zks_010_时序数据变换，获得稳定性数据.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import numpy as np             #for numerical computations like log,exp,sqrt etc\n",
    "import pandas as pd            #for reading & storing data, pre-processing\n",
    "import matplotlib.pylab as plt #for visualization\n",
    "#for making sure matplotlib plots are generated in Jupyter notebook itself\n",
    "%matplotlib inline             \n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.stattools import acf, pacf\n",
    "\n",
    "##分解(decomposing) 可以用来把时序数据中的趋势和周期性数据都分离出来:\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from statsmodels.tsa.arima_model import ARMA\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 10, 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmedCount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>1312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>4579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>6061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>7816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>9821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>11948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>14551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>17387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>20626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>24553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>28276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>31439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>34876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>37612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-09</td>\n",
       "      <td>40234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>42760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>44789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>60120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>64456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>66900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>69044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>71231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>73260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>75138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>75669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>76202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>77672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-22</td>\n",
       "      <td>78635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>79202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>79774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>80427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>81406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>82794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>84186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>86602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>88377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>90308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>92880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>95181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>98088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>101954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>106016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>109936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>114285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>118905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>126054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>134529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>144031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>156433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>169362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>182406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>198178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>199193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmedCount\n",
       "日期                        \n",
       "2020-01-20             282\n",
       "2020-01-21             314\n",
       "2020-01-22             581\n",
       "2020-01-23             844\n",
       "2020-01-24            1312\n",
       "2020-01-25            2015\n",
       "2020-01-26            2801\n",
       "2020-01-27            4579\n",
       "2020-01-28            6061\n",
       "2020-01-29            7816\n",
       "2020-01-30            9821\n",
       "2020-01-31           11948\n",
       "2020-02-01           14551\n",
       "2020-02-02           17387\n",
       "2020-02-03           20626\n",
       "2020-02-04           24553\n",
       "2020-02-05           28276\n",
       "2020-02-06           31439\n",
       "2020-02-07           34876\n",
       "2020-02-08           37612\n",
       "2020-02-09           40234\n",
       "2020-02-10           42760\n",
       "2020-02-11           44789\n",
       "2020-02-12           60120\n",
       "2020-02-13           64456\n",
       "2020-02-14           66900\n",
       "2020-02-15           69044\n",
       "2020-02-16           71231\n",
       "2020-02-17           73260\n",
       "2020-02-18           75138\n",
       "2020-02-19           75669\n",
       "2020-02-20           76202\n",
       "2020-02-21           77672\n",
       "2020-02-22           78635\n",
       "2020-02-23           79202\n",
       "2020-02-24           79774\n",
       "2020-02-25           80427\n",
       "2020-02-26           81406\n",
       "2020-02-27           82794\n",
       "2020-02-28           84186\n",
       "2020-02-29           86602\n",
       "2020-03-01           88377\n",
       "2020-03-02           90308\n",
       "2020-03-03           92880\n",
       "2020-03-04           95181\n",
       "2020-03-05           98088\n",
       "2020-03-06          101954\n",
       "2020-03-07          106016\n",
       "2020-03-08          109936\n",
       "2020-03-09          114285\n",
       "2020-03-10          118905\n",
       "2020-03-11          126054\n",
       "2020-03-12          134529\n",
       "2020-03-13          144031\n",
       "2020-03-14          156433\n",
       "2020-03-15          169362\n",
       "2020-03-16          182406\n",
       "2020-03-17          198178\n",
       "2020-03-18          199193"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data = pd.read_csv('./world.csv', index_col=0)\n",
    "#增加id递增列\n",
    "Data = Data.reset_index()\n",
    "##该步骤很重要！！建立时序数据\n",
    "#Parse strings to datetime type  将字符串解析为datetime类型\n",
    "#convert from string to datetime ###Name: date, dtype: datetime64[ns]\n",
    "Data['日期']= pd.to_datetime(Data['日期'],infer_datetime_format=True) \n",
    "#set_index( ) 将 DataFrame 中的列转化为行索引\n",
    "indexedDataset = Data.set_index(['日期'])\n",
    "#数据反转\n",
    "indexedDataset = indexedDataset.iloc[::-1]\n",
    "indexedDataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\converter.py:103: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23d014e9908>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA/IAAAGJCAYAAAApGAgTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAMTQAADE0B0s6tTgAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8GearUAAAgAElEQVR4nOzdeXydZZ338c8vS5M0SdMtXdOVUijQUkoL6CA4LiM6KrLIMoILPCOOjs4M4zKjzOgj84w6PDKjIoP6gAioyCqoCKMiIpsCpdCV0r1J96Ztki5Jk1zPH+eknNYCCU16kubzfr3OK+e+f/fJ+Z2ypN9c131dkVJCkiRJkiT1DQX5bkCSJEmSJHWeQV6SJEmSpD7EIC9JkiRJUh9ikJckSZIkqQ8xyEuSJEmS1IcY5CVJkiRJ6kMM8pIkSZIk9SFF+W6gLygpKUnV1dX5bkOSJEmS1E/U1dW1pJRKDlYzyHdCdXU1tbW1+W5DkiRJktRPRMTmV6o5tV6SJEmSpD7EIC9JkiRJUh9ikJckSZIkqQ8xyEuSJEmS1IcY5CVJkiRJ6kMM8pIkSZIk9SEGeUmSJEmS+pAeDfIRURoRP42IpRExLyIejIiJ2dqI7PFLEbEgIk7Ped3AiPhxRCzLvvbcnFpBRHwrIpZn6x8/4D2vytaWR8TVB9Quz77f8oj4bkQU9eTnlyRJkiSpux2OEfnvAseklGYCP88eA3wVeCqldDTwEeCHOcH600BzSmkK8A7g+ogYkq1dAhwHTAVOAT4bEccCRMQZwMXAjOw174yId2Rrk4CrgdOBKcAo4PIe+9SSJEmSJPWAHg3yKaU9KaUHUkope+opYHL2+QXAt7PXPQ1sJBOyAS7Mqa0EHgXOzqndkFJqSynVA3cAF+XUbk4p7UwpNQM3kQn2AOcD96aUNmb7uSGnJkmSJElSn3C4p5Z/CvhZRAwDClJKm3Nqq4Dx2efjgdVdqM3Oqf3ugNr5nfie+4mIK4ErO46rqqpe6fNIkiRJknqplBINe1rZ3NjMpsY9bG5sZvLwCqbX9O2Md9iCfER8Hjga+BhQBqQDLzngOB3m2ssXpXQtcG3HcU1NzYG9SpIkSZLypLWtna07W/YL6JsamtnU2PzyuabMuebW9v1ee8UZkw3ynRERnwbOBd6WUtoF7IoIIqI6Z1R+ArAm+3wNMBHIrT1wQO3pV3kdXaxJkiRJkvqAq3++iO8/vpL2VxhuLQgYVlFCdUUJRx1VQXVFCSMGlWS/lnLMqMrD23AP6PEgn52mfjGZEL89p3Qn8AngSxExh8zic48dUPtwdpG6M8mM5HfUroiIe4AqMvfFn5VTuy4irgdagcuAq7K1u4HHIuLLwKbs97u9mz+uJEmSJKmH/GrRRm58bCXHjqrklElDGVFZwojKUqorS6iuLGFEZQlDywdQVHhk77Teo0E+ImqArwMrgN9GBGRWoz8V+Bxwa0S8BLQAl6aUWrMvvQa4KSKWAe3AJ7IL2wHcCswBlnZcm1JaDJBSeiQi7gDmZ2u3p5QezNZWRMQXgcfJLPL3MHBjD310SZIkSVI32r6rhc/fO59BpUX84LJTGDmoNN8t5U28vKC8XklNTU2qra3NdxuSJEmS1G/9/e3P8dN567j2ghM5d1ZNvtvpcRFRl1I66Ac9sucbSJIkSZL6vIcWbuCn89bxtmkjOOeksfluJ+8M8pIkSZKkXqt+ZwtfuHc+VWXF/Ps508nest2vHe595CVJkiRJ6rQv3r+QLU0t/NeFMxnRj++Lz+WIvCRJkiSpV3pwwXp+9vw63n7cSM6eOSbf7fQaBnlJkiRJUq+ztamZL9y7gMEDi/k/55zglPocTq2XJEmSJPU6/3r/QrbubOEbF81kRKVT6nM5Ii9JkiRJ6lV+8cJ6fvHCes46fhTvPdEp9QcyyEuSJEmSeo0tTc38y30LGDKwmKvf55T6g3FqvSRJkiSp1/jX+xZQv7OFb118EtWVJflup1dyRF6SJEmS1Cv8/IV1PDB/A++aPop3zxid73Z6LYO8JEmSJCnvNjc28y8/XcDQ8gF8+Wyn1L8ap9ZLkiRJkvIqpcRVP53Ptl17+fZfzWJ4hVPqX40j8pIkSZKkvLr/+XU8tHAjfzljNH/plPrXZJCXJEmSJOXNpsY9fPH+hQwrH8CX33t8vtvpE5xaL0mSJEnKi5QSX7h3Adt37eW/PzCLYU6p7xRH5CVJkiRJeXHfvHX8atFG3nPiGN453Sn1nWWQlyRJkiQddpsaMlPqh1eUOKW+i5xaL0mSJEk6rFJKfP7e+ezYvZfvXHoyQ8oH5LulPsUReUmSJEnSYXXbU6v59eJNnD1zDO84flS+2+lzDPKSJEmSpMPmDyu28r9/togJwwby5feekO92+iSDvCRJkiTpsKjdtouP/3AuJUUFfO+Ds6kaWJzvlvok75GXJEmSJPW43S1tXHHrs2zd2cJ3Lj2ZqSMr891Sn+WIvCRJkiSpR6WU+OzdL7BwXQN//7ajvS/+EBnkJUmSJEk96juPruBnz6/jL44byafecnS+2+nzDPKSJEmSpB7zyIub+NqDS5g6soJrL5xJQUHku6U+r8eDfER8MyJWRUSKiBNyzj8REfOyjwXZ+oxs7eaIqM2pX5PzuoKI+FZELI+IZRHx8QPe76psbXlEXH1A7fKIeClb+25EuEaAJEmSJPWQFZub+OSPn2NQaTHf++BsKkqMYN3hcIzI3wWcDqzOPZlSemNKaWZKaSbwJWBBSumFnEu+2lFPKX0m5/wlwHHAVOAU4LMRcSxARJwBXAzMyF7zzoh4R7Y2Cbg628sUYBRweXd/WEmSJEkSNO7Zy0dvfZadza1c91cnMWFYeb5bOmL0eJBPKT2aUqp9jcsuA27s5Le8ELghpdSWUqoH7gAuyqndnFLamVJqBm4iE+wBzgfuTSltTCkl4IacmiRJkiSpm7S3J/7hJ/NYtqmJf37nNN50dHW+Wzqi5P0e+YgYC7wZuO2A0pUR8UJE/DwiZuacH8/+o/ursucOpSZJkiRJ6ib/9eul/HrxJs45aSz/602T8t3OESfvQR74MPDzlNKWnHNfAKaklGaQGan/ZURU5NRTzvMDV0p4vbWXCxFXZu/Rr42I2qamptf6DJIkSZIk4Jfz1/PNh5cxo6aKr5w7nQgXt+tueQ3ykfkn+hEOmFafUqpLKbVnn98LNADHZMtrgIk5l0/InjuU2n5SStemlGo6HhUVFQe7TJIkSZKUY8mGBv7xzucZXjGAGy45mdLiwny3dETK94j8mcAA4Fe5JyOiJuf5acAwYFn21J3AFRFRGBFDydwX/5Oc2ociojwiSsjce397tnY3cE5EjMz+AuFjOTVJkiRJ0iHYtrOFv77lGfa2tfPfl5zMmMFl+W7piNXja/9HxLeBs8msEv/riGhKKU3Jli8Hvt8x+p7j5ogYCbQBu4H3p5R2ZGu3AnOApdnja1JKiwFSSo9ExB3A/Gzt9pTSg9naioj4IvA4mV9gPEznF9iTJEmSJL2C1rZ2/vbHc1lbv5t/P2c6cyYOzXdLR7TILOCuV1NTU5Nqa19r4X1JkiRJ6p++/LNF3PT4Sj5w6nj+zznT893OESEi6lJKNQer5XtqvSRJkiSpD7v72VpuenwlcyYO4YvvOT7f7fQLBnlJkiRJ0usyb+12/vne+YypKuX6D5zMgCIj5uHgn7IkSZIkqcs2NezhilufIYDvXDqb6sqSfLfUb/T4YneSJEmSpCPLnr1tXHHbs2xsaOYbF81kek1VvlvqVxyRlyRJkiR1WkqJq366gOfWbOdjZx7F2TPH5rulfscgL0mSJEnqtBsfW8ldz9bylmNH8Jl3HJPvdvolg7wkSZIkqVMeXbqZf39gMUdVl/NfF82ksCDy3VK/ZJCXJEmSJL2mlVt28rc/mkt5SRHf++BsBpUW57ulfssgL0mSJEl6VY179vLXtzxDU3Mr1/3VLCZXV+S7pX7NIC9JkiRJekVt7Ym/v30eyzY18fl3TePMqdX5bqnfM8hLkiRJkl7R1//nRX6zZBPnzhrL5adPync7wiAvSZIkSXoF9z+/jusfWc7McYP593OmE+Hidr2BQV6SJEmS9Cfm1+7gs3c9z4jKEr5z6cmUFhfmuyVlGeQlSZIkSfvZ1LiHj976DO0JvvvB2YwcVJrvlpTDIC9JkiRJ2qe5tY2/uW0u63fs4avnTmfmuMH5bkkHMMhLkiRJkgBIKfGvP13Is6u38dEzJnPurJp8t6SDMMhLkiRJkgD4wROr+MkzazlzajWfO+vYfLejV2CQlyRJkiTx+LItXP2LxUweXs43Lz6JwgJXqO+tDPKSJEmS1M+t2bqLT/xoLgOLC/neh2ZTVVac75b0Kory3YAkSZIkKX+amlv5X7c8zY7de7npQ3M4qroi3y3pNTgiL0mSJEn9VFt74lM/fo6lG5v4p7OO5c+PHZHvltQJBnlJkiRJ6qf+7ReLeHjJJs6bVcNHz5ic73bUSQZ5SZIkSeqHbnlyFd9/fBWnThrKV86dToSL2/UVBnlJkiRJ6mceeXETX7p/IZOGl3PDJSczoMho2Jf4T0uSJEmS+pElGxr42x89x6CyYm768ByGlA/Id0vqIoO8JEmSJPUTmxr3cPnNz9Dc2sYNl5zMpOHl+W5Jr0OPB/mI+GZErIqIFBEn5Jx/JCJWRMS87OMfcmoDI+LHEbEsIpZGxLk5tYKI+FZELM/WP37A+12VrS2PiKsPqF0eES9la9+NCLffkyRJktQv7G5p469veZa67bv5yrkzOG3ysHy3pNfpcIzI3wWcDqw+SO1TKaWZ2cd/5pz/NNCcUpoCvAO4PiKGZGuXAMcBU4FTgM9GxLEAEXEGcDEwI3vNOyPiHdnaJODqbC9TgFHA5d36SSVJkiSpF2pvT/zjnfN4fu12/vbPp3D+yTX5bkmHoMeDfErp0ZRSbRdfdiHw7ezrVwKPAmfn1G5IKbWllOqBO4CLcmo3p5R2ppSagZvIBHuA84F7U0obU0oJuCGnJkmSJElHrP/7Py/ywPwN/OWM0Vz59qn5bkeHKN/3yF8TEfMj4icRkbtp4Xj2H8FflT3XU7X9RMSVEVHb8Whqaurs55EkSZKkXuWOZ9Zy/SPLmTluMF9//4kUFLjNXF+XzyB/aUppGplp8L8Hfn5APeU8P/DftJ6ovXxRStemlGo6HhUVFa90qSRJkiT1Wk8s38Ln75nP2MFlfO+DsyktLsx3S+oGeQvyKaW12a8ppXQdMDkiOlZbWANMzLl8QvZcT9UkSZIk6YiyfHMTf3PbXMqKC/n+R+ZQXVmS75bUTfIS5COiKCJG5hyfB2xMKW3NnroT+ES2Ngk4E7g/p3ZFRBRGxFAy98X/JKf2oYgoj4gS4DLg9mztbuCciBgZEQF8LKcmSZIkSUeM+p0tXHbz0zQ1t3LdB2YxdWRlvltSN+rx7dci4ttkFqobBfw6IpqAE4FfZMN2O7AFeG/Oy64BboqIZdn6J7IL2wHcCswBlnZcm1JaDJBSeiQi7gDmZ2u3p5QezNZWRMQXgcfJ/ALjYeDGnvjMkiRJkpQvza1tfOzWZ1m9dRdXv+8Ezpxane+W1M0is4C7Xk1NTU2qre3qwvuSJEmSdHillPjHO57nnufquPz0SfzLu4/Ld0t6nSKiLqV00H0C871qvSRJkiSpm1z38DLuea6Ot00bweffNS3f7aiHGOQlSZIk6Qhw//Pr+PqvlnLc6EF846KTKHSbuSOWQV6SJEmS+rifv7COT9/xPCMHlXDjh2dTXtLjy6EpjwzykiRJktSHff/xlXzyx88xpLyYWy47ldFVZfluST3MX9NIkiRJUh+UUuJrD77IDb9bzlHV5dxy+amMHWyI7w8M8pIkSZLUx+xta+dzd7/APXPrmDV+MDd+aA5Dygfkuy0dJgZ5SZIkSepDdja38vEfzuV3Szfztmkj+NbFsygbUJjvtnQYGeQlSZIkqY/Y2tTMZTc/zfO1O7hw9jj+zzknUFTo0mf9jUFekiRJkvqANVt38aHv/5GVW3byqbdM4R/ePpUIt5jrjwzykiRJktTLLajbwYe//zRbdzZz9ftO4NLTJuS7JeWRQV6SJEmSerHHl23hilufpaWtnf/+wCzOOmF0vltSnhnkJUmSJKmXuv/5dfzjHfMoLS7k1stO4dTJw/LdknoBg7wkSZIk9UI3PbaSL/98EaMGlXLzZXM4dtSgfLekXsIgL0mSJEm9SEqJrz64hO/8bgVHVZdzy+WnMnZwWb7bUi9ikJckSZKkXmJvWzufu/sF7plbx6zxg7nxQ3MYUj4g322plzHIS5IkSVIvUL+zhb/90VyeWL6Vt00bwbcunkXZgMJ8t6VeyCAvSZIkSXm2oG4HV9z6LHXbd3PpaRP44nuOo6iwIN9tqZcyyEuSJElSHt03r47P3f0C7e3wH+fN4II54/Ldkno5g7wkSZIk5UFrWztfe3AJ3/v9SkYOKuGGS07mpPFD8t2W+gCDvCRJkiQdZtt2tvDJHz/HY8u2cPKEIfz3JbMYUVma77bURxjkJUmSJOkwWrSugStue4a19bv5wKnj+eJ7jmdAkffDq/MM8pIkSZJ0mPz8hXV85s4XaG1v5yvnTufiU8bnuyX1QZ3+tU9E/LEz5yRJkiRJ+2trT3z1l0v42x89R0VpEbd/9DRDvF63rozI73dtRBQAFd3bjiRJkiQdWbbvauFTt8/j0aWbOWn8YG645GRGDvJ+eL1+rxnkI+IzwGeBqojYlFMaCPywpxqTJEmSpL5uyYYGPnrLs6yp38VFc8bxv88+npKiwny3pT6uM1PrvwvMAX6T/drxGJdSuuK1XhwR34yIVRGRIuKEnPM3RcSLETEvIh6NiJk5tZsjojZbmxcR1+TUCiLiWxGxPCKWRcTHD3i/q7K15RFx9QG1yyPipWztuxHhGgGSJEmSesQD89dz7vVPsG77bv7tfSfwlXOnG+LVLV4zyKaUdgA7gHe+zve4C/gP4LEDzv8U+GhKqTUi3g3cAUzNqX81pXTdQb7fJcBx2WurgLkR8XBKaUlEnAFcDMwAWoHHI+KxlNJDETEJuBo4CdgE3AdcDnzndX4uSZIkSfoTrW3tXPurpVz/yHKGV5Twg8tmMWfi0Hy3pSNIVxa7mxURD0bE0ohYERErI2LFa70upfRoSqn2IOfvTym1Zg+fAiZk77t/LRcCN6SU2lJK9WR+AXBRTu3mlNLOlFIzcBOZYA9wPnBvSmljSikBN+TUJEmSJOmQLdvUyHn//QTXP7KcE8cN5mef/DNDvLpdV6aW/wC4DngSaOvmPv4OeCCl1J5z7sqI+CiwBrgqpTQve348sDrnulXA7Jza7w6onf8qr3OZSEmSJEmHrK09cdNjK7nmf15kb1s7V5wxmX94+1RKi51Kr+7XlSDfllLq9mnoEXEJcAHwppzTXwDWp5TaI+Ic4JcRcXRKqSlbT7nf4oBv+XpruT1dCVzZcVxVVfXqH0KSJElSv7Vqy04+fefzPLN6GxOHDeTrF5zIyRMchVfP6fTUejL3m5/UnW8eERcCXwTenlLatyJ+SqmuY3Q+pXQv0AAcky2vASbmfJsJ2XOHUttPSunalFJNx6Oiwl32JEmSJO2vvT1xy5OreOc3fs8zq7fx4TdO5Jd/d4YhXj2uKyPybwT+V0S8COzpOJlSOuX1vHFEXAD8G/C2lNKaA2o1HffVR8RpwDBgWbZ8J3BFRNxDZrG7C4GzcmrXRcT1ZBa7uwy4Klu7G3gsIr5MZrG7jwG3v57eJUmSJPVvtdt28dm7XuCJ5VupGVLGNeefyBuOGpbvttRPdCXI//3reYOI+DZwNjAK+HVENKWUppDZg34DcF/Evlnub00pbQVujoiRZO7F3w28P7t6PsCtZLa/W5o9vialtBggpfRIRNwBzM/Wbk8pPZitrYiILwKPk5mJ8DBw4+v5TJIkSZL6p5QSP3l6Lf/2i8U0Nbdy8Snj+cJfTqOixJ2tdfhEZgF3vZqamppUW/snC+9LkiRJ6kc2Nuzhc3e/wCMvbmbUoFK+et503nzMiHy3pSNURNSllGoOVuv0r40i4rfsv1gcACmltxxCb5IkSZLUq6WU+Om8Or5430Ia9rRy7qyxfPE9x1NVVpzv1tRPdWX+x//NeV4K/BUv37cuSZIkSUeczY3NXPXT+Ty0cCPDK0r47qUn8hfHj8p3W+rnOh3kU0q/yD2OiPuAB7q9I0mSJEnKs+bWNu57bh1ffXAJ9TtbePeM0Xz57BMYWj4g361JXRqRP1ABMKm7GpEkSZKkfNvc2MwP/7Ca255aw5amZoYMLOa6vzqJd88Yk+/WpH26co/8nbx8j3whcCLwUE80JUmSJEmH06J1DXz/8ZXcN28dLW3tjB1cxhfeNY0L5ozzXnj1Ol0Zkf95zvNWMtu+PdXN/UiSJEnSYdHWnnh4ySZuemwlT67YCsCciUO47M8m8fbjRlJUWJDnDqWD68o98j/oyUYkSZIk6XBoam7lzmfWcvMTq1i9dRdFBcE5J43lI382kRk1g/PdnvSaujK1fjTwPeDPs6d+A1yRUlrfE41JkiRJUndaW7+Lm59YxR1Pr6WxuZWh5QP45FumcMlpExg5qDTf7Umd1pWp9d8FngAuyR5/LHvuPd3dlCRJkiR1h5QSf1xZz02Pr+RXizbSnuCYkZVcdvpEzp45ltLiwny3KHVZV4L8uJRSbmj/akTM6+6GJEmSJOlQNTW3cu9zddz25Gpe3NgIwFuPHcFlp0/ijUcNIyLy3KH0+nUlyBdExKiU0gaAiBgB+G+/JEmSpF5j8foGbntqNT99ro6dLW1UlhTx4TdO5INvmMDk6op8tyd1i64E+WuA5yLiZ2S2oXsX8M890pUkSZIkdVJzaxu/nL+B255azTOrtwFw/JhBXHraBN47cwwDB3Ql9ki9X1dWrb81IuaSWewugG+klBb1WGeSJEmS9CrW1u/ih39Yw53PrGXrzhYGFBVw7qyxXHraBGaOG+z0eR2xXjPIR0QNMCql9ExKaSGwMHt+TkSMTSnV9XSTkiRJkgSZvd8feXETtz21mkeWbiYlmDBsIFecOZn3nzyOIeUD8t2i1OM6MyL/NeD/HeT8cOBTwKXd2pEkSZIkHWBLUzM/eXotP/rDGuq276Yg4G3TRnLJaRN405ThFBQ4+q7+ozNBfmZK6bcHnkwp/TIirumBniRJkiSJ5tY2Hl68ibvn1vLIi5tpbU9UV5bwybdM4eJTxjNmcFm+W5Ty4lBXffDXXpIkSZK6TUqJ52t3cPeztfzshXVs37WXgoDTj67mgtk1/MVxoxhQVJDvNqW86kyQb4+I0Sml9bknI2I00N4zbUmSJEnqTzbs2MO9z9Vx99xalm1qAuDoERV87MyjOOeksYwcVJrnDqXeozNB/gbgroi4LKX0IkBEHAt8D/hOTzYnSZIk6ci1u6WN/1m0gbuereXxZVtoTzBkYDEfesMEzju5huljq1x5XjqI1wzyKaVvR8QIMnvIN5PZQ74E+HpK6bqeblCSJEnSkSOlxNOrtnH3s7X8Yv56mppbKSoI3jptJOfNquEtx45w6rz0GiKl1LkLIwYCx2cPF6WUdvZYV71MTU1Nqq2tzXcbkiRJUp+1bvtu7nq2lruerWVN/S4AThg7iPNm1fDeE8cwrKIkzx1KvUtE1KWUag5W68w+8sflHHaE9wkdU1xSSosOuUNJkiRJR5zm1jZ+vWgTP3lmLb9/KbPne3VlCR89YzLnzarhmFGV+W5R6pM6c4/8L8hMpw9gPNCQPT8IWANM6pnWJEmSJPVFi9c3cMcza/npc3Vs27WXooLg7dNGcsHscbz5mGqKCp06Lx2KztwjPwkgIr4FPJpSujN7fD4wu2fbkyRJktQX7Ni9l/ufX8cdT69lft0OAI6qLudv3nwU55xUQ3WlU+el7tKVfeTnpJQ+2XGQUrorIj7dAz1JkiRJ6gPa2xNPrdjKHc+s5ZcLNtDc2k75gEIumjOO988ex6zxg111XuoBXQnyAyPiTSml3wNExOnAwJ5pS5IkSVJv1bFw3Z3PrmVt/W4A5kwcwgWzx/Gu6aMpL+lKzJDUVV35L+wTwI8jomPBuzLg4td6UUR8E3gvMAGYnlJakD0/ArgFOApoBj6WUnosWxsI3AjMAdqBf0op3ZOtFQDfAN5F5t79a1NK1+e831XAR7KHP0op/UtO7XLgn4AC4DfAx1NKrV34M5AkSZL6pabmVh5csIF75tby5IqtpAQjKkv4mzcfxftPrmFydUW+W5T6jU4H+ZTS7yNiMnAMmYXvlqSUWjrx0ruA/wAeO+D8V4GnUkpnRcQc4K6IOCobrD8NNKeUpkTEJODJiPhtSmkbcAlwHDAVqALmRsTDKaUlEXEGmV8uzABagccj4rGU0kPZ73M1cBKwCbgPuBz4Tmf/DCRJkqT+pK098fiyLdwzt5aHFm5k9942BhQWcNbxozhvVo0L10l50tU5L+8Ejk0pfS0ixkTEsJTS/Fd7QUrpUeBg98ZcQHbF+5TS0xGxETgdeAS4EPhwtrYyIh4FzgZuztZuSCm1AfURcQdwEfClbO3mjj3uI+ImMsH+IeB84N6U0sZs7QbgsxjkJUmSpP0s2dDAPXPruG9eHRsbmgE4ecIQzp01lndPH0PVwOI8dyj1b50O8hHxJeBUMlPhv0ZmWvsNwJ919U0jYhhQkFLanHN6FZnt7ch+Xd2F2uyc2u8OqJ3fie95YH9XAld2HFdVVb3iZ5EkSZKOBJsa93D/vHXcM7eOReszO06PG1rGp956NOeeNJaJw8vz3KGkDl0ZkX8fcDLwDEBKaX1EVB7Ce6cDjg8csk+HufbyRSldC1zbcVxTU3Ngr5IkSVKft2dvG/+zaCP3zK3l9y9toa09UVlaxMWnjOPcWTXMnjDEVeelXqgrQX5PSqmtO/5DTiltjQgiojpnVH4CsCb7fA0wEcitPXBA7elXeR1drEmSJEn9woYde3j0pc38/qUt/HbJJpqaWyksCN48tZpzZ9Xw1mkjKC0uzHebkl5FV4L86uyWcym7cvzngVe9P/413ElmJfwvZRe7G8XLC+J11D6cXY11lZUAACAASURBVKTuTOBjObUrIuIeMovdXQiclVO7LiKuJ7PY3WXAVdna3cBjEfFlMovdfQy4/RD6lyRJknq9PXvb+MPKeh5dupnfv7SZpRub9tVm1FTxvpljee/MMQyvKMljl5K6oitB/lPAD4ATgF3A74EPvNaLIuLbZBaqGwX8OiKaUkpTgM8Bt0bES0ALcGnOVnDXADdFxDIy2899IqVUn63dSmZbuqUd16aUFgOklB7JLn7X8QuG21NKD2ZrKyLii8DjZLafe5jMFneSJEnSESOlxIsbG7PBfQt/WFlPS2s7ANWVJZw7ayxnHF3N6UcPN7xLfVSk1LXbv7N7vBeklJpe8+IjRE1NTaqtrc13G5IkSdJBbW1q5rFlW3h06RZ+/9JmNjVmVpofUFTAKROHcsbU4bzp6GqOHVXpPe9SHxERdSmlmoPVurT9XERMASYDRR3/A0gpPfCqL5IkSZLUbVJKrN66i+fWbuO5Ndt5dvU2Fq5r2Fc/ekQF7zlxDG86ejinThpG2QDvd5eONF3Zfu7rwCXAi0Bb9nTi5