nishimotz/200624-python.ipynb

## 200624-python.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# すごい広島 with Python\n",
    "\n",
    "## 2020-06-24 @nishimotz / @24motz\n",
    "\n",
    "### 「アンナほえたワン」を集計してみた\n",
    "\n",
    "過去の報告\n",
    "\n",
    "* https://www.slideshare.net/nishimotz/200429-python\n",
    "* https://www.slideshare.net/nishimotz/191030-annawithpython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dogbarks_xxx.csv\n",
    "\n",
    "```\n",
    "記録時刻（約5分周期。時刻はUTC）, 直前の5分間にほえた回数\n",
    "\n",
    "2019-10-27 12:26:44+00:00,18\n",
    "2019-10-27 22:45:05+00:00,3\n",
    "2019-10-27 23:34:57+00:00,17\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 第1列を datetime として読み込んで index にする\n",
    "* 各列を datetime, barks と名付ける"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('dogbarks_200624.csv', parse_dates=[0], index_col=0, names=['datetime','barks'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-06-22 22:33:26</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-22 22:53:22</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-23 05:57:11</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-23 06:47:03</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-23 06:52:02</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     barks\n",
       "datetime                  \n",
       "2020-06-22 22:33:26      6\n",
       "2020-06-22 22:53:22     10\n",
       "2020-06-23 05:57:11      7\n",
       "2020-06-23 06:47:03      4\n",
       "2020-06-23 06:52:02      6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* インデックス列を UTC から JST に変換する\n",
    "* インデクス列の名前を localtime にする"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = df.index.tz_localize('UTC').tz_convert('Asia/Tokyo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-27 21:26:44+09:00</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-28 07:45:05+09:00</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-28 08:34:57+09:00</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-28 09:19:50+09:00</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-28 09:34:47+09:00</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           barks\n",
       "datetime                        \n",
       "2019-10-27 21:26:44+09:00     18\n",
       "2019-10-28 07:45:05+09:00      3\n",
       "2019-10-28 08:34:57+09:00     17\n",
       "2019-10-28 09:19:50+09:00     19\n",
       "2019-10-28 09:34:47+09:00     39"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = df.index.set_names('localtime')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* date という列を作り、localtime から時刻を取り除いたものを入れる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date'] = df.index.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barks</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>localtime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-27 21:26:44+09:00</th>\n",
       "      <td>18</td>\n",
       "      <td>2019-10-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-28 07:45:05+09:00</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-10-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-28 08:34:57+09:00</th>\n",
       "      <td>17</td>\n",
       "      <td>2019-10-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-28 09:19:50+09:00</th>\n",
       "      <td>19</td>\n",
       "      <td>2019-10-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-28 09:34:47+09:00</th>\n",
       "      <td>39</td>\n",
       "      <td>2019-10-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           barks        date\n",
       "localtime                                   \n",
       "2019-10-27 21:26:44+09:00     18  2019-10-27\n",
       "2019-10-28 07:45:05+09:00      3  2019-10-28\n",
       "2019-10-28 08:34:57+09:00     17  2019-10-28\n",
       "2019-10-28 09:19:50+09:00     19  2019-10-28\n",
       "2019-10-28 09:34:47+09:00     39  2019-10-28"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* ほえた記録そのものの統計量\n",
    "* 下記の count は barks が記録された回数であり、barks の合計ではない\n",
    "* date で groupby できることはわかるが、これが知りたいわけではない"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">barks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-27</th>\n",
       "      <td>1.0</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-28</th>\n",
       "      <td>21.0</td>\n",
       "      <td>17.714286</td>\n",
       "      <td>21.769572</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-29</th>\n",
       "      <td>11.0</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>22.842942</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-30</th>\n",
       "      <td>22.0</td>\n",
       "      <td>15.318182</td>\n",
       "      <td>15.908627</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>19.5</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-31</th>\n",
       "      <td>35.0</td>\n",
       "      <td>16.257143</td>\n",
       "      <td>14.929920</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           barks                                                    \n",
       "           count       mean        std   min   25%   50%   75%   max\n",
       "date                                                                \n",
       "2019-10-27   1.0  18.000000        NaN  18.0  18.0  18.0  18.0  18.0\n",
       "2019-10-28  21.0  17.714286  21.769572   2.0   3.0   7.0  24.0  93.0\n",
       "2019-10-29  11.0  16.000000  22.842942   2.0   6.5   7.0  10.0  81.0\n",
       "2019-10-30  22.0  15.318182  15.908627   2.0   6.0   9.5  19.5  64.0\n",
       "2019-10-31  35.0  16.257143  14.929920   2.0   4.5  12.0  23.0  66.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['date']).describe().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 日付ごとの「ほえた回数」合計を df_by_day にする"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_by_day = df.pivot_table(index='date', aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-27</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-28</th>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-29</th>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-30</th>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-31</th>\n",
       "      <td>569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            barks\n",
       "date             \n",
       "2019-10-27     18\n",
       "2019-10-28    372\n",
       "2019-10-29    176\n",
       "2019-10-30    337\n",
       "2019-10-31    569"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_by_day.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* プロットしてみたい"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,10))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(df_by_day.index, (df_by_day['barks']), label='barks')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* そういえば昨年末にシステムが誤動作していたのを思い出した\n",
    "* 不正な値を削除したい\n",
    "* そもそも5分間に 200 を超える場合がどのくらいあっただろうか？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barks</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>localtime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-27 13:50:05+09:00</th>\n",
       "      <td>214</td>\n",
       "      <td>2019-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27 13:55:04+09:00</th>\n",
       "      <td>330</td>\n",
       "      <td>2019-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27 14:00:04+09:00</th>\n",
       "      <td>331</td>\n",
       "      <td>2019-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27 14:05:03+09:00</th>\n",
       "      <td>322</td>\n",
       "      <td>2019-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27 14:10:02+09:00</th>\n",
       "      <td>285</td>\n",
       "      <td>2019-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27 14:15:02+09:00</th>\n",
       "      <td>348</td>\n",
       "      <td>2019-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27 14:20:01+09:00</th>\n",
       "      <td>351</td>\n",
       "      <td>2019-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 02:18:53+09:00</th>\n",
       "      <td>333</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 02:23:53+09:00</th>\n",
       "      <td>329</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 02:28:52+09:00</th>\n",
       "      <td>339</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 02:33:51+09:00</th>\n",
       "      <td>329</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 02:38:51+09:00</th>\n",
       "      <td>337</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 02:43:50+09:00</th>\n",
       "      <td>336</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 02:48:49+09:00</th>\n",
       "      <td>355</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 02:53:49+09:00</th>\n",
       "      <td>372</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 02:58:48+09:00</th>\n",
       "      <td>382</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 03:03:47+09:00</th>\n",
       "      <td>380</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 03:08:47+09:00</th>\n",
       "      <td>386</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 03:13:46+09:00</th>\n",
       "      <td>381</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 03:18:46+09:00</th>\n",
       "      <td>346</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 03:23:45+09:00</th>\n",
       "      <td>346</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 03:28:44+09:00</th>\n",
       "      <td>350</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 03:33:43+09:00</th>\n",
       "      <td>351</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 03:38:42+09:00</th>\n",
       "      <td>344</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 03:43:42+09:00</th>\n",
       "      <td>340</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 03:48:41+09:00</th>\n",
       "      <td>349</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 03:53:40+09:00</th>\n",
       "      <td>356</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 03:58:40+09:00</th>\n",
       "      <td>378</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 04:03:39+09:00</th>\n",
       "      <td>386</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 04:08:39+09:00</th>\n",
       "      <td>378</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 04:13:38+09:00</th>\n",
       "      <td>417</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 04:18:37+09:00</th>\n",
       "      <td>300</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 04:23:36+09:00</th>\n",
       "      <td>233</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 04:43:34+09:00</th>\n",
       "      <td>296</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 04:48:33+09:00</th>\n",
       "      <td>342</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 04:53:32+09:00</th>\n",
       "      <td>340</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 04:58:32+09:00</th>\n",
       "      <td>373</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 05:03:31+09:00</th>\n",
       "      <td>407</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 05:08:30+09:00</th>\n",
       "      <td>374</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 05:13:29+09:00</th>\n",
       "      <td>343</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 05:18:28+09:00</th>\n",
       "      <td>335</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29 05:23:28+09:00</th>\n",
       "      <td>250</td>\n",
       "      <td>2019-12-29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           barks        date\n",
       "localtime                                   \n",
       "2019-12-27 13:50:05+09:00    214  2019-12-27\n",
       "2019-12-27 13:55:04+09:00    330  2019-12-27\n",
       "2019-12-27 14:00:04+09:00    331  2019-12-27\n",
       "2019-12-27 14:05:03+09:00    322  2019-12-27\n",
       "2019-12-27 14:10:02+09:00    285  2019-12-27\n",
       "2019-12-27 14:15:02+09:00    348  2019-12-27\n",
       "2019-12-27 14:20:01+09:00    351  2019-12-27\n",
       "2019-12-29 02:18:53+09:00    333  2019-12-29\n",
       "2019-12-29 02:23:53+09:00    329  2019-12-29\n",
       "2019-12-29 02:28:52+09:00    339  2019-12-29\n",
       "2019-12-29 02:33:51+09:00    329  2019-12-29\n",
       "2019-12-29 02:38:51+09:00    337  2019-12-29\n",
       "2019-12-29 02:43:50+09:00    336  2019-12-29\n",
       "2019-12-29 02:48:49+09:00    355  2019-12-29\n",
       "2019-12-29 02:53:49+09:00    372  2019-12-29\n",
       "2019-12-29 02:58:48+09:00    382  2019-12-29\n",
       "2019-12-29 03:03:47+09:00    380  2019-12-29\n",
       "2019-12-29 03:08:47+09:00    386  2019-12-29\n",
       "2019-12-29 03:13:46+09:00    381  2019-12-29\n",
       "2019-12-29 03:18:46+09:00    346  2019-12-29\n",
       "2019-12-29 03:23:45+09:00    346  2019-12-29\n",
       "2019-12-29 03:28:44+09:00    350  2019-12-29\n",
       "2019-12-29 03:33:43+09:00    351  2019-12-29\n",
       "2019-12-29 03:38:42+09:00    344  2019-12-29\n",
       "2019-12-29 03:43:42+09:00    340  2019-12-29\n",
       "2019-12-29 03:48:41+09:00    349  2019-12-29\n",
       "2019-12-29 03:53:40+09:00    356  2019-12-29\n",
       "2019-12-29 03:58:40+09:00    378  2019-12-29\n",
       "2019-12-29 04:03:39+09:00    386  2019-12-29\n",
       "2019-12-29 04:08:39+09:00    378  2019-12-29\n",
       "2019-12-29 04:13:38+09:00    417  2019-12-29\n",
       "2019-12-29 04:18:37+09:00    300  2019-12-29\n",
       "2019-12-29 04:23:36+09:00    233  2019-12-29\n",
       "2019-12-29 04:43:34+09:00    296  2019-12-29\n",
       "2019-12-29 04:48:33+09:00    342  2019-12-29\n",
       "2019-12-29 04:53:32+09:00    340  2019-12-29\n",
       "2019-12-29 04:58:32+09:00    373  2019-12-29\n",
       "2019-12-29 05:03:31+09:00    407  2019-12-29\n",
       "2019-12-29 05:08:30+09:00    374  2019-12-29\n",
       "2019-12-29 05:13:29+09:00    343  2019-12-29\n",
       "2019-12-29 05:18:28+09:00    335  2019-12-29\n",
       "2019-12-29 05:23:28+09:00    250  2019-12-29"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('barks > 200')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* これらは誤動作とみなせるので削除したい\n",
    "* 200以下の場合だけを残して df を更新する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[ df['barks'] < 200 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barks</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>localtime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-06-23 07:33:26+09:00</th>\n",
       "      <td>6</td>\n",
       "      <td>2020-06-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-23 07:53:22+09:00</th>\n",
       "      <td>10</td>\n",
       "      <td>2020-06-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-23 14:57:11+09:00</th>\n",
       "      <td>7</td>\n",
       "      <td>2020-06-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-23 15:47:03+09:00</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-06-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-23 15:52:02+09:00</th>\n",
       "      <td>6</td>\n",
       "      <td>2020-06-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           barks        date\n",
       "localtime                                   \n",
       "2020-06-23 07:33:26+09:00      6  2020-06-23\n",
       "2020-06-23 07:53:22+09:00     10  2020-06-23\n",
       "2020-06-23 14:57:11+09:00      7  2020-06-23\n",
       "2020-06-23 15:47:03+09:00      4  2020-06-23\n",
       "2020-06-23 15:52:02+09:00      6  2020-06-23"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2278.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.132572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21.112909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>193.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             barks\n",
       "count  2278.000000\n",
       "mean     20.132572\n",
       "std      21.112909\n",
       "min       2.000000\n",
       "25%       6.000000\n",
       "50%      13.000000\n",
       "75%      27.000000\n",
