Modelling COVID in Alberta.ipynb

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    " "
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size = \"6\">The Spread of Coronavirus in Alberta: A Machine Learning Story</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook:\n",
    "\n",
    "<font size = \"5\">Numbers! Python! SVM! Graphs! Action!</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b><font size = \"5\">Question of the day: Can the spread of COVID 19 in Alberta be modelled and future trends predicted using current data? And can it be simple enough that even I can do it?</b></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size = \"4\">First, a brief introduction into Machine Learning:\n",
    "\n",
    "Machine Learning: data -> algorithm -> learning -> learned algorithm\n",
    "\n",
    "Then: learned algorithm -> prediction, knowing only original data\n",
    "</font>\n",
    "\n",
    "<font size = \"5\">Today, we're gonna give algorithm the new cases per day in Alberta since all of this started, and see if it can tell us what the future holds!</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<font size = \"7\">Let's Rumble!</font>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Credit goes to a live session from Simplilearn, an education company that collaborates with Purdue University and IBM. https://www.youtube.com/watch?v=sHWKN5dakPw\n",
    "\n",
    "Dataset is the Novel Coronavirus Dataset from Kaggle, adapted from JHU: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](surprisetool.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size = \"5\">Prepare the data. Make sure it is clean. Pretty. Workable. Well-seasoned.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('time_series_covid_19_confirmed.csv') #read in the csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>1/30/20</th>\n",
       "      <th>1/31/20</th>\n",
       "      <th>...</th>\n",
       "      <th>`4/7/20</th>\n",
       "      <th>`4/8/20</th>\n",
       "      <th>`4/9/20</th>\n",
       "      <th>`4/10/20</th>\n",
       "      <th>`4/11/20</th>\n",
       "      <th>`4/12/20</th>\n",
       "      <th>4/13/20</th>\n",
       "      <th>4/14/20</th>\n",
       "      <th>4/15/20</th>\n",
       "      <th>4/16/20</th>\n",
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       "    <tr>\n",
       "      <th>Province/State</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",
       "      <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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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alberta</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1373</td>\n",
       "      <td>1373</td>\n",
       "      <td>1423</td>\n",
       "      <td>1451</td>\n",
       "      <td>1567</td>\n",
       "      <td>1567</td>\n",
       "      <td>1732</td>\n",
       "      <td>1870</td>\n",
       "      <td>1870</td>\n",
       "      <td>1996</td>\n",
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       "    <tr>\n",
       "      <th>British Columbia</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1266</td>\n",
       "      <td>1291</td>\n",
       "      <td>1336</td>\n",
       "      <td>1370</td>\n",
       "      <td>1445</td>\n",
       "      <td>1445</td>\n",
       "      <td>1490</td>\n",
       "      <td>1490</td>\n",
       "      <td>1517</td>\n",
       "      <td>1561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manitoba</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>217</td>\n",
       "      <td>217</td>\n",
       "      <td>221</td>\n",
       "      <td>230</td>\n",
       "      <td>243</td>\n",
       "      <td>242</td>\n",
       "      <td>246</td>\n",
       "      <td>246</td>\n",
       "      <td>246</td>\n",
       "      <td>250</td>\n",
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       "    <tr>\n",
       "      <th>New Brunswick</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>108</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>114</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
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       "    <tr>\n",
       "      <th>Newfoundland and Labrador</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>232</td>\n",
       "      <td>239</td>\n",
       "      <td>241</td>\n",
       "      <td>242</td>\n",
       "      <td>244</td>\n",
       "      <td>244</td>\n",
       "      <td>247</td>\n",
       "      <td>252</td>\n",
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       "    <tr>\n",
       "      <th>Nova Scotia</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>310</td>\n",
       "      <td>310</td>\n",
       "      <td>342</td>\n",
       "      <td>407</td>\n",
       "      <td>428</td>\n",
       "      <td>445</td>\n",
       "      <td>474</td>\n",
       "      <td>517</td>\n",
       "      <td>549</td>\n",
       "      <td>579</td>\n",
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       "    <tr>\n",
       "      <th>Ontario</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4726</td>\n",
       "      <td>5276</td>\n",
       "      <td>5759</td>\n",
       "      <td>6237</td>\n",
       "      <td>6648</td>\n",
       "      <td>7049</td>\n",
       "      <td>7470</td>\n",
       "      <td>7953</td>\n",
       "      <td>8447</td>\n",
       "      <td>9840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prince Edward Island</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>Quebec</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9340</td>\n",
       "      <td>10031</td>\n",
       "      <td>10912</td>\n",
       "      <td>11677</td>\n",
       "      <td>12292</td>\n",
       "      <td>12846</td>\n",
       "      <td>13557</td>\n",
       "      <td>14248</td>\n",
       "      <td>14860</td>\n",
       "      <td>15857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saskatchewan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>260</td>\n",
       "      <td>260</td>\n",
       "      <td>271</td>\n",
       "      <td>285</td>\n",
       "      <td>289</td>\n",
       "      <td>298</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>304</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northwest Territories</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>Yukon</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>12 rows × 86 columns</p>\n",
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      "text/plain": [
       "                           1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  \\\n",
       "Province/State                                                           \n",
       "Alberta                          0        0        0        0        0   \n",
       "British Columbia                 0        0        0        0        0   \n",
       "Manitoba                         0        0        0        0        0   \n",
       "New Brunswick                    0        0        0        0        0   \n",
       "Newfoundland and Labrador        0        0        0        0        0   \n",
       "Nova Scotia                      0        0        0        0        0   \n",
       "Ontario                          0        0        0        0        1   \n",
       "Prince Edward Island             0        0        0        0        0   \n",
       "Quebec                           0        0        0        0        0   \n",
       "Saskatchewan                     0        0        0        0        0   \n",
       "Northwest Territories            0        0        0        0        0   \n",
       "Yukon                            0        0        0        0        0   \n",
       "\n",
       "                           1/27/20  1/28/20  1/29/20  1/30/20  1/31/20  ...  \\\n",
       "Province/State                                                          ...   \n",
       "Alberta                          0        0        0        0        0  ...   \n",
       "British Columbia                 0        1        1        1        1  ...   \n",
       "Manitoba                         0        0        0        0        0  ...   \n",
       "New Brunswick                    0        0        0        0        0  ...   \n",
       "Newfoundland and Labrador        0        0        0        0        0  ...   \n",
       "Nova Scotia                      0        0        0        0        0  ...   \n",
       "Ontario                          1        1        1        1        3  ...   \n",
       "Prince Edward Island             0        0        0        0        0  ...   \n",
       "Quebec                           0        0        0        0        0  ...   \n",
       "Saskatchewan                     0        0        0        0        0  ...   \n",
       "Northwest Territories            0        0        0        0        0  ...   \n",
       "Yukon                            0        0        0        0        0  ...   \n",
       "\n",
       "                           `4/7/20  `4/8/20  `4/9/20  `4/10/20  `4/11/20  \\\n",
       "Province/State                                                             \n",
       "Alberta                       1373     1373     1423      1451      1567   \n",
