsRYF4VqAR0irhM68HaSNjR7Ku2Pt43BrbWLbi4GdmZAtTQHyC7mEPgQkeOfuQmg9xS5Pm+IODnrUKTf8c9EjA27V5SioTQbM3YXnjvpMXTKKQMqryXXW/ZH9Ny/O0OszHdsWEKyqa7+BXj7GXLnR34FfPaQqq78hybgzifJAtaQCbS/Bfx0Jz6zdBxGvYiz3ZN4ezHak5Q8xI3xeT1jlrqgShE1oufQg83SmfBQjsPQI9lpmWc1zDTq0laZmUgONJMT9j1G2sLJIiH8YM4vhxVm6AQZw11xXFyuvwvHbvKdgXjspl+GXdgVLj6bmUs9Gqzn5SKycpWOcIaILh/FKrIcvyGiIRolYuTFKrLkRqlEfg/zKpaF/9rzYPbiUx3jmHVEmJVElYiWqQn4W04JAB55+VLwBdfMEMljE0RVwQSon6/9Z7GjgSgRm/qnoh8AcRYVzM68IpASUZKArqPkep3vIWJehkHJVIxajSiKqWdpppmIwy7y3hyKnRkANlSxcpUpVRRQWE/O7xwzqlI8lWDFaUXLgQKqDp/sjM9rMztzICUizURM6mZmBo2EWid2oHloWslzTBbmK+3vefoEtj/6JvY3RSbE8UkslIgAL1cJAz5E5Pin/QAgSySnI987U4gtYspyTcgy+SkHWWSIooD7drIm5A7vMPKVe8llBLmIM3YX9l8cBADs21xFhrMv/R25c8tngYZbfKsrWSN10XLA0ovCFz6Ax2pPA5Dx04NtC18o2RmIYx4iQ7E0H43fa8aSAC3lp2/+dcyHHslOK1VfZBrJwKRvIk0DpxPMW80jqBJGUOHuJ/EFtTcG/gJBwKCe2Got/fEbIu5vGsC/vXLZ6zZJJu/F//bKFW1PeH0wSPN3K/LMwDAduJasVh/AMsr6qRKRbth1jYU+RGR2Zq9iFdukahHOibUSkfwt2iNRIkZRrDI90gej4IJLFlFSGb0ScVlJFirzM+BwSzjZHqF9jGYimiX/348MMlw+FSznaoiqfIuWeyv72cbZ4AWUZumxqjwHsgwcucrViOmCLMtoGSKfhQGbmYeayPmoMTvg5u5GLXMRlYbmFBkiUjtzv4P8DYZqZ15ZngOjTsTUnBNdLJ5CbwQK6YZHKlqax+m6pWg5kF9DrsdLicjiSrKDZyLmLwolIvl7XSN2Qw9XUuXe7m8a8Km0H5yy4f5nz2h3XsWViAmHDxE5/mn1b2VmQ0SmjEgVPrhxCcpyTRiy2PHSOY+dwJW3kcuOg4Aj9AUcAOxvGoTNKaG+OAsbqvKAV75KAvSLlgPv+V+Bv7hsDVEnrr4DkJy4s+97+IHhSRy53KPsNC8KZDn+SkRALVfpThElog+YXuVoG18ksr+JG2kWWNq21iUQm9ONY22juEGkQ46qa0nGXxCmMsiiwjF8NZaHp+CWZDzy8iUIsvdw66q8BMNyHsbk3NAUYVGgKhEz1EVjqecQkSoRR64A9hnUFYWficiGd152ZqZCzCzy21SpFex1412sMt7fCgAYFIqRYY4+9kQQBEWNeDDSXESqcMhCcGUla+32yxAdOpfOy4H1KFfBaItyzNzSnD6MTNsxNUeamQMWHyp5iNtI5rkftG1oZkPEFGlopnbmbht5bw7VzmzUi1i9hHwuelmalVzEFLF7e8JKVYqWAflMiRgvO3PwYpXJxaRELKwHjDkwwokGoQ+nkiQXkZ1X+TprYrdpdl5lo3miXImYMPgQkeMbWQ4pDzHVhogmvQ6fupE2IR9uV20GpWuAvBpS+95+IKznZK3MH9pUCeHS74ELvybqnA89BRhDyJM05wJ3/QJ47zcBQYcP6Y7gd8Z/wYuvHwzrOBLKVA852RINapNkPKjZSi5HW4CZ1AiXnzMWYFpWlSeyoMewnIdX2t1wuiNYqKcITrekbG7cRHNHe7kSMe6c6hiHzSnhFhMdctTvDunr7Ll1AAD9ZHtMjms+pzrGMTBlQ5HovRnzH+692G5/AgMoCk0RFgUsE7HWPKsqJTytqTnlpPhEloCB80omYs/4LFwh/q37LFZhQ8QYqxABVQEZmZ2Zfj5GUKxiHSIL1jF9efiv6wc1FzGyzxLJQBYnmYIdIgL//5XmBBlGzG9mZniE7mPgnGJpPtw6sqiylN2SjBPtYzjcPYcT7THI00phmAqxrigrcDPzlT+Sy8LAdv9NtFzlfM8kpGj/H9jv5uAFYqde7FAlYucsGTyFqkQEQEQFmF+uwhqaU1CJ6MvOHPdMxFDszItAiTivXCVZchHZeZU/QlbaB0OWVTuz5kpEOkTkxSpB4UNEjm+GmoCZQXISX3vDgrs7qBIi1YaIAPCR62uQbdKjZWgGB5rpYkEQ1JbmMCzNA1NzONZGdir3rdADf/wSuWPHl4Cqa0I/KEEAbvgC8Mk/wJlRjNViDx5ovRcT77wY+nMkEqZCLFujaRlOUDILVaVGiqgRO50F+KV7l/JvUXbhM7p/Q6stF8fb4tR0l4R0jVnhdMvIMupwXV0BAGJnXkwL51TgreZhCJCwTWClKoHzEBliEbGcZlrjU4LElF6rhR6v29cKXXDAsOBxsYCdbNe46UKqoI5kuXrC4h/63kF5rhkmvQiXJIc8IGcKQC87s9LMHPshoskQhZ2ZqSQjUCK6RjsBABazdt/jDcuLoBMFtI9a0TMensX66vA0Pv7sJeXf/tSIAoi9fcvSwsBPOMyGiAsb6dVylbO4rq4QJr2IIYtdiXtIdlie1kefOY3HT07ho8+c1j5PK4VRm5kDlKq4XUDfO+T6bOCh+MqyHGQYdJi2u9A+GuXvUPEKQGcCHNPAREd0z5VoZFlRInbOUjtziEpEwDMXMV2UiB5DREWJGKfSQ2WIGIqdeREoEQHF1bVO6MC7vZOwOSPYqNOYUM+Xoj6vclgBiWYTcyViwuBDRI5vmApx6U6fQ5+OUXWnM9XINRtwzxaSAfnTQx7ZgywXseW1kHdQf3+2H7IMbKkrQOWhh0gIc/l6YOfXIju4uu0w3H8Elw1rkSPMoeDlT5GWZzd9M+07A/z8diWIP2noT0AeIqOG5iLu/8fk+7lEQNeYFXXCkNdtf11DFg2vpvEiq5WqL5aX5aCqgCiYpu0uWOYiLEHgRMTB5hGsFrqR7Z4iJ2NV14b0deayFQCAYntvLA9PgSm9VotkgHdJIouatWKnz8dpjSTJGLKQE+kyG11Me+YhMpiluf8MRFFQPnM7Q8xFZApAr2KVOJWqkNeNxs4ceTuzMEUWp47sqvBf1w+5ZgM2U1VWqJZmm9ON77/egvf98DCOdk3DKZP/hyzYML/yif374TvWBC6EcjvVIYNnMzNDKVc5B7NBh+vriwBErqCMJ3HL00phWocDlKpMdpNN3aZfA25axtBxkJyj9Z/1OdTR60SsqySquTPRWpp1BnXwvdjLVRwzgEQGTxPIhlEvhpWnt6Ga/Eyb+iyqstxTiZhKG6Cz4yR/EyBWXKZEnOqJvSJVkkLKRJxS2pkXyxCRrKc2GTrhdMs4r0VmaZSEer4U9XkVUyGKBvU8QSsUJeIiigxLEHyIyPFNgDxEWZbROUp24QPmrSxiPnXjUuhFASfax9U35trtgDEHsA4rQfeBkGUZL9JW5q+UnARaXyM7sPueIgHKkZJbgcEP/QpPu2hO49EfAr+4k+y0nX8B6DwMvOu7cTRhKHmICRgiMiWtpS/5fi4R0DU2i2UCHQLkkRy53TlEWfTaxaGQbY6pBlPZNJRmI8OoQ1EW+RvrnQy/lIETGV1jVrSPWrFDR1VStTcGzNrypLCGDNBKpRHIEQyNwmXL0kJU5JmxSiCL5t+6dwAAVgk9ECGFrgiLkFGrHS5JhigAORaS3+fVzMxg5Sp0A6SW5SKG2NDsCKhErAz/wMPEFI2dWR+5EjHDSj57ZaZ40YhwLM0n2sdw2xOH8cQbrXC6ZexZVQadmQx2vvOB+gXWx/I8M5782GbsbawI/MRjbWQAZMxWPgO88ChXgduFndTSfCjJcxFjkqeVrBurMUQpVfE1RHx8HfDUbuB3n1Nvs44BT+0itz++zudzMkuztuUqizwXkaoQ3TozbDChPNcMQQgw/J9HfXE2sk16zDnduDpCVU9Fy0njum1KzfFLBZgKMbeSxDjlLCFN1G4HyYiPJXPjgEw/f7JK/D6M2ZkXRbEKoLzPr0QXdHDj7SQoV2HnVf7Q7LyKlapk5KvFpFphpO+bXIkYFD5E5CzEZgF6TpDrPoaIIzN2zNhdEAWgulDjHYAkYUl+Bj6wgag0njpEM7r0RqCB/jyaXwn6HBf7LWgdnkG9fhTXNX+H3Ljn697B+RGya1Ulfl30OTzg+Hs4RDMZHP5oqzoka/ptwJ3luOJZqlIRx1IVtuNuylNvu/Dr5Pm5REjPyBSqBbqAXf/XAICquSsoyDRg3OpImoDleKNYuErJLmJVAbEX8VzE+MHiH27LpI3Hy0KzMgNAeUUVpuUMiIKM8b6WWByeFzpRwCO3LUO9QBYwr7ivx6xsQqZgV24LqgiLApaHWJpjhshUZfNLMgD1PXOyC7COKhEioZarMAWgdyYiXbTFRYmo8zqOsGDFKpJTVduHSJ6dbLQYi5eG/7oBYEUlx9rG/GbQTs468LXfnMfdT51A+4gVJTkm/Ogjm/HMJ6+FSIeIO2rNOPLQHjx/31b88O6NeP6+rTjy0J7gA0RAbWYuXUOyseYzr1xlRwM55pPtY0lhefNHTPK0knVjNUaQZmamRPRhZ973NBneeH8VuRD15H4faFuuwjI7F7kSkeYhOowkPiUcKzMAiKKAxkpSrvJuD81FNJiBAvqelUq5iJ6lKgCg06ubWLEuV2HD2IxCv5uasixjkikRsxaJEpGVq8gOUq4Sw/zmUNGJAm5bF/gzTJPzKiUPMS/w4yKBZyKGDB8ichbScZBkDRQtBwoXnoAzFWJlQYa3RSrFuG8nyeh6tWkA3WzBtoLlIu4P+vUvnumDCAlP5fwMgsNKmoK3fV6TYxNFAX+7cxlekbbCKNGT7rkx9Y3VOhJ0ZzluTHaR49IZfS+SYwXbcX/uw+pts8F33JMd2/BV6AUJTl2m8vso9p/FLauJTePVC4OJPLyEcZUqEZmFq5IOEfv4EDFuHGgehgkOrHGFV6oCAAa9Dn0iOfmc6IlPHtR7S6egFySMy9kYQCEuyUS1dmNWb2iKsChQmplzTcAw/XmV+FAiZuQDRQ3kev9ZpVylI0Qlou8hYhztzAaqRIxkeOVpUwqnXMXtQqGbDLSzy+vDf90ANC7JQ0GmATN2F/79jVYcb1NLP2RZxkvn+vCe7x/Er94mSsiPXl+Dv3xpF96/voKolIzqAkUnCti2rAgf3FiJbcuKQl9Ysd8XX3mIgHe5Sv9ZrCjLRlmuCXaXlDQB/L7QLE+LbSD2nyMbh0BybazGkCGLHdM2F3Si4DuzfP1dwPu/5/uL732D3O+DjVSJ2DxowawjyogQZYh4fnFbdukQ0aojg8CyMEpVGCwX0buhmVmaUygX0TMPkRGvcpUQSlVmHW6lhGxRFKsAXu/z68R2nOmaSHgB1ZDFht+8Qz77sk3emxU6UcCPP6rReRVTImpdqgLwTMQw4ENEzkICtDIDqZ2H6MnqilzsXFECSQZ+doSqERtuIc3KwxcDfvC53BL+cL4Pn9a9iuVz75I3pTt/DIjaDV0/sGEJynPNeNDxACTBz/MKArDjq4CUQPXBAM1DLGuMzsYdLj533CkBdtyTHcMEORlzFCwHyhvJ9zI7ijvpBu/+i4PRNyguMlxuCe0jZKiynCoRK/O5EjGe2JxuHGsbw2axFQbJRk7YfQ3FAjBmIlm0tsHYKxEBAINE0XVFqgEg4CLNRXz4WndMB4iAqkRcmT1LNlkEkZQO+ILlIva9o3zudoWYiajamX1lIsbTzhyBElFvgpIUGIalWbb0Qg8JdlmPkgpt7cx/vjSIOToQfeLNq7jn6RPY/uib+MXxLnziP07hwRfOYXTGgYbSbPzmc9vwrQ+tQ16Gx6LUpMECxV8zsydMwTpwDoIgKGrEQ63Jm4uoWZ4W20B8ahexMgLJtbEaQ5gKsa4o0/8m/5X5TprgS8GKvAyU55ohycAFzzbhSChdQ97vZkdjb2WNJdTObBHIELE8N/zSwPVKuYpnQzP9HEgpJaKPIaJSrhKvIWKgUhViZTbqRWQEajRPNmj+7SZ9F6btLjQPJi7HT5ZlfO0372Jqzol1lXl4++vvwfP3bcVjH14PvSjALcmBy57CweZhZ9YanokYMnyIyPFGlj3yEG/x+ZCO0dRtZp7P56ga8Vdv92Dc6iBtvzVbyZ0t/tWIh1tHUWBtx1cNvyI33Potn6rOaDDqRXxm+1K8JG3HA5nf9f0gWQYOfwf4bgPw0udJs3Qc8sY8ERJVqrL+LrKz7osAO+7JjN3lRv4cOeHSlTQQux+1x19n7EKOWY+RaTve6U5etUks6BqfhcMtIdOoU4aHrFylj2cixoUT7WOwuyTszaBW5vrdYWfVzGbTfLfxdk2PzS90GHNFrkFDaTYuynUAAHEo9jY7pkRca6ADvcJ6tY14Ph65iHXF5Pe6Z2LOr53WkwVKRPs0YKcL1tzYDkrJ60ZhZxYE1dLsDP3v2DJAfn/65WKU5WkXucJKP2zzmqYHpmz4xktNONw6CqNexFfeuwJ/+vsduLbOR+6TUQOrFBsiBlL2e5SrAMAOlouYxOUqLE/L37tGyHlaKbqBGAqqldlHHiJA4oraD5LrxSuB238ALNlABiwB8uIAD0tztLmIxkzy2sDitjTTAfUEyN90WZh2ZgBYX0XsmJcHLGrUQEoqEZmdORFKRGpnDjBEVKzMmYawci0TDt0s2mIi6urTCYwz+u+T3TjYMgKjXsQP/scGmA06bFtWhLuurcYNy8nnz18uD2vzYjFVIvJMxFDhQ0SONyNXAEsvCTSvu9HnQ5gSMR2GiNuWFaGxMhc2p4RfHKcfdCuZpdl/LuJLZzrxA8OPYYITaHgvsPmTMTm+u7dUI8esR884W2CxP2n6IdhwK3mTnR0Dzj4LPH838Ngy4JcfB87/Um1L80SDIHK3JONE+xgOd8/B0n6a3LgkjnmIC1hEJwUB6BmfQz3I0MFURk/C6XDWMHgOt6wmdo10szS30oXT8tJsiNQSyJWI8YXlIe4xsiFi6HmIDKmAbNqYLJ1aHVZgaLbcZbkG72ssx0Wpjtw+8G7MbXaDU+T3cjmotTKQanOJqkQsyzbBbBDhluSQfrcdrJ2Z2oqVPERTLmDyM2jQkKiKVQCPIWJoVlcAmB4kC9ZBXZl3oUwUBCr9YBh1Il75+x34uz0N/l9XUSJGqHKwTZFGU8C/nRlQN+1oucr25cUQBODK4DSGLaH/LOOJThTw8B2+v6eQm6sBskF40z/7vm+RbiCGSmugUhUAuPgi4LaRiIQHTgDXfhq47y3gi01AXmBlMrM0n9MkF5GWqyzmhmaqRBx2kb/p+UVJoVBVkIHCLCNckozLAxZyYwk9t0uVIaIkAeM+hoj5deQy1vEC1uB2ZqVUJWOR5CEy6Lqq1tUOHdwJGyJ2jlrxrT+Rc7+H9q7C8lLv95/30Lilv1zSqCwoLkpEPkQMBh8icrxhVua67erJ+zxYJmJdGgwRBYFkDwLA/zveSXYKWS5i51FyQj8Pi82JhitPolHshMuUD3zg37Vvj6LkmA342NZajMm5mBALMFPUiHMbHsZM0TrIWaVkl/mrV4FP/AHY8lkgtwpwWoHLfwB+97fAd5YD//VB4NTTqsUtyiDy/U0D2P7om/joM6fxw5MTEAdJA9/R2Wqtvu3QySqBnFWKmaJGOAzEciKb84PuuCcrXWNW1ItkCCAwywtbLPafxftooPGrTQNpZWlWFk4eJy5VhTQTcZIPEePBgeZh5GIGVXPUglW/K+znMJaS7L8CW4+Wh+YbWYZM7cyXpRrsbaxAq1wFp6wjJ6hTsT0GpkSsdNDNqUCqsvJ1SmyBON2rWJpDaWhW7Mw6NkRkzcyxz0MEPDMRI2yN14evRLSPECXilEm77zFY6QcAONwSRqbtgZ8oWiXiEM1DzK0EMgr8P65wGVFUuOaA0WYUZZvQuISono5cTd6W5r2NFXjsw+sX3B5yczUAzIwARx+PwdElPy3DAUpVALKZDACbP6GW8ggCjQ4IzKZq3tDsBc1EHHCS96hwi1UAssZgakTF0sxiLWZHAWvy/q2GzPQAef8WdEC+R5t8QTLZmYkScdE0MzPo+7xBsmO50IfTneOQ45wz6pZkfOlX5zDndGNbfRE+dUPdgsfsWUV+9me6J4irL1rY2ptnIiYUPkTkeNP6Orn0k4coSTI6aRZTfRoMEQHgtsZyVBVkYNzqwK/f6QWKl5NdXMkJXF1olz11+HV8Vvg9AEB3xw+AnPKYHt+nbqjDqFiM62cfR2PfQ7jz5Eo09j2EXc5/x/4ekbSR1e8CbnsM+Icm4G8PADu+ApSsJgU67QeAV74CfH81aXg++wvyxBEEkTO7F1ts1QpDyBVmYZMN+Js/WrC/Kb75N/t7ROxy/jsa+/4Rf7KRHKT/59hDfi6LkM5RK5YJdNjLyhY8hog7lhchy6jDwJTNO6g7xWkZZuoLdeHElIiTs07M2KMMgucEpGPUis6xWezQX4YAmVjVIhhS5VYSBUaheyQs5VlEzAxBmBuDWxYwkbkUqytyYDRloFWuIvfH2GY3SNVgRbNUoVEaQIloMANla8l1j1zEzhByERU7M8t5imOpChClnRnwUCKGkYlIP6/mMrXLfNSs9CPaBcowy0NcG/hxoqiqvRaRpRkgedQAoKeKQ1EAXvviztBzSl/5MllkinpVfZRZHJJldzEjyzKu+thQUxi+AvSeJsOcDXeH/fzrqvKgEwUMWmwYmIpycy4VGpqpErHPTiITIrEzA2ouonLOZsxSh22poEZkeYgFdd7tyCwT0dIHuJ2xe31mZ84KZGcmg62CzEWmRPR4n9+o68SQxR53981PD7XhTPckckx6fPeuDYobyJOqgkysrsiFJANvXdHA0jwXSyUife/kmYhBWZwraU5ssM8A3cfJdT95iAMWG+wuCXpRUBbpqY5eR7IHAeBnh9tJ+9XKveTO+bmIjlmsPvlV6AUJLaW3QmjcF/PjO9M9AZckwwEDVNOPgB6LG/c/e8Z7cCcIZOh08zeAz58AvnAGuOV/q/ePXFYVH2EGkfuye60TOgAAl+RauKDHIy9filt7GBtodlvcAASclEh24GrXpYU/l0XCyFAf8gUrZAhAEW1SKV1Lmq9tkzDP9GAPtTTvb0ofSzOzMzeUqkPEHLNBKTTgDc2x5UAzOSm8M5cWoiwL38oMABUVVbDIGRAhwz0W41xEamXukCtQW14MQRCwrCQLTYqlOXYKGVmW6UaLjMwpusAK1lyvWJrPoJbmIoajRFQyEafjPUSM1s5MF+ZhtDObpomK1J1XE+SRoaNZ6Ue0VqlQ8hAZHuUqAJRylSNXR5Naqc7U42uX5KI0UwdJJuc5IXHxd8Cll8gA8dOvAbv+kdxeeU1Ilt3FzMCUDdN2F/T+mpnPURXiir0BVVn+yDTqsZLapKO2NJfTc8qpbkXRt+igQ8QRd+SZiACwsXqeEhHwyEVMgXIVDyuzW5JxvG0ML53rw/EhHWS9GZCl2Cr/Z+imSSAlopVmImYtMiUioLzP78whLoN4Wpov9k/hB6+T876HP7A24FyAWZrfuKKBpdkWw0xEZYjIlYjB4ENEjkrnYcDtILtDbEAx/yF00VJTlAm9Ln1+fe66thp5GQZ0jc3izxcHgZW3kTtaXgPcqspp+pWvo9LVi0G5ANn7Ym+nYYM7X7AlQsDBXdEy4MYHaRB5gEayNXcC1rGAx+LL7rVOJEPEC9JSyCAnuac6Yv8B52ugyYaIG4U2GOGI60BTK1zDrQCA2YwKVaGjN6otnf1n8b5Gonx9pWkg7raGROByS2in7zFEHGsAACAASURBVEvzw+TZCQ0vV4ktLA9xi0xVJfW7I3qesrwMdMnk93eyL8YKDI9SFfZ7s8yjXCWWWV0Ts044XBIqMA7RMU0GHoW+P3MVWLlK/1kspUrEjrHgv9d2pZ2Z2Znj18wMRNnODAAGWowShhIx20YWU/rCushe0wealX4Yo8xEZHbmQM3MjHnlKtfUFiDTqMPojAOXBy2RvX4cGKBDxIo8MxpLiTLoRHsI5w3WUeBPXybXd3wZqLoWyKPK4un+kCy7ixmlmbk4a2Emp9tJomoAYNPHIn4NJRcxWktzRr6qRBu8EN1zJQqaKT6BbBRlGSPOX2VKxLaRGdU1kUq5iLRUpRPl2P7om7jn6RN48IVzuOdnJ9HlLiKPiWW5SgjFKkom4mJTIgLK+/wGXSeA+A0R7S43vvTL83C6Zbx3TRn+anPgc4qbqcDhUMuosrkZMbFUInp+RqfBGioa0mcKtBjpP4uGY18idtJ4wPIQG27xm+HHFutsEZMuZJn0+PhWcsLz00PtkKuuI3lEtkmg5yR5UPtB5Jx7BgDw86IvY0l57JUewXKaQh7crb8LuPdN//df+j2xO7/0eb8nfL6UAutFoia6INcrt4VqC4sGXz+XDrkcw3I+TIITG4W2uA00/z97Zx7eRnng/8+MJEu+79uOHTv35SQQCCFASkJJKeGmS7f0+NGlW+i2tKUt7bbbFnrssmxbem13A6W7LS09gIUSjlDClYQcQE7njo/Yju/7kqxj5vfHOyPJtmTrGPmKv8+Tx7Ekj0bSjOZ9v+/3MBLWbqFacqXPG36H19J8gA0Ls7FZZOo77RxrnLoTRaNQ1zmI060QbzGNWgUtSp8tV4k17E4Pe6o7KJLaSLXXC6tcSeBSrvFgkiVaLWLS3984MSTicWWO1wY/LyeJY4o2sY2hzU63Aq5J0NTCmfPEYsBYKNSUiI0HKc0QipdQlIij2pl1EjE59s3M4LNRR5yJGG6xinuIVLdY8ErILRvnwaHDv/Rj5OgorNKPaFQOqgqtOokYghJxRLlKnFnmsjIxad95ZupmrenX7vxUG0uzxXmxp3rsRUxAEIiDHYJgveIr4jZdcasf9zMYejZwwDzEM68Kd0lijhjjRwhvQ7OR5SrTNRdRUyJ2qskRqxABspKsFKbFo6pwVFcjziQlomZnfuy4afS43C0iFiqPxeh663F7P6exilV8dubpq0TMt5/RylVCVG1HiR//7TSnWvrISorjX29ZPm6r9YrCVLKTrfQPudlXE8L3+ViIqRJR+/5UlbAWLy9EzJKIUxjSkT+R0nEI6WhkBRdhQVXHzUME36TlQmhmHolPrislzixzqL6bd+v6RPMxwPu/gSc+hPrMPwDwpHsjZetumpB9MiynaRjk4T8/8E1xkfIMiVDu/1oPv/kwHP8reNycbe3nc384wCPbh0/6JRSWSrUAHFF8k7lQbWHRIPDrldiniIHZpfKJMR43NeHyKKTZxWqtJWfB8Du9JOIhEuLMfGChWHF9eRpatsPFGS0P0b+ZWUehRiLO2pljh73VHTjdCh9O1CY7RReDLSXi7fUliAImT9tZI3YvOLRSlWFKxOwkTqglKEhCuRSjUPtmbSK10qadnzmLx/+j7EVgSQRnP/NM4u8augbHXdEfZWf2FqtMtBIxQjtzuMUqPQ3IqAyqVjJzjH2Nm5fl86s7V49qYQ2r9COaTMSeehjqBdniy8QdCyPKVWB65CI2+pGIuhKx8nwPfY4xctOO/Z9Y7JRMcNN/+kh5nUQc7Ih9zuok47Q31iNAHuIBLeu64o7huXRhYrWmRDx6vge3J0o1kZ6LOB0bmlXVa8PuVpMiamb2h69cRSNHvCSiQYtp5w/A/1wvfk4wVI1ErFZHfz/Wq2Ks+t7hw7FxBg22AypIMiRkBn2Yr1hlGioRM+dBXBImj4NyqZGzrf38Yd859lR1xMxttb+mk61vC5HID29eTmbS+CpvWZa4eqFBLc26EtGWGt12AsHix2/MlquMiVkScaqhu04oDxt95KFU+WzYBRdho6NKNGSZ4qD0iqAPq9FIxAuhmXkkspOt3LpaqGS2vl3ly0U88yrUvYM00EqtksuPpY97LaWxRqiEXOX5Hgad45RLJGYLuX9BhWh1LqgQv6/8mChjuetVWHqLGKSf2wV//jid/7aEp3/2FXYdETZbm8X3lTJXaiZZsjOoWqlSC0K3exmAYO+LbmnWScSJIDSNQmO3nVKEmiI+f0QJgx+JiKKwWTv+Xj7aPOMtzYHyEHUUpQsr5KwSMXbQ8xA/nKTlIZZFloeow5km8mfNPTVRbWdMuJ2oGqlyUpnDAm3SPS8niQHiqdMs1bFSyOhqjEVyg7ghOwQSUTZ5J92Z3UeJt4isuIausck1nxJxsopVorUzh1es4ukUCy0NahYF6cbnNm9els+uB67mqbvX8tM7VvLU3WvZ9cDVoZd+RJOJqOchZi0YX7kKgctVFohcxPdqu8YfE0wSGr125niyEkzMyUjAo6i8F0xhM9AOL2rKwyvu95FTIBwjuiVeJ9BnKPSCsZGxHvQ1i3EqRGVlBijLSiLZZsbu8nCqJcrigbxpXK7iHBAL60An0SkRwWdpHtXQ3N/stU1HhcN/FJFVRyZAlOIPjwu1sxaAWmX0vKheFd9H6c6m2DiDdCtzQtaYkU3TtlgFxPe81nZeYRLjpn/+v0o++the1j/8uuH57/1Dbu7/yyFUFW6/qIgPLg19vrtRy0V87URr5HMTVfUpEWNhZ5Zl32LfbLnKmJglEacaHl0uiiy2XuWrMB9sD6vgIiKc1VSIcy7zDXIDQFciXijNzCNx9xVzkSQ4ceIY9a4kkMzez0kFfuG+kY+WDZHsmBgF2Hg5TToe21nDpT/cwYMvHKO6LcjkJbVQBI/f/QZcfJf4qQeRSxLMuRRu/w0tn36XN3M+TqeaRIarha+bn2J//Oc5uOpFHt+cgISwd10n7wWgRs3Fg7h4h2T3MgDB3pe9Gom4Wj5DcYppQghNo3CuY9DbzCxnj1AiZi8Cs01keHRWcfWiHOLMMtXtA5xumdkraWe8zcyj1Re6vbmhe5ZEjAVUVeWNU21IKCwa1BQOZRui2qacKaz6yQMxWjADaD+NpLjpURNwJxWQqlmY5mQkYJYljuqW5hgpZHQlYomivcZQlIjgtTRLjQcpydTKVcZpaNYVgFazLJRYurVr2rQzawRQiMUqAy1C9dJATswWiUyyxGXlmdy4spDLyjPDu6ZFo0RsCbGZ2R8jylXKshIpTIvH6VHYN0XjPPRMxII08fmt1a7TQS3NL31FjJNzlsKVXx1+nyRdEJZm0cwsJryj7MyH/wiqB4ou8WXtRQhZlqgoMsjSrBPc7acFKTedYBfnjkuKw46VvChJxApNiehtaLalQLJ23LadjmyjfqIUKp8Wt1U+E3tRyoh9kFU3djWOZtJH3a0rEYul1tg4g7ylKsGtzADddq1YZTramYHaOKFMX8LwxdfmHofhRZI/ePE49Z12CtPi+faWEGI1/LB+fhZWs8z5bnvkixAuu+hvgNjYmSG66/QFhFkScarhlsdQJDHo9vXsCiiSSRRgxAL+eYhB4PYo1HUK1cOFqEQEKMtO4prFuey23Ufxc7eBOnwl/z/itvLVc/8YO7J3BMbLaZKAW1cXUpKZQJ/DzW9213L1j97i47/ex6vHmkfZUTxyHHuqO0VzWnUnHtm3KtfeP8T3th3niv86zafqPsRlQ79ga/r92DOWYFWHSD/xe9b/7QbeK/4ZtyUd4TrTfu9+mCT4z4+FaPcyAP7viz/OqoW0qynES04eWeeZEELTKNS3dTNHEqovskZY2Uxm70okjQdJtlm4UrOtvXR0ZluadZI0sBJRtzPPFqvEAjXtA9R1DrLcVE+cs0sMvIoujmqbifni2E53t8Yuj8a/VCXPZ722mGRKsxI5FuOG5qYeBxIKOY5acUOYJCLn3/dGitS2Bz+2PYqK7maKM8vQp30XmOOFQmsCYLUY1M4c4rEw2CosVh3mvKn5/e7NRIxgAuUlEcOYuPmr1AFJkrhygbg27Dw99XIRPYpKS59QeOVrFtG1ZRqJWBWARDz2nLAyj7Qx++MCIBHPd9sZcHqwmKTh43NVFTE0ELUKUccqo8pVkvM0ckf1HdvTBdpiTJ+cAkjkpUZX2rNMIxEbuux09Ivj31euEkEuouIZLkrRF48GJkCU4g+9VEXNQw1AOTSo4ruoWGqLzaJPCKUqAF0D07dYxaOo/E+tOCf1MksdIRVshoEdJ1p4an89kgQ/+kgFybbwSNeEODOXz8vSttUa2U7oKkTJ5LueGo1oHAMXEGZJxCmGV+QruMHxUMD7bnA8xCtycKtxxHDZoXaX+P8YeYgNXXbciorVLEe96jad8Y9XlXGf817c6nBpvD5dUWVz7MjeABgvp+lHH1nJG/dv4H/+3xo2LspBkkSo+md+9z5XPfImv3zjLO39Q7xS2TS8OU2Twj/zfgOPbD/Jlf/+Br/eVYPTrXBJaQa//cyVfOa+bxP/+XfgUy/B4hsAicy2vTzi/jeWyGKVs1hqYxE1zHWdmZiVT7/35TNXzh1xq8RBSUzY12qW5umCnsYzmCWFITkhcCmCd7Ioipg+pBG2r1Q2T9QuTjg8ikpVm65EDE4itvc7cbgiJDFmERRvaK3Md2SJiQKl66PK2wLIzSukV9XUZ50xsjS3iHKoE36lKjrmZfs1NMfIZtfca6dIasOiOESESPrI76kg0BuamyspzxDv81hKRKffpMFqNg23Mo8Tgm4UvHbmqItVQlsIUDQ780DCxGQ+ho1oFA6tYTQz69AbmrVyFYAr5gsL4dtnpl4uYmufA4+iYpYlsrScLZ1EPNbYQ4/dLxdxoMOvjfnLvtc6Enr+5wy2M+ulKnOzErGY/KZ29fuh44xQ9C692ZDn0stVoiYRwbf4Od3KVTRSrksVJEa0duYUm4WybEH+HhlVrhJiLqKjVxDqz/4jPDIvyIO0a8JEzVO0PMQmc2FAx5SuRMyWerikaHJIRLdHodchvhunoxJxf00nOwdE1NYS6Rwyw6+1IRdsjoPOAScPPCPGTp++fC5ry4JnTI4Fn6U5wlxE/zzEWI1jZpWIIcE82TswCx88isqDLxxHN1gqqoQsDV85ePCF41yzxOAV9trd4HZASpHvohUANX6lKiMLDC4kXFSSwQ+Lt3BjXSEvWr856n7pH3YEH8zGCJuX5XPNkjz213TS2ucgJ1lkD+rHiSxLbFiYw4aFOdR3DvLkvnP8+d16znfbeWT7KX7yt9O4A6xSNfU4uP8vvsFdRVEq939wIVfMz/I1cUkSlF4u/n13dMhtkmQX79NftRu+22P46w+Gcx1CvbKlIp/BIQ87TrYymL8WmvbBud3AVyZsX6KFolla+pNKsQa6cI4gETctzsUsS5xq6aOqrZ/y7OAxBdMV9Vozs80ie/MP/ZEabyExzsSA08P5bvuMfA8mE3oe4pUmTUVStiHqbRZnJFKj5lEhVeNqr8ISjuoqVPgpEStG2ODLcxJ56lip+KWzSijGDF7tbupxsEDS8hCzFgolcShIK4H4DLB3UmGpB3zX5UDwF//FmeUJz0MEA+zM5vDamc294n1xJRdH9nyxhq5wCNe+6R6CdpE9TE4Y54ReruLsE+UquUtZV56JLMHZ1n4au+0UpBmfHRkp9DzE3BSbd/ySm2KjLCuR6vYB3q3pZNMSzZr48lf9bMxfC75RrxJx5pKI3lKVkbEeB7VClSU3RVV45Q+dRDzb2k+P3UVqfBTES/4KEac07UhEkVPYrgjiLz81+nOooiiN6rYBDjd084FFOT4l4uE/wPLbfEp0f3TWwOlXxL/a3aD4key2NOEM0