UXoJEmSJHWzhj17eX7tdp5bs53n1mxj3trtbNu1d199WPkA3j1jNGccXc2bpg5ndFVZHruVdDh0ZUT+bGBSSmlXTzUjSZIk9Wdt7YmlGxv3C+3LNjfRcTdscWFw3Jgqzp45mJPGD2bW+CHUDClzurzUz3Rp1Xoyi9JJkiRJ6gY7m1t5dvU2/riynmdXb+P52u3samnbVx87uIy/nD6ak8YP4aTxgzlu9CC3hpPUpSD/j8DPIuJXwJ6Okyml67u9K0mSJOkItGPXXp5eVc8fV9Xzh5X1LKjbQVt7Zrh94IBCZtRUZUL7uMHMHD+YEZWlee5YUm/UlSD/z8BoYCb73yMvSZIk6SC2NDXz9MpMaP/DynqWbGjYN01+8MBi3nLsCE6dNJRTJw1j2uhKigoL8tuwpD6hK0F+FjA1dXW/OkmSJKkfSCmxoWEPf+wI7iu2snzzzn314RUlvGv66H3B/egRFRQUeG+7pK7rSpBfApQD/Wb/eEmSJOlgWlrbWb65icXrG7KPRhavb2DrzpeXlBo7uIxzTxrLKZOGcsqkoUwaXu6idJK6RVeCfCPwbEQ8xP73yH+227uSJEmSeoktTc0syQb1xesbWLS+geWbm9jb9vJE1bLiQo4ZVclfHD+KOROHcMqkodQMGZjHriUdyboS5F/MPiRJkqQjzp69bSzf3MRLG5tYvOHlUfbNjc37XTd2cBlnHF3NtNGDso9KJgwrp9Bp8pIOk04F+YgoBKaklC7t4X4kSZKkHtXc2saKzTtZurGRlzY2Zb5uamL11p2056wGNaCogGNGVvLnx+SE9lGDqBpYnL/mJYlOBvmUUltEjO3pZiRJkqTu0tLazsotHYG9kaUbm1i6qZHVW3ft2/INoLAgmDhsIO84fhRHj6xk6sgKjh1VycRh5a4iL6lX6srU+l9HxH8D3ydnwbuU0qJu70qSJEnqovU7dvPs6m08s2obc9dsY9G6BlpzAntBwMRh5bxt2gimjqzcF9onDS+npKgwj51LUtd0Jcj/dfbrWTnnEjC5+9qRJEmSXltrWztLNjRmgvvqbcxdvY267bv31YeVD+DNx1RzzKjKTGgfUcnk6nJKiw3skvq+Tgf5lNKknmxEkiRJeiU7du/luTWZwP7M6m3MW7udXS1t++pHj6jg4lPGcfKEoZw8YQgThw10qzdJR6zXDPIRUZJSao6Ig+6fkVLa1f1tSZIkqb9JKbF9117W1O96+bF1F/PWbmfppkZSdpZ8aXEBM8cNZnY2tM8aP8QF6CT1K50ZkX8SmEXmvvgE5P5qMwHOT5IkSVKn7NnbRt323ayp38Xa7CMT2neztn4XTc2tf/KaUYNKedf00Zw8fgizJw5h2uhBFLsInaR+rDNB/oMAKSX/bylJkqRX1d6e2NzUvC+gr63PCe3bdrGhYc++kfUOBQFjBpcxo6aK8UMHMi77GD90IOOGlDG0fIDT5CUpR2eC/C3ArIh4LKV0ek83JEmSpN6tqbk1J6i/PKq+dltmVL25tf1PXlNVVsyEYQOZNWEI44ZkQnrHY/TgUkfYJakLOhPkSyPiPGB0RLzrwGJK6YHub0uSJEmH266WVjY3NrO5sZktTc37nm9uask+38Pabbup39nyJ68dUFhAzZAyTps8LDuqXrbf6PqgUu9hl6Tu0pkg/0/Ax4ARwGcOqCXAIC9JktSLpJRobm2nYfdeGvbsZcfuVhr27M0et7JtZ8v+Yb2pmS2NzezMWQX+QEUFwfCKEiYPL+fNU6upyRlRHze0jJGVpRQUOP1dkg6H1wzyKaX7gfsj4hsppb87DD1JkiT1W+3tiV1722ja00pT814a97TS1NxK055WGptb2ZnzvHHPXhoOCOkNuzOvaWn70+ntB4rI7Lc+vKKEWROGUF1RQnVl5jE853l1RQlVZcUGdUnqJbqyj7whXpIk6SA6RsA7AndT88vhe2dL635h/LVqO1ta/2QxuNdSUVLEoNIiBpUVc1R1BYPKiqgsLd53blBpMYPKihhUWkxlaTGDBxYzorKEoeUDKPLedEnqczod5CPiLOC/gMlktpwLIKWU3H5OkiQdcdrbE9t37903/Xz/aegt+53ftquFvW1dS98RUD6giMrSIipKihhVVbrveUVJERWlRVRmv5Znz2XqxfuuqSorpqK0iEJHyiWpX+l0kAe+CXySzL7yr3wDlSRJUh+wY/de1mzdxaqtO1lTv4vVW3eysSETzLc0NbO1qYXW9lcO56XFBQyvKGHskMy2aR3hu6KkmMrSIsoHFFJRWrxfMO8I4+UlRQwsLnSquiTpdelKkG9IKT3UXW8cEYOBR3JODSQz2j8CuAcYDzRkaz9IKf1n9nUDgRuBOUA78E8ppXuytQLgG8C7yCzEd21K6fqc97wK+Ej28EcppX/prs8jSZJ6l5QSW3e2sHprJqSv2rqLNdmvq7fuZNuuvX/ympKiAqorSxhdVcaMmsGZ+8QrBjA8e5/48Jx7x8sHFLq3uSQpL7oS5H8REe9OKf28O944pbQdmNlxHBGfBs5MKdVnfyh+6hXe69NAc0ppSkRMAp6MiN+mlLYBlwDHAVOBKmBuRDycUloSEWcAFwMzgFbg8Yh4rDt/OSFJkrpfS2t7ZoG35lZ2tbRl7iPvWPSt43nO+S1Nzdnwvoum5tY/+X7VlSVMGVHB+KHlTBw2kPHDBjJxWDkThg2kqqzYcC5J6vW6EuQ/DgyLiCZgDy/fIz+im3r5CPCFTlx3IfBhMm++MiIeBc4Gbs7WbkgptQH1EXEHcBHwpWzt5pTSToCIuIlMsDfIS5LUA3bs2sumxj3ZgJ0TwFtyAnhzW865tj8N6M1tnVp9PVcEjKnKTHefkA3oE4cNZMKwcsYPHUh5SVf++iNJUu/TlZ9ks3uqiYh4AzAMyB2BvyYivgIsAv45pbQie348sDrnulXZc69Um51T+90BtfMPvXtJkvq3ltZ2lm9u4sUNjSze0MCLGxp5cUMj63fs6dL3GTigcN+iboMHFjN2cBkVJZn7yTPnCxk4oCjnXOG+57nnqsqKKSlyLV5J0pGrK9vPrY6IMjJT0xMwP6W0u5v6uAy4JaXUMf/t0pTS2sjMbfsEmYB/XG47Oc8PnP/2emsvFyKuBK7sOK6qqnr17iVJ6gdSSqzfsWe/wL5kfSPLNzfttyjcgKICjh5RwRuOGkbN4DIqSoteM4C78JskSZ3Xle3n3gjcBWwkE4KrI+L8lNKTh9JARJSTmfZ+Sse5lNLa7NcEXBcR/zcihqWUtgJrgInA5uzlE4AHss87ak/n1NYcUOMgtf2klK4Fru04rqmp6eJurpIk9W3NrW0s3dDEwnU7WLS+gSXrG1myoYGGPfvfc14zpIw3H1PNsaMGccyoSqaNrmTisHL3JpckqQd1ZWr9tcD7U0qPw75g/5/AaYfYw/uBF1JKS7LftwgYllLamD0+D9iYDfEAd5IZpf9wdrG7M4GP5dSuiIh7yCx2dyFwVk7tuoi4nsxid5cBVx1i75Ik9XmNe/ayeH0jC9ftYEFdAwvX7WDZpv1H2StLizh2VOV+gX3qyEoqS4vz2LkkSf1TV4J8aUeIB0gpPRERpd3Qw+VktpPrUEJmhfwSMtvLbQHem1O/BrgpIpZl659IKdVna7eS2ZZuace1KaXF2X4fyS5+Nz9buz2l9GA39C9JUp+xpamZhesyYX3hugYW1u1g1dZd+10zuqqUNx9TzXFjqjhhzCCOGzOIsYPLXM1dkqReIjKz1ztxYcQTwL+mlH6dPX4z8O8ppTf2XHu9Q01NTaqtrc13G5IkdUn9zhaer93OC2t3ML9uOwvqGtjQ8PICdBEwaVg5x4+t4vgxg7KPKoaWD8hj15IkCSAi6lJKNQerdWVE/pPAPRHRTGbRuBLgvG7oT5IkHaKm5lbm1+7ghdrtvFC7g+drt1O77eU1aYsKgmNGVXLG1OEcPyYT3KeNHuRWbJIk9UFd+ek9hsxWbiPJLHa3ATi1J5qSJEmvbM/eNhavb9gX2F+o3cHyzU10TLKLgKOqKzh31lhmjhvMjJrBHDuqktJit2STJOlI0JWp9fNSSjNzjgOYm1I6qaea6y2cWi9Jype9be0s3diYGW2vy4y4v7ihkb1tL//8rhlSxok1g5lRU8WMmsFMr6miwpF2SZL6tO6aWr+flFKKCPeWkSSpm7S2tbN8805eqN3O/LodvFCb2fqtpbV93zXDK0o44+hqZtQMZsa4KmaMrWJYRUkeu5YkSYdbV4J8Q0ScmlL6A0BEnAY09kxbkiQd2drbEyu27GR+XWZq/PzazCryu/e27btmyMBiTps8jBljq5heU8WMmipGDSp19XhJkvq5rgT5zwE/jYiF2eNpwDnd35IkSUeeHbv2MnfNNp5ZXc8zq7axcF0DTc2t++qDSouYNWEw08dmpshPH1tFzRC3fJMkSX+q00E+pfRkRBwHvCF76omU0vaeaUuSpL4rpcTa+t08s7qep1dt49nV9Szd2LSvXj6gMDvCPpjpYzMj7eOHDjS0S5KkTunSPfIppW3AAz3UiyRJfdLetnYWrWvg6VX1PLt6G8+s3sbmxuZ99dFVpbx7xmhmTxjC7IlDOXZUJUWFLjMjSZJeH5e0lSSpi1JKzFu7nd8s3sQzq+t5fu2Offe2R8CxowZx1vGjmD0xE9zHDi7Lc8eSJOlIYpCXJKmTdja3ct+8ddz21GoWrW8AoKy4kJPGD9432n7S+MFUlhbnuVNJknQkM8hLkvQalm5s5LanVnPP3DqamlupKCni0tMmcO6ssZwwtopip8lLkqTDyCAvSdJBNLe28eCCDfzwqTX8cVU9AMeOquTSN0zg7JljqSjxR6gkScoP/xYiSVKOtfW7+NEf13DH02vZurOFAYUFnHPSWC45bTyzxg9xZXlJkpR3BnlJUr/X1p743dJN3PbUGn774iZSgvFDB/LRMybz/tnjGFo+IN8tSpIk7WOQlyT1Wy2t7dz42Epue2o1ddt3UxDwtmkjueS0CbxpynAKChx9lyRJvY9BXpLUb33twSXc+NhKqitL+NRbpnDRKeMZ41ZxkiSplzPIS5L6pSeWb+HGx1Yya/xgbv/oGxhQ5MrzkiSpb/BvLZKkfqdhz14+c+cLlBUXcu0FMw3xkiSpT/FvLpKkfufLP1tE3fbdfOEvpzFxeHm+25EkSeoSg7wkqV95aOEG7nq2ljOnVvOBU8fnux1JkqQuM8hLkvqNLU3NfP6e+VSVFfMf589wT3hJktQnudidJKlfSCnxz/fMZ+vOFr518UmMHFSa75YkSZJeF0fkJUn9wp3P1vKrRRt574ljeM+JY/LdjiRJ0utmkJckHfHW1u/iyz9bxMhBJXz57OPz3Y4kSdIhcWq9JOmI1t6e+PSdz9PU3Mq3PzCLwQMH5LslSZKkQ+KIvCTpiHbT4yv5w8p6Lj1tAmdOrc53O5IkSYcsr0E+IlZFxJKImJd9XJg9PyIiHoyIlyJiQUScnvOagRHx44hYFhFLI+LcnFpBRHwrIpZn6x8/4P2uytaWR8TVh++TSpLyYenGRv7joReZNLycf37XsfluR5IkqVv0hqn156eUFhxw7qvAUymlsyJiDnBXRByVUmoFPg00p5SmRMQk4MmI+G1KaRtwCXAcMBWoAuZGxMMppSURcQZwMTADaAUej4jHUkoPHabPKUk6jFpa2/mHn8yjta2dr19wIgMH9IYfeZIkSYeut06tvwD4NkBK6WlgI9AxKn9hTm0l8Chwdk7thpRSW0qpHrgDuCindnNKaWdKqRm4iUywlyQdgb718EssXNfAx988hVnjh+S7HUmSpG7TG4L8DyNifkT8v4iojohhQEFKaXPONauA8dnn44HV3VzbT0RcGRG1HY+mpqYufyhJUv7MXbONb/92GcePGcSn3np0vtuRJEnqVvkO8meklE4EZgFbgR9kz6cDrosDjlMP1F6+KKVrU0o1HY+KiopXulSS1MvsbmnjH+94nqLCAv7zwpkMKMr3jzpJkqTulde/3aSU1mS/7gX+C3hTSmkrQETkLi08AViTfb4GmNjNNUnSEeIrv1zMyi07+cxfHMPUkZX5bkeSJKnb5S3IR0R5RAzOOXUx8Fz2+Z3AJ7LXzQFGAY8dpDYJOBO4P6d2RUQURsRQMvfF/ySn9qHs+5YAlwG398RnkyTlx6NLN3PLk6s5ZdJQLjt9Ur7bkSRJ6hH5XMJ3JHB3RBSSmea+AvhgtvY54NaIeAloAS7NrlgPcA1wU0QsA9qBT2QXtgO4FZgDLO24NqW0GCCl9EhE3AHMz9ZuTyk92HMfT5J0OO3YtZfP3vUC5QMK+fr7T6Sw4BXvoJIkSerTIqUDb0fXgWpqalJtbW2+25AkvYq/u/057pu3jv84bwYXzBmX73YkSZIOSUTUpZRqDlZzU11JUp+2t62de5+r475563jbtBG8f/ZBf95JkiQdMQzykqQ+Y1dLK4vXN7Jo3Q4Wrmtg4boGXtzQSEtbO0PLB/CVc2cQ4ZR6SZJ0ZDPIS5J6pe27Wli0roEFOaF9xeYm2nPuCKuuLOGNU4Zx/JhBvG/mWKorS/LXsCRJ0mFikJck5d3O5laeW7OduWu2saAuE9zrtu/e75rxQwfyjuNHcfyYQRw/porjxwxixKDSPHUsSZKUPwZ5SdJhV7+zhadX1fP0ynqeXlXPgnUNtGWH2gsLginVFZx70liOy4b248YMoqqsOM9dS5Ik9Q4GeUlSj6vbvpunV9bzx2x4f2lT077a4IHFvOXYEZwycSgnTxzCcaMHUVpcmMduJUmSejeDvCSpW6WUWL65iT+u3MYfV27l6VXb9psmP6aqlLNnjmHOxKGcMmkoU6orKHDPd0mSpE4zyEuSDkl7e2Lppkb+sKKep1Zs5Q8r66nf2bKvflR1ORefMm5fcK8ZMjCP3UqSJPV9BnlJUpe0tyeWbGjkDyu38tSKrfxxZT3bdu0FoCDguOwK8qdMGsLsiUMZXuFK8pIkSd3JIC9JelXt7YnFGxp4Kjvi/vSqerbnBPcTxlZx/slDOW3yMGZPHOqidJIkST3MIC9J2k9HcH9y+VaeWlHPH1dupWFPK5BZUf6EsVVcODsT3E+eOIRBpQZ3SZKkw8kgL0n9XEqJFVt28sSyLTyxfCtPrti6b8S9sCCYPraK0yYP49TJQ5k9YQiVBndJkqS8MshLUj9Ut333vuD+xPItbGxoBiACpo+t4sI5w3hDdqp8RYk/KiRJknoT/3YmSf3AlqZmnly+dV9wX711177a1JEVvPOE0bzhqGGcNmkYVQMdcZckSerNDPKSdARqa088s6qehxZu5InlW1iyoXFfbdzQMi6aM443HDWMNxw1jBGVpXnsVJIkSV1lkJekI8TetnaeWrGVXy7YwP8s3MCWpsxe7tWVJZw9cwx/dtRw3nDUMMYNdR93SZKkvswgL0l9WHNrG08s28oD89fzq8Ub9y1SN2VEBRefMp6zThjFcaMHERF57lSSJEndxSAvSX3Mnr1tPPLiZh5csJ7fLN5EY3Nma7hpowdx2Z9N4p0njOLokZV57lKSJEk9xSAvSX3AzuZWfvviJn45fwO/fXETu1raADixpoqzThjNO08YxcTh5XnuUpIkSYeDQV6SeqmNDXt4eMkmfrN4I79/aQvNre0AzJ4whLNOGMVZJ4yiZoj3u0uSJPU3BnlJ6iVSSiyoa+A3Szbym8WbmF+3A4DCguCUiUN55/RRvOP4UYwc5CrzkiRJ/ZlBXpLyaHdLG48v28Jvlmzi4SUb2djQDEBVWTFnzxzDW44dwZunjnBvd0mSJO1jkJekw2zDjj37Rt0fX/bylPnJ1eV89IyxvOXYEcyeMISiwoI8dypJkqTeyCAvSYfBSxsb+fkL6/n14o0sXNcAQFFBMGfiUN46bQRvnTaSSS5WJ0mSpE4wyEtSD1m3fTf3P7+O++atY/H6THivKivmfTPH8NZpIzljajVVZU6ZlyRJUtfkLchHRCn8//buPUqvur73+Ps7M7lNriQh4RIiIOEqF1FAreINRbEVLBwv1Fa0Pd56OVWpR61W29W1PK1Fa8uira0ua+ulWGy1IF7RClSQEpBLoASSgElgciMJmYFMZuZ7/th7kiEESEgy+/c8z/u1VpiZvfcM31mfPc9vf5+992/zNeB4YAB4CHh3Zq6IiB8DC4HN9eb/mJmfqb+vF/g8cBowAnwoM79Rr+sCPgucAyTw6cy8bMz/86PA2+svv5KZH9uvv6SkjvNw/yDfvuNBvnnran62fAMAMyb38ObTDuP1pxzC6YfP9pJ5SZIk7ZWmz8h/Drg6MzMifqf++tX1ut/LzCt38T0XA1sz86iIOAL4aUT8KDMfBt5K9cbA0cBMYHFEXJOZd0fEmcBbgJOAIeD6iLguM7+7f39FSe1uYHCIH9y1hm/duor/vGct24aTST1dvO6kgzn35EN46TEHMqmnu+kyJUmS1CYaa+Qz8zHg22MW3QD8/m5865uAi+qfsTwifgKcC3yxXve3mTkMbIiIy4E3A5+o130xM/sBIuILVI29jbykPbZteITr7l3HN29ZxfeW9DEwOExXwIsXHci5Jx/Cq0+Yz/TJXjYvSZKkfa/pM/Jj/R7wH2O+/lREfBJYAnw4M5fVyxcC94/ZbkW97MnWPX/Muv/cad0F+6BuSR0iM1n8wEb+/ZZVXHX7g2zoHwTguQtncd4ph3LOiQdz4PRJDVcpSZKkdldEIx8RHwEWAe+uF/16Zv4iIgL4beBKqkvmR+XYb9/pxz3TdWPreT/w/tGvZ86c+ZT1S2pvKx8e4N8Wr+Ibt6xi+bp+AI6aN413/NLhvP7kQ1k4p7fhCiVJktRJGm/kI+Ji4FeBszJzACAzf1F/TODSiPiLiJiTmeuBB4DDgbX1j3gWOy7RH11305h1D+y0jl2se5zM/DTw6dGvFyxYkLvaTlL76t86xNV3PMQVN6/kp8vWAzB32kR+88VH8KunHsrxB8+geq9RkiRJGl+NNvL1me+3UDXxG+tlPcCczOyrvz4f6KubeICvU52lv6ie7O6l7DiT/3XgXRHxDarJ7t4EvGbMuksj4jKqye7eAXx0P/+KklrIyEhyw/L1XHHzKq6+40EGBoeZ2N3FOScexPmnLuDMow9kgjPOS5IkqWFNPn5uAXAJsAz4UX1mayvwCuCqiJhE9Xi5dcDrx3zrp4AvRMS99frfzswN9bp/onos3T2j22bmXQCZ+eN68rvb63Vfy8zv7K/fT1LrWLGunysWr+Qbi1exauOjAJxy2CzOf94CfuWkg5nVO7HhCiVJkqQdorp6XU9lwYIFuXLlyqbLkLQPbX5sG1fd9iBX3LyS/77/YQAOmjGZN5x6KOefuoCj5k1ruEJJkiR1sohYlZkLdrWu8XvkJWm8rNuylWvuXsP3l/Txk3vWsnVohMkTujjvlEM4/3kLeNGz59Ld5X3vkiRJKpuNvKS2dt/aLfxgSR/fX9LHzQ88TCZ0BZx+xGze8NzqkXE+712SJEmtxEZeUlsZHklueeBhvr+kj+/f1ceytdXj4nondvOaEw7iVcfP5+XHzOOAqd73LkmSpNZkIy+p5Q0MDnHt0nX8YEkf19y9hvX9gwDMmz6JC89YyKuOn88Lj5zD5AndDVcqSZIk7T0beUktaeXDA1y7dB0/vKuPa5euY+vQCADHHjSdC89YyFnHzefEQ2fS5T3vkiRJajM28pJawqZHt/HT+9Zz3b1ruW7pOlasHwCguys4/fDZvOr4+Zx13HwWzultuFJJkiRp/7KRl1SkwaERFj/wMNffu45rl67jtpUbGamflnnorCm8+bTD+KWj5nLmogOZ2etkdZIkSeocNvKSipCZ3NO3hevuXcd1S9dy4/INDAwOAzB9cg+vOn4+L150IC85ai7PmtNLhJfMS5IkqTPZyEtqRGay8uFHuWHZ+vqS+XWseWQrABO6g+cuPICXHDWXFy+ay4mHzqSnu6vhiiVJkqQy2MhLGheZyYr1A9y4bD03Lt/AjcvWs3rTY9vXHzN/Or980iG8ZNFcTj9iNlMn+fIkSZIk7YpHypL2i8zkvrX93Lh8PTcs28DPlq+nb/PW7euPmjeNXztjIWccOYcXHDGbeTMmN1itJEmS1Dps5CXtEyMjydI1W7hx+XpuXLaBG5dvYN2WHY37MfOnc/YJB3HGEXM4/YjZHDh9UoPVSpIkSa3LRl7SHstMHtz0GLet3MQdqzZx26pN3LZyIxsHtgEQAcceNINfOfng7Y377KkTG65akiRJag828pKeUmbSt3krt63cyO2rNlX/Vm5iff/g9m0m9nRx3MEzuODUAzjjyDmcfvhsHwknSZIk7Sc28pIeZ83m6kz7aNN+28pNj7tEfmJ3F8cdPJ3XGjXe/wAAFy9JREFUPOcgTlowk+ccOpOj509ngrPKS5IkSePCRl7qUCMjyf0bBliyejN3rt7Ekgc3c+fqzax9ZEfTPqE7OPagGbz6hPmceOhMTqyb9ok9Nu2SJElSU2zkpQ6wdWiYpX1btjftd67ezF0PbqZ/cHj7NhN7ujj2oOmcddw8Tjx0VtW0HzSNST3dDVYuSZIkaWc28lKbebh/kP/pe4Q7V2/e3rjfu2YLQyO5fZsZk3s4acEsjj9kBiccMoPjD5nBsw+c5uXxkiRJUguwkZda1Ib+QZb2PcI9a7Zwb98j3NO3haVrHmHdlsHHbXfIzMm87Jh5O5r2g2ew4IApRERDlUuSJEnaGzbyUuE29A9yT98jLF2zhaV9j7D0SRr2aZN6OGreNF5+zDyOnj+d4w+ZwXEHz/Cxb5IkSVKbsZGXCvDIY9u4f/1A9W9DP/evG2D5+n6Wrd2yy4Z90fxpvOLYqmE/at40jp4/nYNnTvYsuyRJktQBbOSlcbJxYJAV6we4f30/968fYEX98f71/U9o1qG6j/3Z86bxymPns2j+NBbNn86iedNs2CVJkqQOZyMv7UMjI8mqjY9yz+g9632PcN/aLaxYP8CmR7c9Yfs5UyfyrDm9nLnoQJ41ZyqHz+2tPs7pZVavl8RLkiRJeiIbeekZyExWb3qsund9TNO+dM0WBsY80g1g3vRJHHPQdA6fM9qkT+VZc3pZOKeXGZMnNPQbSJIkSWpVNvLSLmQmmx8bYuPAIA8PbGND/1aWre3ffqb93jVb2LJ16HHfM3faJE45bBZHz59e/5vGonnTmdlrsy5JkiRp37GRV1vLTB7bNsKmR7ex6dFt2xvzx38cZEP/js83Dmxj46PbGB7z3PWx5kydyHMOncHR86ezaP50jq4nmzvA2eElSZIkjYOOa+QjYhHwj8BcYCNwUWYuabYqPZmh4REGtg0zsHWYgcEhBgaHGRgc3t6Yj/7bPKZR37F8iM2PbmNweORp/z/dXcGsKRM4YOpEjjxwKrN6J3JA7wQO6J24/fOFc3o5ev505k6bNA6/uSRJkiTtWsc18sDfAZ/LzC9GxAXA54EXNlxTkUZGksHhEbYNjzA4NMK24WRwaITB7V/vWDc4ZpsnLhu73Y71j20brpv0qkF/dNsw/VuHeHRweHvzvjtN+FgTe7qYOWUCM6dM4Flzerd/PnPKBGZMmVA36xPq5rxq0Gf1TmTG5B5ngpckSZLUEjqqkY+IecCpwKvrRVcAl0bE4Zm5orHC9oFLr1nKlbc9SNZXgydJJoxeHJ6Z1ee7WJYJwyPJ1qGxTfsIQ09yafm+Nqmni6mTepgyoZveid0cOGMyvfXnvZN66J3QzZSJ1dej282om/NZvRMe16xPntA9LjVLkiRJUlM6qpEHDgNWZ+YQQGZmRDwALARWjG4UEe8H3j/69cyZM8e5zD03eracgNHzyhFBAFH/JwhGTzqPnn0eXd/T3cXE7mBCdxcTe7q2f5zYXf2b0BNM7O6uP44u27HdpHqbCWPWTRz7s7q7mNjz+J8/qaeL3ok9dHd5JlySJEmSdldkjs9Z1xJExPOAL2XmCWOW3QR8IDN/8mTft2DBgly5cuV4lChJkiRJEhGxKjMX7Gpd13gX07BfAAsiogcgqtPShwEPNFqVJEmSJEm7qaMa+cxcA9wCvLVedD6wotXvj5ckSZIkdY5Ou0ce4F3AFyPiI8Bm4G0N1yNJkiRJ0m7ruEY+M/8HHzcnSZIkSWpRHXVpvSRJkiRJrc5GXpIkSZKkFmIjL0mSJElSC7GRlyRJkiSphdjIS5IkSZLUQmzkJUmSJElqITbykiRJkiS1EBt5SZIkSZJaSGRm0zUULyK2AmubrqNQ04AtTRehlud+VCZzKZO5lMlc2o+ZlslcymQu+8eBmTlpVyts5LVXImJlZi5oug61NvejMplLmcylTObSfsy0TOZSJnMZf15aL0mSJElSC7GRlyRJkiSphdjIa299uukC1Bbcj8pkLmUylzKZS/sx0zKZS5nMZZx5j7wkSZIkSS3EM/KSJEmSJLUQG3lJkiRJklqIjbwaFxHRdA2S9j//1qWn5t+INP78u1OrspFXCSY0XYDaQ0T4mlaYiJgfEQsAMjM9YCpDRBwZEbObrkNP4HjYhhybyuPYVCbHpj3jC4saFRFnAV+KiL+MiN9vuh61pog4NSImZeaIB0zliIjXAlcBl0fEV6A6YGq2KkXEOcDlwLSma9EOjoftx7GpTI5NZXJs2nO+qKgx9R/sX1K9mN4JnB0RL222KrWaiPhl4L+BWyNiogdMZYiIVwKfAj4MvBWYGRG9zValiHgN8BHgI5n5wM5noTwr1QzHw/bj2FQmx6YyOTY9M76gqBERsRD4BPC+zPwnqnfgtgILmqxLrSUi5gMXA+dRHfzePOaAqbvZ6jreacAlmfl9YApwPHBJRHwpIqaCA/N4isqRwLeBz2Xm9+rX4Q9GxMUR8U7wrFQTHA/bj2NT0RybCuLYtHds5DWuxrw49gEfB66PiO7M3ATcAxzbWHFqGaP7UWb2AX8IfDczLwBWUx0wTcnM4SZr7EQ7HfwEcFlEXEA1QH8JuBR4NvDP4MA8nrKyDLgM+HhEvIQqk3nAHOBtEXFxkzV2GsfD9uPYVCbHpnI5Nu0dG3mNtx6AzNyamVdn5sCYQW0b9UQ/EXFhRPxGU0WqeJNHP8nM64GsPz+b6oDpZwAR8caIeFcjFXamsbl8EvgscCLwncz8eGbeSXWGakZEzGyoxo4UEZMBMvN3qA5e/xO4IjM/QNVwfJXqoEnjx/Gw/Tg2lcmxqVCOTXvHRl7jpp7I5x8i4v9FxDvGLB+dpXcQuKe+f+l91AOeNFZ9H9XlEfGpiPgYQGYORsSU+vOzgbsjoh/4M+C/mqu2c+yUy8cBMvNDwLXAERExo970l6kalKFmKu0sYybbeiwiJgFk5u8Cr8zMv66/HgGmA9MjosvLSvc/x8P249hUJsemMjk27Rs28hoX9UQ+nwV+AAwAZ0XEifXq0TMQG4GPAX8MvD0z7x73QlW0iDgT+DzwZeBWqv3ohwCZ+ejoO7vAN4AtwOsy8/ZGiu0gu8jlFRFxDUBm/oDqTNR3I+IjwPuB92Rmf1P1doqI+BWqybZuqi/p3Trm7MePxmz3G8AbgUszc8TLSvcvx8P249hUJsemMjk27Ts28trvIuJwqoOR99cT+VwGHAE8B7a/4wbQCxxMddByx/hXqhZwBHBZZn4tM78MvAyYGhHfB6jf2T0GeC/w6sxc0lypHWVXufSO5gJ8FLiy/vx/1Zcxaj+qJ9v6MPAO4Abg2vqA6bHRs74RMSmqxzB9GHirzeL+53jYthybyuTYVBjHpn0rfHND+1tEHACcAXwP6MrMoYj4LLAiMz8zZrt5wOTMfKChUlW4iLiQanbnMzPzoXpZN/BD4D8y85J62cx6wiiNg6fI5Rrg30f/ziMifEd9/ETEaVSTpj1GNXnQs4GXjj3jVB9U9WTmqmaq7CyOh+3JsalMjk1lcmzadzwjr/0uMx8G/qu+LGb03qMhqsd+EBEXRMSbMnONBy16Gl+nOgB+X0TMBagnh/oqMHqfGx4ojbsny+UrwAGjG3mgNL4y86bM3JSZW4HfAO6jmkiIiDgnIi7KzD4PlMaP42Hbcmwqk2NTgRyb9h0beY2LzNwMEBE99aIJwP0RcTbwIWBxU7WpdWTmNuBqYCbVM0YPrVdNB46JiAlOhjL+niaXo82lefUB09uAWyJiE9Ul3Tc2W1VncjxsP45NZXJsKp9j097pefpNpL035rKl0Yl81gAfpJrQ56LMXNpYcSrO6P4y9nK3iOiqz2JdFREJnA38NCKuAM4HXlsP2tpPzKVMu8pl7PLRr+t7EG8FzqGabOuuJurtFLuRi+Nhi/I1sEzmUibHpv3He+S1X+z0Yjp6MHMSMJyZd0bEJ4APAKd60KKdjd5HGBE99T2kQfV6NVJPGNRT70fn1d9yR2be22DJHcFcyvQ0uZxE1TDeRTXx05XAmzPz503W3Al2JxfHw9ZSz12wHpiUmQOjZ3PrYxxfAxtiLmV6mlwcm/YBG3ntMxHxYmBC1o+O2KmZfwHwL1SzT14bEacAD2fm/c1VrBJFNVPp/wa2UT1n98uZua5e9wKqe97ekpnXNVdl5zGXMu1GLv8CXJiZ19fLnGxrHOxmLo6HLSSqxwZ+gOqRZXcBfwX0103JC4HL8TVw3JlLmXYjl6/h2LTXvEde+0REvA74LvB3EfF62P6OW1dEzAJ+G3h3Zl5br7vVgxbtrD74/QuqZyz/BHgh1Tu1RMRsqv3onZl5nfe1jR9zKdNu5vLuzLw+qpmaATY3UWsn2YNcHA9bRH2M86fAnwDXAc8DttXHOT3A7wLv8jVwfJlLmXYzl/c4Nu09z8hrr0XENOBvqCboWQ38AfAnmfmtMZfVz8vMNWMvq2mwZBWovgTrC8DnMvNb9bKvAssy8w9Htxndj9yHxoe5lMlcyrSnuYDjYemiegzWl4BLMvN7EXEU1Vnea4CVVFcjPZTVbOgaJ+ZSJnMZX56R177QD3yM6uDl28DngT+KiPPGHKAMQHXA4kGLnsRW4DPA1bFjNufbgEljtnkEPPAdZ+ZSJnMp0x7lYjYt4RHgt+qmZC7wHeAq4HZgAdVZx8kR4TH1+DKXMpnLOHLWej1jEXEAsLE+EFkxZvmX608/GhEPAbOBUyPik74Dp52N2Y82AT/cafU6YE693ZuAQyLir9yP9j9zKZO5lMlc2s+YTAeoT0ZQvVHzm5k5+szr1wNvzMz+hsrsOOZSJnNpho28npGIOBd4J5AR8SPgR5m5GKpn5NbN/Hqqd+IGgZd70KKdPdl+FBHd9f4yGVgXEW8APgqc7360/5lLmcylTObSfnbK9Brgx/UxzpbRpqQ2E5gSEVOBAa+w2L/MpUzm0hwva9Aei4hTgb+mujzmcqo/zD+PatZ6oGrmqSb32Qi8LDPvbKJWleup9qMxB7nDwP8Bfp/qsST3NFJsBzGXMplLmcyl/ewi01nsyHTss7Avosr145nZb1Oyf5lLmcylWZ6R1zOxCPivzLwRuDEiFgBbqO6L/4PM/HlETAFeRXXmYUmTxapYT7sfAQ9RXZr1rsy8u8FaO4m5lMlcymQu7Wd3jnFeB7wduCgz72iy2A5iLmUylwZ5Rl7PxB3A3Ih4PkBmrqR6HuTNwIvqZY8Cr8nMmxurUqV72v2IapKoF3nwO67MpUzmUiZzaT+7k+nPqU5U2JSMH3Mpk7k0yEZez8Qa4AHgNRFxBEBmPgDcD7x8dKPMHGqmPLWIp9qPXlF/fW9mrm6uxI5kLmUylzKZS/vZnUxXZua65krsSOZSJnNpkI289lhmrgU+R/VO269FxOg7bpuBqC+rl57S0+xHRERvU7V1MnMpk7mUyVzaj5mWyVzKZC7NCuca0J6IiAC6MnO4nuDiXcDxwAbgZODc+p5A6Um5H5XJXMpkLmUyl/ZjpmUylzKZS/Ns5PWkIuJAoDszHxqzbEJmbqtnqB8EbgcOAY4FbsvMXzRTrUrlflQmcymTuZTJXNqPmZbJXMpkLmWykdcuRcT5wMVAL/Bd4KbM/Hq97mXApcCHMvPKxopU8dyPymQuZTKXMplL+zHTMplLmcylXDbyeoKIOIHqWZBvBEaAN1BdKnNDZl4aEX8PXJWZ/95gmSqc+1GZzKVM5lImc2k/ZlomcymTuZTN58hrV6YDazLzToCIWAOcC7wiIu4A3pmZGRGRvhOkJ+d+VCZzKZO5lMlc2o+ZlslcymQuBXPWeu1KH7AuIl4QEV2ZuR74FrAMOHv0D9U/WD0N96MymUuZzKVM5tJ+zLRM5lImcymYjbwAiIipETEDIDOXA+uADwALI6I7q+c//ivw8oiY3WCpKpj7UZnMpUzmUiZzaT9mWiZzKZO5tA4beRERrwWuAH4YEX8GkJnvobr14s+BM+tNjwQeo5qZUnoc96MymUuZzKVM5tJ+zLRM5lImc2ktTnbX4SLiHOCTwPuonvt4NfCJzPy7ev1ngEOBg4AZwNsz85aGylWh3I/KZC5lMpcymUv7MdMymUuZzKX12Mh3sIiYB/wtcFlm/qBe9rvA3Mz8+E7bHUw12cWDjRSrYrkflclcymQuZTKX9mOmZTKXMplLa3LW+g6WmWuiemzELfUEFiP1qlNGt4mInsxcA6xppEgVz/2oTOZSJnMpk7m0HzMtk7mUyVxak/fId6CImBERBwBk5tX1DJSjl2b0AVvq7S4C3hsR7id6AvejMplLmcylTObSfsy0TOZSJnNpbYbRYSLiXOB7wLci4tKIOC7icc9+3ADcFRGvo7pH5pox78pJgPtRqcylTOZSJnNpP2ZaJnMpk7m0Pu+R7yAR8Xzgn4HfBB4EPgWsBy4HfpiZWf9R/xtwM/C2zFzSVL0qk/tRmcylTOZSJnNpP2ZaJnMpk7m0B8/Id5ZpwM8y8/rMXAZcBPQDbwKeXW/TRfWH/Bb/YPUk3I/KZC5lMpcymUv7MdMymUuZzKUN2Mh3lo3ACRExHSAzHwH+iOoxEr9TL/s34LmZeW9jVap07kdlMpcymUuZzKX9mGmZzKVM5tIGbOTbXES8LCI+GBEfBu4GfgpcHxEB2/9wPwgcExHT6mUrGytYRXI/KpO5lMlcymQu7cdMy2QuZTKX9mMj38bqySk+TfWYwecAVwL/F7iP6vESc+tNTwOmAENN1KmyuR+VyVzKZC5lMpf2Y6ZlMpcymUt7crK7NhURpwJfAS7MzMURcTDwBeC9mbk8Ir4KLKR6tMQi4K2Z+fPmKlaJ3I/KZC5lMpcymUv7MdMymUuZzKV99TRdgPabTcDfZOZigMx8MCKmAqcDyzPzLRFxPDATWJ2Z9zdYq8rlflQmcymTuZTJXNqPmZbJXMpkLm3KRr5NZeZ9EfH3ABHRk5lDwGpgbb3stcBNzkKpp+J+VCZzKZO5lMlc2o+ZlslcymQu7ct75NtYZg6Mflp/HALWRsR5wGeBWY0UppbiflQmcymTuZTJXNqPmZbJXMpkLu3JM/IdIDOH60/7gUuA2cB5Pk5Ce8L9qEzmUiZzKZO5tB8zLZO5lMlc2ouNfAeIiMhqVsPDgOcCL/QPVnvK/ahM5lImcymTubQfMy2TuZTJXNqLs9Z3kIg4C+jLzNubrkWty/2oTOZSJnMpk7m0HzMtk7mUyVzag428JEmSJEktxMnuJEmSJElqITbykiRJkiS1EBt5SZIkSZJaiI28JEmSJEktxEZekiRJkqQWYiMvSZJ2W0SsiIi7I+LnEbE0Ir4ZES/aje+7KCKOHo8aJUlqdzbykiRpT12QmSdn5iLgC8C3I+KMp/meiwAbeUmS9gEbeUmS9Ixl5jeBy4CLI+KVEfHTiLglIu6IiLcDRMRvAc8H/ioibo2Ic+rlF0fEzyJicUR8OyIOa+43kSSpdfQ0XYAkSWp5NwHnAYuBF2fmcETMBhZHxHcy8x8i4q3AX2TmlQARcSHVGfoX1tv/OnApcG5Dv4MkSS3DRl6SJO2tqD/OAT5f3ws/BMwFTgAe3MX3nEd1lv7miADoBob3f6mSJLU+G3lJkrS3TgPuAP4W+A/g/MzMiFgMTH6S7wngTzPzC+NUoyRJbcN75CVJ0jMWEecC7wE+DRwA3F838WcCJ4/ZdDMwc8zX3wLeW1+CT0RMiIjnjlPZkiS1NM/IS5KkPfWvEbEVmAosAc7JzBsi4kPAZfXHJcCNY77nc8AlEfEHwEcy858iYg7w44hIqmOSzwO3jOtvIklSC4rMbLoGSZIkSZK0m7y0XpIkSZKkFmIjL0mSJElSC7GRlyRJkiSphdjIS5IkSZLUQmzkJUmSJElqITbykiRJkiS1EBt5SZIkSZJaiI28JEmSJEktxEZekiRJkqQW8v8B+D5yWSHNFqQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# plot graph\n",
    "#设置画布大小\n",
    "plt.figure(figsize=(15,5),dpi = 80)\n",
    "#横轴坐标旋转45°\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('confirmedCount')\n",