       "British Columbia              1266     1291     1336      1370      1445   \n",
       "Manitoba                       217      217      221       230       243   \n",
       "New Brunswick                  105      105      108       112       112   \n",
       "Newfoundland and Labrador      228      228      232       239       241   \n",
       "Nova Scotia                    310      310      342       407       428   \n",
       "Ontario                       4726     5276     5759      6237      6648   \n",
       "Prince Edward Island            22       25       25        25        25   \n",
       "Quebec                        9340    10031    10912     11677     12292   \n",
       "Saskatchewan                   260      260      271       285       289   \n",
       "Northwest Territories            5        5        5         5         5   \n",
       "Yukon                            7        7        7         8         8   \n",
       "\n",
       "                           `4/12/20  4/13/20  4/14/20  4/15/20  4/16/20  \n",
       "Province/State                                                           \n",
       "Alberta                        1567     1732     1870     1870     1996  \n",
       "British Columbia               1445     1490     1490     1517     1561  \n",
       "Manitoba                        242      246      246      246      250  \n",
       "New Brunswick                   114      116      116      117      117  \n",
       "Newfoundland and Labrador       242      244      244      247      252  \n",
       "Nova Scotia                     445      474      517      549      579  \n",
       "Ontario                        7049     7470     7953     8447     9840  \n",
       "Prince Edward Island             25       25       25       26       26  \n",
       "Quebec                        12846    13557    14248    14860    15857  \n",
       "Saskatchewan                    298      300      300      304      305  \n",
       "Northwest Territories             5        5        5        5        5  \n",
       "Yukon                             8        8        8        8        8  \n",
       "\n",
       "[12 rows x 86 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Province/State') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cleaning to make it easier for pandas to work with\n",
    "df = df.transpose()\n",
    "df.columns = df.loc['Province/State']\n",
    "df = df.drop(['Province/State'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1/22/20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/23/20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/24/20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/25/20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/26/20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>`4/12/20</th>\n",
       "      <td>1567</td>\n",
       "      <td>1445</td>\n",
       "      <td>242</td>\n",
       "      <td>114</td>\n",
       "      <td>242</td>\n",
       "      <td>445</td>\n",
       "      <td>7049</td>\n",
       "      <td>25</td>\n",
       "      <td>12846</td>\n",
       "      <td>298</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4/13/20</th>\n",
       "      <td>1732</td>\n",
       "      <td>1490</td>\n",
       "      <td>246</td>\n",
       "      <td>116</td>\n",
       "      <td>244</td>\n",
       "      <td>474</td>\n",
       "      <td>7470</td>\n",
       "      <td>25</td>\n",
       "      <td>13557</td>\n",
       "      <td>300</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4/14/20</th>\n",
       "      <td>1870</td>\n",
       "      <td>1490</td>\n",
       "      <td>246</td>\n",
       "      <td>116</td>\n",
       "      <td>244</td>\n",
       "      <td>517</td>\n",
       "      <td>7953</td>\n",
       "      <td>25</td>\n",
       "      <td>14248</td>\n",
       "      <td>300</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4/15/20</th>\n",
       "      <td>1870</td>\n",
       "      <td>1517</td>\n",
       "      <td>246</td>\n",
       "      <td>117</td>\n",
       "      <td>247</td>\n",
       "      <td>549</td>\n",
       "      <td>8447</td>\n",
       "      <td>26</td>\n",
       "      <td>14860</td>\n",
       "      <td>304</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4/16/20</th>\n",
       "      <td>1996</td>\n",
       "      <td>1561</td>\n",
       "      <td>250</td>\n",
       "      <td>117</td>\n",
       "      <td>252</td>\n",
       "      <td>579</td>\n",
       "      <td>9840</td>\n",
       "      <td>26</td>\n",
       "      <td>15857</td>\n",
       "      <td>305</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Province/State Alberta British Columbia Manitoba New Brunswick  \\\n",
       "1/22/20              0                0        0             0   \n",
       "1/23/20              0                0        0             0   \n",
       "1/24/20              0                0        0             0   \n",
       "1/25/20              0                0        0             0   \n",
       "1/26/20              0                0        0             0   \n",
       "...                ...              ...      ...           ...   \n",
       "`4/12/20          1567             1445      242           114   \n",
       "4/13/20           1732             1490      246           116   \n",
       "4/14/20           1870             1490      246           116   \n",
       "4/15/20           1870             1517      246           117   \n",
       "4/16/20           1996             1561      250           117   \n",
       "\n",
       "Province/State Newfoundland and Labrador Nova Scotia Ontario  \\\n",
       "1/22/20                                0           0       0   \n",
       "1/23/20                                0           0       0   \n",
       "1/24/20                                0           0       0   \n",
       "1/25/20                                0           0       0   \n",
       "1/26/20                                0           0       1   \n",
       "...                                  ...         ...     ...   \n",
       "`4/12/20                             242         445    7049   \n",
       "4/13/20                              244         474    7470   \n",
       "4/14/20                              244         517    7953   \n",
       "4/15/20                              247         549    8447   \n",
       "4/16/20                              252         579    9840   \n",
       "\n",
       "Province/State Prince Edward Island Quebec Saskatchewan Northwest Territories  \\\n",
       "1/22/20                           0      0            0                     0   \n",
       "1/23/20                           0      0            0                     0   \n",
       "1/24/20                           0      0            0                     0   \n",
       "1/25/20                           0      0            0                     0   \n",
       "1/26/20                           0      0            0                     0   \n",
       "...                             ...    ...          ...                   ...   \n",
       "`4/12/20                         25  12846          298                     5   \n",
       "4/13/20                          25  13557          300                     5   \n",
       "4/14/20                          25  14248          300                     5   \n",
       "4/15/20                          26  14860          304                     5   \n",
       "4/16/20                          26  15857          305                     5   \n",
       "\n",
       "Province/State Yukon  \n",
       "1/22/20            0  \n",
       "1/23/20            0  \n",
       "1/24/20            0  \n",
       "1/25/20            0  \n",
       "1/26/20            0  \n",
       "...              ...  \n",
       "`4/12/20           8  \n",
       "4/13/20            8  \n",
       "4/14/20            8  \n",
       "4/15/20            8  \n",
       "4/16/20            8  \n",
       "\n",
       "[86 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size = \"5\">We took good care of the data, so now we can look at it!</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fddc339580>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(y = 'Alberta') #Plots out a graph, similar to chi-csm.ca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#some more cleaning\n",
    "di = list(df.index)\n",
    "\n",
    "di2 = [i.replace('/20','').strip('`') for i in di]\n",
    "df = df.rename(index = dict(zip(di,di2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot(y=['Alberta', 'British Columbia'], figsize = (15,5), use_index = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size = \"5\">I promised some Machine Learning, so we should probably get started!</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tweaking a few parameters to personalize the model to Alberta's cases\n",
    "dates = df.index\n",
    "\n",
    "zero_cases = []\n",
    "alberta_cases = []\n",
    "\n",
    "for i in df.index:\n",
    "    if df.loc[i, 'Alberta'] == 0:\n",
    "        zero_cases.append(i)\n",
    "    \n",
    "dates = dates.drop(zero_cases)\n",
    "\n",
    "for i in dates:\n",