       "max     193.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 一日ごとのほえた回数のグラフを書き直す"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_by_day = df.pivot_table(index='date', aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,10))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(df_by_day.index, (df_by_day['barks']), label='barks')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AvJamVsHUGRABFRBATRBAAAAAAPDarCGUUWCUWgIIoA4IIAAAAAB4LUk1dQglWzCA+iCAAAAAAOC1NLUKp5y5ZFsw0pN9QABWQgABAAAAwGuzhlCGgdGAIZRALRBAAAAAAPBaamcNoTRKaMEAaoEAAgAAAIDXshYMZkAAdUcAAQAAAMBridXUFgy2YAD1QQABAAAAwGtpOqsFI2AGBFATBBAAAAAAvJak04dQRsyAAGqDAAIAAACA15JZQyhDowFrOIFaIIAAAAAA4LVsCGX110JDBQRQFwQQAAAAALyW2NktGGzBAOqBAAIAAACA15LUykwJIEJmQAC1QQABAAAAwGuptQqnzICIQgIIoC4IIAAAAAB4bdYWDCoggPoggAAAAADgtTSVgilnLlEQMAMCqAkCCAAAAABeS2a0YARswQBqgwACAAAAgNeS1CqYtgUjNBqk6Qk/IgCrIIAAAAAA4LVZQyiZAQHUBwEEAAAAAK/NGkIZEUAAtUEAAQAAAMBrSWoVzKiASK2UEkIA3iOAAAAAAOC11M6ugJCyQZUA/EYAAQAAAMBrSTpjC4YLIKiAALxHAAEAAADAa6nV1BYMVwExIIAAvEcAAQAAAMBr2RDK6q+FQXZKkyQEEIDvCCAAAAAAeG3WEEpmQAD1QQABAAAAwGuzhlCGRQtGepIPCcAKCCAAAAAAeG3WEMqIIZRAbRBAAAAAAPBaaqe3YLjbB8yAALxHAAEAAADAa9kQSioggLojgAAAAADgtVktGCFrOIHaOFYAYYz5e8aYLxljnjDGfMgY0zbGvNoY8xljzDeMMb9sjGnm923lnz+Zf/3hdfwfAAAAAHC6pVYKplZABPl9CCAA360cQBhjHpT0dyQ9Yq3905JCSW+V9LOSfs5a+1pJNyW9Pf8rb5d001r7bZJ+Lr8fAAAAAMyUVUBUfy1kBgRQG8dtwYgk7RljIkn7ki5L+suSPpJ//QOSfjj/+C3558q//iZjpsSYAAAAAJCbNYSSGRBAfawcQFhrn5P0f0t6WlnwcCDpMUm3rLWD/G7PSnow//hBSc/kf3eQ3//e8f+uMeYdxphLxphL169fX/XhAQAAADglUjt9COVwBkR6kg8JwAqO04Jxj7KqhldLeoWkM5LeXHFXF0VWvWJMxJTW2vdaax+x1j5y//33r/rwAAAAAJwSiwyhpAIC8N9xWjD+U0l/bK29bq2NJf1LSd8j6WLekiFJD0l6Pv/4WUmvlKT86xck3TjGvw8AAADglLPWzhlCyRYMoC6OE0A8Lem7jDH7+SyHN0n6sqRPSfqR/D5vk/TR/OOP5Z8r//onrWVULQAAAIDpXK4wrwIiJYAAvHecGRCfUTZM8g8lfTH/b71X0k9L+iljzJPKZjy8L/8r75N0b377T0l65zEeNwAAAIAd4ForpgUQUUgFBFAX0fy7TGetfZekd43d/E1Jb6i4b1fSjx7n3wMAAACwW9K8aHpaC0YYZNdUmQEB+O+4azgBAAAAYGOGFRDVX3fbMaiAAPxHAAEAAADAW8ncCgi3BYM1nIDvCCAAAAAAeMsNl5y6BYMZEEBtEEAAAAAA8Na8IZTDCggCCMB3BBAAAAAAvFW0YEzbgkEAAdQGAQQAAAAAb7nRDuGcGRC0YAD+I4AAAAAA4C1XATFtC0bEGk6gNgggAAAAAHhr3hDKPH+gAgKoAQIIAAAAAN6aN4SyqIBIWMMJ+I4AAgAAAIC3hi0Yc7ZgUAABeI8AAgAAAIC35rVgDLdgUAEB+I4AAgAAAIC3Fq2AYAYE4D8CCAAAAADeShatgKAHA/AeAQQAAAAAb7nOCioggPojgAAAAADgrWELRvXXjTEKzLBSAoC/CCAAAAAAeGteC4aUreJ0QQUAfxFAAAAAAPBWOmcIpfsaFRCA/wggAAAAAHjLBQvhzAoIowFDKAHvEUAAAAAA8FbqWjBmVUCERombVgnAWwQQAAAAALyVLNKCYQxbMIAaIIAAAAAA4K1FhlAyAwKoBwIIAAAAAN5aZAhlRAAB1AIBBAAAAABvudEOs4ZQZjMgCCAA3xFAAAAAAPCWmwERzDhziYKAGRBADRBAAAAAAPCW24IxcwglLRhALRBAAAAAAPBWsQVjVguGMRqwhhPwHgEEAAAAAG8VWzCogABqjwACAAAAgLfSBSogotAwAwKoAQIIAAAAAN5K3BYMKiCA2iOAAAAAAOCtdIEWjIgAAqgFAggAAAAA3lpoCGVACwZQBwQQAAAAALxVDKGcnj/QggHUBAEEAAAAAG+5IZSzt2AEVEAANUAAAQAAAMBbrrJh5haMwChJ05N6SABWRAABAAAAwFvJAkMosxaMk3pEAFZFAAEAAADAW64FY9YaTioggHoggAAAAADgLVfZwBYMoP4IIAAAAAB4aziEcvp92IIB1AMBBAAAAABvLTKEMgyMBgkBBOA7AggAAAAA3ioCiLkzIAggAN8RQAAAAADwVmqtjJHMzAqIQIklgAB8RwABAAAAwFtJame2X0hUQAB1QQABAAAAwFuJtQpmtF9IbgYEazgB3xFAAAAAAPBWukAFBFswgHoggAAAAADgrSSdPYBSylowBgQQgPcIIAAAAAB4K7VWc/IHKiCAmiCAAAAAAOCtJLULVUCwBQPwHwEEAAAAAG8ldn4AEQaBrM3mRQDwFwEEAAAAAG+lqVUwbw1nmH2dORCA3wggAAAAAHhrkRYM93XmQAB+I4AAAAAA4K3Ezq+AcGs6B2l6Eg8JwIoIIAAAAAB4K6UCAjg1CCAAAAAAeCu1mr8FIySAAOqAAAIAAAAo+dVLz+jxp29u+2Egl7VgzL4PFRBAPRBAAAAAACU/+2+/pg9/9pltPwzkFmnBiAK2YAB1QAABAAAAlPQGCSeyHkkWWMMZBkFxXwD+IoAAAAAASuIkVcI2BW+kdpEhlNmfBEeA3wggAAAAgJL+IOVE1iPJQlswXAUEwRHgMwIIAAAAIDdIUqWWUn6fJFZzWzCiYgjlSTwiAKsigAAAAABy/fwMlgDCH4sMoQyLIZQkEIDPCCAAAACAXH9AAOGbbAjl7PtErOEEaoEAAgAAAMi5AIIZEP5I7CJbMFjDCdQBAQQAAACQowXDP8u0YPBzA/xGAAEAAADkhhUQzBLwRbLQGs68AiIhgAB8RgABAAAA5KiA8E+azm/BiPI1nKnl5wb4jAACAAAAyDEDwj9LVUDwcwO8RgABAAAA5NiC4Z8k1QIVEG4GBK0zgM8IIAAAAIAcAYR/siGUs+/DDAigHgggAAAAgFyPGRDeWaYFg58b4DcCCAAAACAXMwPCO4sNoWQGBFAHBBAAAABAbptbMP71F57Xn/v7n9QgYY5B2TIVEGzBAPxGAAEAAADkhlswTj4E+Pwzt/TcrY6O4uTE/22fJalVuOAaTmZAAH4jgACAFTx780if+uq1bT8MAMCaFUMot3Aie/WwN/IYkElTq2BeBUTIDAigDgggAGAFv/j7T+lvf+jxbT8MAMCauRaMbcwSuHrYzR4DAcSIxC5SAcEMCKAOCCAAYAXdOOEAEQBOIffavo1ZAtduZxUQMTMgRiSp5lZAuCGVyRZaZwAsjgACAFYQJ+lW+oMBAJu1zQqIa1RAVEqtVTjnrIUKCKAejhVAGGMuGmM+Yoz5qjHmK8aY7zbGvMQY83FjzDfyP+/J72uMMf/IGPOkMeYLxpjXr+f/AgCcvP7AKrVZXyoA4PTY1gyIO72B7vaz4ZN9KiBGLDKEkhkQQD0ctwLi3ZL+rbX22yW9TtJXJL1T0iesta+V9In8c0l6s6TX5v97h6T3HPPfBoCtceWxCeu+AOBUGW7BONnXdzf/ofwYkFlkCKWrgCCAAPy2cgBhjDkv6S9Kep8kWWv71tpbkt4i6QP53T4g6Yfzj98i6Rdt5lFJF40xL1/5kQPAFhVXyDjQAYBTZVuv79fyDRjlx4DMIkMoQ1owgFo4TgXEayRdl/TzxpjHjTH/1BhzRtID1trLkpT/+dL8/g9Keqb095/NbxthjHmHMeaSMebS9evXj/HwAGBz4i32CAMANmc4A+JkQ4Brt4cVEPEWVoD6LEltETBMEwVBcV8A/jpOABFJer2k91hr/6ykuxq2W1SpetWYeIWw1r7XWvuItfaR+++//xgPDwA2xx2gbmNPPABgc4ZbME52zs9IC0aSnNi/Wwepnd+C4b7MhQHAb8cJIJ6V9Ky19jP55x9RFkhcda0V+Z/XSvd/ZenvPyTp+WP8+wCwNfGWrpABADarPADyJOf8XKUFY6rUam4LhjFGYWBYwwl4buUAwlp7RdIzxpg/md/0JklflvQxSW/Lb3ubpI/mH39M0o/n2zC+S9KBa9UAgLpx5bGUegLA6VI++T/J1/hrt3ty59h9qutGJAsMoZSyORBUQAB+i4759/+2pA8aY5qSvinpJ5SFGr9ijHm7pKcl/Wh+39+U9AOSnpR0lN8XAGqJGRAAcDptK4C4etjVy863dfmgSwVEiWuDmVcBIWWbMFiPDfjtWAGEtfZzkh6p+NKbKu5rJf3kcf49APAFWzAA4HQqt2CcZMh87bCrV96zr8sH3SLkxrANJlygbpsKCMB/x5kBAQA7iwoIADidtlEBYa3Vtds9PXTP3sRj2HXuZ7BIC0YUGC4MAJ4jgACAFQxnQHCQCACnSfnk/6QGDd/pDXTUTwggKqR28RYMKiAA/xFAAMAKqIAAgNOp3P5wUlfT3QaMh16yL2m0DWTXuZ9BuOAQStZjA34jgACAFRQBBAc6AHCq9MoVECf0Gn/tdleS9NBFKiDGuSKUYKEhlAEXBgDPEUAAwAoYQgkAp1O5+sCV/2/atbwC4oELbTVCQwVEiRtCuUABhMLAnNjPDMBqCCAAYAVuBgRXWgDgdOkPUkX52e5JvcZfPcwqIB4431YzDBRTAVFYpgUjYgYE4D0CCI/c7Q302FM3tv0wACxg2ILBQSIAnCb9Qaq9RijpZGdAnGmGOtuK1IgCKiBKXEXDIlswwsAwHBrwHAGER37t8ef0n7/n9/XYUze3/VAAzJCmtrjCQgsGAKzub/6zS/r1x5/b9sMY0U9S7TWzAOIkZ0C89HxbkrIKCAKIQlEBsegWDGYzAV4jgPDIQSeWJL37E9/Y8iMBMEuclle0caADAKv6na9f1+eeubXthzEiHqTab55sBcS1w55eeq4lSWqEwcggzF3nfgaLV0Dwvgz4jADCI904kST97tev6/GnqYIAfBWXrq5woAMAq4sTq4FnJfNZBUQkSSf22K7e7uqBvAKiFQVswShxLRiLVEAwAwLwHwGERzr9RK0o0D37DaogAI+VDww50AGA1SSpVZJar0rm09QqTuyJVkBYa3XtsKcHzmcVEM2IFoyyZYZQsgUD8B8BhEc6caJz7Uj/1V94jX77a/6VJALIlA8MGXYFAKsphvl6FOS64Y8nGUDc7g3UiRO99FxWAdEIqYAoW2YIZRQEXgVaACYRQHikG6dqN0K97Xse1sX9ht7977++7YcEoAIVEABwfD5uE3IBRPsEt2Bcy1dwvnSkAoL3Fsf9eiw6hJLWSMBvBBAe6caJ9hrZCqb/+i+8Rp/62nV9nioIwDujFRAc6ADAKtxJtk9BrguYXQXESTy2q4c9SSpmQDSpgBgxbMGYf98oNN7NFAEwigDCI504KRL3H//uV+nCXkP/+JNPTr3/Uy/eLQZXAjg55StTlHoCwGqGFRD+vI66x7R3khUQt7MKCBdANKJAPY+qQrataMFYoAIiMFRAAL4jgPBIp58Ub3jn2g39le94QF98rroCYpCk+oF3/55+6TNPn+RDBCAqIABgHdxVfh8rIPa2UAHh1nA2w0AxFRCFZYZQsgUD8B8BhEc6caJ2/oYnSWeaobpx9RtQJ050t5/oxt3+ST08ALl+wgwIADguF+b6NMx3vAXjJB7b1cOuzrYinWllqz+bkRl5n9l1yRJDKJkBAfiPAMIj2QyI4Y+k3Qyntli4YKI3oAUDOGnlK1M+HTgDQJ34OAOiVwQQWRhwEo/t2u1eMYBSYgbEuNRVQCzQghGFBBCA7wggPNKJhy0YktSOQvUGafHCW+aCCd6ggJM3MgOCAx0AWImPMyC2tQXjgXwFp+S2YHB85yxuNCYhAAAgAElEQVTTghEGAQEE4DkCCI9046ToOZSG/Ye9ipDBBRBVXwOwWcyAAIDjcyf7Pm0tmGzBOJkZEOUKiAYVECOSJYZQMgMC8B8BhEc6/eEWDElqR9mPp1PRhtEhgAC2hhkQAHB8sYdDKF3AfFJrOK21unrYLTZgSFkFBDMghlw+tUgFBFswAP8RQHikG6cjLRiuAqJqDoSbAUFCDpw8KiAA4PiKGRA+tWAMTrYF47A7UG+QFhswpDyA4Piu4CogwgXOWrIKCL53gM8IIDwxSFL1k3S0AiL/eHYFBEMogZNWDiB8OnAGgDopZkB4FOSOt2Bs+rFdO+xK0mgFRJhVQFjrz/dlm9wstEVaMMLQiOIRwG8EEJ7our3TFQFEdQUELRjAtsSD4UEhWzAAYDXFDAiPzhj7Yy0YyYYf29XDniSNVkCEgaylws5ZZghlFBjelwHPEUB4otPPAoV2c8kAIuZFFjhpzIAAgONzFRA+nWj3igtCJ7OG8/JBR5L08gt7xW2NfAYYcyAyyw2hDEY2VQHwDwGEJ1ygMDIDogggZmzB4M0JOHHMgACA4/O5BWPvhLZgXM1bMMpbMJr5sINdnQNx0Il1uxsXn1u7eAXEfjPUUX9A+wrgMQIIT3QqAoh2I9+C0a+YAdF3FRDMgABOmjtoDoxfB84AUCeunc2rFozxNZwbPpG9fNDVvWeaIzPAmjteAfF3PvS4fuZffrH43H0bFgogWqFSS4sy4DMCCE8UFRDN4Y+kaMGoGDTpZkbsajoObMJP/Pxn9dHPPTf3fv3SzBafDpwBoE76HlZAuIC5qIDYcDn/lYOuXnahPXLbrldAvHCnp2dudorPl2nBONPMWmfu9gabeXAAjo0AwhPFDIiKFoyZFRA7+uYEbMLvfeMFPf70rbn36+cHpO1G6NWBMwDUidctGI2T2YJx+aCrl48HEHkFxK7OMhgkVoedYQtGusQQSle5clRx7AzADwQQnqhqwWjlLRjdipDBVUUQQADrkaZWg9RWDn0dFyepGqFRFBpmQADAimJPt2AYk21TCIPNv8ZfOayogIh2uwIiTlPdOuoXnxdbMBapgGhlFRAEEIC/CCA84U56qiogquY8dIsKCF5ggXVwpcCdRQKIQapGGCgKAq+u3AFAnbgr/D69jvYHqZphIGOyAGKTj60bJ7pxtz+yAUOSGjvegjFIrA46cVH5ULRgLHDW4iog7vZpwQB8RQDhieohlNNbMNxmDCoggPVwK22rnm/jsgqI4ESujgHAaeVOsH0KIHp5ACFlVRBJurnjLLcB42Xnp1RAeFQZcpIGSarUSnfyEGGZFoyiAqLHBTrAVwQQnuj0R4ceScqvsJrKIZQusOgPUlYNAWvQS7Ln1CIVEP3EFs9Pnw6cAaBOfG3BcAHApisgLh/kAcRYC0YjzE60d7UCIs6/5wdH2RwIVwGxSAuGu5BHBQTgLwIIT3QqWjDc5y6cKCv3qe9qQg6skzvQW7QCohm6/mCefwCwChdApHZ4lXvb4sEwgIg2XOV2ZUoA0aICQpJ0kA+idL8bZqkZEAQQgK8IIDzRrWjBkLIAYlYFhEQbBrAORQCx6BDKKGvBGOzolHIAOK7ylofEk2rO0QqIYKMBRFEBMd6CEWbHgvGOHt+591UXQCTLtGC4GRC0YADeIoDwRDdOFAamKLtz2o2gGDg5fn9nV0v0gHVaaghlkvUIswUDAFZXvsLvS5jbL82ACANtuAKio/PtqLhq7zSivAVjRysg3P/vW0ULRnb7Ii0Y+1RAAN4jgPBEp59orxFOlJftTamAcEMoJSoggHVwQyirAr9x/YHNh1CyBQMAVlW+wj/wpJ2tP9KCsdnX+MsH3YkNGJKKACTe0QDCfc/HWzAW2YJRzICgAgLwFgGEJzpxonZj8seRzYCY04KxwBVbALMtWwHRiIKN9wcDOF2evHZb/9OvP+HNvINti32sgMi3HEna+KajK4fdifkP0nALxi5eYLLWFt/zW52+pNIQygVaMMLAaK8RLvReDmA7CCA8kQUQ4cTte41wpNrB6cZJset4F9+ggHVbdgaEG0Lpy1U7AP77na+/oH/26FO6fqe37YfihfIMCF+qyXpjQyg3XwFREUDkAcguttiWfyfGZ0AEC7RgSNKZVqi7PVowAF8RQHiiGycTAyglqdUIKk+IunGii3sNSbv5BgWsm3sedeN07tXJOL9CRgUEgGW46f63u/GWH4kfRmZAeBLmxklabKHY5Kaj/iDVC3d6MysgdrEFo/x74NZwpksMoZSylfZHC7RTAtgOAghPdPqJ9prTKiCqZ0CczwMIKiCA4ys/j6rmrpT1EzcDYrNXxwCcLnERQHB1VvK0BWNkCOXmNh1du92VtaqsgGhQASGpVAHhWjAWrYBoRlRAAB4jgPDEtBaM9pQAohMnulAEEKS8wHGVn0dVc1fK4gEVEACW506u7nByJGksgPDktXRkCGVolG5oPejVw3wFZ9UQymh3A4hB6Xfi1tH4EMrFAoh9KiAArxFAeKITp5UtGFUzIOIkVZLaYQBRMSMCwHLKB3rz5kDESapmZLItGJ5ctQPgPyogRsWD4evnplodltVPhgHEJjcdXT7IAoiqCogoMDJmV1swqisgFm2/kKQzrUh3WcMJeIsAwhO9KTMg2hUzINznLoDY1T3RwDqVn0dVVUdlzIAAsAp3QnmHAELS+AwIP15L+4PSFgyjjb3GXzlwFRCTAYQxRo0wUG8Hj+/cc8SY8hDKxdsvpLwCgjWcgLcIIDzRiatnQFS1YHTHAoiqFozHnrqht/yT/2/uiRSAzEgFRH/2QZ87QA1DtmAAWJxrwbhNC4Ykj2dAFFswNlfldvmgqzPNUOdaUeXXW2EwUiGyK9z3+579ZhFApNYqWOKM5Uwz0lHMcwzwFQGEJzr9RO3G5I+j3QjVG4xO5e/mJ0cX96e3YHz+mQN9/plbunG3v6FHDJwuy7RguCGUVEAAWEbMFowR5Y0T3lRAJKNDKDdZAfHAhbbMlCv7zShQP9m9i0gu1L/vbFN3eoOi7XipCogWFRCAzwggPDFrCKVUPaF/VguGO4FiQwam+eKzB/p7v/y5uSsnd0U5gDia0zsaJ6maoWELBoCluKu7tGBk4sRqP6/+HHjSbtAfDEORaINVbpcPOpXzH5xGGOzkEEpXJXTvmZYk6bATK0ntwgMoJWm/yQwIwGcEEJ7oTpkBsZdXRZRbKdyE/vMzhlC6++/imxcW8+k/ekG/9vhzOuRKnKSxkG+JGRC+lA0D8F8xA4IWDEnZMYo79vEhzLXWjg2h3GwFxMvOT27AcJpRMLKScle499R7zzYlSbc6sdIlh1DuN7MB7lQoAn4igPBAnKSKEztlCGV2W7kkfJEZEC6kYEUnpnHh1C4e4FQpVxItsgWjEQUbnZAO4PTpswVjRJykxfwrH8LcQWplrYoWjGhDVW5JanX1dm9OBYTZyYtIcdGCkVVAHOQVEMu0YJxpZnM15lUzAtgOAggPuEChagilu22kAiL/+Fw7UmCq2yw6VEBgDncljiGKmUWHUFprFeczIBqh8WZ1HAD/DRhCOWIkgPDgtdS9DzQ2XAHxwp2ektRWbsBwmlG4k2207jlyX14BcXAU50Mol5sBIUlHfS7CAT4igPCACwuqZkC0oqoKiLS4fzOq7hFkBgTm6edv8j5cdfJBr9T3O6sCwlWMMAMCwLKGazhpfZPyGRCN7Gq1D+9F7nhq00MoL+crOGdVQGQtGLt3DOf+P6+jAuIuQR/gJQIID7gZDpUzIIoKiMn+9L1GqNaUhJwZEJhnWAGx/YM+H/QHadHWNGsGhPu+sQUDwLLi/PWCFoxMP0nVbvozA8K9vg9nQAQbeY2/ctCRpNkVELvagpH/DO7NA4hbR30lqZaeASFRAQH4igDCA50ZLRjtaHIIZbdUMdGKAmZAYCXuwMaXyePb1hskOtuKFAameP5UKR+gMgMCwDLiAUMonaydLS2GbfvQguEu6LgAYlMzIIYVEPOGUG7/e3LSxodQHnQGeQvG4v+NMy03A4JjYMBHBBAecCc77cbkj2PWDIi9RqhWI5g5A4IWDEzjDmwYQpnpD7LJ53uNcOZBS58KCOywr1w+1Pf8X5/Qi3d6234otTRswSCASPKBj/t5ubwPr6Xu9b214RkQVw66akaB7tlvTL1PMwwq16yfdi6IakehzrYi3er0l27BcBUQrOIE/EQA4YFZMyCqt2CUZkCE0wKI7DYCCEzTZwjlCLd6rd0IF5wBERQHp9Zu/8AZOAnfuHZHzx90iyu4WI5rwbjTHyj14IR7m9xrqTvO8XEGRFYBsf73yMsHXb38Qltmxkl1I6ye8XXaud+LRmh0Ya+hg84KQyjdFoweFRCAjwggPFCuaBjnbivPgHD3b0VBNgMinnyD6vaZAYHZWMM5qp8PodxrBrNnQBRT0o2i/IDIhyt3wElwrxuE26txrx/WcnXWheD7Pm7B2PAQyisHXb3s/PT5D1LWgrHLFRBRGGQBRL4FgwoI4PQggPCACwuqZkC0GpNT+XtxolYUKAhM3oJRMQOCFgzMUQyh3MEDnCrlFoxFZkA0wkBhmB0QMQcCu2IYXPK6sYrySfauz4Fwv0PuQosPr6P9sSGUm2qzu3LYnbkBQ8pbMHbwGM5dFImCYQVEktqlhlAWMyB2/DkG+IoAwgOLVED0xmZAuLBi2hvUMICg/AzV3Ju8Dwd9PugNUjXDQHvNaGYLxvgMCIkKCOwO955CALGaOBmeSO36JowigGj604IRjw2h3MSgYWttVgExYwClewy7GEC434NmFOjifkO3OrGSVApWqoDgGBjwEQGEB2YFEMUMiH55BkSidpTd3mpMWcNJCwbmYA3nqGEFRLDEDAg3vZ3vIXYDFRDH0x+kumc/m+6/8wHEIHvd9KkCojexhnP9AfNhZ6B+kur+c62Z99vZLRiuBSMYnQGxTAVEK8pmNB3RggF4iQDCA8VQyYoWjEY+6K47KFdApMUVg2wNJ1swsDz3u0ELRiYbQhlqrxHOngFBBQR2mAsgCLdXM0hTveRMtvlg11swJmZAePBeND6EchMVEK6KqGrzWdmuD6GMwkAX9rMZEEm63BBKY4z2m7M3WgHYHgIID3RnVEC42zv9dOT+bkVUKwrUH2uz6A/S4g1zF9+8sBjWcI4aDqGcMwOiGFJmiisyPgxPA06CCy77vG6sJE6sLhYVEPGWH812TbRgeBDkumOm1gZnQJTb+GbJKiC2/z05aYNk+B57Ya+hfpLqbm+gcPH8QZJ0phmxBQPwFAGEBzr9RGFgpr4ZtRvBSAVEtzwDoqICYmRgJTMgMEXMGs4RvcFwDeesqyauRLcRUQGB3eNOnmLC7ZXESaqX5AHEnV1vwUiGK8UlP2ZATNuCsc5Vy+U2vlmaYbYFY9fWPLsgKgoCXdzLnis3jvpLtWBIWWUNWzAAPxFAeKATJ1OrH6Tszbk7bQZENDkDolw+TgUEpukXLRi7dXAzTX+QZEMo57VglEp0iwoIvofYEcyAOJ44SXXPmTyA2PEWjHIAYYyUeBCGV23BkNYbMsdLVECUH9OuiMcqICTpxt2+zBJDKCVpv0ULBuArAggPdOKkuAJQpd0Ix2ZAJKMzIMZOlsrl48yAwDTuKgwnEpmea8FohAsNoWyEgaKQCgjsFldVt2snReuStWBkJ1WHO14B0R+411KjKDBetWAUQyg3sGq5X2rjm8VVSOxaG8Yg3xRjjCmeK7eOYoXLBhDNSHd3POQDfEUA4YFuP9Fec/qPIrsiW54BkRbDi1pRMHEg2KECAgsoKiA8OOjbNmttPoQy0H4zCyCmlb2Wr86wBQO7pscQypWlqVWSWrWiQGdbES0YybCaLNrAsMdVxFMqINK1tmCM/hvTuIBi155rcZIW33dXASFp6RaMMwyhBLxFAOGB+S0YwUhVQ6c/rJhwWzDKJ0ujMyB2640LiytmQHAlU4PUytrsQLjdDGXt9OdOny0Y2GHDFgx+55cVp8PXjnPtSHd6DKGUhq+lPrSy9ca2YARmcxUQc2dA5K22u1alGCe2aE8pBxDLbMGQpP1WxAwIwFMEEB7oLjIDYjA6WLIIIBrZyVL5YLA8L2LXknMsrs8WjEIx+bwRFM/FaXMgylev2IKBXdNjBsTKBsmw5eBsK9JtKiAkDdvZfHgdHQ8HipB5je+TRRvfnAqIYgbEjh3HDdK0aG+8sF+qgFh6C0bIFgzAUwQQHlhkBsR4BcReqQJCGu3HdRUQ51oRWzAwlRumyNX70YNO99yaNgciHkxWQPhw5Q44CQyhXJ37nkVBoLPtaOeHULpVrs0oa2fzoQWjn5f/u6vtYbj+NrtFh1C6Foxdq2SNE6sob28814qKoH/5LRiRjqiAALxEAOGBTpzODCD2GsNNF9ZadQdpcZLkEvLyIEp34nR+r7FzyTkWVwyh9OCq07YVZbdRWAx4ndY7WqxQG6mA2P6BM3AS+syAWFnRvhUFOtduUAFRCnMbofGiHbCfr2N2NtFm1y/NEZrFXWDatbBvkKTF98YYo/PtSNKwHWZRZ/ItGLu2xhSoAwIID3T7i8+AiJNsiFV5CKU0mpC7+17cb+xcco7FuKGLElfvpdHJ5y4M7EwJIMoHj+4qDVUk2BVswVide61thkbnWpFud5kBIWUBROjRFoxyALGJNrvy8M1ZdrcFwxYtGJJ0cT9bW7tKBcQgtbxWAR4igPBAea1mlb3SDAj353AIZfZnOWhwvesXqIDAFOW5Dz5cddq2fpI9Z5rR4jMgGgEzILB7hrNj+J2vMkhSvfndv6ff+tKVia+NtGC0aMHwcQhlnKQjwcBGKiDGVn1O41o0du0EOk5SNYLh9+Z8Pohy6SGUrpqRORCAdwggPLDMDAg3YLI91oJRDhpcCwYVEJimfPIQe3DVadvc86SVr+GUZsyAKPUIuzJRKiCwK4oZEAN+56scdgf6yuVDffXK7YmvlYcPnmuzhrOYAREGisLAi9fRaRUQ63xsi86AcEFIvGPHcYNkrAIiDyDCZVswmlnrBpswAP8QQHhgfgtGNgMiTa26cfZGND6EsjxsstPP7nNhr8EQSlQqB1ZUQCzXglFeEcYMCOwaF9bt2lXZRbm2iqrqw2H1lNHZdqS7/cSLk+5tKb4fkckqIDyoJOsl4zMg1t9m1y+2ocypgHDHdzv2XBuk6cj3xq3iXLoFozV7nhOA7SGA8EDWgjH9R+FOiHqDtLgqO6sFoxMnxYkUFRCoMlIB4UHZ67YVazjDoGiHmlYB0R8MB2QVB6d8D7Ej+gQQM7nBklXfn/KV77Ot7OrsLrdhjGwUCv1owegPRlsw3Idr3YIxtupzmtNcAfH7f/Si/vzPflJ3K37/48QqKn1vLuarOJceQukqIHb4OQb4igBiy+Ik1SC1Mysg9vKBk904KfrSXWBR1YLRjbOKimYUEECgUvng2IerTtvWK1VALDIDwj3vqIDArhm2YPC6UaUIICorIEZbMKQdDyCKmRgereGcaMFYfwVEufJjlqo166fFE88d6NmbHd2425/42iBN1ShVOwwrIJb7N/bnbLQCsD0EEFvWHatoqFKUhMfJsAIimtWCkQUQrShUf5CygggTRodQ8vvRrwggpq/hHJaHRsyAwI5xYR1DKKu5QKEq/C+3YJxrZydVm96E4fP7fz+xaoaBjDFqeNKCMV4BEW0gZF50BkQxhPIUhn23OlnwUP08GZ0BsWoLxpm8yogAAvDPsQMIY0xojHncGPOv889fbYz5jDHmG8aYXzbGNPPbW/nnT+Zff/i4//ZpMN5SUaVduiJbBBZ5stvKqyN68WgLxl4zPNXpOY5nZAYEJ8/Fc6QZzW/BqJ4BwXMMu6GogCC4rDRrBsSgVAFRtGBscBDl3/qlP9R/+6uf39h//7iyMDd7DQ192oJROYRyfa/x/UEqY4bhxjTucZzGsO+gM+t5Uj0DYtkWjL2iAmJ3q4wAX62jAuLvSvpK6fOflfRz1trXSrop6e357W+XdNNa+22Sfi6/387r9keHSlYpV0B0JyogqmdAtBulAOIUpuc4nvIBDUMoSzMgoux5Y8xw48zEfZPyDAgqIHxnrdX/8rEv6fPP3Nr2Q6m9NLVFWMf7SjVXATFrBkSUD6GUpNsbbMF44rkD/frjz+mZG0cb+zeOY5CkxaDFRuhJC8bEEMo8ZF5jONLPQ2wz54T6NFdAHHSy3/uqcGWQ2pFw5uJ+U9IKFRDFDAgqIADfHCuAMMY8JOmvSvqn+edG0l+W9JH8Lh+Q9MP5x2/JP1f+9TeZea++O6BTzHSYFUC4GRDpcAtGc/oazmwGRFB8jTkQGFc+OGYN52gLhjFGe41wzhBKZkDURW+Q6hc+/S198qvXtv1Qaq/8ukFlXbXhDIjJ149+qfT+vAsgNlgBcfMoVmqlf/7oUxv7N46jP1ZN5sPr6OQQys2s4Zw3gFLSQsdwnX6iH3nPp/U7X7++tse3DtZa/Y2f/6w+8ZWrlV+/dZS1YFQHdaNDKFetgBhuwaACAvDNcSsg/qGk/0GSewW5V9Ita617tj8r6cH84wclPSNJ+dcP8vuPMMa8wxhzyRhz6fp1v15QN6EIIGYOocyrHMozIPJQYuoMiCYVEJiONZyj3PPHHRTOCiDKJbqbWNGG9XIH79OGimJxI8ElrxuVXKBQ1aLirqI3o0BnW9lJ1aZaMAZJqsO8HeTDf/DM1LXC21Q+EY8C48V70fgQymLOzxpnaZRbT2ZpFS0Y0//tf/WF53XpqZv69JMvrO3xrcMgtfrtr13X7//Ri5VfP5zRgjH+/XFbMJZew5kfO1MBAfhn5QDCGPODkq5Zax8r31xxV7vA14Y3WPtea+0j1tpH7r///lUfXm24g4JFh1B2xwKLVkVC3okT7TWiyvYMQBpvweDkubwFQ8qec51+9fOmPISSCgj/ud91AojjK88aIoCoNmsGRFULxp3eZoZQHnRiWSv91T/zch10Yn30c89t5N85jvKJZhQaL4Lc3lgFhLvqvu4hlPMGUEqLtWB88DNPS5Keu9VZz4NbE/e7fvOo+ve7mAFR1YKRpEW4L60+hDIKA7WigAoIwEPHqYD4c5J+yBjzLUkfVtZ68Q8lXTTGRPl9HpL0fP7xs5JeKUn51y9IunGMf/9U6C7QguG+1o3TiaGVVSV6bghlVXsGIA0PDoyhBUMaHgS5QG+vGU5fwzmwkzMgOBnzlnv9m1bRgsWNtGDwvlKpmAExawtGGOhMM5Qxm2vBcCd+3/unHtC3v+ycfuHT3/JuI8bIRiFf1nBOzIDIq9zWOQNiYBcKIMLAKAzM1LDviecO9PlnbskY6XnfAohB9v1yrRbjbs2sgKjegrFsC4aUbcK4SwABeGflAMJa+zPW2oestQ9LequkT1prf0zSpyT9SH63t0n6aP7xx/LPlX/9k9a3d8MtGK9oqOIGTmYVEKMnSi6pHwkg+tkMiKr2DEDKDoCkrETRh7LXbStmQJRaMKZdNemXKyBCKiB8Nwwg+D0/Lve9jALDFowpXKDQm9LbLqkYQHi2FW0sgHAnfhf3m/ob3/Owvnrltj7zx35d8ymfiEehHy0Y07ZgrPM1vp+kxfHZPM0wmDpv5YOfeVrtRqC/8h88oOdvddf2+NahX1RATAYQaWpntmAM0lSNUgVEuxHqja9+ib7jFeeXfhz7zZA1nICH1rEFY9xPS/opY8yTymY8vC+//X2S7s1v/ylJ79zAv107i8yAaDfdEMqsBaPdGE5PNsaoFQWjMyDiRHsNKiAwnTs42GtGtGDIDZY0CvKDzbkzIEp9yxIzIHzmrh762ANfN+595mw7YgjlFHe6i1RAZK8b51pRUTGxbq4C4p79ht7yHz6oC3sNfeDT31r473/8y1f1v/6rL23ksTlxaQuGr0MoixkQ62zBGCzWgiFlvytVv0u3u1lbzQ+97hX69pef19XbXa/aotxjuVXRgnGnP5D7dk5bVxuNzcj45b/53fqh171i6cex3wx1xAwIwDtrCSCstb9trf3B/ONvWmvfYK39Nmvtj1pre/nt3fzzb8u//s11/Nt1Nz5Usoprt3ABxHhY0YqCkd7cTj9RuxkyAwJTxfnvxJlWqMEa95vX1Xjfb7sZTr1izgyIenGvf1SCHZ87WTjbirw62fHJYTEDYvL3rQgg8pPuc+1GMTNi3dyV53v2m9prhnrrG16p3/ry1YVL9X/jC8/rQ599eiOPzcnC3Ow1tBEEXoTh40Moh6/x6/t9z4KXxdoJmlFYGfb9+uee11E/0Y+98VV68GJb1kpXDvypgohnVEAclEKJqteRRWdkLGK/SQsG4KNNVEBgCcUQylkzIEoBRKefTAysLL9BpalVb5COVEBw4I1x7k1/rxFy8qzJg879RqjulCvmcWLZglEjVECsTzmAoLKuWjEDYlYLRv66cba9uQqIYQtG1j//X3zXq5SkVh/93POz/lrhymFX3Tjd6PFDPNbOtu33ojS1GqR2bAbE+isg+kucYDcrKiCstfrgo0/pzzx4Qa975UU9eHFfkl9zINzr7kEnnvjeuQGU0pQhlKktvu/HdaZFCwbgIwKILVtkBkQjDBQGJpsBkYcLZeUKiO5g+N9jDSemGbZghPRyazKA2GtOb8Hol8pn3THStg+cMR1DKNenRwXEXLdntGAMxlowzraija3hvHkUZ9s2WtlM8Ifu2Ve7EehWp3oo4Lirhz1JmxuSKUn9ZDgDohGYrVfj9Yufz2QFxFpbMJYJIKJg4nfpD5++qa9eua0fe+OfkCS94mJbkl+bMNxxRWqHKzedkQBiagvGGisgNhTyAVgdAcSWdeJEUWDmvhntNcJsC0Y/UWs8gGgMZ0C4q3zlLRi0YGCce9M/04y8GPy1beOTz9vzZkBEwxksvuyvRzV3IMwazuMrXjdakeLEerdVYdustQttweQZELEAACAASURBVHAntWfbkW5vsALi4n6zmBclZQMNewsMY7XWFuX84yeP61SehRAGwVo3TazCHSu1qrZgrLMCYrDEEMoomAj7Pvjo0zrXivSf5TMRXnFxT5KfFRDSZBtGeS5EZaVQOlzPelxnGEIJeIkAYss6/cmKhirtRqBOnKg3yDZclDXDoHjjLK/pbBFAYAp3UrbXpAVDcgeEw+fh3swWjHTiChktGP7qJ9nPscsWjGMrKiDa2VV1qqdGdeKkeC2oCiD6iVUzHA6RPt/e3BaMm3dj3ZO3XzitRrjQ8cBhd1AcSxxusAKiHOZGoVG85QoId9JcDqPdMoZ1vk/GyWJrOKWsGmP8d+nRb76o/+TbX6ozeXVLuxHq3jNNPefRJozRAGLxCogktbJW65sB0YqmbrQCsD0EEFvWiZOZ8x+cdiOcOgOi1QiLF/FySwcVEJjGHRzsN0NKqTU5hHKvGcyogBg9eIw8md6Oam7lLC0Yx+cq7c61XADBa0eZa6c4144qw5lBMnpld7MtGH3ds98cua1ZcTJb5erh8ER2U0MypdEwN/IgyB1fxyxtpgIiTha/wt+MRtdwWmv1wt2+Xn6hPXK/V1zc86oCwr3uSsN5JM6sAMK9poxvwVjVmWaou2zBALxDALFh//7LV/W5Z25N/XqvYqtFFRdAdAdTtmAULRjD4YLuim6dZkC866NP6H/+6BPbfhinXn+QKjDZ744Pk8e3rTdIRmdA5MM5q69iUgFRJ+7gnRaM4yu3YJQ/R8ZVC9x3tqV+kk60qMRJOtLbfrbVUCdONtLCdeso1j1nxisggoWGSpa3KRx2NlkBYUcCiG0HuUUAUbkFY1tDKEdDo7v9RP1BqnvPjoZLr7jY9iqAmFUBcavTVzMMtFe6eOa477Mb1Hpc+81InThRynv0VGlqmZOBE0cAsWH/529+Rf/vb//R1K93Fg4ggmIGxEQFRDTZgrHXLLdg1OfA+wvPHejSt25u+2GcenE+8yAKg60P/vLB+BBK9xwbv2purR1ZHSeJ76HnykMomVlwPC7MOUMFRCU3/+ElZ7KTw/H+9jgdrZ46l7eybGITxo1jVEBcKVVAHG6wAqJ8Ih6Fgazd7kahfkULxnALxnrXcDaXGUJZ+j168U42HPTeM62R+7kKCF9e48qvDeMVEIedWOf3GpXzLQZrroDYb1a/l2PoI489qz//s59klhVOFAHEhvUG6cwdxJ04Ubsx/8ew1wjV6SfqxmllADE+6b3dCIs3uDpdperG6cSbFdbPHfg1AkMft7LvR3ko2H4zOzEYv2pe1Z9KBYTf3AGutbSjHZcbYOhaMKoGyO0y165wrwsgxsvLB2MtGHkAse45ENbaYghl2aIzIK6OVEBstgXDhbnDSoPt/U65n1fVFoy1zoAYLD4DohmOnqS/cCc7PhqvgHjw4p7u9pONVqwsY9YQyoNOrIv7jYlwJft72fd5bVsw8teqWcfhu+7ZWx3dPIrV5f0RJ4gAYsP6STpz/3xVRUOVdiNUd5CoWxFYtKLhQUWxBaMRKgjMyIDKOugNEt0ggNi4fj7zIAoDTp41OZV8r5l9PP7cdQdHjdJ9G4GhjcVj5ZNA2jCOx50snLYhlL1Bov/4H3xKv/WlK8f677h5Du7ksKq/faQCorWZCoi7/URxYieHUC5RAXFxv6HAbHYNZ3kLhgtmtvla2qtowXAVEOss4R/fujTL+BDKaRUQD+abMJ69dbSmR3k8/dLPcaIF4yjWhb1G5fGpC6AawfpmQEjSEXMgpnK/X3W6WIn6I4DYsDhJdXdGANGNE+0tOIQyq4CYPQOiW2rBkKp3SPusF6fqxiknChvmDoSjwFBGrckWjL0pLRiVe+JDKiB8Vv79ZhPG8ZzWGRC3jmI99eKRvvjcwbH+O+5k3Z0cVrdgDE+szrUbI39vXW7ezUL88RaMRWdAXD3s6mXn2zq/19hoC0ac2CLMDfOe/23OgXCvFa2NV0AsMQNi7BjuxbvVFRDDVZx+bMKIS2FO1RDKC0ULxticlMGaKyCaVEDMQwCBbSCA2LD+IJ25AmjxGRBZlUPWsjF6/2Y03O3dKW3BkEbDiTpwj3W8ZA/rFSc2nwGx/cFfPhjfgjFtBsRwSnppBkQQ8D30WPkKG33Ax9MbJAoDU7y/nJbw0gUAN+4e733n9vgMiMoWjNIQymIGxHpP8m/lV5wvjlVANMPJkvcqVw67etmFts63GxtrwbDWjsyAGFZAbL8FoxxGG2PW3mbXT1I1osW3YJRP0l0FhPsdc4YBhB+DKN1rw/1nW7p5d3IN58W8AqI/dnzqVrEuuiVknjOtvAJixoXAXedWVRNA4CQRQGxYnKQzX/gWDSD2GoFud2OlVhMVE61SH125BUOqZwWEdPwDQczWz9eARUGQzzXY7RPoaRUQ3YkWjOoeYSog/FU+SZ7VDof5XOuWOzk4LTMg3OyGYwcQbgbElBaMwdgQyrOtzcyAcAH+PWcqKiAWqAK6ctDLKyCiYrPHurnQdnwGxFaHUFYEEJIUmvUG9csMoWyMtSm8eLevc61o4kLUvWeaakaBdwHES8+3JmdAHA2HUE48R9wMiDVuwZDElocZ3GvCaXk9Rz0QQGxQmlrFidXRjBe+Tj9Ve8EWDNdH1xp7c2w1wokKiHbew17ekFEH3TwNv3W0ubJPZAdajdKJxGnp5V7VeE+uC/nGw8OqACJbH1ef59iuGZkBUaNqMB+5oM49V+IavbfM4mYwvHjMAOJOd6D9ZlicHI6/92ZrOIdXds9vaAhlEUCsUAERJ6levNvTA+fbOtfaXAXE+Gupm7UQe7YFQ1pvyJykVunYIONZWmObIl68059ov5CkIDB6xYW2nvMkgHAzIB441x4JIJLU6nZvULRgTA6hXO8WDFcBQfg8nfsZ1OliJeqPAGKDXCnZ0Yz1b72FKyDC4g2wqgKiPAMiMCrS9TpVQCR5YCNRAbFpcb71wfVZ+nQC/W++eFk/8O7fO9EDhuzK7vB5NW0GRHHQHFEBURcjMyA4CD2WXj6s1b2/nJbg8s66WjC6A51tRcUJ7PjJVX9qC8Z6A4hhC8ZYBUQUzq2AuH67J2uVtWDsRRsbQul6/YcBRPZnssXfqaotGFIeMq/pcVWF2LNMzoDoTbRfOG4Vpw9GKyDi4hjYBVoX9xtqhKb4PXBcpcm6WjD2G24GBK/90xQzIKiAwAkigNgg96S2dvrws0XXcLZKIUU7GpsBEQZKbdY72elngYYx2Yt3eUOG78pvsqzi3KzyEMrscz9OJL55/Y7+u1/9vL58+VDPH5zcgVR/kKrVmKyAmJwBMVo2LLkKCD++f5jUZwbE2rgKCHfy5HqH687NbjhuAHGnN9C5djQMaCpaMMql93uNUIEZBiDr4q44X9wb24LRmF8BceUwG2L4wPlWNgNiQ0Mo+2Nhrrvi7cMazvH2iGzQ8HoeV9WmjVkaoRn5mWUVEK3K+2YBhF9DKF96rqV+Pr9Mkm7lAURWARGqN/b76GaArK0Fo5gBQQvGNAyhxDYQQGxQ+aSuagJvnKQapHbhCoji4/EKiPzEyQ2pLH+9WaMhlOXNFzfu0oKxScMWjLwCwoPkuxsn+slfery4UnFSIZQbhjZ+YuAeUxkzIOqnfPBOAHE8vWQsgBicjt97FwDcPOof67l82I11tt2YWgEx3oJhjNHZVlTMjliXW0exzrWjiU0CzTBQb85z4OqBCyDyLRgbbsFwYW7kwRYM9/Mab3NdZ8g8/v97nmaYVb+638sX7vR1X0ULhpSt4rx6u+vFcNg4SWWMdF8elrgW4oNyAFGxFtYdN6+tBaOYAcFr/zQ9AghsAQHEBpWfzFU7iIt5DQttwQgqP5ayKgepOoBo1agFo1ypwRaMzern68/cm7wPJ9D/x298WV+5fKi/+6bXSjq5OSBVV6SKCogpMyBG98QHW91dj9nKJ8ms4TyeXpyqFYVqRq5y6nR8P10LhLXDE6RV/zvnSxUQVSdX46X359qNogJjXW4e9SdWcEpuDediFRAvO59twbjbTzYSUI+HucW6Sw9aMKpmQKRrGtS8bAtGo/RcS1Orm0f9Ys3ruAcv7sla6crB9qsg+vnvumsDcqthD0otGM1ocg34IF3u+zNPuxHIGCogZnG/96fl9Rz1QACxQeUn81E8+eLn+pHHKxqqlCsgqtZwStmKtO7YTIlmjYZQlq8204KxWbGbZp9fddrm4C9J+o0vXNY/f/RpveMvvkZ/7fUPShpeMdm0qqters1pogWDCoja6SdpsW2ACojjccNa3byU03LAWp7BcONub+X/zsQMiIohlOO97efa0dpbMG7c7U8MoJSyq+mD0tX0KlcOu2qGgV5ypqlzGxqSKU2eiDd8aMGYMoRynSHz+OyLeVyY1RukOujESlJbOYRSGq7i9GEQpdv04X4P3QUFd2w3rQJiuAVjPRUQxhidaUZUQMzg2mDqcq6A04EAYoPKT+aqFz93MLxIC0Z7RgDRKh3suBkQ5a/VsQLiBlswNipOUjUjU7rqtL3fkRt3+3rnv/iC/uyfuKj//vv+ZHHF5KRCqKqrXkFg1IqCiiGUkwePUcgWDJ/F/z97bx5kS3aXB3653632qrf1ql4k0ZJaaDMCJAYkY2AwIDYzDgZsIhj+GCIYxhMTM/PHjCeCcHhswsyETYDHNrvxGMKYkFmMhgEJWqgREurW0mp1t3p9/ape1at6r+ruuc8fmb+TJ/OezJt5b+bNqtf5RXR0d61Z92ae5Tvf4njYCL3wTQjlcrAcF4Yqs1PZizK3zAO/wT4ZLj7uDKdhBkSGBSO58QwsGOWHUCYDKIHIrpn1vh2eTXFp3YAkSVgPn5sqCAgrsRFnc9F5qOFMvEeyXJ5KMJl9MQ+0vqN2EgAZIZQtADgXQZREtlEVLKlaydKTVsNZVCGSBx1daRQQGWhCKBvUgYaAqBAxBYRg8FuUgEh+PW/BGFtu7Gv1CxRCSVkVktQoIKqGRSGU56CG8+XjIQamg5/60KPQFBlrhgpZWp0FgybfpO+3rSuzFgzBArVRQJxvWK7HTnKTmR4NioFaMGhzcLcoIPgMhmWCKAdTGz1Di51a83CEFgy19BaMO2NLuElNs4bwuNmf4sp6sJGlmtAqgigjO1swB0V5RPUSEJIUkSEEVZZry4DQuPfsOCTHdjNCKIHzREDI2GQKiLgFI62GM2rBKG970jXUpgUjA7T2vlsI5QYXAw0BUSFiGRCCwY82N60cFgw+92GWgAgXO7YXWDAuaAYE+bN3e0ZTw1kxmAXjHNRw0rNBlXSyLGGzo+N0Up8CAgies7QMCDoBBpoWjPMO2/XQ0RWostRYMJaElRg3rLsk+2RoOri8HmzqThace1zPx8hy0WupMVUiD0tgwei1tEpqODcFFowosDr9OTjsm7i8ERIQoQKiiiDK1AyImi0YuiKzFjFCmSRz2nyTBp1XQIQERJoFo6Up2OnquHEOmjAsJ8yAaJMCgiwYNtqaAkNVoCnyTFMM3RdlhVACgQJi0iggUtG0YDSoAw0BUSHmKSBo49Up2IKRlgFhuW4QQpmwYFyUFgy6zqsbrZWdfr9RwUIoz0HwF23y+ft2s62tLAOChVAq8eeqrStNBsRdADNsfGlrs+9ng2KgulpeFn5R8NrJGM/snwk/N5w6uH+7A2BxBQSRCOut9AwIR2jBUEolICzHw9B0hCGUacoMgu/7uHnGKyBCAqICBURyLD0PcxHVzCYRkMzl3OtFLQb8vUT5JGkhlABVcZ4PBYSuytBVGT1DZc/V2cRmljhdlQU1nKECoqQaTiAgIMom+e4mNCGUDepAQ0BUCH7xIcqAIF/lWmv2pCKJbAtGpIBIEhAXKYTSDBUQV9ZbGJpOw8ZWCMtxYyeZdU48zIrEKXc2OxrOVmzBECkgZms4g8URb8EoU57boHzQQrilK00LxpIwkwqICzRG/9OPfQU//e+fFn6ONuxrLXVpAiIWQjmTAeGzuklCS6C0WgakHBOFUBpaZNcUoT91MLFdRkCQdak/qSKEMi61p8rQOslcy/VmrHhAyQqIoi0YHGl0PLQgSeL3lnDPOSIgSO2z2dFiFgxS5xhhCKXPNYwQ0VOmAqKlNWN/FuievEjjeYOLj4aAqBCx/nnBAoMWLDTJZ4EnIIxkDSe3qJhYXszSYVygDIgpp4AAmhyIKmG7PnSuhrPODTQ9G50YAaGvrIrVcoPfL7RgzBAQYgVEnSGeDbJhhbkFLU1uMiCWBJ0QK7IEWbpYJ2Z3xlaqvWIwddBrqdjp6gsTEJQjsdaKMiCS8nLL9WL2LSAKyPNLqnkk9aAohHJeBsRhWME5Y8GoIgMikadDCog67ymyGCWhlkhAiIKMs6AnQig32xoja0S4ttnGjdNJaffTouADV7c6emTBmNjsvoosoD73fWELRukERDP2p4EO/5oQygarRENAVIiYAkJgwRiGkzpVxGWBVA2SNBuWF8kqxTWcSYb5vIIpIDaCIKVVSfDfiKDTCTqNq3PRN06xYKzKhmMmFsIEYQglIyCaDIiLAou3YDRBZACAo/4UT754Uvj7TMdloceaMhsgd54xnDo4HVvwBM/q0HSwZqjYWoKAoCrNXkuFIkuQpNkFvePObnA7ugrPL68C786IFBDpLRhptsybZwEBwRQQhgpJCpQRZSOZp0MbzloVECkWDKXEMV4UZJwFnjQ6GVrYSQmgJFzbbGFsuSzssS5YXOAqr4DoJywYQHytzO6LEi0YIjVjgwg0Tl2Uw8oGdwcaAqJC8M0CohBKsmB0cxAQFELZUpWZgKRoUTFrwTBSpKDnEaSAuLIRTLBNEGU18DwfjhcsDrRzsOgTWzD03AqYv/0vnsDv/PXrC/9+1oKRUBa1NAWThGyTvpavUGsyIM43yILRZEBE+JVPvYIf+9W/KkxM8xs0XZFhOxfnvh+YDjwfGCYOA3zfx9CMFBCLhlBGlkoVkiRBV+IB0K7nw/MxY8Gg+bqsDdIdpoAQWDByKiCIgJBlCT1DrSSEMjUDomYCQqRMUGW5ghrOfCf8vJ3nZGhhJ6WCk3BP2IRxo2Ybhs2pSba7kQLibGJjM4OAcCpRQMiNBSMFrueze7uxYDRYJRoCokLMC6Ecmg6MMKRnHshW0RY0ZhDJMDQduJ4/04IBXIyBJcqACCbQxoJRDfiFn3IOgr8mlgtZip8IbXY0jCx37n1rOR6+dKOPZw/6C//+VAWENpucLcyAUBoFxHkGbSqM5hSMYTC1MQ0zg4rAcjkCQpUvlAWDFArJbJmJ7cL1fPQMDdtdnQX9FcWALJXhgUIyf0nUoANE1jPRIcUioHlzS7BR5Q8rRCAC4tJ6dMq+3tIqquGMj6VEzNTZgmE67gwRDQCyXB4xEtVwFlNAkAUjrYKTQO/drcFi93FZsDm70RZnqTwdCxQQ3Dhie8UyMvKgIZ/TIVKfNGiwCjQERIWgB1uRJYxFIZSmkyuAEgiUD8G/Z98yGsRJst4SKCAugrSKrpEyIG43BEQloEnGUM9PCGVHV2PKHgrZmlfFSZL6ZTq+mQIiRwYEU0A0LRgXBlaYd9LIcCNMrOA+LhIu6Hk+bNdnz4mmXJyKZyDKXEpK03nrxHY3qIBexLLIZ0AAYQW2KyAgkgqIkgkIOmkWBRVS00/a+3azP8VmR4utIdZaakUhlCk1nDWS4bfHttC6UqoCQjCHZIEPfD0ZWakVnISeEbzvouDzVYLPgNjsaBhMHUysoKltI5EBIVRAyOcjA2L/dIJPPHdU2rWcN/B2rIs0nje4+GgIiApBi4+NtpaSAeHkCqAEAs+5IkuxgEkCeXJJJpnMgAAuxsBCE8QVFkLZZEBUAT4E6zyEUI4td6ZadiNcBM5rwqDnSqQwyovUFoyUDAhZihbLQNiC0ZwcnFtQ40tzChaBxto0n/hvf/b6TGUlzWf0nGiqdGFOzFzPZxv85N884Oozd7o6bNdfqLKPJzIAzFgwonE3vrGi+bqsfJI7Y4sRbknMU0DcPDOZ/YKw3tYYuVImknk6okDCVeN4YGJPoDAoNQOClB85lK/8140tF6djG9tzLBhdI3jfRzXXTvIZEETqvHp7BCCyB4kswjSXKiUSEIYWhLGL8l/m4V8/8RJ+7Fc/cy6aRaoAP0ZdhH1Cg7sHDQFRIehh3mxrwtONoenkCqAEAEmS0FJlpoTgYSQUEG1d5j6XXbt1nmCG/uKWpqCjKyxMq0G54E9gzkMI5dR2Yw0YAK+AyF74jhkBsYQCwk0nIJK+Uf5Uh9AoIM43qPFF9H6+UTGPgPiHH30G/+7Tr8U+lrQqXaQQyiEXopgkthlxYKhsc7dI/tBg6kCSgG44llEANMFhFozZEEpgORKVx52Rha2ONpMVBfAbPvF4edif4nKSgGhplYRQJvN0lJozIHzfx8nIFCoMghaMcu51UZNSFug9uxnaY+aFUK6FCohBzQSEzQWuEuHwyvEYQNSuImplsT0fmiIJ799F0Z5TP5uF1+9M4PvAf1giZ+o8g39NLsp43uDuQENAVAiaaDY6YgJiMLVzExBAsCHKyoAgubpIAZGWen2eMLVd9rdsdfTGglERbG7DTadPdcpex5Yzc1q32Q4WgfNIKJKZlqGAMBLkXltTYLleTN1gCVLsmwyI8w0rbHxpaXKjgAgxySAgpnYgk06qAGgOodpnXbk4GRADM/o7k7Yu+jt5AmKRIEo6UKCNU5KAsOZYMMq6N++k2AjomoAobymJm/2pQAFRTQjlbAYEzUX13FMjy8XU9oQb/KBqudwMiKQSJg1EVFBDye4FUUBQ0xYQKSBeOQkUEEkLhp1QQCSDWpcFhbgvYsM4OAuUD7/92esLKSjOO3jSoVFANFglGgKiQvAKCNFkMChgwQCCDVJLEJBEidtZGRAXYWAxHY9tAre6q6thfKPB4hZAKpO91pkB4c0Qa5s5FRBkwVjG75pqwaB0+kRIU/IEs8yO+AblghK+dUUJfMA113B+7Jmb+Pg58BPTZle0saSPJecsRtQpUQjlRZhXAMTIlBkLRiwDIlRADIsTEP2pjXUu00lPZEDQBjYthLIsC8bp2BI2YADZikjb9XA8NHF5Q6SAqNKCERIQNTcynQyD0EZRyKOqSPBKqjI3C2ZA0Lx0cJZPAaEqMlqavJCNqEzYXKMIIyCOyYIR/L+4htMvtQED4GxOixAQp1Ps9gy8fmeCTy1QXXzewZORjQKiwSrREBAVwnY9SFIgNxMNfFT9lRdtXRFaMICAaDjLyIC4GBYMlxEsW53F+9gbZINP4dbCUye71hYMgQKCCIg5KphxCQoIOtlNKhtago2B7fgzJ1eKLMPx/IWC6y4aPvr0DfzLP3ux7svIDV7tcx4yIH7h41/FL36i/tdvkpKHAESk32AqJiBYBoQi1zpuFAFvwUjmygxZe4W2lAVjOI1bKmczIIL/TqvhLC+E0pqrgBARR7cGJnwfswqIloqh6ZR++pvM04nsgPXcU8chASGyYNAYXwaKtmDQfEMn8fNCKIFAzVM3AWG5PiPrmQUjoYAQ1nB64irUZdBasOp2ars4GVn4r953HzY7Gn7rs9dLva7zgEYB0aAuNAREhTBDv3hHV4UntEPTYZVdefAT3/QQfvj99ws/Z2gcARGr4cxOvT5PMG2PKTY2O3pTw1kRYhkQ4URf5wn+RJAB0TNUqLI0VwUzKiMDwokUITzagkWLzdUQEkg6/EYQQfze5/fx2xdoEWZy721LU+B4fq22AdPxzkW2TVYGBD1zyeBkk1mViICQLsyJ2SBDATEMT/d7LZVt7haxYCQVjcmMDMsVn3yzGs6SyLHTsS2s4AT4VqzZ30UZA1c24ifs620Nvg8MS8qoIFiJPJ1IAVHPPXUcql6EIZRSeXOk7XpQZQlyzpBFmm/IgrEzx4IBAF1DjZFudYDPgKD78dWTIAMiSUCYCaVQmQ0YQERAFCWg6TV/YKeDj3ztPfjYl26ei/G7TDQhlA3qQkNAVAjb8WEoMrq6MnNC6/s+BtNiCoi/89778KG3XhZ+jrdgtIU1nOff+xwoIIJr3+5ojQKiIvCnwrToq3NTNrbcmXYXSZKw2dFYpVzW9/L/XgRmSCokQ69EJ5PJRTPAh6fd/ZP31PZS/ePnEXzlrIhQWjVMx8Odc0CsZmVAEPGb3MCIFRAX416gv0URkJoDLoSyo6toafJC71FS0TgbQkntB4lxhimtlt8w+r6P04ktrOAEArJUksQbjcNwsyUKoQTEdp1lYDt+TAWg1hxCOVcBUVoGhF/ohJ9eo+OhBVWWYjafNPQM9VxlQHR1BZoiMRvJOtcUAwR2jej7ir0+eRBlQBQbr/ZD1cm1zTZ+6H33wXI9/O5TN0q9trrBj+sXhVBucHegISAqhOW60FQZHUPFxHZjEsap7cH1fNbZvCwMTl7cuqA1nEEGRKSA6E+dpt6wAlgOV8NZ86IPAKaWi46gMm6zo+NsMi+E0on9exFYjsd87Tw6gnA4/lSHQK/hGyEHYmq7F4LMJPBqn1bJYX+LwLRd3BnbtYeZkQVD5O0nC8YwodpLtsUY6gUiIMLx4epGSxhCaagy+7u2OzpOFsiAGExtrHGbQ0PNZ8GgFoyJtfxr2Z86cD0/1YIhSRIMVRZaMul9T9Y8rreD6+tPyt3QJvN0JEkqNeyxKOg93+kKMiBKzPmxHC93ACUQvC709dtdPZdyomuo56IFg4iE4EAhuK/WDJUpL9n6lFdAeMVenzxYlHw+OA0Ik6sbLXzN1XW8894N/NZnrt9VdktqxOkZ6oUZzxvcHWgIiApBDH9XV+D7wJRbuFMqd5EQyiwY3EQet2BcnAyIoAUjVECEi6C0mrgGiyPaSEhsMVwn0TO2XWG7y2Zbw51RPgWE6XgLLxAtx4MhCHdlsk0+A0JwOlN3fdwqMXXcC1Vlyat9WnMaAFYByw3u02S+wqpB72FWCOXQ25JD7gAAIABJREFUjH+OXjcaozXlAoVQhq/3fVsdnCU20gMzbp3Y7um4PTKL/45ErXbyRJGyDUTjh67KGNvL3xOkXtlMISCA4NRZtB6gsbSjxdckTAFRchAlf0JOUOT6GoVOhibWW+qMxQ4AlBKbjkQ2vnkg0nteACVhrWYFhO/7M3MlqXKoghMQ13A6rs8IirKwaAYE5W5c3WgDAH7offfjucMBPv/6WanXVydoXO8Z6oUZzxvcHWgIiAphuR40VWInqXwOBC2IqiAgeD/9xQqh9JhUjkKLzoNc+W4DyR11RWELwHpDKFMIiI4+twWDt0csGkRpOrOqBiAi8pIZEMlFM1NAFHgNfd/Hy2Ei+EXC1PYurAKi7LrDRUCLvTorhh3XYxvjrAyIqZ2soA3DWi9gCOVgakOSgKubLZwlXvtkeOR211jI/tefOkxaDqSHUIpOd9uaUkoLBlnW0iwYQKCWFK0HaJxLjsW0YSybNBPZ2TRZqo0MPx5a2F0Tb/ADBUQ512U5xUMW6ZnbzRFACQQKiDoJCFaxqvIERHDtfEMLKWBsN/6clJ0BsejYv382xVZHY9//Xe+8iram4Lc+c3FykOaB5oK1VkNANFgtGgKiQlihXDuSWHIKCM53Wgb4gZ5vysiq3Tpv4BUQNFnNywBoUBxsIaxKkey1pvwC1/NhOt5MCwYQLFTmtmBwpMOiORCWIz6REmZACBaPCqsyzb8Z+/MXjvGhf/YJXL89XuSSa4PpuLBd/8LYTXjbQFugaFk1KGytznwbvlZWJKvnLQo8ac68wkpEQFyUBevAdNDTVWx19NkQykR2w05XLxxCaTouLMebVUAICQix3auMFow7ORUQovdtYrlQZGmGIKFDktIzIFx/hvitUwFxPDSxK7BfAMF1lRlCWVQBQfdM0h6Thl6r3hYMEdlGa7qNeQoIr4IMCJUOE4qNVwenE6Z+AIC1loave2gbX3j9tNTrqxO0N+gZ6oXYJzS4e9AQEBWCNitdI1RAcJslmhzKIiBo426ocswjeOEyILT4RNsEUZaPZBq7WqPvlk7dki0YQHCKN7cFg9sgLXrik0ZAXApPw0iGCWS3YBRZoO6fTuD7wNFgusgl1wZawF0UFQS/aa47hNL3fXY9dSap8wRMlgICiDcfsBYMLfJvXxTP8DAMfN5saxhZbmw+nFVAFK+AFika81owgOCEthQFRHjd2QoIWfj8ji0XbU2ZCeOtzIIhIHNVRa6NDD8emthdE2/wy8yAWCRkkeYcUT6FCHXXcIrItq1ucB/FCAiBQtd2PRaOXRZaevB7iiogDs6muLYZD2W9tGawwNK7ATQWrrXUJoSywUrREBAVgjYr7VABwZ/WDtiCpaQQynAgT8onL1QLhu0xpppkek0VZ/kQnWTWJaWmUz+xAkLHxHYzN4ylKCBSTqS2ujq2Ohpe4qwS2RkQ+Sfvs5Sgv/MOei8uShMGLwU2FqxiKwv84q5OCwa9h1sdTUhA8B/jmzDMxLihX6AaTspn2AjnFf5vHJhOLAx6u6tjbGWPO6KfDwC9Fn+6q+S2YHR0pZT7MrJgLKCAsN1YgDUhUkBUEUI5a2erS111MrJSN/iKLJemzBBZT+YhyoDIqYAw1BkL1SohqpzdFFgwaH1qJSwYmlyyAiK8r82iFoyEAgIIcjhOhtZdE0TJKyAuCqHc4O5AQ0BUCCv0lnf1WSk3LVjKyoCgDVRyIyeSuJ1XTB1XoIBoLBhlI+nPVJX6LBiR73j2OaCTkqwg0lEsA2JxC4ahzi68AeBNu128dGvI/j8zA6LAApXkzHX4dP+3j34Jv/vU6wt9LxEP0wtAaALxirG6FRD8KV+dyi7a6F5eb2FiuzNzw+nYZvc0f4pKX2fwGRA1zysf/8pRrpNeslmIxpSgvSKugACKvUeDNAVETFqeYcHQ1IUzbHicji1IUjzoL4msDAiREk0N1zBlKyBEG3FVlmohw23Xw+nYxm5KyKMil9dyFDQpFTvhXyQDAogrBFcJtsaYE0JJ77/tRK9tEEJZsgJCLT72j0wH/amDqwkFxG7PgOP5pRNydYHGqF6TAdFgxWgIiApBNUSUAcFPBoNwMi/PgiEmIGRZSk29XhXyLlxM22NMdVtToKtyo4CoAEl5pFri6U5RZCkgohyQ9HtgbDqM4BstHELpCkMoAeChvR5euhUpIESL5kVaMCIFxGoXMb7v47c+cx3/+Ys3C3+v6/nspOqiKCAoOPE8hFDyi7s6LRhTjoAAZsfn04nFFt38/cksGNSCodYbQnlnZOHHfvUz+I0nX537tYPQZhERENHrn2yvWIqAELRg0EkpbbJEm6tWWRaMsYWNtsbGJBGMjAwI0TgMBJvGQSUtGLMWjDoUEPRepykMSAFRxqn3MiGUeS0YdB8OzHoOcIiY5BUum4IMCEUOMqhonAYA2yu/BUNTgt9TZOwn6+W1hAKCSKBbd4kNg8aCbtOC0WDFaAiICmG5PnRVZqcK/AkHSVu7JWdAiCSUyZOYVWL/dIL3/Mwf48kXTzK/zvf9QAGhRr3RWx2tacGoAFEzQLA4UGtMHp9kZEBENpxsBQQll48XPO1Jy4AAgIf2ujgamGzxbbuzjRlRlWkBBUT4/K9aAXF7ZMF0POxzuRZ5wdu4LkpYleVEJ3HUsFNXjei5UUCEG90rIQGRVBidjm3cu9kBEL8/eTUJEIZQchvsVYM2/V+6Mb8Sb2g6WG9pbBNEf7Pv+xhO4zWcOyEBUSSIksaHtZgFI94wxAJRhQqIckIob56Z7H1NQ2oGhO2iJRiHgUDZUb4FYzaEUq0phPLWINhMpikgSBFUxqWJiJd5YCGUF0QBIVL7sBaMdvxvSFqCHNeDVnILhiRJaKlyobF//zTIZ7q6MauAAILa1rsBlhuEz7Y1BY7nw7sgAdMNLj4aAqJCENPdYSGUcQuGocqF05DTQNYFUZ2hoYoXHKvAy8cj2K6P63ey0/5t14fvx+tEtzp6Y8GoAHwzABBaMGrLgAgWtSLiLE8OyNhysBcuCBZXQGQQELs9AGCVmcIWjGUyIEqutpuHg7NgUXVwWjz8kl+81WVjKIroXpdqb8Hg/cd1EqvMgrExS0C4no/B1ME9W8Gpn8iCQc8KjdV1qSDoef/yQX/u1w6mNnpGEEIJRKSm6XhwPD/WghEpIPJvMKIMiLgCAojuQUfgiyeU1YKxfzrBtc125tcE6wGBBcNy0UlTQLS08kMoRXY2pR4ynMimNIvDImN8GuhgqgiIqElr6UiC7sNhTQoIIn75e31bEEIZfE3cdlOFBQMIg14XUUBsJjMggnvkeHh3HI6Zthfbi1yUXJ8GFx8NAVEhbDd4sLushpMLoTSd0gIogWiCEkkodVWuTTJNKf/jOSe9RJDwG9Gtjt5YMCoAs2DInJe7JtY7qwWDTiszFRCmi71QAbHoxjIthBIAHt7rAogICNv1hcFpwGIZEMMSfN9FsH8aLKpORlZhEoH/+ouigCApsK4obGw5FyGU58KCETw3fL0i/fe9REDEQihdqKFkGogUVHUFl9GG/eXj0VwrE7VgbCQICJF1ggiIkwIbDGEGRCJ/iTZZos1VW1dKIfX2zyYzqf1JpCkiJ7YrPMAAAgtG6RkQQjK3HjsgnWbvzFFAlGEPEbV/zAOzYOQOoQzex7pCjm2B2ufxezfxD771zfgv3rIX+1pdjWeS2J5XugUDCFTCRZ6x/dMpJCmyqhHIBnNSgKA8z6D1DxuvGgKiwYrQEBAVIphgo5M3Xg6XlH0uC1JAiE6SjUQd2Cpx1A8G6dGczSGdrvIKiO2uXmta/N0K2/WgyhKra63TgsEyIFJqOAHgNCOEcmw5jIBYVAFhOR6MlAXP/TsdyBLwYpgDIVo8KkrxDIi6QihJAZH87zzgF4kXTQGhqRIMVYYkFU9CLwtEAnd1hbUV1AEiYEQWDHrW7glP/ZIWDJ6oYwFyddm3uDnl2QwVhOv5GFkueobKAvCSGSy8cmG9FWQoFFGpiGq19dAWyQiIrBDKEhQQY8vB6dieSe1PwlDFIZQTOyMDohILhgctQfxqNSkgqFZxngKiFALC9aCrxU74tdBCJiLqRaBWl6IKO8/z8b/8xy/gr1+9U+j7khDVcGqKjJ/68KMzuWdGMqzV9Uu3YADFSb6Dswl2e8bM4cR2V4ckAceD1RIQvu/jqdeWe19EoLB8poC4IIcLDS4+GgKiQlANpxz6q+I1nHZpAZRAlAEhtmAoNSoggkF6XsI3KSAMbgG02dEyT78bLIbkRkKtsYZzkhFC2dYU6IqcuhHwPB9jy8VmR4csLZcBQQReEoaq4N6tDmvCsIQZEAsoIKZEQKx2M8xnPxycFsuBuJAKCO4kLvABl1N3uAiIDLm80ao5AyK8Dgqh5AmIMUnRDRiqHLdguGICoi5ym59TnsnIgSBicq2lQpElrLfUGQsUX8Mpy1Jo/8v/HvWndpgzEo1jyQW9LZClE9q6iontLuW/Js/6PXMsGKk1nJa4hhOoKoRyNgNCqSkD4mRoQVfl1DVZqQqIBTIgDE3GTteAJOXbmHfJ9luQ4H7q+h38P391Hb/yFy8X+r4kohrO+derJw7IHLcaBURLK5YBcXA2xbWNWTWRIkvY7ug4XvEY/onnb+F7f+FTeO7moNSfS+vBi9SY1+DuQENAVAheYtg1lJkazjIJiKiGc/YtTQ7wqwQREPM2WlHCejwD4nRsNaE4JcN2/cTJRH01nBM7XQEhSRI2OxrOUkgo+t6urqCrq8tlQGQseB7a67ImDNHikfmDc5I4vu/X1oJxcDplf+t+QQVEnIC4IAqIRG5BUR9wUfi+z2xnSRAJfHWjhbOJXXvw66XQgiFSQGx0NKy11HgLRugVJtBrWht5yb2PWTkQEckQzLcbHY0RLdQSkJyLd7p6IQsGWTx4RJ7q4Dpt14MsQdhQwSpil3iuyF41NwMiJYRyklLDCYQhlFOn1MDRtErjOvKIbg1N7PXSN/iLNB2lYZEWjP/mgw/hf//ut+X++rWQUBsUnF/+6EtBO9KfP39rqfGJxoSkwkUETZFidb625+ciLoqirRVrmtk/naSqiXZ7xsoVELQGSZtfFoXpJDIgGgKiwYrQEBAVgj8xaickloOyLRgpNZz0ubo2DIf9YLCcx8TT5oaUHACw1dXh+flrPBvkQ7JKsq5FHxApINIWvpsZTShEOHQMFR1j8Rq7rBYMIAiifPl4BMf14PmY+Vpqwch7Oja1PbZAW70FY4LHrq0DiDYseREPobwYixQrIQUOFqHZ134yNBc+ZfqjL93EB/7Jx4XZNbQRvbIeLGqzrEVVgsbajbaGliazRhYAjOzbbGvoGmqmAqLuEzOaTx+51MMz+xkERMJmsdnWGekiym4AQvtfwRrO5M+g18fkLBhpG8+oKasMAmJOBkRWDWdaBkRLgxsqzsqCsIZTrqeG82RoZeYrKAXH+CxYiQOAPPja+zbxrY9dzv31iyggfN/HHz1zEz0jIJuevn5a6Bp5RNk78/9OoQJCrkIBoeQm+Hzfx8HZlNURJ7HT0wu15JQBer6TrUXLIgjhVpoQygYrR0NAVAiLO1nt6mpsMhiasycmy4DVcAoWEHXWcN5iFox8CoiWxisgAha/Tr/03YjgvoxOGAILRr2biJaaRkDoqTYcIhwiBUT5IZRAoICY2C6u3wkWAMu2YPALiFUrIPZPp3jTbhe7PZ2lfOfF1LnACojwPTM0ee4i9Kd/62n8+K9/ZqHf9/zhEJbjCccsXgEBAHdqsmFMLBeyFLwmG+24woiIk82Ojp6hzmZAJDzdQP0hlO97cAvPHw5S57hBUgHR1hj5MyyJgBApGg2BBSNt40kb/2UaWvZPJ8LQvCQCBUT8tfI8HxM724IBlHsYIFICqIrEsjJWieOhmVrBCUQWjDIUEBROXiXUMDOiCAHx5YM+rt+e4Kc+/AgUWcLHnzta+PeLMiDSMFvDWU0LRquAAqI/cTC2XFzLUkCsuIbzRrj+KDuLZSaEslFANFgRGgKiQtjcxqaTkP4Opk4seXtZzFdA1BVCGSog5sjj0xQQQL2J8Xcj7Bkvt1TLqROAcNErs0DMJDbb6TkgZOvp6GqgMFpgM+96PlzPh66kh3s9FDZhPHczOGUVyYbpZ+UBLeIVWVopAeF6Pg77U1zdaOHqRpt5xvOCD2+sK1OmKCwnHrja1hRMMxahzx708cQLxwtnzxyG8lhR2BmdLF0JCYi6xjUKG5QkKSAgBBaM9ZaKrqGyzTsQkE78+EzPQV0LVmqVeu8D27BdHy8ciVUr9IxR69RGR5sNoTRmCYgiJ5yDqT2rgEgQEI43azkgkAJiGXvQ/tkUl9daczd9uqLACcc9Aq0P0kMoQwKixM2PLaijVOV65qKToYWdbpYCIhzjS1AKiqwnVaBnqIUsGB975hCyBHzfu+/Fex7Ywse/cmvh301jXR4iIamAsL1sS+SiaGni8FURKCspUwGx4hpOuqayFRCW48JQZGaXaRQQDVaFhoCoCCTXpsVAh1NA+L5fugJCzyAg6lJAjEyHnUrPCwhkGRAxBQTVMDYERJlISl8Vub4azomVnrwOhDkgE/H7TyF0XWPxDAhLcN8l8dBuDwDwlVCWn1w0F/UH0wLiynprpRaM46EJx/NxdbONqxut5SwYF0QBkSTb2lp2BsQvfTIIX1u05ePwLJ2AINKG2idqJSDCDe96K16veDq2sWaoUBUZa0b8mTKTLRhq/QoIRZbwtfdvAgC+nGLDoPDENWbBiFQfohYMICAgiuR0DKZOLMgSmM3IsDPC9cqyYMyzXwDRWMevCeiZSLPCrbeD16fMIErRRlyR5ZXbAX3fx8nIxO5ahgJCKaZyy8IiIZSLIKlgmoePfekm3vvgNnZ7Br7lLZfw5YM+bhbMCSLQPZ+HSNBWpIBoa3LucZ3UgVkZEEPTWWkbFFNAlGxJNsMQbqNRQDRYMRoCoiKwAZhTQNDiYmp7cD1/ZsGyDAwua2L2c/mZXyBYGH/rz/3Z0mm7R1xIz7zNIS3OeSk+WTAaBUS5sBJSYK3mGs6Onk7EBRkQtjD8bGRFCohFMyDISpC1ULq8bqCrK+x5SC4e6f9zKyBCAuLqRmulLRjMI77RwrXNNvZPJ4VC5aYXVAHBv18tLb2K7ag/xUefvgFdDVphFtlYRwqI2e+le40pIGoiVqdc20FSAXE2sbERjrtdQ43V+CWzUupesI4tFx1NwYM7XXR0JTUHYiaEMrRg+L6PwdSBrsgxZQcAlgeQx/53NrHx1aMhHrnUi308agkJ3nfLmW19IND7Ma8tKgsBAZEdQAmIsztYGHAKGUzqkbI2P57nw/FmLSl1BCL3Jw5s18+lgPCWDOH0fX8mBLoqJJ/fLLx0a4jnDgf49rddAQB8y1v3AAB/9vxiNowiFgy+htP3g/uiqgyIvAojUgemEXpU17oqG8bUdpkiq3wFRFPD2aAeNARERaCHOGrBUBkBQcnbpYZQhgsHkYezqALipVtDvHA0xBMvLC7BAyL7xU5Xz5EBQTWcnAKiSwqIJgOiTCQzD1SlvhDKaWjBSMNmR4fleMINHVkuIgVE8c18siVBBEmS8Ka9Lp47FBMQiyogrm22MbLKTZbPwkF4mnV1o41rmy2MLDcWQDgPPIl5URQQVkLmHSxCxWPhrz/5KhzPxw+99z4Ai8nhD/vBglSUkUGv35WaMyCmTqQ6mrFgjC1shgREr6ViyBFklJZO0BIn/KsGhSYqsoS3XllLVUDMhFB2gkDFkeViaNpCJeJ2AfvfEy/cguP5+Jtfcyn28eRGP9uCobK/aRH4vo/9s2kuAoLmWP4eJTuLKEMKCCw5QHkWDMp5EI2lq67hPB4Fz+xelgKipAwIkrdnzTdlIRkim4WPPXMIAPi2twcExFsur+HqRmthG0ZEQBSzYLD2jIpaMIooIBRZwqW1FAtGN7hXVmXDuMGpFftVEBBNC0aDGtAQEBUhOdEELRjBZJCWvL0MaLFTRgsGXd9Xj4ZLXdNhqIB4cLc7VwrIFBDc9a8ZKlRZqu2k8G6FnQiTUxW5luAvIDjxm6eAACBswhixEMrFMyDMHAQEENgwXjkOarDSMyDyvYZMAbHZgu8vJ7suAj4lnzYqRYIoafGmytLKFRB3Rhb+/q/8VeEKsmRwYlsXL0LHloN/++lX8bceu4y3Xl0DgMysCBFs12MnYmIFRPCxnqGiZ6i4PaqHWOXbDtYFCojNts6uc2hGn7OSBETdIZRcbeTbrm3gywd9YWUzzWddPWrBAAKyZTgV12HTBuNWjqq9P3n2CFsdDe+6fyv2cRpTWAtGDgvGohkQJyMLluPh2sZ8C0aynQMAa4bprCiEMk2irymrt2BQnSK95yJQC8ay11blBjuJtUIExE08fu8G7gnnBUmS8M1v2cMnv3q80IaUHcDlquGMQrBJ/ZL2nCwDQ1Mwtb1chP/B6RSX1wxhZS4AZtdZlQKC5m5ZqkAB4cYJiLrG8wZvPDQEREVgBEQ40XR1hcmtk5LQMkALGKpf4qEXDKGk4KLnD5e0YIQKiAd3unM3WXSiyi9wJUlCr1XMx9hgPmzXg6ZGE6tWZw2nnZ0BsRkufEUqGCL0OrqCrq5gvMDinZ7TeankD+11QXub5KKZKSByvoZn4SkiJWyvKojy4GyKtqZgo60xb+tBgSBK2lRvtLWVh9o+s9/HJ567hadfK1YNN5sBIfYB/87nbuB0bOPHP/gQux+LbgaPhyZobSsifFneiCpjq5teL1s1+LaD9baGoemwjfspZ8HoGSqmtsfsWabjzoTXAvWFlk0sB+2QVHjbtXUMTQfX74xnvm5oOuiGSgkg2kyfTWxhewUAvGk3CJ596TibhHc9Hx9/7gjf8pZLM5uVmRaMDOl9W1suAyIiF/MoIILfZYosGCkKiDWmgCiJgHDEJ+RKDSGUJG3fXcuyYAT/XvbaitRTLotuzgyIg7MJnr5+im8L7ReEb37LJQxNB5999Xbh310kA4JvwaDvU1M2/sugpc0Sb2nYP5vgasazRHadlSkgwvyHN+12C6kW88C0A2I5sow1BESD1aAhICqCnbBgdHQVE9uF5/mpydvL4B33bOAff9878IFH9mY+VzQDgoKmXjgaLiUPvzUwoatyKPfOlprTiWpyI9guUJ3UIB+sRAiWqsi1ZUBkdc8DgQUDEAeREqHXNVR0DHVu0KkIyZrGNNCGBJiVDVNgVpEWjK6uMHXH6giICa5utiBJEvO23igQRDl1XGiKhLauxBoxVgF6jYpu2pMKCJEP2PN8/PInX8Y779vEex/YWpiAIPsFkBJCGV6LJEnY7hSreSwTE9uLWTB8P1IJnI1tRvp1w/mJnjPL9WJZCUbNJ2ZBfkxwPY9dWwcAYQ7EcBoPfKbn7mxsoz91hErEy+sGeoaKF+eoAJ967Q5OxzY+lLBfAFwLhhspIOa2YKyAgIgUEJwFI7xf02o4DVVBS5NjrSjLgEn0BS0Yq76f6BQ7lwJiWQIi5e+uAoGFav779f+S/SJBQHzjI7vQFAl/9lxxG0ahGk7OIuwU+L6iYON6jmfs4GzK6pJFoMrWWytSQNw4nUCWgDdfXivfgpGo4ayrMa/BGw8NAVERkhYMUiZMbJezYJQXQinLEv7u37hfKCWnAT4vmUDXN5g6sUV1URwNTOz1DHQNFb6fvaCnxVByATQvtb5BcSQ3ZZoi1deCMU8BEW4WTgWT7thyIEvBRqirK7Bcr7BcNE8LBgA8vBcFzCUXj4tkQKy3NSYJX5XCZ/90ylQXl9ZaUGSpsAWjpSq11PrSa1TUtpBU+4gIzT97/hZePh7hxz/wJkiSxHzwRTeDfGK86PXhLQxbXb02BUQQQhlcxwanBvB9H6cTmz1zVBM9DJVGs+PGOQihDN+rN19egyJLeGb/bObrkioHfkwZphAQkiTh4b0uXrw1yryG/+/ZI6iyhA8+Okv8JzMgstoP2ku2YEShefkzIGIhlOF7nDUWJxtTloGVstFUa6iEPh5akKQo90OEolXLaUj7u6tAL4cFw/d9fPTpG3jkUm8mRLVnqPgbb9rGx58rHkRpux5kCakWBh58BgTNoVW0YNDacl5+ke/7OJiTp9IOVZerzIC4st7CdlevJgNCUWYUWw0aVI2GgKgIyRBKkoqOLGemFqxqGImTmHngk5PTutXz4GgwZQ0CADIT/6d2MGElpXdZqfV14sd+5a/wy2Fl30VDsCnjFBCyXEv3OhDfRIhAVazCDAjTRVdXIUkSe76KbhpZBoSSfg1AUgGRzIAo3oKx0dbYCfNKFRDhqY4iS7iy3ipswTA0BYa6+meS7DaFFRDurALCdLxYVsCXbgSb1m997DKAYidlPPh8CrECIrIw1KuAiEg/Fi44DewIruezjAR2f4bzwUwNZ90ZEJbDxo6WpuDRSz1hEOXAdGJk/0YOCwYQkI4v3spWQPzpVw7xvge32c/kkQx1CywY4o0VtT9NFmzB2D+doKXJrDkqC7QeEFkwssbitZZaXghlikRfleVaFBDbHT1zsxyRzMtdWxFrwrJIWqhE+M1Pv4bPvXaKv/8NDwo//y1vuYTnD4d4XWBtykJSZZmFuAUjXDdX0IKRd1ynPJUsBQQQ5ECsKgPixp0J7tlqs8yeMoOrmxDKBnWhISAqgp1UQHCnalVYMLIgWnBkYTC1GRHwwuHiQZRHfROX1losZDCrYsx0Al+yJMUXAW39fCogPvPKHTx1vZgfPQ3Xb49ZL/0qYLvxOjilBtkrYWq7qcnrAHdamZIB0QmVRfR8je1iC+Q8LRhAsBm7sh4sSFIzIIooIFoae/5XUcVpux6OBmbM13p1o1XIgmGGjSUtbfUKCGpjKLppNwU1nPRxwkF/iu2uzj63uAVjCtrDpIVQ0li83dVra8GY2JHtid+M0zO2wbVgABFBlhZCadWUH5Os8H3s2rrQgjGtBOhpAAAgAElEQVSY2jGyPwqhDAmIlIOAhy/1cHA2TSUIr98e4/nDIT4ssF8AIgIifVMmy9JSar/9s6CCMzl/iiA66aQQynl2uLI2XGkSfbWODIihyWpX01CaAsJZnQIiaaFK4pXjEf7RHzyLDz66ix/+uvuFX/O+B7cBILVhJg12RuVsEqSA8H2f5ShVo4AIrkc0NvMgUp5yktKw09VxMlpRCGX4fG+0NTieX9qa2PP80FrXhFA2WD0aAqIiJL3lHT2aDFgI5aoVELkJCAeX11vY6mhLKiBMXFo3mP0ka6OVrHgjnMcMCMrxKCON2PN8/MC//BT+yce+UsKV5YPIglFXCOXYclOT14Fgw9jSZHEGhOUyG0Nnwc285QZfn6cW7aG9QAUhWjQDBVowpk5gwWDPRfUKiMP+FL6PWEr+tc02q+bMg2lIEgaZMjUpIApu2kUhlECcXDgI5a2ERRsJDvsmLq+3oMqSUAFhcQqCra6OkeXWou4KLBghAdGJCAgaz4iU6IX3J09A8K8lW7DWdGKWzI957Oo6jgbmTHNFsumipQV+57PQgtEzxKqBh8Pn/eUUG8afPBt45z/8NZeFn1dlCZIUKQ+djBBKILjvFrVg3DidsgaDeaAcD56Eo+crLQMCAN56ZQ3P7PdLIQiijXgihFJZfQ3nydBinv40KCUREEXqKZcFWagG5uw6xfV8/IPffhqaIuGf/sDjqcQVtT0UJX6TKsss6IoM3w8I/CpbMFo5iWWyJVJOUhp2ewaOB9WTyK7n4yB8vtdb0XhdBnireN2WugZvPDQEREVgUjuVCAjyeDoYmg5amrwSFhwQLziyEEhWVTx6aW1hBcTUdnE2sXFpzcilgJjabizgjBCExp2vAZE80WcleLifPxrgsG/i+u1iEsdlkPTFq4q8tLR0Efi+HzuNTcNmWxcrIEyBAqKghDlvCCUQ2TBE3fVAfgVEf2Jjva2yTdFgBQQEEQ0xBcRmCzfPpsLqQhFM22MKiHmnSGWDNsFFK3lFNZxAgoA4m8YWm60FLRiH/SkurbdSMzJMJxrjyG8uuq+rRtyCMauAmA2hDKwZjufHCYiaU9OT5OVjV4MgymR7U9JmIUkSNjoajgZTWK6XaoWk3Jev3hKT8H/ylSM8tNuN2bN4SJIETZFjIZRZ6f5tfXGy/eB0MlcyThBJrYkIy8qAeM8DWxiazlKHEoS0MEZNlldOQBwPTezMISCK2uzSkMwGqxJZCoj/+89fxOdeO8XPfOTtmSf9rO1hEQIiJ8micSfvrKa0khaMcB08h4A4DNvbeFJahJ2esRIFxK2BCcfzmQICQGlWKL4FLEmYNmhQNRoCoiLQySptVui0c2y56GeculSBot6uwTSQiD96uYfnDwcL+c3oFOrSWiuaCDMWV6bjMYkcj7Z+/jIgSMFSBgv95IsnAAK7yqqQ9GdqsgTb9Uv1FeaB6Xjw/WzZLxDYMO4INmojy0FHC+6tdo6ckbRrAOaHUALAQ+GGRFeTGRDFajgpA4IUUKtQQLCUfG6Tcs9mG5br4TjnImrqUAhlDQqI8H1dVgHBgsi4MeVmf4or3OtC91LRceewP8WVdSM1tyamgAizTVadA2G7HhzPj7VgAME9eToJroWaZ3pcBkRUIRo9q7TBqEMB4YUyZD6z4P6dDgDMkLnJFgwgIFleD6vt0qyQD+x0ocgSXjyaVUAMTQefful2qv2CYHD+dmvOqXBbW0wBYToujgZmrgBKgLdkxlswVFnK3Bi/+/4tAMDnXl3eepiWhUA1nKuciwIFRLYFgyIJlm7BWGENZ9JCRfjyfh//5x8/j+98x1V89zuvZf6MlrZY2GLRDAggGB8jC0aFGRBzxvWjgQlZwlxSarcX5PhUbRm6cRqMZ0EGRPCelqaA4CyokiTF8jgaNKgaDQFRESwnPsHyKoChKU7ergqiBUcWBlNSQPTQnzozklbfn79AoEC2Pc6CMc7YaKUpINqafO4sGDShlzEJfCokIA4H+aXwyyIppabJftXeW3pfs07dgICAOJvMLoAmlsspIMIQyoIZEGaBBeG3v/0KfvA99+L+7fiJZxEFhOv5GJgO1lsa2poCWVoNASFUQIQnX3mDKIMQShlGHQoIi1owFlBACAgIuvcmlovTsR07BVymhvPyeiskIOZnQACrJyDobyKSpaMrUGUpZsGg3JUeF5IqykpRwhOzOjzDlGTf5jIgrm60ocoSXuMICM/zMbQcJkcnbLQ13AgJiLS5WFdlPLDdEQZRfvKFW7BcDx96q9h+wf+MqGIw2xff0RWMFyDbD8+C+TkvASE6kJhY3txx+IGdDra7Oj732p3C15hEWgYEkVr8WPrczQG+5+c/iZfmBIIugqntYmA6cy0YZSkg2An/Kmo4ExYqwr954iV0dBU/85G358oM2e7puF3wpD+ZM5UF/n60mQWjwhaMOXPXUT9QxMxr8NjtGfD84sHIRXEjnJ/viSkgyiEg2AFM+B7oNTRcNXjjoiEgKkIktQsGsQ53Qjuc2isLoAyuoZgCggiSRy+vAQBeSHSh//yffhXf+wufyvwZdKJ/ac2I6gYXUUCcwxpOajE5m9i55esiuJ6PT790AkkKpNirOlW2E80AqmDRtwrQYjsreR0ITotFUnU+AyJPzogIVmICzsI9m2387A++c+aUUJKk8ORu/vNF985GW4MkSejq+bral8XB6QRrLTU27pBkO28VJ9VwtmpRQASvUX/qZKa6J2EnfPfthAKC/nZevk4L1SKn0WQ5u7zeCggawetjOR5T2mx3g4VkUUvJspha8bpjSZKwESarsxDKhAVjaDrs/ebv/chisPr8GHpv+LFDkSXcs9XG9TvR/Ty2Xfj+bN7SZkdj733WXPxQShPGn37lCGstFe99cCvzOnkCIo8FY7oA2U5BsstkQExsJzMMGAje73ffv4XPvbo8AWGlZCEogo3+F2+c4fOvn+En/91TpashyVowTwFR1GaXhqQytkqQyjZJcF+/M8Zbrqxl1o7y2O4axS0YTgEFBBeS7jALRnUKiHn30NFgiktr2YQUABZcWnUVJxGl16rIgEgQy4a6+haaBm9cNARERYikdnTSFCogbDez+qsKFM6ACCWrj14OJOe8p9bzfPzmp1/DswfZqchHoWri8nqLI18yWjBsT5wBcQ5bMAahBcPzo5PZRfDl/T76Uwfvf9MOAMwoTaqA6/nwfCQsGPWkH08Sm6E0bHZ04SnD2Ixq+PLkjIiQtwVjHhQ5X3gaLRzWuU3eSiwYZ1NcS3h9acNyI7cCIgyhrKEFgyeWTgssvpLVkckMiJuhMoS3YCihFL3IuEO+4cvrrcCikqKAIOKP1cuuWAFBp3/8aTdVu51N7DDjI/icpsgwVBkj05k5KSMYNUl2mXoqsWm+f7sTU0CQXY6v4QSAjbYOelyzwqAfvtTFK8fjGOnl+z6eeOEYH3hkd+4mixL+gZAMyxhnOrpauMUHEJNo864JSCogsuuQCe9+YBMvHY+Wvm9pfZQW6MvPRTSmP3vQxz/6g2eX+r1JHIdz7k53ngKiWNBwGkgZu4oQSiLl+Vp1ANg/ncasePOw0y1eGZzMmcoCjYm2G1WGVtmCkceCcXlO/gMAppqpuopz/3QSWDYNNVJATEsmIJRozG8sGA1WhYaAqAiM4Q8HYd6GQBaHVaGIAsL3/bC2TMNez8BGW4spID776h3c7E9Dtjr95x0NplBlCdsdncuAyLBgOK7Qh9/WFFiOt3J7QBb4E+tl6jOffOkYAPCRdwU+zKMVEBAi6StTQKz4JHPCTjGzn4W9FK/lyHLZvdVZMAOirFAwVZbg5nj9KDwqOmVWVlLDeXA2wdVEqvdmR0NLk3GQs4qTWTBUeeW5LPzYUWTzYzluTO3TUuMyXGZNSZAznYKn0Yd9IlyNsKY0RQGhRtkLklS/BQMICIj+1MHp2GIVlYS1VqDQ4cPKeGg1nZiJFBAAcO9WJ5YBQYqjJOFPzx8ArGXkMT2814PleiwvAgBevDXEwdkUH3x0b+518p5q2/Uyw/XaC7ZgsHyXJTMg5lkwgCgH4qnry6kgkiHdBJqL+LGe5tsfef8D+I2/fBV/+MWDpX43DwoR3J1z4q0UzPlJA6tnX4ECgu5rfr3ieT4O+9OYFW8edrp6tRkQtD51PdgeETQVtGDkzPY57Ju5FBCkmqmagLhxOmGHBbRvqEoBwROmDRpUjYaAqAjJiYYWviPLZQqDVaFIBoTpBEnEay0VkiThzZd7eIFTQPynz99g/521cTrsm9jtGZBlCYYqQ5aiIDnh701RQOSVza0SA+5EYZmJ4MkXT/DQXhdvu7YBYDVBlKbgxJ8yIFZtwWCboTkL3921wGuZTJweW7MKiKJqmSItGFkorIAIn/+eoa6mBeN0OrPJliSpUBWnGdZwtjQlDBBd3f0yMh224CuyabddP6GAiJ+C3UxJPC9q/brJKSBaqjiE0nRcdi2qImOjrVXuH05C9MzxFgzKfyB0jYCAIEVH8jnRFKkmAiJ4ZpIExP3bHdweWWzTRc+WyIJByFRAUBMGR8I/8UJAHH/w0d2515m0YGRtrBatnL5xOsVOV5+rJCOwtPuYBcPL9f2P37sBRZaWDqJMy4CIFBDR2DI2XcgS8L/+7cfwtfdt4n/6D1/AqyfiatSioBrFnTl2BCJGvCXHPLskwjsPuoIMiONh0KiQVy0DUAaEVWi8n3ev8+BDKCNlTAUKCHX+WtJxPZyM8hIQpICo3oJB5KKqyOgZamktGLQnYBkQjQKiwQrREBAVgfVchw+2LEvo6AomFEJ5TjMgaHNN1/fIpTU8fziE7/twXA9/+MWbbMLIsh8cDUxcWg8GaPK6L6SAENTm1Y1hCQSE7Xr4q5dv4xse3mGv09EKgigjYoyr4WT+1tVOPLSJmNeCsUcTPde5bYVEGSkggh5rqbCdwXRcKLK0dOq2Gqa3zwNJJzco6K9VvQVjars4GVlC2e21jTbzkM//OV7YghH0tq/ypGRkubh3K2g5KLJptxJ1cCyILNzo7Z9OsNXRZu7BdsH63yOegEgJ6eRDKAFgu1Nc2rwsaIPLj7XrLTVswbBjygAgIMhGvAJCSxIQ9ZyYRQG28Xn0/u14E8YwMZ8R+L8zyw75SEhA8DkQT7xwjAd3Orgv/F1Z4E8UnbkWjMUVEHnVD0AwHyerYieWk0sB0dFVPHZ1HX+9ZA5EWgaEKBB5aDroGip0Vca/+LvvgiQB//1vPb3U7ydQA9C8EMqyMiDSiJcqoCpBZTI/v6QpvrKw09VhuV6hrKIiIZR8DSetQdQKMiA0RYIsZYdQnows+D6wl8OCsd7SoMoSTpZQQNw4ncwl0/ZPJ7h3K3q/1ltq+RYMTgHRZEA0WBUaAqIiWAKpXUdXMTTDDIhaFBD5Q/LIM/vmyz2cTWzcGpr41IsnuD2y8O1vvwJg1lvI46gfD/LpGEoOBcTs7ZhMrT8P4E+sFyUgvnjjDCPLxdc/tIudrgFZWo0CQmjBKEleWhR5uucBYC+8j25xE73oBLSjq4UX8JbjlSKHVXL210cKiNCCoVdPQFA+gUh2e3WjlTuEMlBARBkBq8yBGJkOW4TdHuV75lzPh+v5zN8KcASEE2VAXBEsxltaQBbnxWF/ipYmY72lptaUJhs5trvibJMqIXrmNtoa+hMbZykKiAFXw8m/lgAtWM9HCCUA3LcdvJeUAzHMoYDIskNudDTs9gxGQFiOh7986QQfyKF+AIL5n9RCVg4LxiLz3MHZpNCJNn9dhGSlaRbeff8mPv/6aaEw2CTSrAiKgAwfcZlZ92138KNf/yA+99pyv59we2ihoytzSfCyWjAosHUVBAQwq7ArmhcCBCGUQFHlmZdbxUD3AKlvgWoUEJIkzVW28eHp8yDLEra7+lIWjH/40S/hpzPItLOJjYHp4Bpnn6TMnjJgJhQ5mtK0YDRYHRoCoiIwBUSMgFBwe2TC9fyZUKwqoRcgIGjBRouyRy8FTRhfPRzi9z6/jzVDxXc+fjX2tSLcGpjYW4sGzXkKiKAFo1oLxtFgWgqRMeDYZ1E7Qx48GdZvvv+hbSiyhN2esRIFhCh0ke7RVTPfaZuIJBgBwWVkUKNKl8uP6OhK4c18clO4KNScLRj9RAhlz6i+BWM/DJkUKiA22zgamHPfe9fzYbt+EEJJ48mKqjg9z8d4AQUEI9u4MLR2gtA8OJsKF+PtguG3N8MKTkmS5iggont9q6vnJlPKgigDgiwYd0QZEEYwbotaMACS7K6eHE5r0ElTQKRlQKihRTALD+918eKt4JTyqdfuYGy5ufIfgMiC4ebwtnc0FZabna2UhO/7MYl2XhihjYowsdy5LRiEdz+whbHl4jnOmlkUaSGUmiCPaJwIyKT2hjLGzdsjK1cbhCKVQ9KXZfnLi14i5JjmgiIExA7LOiiSvbNABoTDKSAqen2CiuQMAmIQKdnyYLdnLNWCcWto4frt9AOAfdZwE6mt1kPCuAwk70feMtagQdVoCIiKYLseFFmKdQl3dIWFlZ3XFoxBYsFGTRjP7PfxR8/cxN962xU2Yadt9mzXw8nIirHIXSP7dNq0XeFCkG0YSiAgvu8XPoWf//gLS/+c4dRhJ2iLMtFPvniCt15Zw04o/by83qo/hLKuDIg5C1+Sx/IEBNUydgxeAaGwjUleWG45BESRDAhFltAN/+ZVtGCwUy/BJuXaZgu+H7VBpIEWbYYqs/FkVbks9J5ud7WQxM234BOp0FqJ8eRmX0xAdAqeRh/2p2zRmrbInVFAdPSVt2BE1oV4CKXj+TgemswaROgaKoacAmImhFKpRwExSbFvbbQ1rLVURkDQ6W8yaJIICMo6ysLDl3r46tGQtV8osoSvf3gn13XqSiBpptcoa2PVWcBu2J86GFlu7gpO/rpiIZRWvhBKIAqi/Nxri+dAsJNuNamAmM0jSraGEXlbhg/+9jgnASEIx1wEIlK0StDzS7jZn0JX5dwVnECUj1FYAZFzXjU4AoI9JxlKoWXQmqeAGORXQAABOXO8xBg+mNo4GaUfAEQVnNEctVGmAiIc16khxGhCKBusEA0BURFE0u6uoTKv8CpbMMi3my8DIm7BuLRmYL2l4teefAWDqYPveudVduqctnEiSRrPIs87nU5VQOjlWDDsMMn8IGflYBaGpoO9ngFdkReaCEzHxWdeuY33PxQtYi+tGSuxYEQ1YLwFo94aznkERNdQ0dWVuQqIrqEyYiIvzJIsGKqSPwNindv0BAREtRv5yPc7u9EmL/C8IEraUFMNJ7A6Cwa9p11DxVaBTbto08xXbE5tF7dHlvB1aS2QAcETEMnXxvMCCT5/LVtdHbfHxcLdlkVaCCUQ1ArPZEC0AttgWl1tfSGUs88/EMis79uKqjhp89U14mPMZliDmscK+fBeYEO8PbLwxFeP8bX3bTIL1TzQiWJa5gGP1gJzHZGLxRUQciKEMj8Bce9WG3trBj5XIAfi5eMRfu/z++z/014PLcWC0eUJiBKbAPIqINSyMiBSlB9VoZtQ2O2fBnadeaQbj21GQORfnxTKgGAKTJ8pTKp6fVqanKncI7vivEwQwl7PYFWui6A/ceD76RXs++HzfU8sA0KLBaEvg6S1rgmhbLBKNARERbBdf2Zy7egKY1hXqYCIPHbzFzb9adyCIUkSHr28htfvBGFt3/jILrv2NAnkocBHl6WAcAWLc0LyxHJRkEyuDNkm1agGXrzi7PfTr53CdDx8A3eKdmndWKkCQudOYESy11VAdBqbhr01I54BYYoyIBRGTOSF5XjC8NOiyK+AcBIBeAos18v1bC6K/dMJtlNS8mnjsj8niHLKnZREiqrVKCDome3qKrbDTXsepAW+tdRgEUqqD1EGRHuOVJeH7/u42Z/icjjeiWpKRSGO210NluMtFDy4KBiRpM8SEABmMiACi5AtbM8B6uuNH2eQl/dvcwSEaaOjKzPKg01mgZpPJDy81wUA/PWrd/CF10/xgUfy5T8AUQil44pfPx6d8Pkscj/Qc5us2J2HIKdksQwISZLw7vs38bnX8hMQv/7kK/ipf/8Ue+bYsyknFRCzc9HIcmNVzUwBUUIQ3+2Rhe1ODgWETAqI5e512/UgSdWd8CexliAg0ixnWdgJMyBOqsqAYDWcLmfBqOb1mWetOxqY2O7quVWROz0dJyNzYRKZ7mFqUUrixp0JdFXGbjdaS5epgGhCKBvUiYaAqAim40FP1Ep2dIVtUlZrwcjv2R4mCAggCKIEgO94x1VoisxOjdIUDaTyoHYHIFsBEZ1UVpcBQaqMUggI00GvpWGzs9hE8ORLJ5Ak4OveFBEQe2stnIzMUoK1shDJ0qPXOqrhXHEGhO1CU6Rcpx17awZucRkZTAFh8BkQKgunzIuyFBCaLMPNQeD0JzZbQAPR9VepguDtAUncu9WGLAUnlFngFRAk18xKEy8TY+693uoWV0AkF5MU9pelDClSidifOpjaHq6EP4f89fyilG3guXttq1Nc2rwsRKRfjIBIZED0DBVTOyJJkiRxXb3xY8uBLM1eDwDcv9PB63cm8Dw/qLwWzLX0DOZpo3rkUjD//cZfvgrfB77pzQUIiJCgiaTl8y0YRQiIm2fBvFY4hJLzenueHzTc5FRAAIEN49WTce4AvrHpwveBP/jiAYBgg6rKEuTERlxkBwxCKGfv1zJ88LdHFrYKZEAse6tbrg9NkQspEJZB0uJ382yKawUaMIBgvGxrCm4XyDooVMMpsGAkiamykFaRTDjq56vgJOz2DExtr/DBBxDMqfQMHqUREKcTXNtoxZ6T9XZAKpWxVrQS2T51EcoN3phY+CmXJOk+SZI+LknSs5IkPSNJ0n8XfnxbkqQ/liTphfDfW+HHJUmS/rkkSV+VJOkLkiS9u6w/4jzCdr1Y1SEQl4uuMoRSkqRgIZRjwEpmQABREOV3PX4NgLhfmkfko8sXQkknqa0KazhpoVSG3344tbFmqNhoawuFUH7pRh+P7PVifutLawZ8v/pOaVHPtlZTC8bEcnMvend7Ruy1EbdgFK+xsxyx8qYo8iog+lNbWAFYZQ7E2cTGVkc83rQ0Bfdvd/DCUXagXJQBodSogFCw3dFyKyBEQcAAWBL6zX56InxbV3KTWRHhShaMWYsKI1m5+317AW/1spjYLlQ5TvrxdgJRCwYQBX+KQijrsmB0dHF+w31bbZiOh1tDMySLZ0kGRZaw1lJzWTCubbTR0mQ88cIx1gwV77x3M/d10kbfzmHBWGSuG5pxy2ReBDWcwe+hRph5Vjge73kgyIF4KmcOBOW4kA3DDjfiSURtExkWjJIUEFPbxdhyc1kwZDmocCxDAbGqAEqALFTBOOZ6gVLrSkGyCqCT/mpCKOmZsFyfbaqrVEBkERC3BlMWep0HlOG1SBUnb6NIy2C6cTqJ2S+AiIArw4ZhssO/JoSyweqxzEjoAPgffN//GgDvB/CTkiQ9BuB/BvAnvu8/CuBPwv8HgO8A8Gj4z08A+MUlfve5hy0It+MD81aZAQGEC44cJ5aD6axk9fvfcy9+9gcex/sf2g5/lgJNkTBMObU9GpiQJGC3F03sWTWcdJKapYCYWMsNirR5HZRowVhUCtef2jOLHmLdq27CYN7bhC8eqCGE0sov+w0UEFwGhDmrgOjqambVqwiltWAo+VowziZ2bMM3z86Uhq8eDXKfgPQnTqZn/dHLa3j+cJj5M+gZbWlylAGxMgUElwHR1XGasznCSpG9U0gkJcKLFuTB1+T7+0g+G1kwQoKG+37a7Bm8AoIIiBVWcYq8/jwhNpMBEc5ZZGEzEjWcmiLDduoIoXRTN8z3hU0Yr90eYzh1UlUOl9dbsTkqDbIs4aHdQAXx/od3CiX0zxIQWQqI4DqLZEDQHNwpoF4AwrC5cKNBvy/vWAwAb79nA5oi5bZhUGjo09dPcf32ONygzm4yyZpgxywY4gyIZUMoifjLG8io5qxazkLa310V+Jal42HQwCYKI56HnW4xAsJ2/dzzKo0pQQsGhbVW8xoZana2z9HAzN2AAURr3EWqOPk2tZsp+V/7p5MZxQrN5WVYkEQWjCaEssGqsPDK2/f9A9/3Pxf+9wDAswDuAfA9AH4t/LJfA/CR8L+/B8Cv+wH+EsCmJElXF77ycw4RA8z7GFdpwQBoYJm/sEkmTgPBovQH33tf7LQpK73/1mCKna4eW6iRAkLklctUQJSWAVGiAiJ8jTYXJCACAiO+0KdJr+ogSlENmMpCoFYcQlkg+GyvZ+BsYrN7ZSxIwe8YSmbVaxK+7+OwPy3lWcytgJg4KRaM/Nd92J/i2/6vJ/D7XzjI9fX9qY31dvrf+ObLPbxyPMo8+YieUQWtlSsgiGxSsN3RMTCdXKc0aZV3lIR+82yKjbYWG5cJHV3JXYlImTdEZDCLCvf6sJMmPgMitGCssgljanszdYvZGRDB/5MCIpmXotXkGU5WM/LgqziHKQoIAPhXP/Ie/I/f9tZcv+/h0IbxTY/mt18Awbxrci0YWQREm2VA5B8LRqaDrq7MWBnmIVBAhAQEZ6/Ki5am4K1X1vHF189yff3YclkN8O99YV94QANEcxEF+jquh6ntxcOGdRWytPwGrCgBocj5goazUMSaUAbIQuW4HssLEdUxz8N2Vy8YQrlABgRP1FVkwWjrCsyUtaTn+bg1KG7BABZTrvY5BYPIgmE5Ho4G5owCgtYPZeRAWIlMkqAdpyEgGqwGpTzlkiQ9COBdAD4N4LLv+wdAQFIAuBR+2T0ArnPf9nr4seTP+glJkj4rSdJnb926Vcbl1QKhAoJbMOWRfpaJ/AoIJ5c6o6unExBHfRN7a/FJrmMo8Hxxcn4kA5tdALV08puXlAGxpGzN9XyMLRc9CqFcwIIxCJsQeFBeRtVBlLQQ5u/NukIox5aLtmDzJwLJImmiJ6sFf/JHFoy8gWkqgU8AACAASURBVFBfPujjpeMRPvw1l4tcthBqjsWp7/thBkS8uQMopoB48dYQruezqsF56CdUF0m8+fIaHM/PzIEwmQKi5haMcLNwmkM1ILrXgShgMiuQjWXP5PgbKTmdLGctQU2piAzZqsGCMRWQfvxctJkI5CO7HZ1+JsmcvNa+sjHOqI28Z6sNSYoUEGkE40N7vdxy60dDAuIDj+4Vuk5DqdaCMbYcdBYgUHmpNd2neclgwjvu3cAXXj/NNd6OLRcPX+rh3fdv4vc+f5C6EVeYAiK4tijrJ7o2WZaw1tKWzoAgUq0IAbG0AmLFBASfMXRwlq74moftrpE7A8LzfDie2GIjAk9AOK4PWUJhQi0vWmEDkgi3xxYczy9EQOwsoYCg+1eVJWEI5f7pBL4/23CzUWINLbX10eGi0YRQNlghlh4JJUnqAfgdAD/t+34/60sFH5sZzX3f/1e+77/X9/337u0Vm+zPE0yBAoJY/JYmr3QSAvJLq/pTO5eflJf2JRHI2OKDeFZ1Z+Qvn31NdEWGLC1fw0kb15HlwltiEUF/81pLw0Zbw8B0Cp+KiEie3Z4BSYo2M1VBJAVWWff6aieeYDOU7zmgjQLZMEaWA0OVYyqbjq6yRpU8+OjT+1BlCd/5juWFWHkWp2ZYxyfKgChCQLx+OzjJyrPosd0gIGu9nU1AAMBzh+k5EPwz2iopGDYv6LXphC0YQD7bQloGREuTMbU93OxPUgmIIpWIh/0p1lsq20BGrw9vwZhVQKy3VKiytPIQyuRGk/IQVFlCN6EqoHHq9sgUhgbqqlSLZ3hiO6kKCENVcGW9FRAQppOr6WIe/uv3P4Bf/OF340273ULfR5sr2vRkWzCKh1AOTXchBRffgjEWBJPmweP3bKA/dVjjSBbovvuud17Dswd9PHswEL4WRNDQnMrbr3ist9XYCfIiqEcB4ZeSOZQXZD8aWg4jIIqGUAKB1eB4lK8y2PaKVY0qYb6G7XqwPa+QxakosjIgSH16qYAFgzWELKCAoAyHB3e7QgLi1fC5enAnPubQAUYZCggzkYHVhFA2WCWWetIlSdIQkA+/6fv+fww/fEjWivDfR+HHXwdwH/ft9wLYx10KkQKCFqhlLIiKwlCVfC0YZj4FRK+VHip52J/OsMi0gBAtrmghJJKASpLEQuOWAb9ZKyLTT4J8e2uGyuTKRU5ifN/HQEDyaIqM7Y5eWAHxb//yVfzM738599eLmgFo0WevXAHhCOXvIiQJiLHpzixKafOUJwfC9Xx89Okb+Oa3XMqVgj4PqizPXZzSgoFXI9DJXhELxvU7wcIkj+yTFjlJxQ2Ph/a6UGQJL2QREJwFg7XqrGihwvI+dKVQc4SdkgFBVWwHp1NhBSdQrH3nMBHsFr0+IgVENMZJkhS0eqw4A0JkddtoB60+yVBHesZuDy3hxkmrOYQyDfdtd/D67Uk41i6vNtzu6viOBYhKuveIRMtDQBQh24OAxmLEARC1c/C/r0gGBBAoIADgCzlsGOOQMPrOx69CloAv3jgTqkGSeUQjM4WAaC1fRcgIiBw1nEBwUr0sSW8XCGcsA0xhN3VwcDqBocozNqs82O7qsJx8bQ9MeVY0K8UNFBBaReoHILLfiUD5W0UUELoqY72lLqaACNeSj17qCa23r50EisQHdjqxj2+UWEObbOvTVRmej8rb2Bo0AJZrwZAA/BKAZ33f/znuU/8JwN8L//vvAfgo9/EfDdsw3g/gjKwadyNI2sSDFgqrDqAE8isgclswDFUYQun7Pk5GFvPGsa8PFzeizT87XU05DZ/X3ZwH/GZtmSpO+t5eGEIJAKcFFkIjy4Xni++BZNVkHnziuSN89On8PJ4lkALXVcM5KVD9JlJAJBfMJEXOQzB9+qUTHPZNfORd14pcciryKCCIqOIVEGshGZkW6CoCWS9u5Vj0sN+Zseg0VAUP7HTwfKYCgguhVMuxReXFmFO70GnlnRxBlKLqSyBYhJ6ObZyMrLkWjDyn0Yf9eHCZWAEhHuN2uvpCp2eLIiAgZp+5jbY2E0AJRAqdk5El9OwHBMT5CqEEgPu2Onj19ig3oV4VaLNJpGhWuN4iFoyRmZ/E5WFoUQsGy4AoSEC8+fIadFXGF2/MJyAmod3u0loL738oqJ8WKyDCuSi8p2hc7CVIlvUSLBi3RxZkaTZ4NQ1lKCAs14OmrjCEshUp7A76U1zbbC9UAcqUZznGKlHT1jwQIea41SogKFxYpOSgw58iIZRA8NrcWdCKCwQExNB0Ztalr5yM0dLkGUKEDjBKyYBIKCCYHaYhIBqsAMs86d8I4EcAfEiSpKfDf/5LAP8HgG+VJOkFAN8a/j8A/CGAlwB8FcC/BvDfLvG7zz1EKcC0UFh1ACUQr93KwmBqs01RFnqGgqGAgR2GloQky97hvIhJMH+5IAMCCCeNpS0YZkSCLENAcDWlGwuEATEFhcDmcmm9VVgBMZg6OBmZuU8h2alwzIJRvQLiV/7iZXz06Ruxj00EJEIaSOoYU0AkFt5FJMy/+9QN9AwVf7OE/AcgPB2b8x4wBUR7OQUESZ7znLrQKUlWBgQAvPnSGl7IaMIgsqGlKmwDu6oWDD4Yl+pE81gwIgVEfCHc1hT22qUSEHpcOp+FQPHFExCzBA2r4UzMCUG424ozIATP3JX1Fq4K1CD0upspbTFajRkQWWPH/dsdHPZNeH498y2BXjMiRbNOv8luWCiE0krPuMi8Li5sbrKgBUNTZDx2dR1feH1+FSf/fn3XOwPSV3Q/RQqI0B7C2a94BBaM5QmIrY6eO28gT84Pj9//wj5+6ZMvxz62+hDKqDL94DTdcjYPlHVwkiOIktk8C1hN6IDMSqlnLQuiimQCrS2K1HACQW5OnkyiJPoTB4os4U17gcUiWcX56skY9293Zgijjq5AlaUZAu53n3od/+aJlwpdg5VQatO6sLFhNFgFlmnB+KTv+5Lv+4/7vv+14T9/6Pv+ie/7H/Z9/9Hw37fDr/d93/9J3/cf9n3/Hb7vf7a8P+P8QVS31GEWjJoUEDkGleE0PTWcRxBCObs4PxOc8gZfn77REvmjeSxrwfA8H7dHFh4IvXTL9CfT9661IgtGMQIi+v4kLq0ZhVswhqYD388fgiTKgEieOlWBX/3UK/iNJ1+NfaxIC4auytjqaOzvHFlOrNYWiHJG5hEQU9vFf/7STXzH268USn7PQp7TMVow88+GqgSKgmIWjDADIgdZRUFVWRkQQNiEcTJKVTVMuRBKNfTsriyE0nLZe71ZoDlCZHsA4lYv0aYbANpavkpEz/NxNDBxZSNatIoyMkyB9QkIgijrzoAAgH/8/e/AP/s775z5OC99F4UE07ySN/i1LMwlIHai93XVgc88aEFPz3eWLF2SJHR0tVAGxEhgRcsDQ+MsGAuGUALA4/du4Es3+pm5Sr7vY2JH79e3v+0KVFkSbjSJDI8UEBHhz2OjreUK4ZtYLr7/Fz+Fz1+fJUluj6xC9jtFKRZC+Tt//Tp+9VNxAkLUjlYlyO47MoMMiEUCKIEghBLIZ32zBGuMeeAVEFXWlIoCggmU5VN0TbC9oI2O7GFRA1qcgHjt9oitWXlIkhQEoCfWnb/6F6/gFz7xYqGx2HLcRgHRoDasNgnxDYQgAyIhEQ83SHVIQvnarTS4no+R5ea2YIg2TWkERIdtDouFUALLWzBOJzZcz2cBYiLiJC8GZkQgMAtGgcknUkDMvsaX1w3cGpqFQjJpgXaYk7gQZUCoLPiruknnZGjhlZN4WNl4jow6id2eESkgrAwFxJzN/J88e4Sh6eAj75op4VkYao7FaZQBEb/urEDXJKa2i1sDE21NQX/qzFU15VZAXFmD5wcNG2m/FwieUUmSQinr6kIo6b3WVRlrhlpsISxQQBDSFuR0X877G09GFlzPj8l2RRkZkQIifr/vdPVcao6ykEb6XVprCaXHmhJZbkQn1roS9+yvChPLYSSRCPdtRb7p86CAIFIhy4IBhHNd0QyIgtYJICDlHM+H6/lsbi2aAQEA77hnA0PTwcsn6Q06geQ9eqa2ujq+91334LGr6zNfm6zhJOVI8trWW1ouBcRLx0P89at38BcvHs987vbIyh1ACeTL+eExMt0Zq5jtzlpzqwQp7M4mNo4G5kIBlEAwTgH5whadRTMgHA+O5899RpZBls3pqG8WCqAkbHa0XJbAJPpTB+stDVfC38kHUXqej9duj/HAdkf4vRttLRbC6nk+nj8c4vbIKqSiTSrbGgVEg1WiISAqgilQQNBkUMeJjKEqcweVITudz9eCMbKcGbZVFLQH8FLzYiGU9PFlWjDo1JzCfIbm4tLNyIKhYaMdTMpFvKj9jNf40loLruezyrsi15O3PcMKFwcqJzut2oIxtV0MTQfHQzNGWqXJwdOwt2aw3IPA+5xQQLAMiOx75XefuoHL6wbzIpcBJcfilE7sZtRBBQiI18MAysfDALh5C8I+s31kjznUhJFmw6CFCsmV8xCaZWFsObFT3rzBjSK7EYDYPTcvA2Ie8Um5GbySQqyACP47uYnf7uo4HdsrC/2a2m5hrz+RpaINBZ1yZlnAXjgc4GSBkLY0+L6PsT3fgkGoMwOCyJs8IZRAsNEumgGxqAICCDYaNLcWvS8A4PF7NwEAX8wIoqRDB74y+Wd/8J34mY+8feZr2VwUkuEjlgGRtGBoGFvuXOshzYv7p5OZz90eWbkDKAFAlooRbSMr8PXzJLHImlslyE778vEIrufj6uayFoz8424hBYQa1dVqcvUWjKnAPng0mA1Pz4OtzmIKiP4kroDgD5GOBiamtjcTQElYb6kxBcSN0wkbN768n1VGGEcyq46vRG3QoGo0BERFsF1v5kS/E57YrNVkwZi3YRiYUcPDPPRaKjx/doHeF/jcgSUVEEuetpJUneqMigT+JcErGBbLgKBNqNiCAURpzLl+XriwTcr30kAnMLyvsOoQSt4eQvkFtuvBdv3YonQegpBOTgGRuE/bLAMifTN/Z2Thz54/wne/8xrzG5eBPAnpogwIIF1NJML1sILzXfdvAZhvvcmrgHhwpwtVllKDKKd2XKppqKtUQLgzBEQuBQSFoSXGFfo71ltq6uYtbwjlb376VWy0NXzgkd3o59Milxtvmc0scS10srhIiNkiSLNgZIFeI5FFjhEQTvrG7Ed/+a/wc3/8fKHfmQXTiZ+oi7C3ZrDXuo7WKQLLgGAExBwFhKbktmB4oWJxIQKCa2pZNAMCAB7e66KtKZlNGPT35AnLJAKCKSBSWzCC/59np7x5FoyPB6ez8+OdsYXtXkEFRAGSnq6dH6vsii0GSdDBzwuMKF2MgOjoKlqajNs5MiBEQdfzQG06jluxAiKj3ehoYC5EQGx3dYwtt/B8OAgVEF1DxZqhxg6RXmUNGOLa3/V2PISVn7e/fFCQgGgsGA1qQkNAVARLEDbUqVUBMT8DIiufIAlW75TYOKVmQJACIqOGU+QxBoiAWHxAPA4XAEwBsUR41dB0IEnBSZWuymiHifp5kR1CSQREvtNC03HZe1rEgpE8galaAcE3kLz6/7P35mGSnHeZ4Btn3lmVlXV0d1V3tfpSd0tqWbKEdVuW5ANzGgwYjHfMM7AwwHDuMMsOyzGHZ2B45oEFdoZlxgss2DPgebAHw9hgy4AOy8bI1mW1pO5WV991V+Ud9/4R8UVGRH5xR1Zndef7D7jUmRWZlfnF972/97BsGLbvOI4CwrJgGIZhVXjGz4D49EtXoWhGpvYLwMqACHn/Gl0FRZEbWBcqMRQQhMC5+4A5eQwjILa7CjiWCZVXizyLW6ZLvgSEpLrbE/LCDiogJNWVgj9VFCJ95+QQBYRf/gMA5COEUF7Z6uKzryzjA/fud32O+yGdlBrOAQVEdG91WhAvftyDJpk+0xQQYRtWXTew3OjhzTV/iX5ckO93kPWAYRhbBXFdLRicW/kXNhWOY8Egn81EFgzHpLOraBA4eiZDGHiOxW37qnjpsn8QZZy1nmeJooZYMOjkCCFxw9SH5FB32aOA0HUDmx0llgIiStORE2TQ4fxu73QGBM+xyAss3lgxlW1Ba14Y6qVcRAWE+R4lCaFUdd3+DAwDOR9lm2GYWT5xGzAA2FlgcfaBgDkcIHvtuYm8K4RyybrP+yogBggI8+87XRbjERCeQenYgjHGTmJMQAwJtL7nco5HUeR8u+eHibzAhaZrN2NZMKx0Zc8EwpaZe1owCgIHhqH78+2KuiFlQNgKCJIBkcLO0eyZqeNEQTBZjNdHHhxCad78ViOSCU47S1QLBm0CM+wQSqf8mjD79tQtpgWjq2hoyxo1fK0Y0ijRUzT8p785izvmJ6j+4zTgI2xOt7sKVYlQynGRc0kubpjVXMf3mNe/FmrBUFHN85Gq147NVeyNjBc9Rbflq4BJFkZp1ckC3qrBuAqIAQLC2oQGBbLZk7KAteKPv7QEwzDw/fctun5Oy4DwU0DUSubnIUq6fFrImg49RDlAQ18BQQmh5IIJiKakQjeAS5uDEviksCX9IRP1/RYBcT0tGIMKiHALRtQWDD91QBQQsl+yCIg0Ybx3WEGUfha0vgIiAgHhySMiGRfepoqoVYR+FoxGz8yFipUBwTGxcpJoCgh5hzMgAHPfSb5/STMgANOGESUDws/6FgTSyqJoxlAVIn4KiO2uAlnVYzdgALBJrLg2jGZPtYm0uWoOyw7l64X1DjiWwb5J+t/LzIDof/bfWG5i70Qeb12s4dUYFgxJoSsgoraqjTFGGowJiCHBW28DmCTAZ3/qEXzPPft3/HrKeXprhRMkGyFqCwYwmOmw3VXAMkDZszlkGMZszqBs6HuKeVP2q8PKp2zBWGtJ4FgGM+UcRI5N3YLhPEROFARsxazh5FiGOoWciWnBcJI/yxTVhKzq+LbfeQZ/89qK/TNaDRjHMmCY4VkwyKaFYfrMfhLZL3l/lhs9V6o6QTFENv/RZ97E5a0ufv69xxN1oQchagsGrXM+lgVjs4OFWtF+L6JYMMIaMAiOzVVwcbNDncD2FM1Vk5sX2FSqpDhoy5prij0V0XOraLrZ2OFZV8hha1+AHzosA6KnaPj4ly/i8RNz9kGXwLQ4+bRgeL579R1UQDibTOKgEqCAIAGfis/EjAT0Xt3uxgrwC0JU8vLAKBEQclQLRvQWjJZNQCRXQEiqbmbxpCAgTi1MoKtovgG2hFCJQnz1azj7FgwawWIrIELUjCTYr9FTXSozMsmPQ0BwLIOoHL0z3HPQgrHzBARgrmlhWUBBiFoZTNaCuBkQClFADLWGk05AENVpshDK6M1MTpAMCACYq+ax7FBAnF9vY36y4PseVvNmCwzJYHttuYmjcxWc3DuBN9fbkUlM2ROWT37fTqkbx7i5MSYghgDDMCwmd/Dt3T9V3NEQIoJyjoes6YFTyzgWjHKABaNaEKhkgt90R1I13wpOwLJgpFAtrLdk1Etm33c5z6cLoZQU12GIVocUhGZPRcVnIp0XOEwUhMh2CpLZwbMMNQPi4mYHL1zcwt+93k8Al1X651Jg2aFZMEhw5K1zFVywLBhxpmIE5NBNbAjeFgyeYyHyLHUDv9aS8H9/4SyeODGLBw5PD/z3tIisgKBsAOO0YFzc6GJ/rYCCyKEkclhrhodQ0kgPGo7NlWEYwJmVwYNEzzMl3SkFhGEYA4GjtYieW5rdCHAoIKr+00CeYyFyrC8B8ekXr2KjLePDDxwc+G8MwyDvyciQVA0izw5878kBKO7mNQnI9STOgKC8l2EhlESWrGhGrGybILQjrh0PHZnGXQcmIyn6hgVCOJA1KYoCIqqXvG9FSZcBEbeNyIs75k07mF8ORDdGBoRXjeeXcUHW0bAqzuWGBPKVu+pQQWwmICB4NroCwrnPcRMQxkArz7BB3r+9k/lUxHtUAiJJBgSp4VQ0wxWQnTX6Cgj335HUnycKobRUbHFyfHTdQEvuD7P2VPNYafYb0C5sdHztF4A5+JI1HT1Fh6YbOLPSwrHZMk7srcAwgNPX6FZKL8YhlGNcT4wJiCGALMB+loLrAeITDVJBNBJkQHgnt9sBBx4z7Z+eAeGX/wAABdH/IBAFay0J0+WcdQ3R5e40tCTVpRCZ9HjxwkAICD/MVnKxFRCL9SLVgkEO6m+u9Q+UXs8fAc8xQ0viX2/JKOd4HJurYGnDsmBYf88401ibgLBIjCJl8lfyIbl+43Ovo6to+N+/8UTs64+CqC0YdAtGNALCMAxc3OjYE/fpSi6CAoL+O2k4ajVhvEbJgRiwYOxQBoTpCzZchxD70B6igvAjgclnLiyQLS+wVDWIYRj4g2fP4+hsGQ8cpjepeBUiskr/3tWKxIIxfALCbjsIIHtpiEJA+FkwnH+jrGwYUSfqT5ycw5/96IOZhs3GRc5jwQgL2DNJ+ngKiCQZF64MiATBpE4cmi6hJHJ46RI9ByIO2Uz+VG4FxODjJiIqIJYbPRybNdc1Zw5EUgVEVJuic4/hIiBU3c4F2SmQz0fSAEqCekmMZBWzMyCS1HAOWSFC1j7vuk72XIlCKC0FRJw65aakwjDgsGDkoToa0JbWgwkIm4DrKbi40YGk6ji2p4KT+0xrZtQmDEnVXcO/3JiAGGMHMTon5BsI/QX4+m18vChbh5AgqbcdkBghNZwcwttydAKiKHLUDAhvwr4XBcHsLE/qS1trSXaNVDknpLZgOAmEiUK0QLz+45XA93e2moscQkk2oIdmytjsKAMTaXJQdwbA0bJJgGgT/KQg7/9ivYjLm11X9VuUqRgBIZHOWzkStA1tURy0Gp1ZaeLjX76ID77tAI7MlpO+jEDwXLQWDD8LRkfW7OlH0OObkmpLy6fLEQgIH9UFDQfrRYgcayemO9FTBxUQO2HBIH9LZ9BejWz4Qg7t3o5zgoVaASLP4rb54BwQv0DA5y9s4aXL2/hHDxz0nSh6FSKSDwHBcywmi8KOWDC6CRUQdg0n5frDJmbOtZFUyKZFkrXjesEOoSQWjJCAvTiV0+ReXkzVgmFmQKRRQLAsg9vnJ/Di5WAFRJTPHcMw5r3Iute3JJWq8CCkahD5L6kaNtoy7rICe684mjCSKSDCSWaCto8CQtb0HVdA9AmIdNljU6UceooeKu+3MyAShVDuUAuG6iUg0lswtmKs4eRz67RgACZhtt1RsN1VsDhFb8AA4GpgI8HRx+YqmJ8soJrnIwdRSqpGV0CMMyDG2AGMCYghQPHx+15PkNDIoMN3q6eCZ5lIE7IgC4avAkLkBwgLwNwEBf3OfIgfOwxrLRkz1uG1nOMi++1paFkhlAQTMS0YjVAFRN6WA4Zei/U6Ds+Yh2rv44gC4uJm194UKD4bIJ5jh5cB0TYVKIv1EnTD3Vkd5zBUK4rgWMZu0qAdQEq5QQXER/7yNIoCh598/GiKVxGMqBkQtDwG4rGnfTecIBWcCzVCQIjRMiAiKiB4jsWhGXoThqS4VUqmAmL4Fgxa0F7fthD8vfPKSwn2TxXx2r96D27bNxH4+IJP9swfPHselTyP9wU0qXgVEN73z4mpkrgzCgiiOoobQikGEBC2BYP+2d9yKiA2slJAxLdvXS/0Qyg1ah6JF0WRQ0fRbG93EIgVpZwgAyKXoQICMHMgvn6lQR0S9ENDo/0OM+zRsB9Ls2AURQ4cywQqIMj98NTCJFjGzCEhIN+3WowWDDYGSe/cYxACwjCM6xJCSd6/fWkVENYQJyyIUtHiZ0AIHAvFtmDsQAuGVwHRkFASucRqonKOj6eAsPbh5N48ZzWgXdvu2SrRA0EKCAcBRxpOjs6WwTAMTu6r4tWIBIRXmSeGWOrGGCNLjM4J+QaC7YEbIQsG6UIPOuQE5RN44WfBaHT9Q++KObq8VFK0EAtGeCK9HwzDMC0YFUJARPfb09CU3ATCZFFAV9EiH8bM9zhAAVHpV01GeS7A7GIHBpswCAGh6aZ0H/BP4eZjyEvjYq1pZnAQSeGSIyQpzuSNYxnUS6KtgKBNxgqiO8TtS+fW8eTpFfzYY0dQL8eXV0ZFmIJE0w1X6rUT/e9S8GfoojVB3j9lTrJMBUTwpmc74PtIw1GfJoyeJ6clz3OQdkIBIQ8SEMS2ELbhUyhBwARR1riCyA8QEJpu4DMvX8P77poPbB/IC24vPy2UmGCqKGIjQrp8WvRiTKKdIGq3JBkQxBc9VRIHqhCTIkmA7fWCswUjymS3KHLQdCPSBDLLFoy07+UdC5OQVJ1KXnaUeIoV3pFHRGs7AszvbzXPB2ZAkPvhfK2APdW86/O32ZZRELhY9x8+AslMQPYYLNMnOzTdgGHEO5hnAfL9Tdu+VreI3zCylKih4mQ52AoISktXliCfc699cLnZS6R+IJiMWA1NQIizqv23ydvXcd4asBysR1NAvHatifnJgv09ObG3itNXm6GfVZsQc6zrwtiCMcYOYnROyDcQ/OrfrieIjzLo8N3sKZEaMIB+44C3hjNUAUGt4QxWQIQl0gehJamQVN2+ecZpHKCh2VNcBILzRhAFja5i33RomKnkIGt6pOezFRCWrcAbXnlxo4Npa2pBDu1+PeQCN7wQyvW2SQAtWtaBCxudfiBezCnmTCVnkylRMiD+/MUrKIocNSwwS3AsA8OAr42iZU886KoNIPi7CcB+3fsdFozNjuyb3SGpGnqKHvh58+LWuTIub3UHrkVSdFcLxs4pICwLhpOAoAQ3Pn9hE3/x4lXXY83PevLNbEFg6XVtmo5bpv03h4A5aXPVcAbYzKZK9FYPwzBCPe5xkNSCQSbsNAKFvL9+B+Zta707MFXMPANiNykgJJ9114uCdUjvydEJiCRWlP51aaktGABwfI+Zs0ANsJU1MEz07BFn3WVLUn0VHlVPFaEX5H44V81h72TBVcW50ZZj2S8AKwMisgLC/K7tnSjY61SSbIQsUHaEUKYBeb82QnIg5h/nfgAAIABJREFUyOuMZcGwajhNC8bw3h+BY8AygwqI1YaUqIKToBaxmYnAVkBY+8eZcg4MAyxv93DB2qsdmArKgOhnoLy+3MSxub619OTeKrqKZu/5/KBahBhNATEmIMbYCYzOCfkGgpzAAzds2JaJIAuGpEbKfwBMOWJJ5FyhkoZhhGdAUBUQISGUKQgIMiEm+QGVPI9mQgJCsVKHvS0YQLAX1QmTwPDfMPa9gOE2jLakgmX6TLlTAWEYBi5sdPDw0RkAwLnVtv0aaJ/LKBkGSaDpBjbaMqZLImYqORQEDkvrnb6MOuZhaKaSszc4NAWEMwPCMAw8+eoKHjoynarnPgrs9HafDSoJ75qkSH7LPmoiLy5sdDBREGz55XRZhGH4ZyF4NzlRcMQKbDvnqdQzWzDcYVU7ooAgU17HAWnSej0b9sZex0/+16/il//8FddjgxQQUUDLgCCbzLDDS45nIysg6mW6BeNzr67g7n/513j2zBrlUfHRTUj6EfUcLTwvLANisyOjVhKxUCtklgERd6J+PeEcQkQ5eBJSpaOE36No+ShR4bRg9DKwYEzaB6LB6+5Yzx+1gYFnGSjEgiGpvn/naj7Y/kgqOPdU89g3WcBVR83hRic+AZGkBePAVNFep5K0Q2SBsm3BSKuAMPdQw7Bg5KwaTkXTIQwxNJZhmAF1GmCGUCYJoCSolcRYTUbeDAieYzFdzuFao4el9Q5mK7nAdXrCvgcqOLfaxjGLAAQQOYjSroZ2EhAOwnSMMYaN0Tkh30CwQ3hGSAFhh0YGHHLC8gm88KoJOrIGVTcCWzBov98r7/aCeJajhnM5sW555IkFw0+FEQXkcU4CghwooygWDMMwSZ4QCwaASE0YTSuPolYUIHIslh2PWWvJ6MgaTi1MYKIg2Gy4XzPAsCwYmx0ZumG+/wzDYLFexNJ6O/FhaMZho6BNQJ0ZEK9ebeLKdg9PnJhL8QqigSTt+8keSSjUccdGgaDkk6fixcXNrm2/APqk2qpPDgTZ5ETNgAD6UtBVTxCqt4YzL3ADQV7DQIdiweA5FhMFwSYD/uyrl3Fxo4uNtuxSoKT1W9MyIEimAY1IcsJ8f7wZEAEKiLY8YLv6+pUGVN3AT/23r9nrWBoktS4QhQ5tjQ7zDG92FEwWRSzUiri81Q0NWo2CbsyJ+vWE87AZ5eBJ/jZRmjDasoq8wCaaGDsPGp0MFBDknkYbcHQULZZahWdZaJoBXTd8azgBswkgiPhfbvQg8uZasW8yj6tbPfvzt5lQARHXgrF/qmDeAx0h2jvdjrZQK6AkcpivpQyhLEcL/1USEC2CXcM53BYMgL6urzQlzFaSK0RqRSFWDWezN3hv3lPNY7khYSmkghPoExcvXdqCrOl20wsAHJ2tQOCY0BwImlLbJibHGRBj7ABG/w6+C0G+2DsttQtClENOWEWkF+U8j5ZD7k4O4f4EhKmA8G60vfJuL9IpICwCgrRg5M2MgKgbCSealJpS8lqj+P/asgbdCK45JT7EKEGUhMxgGMZsz3A8huQ/LNaLODhdspsw/IL5TAtG9jcd8v6T6cmBqSKW1jv2ISLuZswpk/QLJyOb98+/ugwAePT4TKJrjwPid/VTkbx0aRsix+LY3CAB4Rfo6sWljY5LlklINb8ciIatgIj+nbZ9vp7n7HlsUubEykj0PYqDlj3ldb8G0kmvajp+5wtnAJjkj5MI9LMbRQWtkWDDCr4kORS+j+VZSB4FhH8IZQ6qbgz42S9tdlASOWx1Ffzsn76Q+vBOpn5x1UB2C4bPugH4ExDbHRmTBQHztQIUzYjc8BOEuBP16wmGYezDfjQLRnSy3a8hIgrIZ9EOoUxJQOQFFhzL2AcrJ+I+P7E6EKWLn8KjmheoiguC5UYPe6p5MAyD+ckCZE3HmqVEW09AQMTJgCDDiv21InTD3BslUQZkgW97yzye+uePJQpYdKIkcsjxbHgGRILXKfIsdMMkxIbZggGQfJ7+etWSVHRkzQ6CTIJaMaYCgrKXnKvmsdzoYWm9jcWA/AfAfG9LIoe/P78JAK59hcizODxTDm3CsAkIx33JXs/V4d7XxxgDGBMQQ0GSGqJhg2xUgg45LUkJnM57UfYoGsIIiKLIQ6UEbIUpIOzqJEog3EuX6NVfBKseC4Ytdw9pHKAhiICIooCwa04D3uO9E3mIHBspxdjZyEFuXgQkL+DAVBGHpkt402HBoIWjmhaM7G865CBLErQX60Vc2OigLSU7RDgJCL8aTpuAOL2COxcmUk02oiJMAfHipW2c2FuhrglRLBi6buDSZhf7aw4CwvpMr/kc6hoh30ca7KRzZ3e9pkPTDXcGhB1iN1wVRF8B4f5b16zQr09+7QqW1jv41jv3Wdfdfy+CbA9RQJuUEdVFWHp+3psBoWr+IZQl8++z7vFWX9zs4PjeKn7hm07gb15bxUefeTP2a3AiqeqIEH3UDAg+eMO62VFQKwpYsKavl7fS2zA6cryJ+vVGjotOQNgWjAgEREeiN0REAflbmgHKemoLBsMwqOTpAc8dWUVRiH6dgmUHJJXdfq9xoiAEKiCubZsEBNCvoCRVnMkUEGzkeyQhThcsxdp6W75ugymOZWK/VhoYxgyBDrVgqPGzLsjnsSNpQ39/8gLrUu9ds9pRZlMSEE1JjTzEaXQVFEXOpV6aq5r5VssNyc7LCkK1IODyVhcMg4F68ZP7qqEWDPJ5dA6BOJYBxzKQteGrG8cYY3ROyDcQpBFUQHAsg6IYXEEZVwHhtTOEKiCszZU37T9Ingw4p0Luxf3zry7jW377aZxZGUzeJiDSZXIDjpKF4YeWbcHov77JGAqIZoSJdF7gcM/BGp6O4PtuSaptrZmr5lwEBFFALNSKOFgv4cp2Dz1FM3vIKRMGM3l8eAoIclg+UC9BUnUsrbcTHSLI83AsQ/3MlEQObVnFalPCC5e28PgO2C8ApwJicIOq6wZevryNOxbotY9+jTJOrDQlyJqOBacCwiYLfAgIiswzDEWRR0HgXJJ/2uScqCGGnQPR8jmETJVErDYl/NaTb+C2fVV8z737AbjVIEpKC0ZR9Ldg1OJmQKhBFgzzM+2VNl/c6GJ/rYAP3beId52cw69+5jRevLQV+3UQkPUzH5OUma3kkeNZzE8OSrjt0DKftWOrI2OyKGK/RUDEDaL8z0+dw7/+9NddP+vKauqJ/U5CsBUQ0VowgGhqv5ZPQ0QUkM8iOcBn0ShSzvHUmu+OrMWqfiUKiBbF8uhEeAhlzz5U7rMCGK9uddFTNLRlLQEB4U8we2FmV3C28m+zI/cVECM0mIqLqbIYIYRStw+yUSE41pE47RlJkBc4V6PaWWs4c8t02e8hoaiVou8DAXMv6L0v76nm7WrdoApOArLP3l8rDqyHJ/dWsdKUAmu6yfDASyyLlh1mjDGGjd27Eo4wkqQA7wRKARWUhmHEJyA8G45QBYTPQUtStUBZsJ8Fg4RK0WoDCdZaEmpFwb7BRcnC8ENfwTAYQpmVAgIAHj46g9PXmlhpBOdANKW+AmK2kh+wYOyp5pEXONxi1XSeX2/7HoQELrq8NA76IaDmZu+gdWM9fa2ZKBiSKCCKIl09UczxMAzgMy9fhWEAjx2fTXrpscBZ3eW09/D8ehtNScWp+UnqY/sWDP9DByGU9jt8vOUcjxzP+lswuvFDKAHzcO9UQBBC1W3BoNeZZY2OpFHJplpRxGvLTSytd/ATjx+1iSnndE5W0ykg8tQQSgUCx4QG/3mDzqSAa6mXBr3VqqbjWqOH/VNFMAyDX3v/KcyUc/inH/8qVeYeBT1Vg8jFzwyYKon48r94Ao/eOmhlCkpNVzUdjZ6KyaKA+Unzex+XgPirry/jk1+74vpZR9ZiTdSvN8h7xLMRLBjW6+pGUOi1JTVRAKV5LWYbwBYhIDIgdCp5wdeCESdsmGdZqJpuDyr8iOpqnkdP0akqLMMwsNyQbAUEIc8ub3UjB8l6EUcB0ZZNdcqUw9ImW8oAcYdDKLPEVCkXKQMibtCmc20cZgsGYO4nnQoI0txC6syTgCjiojZhNChh5HMTfaVmUAUnASEwaLZOEkQZpKSlhVCS/z0mIMbYCYzWCfkGwSjWcAJAJcf7HnJ6iimzLkdswQDMerZ2nAwIywbilZf2QhQQeR8Cgiz2JN+AhrWmjHp5MDcgSROGPZFx3Dg4lkElx0ciIGi+PxoePjoNAKEqiJajNnWumkdTUm1i5YIjL+AW62Z2fq3tG/LEDSmEcr0lgWcZ+zOxOGVey+WtbiIFxIwjTJQG8px//sJV7KnmcZt1Ix42ghQQL102bUJ+Coi8wIJlgkkxbwUnYEpip8s5XwvGdoIQSsAki5wEBDlIOzMMCBnhtUVljZY1SfSSTWRjf3JvFe86OeewjjgsGCkzIAqWjcKZvbDZNif6YdahvMC6fMZhIZSAm4C4ut2Dphu2dWGyKOI3v/cuXNzo4F/82csDOTpR0JW1xMGNEwWB+poF3vwZTT1FPn+1ooiCyGG6LMZuwlhrmVM8Z7VuV9GoFbyjCjsDIgIZVohjwZCTWzBINsV2JzsFRCXvr4CIFUJpkeFkbxGkgABA/Z2NnoquotmtUhMFAUWRw5Wtnk1ShtmoBq4rVgilhnKOt9eljbZ83TIgssR0SfQlvAnkBEGSOce/H3ZLiDfb5+xKC3uq+Vj2Yy9sAiJiDkSzpw4MBshnFUBoCCXQ//w7KzgJTu4Nb8Lwa+sTeXYcQjnGjmD3roQjjFHMgAAsBYTP9Iw23Y/yfE47hZ2676uAsCwYjs2kYRjhCghr89KTvXJo8/cFEhAtyZ6+AyYJAySzYNgZEJ4N0UQxuA7M+/hqyHt8cm8VUyURT78RQkBIqn0tJECJhLxdWO/Yh9WD0+b/PbfW9m3BEDgWyhBqONdaEurl/oFt32TePqwn2fTaCgifAwipbPvy+Q08dmJ2x4LqiNxUpdy4X7y0jRzP4ugsXeLJMEygOgkw8wAYBgMy+OlKzr8Fo2dO6+MeOqdKoseCYXlFr4cCQlapBxBigfiJx4+CYRjUiiIYxmvBMFJnQABu4nOzI4cGUALm+yOp/cDdoDwKe0rq2LzahJMj8+Peg1P46SeO4X+8cAV/+g+XYr6awSaTLBAUQkmm65PW+zVfK8ZWQBBy7eJG/3G7LQOC/N2jTL7jZEC0JPp3IypyPIetrvmZy0QB4WPB6MZs2eBZBopm9Ct4/QiIvH8FNlEPkqkywzDYO5HH1e00CgiGur7TQCwYzsn4qO4L44CE/wYhifXNpYCIoBRKA28I5dnV1kCGQlwQC0YaBQRR61TzfGjLEtC38tIUEJNFEXPVHN5Y8VcH0zIgAGLBGIdQjjF87N6VcITRDxsaLald2UMYOBF1Ou99vpYnA4JhBg/oBLYCwnENqm5AN4LbEIhn2c+PHURArLdlW54NRPPb+4GmgADM6UqWFgyWZfDgkWk8dWYtcNLpDaEETN9rT9FwrdGzFRCVvIDpcg5vrrZ9D0LDquFcb7nff55j7TqwJJveimU78FNAOCXJj++Q/QKAndxNVUBc2sZt+6qB0lLvd8mLixtdzFXyAwfImbL/RKrRVVDN0yfXQaiX3TJbWgYE+b4OWwHRluiHzW97yz780recxLtOmhkfHMtgqugmToJsD1FQoPjxzVDF8M1hXjBT3YkdT1I03xaMvMChJHKu9/zi5qDiBQB+9B1HcN+hKfzSp16xpcNREfcgGAWETJQpa4e3snRhsoDLMQiInqLZ96Wl9f4ab7Zg3KAWjBgtGH7fjcjXxbM2iZ+VAsI3hDKWAoKF5siA8AbQEpADGO3ee80iIPY4psr7Jgu4stW1v2dJWjCiuhRbVkAo+W6bFozdr4CYKovoKlrg51NR6UOOIDj//fBbMPr5PIZh4OxqOz0BYRNN0exx5N7sBBkihTVgEBBV6VGKAgIA9kwUAluHJD8CYqyAGGOHsHtXwhFGkhqinUDQlJX8PC4BQbqbAXMjUM0LYH1ChMgmxKmAoMm7veA5FiLHUiwY5mJ/PtCCIbkOwOUUFoxmTwHHMgObtcmYCogo7/HDR6ax2pTw2jI9YFOzOtKdIZSASUCQKeOBen9afsiq4vSTpfNDrOF0WmCA/g02yaaXYRjMVHK+G1qSM5IXWDx4ZDr28yeFXwuGpht4+co2Ti3Q8x8ISp5GGS8ubnSwf2owBHC6nPMNmmpQZJ5RUC+bSeeE/CIea3cI5c4oIPymvHsnCviBB29xrTXkugnShlDa1i/HZnurI0ckICzVlvXehTVy1DyTxUubXbAMsGfC3eDCsQx+8wN3oSBy+PGPPR+LAOpa9ZVZgkj5aZ7hTauylAT1LtQKuLTVjVwn6lSEkAwUIP6B9nojjgWj6GM3pKGdwoIBmAePLAmIcp6nZkCYipXo18mxDBRNt1UgoQoIiurimpUP5axWnJ8s4PJWLzEBwVntHFHQdii3psoiNjvyyO4L44DYOYmtkAazaSt5BsSwB3cFRz7PtUYPLUnF4cwIiDgWjMFBVo5nI9kvAGDfRAFFkcPhGfq1z5RzgTlifau4+7tvKiDGLRhjDB+7dyUcYfhJm643yjnOl4CIOp13wqsm2O4qgZV/5Ibs9PPSAu5oyAvsAOtOJmzrbZlKAPQUDU1JdVkwolQe+oEoDrwT5YmCYF9LEPwIDBoesnIgnnqdbsPw+mNnrUnPSkNyVXAS3DJdsuV4NCmwMKQazrWW7Hr/AdgVU3E2pU4c31P1vUmTg8mDh6czl5sHwc6A8EyCz6220JE13DFPz38gCFNAXNrsuOT4BNOWWoF2qDOnLPHf43pJhKzpNklH5KrO9gRix9iJGs6on5N6KTeQAZFGAUE+S85D/kZbseW2Qcg5qoN13YCiGYH3g7qHgLi40cHeiQL1sDJXzePXv+sUTl9r4iN/+Wrk19MdggUDMDesQRYMsjlfqBUgq3pgMrsTq47p3UUXAbE7LRhChHR/QraHWTAMw7QopLFgiDzbb8HIKISyJakDqr2uHE95QwKRQy0YhQALhvXZmfMoINZaEq5t98Ay8eqJgXgZEG1HQ8lU0czUscPJdzEB8dDRafAsg8+fXvb9N0kyIHbagkEIviwCKAHz+5MX2EgZEIZhWBYM9+ePYRj8s3ffig++bTHS7/zQ/Yv47E894rumz1ZzrjXUC3kcQjnGdcbuXQlHGKMaNlTO+09Z40zn7eez0/ujERB2BoTDghFFAQGYC7x32rfZUexroKkgyASNZsFIlAHhs+EzLRjhz0daRqJI4vdNFnB4poSnfIIoW56/VyVn1icuN3r2tPDAVP+menC6ZJM0VAWElTyeJQzDsDI4vAoI8yCd9DD0n77/bvzb7zhF/W9k2rpT9ZsEfi0YL14yJ0WnfAIoCcoBCghV07HclLCPUoNYL4vQdIM6eWn0lGQKCFILaakJqDWcPDlgD1sBEb1qkKaASDNN82ZAGIZh10qGgZANkqLbk8+gNc7rrb642aUqXggeOz6HH3zoFvzhF5fw2Veuhb8YmH/HrBUQgHlgpBIQxIJRIgoI83t/MaINg+Q/8CzjUkDEPdBeb5DPQtT9QEHkQlswJFWHbvhn4US7Ls4mGbMhIHgomuFSRcmqDlU3YrVgkLYJuwXD57Fkr0Gr4ry23cNEQXCtWXstNdHXrzYwWRRj1UQ6rysKnA0l5ndbctRwjpY1Nw4qeQFvOzSFJ19d8f03SZRnznV66AoIsZ8BQQiItBYMwCRao1gwJFWHohnUcOgffPgQ7j9cj/T78gI3YNFzYqacw4Yje8QLWSN770ECQhmCHXeMMbwYrRPyDQI/ZvF6I9CC0QtOnPZ7PiA6AdFvwRhUQORCFBAFB2tNsNWRced+82BHy4EgG1inBUDkWYg8i1aEmjMv/GpKJwoiGl0lNJk+bs3pw0dn8OU316ky635Huvl+MwyDuWoOy00JS+sdFATOpTy4ZbpPRlAzIDgm85tOW9YgqbpdM0hwwFZAJKyQ41jfzePRuQp+/wfuxXffs5DouZOi34Lhvtm/dHkbRZHDIR+ZJEEpx/nms6y1ZGi64arpIiDkDi0HotFNRkBMeRolbAWEMwNiBxUQfh5wL5x2FF03oOrGgLw0DgoeC0ZLUqHqBqZiWDAkVYOkhN8PvPV2fooXJ37uPcdxx/wEfu4TL+LyVvihfhgZEIAVYEslIBS7JQiA3egRtQmD/C1v21e1CQjDMHafBYOLbsEAzM9dmAKiv/6nU0A4f2dakL+zkxAg351YCgjWtDq0ZRV5wb82th9CSbFgNHqu/AegH+D78uXt2PYLAOAYBoaBSBaittS3x9RKIjbbysgOpuLiseNzeGOlhQvr9O+xX9B1EJyH4GHXcOZ5085rGAbOrLRQzfOY8QxJkqBWFCMpIIhiJ85eMAlmqzkYhrua2gm/+5LAMQMKiF//7Gv41NcuD+dCx7hpsbtXwhHFqN5oyiJvsa+Dm8VGIgsGUTREIyDIJsdZBUoWwTAFhLc6iXTMn1qYBMv4EBDWBtZrAajk+EQKiJYvASFA1vRQ326zp6ASo+b04aPT6Ck6/mFpk/Jcg4GYs9W8rYA4MFV0KS2cBAS1BYNlI8tLo4IQQF4FxEHrWoY1xXz01tmhb2K88MuAePHSFm7fNxE6bQsiB2mBagR9AmJQatnoqbErOAFg2lJAkI0LIRmcm0TnhH+YcG7kw1AviWj0VMhqX3WQZtqYJ40E1vea+OUnI7RgELtKT9Eh+UyaXNdeFh2Ej4blhmQrBvwg8ix+63vvgqrp+MmPfzVUwTSMDAhyHbTU9M2OjElHfScJn41ClgB9C8ZdB2q4uGlmR9iT/4T2reuBOBYMwCRmOyH3EtuekOJ9cH4es7JgAG51YUcx//+4GRCqZoS2fOQFFgLHUBUQK43eAGFLFGSbHSUSiehFUNCwE7qVz0TWrXrJ/G6Paj17XDxxwgx39rNhJFGeOYliPqYyJS7Iui6pOs6smA0YWbRl1UpCpAwIklmSZDgQB7MVy5bbpOdA+NdwcpA895I//tIS/vyFq0O4yjFuZuzulXBEQRLBR64FI++ff9BMoIDoWzDMzVLYxJVlGXNz5fj9JKQtVAEhuhUQxE4wW8lhvlagEhDkAOU9AIcF/vnBb0NESJewIMpGN54C4m2H6uBZBk9R6jhpE7C5ah4rjZ4VWOg+vCzWiyD3WNoGKE7AVlSQA1W9PKiAEDgmtgd3lNFXQPQ3p6qm45UrDdwRYr8AgjMgSKDaXooCYqZivrc0AmK7qwwEXUVBveyuhaQpILwhi8NCW9JczSZBIEqnjXY/8C3NZp8c1kn9L1EoxAqhVKIqIET0FB0dWbUP6EEWDIKD0yV85DvuwFeWNvF/ff6NwH/bU/ShZUDQUtO3OgomHGRNUeRRL4mRqzjXWhKqeR5HZsuQVR0rTak/Ud/BfJe0IIRv1IHETCXnav2ggailoqqDaMhlrICwA56dBIT194rXgmHmEZlVlv7rF8MwqOYFagbEtUYPcxX3fd8Z6JpIAeFDMntByKO+BSOHnqLb+4NRU8bGxWK9hMMzJTx5mm7D8Au6DoI7hHLYCgiLgFD0TCo4CWpF0Sapg9BIUHmfBKSy3C8HwjcDgnNnQMiqjs2O4lv3PcYYSbG7V8IRhayaHrgsWNUsUaJsEAiaPdOzGMcX6SQ0DMMIVUAA5ia0LQ8qIPJhGRCCOwOCeO1qRREH6yWcp2zYVlv0CXxY4J8fmpTgIKA/EQ0lIHwe74dyjsfdizU89cbqwH/zZkAAwFwlh2sOBYQTeYHDvgnzQEPbAAls9haM1SadAMoLHD7xIw/gQ/dHC1vaDaBtTt9YaUFS9dD8B6BfYUfb3C6TTvtABYR78tJTNMiqnkgBQTbnpNKynwGxswoITTfQVeJlQADmwTULG5w3A4JMtyKFUJL3R9V9686cIBPZ9ZZsBy4G+Xud+La3zOP9b13Ab33hDJ49S8+MAYgFI/tbvsCxUCihZVvdwcaQ+VohBgEhY7qSs9eypfW2fbjbVRYM6+8etV7woSPTePlyIzCsk4QQp23BIMiCmCL3Iue9NYkFg2dJDWf4d79aEAZaMFRNx2pTGmiQyTtsibUEBISfzc6Ljic8c8paL8g6PmrK2CR4/MQcnju3Tt1HKSGNPzQ4h3XDr+E0P4tXG12steRMCYiNKAoIa5+Y5N4cB7MWAeFXxel3X8rx7hYMsg6tBQRajjFGEuz+lXAEkWQB3gkQj2abkn/QkuIdjoG+/LMlqegqGhTNCCUgSjnOlQERWQHhyYDod8wLZsXkansgg2GtJaEkcgObn7JPX3kYWpLqsjwQkNccxn43e2rsVoKHj0zjlSsN+zDYvxbzd3kVED3FtIIcoExPiQ3Dr4Yz6xBKooDwEhAAcOf+yaHfgHcSNHnuS1YAZVgDBmDWSmq6QZVLXmv0IHDMQJYGYH72BI4ZOKyQKUsSmWde4FDO8X0FREAN5zAVEGSdiCozn3YQEEoGCghyyO0OWDDiKSCitCIR0mezI9sHdJKZEAW/8q234ZbpEn7qv35tYK0gGJYFQ+DpIZSbbQU1j11loVaInAGxalUoEwLiwkbHDmfcTSGUtgUj4mfx7bfOAACepijfCFpSegKif11MJodisn9wVnEmUkBYNZxmy0fw46p5foD4X2/L0A06YUtsGLS1NAyEZA4TCnrViVOWpe1aw/xejpoyNgkePz4LRTPw1OuDw5EkGRA72YJBSNhXLjcAZBNACQA1q449TCFDBoATCdSJcUD2XSuNYALCe4/0hlASBcVqUwrNORtjjDgYvVPyDQBTgjZ6NxlvbaYTcQMSAXelJdkERFJAUDIgwhQQedGdAbHlVEBMl9CUVFdvPGBOE6crg4ff5AoI1SZxnIhqwTAVFPHe43sOTgEwk7u91wJ4MyD6r3WxPlgrdXDa3MjTPps8x0DJPAMMYQdUAAAgAElEQVQiWd/6bkS/BaO/O33x8hYqOR4HKX8LL2x/PGU6fG27h9lKHixFncQwDOql3MB0ggSzJanhBMy/2XrLbcFwHqDJpmWYCoi+zDx6DSdgfu+zUEAQrzBZd4gCIk4IZU/RHRkaAS0YDtvLxc0ORI7FXGXwAOWHUo7Hb33vXdjqKvjf/vSFgaA8wzDQU4fVgkG3YJiKOPd7tVAr4vJmN9JGdq0lYaaSw77JAljGrOLsH2h3UQaE9V2J+lm8fd8Epkoi/pZyuCPokO9GqgwI87OQ1WeC3NsaLgsGyYCIZ8HQdCNSBW+1MGjBIJY1KgFhqQCHqYDwrlvk/ncjKSDeuljDREHA5yk2jEQZEM57y5BbQshe8+Ur5oDgyEwlk+etlUQYRjQlLBAvby0JRJ5FrShgteWTAeGj1PaGUBIFhazpoa9tjDHiYPevhCOIUVVAhFkwaNP9KM/X6kUnIEoi52nBiK6AcFb+2XLoomhP9r05EOfX29RNSDlBCKVsSan9QigBBCYgG4YZqhX3pkN8/14Wu0UJIXO+Vpp8+5Zpk+mnpbELQ6jhXG9LmCgII/ldyBr25lRzKyBun5+gEgde7A8I6Lu23RuQEzsxXREzVUAApp2BZB5IioYc796osCwDkWNdlXtZoy8zj5oB0W/vyCIIuOBQMQDm95thor2nxK7iVEAEfQ/IRHajJePSRhfztUKkz40Tt+2bwC980wl84bVVfPSZN13/TVJ1GAaQG1YGBOVzsNmRqQoISdWprS1erDYlzJRzEHkWeycKuOAiIHaPAoIQd1HD9ViWwcNHp/F3r6/6Ni7YIZQpMiAIMZKVmsS2YPQoFgwhTgilOYENC6EETBm7N4RyOSC0lyggpiLYqGjXBYRnQPTvzf0aTud17fYQSsBUTD566wy+cHpl4DMqawkyILidU0AQYvnly9vI8axN/qcFsZuFBVGS/fdOKEBnK3lfBYSs0s8pIu8mlJ0ZEn55EmOMkQS7fyUcQSQJ4dkJVOzMhkHZdDPB4VjkWYicWWm53YlIQOToGRBB8mSAZsGwfl9RoBIQV7e7ePHSNt5+bIZ6DS2fykM/BNWe7Z3IY6Ig4MvnN3wf35Y16AZihwISVcOyR5rfomR2OAkImnz70Iz5PtF61XmOgR6xYiwq1lvyQAPJjQpvBoSuGzh9rYmT+6qRHk82xjR//DKlUs4Js37SvelJ6zOtl/qVlj1Fo3rEcwJLrYjNCnGT/ss5HiLPYr0l9+WlKcgvgWPBs4x96N3smBk3UXJyyHQ5cgYEISDaMi5tdmLZL5z40H2LeNfJOfzqZ07jxUtb9s/J32lYLRheC4akaujI2kBjSNQqzp6ioSmp9vqxWC9aFoz4mQLXG3EtGADw9mMzWG/LA8o3gixqOAnpn9VngjbgSEIYCRwDTdfRlrTQx5kKCPcwoZ+ZM6h+3DdprqPEFhEH5M8X1oLRkb0ZEOZn+Np2DzzLxCYWRxWPHZ/FelvG1xzrDGAN4NJYMIadAWGtza9caeDQTDlW7lkQiKomrIqz0VXAs4wrU2lYmKnkfDMgZE2j3pNEjvMoIPp7zzEBMUaWGL1T8g0AecQVECQ/wIkk9gDzOTmXBSPsgF3KuVswJIq/nIaCx4Kx2ZHBsQyqeR7zkwUIHOMiIP7qFbMi6t237Rl4LjPwL56UrGVbHgYPdDzH4rHjs3jy9IqviqCZUHZXFHlU8jxVAeFVrJDQoT3VPPX9fOToDP7Dd99p2zqcIJtjJcMmjNWWZDcT3OjwtmAsN3uQVN1VfxoEv4YAwzDMRPdQAsKrgCA+06QEhOhqwaBtlnI8N1wFhEUSFiNOeRmGwXRJxFpLtj2saaeNTuLTnOhHI9RoCoggC0Y5x0PgGMuC0Q2t4PQDwzD4tfefwkw5h3/68a/a6w55DcM4uAscOxBgu+2Tl0Fe18WQIEryeSZJ7gemiriw0bUPtGmsBzsN8hmMI0t/+KhJnPvZMPrWhvQhlFk1owgci4LAue6t3QShoRxrtmC05fAK3mqBH1BAXGv0wLEM9d5D1mNao1D4dcVUQFjXXs3z4FkG0ogOppLi0WOz4FgGT77qtmEoaoIMCMe/H/Z7RNbAjqzh8Ey0+3MUELXXZpQsMEc98TAxW8n5kgaSEqCAUH0UEOMmjDEyxI2zGo4QiLdq1OCtzXTCL98g9DnzZqZDnAyIjlMBEWE6CJibpK6i2d7hra5id8zzHIv9U0WcdxAQn3n5Gg7PlKgBQyWRR0/RY1kOwqqT3nlyDlsdBV9Z2qT+9yaltSIq5qp5e6pjPx9FnlrK8ajk+IEGDAKOZfAddy9QGX+ahSAt1lvSTauAWFo3J7yL9egHyXlKQF+jp6Ija9gz4U/kTJdzWG/JLl99VELQD/WyiM22DF03IKka9fCcF1hIO6CAiDPlrZdzWG9n04IBmJvVnouAiEboOEM6o6gxGIbBVEnExc0ONtpypApOP0wWRfzm996Fixsd/MInX4ZhGEOtrxS4wRBKZ0uRE3tsSxndl0xANr0kSG3/VBFrLckOtt1NFowkCoiZSg637av6EhAtSYPIsak+3+SxWb6XlTzvUkAkUawIHAtVM6wQynALhqzqLiXWckPCbCVHvc+949ZZ/NmPPoBjc/F9/7SqZRoIcUqunWEYezo+itlgSTFRFHDPYm2gjlPRdAgxcxx4jgX5c0W1KiWFk0zPKoASiG7BaCQc9iXBTDXnGx7pNyglFgzymNWmZBN2YwXEGFli9E7JNwBGNgPC2gTQQihbCUIozec0Ax3jZEA4AyDJxiFoOgj0N85kM7/VkV3y3kPTJVsBsdGW8eXzG3jP7YPqB8BZHxr98ESu2Y+keeTYDESOxV9/fZn635MqIABTSuolIFo9larGuPeWKdx3aFDhEAYu4uYqDtZaMrUB40YEOVyoNgFhfhYXp6JPWOYnCwMZELafecL/QDpdFiFrukuKnNaCMVUSoeoGGj0lQAGxMxkQcaa89bIZnplFBgTgVl6ZrQ7RCDVnTamsaa6f+WGqlLNtE/sTKiAI7j04hZ9+4hg+9bUr+MQ/XLIn0VlNu50QeW4gA8LZUuREJccjx7OhG1liKSLrByFVX7vWBHDjWzAA857y/NKmq1WCoC2pqfIfAEcIZYbvZdlDQCQJDeVYBl3FtCyGqZ9IHotTBbHc6GHWRzHGsgzuOlCLfC3e6wLcQcM02OoUx7WTjJdR3BemwR3zE3hzzd1AliQDAuh/P4augHCsgZkSEDEsGDvVADZTzvmGR/oNSkWLJCOqtpWmhMMzZYgR1u0xxoiDG2s1HBEkXYCHDZ5jkRfYgQaInqKhq2iJ5NrlHI+2pNoHnrADdjHHe0IodbBM+GSgYB2A/A4DB+smAaHrBj736jI03cB7btvrc83mDahFqSP1Q4vSOuF+Th4PHKnjr7++TGWbG2kUEJU8likWDBoZ8tEP34ufedetsX+HfYDOKIhSVs2bXj2B13Y3wrs5XVrvgGcZ23McBQu1wkBDAEl0D8uAAOBKu270FIg8m/jASZ5zvS2jp/pkQPCcbaEaBryTxCiol3JYb2WogHBYMEzSMxoBwTAMRJ41FRBKtGupl0Rc3IhfwemHH33HEdx3aAq/+KlX7Mq54VgwmIEWjE3bguG+HzAMg5kAWTABzYIB9AmI3aSAEBJYMAAzB0LVDTx7dn3gv7UjNESEgXwes1TFVPICmo79RUdRIfJsLJ+9cwIeroCwmjcc5Ou17R72UPIf0oK31/jgf0cLiCZ7lVHcF6bBXDWPrpXXQpAkAwLofx6HngExJAKiJHIQOCaiBWNnFBCEiKOtt7KqU8Pfyd+BrOmrTVNRNFMOX7fHGCMObqzVcERgeuBGU2pXzgkDBIR3sxcHJYuA2O6asrKwjUZJ5KBoBmRVx5WtLp47t468wIX64cjG2enHdm5ub5kpQVJ1XGv08NmXr2F+soDb5+kBgOWc+bg4TRhNKZxgeefJOVzY6OD15dbg4+3k4/g3ntlqHivNnutg2uqln4A5QW76WSkgSIPCdOXmsGB45blLG2aQIB9jIzY/OdgQcC0g0Z3g8Iy5iXrlSj+wrtFVU01Z7EaJlmyGUPpYMHpDrOHsxGzBAEw1yFpb9u04j4u8wLlCKKNaMAAgz7OQlGghlIC7rpbWYhMXHMvgNz9wFwoih1/581cADCmEkhsModxytBR5MVPJhXqJyUa37gihBPoERFht8yghqQLi7gM1lESOasOIYk8IA/k8FjLM06jkeJdioyuHB0l64WxBCMv6qHoqsLc7CpY2OqkVRDT0VYJhNZwqCoI7IJrU7N5oCggSku20VCla/AwIwNkWM+QWDGsNZBlEzmiKAoZhUCuK4QqInoJKbmcUECQXjBZEKfsQReRnsmraMFatOuTZavi6PcYYcXBjrYYjAknTIY7oBqmc4wYO3l65a7zn49G0CIgoCgoytfnIX76Kd/z63+CFS9v4mXceC30cuWkQAmK7q7imkeRG8tLlbTz1xhrefdseX1KDHGi8REwQbAVEwKbviRNzAIC//vq1gf+W1oKhaIaLWTcryrK7iQnWTd97kEgKQmrdfAoIk4C4sN7BgXq8zQ0J6HPaMIgCYjZgondyXxXVPI8vOialjZ6SaspCDsPrLQk9hT4pGbYCgnw/41owZFW3fbhpO+XN+l/NVonVStEJtbz12KhqDPKeFwTOlmynxVw1j1//rlN289BwMiAGQyi3unQFBIBIk7S1llnhS2wCEwXBzBewDne7qUnAPlglaAZ44IhZx+lV1bUlLTUB3VdAZLcNrOTdFdcdWaO2LgXBOQEPe42EZCUWjD/5ykXIqo733T0f63fGua7wEMrBv029dGMqIGYr7ppwTTeg6ckIiKRKobggdsIDU8VQ629c1IpipBrOnVJAkKEibb31D6E03xNZNW2dsqpjZqyAGGMIuLFWwxGBouq2j2rUYIZGug/e3sCvOHC2YEQhIMiN+fefPY/33L4HT/7s2/GDDx8KfRzZONsWDE8gHCEg/t9n3oSs6b75D4CjrzwGAUEkhkEWirlqHnfun6TmQKQNoQTgyoFI2lriB1sBkVEIZV9Vc7MoIIiFxYBhGDi/3sZizCk26SO/7GgIuNboYaokBlopOJbB2w7VXVLttD5TlwXDp4YzawXEmZWmTbgA5uHFO0kMAyG8rlokjsil22AWRNOCsRkw0fdDXuCsGs5oOTeEgNg/Vcg0If2x43P4xw/dAoBOCKSFwLFQVK8FQ4bIs1TCYzqCBWO16Q6wZRjGtmHsJvsFkKwFg+CRYzO4tNnFOUfAMoBIDRFhsDMgMrVgDIZQxrX9OC0YYa9xokAsGAo03cAfPnce33BwCrftm4j1O6OAtGBEqeH0XveNa8Fw14Tb2TsJiF8xIVEX+/dYgZdZ2i8IaiXBroj3Q6OrJBpEJUFfATEY+us3KCV/B0XTbVvnTCUXWOk5xhhJcGOthiOCUa3hBPqhkU6ksWCUc4LdghGFgHj7sVl84N79+OSPPYjf/MBdkevmyCaGTCN7iu5SQMxV8igIHJ47t4F6ScRbF/2DpohygBbG6YdmT4XAMaEy6nednMMLl7YHWyt6CjiWSbTZs2/y1nMahmEpILIkIMjmKpsD5bqlqrlpFBBcX5671VHQ7KmxGjCAPgHhbMJY3g6u4CS4/1AdFzY6tnqiYVV9JQXZMK+3TDvDsDMgdN3A9/3el/ATH/+q/bNWgqA9Itm/ahEZSTbCTpAQys02aXWI/p7meNalgAg7gBICImkFZxD+j/eewP/8yYczsXZ4IfIsJK8Fo91vKfJippzDRkcOzJtZa0kDhDghIHZTACXQ39AnsQM9esys43zKY8NoS2rqKlJyXfksQyg9Fs9OgqwKLgYB0VdAqPjC6RVc3OjiHz1wMNbvi3xdTDQFBO1vQ9alUR1MJcWsPRwx95CEgEiUAUGIuiGrmxiGwf6pIt66GD+sOwy1ooiNAAWEquloy9qOhVCWczzyAjtQ4w5YGRCU/Sy5T0mqbj+OEBAbbTkzlewYY4zmKXmXQxnREErAXJC8BITXbxvv+Ti0ZRVbEQmIPRN5/LvvPIW37J+M9XsKDgsGbRrJsox94HvXbXOBU1PbghEjA6LVMw/8YZPJd54kNgy3CqLRNVtGkkw2vTLHnqJDN/wDMZOA3PS9UuqkIKTWdAJSazfCmQGxtGESCH51qH6o5gVU87zbgtGIFqh2/+E6ANg2jGbE76MfRJ5FNc9joy2hp2jUjUpOyK4F48XL21hpSvjy+Q28vmz6/DtS/CkvObQSAiJtBoRpwdAdrQ7xLRiStdEL++4Tmfb+DAIoveBYBif20jNx0qJWNKsQ1xz+4K2u7KsWmankYBj9nBga1lryACG+axUQKSa7+6eKmCwKeH3FnSvUlrTQhogwkO90UcgwAyJv7i/IIb2TQAHh3DtFzYBodBX8wRfPY081j3fdNhfvoiPCzoAIuUfShgM3qgKinONREjl7OEL2D0le504pIADgr376Efyvj4Qrb+Nisija9woaWhGUtFmCYRjMVvLU7AZZ1aiDUrIuyKpuP262krfX4/WW+/W1JBX/5i++HktRPMYYwJiAGAr86m1GATQLhtdvGwelHA/DMCe1aQ48Ycg7LBhkGumVEx+aMW0Y777N334BwA4AipUBIamRDvxHZ8tYrBcHCIg0lolZjwKCBGIORQGREQGx3paR41m7+vVGh50BoRl2BefBBAFX87Wi24Kx3Qus4CS4da6CqZKIZ8+uATAzUpIEnjoxXc7ZgY60Gs48z9k1umnx5KvLYBmTMPjYly4AML3UcaenfQWE+R4KGbVgkKlWrRQjhNKyqEg+kyYv+haM7FUKw8TbDpnkl9MCtNlRfO0eMwHBaASmBcNDQNSJAmJnNu9ZgewFkqb7L04VcWG94/pZW06vgLMzIMRsMyCA/r21q8QPoXQrIIIfmxc4iDyLr17YwlNvrOH77zswtEN+1AwIGjl0o2ZAAKZFlHyX09QfCym/J3GQ4+NZ+6JiqiRgs6PYmS09RcMzZ9agW58Z0taSRp0YF7OVHFUBIak6crQQSkcLBhlOkgwIYDBP4m9fW8XvPfUm/uqVweyzMcYIwo23Go4AFE1PvfEdFkoUBYQpd03m1ScTyracrMYzKpwtGFtdesf8Xftr2FPN44HD04HPFTeEsi2pePVqI5JsjmEYvPPEHL54dt31/M2emjj5OMdzqBUF22fZSpEn4Qdy01cysmAsN3qYLucy9bKPMog8V9UN+7AQVwEBmE0YlywCQlI1rLflwAYMApZlcN+hKTx3dh2GYVghlOm+j1Ml0QqhpLdgZKmA+PzpFdyzOIVvvGMP/vvzl9CRVXRk1a7MjXPNQIYKCJFDR1btANipGAoIYlGR1GihxAenSxB5FqcW4qnDrjfumJ9AJc/j2TNr9s+2IxAQfonqPUVDS1L9FRBDCNIcJu7cP4kPP3AQ9wTYAoNwoF7C0oYnAyKBOsgLuwUj4wwIoH9v7SRowXBalaKQLNW8gM+9ugyRY/GBbzgQ63fFQeQWDEoGxI3aggGYA5KVhicDIgGJYLfFDLkFY5ioFUVouoFGT4Wq6fjxj30VH/zPX8IffPE8gH5YatrhQByY2Q2DGRCy6hNCyfVDKFeaEnKWGrK/bruf6+yqqc76+/Ob1N//O184gx//2POpXsP1gKrpA+G/Y2SL3ftNH2GMsgKi4mPBSBJACbg3CMNkdckmqadodsiPV+L7gw/fgr/9uUdDb/I8xyIvsJEIiLWWhO/9vefw+nITPxQhLBMwbRiyprt8u82emoowmKvmcW3b3LCT685UAcFGm+5ExcWNTqID+G4FyzJgGfP9O7/ewVw1Fxgc6YeFWgGXt7owDMOeWuyZiPbdvP/wNK5s93D6WhOKZqT2mdbLYr+GkxpCmY0C4up2F69caeDxE7P44NsW0eyp+PQLV9GW4vvHczyHSp63qzOzqOHsKbpdrRbPgmEqIPy8tl7MVfN4+ZffjW+4JXtv8jDBsQzuP1TH0w4CwgwJ9rFg+EzSCOypm08GxG6zYOQFDr/8rbclDp47WC/iylbPPtxJqgZFM1Kry8hnMsk65QfyGknrU1emrx1B4BwH0Cjff9Io8M137k28j4kCco/UQw4lbUlF2XPdUzeoBQMw161+BoT53iQhWvptMbt3aEHWvM22jP/zU6/gc68uY7FexL/7n6dxZqVlExA7FUIJmAoI2lora/T7kiuEsmlWcDIM49uoQQiIr5zfoP7+P/nKRfzFS1exvosqPDXdwEO/+gX80XNL1/tSbmjceKvhCGCkQyhzPHqK7goAo/lt4zwfwVAVEE4Lhk8iPcMwkW0ktCwML5bW23j/f3wWry838f986B58+13Rqr3euljDREHA50+v2D9r9NIlH89W8zaLHaUSNC74jGs4L252sX8qey/7KINnWVMBsdHG4lSyfvGFWgEtq1WGWG6ihFACZhAlAHzWkkKmrfqaKuWw3OhBN0C1YOR4UwGRdkrwpPU9efzELO49WMOxuTL++EtLaMtaos84OYTwLJO6rpGsO1e3eyiJXKx1PSdw6KkaJJWeoUHDqN43wvDgkWlc2uziwnoHhmFgq6NgIkwB4UdA2Pkx7vV932QBLLP7QijT4sBUEZpu2NasjmSSa5m1YGQaQmkpIHpEAaHGJozIQV/k2EjfB0K0fnhI4ZMEUTMgzIpUTwZEiSggdu/h2g+zFfM+YRhGNhaMXVSx6wWx6H3kL1/Fx798Af/k0cP40x+5H0WRw8/8ydds+/BO1XAC5t6x0VMHhgV+CgiiXpHVPgEB9O+rfgTEGystm6gnuLjRwdJ6B4YBPOOw6I06rm53ca3RwwuXtq/3pdzQ2J27nRGHohkjq4CwLRNSfzFaS6GAcHo0d8aCodsKiDSVcuUcHxhCeWalie/8j89iu6vgYz90H544GT3YiudYPHrrDL5wesVWFKTtfp6zbvJAvxI00xDKDGs4u7KG1aZ0UykgAHODquk6ltY7sRswCBbsJoyubSPYGyEDAgAOz5QwW8nhs6+Y+SNpFRDTZREN6ztCb8FgYRjpg0uffHUFB6aKODxTBsMw+ODbFvHCpW0srbcTTbvrpezkzuT3X9nqxlI/AGZGhmRlQOxWYiEqHjxi2t6eObuGrqJB1nRfBURe4FDJ8b4ExJpPLbTAsbhz/yQOzWRfnzfKWKybZOZ5K1uGEOdpCYhb91Tww48cwsNHZtJdoANE5dfsOS0Y8a6TTMCjhmwemS3joSPTQ7cuEZI+SCVoGIZlwXBfu8CZMvYbVQEhqToaPdXR+JOsBUPgmF1t2yT3iL/6+jK+4+55/Ny7b8VsJY9/87478OKlbfzG514HkP7eHAd+ijO/+xL5maTqWGn27CrPvMBhoiC4nkfXDZxdaeP2eTPg+CtLbhsGyaQSOAZPv+Fu8hllLFk2Wmce1xjZ48ZbDa8zNN2Aphsje6OpkAmFbG4QeoqGJsVvG/35+gvpMAkIMkHsKhq2OjLyAptKOlrKDYZxOvFHz11AS1LxiX/yAO4+EN+7+9jxWay3ZbxwaQuAKUlNc9OZq+ax2pSg6UY/AyJhpgQNWdZwXrRqJHdbmF5a8CyDZk/FSlNKTEDMT5qPu7zVtQmnKBkQgKkAuv9wHa9ebQBIb4kieQqAOc33gnz/eimqOLuyhqfPrOGx47P2xvN9d8+jIHCmzDzBIYsEUWaxBvcVEF3X+xEFZkaGFtmCsZtxeKaEuWoOT59Zs/MygipLZyo53wyI1YBa6P/+Iw/gZ955LIMr3j0ga8kFq12nbd2709ZwChyLn3/vCV+lShIQAqLRU6DpBiRVj50xQSbgUV/fv3//Kfz+D9wb70ITgCwnagAB0VU0GAadHHri5FziHJBRBqniXGn0UmdA8Ls4/wGAfVh/5NgMfvU7T9n3tPfesRff/pZ9eMNqs9lRAqI6GPpLzikk78GJnCeE0rkOm3kS/ee51uihq2h4310LEDl2wIbxzJl1zFRyeOLEHJ56Y23XZCoQstfZSDZG9tjd3/YRhN2DPKIbzpJHIunnt43+fDujgGAYxqrE07DZUXyna1FRzvG2koCGc2ttHJkt43DCadujx2bBsQyefHUFhmGgJaXNgMhBN4D1ltTPgMgyhDLDGs6LCWsodzs4jsG5NfPGdaCezIIx71BAXNvuIS+wsZQzxIYBpA+6qjvWhHxAXZekJCetnj27BknV8cSJvsKomhfwrXfuAxCegk8Due4s1uC8pYC4utWLrbgyW0J0y4JxY9sGGIbBg4en8cWz67YMd6Lgv0ZPV3K20sGLtab5+Hpp8J6U1lKzGzFbySEvsPZUrm1bMEbvM0Vshi1JRdeSfMe3YJjf26j2K4ZhdqS6kYuggAhSp/yH734LPnT/waFc2/XEXIW0dEn9DIiENZy7Of8BABZqRXzsh96G3/3+tw4Q4L/yrbfbw4Qs925h6Csg+uGRRKmSo1grCSnRkczw5ZlyfwAyU3bnSRD7xcm9VZxamMDfOwgIwzDw7Nk1PHi4joePzuDqdg9nV91huqMKEiR+ZaubWS7aGIMYzVPyLoakJmeAdwLeBgg/v21UlHcoAwIwbRhd2VRAxJVDe1EOUUCcW23h0HRyqe9EUcA9izV87tVltGUNupGutYJMGZYbkmOTk90GlNwss7BgkEndzaiAOGfdYBcTvvZaUUBR5HB50/Qg7p0oxJKkOhtg0iogpkMUEORnkkMB0ewpduVYFHzu1RWURG4gePGD95lp9klyU8h1Z2GDI9PbpqTGJj3NEEpLAUHZ6N1oePDINDbaMp47Z3p9kyogSC30qJL4Ow2GYbA4VXIQENlnAGUFpwWjYyk1YtdwxrRg7BR4tt905AebHLqJckr6exOHAiLBd7cgcJkGol4vPHB4mpqrMlEU8Lsfeit+/l0FOXsAACAASURBVBuPD6UC1A+zFAUEISBo90iy7l6xpv/k8cDgun3GUnQcni3hnoNTeOnytp018dpyE2stGQ8cmcbDR819yW6xYRAFhKob1AaRMbLB+A6fMcgCPKqSW7JBIJsYP79tVOxUCCVg3qC6tgIi3e8q5/1DKHuKhstbXRyaSTbFJnj8xCxOX2vitWumJD5NCOWc4ybf7KkQeTbTqSqZPGRhwbiw0UFR5Gwv/s0CjmWwZt2cDyZUQDAMg/nJAi5vdbDc6GGuGu97uX+qgPlJU0WR9vs45ajmDVJA9CwFxGZbxv3/9kl84vlLkZ7fMAw8eXoZjxybGThsnlqYxO983934zrsXYl93lgoIp3w87pqTFziouoGOrI1sJlCWIDkQn37xKoDgxhDvJM0Jr+x3DOBAvYgLVhUnuXfHzVbYCRQEDhzLoNVT0bWaaAoxr1OIqYDYKXB2U5T/PbKdUT7HbgKxHaw0JcgpQih/6OFD+I3veUum1zZquHP/JH747Yd39HfWSzmwjDsDggwNgkIoL1kEhFMdPVMZVEBU8zxmyjnce7AGRTPwwkXTdvzMGZOIfvDINPZPFXGwXsRTb/SbkkYZS+sd+94/zoEYHm78XdEOI00Iz07AtmAQAqJlyl2TbviKIgcyoB1mDSdgThRNAkJOFUAJBGdAkNTeW6bTERCPHTdl5Z/62hUA6RQQ5CC63OyhJSl2lkdWEOwWjCwsGF0cmCru6jCpJCDS4YmCkMpXPV8r2CGUUfMfCEgOBJDu8wa4JfD0EEq3AuKL59bRklQ855N2vdGW8d/+/gJevLQFRdPxypUGlhsSHjs+S/3333Rqb6J1qZ8Bkf7zVxD763gtbgaEtblr9tSbQgGxZyKPQzMlfM3agIYpIJqUZHbAVEBMl28u8jIMi1NFLK13oOsG2tbBftQO6IC5/pRzPJo9xa7Cja2AiJkBsVOIYlMcZXXKsFDK8ajkeFMBkUIBfKBetEnMMbIDxzKol3N2rTfQV2oHhVCSg7dXAdGRNftzfnaljcOzZnj0W618E2LDeObMGm6ZLtkDkYeOTuO5c+v2GWlUYRgGzq+3bVXmpTEBMTTcPKvkDmHUMyDKHgKCsJk0v20UMAyDksiDAYYuKyuIHHqyhu2OktqCUcnxdlK3F+csX1vS/AeCwzMlHKwX7YlgGgXEdDkHhrEsGD01cw8hkb0GTXei4uJG56azXwD9z3/SAEqChVoBX72wha6sYW4iHgEBAD/y9kO4fV81tULGeYCkERCkmpMoIJ4+Y043XrxMr676L0+fw+984az92HrJ/Ey/w4eASAqylmWjgOh/z+JbMMz3bLur3BQKCAB46Mi0bUMKIuGcyezetWKtJeH2+YnhXeQuxGK9aKXSS44p+2jK1Um+UsdWQMS7TmFELRi1kgiRZ22LIQ12QOhNREAAZtDhSrOXKgNijOFh1mOdkAOU2uS+SQ7erhBKx7pdyvE4u9rCI8fMFp3Joohb5yr4+/ObUDQdXzq37qquf+jIDP7ouQv46oVNvM2RVTVqWGlK6Ck6Hjhcx9++vjoOohwixqtExhh1BUQ557FgtCRMFtP5bcs5fujqB8CUd3ZkDVvdDCwYOR6SqtuEkRMkSDCtAoJhGDx2fA4bVihbmom0wLGYLuew0uihJamZT1iEjEIoDcPAhY3OTRdACfQnZGlf+/xkEdtdBbKmY29MBQQAHJmt4MMP3pLqGgCzGYV8z/KUCb5XAfGsRUCcXW1R7U3/sLSJ43sq+O3vuwvf9w2LmKnk8P67FxLbv/wwnWULhuPwFDuE0nrPWpJ6w4dQEpAMkqLIBb5msqml5UCMLRiDIFWcS+vtzGo4h4VK3iT3iQWjGNPXT4jcUVMRCByL43sqeNmHYAWA1k2YAQEAc5W8FUI52vvfmxWzlZwry8AOoaQRENbf7prVwuUcTjrX7UZPwUpTcg3q7jlYw/NLm/jqhS20ZQ0PORQt9x+ug2MZe1CRNQzDwC//j1fw/IXN8H8cAJK1c3xvFfWSOFZADBHjVSJjpPHA7QRoLRhpDwClHDf0/AfAnCiuNHvQdCN1C0bJQ8Q4cXa1hT3VfCYbvMdP9Ke7aVsJ5qo5OwMi682ZXcNJIWTiYL0to6toODBVyOKydhWyVEAQ7EmggMgSJE+BasEQ+n3hlzY7OL/ewSPHZmAYwCueTbqq6Xjh4jbuO1THN5/ah1/8lpP45I89iH//XXcO7ZqzDKEEkisggNFVxGWN+w/VwTLh75W9kfXkQHRkFW1Zy5yU2u0ga8rSRgcdWQXHMiObM1XNC2j2FEcLRrx7FbGyjSLBctu+CbxypeFbJ9gZcXJoWJizFBByihDKMYaHmUp0CwbDMBA4BppuYMpS/TifBzDXbaJ0O+zISrv34BSakoqPPv0mGAa2HRQwral3LkwMLQfi4kYXv//sefzhs+dTPQ8JoDxYL2KhVsClTX/F0xjpMF4lMkYQszgKEDgWOZ5FS+4rINL6bSeLou27HiYKAoer2z37d6ZB2ZHW7cW51XbqAEqCew9O2XkNaSwYQH/KkLbSk4Z+CGU6BcTN2oABOAmIdJ+deQcBMZdAAZElpqzcgzxlmk1+JikanrUCp37k7YcAAC95CIjT15roKhruOjA5zMsFAEwWBLDMMEIok2VAeP//GxkTRQF3LEyG3g/8CAhSwZm0FvpGxb7JAjiWwYX1DtqShpLIjWzGDgl4Ji0YcS0Y5F40iiqC2+er2O4qvlPRUVenDAtzVXNvYiuAb8K63FHGbCWP9bZsV0r2WzDo3zFC3nvXYTtwtNGzGzCOzLoVEADwmVeu4fZ9EwP79IeOzuDFS1vY7ihpX9IAXrxsZg998dy6L0EYBUvrbXAsg32TBczXCmMLxhBxc+yKdhBEwj6qCgjAlDYSBcRaS8JMJd0h519/++34xW8+mcWlBaIgcravdDKl4sK2oshuAsIwDJxbbaW2XxCIPGt75NKSBrPVPFaaw7JgZBNCedEiIG5KC4a1cU5awUmwMDk6CghCTlItGA4FxDNn1zBdzuH+Q3XMTxbwwiU3AUFkkXcfqA35igGWZTBVymWigMi7QijjrTm5m1ABAQC//v5T+Mj77gj8N1MlEQwDuzWGgFgyxhYMNwSOxfxkAefX22hL6kgfcAcsGHEJCBJCOYKv8fZ9ZjbJK1foNoybsYYTML+vsqrb3+dR3v/ejJip5KDphm0HJlN9v/sS+bl3Ha4VRXAsg9WWhLOrLQgc4xo2LdSK2GftWR44Mpjz8MjRaegG8OzZ7FUQL1l7juWGZNuok+D8egcLtYK95l7e7KYiNMbwx3iVyBhygLRpVFDO9xsgTAtGOjXBib1VHJ2rZHFpgXBNI2MeBrwoe6woBBttGY2eikMpAyid+OG3H8IPP3LIdf1JMFfNYa0lY7MtZx5CaSsgUlowLlj+uYXaTUhAWCROWgXEdDkHkWfBMtd/EkwUEDQ/P7EYdGUNz5xZx4NH6mAYBnfMT+ClS1uuf/v80iZmKzmXvWSYuHVPGfsm0/8ukWNtZUtsC4bjPbtZMiAA4OhcJTREUuBY1IrioAKila4W+kbGYr2ICxsdtOXRJiDIgCNpCwbZO41aBgQA3LqnAo5l8PLlBvW/d2QVOZ61LY03C4hSjyhDxhaM0UK/KrWH//L0m/hnn3gRh6ZLOLmvSv335Ds46yEgWJbBdNlct8+utLBYLw2QTfccNNsjHqI0mty5fxLlHI+/G4IN48VL2/Z944s+TVxRcGG9Y+/hFmpm+C9pCxwjW4xXiYzRD+EZXQlaSeTRkjR0ZW1X+W2dnuq0FgxvHSkBYU6zsmAAwKmFSfz8e0+klsySm3yjp6KcyzZzw64YS2nBuLjZwWwlF1t2eyOAt3zZ3pt2XLAsg/nJAmYqueu+kd1TzYNnGZcSgIDYCl66vI21loQHrQDCOxYmcH6945JZPn9hC3cfqO2YbPyjH74Xv/gt6VVZDMOgIHAQOTb2QcqpGrkZajjjYqacGyAgyP8eKyAGsVg3qzhbkjbSBEQlL5gKCCVZC8aBqSJ+6VtO4l237RnG5aVCXuBwdLaMl30UEMNQJ+4GkL0JqW4c5f3vzQhSpfmzf/IC/tWnv47Hj8/ikz/+oO9n1U8BQX622jQVEIcp++T33rEHh2ZKuGdxauC/CRyL+w7VM1dA6LqBly9v4923zWFPNY8vnktGQJAKzoNW5g6pEB3bMIaD8a4oYwSFu4wKyjkeLUmxp027ZbPn3MikDaEkdogBAoJUcE5np4DICnOOPuasMyAYhgHPMukVEDdpBSdgZkAs1otgM/C/HpktZ2YDSoMP3XcQ/98/fht1gk8IiC+cXgEAPHjUJCBOLZjTb7JJX2tJuLDRwd2Lw89/6F8bl5kMOC9wmCwKsckT53s2rqUbxIynGg4Armx1wTJ95c0YfSxOlbDdVXB1qzvSEv9Knoes6djqyOBYJvZnn2EY/MCDt+xIsHUS3LZvwlcBMer2mGGB7E0ubZkKSGLpHGM0MFM2CaLXl5v45+85jt/90FtRDcgkI/dOKgFRzuHqdg9L6x1qVf17bt+LJ3/2UV/i8cEjdSytd2y7bhY4v95GU1Jx58Ik7j9cx3Nnk+VAbHYUNHuqrYAgeVzjIMrhYLxKZAyigBjlDadpwdCwQqZNu0QB4bQwpN2ckA2ut9P73GobIse6ggBHBbOOrI5hTFl4jkkdQnlxo3tT5j8AwP9y/0H82DuOZPJcv/adp/Db33d3Js+VBhNFwZVk7QRRJF3Z7uFgvWhPC+6w5PcvWp7M55d2Lv9hGCiIbKID8VgBEQwySXPiK+c3ccf8xEgT+NcLB6yp3NnV1kgfcgk5vtyQUBRGNywzKW6fr2KtJeH/b+/Oo9uorz2Af68kW973JfESJ7azbyRkIQSyQRZSHpRSeoC+V/qgLWtLgdJS+uh5beEQoD2vgfa8llIetGUrS095ZSvwWFIgaTbibJDFiR07zmI7tuN90e/9MTOybMubrJFGmu/nnDl2RtJopBuNxnfu7/5ONbUPuK25o2fUlVLRwDg3qWloh8shQUnCU/AUpMfjlhUl+OMNi3HzipJhP5OxQyUgkt04cPIsuj3KbwJiOEv1oRkjqYI4Vt+KV3cdH/Z+RtPr2QWpWFKSibqWThw42TzqfTNmwDD6eBl/B1RzKk5T8Fs+yLwJCAufQCW6tS7VkTbe1khApMS5vOOyA5WV5Mbs/FS8ve9kn/WHT7egKDNhzNs3g++MCKYkIBwOdI+hCWVntwc1jW22rYD4wpzxuPyc/KBsKz0x1vKfS5dDYHxMlvqM90xLiMWEjATs1rtSb688gxinDNsXwKri9QqI0eozDaeFE9LhYiQgjCtVbZ09+PRYAxYX+0942Z0xFadHWbM/gsFIQJw62x6VQ/GM45i/YRitnfYcghEf60RynAvdHsUGlBbkcAi+v25an+/pobiHGYJhXKfynQFjpCbnJCE72Y2PDg09TKKsqgFf/PVH+M5zO70zbgx+30a4XQ5MzknCEv37I5BhHhXGFJxZ2rE2JS4GKXEuDsEwCY8UQeadhsjCB+EkPQERaeNt4/STmfQgleeunZmLnZUNOOlzJeNIbXNQ+z8EU2ZirDcxEuwmlIBRARH4EIzjDW3wKKDQgtUjFHwi4v0ju/+JzeyCVG8FxM6KBszMS+3zB3kkufycfFw2d/SJJd/X647Q126m7CQ3Oro9OKsPg9tZeQadPR6cVzxw7DD1nVnIylfZjf5Ep5o6LL2fgZo+PgUi8DsMw65DMIDeCyTs/xD5eptQDpyFy7diOpBzZRHB0hKtD8RgwyQ+OHAaVz++2ftvo4pyMLurGjEzLwUupwOFGQkozIgPqBFlRV0rRPo2Uc9PTxh02l0aG+v+lRyhIqECIsntRItPBcRwc7ZbhVEBMdYGlIZ1s7QmV3/fewKANgNEZX1rUGfACCaHQ7wNDpNNqoAYyzSclTaegtOu3C4HROC96mCYk5+KqjNtONXUjrLqhogdfgEAt64sxbWLJ4z6cW6f7wBWQAxkJL6NRPjmI/VwSG8XdeorIdblPf5b+Sp7bwVEB+JjrbufgUpyuzApMxF7qgdWQNi1CSXQO2OClc99aWSGbkKpJSVyU9xIHqKPxFDOL81CbXMnPj95dsBtL2+vwg1PbUVRZiJev/1CpCXEYFtF/aDb6vEo7DneiDkFvT2mlhRnYsuRevT4DCn+66fVuGTjJtT16zvkq6KuFXmp8X0uHhSkx3MIhkl4pAiyjgiogEh0u9Da2YOTTR1IT4ix9L76MhIQ6QGUQ/tTmpOMkuxEvKknII6daUNXj0KxBZr/DSZHv8pgRgVEjHNsTSiP6Y16jLHKFP3cLidm5qUMqEqarTei/PO2Y2jv8oS0AaVV9K2AiIxjbCgZQ4xqjQREeR1m5qUO2RzN7oxhGFa+yu7b4DkaKyAAYGZ+KvYe91cBYc8eEIBvBQSPdZEuxulArMuBFD/nmcaMGoH0fzAYFZP9h2F8crgOd724C4uLM/DnG89Dbkoczp2Qjm1DVEAcPt2M1s4eb+8pAFhSkonGti7sr9E+owdOnsUPXi7D/pomPPXx0UG3dbSuZcAFtPy0eFQ3tAXU1JKGxiNFkBlXkN0WzgIbGfqKuhbLjzP3Fa9PBTjWGTB8rZs1DpvL63GmpdM7A4ZVh2AAQK6ekTbjBHSsTSgr61sR63Qg10/ZHkWnrywowDcvLB6w3hgn/afNlQCAc4sitwIiUE6HeMuR3TwpH8BbAdHcgfYurf8Dh18MbUKG9t1k5T9yk32miLbyfo7FrLwUVDe04UxLZ5/1LZ32HYJh/GHKBETkc7scyE5y+21WaQzBGEsCIj8tHpOyEvHRob59Gv7rnQPITXHj99ct9FZXzC9KR/npFtT3+6wZjKGexuxbALCkWEtwfHK4Dq2d3bjlmR1Icsfg/JJMPP3xUZxt7/K7rYq6Vm//B0NBejyaO7rR2Ob/McH22YkmvLS9yhYJDx4pgixSekAAwNHayEpAGFcUgzk919qZ49DjUXhn/0mUn9Ya0BRbcApOg3GVwYwhGDEOh3cIUSCO1beiID2eHbBt5M41U/023kyJi0FxdiJONLVjfGocxqfasy+IMRUnKyAG8h2CsbOyAZ3dHiyexAaUQzEqIKxc5u87RXR8lPY+MRKsvlUQSim02HgIhnHhgT0gIt91Sybi++um+r1tXGocMhNjsWjS2JLFS0szsaW8znvO+cnhOvzzSD1uXl7Sp3pwgX7xYrA+ELurGpAQ6+wzdHpcahyKsxLx8eFa/Pive3H4dDM2Xn0OfrBuGprau/HcPysHbKepvQv1LZ3eKTgNBd6pOEc+DKO9qwftXT0jvr+hs9uDO17YhQ1v7EdTe/eoHx9peFYUZF09HjgdYslZFAxG+X5NU3vENKAEfIdgBK8CYnZ+KvJS4/DW3hMor21GekJM0JpcmqEwIx4xTkGKCXOku5wyplkwjtXbdwYMGmiOfpIeyf0fxsqYitNIRFCvtPgYuByC02c7sOVIHUSAhWM8qY12kTAEw3d4YLRWQMzMSwHQdyaM9i4PPMrasTETKyCix/mlWYPO6BUX48S2/7gYl84ZP6bnWFqShZbOHpRVabNl/fKdA8hJduPqRX37Lc0tTIPLIYMOwyirbsSs/NQBf3MtKcnEBwdO46XtVfj2ylIsLc3C3MI0LC3NxBObjqCju2+CoLJOG0I8MbP/EAzt3yOdCaOuuQPrN27CwgfewYNv7EdN48gTFxvfPYD9NU148Etzgnqh1ap4pAiyzh6P5TPAxhekUpEzBScA75Re6YnB+2CKCNbOGocPD9ZiT3WTZRtQGv71vCK8cvNSU2YUcDkcY5oFo7K+lQ0oyWu23hRq3gT79X8wGIkHNmYbyOEQZCVpU3Fq/R9SbHHSNRYz9BkY8tKsW1EU43R4E2/R2IQS0Bph56fF92lE2azP5pLojs6ky3CM6kwe66KfiPgdnjEaS0oyIQL842AdNpfXYcuRetzUr/oB0BIeM/NTsd1PI8quHg/2HW/yXuzov32PAhZPysDtF0/xrr95eSlOne3AKzuq+9z/qD4FpzHMzTCaCojWzm5c/9RWHG9sw3nFmfjdh+W48KH38N3nd+L4MAmM7RVn8N/vH8ZXFhRg9YzcYZ8rGvBIEWSd3R7Ldzz3LRHMSrbu1f7+xqXEIT0hBtPHpwR1u+tmjkNntwe7qxst3YAS0Dqhzy4YeLANhrH0gGhs60JjWxcKM6x7YkyhdUFpFhJjnVg+JTvcuxI2vRUQ1v5OCJfsZDeqzrRhZ2UDh1+MwOTcZGy59yLL91Qxxm9HawUEAMzKT+kzBKO1U09ARGnSZTi9QzB4rKPhpSXEYlZeKj46XIuN7xxEdrJ70NmmFhSlY1dVo3eIu+HgyWZ0dHv8nhNfNC0XNy4vxmPXzutTHbG0NBNzClLx2w8O95klo0KvgCjqVwGRlhCDhFjnsDNhdPV4cOszO7C7uhGPXTMfv/vaAnxw90pcd/5EvLX3JO544dNB+zq0dnbjrj9/iry0eNx36Ywhnyea8EgRZJ09HstngH0TENkRVAGRlhCLnT9eg4VBnqZtwcQMZOrDLiZZuAGl2VyOwIdgHOMUnNTP1HHJ2PvTdZicmxzuXQkbVkAMLSspFluP1qOj24PzipmAGImcCGjya/QoiuoERF4qjtS2eBva9VZA2DMB0TsEw9oVwGQd55dmYuvRenxSXocblxUPWtm7oCgdnd2ePkOeAGB3tTZ8w3cKTkN8rBM/vGT6gOOliOCWFSU4WteKN/bUeNcfrW1BdrJ7wOdXRPSZMFq96zq7Pfj4cC32VDeirrkDHo/Cva/sxnufn8YDV8z2VjAUZiTgvktn4N7107DlSD3e3nfS7+t78PXPUFHfip9fNTfgqU0jkT2PlCbq6vZYPgPctwIichIQZnE6BKtn5OL5rccs3YDSbC5nYE0oPR6FrUe18jj2gCDqxR4QQ8tOdqPboyACLApyYpnCx2hEacZQQauYp/e2ee6flfjWshK0dGhjyu06BCMuxomUOJflz3/JOpaWZOG3H5QjK8mNry4uGvR+RsXX9qNn+vSUKqtqRHKcC0WjPO9cM2McirMTcfeLZbj/b/sBAPWtnX6HcgDaMAxjCEZ3jwe3PrujTzIhxino6lG4/aLJuGbRwCqOaxZNwFMfH8WGNz7Dymk5fT4jb+09gT9ursA3L5xkuyQ8ExBBFgkVEIkRWgFhpqsWFODv+05ibqE5wxsiQYxT0N41sgSEx6Ow81gDXiurweu7a3CiqR2p8TGYmGnfChKi/ow/wKz+nRAuRhPk6eNSkJpgnys/0c5oRBnNFRBLSzOxbuY4PPzm51g4MQMtnfaugAC03iTRHHMKroUTM5CXGofbVk329njzJyclDoUZ8dhecQbf1Ne1d/XgH4dqMTs/ddQzrzkcgoeunIOXtlX1Wb9+kMaa+enx2HmsAUop/PjVvXh730ncvXYqSrKTcKKxDTWN7chLi8fXlvhPoricDty7fjpueHobntlcga8vnQQA2F5Rj+88txNzC9Nw1xr/s45EM/seKU1y2dw8y49l9c3QR9IsGGY6tygDO+5bHe7dCCuXw4HunsGn/lFKSzq8ricdjje2I9bpwPKp2fjhnGlYNS3H1idfRP0ZCQj2gPDPSIDb7cpPtEt2R38PCBHBQ1+egy88ugm3PbsTt64sBWDtKVLN9siX5w75hySRr/hYJz66Z9WIGlouKMrApoO1UEpBRPDTv+1DRV0rfnLZzICee+HEjBEP5y5IT0BDaxc2vPkZnt1SiVtWlHg/7yO1aloOlpZmYuO7B3HF/AKcPtuO65/ahry0eDx53YKorhYbjH2PlCa5aLr1u5e6XU7EOh3o7PEgw8JTTlJoGWVkvpRS2FXViNfKjuP13SdQ3dCGWKcDy6Zk4e51U3HR9Fyk2GjMGtFouF0OiGj9VWigbH187uJiDr+IJsYQjGidBcOQGh+Dx66Zh6t+8wk2vKGVcts5CW9Wg2yKXiOdTePconT8ZWc1KutbUVbViGe3VOKm5SVYMTXH5D0E8vVZh377QTmunF+Au9eOvlpBRPCj9TPwhcc24af/uw+by+sQ43TgD9cvQqZNK9Hte6S0OaNEkuP1yGBMw6mUwu7qRrxWVoO/ldWguqENMU7BssnZuGvNFFw8g0kHopGIi3HqSQgmIPxZMTUb3183FStDcBJJoeMdgmGDq3rzJqTjnkum4f7X9AQEKwCIgs7oA/Hy9io8+dFRnFuUjrvWTBnmUcFhNFdfPiUbG66cHfD3+Yy8FHx5fgFe3F6FxFgnXrhxia37pjEBYVOJbifibXByQCPncgqqz7Rh2SPv4Vh9G1wOwYWTs3DH6ilYPSMXqfFMOhCNRlyMw/LTModTotuFW1aMrpSVrM8O03D6uuGCSdhcXodNB2ttXQFBZJYpuclIdrvw6P8dQmp8DB69Zl7ILqDOKUjFr66dh1X9GkgG4ntrp6KmsR03ryjBrEGaXtpFyI+UIrIOwEYATgBPKKU2hHofSBujmZ7IPyip1/jUOHR0e1CclYRvr5qMNTNykZbAITpEgfriOfm2nlmH7MmYhtMu/QBEBL+6dj6qzrSyqpTIBE6HYF5ROj48cBq/uGqud1hEKIgILp2TF5Rt5abE4U/fWByUbUW6kCYgRMQJ4NcAVgOoArBVRF5VSu0L5X4QcO/66bY5OaCRueeS6bhj9RQkRPm4XaJQWVycicVssEg2k+ydBcM+3yVxMU6U5iSHezeIotZdq6fgS/PycfEM6/fao+GF+tthEYBDSqlyABCR5wFcDoAJiBC7YHJWuHeBLMbpEFudMBIRUfAtmpSBi6bloCjTvuObiSi45hamYW5hWrh3g4Ik1LVi+QCO+fy7Sl/nJSLfEpFtIrLt9OnTId05IiIi6sqhNAAACOhJREFUIgpccXYSfv/1hbacWo6IiIYX6gSEv9ahfeb9U0o9rpRaoJRakJ2dHaLdIiIiIiIiIiIzhToBUQWg0OffBQCOh3gfiIiIiIiIiCjEQp2A2ApgsohMEpFYAFcDeDXE+0BEREREREREIRbSjnNKqW4RuQ3AW9Cm4XxSKbU3lPtARERERERERKEX8pb3SqnXAbwe6uclIiIiIiIiovAJ9RAMIiIiIiIiIrIhJiCIiIiIiIiIyHRMQBARERERERGR6ZiAICIiIiIiIiLTMQFBRERERERERKZjAoKIiIiIiIiITMcEBBERERERERGZjgkIIiIiIiIiIjIdExBEREREREREZDomIIiIiIiIiIjIdExAEBEREREREZHpmIAgIiIiIiIiItMxAUFEREREREREpmMCgoiIiIiIiIhMxwQEEREREREREZmOCQgiIiIiIiIiMh0TEERERERERERkOiYgiIiIiIiIiMh0opQK9z4MSkROA6gI936EWRaA2nDvBI0Z4xidGNfoxLhGJsYtOjGu0YOxjD6MafQYSyyLlFLZI72zpRMQBIjINqXUgnDvB40N4xidGNfoxLhGJsYtOjGu0YOxjD6MafQIZSw5BIOIiIiIiIiITMcEBBERERERERGZjgkI63s83DtAQcE4RifGNToxrpGJcYtOjGv0YCyjD2MaPUIWS/aAICIiIiIiIiLTsQKCiIiIiIiIiEzHBESQiUihiLwnIvtFZK+I3K6vzxCRt0XkoP4zXV8/TUQ+EZEOEflev23dLiJ79O18d4jnfFJETonInn7rr9If6xERdqgdBYvF8RER+UxEykTkLyKSZsZrtoMA4vpV/X0vE5GPRWSuz7bWicjnInJIRO4Z4jmv07d7UESu81n/gIgcE5FmM1+zHVglriKSICKv6Z/XvSKywezXHumsEjt9/Zsiskvfj9+IiNPM1x6trBRTn9tf7f/dSsOzUixF5H398Z/qS46Zrz1aWSymsSLyuIgc0L83rzTztUcbq8RSRJJ9PpefikitiPxyyJ1XSnEJ4gJgPID5+u/JAA4AmAHgYQD36OvvAfCQ/nsOgIUAHgDwPZ/tzAKwB0ACABeAdwBMHuQ5lwGYD2BPv/XTAUwF8D6ABeF+byJpsVgc1wBw6b8/ZDwnl5DE9XwA6frvlwDYov/uBHAYQDGAWAC7AMzw83wZAMr1n+n678b2ztP3pznc70ukL1aJq/45X6nfJxbAJgCXhPv9sfJildjpt6XoPwXAywCuDvf7E4mLlWKq3/4lAM+i33crl8iKJXguG40x/QmA+/XfHQCywv3+RNJipVj2u992AMuG2ndWQASZUqpGKbVD//0sgP0A8gFcDuBp/W5PA/iifp9TSqmtALr6bWo6gM1KqValVDeADwBcMchzfgig3s/6/Uqpz8f+quzHYnH8u/5YANgMoGAsr83OAojrx0qpM/p63/d+EYBDSqlypVQngOf1bfS3FsDbSql6fTtvA1inb3uzUqom2K/RjqwSV/1z/p7+HJ0AdoCf1yFZJXb6tpv0+7ignYSxSVYArBRTEUkCcCeA+4P7Ku3BSrGk4LBYTK8H8KD+PB6lVG3wXmn0s1gsAQAiMhnaRdlNQ+07ExAmEpGJAOYB2AIg1/hjQ/85XOnYHgDLRCRTRBIArAdQaN7e0mAsFsfrAbwxhseTLoC43oDe9z4fwDGf26r0df2N9H4UJFaJq2hDpf4FwLujfQ12ZYXYichbAE4BOAvgpQBeBvmwQEx/BuAXAFoDegHkZYFYAsD/6CXe94mIBPAyyEc4Yyq9w4l/JiI7RORFEckN8KXYnkU+nwBwDYAXlF4KMRjXUDdS4PSs+8sAvquUahrtcVIptV9EHoKWXWqGVg7TPfSjKNisFEcR+ZH+2GcCeTz1Gm1cRWQltIP1BcYqP3fzd7Ad6f0oCKwSVxFxAXgOwKNKqfIR7LrtWSV2Sqm1IhIH7Ti7CtqxmwIQ7piKyDkASpVSd+gn5xSgcMdS//lVpVS1iCTr+/JvAP4wgt0nPywQUxe0K/AfKaXuFJE7AfwcWlxpFCwQS19XYwQxZAWECUQkBtp/hGeUUq/oq0+KyHj99vHQrrAMSSn1e6XUfKXUMmil+Qf1hiNGk4+bzHoNZK046o1eLoX2Bcw/YMdgtHEVkTkAngBwuVKqTl9dhb6VLAUAjovIYp+4XjbY/cx4XXZnsbg+DuCgUmroJkwEwHKxg1KqHcCr8F+CSiNgkZguAXCuiBwF8A8AU0Tk/eC+0uhnkVhCKVWt/zwLrafHouC+UvuwSEzroFUm/UVf/yK0Pmg0ChaJpbHtudB61m0fdseVBZpoRNMCLTv0BwC/7Lf+EfRtCPJwv9v/Ez7NC/V1OfrPCQA+g59GHz73nYhBGiyBjXsiOo7QxlftA5Ad7vcl0pfRxlWP2SEA5/e7vwta851J6G3YM9PP82UAOAKtWU+6/ntGv/uwCWUUxRXaWPOXATjC/b5EwmKV2AFIAjDeZ1svALgt3O9PJC5WiWm/+0wEm1BGbCz1x2fp94mBNjzqpnC/P5G4WCWm+m3PA1il//51AC+G+/2JpMVKsdRv3wDgJyPa93C/edG2QCtnUQDKAHyqL+sBZEIbC3xQ/2l8+MZByyg1AWjQfzc6cW+C9ofnLgAXDfGczwGogdYAsQrADfr6K/R/dwA4CeCtcL8/kbJYLI6HoI25MvbjN+F+fyJ1CSCuTwA443PfbT7bWg+t4/BhAD8a4jmv12N4CMC/+6x/WI+zR//5n+F+fyJ1sUpcoV0NUNAaQRnb/ka43x8rLxaKXS6Arfp+7AXwGPTZh7hEZkz73T4RTEBEbCwBJELrrG98PjcCcIb7/YnExSox1dcXAfhQ35d3AUwI9/sTSYuVYqnfVg5g2kj2XfQHEBERERERERGZhj0giIiIiIiIiMh0TEAQERERERERkemYgCAiIiIiIiIi0zEBQURERERERESmYwKCiIiIiIiIiEzHBAQRERERERERmY4JCCIiIiIiIiIyHRMQRERERERERGS6/wfxkR6DMeJ8jAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,10))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(df_by_day.index, (df_by_day['barks']), label='barks')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* なんとなく折れ線グラフにしたが棒グラフだったかも"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,10))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.bar(df_by_day.index, (df_by_day['barks']), label='barks')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 日ごとの合計ではなく最大を可視化したい場合は aggfunc を max にする"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_max_by_day = df.pivot_table(index='date', aggfunc=np.max)\n",
    "fig = plt.figure(figsize=(18,10))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.bar(df_by_day.index, (df_max_by_day['barks']), label='max')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* リサンプル（1週間）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "localtime\n",
       "2019-10-27 00:00:00+09:00      18\n",
       "2019-11-03 00:00:00+09:00    2864\n",
       "2019-11-10 00:00:00+09:00    1487\n",
       "2019-11-17 00:00:00+09:00    1335\n",
       "2019-11-24 00:00:00+09:00     894\n",
