{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def daterange(start_date, end_date):\n",
    "    delta = timedelta(hours=1)\n",
    "    while start_date < end_date:\n",
    "        yield start_date\n",
    "        start_date += delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26280"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_date = datetime(2013, 1, 1, 00, 00)\n",
    "end_date = datetime(2016, 1, 1, 00, 00)\n",
    "time = []\n",
    "for single_date in daterange(start_date, end_date):\n",
    "   time.append(single_date.strftime(\"%Y-%m-%d %H:%M\"))\n",
    "time = pd.to_datetime(time)\n",
    "len(time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>20</th>\n",
       "      <th>22.44</th>\n",
       "      <th>25.179</th>\n",
       "      <th>28.251</th>\n",
       "      <th>31.698</th>\n",
       "      <th>35.566</th>\n",
       "      <th>39.905</th>\n",
       "      <th>44.774</th>\n",
       "      <th>50.238</th>\n",
       "      <th>56.368</th>\n",
       "      <th>...</th>\n",
       "      <th>178.25</th>\n",
       "      <th>200</th>\n",
       "      <th>224.4</th>\n",
       "      <th>251.79</th>\n",
       "      <th>282.51</th>\n",
       "      <th>316.98</th>\n",
       "      <th>355.66</th>\n",
       "      <th>399.05</th>\n",
       "      <th>447.74</th>\n",
       "      <th>502.38</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <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",
       "      <td>2013-01-01 00:00:00</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-01-01 02:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-01-01 03:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-01-01 04:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-12-31 19:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-12-31 20:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-12-31 21:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-12-31 22:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-12-31 23:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26280 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     20  22.44  25.179  28.251  31.698  35.566  39.905  \\\n",
       "Time                                                                     \n",
       "2013-01-01 00:00:00 NaN    NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2013-01-01 01:00:00 NaN    NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2013-01-01 02:00:00 NaN    NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2013-01-01 03:00:00 NaN    NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2013-01-01 04:00:00 NaN    NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "...                  ..    ...     ...     ...     ...     ...     ...   \n",
       "2015-12-31 19:00:00 NaN    NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2015-12-31 20:00:00 NaN    NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2015-12-31 21:00:00 NaN    NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2015-12-31 22:00:00 NaN    NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2015-12-31 23:00:00 NaN    NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "                     44.774  50.238  56.368  ...  178.25  200  224.4  251.79  \\\n",
       "Time                                         ...                               \n",
       "2013-01-01 00:00:00     NaN     NaN     NaN  ...     NaN  NaN    NaN     NaN   \n",
       "2013-01-01 01:00:00     NaN     NaN     NaN  ...     NaN  NaN    NaN     NaN   \n",
       "2013-01-01 02:00:00     NaN     NaN     NaN  ...     NaN  NaN    NaN     NaN   \n",
       "2013-01-01 03:00:00     NaN     NaN     NaN  ...     NaN  NaN    NaN     NaN   \n",
       "2013-01-01 04:00:00     NaN     NaN     NaN  ...     NaN  NaN    NaN     NaN   \n",
       "...                     ...     ...     ...  ...     ...  ...    ...     ...   \n",
       "2015-12-31 19:00:00     NaN     NaN     NaN  ...     NaN  NaN    NaN     NaN   \n",
       "2015-12-31 20:00:00     NaN     NaN     NaN  ...     NaN  NaN    NaN     NaN   \n",
       "2015-12-31 21:00:00     NaN     NaN     NaN  ...     NaN  NaN    NaN     NaN   \n",
       "2015-12-31 22:00:00     NaN     NaN     NaN  ...     NaN  NaN    NaN     NaN   \n",
       "2015-12-31 23:00:00     NaN     NaN     NaN  ...     NaN  NaN    NaN     NaN   \n",
       "\n",
       "                     282.51  316.98  355.66  399.05  447.74  502.38  \n",
       "Time                                                                 \n",
       "2013-01-01 00:00:00     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "2013-01-01 01:00:00     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "2013-01-01 02:00:00     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "2013-01-01 03:00:00     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "2013-01-01 04:00:00     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "...                     ...     ...     ...     ...     ...     ...  \n",
       "2015-12-31 19:00:00     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "2015-12-31 20:00:00     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "2015-12-31 21:00:00     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "2015-12-31 22:00:00     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "2015-12-31 23:00:00     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "\n",
       "[26280 rows x 29 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "import matplotlib as mpl\n",
    "\n",
    "my_dateparser = lambda x: pd.datetime.strptime(x, \"%Y %m %d %H %M\")\n",
    "\n",
    "path = 'shared-ns1000k/inputs/Aerosol_sizedist_obs/' \n",
    "all_files = glob.glob(path + \"/Tiksi_201*_hourly.csv\")\n",
    "\n",
    "\n",
    "myorder = [2, 0, 1]\n",
    "mylist = [all_files[i] for i in myorder]\n",
    "  \n",
    "li = []\n",
    "\n",
    "for filename in mylist:\n",
    "    df = pd.read_csv(filename)\n",
    "    li.append(df)\n",
    "\n",
    "\n",
    "df = pd.concat(li, axis=0, ignore_index=True)\n",
    "\n",
    "\n",
    "df.replace(-999, np.nan, inplace=True)\n",
    "df.drop(labels=['0','0.1','0.2','0.3','0.4','0.5','0.6'], axis=1, inplace=True)\n",
    "df['Time'] = time\n",
    "df = df.set_index('Time')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "diam = df.columns\n",
    "diam = ([float(ii) for ii in diam])\n",
    "time = df.index\n",
    "data = xr.DataArray(df.values,dims={'Time':time, 'Diameter':diam})\n",
    "datas = xr.Dataset({'SizeDist':data})\n",
    "datas['Time']=time\n",
    "datas['Diameter']=diam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fec954e2550>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/matplotlib/colors.py:1031: RuntimeWarning: invalid value encountered in less_equal\n",
      "  mask |= resdat <= 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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pzCMjbHQQX9FTlF2FtiFV1pRHlYKZTvCDDvkQzFJTJx5nFBxfoxjF+GDIYqz36mNE2VG4MMQF4IJ7tn17U6m6Hl0r3O1dmgAUfbA3vxRvSz4GFzbl7Y1C1U27pyuwr74A95Q9DrUpt6NOYL5+0KUsVqFODeXA4fbq6GKlaUdf7fLDKbMTnmivnevHS2abY+pJyfxkBxtr3t1/it/qb1B2NeF271C368d95sdblJ2Da0Ef36CcdgizmNlGymJiiLY7hLtj6rU+ZlawWPe4yFGNWngFaIVut/DLHFfkABRrN/7rw1XB2urhz5MV6vMXALDb20DTRhXD186/sqvQvXL/czFQZL0lD8evAFB3YlykcPFBBlYdgw+6lBsl1W6AGhXkJsaFTeGpIOTiu/oMP0HTto1H1Htti84y3GLx2ona41aH8PIrBMePUT//Itv3NeVspivMVgOimcMUPcLyGLvTGGcUNlHkoSE6NsamIXVqqDJF1Y+pWhrTCVmsGOoEXAwuhLKt8LrZruxHxHvHz8dgyoAgN3gFysFyDPbqBnvFrizUXXdb5yQaRb8ZY1wdmwAshwpTgg8UddqMccpOU0YuAh/QXC9A0TPkQ9W0Ga9S9Rw+ONyQ5H1F1aEpM9+Mm/bTMgBvPLZj8eFBgvJxM9YKloZqpUJpj50H+MRSLQw7WwHFAOyjJ9k5lZIMbtzILkeaqg26NpTddP88s40RNvUs1hTRtiJYNvuXXY0LUtyrRsqLtbgZ0w0LimFElSnmq4rsvMdtrFB3YoLu9Q337rGAfNxclxybMl/VvH/yAk8+9nbKjmGxCjx8irf1/5iTQQcAM+wzXw8J8usbZD2dMF/V6DojPQOL4zWLZ0O6wOL+5tHr2w8146XlmiN/bIPl1MOj9zE7kdLxD7J7MqVbT5lvxOiViHxqIXXouGa+SKnbjmh1QV0YnFPMo5jZyym7mwpdNZVgdzPERlC1rinnoaIYgsnBFKYZF2dgM0edabxq6pKNIB8pil7TJ72Woqeps2Z8XWcKZffKY6wOxtZ9i66uX8xdDEDVTXkXA48Lm98d5qsR86mMccQbSyk1ePujEb/zC8dfc7vv+fYuf+o/fp4f/XHV895v36XkfVnd8xMPQgghhBBCCCHEveZWqx2ueiusepBnPAghhBBCCCGEEHeRUir9179w82c7vNr3fHuXf/X/zFBKxbfe+s1HJh6EEEIIIYQQQoi7q702vfVqh6uiSDFdCQBat9r2KJRSjyil/pFS6l8ppf6rL2fc15KJByGEEEIIIYQQ4q7zuCP8d7sP4VJK/WOl1Hml1KdfFf6NSqnPKqWeVkr9IID3/inv/fcB3wZ85Zf7DK+SiQchhBBCCCGEEOIu84D17rZ//