    "    alberta_cases.append(df.loc[i, 'Alberta'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#put them into numpy arrays for the model\n",
    "days_since_first_case = np.array([i for i in range(len(dates))]).reshape(-1, 1)\n",
    "alberta_cases = np.array(alberta_cases).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#more probably redundant cleaning\n",
    "index = []\n",
    "for i in range(len(alberta_cases)):\n",
    "    if alberta_cases[i] == 0:\n",
    "        index.append(i)\n",
    "        \n",
    "alberta_cases = np.delete(alberta_cases, index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size = \"5\">Setting up a reasonable prediction. </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "days_in_future = 10\n",
    "\n",
    "future_forecast = np.array([i for i in range(len(dates)+days_in_future)]).reshape(-1, 1)\n",
    "adjusted_dates = future_forecast[:-10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This is for later\n",
    "start = '3/6/2020'\n",
    "start_date = datetime.datetime.strptime(start, '%m/%d/%Y')\n",
    "future_forecast_dates = []\n",
    "for i in range(len(future_forecast)):\n",
    "    future_forecast_dates.append((start_date + datetime.timedelta(days=i)).strftime('%m/%d/%Y'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#more tools\n",
    "from sklearn.model_selection import RandomizedSearchCV, train_test_split\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#training from data #look into how to these are splitted\n",
    "x_train_confirmed, x_test_confirmed, y_train_confirmed, y_test_confirmed = train_test_split(days_since_first_case, alberta_cases, test_size=0.15, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size = \"5\">Support Vector Machine time!</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 40 candidates, totalling 120 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    2.0s\n",
      "[Parallel(n_jobs=-1)]: Done 120 out of 120 | elapsed:   16.5s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score=nan,\n",
       "                   estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3,\n",
       "                                 epsilon=0.1, gamma='scale', kernel='rbf',\n",
       "                                 max_iter=-1, shrinking=True, tol=0.001,\n",
       "                                 verbose=False),\n",
       "                   iid='deprecated', n_iter=40, n_jobs=-1,\n",
       "                   param_distributions={'C': [0.01, 0.1, 1, 10],\n",
       "                                        'epsilon': [0.01, 0.1, 1],\n",
       "                                        'gamma': [0.01, 0.1, 1],\n",
       "                                        'kernel': ['poly', 'sigmoid', 'rbf'],\n",
       "                                        'shrinking': [True, False]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=True, scoring='neg_mean_squared_error',\n",
       "                   verbose=1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernel = ['poly', 'sigmoid', 'rbf'] #type of hyperplane\n",
    "c = [0.01, 0.1, 1, 10] #trade off between a smooth line and more accurate training points\n",
    "gamma = [0.01, 0.1, 1] #the influence of a single training point\n",
    "epsilon = [0.01, 0.1, 1] #how hard are errors penalized, and adjusted accordingly\n",
    "shrinking = [True, False] #ignore certain elements in order to speed up optimization\n",
    "svm_grid = {'kernel': kernel, 'C': c, 'gamma': gamma, 'epsilon': epsilon, 'shrinking':shrinking}\n",
    "\n",
    "svm = SVR()\n",
    "svm_search = RandomizedSearchCV(svm, svm_grid, scoring = 'neg_mean_squared_error', cv = 3, return_train_score = True, n_jobs = -1, n_iter = 40, verbose = 1)\n",
    "svm_search.fit(x_train_confirmed, y_train_confirmed.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'shrinking': False, 'kernel': 'poly', 'gamma': 0.01, 'epsilon': 0.01, 'C': 1}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#parameter tuning\n",
    "svm_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_confirmed = svm_search.best_estimator_\n",
    "svm_pred = svm_confirmed.predict(future_forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=1, cache_size=200, coef0=0.0, degree=3, epsilon=0.01, gamma=0.01,\n",
       "    kernel='poly', max_iter=-1, shrinking=False, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_confirmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  25.37345214,   25.41490848,   25.70510284,   26.49277326,\n",
       "         28.02665775,   30.55549435,   34.32802108,   39.59297596,\n",
       "         46.59909703,   55.59512231,   66.82978982,   80.5518376 ,\n",
       "         97.01000366,  116.45302603,  139.12964275,  165.28859183,\n",
       "        195.1786113 ,  229.04843918,  267.14681352,  309.72247232,\n",
       "        357.02415362,  409.30059544,  466.80053581,  529.77271275,\n",
       "        598.4658643 ,  673.12872847,  754.01004329,  841.35854679,\n",
       "        935.422977  , 1036.45207194, 1144.69456963, 1260.39920811,\n",
       "       1383.8147254 , 1515.18985952, 1654.77334851, 1802.81393038,\n",
       "       1959.56034317, 2125.2613249 , 2300.16561359, 2484.52194727,\n",
       "       2678.57906398, 2882.58570173, 3096.79059854, 3321.44249246,\n",
       "       3556.7901215 , 3803.08222368, 4060.56753705, 4329.49479961,\n",
       "       4610.1127494 , 4902.67012444, 5207.41566276, 5524.59810239])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the predicted data points from the SVM model!\n",
    "svm_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE:  597.2125607166328\n",
      "MSE:  389973.7241245328\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#looking at mean error\n",
    "svm_test_pred = svm_confirmed.predict(x_test_confirmed)\n",
    "plt.plot(svm_test_pred, linestyle = 'dashed')\n",
    "plt.plot(y_test_confirmed)\n",
    "print('MAE: ', mean_absolute_error(svm_test_pred, y_test_confirmed))\n",
    "print('MSE: ', mean_squared_error(svm_test_pred, y_test_confirmed))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size = \"5\">Let's see the results!</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20, 12))\n",
    "plt.plot(adjusted_dates, alberta_cases)\n",
    "plt.title('Number of Coronavirus Cases in Alberta over Time', size = 30)\n",
    "plt.xlabel('Days Since 3/6/2020', size = 30)\n",
    "plt.ylabel('Number of Cases', size = 30)\n",
    "plt.xticks(size=15)\n",
    "plt.yticks(size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aFBEdgeOAqdXmmgp8MiI6V7t2KrACeCD7Xte5JEmSJElqNZZ/sJyJZ06kfZf2dN+5+4YHSI2kWTyimFL6ALi/+rWI6Jv9+GBKaWl27efA9yNiAaWdVl+jFOJdXm3olcAFwISIuATYCbgI+FVKaXG23so6ziVJkiRJUquQUuK2c25jxYcrGH37aMo7/Z/3r0mF2WQBV3Zu1pBsjadSSn/fVGtV83NKIdS3gZ7ADOColNK/9k2mlBZExBHAFcBkSuduXUYp5KrXXJIkSZIktRYzrpzBS7e9xNG/OpptBm1TdDnSv4mUantZ4HoGRGwNfDP7em1K6ela+lwJfL7G5enAyJTSgjyFNieDBw9OM2bMKLoMSZIkSZIaREqJ8aeNZ9XiVYy+fTTRJoouSa1QRMxMKQ2u7V6eHVyfAr4KLAN+UMtiXwG+UMu4Q4BxwFE51pQkSZIkSQWJCE4aexJrlq0x3FKTlOeQ+WOy9r6qs6+qRERbSo/0AawCfgmcD/wTCOATEXFszlolSZIkSVIj+8cV/2DB7AVEBOWbe+6WmqY8AdcAIAGP1XLvE8BW2f1zU0rfSCn9jtJbD9/O+ozOU6gkSZIkSWpcL9/+MlO/PJUZV3oMj5q2PAHXFln7ei33PpG1i4Hrqy6mlJYDN1DaxVXrs5KSJEmSJKnpWPLuEiadPYmt996awy8+vOhypPXKE3D1yNrltdwbQmn31r0ppbU17r2UtdvlWFOSJEmSJDWSVJm49dO3snrZak664STats9zhLfUePIEXKuztmv1ixHRgY92Zz1Uy7hFWds+x5qSJEmSJKmRzLx6Jq/d/RqfvOyTbLnblkWXI21Qngj2baA/MAi4ttr1IymFVwl4uJZx3bJ2aS33JEmSJElSE7HXmL2oXFvJvl/Yt+hSpDrJs4PrUUpnaZ0RETsDREQZ8PXs/kKgttPnPpa1c3KsKUmSJEmSNrE1y9ewZvkayjcvZ//z9iciii5JqpM8Adc1WdsN+GdETASeAg6htHvrrymlilrGDc3uP5GnUEmSJEmStGlN/cpU/nDAH1i7suax2lLTVu+AK6X0APBHSru4ugHH89HurHeAH9ccExF9gf2yr/flqFOSJEmSJG1Cz9/yPE/84Ql2OW4X2nbwUHk1L3l2cAF8AfgP4DlKh84vBMYBB6eUPqil/3nVfr4n55qSJEmSJGkTWDRnEZM/P5nt9t+Ow350WNHlSPWWK5JNKSXg19mnLi4FrsiGvp1nTUmSJEmS1PAqKyqZcMYEKtdWMvKGkZS1Kyu6JKneGmXPYUrpvcZYR5IkSZIk1c+K+StYu2Itx/7uWHrs3KPocqRcfKhWkiRJkqRWrNOWnTjnkXNo0zbvKUZS8Tb6T29EbBcRZ0XEzyLiyoj4Y0MUJkmSJEmSNp2lc5dy2+dvY8WCFYZbavZy/wmOiK0i4gZgNnAN8A1Kh8+fXUvf30XE6oh4Ne96kiRJkiSpYVRWVDJhzASe+eszLH5rcdHlSBstV8AVEf2BJ4FTKT3mGNlnXS7P+vWNiMPyrClJkiRJkhrG9P+azux7ZjPsimFsvefWRZcjbbR6B1wR0Q6YAvSiFGr9BfgkcP66xqSUXgCeyb4eU/8yJUmSJElSQ5h972we+NED7HXmXgz67KCiy5EaRJ5D5s8B+gMJODeldDVARHTcwLgHgD2BA3KsKUmSJEmSNlJKibu+dhdbDNiC4b8fTsT6HsaSmo88AdfIrL23Ktyqo+eydpcca0qSJEmSpI0UEYyZOobVS1ZT3qm86HKkBpPnDK49Ke3eurWe4+Znbfcca0qSJEmSpI0w+77ZVFZU0nmbzvTcpWfR5UgNKk/A1SNr36vnuDy7xSRJkiRJ0kZ69e5Xue6I63j0skeLLkXaJPIEXIuytms9x/XJ2g9zrClJkiRJknJY8s4SJoyZwJa7bcl+X9qv6HKkTSJPwDU7a/ev57hhlB5tfDbHmpIkSZIkqZ4q11YyfvR41ixbw6hxo2jXsV3RJUmbRJ6A624ggNMiYsu6DIiIo4Gh2de7cqwpSZIkSZLq6YGLH+CNB95g+O+Hs+VudfonvNQs5TkX63+BC4HOwPiIOC6ltGhdnSPiCOCG7Oti4E851pQkSZIkSfXUf3h/Kisq2fusvYsuRdqk6h1wpZTejIiLgZ8AQ4CXI+JPVNsNFhEHA3sAJwJHUtrxlYALU0qLG6JwSZIkSZJUu4rVFZSVl9H7gN70PqB30eVIm1yuNxumlH4WEVsBXwG2BL5RdStrH6jWPbL24pSSu7ckSZIkSdqEKtdW8pej/sL2B2/PET85ouhypEaR5wwuAFJK/0Fph9bTlEKsdX2eA45PKf1oo6uVJEmSJEnrdd8P7uON6W+w5cc8c0utR64dXFVSSpOASRGxF6VD5PsCXYGlwNvAAymlGRtbpCRJkiRJ2rBZd87ioZ89xKDPDWKvM/Yquhyp0WxUwFUlpfQ0pZ1ckiRJkiSpAIvfWsyEMyaw1Z5bMew3w4ouR2pUuR9RlCRJkiRJTcd7T71Hm7I2jLp5FO02a1d0OVKjapAdXLWJiO7AftkaT6eU3tpUa0mSJEmS1NrtMnwXvjL7K7TraLil1qfeO7gioltEXJB9dllHn29ROoNrKjAZeD0iro2I9htXriRJkiRJqu6Vqa/w5J+fBDDcUquVZwfXscD/AKuB62vejIgxwE+BROktimTtmUA5MDpXpZIkSZIk6d8smrOIiWdMpMv2Xdhz9J6UtSsruiSpEHnO4Doma6enlD6sfiMiAvhxtUu3AL8E3qAUcp0aEQfnKVSSJEmSJH2kYk0Ft5x2CxVrKhh18yjDLbVqeQKuvSjtznqklntDgL7