2. Area plots_Histograms_Bar charts.ipynb

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    "# Area Plots, Histograms, and Bar Plots\n",
    "\n",
    "Estimated time needed: **30** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "-   Create additional labs namely area plots, histogram and bar charts\n"
   ]
  },
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   "source": [
    "## Table of Contents\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "\n",
    "1.  [Exploring Datasets with _pandas_](#0)<br>\n",
    "2.  [Downloading and Prepping Data](#2)<br>\n",
    "3.  [Visualizing Data using Matplotlib](#4) <br>\n",
    "4.  [Area Plots](#6) <br>\n",
    "5.  [Histograms](#8) <br>\n",
    "6.  [Bar Charts](#10) <br>\n",
    "    </div>\n"
   ]
  },
  {
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    "# Exploring Datasets with _pandas_ and Matplotlib<a id=\"0\"></a>\n",
    "\n",
    "**Toolkits**: The course heavily relies on [_pandas_](http://pandas.pydata.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) and [**Numpy**](http://www.numpy.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) for data wrangling, analysis, and visualization. The primary plotting library that we are exploring in the course is [Matplotlib](http://matplotlib.org?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n",
    "\n",
    "**Dataset**: Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) from United Nation's website.\n",
    "\n",
    "The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. For this lesson, we will focus on the Canadian Immigration data.\n"
   ]
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   },
   "source": [
    "# Downloading and Prepping Data <a id=\"2\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Import Primary Modules. The first thing we'll do is import two key data analysis modules: _pandas_ and **Numpy**.\n"
   ]
  },
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   "source": [
    "import numpy as np  # useful for many scientific computing in Python\n",
    "import pandas as pd # primary data structure library"
   ]
  },
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   "source": [
    "Let's download and import our primary Canadian Immigration dataset using _pandas_ `read_excel()` method. Normally, before we can do that, we would need to download a module which _pandas_ requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
    "\n",
    "```\n",
    "!conda install -c anaconda xlrd --yes\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): ...working... done\n",
      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "installed!\n"
     ]
    }
   ],
   "source": [
    "# anaconda 명령어\n",
    "!conda install -c anaconda xlrd --yes         \n",
    "print(\"installed!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xlrd in c:\\users\\sori-\\anaconda3\\lib\\site-packages (1.2.0)\n",
      "installed!!!\n"
     ]
    }
   ],
   "source": [
    "# 파이썬 명령어 (common way in python)\n",
    "!pip install xlrd\n",
    "print(\"installed!!!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "source": [
    "Download the dataset and read it into a _pandas_ dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data downloaded and saved into dataframe!\n"
     ]
    }
   ],
   "source": [
    "df_can = pd.read_excel('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx',\n",
    "                      sheet_name='Canada by Citizenship',\n",
    "                      skiprows=range(20),\n",
    "                      skipfooter=2)\n",
    "print('Data downloaded and saved into dataframe!')"
   ]
  },
  {
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   "source": [
    "Let's take a look at the first five items in our dataset.\n"
   ]
  },
  {
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       "      <th>1980</th>\n",
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       "      <td>Immigrants</td>\n",
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       "      <td>Albania</td>\n",
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       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
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       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>912</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>3616</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>909</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>957</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
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       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
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       "         Type    Coverage          OdName  AREA AreaName   REG  \\\n",
       "0  Immigrants  Foreigners     Afghanistan   935     Asia  5501   \n",
       "1  Immigrants  Foreigners         Albania   908   Europe   925   \n",
       "2  Immigrants  Foreigners         Algeria   903   Africa   912   \n",
       "3  Immigrants  Foreigners  American Samoa   909  Oceania   957   \n",
       "4  Immigrants  Foreigners         Andorra   908   Europe   925   \n",
       "\n",
       "           RegName  DEV             DevName  1980  ...  2004  2005  2006  \\\n",
       "0    Southern Asia  902  Developing regions    16  ...  2978  3436  3009   \n",
       "1  Southern Europe  901   Developed regions     1  ...  1450  1223   856   \n",
       "2  Northern Africa  902  Developing regions    80  ...  3616  3626  4807   \n",
       "3        Polynesia  902  Developing regions     0  ...     0     0     1   \n",
       "4  Southern Europe  901   Developed regions     0  ...     0     0     1   \n",
       "\n",
       "   2007  2008  2009  2010  2011  2012  2013  \n",
       "0  2652  2111  1746  1758  2203  2635  2004  \n",
       "1   702   560   716   561   539   620   603  \n",
       "2  3623  4005  5393  4752  4325  3774  4331  \n",
       "3     0     0     0     0     0     0     0  \n",
       "4     1     0     0     0     0     1     1  \n",
       "\n",