O0//fufAN94bYBSygD/APgm4FHgO5RSj+59983AbwD/7st0ateRiQchhBBCCCGEEOIu83jcEX72fFApdeaan++9Ll7vfx24/Krg9wBPe++f8d6XwD8HvmVv+5/13r8f+At36lzlrRZCCCGEEEIIIcQboLmF4vbsTT38tPf+J17HoTaAF675/CLwXqXU1wJ/HoiBn38d8d4WmXgQQgghhBBCCCHusuZWi9u+feIot1rcyI2eYum9978K/OqXEvHtuOcnHqou1JnHhx5noE4VPvD42FFXGlMovFKUAwupxWpAe7w2zf4dB2hwkHVzFrlpvlOAV9QZoEBXYGOPiz1BryAfG2zm0KXGG4VX4ELQNbgAbObwWqMs2MSDBlWra9LtKRZ7++29EKXqgAs8xTDAdmryuikeGyu8vvnTThfTw9+5QcViEt1W/s3XYdLfxWVtlisBi3VHckEzn978Lpz6xASzmx8K2zkeUYxufhzTbmNns/3PwQP3s5yCzjLcYnGw3XjEfKqxk+K6OFTY5EfRO5w2Mx5Rx+Dv26DoaXTt8WHAcgXmx9ukfzAj7yvC+d4O3TbzjZgOYHKH14rN0RVeCSxqbUpxrM/lRyMmv9xs7ns3fnBsPvLMjhniK/VB2HqH8FMQnNgkX7V0n9XEa3OWFzsAuJ0ZanNtf/uyY2C9iX8xDSh7ChvFtF91LJ1lLEeGRDf1pBg0ddKbAHfNVVz0FVUbnFF4Dd4AGuyxMcUwoRw4kssaFypsBMuNDvlAU2URZb+pw1VLYROwoULBofjF62czR12Ypj24TWUP3O1dyjdVDWu8Bl0GFAHoEqqOR9Xg4hunpc6OfhybwCI9aIuKkaNONb5bU0VNe1uMoW5df8yd+4GNJSUpAO2w5OxqQGuwzWy9hzdwX3qBX+0qUFD2I8q+OUjvsMVszVBdc6lWK22WKxEu1CymhuUEFpdiehfaFOOYIAnw44IwtlSdGFM0f21wpzdRlYVPPgnAfD18zfP2w8OvwPKjAUG3Q/30M/thutOh6h6cdzAaUV+6dGi/OoW0Ve5/Lnuw1pmxGew2n/shuvToosLRtAll15APDaPJJbaSjJX+jLOVQenmXMzJTbZPw+grH4eyphhlhC+/givLQ+3urZTDlACoN0YEVc18rwlzkz6LNTDPK2abCS09YT41BLnHGUWdKnTVwpumH6syRdkLqDIFo4Cir3BR0+7YCMqOwgVN+1P0DK3RCLe7S77i0JVGlwptm7avGHp8cJCnXoO2YNv2ts9LQN1if5xzVdUFl4ONwaYQhFD1PN6ACx0ojd/bvmwrqg6Uveuva5dZVHg43GZNXPimzDAH39f9Gh94wnZJHR9cd2XfYZaaoqdI+zlRWDOLE+KkoshCllspdcuzfTplPlWUnRt3WkUfyj6YQuHC5nOwhNnxFJ9YytSCCplXGl0339epxr6qCZitNd+PBjPy4RhvYHGs6VvLUcpiGmKK6xvufLx3LqeOU0zb5CP46u7n+f9OvwuvoRg6du/r8Hj2IgA+DrD3r7NYUUS7148B69U+xQh0bUiB0fo287URwxObXDje5MFsvdnPj0q27k9wo4Ld+7ssphrlMhYTRXIlZjlW4BXBeE4U1YSBZXtqCLKKzeEVFlWEdZoLwHw1YbnqUHt/oF1MVDPOuOaUyy5UXYfNFNo2aaha4ANPnezVhcTjI0/R11RtT7B87af6l+29+pqCizxqL9663YQB0LY4e/1fjqsO4CFYgG03436bKOa1es2xqxB3x6FbKO60F4HNaz4fA16+WweXXymEEEIIIYQQQoi7zAP2CBMPX+IUxe8BDyilTgEvAd8OfOeXFuXtk4dLCiGEEEIIIYQQd5mHIz1ccm/i4ZYPl1RK/Z/AbwEPKaVeVEr9F977GvhrwC8CTwH/wnv/mbt1rrLiQQghhBBCCCGEuMu8f13PeLjlwyW9999xk/Cf5w4+QPK13JMrHpRSH1BKfXR7e/uNTooQQgghhBBCCPG6uCP83MvuyYkH7/3Pee+/t9frvdFJEUIIIYQQQgghjszjsUf4uWuPobwD5FYLIYQQQgghhBDiLmtep3mE7ZttP6iU+q5rgj/qvf/olzVhd4BMPAghhBBCCCGEEHdZ83DJI7vlMx7ejGTiQQghhBBCCCGEuOsUFnXbW8utFkIIIYQQQgghhLhtHnBHudXijqXkzrvnJx6qnsMlDhKL0h5bGFToCZIKV2vqPMCrANWrCJMaGzqStGQRxQCoUUFpIpRVbPZ2eL4KqIghduBCbNc2B/LNTJRqV0wHu7w0j9BxjasNZdBko48dlBoCj2pVVHGAKjU+9BDb5ru9CS07qMnrEOXAJk0VqkY13njm05Cws6TW4B3YrsKFNy+q5cSDOaiGvfGM+erwtvKvHHqOtbd5eTpmdkyhRjllkeDCm8+85SsxqT28KGixpiiH9lBYcPo+7BdfwNcVamMVPvs0AOZtD1GOW1R9izpxDJ76XBPe6eCnI/IVGK3sHkSkNMHGGpQlwckTzFdflbY4ourAcqNFPoRgqSA05CuO3U1DCtQZeN3sV006zNYUg7VVilAx34h5e+cSgXYsTq9TDAK2H3BMrubv5o0fYuqGFfksouwFpHthOydDputr2NUB4ShH+Yxjgy2+MMmaUzEGl4X7ccyniipryna5oshXPHWq6U8n+9sEDz8AiyXzNUXVClhMwbYcqla4UGGzg7JfTsDFHhsrUOCNxyvIJymLiUGNcqpXUpRv8qQYGOZrimhHUYw8LnToQuMDjzdNffX35CNo34RSS+kUBLffZVQ917QfX4JwUGCMIy9a6EqhLNhOjQo8qBvHXfYdR5h8ByAf+0OPKzbTJdVOzGA8Y5FHABSTmqBdXbdvNa44Md7iud1muywoWE5gs7/FC8MeysI03GY5bZJcP28oOgcHy0cRxRDK3sH55OOIxVTjg4DlChRDx3KkiY/1mK0FxNua9el5IlOzu7JBsqUI+xllL8KHmuiTTTy7mzfPCBWEuDTe/xyMRiw3u5jcop++ZsPNVVz/mvMe9ODSpSYOY/DWUvZglBRNPNMJxdAxzWasmQSAOtP4tkK5jPT0fQCUHUUxgM1sTjfOWc12ubjdIorqJr9PDHCbS8zFHfIHVii7AfHxY+j5AqKQ+rkX0FmGWywOzmkvPddaTkIG953k4oMZo1mferUEYHaqQz5x6EqDgypLWE5AlwpTQDEElMYbqFPwBmZKU/bA5JqqA1W76b+8hmKkqZqo8VrhV0foXgfVK8nLGOXALBXagu3VKO2beqoAPF57ovhw2sVrKwcO5faHOAAUI4dZarz22K7FpgbbsqjIgfLUJthvO8puc925wfXXddQtSZPy8PG6UHctqlb4yDVluMd0K6K4opMWFJk52GlYUuWGajdkc3iFSFsuxxnDZMHlPOOVnQiymtl6zOKYpZjfuNNarjl827KsQ1zoqScV5cKgS0M6mBNHFdumDUTgoepbqp7GvaoNXqx7wl3FO0bn+IPBCi4CvZJTtjPmqyG7x1UzDnmVYuxgWFKudcmHIeVKzVenzzE76VBO4bs1u8ciHotfAkIWp/p71wVUnYN4TL+P3dpisZGSTyy6aPLq/v4lPjEaUZ5cYb7RbJuvNGlfGe0yGyb0hnNmx4YsJ2BDzWLTEW8ZlivN9qvDHUJt0cpT1YZelvNQ7zzzOsbtVZIr04RwsqQum+MWQ4+yzTW/X85Dj+qXOKvIfdT0O5mDdk1VK5QHF3l85Chp/t+mr93pVN2mvXCxx7UsqmzKuWo34x6AMK2w9fXlb1OPCzwuUuheSR2EqErhIo0d1K95XCHuNA9HWvFw5AHam8g9P/EghBBCCCGEEELca4468SArHoQQQgghhBBCCHEkzh954kHeaiGEEEIIIYQQQohb86/v4ZLyVgshhBBCCCGEEELcWnOrxVEeqCbPeBBCCCGEEEIIIcRt8l5RHeFJ7u7Wm7xpycSDEEIIIYQQQghxl3nAHmHiwR/heRBvNjLxIIQQQgghhBBC3GUehZNbLYQQQgghhBBCCHGnvI6HS8pbLd4Irl+jQ0ucVGjtqRJDEFiSqKa2mjwOKUNHp5OjtaMODCvtGbtRBUCnu2S71nhgPdthq51yuTQkWcmy0KhWjdIePHinaHdyjrW3udRrkcQVRRlQhCFKgQksVRGgjafVyllEEXUegvJEaUVdmv1JqqBVUpcatIfYAhCPlsRhzXIyYNBZkCdNGmurWQTpzfNgXDZp3HO8t8UfjTq3l3+DilOtizzXeoBi4BkPZ5zfjbDdm78lNh8YbNw6FLZYt0Tj5aGw4sSAGKiffobFqQHRZ/e2PdkjHxhUu6Bc72CeasLVZMz8eIdyrWK1tbsfT3BiExYL/HREMW1Tt191Dit9bNqkq87AplD1E4LVJfNFk85iAOGs2X45iZmddJAmqNqTDzTryRZLG/LkRkjRg/7JK/vxz9cDgvz6fGj1l8xnAS5UBNMJ9bnzLKZQPrBGMQjptq+g65T7O5d4YTJARxFoRXB53pzXdEIxBBvv5euKx64XLKMYuzk9yNtTfcJZm3zq8UZR9yxkFm8VZaYgsfvbVsMalMdFGhT4wKNzTdE3LMeKbm/Jop2CB5t6FlPNctVhY40bVM2+LQUeSh2A8hxpElbcVNQpKY1Hqdt/A7NrWUxaf0nHHXQWREHNS8sQ5xS+MKSDHA8E5sZ3CvpOjY7sDb+7GduxmFa1//nEymVeibr/ApcPAAAgAElEQVTcP7jEVpEAMN7YIg2r6/Ydr22z2d7ibL8LQD9ckq94xsmcL3RBWUhUSd21oKHoaYrhwf7LkWG51lwX+2FDQz4E0FRtj+9X5OOY4lJA1QYbad47OEugLf9v/xjKa+okwwUKF0A2WcGXFcvVm99N6b/yUfJpc24oTfXoceZrIdlFTXTNdvl6h7R70D7mJ4cETz8DgJlOqF9+BZt5WlFBsDLGHZug+iWnW+eJVQhAlTWdRzEMSF6JsO0YG8Fy6miHJSvtXVJTEcc13bRpsBaTkGPTsxT3j5mth9hYka71CJ+voG7ySt23CZ/+7H7azGSF+pWzh85zOVK0NwbM1xTx6R6T6WUA5qsGM5mzrDLQULcVxdiCU4Q7mmq9xAURLvL4oKn3daaxmQOrcInDxw5lFcSOwgfoUhEsFC6E5ck+OE9vsMs24KzGFhqsojueM5/F6KApH6UgCCztpLhpeYnr+V6FqzQqPKjnvm2xCrzxkFhs4AmyGhNajHYsYT/fyx7UPUtwg3aq08ppx4fLo+5Zol6BrTQ6cJjg4LidzpJ2XDBKFpTO7Id3ewsWUUzdCjnVvkwrKDgbdVlNdrgYt9mapKRxxeXjAfFkQZXfZGg7KOn1F2y5Dir0bKxdZmeZMLNdpq0lm50tvqA9l8se1Ip4uqAqAsJXtYV2pcLFAe/qPsdvDd6GN7Ax3uJ8J0M7xeJETbBjrju869WkWclirc1iRWN6OSeDDmpcoLQnUJ5iGHEiKICQqqWpU0XdrzGza+LbXCUwhp1NTTSZUW03g6LVZIdqVHPl4YRypSmPetD8u9HZ5tOtFe7vbfGZzQG2Y6lbGr1S4MKUqtuUw2Z7i0A5ShdwadFinM45mVxi4SKs18zqmHOTPuu9GYsy3D+G2Qmai/BqHnVqet0lSnm2qmY8okKHiSx1plGuueZNYHGBJ0gq6uK1fyUpBx6XWdiLx+kmT5p+oWlfoqim0jfI+9RC5LChptMqmAPeKmymafWX120vxN3kUUe71aL5R95qIYQQQgghhBBCiFvzgDvS7RNyq4UQQgghhBBCCCFuk0cd6XWat79u9s1HJh6EEEIIIYQQQoi7zPsjvtVCVjwIIYQQQgghhBDi9h3trRay4kEIIYQQQgghhBC3zQPWH2EVw1G2fZORiQchhBBCCCGEEOIue53PeJDXaQohhBBCCCGEEOL2uKM/40FepymEEEIIIYQQQohbk7davMkppT4AfOD06dNErRKtPO2kIAosZW2IAks7KljWIfPAUoY1vWyJ9wrnFSvpnNBYAHppzrIVAbCWbHM+a7M7T+i3luSLiDit0NphtMc6xbg9Yz3d4vl2n3ZUMAtj5oElMA6jHEUcoJRnmC3Q2rMMHACtpMQmCueaihXHNa5ToownimoAjg22aIcln9xo0Y4L+ukSgNppzr5GfnT7C/w19/ucbF3m84OV28rLqFWyFm1RdjV1v2ba2uV81sVE9qb7lD2FCw7fXxSvLljr7RwKW0wjdNlDPQ2zjZDh1fCJYTlWtLs589Uew5Vxc57THrONgP7kMv14uR9Pcf+Y+Pktlse77B4LqNvu0HGqbkzdclSZpu548LCcxqwNz/Lcdozp96nGNS4yAOyc0MRru1Trfaq2YTmBQTCnE3bIh1AM4fH+ZWaACkLKjsKF1+fDqD1n0Yspugn548dJgoB8zTJfjyg7ml6cU+We09l5PtVZQ09WqF98ab9psZtTqp7D6yak7jqOr17mBT9kuZ7uHycfBlQtg+3UFC5AdSrCuMZajS0CTHxQViarAHBRE6cxDmcCqlZI1Yb11owvtPvgFC5xFAOD71dUPqTVW+I91NaglKfQgPJoczi/xevTy5bsKI9Wt99lBFlNkpZf0nFH2Zx2UHKlnaGVJy9Cxt0Z3isiU99wn7BVkcTVkY4T9Qo6rXz/80O981TO8HD7LBeiDgDvXnmRwl3f7Tw6PMcwmtPOCgDG4QzbqWmZkrrlUA6M8tCyKOOoWgll72D/og+qU5G2i/2wqg1121NWCttxtLo5i0lIfkFT9ppbJB9sncXg+ZU2eK1QXmFjQIE9vopLA/Sw4Ga2H8jIh01bGEzGXD6eUAwUXhuy9TXcygD3ySdZjgP6rYM2bb4eMuz1UL0Odm0IL7+C61VkQUU1HrLYyOh0dzmVXNjfJx8qggUUXUV6rsVyGlKnoMYFka65L71I7kLCwO73b/OpZjOd8ezxNYpB044tVyJcMCE6vwsvweJkj9biJPa5FwDwKwMCY6hffGn/2DaGnZMJ+cSzvGgYp4smTWOYDnZ5uTQo7Sl1jBmUhFFNbjJGkx0uqS46sGjjMcZRLiJMXOMqjTaeILLYWpMkFQsTY0uDDw2qUsynTZs9be+iVdMHL4sIZ5u++Gp/77xCK08rLuklB/ksbi1uldRlQBgdtAUmq6gdzfgkq6hLQ5KWxGFNZCzWasxev+Bij05qWtn118kgXdCNDoerVkW3vWRZNHU1Dg76r1E2pxsVjOL5q+JZEhnLhW7CifQSPbMg1jXHosu0TcHL/S6JqclXAzb628zK+IbnOhjOmbZ3KcoQYxxfOX6el5d9/qg2rGQzTrUuUjrDbBFTV4Zpb5eyDkiCw21hq79krhMeT16g6jmUU5zqXOalzhqg6E132Ymy644ftUuG7QXzUZdiAL1OU1dbrYJumnN5llF0PBPTBqBqKYqewnQrXHZQPvP7emSBJl+BaW+XF3vNeEErR9Qr2DkZ0F/d2SvLZr+VeIbNPBvZFp8al8RZBQNopwX4FB95sLCa7GCUZ6dKKCtDP1pyIrrIhbppw1+M+qT9nHE6Z9c0+awTi8sNPjjo24JWxTBbEBrLYhmjdDM209rjU4V3iiC2e/WoJokrqvjG/dFVLrOotL5ufOpDh9o7dhzWN+5jY4eJLS5wpFGF84raauKwZqU9e83jCnGnHfkZD/ew259eeRPx3v+c9/57e73erTcWQgghhBBCCCHeZDzg0Lf9I6/TFEIIIYQQQgghxO3zCnv0Zzzck2TiQQghhBBCCCGEuMuaFQ/37mTCUcjEgxBCCCGEEEIIcZd5jrji4R5+uqRMPAghhBBCCCGEEHeZhyO+1eLeXR0hEw9CCCGEEEIIIcRd17x18Yg+qJT6rms+f9R7/9EvY6LuCJl4EEIIIYQQQggh7rLXueLhp733P3Gn0nSnyMSDEEIIIYQQQghxl3kU7khvtbh3ycSDEEIIIYQQQghxlzUrHm7/Vgt5xsMbKEtKtPK0opI0rKhDTaAd/WjJlkoB6EQFrbCk9hrnFaN4tr9/L8rZiprtJtEOw3jA82ZAN865GLVJ4opQW6LAUlnDOFkwDmf0kyX9aEmkLUY7AuUIjWVZhwAMkyXeK4x2GOXpxDnWaSpnmnRHJd5DYBxZVAFwqnOZSNc80x+RBRX9eInGU3tNaW9eVKPWHH/NvUGbyWUG7cVt519H5xQ9MK2KtXSHP06mZGlx033qBJQ9HLbe32a9tXMobD5VmDwiA5YTMP0+aMVsQ1GsOB7sbfPypE/3gWMALKcxRR/u626Rmmo/nsUkRBcd5lPDfA1crz50nMVqiMssNta41