Z/W+mlE5JKX2D0llc87M+n86xpiRJkiRJquae79zDW4+8xXFXH0fP/j2LLkcqVJ6Aa4usnVXLvSOzdgXwu6qLKaUPgBso7eI6MMeakiRJkiQp8/5z7/PILx9h8BcHs8epexRdjlS4PGdwVcXCS2u5V/X44QMppeU17j2TtTvkWFOSJEmSJGW22n0rxkwdQ9/D+hZditQk5NnBVZm1m1e/GBFtgQMoPZ74UC3jqh5R7JhjTUmSJEmSWr21K9fy7uPvAtDvmH607ZBn34rU8uQJuN7L2t1rXB8KdMp+friWcZ2ztubOLkmSJEmSVAd3fPkO/njQH1k0Z1HRpUhNSp6Aawals7TOiIjqp9h9OWtXUPsB9Ltk7Vs51pQkSZIkqVWbefVMnvjDExz09YPoukPXosuRmpQ8AdcNWbsN8M+IuCwi7gJOoPR44s0ppdW1jPt4dv/pXJVKkiRJktRKvf2Pt5l6/lR2PnpnDr/48KLLkZqcegdcKaVJwB2UdnH1AS7go7cnLgYuqjkmIrYChmRf78tTqCRJkiRJrdHyD5cz7qRxdN62MyNvGEmbsjx7VaSWLe/fipOBX1MKtCL7/AM4MqX0Ri39vwCUZT/fm3NNSZIkSZJanQ7dOjDonEGcMv4UOvb0vW1SbSKllH9wRBtgS2BFSmnxevoNBLoClSmlB3Mv2EwMHjw4zZgxo+gyJEmSJEnN3Kolq2jfuX3RZUhNQkTMTCkNru3eRu1rTClVppTmri/cyvo9mVJ6oDWEW5IkSZIkNYTnxj3H5f0vZ94L84ouRWryfHBXkiRJkqQm5v3n3mfSZyfRfafu9Ni5R9HlSE2eAZckSZIkSU3IykUrGTdyHOWblzPq5lGUlZdteJDUyrXd2AkiogzYC+gNdOGjw+TXKaV03cauK0mSJElSS5MqE7d++lYWvLaAs+49iy7bdSm6JKlZyB1wRcQOwA+BU4HN6jE0AQZckiRJkiTVsHbVWgCO+uVR9Bnap+BqpOYjV8AVEUOAyZTejBgNWpEkSZIkSa1Uu83aceqEU/2XtlRP9T6DKyK6ABOAbny0G+vc7HYCLgfOB/4beLra9euBzwCf3biSJUmSJElqWRbMXsBfjv4LC99YSLQJIky4pPrIc8j8ucCWlEKrM1JKZ6eUrqp2/56U0u9SSt9MKQ0ETgQWAKcBpJT+vLFFS5IkSZLUUqxZsYZxI8fxzj/foXJtZdHlSM1SnoBrWNbOTCmN3VDnlNIkYHj29fcRMSDHmpIkSZIktTgpJW4/93bee/I9TvzrifTYuUfRJUnNUp6AazdKu7duXcf9//MWxZTSY8A4oAMfPc4oSZIkSVKrNuP3M3jquqc49KJD2WX4LkWXIzVbeQKubln7Zo3ra7K20zrG3ZO1R+dYU5IkSZKkFqVybSWP/+FxdvnULhz6/UOLLkdq1vK8RXF1Nm5ljetLgO7AdusYtyJr13VfkiRJkqRWo03bNnxm+meorKgk2niovLQx8uzgejtre9a4/lrW7reOcbtmbZ5QTZIkSZKkFqFiTQXTfzKd1ctWU755OR26dii6JKnZyxNwPZ21H6tx/VEggGMjok/1GxHRjdLZWwmYnWNNSZIkSZJahLv/827u+959vDbttQ13llQneQKuBygFWYfVuP7XrG0PTI+IL0bE0RHxRWAmsFV2f12H00uSJEmS1KI9c8MzPPbrxzjgqwcwYMSAosuRWow8AddtWbtHROxedTGl9A9KIVcAvYErgKlZ2zfr9iZwad5iJUmSJElqruY+PZfbPncbfQ7pw1G/OKrocqQWpd7nYaWU3oyIw4EOwOIat88BVgGfpRR0VTcTOC2ltCBPoZIkSZIkNVcpJW773G1s1n0zTr7pZMralRVdktSi5DrwPaX0wDqurwE+HxH/BXwC2BpYDvwzpfRI7iolSZIkSWrGIoKTbzqZFfNXsHmvzYsuR2pxNskbDVNKbwDXbIq5JUmSJElqTl6//3X6HNqH7jt2p/uO3YsuR2qR8pzBJUmSJEmS6uDZm57lz4f/mcevfrzoUqQWrU47uCJih+zHypTSW/VZICJ6kwVpKaU59StPkiRJkqTm6Z0Z7zDp7EnscPAODDx7YNHlSC3aBndwRcQYYHb2OS/HGudVjY+IUTnGS5IkSZLUrCx5dwljTxhLp606ccr4Uygr91B5aVNab8AVEQH8F6U3Is4AvpNjje9SeoNi1VySJEmSJLVYKSVuOeUWVi5cyWm3nUanrToVXZLU4m3oEcUjgD5AAr6bUkr1XSClVBkR3wXuAvpFxGEppfvrXakkSZIkSc1ARHDYxYexeslqeu3dq+hypFZhQ48oHp+1s1JK0/IuklK6G5iVfT0h7zySJEmSJDVl81+dD8COh+/IrsfvWnA1UuuxoYBrf0q7t6Y2wFpTKT2muH8DzCVJkiRJUpPy8pSXuWLXK3hh4gtFlyK1OhsKuPpm7UsNsNaLWbtjA8wlSZIkSVKT8f5z7zN+9Hh6DexFv0/2K7ocqdXZUMDVLWvnNcBaVXN0W28vSZIkSZKakeUfLmfs8WMp71TOabeeRruO7YouSWp1NhRwrcrazRtgrarXRqxugLkkSZIkSSpcZUUlN4+6mcVvL+bUW0+lS+8uRZcktUobeoviPErhVu8GWKtqjg8aYC5JkiRJkgrXpqwNu528G4M+O4jeBzTEP50l5bGhHVyzKB0Mf3gDrPWJrH2lAeaSJEmSJKlQqxaXHnra70v7sdcZexVcjdS6bSjguidrD4mIAXkXiYiPAYdSeiPjvXnnkSRJkiSpKZh932z+p8//MOfvc4ouRRIbDrgmApVZv99GRFl9F4iItsBvszkqgQn1nUOSJEmSpKZi/qvzufnkm+m8bWe23nProsuRxAYCrpTSLOBGSo8pHgaMjYjOdZ086zs2G5uAm7I5JUmSJElqdlYtXsXY48cCcNptp9G+S/uCK5IEG97BBfAN4J3s55HAsxFxQUT0XNeAiOgZEV8BngFOzC6/l80lSZIkSVKzU1lRyfjR4/nw5Q8Zdcsoeuzco+iSJGU29BZFUkrvRsSJwN+ALpTehngZcFlEzKJ0EP3CrHs3YGegf7UpAlgMnJhSegdJkiRJkpqhVJnovF1njvnNMex4+I5FlyOpmg0GXAAppX9GxH7ALUD1V0P0yz41RbWfnwFOTin59kRJkiRJUrNUWVFJWbsyPnXlp4iIDQ+Q1Kjq8ogi8K/zuPYBxgD/oHRgfKzjU5n1GQMMMtySJEmSJDVXbz32Fr/f8/d88NIHhltSE1WnHVxVUkqVlA6dvzEiugIHAdsBVedxfUjpvK5HUkoLa59FkiRJkqTmYfFbi7nphJto17EdHbfoWHQ5ktahXgFXdSmlRcCdDViLJEmSJElNxprlaxh7wlhWL1vNmdPOpGNPAy6pqarzI4qSJEmSJLUWKSUmfXYS7z7+LifdcBJb7b5V0SVJWg8DLkmSJEmSalizbA1L3lnCkT8/kl0+tUvR5UjagNyPKEqSJEmS1FKVb17OWfecRZu27guRmgP/pkqSJEmSlHnr0be4ftj1rJi/grJ2Zb41UWom3MElSZIkSRKw4LUF3Hj8jbTv3J7Kisqiy5FUD+7gkiRJkiS1eivmr+D6Y68nVSTGTB1Dpy07FV2SpHow4JIkSZIktWprV63lphNvYuHshZx666n03KVn0SVJqicDLkmSJElSq7b03aUsfGMhI64ZQZ+hfYouR1IOzSLgioiTI+LhiPgwIlZGxEsR8b2IKK/WJyLiOxHxZkSsiIjpETGwlrl2i4h7ImJ5RLwTERdHRFmNPnWaS5IkSZLU/HXr243znj+PPUfvWXQpknJaZ8AVEQuyQGlYjeuHZJ8em768f+kJ3Ad8DhgG/An4LvCran2+BXwfuAQ4DlgKTIuIXlUdIqI7MA1IwAjgYuBC4Ec11tvgXJIkSZKk5u3Ja59kyhenULm2knYd2xVdjqSNsL63KHalFATV/Ft+f3b9ROC2TVPWv0sp/W+NS/dFRBfgvIj4MtCeUij1s5TSFQAR8QjwOnA+8L1s3LnAZsDIlNJi4O5snosi4hcppcUR0aGOc0mSJEmSmqnX7nmNyZ+fTN/D+pJSKrocSRtpfY8oVv0Nj8YoJIcPgapHFD8OdAHGVd1MKS0DJlPa8VVlGHBXFm5VGUsp9Dq0nnNJkiRJkpqhec/PY9xJ4+i5a09G3TKKsnZlGx4kqUlbX8C1LGu3boxC6iIiyiKiY0QcDFwA/D6VovYBQAXwSo0hL2T3qgwAXqzeIaU0B1herV9d55IkSZIkNTNL31vK9cdeT7vN2jH69tF06Nqh6JIkNYD1BVyzsvaM7LG9pmBZ9nkQeAD4z+x6d2BpSqmiRv8FQMdqh9F3BxbWMu+C7F595vo3EfGFiJgRETPmzZtXn/8mSZIkSVIjmffCPNYsX8PpU06nW59uRZcjqYGs7wyuqcBAYAjwVkS8BKyqdv+/IuKrOdZMKaUjcoyD0uODHYH9gR8AVwBfqpq3lv5Ry7119atLn3XdI6V0FXAVwODBg32AW5IkSZKaoB0P35GvzP4K5Z1q3bsgqZlaX8D1S2A00AfoARxY7V4Au+dYr2aQVC8ppcezHx+KiA+AP0fEpZR2V3WOiLIaO6+6ActTSmuy7wuyazV15aOdXXWdS5IkSZLUTPzt63+jR78eDD53sOGW1AKt8xHFlNIC4ABKu6ReBdbw7wFV5Pg0pKqwa0dK52qVAf1q9Kl55taL1DhHKyK2BzpV61fXuSRJkiRJzcBjv3mMRy59hA9e/KDoUiRtIus7g4uU0vsppQtSSv1TSh1SSm34KOQ6IaXUJsenoV5PMSRrZwMPA4uBUVU3I6IjcBylRy2rTAU+GRGdq107FVhB6Uwv6jGXJEmSJKmJe+m2l7jzq3ey64hdOfrSo4suR9Imsr5HFJuMiLgTmAY8R+kNh0OAC4GbUkqvZn1+Dnw/IhZQ2mn1NUoB3uXVprqS0tsXJ0TEJcBOwEXAr1JKiwFSSivrOJckSZIkqQl7Z+Y7jD99PNvuuy0jrx9Jm7L17vGQ1IzlCbh+lLWN+bjeP4Gzgb7AWuA14NuUAqsqP6cUQn0b6AnMAI5KKc2t6pBSWhARR1B67HIypXO3LqMUclGfuSRNENPeAAAgAElEQVRJkiRJTdubD79Jp606cfrk0z13S2rhIiVf+NfQBg8enGbMmFF0GZIkSZLU6q1eupryzQ23pJYgImamlAbXdq9B92dGRNuI6B4RzeLRR0mSJElSy1KxuoKbT7mZN6a/AWC4JbUSGxVwRUR5RHw2IqZExDxgFfABsCoi3s+unx0R/kaRJEmSJG1SKSWmnDuF529+ngWvLSi6HEmNKHfAFRGHAC8DVwPDKJ1VFdU+W2TX/wi8FBFDN7paSZIkSZLW4cGfPsiT1zzJIT84hIFnDyy6HEmNKFfAFRFHAX8DtuejQGsp8BTw96xdWu1eH+DuiDiyAWqWJEmSJOnfPHPDM9z3vfvY64y9OOyiw4ouR1Ijq3fAFRFdgBuAckrh1e3AUKBrSmlQSmlo1nbJrk/JhpYDN2bjJUmSJElqMK/e9Sp9Du3DcX84jogouhxJjSzPYfBfovQ4YgJ+kFL6ybo6ppT+DhwfEd8B/gvoAXwRuCTHupIkSZIk1WrENSNYs3wNbdv7zjOpNcrziOLwrP37+sKt6lJKPwUeorTj67gca0qSJEmS9G8WvLaAPx/+ZxbNWUS0Cd+YKLVieQKuXSjt3hpXz3FV/XfJsaYkSZIkSf+ydO5S/nL0X5j79FxWL1tddDmSCpYn4Oqate/Vc9zcrPUMLkmSJElSbqsWr+L6Ydez9N2ljL59NFt+bMuiS5JUsDwB14Ks3aGe43pn7cIca0qSJEmSxNpVa7npxJuY+/RcRt0yit4H9t7wIEktXp6A6wVKZ2mNruuAKL3CYgylRxufz7GmJEmSJEmsXrKalQtXMuJPI+g/rH/R5UhqIvIEXJOzdlBEXFrHMZcA+2Q/T8qxpiRJkiSpFUspUVlRScctOnLOo+ew91l7F12SpCYkT8B1JfBu9vNXI+KhiBgREZ2qd4qIThFxfERMBy7MLr8LXJW/XEmSJElSa/TgTx5k7IixrF25lrJ2ZUWXI6mJqXfAlVJaAZwErMwuHQRMABZFxFsR8VJEvAUsAiYCQyg90rgCOCkbL0mSJElSncy8aib3ff8+OvbsSFm54Zak/yvPDi5SSo9SCq6qzuOKbK5tgX5Z26baveeAISmlxxqgZkmSJElSK/HCxBe4/Yu30//Y/hz3h+OINlF0SZKaoLZ5B6aUnoyIPYFPASOB/YFtgM7AEkqPI/4DGA/cnlJKG1+uJEmSJKm1eGP6G4w/fTzb7b8dJ4872UcTJa1T7oALIAutJvPRwfOSJEmSJDWIdp3a0fvA3pwy/hTKO5UXXY6kJmyjAi5JkiRJkhraqiWraN+5Pdvuuy2fvu/TRPhYoqT1y3UGlyRJkiRJm8Ky95dx1b5X8eBPHwQw3JJUJwZckiRJkqQmYdWSVVx/7PUsfmsxfQ/rW3Q5kpoRAy5JkiRJUuEqVlcwbuQ43nvyPUbdPIrtP7590SVJakY8g0uSJEmSVKiUEreefSuvTXuNEdeOYJfhuxRdkqRmxoBLkiRJklSoiKD/8P5ss882DPz0wKLLkdQMGXBJkiRJkgqzaM4iuu7Qlb3G7FV0KZKaMc/gkiRJkiQV4vE/PM7l/S/nzUfeLLoUSc2cAZckSZIkqdG9OOlFpvy/Kez4iR3ZdvC2RZcjqZkz4JIkSZIkNarX73+d8aeNZ9vB2zLq5lGUtSsruiRJzVy9z+CKiHuzH6enlC5q2HIkSZIkSS3Zh698yA2fuoHuO3dn9O2jKd+8vOiSJLUAeQ6ZPzRrb27IQiRJkiRJLV+Pfj0Y+t2hDDx7IB236Fh0OZJaiDwB1zxgy6yVJEmSJGmD5r0wj7LyMnrs3IOh3x5adDmSWpg8Z3C9krWeAihJkiRJ2qD5s+Zz3RHXccupt5BSKrocSS1QnoDrZiCAkQ1ciyRJkiSphVk0ZxHXHXEdFasrOPG6E4mIokuS1ALlCbj+F3geGBoRFzRwPZIkSZKkFmLpe0u57sjrWLloJWfefSZb7rZl0SVJaqHqHXCllFYBnwKeAi6LiJsj4tCI8NUXkiRJkqR/uec797DknSWMmTqGbQZtU3Q5klqwqO/zzxHxWvZje2AboGqCCuBDYMUGpkgppZ3rtWgzM3jw4DRjxoyiy5AkSZKkQq1eupp5z89ju/23K7oUSS1ARMxMKQ2u7V6etyj25aNQq6qNbK6t6zDeEwUlSZIkqYVavWw19//wfg676DDKNy833JLUKPIEXHMwpJIkSZIk1bB25VrGjhjL6/e9zs6f3Jmdj2rRD+9IakLqHXCllPpugjokSZIkSc1YxeoKxp08jtn3zOaE604w3JLUqPK8RVGSJEmSpH+pXFvJhDMm8MrtrzD898PZ+8y9iy5JUitjwCVJkiRJ2iiL317MnAfncPSlRzP43FrPf5akTSrPGVySJEmSJJFS6Xjmbn268aXnv8Rm3TcruCJJrdVGB1wRcSBwNPAxoDvQLqV0RI0+WwDlwMqU0vyNXVOSJEmSVKyUEtO+OY3KtZUcfenRhluSCpU74IqIAcA1wP7VL1P7Gxa/AVwIfBAR26WU1uZdV5IkSZJUvOk/ns7D//0wg7/kI4mSipfrDK5s19Y/KYVbUe2zLldk7RaUdntJkiRJkpqphy99mPt/eD8Dzx7IsZcfS8T6/jkoSZtevQOuiNgcmAh0AtYCPwZ2BU5Z15iU0hzgH9lXAy5JkiRJaqZmXDmDu79+N7ufsjvH/eE4oo3hlqTi5dnB9UVga6ASODGl9MOU0ivAmg2M+zulXV7uX5UkSZKkZqrTVp342Ekf48S/nkibslwPBUlSg8tzBtdxlM7ZmpRSuqMe417M2n451pQkSZIkFWjp3KVsvvXmfGzkxxhw4gAfS5TUpOSJ2wdk7Z31HLcwa7vmWFOSJEmSVJAXJrzAr3f8NbPumgVguCWpyckTcFUFVPPrOa591lbkWFOSJEmSVIDnxz/PLafeQq+Bvdj+oO2LLkeSapUn4KoKtnrWc1zVo4kf5FhTkiRJktTInr+lFG5tt/92nHHnGbTv0n7DgySpAHkCrleydmg9x42gdHbXEznWlCRJkiQ1orlPz+WW026h94G9GXPnGMMtSU1anoDrTkpvQzwpInaqy4CIGAMMzL7W52B6SZIkSVIBttpzK4ZdPowxU8fQvrPhlqSmLU/A9b/AEkpnak2OiB3X1zkizgGuprR7ay5wXY41JUmSJEmN4IUJLzDv+XlEBPt9cT/DLUnNQr0DrpTSh8BXKO3iGgA8FxE3AidU9YmIMyPi5xHxAnAV0IFSwPX5lNKqBqlckiRJktSgnh37LDefcjP3/eC+okuRpHppm2dQSunaiOgK/Del8OqUqltZe2217gGsAb6YUro9Z52SJEmSpE3omRufYeIZE9l+yPaccO0JGx4gSU1InkcUAUgp/Ro4CJhCKdiKWj4AU4EDU0p/2rhSJUmSJEmbwjM3lMKtHQ7egTF3jKF88/KiS5Kkesm1g6tKSmkmcHxEdAOGAH2BrsBS4G1gekpp3sYWKUmSJEnaNFJKPHntk+wwdAdG3z6a8k6GW5Kan40KuKqklBYCPn4oSZIkSc1IqkxEm+DUiacCGG5JarZyP6IoSZIkSWq+nvrLU1x76LWsWryK8k7lhluSmrUGC7gionNE9I+IQVnbuaHmliRJkiQ1nKeue4pbP30rZe3LaNPWfQ+Smr+NekQxInoDXwROBHbho4PlAVJEvAyMB65MKb29MWtJkiRJkjbek39+kkmfmcROR+zEaZNOo13HdkWXJEkbLXdUHxFfAl4AvgXsms1V/Q2KbbLr3wFejIgvbnS1kiRJkqTcnh377Efh1m2GW5Jajlw7uCLiB8APq74CFZTCrlnAMqAT0A/4GFCWfb8iIrZIKf14Y4uWJEmSJNXftoO3Ze+z9mb474fTbjPDLUktR6SU6jcg4kDgIUo7tNYClwL/k1KaW0vfrYGvABcC7YBKYEhK6bGNrLtJGzx4cJoxY0bRZUiSJEkSAHP+PoftP749EbHhzpLUREXEzJTS4Nru5XlE8YJsXCUwKqX07drCLYCU0tyU0neAk4FEabfXBTnWlCRJkiTl8PgfHueag69hxpX+T3hJLVeegGsopbBqfEppUl0GpJQmAzdTCrgOybGmJEmSJKmeZl41k8mfn0y/Yf0Y9JlBRZcjSZtMnoBry6y9s57j7sraLXKsKUmSJEmqh5lXzWTK/5tC/2P7c+qEU2nbIdcRzJLULOQJuOZl7fJ6jqvq/0GONSVJkiRJdbT4rcVMvWAq/Y/tzynjTzHcktTi5fkt9ziwLbAncFM9xu2ZtTNzrClJkiRJqqMuvbvw6fs+zTb7bEPb9oZbklq+PDu4/pfSWVqfj4iedRkQEVsAn6d0dteVOdaUJEmSJK1HSom/ff1vPP3XpwHY/qDtDbcktRr1DrhSSncAv6d0Fte9ETFgff0jYldgWtb/tyml+p7dJUmSJElaj8qKSiZ/YTKPXPoI7z7+btHlSFKjW2ecHxHre9vhTcA2wAnAUxFxF3APMIvSWVsdgX7AJ4BjsnUmArdExCEppekNU74kSZIktW4VayqYeOZEnrvpOYZ+byiHX3x40SVJUqNb337V+yk9Urg+CWgHDM8+tYms3wnZJ21gXUmSJElSHVSurWTcyHG8POVljrzkSIZ8Y0jRJUlSITYUNEUd59lQv7rOI0mSJEmqozZt29Brn170H96fwecOLrocSSrM+gKuHzVaFZIkSZKkOlsxfwWL317M1ntuzeE/8pFESVpnwJVSMuCSJEmSpCZm6XtL+cvRf2HF/BVcMOsC2nbwBBhJ8jehJEmSJDUTi+Ys4rojr2PJ20s47bbTDLckKeNvQ0mSJElqBj58+UOuO/I6Vi1exZl3n8n2H9++6JIkqckw4JIkSZKkZuChnz3E2pVrOfv+s+k1sFfR5UhSk7LRAVdEtAF2BroDHeoyJqU0fWPXlSRJkqTWIKVERHDs745l6DtD6bFzj6JLkqQmJ3fAFRFHAP8BHAGU12No2ph1JUmSJKm1mH3vbO6/6H5Ov+10OnTrYLglSeuQK2iKiF8AF1Z9bbhyJEmSJEkAL01+iZtH3UzP/j1Zu3Jt0eVIUpNW74ArIk4Bvl7t0ivAQ8BcYFUD1SVJkiRJrdYzNz7DrWfdSq9BvTjjzjPYrMdmRZckSU1anh1c52XtGuCclNJfG7AeSZIkSWrVnh37LBPGTKDPIX04ffLptO/cvuiSJKnJyxNw7U3pHK2rDbckSZIkqWFtP2R7Bp0ziGG/GUa7zdoVXY4kNQttcoypOnPrwYYsRJIkSZJaq5QSz417jsqKSrpu35Xjrz7ecEuS6iFPwPV61tbnzYmSJEmSpFpUrq1k8ucnc8upt/Dsjc8WXY4kNUt5Aq7bKO3iGtLAtUiSJElSq7J62WrGnjCWJ/74BEO/N5Q9x+xZdEmS1CzlCbiuAD4AzoqI3Ru4HkmSJElqFZZ/sJzrjriOWVNnMfz3w/nEjz9BRGx4oCTp/6h3wJVSmguMANYC90TESQ1elSRJkiS1cPNnzefDlz/klPGnMPjcwUWXI0nNWqSU8g2M6APcCuwFzAVmAh8ClRsYmlJK5+RatJkYPHhwmjFjRtFlSJIkSWqClr2/jE5bdQJg1eJVtO/SvuCKJKl5iIiZKaVa/49A25wTdgUuAnandB5XL+DYekzRogMuSZIkSarNq3e/yriTxvGpKz/FnqP3NNySpAZS74ArIjYH7gUG1rxVxynybRmTJEmSpGbs6b8+zaTPTGLL3bak72F9iy5HklqUPDu4LgAGZT+/Q+nQ+b9TekxxVQPVJUmSJEktQkqJh3/5MNO+MY2+h/Xl1FtPpUPXDkWXJUktSp63KJ6Wta8DA1NKP08pPZhSejml9EZdPvVdMCJGRcRtEfF2RCyNiJkRcXqNPhER34mINyNiRURMj4iau8yIiN0i4p6IWB4R70TExRFRlmcuSZIkSdqQtx97m2nfmMbup+zOmDvHGG5J0iaQJ+DaidJjhr9NKX3QwPWsy9eApcB/AMcD9wE3RMSXq/X5FvB94BLguKz/tIjoVdUhIroD0yjVPwK4GLgQ+FGN9TY4lyRJkiTVRe8DezPmzjGcdONJtG2f6xhkSdIG5Am4lmVtvXdibYTjUkqjU0rjUkr3ppS+DtxIKfgiIjpQCqV+llK6IqU0DRhFKcg6v9o85wKbASNTSnenlK6kFG59LSK61HMuSZIkSarVykUruWH4Dbz5yJsA9PtkP6JNXY8tliTVV56A6/msbbTdTOvYKfYEsFX288eBLsC4amOWAZOBYdXGDAPuSiktrnZtLKXQ69B6ziVJkiRJ/8fitxdzzdBrePXuV1k0Z1HR5UhSq5An4PoLpTcmjmrgWurr43wUtg0AKoBXavR5IbtHtX4vVu+QUpoDLK/Wr65zSZIkSdK/mffCPP540B9ZOHshY+4Ywx6n7lF0Sa1GZWUqugRJBcoTcF0D3AscHBHfbOB66iQijqB0htZvs0vdgaUppYoaXRcAHSOivFq/hbVMuSC7V5+5JEmSJOlfPnzlQ/405E9UrK7g7Olns9OROxVdUqvx6GsfctRlD/Dy3CVFlyKpIPUOuFJKVQe0jwd+GhG3R8SxEdGzwaurRUT0BW4AJqWUrq1eWm3da7m3rn516bOue0TEFyJiRkTMmDdvXm1dJEmSJLVg3XfqzsCzB3LOI+ewzaBtii6nVVi5poKLJz/P6Vc/ytrKxMo1NfcpSGot6v0Kj4io/hsjgGOyDxF1OjQxpZRyvTokInoAU4E5wBnVbi0AOkdEWY2dV92A5SmlNdX6datl6q58tLOrrnP9m5TSVcBVAIMHD3ZvrCRJktRKPHntk+x05E506d2FT/7qk0WX02o8+eZCvjbuSV6bt4yzDurDt4YNoGO5b6mUWqs8jyhGtU/N73X91H/RiI7AFKAcGJ4d/F7lRaAM6FdjWM0zt16kxjlaEbE90Klav7rOJUmSJKkVSylx7/fuZdJnJvHIrx4pupxWY/XaSn5510uM/N3fWbm6gr+ecwAXj9jDcEtq5fL8BpjOOh7T21Qioi1wM9AfGJJSer9Gl4eBxZQOvv+vbExH4DiyXVWZqcB/RkTnlFLVw9mnAiuAB+o5lyRJkqRWqmJ1BZO/MJmn/vwUg84ZxFG/OKroklqF599ZzNfGPcmL7y1h1L69+f5xu9GlQ7uiy5LUBNQ74EopHbYJ6tiQ3wHHAl8BekTEgdXuPZFSWhkRPwe+HxELKO20+hqlHWqXV+t7JXABMCEiLgF2Ai4CfpVSWgxQj7kkSZIktUIr5q/gphNv4o3pb3DoRYdy6A8OretxLcppbUUl/zv9Nf5n2st03aycq88azFG7bV10WZKakOayh/PorP11Lfd2BF4Hfk4phPo20BOYARyVUppb1TGltCB7A+MVwGRK525dRinkqm6Dc0mSJElqnaIsWL1sNSNvGMmep+9ZdDkt3qz3l3LhzU/x1JsLGb7XNvx4xB706OTL7SX9uyi9FFENafDgwWnGjBlFlyFJkiSpAb358Jv0GtiLdh3bUVlRSZuyPEcaq64qKxPXPPw6v7jzRTYrL+PHI/bguL23LbosSQWKiJkppcG13fM3siRJkiRtwMyrZ3Ltoddy/0X3AxhubWJvzl/O6Vc/yo+nPM/B/bbgb189xHBL0no1l0cUJUmSJKnRVVZUMu2b03jk0kfod0w/hn53aNEltWgpJW78x5v85PbniQh+cfJejNq3t2ecSdqgegdcEXHvRq6ZUkpHbOQckiRJkrRJrV66mgljJvDSbS+x3/n7ccxlx9CmrTu3NpX3Fq3km+Of5oGX5/HxnXvyi5P3onf3jkWXJamZyLOD6zAg78FdsRFjJUmSJKnRLHt/GW89+hbDLh/G/ufvX3Q5LVZKiVuffJsfTnqO1RWVXDxid844oA9t2rhrS1Ld5X1Esb6/aVKOMZIkSZLU6ObPmk/3nbvTfafufHnWl2nfuX3RJbVYHyxdxfcmPsudz73Hvn2688tRe7PjFp2KLktSM1TvgCultME9uVF6QHoL4ADgK8ARwI3AOSmllfVdU5IkSZIaw/Pjn2fimRM54qdHcOBXDzTcqmbW+0t5f3HD/XPu7YUr+PnUF1myci3fHjaAzw3diTJ3bUnKaZMcMp9SSsA8YAowJSIuBr4LdAJO2BRrSpIkSVJeKSUe+vlD3Pude+l9UG/2HL1n0SU1GWsrKvn1Pa9wxX2zSA184Mwe23XhhlED2bVX54adWFKr0yhvUUwp/SAihgHHRcTpKaUbG2NdSZIkSdqQitUVTPl/U3jy2ifZ4/Q9GPGnEbTt4AvnoXTw+wVjn+Afs+czat/enLxv7wabu21ZsFfvbrQr8+B+SRuvMX9r3wjsC5yT/SxJkiRJhXv38Xd5+q9Pc+hFh3LoDw6ldOKK7n/pfb427ilWrqngV6fszch9Gi7ckqSG1pgB15ys3aMR15QkSZKkWq1ctJIOXTvQ+8DenP/S+XTfqXvRJTUJayoqufRvL3PlA68yoFdnrhi9D/222rzosiRpvRpzL+gWWdu1EdeUJEmSpP/jtXte4zc7/YYXJ70IYLiVeXvhCk676lGufOBVRh+wA7eeN8RwS1Kz0Jg7uMZk7XuNuKYkSZIk/ZuZV8/kji/dQc9de9Jr715Fl9NkTHt+Lhfe/BQVlYnfnD6I4/fetuiSJKnONnnAFRF9gUuAIUAC7tnUa0qSJElSTZUVldz9jbt59FeP0u+Yfpx808m079K+6LIKt3ptJb+480X+8NBs9tiuC1ecvg99t+hUdFmSVC/1Drgi4t46di0HtgX6VLu2klLYJUmSJEmN6pU7XuHRXz3KfufvxzGXHUObtr697835yzn/xid46s2FfPqgPnxn+Mdo37as6LIkqd7y7OA6jNJOrLqo/vqR+cAZKaVXcqwpSZIkSbmsWbGGdpu1Y5dP7cJZ957FjofvWHRJTcKdz77Lf97yNAC/H7MPw/bcpuCKJCm/vP/LIurwWQO8D9wPfAsYkFK6cyPrlSRJkqQ6e2HCC/xmp9/w/rPvExGGW8CqtRX8cNKznPvXx9lpi07cccFQwy1JzV69d3CllNzHK0mSJKlJq6yo5P4f3s+DP3mQ7Q7Yjg7dOxRdUpPw+gfLOP/Gx3n27cWcc/COfPOYAZT7qKakFqAx36IoSZIkSZvcyoUrGT96PLOmzmLQ5wZx7BXH0ra9//SZ/NQ7fHvCM5S1Ca4+azBH7bZ10SVJUoPxt7wkSZKkFuWRyx7htWmvMfzK4ez7hX2JiA0PasFWrqng4inPc8Njc9hnh25cPnoftuu2WdFlSVKDMuCSJEmS1CKsXrqa8s3LOeS7hzBgxAC22cdzpV6dt5Tzrn+cF99bwrmH7syFR+9CuzIfSZTU8vibTZIkSVKzVllRyT3fvYcr976SFfNXUFZeZrgFTHrybY67/CHeX7KKaz6zH98aNsBwS1KLtd4dXBHxg02xaErp4k0xryRJkqTWZcWCFUwYPYFZd85in8/vQ7tO7YouqXApJa64dxaX3v0y+/ftwW9OH0Svrh6yL6ll29Ajihfx/9m77/Aq6/v/48/7nJOTnOw9SUIGBMIKJGxQFBFQEVerVevCWe1Qa2uttVs7vq1dtrWt2/4cVRS3IgIqQyFsCBACWYTsnZzkrPv3RzAOHIyEQ8jrcV25TnJ/7vvzed/oZQ4vP/f7gNkP6yrgEhERERGRY1K7rZanz3ualvIWznnwHPKvz/d3SX7n8fq456Xt/L8PyrlgfAq/uXCsPiVRRAaFw+nB1dcdGfsjMBMRERERkUHm7Tvfxt3h5qoVV5E6LdXf5fid0+Xl209t5O2iGr41K4s75uYM+gb7IjJ4fFXA9Y0+WGMc8G0gmL4Py0REREREZBDxeX24O9wEhgey8OGF+Dw+wpLD/F2W3zV2uFj02Do2VTTzi4WjuGLqUH+XJCJyXH1pwGWa5jNHO7FhGJn0PIp4CT3B1kfh1htHO6eIiIiIiAxezkYnz1/6PF6Xl28u/SYh8SH+LumEUNHYyZUPf8j+Zif/uCyfeaMT/V2SiMhxdziPKB4RwzASgXuARQfn/yjYWg38yDTN9/p6TRERERERObnVbK3hmfOeoaWihbMeOAuLPg0QgG37W7jqkXW4vT7+e+1kCoZG+7skERG/6LOAyzCMSOBO4BbAwcfB1hbgx6ZpvtpXa4mIiIiIyOCx/dntLLl6CYERgVy18ipSp6rfFsC7u+u46clCIoPtPH39ZLLj9aimiAxexxxwGYYRDHwP+D4QwcfBVglwj2maTx3rGiIiIiIiMji5nW6W/mApiXmJfO25rxGWpBAH4PnCSn74/BaGJYTx6NUTSQgP8ndJIiJ+ddQBl2EYNuBG4MdAPB8HW1XAL4GHTNP0HHOFIiIiIiIy6LTXtBMUGUSAI4Ar37mS8CHhWO1Wf5fld6Zp8vcVJfz+zV1Mz47hH5fnEx4U4O+yRET87ogfXDd6XAHsBv7Mx+FWI/BDINs0zQcVbomIiIiIyNHYt3wfD+Y9yLK7lgEQlRmlcAvw+kzuWbKd37+5i4V5yTxy1SSFWyIiBx3RDi7DMM4DfgWM/OgQ0AH8Cfi9aZqtfVueiIiIiIgMFj6vj/fufY+VP1tJzPAYxl893t8lnTC63F6++/RG3txeww2nZPLDeSOwWIyvvlBEZJA4rIDLMIzTgfuAgo8OAS7gQeBXpmnW9U95IiIiIiIyGHTUdrD48sXsXbqXMZeN4Zx/noM91O7vsk4IzZ0uFj22ng3lTfx0QS5XT8/wd0kiIiecLw24DMOYCNwLnP7RIcAHPA781DTN8v4tT0REREREBoPO+k4ObDjAgn8vYPyi8RiGdicBVDZ1cuXDH1LR5OSBSydw1pgkf5ckInJC+qodXB8AJj3BlgksBu42TXNnfxcmIiIiIiInN9NnsrNWWnEAACAASURBVPvV3eQsyCEuN47vlX5Pu7Y+YXtVC1c/so4ut5cnrpnE5MwYf5ckInLCOtweXCbQCeQCi4/x/6aYpmmOOpYJRERERERkYOuo6+CFb75AyZslXLHsCjJOz1C49Qmr9tRzwxOFhAXZeO6maQxPCPN3SSIiJ7QjaTIfDOQc43of7QQTEREREZFBqvz9cp675Dk66zs5+x9nM/S0of4u6YTy4sb93PHcZjJjQ3n0mokkRTj8XZKIyAnvcAIuPfwuIiIiIiJ94oO/fsCbt75J5NBIFq1ZRNJ49ZT6iGma/Ovdvdz3+k6mZEbz4DcLiHAE+LssEZEB4UsDLtM0LcerEBEREREROfmFDwkn98JcFvx7AYHhgf4u56h1ub386929rCtt7LM5O7o9bChv5pyxSfzh6+MItFn7bG4RkZPdkTyiKCIiIiIicsQq11ZSt6OO8deMZ+T5Ixl5/kh/l3RMVpfUc/cL29hb38HolHDs1r7bF/Dd2cP47uxhWCx6kEZE5Ego4BIRERERkX5hmiZr/riGZXcuIyorirGXj8VqH7i7kho7XPz61SKe31BJekwwTyyaxMxhcf4uS0REUMAlIiIiIiL9wNnkZMlVS9j10i5GnD+ChQ8vHLDhlmmaPFdYyb2vFdHW5eHm07L49unDCAoYmPcjInIyUsAlIiIiIiJ9yt3p5t8F/6alooW5f5rL5O9MxjAG5iN3JXXt/PiFrazd20h+ehT3XTCG4Qlh/i5LREQ+QwGXiIiIiIj0qYDgAKbcOoWUSSmkTErxdzlHpdvj5R8rSvj78hKCAizce/4YLpmYqt5YIiInKAVcIiIiIiJyzDpqO3j5upeZ9O1JZJ6RyaRbJvm7pKO2pqSBH7+4lb11HZw7Lpm7zxlJfFiQv8sSEZEvoYBLRERERESOSfFrxSy5egldLV3knJfj73KOWlOHi3tfK+J/hZWkRjt47JpJnDpcTeRFRAYCBVwiIiIiInJU3E43S+9YyroH1hE/Jp4rll1B/Oh4f5d1xEzTZPGG/fz6tSJanW5umpXFd04fhmOANsUXERmMFHCJiIiIiMhR2f7sdtY9sI4pt05h9r2zsQUNvL9e7K1r5+4Xt7G6pIEJaZHce8EYRiSG+7ssERE5QgPvN5CIiIiIiPiN6TOp31VP3Mg4xl0xjrjcOFImDrxG8t0eLw+u3Mvflu8h0GbhV+eN5tJJaWoiLyIyQCngEhERERGRw9K6v5UXr3yRqvVV3LLzFkITQwdkuPXB3gbuemErJXUdnD02iZ+ek0t8uJrIi4gMZAq4RERERETkK+14fgevXP8Kni4P8/48j5CEEH+XdEScLi/76jt4bHUpz6yvICXSwSNXTeS0EQOvZ5iIiBxKAZeIiIiIiHwhn9fHy9e/zKaHN5FckMwF/72AmOEx/i7rc/l8Jvubneyt72BfXTt76zvYW9fBvvoO9jc7AbBaDG44NZPvzh5GsF1/HRIROVnov+giIiIiIvKFLFYLhmEw464ZzPrZLKwB/v9kwZZONyX17eyr62BvfXtviLWvvoNuj6/3vNBAG5lxIUwcGsXFcalkxIaQlxpJanSwH6sXEZH+oIBLREREREQ+xef18f5v3idnQQ4JYxNY8O8FGMbxb75e19bNxvKmgzux2tl3cEdWQ4er9xyrxSA9OpiM2BBmDoslMy6UjNgQMuNCiAsN9EvdIiJy/CngEhERERGRXs2lzSy+fDEVqyrwOD0kjE04biGR0+Xlw9JG3i+u473ienZWt/WOxYYGkhkbwpzcBDLjQsiIDSUzLoS06GACrJbjUp+IiJy4FHCJiIiIiAgAW/67hde+9RoA5z95PmMvG9uv6/l8JjsOtPJecT3vFdexvqwJl8eH3WohPz2KO+bmMCUzhuz4UCIcAf1ai4iIDGwKuEREREREhK1PbeWFy18gdXoqFzx5AZFDI/tlnf3Nzt4dWqtLGmg8+LjhiMQwrpiSzoxhsUzKiFYDeBEROSL6rSEiIiIiMoi5OlzYQ+zkXphL9z+7mbBoAhZb3z3y19blZk1JA+/vqef94nr21ncAEB8WyKzhccwcHsv07Fjiw4L6bE0RERl8FHCJiIiIiAxCbqeb5T9Zzo7ndnDDxhtwRDkouKHgmOf1eH1srmzmveKeQGtjRTNen4kjwMrkzGgunZzGzGFxDE8IVQN4ERHpMwq4REREREQGmYrVFSy5egkNuxvYPzmJ0+9/F2+gtU/mbu/20OnyYhgwNiWCG0/NZEZ2HBPSIwm09c0aIiIin6WAS0RERERkkPC6vbx959usvX8t7uggll08nKRT0piVGNZnawTarEzKiGZaVgyRwfY+m1dEROTLKOASERERERkkDKvBltUVlEyIZ9PsNO44bxSXTU7HYtGjgiIiMrAp4BIREREROYm5nW5W/mIlQy4dza8/LOP9GQlMGRbHqxeNJTU62N/liYiI9AkFXCIiIiIiJ6lP9toq3LyfkoIEfnnhWC6dlKZdWyIiclJRwCUiIiIicpJxO928c/c7n+q1lXlGBm9coF1bIiJyclLAJSIiIiJykln5i5Ws/eNa9uTHs/WMdH5w/mi+MSkVw9CuLREROTkp4BIREREROQm4O9101HbQFm7nkYRASi4ZzrAzMnn1wrGkRDr8XZ6IiEi/UsAlIiIiIjLAla8qZ8nVS2gHnrg4G4vFwt23Tufiidq1JSIig4MCLhERERGRAcrdebDX1p/W4ooO4p05aUzMiOG+C8aQrF1bIiIyiCjgEhEREREZgJr2NfHk3CdpLG6kOD+ebXPSuevCMXwtf4h2bYmIyKCjgEtEREREZABqDLRSGmxl1SXDGXlmFq9fMIakCO3aEhGRwUkBl4iIiIjIALFv+T5W/vJd3D+Ywh9W7SXgvCx+umAUF05I0a4tEREZ1BRwiYiIiIic4DrqOnjjtjfZ9uRWumMdvP6/LUybOZR7zx9DYkSQv8sTERHxOwVcIiIiIiInKJ/Px0t/WM2mX7yH2elm69QkauYN5e6zc7lAu7ZERER6KeASERERETnBHGhxsnjDfp4vrCDrH5uwR9kJvGcat56Xy7SsWKwWBVsiIiKfpIBLREREROQE4HR5eXN7NYs/KKPtyW0Uj4kld1wSpz20gHMmpxEebPd3iSIiIicsBVwiIiIiIn5imibrSpt4vrCSV7ceIGxXIzOXVZBV7+S6+SM468ap/i5RRERkQFDAJSIiIiJynFU0dvY8grihkvLGTqLcPs5bX0fAuxVEZ0dz9lMXkXlGpr/LFBERGTAUcImIiIiIHAcd3R5e23qA5wor+WBfI4YBUzNj+N4Zw7A/upWNa/Yz/e6ZzLxrJgGOAH+XKyIiMqAo4BIRERER6Ucf7G3gmfUVvL61Gqfby9CYYL5/5nBmhTiIDbKROC4RZ0YMk28sIC43zt/lioiIDEgKuERERERE+sGe2nZ+/eoOlu+qIyzQxnnjU7goP4UxcaG8f9/7vPDbVaTNSOPKd67EEeXAEeXwd8kiIiIDlgIuEREREZE+1Nzp4s/LinliTRmOACt3nTWCK6YOJSjAyt639/LgGf+lcU8jY785ljP/cKa/yxURETkpKOASEREREekDHq+P//dhOX9cuptWp5tLJqVx25zhxIYGArDrpV08vfBporOj+ebb3yRztprIi4iI9BUFXCIiIiIix+jd3XX88pUdFNe2MzUzhnsW5DIyKRzTZ9K0r4mojCiy52cz9/65FNxYgC1Ib8NFRET6kn6zioiIiIgcpZK6dn79ahHv7KwlPSaYB7+Zz5m5CRiGQcWaCt74zhu07m/llp23EBgeyJTvTfF3ySIiIiclBVwiIiIiIkeopdPNn5cV8/iaUoICrPxo/giumj6UQJuVtqo23r7zbbY8sYWw5DDm/H4O9jC7v0sWERE5qSngEhERERE5TB6vj6cO9tlqdrq5ZGIqt83JIS6sp89WY0kjD+Y9iNflZcaPZjDzrpnYQxVuiYiI9DcFXCIiIiIih+G94p4+W7tr2pmSGc1PzsllVHIEAE17m4jKjCIqM4qpt09l7OVjic6O9nPFIiIig4cCLhERERGRL1FS1869rxaxbGctadHB/PPyfOaO6umz1bC7gTdvfZN9y/dxy65biEiNYNbPZvm7ZBERkUFHAZeIiIiIyOdo6XTzl3eKeWx1T5+tO+eP4OqDfba6W7t591fvsvZPawlwBHD6r04nNCHU3yWLiIgMWgq4REREREQ+weP18dS6Cv741i6anW4uLkjl9jM/7rPV3dbNAyMfoK2qjbxr8ph972yFWyIiIn6mgEtERERE5KD3i+v55Ss72FXTxuSMaO5Z8HGfrebSZiKHRhIYFsiU26aQfko6KRNT/FyxiIiIgAIuERERERH21Xfw61d38HZRLanRDv5x2QTmjU7EMAzaq9tZ9qNlbH58M4vWLCJlUgrTbp/m75JFRETkExRwiYiIiMig1eJ089dlxTy2phS71cIP5/X02QoKsOJ1efngLx+w8hcr8XR5mHbHNGJHxvq7ZBEREfkcCrhEREREZNDxeH08va6CPy7dTVOni6/np3L73OHEhwUBYPpMHpr2EAcKDzDs7GHMvX8uMcNi/Fy1iIiIfJEBE3AZhpEN3AFMAUYD75mmOesz5xjAj4CbgFhgHfAd0zQ3fea8XOCvwFSgGfgP8HPTNL1HOpeIiIiIDCyr9tTzi5d7+mxNyojmnnNyGZ3S02erobiB6OxoDItBwU0FhCWFMeysYX6uWERERL7KgAm4gFHAWcBawP4F59wJ/ISeIGwncBvwtmEYo03TrAYwDCMKeBvYASwEsoA/ABbg7iOZS0REREQGjp4+W0W8XVTDkKhP99lq3d/Kip+uYNMjm7jo2YvIvTCXCYsm+LtkEREROUwDKeB62TTNJQCGYTxHz66qXoZhBNETSt1nmubfDh5bA5QCt/BxeHUj4AAuME2zFVhqGEY48DPDMH5nmmbrEcwlIiIiIie41q6ePluPru7ps/WDeTlcMz2DoAArXS1drPrdKtbevxafx8fk705m6Kyh/i5ZREREjtCACbhM0/R9xSnTgHDg2U9c02EYxsvAfD4OpeYDbx4Mtz7yNPBb4FTg5SOYS0REREROUF6fydPryvnjW7tp7HTxtfwhfP/MHOLDD/bZMk2eOOMJqtZXMfobozn916cTlRHl56pFRETkaAyYgOswjAC8QPFnjhcBF3/mvHc+eYJpmuWGYXQeHHv5COYSERERkRPQ6j31/OKVHeysbmPS0GgeW9DTZ8s0TYoWFzHsrGHYgmzMvm82QVFBJOcn+7tkEREROQYnU8AVBbR/slH8QU1AsGEYdtM0XQfPa/6c65sOjh3JXL0Mw7geuB4gLS3t2O5ERERERI5KaX0H975WxFs7evps/f2yCcw/2GerdGUpS+9YStW6Ks751znkX5dP5hmZ/i5ZRERE+sDJFHABmJ9zzPicsS8673DO+dwx0zT/BfwLoKCg4POuFREREZF+0trl5m/v7OGRVfsIsFq4Y24Oi2b09Nmq3V7LsjuXsfuV3YQPCWfhowsZe/lYf5csIiIifehkCriagDDDMKyf2XkVCXSapun+xHmRn3N9BB/v7DrcuURERETkCH24r5Hlu2r7bD63x8cLG/fT2OnioglDuGPux322AF654RVqt9Uy+zezmfydyQQ4AvpsbRERETkxnEwB107ACmQDuz5xfMTBsU+eN+KTFxqGkQqEfOK8w51LRERERI5AYVkjlz/0AV6fidUwvvqCwzQ+LZK7z85lzJAIulq6WH7PcibePJHQhFAWPrwQR4yD4JjgPltPRERETiwnU8C1GmgFvgb8CsAwjGBgAQcfHTzodeAOwzDCTNNsO3jsYsAJrDzCuURERETkMJU1dHDd44WkRDpYfNM0okLsfTq/1+Xlg798wMpfrMTZ4CQyI5LxV48nZnhMn64jIiIiJ54BE3AdDJjOOvhjChBuGMZFB39+zTTNTsMwfgP8xDCMJnp2Wt0GWIC/fmKqfwLfARYbhvFbIBP4GfBH0zRbAUzT7DrMuURERETkMDR3urj60XWYpsnDV03s83Br+/+2s+zOZTTtbSLj9AzO+N0Z+mREERGRQWTABFxAPPC/zxz76OcMoBT4DT0h1I+AGGA9MMc0zZqPLjBNs8kwjNnA34CX6em7dT89IdcnfeVcIiIiIvLVuj1ern+ikMpGJ/+9bjIZsSF9vkbR80UEhARw2euXkTU3C6MPH38UERGRE59hmvrAv75WUFBgrl+/3t9liIiIiPidaZrc9uxmXti4nz9fksfCvJQ+mXPv0r2s+NkKznnwHBLGJNDV0oU91I7FaumDqkVEROREZBhGoWmaBZ83NpB2cImIiIjIAPOnt4t5YeN+vn/m8GMOt0zTpHR5KcvvWU7Fqgoi0iJor24nYUwCQRFBXz2BiIiInLQUcImIiIhIv3i+sJI/Lyvma/lDuPm07GOayzRNnjrnKYpfKyYsJYyz/n4W468Zjy1Qb2dFREREAZeIiIiI9IM1JQ3cuXgL07Ji+PX5Y466J9aBjQdIzEvEMAzST00na14W+dflYwvS21gRERH5mN4ZiIiIiEif2lPbzg1PrCc9JoR/XJ6P3XbkfbEqP6hkxU9XUPJmCZe9fhnZ87KZ/oPp/VCtiIiInAwUcImIiIhIn2lo7+bqRz/EbrPwyFUTiXAEHNH1VYVVrPjpCopfLSY4Npg5v59D2sy0fqpWREREThYKuERERESkT3S5vVz7+Hrq2rp5+vqppEYHH9H1XreXp899Gk+Xh9n3zWbSLZOwh9r7qVoRERE5mSjgEhEREZFj5vOZ3PbsJjZVNPOPy/LJS408rOtqt9Xy4QMfMv/P87HarVz84sXE5sQSGB7YzxWLiIjIyUQBl4iIiIgcs9+9uYvXtlbz47NGMm904leeX1dUx8qfr2T7s9sJDAtkwrUTSM5PJmViynGoVkRERE42CrhERERE5Jg89WE5/1xZwuVT0rh2ZsaXntvd1s0r17/Ctme2YQ+xM/OumUy9bSqOaMdxqlZERERORgq4REREROSordxdx90vbmNWThw/WzAKwzA+97yWihYiUiOwh9pprWxl2h3TmH7HdIJjj6xPl4iIiMjnUcAlIiIiIkdlZ3UrN/93A8MTwvjbpROwWS2fGjdNk+LXinn/vvep2VLDreW3EhQZxFXvXvWFQZiIiIjI0VDAJSIiIiJHrKa1i2seWUdIoJWHryogNPDjt5U+j4/t/9vOqt+somZLDRFpEcy+dzbWQCuAwi0RERHpcwq4REREROSIdLo8LHpsHc1ON8/eMJWkiE/3z6rdVsviSxcTlxvHeY+dx+hvjMYaYPVTtSIiIjIYKOASERERkcPm9Zl856mN7Khq5T9XFjA6JYLu1m7W/WMdnfWdnPn7M0nMS+Tq964mdVoqhkW7tURERKT/KeASERERkcP2y1d28HZRLb9YOIrJ0aEs+/Ey1j2wju6WboadNQyf14fFaiFtRpq/SxUREZFBRAGXiIiIiByWR1bt49HVpSyakUHB/k7+dPqf8HR7yL0wl+l3Tic5P9nfJYqIiMggpYBLRERERL7S2ztq+OsjhZyVEc1dZ42kvbKVMZeNYdod04jNifV3eSIiIjLIKeASERERkS/k9nh5+amtvPHLlSwsbibr7GFYLQYRaRGc+59z/V2eiIiICKCAS0REREQ+w+cz2VDexEv/Wk/bY1uIrOogPthGwY+mc9rt0/xdnoiIiMghFHCJiIiICKZpsr2qlZdW7uW1fQ1UtnUz/oNqRmMw9CczOP+2aYRHOvxdpoiIiMjnUsAlIiIiMojtqW3jpc0HWP76bsLfLiVrRyN5147j9hvyOe2u2USEBmJYDH+XKSIiIvKlFHCJiIiIDDIVjZ28vKWKlzdW0f5+BaPW11BQ3oYRZGP01Xmcevt0YobF+LtMERERkcOmgEtERERkEKht7eKVLQd4eUsVm0qbMC0G41MjmLGhjiAfTPntGUy4dgKOaD2GKCIiIgOPAi4RERGRk1RTh4vXt1Xz8uYq1u5rIKSpmxk7m7lmZwOXfXgdmWmRNJ01iojUCCw2i7/LFRERETlqCrhERERETgJur4+yhk5K6topqWtn3b5G3iuux+P1Mb7Vw7Wb6/Gs3Y/FamHU10cRF2AFICojys+Vi4iIiBw7BVwiIiIiA0hrl5uS2nZK6jp6wqzankCrrKETj8/sPS812sGiGRmcEhDA22f+F3tsMFPvmsnEb00kLDnMj3cgIiIi0vcUcImIiIicYHw+kwOtXb3hVUldO3sOhlp1bd295wVYDdJjQsiOD2Xe6ESy4kJJ7PLRsmQXtnovc84aCUDMCxeTNTeLAEeAv25JREREpF8p4BIRERHxo8YOF5sqmtha2dobZu2t68Dp9vaeEx5kIzs+lFnD48iKDyUrLpSsuBBSo4MJsFrweXzsfmU3hT9eydI392AYBiMvHIlpmhiGwYjzRvjxDkVERET6nwIuERERkeOk2+NlR1Urmyqa2VTRzMbyZsobOwEwDBgS5SArLpQpmTG9IVZWfCgxIXYMw/jCeVf8fAXv/eo9wlLCOPWeUxm/aDwRqRHH67ZERERE/E4Bl4iIiEg/ME2T8sbO3iBrU0UzO6pacXl9ACSGBzE+LZLLJqeRlxrJmCERBNu/+q2Zz+tjz+t7KHywkCm3TiHj9AzGXzOe5IJkhp89XJ+GKCIiIoOSAi4RERGRPtDidLP54M6sj74aO1wAOAKsjBkSwdXThzI+LZK81CgSI4KOaP62qjY2PLSBDf/eQGtFK6GJoXTUdgA9n4SoT0MUERGRwUwBl4iIiMgRMk2THQda2VDezKbyZjZVNFFS19E7nh0fyuwR8eSlRZKXGklOQhg269HvrDJ9Jg9NfYiW8hYy52Qy9/655JybgzXA2he3IyIiIjLgKeASEREROUxOl5cXNu7n8TWl7KxuAyAmxE5eaiTnj08hLzWKsakRhAcd26cVtle3s/GRjex5bQ9XLr8Si83Cgn8vICoriuis6D64ExEREZGTiwIuERERka9Q0djJE2vLeGZdBS1ONyOTwrn3/DHMHBbLkCjHlzaAP1ymz2TfO/sofLCQnS/uxOfxMfS0oXTUdRCWFEbWmVnHfiMiIiIiJykFXCIiIiKfwzRNVu1p4NHVpSzbWYPFMJg3KpErpw1l4tCoPgm1PlrHMAxKV5TyxJwncMQ4mPzdyeRfn0/M8Jg+WUNERETkZKeAS0REROQTOro9LN64n8dWl7Kntp3oEDvfmpXF5VPSSYpw9MkarnYXRYuL2PzYZpLyk5jzuzkMnTWUi569iJwFOdiC9BZNRERE5Ejo3ZOIiIgIUFrfweNryvhfYQVtXR7GpETwf18bxzljkwjqo2buZe+VsfGhjex4bgfuDjdRmVHkLMwBwLAYjPraqD5ZR0RERGSwUcAlIiIig5bPZ/JucR2PrS5lxe46rIbB/DFJXDVtKBPSIvvkMcTm0mYih0YCsOHfG9i1ZBejvzGavCvzSJ2e2mePOoqIiIgMZoZpmv6u4aRTUFBgrl+/3t9liIiIyBdo63LzfGElj68pY299B7GhgVw6OY3LJqeREB50zPN3NXex7ZltbH5sM5VrKrlx840kjE2gvbqdwIhAAhzH9imLIiIiIoORYRiFpmkWfN6YdnCJiIjIoFFS187jq0t5rrCSDpeXvNRI/nRxHvPHJBJoO/bHEFsrW3nr9rfYuWQn3m4vcaPiOON3ZxCWHAZAaGLoMa8hIiIiIodSwCUiIiInlKYOF8t31fJecT0d3Z4+m7e5082HpY0EWA0WjE3mymlDGZcaeczz1mytoaupi/RT0gmKDKLyg0ryr89n3JXjSJqQpEcQRURERI4DBVwiIiLiV6ZpUlLXwbKiGt4uqqGwrAmfCbGhgcSG2vtsnQCrhdvmDOcbk9KICws8prnaDrSx/ZntbH5sM9WbqknKT+L69ddjD7Xz3b3fxbAo1BIRERE5nhRwiYiIyHHn8fpYV9rUG2qVNnQCkJsUzi2nZTN7ZAJjUiKwnIBB0dIfLmX171eDCUn5Scz7yzzGfGNM77jCLREREZHjTwGXiIiIHBctTjcrd9exrKiG5Ttrae3yYLdamJIVw6IZGZw+MoGUSIe/y/wUZ6OToheK2PHsDhY+spCw5DDSpqcR4Ahg1MWjiBsZ5+8SRURERAQFXCIiItKPyho6eLuolmVFNXy4rxGPzyQ6xM6c3ETm5MYzY1gcoYEn1tsRV4eLoueL2P7MdkreKsHn8RGVFUVzWTNhyWHknJtDzrk5/i5TRERERD7hxHpHKSIiIgOa12eyqaKJpTt6Qq3i2nYAhsWHcu3MTObkxpOXGoX1BHuMz9XuoqOug6iMKLqau3jxqheJSItgyq1TGHXxKDWLFxERETnBKeASERGRY9bidPOHt3bxypYDNHa4sFkMJmVEc8mkNM4YGU96TIi/SzyEu9PN7ld3s/2Z7RS/WszQ04Zy2WuXEZ4Szk1bbiJuVJxCLREREZEBQgGXiIiIHJNVe+r5/v82U9vWzdljkjgjN4FTh8cR4Qjwd2lf6J2fvMPa+9fi7nATkhDC+EXjGf2N0b3j8aPj/VidiIiIiBwpBVwiIiJyVLrcXn7z+k4eXV1KZlwIi2+axrjUSH+XdQhXh4uSt0rY/fJu5v91PvYQO6GJoYy5bAyjLx5N+qnpWKwWf5cpIiIiIsdAAZeIiIgcsS2Vzdz6zCZK6jq4atpQfjhvBA671d9l9epq7qJocRE7X9zJ3qV78XR5CIoMouCmAlImpjDp5kn+LlFERERE+pACLhERETlsHq+PB5aX8Nd3iokLC+TJRZOZMSzW32UB0FDcAEDMsBiay5p5adFLRKRFMOG6CYw4bwRpM9OwBpw4IZyIiIiI9B0FXCIi5tq22AAAIABJREFUInJYSuraue3ZzWyuaOa8vGR+vnC0X/tsmT6TqvVV7HxxJ7uW7KJuRx151+Sx8KGFJIxN4IZNN5AwNkGN4kVEREQGAQVcIiIi8qV8PpMn1pZx3+tFBAVYeeDSCZw9NskvtZim2RtYPTTtIfZ/sB/DapB+Sjr5N+STszAHAMMwSByX6JcaRUREROT4U8AlIiIiX6i6pYs7ntvMe8X1zMqJ43cXjiU+POi41tDV3EXxa8XsWrKL6k3V3Fx0M4bFYMJ1E5h480SGnz0cR7TjuNYkIiIiIicWBVwiIiLyuZZs2s9PXtyG22vy6/NHc+mktOP6uF/Zu2W8+8t3KV1Ris/jIyQhhJxzc3C1uwgMD2TCognHrRYRERERObEp4BIREZFPae50cfeL23hlywEmpEXyx6/nMTQ2pF/XdHe6KV1Ryp439pB3dR5J45NwdbhoqWhh6u1TyVmYw5DJQzAs6qclIiIiIodSwCUiIiK9Vuyq5QfPbaGxw8Udc3O44ZRMbFZLv6zlandR+K9C9ryxh7J3y/B2e7E5bCRPTCZpfBLZ87IZNn9Yv6wtIiIiIicXBVwiIiJCp8vDva8V8eTacoYnhPLwVRMZnRLRp2t0t3az7519AIw4bwSWAAvLf7KciPQIJn5rItnzs0mfmY4tqOftiT79UEREREQOlwIuERGRQW5DeRO3P7uZ0oYOrpuZwe1n5hAUYO2TuWu31bL7ld3seWMPFasq8Hl8pE5PZcR5I7AF2vhe2fcIjg3uk7VEREREZPBSwCUiIjJA+HwmLq8PAIthYBhgfPL7I9zx5Pb6+MuyYh5YvoekCAf/79opTM2KOaYanY1OKtZUMPzs4QCs+OkKihYXkZiXyNTvTyV7XjapU1N7z1e4JSIiIiJ9QQGXiIjICazL7WVNSQNv7ahm6Y5a6tu7v/T8Q0IvPgq/er63HAzCDAO8PpNOl5eL8ofw0wW5hAUFHHF97k435avKKV1eSunyUvZ/uB/TZ/K98u8RkRrB7PtmM/9v8wlLCjvKPwERERERka+mgEtEROQE0+J0s2JXLW9tr2HFrlo6XF5CA23MyokjNzkc0+w5zzRNfCaYJvhME7PnYM8xzIPHe77no3M+ccw0YeawWGaPTDjs2jzdHirXVhKbE0toYijb/7edJVctwbAapExMYebdM8mel01Yck+gFTP82HaEiYiIiIgcDgVcIiIiJ4Dqli6W7qjmrR01rClpwOMziQsLZOH4FM7MTWBqVgyBtr7pi3UkfB4f+z/cz77l+yhdXkrFqgo8XR7OeuAsJn5rIsPOGsalr11K2ow0AsMCj3t9IiIiIiKggEtERMQvTNOkpK6dN7fX8NaOGjZXNAOQGRvCtTMzOXNUAnlDIrFYju8nCfq8Pqo3VmP6TFImpdDV0sXDMx4GExLGJpB/Qz4Zp2eQfko6ACFxIQybP+y41igiIiIi8lkKuERERI4Tn89kY0VzTz+t7TXsre8AYFxqJHfMzWHuqASy449/r6qarTXsW7aPfe/so+zdMrpbusmam8Xlb1xOcEwwl79xOUkTktQQXkREREROWAq4RERE+lG3x8vqkgbe2l7D0h011Ld3Y7MYTM2K4eoZGcwZmUBiRNBxq8fr8nJgwwEaihsY981xALx282uUv1dOdHY0o74+iqGnDWXorKG912SdmXXc6hMRERERORoKuERERPpQl9vLlsoWCsuaKCxrZO3eRtq7PYTYrcwaEc+ZuQnMyoknwnHkn1h4tPav20/R4iIqVlVQta4KT5cHi81C7kW5BDgCmP/X+TiiHUSkRhy3mkRERERE+pICLhERkWNQ3dJ1MMxqorC8ie37W/D4ej7mMDMuhAXjkjgzN5GpWTEEBfRvk3jTNGnY3UDF6goqVlVw+q9PJzQhlLKVZaz5vzUkTUii4KYCUqenkjotlYCDIVviuMR+rUtEREREpL8p4BIRETlMbq+PnQfaKCxrpLC8mQ1lTexvdgIQFGBh3JBIrj8lk/z0KManRREdYj8uddVuq+WdH79DxeoKOus7e+qJCiLv6jxCE0LJvz6fiTdP7A20RERERERONgq4REREvkBzp4sN5U29O7Q2V7TgdHsBSIoIIj89imtnZpCfHsXIpHACrJZ+rae9pp3KNZWUryqncnUl+TfkM+6KcVjtVuqK6hh+zvCe3VnTU4nNicU4+AmMgeGB/VqXiIiIiIi/KeASERE5qLqli5W7a3sDrZK6nk85tFoMRiWHc/HEVPLTo8hPjyI50tGvtTibnHS3dBM5NBK3080DIx6gpbylpx67leSCZGxBPb/GY4bH8O3d3+7XekRERERETmQKuEREZFBraO/m9W3VvLS5inWljZgmRAYHkJ8WxQUThpCfHsXYIREE2/v3V2bFmgoq11RSta6K/ev201TSxPAFw/nGS98gwBFAznk5RKZHkjI5heT8j8MtERERERFRwCUiIoNQa5ebt7bX8NLmKlbtqcfrM8mOD+XWM4Yzf3Qi2fGhGIbRL2t7ujxUb66man0VnfWdzPrpLACW3rGUilUVhKeGkzIxhfGLxpM+M733uvl/nt8v9YiIiIiInAwUcImIyKDgdHlZtrOGlzdXsXxXHS6PjyFRDm44JZMF45IZkRjW56GWz+PDYuvpy7X+wfVs+NcGarbU4PP4AIhIi+DUn5yKYTFY8K8FOGIchCaE9mkNIiIiIiKDgQIuERE5abk8Pt7dXcfLW6pYuqOGTpeX+LBALpucxrnjkslLjeyzUKurpYsDhQeo3lxNzeYaarbUULejjlsrbiUkLgSf24cj2sHU708lZWIKyROTCR8S3rt+XG5cn9QhIiIiIjIYKeASEZGTitdnsqakgZc3V/H6tgO0dnmIDA5gYV4KC8YlMTkjBqvl6EMtn8dHQ3EDNZtrqN5czYRFE4jOjqZocREvXfMSACEJISSOSyTzO5m9u7Um3TKJSbdM6pN7FBERERGRT1PAJSIiA57PZ7KxoomXNlXx6tZq6tu7CbFbmTsqkQXjkpmeHYv94KOCR8LZ5AQTHNEOarfX8uKVL1K3vQ5PlwcAi83CkMlDiM6OJnteNpe/eTkJ4xL0mKGIiIiIyHGmgEtERAYkl8fHxvIm3tlZyytbDrC/2YndZmH2iHjOHZfMaSPiCQqwHvZ8XpeXosVF1G6r7d2d1VrRyuz7ZjPjzhkExwbjiHIw8eaJJIxNIGFcAnEj47Dae9YISwojLCmsv25XRERERES+hAIuEREZEHw+k53Vbawuqef9PfV8sLcRp9uLzWIwc1gs3587nDNGJhAWFPCFc3S3dlNXVEfdjjrqi+qp21FHyuQUTv3JqWDAC1e8gOkzic2JJW1GGgnjEsiemw1AaEIo31z6zeN1uyIiIiIicgQUcImIyAmrorGTVXvqWVXSwOo99TR0uADIjAvhawVDmJ4dy5TMGCIcH4dapmnSWdfZG2RZbBbyr8sH4O+j/k5rZSsAVruVmOExpExO6fk5wMpNW28iMj0SW5B+PYqIiIiIDCR6By8iIieMpg4Xq0saWFVSz6o99ZQ1dAIQHxbIKcPjmJ4dy/TsGJIiHPg8PlrKW2jdWE3EtFQAXrnpFXY8uwNno7N3zsS8xN6Aa87v52Bz2IjLjSMqIwrLZ/pyxebEHqc7FRERERGRvqSAS0RE/Mbp8rK+rJH39/QEWturWjFNCA20MSUzmisnpZEXGMD4yakYhsHmxzez/J53aShuoHlfMz6Pj4CQAH7U+iMMi0FURhQjLxpJXG4ccSPjiB0ZS/iQ8N71Rl8y2o93KyIiIiIi/UUBl4iIHDcd3R521bSxpqSB94vrKSxrwtPtwRZgYfzQaG5JiCRmQw3W6kaantxFZVkzFV6TkQ0/wBHtoHV/K20H2kjMSyT3olyis6OJzo7unX/6D6b78e5ERERERMRfDNM0/V3DSaegoMBcv369v8sQEfGLLreX8sZO9tV3sK++g9K6dvbVdVDa1ElnWQvpu5sJa+4mqdNDRHM31HVy1frrSJ+QTOG/Cll6x1Kih0UTMyyGqOwoorOjyb0wF3uo3d+3JiIiIiIifmQYRqFpmgWfN6YdXCIicsTcXh8VjZ2UNnRQUtNOWW07pS1dVO1tJHT1fkJaXIS2dBPa6iK01UXwtWOZedpQUowAWv+1jaBoBzHDoomeOITo7Ggi40IAGL9oPBOum4BhGP69QRERERERGVAUcImIyOdyurxUtTgpr+ugrLqV0nYX5RXN+JYU46npIKSlm9AWFyFtLrbNTcc8K5u8yGAiVu7HGhlIWGoEsWMSic2KZtyV40gcl4iny4P7xkk4ohyfu6bFavnc4yIiIiIiIl9GAZeIyCBkmib17d2UV7RQtruBWqeL2lA7++vbsT28BW+9E1tzF8HtbhwdbrZOTmTXnHSygu3kLy3DiAoiKCWMqDGJJA6L4dqLRpI2LQ2f14fnW1O/8HFCW5ANW5B+9YiIiIiISN/S3zJERE5CTqebfcX1lBU3UFXSRJPPpC47kv3NTqLuX4e9ugNHm4sAtw+APaOiKbxgGCkRQUzZ3oAlzE7gkAhCk8OITovgvLlZ5J09HMMw8HzvlC8MqSxWi3pliYiIiIjIcaeAS0RkgKnfWU/tnkYq9zVRU95CY1Ur7VaDmvmZVDZ1MuT3HxJd2sonu1gdSAtjw/VjSY50kBoeiCPK0RteJWREcml+MtmTh/T0vrp91peurx1YIiIiIiJyotHfUkRE/MQ0TVxtLjobOulq6iJpQhIA25/dzt6VZdRXttByoJ3O2g48NoPaX8yksslJyp/WE7+nuXcej82gPjmU7aOjGRLlIHRGKkHTDaLTwkkYGkXqsGgyRsYRlRjWc8HN0/1xuyIiIiIiIv1GAZeISB/wur1YbBYMw6CxpJGaLTU4G5x0NnT2vi54cAEWm4VlP11O4T/X09XUhXnwEUFsFmofOYvKZiexj2wlcVsDzhAbzpAAuoID6Iy0U7O/hSFRwYRfm0dYUADJQyNJz4omfUg4CeEOrJaDe7au89+fg4iIiIiIiD8o4BIRoWc3FYBhGDgbndTvrKerpYuu5i66W7rpau5i/KLxhMSFsPvV3Xz4lw97wytno5Pu1m4W7b6Frgg7a//6ASV//vDjuW0WPCEB/H1oMFVeH7Eba0hKCqY7M5zuIBtdDhueMDuWqlZSYoKJ/9E0kmNCGBLlYEiUg5TIYOLDArFYjC8qX0REREREZFBTwCUiA5rpM3G1u3C1uwgMD8QeasfZ6KRidQXdbd09Y20uulu7GXXxKOJGxlG5tpJldy3rDa4+CrKufOdK0k9Jp/j1Yl64/IVD1qrPjKA7I4LKDyto29eIy2GjM95BW0owzVaD2X97n26HjWCXi6Crcul2WDFD7cTEOIgPd5AZFsjksEDiT8kkPiyIuPBA4sMCiQ8LIibErgBLRERERETkKCngEpF+ZZomPrcPd6cbi63nE/Z8Hh8HNhzA3enu+XL2vCaMSSAxL5Gu5i5W/X4VrjZXb0Dlancx4boJjLxgJPU763l01qO42ly4O929ay18ZCF5V+VRv6uepxY8dUgt8aPjiRsZh8dn0tXlgaggAlJCIdAKgVYeLaqmrrKB5v0tdF03hnqfSZNh4gq04gq04S0sgw09IVTYpTm94VR8eCCZYYHMO/h9XGhgz2tYEOFBtp7G7SIiIiIiItJvFHB9CcMwcoG/AlOBZuA/wM9N0/T6tTCRw+B1eXu+3F58bl9PjyirhdDEUADqiurobunG0+3B2+3F0+3BEe0gbXoaAJse3YSz0fnxeJeH+NHxjL18LABLFi2hu6W791pPl4fs+dnM/NFMTJ/JH5L/0Btgmd6ex/+m3j6VM//vTNxON/+Z/J9Daj7lnlNIzEvE7XSz6rerCAzr2ZFlD7NjD7Xj6fYAYAu3k3nOcHDYMINseAMteOw29kTb2flhOa2tTkL+diZOi0GH1aDdAo1eH9+rqKPuF2/R1OmGUxIPWT+0qpnYUDtx4YHEnZLOyNBA4sICie19tff+HBRg7Zd/biIiIiIiInLkjI/6zsinGYYRBWwHdgC/BbKAPwD3m6Z595ddW1BQYK5fv77/i+xnXreXA4UHDjkenhpOeEo4nm4P1RurDxmPHBpJaGIo7k431ZsPHY/OiiYkPoTu1m5qttbAwX8FP/p3MW5kHMGxwTgbnT3j0HsOQMLYBBzRDjpqO3qvN02z93XI5CEERQbRWtnas/5nxjNOyyAwPJDGPY1Ub6r+1JjpM8k5Nwd7iJ3qzdUcKDyA6TM/9TX+mvHYgmyUv19O5QeVvccxex6Xm/7D6VisFna/upuK1RX4PD5Mr4nP48OwGMz941wANvxnA+XvlePz+vB5er7sIXbOe+w8AFb8fAXl7356PDQhlEuWXALAC1e8QNnKsk+FWLEjYrn2g2sB+M/k/7D/w/2f+rNPnZ7KNe9fA8DfR/2duh11nxrPmpvF5W9cDsD9affTWtHaO2axW0mYl0XGfafT0e2h6NIX8Do9+AIs+KxGz2t+Ip4FwwAI+M9msBmYgTawWzHtVsysSHy5seAzsWyq6T1OoLXn+/BACAkA08Tl8dHm8tLR7aG920N7l4e2g69O9+FlzCF2KyGBNkKDbEQ6Aj4TVn38Gn/w1WFXaCUiIiIiInKiMgyj0DTNgs8b0w6uL3Yj4AAuME2zFVhqGEY48DPDMH538NhJrbu1m4emPnTI8dN/fToz75pJe3X7547P+8s8Jn97Mk17m3h42sOHjH/0GFnt9loemfHIIeNfe+5r5F6YS9X6Kp6c++Qh45e9cRnZc7Mpe6+M/130v0PGr1l1DanTUtn79l6WXL3kkPEbt9xIwpgEil8v5o3vvHHI+Hf3fRd7iJ3iV4t558fvHDI+6uJR2IJsFL9WzPv3vX/I+LTvTwMrlLxVwvq/r8ewGlhsFixWCzaHrTfgqiuqo3Rlac+YzYJhNbBFBLGntp0Wp5u9B9pobOzEa4AH8AJGZzf3vV4EgNcG5vAosFnAaoDNQnWs4+PxmSlYR8d8avxA9Mfj7ktz6e504QQ6TZNOn48NNgv/+M07dLg8uC7Iwm2Y+KwWvFYDPnrM7vGD4e3CDACsFoPQQBuBNkvPKbtqe8ZnJh3yZwO+j8dDLPSkgh7o8kAX0Nr5/9u77zjJqjL/459vT08OwACDwpJUkCAqKKKwIEkUBYVVogqIgC7+BJFF9GdCjKxgQFRAVERFUAQRkCAgIKIECbtEkTDkMDjMMExgwrN/nFNTt2vuraoO0z3V/X2/Xv2qW/eeuvfU7ToM9fRznrO0ZXdXF5PHdTN5XDerThzDOlMnMHlcN5PGdjNp7Ggmjetmcg5eTRyb9i89Pq6biWO666sKmpmZmZmZ2bDmDK4Kkq4DnoiIfQv71gGmA++OiIuqXjtsMrheWsxDVz+0zP6pG0xl6iunsnDeQqZfO32Z46ttvBorr7syC15YwKM3PLrM8WmvmcaUtaYw//n5PH5zyjBaWqNIsMZmazBx2kTm/WtejwywWpulGVzPvsiMe2aA8rF8iqmbrE73pDHMfnoOMx98nsURLI5gCbB4CUx61cpobDcvPjuXOU/OYTHBkggWB2iUGL/2FLpGj2Lh7AUsmvNSOncXqCsFecZMHY+6xOIFi4iFS9L1uwRdQhIa3YUkliwJZs9fxOz5C5k9byGz5hUe5y9iVsO+BYuWNP19jBnVxUCWcqoFpiYtDRp1L/u8GEQaU3s+OgeVRjF57GjG5fdrZmZmZmZmtjw5g6tvNgJ6pO9ExCOS5uZjlQGu4eLkax/gqTnzCYIISLPwAm6Zw5KbpxOQ90feTu3iulmFfXl/Pr44YMmjz6WA0pIoPLJ0e/H/PkYEKSi1JAenIliyhHT8L/9kSX7toiXpNbXHxUui9W/miuV+60pJMGXcaKaM72al8aNZafxopk2exErjRzMlP58yrru+vXRfes3Ybk+fMzMzMzMzMyvjAFe1VUiF5RvNzMd6kHQYcBjAOuuss3x7NkhueuhfPDhjDkJ0qZ5B1dUFQkjQJSFS8EZ5u0tammlU204JVqKrS4xSyh6SRHdXF2O70/4uwSjV2oiurvT6Ufm5JEZ1pdd2Kf10jxLdXWJUV1d+FKNH9XzePSrv7+rq8Xzp62rnKGSBDQQhJo/LwawJo5k0ppsuT5kzMzMzMzMzG3AOcDVXNn9TZfsj4nTgdEhTFJdzvwbFrw5781B3wczMzMzMzMyspa6h7sAKbCawcsn+lSjP7DIzMzMzMzMzsyHgAFe1e0m1tpaStDYwMR8zMzMzMzMzM7MVgANc1S4F3i5pcmHfPsA84Nqh6ZKZmZmZmZmZmTVygKvaqcAC4HxJO+ci8scB34qI2UPaMzMzMzMzMzMzW8pF5itExExJOwGnABeR6m59mxTkMjMzMzMzMzOzFYQDXE1ExN3AjkPdDzMzMzMzMzMzq+YpimZmZmZmZmZm1tEc4DIzMzMzMzMzs47mAJeZmZmZmZmZmXU0B7jMzMzMzMzMzKyjOcBlZmZmZmZmZmYdzQEuMzMzMzMzMzPraA5wmZmZmZmZmZlZR3OAy8zMzMzMzMzMOpoDXGZmZmZmZmZm1tEc4DIzMzMzMzMzs47mAJeZmZmZmZmZmXU0B7jMzMzMzMzMzKyjOcBlZmZmZmZmZmYdzQEuMzMzMzMzMzPraA5wmZmZmZmZmZlZR3OAy8zMzMzMzMzMOpoDXGZmZmZmZmZm1tEc4DIzMzMzMzMzs47mAJeZmZmZmZmZmXU0B7jMzMzMzMzMzKyjOcBlZmZmZmZmZmYdzQEuMzMzMzMzMzPraIqIoe7DsCPpWWD6UPdjgKwGzBjqTpitgDw2zMp5bJiV89gwK+exYVbOY6PcuhGxetkBB7isKUm3RMQbh7ofZisajw2zch4bZuU8NszKeWyYlfPY6D1PUTQzMzMzMzMzs47mAJeZmZmZmZmZmXU0B7isldOHugNmKyiPDbNyHhtm5Tw2zMp5bJiV89joJdfgMjMzMzMzMzOzjuYMLjMzMzMzMzMz62gOcJmZmZmZmZmZWUdzgMuWIWkTSVdJmivpCUnHSxo11P0yG0ySXiXpNEl3SFos6ZqSNpL0/yU9KmmepOskvX4Iums2KCTtJen3kh6XNEfS3yXt19DG48JGHEnvk3SDpOckzZd0n6TPSRpTaOOxYSOepLXyvx8haVJhv8eHjSiSDsrjoPHno4U2Hhe95ACX9SBpFeBKIID3AMcDRwNfGsp+mQ2BTYF3Av/IP2U+DXweOAHYHZgDXCnpZYPSQ7PB90nS5/wo4N3An4CzJX280MbjwkaiVUnj4RBgV+AnwGeBbxXaeGyYwTdJn/1GHh82Uu0IvKXwc37hmMdFL7nIvPUg6TPAp4B1I2J23vcp4DjgZbV9ZsOdpK6IWJK3zwNWi4jtC8fHAU8DJ0XE8XnfROBh4LSI+Nygd9psOZO0WkTMaNh3NvCWiFjf48KsTtJXgY8BqwBj8diwEU7StsCFwNdIga7JETHH/3bYSCTpIOCn5HFQctzjog+cwWWNdgUubwhknQOMB946NF0yG3y14FYTWwNTgF8XXvMicBFpHJkNO43Brew2YFre9rgwq3sOqE1R9NiwES2XO/keaXZI478lHh9my/K46AMHuKzRRsC9xR0R8QgwNx8zs2QjYDFwf8P+e/BYsZFla+DuvO1xYSOapFGSJkj6d+AI4IeRpkt4bNhI91FgHPD9kmMeHzaSPSBpUa7d+JHCfo+LPuge6g7YCmcV4PmS/TPzMTNLVgHmRMTihv0zgQmSxkTES0PQL7NBI2knUr3Gg/Mujwsb6V4kTUcEOAs4Jm97bNiIJWlV4MvAByJioaTGJh4fNhI9SaqvdRMwCtgPOFXShIj4Nh4XfeIAl5UpK8ymiv1mI1nVWKk6ZjZsSFoPOBu4MCLOLBzyuLCRbGtgAvAm4AvAKcDh+ZjHho1UXwVujIg/NGnj8WEjSkRcDlxe2HWppLHA5yR9t9as5KUeF004wGWNZgIrl+xfifLMLrORaiYwWdKohr+srAzMjYiFQ9Qvs+VO0lTgUuAR4AOFQx4XNqJFxK1583pJM4CfSToJjw0boSRtSsry3U5S7TvGhPy4kqTFeHyY1ZwH7A2sh8dFn7gGlzW6l4Y5vZLWBibSUJvLbIS7l5RO/KqG/cvUsTMbTiRNAC4mFc9+Vy54WuNxYVZXC3atj8eGjVwbAKOBv5K+sM+kXofrMVLheY8Ps54Cj4s+cYDLGl0KvF3S5MK+fYB5wLVD0yWzFdINwGxgr9qO/MV/d9I4Mht2JHUDvyF9Ydk1Ip5paOJxYVa3TX58CI8NG7muB3Zo+DkhH3sn8E08Psxq3ktaZXQ6Hhd94imK1uhU0qo/50s6AXgFcBzwrYiYPZQdMxtM+R+Qd+anawFTJL0vP/9DRMyV9A3g85Jmkv6S8knSHw6+N+gdNhscPyCNiyOBqZLeXDh2W0TM97iwkUjSZcCVwF2kVa+2AY4Gzo2IB3