    "plt.plot(indexedDataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在使用很多时间序列模型的时候，如 ARMA、ARIMA，都会要求时间序列是平稳的，所以一般在研究一段时间序列的时候，第一步都需要进行平稳性检验，除了用肉眼检测的方法，另外比较常用的严格的统计检验方法就是ADF检验，也叫做单位根检验。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: FutureWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAGMCAYAAADHg8H9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAMTQAADE0B0s6tTgAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8GearUAAAgAElEQVR4nOzdd3hUZd7G8e+PUEITkWoIJEhAyKIiTZoIiAqrgougIhaagN0X3FdfF12WXcuuiNh1VQRZXBVxAQu6NlQUFVFQQHoJAURK6C3lef94ZsgQAgRIOEnm/lzXuTJzzpkzz5kAes/vKeacQ0RERERERETyX4mgGyAiIiIiIiJSXCl0i4iIiIiIiBQQhW4RERERERGRAqLQLSIiIiIiIlJAFLpFRERERERECohCt4iIiIiIiEgBUegWERERERERKSAK3SIiEhgzSzQzZ2ZJoed9zSw14vg4M/tXcC2U/GJmnc3MFZXrHo/Qn+XOJ3iNVWY2ML/aJCIiwVPoFhGR42ZmM0JBw5nZLjOba2a98vEt7gRuzcfrHSIU7J2Zjczl2NL8CFL5wcxuMrMVZrbHzOab2RV5fN3tZrbAzHab2SYz+9LMukccTzWzvgXW8ICZWYeIP6OZZrbFzGaa2R1mViqf3+504Is8tisp1KbEHIdaABPzuV0iIhIghW4RETlRY/BhozHwGvBvMzsnPy7snNvmnNuWH9c6ilTgOjOz8A4zawPEnoT3Piozqw+8ENqSgTuAnXl43SDgr8CDwO+AzsC/gdMKrLEFxMzKnOAl4kNbO+BfwD3Ax2ZW+kTbFuac+9U5t/8Er7HRObcnv9okIiLBU+gWEZETtSsUNlY65/4BbAM6hA+aWVcz+9nM9pnZMjO7Ia8Xztm9PNT1dpiZTQpV1n8xs045XjPMzDaY2TYze8zMJprZuKO81RdADD6Qhd2ID2c523SGmb1jZjvNbJ2ZPW1m5SKO/1+oXbtDlfI7crx+hpn9w8xeMLMdoXu6Jo8fydjQ5/ypc+7jPJz/e2CCc+610OvmOueedc69Em4LUAt4JVR1nRHa383Mvgm1b52ZPWtm5SPuYUSoWnybma0PVdD/keNLi7PM7Hsz22tmM4HEHJ9DGzP7zMy2mtlGM/u3mVWNON43VIW/1syWAxvzct0j2OCcW++cW+icex5oD5wHDIh4z8P+bs3sCTN7P8c9VDezDDNrFnp+oFeEmdUws7fM7NfQ5/iFmTWJePnS0M+VodeNCL3uoO7lZtbSzGaF/v6sMbP/zdEGF/qsPg79mZtjZmfn8TMREZGTQKFbRETyhZmVMLMeQGVgf2hfIjAltJ2Nr4qPNbO2J/BW/wu8AzQBvgT+Fa5WhgLPw8CfgJZAaeDyPFzT4bv0Xh+6ThngKuDVHPdYGvgQH5iaAd3x3YEfizhtH3ATvrL8J+AhM/t9jvcbDCwCzgXG4UNv9SO0bzWwAHjSzI7lv90bgDZmdvphjvcA1gN34Xsr9Ajtj8VXx88BrgE6An/O8dqz8ffeCRgYusZlAGYWA7wNpOA/pyfwFfdIFYDngOZAV6A28GyOc6oC/YCeofvIy3XzxDm3HJgOdAu1+Wi/29eBzmYW2UugJ7DSOTcnl7coi/8y56LQ9RYC08ws3HuidehnS/xnPyrnBcysIvA+/nffBP9n/89mdm2OUx8Angqdsw545eifgIiInDTOOW3atGnTpu24NmAGPmDvBNLx4TUFqBo6/gjwXY7XvA5MCj1ODL0mKfS8L5Aace444F8Rz1cBz0Y8Pz30+sah55NynB8Tas+4I9zDOHxFuyGQBoQD9+zQcQd0Dj2+Afg+x+vb4IN2zGGu/zy+Qh35mb0f8bwksAu47AhtnAh8BLwVehwT2l8KyABaHeZ1tYEfgCzgZ+AZoEOOc1KBvkf5PV8DrIh4PgLYAsRG7PsQGBV63BXYDVSOOP6I/9+Ow75Hq9CfofC99Q199okR5xzPdTuErlMyl2MPA7/k5XcLWOjP38Acv8u/RTw/8Gcll/eKwf89aR96npTz/iL+jA8MPR4CrI1se+h+Z+d4z/+NeN46tK9Cfvwd16ZNmzZtJ76p0i0iIifqRXyF7ULge2Cwc25T6NiZwDc5zp8V2n+8fo54/GvoZ7hKXB84UHV0zmUCc/NyUefcInyVsxs+gL2ay2lnAeeEuh/vNLOd+DBcGt9NGzO7NNT1ekPoeH98+M31HpxzGcCmiHs4iJk1xofe/kAfIA54w/wkYA3wX3rkeo/OuTX4KmtLfPUzEfg03JX5cMws2cz+Y2YpZrYj9Nqc97DUObc34vmvEfdwJrDMOZcWcfy7HO8Rb2YTzE8OtwP4BP8FRM2I09Kcc6sinh/1usfI8AEVjvK7dc454E3g6lD7awLnA2/kemGzUmb2UGiowVb8sItyHPo5HsmZwJzQn5Gw3P7+HOnvhIiIBKxk0A0QEZEiL805twxYZmZ9gK/M7Czn3K/4UJPf0sMPnHMuNIw4/CVyZIg6HhOA/8F/idAvl+MV8F2GB+dybL2ZnYHv/vx3fHfrbfgJu5JynJue47nj8EO+koGtoQCN+ZnHPye7m/WbOcLvwRf2YfH70DbazO4D/mJmDzvn9h3mZdOAn/Ah/zf8+Od/5uEeYkKP8/J7GIcPtIPw1fa6+K7UkTOK787xmhP9/ebUEF9ZhqP8bkM/3wCGhoYC9AIWO+d+zuV88L/3G/GT3i0G9uK/IDiWGdPz+vcn8ncR/nxUWBERKSQUukVEJN8455aEJuMaDtyGH7fcKcdprUP7C8ISfGUXODC2uAnwaR5f/2/8GN4PnHMbczk+D18JT80t6JpZU2CPc+6BiH118978XK0ETjOzhs65Rc657WbWFfgKuBRf7T4Wi/D//S+D7zqdTnZYJjSZWT2gp3NubmjfVcf4HouB+mZ2qnNua2hfixzntAKuc6EJ4cws5/HjvW6emF8bvgt+WTo4yu8WwDk3x8xWAlfihyC8foS3aIUfRjE59H618fMdhIWDckzOF0ZYBPQys5IR1e6C/PsjIiIFQN+CiohIfnsaGBCavOs5fJfdkWbWwMxuw08+NaaA3vsF4Coz629mZwKjgVPJY3U01C2+BqEuxLmYiO/O/YaZtTC/1vLlZhaeBGs5cEpoNukkMxvOcYbCCN8Tqmyb2UVm1gAfDk8D9uCrqbkys+fN7B4za2VmCWZ2CX6CtM+dc9tDp60G2ptZTTOrhB/XngbcFJrN+2pyr/4eyYf46vBLoa7qPXNp53LgejOrb2ZdgPvy6bqHU8PMTg+9bjD+M/0aGBs6frTfbdgb+LXj23Dk0L0c6GJmTUNfxozHV7vDfg2938VmVs0iZsCPMBH/5chzZtbQzHoDt1Nwf39ERKQAKHSLiEi+cs59jq843+2cWw1cAfwBmI/vcj3AOfd1Ab33x/jw9gi+K28G8DG+opvXa6S5w6yT7JzbgZ+Yaz9+vO884G+Euh87537Ez1j+D/wEZon4LwKOW6h7+B/wIXFs6D37AtfhK673hYJxbj7Bzzw+BV8l/id+8q/IyvUI/NJZa4CpoXHwfYCL8bNmD8bPjn0sbc7Ez4ReF/gRGBp6n0gD8d3uf8bPQD48n657OKmh7Sv8LPV/By5xzqWHrn3E322E1/Ez0//knFtyhPf7G76XwkxgMv6z3xxxL/uAP+I/2w34mclz3u8O/LJvZ4Xa8yjwF+fca3m8ZxERKQTM/7dcRESk+DE/4Hsx8KJz7tGg2yMiIiLRR2O6RUSkWDGzu/HrLzvgFqAOfikxERERkZNO3ctFRKS4aY+fhfo7oDlwUY5lp0REREROGnUvFxERERERESkgqnSLiIiIiIiIFBCFbhEREREREZECUqwmUitTpoyrVq1a0M0QERERERGRKLF27dr9zrkyhzterEJ3tWrVSE1NDboZIiIiIiIiEiXMbOORjqt7uYiIiIiIiEgBUegWERERERERKSAK3SIiIiIiIiIFpFiN6T6arKwstC550WBmlCih74RERERERKRoi4rQvX//flJSUkhPTw+6KXIMSpUqRZ06dShdunTQTRERERERETkuURG6U1JSqFixIlWqVMHMgm6O5IFzjs2bN5OSkkJSUlLQzRERERERETkuxT50Z2VlkZ6eTpUqVShZstjfbrFSpUoVtmzZQlZWlrqai4iIiIhIkVTsk0x4DLcq3EVP+HemcfgiIiIiIlJUFfvQLSIiIiIiIhIUhe6A7N+/n3vuuYekpCQaNWpE48aNeeWVVw57/gMPPMAbb7xx1Os+//zzPP744yfUthEjRnD33Xef0DVEREREREQkCsZ0F1Z9+/Zl3759zJs3j/Lly7Nq1Sq6du1Keno6gwYNOujcjIwMRo4cmafrDhkypCCaKyIiIiIiclJlZEBxmJarGNzCsenWDZYvL7jr16sH06Yd+Zxly5YxZcoU1qxZQ/ny5QFITEzkscceY/DgwTRo0IC77rqL9u3bM3v2bIYOHcp7771H8+bNue2229i2bRv9+/dnwYIFxMXFUatWLWrUqMGoUaMYMWIEO3fuZNSoUYwbN45///vfnHbaacyfP58yZcrw5ptvcsYZZ/Drr7/Su3dvtm/fzt69e7nwwgt54oknNPZdREREREROusxMWLgQvvsOZs/2P9PSYOXKoFt24qIudBcGP/zwA/Xr16dKlSoH7W/dujWpqals3LiRn376iaeffponn3wSgPfee+/AeSNHjqRy5cosWrSItLQ0mjZtypVXXpnre3377bfMmzePhIQE7r33Xv7+97/zwgsvcOqpp/LOO+9QoUIFMjMz6d69O5MnT6Znz54Fd+MiIiIiIhL1nIPVqw8O2HPmwK5d2efUrAktW8KePVC2bHBtzQ9RF7qPVoU+WY5WUW7QoAHt2rXL9dhnn33GU089BUDlypW54oorDnuddu3akZCQAPhQH35dVlYW99xzDzNnzsQ5x2+//UaTJk0UukVEREREJF9t2uTDdThgf/cdbNyYfbxiRTjvPGjRwgftli2hVi0oLp1woy50FwbnnnsuS5YsYfPmzQdVu2fNmkV8fDzVqlWjQoUKh329cy7P3cBjY2MPPI6JiSEjIwOA0aNHs3nzZr799ltiY2MZOnQoe/fuPc47EhERERERgd274YcfDg7YK1ZkHy9VCpo0gauuyg7ZZ54JJYrxFN8K3QGoX78+l19+OYMGDWLChAmUK1eOVatWMWzYMP70pz8d9fUdO3Zk/PjxtGnThq1btzJ16lR69OhxTG1IS0ujZs2axMbGsmHDBiZNmsTVV199vLckIiIiIiJRJiMDFiw4OGDPn+/HZ4c1bAg33ODDdYsWcM45UKZMcG0OgkJ3QF599VWGDx/OWWedRenSpYmJiWHYsGEMHDiQGTNmHPG1DzzwAP369SM5OZnExETatm1LpUqVjun977jjDnr16kWTJk2oVasWnTt3PoG7ERERERGR4sw5X7GODNg//ODHXIfVquUnrg53EW/WDI4xphRL5pwLug35Jj4+3qWmph60LzMzkyVLltCgQQNiYmICaln+Sk9PJzMzk9jYWLZv3067du0YPXp0sQvOxfF3JyIiIiJSFGzYcHDAnj0btmzJPn7qqb5yHd5atoS4uODaGyQzW+uciz/ccVW6i6C0tDS6du1KZmYme/bsoU+fPsUucIuIiIiIyMmxY4efPTwyYKekZB8vUwaaNs0O1y1aQFJS8R6HnZ8Uuoug6tWrM2fOnKCbISIiIiIiRcz+/fDTTwcH7F9+8d3HwQfp5GTo3z87YJ91lp8ATY6PQreIiIiIiEgxs307LF0KS5Zkb4sXw88/++AdlpgIvXplV7GbNoUjLKQkx0GhW0REREREpAjatw+WLz84WIe3DRsOPT8uDjp3PribeLVqJ7/dR5WeDqtWwa+/wvnnB92aE5bn0G1mlwAPASWAUsCjzrnxZlYdeBWoB+wDhjjnZoZeUw54GWgBZAH3OufeDh0rATwB/B5wwGjn3LMR7zcc6Bd6+ppz7v4TuVEREREREZGiJjPTj6/OLVivXp3dLTyscmW/7vXFF0ODBtlbUlIhq2Dv3QsrV8KyZYduq1f7Gy9f3g84Nwu6tSckT6HbzAx4DejonPvJzBKBRWb2NvAI8I1zrouZtQDeMrN6zrkM4G5gn3MuyczqArPM7DPnXBpwHZAMNAAqAT+Y2afOuUVm1h7oDZwNZABfmdlM59yH+XnzIiIiIiIiQXEO0tJg3TpYv/7gbdUqH6yXLz+4OzhA2bI+SPfsCfXrHxyuq1QJ5FZyt2uXX2cst2C9Zs2h3xiULeu/HejWzf9MSvLhu2TR7qB9rK0/NfTzFGAzvrJ9FVAXwDk328w2AO2AGcDVQN/QsZVm9gXQHRgXOva8cy4T2GJmbwLXACNCx8Y553YBmNlYfAgvNqE7MTGR2NhYYmNj2bNnD/369ePee+896uvMjB07dlChQgUSExN59913ady4MQMHDuTGG2/k/HzsfpGYmMju3btZu3YtpUIzJ3z66adceOGFDBs2jFGjRuXbe4mIiIiIFBdZWbBpkw/POQN15PNff/VdxHNTsiSccUZ2xToyXMfFFaKZw7dv998M5Bas16079PyKFf3NtGrlQ3W9etkB+/TTi3xVOzd5Ct3OOWdmVwFvm9kuoDLQA6gIlHDObYw4fRVQJ/S4DrD6GI41jzj2eY5jPfPS1qLkrbfeonHjxqxbt47k5GQ6depEy5Ytj+taL730Uj63zqtTpw7Tpk3jyiuvBGDs2LE0b978KK8SERERESlenINt2+C33w7eNmw4NFBv2AAZGblfp0QJqFHD58vGjf3PuDj/M7zFxflzCs2M4du2+RC9dGl2oA4//u23Q88/7TQfojt0yA7U4a1q1WIZrI8kr93LSwL/B3R3zn0V6kY+Bd/92+U8PcdzVwDHwu0aCgwNP69UqVKu7S/s4uLiOPPMM1m9ejUtW7Zk2bJlDBkyhN9++40SJUowYsQIrrjiiiNeo0OHDtx9991cdtll9O3bl3LlyrFkyRJSUlJo3Lgxr7/+OqVLl2bbtm3079+fBQsWEBcXR61atahRo8Zhq9b9+/dn7NixXHnllWzbto1vvvmG3r17s2fPngPnjBo1ijfffJOMjAxq1qzJCy+8QO3atfnkk08YPnw4e/fuJT09nWHDhtGvX78D7T3vvPP4+uuvWbduHRdddBHPP/98/n2oIiIiIiJHsW/foSH6SFvObt6RSpaEmjV9YG7e/NAQHX5cvTrExJy8e8yzrVtzD9VLl/qyfU7VqvkQ3aWLr1bXr59duT7ttJPf/kIsr93LmwBxzrmv4EA38nX40I2ZVYuodicA4aXUU4BEIPLY+zmOzT7C68jl2AHOudHA6PDz+Pj4nF8AHKpbN9/9oaDUqwfTph3TSxYtWsSmTZvo0KEDAH369GHAgAEMGjSIpUuX0qpVK5o1a0bt2rXzfM25c+fyySefULp0adq3b8/kyZPp3bs3I0eOpHLlyixatIi0tDSaNm16oIqdm/bt2/PUU0+xdu1a3nnnHXr16kVMxL8Sr732GkuWLGHWrFnExMQwYcIEbrvtNqZOnUrTpk2ZOXMmMTExbNmyhaZNm9KlSxdOP/10AJYvX86MGTPYv38/ycnJzJo1i9atWx/TZyciIiIiEpaZCVu2+IC8cePRQ/S2bUe+XtmyvuIcFwdNmvjAHLnVqOGzZ1ycH0tdaLp858Y5/+GEu4JHhuply2Dz5kNfU726789+6aU+UEcG61NPPfR8yVVeQ/caIN7MznTOLTazJPxs5UuAScCtwIhQBbwmMDP0uvCxvqGJ1C4AhkQcGxyajK0Sfhx3l4hjT5vZs/iJ1PoDw4//Ngunnj17YmYsXryYxx9/nGrVqrFjxw7mzp3LgAEDAKhfvz7t2rVj5syZ9O7dO8/X7tGjB2XLlgWgZcuWLA990fDZZ5/x1FNPAVC5cuWjVtABrr/+esaPH8+UKVOYOHEiEydOPHBsypQpfP/99zRr1gyAzMzMA6F88+bNDBgwgCVLllCyZEk2bdrEggULDoTua665hpiYGMqWLUuTJk1Yvny5QreIiIiIHOCcn7w6Z1g+XKDetMmPpz6cEiV8SK5e3VejcwvRkc/Llz9593rCnPMfwKpVflu9+uCfq1bBzp2Hvq5GDWjUKLv7dzhYJyXBKaec1FsorvI6pnuDmQ3Gz0yehe/ufYtzbq2Z3QNMMLOlwH7g+tDM5QCPAmPNbBl+ybBbnXNbQscm4JcSWxI+1zn3S+j9ZoQmVvs5dOx159wHJ3arIcdYhS5I4THdH3/8MZdffjmdOnUiISEB8BOmRcr5/GhiY2MPPI6JiSEjNKjEOXfM1+rbty9NmzalQYMG1K9f/6BjzjmGDx9O//79D3ndkCFDuPzyy5k8eTJmRtOmTdm7d+9R2ygiIiIi0cE5Pw566dLsbckSSE3NDtKHm2gsrFIlH5Dr14e2bQ8N0uGtWrUiUI0+Euf8YPGcQToyXO/efejrSpWChAQ/cVlCQvbEZfXr+8cVK57c+4hCeZ693Dn3b+DfuezfAFx8mNfswlewczuWia+CH+79RgIj89q+oqxz587cfPPNDB8+nKlTp9KkSRPGjx9Pv379WL58OV999RVPP/10vrxXx44dGT9+PG3atGHr1q1MnTqVHj16HPE1cXFxPPzwwzRs2PCQY926deOJJ57giiuu4LTTTiM9PZ358+dz7rnnkpaWRkJCAmbGF198wbx58/LlHkRERESk6HDOV6bDgToyYC9demhOjInJnkjsrLMOH6KrV/dzcpUpE8x95bv9+2HtWr+U1po1PkRHhuuUFL+2dU5lyvgw3a4dJCb6LSEh+3HNmkX4m4bioWgveFaM3H///SQlJTFnzhwmTpzI4MGDGTNmDGbGSy+9dEzjuY/kgQceoF+/fiQnJ5OYmEjbtm3zNAFdeAK0nK6//no2b95Mhw4dMDMyMjIYMGAA5557Lo888gi33HILjzzyCMnJyZx33nn5cg8iIiIiUvhs2XJoxTr8ePv2g881gzp1oE0bX3ANbw0a+JxYunQgt1BwsrL8+mDhQJ2Skv04vP3666HrVoMfWJ6YCB07Hhymw4+rV1eoLuTM5faLLaLi4+NdamrqQfsyMzNZsmQJDRo0OGgCsGiVnp5OZmYmsbGxbN++nXbt2jF69Gg6d+4cdNMOod+diIiISOGzfz/MnQvffANz5mSH69zm4YqLy15jOnKrVw8iRhoWbeEJyiIDdM5QnZqa+xpiZn5K89q1s7c6dbIf160blUtsFTVmttY5F3+446p0R5m0tDS6du1KZmYme/bsoU+fPoUycIuIiIhI4bBuHcya5UP2rFk+aEf2cq5eHRo2PDRcJyUVsYnIjiQz0wfn8HJa4RnAw49zG0sNfhB57dq+n3xkmA5vtWoVosW4paAodEeZ6tWrM2fOnKCbISIiIiKFULiKPWtW9pYSsXBvpUrQvj20bu23Fi2K0ZLMGRl+DHU4TEduK1Ycukh3TIzv3t22re/qnbNaHR8P5coFcitSuCh0i4iIiIhEqXAVO7zNmXPwbOHJyTBggJ/4unVrv7JUkR4+vG8frFx5cKU6vK1adWgX8FKl4Iwz4KKLspfRCm8JCapSS54odIuIiIiIRIH9++HHHw8O2WvWZB+vVAk6dMgO2OedB6eeGlhzT8y+fbBoEcyff/C2evWhk5XFxvoQfdllB4fqevV81VpzC8kJUugWERERESminPMzg2/cCJs25f5z40Y/Mfb8+dlVbLPsKna4q3jDhkWwip2R4avWOcP10qV+HHZYqVJw5plw5ZWHVqxPP70I3rgUJQrdIiIiIiKFRHq6nwX8cOE5t2Cdnn7ka5YuDdWq+Sp2OGC3bFnEqtjO+cHlOcP1L78c3B/ezAfpbt2gcePsrX59dQWXwCh0i4iIiIgUAOdg584jV6Fz/ty69ejXrVTJryKVkADNmvlAXa2a35fzcdWqULFiEVtxatMmmDfv4HC9YAHs2HHwebVrQ6dOB4frRo38utYihYhCd0ASExOJjY0lNjaWPXv20K9fP+69996jvs7M2LFjBxUqVCAxMZF3332Xxo0bM3DgQG688UbOP//8fGvj22+/zYMPPkhmZib79u0jLi6Ojz76iBIlSjBmzBiuvfZaqlevnutrR4wYwc6dOxk1alS+tUdERESksEhNhW+/hd9+O3KQjizC5qZkSR+Mq1aFc8/Nfny4IF21qq9cFxs7dvjZ22bPzt5WrTr4nKpVoXnzg8P1737nv30QKQIUugP01ltv0bhxY9atW0dycjKdOnWiZcuWx3Wtl156KV/b9uuvvzJkyBBmz55NQkICAD/88AMW+pp0zJgxdO7c+bChW0RERKQ42b8fvvoKpk+HDz6An3/O/byKFX1GrFULzjkn98pz5M9KlYpYFfpE7N3rK9iRAXvRouyJzcz8wPIbboCmTf3a1o0b+4XARYqwqAvd3bp1Y/ny5QV2/Xr16jFt2rRjek1cXBxnnnkmq1evpmXLlixbtowhQ4bw22+/UaJECUaMGMEVV1xxxGt06NCBu+++m8suu4y+fftSrlw5lixZQkpKCo0bN+b111+ndOnSbNu2jf79+7NgwQLi4uKoVasWNWrUOKQivX79ekqWLEmVKlUO7GvatCkAI0eOZN26dfTs2ZPY2FjGjRtH3bp1GTBgAAsXLqR27dpUq1aNmjVrHtPnICIiIlKYrF6dHbI/+cR3FQefAW+4wfdsrlUrO0BXqeInwhb8BGcLFx4csH/66eAluerWhZ49/WLfLVr4oH3KKcG1WaSARF3oLowWLVrEpk2b6NChAwB9+vRhwIABDBo0iKVLl9KqVSuaNWtG7dq183zNuXPn8sknn1C6dGnat2/P5MmT6d27NyNHjqRy5cosWrSItLQ0mjZtypVXXnnI68855xxat25NnTp1uOCCC2jTpg3XXnsttWrV4oEHHmDs2LEHKvUAw4YN45RTTmHhwoVs2rSJpk2bctVVV+XL5yMiIiJyMuzdC1984UP29Om+CAt+xajWraFLF+jaFZo00WTXB8nK8utcRwbsH3+EPXuyz6lZ03944YDdvEpf0TAAACAASURBVLn/tkIkCkRd6D7WKnRB6tmzJ2bG4sWLefzxx6lWrRo7duxg7ty5DBgwAID69evTrl07Zs6cSe/evfN87R49elA2NIlEy5YtD1T3P/vsM5566ikAKleufNgKeokSJZg8eTKLFi3i888/Z/r06Tz44IN8//33JCUlHXJ+5HWrVq1Kjx498v5BiIiIiARk+XIfsKdPh88+y86JcXHQv7/PiZ07F7GZvgva9u1+ke+vvvI/Z8+Gbduyj596KrRtmx2wW7TwXQKiph+9yMGiLnQXJuFK8ccff8zll19Op06dDoyfthz/KOV8fjSxEX2bYmJiyAh15XHOHdO1GjZsSMOGDRk8eDBdunRh2rRpDB069JDzXHgsjoiIiEghtns3zJiRXc1etszvL1kS2rXLrmafdZYy4gFr1sDMmT5kz5zpB7RnZfljZcv6buGRAbtePXUFEImg0F0IdO7cmZtvvpnhw4czdepUmjRpwvjx4+nXrx/Lly/nq6++4umnn86X9+rYsSPjx4+nTZs2bN26lalTp+ZalV67di2rVq2ibdu2AKSlpbFy5Urq1asHwCmnnMK2iG80L7zwQl555RXatm3Lli1b+M9//kOvXr3ypc0iIiIix8s5WLIku5r9+efZM4rXrg2DBvmQ3amThhMDkJnpl+iKDNlr1mQfr1kTevTwlex27fxscVr/WuSIFLoLifvvv5+kpCTmzJnDxIkTGTx4MGPGjMHMeOmll45pPPeRPPDAA/Tr14/k5GQSExNp27YtlXJZbiEjI4ORI0eycuVKypUrR0ZGBjfeeCPdu3cH4I477qBfv36UK1eOcePGcf/999O/f3+Sk5NJSEjgoosuypf2ioiIiByrzEz4+muYNg2mToWlS/3+0qWhffvsanajRqpms2uXX/ssHLC/+cZ3Hw9LTvbfTIRDdt26+tBEjpEVp27B8fHxLjU19aB9mZmZLFmyhAYNGhATExNQywqP9PR0MjMziY2NZfv27bRr147Ro0fTuXPnoJt2CP3uREREJK927oT//tcH7Xffhc2b/f64OLj8crjsMujQASpUCLSZwVu/3gfscMj+8Uf/LQVAmTK+e3i7dj5kt2kDp50WbHtFigAzW+uciz/ccVW6o0xaWhpdu3YlMzOTPXv20KdPn0IZuEVERESOZt06H7CnTvVLeoW7jZ99NtxyC3TrBs2aRXlhdtUqP0PcjBk+ZK9YkX2sShW49NLskN2smQ/eIpKvFLqjTPXq1ZkzZ07QzRARERE5Zs754cbhbuOzZ/v9JUvCBRdA9+6+qp2YGGgzg7VmjQ/Yn33mt1Wrso/Vrw/9+mV3FW/QIMq/kRA5ORS6RURERKTQSk+HL7/0QXvaNFi50u8/5RS45hpfze7aNYqX9Fq/Pjtgf/aZXwMtrH59Px67Qwe/nX56UK0UiWrFPnSHl8cqTmPXo0X4d3asy6WJiIhI0bZ9u1/Sa+pUeP992LrV769TB26/3Qft9u39xGhRZ8OG7Er2jBmweHH2sTPO8IuLd+zoQ3b8YYeYishJVOxDd4kSJShVqhSbN2+mSpUqCnBFhHOOzZs3U6pUKUponUcREZFi79df4T//8duMGb7CDX6YcffuPmiffXYU9obetMmvcxauZC9cmH2sTh248cbskJ2QEFgzReTwin3oBqhTpw4pKSls2bIl6KbIMShVqhR16tQJuhkiIiJSQNatg7ffhkmTfBdy53z1+sILfdC+7LIoLNZu23Zwd/Gff84+VqsWXHedD9gdO2r5LpEiIipCd+nSpUlKSiIrK0vdzIsIM1OFW0REpBhKTYXJk+Gtt/yqVc5BbCz84Q/Qq5efTLtixaBbeZJt2+YHrL/5Jnz4YXaZv0YNP3C9Y0e/JSUpZIsUQVERusMU4kREREROvpQUH7QnTYJZs/y+cuWgZ0+//f73Ubh+9vbtBwft/fshJgY6dfJl/k6doGFDhWyRYiCqQreIiIiInByrVmUH7W+/9fvKl4err/YV7S5d/POosmMHvPOOD9offOAXFi9Rwgfsq67y5f6qVYNupYjkM4VuEREREckXK1b4buNvvZW9hnaFCnDttb6i3aULlC0bbBtPuh074N13fdCePj07aHfokB20q1cPupUiUoAUukVERETkuC1b5kP2pEnwww9+3ymn+Pm+evWCiy/2Y7ajys6d8N57Pmi//z7s3euD9gUX+KDdo4eCtkgUUegWERERkWOyeHF2RXvuXL/v1FP96lU9e8JFF0GZMsG28aTbtevgoL1njx+P3b59dtCuWTPoVopIABS6RUREROSInIOffvLLe02eDAsW+P2VK0P//j5oX3ihX+4rquze7QP2m2/6LuThoH3++dlB+/TTg26liARMoVtEREREDuGcH5c9ebIP28uW+f3VqsGgQT5PduoEpUoF285A7NsHDz0Eo0b54G0G7dr5/vRXXglxcUG3UEQKEYVuEREREQEgM9OvnR0O2qmpfn+tWnD77T5PtmvnV7aKWt9958v7CxbA737nv4G48kr/IYmI5EKhW0RERCSKpafDjBk+aE+ZAhs2+P1nnAF//KOvaLds6ecBi2p79sADD8Do0f5bh5Ej4Z57orBPvYgcK4VuERERkSizdy989JEP2tOmQVqa39+oEdx0ky/cnnOO7zUtwBdfwIABvo99ixYwdiw0bhx0q0SkiFDoFhEREYkCu3b5ZaInT/aTbO/Y4fc3aQJDh/qg3ahRsG0sdHbsgHvvhWef9euePfoo3HUXlNT/QotI3ulfDBEREZFiaudOmDrVB+0PPvA9pAFatfIhu0cP341ccvHf//qyf0qKn4385Zehfv2gWyUiRZBCt4iIiEgxkpXlx2i/+qpfR3vXLj8e+/zzfdD+wx8gPj7oVhZiaWkwbBi88gpUqADPPANDhmhQu4gcN4VuERERkWJg8WIftCdMgDVr/L7zzoPrr/crWVWvHmz7ioSpU+Hmm2H9erj4YvjnPyEhIehWiUgRp9AtIiIiUkRt2QJvvAHjx8O33/p9tWvDfff5sN2wYbDtKzI2boQ77oDXX4dTT/UTpfXtq5nkRCRfKHSLiIiIFCHp6X5CtPHj4d13Yf9+KF8ebrgBbrwROnRQT+g8c85/a3H77bBpE3Tv7idNi4sLumUiUowodIuIiIgUcs7BDz/47uOvvebzoRl06uTDdo8efvixHIN163xX8mnToGpVX+W+6ipVt0Uk3yl0i4iIiBRS69bBv/7lw/aCBX7fmWf6Jb769IE6dYJtX5HknJ8kbehQ2LYNeveGJ56AatWCbpmIFFMK3SIiIiKFyO7dMGWK7z7+8cd+NvLTToNbb/VV7RYtVIw9bqtX+2XAPvoITj/df5vRrVvQrRKRYk6hW0RERKQQmD0bnn8eJk2CHTugZEm4/HI/Tvv3v4cyZYJuYRGWlQXPPQf33usXL+/fHx57zE+aJiJSwBS6RURERAK0cyf83//55aCdg+bNfUW7d28/1FhO0NKlMGAAfPmlX/5r8mS/HJiIyEmi0C0iIiISkI8/9r2dV62Ctm198D7nnKBbVUxkZsKYMTB8OOzd6/vnP/wwVKwYdMtEJMoodIuIiIicZFu3wt13w8sv++W+nnoKbrlFS33lm4ULfRfyb7+FpCT/QbdvH3SrRCRK6Z92ERERkZPonXfgd7/zObBzZ5g/H267TYE7X6Snw0MPwbnn+kHyw4bBvHkK3CISKFW6RURERE6CTZvgzjv9OtuVKsFLL/lirGYizyfz5vkP9IcfoFEjGDsWWrUKulUiIqp0i4iIiBQk5+DNNyE52Qfuyy/3a24PGKDAnS/274c//9nPQDdvHtx3nw/eCtwiUkio0i0iIiJSQNav92O1p0yBKlV86L7mGoXtfPP999Cvn++jf/bZvrrdrFnQrRIROYgq3SIiIiL5zDkYN85Xt6dMgauv9nN79e6twJ0v9u71a26fdx4sXgx/+Ysfw63ALSKFkCrdIiIiIvkoJQUGDYIPP4SaNeE//4Errgi6VcXI11/7sduLF/uQ/corcNZZQbdKROSwVOkWERERyQdZWfDcc35m8g8/hL59fXVbgTuf7N4N//M/0K6dX9j84Yfhm28UuEWk0FOlW0REROQELVsGAwfC559D7drw1ltwySVBt6oYmTHDf8DLl0Pr1n69tUaNgm6ViEieqNItIiIicpwyM+Gxx/wcXp9/7idNW7BAgTvf7NgBt94KHTvCunXw+OPw5ZcK3CJSpOQ5dJtZGTN72syWmtkCM/tXaH91M/sgtH++mbWLeE05M/u3mS0zsyVm1iPiWAkze8rMloeO35Lj/YaHji03s7/mx82KiIiI5JcFC6BtW7j7bqhVyxdjn3kGKlYMumXFxH//C40bw7PPwgUXwE8/wV13QUxM0C0TETkmx1LpfgTIAho4534H/DFi/zfOufpAP2CimYW7rd8N7HPOJQGXAM+aWeXQseuAZKAB0BL4XzNrCGBm7YHewNmhc7qamb4zFhERkcClp8Pf/gZNm/oJs4cN88tDX3BB0C0rJrZu9YuYX3IJbN7sv8n49FNISgq6ZSIixyVPodvMyuMD9X3OOQfgnFsfOnwV8Exo32xgAxCudl8dcWwl8AXQPeLY8865TOfcFuBN4JqIY+Occ7ucc/uAsfgQLiIiIhKoP/0J7r/fZ8Cvv4ZRo6BcuaBbVUy8956fiW7sWLjoIr/+9i23QAmNiBSRoiuv/4LVAzYDw83sezP70swuNLMqQAnn3MaIc1cBdUKP6wCr8/nYAWY21MxSw9vOnTvzeDsiIiIix277dnj+ed/r+Ycf/DLRkg/S0uDGG+Gyy2DXLj9R2ocfQmJi0C0TETlheQ3dpYAzgIXOuebAbcDr+NnPXY5zLcdzVwDH/AnOjXbOxYe3ChUqHOEWRERERE7MuHF+bq8774QyZYJuTTHx3nv+W4xXX4WuXX11u39/sFz/909EpMjJa+hejR/PPRHAOTcPWAk0AjCzahHnJgApoccpQGI+HxMRERE56bKy4KmnoEoV6NMn6NYUA1u3Qr9+vrq9c6fvUv7eexAfH3TLRETyVZ5Ct3NuE/AJfjI0zCwBqAssBiYBt4b2twBqAjNDL408Vhe4AJgWcWywmcWY2Wn4cdxvRBy70czKm1kZoD++si4iIiISiOnT/XrcgwZB2bJBt6aImz7dV7fHjYMuXfxU8P36qbotIsVSyaOfcsAQYKyZ/R3IBAY559ab2T3ABDNbCuwHrnfOZYRe82joNcvwlfJbQ5OmAUwAWgBLwuc6534BcM7NMLM3gZ9Dx153zn1wnPcoIiIicsKefNKvVnXzzUG3pAjbuhWGDoVXXoFTTvFjtxW2RaSYs9Bk5MVCfHy8S01NDboZIiIiUsz88gskJ8NVV8Ebbxz9fMnFBx/AwIGwdq1fDuzFF6F27aBbJSJywsxsrXPusGNjtP6CiIiIyFE8+aT/eeedwbajSNq2za+73bWrn/79xRd993IFbhGJEsfSvVxEREQk6qSl+Ym1mzWD1q2Dbk0R8+GHvrqdmurX3X7pJahzyCqwIiLFmirdIiIiIkfw8suwe7evcmvocR5t3w433eQnSdu2Df75Tx/AFbhFJAppTLeIiIjIYWRkQFIS7N0Lq1drbe48+egj3518zRro3Nl/a6GwLSLFmMZ0i4iIiBynd97xYXvIEAXuo9q+HQYPhosv9n3yX3gB/vtfBW4RiXoa0y0iIiJyGE88AaVK+dAtR/Dxx766nZICF17oq9sJCUG3SkSkUFClW0RERCQX8+bB55/D1VdDzZpBt6aQ2rHDfyNx0UWwZQs895zvXq7ALSJygCrdIiIiIrnQMmFH8cknvrq9ejV06uSr24mJQbdKRKTQUaVbREREJIeNG2HiRGjTBpo3D7o1hczOnXDLLX6StE2b4NlnfXVbgVtEJFeqdIuIiIjk8OKLsG8f3HFH0C0pZL78Evr2hRUroEMHGDsW6tYNulUiIoWaKt0iIiIiEdLTffG2Vi3o0SPo1hQSe/bAsGFwwQWwfr3ve//JJwrcIiJ5oEq3iIiISIS334a1a+HBB/3M5VHvu+/gxhth0SJo3RrGjYMGDYJulYhIkaFKt4iIiEiEJ56A2FgYNCjolgRs3z7405980F6xAv7+d9+9XIFbROSYqNItIiIiEjJ7Nsya5Sflrlo16NYEaO5cX93+6Sdo2hRefRV+97ugWyUiUiSp0i0iIiISEl4mLGonUMvIgL/9DVq0gIUL4S9/gW++UeAWETkBqnSLiIiI4OcHe+MNPyn32WcH3ZoALFzoq9vffw+NG8P48b7KLSIiJ0SVbhERERHg+ef9zOVRV+XOzIRRo3zA/uEHuPdeH7wVuEVE8oU554JuQ76Jj493qampQTdDREREiph9+6BOHShXDpYtg5iYoFt0kixb5tfd/uorP0Ha+PHQqlXQrRIRKVLMbK1zLv5wx1XpFhERkaj3xhvw229w221RErizsuCZZ+Ccc3zgvvNO+PFHBW4RkQKgMd0iIiIS1ZzzE6iVKwf9+wfdmpNg9Wp/o59+ComJ8MorfiC7iIgUCFW6RUREJKp9/TXMmePnEKtcOejWFCDn4OWX4ayzfOAePNgvCabALSJSoFTpFhERkaj2xBP+5+23B9uOArVuHdx0E7z/PtSqBZMmwSWXBN0qEZGooEq3iIiIRK01a+Dtt+Hii6FRo6BbUwCcg9de80uAvf8+3HADzJ+vwC0ichKp0i0iIiJR69ln/YpZd94ZdEsKwObNMGQIvPUWVK8OU6ZA9+5Bt0pEJOpoyTARERGJSnv2QHw8VKkCixZBieLU/+/992HAAPj1V7jySr8IedWqQbdKRKRY0pJhIiIiIrmYOBG2bPFjuYtN4N6500+Qduml/luFCRP8+G0FbhGRwKh7uYiIiEQd5/wEahUrQt++Qbcmn3z9tR+zvXw5dOoE48ZB7dpBt0pEJOoVl+91RURERPJsxgw/n1j//j54F2n798N998H558PatTBmDHz0kQK3iEghoUq3iIiIRJ0nngCzYrBM2Pz5cN11MG8eNGvmu5MXy2nYRUSKLlW6RUREJKqsWAHTpsFll0G9ekG35jhlZsKoUT5oz58PDzwAs2YpcIuIFEKqdIuIiEhUeeYZP6b7jjuCbslxWrnSD0T/4gto0MBXt1u2DLpVIiJyGKp0i4iISNTYuRNefhmSk+HCC4NuzTFyDsaOhbPP9oH7ttvgxx8VuEVECjlVukVERCRqvPoqbNsGf/+7H9NdZPz2G9x0k+8XX6sWTJ4MF18cdKtERCQPVOkWERGRqJCVBU8+CZUrw/XXB92aYzB1KjRu7AP3tdfCzz8rcIuIFCEK3SIiIhIV/vtfWLzYF4zLlQu6NXmwfTv06wdXXAEZGfD66zBxov/WQEREigx1LxcREZGo8OSTUKIE3HJL0C3Jgxkz/GRpq1fDJZf4sdxxcUG3SkREjoMq3SIiIlLsLV4M06fDH/4ACQlBt+YI9u6FYcOgUyfYuBGefdY3XIFbRKTIUqVbREREir2nn/Y/C/UyYT/+6AebL1gArVr5Wd/q1w+6VSIicoJU6RYREZFibds2GDcOmjSB888PujW5yMqCRx+F887zJfm//Q2+/FKBW0SkmFClW