OI1zFcD3tW9yCu9Hhxfw3RLbNzk4WLNc3KwxWJ1g+VIUw4ciaqIdU0xhGpY0w1zZoA5dZyqA3V6fT6MkgVbvZRikLBtIoLZCsnKguW4Q51BNyo4nyiORZcZp3OK1RXM9g6210S23MjwnZpKNelS7YoHexfYXiYsx4P94xR9ha4UQSun9pBlJYFxOK+YlwauKXsTOrRxeKea+mccJVC1Qmzq6Uc5LnHNPmlN1VNknZyFVQxaTZ1ZlBEAzmq0cRjtbloXxO0bZgus10TG3nrjPVFc0U3zL+248YJOkDNodQAwxjFKFgTaoW8yb56lBZ3k5m3AjXTbS/rJcv/zyeQSl7I2x+NLxLq5Zh9rv8SVunXdvg+3z5K7kG7cnGumS4JWTaAtPnZ4IFQ1QVIRBA4bJdStg3pZdSHrFPt1GJprtm47dGFQ7Yp+tmQ5jCh6KWXf4Y3nRHQRAJtC3fZ4BS72KKeYn2hRJ4p+7/JNz3m2qSj6TR76lQHzNUXVARsq7LExsxMtWp+ExVRxX3bQ7+ycUPQfOI5LDIvVhO6THZJuQWJqZhtdFisBK+0ZJ8KL+/vkI4hCyMeeaBazHEOdwWS4S6Acp+LzPF1MSYIao5q8Wax72mHOYk1RtZvLfr5mcJFC+TZBnDDbCAiWI6JWAkAxbREmAfryFZQx2N1d6hTmawrbrsmHAZG2e/nuua97iXkZoZXnilP0ugvaccFZr7ivd5mqNijliQNLElZsJwmBcdRW45wmCSsqZ2jHTX2rrSa3KapWLCYGZWGSzNDKUzvNZZOh8YyTBVfijDSssE6j9/rZcTK/VVUV12glJbl2tOJyPyyOK2xp0MYRRzVae7KoJI0qYlMzLyLCoKkDLvIEsSUJ6+viHsbLQ+MtgCir6CdLjPIkYRPfVf04pxsuGYZztDpom3pxThpWXGj1OBZdpqObdmYzuoxWnuPtKxjlcSg2sm2ulNkNz3WlNWM13WHei9B47ksv0A1ytsqEfrRkI77CTp3wSrtLXgVstLapnSELykPxjNpzrNXcF+zgOzW+MBxPL2NbDuU1p3rblNX1Y7ZOlrPe2uEzvVWqnmeQNu1VGlVMshlbixSXHfQPZUdR9qHdXmL0QX7M1gzRdkK5UnOic4WXe30AduqEbnvJ5Q3Dyf6VpiyTZizVCgq88XSDnN5gTmAcSVATassi9+AAA4OgSZPGsZzFpKZiaGZcsm0iVRPrmlFnTjfK98sojGvKKMBdk8YkqRilCzJTcj5tExnLoowI9vo/TzP+DYwlMpY4rCG+YbEdCB1BbImiGms1dm/oo+ODPIuMRanr+zUVOIKoRkWQhBVGO5ZVyCibs5rt3uLAQtxh/mgrHu5l9/zEgxBCCCGEEEIIca85+oqHe5dMPAghhBBCCCGEEHfdEZ/xcPQ3YLxpyMSDEEIIIYQQQghxl3nAyq0WQgghhBBCCCGEuBM84OThkkIIIYQQQgghhLgTvFf7Lx+43e3vVTLxIIQQQgghhBBC3GUeheUIz3i4g2m502TiQQghhBBCCCGEuMs84I6wikEmHoQQQgghhBBCCHEECneEFQ80z3j4oFLqu64J/Kj3/qNf3nR9+d3zEw9JUAOQhhXtoKR0hkhbOkHBoo5wQUViahJTUzqD84p2UFK4HIAsqAiMQynP0MzphUuU8rTDEqU9kbEkYUWoLUp5BtGCnlmQBRWDaEGg3KG0BLr53A4K6ljjULTCcj9t5d49PHHYpDswjiwsARiGc2Jd004KsqCiFy4xeCyKWRLfNA+GyfLQTNk42KWfLG8r/9KwoqUL6hakWck03iGKajpJcdN9XAS2Ohx2vL3FSjw7FLacQrRr6B7bIJ94ODalGrXIN2qiUc6J1hW+MD3B9v0pAPOpoWrDWrpzOJ4VTbiIWa4oyoEj7hxO22Kq0XGJjUOIHSay5OOA09kF/qi9TrnSIUuXqKzZz05Kjrev8Icrm+Qj8G2LRWOUpxrWhN2Ceu/psvWoTdUCdYPpxVE850qW8nJ/QN2C7ELCau8s57od6gwibSk6mtVgi2G84Nm1lKw8Rj5tzncxNoRZztWsjNKKY8kVnk7HXO4M949T9sDkkCQVS6vIkhKNx3rN3IOtDhorpT1xWOO9wjpFFFqs1dgEXOxphzlEDhSEaU1ZGrppQVUFDOIlgXZcUh7nFUUVEAaWUNub1gVx+8bJnGUdEpv6tveJo5punH9Jx+0EOd0gpxMVBMrhvaIb5nTCnNLduAtoxyWd6OZtwI1045xhfNDurIVXmMRDhsFBu7AZXiJU19en0/E5ni6m+8cMVU0UVzivUOFefcUSRTVhYKkicNlBPHXqGWdLRsniICwDMotdaKK0YpgsmHUjlt0U361RxrERXKHyAXXb4TKLUqDjGmc1y1FMlcGx9u4Nzze47ySLNQedpjxtNyUfg00dXmsWGxm7m5p2ELJcgWlyEE++bpmdbKE8zNc17UdOMuntkpiK3eMh+QjWsx1Wg10ga85nWIMKUGs5u/OUcmQxS81mZ4vY1EzMLp9xx0j3+hMAO2lal3zkcS0LVrFcMehao2xE79ga+RBmGxFJ2vRNi0lIqhXpZAWMht1dfAB5x6OTmmIY7Pc3NnFspFu8nHWJjKWoAnrJkiyo0P1t1tMtLrYyKmfoRTlJUKPxOBTOK2prSIKKwgZ0ooKyDqidpogjCBTFUKOcYhjNqb2mdoZZGZOFJYNoQSsq6cY5pW3S3g5LJvGNy0vcWBY19aUVHdSbMLDowKGNw+yNj+KwphWWJKYmCuv9sZdLLUFgMdpdF3c7zBmF80NhSVTRjQq08gTakQUHg4nMlKSmom0KQl1fE0/TLujIMjQz+npB5QNWzA6lN0zjXUJliXRN29y83RrGC1aiGTtpisbT0gWdOOdSt4XBsxLs8krYp5csScKAQbTA4BlFh8c2o2TBrIiZmgST1lgFJ+KLTXtj1f/P3p3HWJLkh33//iIij3fU0dU9PdMzszN7cpe7OmiLoGlAti5aoiDSEgRaIgF5LUHgWoYO0JJtyAZt6A/f9B80YYLiEpIoCpAEWRZIySBBA4RtGQYNiaQMWhRNiyK53Nnd6Zm+6njvZWYcP/+Rr15VdfVMv+rZ6d7m/j5AoiqzMiIjIyMjoqMzI9mvV9TV5Xp+v13x6vQ+/2QP0m7m5mSMtw6JeehRBb97dh3SDOJcuTVZMTt3X3c3oL9W0R4s+fD0Dj8/f5lw6yXeWgk3pgsqV3hteh8Y2xAALwoKlWRuzk84GRpqn3CitPcybvCU3cReWBLV46Ug92oKQiuRwzxhz4/1+27TMfGRtO7PhpAZqoL4s45SUyVeaI7ZDR2/2h7Q+ERWt+kjA3g3loG2ikxCvNCffiQHzhem9UCfAsmNfR9fZ7S4TV6SLrdrziveFdo6MQlxTE9x7NY9B9Xy0v7GPE3jVy2u/MTDj6jq974/KXr/PPcDD8YYY4wxxhhjzPNG1V61MMYYY4wxxhhjzPtGKHqFVy3sqxbGGGOMMcYYY4zZlgIZe+LBGGOMMcYYY4wx74OrftWCKwxSfKWxgQdjjDHGGGOMMeZp06u9amFPPBhjjDHGGGOMMWZrChR71cIYY4wxxhhjjDHvh6t+TtNetTDGGGOMMcYYY8wVXPFVi+f4kYfnfuChDomYPa1P1D5REFofaVwCIEihdpnapXHiDoEgmYmPALQ+4qUAsONXzMOAqhCkIKJ4V/BSaP0YfhZ6pm7AoeyGDo8ylEBSx9QPwDhByMQPLF3NJERuNAuCy3S5oi4egMaP6XOi1D5vfp/6HoBpGOMKLhOAedW/Yx5M/XDhEZ3T89hG4xNT15Nr2GsGDsKCto6b9D1KqaDUF7fdag+5UR1f2BZvRIa3KrpP3EJv9Kxe32N5wzN78YgX5ie8PrlLenHgaDFGtroJcbfwQn3M/TTdxLN8EeojR3dD0Z3E7qS7cJz+Gvg6k6YgodBOBpbXa26EY25MF6ym+1Qhc22yBODg+gkvNsf0+9DfKLgm0ZeKWBz1Xs/OrONONwMekOYVeVoemQ87oeP6ZMnndgpShOUNz8dnR3xh9gppVlimitzCTAb2qyWrA4/rZ5zcGm+7YQcm7bCpQJoqMXUDlc+U6uw4aQrFw7RKpNbRhoSIshxqKA+NeipUIZOLQ0SofWbpFHWgdaGSAk4Rr0wnPbELzOqe47phXvXULtPlQCqOZZWofSasy6d5b3ZCN95vIW4dpvaZ2r23/K+ksBdWBCns1yu6HJiFnt3QscrVI8PUV0znaVr36tVmfd8vmfiBViL7frz3rvsFx2VyKezL4T6/3L9IcGf3WhUyi9QgviBOqSVThUzwha6FMDuro0qjzOue3eqsbkgzxdWZUgfaOnLQLDmeNBztFKpppKoS111Hpx7djTSzCAo7s46YPLlpGK7BjWbxyPMdXr2Gv9Exnazr/dqR9hMalFSE5U3P8pbivvYjpP3Mi83RWV7dWHHy6hwXYXUDVi9NeHX2BsFlli8Kw75yqz3kJX/Wu6ivdQxxwivXD3ljEZhdX7L80pzXZ/eIxVFJ4iS31D5zMjQA7F5bkIqnzDJhvycPniEJYekAR/vaAWkKqxuCi2Onp98X1AWqo30kFsLHP4oUSPuZpk0Me5lh3Y5prdyojjloVgSXeTCZ0IaEE+WV+SHXqiUH7YqTWLPfrBhywLsCxY1t8bpu8a6MbXVIuOypJ5FhVZHnY3018ZHaZZzo2D67wm7VUfu0LtNjOZ6Ggb1wVgbN4zlRgivjdVnzTkFB1s2Lc0rlMq1PtD5S+7zZX+pC8BfDnzqte86rfWYeeoIUuhyo3dl9PFn33VoXaVy8sB2gahK7rmPfdxzriplEdt2KG9UxrUQqyRSEVX6og7K2E3pu1kfcizMAFqXhw/VbfLC9y50457o/pnWRaYi0PjH3PY1L3KoeXIhnt+qY1gONVLRtZJWFl6v7hEkkRrfpPz5sv+n4YHuHtJupdnteasc64bS/lbJnf3e52T/OoLSFa+2KW5PDzfb+QDm55Xlh94TXmzvsTVfEj7zE0XLBp65/iZvtCS+398c8W7cffQ6IwrLU3JyccNy3xOLJxTG/0xNOZsTrmRfCMce5BaB9y3G3n1FwvNnvUWpHXwJBCg69cO2kunj9gytcrxbcrI/Yq2/RhsQy1lQ+M2SPd2VTjk7L1jZtnQB1GK9zE8bja3E4Px7foY/MexjLdXBn/fkTV9bl3/o45tm6+uSSz6/nfuDBGGOMMcYYY4x5/sgV53h4fgcpbODBGGOMMcYYY4x5ylSv9sTDc/ymhQ08GGOMMcYYY4wxz8JV5njgOX4twwYejDHGGGOMMcaYp0yRr5onHq4wvPKVQ0S+VUQ+e3h4+PidjTHGGGOMMcaYrzAKlPU8D9ssz7PncuBBVf+Bqn5mb2/vWSfFGGOMMcYYY4y5uvUcD9su6ycePi0iP3Nu+cyzPYnt2KsWxhhjjDHGGGPMU3bVVy3GD8vyI6r6ve9Tkt43NvBgjDHGGGOMMcY8A1ea4+E5nuTBBh6MMcYYY4wxxpinTLnawMPz7Lmc4+G8mD0pO2qXAXAowWWCyzhRgis4UQpC7RO1T7Qubi6wk0IsHoCZ65n7jqJCcBkB/Dr8qUoyrYs4UVoX2Q0rWh9xKI1P1C7hRJmHgdonghRutYfsVyuu1UumYQAguELjE5XLTEMEYJUrKskM2TPxw+aYXnRzfu/EnZvj1FOofdoq/wrCrusok0IdEo2LVK7Qvkv4UkGuL267UR3zgerehW3VfGDYhePXKnZ2Vxy+HuivCR8+uMsH5g/4aHObF24esnptPP/uxUyZZW7WR/hz5xNvDXTXhLSfCNPIjdniwnHSVAmhUCrwVaauMv5gYMd3fGD2gOIFEWW37gGYVJEd3xHnUHYTCCxLTVTPbNpzfbrg7cV8PPaOR9uCzi/n/9QNzEOP7iZQyA1cq5ekeaHMMve6Kf0+HJWWue/JrbB8saK/NobPU9hpe5pmzOsmJJalps8B9WfHya1SGqh9pqnGMnNatll56M52VhW8KN4VdFPGlVKNfw8ug4ALhUmV8FUZt/vMTujZDR0O3cRfh0SzZVky766oIzzm3nqYKpv64Uk5Kcx9hxNl4iPTEJn4yF5YMvHj7w9rQ2J6rg7a7jhKkLP7pJZE48ZzrWT82Uqkksv30nW/oivVhYa3CYllrkHYlNNJlVAVcqs4Xzb7alsIUpiFfrOt1ErdJLRWmpDZDR3XJ0vKLDNpBw7mSypRDnyinkXmk5692YqbsxNemJ8QZxDnyk7VsVN1l9K8eLXl+t6CvekKgP5ahZtHfJso08ywA/lGZPGhXfx+z636AbfqB+P57ixYvKwsb0HcKxy/Gnh9eo9KCnEH0rxwsz5iV5rN8V6/cQ/Zjby2c5/Z9SWvH9xHdiIfa99kN3R0WvF2P2e/7jb1//XZgqQOmsLezopmGtG9RJxDvwdHHxzztz+A3Ix5XypYXRe6Fxq6lyakgxnqoNrtCSHjds/KhVSFViLBZVofaUMkFUeXAwfVkmthQZBCUWHiI10OLIaGPgcWQ72po2BsE4OMdU5VZXxV0LpAU0jqGYonFc9yqOhTwFGo1/VWkELtMg59ZPky72xI4/8/nb/3chFUx3bz9H/WnCjTMND6OPaL1m20CwXvx2v3sN2qY88vL2yrQ6bxievNydjGnOvbBJdpXKKSjOcsviB5XHyhkkQrY7lzomQcO67jxeqQg3Dyruc68QMHfsHERWa+py8VM9dzEE6Y+54d19Gc9u/8mI6p79nx3aV4qnW6vSv4qrDvljRNQupClytS9peO/3J7yAequzBL7O2suBaW6/x2DCVQsjCpzurj0ii4MR8/PHn7bPteYvEKvDI74uXqPtfbJaXynHQ1r0/u8Yn5m0zdsE7feJ1WpcL1woM44aBa0qXAkAKLvkZUqR9AaDLX/TGti0T1XP+FzJdOdrmbZ7yxusadOGcogaSOqG4zyV0ujlBnxJ3rh7rCtWrBB6q77NYd89DjXaFymdpnJlWk8pnK5c1764/tt+bx3fbaJypXqMO63+/LppxWPl/os59SBZGzvk1SRy6Oo6EhresWY56lq0wu+Rw/8GBPPBhjjDHGGGOMMU+b6hPN8fBcsoEHY4wxxhhjjDHmGbjSHA/vYzrebzbwYIwxxhhjjDHGPGVXnePBJpc0xhhjjDHGGGPM1q4+uaS9amGMMcYYY4wxxphtqZB1++89PMcPPNjAgzHGGGOMMcYY87Rd+YmH53jkwQYejDHGGGOMMcaYp04ufF76cdRetTDGGGOMMcYYY8y2VK86x8Pza/sXSr5CrWLFkD0wXrQuB7woRYUhe1JxDNMw+BwAACAASURBVMVv9k/Fk9XhRNdhHKUIsXhaiUzdgBYhSKEU2Xxb1YluFk/hJNY0Lq63FWqfcCi1yziUiRsIUgiuANC4dOHnaVzBFer1tqhjGnR9vFWuNul2Ut4xD5I60kPvBqVz5/w4B35J8VC5TFHBu0J5l9E09Yo+NGT1QjjmY/XtzXr4yIeYTXvijnL/a2F/uqJ7AUqA/XrFQb3gY/VtdtuOencYz3F/wLeJl6v7BJc3cb366l1WN8FPEj4Udqv+oQSB94U0Lzg35ut02tPKwH61QhRm9cD9bgLA3eMZjYvkVsGBZmGZaxapYVYP7Ncdx4t2EzdVgXA5/xuXaFzCt4mwFPoDuF4tKG3BNZn7J1PSFP758BJOFMmQpkLcGcP3B0obIk01Xn8ROMktXawuHEeDogIiinNKUkcuDlWQJEg8u1aljL+rCik7+uRRIE0U1n9zoaBZxvh8YRnrTXgnhcV6Pa/L/ldLZfh+W+SaVC7fq+9GBHaq7j0d14my75cAzEI/3h9uYM+vmLqBqRsuhQlSNnXktlax2tRvABmHl0J5qJnJjyhPUync7ndZpmoTVgSWqcJ5JYTMQmvqkEjZcSmKJtOGxMTHzSZtCtN2gCaz03Q4KcxDj2syu5OO6+2SVoRbfkZTJfbaFS/Nj3lpcsTL0yPSFEpTmPrI9Fy8pxYvCdenC2bVmH/D3BGaTNUmaDLddTh44ZjlzcB81vGp5gt8qvnCmFyfyNNC3C3ofmR1Ez7a3sZJIe5nmGZeCMdUErhfxms3rwbmux171Yq2TuxWPe1s4MP1W9yojnkz7Y95GXrm6zS9OjvkaGipZwO7TcfutAOUNFPUw+qmrOsl5fTSKRD3YHkzMOx67nzdDBR25x2TJjKb9dTr+lmzUEkmFU9RN9Y7xbGMNcFlWokUFVapIkjmcGhZ9DWrWNENFUPyxOIZUtiUt8rls7Kn4zH6HLjTzbjXT1h1NX0O9CWsy+lYNzsZ261