J1m/pioeYpGIpYsWAGHRt/dSyK9ajwpkh1TTz3kBJ+DRoQB3c4cnET0X5iI6lieJLT2OahR8xlQrSRKQ5RLjZxRiwI+LlKoqso3/+8o7f1DzM9J4ivXRh6LsHFRLt+kkkP13bT3D3kXi0KGHvUWizxEHdE4Bi4gzCoRpxD213TS1OOgQ02hVU3lqDqXx90fAoQjQVIVQ1YTRkHPQ5y3cUxW31uqknlhWpn98ZkrfW3DiioN+/lO1eRYhELNaSrOSOAbH1rMnm9s5Ee3V7CiKDUggegPsyzx33dexHOfu5wrF2T7CMSRuOUxscLpB/2RbkwoN28N92VFjB67i9c1kuPeDfO4/WIxqXylv1w8oG4feMZoe5xisPYIm54nI8gKsz7AbDoMiofUBIvXNjBT1Yj6xKk8O2n48a41EUqNB2fLVWKEQaebfdWdWHFS0CuIayNIxKykOOoRQd195yOwcYUCnURU5ozKD5uXk0QnKXTI4tzRW5yNgqqqNPc4WKiTiOFMmiTJq0ac7xKLCqEoEc2yJM4PbzPzRJKI0dqZwytWSRgUr1HOKIns+WINXeHgdniVgSGh7ZTItbOlhff5BShXSUuI8xY57DoztSzNjd1iols4gti8VFO9eHMRj/9V5LtJJrjpl2MXzXiViDPXzqzHeizwb2Ye6hfKNDDMygyQmWRlToa4rnrbhCOFrkScbg3NmhKxzSPOZyPcWRXehuYRSkR7l68QRfHAuT3wt+/ALy+Fn62EV74O1W8KAjFzPlz2T/CpF+GrVXD1v4x4lgkWgGgkYvmilfzrLaPt03mp8WJxDES5p9HQlYiJwUlEvZk5xWbGbJp+tEhOsg0F2eugWC5VB3zc9spmzrZGpqx77tB5Xq5sxixL/OTvVmKzBC+pGQ95qTaWFaagqvD6yQgszbqdOVZ5iDCrRAwR0+9smcHQVwmayWT90M+40fk9vu++k33KQiQJbjHvGvY4wxBCHiL4KRGzZ0lEj0cdRvb+s+vTHFXn0qqmcv+LjYa3YcUCNouJWy8q4hsfGj+Py62opMRbgpOHOlZ8RKxwBsCNQw/xbnJwu7zR2F7ZjNOtsCA3iUV5yawuERec7e0ZKLZ0cA1Mm9VvRVFJt4sBVlxukBXAzHniwuca9NpfrlsuyJiZmot4JlgbpV8TYaE3F3GWRDQSe6o6cHoUPphch+x2QFLemEr2UCFJEl02QfgPtZ6Jenuj0N8G/S0oqsQptWiUckdXqx5TY1Ou0utwM+j0MF9vZg41D1GHtliQ2y+sree77DiDqPxcHkEi6kTeZCgR9cnGhBSrOAdJcotFVlt22TgPniT4q1qdYagc/EtVwrVwjShXAbhywdS0NDf1aM3MacNJmcvKNRKxqkOzMX9Z3LH+Sz4VfjBcCHbm1gClKsefF5PgjDIoWWfo83ktzVGXq2gNza0nwO2Mcq8mEFqxSpeahM0ikxIfvbFvhfaettafQT1/cPgi96Hfw5O3w8Ol8JvNsPtRkZUomaD0CvjgD+DzB+Dz78G1P9CiRcyQmC1UeHoETnya+D0xO+r9DQlaJiIZ5SRaxXtk1hZ8sxLj2PXA1aTkawvjsYg76tdIqjGKVbzNzInTLw8RfEWSlYqIRRmZi6jjpcpmNv34LT7y33t4/tD5MRf2PIrK3uoOdtbZefFIE//ynFhMvW/jfJYVjnachYuNi8Tn8drxCCzNup05pkrE2UzEUDBLIk4h+IfKOrGgV2P81H0rAH9vep0cuowNn+2sEStFshnmXjXmQ3XFw9wLXInoUVS+9+LxYWTvHzwbudH5PdYP/YxmMg0LsZ0IhEpKh0teq9qKp+q38vnCkYlTAjx/WEwYblxZiCRJ5CTbmJORgKLKdGZp2WJ6FugUR3Ovg7mI15NcGIR0kE2+AblmadajD4419lLXMfPKRc5oSsR5OUl+TYQHfav2lc+wxlrHMqma3uaqSdzTmYc3tTzE29I1oq9sg2H5NPZkQeDJXTHIRGwRg+FaNZe01DRSRgSD6yTiQZdGIhq80KA3My82aYRGdpgkYoEgEW2th0mIM6GoUB+kOEiPIYzzkoi6EnHi8gKjz0QMo1hFm4T2qglkZo3dxjlpMFnApNm3wpmgtEbQzKxjRNQF4C3e2nW2fUqNVXQl4kh7qJ6LeKK5F+e2rwibYs4SuGoMG7OOGV6soiiqNxNx2KKIXqiy8mOGZ4fpJOLBaHMR00vBmiraVp/4oHARTAdoSsROkslLsY2/wB4CluSnYJYltnnuQXpsA/x2i+/OoT44+yoM9Yrfl98Ot/4avlYFn9oG6/4JMstHbzS1EL5YCTf8XPwenyl+T52Aa4BzEHq1xbLMebxbK4jXzcvE4nbnoFN89+hKxK5a4/fBSyKOr0ScjqUq4CuS1EnEZX4kol6w+bkPlLNpcQ6yJFyP9/3xEGt/uIPvbzvuzRXXoefjf+zX7/Lovh6+8KfD9A95KM1M4J4NAY6xCLBpsbg+7zzTHn5e+awSccpglkScQtBXE0Zeit5RlvKusgCr5OL+xJe4ZG5GwL+PCLoKsXjtuHkps0pEAd12Dv5kL4CEE4thIbYThVBJ6ZDJa33lM38l55Z/CfJX4rRl0aGm8NLR0Y3QsUBLr4N3tCbHGyp8qpuLSkQjaaVZs1Wc2x3zfTECtR0DlElCTWjKWRD8gSMmixmJcd7J18vTQB0bLoYpEb1NhBt8g4yBdu45dRfbrN/iswdvmrT9nGlQVZU3tKiAlW5N3VS2wbgn0CZD8X21xm1Th18e4qj8MCDRaqYg1cYxRScRjVUiNvXYkVEo0xYFws6A0pSIUvtpFmeIa09tkFxEnxJRsx5NRiaiXzuzqkZAVulKxFCKVTQSsUHNpiBtCpe/WSOYoOhKxHDyEHV4y1UqvRbqiuI0kq1mugddVJ6fuJzi8aArEUd+fjnJNublJPFB6V3iTjwrFFg3/hLMIeRp6aT5QJvIlpxhON9tx+7yEGeSKc3Uzpf2s1D3DkgyVHzU8Of0b2iO6LzWIUmQp43H/BcApzoGxfi+W02KulRFh81iYmFeMvc570WRgthFJRPc9F9w6+MiJzE+ffwNm62+sWFnlSBsJwKdmq3WlgYJGd450XXL87FZZBRVy0BNnwA785gkotbMPA3zEHVsXpbPzdd/GIClfuUqesHmV69dxOOfXMPur1/NFzfNJz/VRtegi8d31bDxR29xx1ahTnzh8HnuefIATT0OlkvV/MHyfa89urZjMPIylBFYVphCbooVu8vji6gIFROqRJzNRBwLsyTiFIK+mgAjUyt8asTb1NcwDRhzEgM+EnHexjEf5nCJYgKYzUSMlXJvshCMvNYhAfmpttDJa23lU/n0a7SXbkH59GvIX6rEmZhP54DTS+7FEi8cbkRV4eKSdIozfIUbq7WB72t2zT5Rt1dkzExxNDWdJ13SJpwZY6wEBlCcbNZaml+aYbmIHkX15rvMz0kSeZyjBt5icuNSTTya8tUJ3sOZCY+i8syB8zR02cmQB0jp1DIDy8ZWsocDW858AFKcrULNYCT88xBzAhftlOckcUwpFb+0nTCUeGjucVAitRCHS5SGpJWGt4GkHEgtBlSuSBy7XMWpk4gakUeftpAwCcUqisq42bsBYQm9WMWjtXnXq9mGFB3EDHERWKVaImhm1qGXq7jtolwFsJhkr0V45xSyNOtKxIIAn98H5pj4vuXX4pf1XwxcNBEICRlg1oieGahG1LOBy7ITfZluh34vfpZvjInqbElBCnEmmc4BJ/WdEUaF6O4B/++jymdEdmfjwdjYW42CrkRUk8lLNW7BYkVRGs8r6/nfpU8EfsDdr8PKCEjhxCxRnokqmtonAp2a+yNzHt12F6e04/SSuRnerOr6rkFImyMe12Uwiege8i0oh2JnnobNzP64Yu06VEsiCdIQj1+XwlN3r2XXA1d75wAgFN5f3LSAXQ9cza8/eTEbFwl14t5qoU78wh8P6f3d3GLayTrTcW4x7QTEXNAol50kSWzU1Ig7wiUmvUrE6G3VQWHVRFWzJOKYmCURpxg2L8vnV3euHnVROmCuoCtjJSZlCHb/zJgnczmg5m3x/zHyED2KyjaNlIm3yNP+izZaGK7cm2QEJ699v39ny5LwGsHNVp99RpIwW+O9+XwvHI79IP75Q+I5blw5fLK8WlMivtCciWpNEdaQaRDoPdgoCia6LbkQN7qF2AudRGw+6s3TuXZpLpIEh+u7vQsBMwENXYMMuRWsZlkQxUtvhszAOWg3OR/ij47LJngPZx50m8tXtMb2SziGhEpfyjxDiansnHx6VO04N9rSrNmZT6rFo7M0NZRnJ3GeLOymFFDcIq/LIIhmZkH+kb1QFF+EC408WWkS7825IFEFLm2wH2eSxfdBn7aQMAl2ZogwF9Hbzjw+mTzYKhQTjWSTOZXzrbxWqRAnKAMd0K99dpG0lwYoVwH/XMSpU64SLBMR4FM9vyJb6qVWngNXPRD6RiVpRluaT4+0MnvccPgp8X8DC1X8YTWbWJQvnu/xXdXsqeoIn1zQ3QNH/+y9SR1og61XidsfHV3EMWWgKRG7NDuzUdDLVfSoFt803YDpuh53M1FZ4FqpCpnzeK+2C1UVRHdWkpViLau6vtMeu2IVvZlZtoxpfZ3udmYvZBOSpuq9OrVpzIJNkyxIvF9/ag27Hria+zbOJz3BQoHaxjKpmqVSDVtMewDYYtrDUqmGpVI1ck+9YS67TYuFOvT1E63hqZnts3bmqYJZEnEKYvOyfHY9cDW///QablooVH/xFjMpm7WWrfee8OU8RIO6PWJgnpQXdHXbO2l8WhAtdpfCFf/+xrQoDokVDFfuTQEEI691Kbz/Slak2LJCDOK3H2uOvKkzBFS19XP0fA9mWeLDK4YTGwtzk0mMM9EzpDCQu0bcWDsNLM3toom1P3mcsoD0uVq+0JCX+MhJtrGmVByLM6mlWZ84eZuZd/8U2kcWcfjO0pY+R9ACilmMj1cqm7w2Fx3rZaFoeLqz3NBrQnFmIjWqWHTwWqKMgMclwuiB42oJ83ODKxFBosaiqX4N79siCgAAIABJREFUXGho7nGwwNvMHIE1Fby5iOVOoSoL1tCsRw1ZLbJm61LFhCohK7LnjQDDSMRws48grHZmV0ctAL22QuRwFr0mGuGGtut5iOmlw4tZwkHAXERBIh4410WfwxXoryYUQ24P7f1CFTRKiXhiG4X123CrMp+3f4aOcI0eM7ihWSecvMrqqteF6jg+AxZ+KCbP+Uplk9cJ8Ns95/joY3tZ//Dr4V0HbnlslG1XP2sVySTcBVMV3mKVZMPszIC3NX1/qwk1MQcKKuD6n4if0RaieDOzD439OKPQ4VMi6nmIl2rzIt0hNEyJ6OjxEURGQJ8nJ2aPuVjnUyJOcxIRfNEVOx4MOV+0IC2eL22cx79encZu231ss36LF63fJBORv5lBLy9av8k267fYbbvPMJfduvIsbBaZxh4Hx5t6Q/9Dx2yxylTBLIk4RWGSJdaWZfLRZUmkxVvoGHCyT6qAwouFJWX3T6N/Eq+VeVPA0OVAk0YQk6B7njxwwRKJMVHuTQHo5PVTd6/lp3esDCiFjwZrSjPITbHS63Cz83TslA+6CvHKBdlkjFCjmE0yFVog+Jl4bUA1DcpVbD2CSFEz5439QFn2DSL8Josf0oKsX55BLc16G+X83CRBmL71sLjDmuJTL2XMRU3Koc+Uhqr6VC6zCA8eReXBF44zcq34clmo+nYrywwtkypOT6BWIxFdrWcN2SYglBEeJ31qPOfVrICZiADztHKVI25tcmNgLmJTr4MF3mbmCNusC0UxVE6fIJeC2Zl1JaLVbPKRJ8n5kakfI4QkSd5il4iUiHqxinv8iYus2R8dSUXhP89EIlyVgzcPMYJSFR0BGprnZCZQkpmAW1HZWz35Gc566ZDNIpPm73gZ7IRtXwLgadutHFXLwlfDeJWIM6+h+bT3Wqh9nx38nfi54u9Cy4wME/rcYNA5fFEg3LnBK/IV3OB4KOB9Nzge4hX5iqj3NWbQ7MxdGGtnXpAr2p7PDqVS/fG9cPcbcPFd4me0hSj62HDClYhl7NPOV31Be45OInYOCsJGX9gyUo0YQqkKQNeAWEBJT5wBLjv9e763cXS+qKJAdz1Uvwnv/hq2fxOe+ij88lL4QR6bXxvuSNRpAX0q61JN3Oe81zCXnc1iYv08QYq/djwMYdSsEnHKYJZEnOIwyxLXLhW5AS8cbYINXxd3vPtr6I8yx2aMPMRgk0bAe9t0aiA2GhOh3JsMmGSJy8ozuXFl4ZhS+EggyxIfXi4G8rFqaVZVlecP6a3Mge2VernKW06toKTunSmdi6iqKpkOMTm25YdAOgTMRRSEzPt1XbT0To+szvFwVlMiLsyJh+fuFWHh86+Fr5yBRdeLB1X8PdIXKzGnC1LhfNcsiRgJ/MukdBRJbcyVW3CrMnuVxYaWSaXEm2mSxfk72HzKkG0CXjLmlFpMQVoiSVZzwIfN0xQ9e+waGWXgpKvFX4kYbjOzjoKVgIR1oJEsemjstgdUd+vFKnEm2a+ZeeLyEHVYoyIRQy9WsQ2I1yjpypapinBVDi1RNDPrCFCuAnCF1tI8FXIR9biNgtT4YW230vZvwEArZC/i9KJ7AcIP4/cqEWcWiaj4ZQMvyE2CgXY49bK4c9XHDH++8eYGKvD1Z4+y7XAjLx9t4pXKZrYfa+Zvx1t47XgLr59s4Y2Trbx+ooVvPFvp3Y6iis/b39U4ZecYzkHvokaXgcUqIBa6lxYIS/ORJvuwWKCoCWFdidh+yvis4UDQSER7ylxveZPu0PJlImpjMm+5ioE5mCGUqoBfscp0ViLq+aIWv2Px4JPwzN3wm+vgZ6vhB3nw6DL47Y3w4pdhzy/g1EvCneEZQpUtnJMK2e9ZGPApbnY+xP7kTYa67HRL846TYeQiTogSUVuQmc1EHBOBR9CzmFK4fkU+f3qvgZcrm3noho1YClZD4wF452fwwe9FttHuevHFIclQ/oFRdweaNPrDv4FYD+e+0LB5WT7XLMljf00nrX0OcpKFhXm6KRAnElsq8nlidw1/O96C3ekhPi5IA12EONzQw7mOQeItJq5ZEjhI2ZuL2JrDF+OShIWi5ZgvM2qKob3fSYl6HiRILQ7B/uglEX1WhvzUeFbNSeNgXTfbjzXzictKY7OzEwhdfbGx6y/itVpTYcujYhClK7zaT4PZSlF6AtVtAzTMkogRIZB9RVchHlTn0U9C0MdFAkmS6EssgUFQOgy0M+t5iEpxUCszQFZSHCk2M5VDpb6/UzwgR/991dbT521aJydCEtGaLPIU205ySVwNLzlXUt9p95KfOpy6EtEiT0ozsw6r2UQf7shiLEItVnH0YnOLiaola274zzORiNMmKKFmInpJxAjt7+ArV3H2CRJBIySvnJ/Nk3vr2DkFchGb9FKVNO0zbzzI4jf/AbmvRoxVb/pPLunK54l9TewJt6BthmYi1ncN4nApxJllSjITYd9vQXEJRVKe8ZmC480NALoHXfzTUwfHfIwOKym0qqk0qZnYVCcL5QbsahztagrNU3WOoakQnaqZAWyGKhEBKorSeP9cF4fre7h5lYGq6uQ8UTDS3yKuacWXGLftkbB3ed+nQwOZuJUmClJtXvKwOEOc4w2dGpmZVgLn3ze2XGUgNCVit5aJOK3z/gPlhzr7h+WNAiCbRSxGRjlklkNGmfeflFrMiRNt/OL3z7DN9E0UVagQVdXHZRvtsrtaIxGPNPTQ0usIjZB3iOv8rBJx8jGrRJwGuHRuBllJVroHXeyq6vBTIz4uVh0jga5CLFoD8emj7p5pDcSxQiyVezMRK4vTKEqPZ9Dp4fWTBuR6jsBzB4XK4INLc0mIC7xGsrpYHO9VHQ6cBVou4rmpm4tY19bNHEm8V3E5gVcIh0EnEVuOD5t4X6epY18+Ov1zEXX1Rbl0nvnHfy5u3PxD30RRJ2faRC5koTYpbZhBxTITiUD2FT0PcbeybMzHRQpPmiCC4noMLFZpFiTiCbUkaKkKCBJzXk4SNWo+blO8UMHp+U5RYGDITeZQAxbJgxqXBKlRTBC1XMQrEoRyozaApVnn7KzmySYRNSWiK8pilbHC1zUbXIeaTFbmFCMdRiIcJaLi8eZ4RtTMrEOW/TLRfASPPm6paR8Q1sJJhLdURSNlpAP/S0Kfdv5ffh8UXsSlc8Vne6a1n7a+MFrTZ6gScVg2sIRQHwGs/nhMni/UMX95ViJrStO5qCSd1XPSWFmcRkVxGiuKUllemEqRdk1uJpP1Qz/jRuf3+JbrLgBkPCja9HRKzjG0PMROkpEkiZxkYy3jFcVCiXi4wcB8QB35E2Rp1hf/kvPZ2yDOU38Fm56J2DHgZGDI7ctFjImdOXgzM/iUiNM6E/GWxwRBGAiSLL4/v3AIvtkCn38fPvZn2PyvcMndwo2YMRdMZjYvy+f+my+ngzSOqmXUKjlIEtixcv/NlxvusstJtnkjpkKeE9pnMxGnCmZJxGkAkyzxYf9m2/kfFESBaxDe+XlkG/VamQO3Ms+0BuJZTA1IksSWCs3SbHBLs9ujsO2IUPjctDJ4bkxqgsWr2KlL1gi3KZyL2FZ/GovkwSHZQiMA0uaIQHXF5Qvkx2dp3lfTQUd/GJOvKYiGLjtOl5v/iNuK7BkSua4r/axb2boS8QwoHoq0JsCGrsmdJE9X6GVSOiQU1sni2NrlWRaTMilLjsj/TBxqNc56pSm6TihzxiQRQUzKFWTaErQcUgPKVZp7fVZmKWdxwCzikKE1NK+QxWQtULmKrkSM889EnMBmZh1WSzR2Zr1gQwX3GN9bmg2uQc2mwGBlkOEIR+XQVSvGemabUIxEA29eri8XMdlmYfUcMRl7e5ItzY09Dgppo8JcC42HkLRML1UywYLroPEg6a5mFuWJc3dfTRhqxBmqRDytl6rkJglyuPWYOFaW3RaT5wt1zP/9m5fzl8+u45l71vHsvZfz3Ocu5/nPXc5f/2k9L3x+PY/cXuF9rBMLIPGuuoh3lQVYJQ+fNr8U1vNNKPQ8RDWJzEQrFpOxU2m9XOV4Yy8uj8FlcBNVruLXzKzHnKzxGx+k2CykxgvlX33XoM/ObKQSUbczJwZXIqqqSrddb2eexkrEFR+Bf9gR+L6734BrHvIShePhA5esIu2fT9F/5yu8P+8+AGyywgcWjq3ojBSbFmmW5hMhWJrdQ6IXAmaViFMAsyTiNIFOvLx6rAWHW4GrHhB37H8MBsK0dbidUP2W+H+APESYmQ3Es5ga0FuaXz/Vamgj5DtVHbT3D5GRGMf6+WO3j140R6gR9yiaPezcOyJ0eArC0STUdB22OaGRDpIUMBexOCOB5YWpKCq8ejyM/JEpiDOtfdxleplV0hlh0dvy0+HvTXqpmEi5HdBV6yURZzMRI4N/mRTAEqmOTKmPftXGYVWQbEbbXDKz8+hWE8UvRjQ0D3ZCnyAQTqtFYtI9BvSFhjOyZo01QLnR3ONXqpIdYamKDo1EnOs8BagBSUQ9E3HylYjCBh6VnRl8k4dA0Caf9Wo2+SObfacawlE56Fbm7EXR2+kDlKsAXKG1NMey8CwUNHXb2W27jzsPfwK2XoXk0Uhj1QNPXANbN8Cjy7321rAszbrqt79VjIFnCLzNzLnJPhXi4i0xU+kYNTcItp3/dN8IwMdMO1iQ4p6ac4xBXzNzXqrxxTWlmQmk2MwMuRVONRucyTZR5SoaiehJL+NgfRfga2bWoVua6zvtws4MBisRtUWRMezMdpcHp7a4Na2ViMMgj/gZPkxxNtaWZ1G6ZC1qyeVIigt2/ocxuzcCGxcLpeius+3YneOMEbzt3ZIoUYwV9ExE1+CUzsyfbMySiNMEq+ekk59qo3/IzVun22DBZrGi5BoQ4ajhoH6fyMVJzPYNKkdAnzQGMg9N5wbiWUw+FucnU56diNOt8FooK08hQm9l/vDy/HFXhvVylZc68kRwv73TZxmbamjXw6nDUKEEIBEBPqQpml+a5i3NLTXH+IpZy3q59gejbaGyCbLmi/+3nfRTIs6SiJFCtx8B3Gp6E4BjSglZqUkxKZPyb2g2hETUyJg6JZt+EkblB46Efv8Blza5MUCJ2NTjYIFUL37JiSLfDoS1VbaQ4O6hWGqltn20WlN3D8dNOokYhZ3ZZPHZtFzBz193Zy2gKRHTpqB6yR9elUMIBIERpSo6vOUqR4eVq1y5QJCIu6vacRutfAoDjd0O7nPeiyINV8t4R5myGW55jMvKNBIxnHKVhEwwxQEq9E3v658/dDvzwkwzHH1a3Ljqzpg9n/+C0sjRfzhzg2DbeUNZyUmlmCTJwS/nvTc15xg6iUgSeQaWquiQJMmrRjzS0GPsxnUlYtuJ8XNmo4FGIjaZCnG4FDIS4yjPHn7NLU73a2hOLxU3dteNHVsRDrzFKsHtzF1aHmKcSSbB4Iz2CUditiBMCyrg+p+In0k54vZIIUkoV31D/P/A74xVimpYnJ9MQaoNh0th99lxFrL0UhVbqojoiBXi/I7VWTViUMySiNMEsixx/QoxSXvhcKNQ3XjViFu9F7WQoFuZyzeOeRJuXpbPisLRTP90byCexeRCkiSuX6Fbmo0ZzDtcHrYfE1l/wVqZ/bG6RAzQDp7vRynSwqWnaC5iYp8gUNTM+aH/kZdEHK44+ZB2zu6p6qB7cJqqMRQPa49+G5vkoi7tUlj9icCP05tvW09QmCYGq829jkmdJE9nPPO+yBL70LJcPpYisgUX5aey64GrY3ItKM5IoFbVBv+d0ecR6qUqJ9QSijPig2am6tAnPG/3aq+t6XDUk5vmHruvmTknSiWi2eotTqiQqqkJkInoVSKa8Kowp107M/g1NAcnEZ3tIjuvRc712uSmLLzNjyFMTloNJBH1chW3Y9ii2fLCVFJsZvocbn7xxln2VHVMSituY4+d55X1nL/thcAP+IcdsOIjXDo3E0mC6rYBWntDJEIkacZZmj2KSlWbOIYq+nbCUA+kzoHSK2P6vJuX5fOrO1ePKhQJd24QeDsSvzPfAsD8mifBOfp7bdLhtTMnG9rM7I8VRVpDs9G5iCmFglBX3MPibgyHRiIeHRIE1prS9GGN6+BbmKzvGtQWgiWh/BowKFZhYHwlYteA3sxsGbV/0w6phfDFSmFfvvgu8fOLleL2aFCyDso2iIiktx8xYk+HQZIkrxpx3JZmux+JGEuYrb7Fy9lcxKCYJRGnEXRL844TrQw63bDwOjGJcPbDnl+GviFvHuKmMR9W3znI0cZeAH70kQp+esdKnrp7bcwmjbO4cLClQhw/b59uM4TM2nGilf4hN0Xp8V6V4Vgoy0oiNd6Cw6XQmnGRuHGK5iJmDYmsr4SCMEgHzepI64lheXJzsxJZlJeMW1H523S1NO/fSpn9KP2qjep1/xbc4q2TNG0nyUm2YjFJeBSV5lAnnbPwwulW2HfwEMukaj5d2o51UEzCU/vOYGo+LBSvWiadUShKj/cqEZ2tZ6LfoN7MrM5hQc7YeYggJjhxJplKdyGqbBZtkz0NUe1CW1cPpZJWbJQdYTOzP7TzvEKuorHHjsM13Hbj1EjEDLVXTBoledyQ+VjAaonCzgwimgDGJBFVTSExmFg09SeDXiViCASJrkSMVrkKw8tV/CzNfzvejFNbXHn0tTN89LG9rH/4dV6pnDjFXv+Qmz6HUEdmJQ63FaojNG+pCRaW5IsF7rDUiDOsXKWuc5Aht4LVLJN19i/ixlUfi61CR8PmZfnseuBqnrp7bVRzA//trNIKFtIv+YhQptk7hfppqsGuKxGTY6JEBF8u4mGjlYiSFPtyFVX1FpHt7BBkz5rS0bZ0L4nYaRekjU7yGzGWcA7CkJi/jkUi+pqZZ4iV2Wz1jYklSfxuBD7wLfHz0B8MKZkbiU1LNBLxRCvKWAtYjgkoVQHx3s3mIo6LsK40p0+f5tZbb2XNmjU8/PDDqCGsyv/pT39i/fr1LF26lLvuuovW1tHtOy6Xiy1btrBv375wdueCw/LCVOZkJGB3edhxonW4GnHff4emRuxt0iZTEpRfPeZDf7+vDlWF9fOyuHV10WwD8SwMw7ycZBbnp+BWVF6pjL4t+LlDYlJwQ0VBSBNIWZa8YfIHJE3hcW63cTYKg9A96KREFa8toyQMJUpyviALVI+XPNGhqxFfNuB9n3B0VKG+9iAAP3R/jOK5C4I/1qtEPIksSxSkzeYiRoo3T7XyonIP26zf4uLX/s53h70btl7lzSozEolWM20WYVN3tRmhRPSVqswfp1QFhN1ublYiTiwMpBhUrtJxFpOkMmRJgeS86LYFUCgWQFaZalDV0cVBuvAvXdEsQkm5wh48wdCViI5I7Mzgy0UMRiKqKnH9guBVUooje46JhDXEyYlzADq1duJompn9MaJc5ZXKJu558sCoz6a5x8E9Tx6YMCKxqVt8tik2M/Ea6awCdcs+L0iPEbY83dK8NywSUVcizgwSUS9VWZc5gFTzFiDByr+fsOc3yRKXlWdGPTfQt/OxtSI64q0z3bDuC+LOd34+9TIs/ZWIMSpx0huaT7f0jZ8TFy5iXa7S1wyuAVRJ5uVG8f7orer+KB5ZeKfnInbVRr8PAxrXYLKOmZ2nNzNP61KViUDxGlHqqnpiokZcW5ZBYpyJ1r4hKhvHIM69SsQYk4gQnmPgAkXIJKLT6eSzn/0sS5cu5ZlnnqGqqopnn312zL957733+OlPf8q///u/s2PHDoaGhnj44YdHPe7xxx/n9OnT4e/9BQbRbOtnaQZY+GExuHT2wd5fjb8RXYVYuBoSR3+p63C4PPz5PZHd9PHLSqLa71nMIhC8x/KR6KxF3YNO3jwlBgw3rQpdtr9aK1f5W0+RULoMtIk23ymE+vMNZEjiAhafOwZhNhL+5SrnDwy76zotF3HXmXZ6DSy2iTkUBf76eSS3nd2epTzNJkr8cvpGQVcitp8GxUNh2mwuYqT4y/sN3Oe8Fw8jM4M00l3LKjMaQymlAJi6osxEVDxClQucVIvHLVXRoeciNtq0KIGm6EjExB7x/WJPWxBdM7OOAqFEXCZVY8JDzYhcRK8S0a3ZuibBygz+duYIJ8O6nTlYsYq9C4tbqPrMGdNgvBIX4uSk9SSgallXUeRa+cOvXMWjqDz4wvGA2df6bQ++cHxCrM3nNRKxIC1e2HIB0stpm3szyqdfG2XLi6hcxatEnBl25rOt4vj5O8tOcUPZVZA2ZxL3KDpcpWVzHj3fQ9u828SiR28DHP3LJO/ZCGiCjU41dkrEvBQb2clWPIrK8aYY5SLGSomoxY+4kovpckBinInF+aMX7nxKxEEhStKPXSPKVbylKrlBr7UeReVgnSh9UVR1UiIcphU2aNmIR/4EbcZyNlazyVvw9dqJ0WIzLyZKiQg+JaKuaJ3FKIRMIr799tv09/fzjW98gzlz5vDlL3+Zp59+esy/qamp4bvf/S7r1q0jLy+PW265hcrK4aqY2tpannjiCQoLo/TsXyDQLc1vnmoTBIAsw1VfE3fu+y9huRoLXivzNWM+7KWjTXQOOClItbFxUWxq3WdxYUNvad5T1UFrX+QW05crm3F5VBbnp4iGwhCh25731w9A0Rpx47mpZWnurhPqqXY5G+ISw/vjIOUq83OTmZeThNOj8PpYF+uphncfh3O7cZsTeMB9N2XZSZjHKtBJKxHksGdoeENz9yyJGA7a+4d442QrzyvrabhtW+AHaVllRkPKKgfA5miNLhurowrcDuxYOafmhvw9Ua6RiKckraE5SiVi1qCYXKnRNjN7Nzgf4pKxMcQ86Ty1I3IR3dqkKNU12SSibmeOVIk4jp1ZU660qmlkZ0zA5CJaWEMsVjEyD1GHX7nK/qpWmnqCX3tVRBnQ/powMrcjhL4fBWnx0HZK3KgvBAWw5a2Zm4EsQW3HIE09IX6nzzA78+mWPiQULu97Rdyw6uOTu0NRIjvZyjIth/3t6j5Ye6+4Y/ejYhFxqkBTInaTNCoX0ihIksSKQqFG/P3eOmNzSvXvgNbjsVF5anmIrZqT4KLSjIBjNX1hd8DpEQUn6boS0QgSUS9VCTx/faWyifUPv84Tu2sBeLe2a8IjHKYdClcL4ZKqwFv/ZvjmNy4Wn9WOsQo3J0OJOGtnDoqxk8X9cPLkSSoqKoiPFyf9woULqaoa22J0++23D/u9pqaGkpLhq8Tf/va3ufvuu9m5c2eouzIKHs/MrN/WX5f/65uXlcC87ETOtg2wvbKJW1YVwoLrkLMXI7WdQNnzn6hXfT3wBhU3cvUbSICn7AMwxvv22z21ANyxphgJdca+x7OIHQIdv/4oSLWysjiVQ/U9vHi4kU9EqHh97qCwsW1ZkRfWcbqsIBlZEqRS74o1pNTuRKnZhbrqkxHtRyxgbxLh9x3xJaSHew7mVWAC1MYDKCP+9toluZxt7efFI41sWWGArTLW6KpFfu27SMA7cz9Pw9EcPpydOO7nLWctQGo+gqf5GAWpCwFo6BwM6TgZ7/i9UPDcgQbcisqKwlSK4sRgSkU0aqpI4vqgKGNeTyJFekYOXWeTSJf68bSdhbzILJ1S0xFk4KRSDJJMaUZ8SJ/r3Ewx3tnnKGILoDYdHnUuhYohl4didx2YIC5viWHHlZxfgXRuFxVyFdVt/cOOW12JmOIUiwVKUj7qJBzPmhCRQ3Vd7D7TyprSjLCsj7I5XoxbhvoDH2edtZiAejWbvOS4qX/OmuPFd/NQ/5jHk9R8FBlQshcb97mllSLHJSE5+xlsOBrSnzT3DOLxxHbSdl6zNOalWFFaT4jXrZWJBfo8Ey0ySwtSOHq+l3fOtnNTCIVqJOWJ973nfMTn8VTC6eY+1snHSB5qRrWloszfHJPv4YnElfOzqDzfyxunWrjppk8i7/oxUvtpPMf/Cou3TPbuASANdCAjlIg5SZag3zfRjCG2H2tmn0beP3vwPM8ePE9eipVvX7+Ya5dGOWZLLkK2pSE5uvE0H4P8FdFtbwSk9jPIwGm3yLm7eE5awPfAIkNuspWWviFq2/pISy1GBtSu2qjPT6mvWWwrMXvUtrYfa+Zzfzg0SoGtRzj88u9XRv8ezwAEPH6vfADTqRdRK59FufxLxmT16pueLwqzjjX2Ut/R740g8odk7xLXBmtqzMcyclwiEqDYeydl3DSZCPU7K2QSsb+/n6KiIu/vkiQhyzI9PT2kpo7fktPV1cWf/vQnHnnE56V/5pln6O/v56677oqKRDx6NLSB0HTFyNd3UY7E2Tb4w65TlElCYZA253bK2x5CeeeXVCasx2MZbddK7KxkkaMHtyWFw20ytAfOw6jqcnGovgezBEvjuzl0KEa5GbO4IDDW+bkqQ+FQPfx571lWxI+jog2A9kEP+2vE35Wbu8I+VktSzdR0u9nRmc3NgLvqTY4ePGiM1dAAuBqFcrvTnBv2azM7zFQAtJ/hyHvvoJh91t8yi7Axv3WqlT3vHSDePIU7tlSF+Xu+QoprgL7MCn7TswZwkqz0j/uelJpyyASaj7yBJ04Ee5+obw3rvZzp15fx8ORukad3aY5K48EdFAOKyUbDknvIqnuJOEcrJ2pbcbXE4DoxMEitmke6dJbaA6/TXeCOaDMFJ18nHzihFJObYOLU8dA+U0+3OE9e68jk+zJIvec5uu8tPNbwmwGb+90sk0RESMNgHP0GXVcLzUXkIRqa/3iuhUOHfBEFesyd3CVy9Rr7oWWCr+d7Gxw8f0jY8V451sIrx1rIjJe5a2UKa4tCU/HMs7tJBeqqTtPpHL3/uWf3UIQgEQc7Gjl0KAyL6yRAdvWzCpAUF4ff349qChzqP79qPylA3VAyHQZ+bguSy0nuOIytfhewZtzH97bUc+hQbFXrx2o0y+ZgFwOd75MM1NnFNSvYd3BZkoejwIvvnqGU8fcvobufxYCr8xxHp/m41qOqnG3t40emNwFoy9tA/bFTk7tTBqBQEsq4N0+28P4jWFawAAAgAElEQVRxleLi68k/83scr/2Qk46iKTE2qxhoRwYG5WTOnqgcN4c73DHE3gYHj+wZ3crc3DvEvX84xFcvSwv5uxPEsXKizUmXQyHdJrM4O45FSWWkOA5Q/+42OuYYq/Isrz5AGrCvW1wn090dHDoUWHWdHqfQAuw6dILEeBcLgaGWMxyL8vzMP3uEAqDdYaLOb1seVeVfXmwbM8LhX/7vCFnOJkxT4FibChh5/JblX0l609v0/vWfqb74u4Y+14IMC6c6XPx2x0E2l4+OKypprCYLaOwajPlYpszuIR1oqD5Jmzq9rxexQsgkoslkIi5u+EDHarXicDhCIhEffPBBVq1axYYNGwDo7Ozkxz/+MY8//jhmc8i7ERDLly/HZBqZ1TT94fF4OHr06KjXl1LYz5+O7eJoq4uSBUtEq1TFCtS6P2NuO8kK+zuoa742anvSGy8CIC/YxMpVFwV93qeeEV8Y163IZ8PaCoNf1SwuFAQ7fv2RN9fB/xx5kxPtLnJKFwZceRoLW3fWoNLGJaXpbFoX/JgOhsvrjlOzr44TyZdykymOOEcHK0tSIaMs7G3FAu9tF+UniSUVLF25Muy/V/cVIPU2siIbKPH9fYWq8tP3d1LXOci+7mTKshPJSbaGrRCaCEjv/wa54xCqJYGEO56g44/NgJMrVsxj5bKxV4ulgXVw/jXyLb1cVrGQn7+7n16PmZUhvJehHL8zHccae6ntaSbOJPHZ6y4m/SWReyit+zxFG74B6rfA42SpUQ2AI9Cb2Ebt0VxWcZa5qQpqBOcAgHxSkEon1Tksm5MZ0ucPsNDp4auv/Y1mZzzuvLmYu2tYnq1AWfj78e6Z8xRri37z1t0AiVlhbyMg4q6Fqj+yQq7il07Z+9o8Hg+ut94CIMcsFKQFCy8mf3lk72Ek2H6smf/YM1rx0WlX+I893SErPuTTOdAGJQXZzAnw2UlNvwegQc1m0+qlzM8JLfNy0qC4QXOgViwuh4QA+dSqivyaaCktvngzxfnGfW5S2+XQcZi16b3kpVhp6R0KOKmWgLxUGx/ddEnMrwuO9/cDdlYvmkvS68JuXLRqI51twcf43fFtPH/qfc70ENo53V8AO8Hi6GTl8qWTUjJkFGraB0hQqrjW8h4AmZvuI9PAY2SysMyj8PCe1+l1uJEyS8nZ8m3Unz1DYvcpVqb1wtyrJncHXXZMLwwBYE3LYdWqVUEfGskYwqOofG77m0Hvl4Anjzu4+7pLQzontx9r5qFtJ2juHfLelpdi5fclK0lpP8AcSxfFEV5Xg0F+R1znjrnyiDNJ3LrhIqyWwK9/0ZkjnOxoxJSSy7yVxbAHrI42Vq5YDnLk4y6pUbR6Z5YsJsPv9e2t7qDDPoZdFuiwK7hS53BRWfDegAsBQY/fgn9F/e/1pDe9zco8E+QZV6p3Q081j7x6mtP9cXw9wHEpnxL7UVC2hHyDj9uRkOoKoQmKctIojPFzTTXon/14CJm9S01N5cyZ4aUDAwMDWCzjX4Sffvpp3nvvPZ577jnvbT/4wQ+47bbbWLx4cai7EBQmk2lGT/JGvr75eaksyU/heFMvfzvRxkcvmQOYRDbi03ch7/sVXHYv2EaQu1U7AJDnfxCCvF/dg05eOCIyIT65rnRGv6+zmBiMdX4WZiSypjSD/TWdvHKslbuvDI+8e+GwOFZvWlUU0bF6cWkGT+6rY3+DA6nwIqjbg6l+D2TPD3tbsUCuU0wik4uWRnYuFqyG3kZMzYeh7Iphdy3KS6auc5Cfv+GLpchPtfGdLUvYrDU4Tzq6zsFr3wFA2vgdpMxyzraJvJ2F+anjvyc54voit51iTqbIlGzqdiBJMnKIk+KZfn0ZC88eFAUE1yzJIzPBAjVvAiAvuNZ3DYlyEXAslGQmcUjJAxNIndXIkX4OrXozcwmX5CWH/HkmxZsoTIunoctOT+piMrtrMLVUwvxNYe+CvekksqTSI6eSmpIb9t8HRfHFACyS6uno6cWlgE2bsLm0DK0Eh1BpyWlFQa/9RsOjqHzvxZNBFR8S8L0XT3LtsoLxJ8NxQpEgux0B99/deQ4ZqFdzKMpInPrnq8kE5nhw2zG5B8EUILerrxnsnSDJmHKXGPu5aYU8ppYjfPeGr3LPkwe0eAIf9E/kO1uWEGeJ3TmuQyc5ShMcSIMdgIScsxDaTgf9Dr60PAuTLFHfZaepd4ii9DGKtgCSc0G2ICkuTINtkDYNmryD4GzbIDeY3sEquSB3GabC1VNCpRctTCZRsPDi0SZ2nulgzdyFsPoTsH8rpt2PwryrJ3cH+4Vi1qWaSEnJCOm7JpwxxP7ajmGE30joOaW3b93LkvwU8lLiyU+1kZdq8/5Mtol5+SuVTQFtuy29Qzx6LIGfx4HcdNjY7xaP25tRW6PksbI0nQRbYKU1wBytXKWh24EprQxks3Z+tkJqUdC/GxcDgsiUk3OHvb72gdDKBNsHXFP/OjJBGHX85i+D5bfB0b9gevth+OhThj3XNUvzeOTV0+yp7mTIo5IQN+Lao5VuyQnpsR/LaK3esmtgwsZN0w0he9iWL1/O4cO+JqeGhgacTue4KsQjR47wwx/+kB//+MdkZflW3rdt28bvfvc7Lr74Yi6++GLef/99PvvZz7J169YIXsaFB71gxdvSDLDkJshaCI4e2DfifexvhSZNjjtvY9Dt/uW9BobcCkvyU7zttbOYRSzhPZbDbGk+09LH8aZeLCbJ2zgcLvRylWONPbiL14kba3dHtC2j0T9op1AVK6ZZpZFlwQUrV3mlsolXj49ejdUzYaZEuLSqwgtfEKHGcy6DSz5DY4+dQacHi0miJHOcCSMMa2jOS7JgkiWcHoW2/uCD9FkION0Kzx8SiqDbLi6C8++Ja0t8ugjYngAUpMVTo4pz290+dgZzUNi7oUfYiE+pxWGVL4Gvobneqi0sRFiuorYcB6DVZrDKObUYNTEbi+RhCeeo6/Q1NItMRBWbQzvXJ7BYZX9Np3GlHXqxSpB2ZqWzFhCxD0nW2BNehkAvVwnW0NyilRBmzgNLeAr9caFfF5qPsnlxNr+6c/WogojcFBu/unP1hCwoqapKo1Z4VewWC2ekl/hauYMgyWpmuVY+sbc6hONIliFFez3TvKH5TEsfH9GszKy6c0YQiDquWihaWt88rRVCrfs8yGaoeQvOvz+Je4a3VKWLZPLCdM6EglBLBg/X9/DU/np+8tppvvbMET7xxH6u+cnbLP/uqyz7znY2/uhNvvDH0QQiiO/eSlWUhaktlYL4Mwo99aC4cElxNJLJmrljzyP1huaGrkGhPNSJw2jLVfq1eIOk4Qt2Ocmh2cBDfdwFi6seAEmGUy8Zek7Oz0miOCMep1th55n20Q/Qi1Umop15vGv0LEInEdesWUNfX59XTbh161bWrVuHyWSiv78fl2s0u9/e3s5nP/tZ7r77bpYuXcrAwAADA6I9cMeOHfz1r3/lueee47nnnmPZsmV8//vf54477jDopc1sXL9CDIT2Vvs128omX1Pznl+Aw6+W/KxQIZJfEbStSlFUntwnvrg/flnJuDkfs5iFEfjQsjxMssSRhp5R7aJj4flDYhJw1YIc0hKCr3SOhaL0eLKSrLg8KtWJmnX/3NQgEZtqT2KRPNixkpw1J7KNBCARPYrKgy8cD/hwfcD54AvHjWsCjBQH/heq3xQNyzf+EmSZM63iYj43KxHLWM3MOtJKheLHM4S55xx5KWJg2NA1OPbfzYLXT7bSNegiJ9nKFfOy4Oxr4o7yq6OyGYUDm8VEX4KmFuqMkERsFcd6I1n0ksj8nPBIxPJsMZA8ppSKG5oiIxHjOkVeWV/KvIj+PigkCUlTllXIVcO+Q10KpDKAyaONEZInTmEc6mQ4pMfpZFKgdmZVxdQnSGJn8jRSlsXpDc3BSETtO9rA4HovMsogLhncDmg7yeZl+ex64GqeunstiVZxbm/9xEUTpkjvGnR5m7sz7CK/kxAbzC8rF5bDPVUh5mDOkIbmgbqDLJdr8UhmWP6Ryd4dQ7FhgSARjzT00N4/BGlzYLlW1Lnzx5O4Zwh1MKJUJTfFeKIpVPLqH68s44ub5nPHmmKuWpDNwtxkUmxiAaV/yE1V2wBOd/Csw1o1lz41HsntgHYDszQ7xHW6jjxUZC6ZO7YlWCcR6/XFrzStYLE7WhIxcDvzJXMzyE+1EWx2KyEcOZfMzYju+Wc6subDCo2veeOHhm1WkiQ2LhLEb8CWZofezhx+LnXYGO8aPYvQSUSz2cz3vvc9vvOd77Bu3Tq2b9/O/fffD8ANN9zAW1r2jj+2bdtGR0cHjz76KKtXr/b+AygqKhr2z2q1kpWVRUpKikEvbWajOCOBlcVpKCq8fLTZd8fSmyFrgTjR9vupEfUJ4LzgNqy3zrRxrmOQZJuZG0NpupvFLAxAVpKVddpEYFuIakRVVXn+sJgERHOsSpLERSViRWuno1ysdvfUR78KagB66jXyw1wkFBSRQCcRO6u8K3iGKoRihe562P4t8f+r/wUyywGhvgCYH6qaTJYhe4H4f9tJCtOFcqChK7CiaRY+PP2+aD2/eXUhZpMc0jUkFlDShXLPMtga2Ypwi7AyH/cUI0tQlp0Y1p/rSsQ9do186Dgb0X6k9gkbvjNjYdh/Oy4KRR7sCrma2g4/EtGjki9p53FClk/RNwEwVPGhK/FcAb63+lsxeYbwqBKm9GlEIo6rRBTHLbkRqtDHgiyLBWXwOlRMssRl5Zks1L5b6zsn7jtSVyFmJVmxdJwWN2aHdp5cpuWW7a3uQFVDWPiaISTioqa/AtBRtAkSZ1Z2W06KjSX5Yi74tq5GvPyL4ufJbdA2iQUymhKxmyTyUozPAg6V5Pra5kV8cdMC/u3WFfzvXZew/UtXcuS713LswWvZcf9VfO4D5WM+j4rMMbVU/NJ0eMzHhoUOcZ077c5FlmD1nLEVYzqJeL7bLhau0zUSMZoxuKp67cwjSUSTLPGdLYEXZvwjHKZaNviUxFVfBckkxoZ1+wzb7KbFgkR8/WQrykgxg65EtE2EElGbZwwFLgWaRRgkIsCmTZt49dVXeeihh3j55ZeZP1/Ye15//XU2bRo9sfjUpz7FqVOnRv0LhN/97ndceumlEbyECxcBLc2yCa78qvj/nl+Ig1/xePMQmXdN0O39bo/40r79ouLROQSzmEUM4TuWQ7PRHqjrpr7TTmKcyXvBiRS6pXlfg8NHuk0BNaKzRXxX9iSURL6RhAzfyq42WTRUIWQ0zh+A/7ke/vL/wNkHRZfA2nu8d59pERPusIoTdEVL2wmK0mZJxFDQ1jfEG6eEHei21UXQ3+ZTs5ZPbCZVRlYunar2eXdWh7+BZhEOfUItoTQz0ZsXGCp0JeLBDoum5FN9VtMwkDskFFam3BgoyzR7eYVURU27T2Xr8qjkSZpCK2Vic04NVXyYdRIxgIJYU6w0k0Fuengq00lFnLavziATFC+JGIPjBaBAC4pvHN46OTdLHO817ROnvtBJxMI0G7SdFDdmhUYiXlSSjlmWON9tD+17Xbf0T2M7s7t2Dx8eegkAadWdk7w3scEGzdL8lk4i5iyCRdeL/+96dJL2Chj0KRFHRgAYAX+Sa+R3ZygkV6LVTHl2EuvnZY/7XJVedb3xJGKtmsfSglRvPmMw5KXYsJgkXB6Vll6HMUpEZ7/vWpE42nm3eVk+v7pz9ajoi7zUiYtwmBHIKINVHxP/f+MHhm32krkZJFvNtPc7OdTg11LucYFLWySNn4C4tVkl4rgIW96Sm5vLpk2byMiYlfpONj68PB9JgvfOdXkHYQAsu1Xk6Nj/P3vvHd/Gfd+Nv++wCZLg3uAQh/awliVbsmXLtZ3Eo5n1SPJLHDuNnbROk7RJ8zz95UnapHXTPrHTjMZO0zRRPNKMOh5R4iEP2ZIla1KTpMS9Jziwcff88fne4Qhi4wCC1L1fL70AHsA7iADuvt/39z0mgSNP0OTPNQmYbEDNtrD76p1wypPGj+1MgbTQoCEJ3LKmAgYdh4vDM2gbjr3qI2W13bK2AhZjatZKKfvzeM8UxLpraWMW5CIaJmkw5rFFX1GOiRBLc1Znwpx6Guh6E+g/CuhMzMYcfH/bmJ05oVw7iUQcuYAapkTsn9JIxGh49mQ/AoKIjfYCUn1eepUeqFgP5CWXP5osagot6GK5iEmRiIyMuSDUork88dZeSYnYP+VCoJy1ECZqaXZPo0ygybDVngZlGbMzN/KDGBkJWoB8AoJKREmBlSGkOhmeB1mJGOZ7O0UZen1iKSpt6meUpQ3RlIgBX9BiWL42PceXmnxD8nIbSkgZpCSj0w1JGV9pswRVZnHama0mPTbaSZUSl6V5GSgRPa8+AgMXwJxoQvGG9yz2y0kL9qwk8ueNttFgtMquL9Bt6y/JrbAYYCTilJibFjszECS5QknKREiueBZx+szMpRGykJASGIl4WazEtvrYPIGO51DFFnd7JpxAYT09kIoSUcpDNFiD59kQ3LquEtc1U0/Dn26qwlMP7MDBL9+oEYiJ4rq/BngD5ZV2HVRll0Y9j+tYpME8S7NLQSgmYWcOCCIOXRrHsyf7cejSeOzIJi0TMSaS9MhpyAZU2MzySfqF0woFl1KN+Pa/AUd+RPerNgK68ArDfe90QxSB3c0laChJzO6lQUOqsOUYcD27aDx/KrpCwBcQ5M/7nVelPjFeV22DQcdhbNaD0SJqOkXXmynvN1XkzTHlUllLajsKIRFjDS4BGmCeG3RkJhdxqode28BJoPW/g9u3fpJWHRlJIIoiOiQ7cyJKRNbQrLQz92tKxIgQRRH//S5ZmT+8hYWcy1bmyEr2dMFemKMgERPMRRQEORPxQhKlKgBQZDWiyEqZq+N5jNgYSky54Rs6T78mFqK8LA0krLUYnlyy8lrHgwQnKRElEjHzESWRJsMJl3ZIJGK4YhXWBNorlqEyDcqgtCGaymG8Awh46Tm2JPNwY0G6LoQUKyyKEtFB72tDrjeYZVYa/3VPsjQfuhwPibhElYjsOhnoPwFzL41PeA4Qh1vp+smuk8sFm2sLkGfWY9Lpw2lJjVSzBWi4DhD85LRaBAhOKnuYQHqUiBKUOaWP3bUpYZIr2iKOhL033kx3hlrJsaYGWCZip1ARd66gvVCRiygrEVP4PMulKuHz/yVcGiVV252bqrGzsVizMCeDglpqTwcoGzGeSIk4cNMaeu9eOT8S3CjlIZryE87l3n9mELseeRV3P3EYDz99Enc/cRi7Hnk1eomkpkSMCY1EXOKI2Gy77kMkNXZNAKd/yTaGP0G6fQH88iit6n1sh6ZC1LA4CH6WB6NmGx3sGMP4nBcluUZc25h6FpDZoMM61vD4TqCZGsemugFHX8r7TgXlXjp+Xs3q1HYUQiLGM7gUAfz98+fx/h+8hTP9jtSOHwuPrgce3wM8fr0cWg4AeOffafujpP4acLgx5w1Az3OoT2ShQ1K0jLWjxkZkkFasEhlnB6ZxcXgGRj2P2zdUEREnx2FkNg8RAGqKLOgSGPE2nqAScbIT8DnhgRFdYkX8WZohaGQ5il0GpgpOUIk400uW6g6xRiYkVUcN5SLWOC/A7aMJoU8QUYFJenwRSERAORm+GoU5ZG37xh1rE1N8xKVELJEVLUsC0VQOkpW5bE3yebixEFKuIqGeKRG7xjOoRJwiJeJqHZvQ5dcE86jigLJcJWYuoo0tPDqWmBKRXSd1T+wBLxDpaxY90D2xZ951crlAr+OxmynFXrs4Gnxg11/R7bH/AubCtLemGd5pOqYDuSjNVT8TUQkppzRZkivSIg7PAd+/ZzN279hJpVW+OZn8Swk+N0QHzSU7xUpsq4/PcmovovN276QrmIk43Q/4vcm9jgilKkoEBBGdrISsKZFFaQ0Lcd2XyDnU/RaVIaqAPS1l4DngwtBMcLzuZnORBPMQ958ZxIP7ji/Igh9yuPHgvuORiUQ5E1EjESNBIxGXOCI2284MABtCGtuGWkntE7Jq+fzpQUw6fagusGBvivlyGjQki5tWl8Ns4NE5NoezA9MRn/fsCRr837ahigofVMAWZmk+MuAPBs4voqXZ7RhFIehvUF6fov1Ryr6a6gHmSKkRaXBZaTPjB/dsxjffvw55Zj1O9zlwx/cO4h+eP4c5jz90z+rgA09QoU048Hp6HMFSlbibmSUU1MkNzXUcrWr2T7niC+HPFkhZkf3H036o/36XJgE3rymHLcdAWZrOcSIc7NvTfvxQKJWIYqJKRJZd2C7aEYBOLo1IFNIk47SfTXBGzic0wfENEik0YKwHx6VH7WC0E4m4kb+E7nEnRFFkdmYpEzGzdmYlaDJcgjvYQtGBtpEYvxGCKCSiyLKzeoWlpkSMkomY7jxEIGy5CgDUFxNhPjHnhcPpS9/xFZDieOpFNi6Ns1RFwubaQhh0HIam3eiORX5K34PZoXkKzGzHqe3fhk8k9Y10CpFufaIOp7Z/e5FeWfqwp4VIoNfaFCTiihvIiu930UJjhuGbIRLRZypSbfyZTigVjf/y4Q2wmnQQRFAMEK+jiBJg3jkgaUx2goOIaTEHhaVVKI6TZK1hSsS+CSdgLWUZuCKVHCaDCKUqSvROOOENCDDpeVQvpcWnbER+FbD1Prp/4JuqqBELrUZsrSMlq6xGlOzMlvitzAFBxNefO4dwr0ja9vXnzoV3XclKRK1YJRKy/wyoISqUzbYvtCrY9EfXA6/90/wnuyZJ7ROyavnzQ10AgHuurtXk3BoWDVaTHntXEYn9XARLs9Prxx/P0Sqjmg3im1m5yrHuSUDKRexWJ98jGYx2EfkxJBajsCDFFjKzjTJSAWAwmH8VyS7z3g2VuPfqOrzyxetx24ZKCCLw44OduPk7b+DVC8MRDpICNnwEuP+V8I/d/4q8GNLB8hATzrVTNDSXezrBcYDbJ2B8LslV7sWAlBV5+pm0HsbjD+BZ9t37UKiVecX1gC56SHo6UGkzo4cjElFIVC3ByJizATv0PJd0VIdUrnJ8Oo9WwQXfPPVWLPBj9Nxxa4r5plHA1VAUg9TQ7PULALCoduZQSIuUr5wP07oYDfrIJKIwQSTisspEZBb8tDQzK1G1MBfRatKjnLXOdiqavtMJSSFS7mU5aHHmIUqwGHW4yk7X8JiWZmspLU6JQlCxlOUICCI+c6oR93i/Gvbx93u/gc+casxM/EgGcT0rVzndN4XxWQ9t5DhgN8tGPPI44I684JwOCKydmVtCjdiSovFDW+z4s60Uj/DkEUbYS9moapSryHmIFdjWEP/fp5Y1NPdOOun9LWARDsmWq8hKxMiiGGk8uaI0F7w2700du/6KrtN9R4NjxhSxdzWRwC9LuYjuxJuZj3ROLFAgKiGCrj9HOicWPihfo2dUs2kvN2gk4jLA7RvCtDSHVfewL4FC3XOqdwqn+hww6njctc2egVerQUNk3L6RLG7Pnx4MO8l86dwwnN4A6opzsMmeIrmmgNTQfGFoGq7qnbRxEZWI032UoTZotKujXAqxNEuIZpcpyzPje/dsxn9+chuqCyzon3Lhvp++i4d+cYxa9BgSDisOh7G2kA0LL01tch5iEmqyUrKEG8bbUJZHE+Ssz0VUZkWe+TVtO/PrsGpytfDq+RFMOX0ozzdhdzNrd5QGhM2Zz0MEyNbmzqsHAOjmRmhAFy+kUhXRjvoSK4z65IY8jUyJ2DE6p1BuxD/pypmiz7eroDmp48eFyo0QwKOSm8BIfxe8gRASMW/xScSrVxQh16THyIwHrYnEJERSIgoB8NMU+zBtrky5ZCujkFUOYYg6pZ05nZCvC/NVSJIaMRO5iAFBxBC7nhTMsriCBJWIALBDYWmOCl7HWtaxZHIRpYnwzfy7AILzWUGk63XUifASRnm+Gasr8yGKwJvtCuvyqtuB4mayNx77z4y+Jp2L4iEMuSUZPa5auHs7zfNevTBC4zhJjaxGuYqimfnqOPMQAcAukYgT7PwuWZqTLVeRSMQwzcwSOkbp3KZZmVVCXjmw/X66r5IaUVp0PHx5HDNuHwmhAMAS/9zvtLLdOQpGZsIQjdI1WvADfk/cx7ySoJGIywC3rKVm2wtDM7LlL151z88P00n6fRsq45aea9CQLuxZWYZckx79Uy6c6J1c8PjvTtKg/86NVaraAsvzzagusEAQgdP8GgAclTjMDKl2jETgH6GGyumcenV2KE0W+09Ef14Y3LCyDC994Tr8+fUroOM5vNg6hJv+9XX8/FAXXjydRFhxOEiFKpZC4LbvUAlUbhmpRhjak1UiAkAZU7aMXpCtK33ZTiIqsyJZmDvmRsOqydXCr44RIfOBzTVEKLsmaWUZABr3qn68eFFYVIpxkZHHiTQ0MzvzBbEWLcl8bhiamBKxa8wJoWIDbRyKMxfRNQmrl94/sSQxhVVCMFoxkdNAx+k/Bo9fQC6cyOfY5zx/8VsnTXodrmuhyfe81sVYMNAkc0GxyswgOMEHn6iDzrZ4du2kYIoQ2u52BG186bQzA0EVUki5yopSiURMfy7i6IwHAUGEnudgnGynjQkqEYH55SoxoyrkhubFzT2OFyMzbugQwG36wwAo//Orvk+hVWzAiGjDuJgvP2+5YQ9TI752URGBwPPArs/T/UPfB3yZ+38bvTQuNdtKYzwzO9FcnoetdYUICCJFl0hq5KHTlH+cAnwj9P3tFCqxLRESkRXeDc+44fEHFOUqyZKIse3MkhJRurZrUAHXfp4asQdOABd/n/LuGkutqC/OgS8g0iJCnEpEty+AXx/rw4d++Db+8ffxOUbK8sJEoShzebVylbDQSMRlAFuOAdcx1chzp8NN3vmQW8LknFdWL35UK1TRkAUwG3T4kzWSpXn+Z3lizovXWTbOHZvUnzBKluYjgwGggtnIuhbH0myYItumr1Al+2MEJWK8yDHq8bfvWY3nPrcLG+0FmDhv1UUAACAASURBVPH48XfPnsVDTyYRVhyK0YtA+0t0/xMvUrbKAweAz5+RQ/CpmZku4sk07MqT0tELcv5O/1SWl6vEmRWpFkZm3HL21Ac3Myvz5dfI9le6CihYPKV6TaFF0dAcJ4nonpabe88LtckpWBmqCywwG3h4AwLGpIbmeMtVRmgQ2yeWoLgovRa42RJSleSOn4bHJ6CcYwsxJltCRRXphBRZ8fL5BHIRDWyAH6pEZGrcAbEYFYXJWdUXDVImYqiydphZmfOraVElnYhUriIrEdNvZ+5neYgNeQK4aVZ2kkAzs4Sragtg1PMYnfHgcqzXvcQamsvyzLiZfxeV3CQmRCtu8n4bTwb24k7v32OX57sYQrH8vOWGPS00t3mjfWy+O2X9R+g7MjsMnHoqMy/G54ZRoM9rXtHSzY+/ezvZhZ8+2guhuAXQmwHPNBWRpQDnIC1+T+XUJZQzWGQ1Iseogygyh4haSsQ47MyaElFFWEuAq/+c7h/4VsqkNMdxuImpEZ860oOOHlr0EUzhMxHbhmfwf353Ftu/+TK++N+n8G73JHgOMEVxoHCgyJywTeK8LriAmYgD5gqCRiIuE0jNts+fGgiuwlpLaSWmamNYdc8v3+2Fxy9gbVU+NteqZw3VoCEVSJbmF1oH51ljX2wdhF8Qsa46Py0X/i3sO3CsZxKo20UbuxfH0myb6wIAGMoTt3WFRcUGABwVLqWgrlxTlY/fPHgN/s8da6I2OwNRwopDcfBR+q1VtwWVNxwH6IPK6KFpN2Y8fmpmLk6CLJAbmttQU0C5flmvRIxTTa4W/udEPwKCiKtqC4Lfr3ZmZV6EVmYl7EU56BLZhCDeXMQRigQY44sxhbzkyGcGnuewooRZmvkVtHH4THyDZJZv1y5UoyLNmX18zWYAQPXceXgDQlblIUq4YRW1Lp4bnJYLNWJCGsj7Qoh/NsnsXWp5iEBkJeKIVKqyNv2vQVmuolhgkrJDuzJAIg466DOw1SqphyqSIk/NBp08jo1paV5iJOL2hiI8ZNoPANgX+BN4IF0bOXhhiD4RXuLYXFeIPJMeE3NenFZGIOiNwDV/QfffeiwzJTkuOp/6RR6FhUvTzgwA711fiTyzHn2TLhy8PBU816RYrqKfogW+/OrElMQcx8HOFnd7JpwKJWKSkS0xilVEUcQljURMD675C8CUDwy3Aud/l/Lu8sw0Xn+zfQzHLnQBAB4/OiGLFNy+AH5znFSHN3/nDfz07S5Mu/2oKbTgr29ZicN/uxeP3bUJHLBgziL9/LXb10TugzBGuE5rAKCRiMsGN60ph0nP47Ky2dZWTWqeBw4sUPcEBBH73qEB+Md31qWtMVKDhkSxq6kUNosBozMevNMZnAw8e5JUCnduTI9tbQtrAjvePQmh7hrauBi5iAE/yvx0gcyvXq3OPk25wZypFLNvdDyHleX5YdvOJMSd0TTZHSwLkcLSw6CNqRCTzrUrqCMiIuDFKiNZS7M+EzGDEEVRtjLLhSqiGMxDXHQS0YIuIUElIrMynwuQgjIVOzMQnGy0ull7pHc2vtfCFF4XRXva24MLmnYAAFYJ7XA4vajMQhKxyGqUM2jjtjTLmYghtsUpRalKwRJTYRkjFKtkKg9RgmRnVBAIShIx3S32g1P0nq41MuV6EnmIEnauIGInZrmKbGfuT/pYmYSu/12sFy/CI+rxc//N8x6LayK8hGHQ8bi2id7XeZZmANj8ccBSRAq6c/+T/hfjpPPpJHLTviCUTliMOnzgKvoOPH20R51yFbcDVh/9fWqbE49asRfR37N30pVasYooKpSI4UnEkRkPZjx+8BxQX5KT+DE0REZOEbDjIbr/2j8CQiDpXe0/M4hHXw7mpds4WtTqd5vwmX3H8cmfHsHV33oFX/glqQ51PIdb1pbjv+7bjjf++gZ89oYmlOWbceu6Svzwo5tRETL+qrCZ8cOPbsat66JEvUQrQNOgkYjLBbkmPW5cRSfM55WWZr2JVD3APHXPG22j6J1wwWYx4I40kTIaNCQDo57He9YRYSBZmvsmnTjaNQmOC6pu1caqyjyYDTym3X50WZk6Y+xiMF8lQ/COd8IAP1yiEVV1TertuIpUSslampWIN3sp5vPe/i4gBoAVNwDVWyI+rV0uVUmSCOJ5oIQscitEIsuyXokIkGpcsqEaJAUmF1RmqYTWfgfahmdh0vO4jRV1YfgsMDtEx6rdqerxEoW9MCdxO7Oimdmg41CfZDOzBKmhuX3MHVRuDMWedAnDkhKxJu0kYn7dJnhggI1zYrDzHCqQfSQiEAxMj9vSLLUzBzzzJyVMqdIrlqFqqU3qjezzGKpwGM5QM7OEMOUqtcU54DhgxuPH2Gx6W+wHmBKxiWP5hEnkIUrYycpV3omVi7jElIg4/H0AwKGcPRjFfNdQXBPhJY5gLmLIWMxoBa7+DN0/+Gj6G1RZM/OkmIcK29LOkL+LWZr/eHYYM4XsepbCArOX5SGOiAW4qrk24d+XYmb6JpxBO/PcaPjiqWhwTwEBds6KUKwiqRDriq0w6ZdQGddSwY4HAbONFlDP/japXQQEEV9/7tw8sYIN9FlwiHTtPHBhFA6XD9UFFnzp5hYc+sqN+NHHtuL6ltIFjdu3rqvEwS/fiHVVlB/7metW4OCXb4x93tSUiFGhkYjLCBK58pzS0hwBPzvUBQD48JaapdVoqOGKgPRZ/v2ZQfgCAn7Hsjt3NBQvWE1SCwYdj401NEA/OsIBZWxglWFL80Q3Kag6UYWyfBUnxinmIioRb/ZS1OfNDAPHf073d38x6n7amRIxaRIRAMpI1Vnp7QJAWVzpVtmkDFs1sPoOur/9fqBwBQAR6Dui6mEkFeItaytgs5B9RFYh1u8OZtItEmoUJKIYr52ZKRHPC3VoKLHCoEttuCMpES+NzgKVrFwlDuWGyGzVHZw9/eVlOgM69ZSj6ul5V6FEzK6FwptW0+Tu0KVxzHnisCEaFOdBZS7iPDvzElMiSosDSoWDKMr297SXqkgIU65i0uvkTLOu8fRamiUlYrWPWRdTUCJutNtg0vMYm/XKmWdhYWNqa8cSUCJO9QDnyBb4b05SIX79jrV47K5NeOqBHfFNhJc4rmck4qm+KUzMhZDa2x+gif5wazBbOU1wT5OLYRJ5KM9fYuebEKyuzMcmewH8gog/TLKokMFTSROxfR2tdMtVorE08QU7uaF50klxBlLuXaKWZmnR32SLOG6RmpkbtVKV9MBSEIwaePlrwH++D+g/ntAupEZ6JSQlogPBz9eXb12JN/7mBnzuxmaUxfhO6ngO69kcz6Dn41NumyJkF2sAoJGIywo3rCyD1ahjzbaRa817xp1ygP69WqGKhizEjhXFKMk1Ycrpw4/fvIx9h2iyeMem9A6WJZvdse5JoP5a2phhEnG2n0iHEaNd3ZgBJYmYInm2vaEIlTZzxFzEuDKaDn+flEU124H6XVGP1z7ClIgp5NpJk1PbLJFQsx4/pl0ZyFFKFRJRVbMN2Pwxui+RryrA7QvgWdZ6LluZgSCJ2Pwnqh0rWZTlmdCvo4UFbm6ESlOiQRBkRdd5sTa1zw1DYxkNXDtGZiFKDc2xylVmR6FzjUMQOczkNmbEbjicR+RTzugpRSZidpEMjaW5qC/OgTcg4M32OJTeesXkwB+cWIhTXQDIzlyVQJB/VkBWOMwEz8eOXio44A1AcXNmXkeEchXJ0tw5ml4SUVIiFrtYqUMKSkSTXoet9XQNj2pplpSIM4Mp2e0ygnd+BIgBjJXtxDFPDaoLLPj4zjrcuakaOxuLl6WFORSVNgtWVeRBFLHwfJFTBGz5BN1/5RvAT29LmLCIFzOTlCc9w+XLWW1LGXdvp6iPfz9nhMgbSMWXZA7heDddb515DUmNW2slEnGCLRIVMjVjouUqMazMgFaqkhFc/RmKGnD0Ad0Hg7FFcSKciymfm69EBICqAktC58C6YkX2ZjzQlIhRoZGIywgWow43sWbb509Fbkbd9043RBG4rqVUHihq0JBN0PGcLDt/ZP9FDLAVqUdfbo+/9TcJSCTi8Z4poI6RiBnORRRGyRYyY61Xd8cV6wBOB8yNpGzj0vEcvnY7kRWRLt9RM5pck8DR/6D7u78YjFwIA1EU0c4Gfc2p5NqVkhJRP3YRJblGAGzVO5vhmQ0qk6q3Apvuofew9zAw2hb9d+PEK+dH4HD5UGkzy9lT8MwAPYfoftNeVY6TCnieQ0FBMcZEOifEtDQ7egDvDPycAZ1iBVaqQCI2lFjBc8CM24/JfEZ0DJ2OTsiP0oJAr1iKwoLwjYJqw8kamqvmzmetEpHjuMQszTwfJBKlcpWATz6P9aF06SmDpKwlUQiqK6U8xJIWKo7IBGKUq3SmWYk4MOWGBW5Y5qRm5uRJRADYuYIszVHLVXLL6TwqBoDZBFrCMw3PDHD8ZwCAX+pvBwC8b0PlFZlhLqkRXw+1NAPAzs8BOiOpEbveTJiwiBeuSfqsuI3Lo4jytg1VyDXp0THhw1wBa0RPslzFz8atpvLkFj+CmYjs/C6Xq6SPRExGMakhDkz1AOMdwPoPB7e1/ors8gMn4iKqw7mY8rFQiZhoI30dI6u7x+Mc+2uZiFGhkYjLDLezPKvnTw+EbUZ1+wL45bu9AICPaypEDVmK/WcGZbWsEiPTHjy473jaiMSraolE7BiZhaNsGzvoWTlQOxMwOUgpF1BbiWKwBMP6B1JfqY8UVgwA915dG91i9c7jtLJXvg5ouSXqcYanPZhx+6HjudQWPcrY5HS8HXYbTdD7422HXSwMniSSIa+K1GR5FUFl4Al11Ii/OkbXgw9srg6Svp1vAIKfVEpFK1Q5TqqoKcpBt9TQHItEZGRMN2+HH/qUS1UAUjlJaomLqCUSwjkenZAfIWVXm2hPWwxDKAz2rQCAleJlVHPsHJplmYgAsJdZmg9cGImvxT20XMXRB04U4BENgLUsucKlxYRBcS6TVA7DGWxmViJMuUp9cfqViB5/AGOzHqzgBsFBBHJKAGtxSvvcwUjEdzonIET6XPE6OpcC2Z2LeGIf4JmGUNSE7/U2AABu25BdquJMYU8LnS9ebxud/75O9VB2b7OicObMrxMiLOKFd5bszII58fbwbITVpMcdm+jacCpAn69kylUCgoj8OSL7yhqSy3KV2pmnnD7MuH1AYT09kKgSMUYzM6ApEdOOR9cDj+8BjvwouM05Bjx+PW1/NHbxTqjbiYeAfI7G6w7RmnQjvWSb15SI6mCJjbo0xMLulhLkm/UYmfHgaNdC4uN3pwYw5aQg0htWRT7JatCwWJACdcNBGjp+/blz8U08E0SR1YgVjKg6Pm4ASlg+U/fbqh8rEgqdNGgylreov3NpsqhCLiIQDCt+6oEdeOyuTfjo1WRBOdI1ETlv0DMLvPNDur/7C1FViEDQylxXnJNaCLatVm5o3pQ7CWAJlKv0H6PbGkXpzFXM0nzqKVJjpYCRaTdeZ2T9BzeHsTIvciuzEjWFFkW5SoxcxCHKQzztI7uWGnZmIJih1DHuCyqmhqJYmpmKtE2sRkWGlHLFdasxLVpg5nywcWygnIUk4rb6IuSZ9Rif8+Jk72TsX5DKVSQlIiMH+sQSVBYswYZNnlc0NLO8JZlEzFAeooQw5SoNTKWTzkzEYYcHALBGz4i8FFWIALChpgAWgw4Tc160jUTJsZIbmvtSPmZaIASAw3SdPFN7L5w+EbVFOVhfnRlFc7Zha30hck10vjgz4Ag+IBEWF54PbpsbTYiwiBfCLKlbxZzUiO5swj2sYOUPE4pcxARxfsCBWtDCfk1Tcn9vq0mPIitziEy4VFAilod9eNrtw8gMnXcaNRIxPfjAEwCvD/8Yr6fHYyDU7SSpEAFgBnS9T6aRXrIzT8x5iayOBS0TMSo0EnGZwaTX4Za1NNF6/vTCFdZ9h+mE/NEddVdEloqGpYdwgbpKiAAGHW4c6UyPOnCzbGlehFxE5wTyBcozLbSnYSKpYrmKBB3PYWdjMe7cVI2/ec8qWI06tA3P4mDHWPhfOPZTsjMXNQJr/jTm/ttYqUpLWYpEkKKheZ2Bzo392U4i9r0LAOi2rMGzJ/tx6NI4Ak03U+vg3CjQ9oeUdv+bE/0QRLLxr5BCxkURaJdIxMXPQ5RgL8xBp8BIxPFYSkQiEc8EamDU8bKFJVUEy1XmFOUqUUhEljF3UbBnrPijoTQPp4WgetTDmQFz9tnvDDoeN6ykhcy4LM2yEpF9Z6ekUpUyVC61ZmYJoSqHkQw3M0uYV65CE6sGSYk4NhdZ0ZciJCX4JjOb+KdQqiLBqOeDuYjRLM3Z3tB84QX6jFsK8bhjOwDg9o1XppUZoPPFtU1E3s1raVaBsIgXnIsWOwwpqmWzCeuqbVhXnY9TfkbaDZxMODO7ta0DeZwLAjjoipN3LtgL6TzeM+EEClgmYsIkIvtsWEvDPiypEMvzTchfBrmWWYkNHwHufyX8Y4X1VNYXB5RuJ6lUZVY0o8SWm3QjfZ7ZIJPVcakRNSViVGgk4jKE1Gz7YusQ/AFB3n6ydwqn+xww6nn82Tb7Yr08DRqiIlygbirPSxSbaxXlKnIu4sG0HCsUAZYrMygWwV4RfhCUElQsVwmHfLMBH95K55afHOxc+ASfG3j73+j+rr8iW1kMdMilKiqsGrOG5hUiWXj7sjwT0dVFLcxfPmzAw0+fxN1PHMauf3kTl6spHysVS7MoinIr87xClbF2yhTUmYIkehbAXqRUIsZnZ74g1mJFqRX6FJuZJchKxJFZoCJGQ7MoAqyZuV2syZiduSDHiIu6YBTCLJ8fU+27WJAsza+cH479ZAMjgv2MRGQ2N1IiLrE8RAnKvCW/h757QDB2IlMIU65SU2iBnufg8QsYmk7PtXaQlaqs1El5iKmTiACwszGOXERZiZilDc2HfwAA8Gz6BP7YTtfA2zZkn6I4k9jDFh1eu6hYdIhGWNz/Cj2uEgxeIhFNtjSMzRYRd2+vxQWxFgHwZDtNkFgfuETNzLPmSkBvSvp11LDFvr5JJ1DISM3JRNuZoysRNStzpiGNvdgYZLwD+PFNcvFdLEhup3/7U7LbG6yFKTfSy5bmeHIRtUzEqNBIxGWIaxqLUWQ1YmLOi7cVg6ifHeoCANy2vlJm4jVoyDbEG5SbaKBuvJDKVU72TsFvv4Y2DrUCrsiN52rB0UcX1stiVXrUNeVrKYDcNZn4Cm+c+MQ19eA44MDFUXnAJuPUk5RflF8NbPizuPYnKRFVGfQxu1ylj/7v2ZyJeODoSVhcQwiI3Dxl2ZDDjQdame2v/Y/AdHL5oKf6HOgYmYXZwON9ypwtycpcdw1gzJ7gcXthDrrkTMQodmbvnEwyXhBq0aKSlRkI2p86RmaDSsRIduaZIcA9hQB4XBYrM6ZEBICR/KCSjefSoyJTA3tayqDjObQNz8Ye0BukYhVJiUiTy16xDFXLQYk4epGKPswFmbef87wi6oIszXodL2eAdo2lx9IsOQ7qBFrUUYtEjCsXMZuViP3HqNiKN+DVvDvg9QtoLLViVYV657KliD2sXOVk7xSmnN4wz0jvYonZR2NAa2F4gmqp4o6NVeANFrQJbDExgXIVURThHLxI94saU3odUi5ir1KJ6HHQeDVexChWuSSRiKUaiZhWWEvpPajaCNz2Hbq+5BQDBfUUIfGTW4BLB+LalY7nsKGYzuOm3KKUXZR1ieQihkaOaJgHjURchtDreLxn3XxL88ScF8+fpsnmx3ZqhSoashehgbqhSDZQN140l+Uiz6SH0xvAhTkr2W4hAj2H03I8JZwDpAIZNdnTEzegNwVD+1W0NCtRX2LF3lU0yP7p2wo1YsAPHHyU7l/zl3G1j4qiiPZhunirQgYxJWLBLJFQ2ZqJGBBE7P/DiwCANrEGTgQJKBHAZbEap7hVVLpy6smkjiEVqty6tmK+rScL8xCBkEzEuVHAPR3+iSPnAYiY1hVhHDZVSlUkSBOPoWk3ZguZWszRG754iTUzd4kV8MCIikwQXVM9wMAJFOYH/8+5AUdaSgbUgC3HgG3MevpyLDViRDtz6RJWIirylpSlKouhHJUamg98E+in4i2pyOpymkjEgSkXTPCi2KteJiIArK+2wWrUweHy4fxQhPOEjSkRHVmoRDxEKkSs+yB+1RYAQCrEK9XKLKHSZsHK8jwIIvBGuyIuRSIsKjcBPLuW5RRHtLQmizyBPku2ouVFIuaZDbh9YyVahcTLVS6PzaHUS46G3KrUvr/BhmYXLWBK718i5SoxilUujWpKxIzAVg18/gzwwAFg6310+4XzwKcPkMPLMw384kPAiV/Etz83E3FYUo9mkXIRu+MhEaVrtGZnDguNRFymkCzN+88MweMP4JmjvfD6BayvtmGTPfvykTRokBAaqKuE9HMygbrxguc5bKql78iJebmI6bc0i2NtAABnXhobcdOQixiK+3bVAwB+faw/qBg4+xua+OeUAJs/Htd+Rmc8mHb7wXNIrZlZAlO6mByXoUMADpcPsx5/6vtVGUc6J1DnJhLqlLBwdV8EsM97Hf1wYl/C1nS3L4DfnaSJ+4e2KKItvM6gdb85e/IQASo9Eox5GBXzaUMkNSLLQ2znaLFMrVIVgEivklyya12a5oFCNukKp0ZkVuY2oRocB5TlJW/zihusZOAzfV+RPxJ60ZuWkgG1cNNqmpC/ciEGiagPJRGlYpXS5ZGJOLJIzcwSpOvCzCBw+hkAtCAEpE+JODDlQgM3BB4CYLZFtCAmCoOOxza2yBjR0izbmWMrEQOCiEOXxoO5tGnKiAQAOPqAs78FAMxc9Wm80U6kyO0br8xW5lBIasR5lmaJsPj0AaCG2umx92tBolgF+L1uWEHnnuKy5fde3L29FmfEegCAry/+seHRzgk0cCRQ0ZU2x3h2dEjK516J3Em0XEUQgFn2uYhhZ9ZKVTIAvSm4IMZx9HNOEfCx3wLrPwwIfuDZh4AD34o9hpWcYCrkO9cmZGfWilWiQSMRlym21RehPN+Eabcfj79+GU+8SROue3fUXvGrmRqyH8pAXSUqbOakA3UTgWRpplzEXbSxK/3lKhYH2TADxU3pO4g0WWRqk3Rg54pirK7Mh8sXwFNHemlw9+a/sgcfAozxFV1IVub6YivMhhSamSWwhmYu4MU6M00us7FcZWTGjU1cBwDgpBj+s/BCYAf8+hyy7iZY/PPSuWFMu/2ospnl/DAAtJ+AB7DZ5RKabAHHcbAX5qA7Vi4iU3Sd9NIEUk07MwA0lRGxcml0Nnq5ikQiinaU5ppgUCmXMSoUJQPy2F16TOWSAbUgkYjvXJ7AdLS2RKUS0ecmsgukRKxaskpERd6SpETMdB4iU69CpyC5W38FDJzEFkM3qjGKzjTamZs51o5cukpVBeZOZmk+fDkSicjszDMDdH2KgP1nBrHrkVdx9xOHg7m0j7yK/WeSi5GIiSOPk629fjd+P14GX0DEqoo8NKVaLLZMcH0LkYhvtI3Ot6pLhEX1Fvo5UsxEkpgYpUWOgMihuHh5ZSICwCZ7AWYKaQEjERLxiIJERLE6dua+SRdEUVTkIsZJIrom6LsDhFWhun0B2cKqKREXEXoTjUV2f4l+fv0R4LefAfzhIgoYVFQi1iZjZ9aUiGGhkYjLFDqew5oqUmz860ttmJijwfmjL7Wlb/CjQYOKkAJ1n3pgBx67axOeemBHyoG68UImEZVKxMGTkS2UaiDgR4GbJlTmcnWyocJCIhEHT0WdPKUCjuNw37X1ACiL1X/+BQrsN+UD2+6Pez/trFRFtQEfz8tqxO25tGKdjeUqZVYD1vNkBT8phCcRnTDjDxx9NgPHfpbQ/qVClQ9srpmv6JWtzHuzsoxjnqU5UkMzI2PO+O0w6YO5bmohbLlKuAkrK6hoE2oyl4e44SN4+4Znwj709g3PqFoyoBbqS6xoLLXCL4h4Xdm6GgqpWMXnJAs5gDnRhGk+P235uGmHPEGZCwbNZ7qZmalX8cuPBrc5x4DHr8d7D92Ft8wPo3M8fUrEJl7dUhUJUi7iWx1j+O2JMArC3AqA40kNMxf+c7f/zCAe3Hdczm6UMORw48F9x9UfS3tmgXd/Svd3flaOILptw/JTviWLrfVFsBp1GJv14uxAmPGYRCL2vavqcSfG6L1wcHnQ6VRY0MwycByHrdt3IyByyPGMQowza/ndzlHUcUxFnuLid1WBBRwHuHwBjM16FQ3NccZwSCpESxGgW9i83DU+B0EE8sx6lOZmwBmgITI4Dtj7d8Dt3wU4HXD6aWDfByLnX6qoRKwrpoXg/ikXfIEYcyCtWCUqNBJxmWL/mUEcuLBwYDQ87UnP4EeDhjRAx3PY2ViMOzdVY2djcdoszKHYZC8AxwG9Ey6M8CVkqxAFoPed9B10qht6+OESjSipSW1FNypKVwF6M2WSxGq5TQG3b6xCSa4Rgw4XZl5+hDZuf4Bsa3FCUiKqqiYrpVzEdQaysWVjucr2vDHkcS7MiSa0iTURn/fjWVLJ+lp/i++9eAxDjtgtqkMON95kFrl5rcwA0P4S3WZZHqIEe1EOOgVJiRjGziyKwBDZmc+LdWgqy1X9nNE0r1yF5ciFZkiJIjDCSMQMNjPvPzOIb71IxxVEbt7tt168kLXX/ZvWMEtztFxEqVjF71bkIZahPM+cseuC6pAmKFPdVDgFAGXq5ALGDYV6NRQir8fD3ofQM+6EP9ZkK0HMefyYdvvRzEkkorr/775JJzgALp+Av3omjIJQpyciEaCg/xAEBBFff+4cwpnspG1ff+6cutbmk09SkURRIyaqb8BbHZT7d6W3Mith1PO4pqkEQIilWYJkZx4+E4w+UAHT43RumtPFP35ZarhtazMugxT8l1vfjvn8/ikXBEc/TJwfos5IDoYUYNTzqMyn83zvpDNxO3MCzcyaIy9LsOX/A+79b8CYBUH+oAAAIABJREFUB3S9CfzHLeGVp24H3aqgRCzLM8Gk5xEQRAzEGv9rSsSo0EjEZQhp8BMOaRv8aNCwjJBnNmAlI66Od08B9ZKlOX25iFIeYqdYifqSNFqXdAaggmWjpTEX0WzQ4aM76rCLP4PCyVbKNNvxUEL76GBKxGYVyzEkxUsjaOKYjeUquoFjAIAzYgOEkMs0x/7984c2YO9N78Vlzg4zvBh8ax92PfIqPvvkcRztmiA7kAJSrtc3XzgPQQS21hXImWcAiFCeuESEQsP1af4fJoeaQgu65YbmMAS4ow/wOBDgdLgkVqluZQaCJOKlUYUScayd1GTK1+GdQYDTo0usyEhmn3TdHxPzMSLa0Co24Ku+T6FVbMCIaMO4mJ+1133J0nzg4mhkskqpRJxUlqos0TxEgCZOQHBxqrA+mMGUKWz4CHD/K2EfEj/1Mvbz18EviKovtgw6aH8rdeorEfefGcTnnjyxgABcoCAMaWgWBBHD02682zWB//vSxQUKRCVEkB37SGeYUqVkIASAw6xQZceD2H92BAFBxLrq/PnnaQ3BXMS2MApSm52srIIfGGpV7ZjOKSIs3YblSyLacgxwFNAia8ep2BEpyjxErrAB4FNXaNYocxETtTPLpSrh7eYdWjNzdqJpL3DffsqpHbsI/HgvNdQr4VZPicjzHOzsc9YdKxdRWaySJufWUoZGIi5DHOmcyOzgR4OGZYjNzNJ8vGeS2sSAhLPnEsFsPymIOsVKVKd7Yly1mW7TSCICwL1X1+Fz+mcBACMtdwHWkrh/VxRFWYmoan4Na2iu8nYByM5MRGkAVbrqWtgs8205Ui7oR7ba8bm9Lai76c8BAJ/MeRN+QcQLpwfx4X8/hPd99yCeOdoDty8wL9frudM0YW4fmZuvTOtgRIJ9B2DOT///MQnYi3LQKduZwygRmZV5yFAHH/Tqks8Mkp25e9wJX04pUz2IwUw7QM5DHDHa4YM+I0pE6bo/hGLs8nwXd3r/Hk8G9uJO799jl+e7GERx1l73N9cWojDHAIfLRzm04SBnIgaViFSqskStzEBQiThO+acoW6RSFRnz1Tk8x6G+OD0Nzf1TbhjgRx2YAlMlJWI8CsL//T9n8MKpAVz2EiG074+HsPdfX8Pq/38/rv7WK/jQvx/C9w9EKG4KwchMbPV3XGjbD0x20iR50z14np2nNRXiQuxZSc27J3omg8VtEjgOqGZqRBUtze4ZIqj8piLV9pmNKGvZAQDQDZ/GTLSMWgBHupR5iOrkeEu5iL0TSiViT3zlcQkoETVkGSrWAfe/DJSvJzL4p7cBF14MPu5SLxMRAOrizUU0Kj4rvvTEeixlaCTiMkS8gxrVBj8aNCxDbKllJGK3Ihdx4MR8xZGKcA0SiThqssOoT/OpOQMNzQBQOnkSO/hz8Io6POa6NaHfHZ31wOHygeeCxI06L4omqwXObugQyMpMRPTT5GfFpuvxkW1kOd7VVBI2F1S36W6AN6DJ145X7i3BXdvsMBt4nBucxpd/3YrNf/8SPhMm12va5ZuvypFIxKa96f//JYl5mYjOsaDFRQJrZr4oUpZSSxrKCCptZuQYdfALIq1ih7M0jxKJ2MnVyr+Tbiiv514YECSEOPbzwudlC3Q8hxsYMfByJEuznv0Nfc55zcxVS1qJGKIwW6xmZmspkFtGn2WOWZtzigFrKRrS1NA8OOVCHTcEPQI0UctXp0k3nkX0sVkvPvvUCbw2SN+L2dEeXBqdg8cvgOeA6gIL1lTGt5CiWh7noe/T7dZPYsSjkwth3rdey0MMRXWBBc1luRBE4CCzfM9/AstFDFUzpQBhlt4P0VKo2j6zEfa1RCKuxmU8ezJ6czmVqrBFgBRLVeTjF9H5vHfCxezRHOB3BfMOo0EiEa1lYR/WSMQsR34VcN/vKU7H5wSevgd450f0GFOLYy5CUVaCqC2Ok0Q0WCg7F9ByEcNAIxGXIeId1CzZMHINGjIASYl4ut8BT24NkF9DFpn/uDktzcbceDsAwJmfxjxECfPKVQLpO87B/wsA+E1gN56+KCRkietgKsTaohx1mpkl2OyAwQpe9KGeG8q+TESvM1iyUL0FbUP0d7h1XUX4XFBrCbDyPQCAxr7f4J8+uAGH/3YvvvreVaguMMPpDf/+zou28LqBzjdoQ5bmIQKkRJyDBaMis5SFqhEZiXjUnZ5mZoAC6GOWq7A8xPMBUhFV5Kf/WrvUr/vBXMQIk0XZzuyS7W1LXoloDJnMlme4mVmCrRr4/Bng068BdTtp23VfBmzVspVWbRJxwOFGE8cmhqUrVStyipckryvKQWFlPQDg1toAfnH/1Xjjr2/AxX94D976yo147i92odJmRqRXxYEWB7Y3qKBMGzhBLgdeD2z/NH7fOgRBpGxmu8rFUMsFsqU5XBlTjUQiqliu4iTyQpcbv5tiKYJji2LV3DhefCeyHXx81oOOkVmsSJcScdIJ6I3BxYV4chFnJTvzQhIxIIhyy7xGImYxTHnA3c8AWz4BQAR+/zfA/r8FZtjnrPewKoeple3MMa5rHBeMHdFyERdAIxGXIbY3FGVu8KNBwzJFfXEOiqxGeP0Czg7OBNWIw2eA0+EbUFNBzgzLeCtRZzAWFSXNgMFK8vyx9vQcY6iVLFocj3eqPo6AIOJnh7ri/vW2YSkPUWUiiOeB0hbaN9ePsVkvXBGItkXB4ClADFDof341Lg7R32FVRZS/w+aP0+3pZwC/BwU5Rnz6ukb884c2Rj2UFG1x4cgf6bOQWx7My8xC5JsNsFkM6IqUi8gsxWcDNbAYdKgpTI9KbV4uYiUjEQeVJCKRwMfdpCLKRCbiUr/u724ugUHH4fLYHP1dQzGvWIWUiL1iaUb+tmlDaP5hppuZldCbaMIk5f+yydqKkvTYmQenXGjmWKGJiqUq8ZLk//TBDXj/9dsBAPX6KVzbVILa4hwYdDQt0vEcvnY7kbqRvlNfu32NOqU+h1gW4toPAPlVCiuzpkKMBMnS/HrbKITQnFcprmWyC5gLo1RMAnoPxSyY8pY3iQhTHgKFtJCtG25Fa58j7NOOdtHfo0WvTjOzBEkh1is5RBLJRYxiZ+6fdMHjF2DU86gp1Ij5rIZOD9z2KHDt5+nnwz8IEnidrwMDJ2nhJd7W7jCok5WIcYgI5IbmmaSPt1yhkYjLENEGP9LPqg1+NGhYpuA4DptrC1GNUXS3vgXYaoMPnvm1KhcyGa5JWH00KLNWZqCdk9cFbZgD6qsqAQBvkgoRa9+P9+2hielT7/RgzuOP69fbmfWkOR2rxtnc0CypJ2q2wuHyY2ialDUt0UjExhtpxd41CVx4Xt48NuuJ65DGrlfpTtNNqimC0gV7kQVdckOzgkT0ueRsufNCLZrKcsGn6Ronk4hKJeLIOSDgo/Dt0YsAgHN+UlGU5ZvS8jqUWOrX/TyzATtWFAOI0NIsKRHnRsnKDsnOvEyUiHozULRi8V6LBGWJmCgGlYixFBsJYsDhQjOvfqlKQmS6pHKa7g/73FvXVeKHH90cNtP04b3N82Ilksb0AHD2N3R/50MYdLhkguZ9GokYEVvrC5Fj1GF0xoNzg9PzH7QUAMXNdF8l14jZR5lsOYXh8/aWE3TVmwAA67lOPHU0/Pj2aNcEjPChXGTqP5WViANTbirZKmDj7riUiEzFHqZYpWOUCKAVJdasvQZqUIDjgLceXbjdNQk8fj3w+B7g0eQXvGuL6LrWMz63oIRwAbSG5ojQSMRlikiDHymUX5XBjwYNyxyb6wrwlvlhvP/ovcCb3w4+MDeqyoVMxhiRH0NiIarKw+e5qI505iKOdQBnf0v3d30BN64qQ11xDqbdfvzmeF9cu2hnduZ0lGOgjIja9UYiEbMqF1EKg6/ejAtDNDmqLrAg32yI/Du8Dth0D90//nN5c7yqnOoxVhiUxXmIEmoKIpSrjF4ARAEufQFGUZCezw1DYykNQDtGZ1mjrg0IeOk1THUBfhcEnQndYjmKrUZ17fhRsNSv+1JL88vhLM1SsQojaB1iDqZhXeJKRMVn1FarSrtpyqjeCuhMwNwIMNYuZyKSkkc9xfbglBvNnEQiqrdwlhCZLrUzzwxGbN68dV0lDn75Rjz1wA48dtcm3LKW2e4vjCxUwCWDI49TTErdtUDVVXjhNNn2ttcXLe3Pdpph0utwTSOpAl8P19Jcw8pVVLA0z7h9yBeJhMovWv4kIiqJRFzLd+J3JwfCLvwe6ZyAnRsBD4FIljAW4mRQlmeCUc8jIIiUbSqXq8RBIs5JJOLC90jKQ2zUrMxLBx94giIewoHX0+NJoqbQAo4D5rwBjM95oz9ZViJqJGIoNBJxGSN08BMulF+DBg2RsaW2EA97H4IfESZ3KV7IJIhjbQCAS0KVLLNPO9JJIr71HQAi0HIrULEOPM/hk9fUAwD+862umJMvURTRNsLszGkox5CUiI0gQjO7lIhMOVG9FReH47AyS9h0L91efk1Wx8ajytmYP4ucqTYKj15xQ0ovPROwF1nQLYZRIg5RHmKPoQEAl5Y8RAlKJaIIzLc0szzEubwVEMBnpJlZCem6/4tPbcPnr7bhF5/atmSu+3tX00T0WHeY1lU9I1TcpAjqFctg1PEothoz+RLVhVKJmC0KYIMZsJPNF11voiTXiFyTHoLIGlNVgCiKGHYo8tRUVCICCZDpeZUAOFoAcEYO7NfxHHY2FuPOTdX45vvXw2rUobXfgReV7fbJwDsHvPufdH/nZwEAzzMS8baN2f99XWwEcxHDLDqoWK4yPO1GIehabM5fqHJbdmAulav03Zj1+GV7vYRZjx9nBxyKPMRG1c5fPM+hhpVl9U4647czB/xB63oUErFJzZI+DenFho8A978S/rH7X6HHk4TZoJOzquNuaNaUiAugkYjLHMrBT9hQfg0aNETEhpoCvIDduNPzjfBPSPFCJsE9ROqay2KlHPibdkgk4lAr2TDVwlQvcOppur/7S/LmD2+1I8+sx+WxObzWFr1pb2zWiymnD5zazcwSmBKxwt8HPfzon8wSEnF2BHD0AOCAqqtwfpAmLivjIRGLGoD63QBE4OSTAOJT5XxzPbOOVm8FcrIzL08Je1FOsKF5QqFEZHmIZwKsmTmNSsTaIrJEzXkDZDdXlquwZuaxHLKmLkbxh47nsGNFMXbXWrBjxdK57tcU5mBVRR4CgriwMMEwX5XVJ5aiwmZOm2U9rZjqWRiFMd2nbkRGKqjfTbddB8FxHOpL6Jp0eVQdS/OU04dS/xBMnA+i3jI/KkQlxLWIrjMECYfp+BTyJbkmPHAdfbf/5Q8X4QuEVzDGhZNPEile2AC03IreCSdO9k6B54D3LAHSf7EhkYjHe6bgcIWMYZQkYiy7YgwMOTwo5BiBkFOc0r6WBNiiWJU4jHzM4qkjvfMePtY9CUEErrIy0k4lK7OEGjYG7p1wxq9EdI4DEGkxNMx7pDUzL3XwIbepQ5pr9YzHIBGl7GLPdPTnXYHQSEQNGjRoiACLUYe1VfmKLek5ZXqGSL00ZqrNmPURRSvIhul3q9s4feh7ZM+q3w3Yt8mbrSY97tpmBwD85GBX1F20MxVibVEOLMY0/D3yawCDFXrRjzpuGH3ZQiJKVubSlYA5HxeZnTkuEhEIFqyc+IVsz4ulylnnPEobsriVWYmaQkuwWMU5DrhImSY1Mx9x0uQ7nUpEo56XFcMdIyHlKiNEIvbp6wEg40rEpQ5JjfhSaC6iYf7iSu9SbmZ+dD1FYfz8T4PbvHPqRmSkgpBcxIYSmnirlYs44HDJVmautIXKrtKAuBbRJUvz9MDCxyLg/t0rUJJrRNe4E08f7Y39C+EgCMDhH9L9HQ8BvA4vtJKya8eKYpTmpT9HdamjpjAHTWW5CAgiDraHFKiUryNbvmtyYQFXghiemkE+x4iGJbDQljIshRTTAWCjvhsne6dwXpE7ebRzAgCw2crUuyqTiHZWiNY74QoqER19gBAlTkEqVckpWRALIYqiRiIuVVhLySpftRG47Tt0m1tG21OENIbrjkUiGjU7cyRoJKIGDRo0RMFVtYUYF/Mxoy+iC9iuLwQfVGllip+gTESXLYPB+jxP/x+AylXUaJyeHQWO/Rfd3/3FBQ9/fGc9eA442DEmtw6Hg5yHmK4BH8/LFroWri977MxSflP1VrJ0s7/D6sr8KL+kwOrbiRh29ACdr8mbI6pyVpeQ/RlYMiSivTAHc7BgFAW0YeISKU1kJaIdVqMO1QXpzROTbFHzylWGWoFhamZuRw2AzDQzLydIuYhvXByF169QeRnmE4a9Yimq0vwepw1pzHpSBdVbqOhFykVkk61OlRqaB5R5iCXqWpkTRhIkYq5Jj7/cS8Udj73cHndZ2Dy0/4HOXWabnGcbbGWuSnx/Vyiub4lgadYbg4s7KVqapyeIoBLA0/t1JYBZmt9fQYrwp48E1dFHGIm4gh+iDWqTiEWKhua8SoA30MJ0hAIkAIpSlYXZjKOzHky7/eA5yBmvGpYIbNXA588ADxwAtt5Ht58/Q9tThKxEjGVnlpSImp15ATQSUYMGDRqiYEtdIYZQjI/n/4QuYDd9DVj5Pnrw3Z+kfoCAHzmzNEDjS1pS3188kOx0+YoLceuvUrfTHf4B4HcBVZuBFXsWPGwvysGt68iK+pODnRF3IykRm9OoJkMZ5SI2c/3ZU6wiTXZqtqBv0oVZjx8GHRf/wNdgATZ8mO4rClaACKqcvqNEhFuKgKpNKv5H0oca1t7YKTA14kQnMDMEuCYgcDp0iNVoKs8Dl+aMOSmgvWN0FihpIdLFOwOMEJnZ6iUiQMrd0RAfNtYUoCTXhBmPH0e7JoIPhLEzL1klYhqznlSBwQzUMBV515toYEVCapGIgw4XmtLQzJwUYjQ0R8Jd22pRV5yDsVkP/iPKtSwiDn2fbrd8AjDlomtsDmf6p6HjOfkaqSE2JEvz622jC1tWq1m5Sl9q5SozjER06/Oyo/woE2DlKrusRGz/5kQ/XN4A3L4ATvaR+r/IzVS4xY2qHlpqaO6dcNLf20YLclHHpXORSURJhWgvysmc00eDetCbgpmbHEc/q4DaYtbQPBHjuqYVq0SERiJq0KBBQxRsqSsEAJwecsHpY3aKG75Kt2d/Kxc6JI2pbuhFH9yiAfmVDantK15IdrpTTwW3Ocfm2+n63gV8cSr0+o8DP7kVeOdH9PN1X4oYtH3ftfR//O3JfozPesI+J+1KREBuBG3h+zAy41G1eTQpCIKiVGWLrNRsLM2FQZfApfqqj9HthecB50T053a8TLdNe5fM5Mhi1KEk14QuQdHQzKzMk+ZaeGBESwYsS0El4hyg0wNla4IPGnLQOkvq0SVLdC0SeJ7DjauIGHjpnMLSrJ9PIvaKZahcqkrEeVA/60kVKHIR69lkq2tMncUWUiKyDEIVm5mTgqRocSRGIhr1PL54MxGgj79xOeK1LCwGTwNdbwKcDtj+aQBBFeK1TSUoWsplQRnG9oYiWAw6jMx4cG4wxBkiNzSnpkR0O8gq7TMVprSfJQWmRCydOQ97kQUzbj9ebB3E6T4HvH4BtVYBeicj7orUJRFrZSUiG3/GU64i2ZnDlKpcYlmuWqmKBiXqihK0M3sju6euVGTZqEWDBg0asgtVBRZU5JsREESc7nPQxop1wNr30/3X/jG1A4yTlblTrER9SRqVd0pEs9NJ+PFe4FvVwA+uAX77IBGEPYcpuysUp54Geg4BvjlqPm55T8TdbqkrxIYaG7x+AU++E35luX1EIhHT+PdQkIiiCAxOudN3rHgw3k6qQL0FKFuLCywPMa5mZiWqNgEV66lx9PQvoz9XJhGXhpVZAuUiKspVGIl4WVcPIL15iBLmKREBedIFALDZMThN7cJaJmLi2Mssza9cGA6qixYoEUtQtZT/tmnMelIFilxEyc48NO2G05uEdTcEQ1NzaOKYfXixSURZiRi/nVnCbesrsa46H7MeP753oCP+Xzz8A7pd+35ZZSW3Mm/QClUSgUmvwzWNVKSxoIypejPdDp0G/AmQvCHwzxKJKFiugDxECUyJyE1cwseuov/3j964hJ8d6gIA3FrJCJWcEsBSoOqh7UV0rh+d8cDtC8RXrjLL3vsw589LWh6ihjCQyOqRGQ9c3igiArlYRVMihkIjETVo0KAhBiQ14rHuyeDGPX9LTXAXnicLcLIYawcAXBIr5aDftCOane6qjxGplFMCiAGyZ556Evj93wA/uYWIxe9tB56+F9j/VcpAbP1V8PfXfRAYOhXResJxHD61i9SIPzvcvUABOD7rwcScFxyX5kEfa2hu4IaooXmxcxEltUTVJkCnxwWmRFwVbx6iEldJBSs/j9xMOTsCDJ6i+403Jn6MRQQ1NEt25styHuJpH03Im9PYzCyhkVk8R2c81Awq5W8BCIgi5tigVCMRE8fu5hIY9Tx6J1zygoKyWGUC+XDBvLTzJtOY9aQKFLmIBc4uFOYYAKijRvSNd8PCeRHgDXKBw6JBzkRMTIkIkGr2K7dSLMa+w91kv4yG/uNUYiYt7ux8CADQPjyDC0MzMOg43LJGszInCtnSHEoiFjZQVEfAm5pjxEUFIroroZlZgrUYsFERXlOAimnahmdlsnuqj8oA1c5DBACbxYA8Ey1y9006U1YiSnbmRo1E1KBAQY4BeWb6nPVGizSSlYgaiRgKjUTUoEGDhhjYzEjE40oSsXQlsJ7lzx34VtL79o5cBABcFitRV7wYoc8hdrpt9wMf/TXw1x3AX50D7noKuP7LQMutQG4FABEYu0jk6eHvA8/9pTzIBgAc+IeYDaPvWVeJ8nwTRmc8eIENSiVIZSI1hZb0NDNLsNkBYy4MkBqaFzkXUcptqt4CALKdOe5mZiU2fJiaKYfPRCa4OxiJXLkxbI5QNsOuVCKOX5JJxEOztC0TSsQ8swEV+WZUYxQD5w/Ns9tyjj6s5Tqxw9yDHGfiCqcrHTlGPa5l6qKXpZZmnYEWbQD0CEQaVBUscYI2TVlPqiA0F7FEvVxEi4NUex5bI0UBLCaUxSqRFlyiYFdzCXY1lcAXEPGvf7wY/cmnngZ636HFudqd8rn+OXYNvK65FDZG1mqIH3tW0vXrWM8kLehI4Dj5b5yspdkXEKD3UAagIb8kpde55MDU9W+9uXDBucJHpHufTv0SII7jUCNZmidccSoR4yARNTuzBgU4jouvoVnLRIwIjUTUoEGDhhjYXEt2jXc6x/HsiX4cujSOgCASucbpgPY/Ar1Hktq3b4gmHiPGWuSaMjihimWn4zhSxax6L2VA3vMM8KWLwBcvAvf8Elh9J4AI5RUxGkaNeh4f31kPAPiPg53zAtE7WKlKSzqtzAD9/5QNzZOLrUQMkogefwCX2WQ9YTszAFgKqakZIDViOMhW5j9JfP+LDFIiMhLRNQGMkiriTKAWeSZ9xnIIG8useMv8MFb/7nbgfz4jb+f8Trxg+l94Gl+JSqZriAzJ0vyylIvIcUSMA5gRLbAYdLBZNMIlrVDmIjISsWs8NRIxIIgocVERCbfYpSoAkMdIkIAHcI5Hf24EfPlWUrU/e2oAZwcc8x+USswGTgJnfh3cvvK9wMAJiJPdwVbmjZqVORnYi3LQUJKDgCDisZfbguMzQJGLmFy5yuiMB4WgMYk5L0uiBjIEoYJIxLV814LHGngivn/XmxP8W6sIeyEtyvVMOINq5WhKxDmmQs2d/x7NuH0YmqaoGs3OrCEUdUV0XeuOdl3TlIgRoZGIGjRo0BADfYxgmvUE8PAzJ3H3E4ex65FXsX8wB9h0Nz3pwDeT2rdu8hIAwFugbjh1TCRrp8urAFpuAf7sZ8CnXwv/nDgaRu/ZXguTnsfZgWkc6QwWgEhKxKYMWFLlXESuD32LaWf2uWQ1HWq24tLIHAKCiHyzPvl2382sYKX1V4A3ZJVVCACXXqX7SywPESCVqhNmjHMs6F4U4DXYMIQiNJXnpr2ZWUJTaS4e9j6EAOYrZqWjB6CLSqZriIy9q0lddKJ3CmMhpRU2bg6VBeaMvc9XLJS5iEwZdHk0NRJxbNaDRlCpiqlyTYxnZwB6I2BlSuwkLM0AsL7Ghts3VkEUgX/eH6JGlErMHr+eysskvPR3wON7wD22AZdH52DU87hp9UIVlYbY2H9mEMPTdI74yVtdwfHZmcFgQ3OSSsShaTeKOCIROesVZGcGcJFfAQBYxy1sH2/giEQ85SqZN35TC/YiRUOzpEScGYycbRlBiSiVqpTmmbRFJw0LMO9zFglyJqJWrBIKjUTUoEGDhijYf2YQf/nUQkvokMONB/cdx+sVnwB4A3D5NaDrrcR27pqC2UMTC11pc+ovNlGoZqdLvGG00GrEBzZTht1P3goOUtszpUQEZBKxme+TieJFweBpQPDTZNZmV5Sq5CdPlNRfR4NvzzRw/nfzHxs4SQo+ky1oWVxCsBfSwK9TCE4Yhi2NALjMfG4YGsty8aywC9+s+l7Yx3/Q9KOYZLqG8Ki0WbCuOh9V4ihOHD5An9kATSBXcIO4xtJHCq8I2asaVEDNVpaLOIr1Zpqkp6pE7J9yoZknso4vW+RSFQlKS3OS+NLNLdDzHF5vG8XblxRkYbQSM16P55u+DgC4YWUp8swayZEo9p8ZxIP7jsMZUowgjc9emWbv7XgH4JoMs4foGHa4UQCmQMq5gopVAHQbKe+wkRuABcriOREN3BAAoEuswMiM+qV0khKxd9IJWEtYJq4IOPoWPtnvDb631vnRLJKVWWtm1hAOsp05GomoKREjQiMRNWjQoCECAoKIrz93DuHMGtK2r7w6DeEqpvo68M3EcpVYM/OwWIDKsqWVSwcg5YbR+66tBwD88dwwelgmiTToy0Q5BsooFL+Z619cO7PCygyOk/MQV1WmQIjxPHDVR+n+8RBLc8dLdNu4Z/EzyZJAVYEFHAdcCgRaqZ3rAAAgAElEQVRLCIYCVEDTkoz9O0lIE5PgKjYNqQSmRSzOXeKZfYuMvavK8Zb5YfzJwT8jJZcoAACscOMfRj4bM3tVQ4rQmwD7dgBAi4tKmFLNRBycdKGJY4q/xW5mliA3NCenRASAumIr7rm6FgDwyO8vBCM6opSYife/jH8eIMvobRvUz5Zb7ohnfPa//zgIsYgUdcmoEZVKRFxJxSoAbKV2DImF0HEiVnPBxZoizMDG0TWvS6xAWZ761zm7MhOR44AC+m5hsmvhkyUrM6+nKBcFOrRmZg1RUMc+Zz1aJmJS0EhEDRo0aIiAI50TGHREXmUVAQw63DhR9ynK6+p+ixSJ8UJqZhaqMtfMrCZSbBhtLs/DdS2lEEXgp293YWLOi7FZL4AMhWCXSg3NgxibnoU/IKT/mOEgTW5qKAT+QiqlKkpsugcAB3QfpAISCXIe4tKzMgOUqVmZbw7mIgLg3WSpaskE+cwgTUzOOowQrUEyvcvYjBHRhrwirWk1Fdy0uhwPex+CXwyxi0vi3BjZqxpUAMtFLJs4CgCYmPPOL69IENMjXcjl3GT1l8idxYZ0vUpBiQgAf3FjM3KMOpzqc+D3Z4aiPJM+wB0js+iZcMJi0Mn2fQ3xI97x2Vj+OtrQfzzhYwxNu1GAK5NE3N5QhA4dxeys54NuEcnK3C+WoMiWj+0N6is0ayUSUSq8i1auIlmZrWW0eKrApVGNRNQQGRJZ3TfpipztKSkRAx5SvWqQoZGIGjRo0BAB8do0+oRCYOsn6YdE1IjjRCIuXjOzCkjREi2pEZ852oOnjtAAsSTXCLMhjc3MEmw1EI25MHIB2MVBOYA745CbmSm/SVYipkoi2mqApr10/8Q+unVOBEnLxr2p7X+xMNWD3bn9CCiKfRr9l7CW68QaXM6YxbU0z4Q8kx4DYjHa7zkkk+kPWf4Fuzzfha2iPiOvY7liXXU+DufuxZ3eb4R/QhzZqxpSBMtF1Pe8hbJcIwCgKwU1ojBMJUiTZjvlEWYDJDuzI3klIkDngwd2EzH67T9chE9alLKWBieiZWuAqk1Abhn2d9Hje1eXIce49BThi414x2dDeWvpTl/i5SrDDoUS0XJl2Zl1PIeKlaREVuYirmClKp1CBb52+xroePWzaWtYZMmM2w+H0wcUMhIxXLlKhFIVALikKRE1REFVgQUGHQdvQIg8/jcpxuGapXkeNBJRgwYNGiIgXptGWZ4Z2PUFQG8B+o4C7S/F9XuBEQphvyxWoX4pKhFVwPUtpajIN2POG8C3/9AGABib9QaD0dMJjpMbQlu4RcpFnBsLrq5XXYUpp1cezLSUq2DNlaz2J58EAn4qVBEFmszGqRjNOjy6Ho+M/wW+anhK3mTDHF4w/S8U/+LmjFlcOY5DI5uctI/7ZDJ9cNoDLwwZa4leruA4Tm5pJjC7uKgVqmQM1VvkXMRdBaT2TcXSbJyihbPZ/CZVXp4qUMHOLOGB61ag2GpE59gcnjnaSxtt1cCKPXR//Yfw/9q78/i26jvf/68jyfsi77HjJXEWAiEbIYQtLSkEJpdboKW/0mkf3OkC3MIwM2WgHQbmtiHN/FrSQktn7pQZuJd2WqYtDHQyDXSgJWwlQAMEQkx2x4kTL3HifZNlS+f+cXRkObblTbIk+/18PHjI0jmWvyHfyNLnfBZuewXza3v51QGrj59KmSdn3GW0pYG+v3XvTqzdDNDU3h0s3Z1tmYgAi1ZamcirkgaDd3Y/xPJzVrBxWXQmiqclOynItC5In2jtGV8m4llDVbwD/mCvOwURZSROhxEMWI86odmZZFWagYarnEVBRBGRUaytzKPEncpoH1kNoMSdapVzZM2BtbdaB8aZjTjQZH2gakgqJyc9TrIyptmLHzWOeAXQbowe9UBiYYz7ItrZEQXnQFpOsJS5LDctMo32l1xrffjparTKmI8E+nMlaCkzADc+js+IjxJXu+zeLpvq8Q4Eyz2LFUScsg3nFdFsZtNMDubclXyb29hrVjKQVjju3qsyBSF9ET+WbGURTiWImN1ptVXw5Z8z9bVFSgQGq9gyU1z85ZVWgPRHOw7T4x2w3gvY2d/ll4BhsLu+l7q2XjJTXKxfon08GWO9PwPr/dnS1ZdZw+96mkcOQoXR224NyTExIC1nCqtNUCVWz85FnOSpL6/iR3+6is8tsAZczVu8Iqo/ujwvMFylpSd8JmIwiDi0JcCx5m58fpOsFBdFWZMdGigz3fgmNGu4ykgURBQRGYXTYbDpuqUAI75RNWFoOcfld0FSBjR8AAeeD//kfh9J7UcBGMhdGLlFJxC7MfpI7BDs5u37Ru9VEglFMZ7QbH+4jHQps82VDCs+Z3395j/A3n+3vk7kIOKKm3h53S9HPjbNJa52hoPdwL0x0KMrM8WlaasRcNnCAtqTirjU8yPevfpZnvB8ghu8W/D8xZ7EzaRNNIG+iCv69wJTCyLO8VpBgOSSpVNfV6SEBhEnmKk2ki9cPI/yvDROd/bxxBs1VnuFzgbrAsfcCwB47kMrYHn10jnT07pjBhrr/RkE3p8lp0FxIDt9AiXNpmnS32kFEf0pOeCYhX9P2XMhoxDD9HFxRiM3rCol3xPIsM2L7vvW8kCGWG1Lz+BglREzEQPlzKNMZl5QlIlhKHtdRmYPVzkedrhK4P24hqsMoSCiiEgYG5eV8OjNq0fMKkpNcrCqPGQaXEYBXHK79fUr3wF/mEEdbcdx+PvpM5NIK5wf2UUniPE2Rt9V0xK9RQSGq5xjnKSuLcybiGgJTmZeDURwqEoou6T5+E7w91sfZisuidzzx8CcbOvfoz0JOVYlrnYQ0c5EtIOIykKMjNQkJ+sWF+AliZ+/bfW6zEpNIjMjQXvIJqJAX8SyjvcAk2OjlX2NwdvvY77fCkBklZ8fqdVNXVYgiDjQC72tU366ZJeDr19jtcn459eO0nXkTetAyUpITsfvN/ntXivD/pMrolMOOluEe38Gg78nKLWGlk1kuEqHZ4C0gXYAjIzZ1Q8xyDCC2Yg0vG+9p22xLn6TH+Ugop2JGFrO3NM8PJAzSjlzcDLzdAzpk4RlD7U8Hi4TMTnwftyrcuZQCiKKiIxh47IS3rj3Sn552yX86E9X8eQta1lRmo2n38+9z36IGZq9cOlfQEo2NH0E+7aN/qRnjgBw1CxmXkEEA0YJZLyN0cd73qQEJzQ30tjaEb2fMxK/P2Qys52JaK1hSXF2ZH5GW601VS5Qtg2A4YCm/VD//rQNIYm0wuIymkw3Vf5KHkq6g71mJd7UgmkvcV1YaAWzqk934febwaC4+iFGzobA5Nrn91rZWzlpSdHNTpahAn0RU/paWGTUUXO6e+jvvHE63XiCHKMbn2ngLjtv7G+YLkmpkF5gfR2BvogA162Yy/lzs+nqG2D/rt9ZD5ZfDMA7x1o41dFHdqqLjy1WKfNUnf3+7Je3XcJnVltZyg/+1wFrrwZ+vwYv2o3DqY7BoSqOWdgPMSgYRNxj/fsY8FgXIu3AXpTYmYgnWnqtUvJUt3Xg7PcsXU3W7VmDVY5oqIqMgz0JvDZsJmJgDykTcQgFEUVExsHpMLh0YT43rCpl3eJCfvC5VSS7HLx26PRgA3WA9Dy49E7r61cfBL9v5CecCZOZp2hCg2uixV2GLymTJMMHLTVjnx9JLUfB0241bZ6zDL/fjHw58yPL4bH1cHr/4GM+Lzx2hfX4NA0hibSi0gWs7/9Hrvdu4X93fowbvFto++ruaS9xrchLJ8lp4On3U9fWG+zvWZytIGKk2cNuT7T2Ts/gJbGE9EW81LGfzr4Bmru9E36a9lqrHLreUYKRlBbRJU5ZBPsiAjgcBvdutC5QZTQFst8CQcTnPrT27Z+cX0yySx/DIiH0/dmlC/O5+5olJLsc/LGmhVcPnQ62C6FhD/j6x/Wcje0ecoxA0GBWBxFXWbcNe6DZuvhNbiU4oztRPNirrjUQ3BltuEq3HUQcJRNRQUQJoyI/pGx+NMnqiTgS/fYSEZmERUVZfCNQsrTluX1Dm/Jecgek5sCZg7D3mZGf4Iw1iXg2T2ae0OCaaDEMBgJN/t2dR/BPZ4aTnRVRshKcSdS19dLt9ZHsdFBZEKHA8o2PW1kDI5nmISSR5HQYFORkY3fDyk1PpjAnQtmbE+ByOpifP5iN2NBu9dVUJmJkvFDVwN8+u3fY49M2eEks8z8OwPqUg8Dk+iL2N1r9b0+lVERuXZESwQnNto8tLuCqBWksIRD0KL+YAZ+f/wrs2U+u1FTmaCnNSePPLrGCTt974SD+3Eork23AA6c+GtdzNHZ4yCNQvpg+S8uZYTAT8dQ+aAr0sM6P/nR1O0PsZGuv9b5stOEqXcODiH6/ydEzCiLK2Ox91t7bT3vPKBcYlIk4IgURRUQm6SvrKlkzL5dur4+/eebDwQBUqhsu/yvr69ceBN/AsO/1n7YyEav9c4NXwmabcI3R7ftDBtdESVKxtYaFnKCpsy+qP2sIu8l7oNTK7oe4sCiTJGeEfj2vuMkaNjKSaR5CEmlluYOBujnZqcSqwjV0uMpgT8Q4y7RKQPbgpZH+Wqdt8JJYAn0RL+IjwJxUENHRbF04a8+Iw0FidgZze+SCiIZh8Hcre3AaJif8hTx1sJ8f/P4QZ7q85KS5uGzhLM5umwZ3fmIRWSku9jd08JsPG0P6Io6vpNnKRFQQkZwK66K4vx/2P2c9FuV+iGBdiHM6DLwDfk539Y2cidjfC32BNjQhrUzq2nrx9PtJdjooz9XvYhlderKLwsD07uMto/xeC2YiqidiKAURRUQmyekweOizK0lLcvLW0WZ+/nbIm5u1X7X6LLUchT3DJ8n6z1hBxHpXKYWZKdO15LgzWmP0Yncqj968mo3Lot943mFPaJ7u4SrByczWhxu7H2LESpmHcZx1m7heqGrg/dr24P0DjZ0xK3EdHK7SHeyJWOyevf+mIyUuBi+JpXQ1uNLI9rezyKjj2CSCiJntVilkX+7iSK9u6iJczmxb0FsFwHvmYu59di8/frUagD6fyY79pyL6s2So3IxkvnrFAgAe/v1BfCXW8LLxDlcZkomYNouDiIYBcwMlzbWBIUHTkInocjqCGf0nWnogd751IDQT0c5CdKYM9kwEjgQGnVUWZOCK1AVZmbGCfRFHK2nWdOYR6V+WiMgUzC/I4G//mxWEevC/Dgx+uErJhHV3WV+/9j0YCOkh5WnH1WO9+fHlLcYwYjNZNl6M1Bj9jXuvnJYAIhAcOrLYqONka+/0/Mx+DzQGyjQDQcSoTGYG6wp9ZhHMXQmf/KF1m1k07UNIIuWFqgbueHI3vf1D+43GqsR1YWD6Y3VoJmK2sh+mKi4GL4klpC/iJY79k8pEzO89BoARuGgTV6JQzgxwZt9rALzrXzLk8V6vT+X40+Ar6yopzErhREsvr3QFyuhPji8T8VS7h9xgJuIszxq1S5pt05CJCCHDVVp7rIxIGJqJGFrKHPI+ulr9EGUC5gWCiMdHG66inogjUhBRRGSK/scl87hsYT69/T6+/u97Bsvr1txivblpr4X3fz74DYHJzKfMHIoKCmKw4vhzdmP0aJcwD1E0OKG5vnmaJjQ37rXKg9Lzg1fYD0R6qIrNXQp3VcFtr8Car1i3d1VN+xCSSIjHElf7g8r+xo7gwAn1RJy6uBi8JIPmfwyASxz7Jh5E7D5Dtr8NgIzSOJrMbItCJqJvYIC0pvcBeM9/zojnqBw/utKTXXztKivz9cEPA32GzxyyBpqNobHDQ64Gq1js4Sq2achEBCjPsy7G1Tb3hpQz14I9Hb47/GTmhQoiyjgEh6uMFkRUT8QRKYgoIjJFDofB9/6/FWSmuHj3eCv/942j1oHkdPjYPdbXrz9kZZ/B4GTmWdwPMa5kl9LnzCDJ8OE5dWh6fmawlHkNGAZ9A77gB/Nzi6MwIMSVMnil3jCs+wkoHktcFxRaH047PVbv0xSXg5z0pGn7+TNVXAxekkGBvoiXOPZzrLlrYkOoTlsDWU74CykuiMOATGgmohmZoF7V+2+TQS+dZhoHzfJhx1WOPz0+d1E5lQUZHOlJoz1lLmBC/ftjft+pDg+5GqxiCc1EdLgga3qqREbMROzrgN5W6+uuQEuAUSYzLyyM0IA6mdHGLGdWJuKIFEQUEYmAstx0/td/tzIsHvrdIQ6fCrz5XP1F6wNKZz2891PrsUA/xKNmSXCyq8SQYdCVZZXnJDVPVxBx6FCVI01d+Pwm7rQk5mQnZoBvOsRjiWt6sovSnMHy5RJ36qxvURAJ8TJ4SQJKV2O60igwOigbOMGpCfwb8wYmMx82S+MzS9fOROzvAU9bZJ7z5B8BeN+/CH+Yj1sqx4+uJKeDe66xMkF3egLZbGOUNHsH/Jzp8ioTsa3WCrh6Ohh81TWhYY/1eFttVH98eSC4c6Klx7oon1EUWFegpNkuZw5pzWKaZrAnosqZZTzm5Y+3J6IGq4RSEFFEJEI+d1E565cU4h3w8/V/38OAzw9JqfDxr1sn/OFh8PZY5TRYQcR5ykSMCwP5Vs+qrM4j0/MD7Q8xpVaz94Mh/RAVgBpdvJa4hpZNnT0kSCYvHgYvSYArBSO0L+Lp8Zc099RZQcTjjnKyUuMwSzcpbXB4RoRKmud27AFgtxl+kIzK8aPv2mUlLC91896ANWhlrOEqTZ0eHPhxE9jjs3WwyiPL4bH18Ph6gg1D/D547Arr8UeWR/XH2+XMwV7VuYEgcOtZQcSQTMTmbi9tPf0YxmC/YpFwKvKsZI769l76BnzDT7AzERVEHEJBRBGRCDEMgwdvXEF2qos9J9v559esSYysutkqxehugnf+D2YgE7HanKtMxDiRVGJlPM3pq8GMUDnbqLqbobXG+vqsoSrRm8w8M8RriWtlweDFAJfDoT5nERTzwUsyKNAX8VLHR9Q0jz+IaDYdAKAlfUFUlhURwZLmyAQRC1o/AOC9s4aq2FSOP30cDoN7N57LB36rl5/vxDthy9ZPdXhw04XDCJyTljsdy4w/Nz5ulS+PxOGyjkeRnYnY0N5Lv88f0hfRDiLa5cxFwe+xS5nLctNITXJGdX0yMxRkJpOe7MQ0oW6k4YopKmceiYKIIiIRVOxO5YHrzwfgRzsOs6++A1zJcMW91gk7H4FmK9stzeGjOFtZCPEgq9y6or7QPBkcjhE19YEsiPxFwQ8ng0HEKPRDnEHiscT1haoG/mP34FTXN46cYd3WlzV5NYJiOnhJBoX0RaxpGv8HqtQ268JZt3t6prpOijuCE5o7GzHajmPi4AP/wrh5rZrN1i0uIHvBGgZMB86eJmg/Oeq5je195NmTmVPd4BwlkDbTrbgJbt0x8rFbd1jHo6gwM4XUJAd+E+rbegf7ItqZiN2nrduQIGK1XcqsLEQZJ8Mwgn0Rj49U0pyswSojURBRRCTCPn1BKdcsnUO/z+Tupz/AO+CHFX8KeQuhpxnD3w/ANWkHcOgDRFxIKrb6Wc43Gqk7M/bkxikJljJfGHzoYKM1FXqJMhHHFE8lri9UNXDHk7vpCAxVsTW2e7jjyd0KJMrMUnohA45U8o3OYJ/DMfW2ktZnfdj354+clRcX7L6I7REIIp6w+iEac87n+zevi4vXKoG7/9tKDphWIOrkR2+Mel7jkKEqs7Qf4jCOs26jzzAMyuzhKi29g+XMwzIRB8uZ7UxE9UOUiQgOVxlpQnNK4OK+MhGHmKWXVkREoscwDP7/Ty/nnWMtHGjs5B9fPsw9a9Ng5efhlb8PnneVbyfUfwCY1htV+yqrTL/sUrqNdDLooe3kASi/PHo/yx6qUmoNVWnr8XKqow9QEHG8Ni4r4eqlxeyqaaGp00NRllUWOJ1ZPT6/yebt+xipKM7EyjbavH0fVy8tVraRzAyuZDqLLiS3cScFZ3YBnx37e05bPYDrzTzy8+I4IGMHESNRzlxrBREpXxsXr1ViWV7m5g+5K6D9GHv/uIOyyz8/4nmnOjwaqmLLKLQy/bJLYfWfwe6fWdm6IcNMoqk8N40jTV3WhOZ8O4gYGOgywmAVBRFlMuz+9MdHDCKGlDObJqhvOaAgoohIVBRmpfD3n1rOnb/YzY9freaeN4e/Wc3yt1kNqm0PRDkDTkZnGDSlzKfSsw9vw0dAlIKIpgl171lflw3th1iWm0Zmin4tj5dd4horu2paaGgffbKqCTS0e9hV0xLTdYpEkmPBx6BxJ4t7reFhLucYmUlnDgJwxF9KScgU87iTHcFy5hNvW7cVlwCxf62SQedeuB5e/g15bXt5+2gzlywY/vfS2O4h1y5nnq1DVWzuUrirCpzJVvDkwi+DzwuulGn58XZfxNqWHlgUEkTs67SmqcOQTMRqBRFlEirywkxotsuZTb+155LVyx5UziwiEjX/fUUJn1xRgs9v8p20uzHPalAdvJY1DQ2qZWydWVbTdVfzwej9kJaj0NtqvSGfswyAAw1WKbP6ISaWps7RA4iTOU8kEWSdsx6AtcY+6kdqQn+209br6WGzjLk5cdwDOFKZiP290GBNZiYwzVriR+G51gXC5UYN3/vtRyMOUrPKmZWJGORKGcy+MoxpCyAClAfLmXvAXQ6GAwY80FhlnZCUEcwU6+4boD5wYU+TmWUiKgJDLmtbRhgYlpxB8BOb+iIGKYgoIhJFW25YRkFmCo+1ruEn5/2fkU+ahgbVMrb+/HMAyOo4Er0fYmchFq8IvhE/eEqTmRNRUdb4AiLjPU8kETjKLsRDMvlGJ41HPxjzfPO0NZn5sFnKXHc8ZyKWWbcddWEn946pbjf4ByCzeHCarMSPgnPwJ2eSbvTRW1fFix+dGnaKVc5s90Sc5ZmIMVaeZ71mnGjtBWfSYMbwyV3WbeZgKbM9VKUgM5mc9ORpXacktnkhmYjDLiwYxmA2ovoiBimIKCISRbkZyXz3Rmvy77OBCa5+0xhy+2b1mdgsToZIKram/hZ6jkXvhwRLmdcEH7LLmdUPMbGsrcyjxJ06bPKqzQBK3Fb/M5EZw5XM0TQri9p/9PUxTzebAkFEf+mwASNxJTsw5MTbBX0dk3+ewFAVKi5W76x45HDgKF0NwEpHNd9/8QADPn/wsGmaVjlzMBNRr9+xZA9WOWmXmdqB+RN2EHH4UBVlIcpEzc1Jw2GAp9/P6c6+4SfYfRH7Oqd3YXFMQUQRkSi7eukcLqnMp9nMpsl0s9es5P7+W9hrVtJkurnn+XpNcY0DWRVWsHeurx5zYIQ3EZFw1mRmv9/kUKMyEROR02Gw6Tor8Hx2qMC+v+m6pRqgIDPO6fyLAMhq/GP4E/s6cXScBKAlbT6pSc5oL23ykjMgNcf6eiolzXYQsfziqa9JoiMw1OzipKNUn+7m2d0ng4fae/vpG/CTp56IcaEiMPCiudtLd9/A4ABC+71UZlHwXA1VkclKdjmYG+jZezxcX0RlIgYpiCgiEmU+v0lNcxeN5LOu7x+4wbuFX/iu4gbvFtb1/QON5LN5+z58/imUUMmUlZQtoMNMI8nw0VV/IPI/YKAPGj+0vg4EEevaeun2+kh2OphfoGbNiWbjshIevXn1sAyrYncqj968mo3LSmK0MpHoGQhMry/v2B2+9PeMNZm5ycwhM7do9PPixVSHq5hmSBDxksisSSIv8Pv3iswTAPzw94fx9PsAqx8iQKFTPRHjQXZqEu60JABOtvZCbiATsavRus0YfF2xy5kVRJTJGNeEZvVEDNIYSBGRKNtV08KpDiuzzUtSyBEjeF9TXGMvNdnFIaOcFRyivbYKMs+P7A84VWVNNUzLhbwFAOwPDFVZVJRJ0lhTTiUubVxWwtVLi9lV00JTp4eiLKuEWRmIMlNlLVhL75vJZPvb4fQBKDpv5BNP25OZ51ISz6XMNncpNH0E7ZMMIp45bA3OcqVC8fLIrk0iJ9BOJLe7moXZJtUdHv71zWN89YqFNAYGc+Q7usCPgohxoDwvjfa6fk609LDk7D6jI5QzK4gok1GRl8FOmsNPaFYmYpA+sYiIRJmmuCaOU6nzAfA27I/8k58M9EMsvTDYK+ugSplnBKfD4NKF+dywqpRLF+YrgCgz2vziXN71W4Oo+qtfG/3E0KEqOXE8VMU21QnNdhZi6YXg0mCHuJVVDNllGKafb662LvD++NVq2nv7ORXIRMxBg1XiRXBCc2vPYCaiLTBYpd/nD2aQKYgok1FhD1dpHmFCc0rgPbp6IgYpiCgiEmWa4po42jMXAeBqPhj5J6+z+yGGDFU5paEqIpJYCjNTeM+whqv0HQ4XRLReRw+bZYmRiTjVcuYTb1u35Wsjsx6JnsBwlY9nnOCcOZm09/bzz69V09DuwYGfDL/KmeNFeSC4c6Kld/jE80Am4vHmbgb8JpkpLoqzE+C1RuJOsJxZmYjjoiCiiEiUaYpr4ujPXwJAZseRyD+53Qg8ZDLzQU1mFpEEYxgGJ3OsvnLJdW+B3z/yiYFMxCOzJhMxMDFW/RDjX+D3sKP+Xb7xJ+cC8MQbR3nt4Gmy6cZBYE+n5cZqhRJQnmu9dtS29EBWCThC2gIFgoiDk5kzMDQVXSahIhisDtcTUZmINgURRUSiTFNcE4er2Ortldd3AsPnjdwT97RAS7X19VwrA8LT76PmjFU2cV5JduR+lohIlPXPWUmPmUJyX2swWDiEtwdajwNw2F/K3JwEyA6aSiZiT0twkIwyERNAYLgKdbvZcF4RCwsz6Bswef9EG7mGFZDqJJ0X9p+J4SIFoCwQ3DnZ2gMOB+SUDx7sagJCg4gqZZbJsSeBn+ny0tU3MPRgsgarnE1BRBGRaaAprokhb848Osw0nPhJ6Z5kSdtI6ndbt7mVkGGVRx1p6sLnN8lJT6IoKyVyP0tEJMoqCnOCfRE59sbwE5oPAyYtZibNZFPiToRMRDuIOIlMRLsfYsE56qOXCEpWgeGAjjpefXcP1acH+6DlBvohtvgzucyUVqwAACAASURBVOPJ3bxQ1RCrVQohPRFbejBNc2hJc/UOICSIqH6IMknZqUnkpltZrrVnT2i2eyJ6lYlo03RmEZFpoimu8a8sL4PDZhkXGodJ6zwWuSe2h6qMVMo8J0vlNyKSUCoLMnjbfx4fd+6FY3+Ai//n0BNC+iE6DCMxLpRkBy7m9XWApwNSJ5AhbgcRyy+O/Lok8lIyoWgpnKrixRefB1YFD+UZ1u/mVqzAwebt+7h6abHeq8VIWaCcOcfbSMfRd3GnhAQK9/0nXPA/MBs+oBSDRUUXxmiVMhNU5GfQ2tNGbUs3S+eGvP4HB6soE9GmIKKIyDSyp7hKfCrNTWO7v4wLHYdxtddE7onr7MnMIUHEU5rMLCKJaX5BBk/6rTYdHN9p9UV0hBQ42f0Q/aXMyU7F5UyA4qeULEhxQ187dDZMLIhYqyBiwildDaeqqPAcIDSImBsIIraYWZhAQ7uHXTUteu8WI6lJTuZkp7DT+zX4+VkHu8/AY1fwI4BUqC6KYAWJzDoVeensOdFm9d8MpcEqwyTAb3QREZHpkZni4mSSVSpTVLsd6t+f+pOaZshk5sGr5AeCQ1XUD1FEEsuCggz2mgvoMVOgp3l4X8RgJmKCDFWxuSfRF3HAO9iyokJDVRJG4KLeKmPoIDW7nLmVwYy3pk7P9K1LhinPTedr3j/HbzjPOmIC0G86uWfgTuYF+ieKTIa9f44PK2dWT8SzKYgoIiISoj1zAQCp/e0Ye5+a+hO2HrM+ZDuSoHh58OEDDR0AnFuiTEQRSSw56clkpKeN3hcxEFQ8bJZS4k6AoSo2e0Jz+wSCiI0fwoAH0vIgf1F01iWRF7iot9xRMziNGYKDVVrNwd/NRVkJtIdnoPK8dP7Tv45nV/9sxOOf8n6bD/P+JDEyniVu2cNVhmciqifi2fQvTUREBKCtFurfpzAjKfiQUfVrqP/Aykhsq53c89qlzMXLIcn6INLa7aWpsw+Ac+YoiCgiicfuiwhYfRFtA33QchSAw/6yxMpEtIOIExmuEtoPUf1tE0fReZhJGWQZvSwyBv++g5mIZhYGUOK2+ldL7JQH+iKe6rAzQh1n3cIiDVWRKbIzEYcFEZWJOIx6IoqIiAA8YmUJfg2rAtkwgB6r307QA+0Tf95gP8ThpczleWlkpuhXsYgknsr8DN4+MUJfxOYjYPrpcWTQRE6CZSJOopy59m3rtnxt5Ncj0eNwYsy9AI6/wSrHEQ77yjAJyUQMDFbZdN1SDVWJsbJAcOdQVypkFln/Tlf/Gez+GZ1Nx2n2ZHOlgogyRXYmYl1rLwM+/2Bmq3oiDqNMRBEREYAbHweHFdCzk0kGPzYYcMW9VnRxok4G+iEOmcxslTIvmaN+iCKSmCoLMvjQXIDXSA30RdxvHQiUMtc6KgAjwTIR7SDiODMRTXMwE1H9EBNP6WoA/mJJO8WBYLc9WIW0XB69eTUbl5XEanUSUJ5rBXc+7MiAu6rgtldgzVfgtlf4av5PaSRfmYgyZXOyUkl2ORjwm9S3hfRBVSbiMAoiioiIAKy4CW7dMcpBE17bCo9eDrseB884MxJ9/dCwx/p6hMnM56kfoogkqPkFGQzgYl9SIBvR7ot4+hAAB31WafBcdyIFEe1y5nFmIrYdh65TVs/buRdEb10SHYGLexU9+3jj3iv55W2XcF52PwBbvnCFAohxojzPeg2pa+vF50gOudJrcOCMF4CFhQoiytQ4HAYVI5U02z0RB3rBNxCDlcUfBRFFREQC3qw+A4DfNIbcnir+BLjSoOkj+O3X4eFz4T//wipVDpedeKoKfH2Q6oa8BcGH9zfYk5kVRBSRxFRZkAHAG/3nWg/YfREDmYh7+4sBKMmZweXMtYEsxJKVkJRAwVKx2G1GTu3DOdDLpQvzyfJblQLOjIIYLkxClbjTcDkM+n1mSF9EaOn20tJtBREXFGbEankyg9hBxOMt3YMPpoQEqFXSDCiIKCIiAsALVQ3c83w9TaabvWYl9/ffwl6zkibTzaeO3chL174KG7dC4bnQ3wPv/xwevxL+5ePw7k+gb4SpbVX/Yd3mL7Z6hQF+v8mhQCbiuQoiikiCmh8IIr7ssSc0B/oinj4IWENVkl0O8jOSY7XEibMzET3t4ytdCx2qIoknuxQyi8H0WVUDfj/0tljH0jVMJV44HQalgeEqJ0IyxI40Wf9GS3PSSE9Wf2mZumAmYnNIJqIrxco2BwURAxREFBGRWc/nN9m8fR8N5LOu7x+4wbuFX/iu4gbvFtb1/QON5PPNF+vwrf0q/Pnb8OUXYPlN4EyGxg/hubus7MTn/hoaPhx84kP/Zd2GZCuebO2lx+sj2eVgfr6unItIYspMcVGYlcKH5gJ8rjQr+NL4oTVYBTjsL2WuOxUjkSYWp2YPlq51Nox9frAfooKICckwBvsV170Lfe1g+q37aQoixhO7L+KJ1t7gY9WnrYCO+iFKpMzL14Tm8VAQUUREZr1dNS00tFslMl6SGBypYuAlCRNoaPewq6bF+tAx71L4zONw9wG45u8hb6F1dfLdJ+BfPgb/dDG88h1orraepvkw1H8A9e9zrNoaPrCoMHNw8puISAKqDPRFbM6zBlTw/s/B30+/M5168ilJpH6INnegpLn9ZPjzPB1w6iPra2UiJq7AcBXq3oOeQBZicha4EiiDdhaw+yKOlImoIKJESrCcufmsIKJ9cWmkqqNZSJ9eRERk1mvq9Ix90kjnZeTDZX8Jf/ke/Nlv4PxPW4+fPmANYjF91v2+DnjsCnhsPR//7ZUAnKuhKiKS4CoD2dSH0lZaD+z5FQCt6fMBI7H6IdqCw1XGmNB88h3AhJx5kFUc9WVJlNhDz06GBBFVyhx3yoKZiAoiSvSEZiKaoT3P7UxEr4KIoCCiiIgIRVnj+6A76nmGAQuugM/+FK59CIxRfr06XPx0zv2A+iGKSOKrDAwzeIfzrQcC/aLqXBVAgk1mto03iHhil3VbcUl01yPRNfcCwID2WjhtVQooiBh/ygMZYspElGgqy03HMKCrbyA4tAeAZJUzh1IQUUREZr21lXmUuFMZrXOXAZS4U1lbOY4PFmtvg9teGfnYrTv4eY9V9rakOHtSaxURiRd2X9fXu8ogKT34eMtACpBgk5lt453QfOJt67Z8bXTXI9GVmg2FS6yvD//Ouk3Pj916ZETlwcEqVk/EHu8AdW3W14sKFUSUyEhNclKcbf3eGtIXMSVw4V+DVQAFEUVERHA6DDZdtxRgxECiCWy6bilOx0QHBDiG3PYN+DkW6LOiTEQRSXQLApmIR5r7MEMy8jJ6raEkc3MSMRPRDiKGyUT0++Dku9bX5cpETHh2SXP1q9atgohxx85EPNXpoW/Ax9HT3QDkZySTm0gT4CXu2XttaBBRmYihFEQUEREBNi4r4dGbV1PsHp45k+xycOG8CZQ3ZRRCZhHMXQmf/KF1m1lETW86Pr9JbnoSRVkpEVy9iMj0q8hLp8w4zby+Q3iyK4OPnzuwj/ONGiq9h6GtNoYrnITxZCKe+sjKSEnJhqLzpmddEj32cBW735kmM8ed/Ixk0pOdmCbUtfYGS5kXKgtRImzeSMNV7MEq6okITDCIeOjQIT7zmc9w0UUXsXXr1qHNJkfx1FNPsW7dOs4//3y+8pWv0NTUNK5jIiIi023jshLeuPdK/u2Wi7jrYjdPfuUiVpRm4x3w80+vHBn/E7lL4a4qq6x5zVes27uqqOqy3uwuKc7CMCaa1SgiEl9Sk5y8kfI1nkv5X6S9/3+Dj2ebXTyf8nfMf/ZaeGR5DFc4CcGeiGGCiCf+aN2WrQGHM/prkugqWzP0vjIR445hGJQHh6uEBBHVD1EiLHS4SpAyEYcYdxDR6/Vy++23c/755/Pss89SXV3Nr3/967Df8+677/KjH/2I733ve+zYsYO+vj62bt065jEREZFYcToMLlmQz8cq0rh0YT5/s9HKMvnFH2s5GTIVcEyuFGvgCli3rhQONnYAcK76IYrIDPHjvHvpN4cG0oKdHxwuuPHx6V/UVNhBxN5W8I7ymm8HEcsvnp41SXQVLQVXSBVCem7s1iKjKs+z+yL2UH1aQ1UkOoLlzEMyEe3pzAoiwgSCiK+//jpdXV3cd999VFRUcPfdd/PMM8+E/Z6amhoeeOABLrvsMoqLi7nxxhupqqoa85iIiEi8WLe4gMsW5uP1+XnkpcNTeq4DjVYZxBL1QxSRGaK+4jo+5f32yAdv3QErbpreBU1VqnvwA2Nnw8jn1CqIOKM4k6Bk1eB9T0fs1iKjKgtmIvZoMrNEzbzAwLDjLd2DDyoTcQjXeE88cOAAK1euJC3NugKwZMkSqqurw37PZz/72SH3a2pqmDdv3pjHJsrn803q++Kd/eeaqX8+mdm0fyWRnb1/77l6MW9WN/Pr3Se5dd18Fk/yTevBQBBxcWGG/m1I1Oj1V6bT/Lx03g98bWJgYOI3DRyGic/vh0nsw1jvYUdWCUbzYXyttZAzf+jBjnqc7bWYhgN/yQWT+vNJ/DFKV+MITNz2172HOYW/11jv35mqLDDtveZ0N8earQDPgvw0/X+OsNm+f8tyrJ7lpzr66PZ4SU1yYiRl4ADMvg78M/j/y3j/zscdROzq6qKsrCx43zAMHA4H7e3tuN3uMb+/tbWVp556iu9///sTOjYee/fundT3JYqZ/ueTmU37VxJZ6P5dOzeFXfV9bHrmHf7msomXOnX0+Wnq7APA03SUD1o020yiS6+/Mh38HR6azWyaySHVPYcXk65iYdMLzHe2cPxYE/2nPpj0c8dqDy82sskGaqvepqV9aPuJnPpXWQj0Zi1g//7wCRUS/5J7GnF5O8hq82J/0vVXv8ahV5/GAAaSs/GmF0/qufUaHFn9bR4Adh5uot9nkuo0OFVzgCb1mI6K2bp/TdMk3WXQM2Dy+7fepzzbRV5DM5VA55kGDn8w+d9pM8W4g4hOp5Pk5KHj01NSUvB4POMKIm7evJkLLriA9evXT+jYeCxfvhync+Y1Nfb5fOzdu3fG/vlkZtP+lUQ20v7dXNLJtf+4kz/W9eEoqGRF2di/+0K9dbQZaKIiL41L16yOwqpFLHr9lenkLuvmO2+08Qnf/+a9Ozfy9n/u4+6Tl3PP5fP480uXTuo5Y72Hjdpz4Mx7zMt1UbFq1dBjTU8DkHrOeladdUwSj3PL8EnMzv5Olv7h9uB93zdbJvScsd6/M1VqcSdb39xJd7813HXRnCwuuOCCGK9q5tH+hcqdb/JRQwfphRWsOq8IUuvgA8hKMWb06779dz+WcQcR3W43hw8P7QXV3d1NUlLSmN/7zDPP8O6777Jt27YJHRsvp9M5ozf4TP/zycym/SuJLHT/njc3h09fUMqvd9fx8O8P8+StE+uFdbjJKr1ZUpytfxMyLfT6K9NhXkEmTodBR7+DM70DNHR4AIPi/Jwp77+Y7WF3OQCOzgY4++ef2GUdq7hk+DFJPDc+DtvuAP9A8KFgXpvDBZ96dNJ7UK/BkTWvYGgrmcVzsvT/N4pm8/6dV5DORw0dnGjzWP8P0qyMdMPbPWv/n4Qady3V8uXL2bNnT/D+yZMn8Xq9Y2Yhfvjhh3znO9/hBz/4AQUFBeM+JiIiEm/+esM5JDkN3jhyhjePnJnQ99r9EM/VUBURmUGSnA4qAtMsa850U9/WC0BpTloslzU19oTmjrqhj3t7oPFD6+sKDVWZEVbcZA0AGkkiDgaawTJTXOSmDyYwJTkNfH4zhiuSmaoizxqucqIlMKE5OfDeva8zRiuKL+MOIl500UV0dnYGMwYfe+wxLrvsMpxOJ11dXfT39w/7njNnznD77bdz2223cf7559Pd3U13d/eYx0REROJReV46X1hbAcDWFw9imuN/83ogGETMHuNMEZHEMj9/MIjY0G71LStJ6CBiqXV7dhCxfreVsZZVEsxWlJnEcdatxJMXqhro6hvMGH363ZOs2/oyL1SNMkVdZJLsC2PHAwN8gtOZvQoiwgReIV0uF1u2bGHTpk1cdtllvPjii9xzzz0AXH/99bz22mvDvue5556jubmZRx55hNWrVwf/G+uYiIhIvPqLKxeTluRkz4k2frfv1Li+x+83OXTKeuOxRJmIIjLDzC+wsjY+PNFOj9ea7ljiTo3lkqYmmIlYP/TxWmt6L+UXg4Y5zBwZhZBZBHNXwid/aN1mFlmPS1x4oaqBO57cTb9v6MXbxnYPdzy5W4FEiah5gQtjx4OZiIEgYl8XTCCBYKYad09EgA0bNvC73/2OvXv3snr1avLyrEa0L7/88ojnf+lLX+JLX/rShI+JiIjEq8KsFL6ybj7/9Eo1D714kA3nzcHpCP9h8kRrDz1eH8kuRzBjR0RkplgQCCLurLbaPORlJJOalMB9o+wgYk8z9HsgKRAQDfRDpFylzDOKuxTuqgJnshUcvvDL4POCKyXWKxPA5zfZvH0fI4VuTKwelpu37+PqpcVjvh8TGQ87E/FkSy9+v4nDzkQ0fTDggaQEzrSPgAnnas+ZM4cNGzYEA4giIiKzzf/8+ELcaUkcbupi2/t1Y55vlzIvLsrE5VSZlIjMLHYm4slWqx/i3JwEzkIESMuFpMAFH7uk2e+HE3+0vlY/xJnHlTKYXWoYCiDGkV01LcE2CSMxgYZ2D7tqJjZFW2Q0Je5UXA4Dr89PY4dnMBMR4PhbsVtYnNAnGRERkQlypyVx+xULAfjhS4fwDvjDnm8PVVEps4jMRJWBIKKtxJ3gWRqGMbykufkweNrAlQbFK2K3NpFZpqlz9ADiZM4TGYvL6aAs1/o9dry5BxxOa1o7wN6nY7iy+KAgooiIyCR86bL5FGalcLK1l1/uqg17rh1EPE9DVURkBprrTiPZ5Qi5n+CZiDA8iGj3Qyy9EJxJI3+PiERcUdb4Xk/Ge57IeFTkZ1DKabpq3oH6D8Bv9fvl0AvW/fr3oS38+/+ZSkFEERGRSUhLdvJXVy4C4B9fPkKPd2DUcw80dgDKRBSRmcnhMJiXN5h96PX58fkTvPn82ROa7X6IKmUWmVZrK/MocacyWrdDA6v8dG2l2q1J5FTkpbEz9Wtc/cbn4LErwO7K2dtq3X9sPTyyPJZLjBkFEUVERCbpcxdVUJ6XxpmuPn6y89iI53j6fdSc6QbgXAURRWQGeqGqgdqW3uD9X+46wbqtLyf2xNRgEDGQiXgiZDKziEwbp8Ng03VLAYYFEu37m65bqqEqElHz8jL4mvfP8THKkDCHC258fHoXFScURBQREZmkZJeDu68+B4B/ea2a9p7+YeccaerCb0JuehKFWWrULiIzywtVDdzx5G76zuoN29ju4Y4ndyduIDFYzlwH3c3QfMS6X3ZR7NYkMkttXFbCozevpvisVgnF7lQevXk1G5eVxGhlMlNV5Kfzn/51fD3nByOfcOsOWHHT9C4qTrhivQAREZFEdv3KUv751aMcPNXJP79ezb0bzx1y3J7MfG5xNoahq+QiMnP4/Cabt+9jpMJlEytLaPP2fVy9tDjxsoRCy5ntqcwFSyBdJZMisbBxWQlXLy1mV00LTZ0eirKsEuaEe22RhDAvPx0gZDK4A/CH3M5eykQUERGZAqfD4Ot/sgSAn+ysoalj6HTAg+qHKCIz1K6alpAPWMOZWB/AdtW0TN+iIiV0sIodRFQ/RJGYcjoMLl2Yzw2rSrl0Yb4CiBI15blWEPFYbzr+jCKYuxI++UPrNrMIMgpjvMLYUSaiiIjIFG04r4jVFTnsrm3jH18+wpZPLQseG8xEVBBRRGaWps7RA4iTOS+uuMus2+7TcOwP1tfqhygiMitkpLgoyEyhsSuffTftZFlFIRgGXPhl8HnBNXtbFCkTUUREZIoMw+Abf2KVMf9yVy21zT3BY3YQUZmIIjLTFGWljn3SBM6LK2m54Aqsu+4967b8ktitR0REppVd0ny83WcFEMG6ncUBRFAQUUREJCIuXZjPxxYXMOA3+eFLhwBo6fZyurMPgHPmKIgoIjPL2so8Stypwyam2gygxG31LUs4hjFY0gyQng/5C2O3HhERmVYVeYEgYkt3jFcSXxREFBERiZC/CWQjbvugjgONHRwI9EOcl59ORoo6iIjIzOJ0GGy6binAsECifX/TdUsTt2+ZPVwFoHDJYCaKiIjMeHYQMbTCSBREFBERiZjlZW6uXV6MacL3XzjIi1WNABRmpuDzjzS/VEQksW1cVsKjN6+m2D20ZLnYncqjN69m47KSGK0sAkKDiH5f7NYhIiLTzi5nrm1REDGU0iJEREQi6O6rl/BfexvZcaAp+Ni7x1tZt/VlNl23NLE/UIuIjGDjshKuXlrMrpoWmjo9FGVZJcwJm4HYVgs9zeBwDj52+iDUfwCYVmlzTkXMliciItEXLGdWJuIQCiKKiIhE0JGmTkbKOWxs93DHk7sTPzNHRGQETofBpQvzY72MyHhk+fDHPO3w2BWD9x9on771iIjItKsIZCI2tPfiHfCT7FIhL6icWUREJGJ8fpPN2/eNeMwOLG7evk+lzSIi8ezGx8Fxdq5F4HXb4bKOi4jIjFaYmUJ6shO/CXVtvbFeTtxQEFFERCRCdtW00NDuGfW4CTS0e9hV0zJ9ixIRkYlZcRPcumPkY7fusI6LiMiMZhhGSEmzJjTbFEQUERGJkKbO0QOIkzlPRERizXHWrYiIzBbleRqucjb9NhQREYmQoqzUsU+awHkiIhIjGYWQWQRzV8Inf2jdZhZZj4uIyKwwzw4iarhKkAariIiIRMjayjxK3Kk0tntGHK5iAMVua2qpiIjEMXcp3FUFzmQwDLjwy+Dzgisl1isTEZFpMi8wXOW4MhGDlIkoIiISIU6HwabrlgJWwDCUfX/TdUtxOs4+KiIicceVYgUQwbpVAFFEZFYpVybiMAoiioiIRNDGZSU8evNqit1DS5aL3ak8evNqNi4ridHKRERERERkvOblZwBWT0TTHKnOaPZRObOIiEiEbVxWwtVLi9lV00JTp4eiLKuEWRmIIiIiIiKJoTQnDYcBvf0+Tnf1qa85CiKKiIhEhdNhcOnC/FgvQ0REREREJiHZ5aDEnUZdWy+1zT0KIqJyZhERERERERERkWGCw1XUFxFQEFFERERERERERGQYO4hYqwnNgIKIIiIiIiIiIiIiw5TlWkHEnUfO8FZ1Mz7/7B6woiCiiIiIiIiIiIhIiBeqGnj8D0cBePd4K59//G3WbX2ZF6oaYryy2FEQUUREREREREREJOCFqgbueHI3bT39Qx5vbPdwx5O7Z20gUUFEERERERERERERwOc32bx9HyMVLtuPbd6+b1aWNiuIKCIiIiIiIiIiAuyqaaGh3TPqcRNoaPewq6Zl+hYVJxREFBERERERERERAZo6Rw8gTua8mURBRBEREREREREREaAoKzWi580kCiKKiIiIiIiIiIgAayvzKHGnYoxy3ABK3KmsrcybzmXFBQURRUREREREREREAKfDYNN1SwGGBRLt+5uuW4rTMVqYceZSEFFERERERERERCRg47ISHr15NcXuoSXLxe5UHr15NRuXlcRoZbHlivUCRERERERERERE4snGZSVcvbSYXTUtNHV6KMqySphnYwaiTUFEERERERERERGRszgdBpcuzI/1MuKGyplFREREREREREQkLAURRUREREREREREJCwFEUVERERERERERCQsBRFFREREREREREQkLAURRUREREREREREJCwFEUVERERERERERCQsBRFFREREREREREQkLAURRUREREREREREJCwFEUVERERERERERCQsBRFFREREREREREQkLAURRUREREREREREJCwFEUVERERERERERCQsBRFFREREREREREQkLAURRUREREREREREJCwFEUVERERERERERCQsBRFFREREREREREQkLAURRUREREREREREJCwFEUVERERERERERCQsBRFFREREREREREQkLAURRUREREREREREJCwFEUVERERERERERCQsBRFFREREREREREQkLAURRUREREREREREJCwFEUVERERERERERCQsBRFFREREREREREQkLAURRUREREREREREJCwFEUVERERERERERCQsV6wXMBWmaQLg8/livJLosP9cM/XPJzOb9q8kMu1fSWTav5LotIclkWn/SiLT/p297L9zO842GsMc64w45vV62bt3b6yXISIiIiIiIiIiktCWL19OcnLyqMcTOojo9/sZGBjA4XBgGEaslyMiIiIiIiIiIpJQTNPE7/fjcrlwOEbvfJjQQUQRERERERERERGJPg1WERERERERERERkbAURBQREREREREREZGwFEQUERERERERERGRsBREFBERERERERERkbAURBQREREREREREZGwFEQUERERERERERGRsBREFBERERERERERkbAURBQRERFJcK2trezevZuWlpZYL0VEREREZigFEePUoUOH+MxnPsNFF13E1q1bMU0z1ksSCau1tZUrr7ySkydPBh/TPpZE8dJLL3HVVVexdOlSPvvZz1JdXQ1oD0tieP7557nmmmv49re/zSc+8Qmef/55QPtXEs8tt9zCr3/9a0D7VxLDli1bWLJkSfC/q6++GtD+lcTy0EMPcfvttwfva/9KOAoixiGv18vtt9/O+eefz7PPPkt1dXXwDZVIPGppaeH222+nrq4u+Jj2sSSK2tpa7r//fu655x5ef/115s6dy9/93d9pD0tC6OjoYMuWLfzbv/0b27ZtY/PmzTz00EPav5JwfvOb3/DGG28Aeg8hieOjjz7iscce45133uGdd97hP/7jP7R/JaEcOnSIX/ziF9x///2AXn9lbAoixqHXX3+drq4u7rvvPioqKrj77rt55plnYr0skVHdfffdXHvttUMe0z6WRFFdXc1f//Vfc+2111JQUMDnP/95qqqqRjjwPQAABSVJREFUtIclIXR3d3P//fdzzjnnAHDuuefS3t6u/SsJpa2tja1bt1JZWQnoPYQkhoGBAQ4dOsSaNWvIzs4mOzubzMxM7V9JGKZp8q1vfYsvfvGLVFRUAHr9lbEpiBiHDhw4wMqVK0lLSwNgyZIlwdI6kXi0ZcsWvvjFLw55TPtYEsUnPvEJPv/5zwfv19TUMG/ePO1hSQglJSVcf/31APT39/PEE09wzTXXaP9KQtm6dSsbNmxg1apVgN5DSGI4ePAgpmnyqU99ihUrVnDLLbdQX1+v/SsJ4+mnn+bAgQOUlZXxyiuv0N/fr/0rY1IQMQ51dXVRVlYWvG8YBg6Hg/b29hiuSmR05eXlwx7TPpZE5PV6eeKJJ/jCF76gPSwJ5cCBA1x++eXs3LmT+++/X/tXEsbbb7/NW2+9xTe+8Y3gY9q/kgiqq6tZvHgxDz/8MM8//zxJSUl861vf0v6VhNDd3c0jjzzCvHnzaGxs5Cc/+Qk333yz9q+MSUHEOOR0OklOTh7yWEpKCh6PJ0YrEpk47WNJRI888gjp6encdNNN2sOSUJYsWcJPf/pTFi1axH333af9Kwmhr6+PTZs28cADD5CZmRl8XPtXEsH111/P008/zYoVKygvL+eb3/wmO3fuxO/3a/9K3Pv9739Pb28v//qv/8qdd97JE088QUdHB88++6z2r4SlIGIccrvdtLS0DHmsu7ubpKSkGK1IZOK0jyXR7Ny5k1/96lc8/PDDJCUlaQ9LQjEMg6VLl/Lggw+yY8cO7V9JCD/+8Y9ZtmwZ69evH/K49q8kouzsbPx+PwUFBdq/EvcaGxtZsWIFOTk5ALhcLpYsWUJfX5/2r4SlIGIcWr58OXv27AneP3nyJF6vF7fbHcNViUyM9rEkkhMnTvD1r3+dBx54gEWLFgHaw5IY3nrrLbZu3Rq873Q6AViwYIH2r8S97du38/LLL7NmzRrWrFnDc889x+bNm9m2bZv2r8S97373u/z2t78N3t+7dy8Oh4MlS5Zo/0rcKy4upq+vb8hj9fX13Hvvvdq/EpaCiHHooosuorOzk23btgHw2GOPcdlllwU/GIgkAu1jSRQej4evfvWrbNiwgauuuoru7m66u7tZs2aN9rDEvQULFvDUU0/x1FNP0dDQwMMPP8zll1/O+vXrtX8l7v3iF79g+/btbNu2jW3btnHllVfyV3/1Vzz55JPavxL3zjvvPB555BHeeecd3nrrLbZs2cKnP/1pLr/8cu1fiXvr16+nurqaX/7ylzQ2NvKzn/2M/fv3s27dOu1fCcswTdOM9SJkuJdeeol77rmHjIwMfD4fTz75JIsXL471skTCWrJkCTt27Ag249U+lkTw0ksvceeddw57fMeOHRw4cEB7WOLeH/7wB7773e/S2NjIunXreOCBB8jLy9NrsCScv/3bv2Xt2rXceOON2r+SEB5++GF+9atfkZGRwYYNG7j77rtJT0/X/pWE8MEHH/Dggw+yf/9+CgoKuO+++9iwYYP2r4SlIGIcO3XqFHv37mX16tXk5eXFejkik6J9LIlOe1gSmfavJDLtX0lk2r+SyLR/ZTQKIoqIiIiIiIiIiEhY6okoIiIiIiIiIiIiYSmIKCIiIiIiIiIiImEpiCgiIiIiIiIiIiJhKYgoIiIiIiIiIiIiYSmIKCIiIiIiIiIiImEpiCgiIiIiIiIiIiJhKYgoIiIiIiIiIiIiYSmIKCIiIiIiIiIiImEpiCgiIiIiIiIiIiJh/T+fP9YhHn4itwAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 10))\n",
    "plt.plot(range(len(y_true_xgb)), y_true_xgb, 'o-', label='真实值')\n",
    "plt.plot(range(len(y_pred_xgb)), y_pred_xgb, '*-', label='预测值')\n",
    "plt.legend(loc='best')\n",
    "plt.title('预测结果')\n",
    "plt.savefig('./figure/CO2排放强度预测结果.png')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 煤种标准化工程"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [],
   "source": [
    "new_values = total_data.groupby(['煤种', '入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new']).CO2_em_air.mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [
    {
     "data": {
      "text/plain": "      煤种  入炉煤低位热值_new  燃煤挥发份Var_new  燃煤灰份Aar_new\n0    无烟煤     17050.00          6.51    31.330000\n1    无烟煤     18440.00          9.13    21.240189\n2    无烟煤     19335.65          7.06    21.400000\n3    无烟煤     20125.07          5.70    29.850000\n4    无烟煤     20463.30          5.70    29.790000\n..   ...          ...           ...          ...\n622   贫煤     21772.91         10.66    26.320000\n623   贫煤     21907.00         10.64    28.100000\n624   贫煤     22042.72         12.96    25.690000\n625   贫煤     23215.00         11.00    19.310000\n626   贫煤     23791.00         11.00    19.310000\n\n[627 rows x 4 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>煤种</th>\n      <th>入炉煤低位热值_new</th>\n      <th>燃煤挥发份Var_new</th>\n      <th>燃煤灰份Aar_new</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>无烟煤</td>\n      <td>17050.00</td>\n      <td>6.51</td>\n      <td>31.330000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>无烟煤</td>\n      <td>18440.00</td>\n      <td>9.13</td>\n      <td>21.240189</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>无烟煤</td>\n      <td>19335.65</td>\n      <td>7.06</td>\n      <td>21.400000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>无烟煤</td>\n      <td>20125.07</td>\n      <td>5.70</td>\n      <td>29.850000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>无烟煤</td>\n      <td>20463.30</td>\n      <td>5.70</td>\n      <td>29.790000</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>622</th>\n      <td>贫煤</td>\n      <td>21772.91</td>\n      <td>10.66</td>\n      <td>26.320000</td>\n    </tr>\n    <tr>\n      <th>623</th>\n      <td>贫煤</td>\n      <td>21907.00</td>\n      <td>10.64</td>\n      <td>28.100000</td>\n    </tr>\n    <tr>\n      <th>624</th>\n      <td>贫煤</td>\n      <td>22042.72</td>\n      <td>12.96</td>\n      <td>25.690000</td>\n    </tr>\n    <tr>\n      <th>625</th>\n      <td>贫煤</td>\n      <td>23215.00</td>\n      <td>11.00</td>\n      <td>19.310000</td>\n    </tr>\n    <tr>\n      <th>626</th>\n      <td>贫煤</td>\n      <td>23791.00</td>\n      <td>11.00</td>\n      <td>19.310000</td>\n    </tr>\n  </tbody>\n</table>\n<p>627 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coal_df = new_values.reset_index().drop(columns='CO2_em_air')\n",
    "coal_df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "outputs": [],
   "source": [
    "coal_params_dict = dict()\n",
    "for coal_type in coal_df['煤种'].unique().tolist():\n",
    "    options = coal_df[coal_df['煤种']==coal_type][['入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new']].values\n",
    "    coal_params_dict[coal_type] = options"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [
    {
     "data": {
      "text/plain": "{'无烟煤': array([[1.70500000e+04, 6.51000000e+00, 3.13300000e+01],\n        [1.84400000e+04, 9.13000000e+00, 2.12401894e+01],\n        [1.93356500e+04, 7.06000000e+00, 2.14000000e+01],\n        [2.01250700e+04, 5.70000000e+00, 2.98500000e+01],\n        [2.04633000e+04, 5.70000000e+00, 2.97900000e+01]]),\n '烟煤': array([[1.277100e+04, 2.126000e+01, 3.355000e+01],\n        [1.500000e+04, 2.346000e+01, 1.904000e+01],\n        [1.610000e+04, 2.333000e+01, 1.873000e+01],\n        ...,\n        [2.348751e+04, 2.927000e+01, 2.097000e+01],\n        [2.365000e+04, 2.887000e+01, 7.910000e+00],\n        [2.365614e+04, 2.927000e+01, 2.097000e+01]]),\n '褐煤': array([[1.059800e+04, 2.476000e+01, 2.179000e+01],\n        [1.129000e+04, 4.764000e+01, 3.079000e+01],\n        [1.160400e+04, 4.758000e+01, 3.025000e+01],\n        [1.172435e+04, 4.601000e+01, 3.673000e+01],\n        [1.203000e+04, 4.726000e+01, 3.119000e+01],\n        [1.213546e+04, 4.642000e+01, 1.113000e+01],\n        [1.217290e+04, 4.642000e+01, 1.113000e+01],\n        [1.219256e+04, 4.642000e+01, 1.113000e+01],\n        [1.221131e+04, 4.642000e+01, 1.113000e+01],\n        [1.230939e+04, 4.642000e+01, 1.113000e+01],\n        [1.233780e+04, 4.642000e+01, 1.113000e+01],\n        [1.267400e+04, 4.324000e+01, 1.237000e+01],\n        [1.278700e+04, 4.884000e+01, 4.117000e+01],\n        [1.295100e+04, 2.228000e+01, 1.287000e+01],\n        [1.299880e+04, 2.256000e+01, 1.716000e+01],\n        [1.311100e+04, 2.367000e+01, 2.107000e+01],\n        [1.313000e+04, 2.417000e+01, 1.630000e+01],\n        [1.318000e+04, 2.445000e+01, 1.794000e+01],\n        [1.320830e+04, 2.451000e+01, 1.429000e+01],\n        [1.325722e+04, 1.703000e+01, 3.660000e+01],\n        [1.327000e+04, 3.204000e+01, 1.709000e+01],\n        [1.327300e+04, 2.364000e+01, 1.622000e+01],\n        [1.327300e+04, 2.458000e+01, 1.261000e+01],\n        [1.332771e+04, 4.090000e+01, 2.507000e+01],\n        [1.333064e+04, 1.680000e+01, 3.741000e+01],\n        [1.335883e+04, 2.301000e+01, 1.841000e+01],\n        [1.336864e+04, 2.301000e+01, 1.841000e+01],\n        [1.343787e+04, 2.336000e+01, 1.705000e+01],\n        [1.344000e+04, 4.782000e+01, 2.290000e+01],\n        [1.345749e+04, 2.388000e+01, 1.652000e+01],\n        [1.357000e+04, 1.799000e+01, 2.177000e+01],\n        [1.364000e+04, 2.526000e+01, 2.108000e+01],\n        [1.365410e+04, 2.232000e+01, 1.171000e+01],\n        [1.369000e+04, 4.771000e+01, 2.205000e+01],\n        [1.382000e+04, 2.420000e+01, 1.104000e+01],\n        [1.389597e+04, 2.232000e+01, 1.171000e+01],\n        [1.390000e+04, 3.683000e+01, 4.441000e+01],\n        [1.395400e+04, 2.310000e+01, 1.011000e+01],\n        [1.396000e+04, 4.665000e+01, 1.890000e+01],\n        [1.400000e+04, 4.520000e+01, 1.364000e+01],\n        [1.404100e+04, 2.346000e+01, 1.046000e+01],\n        [1.410900e+04, 4.520000e+01, 1.364000e+01],\n        [1.412200e+04, 2.478000e+01, 1.916000e+01],\n        [1.419900e+04, 4.733000e+01, 1.697000e+01],\n        [1.433937e+04, 2.476000e+01, 3.371000e+01],\n        [1.440000e+04, 2.589000e+01, 1.643000e+01],\n        [1.442729e+04, 4.474000e+01, 1.193000e+01],\n        [1.446814e+04, 2.484000e+01, 3.331000e+01],\n        [1.448810e+04, 3.554000e+01, 1.171000e+01],\n        [1.458200e+04, 2.834000e+01, 2.320000e+01],\n        [1.460000e+04, 2.714000e+01, 4.346000e+01],\n        [1.462400e+04, 4.613000e+01, 2.700000e+01],\n        [1.463500e+04, 4.613000e+01, 2.700000e+01],\n        [1.464000e+04, 4.439000e+01, 1.684000e+01],\n        [1.470100e+04, 2.210000e+01, 4.588000e+01],\n        [1.481078e+04, 4.501000e+01, 1.325000e+01],\n        [1.489878e+04, 2.386000e+01, 3.161000e+01],\n        [1.507938e+04, 4.501000e+01, 1.325000e+01],\n        [1.512117e+04, 2.355000e+01, 1.472000e+01],\n        [1.517400e+04, 3.126000e+01, 1.696000e+01],\n        [1.523800e+04, 2.492000e+01, 2.378000e+01],\n        [1.524041e+04, 2.355000e+01, 1.472000e+01],\n        [1.528927e+04, 2.345000e+01, 1.554000e+01],\n        [1.534700e+04, 2.492000e+01, 2.378000e+01],\n        [1.536708e+04, 4.501000e+01, 8.590000e+00],\n        [1.540000e+04, 2.450000e+01, 2.085000e+01],\n        [1.560165e+04, 2.345000e+01, 1.554000e+01],\n        [1.562100e+04, 4.409000e+01, 1.019000e+01],\n        [1.568455e+04, 1.865000e+01, 3.545000e+01],\n        [1.599544e+04, 1.865000e+01, 3.545000e+01],\n        [1.619823e+04, 2.032000e+01, 3.297000e+01],\n        [1.619823e+04, 2.075000e+01, 3.310000e+01],\n        [1.619951e+04, 1.790000e+01, 3.976000e+01],\n        [1.620200e+04, 1.268000e+01, 4.012000e+01],\n        [1.638000e+04, 2.264000e+01, 2.024000e+01],\n        [1.644918e+04, 2.061000e+01, 3.224000e+01],\n        [1.644918e+04, 2.087000e+01, 3.238000e+01],\n        [1.660450e+04, 3.484000e+01, 9.590000e+00],\n        [1.662400e+04, 1.287000e+01, 3.909000e+01],\n        [1.667800e+04, 1.320000e+01, 3.884000e+01],\n        [1.701000e+04, 2.721000e+01, 4.295000e+01],\n        [1.711359e+04, 3.560000e+01, 9.440000e+00],\n        [1.721702e+04, 3.266000e+01, 6.030000e+00],\n        [1.732699e+04, 3.266000e+01, 6.030000e+00],\n        [1.769205e+04, 3.632000e+01, 8.880000e+00],\n        [1.783200e+04, 3.564000e+01, 2.418000e+01],\n        [1.792600e+04, 3.563000e+01, 2.488000e+01],\n        [1.802919e+04, 3.526000e+01, 7.680000e+00],\n        [1.811583e+04, 3.348000e+01, 1.236000e+01],\n        [1.815944e+04, 3.348000e+01, 1.236000e+01],\n        [1.834900e+04, 3.542000e+01, 1.152000e+01],\n        [1.862400e+04, 3.951000e+01, 1.937000e+01],\n        [1.877383e+04, 2.676000e+01, 3.448000e+01],\n        [1.877602e+04, 2.676000e+01, 3.448000e+01],\n        [1.882100e+04, 2.678000e+01, 3.445000e+01],\n        [1.884200e+04, 2.685000e+01, 3.451000e+01],\n        [1.896000e+04, 3.951000e+01, 1.937000e+01],\n        [1.903900e+04, 2.580000e+01, 2.420000e+01],\n        [1.908760e+04, 3.426000e+01, 4.580000e+00],\n        [1.918000e+04, 2.670000e+01, 2.480000e+01],\n        [1.922827e+04, 3.426000e+01, 4.580000e+00],\n        [1.924675e+04, 3.243000e+01, 7.700000e+00],\n        [1.927600e+04, 3.200000e+01, 7.700000e+00],\n        [1.959900e+04, 3.514000e+01, 1.065000e+01],\n        [1.964010e+04, 3.446000e+01, 4.600000e+00],\n        [1.965200e+04, 2.990000e+01, 2.406000e+01],\n        [1.974233e+04, 3.422000e+01, 2.892000e+01],\n        [1.976235e+04, 3.414000e+01, 2.934000e+01],\n        [1.977612e+04, 3.446000e+01, 4.600000e+00],\n        [1.993700e+04, 3.514000e+01, 1.065000e+01],\n        [1.997000e+04, 3.533000e+01, 9.050000e+00],\n        [2.003000e+04, 3.948000e+01, 3.080000e+01],\n        [2.006000e+04, 3.911000e+01, 3.080000e+01],\n        [2.011300e+04, 2.560000e+01, 2.312000e+01],\n        [2.017338e+04, 2.979000e+01, 1.814000e+01],\n        [2.025484e+04, 2.979000e+01, 1.814000e+01],\n        [2.028500e+04, 3.009000e+01, 1.125000e+01],\n        [2.057100e+04, 3.147000e+01, 2.478000e+01],\n        [2.062600e+04, 2.627000e+01, 2.050000e+01],\n        [2.066423e+04, 2.752000e+01, 2.014000e+01],\n        [2.067360e+04, 2.840000e+01, 2.165000e+01],\n        [2.068200e+04, 2.960000e+01, 1.603000e+01],\n        [2.068600e+04, 3.124000e+01, 2.445000e+01],\n        [2.070300e+04, 3.000000e+01, 1.125000e+01],\n        [2.073600e+04, 2.627000e+01, 2.050000e+01],\n        [2.075090e+04, 2.780000e+01, 2.254000e+01],\n        [2.076000e+04, 2.977000e+01, 1.291000e+01],\n        [2.078500e+04, 3.871000e+01, 1.575000e+01],\n        [2.083648e+04, 2.780000e+01, 2.254000e+01],\n        [2.089200e+04, 3.252000e+01, 9.680000e+00],\n        [2.089200e+04, 3.255000e+01, 9.380000e+00],\n        [2.089200e+04, 3.262000e+01, 1.026000e+01],\n        [2.089200e+04, 3.324000e+01, 8.560000e+00],\n        [2.090000e+04, 3.100000e+01, 1.981000e+01],\n        [2.093990e+04, 2.840000e+01, 2.165000e+01],\n        [2.094100e+04, 2.977000e+01, 1.291000e+01],\n        [2.094900e+04, 3.100000e+01, 2.007000e+01],\n        [2.107400e+04, 3.830000e+01, 1.525000e+01],\n        [2.110000e+04, 2.470000e+01, 2.599000e+01],\n        [2.114300e+04, 2.580000e+01, 2.196000e+01],\n        [2.114300e+04, 2.580000e+01, 2.197000e+01],\n        [2.121740e+04, 3.279000e+01, 1.334000e+01],\n        [2.127156e+04, 3.844000e+01, 1.186000e+01],\n        [2.134680e+04, 3.885000e+01, 1.243000e+01],\n        [2.137900e+04, 2.944000e+01, 1.436000e+01],\n        [2.147400e+04, 2.944000e+01, 1.436000e+01],\n        [2.166129e+04, 3.124000e+01, 1.849000e+01],\n        [2.176000e+04, 3.213000e+01, 1.785000e+01],\n        [2.208167e+04, 3.176000e+01, 1.816000e+01],\n        [2.214783e+04, 3.736000e+01, 1.390000e+01],\n        [2.219619e+04, 3.736000e+01, 1.390000e+01],\n        [2.240000e+04, 3.052000e+01, 1.785000e+01],\n        [2.248200e+04, 3.010000e+01, 1.125000e+01],\n        [2.261900e+04, 3.047000e+01, 1.303000e+01],\n        [2.274200e+04, 3.028000e+01, 1.057000e+01]]),\n '贫煤': array([[1.695900e+04, 9.310000e+00, 4.477000e+01],\n        [1.742404e+04, 1.058000e+01, 2.268000e+01],\n        [1.742931e+04, 7.900000e+00, 3.840000e+01],\n        [1.799800e+04, 1.175000e+01, 2.981000e+01],\n        [1.875700e+04, 1.185000e+01, 3.122000e+01],\n        [1.912518e+04, 7.810000e+00, 3.145000e+01],\n        [1.928076e+04, 7.930000e+00, 3.137000e+01],\n        [1.935228e+04, 1.119000e+01, 3.202000e+01],\n        [1.938269e+04, 1.127000e+01, 3.192000e+01],\n        [1.983535e+04, 1.152000e+01, 3.052000e+01],\n        [1.986900e+04, 1.161000e+01, 3.042000e+01],\n        [1.994000e+04, 9.370000e+00, 3.426000e+01],\n        [1.994300e+04, 9.370000e+00, 3.426000e+01],\n        [2.003700e+04, 1.125000e+01, 3.067000e+01],\n        [2.024590e+04, 1.058000e+01, 2.654000e+01],\n        [2.028730e+04, 1.120000e+01, 2.698000e+01],\n        [2.031000e+04, 1.123000e+01, 3.357000e+01],\n        [2.031700e+04, 1.125000e+01, 3.067000e+01],\n        [2.036000e+04, 9.450000e+00, 3.077000e+01],\n        [2.057000e+04, 1.185000e+01, 2.786000e+01],\n        [2.075500e+04, 1.174000e+01, 2.817000e+01],\n        [2.086230e+04, 1.040000e+01, 2.583000e+01],\n        [2.092670e+04, 9.510000e+00, 2.515000e+01],\n        [2.096500e+04, 1.258000e+01, 2.965000e+01],\n        [2.097590e+04, 1.017000e+01, 2.491000e+01],\n        [2.098100e+04, 1.258000e+01, 2.965000e+01],\n        [2.101000e+04, 1.209000e+01, 2.169000e+01],\n        [2.101980e+04, 9.410000e+00, 2.489000e+01],\n        [2.103908e+04, 7.010000e+00, 2.714000e+01],\n        [2.105200e+04, 1.074000e+01, 3.136000e+01],\n        [2.106690e+04, 1.034000e+01, 2.481000e+01],\n        [2.107710e+04, 1.017000e+01, 2.478000e+01],\n        [2.110900e+04, 7.670000e+00, 2.597000e+01],\n        [2.110900e+04, 1.209000e+01, 2.169000e+01],\n        [2.119000e+04, 7.170000e+00, 2.591000e+01],\n        [2.119400e+04, 7.190000e+00, 2.597000e+01],\n        [2.119433e+04, 7.010000e+00, 2.667000e+01],\n        [2.122400e+04, 1.256000e+01, 2.636000e+01],\n        [2.126600e+04, 7.260000e+00, 2.567000e+01],\n        [2.126900e+04, 1.174000e+01, 2.817000e+01],\n        [2.157900e+04, 1.189000e+01, 2.689000e+01],\n        [2.174500e+04, 1.074000e+01, 2.850000e+01],\n        [2.176688e+04, 1.062000e+01, 2.687000e+01],\n        [2.177291e+04, 1.066000e+01, 2.632000e+01],\n        [2.190700e+04, 1.064000e+01, 2.810000e+01],\n        [2.204272e+04, 1.296000e+01, 2.569000e+01],\n        [2.321500e+04, 1.100000e+01, 1.931000e+01],\n        [2.379100e+04, 1.100000e+01, 1.931000e+01]])}"
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coal_params_dict"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "outputs": [
    {
     "data": {
      "text/plain": "     地区 所属集团             投产时间  机组容量 机组类型 参数分类  冷却方式   锅炉类型      时间  \\\n0    北京   华能   1998/1/20 0:00   165  供热式  超高压    水冷     煤粉  2016.0   \n1    北京   华能   1998/1/20 0:00   165  供热式  超高压    水冷     煤粉  2016.0   \n2    北京   华能  1998/12/20 0:00   220  供热式  超高压    水冷     煤粉  2016.0   \n3    北京   华能   1999/6/26 0:00   220  供热式  超高压    水冷     煤粉  2016.0   \n4    辽宁   大唐   2009/4/30 0:00   300  供热式  亚临界    水冷     煤粉  2016.0   \n..   ..  ...              ...   ...  ...  ...   ...    ...     ...   \n847  新疆  NaN              NaN  1320  纯凝式  超临界  间接空冷     煤粉     NaN   \n848  辽宁  NaN              NaN   700  供热式  超临界    水冷     煤粉     NaN   \n849  内蒙  NaN              NaN   700  供热式  超临界  直接空冷     煤粉     NaN   \n850  山东  NaN              NaN    40  供热式  超高压    水冷  循环流化床     NaN   \n851  浙江  NaN              NaN    70  供热式  超高压    水冷  循环流化床     NaN   \n\n              发电量  ...           标煤量   出力系数  煤种  入炉煤低位热值  燃煤挥发份Var  燃煤灰份Aar  \\\n0     51841.70000  ...  2.580497e+05  75.84  烟煤  23380.0     27.59     9.94   \n1     47387.95000  ...  2.126813e+05  74.50  烟煤  23380.0     27.59     9.94   \n2    115498.04000  ...  4.410925e+05  78.76  烟煤  23380.0     27.59     9.94   \n3    120884.07000  ...  4.707218e+05  81.41  烟煤  23380.0     27.59     9.94   \n4    111218.55000  ...  3.726990e+05  71.27  褐煤  14122.0     24.78    19.16   \n..            ...  ...           ...    ...  ..      ...       ...      ...   \n847  704381.26290  ...  2.283076e+06    NaN  褐煤  19970.0     35.33     9.05   \n848  350000.00000  ...  1.328747e+06    NaN  褐煤  14640.0     44.39    16.84   \n849  385000.00000  ...  1.362009e+06    NaN  褐煤  13960.0     46.65    18.90   \n850   17000.00000  ...  1.810834e+05    NaN  烟煤  21060.0     19.12    20.27   \n851   35788.81469  ...  3.502535e+05    NaN  烟煤  22021.0     19.12    21.77   \n\n     CO2_em_air  入炉煤低位热值_new  燃煤挥发份Var_new  燃煤灰份Aar_new  \n0      0.235066      23380.0         27.59         9.94  \n1      0.226207      23380.0         27.59         9.94  \n2      0.220954      23380.0         27.59         9.94  \n3      0.216298      23380.0         27.59         9.94  \n4      0.238755      14122.0         24.78        19.16  \n..          ...          ...           ...          ...  \n847    0.196452      19970.0         35.33         9.05  \n848    0.185688      14640.0         44.39        16.84  \n849    0.181214      13960.0         46.65        18.90  \n850    0.347570      21060.0         19.12        20.27  \n851    0.253057      22021.0         19.12        21.77  \n\n[852 rows x 21 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>地区</th>\n      <th>所属集团</th>\n      <th>投产时间</th>\n      <th>机组容量</th>\n      <th>机组类型</th>\n      <th>参数分类</th>\n      <th>冷却方式</th>\n      <th>锅炉类型</th>\n      <th>时间</th>\n      <th>发电量</th>\n      <th>...</th>\n      <th>标煤量</th>\n      <th>出力系数</th>\n      <th>煤种</th>\n      <th>入炉煤低位热值</th>\n      <th>燃煤挥发份Var</th>\n      <th>燃煤灰份Aar</th>\n      <th>CO2_em_air</th>\n      <th>入炉煤低位热值_new</th>\n      <th>燃煤挥发份Var_new</th>\n      <th>燃煤灰份Aar_new</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>北京</td>\n      <td>华能</td>\n      <td>1998/1/20 0:00</td>\n      <td>165</td>\n      <td>供热式</td>\n      <td>超高压</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>2016.0</td>\n      <td>51841.70000</td>\n      <td>...</td>\n      <td>2.580497e+05</td>\n      <td>75.84</td>\n      <td>烟煤</td>\n      <td>23380.0</td>\n      <td>27.59</td>\n      <td>9.94</td>\n      <td>0.235066</td>\n      <td>23380.0</td>\n      <td>27.59</td>\n      <td>9.94</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>北京</td>\n      <td>华能</td>\n      <td>1998/1/20 0:00</td>\n      <td>165</td>\n      <td>供热式</td>\n      <td>超高压</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>2016.0</td>\n      <td>47387.95000</td>\n      <td>...</td>\n      <td>2.126813e+05</td>\n      <td>74.50</td>\n      <td>烟煤</td>\n      <td>23380.0</td>\n      <td>27.59</td>\n      <td>9.94</td>\n      <td>0.226207</td>\n      <td>23380.0</td>\n      <td>27.59</td>\n      <td>9.94</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>北京</td>\n      <td>华能</td>\n      <td>1998/12/20 0:00</td>\n      <td>220</td>\n      <td>供热式</td>\n      <td>超高压</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>2016.0</td>\n      <td>115498.04000</td>\n      <td>...</td>\n      <td>4.410925e+05</td>\n      <td>78.76</td>\n      <td>烟煤</td>\n      <td>23380.0</td>\n      <td>27.59</td>\n      <td>9.94</td>\n      <td>0.220954</td>\n      <td>23380.0</td>\n      <td>27.59</td>\n      <td>9.94</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>北京</td>\n      <td>华能</td>\n      <td>1999/6/26 0:00</td>\n      <td>220</td>\n      <td>供热式</td>\n      <td>超高压</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>2016.0</td>\n      <td>120884.07000</td>\n      <td>...</td>\n      <td>4.707218e+05</td>\n      <td>81.41</td>\n      <td>烟煤</td>\n      <td>23380.0</td>\n      <td>27.59</td>\n      <td>9.94</td>\n      <td>0.216298</td>\n      <td>23380.0</td>\n      <td>27.59</td>\n      <td>9.94</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>辽宁</td>\n      <td>大唐</td>\n      <td>2009/4/30 0:00</td>\n      <td>300</td>\n      <td>供热式</td>\n      <td>亚临界</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>2016.0</td>\n      <td>111218.55000</td>\n      <td>...</td>\n      <td>3.726990e+05</td>\n      <td>71.27</td>\n      <td>褐煤</td>\n      <td>14122.0</td>\n      <td>24.78</td>\n      <td>19.16</td>\n      <td>0.238755</td>\n      <td>14122.0</td>\n      <td>24.78</td>\n      <td>19.16</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>847</th>\n      <td>新疆</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1320</td>\n      <td>纯凝式</td>\n      <td>超临界</td>\n      <td>间接空冷</td>\n      <td>煤粉</td>\n      <td>NaN</td>\n      <td>704381.26290</td>\n      <td>...</td>\n      <td>2.283076e+06</td>\n      <td>NaN</td>\n      <td>褐煤</td>\n      <td>19970.0</td>\n      <td>35.33</td>\n      <td>9.05</td>\n      <td>0.196452</td>\n      <td>19970.0</td>\n      <td>35.33</td>\n      <td>9.05</td>\n    </tr>\n    <tr>\n      <th>848</th>\n      <td>辽宁</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>700</td>\n      <td>供热式</td>\n      <td>超临界</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>NaN</td>\n      <td>350000.00000</td>\n      <td>...</td>\n      <td>1.328747e+06</td>\n      <td>NaN</td>\n      <td>褐煤</td>\n      <td>14640.0</td>\n      <td>44.39</td>\n      <td>16.84</td>\n      <td>0.185688</td>\n      <td>14640.0</td>\n      <td>44.39</td>\n      <td>16.84</td>\n    </tr>\n    <tr>\n      <th>849</th>\n      <td>内蒙</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>700</td>\n      <td>供热式</td>\n      <td>超临界</td>\n      <td>直接空冷</td>\n      <td>煤粉</td>\n      <td>NaN</td>\n      <td>385000.00000</td>\n      <td>...</td>\n      <td>1.362009e+06</td>\n      <td>NaN</td>\n      <td>褐煤</td>\n      <td>13960.0</td>\n      <td>46.65</td>\n      <td>18.90</td>\n      <td>0.181214</td>\n      <td>13960.0</td>\n      <td>46.65</td>\n      <td>18.90</td>\n    </tr>\n    <tr>\n      <th>850</th>\n      <td>山东</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>40</td>\n      <td>供热式</td>\n      <td>超高压</td>\n      <td>水冷</td>\n      <td>循环流化床</td>\n      <td>NaN</td>\n      <td>17000.00000</td>\n      <td>...</td>\n      <td>1.810834e+05</td>\n      <td>NaN</td>\n      <td>烟煤</td>\n      <td>21060.0</td>\n      <td>19.12</td>\n      <td>20.27</td>\n      <td>0.347570</td>\n      <td>21060.0</td>\n      <td>19.12</td>\n      <td>20.27</td>\n    </tr>\n    <tr>\n      <th>851</th>\n      <td>浙江</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>70</td>\n      <td>供热式</td>\n      <td>超高压</td>\n      <td>水冷</td>\n      <td>循环流化床</td>\n      <td>NaN</td>\n      <td>35788.81469</td>\n      <td>...</td>\n      <td>3.502535e+05</td>\n      <td>NaN</td>\n      <td>烟煤</td>\n      <td>22021.0</td>\n      <td>19.12</td>\n      <td>21.77</td>\n      <td>0.253057</td>\n      <td>22021.0</td>\n      <td>19.12</td>\n      <td>21.77</td>\n    </tr>\n  </tbody>\n</table>\n<p>852 rows × 21 columns</p>\n</div>"