       "[5 rows x 43 columns]"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "Let's find out how many entries there are in our dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "button": false,
    "new_sheet": false,
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    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 43)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the dimensions of the dataframe\n",
    "df_can.shape"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "#### 1. Clean up the dataset to remove columns that are not informative to us for visualization (eg. Type, AREA, REG).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
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   },
   "source": [
    "Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to `Introduction to Matplotlib and Line Plots` lab for the rational and detailed description of the changes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Afghanistan</td>\n",
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       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
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       "      <td>71</td>\n",
       "      <td>69</td>\n",
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       "      <td>...</td>\n",
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       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>Developing regions</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>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           OdName AreaName          RegName             DevName  1980  1981  \\\n",
       "0     Afghanistan     Asia    Southern Asia  Developing regions    16    39   \n",
       "1         Albania   Europe  Southern Europe   Developed regions     1     0   \n",
       "2         Algeria   Africa  Northern Africa  Developing regions    80    67   \n",
       "3  American Samoa  Oceania        Polynesia  Developing regions     0     1   \n",
       "4         Andorra   Europe  Southern Europe   Developed regions     0     0   \n",
       "\n",
       "   1982  1983  1984  1985  ...  2004  2005  2006  2007  2008  2009  2010  \\\n",
       "0    39    47    71   340  ...  2978  3436  3009  2652  2111  1746  1758   \n",
       "1     0     0     0     0  ...  1450  1223   856   702   560   716   561   \n",
       "2    71    69    63    44  ...  3616  3626  4807  3623  4005  5393  4752   \n",
       "3     0     0     0     0  ...     0     0     1     0     0     0     0   \n",
       "4     0     0     0     0  ...     0     0     1     1     0     0     0   \n",
       "\n",
       "   2011  2012  2013  \n",
       "0  2203  2635  2004  \n",
       "1   539   620   603  \n",
       "2  4325  3774  4331  \n",
       "3     0     0     0  \n",
       "4     0     1     1  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.drop(['Type', 'Coverage', 'AREA', 'REG', 'DEV'], axis=1, inplace=True)\n",
    "df_can.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Notice how the columns Type, Coverage, AREA, REG, and DEV got removed from the dataframe.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### 2. Rename some of the columns so that they make sense.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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",
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       "\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>Country</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "      <td>340</td>\n",
       "      <td>...</td>\n",
       "      <td>2978</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</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",
       "      <td>...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>...</td>\n",
       "      <td>3616</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>Developing regions</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>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country Continent           Region             DevName  1980  1981  \\\n",
       "0     Afghanistan      Asia    Southern Asia  Developing regions    16    39   \n",
       "1         Albania    Europe  Southern Europe   Developed regions     1     0   \n",
       "2         Algeria    Africa  Northern Africa  Developing regions    80    67   \n",
       "3  American Samoa   Oceania        Polynesia  Developing regions     0     1   \n",
       "4         Andorra    Europe  Southern Europe   Developed regions     0     0   \n",
       "\n",
       "   1982  1983  1984  1985  ...  2004  2005  2006  2007  2008  2009  2010  \\\n",
       "0    39    47    71   340  ...  2978  3436  3009  2652  2111  1746  1758   \n",
       "1     0     0     0     0  ...  1450  1223   856   702   560   716   561   \n",
       "2    71    69    63    44  ...  3616  3626  4807  3623  4005  5393  4752   \n",
       "3     0     0     0     0  ...     0     0     1     0     0     0     0   \n",
       "4     0     0     0     0  ...     0     0     1     1     0     0     0   \n",
       "\n",
       "   2011  2012  2013  \n",
       "0  2203  2635  2004  \n",
       "1   539   620   603  \n",
       "2  4325  3774  4331  \n",
       "3     0     0     0  \n",
       "4     0     1     1  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1. rename with mapper & index\n",
    "df_can.rename(mapper={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, axis=1, inplace=True)\n",
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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",
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       "    .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>Country</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "      <td>340</td>\n",
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       "      <td>2978</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>...</td>\n",
       "      <td>3616</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>Developing regions</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>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country Continent           Region             DevName  1980  1981  \\\n",