KztoO83hD9gzZX3i/WFXQ7EiDJya/uR61S1RSaHyi8mM+i4yd5viIPkeQzI6/WH+d9pU+0N6/VMectjVOyoXrWJ3r/0QNFB37S8el5kGebf7WukhXKlblYht6Fr+jksTED1SuMPcdWR2NRHZ8x0ziujw7gsv0OsbTyuU6MqujUAi+IE5pJVGFjLjCUWweefyX2/t8sLpLPYkcTJZM/diXOR4ahuIp2bEcztrjvJMhw0G94MVwuNkuVSbuFl6f3qWVyAvtCbl1pOR5rb7Lnl9S1tfzNE8XqcYNwp1ujpPCqq/oYmCxaHHLgfoEQshMZaCSzN24Q/tWx9v3d3gz7XN7OedOP2eZahaxZpkaTtbnmaLH+bFfsUl7cez7Jdf9gtrldZ5nDpoVft0v9uf6yE6UIb/7/4WqjmUzq0NEN/Gcbj+NRx7Rdsk6abGM+dKlwJA991ZT7sVxMeZZUpXtl2ed2PfAnngwxhhjjDHGGGOeMpvjwRhjjDHGGGOMMe8rfY4HE67CBh6MMcYYY4wxxpinTt71FfeH2eSSxhhjjDHGGGOM2dr5eUp+o7OBB2OMMcYYY4wx5hm4yhwPz/NbGTbwYIwxxhhjjDHGPGXKFed4eI5HHmzgwRhjjDHGGGOMedrWn8nc3vP7WoYNPBhjjDHGGGOMMc/AVQYenuMHHnDPOgHvVT8ESnE4KRR1dPniWIqT8fKk4nHrSxXVb96lKetRplKEViKVZFSFymUQyMVt9jv9uSgNi1ifi8uRiieqI6mjIOz4jsplnChHqd2EPZ219DS+2mV2Qg/AUAKti6Ts8ChdrvCiOApDeecxomWqWaZ6s74oDUPxW+XfkALXXQGvBFc4Li0iSirvXDTUQ24ubjvwJ3xtdXbMMp9wc35CmWf05Y6dqifOFHUQ1vny4TBej7aJAEymA3WbuOmPqSRv4rrWrEh747oWofXxwrFdEoLPlEkBIBehqNC6SJCM6wsxe24f7QDQ3Z3QSkSD4u5XaHb0WtHlimk1MA0DcVnhv/ZrUAcIiLt8m59eS82CX0GcKzu+Q+pMOx3oVxWlUf756kWKCqFThjljnEDaS7Q+4WVMdyqOrMJqqLhwuVUQPSuLqoKsy7XrBd+dVVYluU2Zz9kRk0eLoBVIGvfzoYzbVPC+sOhrUh7LL8Dx0DBkzxADKXuyPvfVxFeELlfE7BnydvfmqXDuXngSlWQO/Am1G+OpXWYeOq77E6a+Z+r7S2Fan3Drcrmt+FCdE3UsxEe5Ja+bmkE95RHNjhe43e1w2E0AKDqW40Wscb4QfOHttMtO1dPHQGmUqkqb8C4UapeYnzsXN0nMmx7fZg6aFX0JNC7RTgd26p6DZoEDHI5+CLw0PebW5IgXm2N2qo60W/CdY+IGJm64lObuBgQpm/tt2B3vy0kT8U0m7WVuzY9QD/Nm4OPVCR+vTgBI6tBa0VmmnQ0M1wqvV3fGuCYZCYV9vwTgdj5twxzXZwucKN0QKCrsTTteCsfs+yVfjPs4KUx8ZBrGOvK1yb1NW9CGRB0SIGgY6+I0ZjdlUvDduo5zkFqIMxhmQqkAgRdmJ9Q+U1fpbPbtdZCheJapps+BLlUMydOvK7GkjiGNed/FilVX0Q+BblWzHOp1G3zWJjpRYvKULLiVR1aek1xz3LecDA1p8BytWha5oSAk9RRk3f5X3AjH71BCzaP0MdAPYdO+AKTsoEDJjlzGdgSgcYnGJZworR/vP3EFVWFIl+u11kVauXjvOFGCy3zD9F+s1y/WM0k9lWR2fLfZFlymqKMUYaE1R1qzKA1vpj3eTjtE9SzL2ClZ5Yq3u/kjz/W0jZv6gVgcH6zfptNq3Ob6zb3c5cAy1RzFlviIvtcq16yGipPSX3gv+zT8Ud8+8vgvhiNe9omqyuzXHdN1vbJYNSxThWbh6ORc2CbjBsdrzT1ad9bvOe2PvNLcJ6pn5nukjG3+9XBC1MBhHm/uvE7fvX6KurF970tgWFbE5EnLQNprqY8KTZ0oOCpJ3O52CHdOSIvAW3GXu8czHgwTuhw46lqOYsPJuh+cB0/wl9uLPb9k3602feFYPNebE1QFJ7pZ4LQOqR6Zb5vzHhw5jWUyZUdct6VaHFouX4cLYV3ZlOVUHIuhIWfH8bLlTjfjTjd712Mb835S1v9G3HJ5nkce7IkHY4wxxhhjjDHmGbjSHA/PMRt4MMYYY4wxxhhjnrYrfk7z/RijEJE/BPwB4Cbw/ar6v7wPh3n+X7UwxhhjjDHGGGOeN8r4+vO2y7aTS4rIXxWRt0Tknz60/ZtF5JdE5JdF5C8CqOqPqup3An8c+KNf5lPcsIEHY4wxxhhjjDHmGdArLFfww8A3n98gIh74fuD3A58EvkNEPnlul+9e//19YQMPxhhjjDHGGGPMM3ClJx62HH1Q1X8I3Hto8zcAv6yqv6KqA/C3gT8oo/8G+AlV/bkv57mdZwMPxhhjjDHGGGPM03aVxx3OBh0+LSI/c275zJZHewX4/Ln1N9bb/izwTcC3icifek/n8y5sckljjDHGGGOMMeYZeILJJX9EVb/3CQ71qAOpqn4f8H1PEN+V2MCDMcYYY4wxxhjzlClP9XOabwAfOLf+KvDFp3Vwe9XCGGOMMcYYY4x56q74VYv3Nkjxj4GPiciHRKQGvh34+1+Os9jGcz/wkJIHYBYGAPocyCoUdRQEJ+PV6XIgqaOokNRvtp9uUxU8SiXpbNRJIRaHqlDWT6b0JXCYpxwPDQCrXJHUkdQxlMCQx4dI5r7DrUvG3X5OXwJ9CcQypnfInpjHdDQuAZz9LQYywjLVTNxA49Im3kc5Ghru95PN+mGecrRO3+N0KXDNNSDgRLkbd8a0ZP+OYUqlqLtY6vfdkkaqzXq61rJfd9AUmjZS+wwO0hSGEkjFs+smLGONcwWAKmSaOvKCX2zyBKCoQJspgyf2YVw/x6/AOwWniFOGGFgta1qJ47XOynKoWB2143HuBPb8EgSmXxCQ8Tp3OXCjXTALPXSedGOGS4omQdPlWyWvy1hZVIQl5GnBScHXhUkTKZ0Hp/za4jq9VoRlQT2bCiPMEkkdsj6dVV9RVBiSR88froBkyCrE5BFRGp+IxRNWgl+d5YdmRy5CUSFnR16XX3WK5HG78wUtjmF9jbu+IiZPlytWuWaxasjqiMmzHKp3LQtme10K9DmwTNXjd15Thfm6bntSlWSu+xOCy3hRgsvs+RU7fkUrkVbipTCtj3i5WsuWy8V7JKsQi+d22uM4j/XTsbY8yNNLYTsV7qxmmzK5LPUmThHFucIbwwH7zYoUPRqU4MsmvK8KrY9MfX92DtOBWTUwnfZMw8Aq1zgp7Ex6dque6/Vys29/1PDK5JCDesFBWHCzPqZ4qO8Le2HJXjjbd3N+E6X2mbQ+79xAyY5rkyWzaY/fH5iGON6/otz0c276+ZgPXYMkgVCoq0SZFF4Lh+P9WWecVw7cCQCfT3sAHA4t19oVqXj6vuJeP2Gn6diRTCuR+3HG3A8UFVo/XtMPtW+TimNYVdQuE6SM9Zkfr23c1bFOmiakjNtUQINSKtAA9eG4z7waaEMkF0eXxvZIkiOqp8uBk1TTp8CirxGBwzhhWRq6HBjWbd2ir0ldRS6O3Hv6PqAqY12qgl+3BSk5tAh+6QgLR5crjrtmrM+ysFrWrPJYX/Y5kMoYvkuBD1R3H1NSzXldV5Hz2TUFSNmjRaAIWsY2CaBxiYkfcKKE9bVyXjfX8GEOfcd65F9v+0vbijpiccxczyvh/ma7F6UvgZwdd9Oct/Oce3nOF+M13hp2WZaGB3nKoIEHccrtxc4jj7lIDVEDlWSKOj4Y7nNcJvRa4aUQ1VHUsYw19/vJugzXdFpfiOfBMGHR1zwoibLOo6hj26xFWPQ10/pyvX09nHDTz/Gu8FJ7RC3rvt8Q6FNAsyMeneu7Cbhe+HDz1sV8yg4XhZer+xyVCderxdhYqLDjVtyJc94axjxIebwuh90EBBZDzXFq0ejJ2eGPA+oEF8c+YKeBWjJvrnYo8wa/8NyPU1YnDcdDw5A9q75iEWuWcZ0v3Vg+zj8qnlVoJTJ1ebyGKLk49qsVcX2/nvblVIVUHA/O9WMfRaKgeSyPQ/abcxsjOZc/j3hk3YdCyo4hBroc6GIY1xc195ZT7i0vt0vGPDXKuvHdchn/TfrYOR5E5G8BPw18XETeEJE/qaoJ+DPATwK/CPwdVf2Fp3Wq9qqFMcYYY4wxxhjzDFzlVYtt53hQ1e94h+0/Dvz49kf88nnsEw8i4kXke55GYowxxhhjjDHGmK8aT/Zli+fOY594UNUsIr9NRET1KU59YYwxxhhjjDHG/AalerWvWjzPtn3V4p8APyYi/yOwON2oqn/vfUmVMcYYY4wxxhjzG5wWG3g47wC4C/zuc9sUsIEHY4wxxhhjjDHmSVzlnYJx30+LyB87t/WzqvrZL2ua3gdbDTyo6p94vxNijDHGGGOMMcZ89dh8qeIK+z9+csn3i4j8lKr+nsdte5StPqcpIl8jIj8lIv90vf5bROS7nyy5xhhjjDHGGGPMV7nnZGJJEWlF5AC4ISLXRORgvXwQeHmbOLYaeAB+CPiPgQigqj8PfPvVk2yMMcYYY4wxxhjguRh4AP5d4GeBT6x/ni4/Bnz/NhFsO/AwVdV/9NC2tGXY911R4cX6iKJCzJ6ijr4EynqG0KJClwNDCQzFs8wVL9VHAKTiN/E4Ga+mqpBVyNmRsiOpw6EUFVa55l6a0Q0VsQQWuWHIgVQcy1QzFI9DaSXipABwp5uyyhXL1JB0zPJVrOnzWRpP0zloYOgDqXhScdyqHzD1PcO5dD5sMTQcrtrN+hvDAcd9+477nxeTp5Fqc/63u53HzqyqQZGHJkHZ993FeOeB4DIuZHJ243kWyBOlyxV9Gd/yOVq1lDLmSe0z3in7TmldPDu/WIMDOQmUVaDL1YVjVYsx71AIvjAMgXJY00okFkcJwmLZIEfjMZt78FI4RJ0y/6LiqgxAKo6X2iN2QwdOSdOAFJDBwfJy/o/loaK6GwgdEJS+VPgqE1zBrcO8udihzwGXQTK49Z2zv7dglSqGPO7XLWqKOkp2rIsOwJiGDCm78Xr5xG7ds+orfAfVyblE5fG6qAIqpOhRBcmCG6Cow7uCJmHVV5QiDKuKNHi6XHEYJ/SrirIu/6uufteyYLbX5UDKjly2rXYhq+OV+v57Om7lMjtuIEhh4gaKCjtuRUWmknF5WOMT7oqtWy5C1LP7JGpgWWo+3x1wXMb66O20y524cynsg1Jz72TKEMfwd+KcXE7LsuCd8rnVdfaqFXnw4C+mraoSEx+ZuuFsW8gEKbRVZCieoYxtw07TMQ09c98R1+cY7gdeqh8wdQNz3/FqfQ/fC9Pb8Hp9h9frO5fSrE7ZrVb0eaxXcgMlO16bP+ADew/Y212ObUfSS3XqyaLFrcZy4JwiCq+GinQu/z4Qxnmcf214AYDDVcs0DER15C7wxQd7TEPECzgpRB3rhuPUMvFjPrxe3aH2GY4DrU94V5AkEBR1kHfHysjXmdyO6dkUBx1/3/n1gTQZ86n2mSF6FkMz7huFZWlYpYpFrEnZ0cdA5TMnseFOnLOKFTmPbXLfB1h5ShYYHKmrNmWnqOBkbGdz9GN9voCwZGxbh0BbRVAhL8d2IBVHQcZry9jOf6o+vHStzDvLXSBnRzectas5OSiCRkcpQhrGMu5EcaKEcw2UEyWdtvFbKCqk4qkk4NALfbC47rtNXc/r4fgsPTpe4zR4bqc93oz73I57fGnY526ccZxbTnLLstTc66fcPZo98tgnqaEg5HU/7PUQeJCn3EtzulKxKPX4c6h5sJxwp5uySA3LcrEdvNtN6ZY190rFEAMlO4b1vavJ0fUVN2aLS8d/wY/9zlwcr0/ukNXx6+kYLRCLh8HhF56jsuJuWaCDQxQ+EO7hOctzHRxhIbzkDznOE15v7iAK4gqtRH59dcCXVntjnqYxXYu+BoUuVpzEBi1j3VodCb7PiEIfA0dlQiWJw25CnjeEI8ciN9B5ulSRiyMNgSEFVnHdd1x6UnZwrgx0Q0UtaXP9oo79aU+hGypS9sR1PzepIxXH0erd+62ShJI8fQoMMWz6Tj6ctWFjebzcX5s0kZwccQisUkUfAyl56BzHq5bjxxzbmPedyvbLs0qi6n+vqh8C/gNV/bCqfmi9/FZV/R+2iWPbHvAdEfkI63EWEfk24EtPlmxjjDHGGGOMMcaMn9TcbnnGTz0AvCkiOwAi8t0i8vdE5F/eJuC2Aw9/GvhB4BMi8gXgu4A/9URJNcYYY4wxxhhjvto92RwPnxaRnzm3fOYppvg/VdVjEfntwO8D/jrwA9sE3PZzmqqq3yQiM8CtD/ahJ0ysMcYYY4wxxhhjrv4KxTP7qgVw+n7THwB+QFV/TET+0jYBt33i4X8CUNWFqp6+ePd3r5REY4wxxhhjjDHGbIhuv3wFvGrxBRH5QeCPAD8uIg1bjim86xMPIvIJ4FPAnoj84XN/2gVsJhZjjDHGGGOMMeZJfGUMJlzFHwG+GfjvVPWBiNwC/sNtAj7uVYuPA98C7APfem77MfCdT5BQY4wxxhhjjDHGwDP9WsW2RGRXVY8YHz7439bbDoAe+Jlt4njXgQdV/THgx0TkX1XVn35vyTXGGGOMMcYYY8zG8/HEw99kfCDhZxlTfH60RIEPPy6CbSeXvCsiPwW8qKq/SUR+C/Bvqup/fsUEG2OMMcYYY4wxBq428DDu+2kR+WPntn5WVT/7ZU3Tw4dV/Zb1zyf+wMS2Aw8/xPjuxg+uD/jzIvI3ARt4MMYYY4wxxhhjrurJ5nh4Jl+1EJEA/H7gE+tN/wz4SVVN24Tf9qsWU1X9Rw9t2+oA2xKRPyQiPyQiPyYiv3fbcFWVUIWvmbwJQC6OpI5FrglSKCo4UYbs6VIgFcdQAv/S9NcAKAiqUFTwlDFSFVLxaDl7gsSJoiqscsXdOKMU4SQ33B+mdDnQ5cAyVXQ5EFxm5nqcjKXosJuwyvV6vwqALgX6FCgq9CVs0nKSW0p3Nh70kfo2O65jKP4d8+Bo2bLq683655YHLM6tv5ucz4pAKo4Hw4RcHE14l8vrQMrFTS+4ixtKGPPQhcJw3NDlgItCaZQuBWahB2Bx1DKk8dym1TBeL2DPLzdxHQ8NKPiV4FaOLl8cL3MRVEGSw7tCHjx+6ZhJBCDOPfG4xi/Hc52+rbwWDsFDcz9RNYnGjed7ozrhRnUMTSZNHblxSBJcd/lWyTiWqWb65pgGkrDMY757V6gOHWRhFSv6EihBcBn8agz/0f27nPQNcX3+ejiGLUVw57JfCqCQsqdk4Ua75HqzoF/U+B7qo3OJ8gXvFBEQVyhxTHdYCmEplHNPRQ1DIEWPDo4Sx3w9iTW6DFQuk6MjDZ553V86d3N1fQ7E4vEP3SvvphsCv3Xyufd0XIfSSiG4zG+f/xJdrmhdJPLOdYpDL5SVbcTiWaRms95pxSI1fHG1z/00A+BemvMgTi+FfTPtsjpuWZ2M4d/qd1gONSJKKYKI8rnFNa6FJSQHAiJnrXTwhamPzNxZWe2GimWqyOr4/Mk+w7qenYZIJYXGRR6s61XfCwd+wdT37LgVL4Rj2reF9n7h65ov8XXNly6lWbLwNbPb5DLeY2kGzhc+Nf8iv3n/i1ybLDmJNX64nFd58PheNu90hvuBiTT0OZBXgZKF18IOAL/avwDAEMf0p+Jh4Vl9aUZwhahQ1DH347nf6WZU6wr6pj9hv+7wS4eTQiwe0nhMDRBmEd+BZmGYr9PSjecWOqhPlPrOgrKfOIk1RYWUPSfdWFe5QbgT55z0DV2siMkjjHX5UDxf7Pc46sd5qBepoWSHDIIWN6ajc4iM9VVZ50VRQaMDFaoF+A6G4hFRbk3Hj2pJ5xmyP8v74mh9pEsVL/n55Qw370iTUJK70Bc47fvI6d8Gv+nPnDq9Xt4XYvSUcrm+iHq5jonFb/o8TpSk5/sgnlWumbl+U/4B+jL2ncrguRN3uJ12uZ9m3B52OYwTVqXmME1Y5oZ73ZTh6NH9n6OhoSsVJ7khqmMiDYd5wp0457i0PChTjvKE5apmsWy4s5jxIE44zpOL8axaylHN23nO0AdKEjod+3bSOWIXeGV6eOn4L60b/5QdH2tu02nF59MMZOwzkIXqSHi7ZL6YHG7pKbWy7yL+XP67lad+ADf9kqieD1Z38F3Gh/G+v9PPuN+NaS7re6SPAZeg66uxP7m+XJIBVcIi0/eBwzzFU1gNFa5PhG5suxgcXQqIKHlwxOI2fRfXC2kIcK5pi8kzlYG47k+vcs2QPffTlBjH+zdlzzLWDCkw5MCqqx553U75XtAkpOxIyW/qxKrKF/Yrj3hX/tpkScljWR7S2PfJ6/5RzhfLvzFP37o/sO1yxf7Zly2VIi8DvwD8BeBl4BXgPwJ+Yf23x9r2TrsjIh9hPR4jIt8GXO6JXU7gXxWRt0Tknz60/ZtF5JdE5JdF5C8CqOqPqup3An8c+KNbpssYY4wxxhhjjHkuXelzms/Ofwn8gKr+TlX991X1u1T1dwDfD/xX20Sw7cDDn2Z8zeITIvIF4LuAf2+LcD/M+LmNDRHx6wT+fuCTwHeIyCfP7fLd678bY4wxxhhjjDG/MekTLM/GNz7q9Q5V/T7gG7eJYKs5HlT1V4BvEpEZ4FT1eMtw/1BEPvjQ5m8AfnkdJyLyt4E/KCK/CPzXwE+o6s9tE78xxhhjjDHGGGPeV6t3+dvyXf62sdXAg4jsA58GPggEkfU7oqp/bpvwD3kF+Py59TeAfwX4s8A3AXsi8lFV/cvvkJbPAJ8BeO2115g9QQKMMcYYY4wxxphnSXjmr1Bsa09E/vAjtguwu00E237V4seB/wv4f7gwfcwTedSMGLp+TOP7Hhd4/amQzwJ8/dd/vW41vGKMMcYYY4wxxnylecSkqI/x1D+nCfzvwLe+w9/+4TYRbDvw0Krqn99y38d5A/jAufVXgS9+meI2xhhjjDHGGGOeD1d54mHc96l/TlNV/8R7jWPbySX/hoh8p4jcEpGD0+UJj/mPgY+JyIdEpAa+Hfj7TxiXMcYYY4wxxhjz/Hk+JpbcEJEXReSviMhPrNc/KSJ/cpuw2w48DMD3AD8N/Ox6+ZktEva31mE+LiJviMifVNUE/BngJ4FfBP6Oqv7ClukwxhhjjDHGGGN+Q7jK5zSv/FLGl98PM/47/uX1+v/H+MXLx9p24OHPAx9V1Q+q6ofWy4cfF0hVv0NVb6lqpaqvqupfWW//cVX9GlX9iKr+F1um4ZHm7QDAJ5svUhC6GOhyRSqeglBUCFJQFZI6kjoOhwm/d3ICQFGhFIcqdDq+eVLyuK9mYdXVpOypfd6EX6SGYQiscs2QPV0OrFLFKlWk4qhdBiAVzzJVLLuae/2Uo6FhmSoAhuiJyY/x5Xqz/504R7PgpFAQPhgesOM7yru8+zN0gWFZbdZ/7eiA1bLeKv9kPZuJhkJR4XhoKCrcbE/eOVBdUH9xyO2mn19YT1PHUPx4jIUnSKF+AKVWhuL5utmvjzsuA/1iTOtiqClFeLs4Xq7ub+I6XrZoEiQLki7nQ3WiqArk9fkkhyShkkLlCt01hySHH4sKYanc8A6c4qJybbbiWrVgXo07vBCOCW3CdwVU8UuHi5ePW1QYsqe9O8bjO0flMiULbYjjRDECOTsO4wQVCCso60vztTtf4mTVMK3H4zZ3HRkBFXx/dhzXj+uqULLj9eld9qsVdB4pUC3OrkU1SQQ/lj/nFM2OkhxhCeEEHIp34/65D5TkcCsPq8Ai1hwOLUThoF2hRdDo+ND83jsWBbO9XBw5b1vljkTgd7bvbXi7rJuoSgr/xiTR5UAtma5Ujwl5Naps7nmARWlY5JrbqzlvDTsA3Esz3ljtXwr7dtqFhUcXY5q+cLJH34/1ce4DgR9XxwAAIABJREFUDuXtxZypH6B3oGPenAq+0OeLbw4Oi5q3T+YcnbS8dbjDSazpy7hPXwIe5V6ejml3MHM9L4Rj9v2SB3nKzueV1YHjI2HOR8LF+g3G+/Kb5v+M2icA0kxpm8hvmnye3zb7Vfabjj4HpCjVuk04NZn3m/+1qFxm91fG35M63MKj+ezkPrccHy4c+oplqnkwTPBLx+RNT+0Sd0tDpxUH4QRH4ahv2Q3jxNOtZF5qDymN4kRZDPX4HqkKkmE+6/AdlOOa3IzH84MiBXwHsy/0yJ0HtHsdQ/ak4hBRhmHMR0nCl7o9UnZkFVSFnUnHbt3jUN5c7bLsapwrnKSGEsfyL66gTUEGR+MTMY3tKIAThehAFBehOYQhe3J2fM38NhIKsp5pKpYxD4oKO+Hd20nzaJLG++l83VTi+j4uUJJDo5CKo6iQ1I/XaC34jBbHEC+/ubsq9aV6JmXPMo2N4FA86Vyd0eWKoXgqLt4vq1yNfafouDPMebPf40GccH+YcjS09DlwL844yQ3HXTO2aY8Qiydq4Ci1HKcWgMM05V6ccTfu8Hba5STVpMGTFhWrruYotnxhuHYhnq6vcCvH3bxDiQ7Njk4rcnG46NDoebW9f+n4L/rxvPuTmo9VbxPV8/l0HecL15oVeCUs4Oe6V/h/h5eQJOSdMS923LnJ5BV23ig06+uw71a4IVOHTNTAKlYs47pfmcbrmnpPWKyvQXGIV0oRfAeuS2gQ4knNgzxl0EBMHvXrvoPL+E5Y9RW1z1DG+6ysf4YV5OFi25aTo5bMcakpCMtUU4rj84tr5ORZdDXHXUPKY/+7qJCW794mSRrPPSVPTo4qjHkzawZk3SctKhfK56mXpsdj/VPG+qREN/7rLQk5OXK6WttszJfdc/TEA3BDVf8O63kf1w8V5HcPMtp2jodfYMvPZBhjjDHGGGOMMeYxrjqg8OwHHxYicv00JSLyjcDhNgG3HXjIwP8tIv8rsPm/2Cf8nKYxxhhjjDHGGPNV7zn5nOapv8A4P+NHROT/BF4A/q1tAm478PCj68UYY4wxxhhjjDFfDs/Rq4Kq+rMi8juAjzO+tPRLqhq3CbvVwIOq/vX3kL4vOxH5VuBbP/rRj3LtsXsbY4wxxhhjjDFfWUTZzF20lWf8dISI/Avge1T1L5/b9j+r6rc8LuxWs6mIyMdE5O+KyD8TkV85Xd5Dmt8TVf0HqvqZvb29Z5UEY4wxxhhjjDHmvbn65JKfFpGfObd85immNgK/S0T+moicfs3glW0CbjuN618DfgBIwO8CfgT4G1dNpTHGGGOMMcYYY0ZP8DnNH1HVrz+3fPYpJnepqn8U+EXg/xCR19nyOYxtBx4mqvpTgKjq51T1LwG/+4mSaowxxhhjjDHGmKs98fDsJ6IUAFX9b4H/BPhJ4NVtAm47uWQnIg745yLyZ4AvADefIKHGGGOMMcYYY4z5yhhMuIr/7PQXVf0pEfl9wL+zTcBtn3j4LmAK/DngtwH/9rYHeL9NqwFV4ZOVMmRPLo4he05ijUMZimeZKrpUsUoVQQpOlErGMZch+01ci9LgREFhmWrKMP5tVvfULpGyJxXPUDwiylFqKSqsUsViqOlihRNl4gcG9fQlUFSIQ2CZKo76lkUcX4WJMRDTGH+XKwBOYs3bwxxJQlGHQ7nlA61Edqued1KivzAb6u07u5S43aWdNOMkpJKFXBxJHVmF37zzhXcMIwuPi+8++2quoXYZAL90tCGRp4BXToaGf23yq0RNuKWD5Xgt7h9PycXRaeBT9e1NXMOyQgaHZEDg6/bfuHCs+ljJxeGSsOpq/DRS2sJx+f/Zu7cYS5L0sO//LyLycm5V1VXdPT235eyVK5K7JJe7K9oERRE0AdrA2gIsSCJs0DAELwzDMmQ/2S+GXgS/2PC+GBAWlmHwRYZhPdhr0LAAG6YlwDJWlGhJxF5ndmd2Znr6VlXnlicz4+aHPF3VNdUzXTU7083SfL/GQdXJcyLjkpERUdGZkSW3ymNsn4d4tid1tpDIUA0ruUzKjk+V93hhdAzAvl2xM20xIRNrQ7l8fB4ThjY6inWimidMJ1x3S6K3lDZSLIbvGZNoQoGJGdtmYjVs/7XJ9+nXxcn+3BosGVvEM4vMFGswHlIyGJt4sTqiiQV4wfTQ75wei1duPKCyAR8szkVIQs6C6cD2kBBe2J0jAjkIuTeIF6QXUh7qgEThS7tvkIOBLPzqzqvve6zVxQztk/DCeHHhMCFetIl+bz5ZfBbMtlKNnafPljaXWEnYx6xo5LftwGX03rHo65P3TSppQsm6L7nXzgDYpJImFOfCHsYJpjWYzZDfLjpC64b6uHBURaDzjsp4TGsgCMW2fYGhnCLCvl2dbDNHBcv5iHBc4d8e0wXHYTdm2VcnZXGcxgDYDdTG80pxnwO74u/PP8fstQbbv/dIoFjDr5Qln5gO7UY+6Hjl2iGvuCNeKR4wdR2tL6gPI5OiJ23/AfzOJ79DqjLZCzt1S7UYtvfJUSwMpNOyP+5HAIhJrHzJypcUa6FYgZPEO2GHwzBl1zYUEils5PlySJMReLE6Ju8E+ug4nk8wvSBeIMPuqKU+zNiVwbZDfNVxoljB9O3tAtUi7IxbnEn00fH87oKwGY6hbU/TB0Nbd2u6xGwb2z5afO+YVj3HXY3YYXtOhnLWD2moWkI0tGHoP2Pa5j8L1TxTzROljRiT+ZemP8CVkWwztQ0A7BUbAF6o5mf6c3VBJpODIaV3ne+SkTgcB/GGnIV1rOji0F+b7bPfShuJvT0fHvDJ0Oaz53sfhzHUu3+HoX9qY0Ep8UyYJmzHZr1h6WvWsWITC9owjK0iwrwfcezHdN695wA+bdvBo37CYTec+11yzPsRd9oZ98OMZahJ3iK9oW9KjroRD/rx2Xy1DtsJh2ECawdJaFKFDxbTQ47CJ8oH5+IfybbzXzk+7YbfX+8PKIrIL+/9BOpIsYIfds/xw+450kHP6HrDvVgzkf40H6PE6K7HI9jtmFbaQF141rkkZEPphvNjPDoNV6wgdJbaBkx5WsbZGYplwM4dXSpoc0GMBvfabdwGUjZkA6F3OEnYOtJ7R9eetgNszv4/ZvKGQiLvxF3CdhwO8NZyl9hbfDuMgbvgcDYNfd0Txq2Sh7FqUUSSN9yYDu39wXiNbOvjMI45XxdfHM2HMY03hGjIaRtXkbEuYd1lVvZT6sN3mVstnlkaRT6//fUtEfnSwxdwAPyvF9nHRZ9q8e3tryvg3710SpVSSimllFJKKXUV/SfA14H/avv+3dMgT1yG4X0nHkTkGznnvy4i33rMzsk5/+sXTKhSSimllFJKKaUedTVutfhvReRWzvk3AUTk3wH+TeDHwN+4yA6edMXDwydX/JcfMIFKKaWUUkoppZR6t8veQvHsJin+FvCvAIjInwP+C+CvAb8EfBP4i0/awftOPOSc/2j78w9F5Mb293s/XZp/eiLyNeBrn/nMZ5g+68QopZRSSimllFIfxNW44sHmnA+3v/9l4Js5578L/F0R+eOL7OB9V3KRwd8QkfvAd4Hvi8g9EfnP3y/cRy3n/K2c89d3d3efZTKUUkoppZRSSqkP7mo8StOKyMOLFn4L+D8f+exC60Y+acn0vw78GvCVnPNBzvka8GeBXxOR//iyqVVKKaWUUkoppRQIV+OpFsDfAf5QRP5nYAP8fQAR+Qwwv8gOnjQ78XvAb+ec7z/ckHN+TUT+beDvAf/1B0m1UkoppZRSSin1sXbZKxme0eRDzvlvisj/ATwP/L2c88OUGIa1Hp7oSRMPxaOTDo9EfE9Ezj+MXSmllFJKKaWUUhdymSsZZPjxe9sLAR76Zs75mx9qoh4j5/wPH7Pt+xcN/6SJh/4DfqaUUkoppZRSSqn3c/mrGH4/5/yNjyAlH6knrfHwiyKyeMxrCXzhaSTwSUaFJ2VhJBVtdPS9Y+VLQh6ytgkFIRtiElIWahdOPgNICBmIybBMNZZEjoZFXyFBCN6yX23YLTakLCSENg4Xe/TJYrZTVD5YumApTaSQxDKN6JNlEwqiNyy7mvWmYtMPYUNvSUnooyXlYe5q3tcs/AjTGpahorSRqakBuFEv37sQOkMOcvI2H1aczIc9gTMJANNYuugwkgnR8Iuj198zTHlkkPD++zUBxq4nRYPt4JOTB/TXEiShC47nbc3tuMF2gmmH4xHWJc5GXrY9z1l7mp/eYHpDHGXiNPKX9r79rrgyMRpsYwjecvPakjiN3I0zbrglJgzp8XtDXrs9g8+ZYuRJhVDbwC0352a54KXykFICO1WL6SK2TRQriKN0Lo8xn5ZxddgTR5mZbQG4Wa+QBG4ljIpAnywSM+M7Af/8MGf3CTcnB0MXhvm/XIAzkVHVs/rE2RZIEhiTMDZjSRz1YwBSCY9UZ35u9x1qG4byMNubwfKQf7cZ0vy//ewfkBOQ5ORYARjJxDTs7KvjV4cqFIQv1G++/8FWF+KDJSbDZyd3LxwmxSc10RfTZktlAou0oTSBWjyWxMy0zEx77vt9cvTpQusEnQjBMu/rk/ddKkhZWG8qFr4CoIkFa1+eC7uKNbYVyvkj7daywEjGNoYbozVVMVRU2wpufbZcRDKWzK/Wd062lUcCRyXlfcf4LcNxO+L+ZsLRZsQL1ZxCIos4pHd0DwoJfKpYUkvgn9x7Efvd19/3fyDcavj5hdlwfoxnHftVw8wkSiKVCazakvqNOTdHS1apY5U6AH57958TxwmJwgvjBeN3TufxTQDMacQPmgkAVRXooqMNBRKHNgHgbX+N+37Kjm0xkpkUPTfc0F+kDLu2wY38cH4vC0wYwtoeZkVHsc64RiiaIc7R3Q63hnLuKe6viC9cx5rErdGSlIWvXn+dHIfj5JptP2gytQs4m3hutCRkw07ZUtuALSLTsqOLDltEsoEUhWuzhmxgp2hJ0eDjaZsvXiAJts+UxxFDRkzmS+Uhsi2b3XIDwDXXkLLwXDE/ab/UxWWTycGQ0yPnXgJJMtT/7csny9LXrOIwvno4bhmVnuyF+Ji2ymfLOlVntoVoaMIwDmqjIzxyzB6Oh/psz4Tp0/BeMix8xdLXJ+OxdV+RsnBvM2ETC3KSM33iuxUSWfiK43YEgJXMwlcsfM19Px3y1RtML7C2rPuSw25ytsy8xXhoUoXdDJGtU0XbFthWQDIzu3nPNEgSCnFYMj9qbvDczpLP1e9w6+acbOG7q1v8aHOdT798l08dPOBunFFIPA1fRaofvMNhrOizoySBARF4EKYYyeTt8am37eZk2lEtMkUd2Cs37O02WJcIE0ilQ2KiWArLWNNsj1m4c5dsIGRDdpnoDc4kysrT9e6kHSjnQOTMXxQ5CRGzTU86GXfPlyPIEJuCfl0QouH6aA2A2Tzh/M1AFnZGLWThk7Nhcf3r1RqzbRd8tPhkzwV9pbqHBAEvhGhO/8jLMB51jEfd+8et1EfpMgtLPvsFJn8qT3qc5vmzVymllFJKKaWUUj+1Z7xo5FNzuf/SUkoppZRSSiml1IdDJx6UUkoppZRSSin1kdGJB6WUUkoppZRSSn0UJF/yVosrPElxJVdiEpGvicg35/P5s06KUkoppZRSSin1wXwMFpaEKzrxkHP+Vs7567u7u886KUoppZRSSiml1Afy8KqHi7yuMr3VQimllFJKKaWUetr+BbiS4aJ04kEppZRSSimllHoWdI2Hq2HsPDEM2Wh8SegtbSjYhIKE0AfH2pfEbIjJUJrAypcn4ftoAUjRcBwnzGxLDsLGlxCFHIVrZcOOazGS6aOljcN8TRsLjGScJHIWUjKY7TUw8ziijQWddxAM666k7xxdGMLmKKQkQ7gsAGx8wcqXSIQ+OZykIY3Z8qnRvfcuhG34h4pjg3HxQuWXt5W3WAobXzBynt47XnHH7xmmOgTXnN2WSBylRzZu9ysCEuErk9dIo4R44WC8xufIa2EHCZBG27S2hlERuGmnFHJaNSUKJIgHnnp/w88X4zNxx2ooQ+PB2MTnr91hst9wGKcYEiZkUgnlS2sA+tlp3rMVpkXPzLTUEvhUcZeZaRkVnvKtY8pFRCLknXCuHLrk6KLDTw22CXCzxWdLUQf2ig1kKFZQO08XHMZnXBP4zZ//LgDXrUFMZrWsh/QYuOYaZnXHf/Sv/cFJPKaHbIeydDayTDXH/QiKTCwgFqdpulkucSaRAWMSCOQsmDAck5DsNvNycrmW8cPvhYkn9eHz5TGYDEl4xfn3rAvq4nywxGD4TH3nwmFS/Omb6IhhnUoqE/jD9oDSRGrxFBKY2Q0zuzkXZuWr07py0Xi8YeNPK6PPlj5ZfO8I6TQf5jHXCTaxJJUZN5yi1M5jW8GahGvg+dEcZxI+W2wLxUKw5nQ/IuBM5JadnmyrjsGtDfV9GN/JNJuSVVuxXI74lcmPMJI4jFO67BnfG9ra5+0EgAf3ZsTj4/ft3M0QhN+YfA+AnVHLjmuZiaPHYiSxWVdIs+HXd3/AMgeWeWhHXnFHYDPSG74we4vicDgGKQvJgatP25uj5dDeFS6y7ks2viCb4bxPCG90BxyFMWPpKCSyXzX0eTh2i1yyZxuKImIkIa0hGzCtQQKUNpKckA1U86EddouO6jhjNj1yuCBOCqxJfHpyjxANv7XzJ4gbCsZ2w/HMeThmO6OWmWtpo2PmOmoXGFU9ThIxGa7NGnCZHA0H4zVIZla0pGgIyeAk4ZMd2nzAbTK2SyQEZyM37XQ4Ji6zX6yJybDrGnIWXi4fkNLZvlA9mRiQxJm6noMhmwxRhr4iCjEZVr7kuB/TBncybpkUPRIMOZ4v+5As8zg6u217rAG66OgfaWdCNkObwdm2p43DeEgStMHRhKGdcZLooyVlw6KrCdngXITx48c/RjIT02HIrNoKgJHp6aNl3tUc+skw7oqC9ILdGHrvzowZYRi/GQ9NKinmAl5oc0FsCuwGpEz4fP7/9bo89KUPxzy7tuH78xt8bvcee3bNl278BD+BHxzf4K1mly9ee5t/+eA1XikO2Ten/bBIJr5zlzfCPj5bxtu20EjmTb9PHxx9GMpwXPQA3JwtKReR/VnDL+78hD9zcIe9yYbuJY+ERDaCRFiEmnWqiH44RlmG/iCbId7aBvYmG0JvkW285SoP9eRhowgg0CZHQrZ1wVAWgdgMx85sDDSOEC0vjofxpmkv0N8luDleQRI+OboPwPVqhdmmpYuO+Jh+8+XycDiu27o8lCMQhVERGBXnx3hKPU1yyddVpVc8KKWUUkoppZRSz8IVvorhMnTiQSmllFJKKaWUegau+qKRF6UTD0oppZRSSiml1NN22cUlr/AkhU48KKWUUkoppZRST5mwXWvnY+DKLy6plFJKKaWUUkpdSfkSr8Hvicg/euT19aee5g9Ar3hQSimllFJKKaWetny5NR623/39nPM3PqIUfWR04kEppZRSSimllHoWrvC6DZehEw9KKaWUUkoppdQz8HF5qsWVXONBRL4mIt+cz+eUJpDTkI02OHJv6aOl9QUhGUIyNH1JTEKIw/cWXX2yr5gMAqRomMcxtfRIMPTRQhIQ2CsaxrYHYOXL4bPt7ykL1iREMvJIrTnyE5pQ0PoCvNB1jtRZOn92richhG36e+/YhGJYZQRwJgLQ5YJPlXffp0AyYk/jLtZQ1OFCZRnzEHd5BG1XUNtA3xVct+9dNarjTLE6u+07vuUftNdP3ts+U5hIWXkkwheqt7E7PWT45Wtv8maM/KC7RSpg/6X5kI0gTMpuiEOK050HIZWZg5sLrs/W5/NQDscgFfDCwZxPjh7w8rVj1qlimUbYLpN2An/uE68CsLmZMUD0llQI18o1hSR8tty0DXum42a9Irz2Om7liQXUs+5cvEtf0/Ql/Uzo9yt+/uXbHMcx47pntK0vxQoqF+iiIztBUuYvHPyT4TMMmEw6qrb5hxfKIyZFz1/be+MkHtdCrMCaRFlE5mHM2pdQJVIFcXQ2Xc4kUjTkLEMdzsOiNdlCn4b6J2Uim4wYkAhkobQREUDgeTtBTIYg7Ej1nnVBXVyMhhQMN+ziwmHyh7DYUMiWZa4xkvnGj38LI4laPIVEavHU4s+FWfQVIV+ue8hJaPvT8zZmc9K2jd0QhyVT2fNt0yLUxFE6mfGvXUASFCZiW7hZLolZaGKF7aCcw269OQlvJGPf1WtXxxnbwvhOwnUZ3znariAtSj5b3gPgbr/D/dTi2kTMBoPBkpHFkI/3W+zJT4afXyyGPE7LDiOJkRRYEl1y5PVwvv3l2U9YJsNyWx61JPCC6YXPVO8giwaAlIUwzYxH/Uk8/aI8yeOyqem9JTkIE2hCyRubfeZ+REIY247dYsObfh+Ad8IOe3bN7rjd5kcIuxEThvM+ZaE9EPq9RLZDx5NFqI8ipjlNw6To+XR1hy44vlTNseXQN9luOLYhWpxJvDw9ppDI2pdMXEdpAtOqJ2RDiIaXdubkIpG9sFN0YOCaa0hRSFlwJuGjhQQ5g+kT2Ql9tFgzHN+UhGwz14sVMclJ/X3RzvHBvvcBU49nMyQZ+ost8YLYjAlAGPqQPlrmfc1RO6INBX0ayrq2Yeij4/n2wmfDKtTntj8cd/TB0cXTMVEb3HCsOXvitcFR2ghR8MnSRoeRTMiGlIZ0d94RkmVa9xTTnsdJWdizDTtlezIerI2ndoFlX3HUjxlZDwlsL9jNMG5c92f7QImC3cA8jCgXIL2hSSWyttgORtOOJpXn4p+njjfCEjcd6uznq7d5Z77Dn5ncxmfHL05+gt+BO/d3uL+e8On6Ll8dv8rPF2P2zWnbmqMhB893Ni/is6VAEB+pnedHzQ0Wm4p1N8Rf2OFcvTVe4jaJF2dzfn38fb688zov7xzz0sv3scsNqbKkAuZ+RJNKYusQVyAZ5l0NJiMmM3YdN8crchaMG46T2+Thj6bTKoSYTJNLzLZRj8kwKjx0BjKYXhAvxGC4UQ4DyictrjeMVeDF8TE5wUvlITC0IcYMgUM09I9pB27ZORKGBD6sq8YmJAilC5TuYmNmpT4Sl1nf4YpPUFzJiYec87dyzl/f3d191klRSimllFJKKaU+mI/J5IPeaqGUUkoppZRSSj1lwsfnVgudeFBKKaWUUkoppZ4FnXhQSimllFJKKaXURyKD5EvMPFzhSQqdeFBKKaWUUkoppZ6FKzyZcBk68aCUUkoppZRSSj0Dl1njQZ78lT+1dOJBKaWUUkoppZR62q74kyouQycelFJKKaWUUkqpZ+BST7W4wpMUOvGglFJKKaWUUko9C1d4MuEyzLNOwE/LSCbF4W6X3jvoDL13dMESkiFEQ0xCSoYuOFI2NH1xEj7koQiiN8zjaLtR8MECIJKZ2g5DJmVh40tiMuRkWPUVfbI4kxDJ5Dykw2fDnW6HRVfT9Q7pDbG30FqCt9uYBRFIWWjjMP/jgyUmAwlCMpjt9NcyjrhhlxcuE9vBqPIX+m6IQ/7r44z3Q5nF3lKLfc8wk9ueojl7hnx78wrfa58/eV8fevaLNbvjFtfAC9bwqefukw382uwHfLe/yavtTdIo8dXnXh9KJAm1DQCYR6qmRIFp4DN7D9ip2nPpiZWwP20I08SvXv8xu67h5ckxXSq43e9h+0w56/ida/90+P7NftivyWwOLAfFGp8Nh2FCIZmZSXxh9iZmVGN8JI5gb7I5F+9xP2LTFYQamucKvnLtdeZhzKjwJAzZQrUY6k0fLZt9i133fL64C0CTt3n1sv0JN9yS0sQz8UiEMB7q4qTsWYeKmAzGRWINsT797nL7JvSWEA05CiDk4Qd9svzSt/8KxcgjRQKXMD0gmdoGcoZs8lD+GUyEQnR+8sMQeksKhh1zvg6/l5w+nDv5HoQpmzi0ews/wkoiYYh5eL3bqqvo43u3AY9NaxZiPN2Xz5aUBVtE9srNdttpu/aoe92MXCfytqqlLEgUShsxAcamZ9MVrGIFaTivbo3OtomVnG3zymXCNTB5qyOWQo5CDAazMdSSWMWaO/0OTRJieVrOYxMR/+Ryb28O+Xh4foydZxNLCnHsG08TKqQ30HaMpOI4Da+hHATbGLLAgV2T7gxtQhsdaSewMzqtI3Y57D9EQ78a+p84yvidzHFXc9iNOezGLNKIsempTOC7q6Et/l73PGPp+eLB2zhJQzlda4lVBoEmFGxugruxYfnScLzTqMC2CUKE6QTTRV4Yz/lEcUjnHQdmgiuHtksyjG1PiIax87w8OgKgD0OaSxOpnacLDp8sr0weYMqIRKG2HmzmZrkgJ8GaRGki1gzplChkK8TK0MXTNigngyRh1zZD/cUQsuE5G/H+cnVWbSXODnqTwPYcpDdIFDrvWGxqjjYjuuBO2ofa+qEPy+fPmTZuz9lHiDCMc4AuWFp/Oh5bdRXpMfsBcJKQPNStLrrteKwgRIORdNL2TMqOnen5/hqg844bdsH1ck1ZDHW4kKF9avuCJhTsFC0YMB1sTxmW7dk8kIZx1jvtDsaDBGEVa2xjMAH2p8259gigyZm344jJuAPgF8tI3zp+przHvTDjheKI/obH3K6YNyNeKI54pZgDMDblyX7ydsz7zxYvYsl4MrmwHIwabm92aBYjmsUwFnDb82m/aDA+caNa8sUSfqH+CZ8YH/LF/bdhvcFPLWGWWfmKIz/BLhxSFkiE43Z0clN5ZQI36hW2iBg7VBoT8mMvFX8QZxQSaEKJT5ZJ0UN8ON4RJAgpGHZdM+TrCX+R2H4YJ75YHSNR2LcrAGa2xZq8PTTy2H5z33ZIEiRDDMPYyNqERKGygWo79lTqWRCG/vSir6tM/6JQSimllFJKKaWeNl3jQSmllFJKKaWUUh8lXeNBKaWUUkoppZRSH518hWcTLkEnHpRSSimllFJKqaftkms3fDirfz0bV3JxSRH5moh8cz6fP+ukKKWUUkoppZRSH0y+5Os14KrzAAAgAElEQVSKupITDznnb+Wcv767u/usk6KUUkoppZRSSn0gki7+usr0VgullFJKKaWUUuppu+JXMVyGTjwopZRSSimllFJPmfDxearFlbzV4t1ybwHoeofphc472r7AJ0uIBmsym7ag846QDc2mOgkboiVlgSwc+TERg+2EPliMF1IY9p0Q+mjpo6XxJSkI666kC24ID6QshGxYh4q77ZTFpqZvCiQKqbeIF5I/LfKcIWehDQUAvh/mgbKDPtmT/S5jzU3bvHf+DWcqYXWUqV24UNn1foizXGZSMNxdT59YoW0bcZuzX/pnzUv8sLl58t7Nez4/us21UUOxBoPw52/8ABPhK9Vd/unmE/xofZ1k4auzHwFgWqGNj58LK6c9L4+PHvtZuw9fOniTXCZ+ffY9fLbsFRt8trzWDHGMRz1fqd8BYOfaUJZ7uw2rl2HqWtrsuN3u8iANdeMrox+RmgZpe8IIJmV3Lt476xmbRY2J0M+E54o5q1gRouFBPx7KqstsQkHbFzS3BL8/Ij66LEw0pGq4bspEqKWnT/ZMPBKhvxaHtFctx36ESEZsJllIxel316EiJEPuLSIgvUAavpMNrHzJrO4oy4DYjJjhOCaXGbuemA2ShvTl+C9E8/CnRgqG7A3mEr1LTj/9Meii407Y5diPuV43fOfuTdapok0FbR5e79b05Xuei++ZVm8w5vQawC45uuiwJjErWgBCtoTH5Ont1Q6mjMRyeB+SIQuUJmJ6KCTSNiVdckgGE+Cl+rQ96Lzj0/XdM/sMoyGe4rjFdhlTDGlLZeLHfpe3uj3WocQIrF6w1OLZ5I5aMtltz4vzRXNCbm3OvJ8WHcswtB+fcDOaUJBNJl/fA+DH/gY/9jce2QGkMrPOJfLKywDM2xF27LlWne7bdMP5uG4qWFvq0hP2AvFaYNVVTIue++sJd/wuM7PBSuZuOwXg28efJGH4Cwd/xF6xQTyMqp44TsQRrPqK7pbnhYM56xeH+OLIkY2Qdkb0z+9gOs/nJncYm55uPRygh3U4udPfXxods+s2GMn4ZNjEAiMJZxJddOQMn6gOKUaBbDNGEtlk9u0KklCYRG09RvLJACwVQiyFjT89ECkKhNM+t0klfXDMTEH0Z9tOdQGPaY6MP+2jJA19kPeWZlOybira3rHxQ12YuB6JkPvz53UbC+730zPb4iP9Su8dXTg9Zk1f0kdHxNBlf/q9OIzdAFrvWPclIVmWfYX3jsoERDIhG6ZFz07VPjarTV9yYFoOyhXTqgfAZ8tusaHrhvZux27ILmP7bb+Z5eSz00IZyuV+O8F4sK3woJ9iO3Ab2K8bPlfeORe/z8Kr/U12RkP6RlIR22Hf3928QMJw7daS6lDo5hUT09FsxwM+x9PoXcbu7vK9BzcYm442C6tPzjgo19zfTGBlYTPst7aPjAXzcL5WUvCiW3DNNbxcH5KvzWj3DNxoOepG3Ol3GL8tmGt7FE1muRxBFHISpq5nYocxhN2295t9g2ROxhMAOQmHYYLPjkVfse5LnCSww3ckDH9o5WgoJJIQsn3/vjGWIHEYaxGEWoY60qTytIyDJaXzS+/VkhHPdhxukSjDfzLbjJF8qX5ZqQ9ffvhH4cVeV3jmQf+yUEoppZRSSimlnrbtUy0u/PoIkiAinxKRvy0i/9NHsPsTOvGglFJKKaWUUko9Cx/BEy1E5L8Tkbsi8s/ftf13ROR7IvJDEflPAXLOr+Wc/+qHkZX3oxMPSimllFJKKaXUM3CZKx4uMfnw3wO/cyYeEQv8N8C/Cvwc8Lsi8nMfXk7en048KKWUUkoppZRST5lkkJQv/LqonPP/DRy+a/NXgR9ur3Dogf8B+Dc+vNy8P514UEoppZRSSimlnoXL3GoxzD38noj8o0deX79gTC8CP3nk/ZvAiyJyICJ/C/hlEfnPPoQcPZY+TlMppZRSSimllHoGPsCDVX4/5/yNDxLVY7blnPMD4N//APu7FJ14UEoppZRSSimlnrYMXOIWip/Sm8DLj7x/CXj7aUWut1oopZRSSimllFLPwuVvtfigvg18VkQ+KSIl8FeA/+Wn2uMl6MSDUkoppZRSSin1DFzmqRbbeyWeuMaDiPwd4P8BflZE3hSRv5pzDsB/CPzvwHeA/zHn/CdPK59XfuKhjQWE4RDEaCAKvnX0TUFMhpyFygVSNKRo6KPFr8qT8E1fEIOBDHe7GT47yNBviu2RzXTJ0aYCHyxNW7LpC3IwtH3Bsq/ooqPtC7y3LPuK437E3fWUzbqE1uLWgmwsxcKQejtE3A1pS9sXQOwsKQvZZtrg6NNwJ8wqVsze50hJ5swdO5LAmnSh8rM2cZQa3DqSe8tyU0OGefLvHV/KxPLsLULLUHOnnZ28N53nz49fZep6JML95Pni6A2ywC075U8WL7AKJXYjfGX0YwBSmVm09bn48k7g5u4SJ5Gjzfjc55vnMn9x/9swivxKdZ93+j2aWNCkkp+s9ghjQ+0C182w751RS5szL87mdLcClswN23K3nfJ22OPtUHPDroe8tp5Uwn61ORfvclPBxiIRNjchYnin3WHejGhChQlgQmbjC5pVhZ9Bu1/wqj8A4DAJOcrpWZjhQZydy2MYg+x6ShuZuh4AJ4mchmOQ7enUp5FEnyx0hhgNJghkIUwgjKDxJb/78reZVD2ujBibkQRs62HTlkjc7jcIphN8Do+tB+pyZF5gGntyvl9I99M30atYDudEKLlRLymLyHEacy/MOIxTDuP0XJhNU14unYC0hpRO09slR86Cs4nKDHXoXjulDcW5sIerMaZI2H4bNjpSmQnJkC1EBOMSS18jESZvddwsFyfhQzT89uTVM/uMJbgGsgjZgJjMaNxz45OHvO6vc6+dUZrIcSpZfDaxZzfEnFkmQ3bDORVGQ/1/3Dnw8vWjM+9LE2hjwSq1ABx1I3J12g6/3h/wej+c+y+5glhnipsbftzfYP3ZPQDmTc1k3PHK5HQRardteuKiBAu/dPNtptcbXnzpAc4mXhgd8+B4yhvdATu2pYsOJ0O8Pzi6zoM44TfrNZUNZAu7oxaz2xNqqGzg4NaCn9t7h/TJBoD2oCBMLH5vRL9XYJqeL49+RJscrM7enRnLbR/McIyaWPJcsaD3jnvdjJQNm1BQO48Pll3bMB11mCqehLvpFuQoTMoOZ+LQBxogCv3MYCIsm5qu38adQaLQpIqud8zDmE1fMJKK1Nlzx0ldQBbEnPYjtpVhXJXB9ILphX5TEDYFflUSk6ENw/HwyWK8IP58e3HUjjjuRmejgpOwIVi6/rQ9aNuCVVdyL+xwO7ZnwrXBkS10fhhv9clydDzBt45NLHE20Ud7kqbH8d6yu22ixkVPItGmghvlipzMSf8qNpMcQz0EQnu2zRIvSIbDzfik/77bzTA9xArGzvML5fkx2Do7bvtrHNTNyTaztizTiB+tD7gXZvzGCz9k542MXToihn+4+RQA99Mj5REFObjG/M6MF4ojUobFz1iMpKHNXVrsckjY1HUAHPoxYWxZh2ooi2wotu1yLh3Ww0vPHbHuS15fXWNyO5N3hr4hzocxc36kT/De0W/PyeZ5IRX5zOdkYR7H3PZ7tKE4PX9NJgcz/PHkBRI0qcQHizxh2Go7kLjdjTd4huO8jPVJf9V5d+54DfkFs62juTfghRSHMgrJENKV/3NIXWUZyPnir8Hv55y//Mjrm+d2m/Pv5pyfzzkXOeeXcs5/e7v9D3LOn8s5fzrn/DefYk51jQellFJKKaWUUupZ+ACLS15JOvGglFJKKaWUUko9C5eZeLjCkxQ68aCUUkoppZRSSj1lQkbyx2Pm4Ure1CQiXxORb87n82edFKWUUkoppZRS6vIykC7xGuYdnri45J9GV/KKh5zzt4BvffnLX/73nnValFJKKaWUUkqpD+JyVzwAw+KS3/go0vJRupITD0oppZRSSiml1JWWucp3T1yKTjwopZRSSimllFLPwuWveLiSdOJBKaWUUkoppZR6Bi7zOM2r/OjNK7m45KMWfQVeAIidw7ZCXjvyxtFHC8BO1ZI6SwyGkA326HS+pfOOHIUchXeaGT5bTC/kYEhVQmzmKIxZhJoQDX3nWC1rchRCZ9l0BfNNTegdvnN03jHva5brmrQqMK3BbqBYGkwP0g5FLp0hJ6GwEWvSkJjWUtkAAjnLkDdgE0sOTP3ehZCE7E8PpSTw27w/iZHM68Hg2ohZONpVSY7CvfcJnwqLpHfth8yby93TDSHxipthJBNr+EmYUUog7wQA3lztcrQZ4xrh54vxsN+9wPFqfC6+3YMVN0Zr1qHiaD1ildqz6RklPlUssUXiph1zp52x9DUP+im3D3eJhdBHS+I00fPk2K8azMTTpoJ9Y7i93OFemPEn3Ysn34sHU+Ioca1szqVrs6yxK4PEoRG472fcbyd084qFr7A9ZCOsNhWps/idRLtneK2/CcB3+5tDOZrTFuT77S2Wm+pMPP0uzHY22O33ZkXLpOhJrQOB9Mj0YWUCG18gQYjRIEHIGbKB7hqs+5L/YO9NShewNoFkUjHUmTYWQxg/7EuCwbbC0bvKW30wEgTTCQ/S5OJhup++iV74Ea+v9wnZ8KCbkpIwj2Pmccwdv8sdv3suTI5CFy83L22CkJKcvF+HinVfkvLptjvrGcfN6FzYbllhTObhVzs/1O0mFEiCQiLGZBa+xnaAwMR0p+lNwot2dmaf/Y6QCvD7I0yAybijbQt+4fptPlveoU+W56oFTSqR51pqCRwmj88GRhGAMILvh57vh/5cmq/VG/6vVngjLAFY+Zqp6/heMCd5KHd60nRou7+7ep7vrp4HYCQVuMzetOGN7oD5p4ay3ixq6iLwl/b/39Ny3Z6PdmEhw365pi49X73xOiEaprYjNo473Q619KxiyU45nLPH8zHrVFFJwSqUYDO/cfOHTGctGHiwnvCz+3cpJDGbDmH8VOh2hVQIsTSQMrfcksM0xS2HfiGE4WcqYeErYjDc3uxyr5/xXDHH2sRxVxOyYdMXTIueriuwknh555hiNPQDEgy37BKSMHaeQhIxGRAgw2ZfWL1g8a2j3xTDsd62a8tY07UFD/yEZlMOZdVcrN9Tj+gNJgi2OO0fJQBhOBltJ5gAuTewttilI0VDjEM9D9lg/NC+vduiqzlsz/fpMQ1hozf4/rSdiZ2l9457YcaPw85peiTTbtuj0Dv63tGEgnRYwcZy1I+JSWhCwWE7HtqPxwjesmsqmliyX224Hxvu9VN+prpP9AYjmW7boWYDEhnatM3ZemV6IVlYrEZkM5yjd5rpEE6gNGE4x99lmWoOw4S9cnOyrb5neLPf58fza/yovcFnR3eYvNlSzIfyvO33APiBPy2PYtqTrk2pbzsO7Io2W+IIjvoJfbSUS5BtW9xv8/OT1R7ZCXfbIZ3rXLAKNQ/8jDAtkQSf3rkPwL31lNH9gPQe22XcwmDiUAdWoaRLDt864sad5DmXZweFeXuOvtPt0vQFKQvOJKgS0stJW09rOfITchbyE05f2S6sZ8iIhwdhyMsmFvhtm9RvClif35EBbDfUZ+mGOp+iIdtM40saX75/5Ep91HK++Gu4L0MXl1RKKaWUUkoppdSTSebcf+hegC4uqZRSSimllFJKqYvIl1vj4QrfaqETD0oppZRSSiml1NOmT7VQSimllFJKKaXUR0n0qRZKKaWUUkoppZT6yOitFkoppZRSSimllPpIZODyi0teSVf+cZpKKaWUUkoppdTVk5F8iZc+TlMppZRSSimllFKXcvlbLa7k4zSv/BUPm1Bg/DYbjaVYgV1azMrS9gU5C9Oih40lecvGF7i1nISPwZCTIFG4v5pyHMfYHsQlmARsEZn7EUf9mOAtcV2QFwViIPWWritoNhWxteRVQRcci7amb0rswmLXhnIBxRJsC7Yd0ipByEkwZNz24a3SC9Yk8jQQsqHxJQA+v/9hkl4w7dnvrNryQuXXecer/gYSEuWxwNohUXgnzt4zTKwMJp49QSoTODqanKZp0wHQRkeo4bvd86xTRTH2JBJ9tByvR9judB97N5f0i/PpdjZRmsj9fkJ7XPMPup0zn+ciYRFSFAwGZyKrUPF2u4s/rvBTYdMVHKZ+2J8kjtOI/XKNLRKJoT4s5iOWccT321u02QJg2kDe9eyX6/MFsba4RnDtUBZvtPvcWc5wx461L8kGkoO+KYYn5VSJMIaJGTL9R+tPkg2QhvhNgO8sn2ezqs5E42eZnVGLT4Y+DekqbURWw+/yyKGY2o51X2I6IUWD6QQymA78TqLZnJZvBnIUsgHjhZUvicFgW+FuXCG94BpoPiYL3nzUTC+4jfCO37twmA/wXOdzjruaO5sptfWsfIkPluM4Zhlrbve73O53z4VJvaXzl5yXTkLOp23rKlS0XYEPli4N+zpaj/Deng+7tviNw/ht2LbC9IJPFtsO23znWPmS+jhz/JmaSvxJcGOGOnqUmpNtYTScU8ZHjM+8vHtMvF+z41p+oWzYr9bsug3rXGJMxmfDYSp4K+6CgClLSPDH7Uv8cfvS+exm4VvHv8w/7m4B8PZ6hxdHx/zjzc8AsNzUjEcdcTTk/dXFAa8uDk53IJmdumURarprDwvNYU3i1+rT9vxhGzm6I5DgbjfDB8un67ssN/WwK294ZzMjYjjsJpjtqCRuHKVEAO51M1Kd+OrkVQoXyQZWi5qfGR9yv5/Q9UM6s4CfCO31glSc5vUdv4vbNoMpDunLBhZdTWgK3lrucqedMTEdhYk8aCa0sWDdlTiTiN1w3J8fzSlcJCSL9EItESRT20CXHDELpt8ewyn0O5BaC+tt+oIgCY7CmNQ67rUzYjt8Zjen9U9dUBQkgrWnjY1rQaKAgEQwHqQ3mM5gGyElIW77rTYWSHj8rjvvmDf1mW0CtN1QseLGEf0jY5ck9F3BfT/jLX/tZLORfDIeir3Bt47Gl1QPDGZjubOZ0rUlG1+y6krm69Fj05NaRyVD3M5EmpxZh4p9u4IwpOMojMEL2Q75Dr3FLc6Or4qFkB30y3Ionx4erCfEamiznYmPjX+Rau73U/7s7msn28bvwOubAx7cn/Facx0jmfLOknIO98IO/9betwH4/7btCsDnb92hvTWmvge1hKHNSnC/HZOyUKyG4wZw2A1lcftwF0nwoJngc+Be3OFeP+MnzTUkJrLAjXKFlcz8aEz91gKMGfLTyFBPWsvcj1iGGhqHWWzPSQtSJuSRwYgEYRMLfrzeZ91UWJPYr9aITUgUJG/b516410/xwSLhCedvBtsJtfHYTri9rSOLUBP6oX1JG4dtzo+ZEw/jG8Y7phNib5EodMHRBf1/WPWM5Xzx1xWmZ5pSSimllFJKKfW0fYzWeNCJB6WUUkoppZRS6hm41OM0r/BFDzrxoJRSSimllFJKPW05Q7rMJQ9Xd+ZBJx6UUkoppZRSSqln4WNyxcOVX1xSKaWUUkoppZS6ch6u8XDR10Afp6mUUkoppZRSSqknE/Ll1ngYLnm4ko/T1IkHpZRSSimllFLqWfiY3GqhEw9KKaWUUkoppdTTloF0hWcTLuFKrvEgIl8TkW/O5/NnnRSllFJKKaWUUuqDyfniryvsSk485Jy/lXP++u7uLp13mB58DpjWUKygWAluLfS9QyRTW49dG1hbuugoFqf7ir0lewu9YXk8xmeLRBhNOsqRx7nEylesfEXyBru0FMcWbILGEtYFvilg7XALQ9sXtF0BK0u5EIo1VMeZcgHGg3gBQAKQQSTjkwXAdEJpI3YUAFj3JZvcYSTT5fCe5SFRMJ2cvHebxGZVXags/cb9/+3deZAk6Vnf8e+TV1X1PTN7zGq1ulYSksACWQIjDMYYRIAxVgCBzSkMJmREIMBB2HIgg7GwHRAQGHBwWIbAgDnMbRljWyGQBFohJCEk5F3tfczO2XfdlefjP7J6umdndnd6p4/snt8noqO7jqx6M3+ZT2a/9VYmnxrfSZBXJF0IRwFUxrn85NNPZBBkV6/4vrb9ntXiLBUVvayFR/DQ5DQrxQIznZRRldGJc8abbazcnv6O+R7hMLzqdQfjFpUby+M5oo2IP+l9+pXNqYyLZUQ1qad95exFiirgTHeJsBeSz8BklPBoPg/ASxdXuVgsciIaATCpYrpVSbWZMKoShkWLi+UC0UtehJuxeHLIfDi5ql1BFhANIRo7VPBw9xZ6q3O0Vo1JHuMBFG3Dh3E9QeRUMcTTmX5wcDtWcTm7IIMH1m+FXnzF+xTzFXFQUlYBkzIixKnciLsBmO880QxxUNAbt4mGRlUY4aRePlEK1UxJNkzq9zLHK8Pd8AjCsTHIWlRZSDSCJ8uEIK2nz92QGxeNIJzAmezU9U9U3fiyXx3OsjGcYTbKyKqQdBKzkc+ymXcu/zyVTQLSYncD4qyAakd718Yz5HlIPonYyGYASPst/BrrUzgKsG5Me72uK5NRQjSGrIjorFfkHuLdmH7apr2a89SXCIJ6I3j/+Nbt+1JobzhWOlVkvHxhmfZywGY+w4lghue3N4mtpG05+TBmrZxlrZzlT/uvwAuDKCJK4cP9u/lw/+6r2ryRdrjn4kv44ODlAFxcX+AFrTU+MbyrnofpvKZL9fa80ptjpTe3/QJlPROtoCBfqOc7TI3AnlJbp9v3/NmKoDA+uXwHo3HCTJAx3mwzrhIsNy70Flgr5rgwWGA9nZlmYsyH47p9w3loVZyOekyyGCvBRxEvaK1xcTTPuL+jfoeQzRv5bABpymbV4ZHJbST96ePFdgDjPIZRyHpvhtXxLKUbYeD0B23WJx1G/TaTMsKLgNIDFqIJZRkwKmKCzJh4iBUB7TBnUsbkRXi5JlZx/WNpQDioD1csDwhSYzWdg0nAyngWJiEVFeFYtWrXpicr29qGoN52rDTc6u3aSghyI8iMIAevjLKq85gUEWF27ZdO04jJKLnivjCsyCYRF8sBNgnx/MrD0HISsp7NslosXHH/ME3qdbYIYBzSn7SYuQTRwLi4uUDZjxmmCZMsJlu+uqYBBP36GGEuTAGIDUZlQmgOhTHIE1Ym8wTjEKsgTKGcRCTdK9er9np9PBdtRoRpvb0Ohy2KOSfIIauuXTtXigWeHC7xhtkHLt83d6HgieEJgtWERzZv4Ux6Cr+0SnvDeSy9lbujOe7NR3ys94LL03z2iSdIFwI6a3VmD0yeR5jCxniGJCxpbTjB9JDxYq9ejvlamyCtGKYJ58oxj2e3sJLOcaa/RNjPsMo5EQ+Jw5LgYgt/5Ey9zPKKaQkhnBgXhgv1sVg3JN6cbpMlRK3iitpuaV1rz/UXKYYJYeAsRPVxVJBZfexTQDAOWB7PU1V2xfHgtVhVL/f5YIyVsJzX85ZVEdV0PQpHAWF6dR3oV9F0Pa7X6XBiMAkIMmOcxozT+KppRA7OLjodjnjng75qISIiIiIiInLQnCPdmbAb6ngQEREREREROQy7OcfDEe6jUMeDiIiIiIiIyEFzB6+e/XnbE+xbU/bbkTzHg4iIiIiIiMiRt/vzO7zJzD664+fNh9n866URDyIiIiIiIiKHYfdftfgVd//JfWrNvlHHg4iIiIiIiMhB2/WVKo7uVy3U8SAiIiIiIiJyGHRVCxERERERERHZNzdJx8ORP7nkKEsIcmOtmhBOjGgMcR+CAvJJ3a8S4LQ2jWQjJCtDZi9uh+t5iA1DwokRrsSEOEEOt8wPieOSVpLTy1p0szbVJKK1YbQ2IIgqko2QsBsRdCPifkA0NPJJRJ5FxL2QeADxAMLMSXpOUNbtAggKA4O8ChmkCQDR2JiJcszq9k3SmLNFTmwlAy+edhlYCdHILt9ur+d4N7mu5eejiE/27sTykva6Ew8MK4wz2amnnSYal8TDK8++OigT2svbq9PgpQucKYZ0Jx2oYDPvcDY7SScu6HrOqfaIzhMxVm6/RhKUBOOrV8l0eYbNrM3GeIaka9xz6cVXPB6MQj40vptwGALwms7jLCVjVlYXiIZGlYB3Yz4yrqf7gsUHOZ+fIA4KyjxgWLR4JF8i7oV0iw4L0Zj7JnfSe81pqtmE0/N9WkF+VbuCiZEMIMidaAznNxdJzsfMnXPSIsQqKNtGtBlipUHgeAj9sg3A6mQGywLCYT3PYe5sLs8T9wJe8r+//fL72FxO6QHusDGeYSEaMypi4gFQWf3aW8u9aJN22ySb4OOIpAdkAThYUsFge5uoKsPMqSIIJzDKYhhEJH24Pz1NPKy3p55f37okzywe1sv5gcHt1z1NkNqzP+lZdDdmGfTaJEHBKE+oegkr2Txr6RyjosWoaF09kRt5Ee7qfaKxUU62+7LXhrOUaUg1jnhysARA0I2uuW9NekayGXDyk4P67fsx4QSGacLsk0O6xQydCyGbww5JN+PEA2NSjy9PH0d1IXn35mdcvm/uQsXivV08CihmjDtbGyR9eKJ/gtwL7kg2yT3k1nBIcj7hfHGC88UJfuf+z8ImIdVohBt8dPUuPrp611VtXhvMsvLQKT608sK6zRc73Br1eKh3KwDhWsxw2Cabr7fvtN8i7W8v63AUsDnpcEfSperU7Q8ndrn+b9laA2YuZASTgOEDSxSjmH7VpnUhppe3CTKj3+3wSHo7y8sLnNk8sb1sqF97eXO+XtaUjAYtwgkEk4A74w2GWQv628szqKAKIZ8DX5xjuZznocFtdFbqtnlerxtWwTiNsdIo1tv0Jy1WigXCoKLoJ6yPZrDNmLXRLJYFVBixlaTjmM20QzQy1qoZLDc6Yc6oiMnyiK1y69OZD9KAeGisVUMsN+I+XBzPE44D1oazBOOAQZUSja9et+SZWWmEqeG+XWuCoq49Vk2PWQwsN8IMwgyqPKQs6vV6VMSEKVfsh7YU4/iKmgAQhSXlKObJokWQGzbeUWcMGIVcmswzqq7c70yyuN4+soBwEPkN0tUAABplSURBVDIYtJk7VxD3IT8/Q7wWMRonZOOYzsVr1664W7d5MRqRlREzFjIqYiZVDFnAymiOlfEsUd8IJ3W9Dgch0ejK12mvV4QTaK0ayaDeJsphTJVUxGNnPZ255vtfyJe40Fvg7mgOgDNFn/bZAU9uLNG5aCwvL/CRtRdQbm4yc7Hg7KTejv9i/GLuX7/t8ut8WvsCRcforBQMq4SP9V5AmMFg1KKoAmYv5IST+rn9tdl6ufcDwklBmkbcM34h9w7uZGU8y/L6ApYVRBPnZDgkDkrmzhrVeAy9Pkm3uDz/0ch4cvUEl/rzJJvQXq/vb21CkhR4uX38Fo4DViezbKzPQhpcrmueh0Qjw8p6+cZDWB7OUZYB9iz/d1lV/yRWHw+vZTuW83T9DYfBFceVW9aqGayAIKvrbJhCMA6xwkgnMekkvnoikYPiDlV1/T9HuJNCIx5EREREREREDsMR7kzYDXU8iIiIiIiIiByG3XQ8HOE+CnU8iIiIiIiIiBw0991dTvMIU8eDiIiIiIiIyAFzwL161uddOcXRpI4HERERERERkYPm7G7Ew9Htdzj6V7UQEREREREROXq8/rrF9f7UPQ9vMrOP7vh58yHPxHXRiAcRERERERGRg+bUl8nczfPhV9z9J/enQftHHQ8iIiIiIiIiB85vmstpHvmvWqRpRJDDShkSTiAeObMXnaQHnoZkWcSgaBH3oLUGo0lCmO4INzeCzAgnRnvZGFUJ8QDumOkThSWduKA76XBxYwHLjIXHnTADCyDpQrJptFYD5p+A+TOOlwHFMKK9DEnXiQeOlYBBZ82JB/XbegA49CctRuPW9M76V5mHjLIEBx7MbyEwZ/IMK2SQGe21nbcr4v71RWtZwIPrt2B5ydy5jHAElhtPjE8+7TTJkxskm+kV9y2P5znxwHZvXf+ugI+np+n2O8Qj6AQ5j49OAfBQvsBSMubUvSVhvuM1RnOE2dXvN/9QyJPrJ+j2OyRduHDuyra11ow/Xnslcbee55fFm7xq/jzhSgJWPyfuhfz5xt0AfG77cc6kJ0mrmGoYs5rN8sf9TyfuwbhMOBEP+UTvLjZeHhCkBSdbI9Iqvqpd0RjaGxVWQVBAttZm7iyc+vAKkyzGKqCCzgqQB3VdMYitBODM8iksN8LporQSwo2I1gacOt3dXt4zOVkR4W5s9GdYjMb00zatLkST+mfL+XQRG4S0uk7YD5m9WBEN6veOWgXJZsCPr99NhVFmIRh1Ow0Ggw7JZkDSc+4fP49wBLMXC9bLmadbFWQXohG0150z/RPXP83k2Z/zrHox3k2IrSQrQiw3unmHzazN8niW5fHsVZNYBUUR7uptoiFYb7sve9Br40VAOAw5e6HeZuNuQJFe3d/dWYEgh/DiOgCt5ZCkC6N+i3C1z8PDW1l6uCJbbxNeWCdeH7G5Y708OTNirRryibU7t19zOaf6xH3E5zepImhbTtJ1ziyf5N68YCkcsZrPcWtQsfSAczY7yYPj08zeM1tvM0C2CBfOn+DC+aszG250uP1DcPaR2+r3u1AXm7MbSzyUD4i7RrXaIszq2h10I4Lu9rx3LhndfofnJ+sQ18+JBuBuV7xPe2362Ic/RdKDpQcNhhHn0hO012AlnSMeGN6Lef/ySwmXE0YPLwL1fmbiMQ/lA8oLHawf0ata+DAiHkDUD1gKRiRRcXmeoxTigRONnSqE/suXWC/mONdfZOGxcd2ovG6jFTDutYl7AfFmyGic8MjkNsoqIN4I6fc7xL2AzWGHYBwQW0nuIb6ZsDnqEA3gXH4CHDphxvpkhjyr22alXa7f7VUjGsLjRUw0DAjTejmHY2PQaxONjJWqJLjG/kOemTnEfSir7WOGeADhpN4mcShbgNX3RUNguF0bhlnr8j7sKsMQK65cnydZTDgIOVcsEU6MYOfjgRNkAU90TxDY9vFE5UaZh8Q9CIcByYZR9WNmH+0SpjD3eEA8gHIc4YOYzqVrNyeaHn+1LaeXt5ixmHEe80R2C9Go3r+e31wk7tfzHuT1+3VWrjz+mj2fUSXQXoe4XxDk0L4QEWQBSb+in7Wu+f5PTk7Q29yuWx9Jn0cwnDDqdVh6pMS6MQ8/drpu46Uh/bwNwHs3XsHK8uLl6U6GA/JZI1kf82RxinvXTuMB5GnE+mMniPsZ4XRTTc7Xxy5x3wjyimKY8Edrr+b+7m2s9WcpuwlEAfGgZD6ckJYRp+6tAy0uLROO8/qYt4JwDMGjM/S7HWaWnRMP1AdwSw/nzLYyfEeWYQrrkxnC820InKIMuDhZxIYh0aiuHUkPWhuwuj5P2U2IRtfO7fJrZvVx0nKxAAbrab3vGuQtPA8uv280vHralWKBMId4WOdmJQSpEWZQjiLKkT6HlcPlVXXdP0e5k0JbmoiIiIiIiMhBqy9rcditOBDqeBARERERERE5cL67q1ocYep4EBERERERETlo7nhZ7uLpR7eTQh0PIiIiIiIiIofBd3FVC9TxICIiIiIiIiLXy8F381WLo9vvoI4HERERERERkYPmVLsa8eDsZnREsxz5y2mKiIiIiIiIHDGrJQUTf5bryU6NfUhVdzxs7Gur9ok6HkREREREREQOkLv7i3gFj3H/dT3/ce7nxbwCP6JnmNRXLUREREREREQO2Cf4YLDIyWriI9o287TPG/uQAT3O8diRHThwZBu+xQzcYK2aIR5APKyYOzMmHoBlRtlNWBnPEuaQz9XTzFxKST2vb7hNf0M0rv8MUzjd7pLlEWVlDEYtyiIEN6oIohEEYUWYQlCAFUAF7fUSzwKsCAgqiCZOPHLiQUnRNsoEgq23jeqOqjSL2eqz8gBmwowoKRmnMUlUcqlYZDEaMx88fVRJr27zlmCYXXH7mXjkTLIYG2cEWf2doaCASRk//TSr65St8Ir7ulkb29H3NjkJT+anwI144CzFI86PFsirgHP5CW5tDZg9NyacbE8zTBPc2BpCdFmr66SjmDILSXpOfOnKtsUDONdfxKZXojkZxLyu8xhBZngAVtbL50x3CYAXRW36RZtRmWCTgEHe4onRSayC2SjlZDhkVCaM7iwZvHCW+ejaCzNMob1aEI1KPADygNkLBX7+ElUZMHe+xCqoposqaJVUCSyF9XCqspsQFPX6VC93x3IjyOHvP/++7fmPC5KowN0o0pC56UILcifItldhgE6YEw0DWj0nnBgYxENIBk4UVURjWAxHhFbhbljglDP18i7zgGgESb/ifLpINAEPjLVy/torguyOQTZvTIrr7+8NUhj7dW7MT/e2DnG/rh9pERGO6r9XhnP00ja9tH3VNOEoIB/vrl86yCBId6yMmwlhPyLuGvTqbTbZBM+vrmUn7q/X6eLsufp5vbp+emX4ZpfHeydprxdQGMWZs1hRkVbbdWCpNeG+bJbNYefyfa1Hl4G6Xp24b0S3nGHxsRRfbXF/dgexFZwfLxFbwPyZlEkV8+H1F3L6gz0WHp/OUw7BRkywcXU9DFdjTrzvcdoX6g08GkG/7DAZJvx1dgcnHqyI+gHxqOLj2YTWSkBrZZqD53gAVREwqWJsUL9GZwWyov57UNXLJB5O6/LSIpbX271V8KneaYIMlkdzRAOYORfy2KeeR+eScWJaPoJJwJP5KT6e3klrJSDZCHiyOIWlAZ01J8jhYrFIKyyo2nUBDycVs+cL5s/WO6uiXWe6fGmRaG1A7gWU05wNgvWY1hq0V6DYaLOZzxCFJcmmwcU20RCytTaETmwlvaJNshGQZRFBDvf0XoZNX2+QJhTDqN5/V/U6ZUW9bw4zOF8sYVW9TyjLgGgIVRpiBVwsZkj6bO/b5fo95XOzsl3XDY/qHKDezxSdOge8PvYCGGcx0dgvH9PsFPVDeMr2Hli93k08oYqcqrN9CTkLnCA1RpOEthXb7akCLKhI+hANjdYGxOsRdmmNpUcKZpad1sZ0Wx0HzF4quJZkUP/uljOsjmYJMNaGs/xV7y6STSPfbJGtdGj16nWuiur958kPnL3ideK1IfHA6ayWtC8MCPJ6+082jdmHNxhlyTXfv1+08XT72Onh9HaqcxcINmLiQUncD5h5vK419ug5sunBw/nhAgy3p0usJJ+F4OI6/bJNt9+h1XW8NFjICc+uXp7XuLednwcGacCTgyXWBrOMBy3CYYDHIVY5S+GQtIiIN7f3OVUSEg+dIDfaG/Vxj/djkp7TWq0PmjuPrHHb7ODydgx17czKkGgIJBVh4KynnXq7TiGa1Nt30nd8GGFZQNy/5mK7zK0+3nskvZ1oBKvjWQAGeXL5vVub9THsVcu+ahNk222zEsLJ9PjJ7coDKZEDdr2jHo76aAfQiAcRERERERGRQ/Fsox6Ow2gHOAYjHkRERERERESOomcb9XAcRjuARjyIiIiIiIiIHJqnG/VwXEY7wBEd8WBmX2lm7+x2u4fdFBEREREREZHn7OlGPRyX0Q5wRDse3P1/uvubFxcXD7spIiIiIiIiIjfkE3wwGLDJxOszz2+Ndvg49xzJ/9mf6ljMhIiIiIiIiMhR9dRRD8dptAPoHA8iIiIiIiIih27rXA8bvnJszu2w5djMiIiIiIiIiMhRtTXq4eN84FiNdgCwozwvZrYCPHHY7Wi4W4DVw26EXKY8mkNZNIeyaA5l0SzKoxmUQ7Moj+Y4qCxe6O63HsD7NIKZGfCPgf+ujgc5Mszso+7+usNuh9SUR3Moi+ZQFs2hLJpFeTSDcmgW5dEcykJ2Q1+1EBEREREREZF9o44HEREREREREdk36ng4/t552A2QKyiP5lAWzaEsmkNZNIvyaAbl0CzKozmUhVw3neNBRERERERERPaNRjyIiIiIiIiIyL5Rx4OIiIiIiIiI7Bt1PBwDZvbKw26D1MzsRWbWnv6t7euQmdn8jr/tMNtyM1ONahbVqeZQjWoO1anmUI1qFtUp2SvamI84M/tPwB+Z2YsOuSk3NTP7EjP7C+CngN8HcPfqcFt18zKzLzez9wI/Y2ZvB3Cd0OZQqEY1h+pUc6hGNYvqVDOoRjWL6pTsNXU8HDHX6Gk8CWwAX2JmrUNo0k3PzO4C3gH8qLu/EZg3s68+5GbdlMwsMLPvoM7jx4CfAV5vZt92uC27eahGNZPqVDOoRjWD6lTzqEY1h+qU7JfosBsg18/MbKun0cxCdy+BDwHvA75x+vf/O7wW3jx2ZgG8BPgE8J7p7QvAQ2YWu3t+KA28Sbl7ZWZngK9394cBzOw9wNLhtuzmoBrVLKpTzaMadfhUp5pDNaqZVKdkv2jEwxFhZt8F/J6Zfa+ZPc/dSzNLgC+jHo72XuDrzOyrzezWQ23sMbcji39uZkvAp4AT1EPRHqMuzP8a+PVDbOZNw8y+08y+Zsdd7wEeNbNwevuVgIYG7jPVqGZRnWoO1ajmUJ1qDtWoZlGdkoOgjocjwMy+CvgW4KeBzwS+38xe6+4Z8FF3XwUeAr4b+PeATvyyT56SxauB/wAsuPvXAX8B/KK7vwH4JuC1ZvaF0+mUyR4zs3kz+3ngB4FfNrOtEVzF9DuhW98LbVFns3Na5bGHVKOaRXWqGVSjmkV1qjlUo5pDdUoOkjoejoa/Bfycu78X+CHgMeAt08e+wsz+DHgb8AfUQwR7h9HIm8RTs3gUePv0sQXgXoDpsMA/BF48va1e4j3m7n3g/e5+mnpZ/8z0IZs+7mYWA3cBHzOz55vZt289dhhtPsZUo5pFdaoBVKMaR3WqOVSjGkJ1Sg6SOh4a5Kk9hztuPwp8A4C7PwH8L2DJzF5PfebfD7r7Z7n7m4DT1MOh5AbsIos/BObM7POmj/0LM/syq8/++8XAnx9cq4+vZ8jjXdPf3wt8vZm9bDp0dqvH/tOAU9SfYL1r+rd66feIalSzqE41h2pUc6hONYdqVLOoTslBU8dDs8Q7b+zoSfwdYGRmb5zevgD8CfB3gF9z97ftmOyr3P2v9r2lx99usngf8Hnu/pvAfwO+Hrgb+FJ3f+BgmnvsXTMPdx+aWeDuF4GfBX5hen8xferdwKuoPy35Cnf/0Z3Ty+6Y2S3T3yGoRh22XebxPlSn9s3TZaEadfDsKZfEVJ06PLvM4n2oRu2rp8tDdUoOijoeGsDMXmdmvw38mJl9/taBi5lt5bNBfdKjt5iZuXsXmAXa0zPPhlvPdffJYczDcfEcs5gBFgHc/WeBN7v7t7n7uUOYhWPlmfLYkQkA7v6vgBeb2evN7LSZvYL6DNmf7+5vcfcLBz8HR5/VZszsN6iHIOP1WeB3ftqhGnVAbiAP1ak99mxZqEYdLDP7m1afef8dtn1CPNWpQ/Acs1CN2ifPlIfqlBwkdTwcoukG/yPAz1MPM7sEfBfwAqgvZzN9agf4v9Q9wu80s+cBrwGK6fPKHc+V52APsrh8kOLu6QE2/Vi6njymB4pzTA9Upn4UuAf4U+C0uz/u7h8+2NYfL14bTW/eamZvgcuXodv6tEM16oDcYB6qU3vo2bJQjToY0/3F24HfAH7T3d+0owMoUJ06OHuQhWrUHrqePFSn5CCp4+EQTQvw+4E3uPsvA79Efamala3nmNk7qHuFbwe+j/ofsF8HNoEfOeg2H1fKoll2kcfvAJ8xvf3lwFuBnwA+3d3fd8DNPpamBy53UK/v/5T6E6qlHQcv2i4OkPJojuvI4t+iGrXvpvuLBPiAu/8CgJm9xurvp/v09g+j7WLfKYtmuc48dCwlB8b09ZyDZfUlgSbu/tRL0nwB9XfazlNfruZ/AB8B3gn8oLs/vOO5Mzs+ZZHnSFk0y43mYWavAvru/uSBNvyY2ZnD9BORanr/H1CPOnkbMAR+DlgF/gvwA+7+yI7X0HaxR5RHc9xoFqpRe+ep+wszmwV+l/pqCH+H+p/ZLvDbwHvQ/nvfKItmudE8VKdkP2nEwwGx+jq5v0fdy/vPzOzE9P6tDNaBf+Lur6f+LtW3Are6+ze4+8M7v4Ol4nxjlEWz7EEeWydzu087yufuWjns+Mfq5cCj7n4WeDfwncC73H04zeERbRd7S3k0xx5koRq1R55uf+HuQ+BXgc8Evs/d/wH1MPE3Aqe0/957yqJZ9iAP1SnZd+p4ODgZ9dmTv4n6k9uvhe1zB7j7vV5fzxjqIebzQA6Xv4el7x3uHWXRLDeaR3ngLT6erpnD1Hng5Wb2LuDHqXN4bOtBbRf7Qnk0x41moRq1d542C3f/NeAfufufTu96D3AS7b/3i7JolhvNQ3VK9p06HvaRmb3JzL5w+p3PlPryNO8BHgReN/2k5FrXwf1S6mz6cMWJDeU5UhbNojya4XpzoO7sOU99ffXXuvtXAs83s9eCctgryqM5lEVz7GZ/4e7rOyZ9A/X32AegLPaCsmgW5SFHjc7xsMem/yidpj5RTgU8Qn25pu9x99Xpc14GfAv1d7D+3fS+FvAF1GeSPQf8S3e//+Dn4PhQFs2iPJphlzmk7v7D0/sWvb7kGde6Lc+N8mgOZdEcN7C/CIDPB34KOAO8TfuLG6MsmkV5yFGmEQ97yLYvZzYPnHP3L6b+vuc68J+3nufuDwF/CTzPzF46/ceqoj7hy79x93+oYnBjlEWzKI9meA453DHNocP0Mme2fZ17/WN1g5RHcyiL5riB/UWb+lPcc9T7izdqf3FjlEWzKA856qLDbsBxYPVlad4BhGb2R8ACUAK4e2Fm3w2cN7MvdPf3T+//fTN7JfB/gDngi9z9k8AnD2Umjgll0SzKoxn2KgfgUxqSeeOUR3Moi+bYoyz+nrvfR/0psDxHyqJZlIccFxrxcIOsvmzNXwIngIeBH6Y+WcsXmdnnwOXr6L4D+KEd030t8HbgvcCr3f1TB9vy40dZNIvyaAbl0CzKozmURXPsYRb3HWzLjx9l0SzKQ44TnePhBpnZFwAvcvdfnd7+WepPZsfAW939tdPhl7cBP039narHptPh7n92SE0/dpRFsyiPZlAOzaI8mkNZNIeyaA5l0SzKQ44TjXi4cX8J/JZNr38L3AO8wN3/K/WQqLdOh18+Hyjd/TGoC4GKwZ5TFs2iPJpBOTSL8mgOZdEcyqI5lEWzKA85NtTxcIPcfeTuqW9f//YNwMr0728FXmlmfwj8BvCxw2jjzUJZNIvyaAbl0CzKozmURXMoi+ZQFs2iPOQ40ckl98i0J9KB24F3Te/uA98PfAbwmLufO6Tm3VSURbMoj2ZQDs2iPJpDWTSHsmgOZdEsykOOA4142DsVEAOrwKunvY8/AFTu/gEVgwOlLJpFeTSDcmgW5dEcyqI5lEVzKItmUR5y5OnkknvIzD4X+OD055fc/RcPuUk3LWXRLMqjGZRDsyiP5lAWzaEsmkNZNIvykKNOHQ97yMyeD3wz8BPunh52e25myqJZlEczKIdmUR7NoSyaQ1k0h7JoFuUhR506HkRERERERERk3+gcDyIiIiIiIiKyb9TxICIiIiIiIiL7Rh0PIiIiIiIiIrJv1PEgIiIiIiIiIvtGHQ8iIiIiIiIism+iw26AiIjIzcrMTgF/PL15GiiBlentkbt/3qE0TERERGQP6XKaIiIiDWBmPwQM3P3HD7stIiIiIntJX7UQERFpIDMbTH//XTN7v5n9lpk9aGY/YmbfaGYfNrNPmtnd0+fdama/a2Yfmf787cOdAxEREZGaOh5ERESa7zOB7wH+BvDNwMvd/XOAXwDeOn3OTwH/0d0/G/ia6WMiIiIih07neBAREWm+j7j7BQAzewR49/T+TwJfNP37S4BXmdnWNAtmNu/u/QNtqYiIiMhTqONBRESk+dIdf1c7blds78sD4PXuPj7IhomIiIg8G33VQkRE5Hh4N/BdWzfM7LMOsS0iIiIil6njQURE5Hj4buB1ZvbXZnYf8B2H3SARERER0OU0RURERERERGQfacSDiIiIiIiIiOwbdTyIiIiIiIiIyL5Rx4OIiIiIiIiI7Bt1PIiIiIiIiIjIvlHHg4iIiIiIiIjsG3U8iIiIiIiIiMi+UceDiIiIiIiIiOwbdTyIiIiIiIiIyL75/zWXCmc+9qchAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "datas['SizeDist'].plot(x='Time', robust=True, yscale='log', norm=mpl.colors.LogNorm())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_weekly = df.resample('w').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "diam = df_weekly.columns\n",
    "diam = ([float(ii) for ii in diam])\n",
    "time = df_weekly.index\n",
    "data_w = xr.DataArray(df_weekly.values,dims={'Time':time, 'Diameter':diam})\n",
    "datas_w = xr.Dataset({'SizeDist':data_w})\n",
    "datas_w['Time']=time\n",
    "datas_w['Diameter']=diam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fec9516aeb8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "datas_w['SizeDist'].plot(x='Time', robust=True, yscale='log', norm=mpl.colors.LogNorm())"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}