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "outputs": [],
   "source": [
    "new_use_data = total_data.groupby(use_col + ['煤种'])['CO2_em_air'].mean().reset_index().drop(columns=['入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new'])\n",
    "new_use_data.rename(columns={0:'CO2_em_air'}, inplace=True)\n",
    "new_use_data['coal_params'] = new_use_data['煤种'].apply(lambda x: coal_params_dict.get(x))\n",
    "new_use_data.drop(columns='煤种', inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "outputs": [],
   "source": [
    "new_data = new_use_data.explode(column='coal_params')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "outputs": [
    {
     "data": {
      "text/plain": "      地区 机组类型 参数分类 冷却方式 锅炉类型  机组容量               coal_params\n0     上海  纯凝式  亚临界   水冷   煤粉   320   [12771.0, 21.26, 33.55]\n0     上海  纯凝式  亚临界   水冷   煤粉   320   [15000.0, 23.46, 19.04]\n0     上海  纯凝式  亚临界   水冷   煤粉   320   [16100.0, 23.33, 18.73]\n0     上海  纯凝式  亚临界   水冷   煤粉   320   [16190.0, 23.33, 18.73]\n0     上海  纯凝式  亚临界   水冷   煤粉   320   [16641.0, 19.13, 39.12]\n..   ...  ...  ...  ...  ...   ...                       ...\n646  黑龙江  纯凝式  超高压   水冷   煤粉   210  [23253.68, 23.72, 18.45]\n646  黑龙江  纯凝式  超高压   水冷   煤粉   210    [23380.0, 27.59, 9.94]\n646  黑龙江  纯凝式  超高压   水冷   煤粉   210  [23487.51, 29.27, 20.97]\n646  黑龙江  纯凝式  超高压   水冷   煤粉   210    [23650.0, 28.87, 7.91]\n646  黑龙江  纯凝式  超高压   水冷   煤粉   210  [23656.14, 29.27, 20.97]\n\n[208875 rows x 7 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>地区</th>\n      <th>机组类型</th>\n      <th>参数分类</th>\n      <th>冷却方式</th>\n      <th>锅炉类型</th>\n      <th>机组容量</th>\n      <th>coal_params</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>上海</td>\n      <td>纯凝式</td>\n      <td>亚临界</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>320</td>\n      <td>[12771.0, 21.26, 33.55]</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>上海</td>\n      <td>纯凝式</td>\n      <td>亚临界</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>320</td>\n      <td>[15000.0, 23.46, 19.04]</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>上海</td>\n      <td>纯凝式</td>\n      <td>亚临界</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>320</td>\n      <td>[16100.0, 23.33, 18.73]</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>上海</td>\n      <td>纯凝式</td>\n      <td>亚临界</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>320</td>\n      <td>[16190.0, 23.33, 18.73]</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>上海</td>\n      <td>纯凝式</td>\n      <td>亚临界</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>320</td>\n      <td>[16641.0, 19.13, 39.12]</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>646</th>\n      <td>黑龙江</td>\n      <td>纯凝式</td>\n      <td>超高压</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>210</td>\n      <td>[23253.68, 23.72, 18.45]</td>\n    </tr>\n    <tr>\n      <th>646</th>\n      <td>黑龙江</td>\n      <td>纯凝式</td>\n      <td>超高压</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>210</td>\n      <td>[23380.0, 27.59, 9.94]</td>\n    </tr>\n    <tr>\n      <th>646</th>\n      <td>黑龙江</td>\n      <td>纯凝式</td>\n      <td>超高压</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>210</td>\n      <td>[23487.51, 29.27, 20.97]</td>\n    </tr>\n    <tr>\n      <th>646</th>\n      <td>黑龙江</td>\n      <td>纯凝式</td>\n      <td>超高压</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>210</td>\n      <td>[23650.0, 28.87, 7.91]</td>\n    </tr>\n    <tr>\n      <th>646</th>\n      <td>黑龙江</td>\n      <td>纯凝式</td>\n      <td>超高压</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>210</td>\n      <td>[23656.14, 29.27, 20.97]</td>\n    </tr>\n  </tbody>\n</table>\n<p>208875 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.drop(columns=['CO2_em_air'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "outputs": [],
   "source": [
    "norm_data = pd.concat([new_data, new_data.coal_params.apply(pd.Series, index=['入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new'])], axis=1).drop(columns='coal_params')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "outputs": [
    {
     "data": {
      "text/plain": "      地区 机组类型 参数分类 冷却方式 锅炉类型  机组容量  CO2_em_air  入炉煤低位热值_new  燃煤挥发份Var_new  \\\n0     上海  纯凝式  亚临界   水冷   煤粉   320    0.266602     12771.00         21.26   \n0     上海  纯凝式  亚临界   水冷   煤粉   320    0.266602     15000.00         23.46   \n0     上海  纯凝式  亚临界   水冷   煤粉   320    0.266602     16100.00         23.33   \n0     上海  纯凝式  亚临界   水冷   煤粉   320    0.266602     16190.00         23.33   \n0     上海  纯凝式  亚临界   水冷   煤粉   320    0.266602     16641.00         19.13   \n..   ...  ...  ...  ...  ...   ...         ...          ...           ...   \n646  黑龙江  纯凝式  超高压   水冷   煤粉   210    0.278763     23253.68         23.72   \n646  黑龙江  纯凝式  超高压   水冷   煤粉   210    0.278763     23380.00         27.59   \n646  黑龙江  纯凝式  超高压   水冷   煤粉   210    0.278763     23487.51         29.27   \n646  黑龙江  纯凝式  超高压   水冷   煤粉   210    0.278763     23650.00         28.87   \n646  黑龙江  纯凝式  超高压   水冷   煤粉   210    0.278763     23656.14         29.27   \n\n     燃煤灰份Aar_new  \n0          33.55  \n0          19.04  \n0          18.73  \n0          18.73  \n0          39.12  \n..           ...  \n646        18.45  \n646         9.94  \n646        20.97  \n646         7.91  \n646        20.97  \n\n[208875 rows x 10 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>地区</th>\n      <th>机组类型</th>\n      <th>参数分类</th>\n      <th>冷却方式</th>\n      <th>锅炉类型</th>\n      <th>机组容量</th>\n      <th>CO2_em_air</th>\n      <th>入炉煤低位热值_new</th>\n      <th>燃煤挥发份Var_new</th>\n      <th>燃煤灰份Aar_new</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>上海</td>\n      <td>纯凝式</td>\n      <td>亚临界</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>320</td>\n      <td>0.266602</td>\n      <td>12771.00</td>\n      <td>21.26</td>\n      <td>33.55</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>上海</td>\n      <td>纯凝式</td>\n      <td>亚临界</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>320</td>\n      <td>0.266602</td>\n      <td>15000.00</td>\n      <td>23.46</td>\n      <td>19.04</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>上海</td>\n      <td>纯凝式</td>\n      <td>亚临界</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>320</td>\n      <td>0.266602</td>\n      <td>16100.00</td>\n      <td>23.33</td>\n      <td>18.73</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>上海</td>\n      <td>纯凝式</td>\n      <td>亚临界</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>320</td>\n      <td>0.266602</td>\n      <td>16190.00</td>\n      <td>23.33</td>\n      <td>18.73</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>上海</td>\n      <td>纯凝式</td>\n      <td>亚临界</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>320</td>\n      <td>0.266602</td>\n      <td>16641.00</td>\n      <td>19.13</td>\n      <td>39.12</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    </tr>\n    <tr>\n      <th>646</th>\n      <td>黑龙江</td>\n      <td>纯凝式</td>\n      <td>超高压</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>210</td>\n      <td>0.278763</td>\n      <td>23253.68</td>\n      <td>23.72</td>\n      <td>18.45</td>\n    </tr>\n    <tr>\n      <th>646</th>\n      <td>黑龙江</td>\n      <td>纯凝式</td>\n      <td>超高压</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>210</td>\n      <td>0.278763</td>\n      <td>23380.00</td>\n      <td>27.59</td>\n      <td>9.94</td>\n    </tr>\n    <tr>\n      <th>646</th>\n      <td>黑龙江</td>\n      <td>纯凝式</td>\n      <td>超高压</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>210</td>\n      <td>0.278763</td>\n      <td>23487.51</td>\n      <td>29.27</td>\n      <td>20.97</td>\n    </tr>\n    <tr>\n      <th>646</th>\n      <td>黑龙江</td>\n      <td>纯凝式</td>\n      <td>超高压</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>210</td>\n      <td>0.278763</td>\n      <td>23650.00</td>\n      <td>28.87</td>\n      <td>7.91</td>\n    </tr>\n    <tr>\n      <th>646</th>\n      <td>黑龙江</td>\n      <td>纯凝式</td>\n      <td>超高压</td>\n      <td>水冷</td>\n      <td>煤粉</td>\n      <td>210</td>\n      <td>0.278763</td>\n      <td>23656.14</td>\n      <td>29.27</td>\n      <td>20.97</td>\n    </tr>\n  </tbody>\n</table>\n<p>208875 rows × 10 columns</p>\n</div>"
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "outputs": [],
   "source": [
    "for col in num_cols:\n",
    "    norm_data[col] = np.log1p(norm_data[col])\n",
    "    # total_data[col] = (total_data[col] - total_data[col].min()) / (total_data[col].max() - total_data[col].min())\n",
    "norm_data_dummy = pd.get_dummies(norm_data, columns=object_cols)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "outputs": [
    {
     "data": {
      "text/plain": "         机组容量  CO2_em_air  入炉煤低位热值_new  燃煤挥发份Var_new  燃煤灰份Aar_new  地区_上海  \\\n0    5.771441    0.236338     9.455011      3.102791     3.542408      1   \n0    5.771441    0.236338     9.615872      3.197039     2.997730      1   \n0    5.771441    0.236338     9.686637      3.191710     2.982140      1   \n0    5.771441    0.236338     9.692211      3.191710     2.982140      1   \n0    5.771441    0.236338     9.719685      3.002211     3.691875      1   \n..        ...         ...          ...           ...          ...    ...   \n646  5.351858    0.245893    10.054262      3.207613     2.967847      0   \n646  5.351858    0.245893    10.059679      3.353057     2.392426      0   \n646  5.351858    0.245893    10.064267      3.410157     3.089678      0   \n646  5.351858    0.245893    10.071161      3.396855     2.187174      0   \n646  5.351858    0.245893    10.071420      3.410157     3.089678      0   \n\n     地区_云南  地区_内蒙  地区_北京  地区_吉林  ...  机组类型_纯凝式  参数分类_亚临界  参数分类_超临界  参数分类_超超临界  \\\n0        0      0      0      0  ...         1         1         0          0   \n0        0      0      0      0  ...         1         1         0          0   \n0        0      0      0      0  ...         1         1         0          0   \n0        0      0      0      0  ...         1         1         0          0   \n0        0      0      0      0  ...         1         1         0          0   \n..     ...    ...    ...    ...  ...       ...       ...       ...        ...   \n646      0      0      0      0  ...         1         0         0          0   \n646      0      0      0      0  ...         1         0         0          0   \n646      0      0      0      0  ...         1         0         0          0   \n646      0      0      0      0  ...         1         0         0          0   \n646      0      0      0      0  ...         1         0         0          0   \n\n     参数分类_超高压  冷却方式_水冷  冷却方式_直接空冷  冷却方式_间接空冷  锅炉类型_循环流化床  锅炉类型_煤粉  \n0           0        1          0          0           0        1  \n0           0        1          0          0           0        1  \n0           0        1          0          0           0        1  \n0           0        1          0          0           0        1  \n0           0        1          0          0           0        1  \n..        ...      ...        ...        ...         ...      ...  \n646         1        1          0          0           0        1  \n646         1        1          0          0           0        1  \n646         1        1          0          0           0        1  \n646         1        1          0          0           0        1  \n646         1        1          0          0           0        1  \n\n[208875 rows x 45 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>机组容量</th>\n      <th>CO2_em_air</th>\n      <th>入炉煤低位热值_new</th>\n      <th>燃煤挥发份Var_new</th>\n      <th>燃煤灰份Aar_new</th>\n      <th>地区_上海</th>\n      <th>地区_云南</th>\n      <th>地区_内蒙</th>\n      <th>地区_北京</th>\n      <th>地区_吉林</th>\n      <th>...</th>\n      <th>机组类型_纯凝式</th>\n      <th>参数分类_亚临界</th>\n      <th>参数分类_超临界</th>\n      <th>参数分类_超超临界</th>\n      <th>参数分类_超高压</th>\n      <th>冷却方式_水冷</th>\n      <th>冷却方式_直接空冷</th>\n      <th>冷却方式_间接空冷</th>\n      <th>锅炉类型_循环流化床</th>\n      <th>锅炉类型_煤粉</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.771441</td>\n      <td>0.236338</td>\n      <td>9.455011</td>\n      <td>3.102791</td>\n      <td>3.542408</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>5.771441</td>\n      <td>0.236338</td>\n      <td>9.615872</td>\n      <td>3.197039</td>\n      <td>2.997730</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>5.771441</td>\n      <td>0.236338</td>\n      <td>9.686637</td>\n      <td>3.191710</td>\n      <td>2.982140</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>5.771441</td>\n      <td>0.236338</td>\n      <td>9.692211</td>\n      <td>3.191710</td>\n      <td>2.982140</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>5.771441</td>\n      <td>0.236338</td>\n      <td>9.719685</td>\n      <td>3.002211</td>\n      <td>3.691875</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</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>646</th>\n      <td>5.351858</td>\n      <td>0.245893</td>\n      <td>10.054262</td>\n      <td>3.207613</td>\n      <td>2.967847</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>646</th>\n      <td>5.351858</td>\n      <td>0.245893</td>\n      <td>10.059679</td>\n      <td>3.353057</td>\n      <td>2.392426</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>646</th>\n      <td>5.351858</td>\n      <td>0.245893</td>\n      <td>10.064267</td>\n      <td>3.410157</td>\n      <td>3.089678</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>646</th>\n      <td>5.351858</td>\n      <td>0.245893</td>\n      <td>10.071161</td>\n      <td>3.396855</td>\n      <td>2.187174</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>646</th>\n      <td>5.351858</td>\n      <td>0.245893</td>\n      <td>10.071420</td>\n      <td>3.410157</td>\n      <td>3.089678</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>208875 rows × 45 columns</p>\n</div>"
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_data_dummy"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "outputs": [],
   "source": [
    "new_xgb_data = xgb.DMatrix(norm_data_dummy[feature_cols])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "outputs": [],
   "source": [
    "norm_data.drop(columns='CO2_em_air', inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "outputs": [],
   "source": [
    "norm_data['co2_pred'] = gb_model.predict(new_xgb_data)\n",
    "normaled_data = norm_data.drop(columns=['入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new']).groupby([x for x in use_col if x not in ['CO2_em_air', '入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new']])['co2_pred'].mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "normaled_data.reset_index().to_csv('./data/去煤种化数据.csv', encoding='utf-8-sig', index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.13 ('py37')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "993bd31d5df1020fab369d79a34ff0a2a159e1798f3e25d3ad4b7751d38184c9"
   }
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 },
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}