       "0     Afghanistan      Asia    Southern Asia  Developing regions    16    39   \n",
       "1         Albania    Europe  Southern Europe   Developed regions     1     0   \n",
       "2         Algeria    Africa  Northern Africa  Developing regions    80    67   \n",
       "3  American Samoa   Oceania        Polynesia  Developing regions     0     1   \n",
       "4         Andorra    Europe  Southern Europe   Developed regions     0     0   \n",
       "\n",
       "   1982  1983  1984  1985  ...  2004  2005  2006  2007  2008  2009  2010  \\\n",
       "0    39    47    71   340  ...  2978  3436  3009  2652  2111  1746  1758   \n",
       "1     0     0     0     0  ...  1450  1223   856   702   560   716   561   \n",
       "2    71    69    63    44  ...  3616  3626  4807  3623  4005  5393  4752   \n",
       "3     0     0     0     0  ...     0     0     1     0     0     0     0   \n",
       "4     0     0     0     0  ...     0     0     1     1     0     0     0   \n",
       "\n",
       "   2011  2012  2013  \n",
       "0  2203  2635  2004  \n",
       "1   539   620   603  \n",
       "2  4325  3774  4331  \n",
       "3     0     0     0  \n",
       "4     0     1     1  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2. rename with index OR column\n",
    "df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True)\n",
    "df_can.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Notice how the column names now make much more sense, even to an outsider.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### 3. For consistency, ensure that all column labels of type string.\n",
    " df_can[2010]을 불러올 때, int64 type의 2010 컬럼인지, 2010번째 컬럼인지 헷갈리기 때문\n",
    " → 사실은 헷갈릴 이유가 없음. df_can[--]은 컬럼 이름으로 불러오는 방법이기 때문에, 컬럼 이름이 2010 (type=int)인 것이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[True,\n",
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       " True,\n",
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       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False,\n",
       " False]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# column label의 type 확인\n",
    "[isinstance(column, str) for column in df_can.columns]   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# column label 전체가 str type인지 확인\n",
    "all(isinstance(column, str) for column in df_can.columns)    # boolean의 모든 값이 true인 경우 true 출력"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Notice how the above line of code returned _False_ when we tested if all the column labels are of type **string**. So let's change them all to **string** type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str,\n",
       " str]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mapping 방법 1\n",
    "df_can.columns = map(str, df_can.columns)\n",
    "[type(x) for x in df_can.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mapping 방법 2\n",
    "df_can.columns.map(str)\n",
    "all(isinstance(column, str) for column in df_can.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### 4. Set the country name as index - useful for quickly looking up countries using .loc method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "      <td>340</td>\n",
       "      <td>496</td>\n",
       "      <td>...</td>\n",
       "      <td>2978</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</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",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>3616</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>Developing regions</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",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</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>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Continent           Region             DevName  1980  1981  \\\n",
       "Country                                                                     \n",
       "Afghanistan         Asia    Southern Asia  Developing regions    16    39   \n",
       "Albania           Europe  Southern Europe   Developed regions     1     0   \n",
       "Algeria           Africa  Northern Africa  Developing regions    80    67   \n",
       "American Samoa   Oceania        Polynesia  Developing regions     0     1   \n",
       "Andorra           Europe  Southern Europe   Developed regions     0     0   \n",
       "\n",
       "                1982  1983  1984  1985  1986  ...  2004  2005  2006  2007  \\\n",
       "Country                                       ...                           \n",
       "Afghanistan       39    47    71   340   496  ...  2978  3436  3009  2652   \n",
       "Albania            0     0     0     0     1  ...  1450  1223   856   702   \n",
       "Algeria           71    69    63    44    69  ...  3616  3626  4807  3623   \n",
       "American Samoa     0     0     0     0     0  ...     0     0     1     0   \n",
       "Andorra            0     0     0     0     2  ...     0     0     1     1   \n",
       "\n",
       "                2008  2009  2010  2011  2012  2013  \n",
       "Country                                             \n",
       "Afghanistan     2111  1746  1758  2203  2635  2004  \n",
       "Albania          560   716   561   539   620   603  \n",
       "Algeria         4005  5393  4752  4325  3774  4331  \n",
       "American Samoa     0     0     0     0     0     0  \n",
       "Andorra            0     0     0     0     1     1  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.set_index('Country', inplace=True)\n",
    "\n",
    "# let's view the first five elements and see how the dataframe was changed\n",
    "df_can.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Notice how the country names now serve as indices.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### 5. Add total column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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",
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       "\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>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "      <td>340</td>\n",