0RERIq1sWP9+tx33lkIlwlbuxZuuAE+/RTOPNNPlNasWdCtEhGRfKRKt4iIiBRbmZm+a3m1anDNNUG3Joe334azzvKBe/BgmDNHgVtEpBhS6BYREZFi6733YMUKn2ljY4NuTcjOnTBwIFx5pZ9OfcoUeP55KF8+6JaJiEgBUPdyERERKbaeeAJKloSbbw66JSGzZ0OfPrB0KVx0EYwfD6efHnSrRESkAKnSLSIiIsXKunXwwgtw6aW+53avXoVgxa3MTHj4YWjTxq+9PXo0fPCBAreISBRQpVtERESKNOfg559h2jS/zZ7t95cs6YvJDz0UbPtISfFLgX3xBSQnw2uvwTnnBNwoERE5WRS6RUREpMhJT/cZNhy0V63y+ytVgt69oXt36NLFPw/Um2/6AeVbt8Jtt8E//gFlywbcKBEROZkUukVERKRI2LbN98ieOhXef98/B0hIgDvugG7doH17KFUq2HYCsGMH3H67H7NdvTq8+67v7y4iIlFHoVtEREQKrZSU7Gr2jBm+wg3QvLmvZnfr5lfdKlTrb3/zjZ8sbcUK6NoVXnkFatQIulUiIhIQhW4REREpNJyDH3/01exp02DuXL+/dGno3NmH7Msvh1q1gm1nrjIy/GRpf/mLH1D+1FNw662F7BsBERE52RS6RUREJFBZWfDZZ/D22z5op6b6/aedBjfc4IP2xRdDxYrBtvOIVq2C666Dr77ypffXXoPGjYNulYiIFAIK3SIiIhKIlSth3Di/paT4ffXqwdChvut4mza+YFzovfaaXwh8+3a46y5f7Y6NDbpVIiJSSBSF/5SJiIhIMbF7t69ojx3rq9vgu4rfd58fBt2oURHqjb1tm+8+PnEi1KzpZyq/5JKgWyUiIoWMQreIiIgUKOfg22/9fGKvv+4LwqVLQ69e0L+/X0s7JiboVh6jr77y3clXrfKDzF9+GapVC7pVIiJSCCl0i4iISIHYsAEmTPBV7V9+8fuaNPFB+9proUqVYNt3XPbvh7/+FR56CMqUgeee8+twF5nyvIiInGwK3SIiIpJv0tP9Gtpjx8J770Fmpp8Q7fbboV8/OPfcoFt4AubP9zO7/fijv5GJE31/eBERkSNQ6BYREZETtmCB7z4+YQL89huUKOFnHO/f388+XqZM0C08AZmZ8Pjj8Kc/+cfDh8P99/s+8iIiIkdR4lhONrM/m5kzs8ah59XN7AMzW2pm882sXcS55czs32a2zMyWmFmPiGMlzOwpM1seOn5LjvcZHjq23Mz+eqI3KSIiIvlv2zZ44QU47zy/OtZjj8Epp8CDD8Lq1TB9uh+3XaQD94oV0LEj/PGPkJjox3L/9a8K3CIikmd5rnSbWVOgFZASsfsR4BvnXBczawG8ZWb1nHMZwN3APudckpnVBWaZ2WfOuTTgOiAZaABUAn4ws0+dc4vMrD3QGzgbyAC+MrOZzrkP8+F+RURE5ARkZcGMGb77+OTJsHcvlC8Pffv6qna7dsVkeLNz8NJL8D//A7t2+f7xjzwC5coF3TIRESli8hS6zawM8AxwLfBZxKGrgLoAzrnZZrYBaAfMAK4G+oaOrTSzL4DuwLjQseedc5nAFjN7E7gGGBE6Ns45tyv03mPxIVyhW0REJCDr1/vu4y+95NfXBmjb1gftXr2gYsVg25ev1q+HgQP94PT4eJgyBTp3DrpVIiJSROW10j0S+FcoPANgZlWAEs65jRHnrQLqhB7XAVYfw7HmEcc+z3GsZ26NMrOhwNDw80qVKuXtbkREROSoMjPhww/hxRfhnXf88xo14J57fNhu0CDoFhaASZNgyBDYsgWuvx6efBJOPTXoVomISBF21NBtZq2BFsC9uRx2OU8/wvH8OpZ9knOjgdHh5/Hx8TnbIyIiIsdozRrfffzll/1jM7jkErjpJr8kdalSQbewAKSlwW23wWuvQdWqvu98jx5Hf52IiMhR5KXSfQHQEAhXuePxXb0HAphZtYhqdwLZY75TgEQg8tj7OY7NPsLryOWYiIiIFID0dL/E14svwgcf+LHbtWr5SboHDICEhKBbWIA+/NCX7tet81Ot//OfvqQvIiKSD446e7lz7hHnXJxzLtE5lwikApc456YDk4BbAUITqdUEZoZeGnmsLj68T4s4NtjMYszsNPw47jcijt1oZuVDY8n7A6+f8J2KiIjIIVas8Cth1akDf/iDD9yXXuq7k69aBSNHFuPAvWsX3HILdOkCO3b48v6UKQrcIiKSr050ne57gAlmthTYD1wfmrkc4FFgrJktA7KAW51zW0LHJuC7rC8Jn+uc+wXAOTcjNLHaz6FjrzvnPjjBdoqIiEjI/v0+W774Inz8sd+XkOBXwurXz1e4i72vv4YbboDly+GCC2DcOL8kmIiISD4z54rPMOj4+HiXmpoadDNEREQKpSVLfNAePx42boSSJaF7dz9W+6KLoMRR+78VA/v2wYgR8I9/+MHpjzwCd9wRJTcvIiIFwczWOufiD3f8RCvdIiIiUojt3evnBHvxRfg8tDZIUhIMG+bX1o6qntQ//eRnJP/pJ2jWDF59FZKTg26ViIgUcwrdIiIixdCiRfD88z5XpqVB6dLQu7eval9wQZQVdjMz4dFH4YEH/AxxI0bAffcV02nYRUSksFHoFhERKSbS02HqVHj2WfjsM7+vUSM/A/n11/uVsKLOsmVw441+DHfDhjBhAjRvHnSrREQkiih0i4iIFHGpqb77+Isvwvr1voB7zTVw881w/vl+ne2os3gxPPkkvPIK7NkDd90FDz0EZcsG3TIREYkyCt0iIiJFkHPwySfw3HO+up2ZCbVrw4MP+nW1o2qsdphz8NFH8MQT8P77fl+TJjB6NHTsGGzbREQkail0i4iIFCFpaX728eee87ORA1xyiV9u+tJLISYm2PYFYvdu+Ne/fNheuNAPWO/RA+68M4pL/SIiUlgodIuIiBQBc+b4oP3aa7639Gmnwd13w+DBfjbyqJSaCs88A//8J2zZAqecAkOHwm23Qd26QbdOREQEUOgWEREptPbsgTff9BOjffed33feeX6s9lVXRfHw5G++8VXtSZN8v/r69eEvf/ETplWsGHTrREREDqLQLSIiUsgsW+aX+3rlFV/ALVvWj9O++Wa/vHRUSk/3C46PGQPffuv3de7sJ0jr2jXK1kATEZGiRKFbRESkEMjIgPfe813IP/zQ72vQwC8tfcMNULlysO0LzObNvvv4M8/A2rUQG+sXG7/jDmjcOOjWiYiIHJVCt4iISIB27vR58plnYM0aPxFajx5+YrROQ719ngAAIABJREFUnaJ4DrCFC30X8gkTfD/7uDg/NfugQVG64LiIiBRVCt0iIiIB2LvXV7Uffhg2boTTT4c//9kXcWvVCrp1AcnKgg8+8F3IP/rI72vZ0nch79nTL0AuIiJSxCh0i4iInETp6TB2LPz1r763dFycD9/9+0Pp0kG3LiB79/oB7GPG+HXQYmLg6qt92G7VKujWiYiInBCFbhERkZMgMxMmTvSTbK9YAdWqwejRMGRIFM9CvmOHnzHuscdgwwY/cP2ee+DWW6F27aBbJyIiki8UukVERApQVha8/bafEO2XX6BSJfjb3+DOO6FChaBbF5DNm+HJJ/22davvTz9mDAwcCOXLB906ERGRfKXQLSIiUgCcg+nTYfhw+PHH/2/vzuOjqu7/j79OEggQlkAWECKKgLiVuteFoqKAAiJoW5eqdfm22pbautal1rbaqrW1WpevlopLv4rLrzUqVlyQFkVQ61JXCLJDkISwmo0kc35/fGaSISSQQG5uZub9fDzuY+69M9EzeZwb5j3n3M+xLHnDDXDllSlciby42Ea1H3wQysthyBD4wx/g3HMhMzPs1omIiARCoVtERKSNzZ5tYfuttyxLXn45XHst5OeH3bKQLFkCv/+93be9dSt87Wtw/fVWHC1DH0VERCS56V86ERGRNvL22zaaPWuWZclLLrHwXVAQdstC8skncNttMH26zbM/6ij7BY0fn8JroYmISKpR6BYREdlN//0v3HgjvPACpKXB+efb8l/77BN2y0Ly7rvwu99BYaEdn3SSjWwff7zCtoiIpByFbhERkV20cKEVSHv6aTv+9retOvn++4fbrlB4D//6l4Xt116zc5MmwXXX2VrbIiIiKUqhW0REpJWWLbNw/dhjNmt6/Hhbd/uQQ8JuWQi8hxkzLGzPn29D/d/9rt3EftBBYbdOREQkdArdIiIiLbR6tWXLqVOhpgZOOMGW/zrmmLBbFoK6OnjmGbj1VvjoI+jc2W5iv+aaFJ5XLyIisj2FbhERkZ1YvNiKbz/yiBXfPuoo+O1vYdSosFsWgq1bbYj/9tvhiy+gWze44gpbC61//7BbJyIi0uEodIuIiDTj44+t+PaTT9o08qOPtuLb48alYD2wqiqYNs1+IStXQna23dB+2WWQkxN260RERDoshW4REZFG3n7bppE//7wdjxljxbdHjkzBsF1ZafPpb78dioshL8+C949+BD16hN06ERGRDk+hW0REBKsHNnu2he1Zs+zc5MlWfPuII8JtWygqKuDBB21e/ZdfQt++8Mc/2n3bWVlht05ERCRhKHSLiEhKi0Qaim+//Takp8O551rx7QMPDLt1IfjqK/jf/4U//AFKSuw+7bvvhu9/H7p2Dbt1IiIiCUehW0REUlJtbUPx7Y8/tuLbl14KV1+dosW3N2+G++6z0eyyMigosOOLLoIuXcJunYiISMJS6BYRkZRSXd1QfHvxYpspfeWVVoA7JYtvb9wI99wDf/oTbNgAe+1lw/7f+x5kZobdOhERkYSn0C0iIimhvNzqgf3hD7bedu/ecNNN8JOfpGjx7Q0bbNr4XXfBpk0waBDccQecd54N+4uIiEibUOgWEZGktnEj3HuvZcuyMujXz7LlJZekaPHtsjIb1f7zn2HLFhgyxML3OedAp05ht05ERCTpKHSLiEhSWrvWgvZ991m23HtvuPlmuPDCFL1FubTU7te+7z4rlrbffvCLX8CZZ0KGPg6IiIgERf/KiohIUtmyxZaRvvNOqKqC/fe3Zb/OOitFB3LXrrU59fffb8uAHXgg3HgjfOtbVqpdREREAqXQLSIiSaG2FqZNszxZUgIHHQS//jVMmgRpaWG3LgRr1tga2w88YN8+DB9uv5zTT0/RX4iIiEg4FLpFRCThvfKKVSD/5BPo2xf+8hdb6SolB3KLi600+4MPWqn2Qw6BX/4SJk5U2BYREQmBQreIiCSszz6Dq66Cl16y+7RvuAF+/vMULZC2erXNq5861cL2YYdZefYJE8C5sFsnIiKSshS6RUQk4ZSUwK9+ZSPadXXw3e/a0tIDB4bdshCsWtUQtrduhcMPt1/OuHEK2yIiIh2AQreIiCSMqipb3ep3v4PNm+HYY61g2pFHht2yEKxcCbfeCg89ZGH7yCNtZPuUUxS2RUREOhCFbhER6fC8h6eftqnjy5fDoEGWNc84IwXz5YoVDWG7pga+8Q0b2R47NgV/GSIiIh2fQreIiHRo8+fD5ZfbY69etvrVlCmQmRl2y9rZ8uUWtqdNs7B91FEWtseMUdgWERHpwBS6RUSkQ1q2zNbXfvJJq0I+ZYrNns7NDbtl7WzZMgvbDz9sYfuYY+wXMXq0wraIiEgCUOgWEZEOZfNmu2f7rrusCPeECXDHHbDffmG3rJ0tXWq/iEcesUXIjz3WRrZPPFFhW0REJIEodIuISIdQWwt//astKV1aCsOHW5G0E08Mu2XtbMkSC9uPPmq/lG9+00a2R41S2BYREUlACt0iIhK6mTPhyitt3e1+/axG2Pe+Z9PKU8bixfDb38Jjj9k6aCNH2sj28ccrbIuIiCQwhW4REQnNwoXw05/Cyy9D165w441wzTXQvXvYLWtHX3xhYftvf7OwfdxxDWFbREREEp5Ct4iItLstW+Dmm+2+7ZoaOPdcm1G9555ht6wdFRXBLbfA449DJGIh+1e/stAtIiIiSUOhW0RE2o33MH06XHUVrFkDhx4K994LRx8ddsva0eefW9h+8kkL2yeeaDeyjxwZdstEREQkAGlhN0BERFLDRx/ZIO53v2tVyR94AN55J4UC96efwllnwYEHwhNPwEknwZtvwmuvKXCLiIgkMYVuEREJ1IYNcNllcMghljEvvdRmVl9ySYoUSvvoI/j2t+Ggg+Cpp+Dkk2HePLuR/dhjw26diIiIBEzTy0VEJBCRCDz8MFx7LaxbZyPa995rU8pTwocfwm9+A88+a8cTJtg08iOOCLddIiIi0q4UukVEpM298w5MmQLvvgt9+9qS0+eeC2mpML/qvfcsbD//vB2fdpqVZT/ssHDbJSIiIqFIhY8/IiLSTkpL4X/+B77xDXj/fbjiClsW7PzzUyBwv/sunHoqHH64Be7TT4cPPoDCQgVuERGRFKaRbhER2W21tVYY7cYbYeNGGDUK/vxnqxmW9ObPt5Htl14C5+z+7V/8AoYPD7tlIiIi0gEodIuIyG554w2bSv7RR7bO9tSpcMYZlj+T2ty5FrZfecXe7FlnWdhOiW8aREREpKWSfbKfiIgEpLjYlv8aORIWLIAbbrAlqL/1rSQP3HPm2HJfI0bYcl/nnGPLgU2frsAtIiIi29FIt4iItMrWrXD33TbI+9VXMH483HUXDBkSdssC5L1NH7/tNhvaT0uD886zbxqGDQu7dSIiItKBtWik2znXxTlX6Jwrcs596Jyb6ZzbO/pcfvR4kXPuE+fciLif6+acm+6c+yL6s6fHPZfmnLvHObc4+vyPGv0/fxF9brFz7ua2ebsiIrI7XnnFblW+5hqrSv7CCzBjRhIH7poa+L//g69/3b5dmD8fLrjAhvYfe0yBW0RERHaqNdPL/wIM894fDMyIHgPcBsz33g8FLgQed87FRtCvAqq990OAscD9zrne0efOBQ4A9gWOBK5xzu0H4JwbCZwNDI++5hTn3NhdfI8iIrKb5s+HceNg7FhYsQJuuQU++cSWnk5KFRVwzz0wdKiNaC9ZYqXYlyyxxceHDg27hSIiIpIgWhS6vfdV3vt/eu999NR8YJ/o/neA+6KvexdYC8RGu8+Me24pMAc4Le65B7z3dd779cDTwFlxzz3ivS/33lcD07AQLiIi7WjOHBg9Go4+GmbOtFphsfu3u3QJu3UBKCuzefMDB8Jll1n4vvlm+6bhj3+EgoKwWygiIiIJZlfv6b4MeME5lwOkee9L455bBgyM7g8ElrfiucPjnvt3o+e+tYttFRGRVvAeZs2yrDlnjt2+fO65cP31sP/+YbcuICtWwJ13Wun1igrYe2/49a/hwguhW7ewWyciIiIJrNWh2zl3PTAUuBToCvjGL2l07AN4LtaWK4ArYse9evVqutEiIrJT3tto9m9+Y9PJMzLgoovguuuS+J7tTz+F3/8ennjCFhsfPhyuvdbW2s5QrVERERHZfa1aMsw5dxVwOnCK977Ce18WPZ8X97K9gBXR/RXA3m38XD3v/Z3e+4LY1r1799a8HRERwcL2c8/BkUfafdvvvQeXXAKLFsFDDyVp4J47FyZOhIMOsoJoI0ZYdfIPP4Szz1bgFhERkTbT4tAdHVU+Gxjtvd8Y99QzwI+jrzkC6Ae82cRzg4DjgOfjnrvEOZfunOuD3cf9VNxz33POZTnnMoGLgCdb//ZERKQ5kQg88wwcfDBMmmSF0X7yE6sV9sADNsM6qUQiVmp9xAjbZsyAyZNtWH/2bDj55CRfYFxERETC0KKv8p1zBcAfgSXAbGcfSqq9998Afg78zTm3CNgKnOe9r43+6B3ANOfcF0AE+HG0aBrA34AjgKLYa733nwN47//lnHsa+Dj63JPe+5m78T5FRCSqrg6eegp++1v47DO7ZfnKK+Gqq6Bfv7BbF4CaGpg+3aaRf/opdOoEF18MV1+tJb9EREQkcK6hIHniKygo8KtWrQq7GSIiHVJNDTz+OPzudzZ1vHt3mDLFVsLKy9v5zyec8nL461+tQNqKFfaGL70UfvYzGDAg7NaJiIhIknDOrfbeN7vEiW5aExFJclu3wiOPwK23wrJl0KsX/PKX8NOfQp8+YbcuAMXFcN99Nkd+/XrIz7dh/R/+EHr3Drt1IiIikmIUukVEklRVlRVCu+02WLXKAvYtt9jodlIu9vD++/CnP9nc+ZoaGDzY3vAFF0DXrmG3TkRERFKUQreISJLZssVmVd9xB6xZYwO9t99uA709eoTdujZWVwcvvGBhe84cO3fccXD55TBhAqSnh9s+ERERSXkK3SIiSWLRIptV/fDDsHkz7LGHZdEf/MCKpSWVLVvsjd59t5Vb79QJzjvPwvYhh4TdOhEREZF6Ct0iIgksEoFXXoF77oF//tPOfe1rtvTXeedBly7htq/NLV9ub3bqVPtmIScHbrgBfvxj+5ZBREREpINR6BYRSUCbN8Ojj8K990JREaSl2ZLTl11ms6uTbrnpefNs2P7vf7dvGg44wKqQn3uu7tcWERGRDk2hW0QkgSxaZEH74YdthnWfPnDNNfCjH8Fee4XdujZWU2Mh+6674O237dzYsTaFfMyYJPxmQURERJKRQreISAcXicDLL9us6pdesnPDh9sU8nPOScL7tTdssOnj99xjZde7dIHvf99Gtg84IOzWiYiIiLSKQreISAcVm0J+zz02wp2WBqefblPIR45MwoHeRYusMNojj0B5OfTrBzffDJdcAnl5YbdOREREZJcodIuIdDBFRQ1TyL/6yqaQ//zntuRX0k0h9x5mz7Yp5DNm2PHBB9sU8jPPhMzMsFsoIiIislsUukVEOoDYFPI//xlmzrRzw4fbqPY55yRhrbDSUhvRnjrVRridg1NPtbCdlJXgREREJFUpdIuIhGjzZsue997bMIX8jDMsbH/zm0mWPSMR+Ne/4C9/gX/8wwql5ebCVVfZFPIhQ8JuoYiIiEibU+gWEWln3lsx7r/+FZ580m5f7tMHrr3WppAPHBh2C9tYSUnDqPYXX9i5UaPgBz+ASZM0hVxERESSmkK3iEg7Wb8e/vY3C9uffGLnjjjCBnmTbgp5JAKvv26j2oWFNqqdl2frm/3P/8DQoWG3UERERKRdKHSLiATIe5tRPXWqzaiurobsbJgyxbLn178edgvb2JdfNoxqL1li5046yUa1TzsNOncOtXkiIiIi7U2hW0QkALHs+dBDDTOqR4605abPOCMJR7Vfe81GtZ97DmprIT/fSq5///sweHDYLRQREREJjUK3iEgbqauzCuRTp8ILL9hxXh5cfTVcfDEMGxZ2C9vYl1/aumZTp8LSpXZu9Ggb1Z44UaPaIiIiIih0i4jstuXLYdo021atsorjY8bYIO+ppyZZ9oxE4NVXbVT7+edtVLtvX7juOpsvv88+YbdQREREpENR6BYR2QVbt9po9tSp8Mordu92QQH88pdw4YWw995ht7CNLVwI06fDo4/CsmUN3yz84Af2zUKnTmG3UER2k/eeSCRS/7iz/U6dOtGzZ8+wmy0i0uEpdIuItEJRkVUff/RRWwkrPd3qg33/+zB2rB0njeXL4amnLGx/+KGd69cPbrjB5ssPGhRu+0RkG+Xl5SxatIiioqJttsWLF1NdXb3TEL0rsrOz2XvvvRk0aFCTj927d2/jdykikngUukVEdmLdOqsP9thjMGeOnRs8GC6/HC64wHJo0li7Fp5+2hYQf+stO5edbSH7rLPg+OMhQ/90iISlpqaGZcuWbResFy5cyOrVq7d7fd++fRk2bBhZWVk450hLSyMtLa1N9quqqli+fDlLly7lo48+IhKJbPf/z83NbTaU77XXXnRNqqqSIiJN0ycnEZEmrF4Nzz5ry3z9+992K3PnznD22TaqfdxxkJYWdivbyIYN9kanT4fZs+3Ndutmb/ass2wIPzMz7FaKpAzvPWvWrKkP0/HhesmSJdTW1m7z+u7duzNs2DBGjhzJvvvuW78NHTqUXr16tUuba2pqWLVqFcuWLWPp0qXbPb733ntNjqb369dvuzB+8MEHc9hhh5GWNH9kRSTVuV2dTtQRFRQU+FWrVoXdDBFJUF980RC058+3c126WOY8/XS7dbl373Db2Ga++soKoT35JMycCTU19q3CKadY2J4wAbKywm6lSEqorKzktdde47nnnuP999+nqKiI8vLybV7TqVMnBg8ezLBhw7YJ1vvuuy99+/bFORdS61umurqaFStWNBvK165du83r8/PzGTduHOPHj2fMmDG6d1xEOjTn3GrvfUGzzyt0i0iq8h4++cRC9j/+AR99ZOe7d7fMecYZcPLJdpwUqqvhpZdsRPuFF6Cy0obrTzzRgvbkyTaVXEQCt2HDBmbMmEFhYSEzZ86koqICgD333LPJYL3XXnuRkcS3dlRUVLB8+XKWLFnCm2++yYsvvsjHH38MQEZGBiNHjmT8+PFMmDCBfffdN+TWiohsS6FbRCSO9/Duuw1Be9EiO5+TYwXRTj/dMmiXLuG2s83U1sLrr1vQfvZZ2LTJzo8YYVPHv/1tyM8Pt40iKWLlypU899xzFBYW8q9//Yu6ujqcc4wYMYJJkyZx2mmnMXjw4LCb2WEsX76cf/7zn8yYMYPXX3+dqqoqAIYMGVIfwEeOHEnnpFqXUUQSkUK3iKS8ujp48034+98td8b+TPTvbyH79NPhm99MovpgkQjMnWtTx595BkpL7fyhh1rQPvNMGDgw3DaKpADvPZ9++imFhYUUFhby3nvvAZCZmcno0aOZPHkyEyZMIF9ffO1URUUFs2fPZsaMGbz44ousXLkSsPvZx4wZw/jx4xk3bhz9kqqypYgkCoVuEUlJ1dU2wPuPf1jl8Vju3GcfmzZ++ulw5JFJVAwt9oaffdbu1Y7dH7nffjZ1/MwzYdiwcNsokgLq6uqYP38+zz77LIWFhSxevBiwpbUmTJjApEmTGDt2rJbS2g3eez755JP6AD5v3rz6yumHH34448ePZ/z48SrGJiLtRqFbRFLGpk3w8suWOV94ATZvtvMHHWQh+4wz4Gtfgw5eb6jlNm+2e7SffRb++U/YssXO77ef3Z/9ne/A17+eRG9YpGOqqqpi1qxZFBYW8vzzz1NSUgJAQUEBkyZNYtKkSYwcOZJOnTqF3NLkVFZWxsyZM5kxYwYzZ85k48aNgC2XNm7cOCZMmMDo0aPp0aNHyC0VkWSl0C0iSW3ZMgvYzz9vS3vV1Nj5I4+0oD15MiRVzZ21a23ovrAQZs2CrVvt/De+AZMm2bbffuG2USQFbNy4kRdffJHCwkJeeuml+mrjBx54YH3QPuywwzp8VfFkU1tby7x58+pHwT/99FPAqr9PmTKFW265hW7duoXcShFJNgrdIpJUIhF45x0L2i+8ANHitmRmwqhRMHGiVR4vaPbPXgJavNhGswsL4a23rBpcRgaccIJ9qzBxIgwYEHYrRVLCggULuPnmm3n66aepra3FOccxxxxTXwht6NChYTdR4ixbtowXX3yRRx55hP/85z8MGTKEhx9+mBEjRoTdNBFJIgrdIpLwysvhtdcsZM+Y0XC7cl6eBexTT4XRo5NoaS/v4YMPLGQ/+6ytawa2bvYpp1jQHjdOy3uJtKNY2J4+fTree0444QTOPvtsJk6cSN++fcNunuxEXV0dd999NzfccAPV1dVcdtll/O53v9Oot4i0CYVuEUlIxcUWsJ9/3mZRR1eK4cADLWRPnGhTyNPTw21nm6mttRLrhYW2LV9u5/Py7M1OmgQnnZREa5mJJIbGYXvMmDHcdNNNHHPMMWE3TXZBUVERF154IW+99RaDBw9m2rRpjBw5MuxmiUiCU+gWkYTgPXz4YcP92dGVdcjIgJEjLXeeeqpVH08amzdbxfHnnrM3XlZm5/fe20azJ0+GY45Jom8WRBLHggULuOWWW5g+fTqRSERhO4nU1dXx5z//meuvv56qqip+8pOfcOutt5KVlRV200QkQSl0i0iHVV0Ns2dbyJ4xA6LLrpKdbbOnTz0VTj45iWZR19baDemvvAKvvgpvv22LiINVGZ882Ua0hw9XxXGRkChsp45FixZx4YUXMnfuXPbZZx+mTZvGcccdF3azRCQBKXSLSIcRiVjhs9des23OHKiosOcGD7bR7IkT4dhjISlW1vEeFi2ygP3qq/YNQ2wds549rRDa6NF2n3ZSDeGLJJ6FCxfWTyNX2E4ddXV13HvvvVx33XVUVlYyZcoUbr31Vq2jLiKtotAtIqFatqwhZM+aBevW2fnOnW3m9MknW9Deb78kGdwtK7M3+uqrNqK9YoWdT0+3Zb1Gj7btyCOT5JuF1FNXV0d5eTkVFRVt+hiJREhLSyMtLQ3nXP1+a7f4n01PTycrK4sePXo0ufXs2bPZ5zIyMsL+VbcLhW0B+OKLL7jooot44403GDRoENOmTeP4448Pu1kikiAUukWkXZWV2W3KsaC9ZEnDc4ccYrXATjwRRoywYtwJr7oa5s5tGM1+/30b4QYYOtQC9pgxcPzx0KtXqE2VbW3dupUNGzawfv16ysrKtnlsbr+srKx+Pebd0bVrV7KysujWrRtZWVl07dqVjIwMIpEI3nsikchOt5a8LvYFQSQSaXUbu3Tp0mxI79WrF7m5ueTm5pKXl7fNY25ubkLcG6uwLY1FIhHuu+8+rr32WioqKvjxj3/MbbfdplFvEdkphW4RCVRFhRXdnjXLQvYHHzRkzn32sYB90kk2kzovL9y2tgnvbQmvWMiOnyPfp4+94TFjLGzvtVe4bU0xNTU1FBcXs3r1alatWsXq1at3GKa3bNnSov9ur169yMnJoU+fPvTp04fs7GyysrK2Cc2teezatStpaWkB/zYaeO+pqKhgy5YtTW6bN29u9rmmXlMVW0pgB7p27dpsIG98Li8vjz59+pDeTgUDG4ft0aNHc9NNN3Hssce2y/9fOr7Fixdz0UUXMWfOHPbee28eeughRo0aFXazRKQDU+gWkTZVV2eVxWMj2XPnwtat9lxOTkPIPvHEJLlN2XsoKoK33rJ7sl97Ddassec6dbIh+9iU8UMOUaXxgFRWVtaH6fgt/tzatWvZ0b9pPXv2pE+fPvUBOj5IN7Wfk5NDdnZ2ykyzbqmamho2bdpEWVkZpaWlrFu3rv4xfj/+cWezA5xz9O7dm7y8PPLy8sjPz6/fb+o4NzeXTq28PWPhwoXccsstPPHEEwrbslORSIT777+fn//851RUVPDDH/6Q22+/nR49eoTdNBHpgBS6RWS3RCLw2Wfw739b3pw9GzZtsue6drXlvE46ybbhw6EdB/CCUVUF//mPfZvw1lu2xW5EBzjooIaQPXJkksyRD9fmzZt3GKZXrVrF+vXrm/zZ9PR0+vfvT0FBAQMGDKCgoKB+69+/f/0oau/evVsd0qTtVFZWtiikx7aysrKdTolvLqQ3DuiRSIQ777xTYVt2yZIlS7jooov497//zV577cVDDz3EiSeeGHazRKSDUegWkVbZtMlWsnrrLZg3z/ZjITstzep/xUazjz4aMjPDbe9uW7vW3uzcuba99x7U1NhzGRk2en3MMVZSfcQI2GOPcNubYKqqqli1ahUrV65kxYoVrFy5sn6LHW+OVXRvJDMzs8kwHX+ub9++7TYtWdpPXV0dGzZsoKSkpD6Ix+83Pl63bt1OQ7rCtuyqSCTCAw88wDXXXEN5eTmXXHIJd9xxh0a9RaSeQreINCsSsZnT8+Y1hOzPPmu4JzszEw4/3ML1iBFw3HEJvmZ2bNg+PmQvXtzwfHZ2Q8A+5hg44giNZO9AXV0da9asaTZMr1y5kpKSkiZ/NjMzk4EDB7LnnnvWb40DdU5ODi4pStpL0CKRCOvXr28ylG/evJnJkycrbMtuW7p0KRdffDGzZ89m4MCBPPTQQ5x00klhN0tEOgCFbhGpt2ULvPOOhevYtmFDw/N77mkB++ijLXMefLAt7ZWwysvtDcdC9rx5sHFjw/NDhljAjoXs/fdPgvnxuy8WYL788svtttWrV9cH6+LiYurq6rb7+bS0NAYMGFAfpuPDdWw/NzdXgVpEEk4kEuHBBx/k6quvpry8nPPPP59Ro0ax7777MmzYMPr06RN2E0UkBArdIinKexvEjY1gz5sHH39sg71gYfrQQxtC9tFHQ0GzfyoSQG0tLFxoS3b95z/2xj/4wCq/gb3hww/fdiQ7Pz/cNrezr776qskg3Xhbu3YttbW1zf538vLydhio99gARGOVAAAYlUlEQVRjDxUfE5GktmzZMi6++GJef/31bc7n5OSw77771ofw2P6QIUPo2rVrSK0VkaApdIukiLo6y5qzZzcE7fj6X/37bxuwDz0UunQJr727parKlu364AML2R98AP/9r52Pyc3ddhT7sMMS+A1vL36N6fXr12+3X1ZWxtq1a+uD9Jo1a3ZYQbpz587069evRZs+OIqI2HJ8CxYsYMGCBRQVFVFUVMTChQspKiqitLR0m9c65xg4cOB2YXzYsGHsueeeqk0hkuAUukWSVGwkO7Zc9OzZDTOnY/W/4kP2wIGQkLN5t2yxQB0L1++/b/dlx4/E5ubatwiHHmpv/NBDYfDgDv+Gvfds2bKl2eC8o/2dLcEE9iEvLy+vRUE6Oztb071FRNrIhg0b6oN4fBgvKiqisrJym9dmZmYyZMiQbcL4PvvsQ//+/dljjz3o3r17SO9CRFpKoVskiaxbB6+/3hC0ly+382lpVvNr9GirKn7kkbacV8JZt86CdfwI9qJFDZXdwG48jwXr2OOAAaEH7FiALisrq18Kqbn9+OOaWKX0HYitYRxb+iq2nnRz+7HjvLw8TfMWEelAIpEIq1ev3i6IFxUVsXTp0iar8Pfo0YP+/fvXh/DYfvzxHnvsQZYKf4qERqFbJIFVVcGbb9r62K++ahk0dskOHWoBe/RoOOGEBKsqXlsLS5bA5583jGK//z6sXLnt64YObQjWsZCdmxt487z3bNq0ibKysvpw3Hh/VwN07969yc3NJTc3l5ycnPqtcWCO3+/VqxdpKvAmIpLUqqurWbJkCQsXLmT58uWsWbOG4uJiiouL6/c3xhcDbaRnz55NBvL44/z8fLp3766ZTSJtTKFbJIFEIpZBX33VgvYbbzTcppyTYyE7FrT32ivctrbIpk1W3GzBgm23L75oWAsbID0dDjhg29Hrr38devbc7SbU1tayfv36JgN0U8exramq3I01DtA72+/du7dGnkVEZJdVVlZuE8bjA3n8/qZNm5r9b2RmZpKbm0teXl6LHnNycvRvl8hOKHSLdHArVjRMF581q6H4WWYmfPObDVPGDz64g65mFYnYCHXjYL1wIaxZs+1r09LsXuv99rNt2DAYPhwOOqjF8+ErKiq2W4e3qeNYeN7RqEBMeno6ffr0qQ/I8SPQsU0BWkREEkV5eTlr1qzZLpCXlJSwbt06SktL6x+3bNmy0/9e7Evm5oJ5/L+ROTk5mqElKUehW6QDqK2F1astYK9YYfdiL1kCc+bYLcsxhxzSELJHjOhg92VXVkJRUdPhulFRGHr0aAjW8dvgwfZtQpzy8vJmg3NTobqiomKHzezcufM23843Ds1NnevZs6c+HIiISEqqrq6uv10qPozv6HFns8FiX2Y3DuPx/xY3Pte7d29VcZeEpdAt0g62bGkI0/GPsf3VqxvWx443cKCF7NGjYdQoyMtr/7bXq662Bi9bZo1etmzb/dWrty1oBlbUrFGwrh40iNKMDEriQnNJSUmz+zsL0ZmZmeTl5ZGfn09eXt52+42Pe/TooXvVREREAhKJRNi0aVN9CI+fXRZf56TxLVxNFYmLFysaGh/Ms7Oz6d27d/3W3HFWVpb+7ZdQKXSL7KZIBL78sukwHXtsbgZzbq7dez1woG2N9/Py2rHodmVl06E6dlxc3OSP1eTksG7AAEr79qWkb19Ks7Mp6dKFEqB006btQvTmzZt32IzOnTuTn59fH5SbC9OxfRV8ERERSWyxoL6jYN5UUG9JgVKAjIyMHYby+OMePXrQrVs3srKytnns1q0bXbp00WeODsR7z9atW8lsNEuyI1LoFtmBykobwI3fVq3a9njNmm2XhI7JyLCB3vgwHR+qBw6Ebt3a6Y3U1UFpqX07UFxsIbpxsF67FoAKoDR+69GDddnZlGZlUdqpk52rqWFdZSWlmzbt9J7o9PT0+pAcH6Sb29dItIiIiOyM957Kyko2bNjAxo0b2bBhQ/3WkuPy8vJW/z+dc80G8sbn4h+7dOlCWloaaWlpOOfqt7Y4bm5LT0/f4fM72mKfw+I/jzU+t7PHxucikQiVlZWUl5dTXl5ORUVF/X7j45Y+V1FRQbdu3VpUdyBsOwvdqgIkScl7KCvbPkA3DtcbNjT9885B3762/PMhh0BBwfbBul8/K7od6JtYv96C9Nq19hjbosd+zRq+WrOGkrIySr2nBGwEOrqtA0ozMynNyKC0WzdKt26lsvE3CFu22BaVkZFRXxzl0MGD6/fjA3R8iM7Oztb90CIiItKmYgG4W7duDBgwoNU/v3XrVjZu3LhdIG8c7ioqKrbZb/z45Zdf1h9XxZaUkTbTuXPnbb7A6NWrF/37968/7tGjR9hNbBMK3dLheQ/l5ZY/Y1tZ2bbHsW3dOgvUxcV2i3JTuna1MD18uD023goKLFB36hTgm6qqgnffbTJIVxYXU1JcTOm6dZTU1dWH6PhAXQKUOEcpULWT2Spd09LIy8khLy+PA+Lug44P0/HnsrOzNRItIiIiCS3+dra2Ej+a2zi0e+/x3hOJROr3d/dc7Liuro5IJNKqrbmfiRXBi5/tHNtv6WNz52JBOT5E7+w4VVaCSY13KaGqq7Np3BUVFp5jjxs3Nh2cm9packtPWpqtZd2/P+y/v4XnpkJ1797teB91MzYsWcLvR45sMlB/tZOf7ZKZSd++fembn8/XmpnGHb91a7c57iIiIiLJKy0trT40irRGhw3dzrmhwKNALrARuMB7/1m4rUo+sUAcC8Pxwbilxzs719yIc3MyMiw89+kDQ4bYY+Mt9nz81qNHB13Hugl1ffpwW3S/U0YG+Tk55OXnM3SPPXZ6X7QqdIqIiIiIJI4OG7qBB4G/eO8fcc59C3gIODrkNu22jRuhsNCmTMe2SKRtjuvqWh6WdzUQNyU9HbKyrGhYt26237t3w3H8Fntd1672mqYCdffu4Y9EB61Pfj5FRUXk5eXRq1cvhWgRERERkSTVIauXO+fygSIg13tf6yyRrAGO8t4va+7nEqF6+eefwwEHtN//r6lA3Jr95s7FHwd677OIiIiIiEgHlqjVy/cEir33tQDee++cWwEMBJbFXuScuwK4Inbcq1evdm5m6+25J7zwgo3kOmfToWP7u3ucnr59KO7UKflHjUVERERERDqqjhq6ARoPwW8XHb33dwJ3xo4LCgo63rB9I927w4QJYbdCRERERERE2kNHLTu1EihwzmUARKeX7wmsCLVVIiIiIiIiIq3QIUO3974E+AA4N3rqDGDZju7nFhEREREREeloOvL08kuAR5xz1wObge+F3B4RERERERGRVumwodt7v5AkWCJMREREREREUleHnF4uIiIiIiIikgwUukVEREREREQCotAtIiIiIiIiEhCFbhEREREREZGAKHSLiIiIiIiIBEShW0RERERERCQgCt0iIiIiIiIiAXHe+7Db0Gacc9VAadjtSELdga/CboRIEtM1JkFS/5KgqG9JkNS/JJHkee8zm3syqUK3BMM5t8p7XxB2O0SSla4xCZL6lwRFfUuCpP4lyUTTy0VEREREREQCotAtIiIiIiIiEhCFbmmJO8NugEiS0zUmQVL/kqCob0mQ1L8kaeiebhEREREREZGAaKRbREREREREJCAK3SIiIiIiIiIBUegWERERERERCYhCt7QL55wLuw0iIrL79PdcRBKF/l5JR6HQLe2lU9gNEEl2zjn9TZdAOOf6OucKALz3Xh9kpa045/ZxzvUJux2StPT5UzoEfUCTwDnnTgIec87d5Zz7WdjtEUk2zrlDnXOZ3vuIgre0NefcKcCLwNPOuSfAgne4rZJk4JwbBzwNdA+7LZJ89PlTOhJ9OJNARf9BvQv7wPYpMNY5d1y4rRJJHs65CcB/gA+dc50VvKUtOedOBO4ArgPOBXo557qF2ypJBs65k4Hrgeu99ysaz57QbArZHfr8KR1NRtgNkOTlnBsI/Aq43Hv/qnOuFzAeKAi1YSJJwjnXF7gKmAScD7znnDvMe7/VOZfuva8Lt4WSBI4A/hj9G34gcADwR+dcFvBD7325c85p5FtaKhqmBwH/BC7w3r8S/bxwtnOuDtjsvf+L+pTsKn3+lI5IoyHS5uK+nV4L3ATMjQaATUARsF9ojRNJArFrzHu/FrgBeNl7/y2gGAveXRW4ZVc1GmF0wP3OuW9hIekx4F5gMPB/oKnm0jreLAHuB25yzn0T61f5QA7wPefcVWG2URKTPn9KR6bQLUHIAPDeV3vvX/LeV8QFgBqiRS2cc+c4584Pq5EiCaxLbMd7Pxfw0f2xWPB+B8A59x3n3CWhtFASWXz/uhW4G/gaMNN7f5P3/lNsdkXP6AiSSIs557oAeO+nYF/k/Bv4u/f+SuxLxOlY+BZpLX3+lA5L08ulTUWLVpznnFsDFHnvp0XPd/Le1wBbgZXR+wQvB84Lr7UiiSd6H+SPnXMLsGmYN0enk3f13ld678c6555xzpUDJcDEcFssiaRR//rKe/9r7/210b/t1zjnenrvNwMTsA+wtWG2VxKHc+5