Ibjw0bcSJiBnBNcV+u4Qjw54iYk/d5fNiIIum3pALz/0PK1Non/xwREUsA/z9VHzjAZT1ExMy8KtYpwEWkulvfJgW5zEaSaaRMlaLa8/WBh4FvkP6R+QywKnAL8LaIeHqQ+mg22HbJj98tOeZxYSPZzcBBpLopi4AHSWPg1EIbjw2zah4fNtLcR6pPtzapcPzdwAER8fNCG4+LXlKEi++bmZmZmZmZmVnncg0uMzMzMzMzMzPraA5wmZmZmZmZmZlZR3OAy8zMzMzMzMzMOpoDXGZmZmZmZmZm1tEc4DIzMzMzMzMzs47mAJeZmZmZmZmZmXU0B7jMzMzM+kjSNZJCUgx1X8zMzMxGsu6h7oCZmZk11yR48hIwG5gFTAduBW4ELomIeYPUvY4laQ3gAOBtwGuAVYBRpHv6CHBIA1UHAAAV9klEQVQPcDNwdUT8z1D1s5NJWg/4d2BLYHPg5cBqwCTSfX4AuB44s7f3WNJkYAYwBjgmIk6saDcO2A/YE9gMWIM0dp4i/Y6vBs6PiMfbvO7fgK1I42y3wv7VgV2B7fN7XR+YmN/ng8B1wBkRcU8v3qOAvYEPAq8HVgf+BdwN/Ip03xa1OMd4YGdgR9LvYUNgZWA+8DjwN+AXEXFVu/3K530LcCjwVtLvdT7wEHABcGpEzOjN+czMzPpLEf6Do5mZ2YqsD9lBzwM/A74YEbOWQ5c6nqSPACeSAi3tODwiflhynmtIX/CJCA1YB4cJSecA+7TRNIBTgSNaBWwK594bODc/fXVE/KOkzU7A6cArWpzuqIj4ThvXXAN4EhDwkYg4Pe8/GTicFCBtZgnwbeDYiFjc4lqrAOeRAlNVbgX2jIhHKs7xftJ9bedzfhlwQEQ826JfAk4CPkG6D2WeBvaPiKvbuK6ZmdmAcAaXmZlZZ9mzsC1gJVLm0euB7YD1SNkZRwLvlbRfRFw/2J1ckUn6OHByYdftwEXA/cA80v3bBNiGlPEiKgIXEbH98uzrMDEXuAW4jZQx9BSwAFiLlFm0K+ke/ycpEHNAm+d9d368ryK49R/AOcDovOtPpCDOo8BYYG1SRtc7evFeds99DdJnpmYT6p+Ru0hZYf9LCjZPA95Fep9dwNGkcXto1UUkjQEuBLbNux4lBer+CfwbcDCwMbAFcKmkt0TE7JJTrU89uPUk8EdSVuIzpOyybUnZbeNI9+HKfK65Te7B14Gj8vaLwI+Bm/J13kvKiFwDuFDSthFxe5NzmZmZDRhncJmZma3gihlczbKEcmbFrsB3gA3y7tnA1hFx13LtZIeQ9HLStLjxpCDF4RFxapP2awIfAu6NiN8OTi+HD0kbAI9ExIImbXYE/kAKOgG8OSJubHHeUaQgzVTgmxHxqYbjm5Cym8aSpvTtVZVNJGkssFJEPNPG+/k9Kch1c0S8qbD/cuBZ4NsR8feK176PNK2w9gfmnZr06UjSOCa/j50jYmbh+Djgd8Db864TI+KYkvN8jhS4+gZwaVnWWL5XV5KmGQIcHxFfrOjX5sDfSUG+WcB2jVNLJR0H1F5/M7BV+AuHmZkNAge4zMzMVnDtBrgK7acAl5BqH0GqM/SaiFiyfHrYOSR9DDglPz03IvYdyv5YIum7wBH56Zcj4gst2m9PysgC2LYxS1HSX4E3k6YEbhsRNwxAH8cDz5GCo5+PiK8Ujq1SDEA1OceJpAwugLMi4sCSNt3AE6R6WwFsVhagljSNVNtrIjkjLiKea2jTbr92o56R9khErFvR7gJgj/z0YxHxg5I2ItX1qgUAd4uIS1r1wczMrL+8iqKZmdkwk6cq7U2aHgVpKlNpHSRJ4yXtKen7km6U9JykhZJmSbpL0g8lva7qWpL+s7aKoKSjqto1vOaiwms2Ljm+vaSfSbpP0hxJL0l6StKdks6TdIikl7VzrRIbFbav7eM5in1tuoqipIMK7/WgvG8TSadJekDSvHzPr5K0Xw4OtHPdVSR9StKVkp6QtEDSi/menS3p/TnLp9k53iHpTEn3S3pB0tzcpzMl/Xuz1y4Hdxe22/nd1qYnzgB6BK8kbUsKbkEqnt7v4Fb2NlJwC+D3xQPtBJGy3xS2N6tosyMpuAVwVVX2Zc44Oyc/HQu8p6RNu/26lDTdEGCdHCTvIRf13zU/nQ2cWdGvAL5X2NVODTYzM7N+c4DLzMxsGIqIJ0k1e2oOrmh6N3A+qUD2m0hTvrqBKaS6Qh8Fbpf0tYrX/wKYk7crawrV5Cl/tS/J1xdXlJPUJelHpMycA0irvU0k1VBaA9iUVOPnR8CnW12rQrGW1uqVrZaTHOS6FTiMVPh8HOme7wicDfy0jXMcCDwMnADsRJpaNgaYQLpn+5F+L8dWvH51SVeRghoHAq8i1U8an/t0IPBnSWdIGl12juXglYXtp9pov3t+vKQkM/FDhe0z+tWrnmpBtYf7sarmC4Xt8RVtdilsX9bifMXjvakl1kOeulisu1XWt7dSn0Z6XYs6XZcXtnetbGVmZjaAXGTezMxs+DobqNUm2lrS6IhY2NBmPKlG0R+pFwFfSCoAvgUpE2w08BlJzzSuNBcRL0g6mxSw2VjSNhHxlyZ9Oph6kOlHDcc+DhySt58nBWluI9X6mUAqoL8VsEOL993MA4XtAyV9p6I49/LwDmAv0vv5Pum9BWlxgA+R7vOBkq6LiJ+UnUDS0aTVH2v+QppaNp10X9cjBSJ2oGSFO0lTgb9SDyjdTcoq+gdpOt+mwEGkQuYfJv2/4kF9erdtkvRGUoF5SPfjghbtNyEF5aAhkyrbLj/OBf4maR3Sogu7AesA84GHgCuAkyPiiTb6KFKheOhZXL63XlPYnt5Gm9J6XgW3VLyuV/J0x1rAdy6pnlif+xURz0qaDqwLrCZpWjs1zszMzPrDAS4zM7Ph607StKOJpADRJsAdDW0OAq6MiEVlJ5D0WVKWyEbA8ZJ+HBEvNDQ7lRTggpTFVRrgykGCWibZLHpO16q9tnZsq7KV8fJ5ppAyjfrit6RV4Ebnc9ya6z9dHBEP9fGc7dqHtGLj2xu+7P9S0mWkTDqA/wKWCXBJegspawtSkOagiDi35DpflbQWKUjV6MfUg1ufB77WmAEl6QTSfdqFFHA7JyJaZRK1lANTG+an3aSpiNuRVgat/T/plyLithanqmVSLSAFqYrXWJn6+7sfeCdwFikjsWYcsHn+OULSIRFxdotrbkV96mRZUK1dxSzHqrpUGxa2H25xvseAxaTg5gaS1MeC7ocVti+rqNfXm35BCuDVanltSFoUwMzMbLnxFEUzM7NhKk87eqywa5kpeRFxWVVwKx+fTpq+CDCZ8jo/t5FWSwPYS9JKFafbGVg/b/8iIuY1HK9l5VxbFdzK15sdEbdXHW8mIh6m5/TGVwInAw9KelrSJZKOk7SzpDF9uUYTC4H3lWWyRMQF1AODG0tau+T1X6Ke/XZkRXCrdr7HG1cilLQF9QLhP4mIr5QFMiJiDrAvKdAI8Mkm76k3DiBlZ11ACm5+j5TR1k0KvO4TEV9q4zy1ANfVua9FxfpdU4FzScGtO0iBw31I2Vy1ulzjSQHGPWiuds1Z9LF2m6R9SWMA4GlKgpjZyoXtGc3OmcduLQOxmxTM7m2/XgF8prDrG/3tV1YseL9yZSszM7MB4gCXmZnZ8FYsMr1qH89RLNK9VUWbU/PjBGD/ijbF7JXTS47XavpssDxrP0XEt0i1vB5sODSNlPHzRdKUzackndAkYNdbF0fEA02OX13Y3qR4QNLqpCLnkPrdl9pSHyxsn1jZiqXFyf+Qn24naWyz9v30AikTqzG7cBl5Kl3tM1iWSVUMpKxNqhn1E2CLiDgpIn4dESdHxDakTL6aH0ma0OTStQDXZSXTfFvK2WvFz/zHI+LFiuaTCtvz2zh9MVA8uZf9mkgKONbe+/cj4uaK5oPWLzMzs75wgMvMzGx4K/5bX7XS3zRJ/yXpCkmP5dX4aiv/BT2/zJZNe4O0mlst42eZYvOSVqOe/XVTRZHuP+bHjYErJe0uqaoQd79ExPmkaVNvJ9XDuoM01atoFVINs9skbUj//a3F8ccbrl1UXNXw4oopZK1smx9fAl4taY9mP9QLio+l71NCl4qIT0eEIkKkaYIbkuquzQKOAe7IBfSb2Y30mQ7Ka2E1/r/tQ8DhFffrs9QzD1cD3l92wZzhtGl+2uvpiXnFz4uoB3l+EBGN03MHnaRRpDp9r827biVlubWjL9MgzczMlivX4DIzMxveihkt/2o8KGkf4DSg3SylKWU7I2KupJ8D/w/YXNIWEXFrocmBpJX+YNni8jXHkgI5a5JqM20HLJB0C2n63tWkaWm9zqCp6PNiUubQFQA5mLY5qUj7/tSLaq8PXCjptf28dqtpXQsK2+MajhUDi/fQN+vlxzG0KOReojHg1i8RsYBUI+t+Sb8kTfvbDDhT0rMR8YeKl9Yyqf4eEY+XHG+sD3dWvlZZH0LSGcCWedfOlH82a9dcRD2rrS25qP8V1AOE5wFHtHjZHOr3exz1VUqrFIPAje+/ql9dwJnU39t9wK4R0Swzq9iPdgLPve6XmZlZfziDy8zMbJjKGRrFwMizDce3I2Vw1IJbtwL/DXyEVINpz8JPzSiqnVbYbsziqq2OOIeU7bWMXB9rc+AU0iqKkLKHtiFlUl0GPCbpE7lg/YCKiHkRcUNEfJ2U1fKpwuGNSPWb+qMvWVc1xcBiq4BHlf5MtRzoemRL5emQhxd2fbGsnaRx1KdpVmVSPd/w/NbSVnXF1QBfWdGmlnl4XUQ0nr9Sntp6BSlwBymLa/8cWG2meI2m04oldVP/bCwiLSrRql8ijdUP5F0PADu1scph2/0qadP2fTMzM+srB7jMzMyGr82o19Z5Ebi74fhx1P9f4LCIeENEHBsRp0fEuRHxu4j4HfWpg01FxJ3UC6XvX6tpJGlbUoAI4FclhcGL53gmIj5Oqoe1NWnK1O+oF9KeBnybnsG0ARfJN4HLC7t3Wp7XbGF2YXtSZavmavf94dpUwV78XNO/7rf0F+pZPlvm2lCN3kb981wV4HqMnkGeWRXtyo4vEwDMqzLWpoe2PT1R0mTSZ+cNedflwF5tZgAWF1hYr0Xbf6MedL6/zRUUT6EecJ4O7FiRDdeffkF9BcXG15qZmS0XDnCZmZkNX8Vi7zcUv1znFQJrNZluiYiqaYPQ84tqK7Vi81OAvfP2IYXjZcXllxERCyPir7kw+J6kFSAPpj6N71BJm1WfYcBcVdhecxCuV6W4GubGfTxHLYixtqTSqaZDJQdmaoEpUT4VtjadbnpElBakz+e5s7Cr1fssBrXKgmHvol7So60Al6RJwKXUi+FfDexRNVWyRLH/b2zRtnj8zspW9b59h3q23GOk4NYjA92vvChC7b8bM9rIDjMzM+s3B7jMzMyGIUkvp+c0wR83NFmV+hf3Ziv7QSrE3q7zgOfy9qF5mtZe+fntEXFLL861VES8FBE/Bb5X2L1NX87VSy8Vtvs6NXAgXE+9sPduuYZSb12bH0cBuw9IrwZI/pysnp8G9c9Q7bhIBeahvLh80aWF7TdUtlr2eFmWUS2odmdEPNTiXOSsxYupfzavA3ZvUduqUTFrsNXYe0dh+7IWfftv4Mj89ElScKtxJdFmrqEeYN6uxQIQxX5fWtnKzMxsADnAZWZmNszk6VG/pl5g/h6gcdW2uYXtqtpDtXMd1e618xf5n+WnWwNfo15sulmWWLseLmz3erEcSdN6+ZJiIKhxiuegiYhnycXwSQXLD2nSvMpZhe0vVEwDHCoHU59q9/eIeKnh+JuAl+XtVplU51APBh6QsxWXkYNmxft4WcPx0dQDSC2zt3KNsAtJixQA3AC8KyLmVr+q1J+o18vbWdKmZY3yZ3nf/HR+vnZV375CWqkS4GlScOv+3nQqTy2uFdmfAhxUcS2RFpuoObc31zEzM+srB7jMzMyGCSW7ArdQrxs0m1T7p0eB84iYRVrFDuCNkoqF5Gvnm0QKjK3dy64U62PVpkPNBX7ZpO8vl3SipPWbtJlAWo2xpnSaWguflHSTpH1yQKLqWt2Svkq97tZi4Fd9uN5AOi73A+C7kvauapjv55uK+yLiRuC3+emGwEWS1mhyjm5Je0g6vKpNM5K2lnRos/uc2+0PfL2w6/slzWqZVLNJmUSVIuI+4Bf56frADyoy3r5KfQXFR1l28YPtqU9xbBrgykG035JWYgS4kbQqYa+z/iJiUe4bpOmaZ0nqsYplvqc/A2pBylMiokfWW6Ht54DP5qfPkgrK39vbfmVfph48/Lqk15a0+QL16Zk308uVJ83MzPqq13/5NDMzs6EjaY/iU2AyMBV4PbAd6Qt9zWPAfhFxV8XpvgecnLfPk/RL0lS4F4DXkDI01iRl/hzQbh8j4h+S/gTsUNj96xxUqzIWOBo4WtLNwJ9JmWfPk+okvZpUU2yt3P7Pua99sSUpmDFb0rWkYMRjpCmIU4BNgf+g5738RkQMWQYXQET8TdKxwInAOOBcSUeQgi+PkP5wuS6pttrOpOy5mxpOczApuLUZ6ffzoKTzgL8CM/J5Xw5sAexC+mw1Tm9t1zRSzbWTJF1BWrHwcVKtrUm5H+8CXld4zXnUMwCLagGuy9os1H4MKci7PvBhUhD3LFIg62WkzKetc9tFwAdLssZq13yKZe9jozOBd+btF4AfAju2WuwzL+JQ5ofAe0m/yy2AOySdBvyTVFj+w9Rrsd0NfKXsJJIOJQWlak4BNpC0QfO3w/URMaOkv7flqY7HksblDZLOIN2fSbnPu+Tmc0iLV7RT+N7MzKzfHOAyMzPrLBe00eZ5UlDqixHxfJN2p5AyLd5PCo58MP8UXQh8lF4EuLJT6RngajU9sfgleEvqmTVl/kTKSuvLF+d/kgIsE0nBrN1pXo/qReC4iDixD9cacBFxkqRZpJUkJ5FqPVXVIlvSuCMiZkvahvT72Ie0KuEBNP/9PtGvTqcg7HvzT5WXgG+S7nWP36uk9UgBV2iz0HtEPC1pF1LA7HX556SSpjNJQeBrS47VPhcXt/FZ27qwPZkU8GpHaQQsIl6S9B5S/3ckZVGWBbFuBfZsEjxu/Gx8qc1+7UB1ptxngDHAJ0jj6MiSNs+Q7uvtbV7PzMys3xzgMjMz61wLSVO2ZpNqU91Kyka6OCLmtXpx/tL+AUmXkArSb04KeDwD3A78PCJ+DdAqE6XElYXtuyPihhZ9mS5pHdKUwB1yX9YhBXEWkDJ/bgHOjoiLe9uZwnXOkPQLUtDgraTsmA1IBc7HkqZSPktaMe4q4JwVbQW4/B4uBD5CqhG1IbAK6T49SrpPv6eiJlNEvADsK+kEUmDrraR7vRKpltOTwF2kAukX9rIQedFFpEDl9qRMpPWBNUhZYfNJheTvJAVSzo6IqkDae/LjInox3S0i/ilpS9J73IeUmbc6KcPqPlIx+B+UBYElvY76KoBtBdUGWkTMlLQzaTXSD5LGxGqkoNxdpCzEn+YpjYPZryBN9f0NcBgpc3RN0u/0QeB3wA/LMsDMzMyWJzlr2MzMzAaapEOoZ20dFRHfGcr+WOeSdBUpIHlNROzQqv0AXfPzwPGkgOdq7QSMzczMbGi5yLyZmZktDx/Nj/PouXqfWdskrUTK/oLBzaSq1d+60sEtMzOzzuAAl5mZmQ0oSe8G3pCf/jIi/jWU/bGO9k5gdN4elACXpDWpf36HZHqimZmZ9Z6nKJqZmVm/SBpPquPUTSrmfQypntNLwMb9qOFkZmZmZtYWF5k3MzOz/loDuLRk/6cd3DIzMzOzweAAl5mZmQ2k54F7gBMj4vyh7oyZmZmZjQyeomhmZmZmZmZmZh3NRebNzMzMzMzMzKyjOcBlZmZmZmZmZmYdzQEuMzMzMzMzMzPraA5wmZmZmZmZmZlZR3OAy8zMzMzMzMzMOtr/AWhtHtHlb5TQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20, 12))\n",
    "plt.plot(adjusted_dates, alberta_cases)\n",
    "plt.plot(future_forecast, svm_pred, linestyle = 'dashed', color = 'purple')\n",
    "plt.title('Number of Coronavirus Cases in Alberta over Time', size = 30)\n",
    "plt.xlabel('Days Since 3/6/2020', size = 30)\n",
    "plt.ylabel('Number of Cases', size = 30)\n",
    "plt.legend(['Confirmed Cases', 'SVM Predictions'])\n",
    "plt.xticks(size=15)\n",
    "plt.yticks(size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size = \"5\">The results are in!</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM future predictions: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{('04/17/2020', 3096.790598544292),\n",
       " ('04/18/2020', 3321.442492459283),\n",
       " ('04/19/2020', 3556.790121497001),\n",
       " ('04/20/2020', 3803.0822236835634),\n",
       " ('04/21/2020', 4060.5675370450676),\n",
       " ('04/22/2020', 4329.494799607634),\n",
       " ('04/23/2020', 4610.112749397367),\n",
       " ('04/24/2020', 4902.670124440383),\n",
       " ('04/25/2020', 5207.415662762781),\n",
       " ('04/26/2020', 5524.59810239069)}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('SVM future predictions: ')\n",
    "set(zip(future_forecast_dates[-10:], svm_pred[-10:]))"
   ]
  }
 ],
 "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": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}