       "2019-12-01 00:00:00+09:00     683\n",
       "2019-12-08 00:00:00+09:00    1048\n",
       "2019-12-15 00:00:00+09:00     768\n",
       "2019-12-22 00:00:00+09:00    1345\n",
       "2019-12-29 00:00:00+09:00    1652\n",
       "2020-01-05 00:00:00+09:00    1759\n",
       "2020-01-12 00:00:00+09:00     884\n",
       "2020-01-19 00:00:00+09:00    1121\n",
       "2020-01-26 00:00:00+09:00     717\n",
       "2020-02-02 00:00:00+09:00    1341\n",
       "2020-02-09 00:00:00+09:00    1218\n",
       "2020-02-16 00:00:00+09:00     580\n",
       "2020-02-23 00:00:00+09:00    1447\n",
       "2020-03-01 00:00:00+09:00     925\n",
       "2020-03-08 00:00:00+09:00    1763\n",
       "2020-03-15 00:00:00+09:00    1822\n",
       "2020-03-22 00:00:00+09:00    1699\n",
       "2020-03-29 00:00:00+09:00    1398\n",
       "2020-04-05 00:00:00+09:00    1445\n",
       "2020-04-12 00:00:00+09:00    1412\n",
       "2020-04-19 00:00:00+09:00    2520\n",
       "2020-04-26 00:00:00+09:00    1885\n",
       "2020-05-03 00:00:00+09:00    1429\n",
       "2020-05-10 00:00:00+09:00    4167\n",
       "2020-05-17 00:00:00+09:00    1367\n",
       "2020-05-24 00:00:00+09:00    1230\n",
       "2020-05-31 00:00:00+09:00     810\n",
       "2020-06-07 00:00:00+09:00     382\n",
       "2020-06-14 00:00:00+09:00     192\n",
       "2020-06-21 00:00:00+09:00     202\n",
       "2020-06-28 00:00:00+09:00      53\n",
       "Freq: W-SUN, Name: barks, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['barks'].resample('W').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ser_w = df['barks'].resample('W').sum()\n",
    "fig = plt.figure(figsize=(18,10))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.bar(ser_w.index, ser_w, label='barks', width=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* リサンプル（1か月）\n",
    "* 合計の場合は Resampler の sum() メソッド"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "localtime\n",
       "2019-10-31 00:00:00+09:00    1472\n",
       "2019-11-30 00:00:00+09:00    5754\n",
       "2019-12-31 00:00:00+09:00    5399\n",
       "2020-01-31 00:00:00+09:00    4980\n",
       "2020-02-29 00:00:00+09:00    4241\n",
       "2020-03-31 00:00:00+09:00    7543\n",
       "2020-04-30 00:00:00+09:00    7304\n",
       "2020-05-31 00:00:00+09:00    8340\n",
       "2020-06-30 00:00:00+09:00     829\n",
       "Freq: M, Name: barks, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['barks'].resample('M').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ser_m = df['barks'].resample('M').sum()\n",
    "fig = plt.figure(figsize=(18,10))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.bar(ser_m.index, ser_m, label='barks', width=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 上記グラフ、このままだと横軸が1か月ずれて見える\n",
    "* Series の index の各 day = 1 にリプレースする"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "localtime\n",
       "2019-10-01 00:00:00+09:00    1472\n",
       "2019-11-01 00:00:00+09:00    5754\n",
       "2019-12-01 00:00:00+09:00    5399\n",
       "2020-01-01 00:00:00+09:00    4980\n",
       "2020-02-01 00:00:00+09:00    4241\n",
       "2020-03-01 00:00:00+09:00    7543\n",
       "2020-04-01 00:00:00+09:00    7304\n",
       "2020-05-01 00:00:00+09:00    8340\n",
       "2020-06-01 00:00:00+09:00     829\n",
       "Name: barks, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_m = df['barks'].resample('M').sum()\n",
    "ser_m.index = ser_m.index.map(lambda t: t.replace(day=1))\n",
    "ser_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,10))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.bar(ser_m.index, ser_m, label='barks', width=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* ところで最初と最後の月は日数が少ないので、一日平均にしたほうがいいのでは？\n",
    "* 単純に agg('mean') すると「記録された値の平均」になってしまう\n",
    "* まずリサンプリングで1日の sum を作り、それをさらに1か月ごとに mean のリサンプリングすればいいのでは\n",
    "* 本当に 0 だった（記録がなかった）日を除外して平均を取ってしまうが、そういう日はほとんどなかったので、よしとする"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "ser_d = df['barks'].resample('D').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "localtime\n",
       "2019-10-27 00:00:00+09:00     18\n",
       "2019-10-28 00:00:00+09:00    372\n",
       "2019-10-29 00:00:00+09:00    176\n",
       "2019-10-30 00:00:00+09:00    337\n",
       "2019-10-31 00:00:00+09:00    569\n",
       "2019-11-01 00:00:00+09:00    551\n",
       "2019-11-02 00:00:00+09:00    309\n",
       "2019-11-03 00:00:00+09:00    550\n",
       "2019-11-04 00:00:00+09:00    253\n",
       "2019-11-05 00:00:00+09:00    481\n",
       "2019-11-06 00:00:00+09:00    177\n",
       "2019-11-07 00:00:00+09:00      6\n",
       "2019-11-08 00:00:00+09:00    104\n",
       "2019-11-09 00:00:00+09:00    260\n",
       "2019-11-10 00:00:00+09:00    206\n",
       "2019-11-11 00:00:00+09:00    232\n",
       "2019-11-12 00:00:00+09:00     58\n",
       "2019-11-13 00:00:00+09:00    193\n",
       "2019-11-14 00:00:00+09:00    284\n",
       "2019-11-15 00:00:00+09:00     95\n",
       "2019-11-16 00:00:00+09:00    100\n",
       "2019-11-17 00:00:00+09:00    373\n",
       "2019-11-18 00:00:00+09:00    255\n",
       "2019-11-19 00:00:00+09:00     26\n",
       "2019-11-20 00:00:00+09:00    148\n",
       "2019-11-21 00:00:00+09:00     87\n",
       "2019-11-22 00:00:00+09:00    115\n",
       "2019-11-23 00:00:00+09:00    216\n",
       "2019-11-24 00:00:00+09:00     47\n",
       "2019-11-25 00:00:00+09:00     19\n",
       "                            ... \n",
       "2020-05-25 00:00:00+09:00    141\n",
       "2020-05-26 00:00:00+09:00     48\n",
       "2020-05-27 00:00:00+09:00     55\n",
       "2020-05-28 00:00:00+09:00    371\n",
       "2020-05-29 00:00:00+09:00     34\n",
       "2020-05-30 00:00:00+09:00    128\n",
       "2020-05-31 00:00:00+09:00     33\n",
       "2020-06-01 00:00:00+09:00     90\n",
       "2020-06-02 00:00:00+09:00     52\n",
       "2020-06-03 00:00:00+09:00     86\n",
       "2020-06-04 00:00:00+09:00     53\n",
       "2020-06-05 00:00:00+09:00     19\n",
       "2020-06-06 00:00:00+09:00     37\n",
       "2020-06-07 00:00:00+09:00     45\n",
       "2020-06-08 00:00:00+09:00      6\n",
       "2020-06-09 00:00:00+09:00     18\n",
       "2020-06-10 00:00:00+09:00     62\n",
       "2020-06-11 00:00:00+09:00     27\n",
       "2020-06-12 00:00:00+09:00     39\n",
       "2020-06-13 00:00:00+09:00     20\n",
       "2020-06-14 00:00:00+09:00     20\n",
       "2020-06-15 00:00:00+09:00     84\n",
       "2020-06-16 00:00:00+09:00     11\n",
       "2020-06-17 00:00:00+09:00     21\n",
       "2020-06-18 00:00:00+09:00     20\n",
       "2020-06-19 00:00:00+09:00     30\n",
       "2020-06-20 00:00:00+09:00     26\n",
       "2020-06-21 00:00:00+09:00     10\n",
       "2020-06-22 00:00:00+09:00     20\n",
       "2020-06-23 00:00:00+09:00     33\n",
       "Freq: D, Name: barks, Length: 241, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "localtime\n",
       "2019-10-01 00:00:00+09:00    294.400000\n",
       "2019-11-01 00:00:00+09:00    191.800000\n",
       "2019-12-01 00:00:00+09:00    174.161290\n",
       "2020-01-01 00:00:00+09:00    160.645161\n",
       "2020-02-01 00:00:00+09:00    146.241379\n",
       "2020-03-01 00:00:00+09:00    243.322581\n",
       "2020-04-01 00:00:00+09:00    243.466667\n",
       "2020-05-01 00:00:00+09:00    269.032258\n",
       "2020-06-01 00:00:00+09:00     36.043478\n",
       "Name: barks, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_d_m = ser_d.resample('M').agg('mean')\n",
    "ser_d_m.index = ser_m.index.map(lambda t: t.replace(day=1))\n",
    "ser_d_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,10))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.bar(ser_d_m.index, ser_d_m, label='barks', width=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 時間帯別に集計できないか？\n",
    "* 最初の df を作るところからやり直す\n",
    "* date と同じように hour という列を作る方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('dogbarks_200624.csv', parse_dates=[0], index_col=0, names=['datetime','barks'])\n",
    "df = df[ df['barks'] < 200 ]\n",
    "df.index = df.index.tz_localize('UTC').tz_convert('Asia/Tokyo')\n",
    "df.index = df.index.set_names('localtime')\n",
    "df['date'] = df.index.date\n",
    "df['hour'] = df.index.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barks</th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>localtime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-27 21:26:44+09:00</th>\n",
       "      <td>18</td>\n",
       "      <td>2019-10-27</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-28 07:45:05+09:00</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-10-28</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-28 08:34:57+09:00</th>\n",
       "      <td>17</td>\n",
       "      <td>2019-10-28</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-28 09:19:50+09:00</th>\n",
       "      <td>19</td>\n",
       "      <td>2019-10-28</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-28 09:34:47+09:00</th>\n",
       "      <td>39</td>\n",
       "      <td>2019-10-28</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           barks        date  hour\n",
       "localtime                                         \n",
       "2019-10-27 21:26:44+09:00     18  2019-10-27    21\n",
       "2019-10-28 07:45:05+09:00      3  2019-10-28     7\n",
       "2019-10-28 08:34:57+09:00     17  2019-10-28     8\n",
       "2019-10-28 09:19:50+09:00     19  2019-10-28     9\n",
       "2019-10-28 09:34:47+09:00     39  2019-10-28     9"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">barks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>124.450793</td>\n",
       "      <td>3.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>91.0</td>\n",
       "      <td>135.00</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>85.333333</td>\n",
       "      <td>95.059630</td>\n",
       "      <td>13.0</td>\n",
       "      <td>31.50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>121.50</td>\n",
       "      <td>193.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>34.0</td>\n",
       "      <td>12.794118</td>\n",
       "      <td>12.902383</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>8.5</td>\n",
       "      <td>13.00</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>172.0</td>\n",
       "      <td>21.267442</td>\n",
       "      <td>23.062298</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>13.5</td>\n",
       "      <td>26.25</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>231.0</td>\n",
       "      <td>23.437229</td>\n",
       "      <td>22.799966</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>249.0</td>\n",
       "      <td>21.630522</td>\n",
       "      <td>20.278306</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>194.0</td>\n",
       "      <td>19.912371</td>\n",
       "      <td>19.007431</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>13.5</td>\n",
       "      <td>27.00</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>121.0</td>\n",
       "      <td>20.264463</td>\n",
       "      <td>21.058557</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>169.0</td>\n",
       "      <td>20.331361</td>\n",
       "      <td>26.346076</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>26.00</td>\n",
       "      <td>184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>164.0</td>\n",
       "      <td>22.957317</td>\n",
       "      <td>19.824800</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>18.0</td>\n",
       "      <td>31.50</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>173.0</td>\n",
       "      <td>17.890173</td>\n",
       "      <td>17.138443</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>236.0</td>\n",
       "      <td>21.305085</td>\n",
       "      <td>21.927544</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>29.00</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>244.0</td>\n",
       "      <td>19.713115</td>\n",
       "      <td>18.909131</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>127.0</td>\n",
       "      <td>15.314961</td>\n",
       "      <td>14.911219</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.50</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>83.0</td>\n",
       "      <td>15.409639</td>\n",
       "      <td>19.582867</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>33.0</td>\n",
       "      <td>13.363636</td>\n",
       "      <td>11.556433</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23.0</td>\n",
       "      <td>11.565217</td>\n",
       "      <td>9.985563</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.50</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.0</td>\n",
       "      <td>12.111111</td>\n",
       "      <td>13.504115</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>2.362908</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.25</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      barks                                                         \n",
       "      count       mean         std   min    25%   50%     75%    max\n",
       "hour                                                                \n",
       "0       1.0   3.000000         NaN   3.0   3.00   3.0    3.00    3.0\n",
       "2       2.0  91.000000  124.450793   3.0  47.00  91.0  135.00  179.0\n",
       "3       1.0   3.000000         NaN   3.0   3.00   3.0    3.00    3.0\n",
       "4       3.0  85.333333   95.059630  13.0  31.50  50.0  121.50  193.0\n",
       "6       5.0   3.000000    0.707107   2.0   3.00   3.0    3.00    4.0\n",
       "7      34.0  12.794118   12.902383   2.0   5.00   8.5   13.00   48.0\n",
       "8     172.0  21.267442   23.062298   2.0   6.00  13.5   26.25  139.0\n",
       "9     231.0  23.437229   22.799966   2.0   7.00  16.0   31.00  132.0\n",
       "10    249.0  21.630522   20.278306   2.0   6.00  15.0   30.00   89.0\n",
       "11    194.0  19.912371   19.007431   2.0   6.00  13.5   27.00  106.0\n",
       "12    121.0  20.264463   21.058557   2.0   6.00  12.0   27.00  120.0\n",
       "13    169.0  20.331361   26.346076   2.0   5.00  11.0   26.00  184.0\n",
       "14    164.0  22.957317   19.824800   2.0   7.00  18.0   31.50  122.0\n",
       "15    173.0  17.890173   17.138443   2.0   6.00  13.0   24.00   99.0\n",
       "16    236.0  21.305085   21.927544   3.0   6.00  12.0   29.00  108.0\n",
       "17    244.0  19.713115   18.909131   2.0   6.00  14.0   27.00  144.0\n",
       "18    127.0  15.314961   14.911219   2.0   5.00  10.0   20.50   79.0\n",
       "19     83.0  15.409639   19.582867   2.0   5.00   9.0   17.00  145.0\n",
       "20     33.0  13.363636   11.556433   3.0   4.00  11.0   20.00   53.0\n",
       "21     23.0  11.565217    9.985563   3.0   5.00   8.0   16.50   40.0\n",
       "22      9.0  12.111111   13.504115   3.0   5.00   5.0   12.00   46.0\n",
       "23      4.0   5.250000    2.362908   2.0   4.25   6.0    7.00    7.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['hour']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_by_hour = df.pivot_table(index='hour', aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>barks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      barks\n",
       "hour       \n",
       "0         3\n",
       "2       182\n",
       "3         3\n",
       "4       256\n",
       "6        15\n",
       "7       435\n",
       "8      3658\n",
       "9      5414\n",
       "10     5386\n",
       "11     3863\n",
       "12     2452\n",
       "13     3436\n",
       "14     3765\n",
       "15     3095\n",
       "16     5028\n",
       "17     4810\n",
       "18     1945\n",
       "19     1279\n",
       "20      441\n",
       "21      266\n",
       "22      109\n",
       "23       21"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_by_hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,10))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.bar(df_by_hour.index, df_by_hour['barks'], label='barks', width=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

## comment.md

      
    Raw
  

              comment.md
            
          
上記「200 以下」と書きながら 200 未満でフィルタリングしていた箇所あり
なぜ6月にほえた回数が減ってきたのか？ドッグトレーナーの指導を受けはじめた（詳細は別の機会に）