Q4FPvfVW08GO19/4nzrl/eO9nA0RrUvQAekRrU3jNpJCW0OdP6eg00i1tJlq04m7gNaACOMk597Xo07FvGjcCNwK/Bi703i9o94aKJCjn3EjgIeBx4EPsGpsF4L2vjI0gAf8AvgLGe+8/DqWxknCa6F+jnHOvA3jvX8NmUbzsnLseuILoPd1htVcSh3PuVKzg47vRLwir40a8Z8e97nzgO8C93vuIAre0hD5/SiJQ6JY24ZzbG/tDdoX3/m/YvVqDgIPAvr2OvrQbsAf2B++T9m+pSEIbBNzvvX/Se/84cDyQ5Zx7FSA6gjQM+BEwxnv/WXhNlQTUVP/qFutfwC+AGdH9b0enmYvsULTg43XARcB84I1o8K5yzsWm+2ZGl6a7DjhXgUhaSp8/JVE4fYkobcE51xv4BvAKkOa9r3XO3Q0s897/Ke51+UAX7/2KkJoqkrCcc+dgFVlHeu+/jJ5LB2YBL3jv/xg91ytaOEakxXbQv14HCmN/y1WtXFrLOXcEVsiqCiuaNhg4Ln6mRDScZ3jvV4fTSklE+vwpiUIj3dImvPcbgLei08Fi9/jVAl0BnHPfcs6d6b0v0R88kV32DPbB4nLnXC5AtEjMdKBn7EUK3LKLmutfTwC9Yy9S4JbW8t6/673f5L2vxpY3XIwVUMM5N845d4H3fq0Ct7SWPn9KolDoljYTLa6Dcy5WoK8TsNw5Nxa4Fng/rLaJJINoMZiXgF5YUasB0ad6AMOcc50aLfck0mI76V/7qn9JW4gG7+8BHzjnNmHTgd8Ot1WSyPT5UxKBqpdLm4mbchgrWlECXIMVr7jAe78otMaJJJDYtRQ/jdc5lxb9Jv9F55wHxgLznHN/B84ATomGJpEdUv+SoDTVt+LPx46j93N/CIzDCj5+HkZ7JbG0oH/p86d0WArdsssafWCL/SEcjv3R+xTrX/sAh+oPnkir9AQ2AelAbfzoYrRQ2nLv/U+dc7Gqv/d5778IoZ2SmNS/JChN9S0XXQos9vngc6zQ1RRgnArySSu0pH/p86d0SCqkJq3inBsBdIot8dEoeB8FPIVVHn3DOXcwsMF7vzy8FosklmgF3+8DNcBbwOPe+3XR547C7rs923v/ZnitlESl/iVBaUHfego4x3s/N3pOBR+lxVrYv/T5Uzos3dMtLeacGw+8DDzonJsIVlDHOZfmnMsGfgxc6r1/I/rch/qDJ9Jy0Q8Vf8DWG50DHI2NCOGc64NdYz/w3r+pe2ultdS/JCgt7FuXeu/nRiviA2wOo62SeFrRv/T5UzosjXRLizjnugP/ixWjKAauBn7jvX8+bmp5vve+JPZhTRVuRVouupzJNOAv3vvno+emA0u89zfEXhO7xnR9SWuof0lQ1LckSK3tX6DPn9Ix6Z5uaaly4EZgAxAB+gC/jBbfKYy+pgL0x05kF1UDfwLmOOcyokuffATkxL1mC+gak12i/iVBUd+SIKl/SVLQ9HLZIedc79g30977Zd7W2dwCPA48BPzCOXeUc24c8LO4aWMi0gJx19gm7/0s732Nb1hrdF3c684ELtU1Jq2h/iVBUd+SIKl/SbLRSLc0yzl3GvADwEer2M723r8Ptiaic+5xoAyYCWwFTvDe1zX7HxSRbTR3jTnn0qPXUhdgnXNuMvAL4AxdY9JS6l8SFPUtCZL6lyQjjXRLk5xzhwL3AL8BngZ6Ab+PVi8HLHhjhSw2Asdr2Q+RltvRNRb34aEO+CnwM+As731RKI2VhKP+JUFR35IgqX9JstJItzRnKPCW9/5t4G3nXAHwFXYf99Xe+/8657oCo7FvGD8Ls7EiCWin1xjwJXY/2yXe+wUhtlUSj/qXBEV9S4Kk/iVJSSPd0pxPgFzn3OEA3vtVwJPAe8Ax0XOVwMne+/dCa6VI4trpNYYVizlGHypkF6h/SVDUtyRI6l+SlBS6pTklwArgZOfcIADv/QpgOXBC7EVxRS1EpHV2dI2Nih5/4b0vDq+JksDUvyQo6lsSJPUvSUoK3dIk730p8BfsW8XvOudi3y5uBlx0armI7KKdXGM457qF1TZJfOpfEhT1LQmS+pckK6cl7aQx55wD0rz3ddGCFpcABwDrga8Dp0XvqRGRXaBrTIKk/iVBUd+SIKl/STJT6E5xzrk8IN17/2XcuU7e+5popfKtwMdAf2A/4CPv/cpwWiuSeHSNSZDUvyQo6lsSJPUvSTUK3SnMOXcGcBXQDXgZeNd7/0z0ueOBe4FrvfczQmukSALTNSZBUv+SoKhvSZDUvyQVKXSnKOfcgdj6h98BIsBkbArPfO/9vc65qcCL3vvCEJspkrB0jUmQ1L8kKOpbEiT1L0lVWqc7dfUASrz3nwI450qA04BRzrlPgB94771zznl9MyOyK3SNSZDUvyQo6lsSJPUvSUmqXp661gLrnHNHOefSvPdlwPPAEmBs7A+d/uCJ7DJdYxIk9S8JivqWBEn9S1KSQncKcc5lOed6AnjvlwLrgCuBgc65dO/9OuD/ASc45/qE2FSRhKRrTIKk/iVBUd+SIKl/iSh0pwzn3CnA34FZzrnbAbz3P8RuMfg9MDL60n2AKqxqpIi0kK4xCZL6lwRFfUuCpP4lYlRILQU458YBtwKXY2sdvgT8ynv/YPT5PwEDgH5AT+BC7/0HITVXJOHoGpMgqX9JUNS3JEjqXyINFLqTnHMuH3gAuN97/1r03E+AXO/9TY1etwdW3GJNKI0VSUC6xiRI6l8SFPUtCZL6l8i2VL08yXnvS6LLL3wQLVgRiT51cOw1zrkM730JUBJKI0USmK4xCZL6lwRFfUuCpP4lsi3d052knHM9nXO9Abz3L0WrQ8amNawFvoq+7gLgR8459QWRVtA1JkFS/5KgqG9JkNS/RJqmjp6EnHOnAa8Azzvn7nXO7d9ovcP1wOfOufHYfTavx30DKSI7oWtMgqT+JUFR35IgqX+JNE/3dCcZ59zhwP8BFwNrgDuAMuBpYJb33kf/KD4LvAd8z3v/WVjtFUk0usYkSOpfEhT1LQmS+pfIjmmkO/l0B97x3s/13i8BLgDKgTOBwdHXpGF/CM/WHzyRVtM1JkFS/5KgqG9JkNS/RHZAoTv5bAQOdM71APDebwF+iS3HMCV67lngEO/9F6G1UiRx6RqTIKl/SVDUtyRI6l8iO6DQnQScc8c7565xzl0HLADmAXOdcw7q//BdAwxzznWPnlsVWoNFEoyuMQmS+pcERX1LgqT+JdJyCt0JLlqM4k5s+beDgBnAz4HF2DINudGXHgF0BWrDaKdIotI1JkFS/5KgqG9JkNS/RFpHhdQSmHPuUOAJ4Bzv/fvOuT2AacCPvPdLnXPTgYHYEg1DgXO99/8Nr8UiiUXXmARJ/UuCor4lQVL/Emm9jLAbILtlE/C/3vv3Abz3a5xzWcCRwFLv/dnOuQOAXkCx9355iG0VSUS6xiRI6l8SFPUtCZL6l0grKXQnMO/9YufcVADnXIb3vhYoBkqj504B3lWFSJFdo2tMgqT+JUFR35IgqX+JtJ7u6U5w3vuK2G70sRYodc5NAu4GskNpmEiS0DUmQVL/kqCob0mQ1L9EWkcj3UnCe18X3S0H/gj0ASZpWQaRtqFrTIKk/iVBUd+SIKl/ibSMQneScM45b1Xx9gQOAY7WHzyRtqNrTIKk/iVBUd+SIKl/ibSMqpcnGefcScBa7/3HYbdFJBnpGpMgqX9JUNS3JEjqXyI7ptAtIiIiIiIiEhAVUhMREREREREJiEK3iIiIiIiISEAUukVEREREREQCotAtIiIiIiIiEhCFbhEREREREZGAKHSLiIiIiIiIBEShW0RERERERCQgCt0iIiIiIiIiAfn/leZM/QUqBCEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#我们选择前40天数据\n",
    "indexedDataset = indexedDataset.head(40)\n",
    "rolmean = indexedDataset.rolling(window=7).mean() #以星期为单位给出滚动平均值\n",
    "rolstd = indexedDataset.rolling(window=7).std()#以星期为单位给出滚动标准差\n",
    "#print(rolmean,rolstd)\n",
    "\n",
    "#Plot rolling statistics\n",
    "\n",
    "#设置画布大小\n",
    "plt.figure(figsize=(15,5),dpi = 80)\n",
    "#横轴坐标旋转45°\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "orig = plt.plot(indexedDataset.ix[:,0], color='blue', label='Original')#原数据\n",
    "mean = plt.plot(rolmean, color='red', label='Rolling Mean')#平均值\n",
    "std = plt.plot(rolstd, color='black', label='Rolling Std')#标准差\n",
    "\n",
    "plt.legend(loc='best')\n",
    "plt.title('Rolling Mean & Standard Deviation')\n",
    "plt.show(block=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们需要确保两个滚动的统计数据即:平均值和std. dev.保持时不变或随时间不变。因此，它们的曲线都必须与x轴平行。\n",
    "所以我们的数据是不平稳的。   \n",
    "\n",
    "为了进一步增强时间序列不是平稳的假设，让我们进行ADCF测试。  \n",
    "\n",
    "\n",
    "单位根检验是指检验序列中是否存在单位根，因为存在单位根就是非平稳时间序列了。单位根就是指单位根过程，可以证明，序列中存在单位根过程就不平稳，会使回归分析中存在伪回归。\n",
    "\n",
    "ADF检验全称是 Augmented Dickey-Fuller test\n",
    "\n",
    "ADF检验就是判断序列是否存在单位根：如果序列平稳，就不存在单位根；否则，就会存在单位根。\n",
    "所以，ADF检验的 H0 假设就是存在单位根，如果得到的显著性检验统计量小于三个置信度（10%，5%，1%），则对应有（90%，95，99%）的把握来拒绝原假设。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* from statsmodels.tsa.stattools import adfuller  \n",
    "\n",
    "adfuller函数的参数意义分别是：\n",
    "\n",
    "    x：一维的数据序列。\n",
    "    maxlag：最大滞后数目。\n",
    "    regression：回归中的包含项（c：只有常数项，默认；ct：常数项和趋势项；ctt：常数项，线性二次项；nc：没有常数项和趋势项）\n",
    "    autolag：自动选择滞后数目（AIC：赤池信息准则，默认；BIC：贝叶斯信息准则；t-stat：基于maxlag，从maxlag开始并删除一个滞后直到最后一个滞后长度基于 t-statistic 显著性小于5%为止；None：使用maxlag指定的滞后）\n",
    "    store：True  False，默认。\n",
    "    regresults：True 完整的回归结果将返回。False，默认。\n",
    "\n",
    "返回值意义为：\n",
    "\n",
    "    adf：Test statistic，T检验，假设检验值。\n",
    "    pvalue：假设检验结果。\n",
    "    usedlag：使用的滞后阶数。\n",
    "    nobs：用于ADF回归和计算临界值用到的观测值数目。\n",
    "    icbest：如果autolag不是None的话，返回最大的信息准则值。\n",
    "    resstore：将结果合并为一个dummy。\n",
    "————————————————\n",
    "版权声明：本文为CSDN博主「敲代码的quant」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。\n",
    "原文链接：https://blog.csdn.net/FrankieHello/article/details/86766625"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey Fuller Test:\n",
      "Test Statistic                 -0.111105\n",
      "p-value                         0.948321\n",
      "#Lags Used                      0.000000\n",
      "Number of Observations Used    39.000000\n",
      "Critical Value (1%)            -3.610400\n",
      "Critical Value (5%)            -2.939109\n",
      "Critical Value (10%)           -2.608063\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Results of Dickey Fuller Test:')\n",
    "dftest = adfuller(indexedDataset['confirmedCount'], autolag='AIC')\n",
    "\n",
    "dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
    "for key,value in dftest[4].items():\n",
    "    dfoutput['Critical Value (%s)'%key] = value\n",
    "    \n",
    "print(dfoutput)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*对于一个时间序列是平稳的，它的ADCF测试应该有:\n",
    "* p值【p-value】较低(根据原假设)\n",
    "* 1%、5%、10%置信区间的临界值【Critical Value】应尽可能接近测试统计数据【Test Statistic】  \n",
    "* 从以上ADCF测试结果可以看出，p值(最大值可以是1.0)非常大。  \n",
    "* 此外，临界值与测试统计数据没有任何接近之处。因此，我们可以有把握地说，我们的时间序列在此刻是不平稳的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 三、时序数据变换，获得稳定性数据  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1构建稳定性检验函数：  \n",
    "    #滑动平均值  \n",
    "    #滑动标准差  \n",
    "    #Augmented Dickey–Fuller ADF稳定性检验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#构建稳定性检验函数\n",
    "def test_stationarity(timeseries):  \n",
    "    \n",
    "    #Determine rolling statistics\n",
    "    movingAverage = timeseries.rolling(window=12).mean()  #滑动平均值\n",
    "    movingSTD = timeseries.rolling(window=12).std()     #滑动标准差\n",
    "    \n",
    "    #Plot rolling statistics\n",
    "    orig = plt.plot(timeseries, color='blue', label='Original')\n",
    "    mean = plt.plot(movingAverage, color='red', label='Rolling Mean')\n",
    "    std = plt.plot(movingSTD, color='black', label='Rolling Std')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.legend(loc='best')\n",
    "    plt.title('Rolling Mean & Standard Deviation')\n",
    "    plt.show(block=False)\n",
    "    \n",
    "    #Perform Dickey–Fuller test:   Augmented Dickey–Fuller ADF稳定性检验\n",
    "    print('Results of Dickey Fuller Test:')\n",
    "    dftest = adfuller(timeseries['confirmedCount'], autolag='AIC')\n",
    "    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
    "    for key,value in dftest[4].items():\n",
    "        dfoutput['Critical Value (%s)'%key] = value\n",
    "    print(dfoutput)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2  Differencing差分方法-Timeshift transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23d02589708>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Estimating trend 估计趋势\n",
    "indexedDataset_logScale = np.log(indexedDataset)  #taking log \n",
    "plt.plot(indexedDataset_logScale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23d0265abc8>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Differencing--差分\n",
    "##这里采用的是一阶差分：一阶差分就是离散函数中连续相邻两项之差。\n",
    "datasetLogDiffShifting_1 = indexedDataset_logScale - indexedDataset_logScale.shift()\n",
    "plt.plot(datasetLogDiffShifting_1)\n",
    "\n",
    "#二阶差分：二阶差分就是一阶差分再进行一次差分。\n",
    "#注：以下内容可以看出，二阶差分后数据已经稳定，所以ARIMA模型参数d=2。\n",
    "datasetLogDiffShifting_2 = datasetLogDiffShifting_1 - datasetLogDiffShifting_1.shift()\n",
    "plt.plot(datasetLogDiffShifting_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmedCount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>5.641907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>5.749393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>6.364751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>6.738152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>7.179308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>7.608374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>7.937732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>8.429236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>8.709630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>8.963928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>9.192278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>9.388319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>9.585415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>9.763478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>9.934308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>10.108589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>10.249769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>10.355804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>10.459554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>10.535078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-09</td>\n",
       "      <td>10.602468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>10.663358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>10.709718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>11.004098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>11.073738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>11.110954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>11.142499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>11.173683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>11.201770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>11.227082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>11.234124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>11.241143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>11.260250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-22</td>\n",
       "      <td>11.272572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>11.279757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>11.286953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>11.295105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>11.307204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>11.324111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>11.340784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmedCount\n",
       "日期                        \n",
       "2020-01-20        5.641907\n",
       "2020-01-21        5.749393\n",
       "2020-01-22        6.364751\n",
       "2020-01-23        6.738152\n",
       "2020-01-24        7.179308\n",
       "2020-01-25        7.608374\n",
       "2020-01-26        7.937732\n",
       "2020-01-27        8.429236\n",
       "2020-01-28        8.709630\n",
       "2020-01-29        8.963928\n",
       "2020-01-30        9.192278\n",
       "2020-01-31        9.388319\n",
       "2020-02-01        9.585415\n",
       "2020-02-02        9.763478\n",
       "2020-02-03        9.934308\n",
       "2020-02-04       10.108589\n",
       "2020-02-05       10.249769\n",
       "2020-02-06       10.355804\n",
       "2020-02-07       10.459554\n",
       "2020-02-08       10.535078\n",
       "2020-02-09       10.602468\n",
       "2020-02-10       10.663358\n",
       "2020-02-11       10.709718\n",
       "2020-02-12       11.004098\n",
       "2020-02-13       11.073738\n",
       "2020-02-14       11.110954\n",
       "2020-02-15       11.142499\n",
       "2020-02-16       11.173683\n",
       "2020-02-17       11.201770\n",
       "2020-02-18       11.227082\n",
       "2020-02-19       11.234124\n",
       "2020-02-20       11.241143\n",
       "2020-02-21       11.260250\n",
       "2020-02-22       11.272572\n",
       "2020-02-23       11.279757\n",
       "2020-02-24       11.286953\n",
       "2020-02-25       11.295105\n",
       "2020-02-26       11.307204\n",
       "2020-02-27       11.324111\n",
       "2020-02-28       11.340784"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexedDataset_logScale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmedCount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>5.641907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>5.749393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>6.364751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>6.738152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>7.179308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>7.608374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>7.937732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>8.429236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>8.709630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>8.963928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>9.192278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>9.388319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>9.585415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>9.763478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>9.934308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>10.108589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>10.249769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>10.355804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>10.459554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-09</td>\n",
       "      <td>10.535078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>10.602468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>10.663358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>10.709718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>11.004098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>11.073738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>11.110954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>11.142499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>11.173683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>11.201770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>11.227082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>11.234124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>11.241143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-22</td>\n",
       "      <td>11.260250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>11.272572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>11.279757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>11.286953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>11.295105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>11.307204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>11.324111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmedCount\n",
       "日期                        \n",
       "2020-01-20             NaN\n",
       "2020-01-21        5.641907\n",
       "2020-01-22        5.749393\n",
       "2020-01-23        6.364751\n",
       "2020-01-24        6.738152\n",
       "2020-01-25        7.179308\n",
       "2020-01-26        7.608374\n",
       "2020-01-27        7.937732\n",
       "2020-01-28        8.429236\n",
       "2020-01-29        8.709630\n",
       "2020-01-30        8.963928\n",
       "2020-01-31        9.192278\n",
       "2020-02-01        9.388319\n",
       "2020-02-02        9.585415\n",
       "2020-02-03        9.763478\n",
       "2020-02-04        9.934308\n",
       "2020-02-05       10.108589\n",
       "2020-02-06       10.249769\n",
       "2020-02-07       10.355804\n",
       "2020-02-08       10.459554\n",
       "2020-02-09       10.535078\n",
       "2020-02-10       10.602468\n",
       "2020-02-11       10.663358\n",
       "2020-02-12       10.709718\n",
       "2020-02-13       11.004098\n",
       "2020-02-14       11.073738\n",
       "2020-02-15       11.110954\n",
       "2020-02-16       11.142499\n",
       "2020-02-17       11.173683\n",
       "2020-02-18       11.201770\n",
       "2020-02-19       11.227082\n",
       "2020-02-20       11.234124\n",
       "2020-02-21       11.241143\n",
       "2020-02-22       11.260250\n",
       "2020-02-23       11.272572\n",
       "2020-02-24       11.279757\n",
       "2020-02-25       11.286953\n",
       "2020-02-26       11.295105\n",
       "2020-02-27       11.307204\n",
       "2020-02-28       11.324111"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexedDataset_logScale.shift()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23d028f4188>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "example1 = indexedDataset_logScale.diff(1)\n",
    "plt.plot(example1)\n",
    "example2 = example1.diff(1)\n",
    "plt.plot(example2)\n",
    "example3 = example2.diff(1)\n",
    "plt.plot(example3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey Fuller Test:\n",
      "Test Statistic                 -1.981448\n",
      "p-value                         0.294766\n",
      "#Lags Used                      1.000000\n",
      "Number of Observations Used    37.000000\n",
      "Critical Value (1%)            -3.620918\n",
      "Critical Value (5%)            -2.943539\n",
      "Critical Value (10%)           -2.610400\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "datasetLogDiffShifting_1.dropna(inplace=True)#滤除缺失数据。\n",
    "test_stationarity(datasetLogDiffShifting_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey Fuller Test:\n",
      "Test Statistic                -7.049674e+00\n",
      "p-value                        5.568438e-10\n",
      "#Lags Used                     1.000000e+00\n",
      "Number of Observations Used    3.600000e+01\n",
      "Critical Value (1%)           -3.626652e+00\n",
      "Critical Value (5%)           -2.945951e+00\n",
      "Critical Value (10%)          -2.611671e+00\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "datasetLogDiffShifting_2.dropna(inplace=True)#滤除缺失数据。\n",
    "test_stationarity(datasetLogDiffShifting_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "差分后平稳性都要比原数据好很多。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3 MA moving average 移动平均法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23d0255f108>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Estimating trend 估计趋势\n",
    "indexedDataset_logScale = np.log(indexedDataset)  #taking log \n",
    "plt.plot(indexedDataset_logScale)\n",
    "\n",
    "\n",
    "movingAverage = indexedDataset_logScale.rolling(window=12).mean() #计算log后数据的滑动平均值\n",
    "movingSTD = indexedDataset_logScale.rolling(window=12).std()\n",
    "plt.plot(movingAverage, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmedCount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>5.641907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>5.749393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>6.364751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>6.738152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>7.179308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>7.608374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>7.937732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>8.429236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>8.709630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>8.963928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>9.192278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>9.388319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>9.585415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>9.763478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>9.934308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>10.108589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>10.249769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>10.355804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>10.459554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>10.535078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-09</td>\n",
       "      <td>10.602468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>10.663358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>10.709718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>11.004098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>11.073738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>11.110954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>11.142499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>11.173683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>11.201770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>11.227082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>11.234124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>11.241143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>11.260250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-22</td>\n",
       "      <td>11.272572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>11.279757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>11.286953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>11.295105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>11.307204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>11.324111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>11.340784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmedCount\n",
       "日期                        \n",
       "2020-01-20        5.641907\n",
       "2020-01-21        5.749393\n",
       "2020-01-22        6.364751\n",
       "2020-01-23        6.738152\n",
       "2020-01-24        7.179308\n",
       "2020-01-25        7.608374\n",
       "2020-01-26        7.937732\n",
       "2020-01-27        8.429236\n",
       "2020-01-28        8.709630\n",
       "2020-01-29        8.963928\n",
       "2020-01-30        9.192278\n",
       "2020-01-31        9.388319\n",
       "2020-02-01        9.585415\n",
       "2020-02-02        9.763478\n",
       "2020-02-03        9.934308\n",
       "2020-02-04       10.108589\n",
       "2020-02-05       10.249769\n",
       "2020-02-06       10.355804\n",
       "2020-02-07       10.459554\n",
       "2020-02-08       10.535078\n",
       "2020-02-09       10.602468\n",
       "2020-02-10       10.663358\n",
       "2020-02-11       10.709718\n",
       "2020-02-12       11.004098\n",
       "2020-02-13       11.073738\n",
       "2020-02-14       11.110954\n",
       "2020-02-15       11.142499\n",
       "2020-02-16       11.173683\n",
       "2020-02-17       11.201770\n",
       "2020-02-18       11.227082\n",
       "2020-02-19       11.234124\n",
       "2020-02-20       11.241143\n",
       "2020-02-21       11.260250\n",
       "2020-02-22       11.272572\n",
       "2020-02-23       11.279757\n",
       "2020-02-24       11.286953\n",
       "2020-02-25       11.295105\n",
       "2020-02-26       11.307204\n",
       "2020-02-27       11.324111\n",
       "2020-02-28       11.340784"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexedDataset_logScale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmedCount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>7.658584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>7.987210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>8.321717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>8.619180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>8.900050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>9.155921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>9.384874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>9.595026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>9.770513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-09</td>\n",
       "      <td>9.928249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>10.069868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>10.196322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>10.330970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>10.454997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>10.567286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>10.667969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>10.756727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>10.836060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>10.908667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>10.973214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>11.032053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>11.086868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-22</td>\n",
       "      <td>11.137636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>11.185139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>11.208710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>11.227158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>11.243512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>11.258646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>11.272571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmedCount\n",
       "日期                        \n",
       "2020-01-20             NaN\n",
       "2020-01-21             NaN\n",
       "2020-01-22             NaN\n",
       "2020-01-23             NaN\n",
       "2020-01-24             NaN\n",
       "2020-01-25             NaN\n",
       "2020-01-26             NaN\n",
       "2020-01-27             NaN\n",
       "2020-01-28             NaN\n",
       "2020-01-29             NaN\n",
       "2020-01-30             NaN\n",
       "2020-01-31        7.658584\n",
       "2020-02-01        7.987210\n",
       "2020-02-02        8.321717\n",
       "2020-02-03        8.619180\n",
       "2020-02-04        8.900050\n",
       "2020-02-05        9.155921\n",
       "2020-02-06        9.384874\n",
       "2020-02-07        9.595026\n",
       "2020-02-08        9.770513\n",
       "2020-02-09        9.928249\n",
       "2020-02-10       10.069868\n",
       "2020-02-11       10.196322\n",
       "2020-02-12       10.330970\n",
       "2020-02-13       10.454997\n",
       "2020-02-14       10.567286\n",
       "2020-02-15       10.667969\n",
       "2020-02-16       10.756727\n",
       "2020-02-17       10.836060\n",
       "2020-02-18       10.908667\n",
       "2020-02-19       10.973214\n",
       "2020-02-20       11.032053\n",
       "2020-02-21       11.086868\n",
       "2020-02-22       11.137636\n",
       "2020-02-23       11.185139\n",
       "2020-02-24       11.208710\n",
       "2020-02-25       11.227158\n",
       "2020-02-26       11.243512\n",
       "2020-02-27       11.258646\n",
       "2020-02-28       11.272571"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movingAverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey Fuller Test:\n",
      "Test Statistic                 -4.976347\n",
      "p-value                         0.000025\n",
      "#Lags Used                      0.000000\n",
      "Number of Observations Used    28.000000\n",
      "Critical Value (1%)            -3.688926\n",
      "Critical Value (5%)            -2.971989\n",
      "Critical Value (10%)           -2.625296\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "datasetLogScaleMinusMovingAverage = indexedDataset_logScale - movingAverage\n",
    "datasetLogScaleMinusMovingAverage.head(12)\n",
    "\n",
    "#Remove NAN values\n",
    "#.dropna() :该函数主要用于滤除缺失数据。如果是Series,则返回一个仅含非空数据和索引值的Series,默认丢弃含有缺失值的行\n",
    "datasetLogScaleMinusMovingAverage.dropna(inplace=True)\n",
    "#datasetLogScaleMinusMovingAverage.head(10)\n",
    "test_stationarity(datasetLogScaleMinusMovingAverage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4 指数加权移动(平均)法 ewm(a)  \n",
    "     Exponential Decay Transformation  指数衰减转换法    \n",
    "ewm:https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.ewm.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23d026944c8>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pandas 指数加权滑动（ewm）, 指数加权滑动平均（ewma）  \n",
    "# halflife的值决定了衰减因子alpha：  alpha = 1 - exp(log(0.5) / halflife)\n",
    "exponentialDecayWeightedAverage = indexedDataset_logScale.ewm(halflife=12, min_periods=0, adjust=True).mean()\n",
    "plt.plot(indexedDataset_logScale)\n",
    "plt.plot(exponentialDecayWeightedAverage, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey Fuller Test:\n",
      "Test Statistic                 -3.081661\n",
      "p-value                         0.027940\n",
      "#Lags Used                      0.000000\n",
      "Number of Observations Used    39.000000\n",
      "Critical Value (1%)            -3.610400\n",
      "Critical Value (5%)            -2.939109\n",
      "Critical Value (10%)           -2.608063\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "datasetLogScaleMinusExponentialMovingAverage = indexedDataset_logScale - exponentialDecayWeightedAverage\n",
    "test_stationarity(datasetLogScaleMinusExponentialMovingAverage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5 Decomposing-分解法\n",
    "#分解(decomposing) 可以用来把时序数据中的趋势和周期性数据都分离出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey Fuller Test:\n",
      "Test Statistic                 -3.586776\n",
      "p-value                         0.006019\n",
      "#Lags Used                      1.000000\n",
      "Number of Observations Used    32.000000\n",
      "Critical Value (1%)            -3.653520\n",
      "Critical Value (5%)            -2.957219\n",
      "Critical Value (10%)           -2.617588\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "import numpy as np             #for numerical computations like log,exp,sqrt etc\n",
    "import pandas as pd            #for reading & storing data, pre-processing\n",
    "import matplotlib.pylab as plt #for visualization\n",
    "#for making sure matplotlib plots are generated in Jupyter notebook itself\n",
    "%matplotlib inline             \n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.stattools import acf, pacf\n",
    "\n",
    "##分解(decomposing) 可以用来把时序数据中的趋势和周期性数据都分离出来:\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 10, 6\n",
    "\n",
    "\n",
    "\n",
    "#Seasonal decomposition using moving averages\n",
    "## 返回包含三个部分 trend（趋势部分） ， seasonal（季节性部分） 和residual (残留部分)\n",
    "\n",
    "'''seasonal_decompose(x, model='additive', filt=None, freq=None, two_sided=True, extrapolate_trend=0)\n",
    "    Seasonal decomposition using moving averages\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    x : array-like\n",
    "        【Time series】. If 2d, individual series are in columns.'''\n",
    "\n",
    "decomposition = seasonal_decompose(indexedDataset_logScale) \n",
    "\n",
    "trend = decomposition.trend\n",
    "seasonal = decomposition.seasonal\n",
    "residual = decomposition.resid\n",
    "\n",
    "plt.subplot(411)\n",
    "plt.plot(indexedDataset_logScale, label='Original')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.subplot(412)\n",
    "plt.plot(trend, label='Trend')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.subplot(413)\n",
    "plt.plot(seasonal, label='Seasonality')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.subplot(414)\n",
    "plt.plot(residual, label='Residuals')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.tight_layout()#固定布局，tight_layout会自动调整子图参数，使之填充整个图像区域。\n",
    "\n",
    "#there can be cases where an observation simply consisted of trend & seasonality. In that case, there won't be \n",
    "#any residual component & that would be a null or NaN. Hence, we also remove such cases.\n",
    "decomposedLogData = residual\n",
    "decomposedLogData.dropna(inplace=True)\n",
    "test_stationarity(decomposedLogData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmIAAAGbCAYAAABnI/yqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8GearUAAAgAElEQVR4nOzdd3xT5fcH8M/TdFFG2aMUypBRwLJlD5mCsqoMFRkqiAqIG0X4gvwUUBRFUARRlNGqICjLAaiAgAKCDEFmgdICbaGldLc5vz9OUkJnmtzkps15v155JU3ufe6TmzQ5ecZ5FBFBCCGEEEI4n4feFRBCCCGEcFcSiAkhhBBC6EQCMSGEEEIInUggJoQQQgihEwnEhBBCCCF0IoGYEEIIIYROJBATophQSkUopXqZbs9USq0y3a6tlLqllDLoW0ORF6VUHaUUKaU8NSqvu1IqUouybDh2F6XUf3bsv0QpNV3LOglR3EkgJoQTmYKpFFPgdEUptUIpVcaeMonoIhGVIaIsreppZqofKaUG5rj/A9P9Y7Q+phV1Ukqp95RScabLWiv2aaqU+lkpdUMpFa+UOqiU6m96TLfAxhFMr0uS6T0Wp5TarpQarkXZRLSLiBpZWY8xSqndOfafQESztaiLECWFBGJCON8AIioDoAWAlgBe07k+hTkFYLT5D1PLzlAAZ3WqTx8AIwE0BxAA4FMr9tkI4BcA1QBUBTAZwE1HVVArdrSiNTe9xxoBWAFgkVLqf5pVTAihGQnEhNAJEV0B8BM4IAMAKKUGKqWOm1ptflNKBRdWTs6uL9N+s5VSfyilEk0tQZUtth+llLpgai2ZbtnlmY+NADoppSqY/r4PwBEAV3LU43Gl1AlTq9NPSqkgi8c+VEpdUkrdNLVGdbF4bKZS6hul1Fem+h5XSrUpoD6ZAFIAXCGiNCL6pZDzUxlAXQDLiCjddPmDiHYrpUoD2AogwNSCdEspFaCUukcptdf0OkQrpRYppbwtyiSl1ASl1GnT812slFKmxwxKqflKqVil1DkA9+eoz1jTeUpUSp1TSj1l8Vh3pVSkUupVpdQVAF8opUqZWiZvKKX+BdC2oOdriYhiiWglgKcBvKaUqmQ6jr9SarnpuV1WSv2fqd4+pufczKJOVUytuFVzth4qpaYqpc6ansu/SqkhpvuDASwB0MF0TuNN969QSv2fxf7jlFJnlFLXlVI/KKUCrDnHQpQkEogJoROlVCCAfgDOmP5uCCAMwBQAVQBsAbDRMgAogkcAjAW3/ngDeMl0jCYAPgbwKIAaAPwB1CykrFQAPwAYYfp7FICvcjyXwQBeBxBqqvsu03Mx2w8OOCsCWAPgW6WUr8XjAwGEAyhvOtaiAupzwlTOMiu/mOPA53iVUmqwUqqa+QEiSgK/BlGm7t0yRBQFIAvA8wAqA+gAoCeAZ3KU+wA4KGoOYBiAvqb7x5keawmgDYCHcux3zfR4OfBrtEAp1cri8eqm5xcEYDyA/wGob7r0hUXrZBF8D8ATwD2mv78EB7R3merZB8CTRJQG4DsAD1vsOwzA70R0LY9yzwLoAn4fzQKf4xpEdALABAB7Tee0fM4dlVI9AMwxlV8DwAXwe8BSfudYiBJDAjEhnG+DUioRwCXwl7K5y2g4gM1E9AsRZQCYD6AUgI42HOMLIjpFRCkAvsHtVreHAGwkot1ElA5gBgBrFpz9CsAopZQ/gG4ANuR4/CkAc4joBBFlAngbQAtzqxgRrSKiOCLKJKL3APiAu83MdhPRFtM4t5XgL95clFJe4FbEZ8DBymcWLVF/KKUG5NyHeEHdewFEAHgPQLRSaqdSqkF+T5aIDhLRPlN9I8Ddn91ybDaXiOKJ6CKAX3H7HA8D8AERXSKi6+Bgw7LszUR0ltjvAH4GBzNmRgD/M7X2pZjKe4uIrhPRJQAL86t3Ac8nA0AsgIqmQLQfgClElGQKsBbgdqC9BncGYo+Y7sur3G+JKIqIjET0NYDTuB3sFeZRAJ8T0d+mAPA1cAtaHYtt8jvHQpQYEogJ4XyDiagsgO4AGoNbXQAe73TBvBERGcHBWmEtVnmx7DZMBmCeEBBgKtN8jGRwi1GBiGg3uKXrDQCbTAGCpSAAH5q6teIBXAegzHVXSr1o6o5LMD3uj9vPO6/6+qq8x0f1AOBPRKvAgWs9cDBWDkADALvz2AdEFElEE4movqmuScjRqmdJKdVQKbVJ8YSKm+DAsnKOzaw6x7B4TU1l91NK7TN1x8UD6J+j7BgiSrX4u8DyrGEKYKuAX5cgAF7ggNT8en0Kbj0FgB0ASiml2pkC6RYA1udT7iil1GGLcpoh93nKT873+y3we9Hy/Z7fORaixJBATAidmFpDVoBbvgAgCvwlCYBnBwKoBeCyhoeNBhBocYxSACpZue8qAC8i7wDmEoCniKi8xaUUEe1RPB7sVXDLTgVTN1UCOFArKk9wlxpMwcpAcOvZfgBfEtGNwgowtSotBgcNQN4tgp8AOAmgARGVA3e7WlvfaPDrZlbbfEMp5QNgHfg1r2Y6F1tylJ2zPvmWVwSDwOftL/BrlQagssVrVY6ImgLZPwC+AbeKPQIOvBNzFmgK0pYBmAigkum5HLN4LoW1tOZ8v5cGvxe1fL8L4fIkEBNCXx8A6K2UagH+8rtfKdXT1ILxIvgLc4+Gx1sLYIBSqqNp7NksWB9gLATQG8DOPB5bAh4M3hTIHgw+1PRYWXAQEAPAUyk1Azw+yha7wa1lb5qCSA9wl1VDcJdeLkqpCkqpWUqpu5RSHooH7z8OYJ9pk6sAKpm6Xc3KgmdV3lJKNQYPdrfWNwAmK6UCFU9wmGrxmDe4WzYGQKZSqh94fFZh5b1meh6BACZZWxGlVEWl1KPgwHOeqXs4Gtwd+p5SqpzpnNRXSll2va4Btzg+iny6JQGUBgdbMaZjjcXt4Bbg8xpYwBjHNQDGKqVamALUtwH8aeoKFsJtSCAmhI6IKAbcwjSdiP4Dp2X4CDyeZwA41UW6hsc7Dv4iDwe3tCSCx6mlWbHvdSLabhpzlfOx9QDmAQg3deUdA49DAnhM11ZwGowL4MH/l3KWYWX9E8CBS3twi8oRAH4AWgF4XCk1Lo/d0gHUAbANHFwdAz/fMaYyT4InFpwzdbEFgCc3PAI+P8sAfF2Eai4DP+d/APwNHvxurn8iOHXGNwBumI7xQyHlzQKft/PgAGqlFXX4Ryl1CzxJ4UkAzxPRDIvHR4GDwn9N9VgLHjBvruef4O7bAPBrlwsR/Qsec7cXHHTdDeAPi012ADgO4IpSKjaP/bcDmA5uIYwGT0YYkXM7IUo6lcdnqhDCTShOJhsP7oI7r3d9hBDC3UiLmBBuRik1QCnlZxqTMx/AUfCMQiGEEE4mgZgQ7mcQuFsvCjzTcERe3Y1CCCEcT7omhRBCCCF0Ii1iQgghhBA6sXVBWV1VrlyZ6tSpo3c1hBBCCCEKdfDgwVgiqpLXY8UyEKtTpw4OHDigdzWEEEIIIQqllMp3RQzpmhRCCCGE0IkmgZhS6j6l1H9KqTNKqal5PN5YKbVXKZWmlHopx2MRSqmjpvXKpJlLCCGEEG7D7q5JpZQBvHxGbwCRAPYrpX4wZV02uw7OJj04n2LuJaJcmZeFEEIIIUoyLcaI3QPgDBGdAwClVDg4T1F2IEZE1wBcU0rdr8HxhBBCCGGSkZGByMhIpKam6l0Vt+fr64vAwEB4eXlZvY8WgVhN3LluXCSAdkXYnwD8rJQiAJ8S0dK8NlJKjQcwHgBq165tY1WFEEKIkiUyMhJly5ZFnTp1oJTSuzpui4gQFxeHyMhI1K1b1+r9tBgjlterXpQssZ2IqBV4geBnlVJd89qIiJYSURsialOlSp4zQIUQQgi3k5qaikqVKkkQpjOlFCpVqlTklkktArFIALUs/g4EL51iFSKKMl1fA7Ae3NUphBBCCCtJEOYabHkdtAjE9gNooJSqq5TyBjACwA/W7KiUKq2UKmu+DaAPgGMa1EkIIYQQwuXZHYgRUSaAiQB+AnACwDdEdFwpNUEpNQEAlFLVlVKRAF4A8IZSKlIpVQ5ANQC7lVL/APgLwGYi+tHeOgkhhBDCeSIjIzFo0CA0aNAA9evXx3PPPYf09PRc20VFReGhhx4qtLz+/fsjPj7eprrMnDkT8+fPt2lfPWiSR4yIthBRQyKqT0Rvme5bQkRLTLevEFEgEZUjovKm2zeJ6BwRNTddmpr3FUIIIUTxQEQIDQ3F4