       "      <td>496</td>\n",
       "      <td>...</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "      <td>58639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</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",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "      <td>15699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "      <td>69439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>Developing regions</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",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</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>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Continent           Region             DevName  1980  1981  \\\n",
       "Country                                                                     \n",
       "Afghanistan         Asia    Southern Asia  Developing regions    16    39   \n",
       "Albania           Europe  Southern Europe   Developed regions     1     0   \n",
       "Algeria           Africa  Northern Africa  Developing regions    80    67   \n",
       "American Samoa   Oceania        Polynesia  Developing regions     0     1   \n",
       "Andorra           Europe  Southern Europe   Developed regions     0     0   \n",
       "\n",
       "                1982  1983  1984  1985  1986  ...  2005  2006  2007  2008  \\\n",
       "Country                                       ...                           \n",
       "Afghanistan       39    47    71   340   496  ...  3436  3009  2652  2111   \n",
       "Albania            0     0     0     0     1  ...  1223   856   702   560   \n",
       "Algeria           71    69    63    44    69  ...  3626  4807  3623  4005   \n",
       "American Samoa     0     0     0     0     0  ...     0     1     0     0   \n",
       "Andorra            0     0     0     0     2  ...     0     1     1     0   \n",
       "\n",
       "                2009  2010  2011  2012  2013  Total  \n",
       "Country                                              \n",
       "Afghanistan     1746  1758  2203  2635  2004  58639  \n",
       "Albania          716   561   539   620   603  15699  \n",
       "Algeria         5393  4752  4325  3774  4331  69439  \n",
       "American Samoa     0     0     0     0     0      6  \n",
       "Andorra            0     0     0     1     1     15  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can['Total'] = df_can.sum(axis=1, numeric_only=True)     \n",
    "df_can.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now the dataframe has an extra column that presents the total number of immigrants from each country in the dataset from 1980 - 2013. So if we print the dimension of the data, we get:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data dimensions: (195, 38)\n"
     ]
    }
   ],
   "source": [
    "print ('data dimensions:', df_can.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "So now our dataframe has 38 columns instead of 37 columns that we had before adding Total column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "button": false,
    "collapsed": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1980',\n",
       " '1981',\n",
       " '1982',\n",
       " '1983',\n",
       " '1984',\n",
       " '1985',\n",
       " '1986',\n",
       " '1987',\n",
       " '1988',\n",
       " '1989',\n",
       " '1990',\n",
       " '1991',\n",
       " '1992',\n",
       " '1993',\n",
       " '1994',\n",
       " '1995',\n",
       " '1996',\n",
       " '1997',\n",
       " '1998',\n",
       " '1999',\n",
       " '2000',\n",
       " '2001',\n",
       " '2002',\n",
       " '2003',\n",
       " '2004',\n",
       " '2005',\n",
       " '2006',\n",
       " '2007',\n",
       " '2008',\n",
       " '2009',\n",
       " '2010',\n",
       " '2011',\n",
       " '2012',\n",
       " '2013']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finally, let's create a list of years from 1980 - 2013\n",
    "# this will come in handy when we start plotting the data\n",
    "type(range(1980, 2014))                         # range \n",
    "years = list(map(str, range(1980, 2014)))       # range -> str -> list\n",
    "years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Visualizing Data using Matplotlib<a id=\"4\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Import `Matplotlib` and **Numpy**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version: 3.1.3\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.style.use('ggplot')   # Optional: for ggplot-like style\n",
    "\n",
    "print('Matplotlib version:', mpl.__version__)  # check version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Area Plots<a id=\"6\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "In the last module, we created a line plot that visualized the top 5 countries that contribued the most immigrants to Canada from 1980 to 2013. With a little modification to the code, we can visualize this plot as a cumulative plot, also knows as a **Stacked Line Plot** or **Area plot**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Country</th>\n",
       "      <th>India</th>\n",
       "      <th>China</th>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <th>Philippines</th>\n",
       "      <th>Pakistan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>8880</td>\n",
       "      <td>5123</td>\n",
       "      <td>22045</td>\n",
       "      <td>6051</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>8670</td>\n",
       "      <td>6682</td>\n",
       "      <td>24796</td>\n",
       "      <td>5921</td>\n",
       "      <td>972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>8147</td>\n",
       "      <td>3308</td>\n",
       "      <td>20620</td>\n",
       "      <td>5249</td>\n",
       "      <td>1201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>7338</td>\n",
       "      <td>1863</td>\n",
       "      <td>10015</td>\n",
       "      <td>4562</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>5704</td>\n",