MGDcfr0aZw6dQq3bt3CtGnT7tguMzMTAQEBWLt2baFlbtmyBeXLl3dUlV2KZNYXQgghhM127NgBX19fjB07FgBgMBiwYMECfP755/j4448xdOhQDBgwAH369EFERASaNWsGAEhOTsawYcMQEhKC4cOHo127dtnLF9apUwexsbGIiIhAcHAwxo0bh6ZNm6JPnz5ISUkBACxbtgxt27ZF8+bN8eCDDyI5OVmfE2CnYrnWpBBCCCFymzIFOHxY2zJbtAA++CD/x48fP47WrVvfcV+5cuVQu3ZtZGZmYu/evThy5AgqVqyIiIiI7G0+/vhjVKhQAUeOHMGxY8fQokWLPMs/ffo0wsLCsGzZMgwbNgzr1q3DyJEjERoainHjxgEA3njjDSxfvhyTJk2y+/k6m7SIiWzh4cDJk3rXQgghRHFCRHnOFjTf37t3b1SsWDHX47t378aIESMAAM2aNUNISEie5detWzc7SGvdunV2MHfs2DF06dIFd999N1avXo3jx49r9IycS1rEBADAaARGjQJCQoD9+wGZCS2EEMVPQS1XjtK0aVOsW7fujvtu3ryJS5cuwWAwoHTp0nnuR2RdylEfH5/s2waDIbtrcsyYMdiwYQOaN2+OFStW4LfffrPtCehMWsQEACA2FsjIAA4eBNav17s2QgghiouePXsiOTkZX331FQAgKysLL774IsaMGQM/P7989+vcuTO++eYbAMC///6Lo0ePFum4iYmJqFGjBjIyMrB69Wrbn4DOJBATAIAoUwpeDw9g+nQgK0vf+gghhCgelFJYv349vv32WzRo0AANGzaEr68v3n777QL3e+aZZxATE4OQkBDMmzcPISEh8Pf3t/q4s2fPRrt27dC7d280btzY3qehG2Vt06AradOmDZlnVghtbN0K9O8PvPAC8P77wFdfAY89pnethBBCFObEiRMIDg7WuxpFlpWVhYyMDPj6+uLs2bPo2bMnTp06BW9vb72rZpe8Xg+l1EEiapPX9jJGTAC43SI2cSLw66/AzJnA8OFAMf9/EEII4aKSk5Nx7733IiMjA0SETz75pNgHYbaQQEwAuB2IBQQAb73FrWOffw5MmKBvvYQQQpRMZcuWhfRuyRgxYRIVBVSqBPj4APfdB3TqBMyeDZgmpwghhBDCASQQEwCA6GhuDQM4dcXbb3Nw9vHH+tZLCCGEKMkkEBMAOOgyB2IA0LUr0KcPMGcOcPOmfvUSQgghSjIJxAQADsRq1LjzvrfeAuLi9EkQKIQQQrgDCcQEjEbgypU7W8QAoE0bYMgQ4L33OCATQggh8mIwGNCiRQs0a9YMAwYMQHx8fIHbjxkzBmvXrgUAdO/ePXvQfv/+/Qvd1xoRERFQSmH69OnZ98XGxsLLywsTJ060u3wtSSAmEBPDCVxzBmIAD9hPTATeecf59RJCCFE8lCpVCocPH8axY8dQsWJFLF682KZytmzZgvLly2tSp3r16mHTpk3Zf3/77bdo2rSpJmVrSQIxkZ26ImfXJAA0bQo8+ijw0Uc8oF8IIYQoSIcOHXD58mUAwOHDh9G+fXuEhIRgyJAhuHHjRoH71qlTB7GxsYiIiEBwcDDGjRuHpk2bok+fPtlrTO7fvx8hISHo0KEDXn75ZTRr1izPskqVKoXg4ODs1ravv/4aw4YNy348JiYGDz74INq2bYu2bdvijz/+AAD89ddf6NixI1q2bImOHTviv//+AwCsWLECoaGhuO+++9CgQQO88sor9p0oE8kjJu7IIZaXmTOB8HCeSfnRR06rlhBCiKKaMgU4fFjbMlu0sHqwcFZWFrZv344nnngCADBq1Ch89NFH6NatG2bMmIFZs2bhAyvLOn36NMLCwrBs2TIMGzYM69atw8iRIzF27FgsXboUHTt2xNSpUwssY8SIEQgPD0f16tVhMBgQEBCAKNOX3nPPPYfnn38enTt3xsWLF9G3b1+cOHECjRs3xs6dO+Hp6Ylt27bh9ddfz17U/PDhwzh06BB8fHzQqFEjTJo0CbVq1bLq+eRHAjGR3dKVXyBWvz7wxBPAp58CL74I1KnjtKoJIYQoBlJSUtCiRQtERESgdevW6N27NxISEhAfH49u3boBAEaPHo2hQ4daXWbdunXRokULAEDr1q0RERGB+Ph4JCYmomPHjgCARx555I7ux5zuu+8+TJ8+HdWqVcPw4cPveGzbtm34999/s/++efMmEhMTkZCQgNGjR+P06dNQSiEjIyN7m549e2avh9mkSRNcuHBBAjFhP3OLWPXq+W/zxhvAihXAm29yxn0hhBAuSKdp7uYxYgkJCXjggQewePFijB492q4yfXx8sm8bDAakpKSgqOtje3t7o3Xr1njvvfdw/PhxbNy4Mfsxo9GIvXv3olSpUnfsM2nSJNx7771Yv349IiIi0L1793zrlJmZWcRnlZuMEROIigIqVy54XcnAQODZZ4EvvwROnnRe3YQQQhQf/v7+WLhwIebPnw8/Pz9UqFABu3btAgCsXLkyu3XMVhUqVEDZsmWxb98+AEB4eHih+7z44ouYN28eKlWqdMf9ffr0waJFi7L/Pmzq0k1ISEDNmjUB8LgwR5NATNyRVb8gU6cCfn7A//7n+DoJIYQonlq2bInmzZsjPDwcX375JV5++WWEhITg8OHDmDFjht3lL1++HOPHj0eHDh1ARNldhflp2rRpnq1zCxcuxIEDBxASEoImTZpgyZIlAIBXXnkFr732Gjp16oSsrCy761sYVdRmPlfQpk0bkoVCtdO2LbeIbd1a+LYzZnBKi7//Blq2dHzdhBBCFOzEiRMIDg7WuxpOc+vWLZQpUwYAMHfuXERHR+PDDz/UuVa35fV6KKUOElGbvLaXFjGRa3mjgrz4IlChAmCRI08IIYRwms2bN2cnj921axfeeOMNvatkFxms7+aysjirfl45xPLi7w+8+ip3U+7ZA5gmrgghhBBOMXz48FwzIIszaRFzczExvMSRtS1iADBxIlCtGvD660Ax7NkWQgghXIYEYm6usGSueSldmtNZ/P47sG2bY+olhBBCuAMJxNxcQcsbFWTcOKB2bWDaNGkVE0IIIWwlgZibs6VFDAB8fHjpo/37ge+/17xaQgghhFuQQMzNmZc3Kiirfn4eewxo1IhnUDoh1YoQQggXZTAYsmcyDhgwAPHx8QVuP2bMGKxduxYA0L179+yFufv371/ovtYwGo2YPHkymjVrhrvvvhtt27bF+fPnAQBvv/12vvvNnDkT8+fPt/v4RSGBmJuLigKqVgW8vIq+r6cnMGsWcOwYLwouhBDCPZmXODp27BgqVqyIxYsX21TOli1bUL58ebvr8/XXXyMqKgpHjhzB0aNHsX79+uxyCwrE9CCBmJuLiir6+DBLQ4cCzZtztn2LdVGFEEK4qQ4dOuDy5csAeNmg9u3bIyQkBEOGDMGNGzcK3LdOnTqIjY1FREQEgoODMW7cODRt2hR9+vRBSkoKAGD//v0ICQlBhw4d8PLLL6NZs2a5yomOjkaNGjXg4cFhTmBgICpUqICpU6dmL1D+6KOPAgDeeustNGrUCL169cJ///2n5amwiuQRc3PWLm+UHw8P4P/+DxgwAPjiC2D8eO3qJoQQomimTJmSvWaiVlq0aIEPrFxMPCsrC9u3b8cTTzwBABg1ahQ++ugjdOvWDTNmzMCsWbOsLuv06dMICwvDsmXLMGzYMKxbtw4jR47E2LFjsXTpUnTs2BFTp07Nc99hw4ahc+fO2LVrF3r27ImRI0eiZcuWmDt3LhYtWpR9jg4ePIjw8HAcOnQImZmZaNWqFVq3bm1V/bQiLWJurihZ9fNz//1Ahw7AvHna1EkIIUTxYm5lqlSpEq5fv47evXsjISEB8fHx2Qt9jx49Gjt37rS6zLp166JFixYAgNatWyMiIgLx8fFITExER1M28UceeSTPfQMDA/Hff/9hzpw58PDwQM+ePbF9+/Zc2+3atQtDhgyBn58fypUrh4EDBxb1qdtNWsTcWGYmcPWq/YGYUsDgwZxxPz4e0KB7XwghhA2sbW3SmnmMWEJCAh544AEsXrw4z4W2i8LHxyf7tsFgQEpKCoqyPraPjw/69euHfv36oVq1atiwYQN69uyZazullF31tJe0iLmxa9c4q749Y8TMzOubnjhhf1lCCCGKJ39/fyxcuBDz58+Hn58fKlSogF27dgEAVq5cmd06ZqsKFSqgbNmy2LdvHwAgPJ+ZYn///TeiTPmZjEYjjhw5gqCgIACAl5cXMkyDmrt27Yr169cjJSUFiYmJ2Lhxo131s4W0iLkxc+oKe1vEgDsDsQ4d7C9PCCFE8dSyZUs0b94c4eHh+PLLLzFhwgQkJyejXr16+OKLL+wuf/ny5Rg3bhxKly6N7t27w9/fP9c2165dw7hx45CWlgYAuOeeezBx4kQAwPjx4xESEoJWrVph9erVGD58OFq0aIGgoCB06dLF7voVlSpKM5+raNOmDZlzjgjbbdwIDBwI/PUX0LatfWVlZfHSR5MnA++8o039hBBCFO7EiRMINv8adgO3bt1CmTJlAABz585FdHQ0PvzwQ51rdVter4dS6iARtclre2kRc2O2Lm+UF4MBaNhQuiaFEEI41ubNmzFnzhxkZmYiKCgIK1as0LtKdpFAzI1FR/NA+2rVtCkvOBg4eFCbsoQQQoi8DB8+HMOHD9e7GpqRwfpuzJ6s+nkJDgbOnwdSU7UpTwghhHWK4zCjksiW10ECMTemRQ4xS8HBPAvz1CntyhRCCFEwX19fxMXFSTCmMyJCXFwcfH19i7SfdE26MXuXN8qpcWO+PnECCAnRrlwhhBD5CwwMRGRkJGJiYvSuitvz9fVFYGBgkfaRQMyNRUcDWq7k0LAhjzmTAftCCOE8Xl5eqFu3rt7VEDaSrkk3pVVWfUulSgF160ogJoQQQlhLAjE3dfUqQKRtIAbwODEJxIQQQgjrSCDmprTMIWYpOJgH62dlaVuuEEIIURJJIOamtFzeyFJwMJCWBkREaFuuEEIIURJJIOamzC1ijgjEAOmeFEIIIawhgZibioriGY5Vq2pbrgRiQgghhPUkEHNT0dG8tJGnxglMypcHqleXQEwIIYSwhgRibkrrrPqWGjeWQEwIIYSwhgRibsqRgZg5hYWstiGEEEIUTAIxN6X18kaWgoOBhATgyhXHlC+EEEKUFBKIuaGMDCAmxrEtYoB0TwohhBCFkUDMDTkqq76ZBGJCCCGEdTQJxJRS9yml/lNKnVFKTc3j8cZKqb1KqTSl1EtF2Vdoz1E5xMwCAoCyZYGTJx1TvhBCCFFS2B2IKaUMABYD6AegCYCHlVJNcmx2HcBkAPNt2FdozFHLG5kpJWtOCiGEENbQokXsHgBniOgcEaUDCAcwyHIDIrpGRPsBZBR1X6E9Ry1vZEkCMSGEEKJwWgRiNQFcsvg70nSfpvsqpcYrpQ4opQ7ExMTYVFHBoqIADw/ts+pbCg7m4yQkOO4YQgghRHGnRSCm8rjP2gxSVu9LREuJqA0RtalSpYrVlRO5RUVxVn2DwXHHaNyYr2WcmBBCCJE/LQKxSAC1LP4OBBDlhH2FjaKjHdstCcjMSSGEEMIaWgRi+wE0UErVVUp5AxgB4Acn7Cts5Mis+mb16gHe3hKICSGEEAWxe8lnIspUSk0E8BMAA4DPiei4UmqC6fElSqnqAA4AKAfAqJSaAqAJEd3Ma1976yQKFhUFtG/v2GN4egINGkggJoQQQhTE7kAMAIhoC4AtOe5bYnH7Crjb0ap9heOkp3NWfUelrrAUHAwcPuz44wghhBDFlWTWdzNXr/K1o7smAQ7Ezp0D0tIcfywhhBCiOJJAzM04Oqu+peBgwGgETp92/LGEEEKI4kgCMTfj7EAMkHFiQgghRH4kEHMz5qz6zhgj1rAhL3ckgZgQQgiRNwnE3ExUFCdydUZOXD8/IChIAjEhhBAiPxKIuZmoKKB6dcdm1bcka04KIYQQ+ZNAzM1ERTmnW9IsOBj47z8gK8t5xxRCCCGKCwnE3IwzljeyFBwMpKYCFy4475hCCCFEcSGBmJtxxvJGlmTmpBBCCJE/CcTcSFoaEBsrgZgQQgjhKiQQcyNXrvC1M8eIVawIVK0KnDzpvGMKIYQQxYUEYm7EnEPMmS1igMycFEIIIfIjgZgbcWZWfUuNG3MgRuTc4woh7EMEpKfrXQshSjYJxNyIXoFYcDBw4wZw7ZpzjyuEsF1mJjBgANChg/yIEsKRJBBzI9HRgKcnULmyc48rA/aFKH6mTAE2bwb+/hv45x+9ayNEySWBmBsxZ9X3cPKrLoGYEMXLxx8DixcDY8fy58X69XrXSIiSSwIxN+LsHGJmgYFAmTISiAlRHGzbBkyeDNx/P7BsGdC5M/Ddd3rXSoiSSwIxN+Ls5Y3MlLo9YF8I4bpOnQKGDuVW7DVreE3a0FDg2DHg9Gm9aydEySSBmBtx9vJGliSFhRCu7cYN4IEHeBzpxo1AuXJ8/5AhfC3dk0I4hgRibiItDYiL0zcQu3wZSEzU5/hCiPxlZHBLWEQEB1x16tx+rHZtoHVr6Z4UwlEkEHMTeiVzNTMP2JcM+0K4nilTgO3bgaVLeUxYTqGhwJ9/ApGRzq+bECWdBGJuwhyI6TFGDOAxYoB0TwrhahYv5lmSL78MjBmT9zahoXy9YYPTqiWE25BAzE3olczVrH59HnsigZgQruOXX4DnnuPErXPm5L9d48Z8kXFiQmhPAjE3oXcg5uUFNGgggZgQruLkSR4X1qQJsHo1z5AsSGgo8PvvQGysc+onhLuQQMxNREVxMFSpkn51kJmTQriG69e5FczbG/jhB6Bs2cL3CQ0FsrJ4RqUQQlynGakAACAASURBVDsSiLmJ6Gh9supbCg4Gzp6VRYSF0JN5huTFi7lnSBakVSueQSndk0JoSwIxN6FXVn1LwcH8i1oSQwqhDyJg0iRgxw7Omt+pk/X7KsU5xX7+WdLQCKElCcTchKsEYoB0Twqhl0WLgE8/BV59FRg1quj7h4ZyTsKtW7WvmxDuSgIxN6HX8kaWGjXiawnEhHC+n3/mfGGDBgFvv21bGZ06AVWqSPekEFqSQMwNpKby8iV6t4iVLg0EBUlSVyGc7eRJYNgwoFkzYNUq28eKGgwcyG3axJ8rQgj7SSDmBvTOqm9JFv8Wwrni4ngNSR8fniFZpox95YWGArducSZ+IYT9JBBzA3rnELMUHMy/zo1GvWsihHt45BHg0iXOih8UZH95PXrwguDSPSmENiQQcwN6L29kKTgYSEnhqfNCCMdKTuaxYS+9BHTooE2ZPj7A/fcD338PZGZqU6YQ7kwCMTfgai1igHRPCuEMcXF8bW2uMGuFhnKG/d27tS1XCHckgZgbcIWs+mYSiAnhPOZArHJlbcu97z7A1xf47jttyxXCHUkg5gbMOcSU0rsm/IVQubIEYkI4gzkQ0/pHWJkyQJ8+PE6MSNuyhXA3Eoi5geho1xgfZiZrTgrhHOYFuh3RGh4aCkRGAgcOaF+2EO5EAjE34ApZ9S2ZAzH5JS2EYzmqaxLgRcMNBumeFMJeEoi5AVcMxK5fv/1rXQjhGOZArGJF7cuuWBHo3p0DMflRJYTtJBAr4VJSgPh41+qabNyYr6V7UgjHio3lnF9eXo4pPzQUOHVK/peFsIcEYiWcK2XVN5OZk0I4R1ycY7olzQYP5mvpnhTCdhKIlXCulEPMrFYtwM9PAjEhHC0uzrFpawICgPbtJcu+EPaQQKyEc8VAzMND1pwUwhliYx2fPzA0FPj7byAiwrHHEaKkkkCshHOl5Y0sSQoLIRzP0S1iADBkCF9Lq5gQtpFArISLigK8vR0za8oewcG8EPGtW3rXRIiSy9FjxADgrruAkBAJxISwlQRiJZwrZdW3ZB6wf/KkvvUQoqRKTwdu3nTO0mZDhvC6k1evOv5YQpQ0EoiVcK6WQ8xMZk4K4VjXr/O1MwKx0FDOJfb9944/lhAljQRiJZyrLW9kVr8+Z+WWFjHnS0sD6tUDPvxQ75oIR3JkVv2c7r6b/6cljYUQRSeBWAnnqi1i3t48tkRaxJxv82bg/HlgzRq9ayIcyZHrTOakFHdP7tjBCaSFENaTQKwES04GEhJcMxADZOakXlau5Ov9+4Fr1/Sti3Acc4uYMwIxgLsnMzI40BdCWE8CsRLMVVNXmAUHA2fO8Ie3cI64OP6i7NaNx/T89JPeNRKO4syuSQBo144/a6R7UoiikUCsBHPFZK6WgoOBzEwOxoRzfPMNB77vvw9UqwZs3ap3jYSjOLNrEuBEzYMHAz/+yK3xQgjrSCBWghWHQAyQ7klnWrkSaNoUaNkS6NePvzSzsvSulXCEuDigVCm+OEtoKAdhP//svGMKUdxJIFaCuXog1rgxX0sg5hxnzgB79wKPPcaDq/v1A27cAP78U++aCUdwRjLXnLp1AypUkO5JIYpCk0BMKXWfUuo/pdQZpdTUPB5XSqmFpsePKKVaWTwWoZQ6qpQ6rJQ6oEV9BIuOBnx8gPLl9a5J3sqU4QXAJRBzjlWrOAB79FH+u3dvTiEi3ZMlkzPWmczJywsYOBDYuFHGfgphLU97C1BKGQAsBtAbQCSA/UqpH4joX4vN+gFoYLq0A/CJ6drsXiKKtbcu4k6umlXfksycdA4iDsR6dctA4I9fAgsWoEJqKg6WrokriwOApACgZk1+wwRY3Pbz07vqwkbOWGcyL0OGAF9+Cfz2Gwf7QoiC2R2IAbgHwBkiOgcASqlwAIMAWAZigwB8RUQEYJ9SqrxSqgYRRWtwfJEPV80hZqlxY+CzzwCjkQf7CsfYtzMdPc6uwPvxbwPjLgCtWwPNm6P8vij4nT8I+uQHqNSU3DuWL387OLMM0AIDgaAgoE4d3saVo303FRcH1K7t/OP26cPx+3ffSSBWVKmpqbhy5QoyMzORlZVl9cVoNN5x2/y3+XbOS36P1ahRA/3794ef/ABzKi0CsZoALln8HYk7W7vy26YmgGgABOBnpRQB+JSIlmpQJwHumrz7br1rUbDgYB7cGxmpz5dGiZeWBnzxBRq+PAdLcRFZde4BvvqYB4gphfh/gBYtgC8+JowZksDRe1QUcPly7tu//spvqszMO49RtuztoMzy2ny7ShXbAzUifoMkJvIK8UlJQIMG0lJnBS26Jk+ePIkjR44gIyMD6enp2deWt/O6rlo1HStWZOD69QwABP4Njuxry9v5XQOAv78/AgMDUbNmTdSsWTP7dpUqVeBRDH+5GY1GXLlyBefOncP58+dx7ty57Mv58+dx+fJlvauI0qVLY9CgQRgxYgT69u0Lb29vvatU4mkRiOX1CUtF2KYTEUUppaoC+EUpdZKIduY6iFLjAYwHgNryjW2VqCj+derKLGdOysuqobQ0YPlyYO5c4NIlnDO0x7Jun2Lqr33vCIpCQriBa8tWhTFjy3PrVpMm+ZdrNAIxMcClS8CFC3yJiLh9vXMnrzRtqVSp24FZUBC3qmVk3A6ubt26fTvnfUlJHIxZqlePm1uaN9fsdJU0WVk8EcOWQOzChQsIDw9HWFgY/vnnH6v28fb2hre3N7y8vODt7Y2MDC+kpnpj715PlC7NAZMyve+Uxfsv532W10SEGzduIDo6Glk5pvZ6eXkhICAgV4Bmebty5cp3HAu4M8gr7D7LwNDykvO+vLaJi4vLM9A6f/48UlNT73j+NWvWRL169dC7d2/Uq1cPNWvWhLe3NwwGQ66Lh4dHnvfnfNzDwyPXbctLfvcfPXoUYWFhWLt2LdasWYMKFSrgwQcfxMMPP4xu3brBYDBY9X4QRaNFIBYJoJbF34EAoqzdhojM19eUUuvBXZ25AjFTS9lSAGjTpk3u/xxxh1u3+PvQ1bsmLQOxvn31rUuJkJrKfb1z53JLVseO+GPsZ+j8Zm9seVXl+klknj25di03dHkW9ong4cEJyKpVA9q0yXub+Pi8g7QLF4ADB7jPTCmerVG27J3XAQG577O8bTQCr78OdOgALFt2e+aBuEN8PMev1s6avHr1Kr799luEhYVhz549AIB27drhgw8+QI8ePeDr63tHoGV5bTAYcgU8CQncEDpsGDB/vn3PJSsrC1evXsXly5cRGRmJy5cv33H7n3/+webNm5HsosnLypYti/r16yM4OBj9+/dHvXr1si9BQUHw8fHRu4rZunfvju7du+Ojjz7CL7/8grCwMISFheGzzz5D9erVMWzYMDz88MNo165drtdc2E7l9WugSAUo5QngFICeAC4D2A/gESI6brHN/QAmAugP7rZcSET3KKVKA/AgokTT7V8AvElEPxZ0zDZt2tCBAzLBsiCnTwMNGwJffcXpClyV+cvioYeATz/VuzbFWEoKBybz5nFTaOfOwP/+B/TsidAHFfbs4e7fvAKt774DHnyQG7O6dHFCXdPTeXqdrR/kV6/yN/zOncDkyfxN7+WlbR2LuVOngEaNeIJGfrFqfHw8vvvuO4SHh2P79u0wGo24++67MWLECIwYMQL16tWzqw79+wMnTwJnzzp+CCERISEh4Y4gLS4uLs9goSj3WbbQ5fd3Xvf5+/tnB1sVK1Ys1kFLcnIyNm/ejLCwMGzZsgVpaWmoW7cuRowYgYcffhh3u/r4FxehlDpIRHn/es3ZpGrLBRxgnQJwFsA0030TAEww3VbgmZVnARwF0MZ0fz0A/5gux837FnZp3bo1lVTXrhFdvGh/Ob//TgQQ/fKL/WU5WqdORF266F2LYio5mWjBAqLq1fkF79qVaPt2IqORiIji4oi8vIimTMm/iIQEIk9PoqlTnVRnLaSnEz3/PD/nLl2IoqP1rpFL+eMPPjVbt955f1JSEoWHh9OgQYPI29ubAFC9evVo2rRpdOzYMU3rsHQp1+HvvzUtVugoPj6eVqxYQX379iWDwUAAqGnTpjR79mw6c+aM3tVzaQAOUH4xVH4PuPKlpAZiMTFEtWsT1ahBlJRkX1lhYfzqHj+uTd0c6ckniSpX1rsWxUxSEtF77xFVq8YvdPfuRL/+mmuzTz7hhw8eLLi47t2Jmjd3TFUdas0aIj8//qf54w+9a+MyfviBX/f9+4nS0tJo48aN9Mgjj1Dp0qUJANWoUYOmTJlCf/75JxlNQbvWYmI4wH/xRYcUL3R29epVWrx4MXXu3JkAkIeHB4WFheldLZclgVgxkJlJ1KcPf3ABRO++a195773H5dy4oU39HMlc15gYvWtSjJibG3r04ObPfHTsSNSkSXYDWb7eeYeLi4zUuJ7OcOQIUf363PS3eHHhT9YNzJt3loBPqE+fIeTv708AqGLFijR+/Hj69ddfKTMz0yn1GDSIG2szMpxyOKGTixcvUteuXcnb25t+zeMHoSg4ECt+839LqJkzeX22jz/mQetz5/KkMVtFRfFkNX9/zaroMOYB+4sW8VAnUTjjyJEw7twJbN8OdO2a5zZnzwJ79txe0qgg/frxdbHMsn/33TwJoE8f4NlngTFj3O6NlJCQgA0bNuCZZ57BXXfdhVdfrQ/gaZw4cRBDhw7Fpk2bEB0djU8//RTdu3d32uy3UaOAK1f4bSpKrlq1amHDhg2oX78+Bg8ejOPHjxe+k7gtvwjNlS8lrUVs40ZujRg7ln/M//UX//3mm7aX+cgjRPXqaVdHR0pKIurZk59z9epEH3zAQ59E/rZt20a1a9emV155hQ4dOpRn99LMmURKWTfm0GgkqlWLKDTUAZV1lqwsolmz+Em3bEl07pzeNXKYjIwM2rt3L82aNYs6deqUPV6nTJkyNGDAAOrd+yPy9PyPsrL0bR1MTSWqUIE/j0TJFxERQTVq1KBatWrR5cuX9a6OS4F0TbquM2eIypfn7w3L4GPQIKJy5XiwtS26dyfq3FmbOjrLr79yvSUgK9yePXuof//+5OnpSQCocePGNGvWLDp16hQRcWBVvz7RvfdaX+ZTTxGVLUuUluagSjvLpk38T1WxItFPP+ldG82cO3eOlixZQqGhoVS+fHkCQEopatu2LU2bNo1+//13SjO9eOPG8f+QK5gwgahUKaKbN/WuiXCGQ4cOUZkyZah58+aUkJCgd3VchgRiLiopiQdIV6hAdPbsnY8dOcI/7F9/3bayGzYkGjbM/jrqIWdAtmCBBGT5iY2NpSVLllC3bt3ItDoFtWrViiZOfJeAi/T559aXtWEDn/MdOxxXX6c5c4bo7rv5n+itt7i1rJjatWsXNWjQgMBJsKlWrVr0xBNP0Ndff02xsbF57jNkCFHTpk6uaD727OH31Rdf6F0T4Sw//fQTeXp6Uq9evbJ/HLi7ggIxu/OI6aEk5BEj4qEsK1cCmzZxzp2cHn4Y2LgROHcOqFq1aOWXKwc88QSwYIEm1dXF778Ds2bxyjrVqwOvvgo89RSPfSsJUlI4iWrZstqUd/nyZXz99dcIDw/H/v37AQAdO3bByJEP46GHHkKVKlUK3P/WLaBiRWDKFOCdd2yrAxEhIyMDycnJSEpKQnJycq7bqamp8PLygo+PD3x9feHr65t9O6/7PD09bcvDlJQEjB8PrFkDDB7MK1GXK2fbE9PJmTNn0K5dO1SoUAGTJ09Gnz590KhRo0LPR7dufP37706oZCGIOKdhrVrAjh1610Y4y4oVKzB27FiMGjUKK1asKNa51LRQUB4xCcR0smQJ8PTTwIwZHGzk5b//eLWZ554D3n/f+rITE/n7Zt484JVXtKmvnnbu5HO0YwcndDcHZMV5ucErVzh5qtEIHDzIKwtpJS0NqFr1DIKCwpCZGYYTJ07AYDCgd+/eePjhhzF48GCUyycg6dmTEBWViM2bYxEbm/8lPj4+zyArKSkp13I09lJKZQdnvr6+GDVqFObMmWPdBzsRsHAh8OKLQP36wPr1BS/h5EJu3LiBDh06IDY2Fn/++Sfq169v9b7NmnFC13XrHFjBInjzTc4vfOGCLGXmTmbPno0ZM2bgjTfewOzZs/Wujq4kEHMxf/3FX8I9enBrWEETmB5/nH/Qnz3LS/RZw5xVe+VKYORIbersCnIGZK+8AkyYUPwCshs3gO7dgTNnOMn8gAH8hanVD8b164HQUGDLFuC++whHjhzJXjvwwoUL8PX1Rf/+/VG1atVcAVZMTByysjLyLNfT0xOVK1dG5cqVUb58eZQuXRp+fn7ZF2v/9vHxQWZmJlJTU5GWllak64iICGzcuBEvv/wy5s2bZ/2v7J07ORv/rVvA55/zbReWkZGBfv36YefOndi2bRu65jMzNj/VqwMDBwJLlzqogkV07hzHwW+/Dbz2mt61Ec5CRBg/fjw+++wzfPrppxg/frzeVdKNwzPrO/tSnMeIXbvGs9OCgojyGd5xh/PnOT3ShAnWH+PXX3lMxvbtNlbSxe3ceXuWZdWqnIfM3gS4znLrFlGHDkTe3kQ//0z0/vv8PD74QLtjDBnCeV5z5m4yGo20Z88emjRpEgUEBFCVKlUoODiYunTpQkOGDKEnn3ySxo2bSsB8Gj16BW3atIn27dtHZ86cofj4eIcl/iwKo9FIzzzzDAGgN4s6rTgykk8+wFn509MdU0k7GY1GGj9+PAGgL2wYWGU0uuZKCZ07EzVuLGne3E1GRgb169ePDAYDbdq0Se/q6AYyWN81ZGYS9epF5ONDdOCA9fs9/TR/sFo7G3/NGn5l//3XtnoWF7t28fkEiLp14/PrylJTOWmvhwfRunV8n9HIM2S9vIj+/NP+Y1izpFFBjEaiOnWIBg60vy6OkpWVRaNHjyYA9N577xVt57Q0okmTKHtppKgox1TSDgsWLCAANNXGSCohgZ/e/PkaV8xO5hzEf/2ld02EsyUmJlKrVq3Iz8+P9u/fr3d1dCGBmIt4/XU+4599VrT9Ll8m8vUlGj3auu3nz+fjxMcXuYrF0rJl/HznzNG7JvnLzCR66CGu5/Lldz52/Tq3kAYF8W17WLukUUGeeYaodGkOHF1VRkYGDR06lADQJ598UvQCVq/mpZGqV+cmVhexceNGUkrRkCFDKMvGmZ5nz5JLzlK8cYN/hE6apHdNhB6io6OpTp06VLVqVTqbM02AG5BAzAWYUwM88YRt+7/wAreknDhh3bZ+fu7TBWA0Eg0dyq2G9gQgjmI08usOcDdqXv78k1uyBg2y73WzdkmjgmzaRMViwfi0tDR64IEHSClFX331VdELOHqUqEEDIoOB+4h1/of5559/qEyZMtSqVSu6deuWzeWYE0L/8IOGldPI0KG8rqyL9goLBzt58iRVrFiRGjRoQDFutqadBGI6O32ak7O2bk2UkmJbGVevcivF8OGFbztiBNFdd9l2nOIqLo6oZk2iRo1ca7yY0ciLHgNEb7xR8LYffMDbvf++bcc6c0ablsGkJG65eP55+8pxhpSUFOrRowd5eHjQ2rVri15AfDwPqgM48Z5OWUevXLlCtWvXpoCAAIq0c8HPrVv56ezZo1HlNGReRcQVg0ThHLt37yYfHx/q0KEDJbtRgkgJxHSUlMR5JStW5IH39pg2jV+xw4cL3q5bNx7+4m62bePz88wzetfktv/7P67TxImFN7gYjRwTeHoS7d1b9GMVZUmjwvTty0FtcZCYmEgdO3YkLy8v2rx5c9ELMBqJ5s3jJufgYKcPrkxOTqb27duTn58fHdSgSXflSn7P/fefBpXTWHo6UZUq3E0v3NfatWuzu+CdtQC93iQQ04nRSDRyJH85bt1qf3nXrxP5+xc+kLpBA+tazkoic+uTK0zOWbyY6zJypPWJ3W/c4MHytWsXbXkrW5Y0Koi5da64DOWIj4+nVq1aka+vL+2wdWmA7ds5SihThujbb7WtYD6MRiONGDGCANB3332nSZnm186aWdl6mDyZZw3bOx5SFG8ffPABAaBJkya5xIxsR5NATCfmL+JZs7Qrc/ZsLrOgGXalSxePbiVHSE0lCgnhtBZXr+pXj9WrOQAfMKDo42H++ovHiw0YYP2wJfMyMkVZ0qggp05xeYsWaVOeM8TExFDTpk2pdOnStMfWfrlLl4jat+cn/8ILDh/MNHPmTAJAc+fO1azM6dO5cc9VGxoOHODT++mnetdE6O2FF14gADTf1ab4OoAEYjrYu5e/TPv313aZu5s3ebBr7975Pw4Qvfuudscsbo4e5TFODzygz/jrjRt5/Hf37raPCVy4kIqUguDpp3lmrZZr7N51F9H992tXnjNERUXRXXfdRf7+/vT333/bVkhaGvclm1NcREdrW0mTNWvWEAAaM2aMpi0CTz9NVKmSZsVpzmjkCSWdOuldE6G3rKwsGjZsGAGg5557jjZv3kzxJXS6vwRiTnb1Kg8cr1u3aN1L1jKnp/jtt9yPnTzJj61apf1xixNz98ySJc497m+/cUDUpo19476NRqIHH+TxYoU17qSl8RjEESNsP15eJk0iKlWq+C24fuHCBapduzZVrlyZjh8/bntBq1bxCahRg5PWaWjv3r3k4+NDXbt21XxR5GHDiBo21LRIzc2Zw/+fZ87oXROht5SUFHrooYfIy8uLAJCHhwe1atWKnn/+edqwYQPFOeJLVAcSiDmR0chZ3319iWz9QV6Y5GT+bujSJXeLz44d/KraOkympMjK4lbDUqU4OHWG/fuJypbl8d5azMyOjyeqV49XYihovM/69fya2zJOvSDm2XdajG90ttOnT1P16tWpRo0adMaeb/sjR7hp0NOTo3sNWq4iIiKoatWqVL9+fYdM4e/Zk9OYuLKLF7nrfuZMx5R/9SpP3hHFR1JSEm3fvp1mzJhB3bp1Ix8fHwJASikKCQmhSZMm0dq1a+mqnmNO7CCBmBMdOkR2pSCwlnn82Y8/3nn/qlV8v7OCD1d2+TK3FLVu7fi8Rf/+y91BQUE8zEgrBw7wwOb778+/izs0lMfE5VzSyF7JyRzITp6sbblFkZnJ5/OPP4jCwnhy4zvvcLLSzZt5PF1ERN4pS44dO0aVKlWioKAgumjPVNL4eE7wBvDAPTu6KhMSEqhZs2bk7+9PJ6xJCmiD5s25mq6uZ0/+oaH18IG0NG6RBoi+/17bsq3xf//HiZWFfVJSUmjnzp00e/Zs6tWrF/n5+REAAkDBwcE0YcIECgsLoygXXB0jLxKIOdE77/BZtTMVUKFSU/lLv02bOz/I3n2Xj6/lWKHibN06Ph+vv+64Y5w/z13R1apxzjitLVrEz2HevNyPXb/Ogdpzz2l/XCIe4+jInHS3bnEQ++OPPHh72jSixx4j6tqVZ496evJzt+ZSujTvc889HLiOHUs0atRB8vUtR9WqNaBVq6JtD5KzsogWLODBh5UqEdmQsywzM5P69+9PBoOBfv75ZxsrUrjAQKIxYxxWvGZWrODXbfdubcudOpXLDQzkH2Ja/jAqzOrVfGx3y+PoDGlpabRnzx6aO3cu9evXj8qWLZsdmLVu3ZqOHj2qdxULJIGYE/XuzQNRnWH5cn4F16+/fd/zz/MXkhvMBrba449zN4gjVrKJjuYP3fLlif75R/vyiW6vHGAw5P7SWrKE3wNFWbu0KMxB4KlT2pR36RKPZWvZkuOZnMGUwcA/MLp0IXr0UQ6glywh2rKF6PhxHnd38yan1di3jxODLl/OY45eeIFThfTty+UHBnKQCvxBQGkCmpGvb6x9KTmOH+cmVnNekhs3rN51ypQpti/JVASlShG99JJDD6GJmzd5BZCnntKuzB07+H/9ySc5j1rp0hzUO2MG6YkTfDx+z0l6DkfLyMig/fv307vvvkvVqlUjX19fWrJkicumwpBAzElSUnhsmKNaJ3LKyOCcYc2a3e62Gj6c7xO33bzJObaCgrRdf3P/fj73fn6Oz2IeH8/PoWbNO8efderEY9Ic9dljXrfwww/tL+vKFR5EXqYMt7Q9/TQHUKtXc4B58aL23atGI7cOf/XVdvLy8iGl2lBoaKJ9haan8+Amg4FfECtat5YsWZI9M8yRkpLI5dddtTRyJP+IsXV2sSXz6hoNG3JLKxHRl1/y+XDUWDSzpCT+LKhcmbvNARmj5kxXrlyhvn37EgB66KGH6EYRfiA5iwRiTvLLL+T0ZKJr1vAxw8L4765dObO+uNPevfy9+dhj9pd19iy36gD8wevAXqY7HDzIv7b79ePA2xwkvf22Y4/bqBG3MtkjNvZ20Kp1V5S1eEFtDwIG0u7dGjSR7N9P1LgxvwjPPnv72z+HrVu3ksFgoP79+zs8i/jFi1ydpUsdehjN/PQT19fe/LlGI4+V9PLKvd7sY49xXrXff7fvGAUZO5Zb4n76iVvCilMwXFJkZWXRO++8Q56enhQUFER7bVmexIEkEHOSV17hD4JEO39wF0VWFn/BNWzIrQl33aV9GoOS4n//43d8eLht+8fE8MB1Ly/u/pk2zflj8T7++PaH/KxZ/OF/4YJjj/n88zw0ytY1POPjuTfPx0f/VoL58xcRAAoIeEGbVsTkZD5B5oFBOZpG//nnHypbtiy1aNGCbjphHUvzZCGNkvQ7XGYmzwAvbLWQwixbxs/7nXdyP3bzJr80NWs6ZrUBcwvY9Om376tfn9PPCOfbt28f1alThwwGA82dO5eytEzkaQcJxJykZUtukXI2c/qC5cu5xeGFF5xfh+IgI4OoXTvuCinKJLqkJKK33uKF2z08iMaN4xmZejAaufvZYODVeLRa0qggP/9se0vvrVvcferp6RrLThER9egxmQDQ+PEajtXasYPXpfLw4IFtaWl0+fJlCgwMpJo1a9q9kLe1zK3yjmz90drLL/P749o12/Y/eZI/93r2zH9m8cGD/ANq4EBtu/GPHuUfZffeER2+GgAAIABJREFUe+c4tOHD+e0gCpeZSTRlirYzXG/cuEFDhw4lANS7d2+6cuWKdoXbSAIxJ7h2jc/m7NnOP7bRyC0ONWpQkbKxu6PTp3lA7b33Fr7iQWYm0WefEQUE8HkdOJDHaustIYHHAWq5pFFBUlP5i66oi6mnpPCXo4cH0TffOKZutkhNzaQyZe4nwEAbN/5Y+A7WSkjgPiqAEps1o5aNG1OZMmXo0KFD2h2jEOHh/L44dsxph7TbkSNc54ULi75vWhp/9lWsWPhM9QUL+DgffWRbPXNKTOSe6WrVcmc0MSfdLqYpr5zqtdf4XAUE8GeNVoxGIy1dupR8fX2pWrVqDp2pbA0JxJwgLIzP5r59+hzfnHwT4HFjIn/m2ab5LQNlNPIyRU2b8nbt2jlmxqU9jh3jwCifYUmaGziQU0NY25qQlsYpJAAeMO1qvv32JgHNyde3nObT3jO/+44e8PYmD4A2jx3r1EUfzfkFXaABoEhatCBq27bo+73yCuWaOZ4fo5GXPfP25i5cexiNRI88wj8y8kqe/dtvXK8tW+w7Tkm3bh2RBzJpQd0PaQTW0NKPNZ6tQ5xPsGnTpgSAXn31VUp3dFLJfEgg5gSPP85dXnottGs0chcQQPTrr/rUobgwGomGDOGuipwfyH/+yd3L5iE/334rqUCIOEElwFP0C5ORQfTQQ7y9qya25P+Xi6RUDapVK0izrguj0UgTJ04kAPRxixZ8Ejp1ctpaPrNm8SF1+q6x2fvvW//+Mtu+ncdIjh9v/T4xMdzy0qiRfWN5P/2UCuwBuXmT6zZrlu3HKOlOnCCqVTqO9vj3zW5FOO95F2UuXc6/5DSUlJRETz31FAGg9u3b07lz5zQt3xoSiDmY0cjL0ISG6luPffv4l6UjBqSWNDEx3JXbpAmPtz59mnN1ATz2atGi4vdl5kgREXxu3nuv4O2ysniWmjXb6o0Hth8gLy8/ateuHSVrsKjmggULCAC9+OKL/MGwciWRvz/3h3/xhf2VLsTkyTyWsbiJjr49vM4asbG3A6qitgr/+isHSbYmvT10iCee9OlT8PCG4ODiscKBHhISiAbVOUwRHnXJ6OVFtGQJ7Xv1OzqAVvzhUbs2N+9qkdfEwjfffEPlypUjf39/+tbeqbpFJIGYg5kX2nbVX/8ib+ap861b82BhPz+e+SSrEuStSROiXr3yf9xoJJowgc/pm286r172GD2ayNNzPSmlaOjQoXbNsFq/nssJDQ29s5yLF3lQIkA0Y4ZDm1gffZSobl2HFe9Q/frxD9rCXgLLFm1b1/OdPp1fjlWrirZffPztGZiFTS547DH+sSfuZDQSzW8bRkkoRamVA7JnGmdlETUJNtKEoC1k7NiRX6Dq1XnAnYapCM6dO0ft2rUjAPTUU09p8gPMGhKIOdhHH/GZdFLvg9DQ88/zDMTx44mKyZJlunnppfzTsxiNPFsXIHr11eLTnXvpEidhbtnyXQJAr9u4Ftb+/fupVKlSdM8991BSXnk+0tOJnniCT9CYMQ5rbu3b17axVvm6cYMTv/30E+fEWLWK++Xef5/75V57jZvhnniC8+YMHMgzNNq3J7r7bs6r07YtR1kjR3K26zff5NaO8HDOZ3LoENHFi/TNiiQC8h5zZWnpUipwjKc1MjKIOnfm5MLWLktmNHKXu8FAtGtX4dsvXMj1dNKE2eIhI4P2dXmRCKDIup1zzXJYuZLP2fcbjNx02bMn31GpEr/fNErUmp6eTq+++ioBoGbNmtFxJ8zCkkDMwQYOLL6/Qt2d0ShLkVhrxw7+xNiwIfdj5haGSZOKTxBm9vrrRICRhgwZTwDo8yJORY2IiKBq1apRnTp1Ch5rZjRyineA+7UckFesTRui++6zsxCjkfNfjBzJUWpBC3x6eXHXa0AAT+Vt3pyoY0de623wYKJhwzg6bN2aZ3uULVtgecnwpTi/QC6nRw/OFbN4MbeaJCZmp6ro1avwlrPCXLxIVKECV82aIUnmH9x5rfmalz178v9/cUsxMRTbogcRQD82eJaMqblPekYGf5fec4/F58jevTzLAuB+92nT7lxexA4//fQT1ahRg3YUFv1rQAIxB0pP58+WogwYFaI4SkvjFoScawPOncufJI8/bv+Xox4SEnhcYOfO6dSrVy/y9PS0+oM5Pj6emjZtSv7+/tb/ql6+nJtVWrbUvBm2bl3unrTJ1aucEbVhw9tfek8/TbR5M7eKHTrEi45GRnLLhK2temlp3BJy7BhPL1y3jpu55syhrc1eoq88x1JG/wFEHTpwXgpTkGZUis77NKT13sMoYerbPCUxKsquyN+cg7Gw3It//cUx5wMPWP8eT0ril3naNJurV3IcPEgZAbUpBT40reYXBY7rM6+fmyv586FDPJBXqdsJMzX4/5GuyRIQiO3eTZos0SFEcTBkCI/jMX/3mbtfHn5YvxnDWjCvWLBq1Q0KDg6mChUq0MmTJwvcJz39duC2ffv2oh1w61YewB8URPTvv7ZXPIdy5Yq41m1mJtGPP3IaeE9Pyp7luWKF83KjWDCnfVi92nSH0chNVz/8QD92epPWIpRuVat3Z0ta1arcwvjKK5y7599/i/RmfPZZLmbz5rwfv36dG/Nq1+b1LIuieXP7lwcr9r76ioy+vhTlVYu6ld5faFdwSgqPrevRI58N/v2XB+AZDDxr4umneTaRiysoEFP8ePHSpk0bOnDggN7VAADMnAm8+SYQGwtUrKh3bYRwrM8+A8aNA44eBf76C3jiCWDwYOCbbwAvL71rZ7uMDCAkBDAagY0bz6Nz53YoV64c9u3bh8qVK+fanojw5JNP4vPPP8eKFSswevTooh/04EHg/vuB9HTghx+Azp3teg7p6YCPD38eTZ9eyMaXLgFffAEsXw5cvAhUqgSMHg08+SQQHGxXPexhNAJ16wJNmgBbt96+f/t2oFcv4KmngCVLACQkAEeOAIcP374cO8YnAQBKlQLq1+c3pcFQ4CVLGfD7bgOSUw3o2ceAUmVMjxmNoKws7N2VhZgrWejaKQsVymUBWaaL0Xj7ds5L48bA+PF4clV3bPheISYGUEqXU6qfjAzgpZeAhQtxolp3dL36DVZsqoL77y981/fe41337gXat89no3PngHnz+H0MAOPHA9OmATVqaPYUtKSUOkhEbfJ8TAIx+3TqxO+3v/7SuyZCOF5kJFCrFtCzJ7BjB9C3L7BhAwcAxd3GjcDAgcCiRUCrVntx7733om3btti2bRt8cjzBt99+G9OmTcP06dPx5ptv2n7Q8+eBfv2AiAhg9WrgwQdtLurKFf4OWrwYeOaZPDbIyAA2beJo+scfOZDo3ZuDr0GDXOZFfOP/27vzMLnKKo/j35MNEggJZMEYNsEIsnfbyQgDgsbAGFFAZWkMi4wgKjogA0ZxZxVFXBAGolEchcC4sRjFBkEMCCRhCxBCMOwJIWmyAFm7c+aPU0Wa0Hstt+69v8/z1NOp6ltV56Srq06/73vP+zW46KJ4rY0aBc3NUSRvtVXUroMGdXDH9eth7tyNhdnTT0NLS8fFUpvLmlWtLHiylS0GtrLD6FZswwbo04fmFX156eU+jBzVlxHbdlLQ9emz8d9mcM89sGwZy0e+i2+/fBpnPHAiO9bl6C/1xYvh6KPhrrt46ANn0vC3S/j6t/rxzW927+6vvQY77AAHHgg33tjFwS+8ABdcEK/r/v3hi1+Ec86puZGRzgqxxKcZe3OplanJ5ctjdLSXJ1qJpNLee8dUzkEH9X4j8Fq0YYP7wQe7Dx8ev9vTpk1zwCdNmuQb2qxDuu666xzw44477k2399rSpbHA3cz9hz/s9cM8+mj8XK6/fpNvPPlknMq67bb+xl4yX/uaewJNLbuj2A7o+9+Pn8kRR5TWqqK7ijtunH9+XL/77pitPfLIXixDW7XK/Ve/8lf3jjYMLf03cz/hhFjBn7azWXrqvvuiv8fAgT7vm7/p8dq6ouJ5LY880s07PPVUnGBiFnP0551XkRNiegutEauM4kJPdbKXPLnmmlgzW0PvcWUza1b8Tk+eHNfPO+88B/w7hcZoM2bM8M0228wPPPBAX1POjfFWrYpP/OLK8V6c9VBcX3XbbR5F1ne/615faJDZt6/74YfH3l3ry7+NTLmNGxfrq4od7Kuxf+6GDbHWsW/f2IB6u+3i5IdSOiasXete3+9hv7vucxvPGN1rrzgTNIsNC3/2s9hDaqedfEnTgz5qlPsuu/Tu/7C5OU4OOu64Ht5xzpyo3ovduS+7rOyNYXtDhViFfO5zcfJGOd+PRSRZkybFGuBnn40ti0444QQH/KKLLvJhw4b5mDFjfGkltq9oaYn+HxBtH3r44TH9quf8S3zfX9tznL+xkH3cuKhiXnyx/PFW0OWXR/gDBkQnjGqdjbtihfvOO2987lmzSn/MsWOjn6+/+mqcHVpXF0+wxRbRnmP27NKfJGkrVmzs5jxhgq9btNQPOCA+H7s9otWOs8+OHRd61aPzvvuizwlEVT1lSqJ/hKgQq5AxY9wnTkw6ChEpp2efjUJs0qS4vmbNGn/f+97ngA8fPtznd7cDaG9s2BCdSiE2Pe2qyd0LL8R05n77vVF8rd3rPTEaVqNTj92xZElMRw4bVv0a8v77o2vGVVeV5/E++9mYKXujmNywIZ7k5JPdBw6Mn9vYsTE3msCZqiV57rno9LzVVv5GN+eWFv/iF+PqtdeW9vALF8bv4imnlPAgt98eDYYhPrSvuy6RPjsqxCqguPfeZZclHYmIlNvkyfH7XRwRWbp0qZ9wwgl+7733VieA666LIZndd4/KsK1Fi6K76IEHxnoYcN9nH7/jkAt8F+Z7ldoiVdxvfvPG7jdVV85WLMW1Z+12Q1m2LH6We+wRBw0ZElMt06fXdlH2wAPRsK5fv5jLPfZY95kz3X1jd/wzzyzPU332s1GUl7RDwYYN7jfdFNPCEAtdb765quv1VIhVwJQp8b/36KNJRyIi5bZ8eSzaP/jgBNdW33FHfDCPGhXbGlx5ZQRULL723DO2Cyp8wp91VgywSG155BHvel/LDRti36RPfnLjbgYDBsSc5kUXxfRl0t2SW1vdb7ll476pW24Z1VabHl4PPhivwYMOKt8uXgsWRK1XlsKutTWG6d75zshhv/2qtshbhVgFHH10nHyU9RNgRPKquE7p5psTDGLOnFjfUlzzteuusXF4O38BnnRSNNuV2rJ+fRQnZ5zRzTusWuX+17/GAql99tn4sx8+PEaepk6NTVKrZfXqGHl497v9jfVW3/te/LXSRnNznNwwerR7Zzt99cbxx8d6szLtbBRV4tVXR7AQ/6cV1lkhpj5ivbBhA4wcGb0Yr7kmsTBEpILWr4c994wWUXPmQL9+CQWycGE0azvgANhrrw47g37kI9FS6cEHqxyfdKnYq3fGjF7c+aWX4Lbb4K9/haamuA7RePeQQ+Jy0EGwxRZlixeILuVXXBGN6V5+Gerq4Kyzoj/YJt2bW1vhsMOit+Bdd8G//Vt5Q3n8cdhjj2hUXErbvrdYswauugpOOgmGDCnjA7+VGrqW2ezZ0NAA//u/MGlSYmGISIXdeGPsHHDllXDaaUlH07n9949mp7fdlnQksqkzzoApU2JDgJIKevfYQeCvf43LXXdFMdG/f3QXnzAB3vnOKCqGDn3zpbsNe598Ei67LEYZVq+GiROjAHv/+9v9I6C1NXbYuOaa2PXgM58pIb9OfPzjUeg9+2w0900bFWJldvHF8JWvwKJF8La3JRaGiFSYOxx8MDzxBMyfX9sfALvuGoMW06YlHYls6te/huOPj5HVPfcs4wOvWRPDbMXC7OGHOz52883bL9Da3nbffbHdVv/+EfCXvhT7TXWgtRU+9akYlPjWt+h25/zemDULxo6NXY3OOac8j7lsGUyeDN/+duU/y1WIldn48bBkSWx1JiLZNnMmjBsX29idf37S0XRs2DA49tiYSZLaMm9ebD85dWoULhXT3BxTlytWwPLlb750dtuyZbFP5zbbxP5Yn/98l5VJS0tsT3rttfF7ce65Fcyr4NBDo9Z8+unYTrQUd90VM1qLFkUORx1Vnhg70lkhltSqh9RatSr+ADn99KQjEZFqGDsWjjsuNiI+7TTYbrukI3qr1tb4LB02LOlIpD1jxsDgwTGqU9FCbNiw3r8I1qyJvTI3Wf/VnpaWGDCbNi32BZ08uXdP2VNf/WqMUE+dGrVib6xfHyNgF10EO+8c24KOHVvWMHusT7JPnz7/+Ef84TBhQtKRiEi1nHdefE7dcEPSkbRv+fKYRh0+POlIpD19+sB73hOjqzVr8827VYStXw+f/GQUYZdcUr0iDOB974u1kJdcEnH01L/+FRuJX3BBrM9/8MHkizBQIdZjTU0wYEC8IEQkH3beGUaPrt0zEpcuja8aEatdY8fGtNq6dUlH0nvr10NjY/xBcumlcPbZ1X1+sxgVe+65mE7sLvc4mWDffWOa+IYb4Oc/hy23rFysPaFCrIeamuLklEGDko5ERKqpvh4eeCDpKNrX3BxfVYjVroaGKMIefTTpSHpn3To45hj43e/ipMovfSmZOCZOhH32ianF1tauj1++PJYWnHRS/A4//HDl14P1lAqxHli8OBboa1pSJH/q6uLsyVWrko7krYqFmKYma1dxCqympyc7sG5dtA/7wx/gxz+OdhxJKY6KzZsX7fU6M2NGjIL93//FdOTf/gY77FCdOHtChVgPFPvzqBATyZ/6+mjmXItnS2tqsvbttFOclJjgCf+9snZt9PC68cY4I/cLX0g6oohnzBi48MKYdtxUSwt84xvR57ZvX7j77ije+vatfqzdoUKsB5qa4hepri7pSESk2oq/97U4PampydpnFtOTaSrE1qyBj30MbrklmrV+7nNJRxT69o2TBB54AG699c3fW7AgFuSfd16c2fnQQ+Xv9F9uZSnEzOw/zGyemT1lZm85h8LCjwvff8TM6rt731rhHoXY+PG1W1WLSOVsv30UOrW4YL+5OTq2Dx6cdCTSmbFjo6nr6tVJR9K1NWvgyCNh+nS4+urKdczvrUmTopXMhRduvO3Xv46pyLlz46zOX/4yHb8TJRdiZtYX+CnwIWB3oNHMNm3F+yFgTOFyKnBlD+5bE+bOjS3fNC0pkk9mtbtgf+nSWB/WwTaUUiMaGmKBeWcN8GvB6tXw0Y/GaNPPfw6nnJJ0RG81YECctfmPf8Cf/hQtNY4/PhbyP/xwnFiQFuUYERsHPOXuC9x9HTANOHyTYw4HflXYhPxeYKiZjermfWtCU1N8VSEmkl91dTGiUWstCJqbNS2ZBg2Fvuq1vGB/1arYQP622+AXv4CTT046oo59+tMwYkRsOH799bEh+B13wI47Jh1Zz5SjEBsNPN/m+guF27pzTHfuC4CZnWpms8xs1pIlS0oOuqeammIv1Z12qvpTi0iNqK+PXkqPP550JG+mQiwdRo+OnYNqdZ3Y669HUXPHHdF368QTk46oc4MGxfZK++4bI2Nf/3qJm6onpByFWHuD4Zuex9DRMd25b9zofrW7N7h7w4gRI3oYYmnWrYM774RDDqnq04pIjakvrG6ttenJ4tSk1LZaXrD/2mvw4Q/D3/8em3gff3zSEXXPqafGus399ks6kt4rRyH2ArB9m+vbAQu7eUx37pu4f/4z/lLQtKRIvu2ySyz+rbVCTCNi6TF2bKw5fvXVpCPZaN68ONNwxgz4zW+iAapUTzkKsZnAGDN7h5kNAI4FbtrkmJuAEwpnT74XWOHui7p538Q1NcWZku9/f9KRiEiS+vSJaZBaOnPSXYVYmjQ0xM+sFl5D7rEOrL4enn8+eoUde2zSUeVPyYWYu7cApwO3AnOBG9z9MTM7zcxOKxw2HVgAPAVMAT7X2X1Ljancmppg3DgYMiTpSEQkaXV10ZuoO9urVMPKldHAUlOT6VBcsJ/09OSKFTHydfLJ0Wfr4YdjalKqryzL2tx9OlFstb3tf9r824HPd/e+tWTZsviF+drXko5ERGpBfX2cWTZ/Puy2W9LRqJlr2owcGdvsJHnm5D//GUXY889HH65zzlF/zCSps34X/va32NZE68NEBGpvwb4KsfRJasF+a2sUXgceGNdnzICvfEVFWNJUiHWhqSkW59b6FgkiUh277QabbVY7hVhxn0lNTaZHQwM89VTMuFTLiy/GgMK558JRR8X0+nvfW73nl46pEOtCUxMcfDD07590JCJSC/r3h733ro3F1qARsTQaOza+zp5dnee7+eboOH///bE4/9prtea5lqgQ68SCBXHRtKSItFXc6sjb7XpYXSrE0uc974mvlZ6eXLMGvvCF2K5ohx2i8DvpJG2FVWtUiHVC2xqJSHvq6mD5cnj22aQjiUKsTx8YOjTpSKS7tt46dmqp5IL9xx+Ps/0vvxzOPDMW6O+6a+WeT3pPhVgnmppid3e9eEWkrVpasL90aXywa8F1ulRqwb47TJkSj//SSzB9OvzgB7GuUWqTCrEOtLbGGZMTJmgYV0TebK+9ovCphUJMzVzTqaEBnnsOXn65fI+5bFksxD/1VDjgAHjkEfjQh8r3+FIZKsQ6MHt2vKg1LSkim9p8c9h999pYsN/crDMm06i4YL9co2LLlkVxd+ONcMkl8Je/xAbjUvtUiHWguD5s/Phk4xCR2lRcsJ+0pUs1IpZGdXUx21KuQuzMM2PN4t/+BmefHesGJR30o+pAU1PsKTdyZNKRiEgtqquLNTiLFiUbh6Ym02nw4OhJV44F+3/+M1xzDUyevLFZq6SHCrF2vPYa3HOPpiVFpGPFBftJT09qajK9xo6NEbFS2qCsWAGnnAJ77AFf/3r5YpPqUSHWjrvugvXrVYiJSMf23Te+Jjk9uWoVrF6tEbG0Kp7ZuHBh7x/jv/87RmV/8QudGZlWKsTa8fzz8cZ2wAFJRyIitWrwYBgzJtkRMTVzTbfigv3eTk82NcHPfhbFWPGxJH1UiLXjM5+BxYth4MCkIxGRWpb0gv1iIaapyXTaZ59og9KbBfuvvgqf/nT0ufz2t8sfm1SPCrEOqDmiiHSlrg6eeQZeeSWZ5y9u+K0RsXQaOBD23LN3hdjkyTF7M3VqtFOR9FIhJiLSS8UF+w89lMzza2oy/caOjanJnizYv/NOuOIKOOMM2H//ioUmVaJCTESkl+rq4mtS05MqxNKvoSFGVJ95pnvHv/46/Od/wi67wPnnVzQ0qZJ+SQcgIpJWw4fD9tsnV4hpajL9Ghri68yZ8I53dH38uefCggUxKjZoUEVDkyrRiJiISAnq65M7c7K5GbbaCvr3T+b5pXR77QUDBnRvndjdd8OPfwyf/zwcdFDlY5PqUCEmIlKC+nqYNy8aQVebuuqn34ABcfZkV4XY6tVw8smw445w8cXViU2qQ4WYiEgJ6upiofUjj1T/uZcuVeuKLBg7FmbPhg0bOj7mG9+AJ5+MvmFbblm92KTyVIiJiJSgeOZkEuvENCKWDQ0NsHIlzJ/f/vfvuw9+8AM49VQYP766sUnlqRATESnB298OI0aoEJPeKy7Yb296cs0a+NSnYPRo+N73qhuXVIcKMRGREpglt2BfU5PZ8O53xxmQ7W119J3vwNy5MGVKnJgh2aNCTESkRPX18OijsHZt9Z5z3brY5kYjYunXr1+sNdx0RGz2bLjkkhgRO/TQZGKTylMhJiJSoro6aGmJYqxaitsqqRDLhrFjY3q7pSWur1sXBdi228b6MMkuFWIiIiUqLtiv5vRksZmrpiazoaEhWlTMnRvXL7gA5syBq66CoUOTjU0qS531RURKtPPOMGRIdRfsa3ujbGm7YL+1FS68ECZNgsMOSzYuqTyNiImIlMgM9t23uiNiKsSyZcyYWIx/zz0xJTlsGPzoR0lHJdWgETERkTKor4f/+Z9Y49OvCu+smprMlj594D3vgalTo7Hr738P22yTdFRSDRoRExEpg7q6WOMzb151nk8jYtnT0BBF2DHHwJFHJh2NVIsKMRGRMqj2gv3mZhg4MC6SDZ/4BHzgA/CTnyQdiVSTCjERkTLYddcoiqq1YF/NXLNn3Di4/fbYqUHyQ4WYiEgZ9OsHe+9d3RExTUuKpJ8KMRGRMqmvjxGxDRsq/1wqxESyQYWYiEiZ1NfDypXw9NOVfy5NTYpkgwoxEZEyqauLr9WYntSImEg2qBATESmTPfeMtWKVXrDf2grLlqkQE8kCFWIiImWy2Wawxx6VL8SWLQN3TU2KZIEKMRGRMiou2Hev3HOomatIdqgQExEpo/p6WLIEFi6s3HOoEBPJDhViIiJlVI0F+yrERLJDhZiISBntsw+YVXadmDb8FskOFWIiImW05Zax3VElCzGNiIlkhwoxEZEyq6ur/NRkv34weHDlnkNEqkOFmIhImdXXw3PPbZxCLLdiV32zyjy+iFSPCjERkTKr9IJ9ddUXyQ4VYiIiZaZCTES6S4WYiEiZbbMN7LRT5Rbsa8NvkexQISYiUgGVXLCvETGR7FAhJiJSAfX18OSTsHJleR/XXYWYSJaoEBMRqYD6+vj68MPlfdyVK6GlRVOTIlmhQkxEpAIqtWBfzVxFskWFmIhIBYwaBW97W/kX7KsQE8kWFWIiIhVSiQX72mdSJFtKKsTMbBszazKz+YWvW3dw3H+Y2Twze8rMJre5/Vtm9qKZPVS4TCwlHhGRWlJfD489BmvWlO8xNSImki2ljohNBm539zHA7YXrb2JmfYGfAh8CdgcazWz3Nodc5u77Fi7TS4xHRKRm1NdDayvMmVO+x1QhJpItpRZihwPXFP59DXBEO8eMA55y9wXuvg6YVrifiEimVWLB/tKl0KcPDB1avscUkeSUWoht6+6LAApfR7ZzzGjg+TbXXyjcVnS6mT1iZlM7mtoEMLNTzWyWmc1asmRJiWGLiFTeTjtFwVRsPVawAAAVgElEQVTOBfvNzbD11tC3b/keU0SS02UhZma3mdmj7Vy6O6pl7dzmha9XArsA+wKLgEs7ehB3v9rdG9y9YcSIEd18ahGR5JjF9GS5CzFNS4pkR7+uDnD3D3b0PTNbbGaj3H2RmY0CXm7nsBeA7dtc3w5YWHjsxW0eawpwS3cDFxFJg7o6uPxyWL8e+vcv/fG0z6RItpQ6NXkTcGLh3ycCN7ZzzExgjJm9w8wGAMcW7keheCs6Eni0xHhERGpKfT2sXQtPPFGex9OImEi2lFqIXQxMMLP5wITCdczs7WY2HcDdW4DTgVuBucAN7v5Y4f6XmNkcM3sEeD9wZonxiIjUlHIv2FchJpItXU5Ndsbdm4Hx7dy+EJjY5vp04C2tKdz9+FKeX0Sk1r3rXTBoUKwTO+GE0h9PU5Mi2aLO+iIiFdS3b4yK3XNP6Y+1alU0h9WImEh2qBATEamwj3wEZs6Ep58u7XHUzFUke1SIiYhU2LHHxtdp00p7HO0zKZI9KsRERCpsxx1h//3huutKexyNiIlkjwoxEZEqaGyMPScfe6zrYzuiQkwke1SIiYhUwVFHxR6RpYyKFacmVYiJZIcKMRGRKth2Wxg/Pgox966Pb49GxESyR4WYiEiVHHccLFgA99/fu/s3N8NWW5VnqyQRqQ0qxEREquTII2GzzXo/Pbl0qUbDRLJGhZiISJUMGQITJ8L110Nra8/v39ys1hUiWaNCTESkihob4aWX4O9/7/l9tc+kSPaoEBMRqaLDDoMtt+zd9KQKMZHsUSEmIlJFAwfCEUfA734Ha9f27L7a8Fske1SIiYhUWWMjLFsGt97a/fusWwevvqoRMZGsUSEmIlJlEyZEQdWT6clXXomvKsREskWFmIhIlfXvH532b7oJXn+9e/fRht8i2aRCTEQkAY2NsGpVFGPdoa76ItmkQkxEJAEHHADbbdf96UkVYiLZpEJMRCQBffrAMcfAX/6ycf1XZzQ1KZJNKsRERBLS2Ajr18Pvf9/1sRoRE8kmFWIiIgmpr4cxY+Daa7s+trk5epANHFj5uESkelSIiYgkxAyOOw7uvBMWLuz8WDVzFckmFWIiIglqbAR3uOGGzo/T9kYi2aRCTEQkQbvuCnV1XZ89qUJMJJtUiImIJKyxEe6/H/71r46P0dSkSDapEBMRSdgxx8TXadM6PkYjYiLZpEJMRCRhO+wQDV6vvTbWi22qtTU2CVchJpI9KsRERGpAYyM8/jjMmfPW7y1bFgWapiZFskeFmIhIDTjqKOjbt/1F+2rmKpJdKsRERGrAiBEwYUKsE9t0elKFmEh2qRATEakRjY3wzDNw771vvr24z6QKMZHsUSEmIlIjjjgCNt/8rdOTxRExrRETyR4VYiIiNWKrreDDH44u+y0tG2/X1KRIdqkQExGpIY2NsHhx7D9ZtHQp9OsHgwcnFpaIVIgKMRGRGjJxYoyMXXvtxtuam2Na0iy5uESkMlSIiYjUkIED4cgj4fe/h7Vr4zZ11RfJLhViIiI1prERVqyAP/85ri9dqkJMJKtUiImI1Jjx46OvWPHsyeLUpIhkjwoxEZEa069fdNq/+WZ47TVNTYpkmQoxEZEa1NgIq1fDH/+oQkwky1SIiYjUoP33h+23h6uuip5impoUySYVYiIiNahPnxgVmzEjrmtETCSbVIiJiNSoxsaN/1YhJpJNKsRERGrUPvvAbrvFvzU1KZJNKsRERGqU2cZRsZEjk41FRCqjX9IBiIhIx846K0bFdtkl6UhEpBI0IiYiUsO22AKOPjrpKESkUlSIiYiIiCREhZiIiIhIQlSIiYiIiCREhZiIiIhIQlSIiYiIiCREhZiIiIhIQlSIiYiIiCSkpELMzLYxsyYzm1/4unUHx001s5fN7NHe3F9EREQki0odEZsM3O7uY4DbC9fb80vgP0q4v4iIiEjmlFqIHQ5cU/j3NcAR7R3k7ncBr/T2/iIiIiJZVGohtq27LwIofO3ptrTdvr+ZnWpms8xs1pIlS3odsIiIiEit6HLTbzO7DXhbO986t/zhdMzdrwauBmhoaPBqPreIiIhIJXRZiLn7Bzv6npktNrNR7r7IzEYBL/fw+Uu9v4iIiEhqdVmIdeEm4ETg4sLXG6tx/9mzZy81s2d7+FxpMhxYmnQQCUhr3mmNuxR5zBnymXcec4Z85p3HnKE6ee/Y0TfMvfezfGY2DLgB2AF4DjjK3V8xs7cDP3P3iYXjrgMOJpJdDHzT3X/e0f17HVBGmNksd29IOo5qS2veaY27FHnMGfKZdx5zhnzmncecIfm8SxoRc/dmYHw7ty8EJra53tiT+4uIiIjkgTrri4iIiCREhVhtujrpABKS1rzTGncp8pgz5DPvPOYM+cw7jzlDwnmXtEZMRERERHpPI2IiIiIiCVEhJiIiIpIQFWKSKWZmSccg0hm9RrPPzLY1s/5JxyHpoEIsQ8zsUDM7I+k4qs3MdjazPQE8RYsezWzLpGOoNr1G0/UaLYWZ7Wpm+5nZ5knHUk1m9iGiWflWheuZL7zz+F4G5Xs/UyGWEWZ2CHAh8HDSsVSTmX0UuAX4ppn9ysw+YWaDk46rK2b2YeCPZnZQ0rFUi16j6XqNlqJQjPwB+Aowo/hBnfWipPAaPw8YQbzWM1945/G9DMr7fqZCLAPM7EBgOjDJ3e8ws6Fm9rasD40X9if9InCMux9F/EJ8EzjezIYmGlwnzGwfYCrwFHBmHt7A9BpN12u0FIWf9Y+AT7v7R4GFwL6Q7aLEzN4P/BQ4Bdgb2KI4CprVAjSP72VQ/vczFWLZMB94DTiw8EL4PdEX5RYzm5jVNwFgJbCO2DoLd78UeAF4F1APNfsG+DTwZeDrwJ+Bs3PwBjYfeBW9RtPyGi3FSuBkd7/HzEYD/w6cbmbTCj/rzH3umFk/YAhwvLs/CAwCBlLYOSbDBWge38ugzJ+56iOWcmbW191bzWx7YBawNXC6u19tZl8CPggc7e6vJRpohZjZZGIa4CHiw2074AlgrLt/IsnY2mNm5u7e5ue2DfAJ4HDge+5+Z+HDa7G7tyQbbXmYWR9331B4jc4GhpLP1+jDwBhq/DVaqjav8T7AWcAAd7/AzM4EDiX2FH412SjLz8z6uXtLm9/t9wG/BD7h7g8kHF7ZtfNetjVwFBl+L4PKfOaqEEspM/sgcASwHGhy979bbLZ+lLv/qM1x04H/dvfHEwq1rDbJ+3fAs8DHgfcCq9399MJx04AT3X1tUrG2VVhHcSTwInCHu9/Z5nvDgY8BHwBeAd5G/GX9egKhls0mOf/D3W/LyWu0bd5/AuYCxwL7Aatq9TVaCjPbG9jg7o9ucnt/d1/f5vqfgC9velxadZJ3sUi5CHjS3X9R/ABPJtLy2eQ9+M/ufneb72XyvQwq+5mbuSHiPCi80f+A+Kt6BfAtM9vN3Rdu8oI4BhgFLEkm0vLaJO+VhX8Pcvcp7v6fbT7gPgVsT4mb2peLmY0jYr0TWAT81syOLn7f3Ze6+9WAEW9i30n7G1c7OU8zs0/m4DXaNu+XiHUk4wuv0ZOJ9WI19xotRWFh/kPAaWZW3+Z226QIO5r4Wb9c/SjLr6O84U1TkU8AXzazzTJShG362XO+me1S/H4W38ug8p+5qX8TyBszGwmcBHyxMPQ7GHgnsSaheExfoBE4lxgWT/2HXCd5jyTW3BSPO5lYDP3hGnoD2Ba4z91/DWBm/wJ+ZGYb3P23hdsOBfYHPpiR0YKOcl7r7r81swHA0WToNVrQXt4/LHwQX1+4rRZfo71iZgOBscBXiTVSRxcGgx4sFiNmNoiYsjqHmLJJfSHWQd4UpyCL0/Hufo2Z/Tvx4fxMYgGXQSfvwUM2Oe4QMvReVo3PXI2Ipc9S4GfATIDCWos+wCHFAwp/eb0CHOHujyURZAW0l7fRJu+CW6m9N4DngPVmth2AuzcB/wVcWXiTBrgPeF+NxV2KTnN293XEz/TwDL1G4a15/5XI+/JC3huIRc219hrtFXdfDfzK3S8GLgO2JIqShjaHtQCvAx/Lys+6g7yPKuZd+DkXfcbdn6l+lGXX5WdPwUzgoCy8vgsq/pmrQixFCkP9G9z9Vnd/vc2ZGc8Sb3aY2UcLQ6bT3X1ectGWTw/y3tvdX3T3+clF267HgAHAV82sXyGfJqLf0HsB3H25uz+dZJBl1mXOwK3u/mRiEVZGd37Wi2rwNdprxSKjMNJ1HjAY+ISZjTKz44Hd3f23WXk/KuoqbzMbW/h+6hdi9/A9eJm7L0gu2vKp1meuCrEUaecXuvjzex5YbGaHAd+m8ALJim7m/S1gVTXj6o7CFEUL8GnijLmfAO8ofHswsGNSsVVKN3LeCbLxAdVWHn/WbRUWoy8mipIW4FpiXc36Tu+Ycp3kXXPvR72V5vfgUlTrM1drxFKozfqD4uLPvkQjwblEg7mnkouucrrI+/hay9s2tm0Y4O5rCws+LwW+YWZDiA/rxmSjLK885gz5zLtNzm/6vXT3xWa2CtiDmKLKxNmwRXnMu6OcqfH34FJ1M++SP3NViNUwM9sNWNt2yqo4VGrRxXmiu58NNBML1o/LwpRHWvM2s/HAQcRfhTe4+4LCX8vrzGwCMI44a24MsAPwVNrXjuQxZ8hn3p3k3Fr4vfyQu59TWMy8JXBoFoqRPObdjZxr8j24VEnlranJGlUY8nwc+HShMAFiqNTM9gC+SyzwhuhV9MGM/CKkMu9C3JcS3eO3Bn5qZsMLv8B7ABcQH8bu7k+6+20Z+GDOXc6Qz7y7kfN3gfvhjcXM3/DoMJ9qecy7mznX3HtwqRLN2911qbELsZ7kUuIN/YfA14Bd23z/QODfCv/un3S8ec8bGF34xTygcH04sf/amML1A4D3Fv5tScernJV3BXPuk3S8yrtqOdfMe3Da81Zn/RpksW/ZO9x9fmFU6BzgX8AfvM2QtxW21EgqznJLa95mtgWxrcVfvNAl3cx+V7g+pc1xmeisDfnMGfKZdx5zhnzmncecIfm8NTVZQ8xsFzPbkfhLej6Auz8BfA/YBfiYmW1lZh83sx1qqRgpRVrzLsS9k7u/7u43eizS7l/49lMUziAys0PNbFQW3rjymDPkM+885gz5zDuPOUPt5K1CrEaY2ZHADcAvgAvN7KTi99x9LlGUDAOuJ3Z5H9TOw6ROWvNuE/dUM/t+MW7fuKXLEmCFmR0OXAxsnkigZZTHnCGfeecxZ8hn3nnMGWorb501WQPMbCvgy8AXgAXE9hCNZjbU3X8IUZSY2RqgjujA/kRiAZdJWvPuTtxE1/+rgIXE6c2pbtaax5whn3nnMWfIZ955zBlqL28VYrWhBXgRWOjuL5nZrcS2Cp83syXu/huLXkTDiFOlM7FNCOnNu7O4l3rsM/gSsa1LJk7rJp85Qz7zzmPOkM+885gz1FjempqsAe6+CniSGCId7LER8IPAH4E9LTYUfRU43VN+anRbac27i7j3KBz2F+CQrLxx5TFnyGfeecwZ8pl3HnOG2stbhVjCzN7Yu+prwEPATwovjFeBfxCNIUd4dPVdl1Sc5ZbWvLsTt5mNdvclnvLeUUV5zBnymXcec4Z85p3HnKE281YhlpDii8E9+ocUzsa4jFgg+GczexfwAWJxek2cJVgOac27h3GvTSrOcspjzpDPvPOYM+Qz7zzmDLWdt/qIVZmZbQ+8BrzqhTYMZtbf3deb2U7EVNwXgJ2JrVHOcPeHEgq3bNKad1rjLkUec4Z85p3HnCGfeecxZ0hH3irEqsjMjiDO1FgJ3AvMdPdbCt8bTzQw/YK7P1lYH9XPC83l0iyteac17lLkMWfIZ955zBnymXcec4YU5e01sL1AHi7ElglzgPcCewEnAjcSp8UC/BP4eNJxKu90x62clbdyVt55zzlteat9RfW0APOAh9x9jZk9BywHPmVm84CDPbr6mhdeJRmR1rzTGncp8pgz5DPvPOYM+cw7jzlDivLWYv0qcfflxALAXxeurwDuAqYDhwItZtYn6RdEuaU177TGXYo85gz5zDuPOUM+885jzpCuvFWIVZCZHWxmp5jZGYWbTgZWmVmxa/wyYCbR1Xegu29IKNSySmveaY27FHnMGfKZdx5zhnzmncecIb15qxCrEDObCFwB9Af+y8yu9FgEeAEw1Mz+YGZDgd2J02X7d/xo6ZHWvNMadynymDPkM+885gz5zDuPOUPK805iYVrWL8QpsPcA4wvXhwAzgHcS+1cNBKYSQ6azgH2TjjnPeac1buWsvJWz8s57zlnIW4v1K2MtcL67325mA4BVwGpgpLs/Vfj3yWa2OdDXY3uFLEhr3mmNuxR5zBnymXcec4Z85p3HnCHleWtqsozMbAcz6w8sc/fpAO6+zt3XEzu8txaO27+wSHBNrb0geiOteac17lLkMWfIZ955zBnymXcec4bs5K1CrEzM7MPE2RhXAP9rZrsVbh9QOGQIMMjMGoFfASMTCbTM0pp3WuMuRR5zhnzmncecIZ955zFnyFjeSc+Npv1CzD9vTzSOOxjYFjgLWAjs0ea4S4Em4O9tb0/rJa15pzVu5ay8lbPyznvOWc078QCycAH6AlcDo9m4bdQXgReBXQvXzwaeBXZLOt68553WuJWz8lbOyjvvOWcxb+01WQIzeyewNTEXfQUw290vafP9c4A9gFOAfYCX3P35JGItp7Tmnda4S5HHnCGfeecxZ8hn3nnMGTKcd9KVYFovwGHAI8Sw5+XAR4FngK+0OWYnYErSsSrv9MatnJW3clbeec8563mrfUUvmNn+wPeBRnd/0MyuBsYR3XrvtdjFfRpwAFBnZtu4+yvJRVweac07rXGXIo85Qz7zzmPOkM+885gz5CDvpCvBNF6IH/5Jba6PAP5U+PfOROO4K4jGcXslHW/e805r3MpZeStn5Z33nPOQt9aI9UKh+t7C3VcW/j0KuBmY6O6LzGxHYtHgFh4bjWZCWvNOa9ylyGPOkM+885gz5DPvPOYM2c9bfcR6wd1b3X1l4aoBy4FXCi+IScBXgf5pfEF0Jq15pzXuUuQxZ8hn3nnMGfKZdx5zhuznrRGxMjGzXwKLgEOIIdQ5yUZUHWnNO61xlyKPOUM+885jzpDPvPOYM2QrbxViJTIzI3Zxn1v4Ot7d5ycbVeWlNe+0xl2KPOYM+cw7jzlDPvPOY86QzbxViJWJmZ0EzHT3x5KOpZrSmnda4y5FHnOGfOadx5whn3nnMWfIVt4qxMrEzMxz+J+Z1rzTGncp8pgz5DPvPOYM+cw7jzlDtvJWISYiIiKSEJ01KSIiIpIQFWIiIiIiCVEhJiIiIpIQFWIiIiIiCVEhJiIiIpIQFWIiIiIiCfl/m3VLXMVu8tUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey Fuller Test:\n",
      "Test Statistic                 -3.586776\n",
      "p-value                         0.006019\n",
      "#Lags Used                      1.000000\n",
      "Number of Observations Used    32.000000\n",
      "Critical Value (1%)            -3.653520\n",
      "Critical Value (5%)            -2.957219\n",
      "Critical Value (10%)           -2.617588\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#我们将original数据 拆分成了三份。Trend数据具有明显的趋势性，Seasonality数据具有明显的周期性。\n",
    "\n",
    "\n",
    "#Residuals是剩余的部分，可以认为是去除了趋势和季节性数据之后，稳定的数据，是我们所需要的。\n",
    "decomposedLogData = residual #留下稳定的数据\n",
    "decomposedLogData.dropna(inplace=True)\n",
    "test_stationarity(decomposedLogData)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 四、构建模型\n",
    "#### 4.1 自相关图和偏自相关图的分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "#acf   from statsmodels.tsa.stattools import acf, pacf\n",
    "ax1 = fig.add_subplot(211)\n",
    "fig = sm.graphics.tsa.plot_acf(datasetLogDiffShifting_1, lags=20,ax=ax1)\n",
    "ax1.xaxis.set_ticks_position('bottom')\n",
    "fig.tight_layout();\n",
    "#pacf\n",
    "ax2 = fig.add_subplot(212)\n",
    "fig = sm.graphics.tsa.plot_pacf(datasetLogDiffShifting_1, lags=20, ax=ax2)\n",
    "ax2.xaxis.set_ticks_position('bottom')\n",
    "fig.tight_layout();\n",
    "#下图中的阴影表示置信区间，可以看出不同阶数自相关性的变化情况，从而选出p值和q值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}