       "      <td>1527</td>\n",
       "      <td>10170</td>\n",
       "      <td>3801</td>\n",
       "      <td>668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  India  China  United Kingdom of Great Britain and Northern Ireland  \\\n",
       "1980      8880   5123                                              22045      \n",
       "1981      8670   6682                                              24796      \n",
       "1982      8147   3308                                              20620      \n",
       "1983      7338   1863                                              10015      \n",
       "1984      5704   1527                                              10170      \n",
       "\n",
       "Country  Philippines  Pakistan  \n",
       "1980            6051       978  \n",
       "1981            5921       972  \n",
       "1982            5249      1201  \n",
       "1983            4562       900  \n",
       "1984            3801       668  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.sort_values('Total', ascending=False, axis=0, inplace=True)   # Total 수로 상위 5개국 선정\n",
    "df_top5 = df_can.head() \n",
    "\n",
    "df_top5 = df_top5[years].transpose()       # 5개국의 1980~2013년 값만 추출\n",
    "df_top5.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Area plots are stacked by default. And to produce a stacked area plot, each column must be either all positive or all negative values (any NaN values will be defaulted to 0). To produce an unstacked plot, pass `stacked=False`. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e9u3bx8iRI20zq0qiSZMm7Nq1izNnzhAfH1+iZNVzzz2Ht7c3Dz30ECEhIZw5c4adO3fy7rvvEhwcDMB7773H4sWLOXz4MCdPnuSHH37AxcWFO+64o8SxCiGEEI4kCSohhBBClNjHH3/MQw89xBNPPEGXLl1ITEzk6aefLnJJka7rLFmyhJMnT9K2bVuefvppJk6cyC233GLXLjAwkIiICJo0aWJXP+pG9evXJygoiPPnz9O5c2cGDRpEQEAAy5YtK7fnLCld11m/fj0DBw7k5ZdfpkWLFgwaNIigoKAi60fdyGQysWrVKlxcXOjRoweDBw9m2LBhRc6CatiwIRs3buT48eN07NiRRx55hE6dOvHLL7+U9dHytXDhQrp168Zjjz3G3/72Nw4fPszGjRvzzBb75JNPmD59Ou3atWP16tUsXryYli1bFthveno6r776Ku3ateOuu+5i6dKlfPfdd7z11luFxtOiRQsiIyPp3bs3b7zxBm3btuXee+/lp59+4r333rPVHJs5cybDhw9n3LhxtGnThjVr1rB8+XK7JN4XX3xBw4YN6dmzJ0OGDGHo0KE0b968xO/RW2+9ha7rtG7dmrp163LgwIFiX+vt7c2OHTto2rQpI0aMoHnz5gwfPpyoqCgaNmwI5CaMP/jgAzp37kyHDh3YunUrq1evxt/fv8SxCiGEEI6kqdIUARBCCCGEuEnv3r3x8fFxaIJIVC2RkZF06NCBAwcOEBAQ4OhwhBBCCFGFSZF0IYQQQpTYgQMH2Lt3L3fddRdZWVnMnz+fkJAQ1q1b5+jQhBBCCCFENSQJKiGEEEKUmKZpfPXVV7zyyisYhkHLli1ZsWIFAwYMcHRoQgghhBCiGpIlfkIIIYQQQgghhBDCoaRIuhBCCCGEEEIIIYRwKElQCSGEEEIIIYQQQgiHkgSVEEIIIYQQQgghhHAoKZJegIsXLzo6hHJRp04d4uPjHR2GqMFkjInKIONMVAYZZ6IyyDgTFU3GmKgMMs5Eafn7+xd4TmZQCSGEEEIIIYQQQgiHkgSVEEIIIYQQQgghhHAoSVAJIYQQQgghhBBCCIeSGlTFpJQiIyMDwzDQNM3R4RRbbGwsmZmZjg5D1GAyxqoupRS6rmOxWKrV7y0hhBBCCCHEX48kqIopIyMDJycnzObq9ZaZzWZMJpOjwxA1mIyxqi0nJ4eMjAxcXV0dHYoQQgghhBBCFEiW+BWTYRjVLjklhBBmsxnDMBwdhhBCCCGEEEIUShJUxSTLY4QQ1ZX8/hJCCCGEEEJUdZKgEqIM4uLiePHFF+nevTs9e/Zk5MiRnDx5stz6Dw0NJTw8vNz6E0IIIYQQQgghqiJZsyZEKSmlGDNmDMOGDeOrr74C4ODBg8THx3PHHXeUyz127tyJu7s7nTt3znMuJydHlp0KIYQQQgghhKgR5NOtEKW0Y8cOnJycGDVqlO1YQEAASinee+89QkJC0DSNV155hcGDBxMaGsrXX3/Njz/+CMCUKVNo27Ytjz/+OF27dmXYsGEEBweTk5PDN998g4uLC/Pnz8dkMrFs2TLef/99Fi5cSK1atTh48CCtW7dm06ZNrFq1itq1a2MYBvfccw+rV6/G19fXUW+LEEIIIYQQQghRYpKgEqKUjh49Sps2bfIcX7duHVFRUQQHB3PlyhUefPBBunXrVmR/vr6+bNy4kXnz5vH1118zffp0Ro4cibu7Oy+88AIACxcu5NSpUyxatAiTyYS3tzfLly9n7NixbNu2jVatWklySgghhBBCCCFEtSM1qIQoZ7t372bIkCGYTCbq1q1Lt27d2LdvX5HXDRgwAIC2bdsSHR1dYLtBgwZhMpkAePzxx1m6dCkAv/zyC8OHDy+HJxBCCCGEEEIIISqXJKiEKKXmzZtz4MCBPMeVUvm2N5vNducyMzPtzru4uABgMpmwWq0F3tfNzc32dYMGDahbty7bt2/njz/+oHfv3iV6BiGEEEIIIYQQoiqQBJUQpdSjRw+ysrJYsGCB7VhkZCS1atVi1apVWK1WEhIS2LVrF+3bt6dBgwYcO3aMzMxMkpKS2L59e5H3cHd3JyUlpdA2I0aM4JVXXuGhhx6yzawSQgghhBBCCCGqE0lQCVFKmqYxZ84cfv/9d7p3706vXr0IDAxkyJAh3HnnnfTr14/hw4czZcoU6tWrR4MGDXjooYfo27cv48ePJyAgoMh79OvXjw0bNtCvXz927dqVb5v+/fuTmprK448/Xt6PKIQQQgghhBBCVApNFbQeqRx9+eWX7N27F29vbwIDAwGYP38+ERERmM1m6tevzz/+8Q/c3d0BWLFiBb/99hu6rvPMM8/Qvn17IHd2yvfff49hGPTp04chQ4YAEBcXx2effUZKSgpNmjTh5Zdfxmw2k52dzezZszl16hSenp5MnDiRevXqFSvmixcv2r1OS0uzW1pVXZjNZnJychwdhqhA+/bt4z//+Q8rVqxwyP1ljFV91fX3143q1KlDfHy8o8MQNZyMM1EZZJyJiiZjTFQGGWeitPz9/Qs8VykzqHr27Mlbb71ld6xt27YEBgYyffp0brnlFtuH6/PnzxMaGsqMGTOYMmUK3333HYZhYBgG3333HW+99RaffvopO3bs4Pz58wD89NNPDBw4kFmzZuHu7s5vv/0GwG+//Ya7uzuff/45AwcOtFuKJURNMHv2bMaOHcubb77p6FCEEEIIIYQQQohSq5QEVatWrfDw8LA71q5dO1u9nObNm3PlyhUAwsPD6d69O05OTtSrVw8/Pz9OnDjBiRMn8PPzo379+pjNZrp37054eDhKKaKioujWrRuQmwwLDw8HYM+ePfTs2ROAbt26cfDgwQILWAtRHY0fP57du3fTpUsXR4cihBBCCCGEEEKUWpWoQfXbb7/ZlvFduXKF2rVr2875+vpy5cqVPMdr167NlStXSE5Oxs3NzZbsut7+5r5MJhNubm4kJydX1mMJIYQQQgghhBBCiGIwOzqA5cuXYzKZuOeeewAKnOGU33FN0wrtuyTXbNq0iU2bNgHw//7f/6NOnTp252NjYzGbHf52lUp1jVtUHzLGqjYXF5c8v9OqG7PZXO2fQVR9Ms5EZZBxJiqajDFRGWSciYrg0E+VW7ZsISIignfeeceWOKpduzYJCQm2NleuXMHX1xfA7nhCQgI+Pj54enqSlpaG1WrFZDLZtb/eV+3atbFaraSlpeVZanhd37596du3r+31zQXfMjMzbbO0qhMpYC0qmoyxqi8zM7PaF7GUQpyiMsg4E5VBxpmoaDLGRGWQcSZKy+FF0vMTGRnJr7/+yhtvvIGLi4vteKdOnQgNDSU7O5u4uDhiYmJo2rQpd9xxBzExMcTFxZGTk0NoaCidOnVC0zRat25NWFgYkJv06tSpEwAdO3Zky5YtAISFhdG6desiZ10JIYQQQgghhBBCiMqlqUqoGv7ZZ59x6NAhkpOT8fb2Zvjw4axYsYKcnBzbjKZmzZoxbtw4IHfZX0hICLqu8/TTT9OhQwcA9u7dyw8//IBhGPTq1YtHH30UyF1+99lnn5GSkkKTJk14+eWXcXJyIisri9mzZ3P69Gk8PDyYOHEi9evXL1bMFy9etHtdFbZpb9asGcePHy92+9DQUL755ht++OEHgoKCOHbsGOPHj6/ACMVfkcygqvqqwu+vspK/0onKIONMVAYZZ6KiyRgTlUHGmSitwmZQVUqCqjoqKkFl/DgbFXuh3O6n1W+APqrw5FFZElRCVBRJUFV9kqASonhknInKIONMVDQZY6KiWZL24uHlRTxNHR2KqIYKS1BJZeNSUrEX4FhU+fVXgrahoaHMmDEDHx8fjh49Stu2bfn888/RNI2QkBCmTp2Kr68vbdq0sV2zaNEi9u/fzwcffEBQUBCzZs0iKysLHx8fZs+eTd26dcvtWYQQQgghhBCVT89JgoSzwK2ODkXUUJo1Fc/LKyHOit54Eoazr6NDEjWIw2pQibI5ePAg7777Llu2bOHs2bOEh4eTkZHB66+/zrx581ixYgVxcXH5XtulSxdWr15NUFAQgwcP5ssvv6zk6IUQQgghhBDlzT0hCP341zilHnF0KKKGcrsaiqayAQOv2F8cHY6oYSRBVU21b98ef39/dF2ndevWREdHc+LECRo3bsztt9+Opmk89thj+V4bExPDE088QZ8+ffjqq684duxYJUcvhBBCCCGEKE+akYlLygEAvGKXgDIcHJGoaTQjE9drO7Hq7miAc2Y0TmknHB2WqEEkQVVNOTs72742mUy2GkDF2aXw7bff5plnnmHz5s18/PHHZGZmVlicQgghhBBCiIrnknIQXWWhNDMmIw3XqzscHZKoYSxJ4ehGOprK/eypAK/YRZIMFeVGElQ1SNOmTTl37hxnzpwBYOXKlfm2S0pKws/PD4AlS5ZUVnhCCCGEEEKICmJJ3ouhudiSBx5XgtAM+UO0KCcqB7fEbRi6G7rKHVcaYLKmYLm207GxiRpDElQ1iMViYdq0aYwaNYohQ4bQsGHDfNtNmjSJ559/nkceeQRfXylqJ4QQQgghRHWmZ1/BOf0USv/fHliaysH98moHRiVqEktyJCZrUr6be3kmbJBkqCgXmlKqJBvI/WVcvHjR7vXN27QbP87O3cmvnGj1G6CPGl9u/V1nNptty/+EqAgyxqq+m39/VUeyZbaoDDLORGWQcSYqgnvCJtwSNwM6Gv9bbqWAhMavy05romyUge+5z9CtKehGer5N0jw7klJ/aCUHJqojf3//As+ZCzwjClURySQhhBBCCCGEKBFlYEmOQOlu6EZantNesb9wtdE/HBCYqClcUg9hzr6MVXcvsI1rcgRpPr0wnGtXYmSippElfkIIIYQQQghRTTmln8aUczXfpVfXd1ozp5+u7LBETaEUbolbMTQXTEZqoU29Yn+ppKBETSUJKiGEEEIIIYSopizJEShMmPKZPQW5y/y8L/0iO62JUnFKP4VT5nmUVvjiq9xk6Hmc0k5UTmCiRpIElRBCCCGEEEJUQ5qRgSXlIIbuWnAbwGRNwpK0p/ICEzWGe+IWDM0JvYjZU5CbDPWKXSTJUFFqkqASQgghhBBCiGrIJeUAmspGU9lFtvWIXwNGViVEJWoKc8YFnNNPoHRntGK0z02GpuB6NbSiQxM1lCSohBBCCCGEEKIasiTtxdBd0FVmkW11lY1H/MZKiErUFG5Xt6I0M7q16NlTN/K4shHNKHpMVjemrMuYsi6jWVNAWR0dTo0ku/hVI3FxcUydOpV9+/bh7OxMo0aNuP/++wkKCuLHH3/M0/61117jxRdf5I477nBAtEIIIYQQQoiKYsqKxznjzJ87qxUvGeCatJM03/swzF4VG5yo9kxZ8bikHMTQ3TCpnBJdq6kcPC6vIrn+sAqKrvK5Xt2OZ/xau2OGbkHprhgmNwzdDWXK/Vrpbhgm1z//7Yahu6JM14+5gmZy0FNUfZKgKqXZu2K4mFR+U2T9vZwZ3/WWAs8rpRgzZgzDhg3jq6++AuDgwYMEBwcXeM306dMxm83k5JTsF4oQQgghhBCiarMk70WhoRvpJbhK4Rm7hGsNxlRYXKJmcLv6O5R4fP2PJXkvqT69MZxrl29gDuCUfgaP+PVYdbfcmWGaDuigFJo1DZORhkkpQKEpA7AWuiSy5IktN5Ru+UsktiRBVUoXk7KIiivdD2tp7NixAycnJ0aNGmU7FhAQQFJSEjt27GDs2LEcPXqUtm3b8vnnn6NpGkOHDuU///kPAQEBNGvWjDFjxrBp0yYsFgvff/89devWJSgoiFmzZpGVlYWPjw+zZ8+mbt26lfZcQgghhBBCiBJSBpbkvRi6a4G79+VHA5zTT2DOOE+OpWHFxSeqNT0n6c/lo66YilEcvSDesQtJbDS+HCOrfJo1Ba9LC1G6M7qRiYY17/I+VXgfuakr058JJu3PxFZ6iRJbCp3Ld7wPWnGqgVVfUoOqmjh69Cht2rTJ99zBgwd599132bJlC2fPniU8PDxPm7S0NP72t7+xadMmunXrxoIFCwDo0qULq1evJigoiMGDB/Pll19W6HMIIYQQQgghysYp/SSmnGtoqohPxgXwurQQSnmtqPlcr+4ADLQyFI8L7uwAACAASURBVNXXAKfMCzilHS+3uCqdMvC+tAjdmoKC3ORUKWiAjhVdZaGrzD//yUA3Mv88lo2WT3JKAQYmDM0ZQ3ep8ckpkARVjdC+fXv8/f3RdZ3WrVsTHR2dp42zszP9+vUDoE2bNpw/fx6AmJgYnnjiCfr06cNXX33FsWPHKjV2IYQQQgghRMm4JkXkFq9WJV/RoQHmnCu4pOwr/8BEtadZ03G9Foahu6FT9O6QhVGAV+wiUEb5BFfJ3BJ/wzn9BIbJDZORUen3vzGxpXTXSr+/I0iCqppo3rw5Bw4cyPecs7Oz7WuTyZRvzSmz2Yz2Z8b1xjZvv/02zzzzDJs3b+bjjz8mM7Pm7bYghBBCCCFETaFZ03FJjcLQLGXqx/PySihh8WtR87leC0NXWWhG2ZJTkJtgMVlTcb0aWvbAKplz2jHcr/yGVXfHZE1xdDh/GZKgqiZ69OhBVlaWbWkeQGRkJGFhYWXqNykpCT8/PwCWLFlSpr6EEEIIIYQQFcsl5QCayilzAkE3MnFL+K2cohI1gpGF27UdWHU3dMpvQzCPKxvRHDADqbT0nGt4XVqM0i3oJajxJspOElTVhKZpzJkzh99//53u3bvTq1cvAgMDqV+/fpn6nTRpEs8//zyPPPIIvr6+5RStEEIIIYQQoiK4Ju3B0C3olH3lg/vVrWjW0hfBFjWLa1IEujW13OuTaSoHj8uryrXPCqOseF1aiKYyQRloRVVAF+VKU0qq4+Xn4sWLdq/T0tJwc3OzvZ69K4aLSeWXVfb3cmZ811vKrb/rzGZzvkv+hCgvMsaqvpt/f1VHderUIT4+3tFhiBpOxpmoDDLORFmYsuKofe7T3GVHZdhd7ToFZLq3IumWkWUPTlRvykrts9PRjEx0o/x3q1dAQuPXMJxrl3vf5ckjfi1uV7eX289Yeckx+3LlttcdHUa58Pf3L/CcuRLjqFEqIpkkhBBCCCGEEAWxJO1FoZVbAkEDXFIPYcqMxepStpUZonpzSdmPKecqVr3i/qjpHfsziY1errD+y8o5JapKJqf+SmSJnxBCCCGEEEJUdcrAkvwHhu6KRvnuiuYVu7Bc+xPVjFK4J27F0C2YKqjmkgY4ZV7EKe14hfRfVqbsBLziluTuXijJKYeRBJUQQgghhBBCVHHOaccxWZOgnGviaIBTVixOKYfLtV9RfTinHcWcFYvCVKH3UYDXpUWgyjfBWmZGNl4xC9CUgVJWNEfH8xcmCSohhBBCCCGEqOIsyREozYypguoDecctBWUt975F1eeWuBVDc6nwZW0aYDJScb26o0LvU1Ke8atxyorB0JwxqbJvPiBKTxJUQgghhBBCCFGFadY0XFIOYeiWiukf0I003K5ur5D+RdXllH4G54wzKM2p0u7pkbARzciotPsVxpK0F9ekcKy6h9SdqgIkQSWEEEIIIYQQVZglZR8aVjRrxc7ucE8IrjKJA1E53BK3oDQndCOlEu9qxSNuVSXeL3+mzEt4Xl75Z92pynx+URBJUFUT0dHR9O7d2+5YYGAgX3/9daHXRUZG8vbbbwMQGhpKeHh4ie/dtWtXrly5Uujx/fv3061bNw4ePEhQUBCzZ88u8X3yExoayqhRo8qlr+I4ceIE/fr1o3///pw5c8buXGpqKpMnT6Z79+7079+fBx54gAULFpTbvb/99lvS0/Ofsj106FDuuece+vXrx3333cdPP/1UYD+vvfYax44dA2DWrFnFuvfIkSO5du1ayYMupYLGbmBgIHfccYfd9tvNmjUrcf83Pnd+PzsVpaCflcIMHTqUffv2Oez+QgghhKj6LEkRGLoFnewKvY+GFY/Ljk8ciMphyozBJe0ohu5SqXWXNMCS8gemrPgi21ZYDEYm3pd+RqGBkS11p6oIs6MDqK72haeSklx+xd08PHXadXYvt/6ua9++PQEBAQDs3LkTd3d3OnfuXK73OHToEOPGjeOrr74iICCAgIAA+vfvX673qCwbNmzg/vvv57XXXstz7rXXXqNx48Zs374dXddJSEjgl19+ydPOarViMpW8wOCcOXN47LHHcHV1zff87NmzadeuHYmJidx9990MHz4cZ2fnPPeePn267fXnn3/OK6+8UuS958+fX+J4K4qvry/ffPMNU6ZMKfG1SimUUsV+7uLIycnBbC79r8rrMem6/D1ACCGEECVnyryEU+YFrHr5f1bIjyX5D1J9+mA4166U+wnHcU/8HYUJ3VqypW1L93kDMLRd2f7A7XVpIYmNXy5TH6WiFJ5xyzFlx2PorphUxexcKEpOElSllJJscOVy1SkiOHToUDp06EBoaCjXrl0jMDCQrl27smPHDr744gs++OAD5s+fj8lkYtmyZbz//vs0bdqUyZMnc+HCBQDeffddOnfuzJUrV3jppZdISEigffv2KFXwTiHHjx9n4sSJzJo1iw4dOgCwaNEi9u/fzwcffMDEiRPx9PRk3759XL58mSlTpjBo0CAMw2DKlCmEhYXRqFEjlFI8/vjjDBo0iJCQEKZOnYqvry9t2rSx3SsxMZFJkyZx7tw5LBYL06ZNo1WrVgQGBnLu3Dni4uI4deoUU6dOZe/evYSEhODn58e8efNwcrJfU33w4EEmT55MRkYGt956K4GBgURERDBnzhxMJhNhYWEsXbrU1v7MmTNERkbyxRdf2BINtWvX5qWXXgJyZ3rNmDGD+vXrExUVxZYtW1i2bBlz584lKyuLDh068NFHH2EymZg8eTL79u0jIyODgQMH8tprr/Hdd98RGxvLsGHD8PHxsbv3zdLS0nB1dbUlwZo1a8a4cePYunUr77zzDtOmTePtt99m7dq1ZGRk0K9fP1q0aMHs2bN59tlnuXjxIpmZmYwZM4annnoKyJ15s379elJTU3nqqafo0qULe/bswc/Pj7lz5+ZJmgUFBTFr1iyysrLw9fXl888/p27dugQGBnLhwgXOnTvHhQsXeO655xgzZgwAM2fOZOnSpfj7+1O7dm3atm2b7/P9/e9/Z/HixfzjH//Ax8fH7tw333zDokWLABgxYgRjx44lOjqap556iu7duxMREUHr1q3tnvuNN97AarXy+uuv53mmM2fOMGXKFBISEnB1deWTTz6hadOmTJw4kVq1anHw4EHatGmDh4dHgc+Vn5tjmjt3LidPnmT69OlkZWVx66238umnn+Lubv8/mvmNjevfn2HDhhEcHExOTg7ffPMNTZs2LdHPqhBCCCGqJ9fkvSg0dKPyPkR7xf7C1UYvVdr9ROXTs6/gkrIfw+SGyVr85W2xyWYiL7gB0KlRGrf5lm5WX+7ukRdxSjtGtlvzUvVRWq5JYVhS9mM1eZTo2UXFkz/p1yA5OTmsXbuWd999lxkzZtida9SoESNHjmTs2LEEBwfTtWtX3nnnHcaOHcu6dev49ttvbR+GP/30U7p06UJQUBD9+/e3JbDy8+yzz/L+++/TpUuXAtvExsaycuVKfvjhBz766CMA1q1bx/nz59m8eTPTp08nIiICgIyMDF5//XXmzZvHihUriIuLs/UTGBhIQEAAmzZtYvLkyUyYMMF27uzZs/z444/MnTuXl19+me7du7N582YsFgubN2/OE9PEiROZMmUKmzZtomXLlsyYMYM+ffrY3qObE0THjh2jVatWhc6CiYyM5I033mDLli0cP36cVatWsXLlSoKDgzGZTCxfvhyAN954g/Xr17Np0ybCwsI4dOgQY8aMoX79+ixZsqTA5NT48ePp27cv9957LxMnTrQlqNLS0mjRogVr1qyx+z689dZbWCwWgoODbUsuAwMD2bBhA+vWrWPu3Ln5Lgc7ffo0o0ePJiQkBC8vL9atW5enTZcuXVi9ejVBQUEMGTKEL7/80nbuxIkTLFiwgLVr1zJjxgyys7PZv38/q1atIigoiDlz5hS6pM3d3Z2///3vfPfdd3bH9+/fz+LFi1mzZg2rV6/m559/5uDBgwCcPHmSoUOHEhQUxKeffprnuQt6pn/961+89957bNiwgbfffps333zTdr9Tp06xaNEipk6dWuBzFebGmNzc3Jg5cyaLFi1i48aNtGvXjv/+9795rslvbFzn6+vLxo0bGTlypG15ZEl+VoUQQghRDSkrLsl/YOhuaFTOH6I0wDnzPE5pJyvlfsIx3K5uA0Czlqzm2PZT7jiZFE4mxfrDtSjL30cV4HVpMajyW5lUFHNGNB6X12LV3SU5VQXJDKpqQtOKXhX74IMPAtC2bVvOnz9fZPtt27bZahUBpKSkkJKSQlhYGHPmzAGgb9++1KpVq8A+evTowcKFC+nZs2eBy9oeeOABdF2nefPmXL58GYDdu3czaNAgdF2nXr16dO/eHchNAjRu3Jjbb78dgMcee8xWb2n37t18++23tvsmJiaSlJQEQK9evXBycuLOO+/EMAx69eoFQMuWLYmOjraLJykpiWvXrnHXXXcBMGzYMJ5//vki368bzZw5kzVr1pCQkMDevXuB3OWUjRs3BmD79u0cOHDA9j3JyMigTp06AKxevZoFCxZgtVqJjY3l+PHjtGrVqsh7Xl/il5CQwMMPP0yvXr1o2LAhJpOJgQMHFivuuXPnsn79egAuXrzI6dOn8fX1tWvTqFEj27LQtm3b5nn/AGJiYnjxxReJi4sjOzubRo0a2c716dMHFxcXXFxcqFOnDpcvX2bXrl088MADtplY/fr1KzTOZ599lv79+9t9X3bv3s0DDzyAm1vuX2wGDBjArl276N+/Pw0bNqRjx44F9pffM6WmphIREWF3j6ysLNvXgwYNshvT+T2Xv79/gfe8MaaIiAiOHTvG4MGDAcjOzs433sLGxoABA2zxX/8eluRnVQghhBDVj3PaMUzWFKx6/iUgKooCvGIXkXDbZNBkTkNNo+Wk4Jq0B8PkiqkEy/uSMnT2X3Sl1S1ZHLjowoVrZg5dstD6ltIV1tcAk5GK69XtpPvcW6o+SnQ/a1pu3SndLJsBVFGSoKomfHx88hSxvnr1ql1i4Ho9IpPJRE5OTpF9GobBqlWr8q15VJyEGMAHH3zA5MmTefPNN5k2bVq+bW6sk1ScJUgF3Tu/a6+3dXFxAUDXdcxms+24rutYrWVfitmsWTMOHTqEYRjous6ECROYMGGCXQHv64mT67EOGzbMbkYOwLlz5/jmm29Yu3YttWrVYuLEiWRklOyXY+3atWnTpg179+6lYcOGuLi4FKvmVWhoKNu2bWP16tW4uroydOhQMjPz7gRz/b2E3LGUX3xvv/0248aNo3///uzatYtPPvmkwOuvv//FHVMA3t7eDBkyhB9++MF2rLCxc+N7n5/8nskwDLy8vAgODi5WnwU9V3FiUkpx77332s00u1lRY+P6/W++d0neVyGEEEJUL5akCJTmhMnIfyOdiqIBJmsylqTdZHh3q9R7i4rndi0UVA6aUbL/jww9446hINuae52mKTYc9aJl/QxMZchjeiQEkeHVBWWylL6ToigDr9gl6DlJGLoLJqpOuR7xP5IOrybc3d2pV68e27blTsVMTEwkJCSk0KV1+fWRkvK/aYz33Xcf8+bNs72+vlyqW7dutuVov/32G1evXi2wT13X+eKLLzh16pRdkqIonTt3Zu3atRiGweXLl9m5cycATZs25dy5c7Yd9FauXGm75sa4QkND8fX1xdPTs9j3vM7Lywtvb2927doFwLJly+jWrfD/8DZp0oS2bdvy8ccf25IDGRkZBSZNevTowZo1a2y70SUmJnL+/HmSk5NxdXXFy8uLy5cvExISYrvGw8PD7vtTkPT0dA4ePMhtt91WZFsnJyfbUrTk5GS8vb1xdXXlxIkTtplfpZGUlISfnx8AixcvLrJ9t27d2LBhA+np6aSkpBSYFLrR888/z08//WR7v7t168bGjRtJT08nLS2NDRs20LVr13yvvfG5C+Lp6UmjRo1YvXo1kJtEioqKKjKu0ujYsSPh4eGcPn0ayP0enjxpP22+sLFRkJL8rAohhBCietGsqbikHsHQnItuXEE849eBkVV0Q1FtaEYGrtd2onR3dFX8+lEZ2Rrh59xoWjebo7G59X2V0khMM7EnuvA/FhfNisflX8vYR+Hcrv6OS9oRDN2t0hO+ovhkBlU1MnPmTN566y3+7//+D4BXX321WEmK6/r168fzzz/Pxo0bef/993nvvfd466236Nu3Lzk5OXTt2pWPP/6Yf/7zn7z00kvcf//9dOvWjQYNGhTar4uLC3PnzuWxxx6jbt26Be5Cd6OBAweyfft2evfuze23306HDh3w8vKyFT8fNWoUvr6+dOnShSNHjtie99VXX6Vv375YLBY+++yzYj/7zT777DNbkfTGjRvnqdmVn+nTp/Pee+9x9913U6tWLSwWS4E7zTVv3px//etfjBgxAqUUZrOZDz74gI4dOxIQEECvXr1o3Lix3Y6KTz75JE899RT16tXLtw7V+PHjsVgsZGVlMXz48AKLjN/oySefpG/fvrRp04bAwEDmz59P3759uf322/nb3/5W5PUFmTRpEs8//zx+fn506tSJs2fPFtq+TZs2PPTQQ7bleAUllm7k6+vLAw88YFvW2aZNG4YNG2ZbzjhixAgCAgLyXYJ443O/8cYbBd5j9uzZvPnmm8ycOZOcnBwGDx5M69ati4ytpGrXrs2nn37KSy+9ZFtG+K9//Ys77rjD1qZ169YFjo2ClPRnVQghhBDVhyU5Eg0rmgMTRJrKxiN+PSn1BjssBlG+XK/tRjcyMDSXohvfYE+0G5k5Op4WUPxv5pWGYvNxT9o3SMfFXLqCVBpgSYkkLasPVuc6peqjME7pp3BPCMJqkrpTVZ2mZNunfF28eNHudVpamt2SnX3hqaQkl18xNw9PnXady3/rWLPZXKzlfo6QmpqKu7s7V65cYdCgQaxcuZJ69eo5OixRQlV5jIlcN//+qo7q1Kljm5EoREWRcSYqg4wzUVw+52ZhyklEd3CtHIVGwm1vYJi9HRqHKAdGNrXPfgLKiqkEu0LmGDBjSz28LQYXk8xY81ka2KtZMn2alT75o4Ac51tIbPxKqfvIj56TjE/052gqB83IRKPyCrKXpxyzL1due93RYZSLwur4ygyqUqqIZNJfzejRo7l27RrZ2dlMmDBBklNCCCGEEBVFWUErumalqBrMmRdxyorBqleFzxwKz9glXGvwnKMDEWVkSf4DkzW5xEX3D1x0JSnDREu/HKKv5le3SrH9lAddG6fh4VK6BJAGOGXF4JR6lGz3FqXqI29YVrxif0G3pqE052qbnPorkQSVcJj8lrEJIYQQQojy5Za4BbfEbVxp9BKGk2/RFwiHsyRFoNDRjeLvsFZRNMA5/STmjGhyLI2KbC+qKGXgdvV3DN21RDWYlILtp92p425lX7RTAa00cqy5S/0GB1wroE0x7gV4xy4mvsmUctk90v3KJpzTT/25tM/xP0uiaFIkXQghhBBCiBrKlHkJ94RgdCMN75gfcz9tiqpN5WBJ3oehu1KV9ur1urRQxk815pIahTk7wa5+VHEcj3chNtmJO+rmkGktOH2ggIhoV+JTSz9TUwN0Iw23q9tK3cd1zqlHcE/cglWX5FR1IgkqIYQQQgghaiJlxStuKUozo9BwyorFkhzh6KhEEZxTj/45c6rqLEfSAHNOIi4pkY4ORZSGUrglbsHQLSWqPQWw/ZQ7Hi4Gh2OKWnyloYCgo2WvVeaeEIxmLX3tNT07Ea/YxRi6W5WYhSiKTxJUQgghhBBC1EBuV3fglHkBNKc/PzqC5+Vf0ayyxXpV5pocgaE5lWgZVmXxjPsVlGyOU904pZ/AKfMiipLNbrp4zcypBBda1s8mKbPoa5XSOHTJhejEgpYCFpcVj8srS3epysH70s9oKhulrFVqFqIomiSohBBCCCGEqGFMWfG4XwnGqrvbzyBQOXjGLnZcYKJQWk4yzqlHUZqzo0PJl64ycU/Y7OgwRAm5J27F0JwxlXA20bZTHjibDM5fLX7aQNcU6454l2k1qAZYUvZhyrxc4ms94tfhlHkeQ3PBpDJLH4RwCElQVSONGjWiX79+9O7dm3HjxpGenk50dDS9e/fOt/0nn3zC1q1bARg6dCj79u0DYOTIkVy7VrridT/++CNLliwp3QMIIYQQQoiKpww845YDoBn2H9A0wJJ2BKe0kw4ITBTFkhyJhoFmZDk6lAK5Xf0dzZri6DBEMZkzonFOP1nipGdimomoSxbu9MvmUlLxZ0QZSiM60YmjcS4lDTUPr9ifS9TeJXk/btd25hZFl6V91ZLs4ldKmzdvJjExsdz68/HxoU+fPoW2sVgsBAcHAzB+/Hh+/PFHHnzwwQLbv/7665jNZnJy7Kfhzp8/v9Rxjho1qtTXCiGEEEKIiueatBvnjNMF7lylAO9LPxPf5E3Q5ONAlaFU7vI+3RW9Ci7v+x8Dz9jlJPnL54LqwC1xK0ozoxslSyqGnnEHIK0UuVJdU2w44k2zunGYSjklRgOcsi7hlHKEbI+WRbY3ZV3GM25Zbt0pKYpebcl/kUopMTGRixcvOuz+Xbp04fDhwwBYrVZef/119uzZg5+fH3PnzsXV1ZWJEydy//33M2DAALtru3btyvr160lNTeXJJ5+kQ4cOREVF0aRJE2bNmoWrqytdu3bl4YcfJjQ0FIDZs2fTpEkTAgMDcXd354UXXmDo0KF06NCB0NBQrl27RmBgIF27dsVqtfLhhx+yc+dOsrKyGD16NCNHjiQ2NpYXX3yR5ORkrFYrH330EV27dq30904IIYQQoqbSs6/iHr8eo5CdqzRAM9Jwj99Aat1BlRugKJA58yLmrFisurujQymUBrikHcaUeQmri5+jwxGFMGXF4ZJ6KPf3gSp+giotS2NPtCvN6mVzNLbky00NpRGfauKPC650alT6ZKsCvOOWEO/+FmiF1MAysvC+tODPr3Ok7lQ1Jkv8qqGcnBxCQkJo2TI3k3z69GlGjx5NSEgIXl5erFu3rth9nTx5kqeeeopNmzbh6enJDz/8YDvn4eHB2rVrefrpp5k6dWqBsaxdu5Z3332XGTNmALBw4UI8PT1Zt24da9eu5eeff+bcuXOsWLGC++67j+DgYIKDg2ndunUZ3gUhhBBCCGFHKTwvr0DDAJVdZHO3a6GlqvEiKoYlOQKFXm12HfO6tNDRIYgiuCX+DujoJdy5b/c5d7KtOq5moJTpHg3FpmNeZFlLdTnX76wbabglbiu4kVJ4Xv4VU1Zs7kwxqu7yWFE0SVBVIxkZGfTr148BAwbQoEEDRowYAeTWpgoICACgbdu2REdHF7tPf39/OnfuDMCjjz7K7t27beeGDBli+3dERP5bEl9fYti2bVvOnz8PwNatW1m6dCn9+vVj0KBBJCYmcvr0adq3b8/ixYsJDAzk8OHDeHh4lPAdEEIIIYQQBbEk/4FL2jEMzQVdFedDmsL70o+UqZqxKB8qB0tyJIbuWi1mf2iAU3YczimHHB2KKICeffXPMWXJTVoXU7YVws64catvNvsvln43PoVGSqZO6Omyzwh0v7KpwN1HLcl7cE3ei6F7YCphIk5UPbLErxq5sQbVjVxc/leAzmQykZGRUew+NU0r8HVBX9/I2dnZdt8ba129//779OzZM0/7ZcuWsXnzZiZMmMALL7zAsGHDih2rEEIIIYTIn5aTjEf8GgzdrdjFgTXAnB2P5VoYGbXuqtgARaFcUg+jG+lYNYujQyk2BXjFLSXefUrhy6+EQ7hd3Q4YeTZKKErkBVdSskzc6ZnD2StlTZcqfj/pSefGabg7lyURbsXj8kqS/UbYHTVnxuB5eVXuEsYS1tgSVZPMoPqLu3DhAnv27AHg119/tc2mAli1apXt3x07dix2n/fddx8//vgj2dm5U8tPnjxJWloa58+fp06dOjz55JP8/e9/58CBA+X4JEIIIYQQf12el1flfhBVJV9P4xm/VnZlczBLUgSG5oxJFf8PzY6Wu/wqvfDlV8IhNGsqlqTw3ILh5BR9wZ8MBTtOe1DPM4fI6NLPnrohErKssOWEVxl7AUvKfrslyZo1A69LC1CYoIRJOFF1yQyqv7hmzZqxZMkSJk+eTJMmTRg9erTtXFZWFoMGDcIwDL744oti9/nEE08QHR3NAw88gFIKX19f5s6dS2hoKF9//TVmsxl3d3dmzpxZEY8khBBCCPGX4pJyEEvqQawmD0ylSjRZ8br0C9caPFfusYmi6TlJOKcdw9DdoFhLM6sW9yubSPfuijK5OjoU8SfXa2HoKgujhJOWjsa5EJ9qputtGcQll99svl1nXbnrthR83cpQkArwiv2ZxMYTcutOxS3FlH0FQ7dgUpKgqik0pWTReX5u3qEvLS0NNzc32+vNmzeTmJhYbvfz8fGhT58+5dbfdWaz2W7p3Y2io6MZPXo0v/32W55z13f68/X1LfeYRM1S2BgTVcPNv7+qozp16hAfH+/oMEQNJ+NMVIbyHmeaNQ3fc5+iKSuakV6m+kWJtzxNtnuLcotNFI9b4lY8EjZgYC7RbJeqQgEZHu1J9nvc0aEIACOLOmc+RqEVe7nvdd/urM3VdBM5BqRmld+yTQ1FwC2ZPN6h7J+fE/1GYc5JwDN+bRmS8tVPjtmXK7e97ugwyoW/v3+B52QGVSlVRDJJCCGEEEKIkvCIX4duTcXQXcpUu0MB3rG/EH/bW6CXx9IeUSxKYUnai6G7ohv5F4Gu6nKXX0WSmtUXw7m2o8P5y3NNCkc30rDqJZsBFZ3oxNlEZzrfmkn4WZeiLygBhcaBGAv33G7G37v0Sdjc31OL0FQ2Vt39L5Oc+iuRGlR/YY0aNcp39hTArl27ZPaUEEIIIUQV5px6DNfkiD8Lo5etdlFuPaEMPC6vKZ/gRLGYM6MxZ8ehqsXefYXzjl3o6BCEsuJ2dVupfidsO+WOxcng9OWKSRHommL96QQZ4QAAIABJREFUEe8y9aEBuspEaU7VNqErCicJKiGEEEIIIaoZzcjE8/IKDN2CXsJlPIVxTd6NKSOm3PoThbMk7UWhoxtpjg6lTDTAKfMCTmknHB3KX5olORJTzjUUJaviE59q4nCshZb1s4lPq5gZlIbSOJ3gzPHLzmXvTBloGGXvR1Q5kqASQgghhBCimnFP2ICecxWFXu5zb2pdmg9KPvxVOCMbS8o+DN21Bsyfyl1+5RW7qFQ7SYpyoAzcEn/PLRpewtlFoafd0TVISq/Ykahrig1HvEtcvD1PP2SXT0CiypEElRBCCCGEENWIU/pp3K6FYZg8MJXzzBsNMOUk4np1e7n2K/JyST2EbmTUmISOBpisKbglbnV0KH9JzqlH/lwuWrLi5imZOnvPu9GifjanEiq2/pyhNGKTzey/KDs+ivxJgkoIIcT/Z+/OgyStzwPPf3/vm5lvnpV1NQ0N9AVCAgQCAUJIXl2GGGscjvV4FI5QKNbWjG3ZI0/MriZCu2tt+J+ZtdY7XlsyRoxHJwIsIY6mRSMOQR9AH/RZ1Wf1Wfd9ZR15v/n+fvtHdrdo6KOqOzPfzKrnE9F/dFVWvk9VvpmV71PPIYQQolFol8T4C+Wh6F7lWvveLz71OlZprmr3LyA8tx+tQtjm2uaH1ZvY9GY5d2rNGGKpbWjlLHpz3+6+KCWtsC2gBrV8CsOvTyRwl0ZeVlSYJKgayM0338wjjzzCF77wBb72ta+Ry12+dPNDH/rQBz42OjrKn/3Zn13ya2ZnZ3niiSeuNVQhhBBCCFEFsenNBNwpDDZqkXNmFkfTNPqzKt7/8maVZgnlTmPUUtyYKOdOrQXzPQQLAxgVWNTXFT3Y3RdjfbvLkeHanIsGxVzeZndfrCbHE41lcWewOC8+toGAO1mx+ysF20mv/IPL3iYcDvPGG28A8B//43/kySef5M///M8XdZzrr7+eH/zgB5f8/NzcHE8++SRf/epXF3W/QgghhBCiugL5IaIz7+DZ8aqvV1dAKN9HKH2UYvzOqh5rOQrPHUBhULrgdygVd/7cyRynGPuI3+EsC9HUW2gVXPTChAMDUbKuRWvUpbuGk9AUhq2n49x3c5ZIsJqJdtFopILqKgXcSUL5nor9W2yy6xOf+AS9vb0A/Pt//+/5nd/5HT7/+c/z9NNPf+C209PT/N7v/R5vvvkmAwMDfOELXwDgxIkT/O7v/i6PPPIIDz/8MN3d3Xz729+mr6+PRx55hP/6X/8rmUyGP/zDP+Rf/at/xW//9m/z+uuvAzAwMMBnP/tZvvnNb/L5z3+eL3/5y1es6BJCCCGEEFfJlEiMP19er+7VZuNbeej1s6CLNTnesmEM4fn9aCuCRcnvaKqifO48A1qGWVdboDCMkz2JsZxFpZi0gR29MW5oKtE5UIHNeotgUBRKirfOJGp6XFH/JEHVgEqlElu3buUjHyn/ReLv//7vee2113jllVf48Y9/zPT09Pnbjo+P80d/9Ed885vf5OGHH77gfp566in+5E/+hDfeeINXXnmFG264gW9961usWbOGN954g7/+67/GcRx+9KMf8frrr/Pcc8/xX/7Lf8GYcpa7p6eHP/7jP2br1q00NTXxyiuv1O6HIIQQQgixjERTbxEsjmJUoGbr1RVgmSKJiY01Od5yEcz3nW3TXAq7+y5OAZYuEJ+U64Nqi6bewqjAomfSHRsNk8oGWN1aomT8ORd39USZyTVGSkIbGJoJkneX7vO2HkiLXwPJ5/M88sgjADz44IN8+ctfBuDHP/4xr776KgDDw8P09PTQ2tpKqVTiS1/6En/zN3/DQw899IH7u++++3j00UcZGRnhi1/8IuvXr//AbYwx/O3f/i27d+9GKcXo6CgTExNAeSbWRz/6UQDuvvtuBgYGqvJ9CyGEEEIsZ3ZxjNj0Vjw7hl3FweiXEp7vIJf8FKXwTTU/9lIUnj+Awcaq8AZGAGPKF9J2nVzzR+beJZf8FJ6zwu9QliTbncJJH0ZbMWyz8LZfY+Cd7hgtEY9DQ37NQVNoY3jzZJIvfSzlUwwLky0qnu1s4fSkg8JwU7PLLe0FbmkvcnNzkUCdPN+WAklQNZD3zqA6Z+fOnbzzzjts2rSJSCTCl770JQqFci+7bdt87GMfY9u2bRdNUP2bf/NvuPfee9m8eTNf+cpX+Lu/+zvWrFlzwW02bNjA1NQUr776KsFgkAcffPD8/TuOc/52tm2Tzy+tDSRCCCGEEL4zmqaxFzDKQnn+zStKjjzF1Nr/A5RciV0TXcSZP4S2w1VJNr7bF2XrqQR/+VsTJCO1qbS7kuToU0yv/gYoqTyptGjqbUCh9OJGrfROhxiaDfGJNXn29IWrE9wCGKBzyOG31gW4vqk+212HZoL87EAz6aLNfTcXGJu3yZds3jodZ9tpRdDWrGstcktbkVvaC6xMlORUvwbyG6bBzc/Pk0wmiUQinD59mgMHDpz/nFKK7373u5w5c4bHHnvsA1/b19fHmjVr+JM/+RMeeeQRurq6iMVipNPpC+6/vb2dYDDIjh07GBwcrMn3JYQQQgghIDK78+x2rpBv84oUYHtzRFNbfTn+UuJkjmKZAkpX/rHUBnb2xsi6Fi8fa674/V8NBQTcCcLz+/wOZcmxSnOE587NMvMW9bXbu2NEg5rT43aVolsohaXgtRNJn+P4IGNgb3+E77/bhkFx/+oi+wdCDM4EmEjbGCDheNzQpBlLB3n1eBOPbV/B/7v5Op7tbGb/QKRh2hfriVRQNbjPfe5zPPXUUzz88MOsX7+ej3/84xd83rZtHn/8cb761a8Sj8f57d/+7fOfe+mll9iwYQOBQIDrrruOb3zjG7S0tPDAAw/whS98gc9//vP85V/+JX/8x3/MF7/4Re68805uvfXWWn+LQgghhBDLku1OEZ/6NZ4Vw17kdq5qiE1vJp+4Dx2sj+RHI4rM7UcrB8tUvhqueypEKhugPebRNebQNeZw+8r62BKYmHiJQuyjGDvidygNzy6MEJnvwJnvBDRqkUsMxucDnJgI8/Gb8xwY8K966hxtFKcnQnRPhVjfVh8LGVwPNh1NcmAwytpWF6Vgd6/zvlsp5gs284Vz/zO0xjyiITg14XBouHyut8dK5XbAtgLr2oqytfAKlDk38VpcYHh4+IL/Z7NZotHo+f/HxzYsevPe5ZSC7aRX/kHF7u+cQCBAqVSf5ZJiaZBzrP69//WrEbW3tzM5WbnXXCEuRs4zUQsLPs+MoXn4hwTz/RijsPB/G5oBSs6NpG76S2nXugqWm6Kt7+/QdrQq7X3PdDRzZtLBsTVzBZu4Y/hfPzOOE/D/cs8AhdhHmbvhK36H0pCs0hzO/EHC8wfKyxJQaCsKaOxFtvdtOJTk0HCEG5pKDMz4NX/qQpYyrEx4fP3TE76/tExnbX5+oIWRuSD33ZynazRI1l18pVnA0rTHNMqCiXmbklYXzq9qOzu/aoF3XQq0Mr32m4uOox6tWrXqkp+TCqqrVI1kkhBCCCGEEADhub2Ect14dhzbW/jw42pSQLAwhJM+SCFxj9/hNJzw/AEUBuVVfm5rpmDRNRrmjhuKHB4uV3rM5RWbTyX417fPVfx4i6UAJ3OEQK6fUmS13+E0Bl3EyRwjPN9BKHsKhUFbETwrhqUzV1VVOZe3ODgU4fbrixwZeX9FkH+0UYzMBTgyGuauG/yba3xi3OG5znKF6G+tz7Ozx0Ff5YbDkrYYnf9Ni18k4NEWNzK/6gokQSWEEEIIIUQdsUqzxKdewbOidZOceq+m8Q1MRj+Csf1vD2oYRhOZO4C2olXZ3tcxFMEz6n0X04ZdPVHuWZVlVbI+qt2To08ztfb/lGH7l2I0wVwv4fkDOOkjWKaAViG0FUPpPNYiq6Xeb1dvDH1202O9Ucrw+vEmbl+Zr/lWPG1gy6k4204nuC5RYmVCs727sq9vuZLN4My5/xkSjkdL1DCeDnJyonysWMjjlvYit7QVuKW9QHOdLDqoJUlQCSGEEEIIUS+MITG+EaVLGFWHV5EAxiUx/oK0ay1CMN+HXZrGsyrfcm8M7BuIsipZ4vjoe1u2FEoZXjzcwn/49ASWz5UZ5WH780RSb5Fr/by/wdQZuzhBeL6D8HwHdmkGg422wngKbF0Ac+2zmQolxd7+KLescOkaDVUg6soyRjGTs9nbH+WhtZVP4l5Ktqh4trOF05MOd1xfYCJtc3i42j+fRcyvOpusunlllUOqE5KgWiAZ1SWEaFTy+iWEEI3DSR/EyR7Hs+LYuv6qp+C97Vq9lCJr/Q6nIYTn9mOwq1I91TsdYjIT4BNrCwzPXnh5d651andfbS/6Lyc+/SaFpvvQgSa/Q/GV8jKE04cIz3WUN3WiMFakXDmpsxVfjLBvIEq+ZJEMGwz12UemMGw5leDeG3OEazBMfGgmyM8ONJMu2nxqXZ69fSFcXfvqPoNiKhNg6uxDfm5+lWXBgcEou/tj2Jbha2tdgsH6mBtWLVJbuUCWZckgaCFEwymVSliWvNQLIUQjUF6axMQmtBWt2+TUeyVHnwazuPX2y5HSBZz0YbQVrkpaYN9AFCegOTV28d/3CsMbJ5qYrZuV95qm0Z/7HYQ/TIlQ+ijJkado7/l/SEy8hO1O4FkxQGHpLHYVkpiehp09MW5qLnFwqP6qp84xKHKuxTs98eoex8De/gjff7cNg+L+1UV29ji+JKcupjy/KsDwbABXKyIBj5taWPLJKZAKqgULh8Pk83kKhQKqgSaXOY5DoVAf62XF0iTnWP0yxmBZFuGwzAgRQohGkJjYhNJ5jKrfC8hzyu1aGWJTb5Bp/x2/w6lr5VlCRaoxTSZbVBwdDfORlS5HRi5+3hgUrja8fKyZr9w3XYUoFkcBoXwvwcxx3NhH/A6n+owhUBg428J3EEvnMCqItiMoXcTS1R8KfngkzGze5sPX5xmcqfcUgGFHd4xPrs6QCFf+WeN6sOlokgODUda2uljKsLu3fgbGX0yuZJNyl0fFYb2fnXVDKUUkEvE7jEWTldmi2uQcE0IIIa5dKHOMcPpQXW3tW4jozNvkmh5Ah9r8DqVuhef2o5WDZSr/B73OoUh5ff0V/n5ujKJrzKFrzOH2lf7/YdEAybFfMLn2W2AtzaoQy00Rnu8kPH+AgDuJwTq7hS+CrXPYnluTOIyB7d1x2mIeBwfqP/kNCk8bNp9q4vfvmrnyzRdhOmvz8wMtjMwFue/mPF2jQbKuXdFjiGtTHzVsQgghhBBCLFPKy5EY34i2IlgNlJwqMyRHnixfBYsPsNxpQvkejKp8XcC54ejXN5U4NnrlJI+lDJuONlMo+d8NogBL54lNvep3KBWldJ7w3D6aB79Pe99/Iz79aywvc7aFT2PrDPY1buJbrDOTIUbng9yywqXgNcblvwH2D4SZSFcueXRi3OHx7e2ksja/tT5Px6Ajyak61BhnqBBCCCGEEEtUfOoVLC+NgTodXXxpCgi644Tn9vkdSl2KzO3HAFYVkhIDM0HG00FWt5Tw9JXPHG0Uc3mLzacSFY/lakVn38UuTPgdxrUxHqHMCZpGn6G9529oGn+BQGEYz46hsbF0DltnfHtuv9MTJ+5oTow0UvOUAgWvn0he8z1pA2+ejPPUvlaaIprbVpbY3h1Gm0Z7tV0eJEElhBBCCCGET4LZ00Tm9qGtWM0rKyopMfkSyquPLXF1w2jC8wcwVhRVhQlUe/ujhGxD98RiqkAMu3qiH9j25x9zdth+Y1bgBXM9tPX+N5pHnsDJHEUr53w7p+1lsPB3icDIXIAzkw4fXukyW6iXx3xhjFEcH3PoT119C2i2qHhybyvbTie4/foCxsChOh4SLyRBJYQQQgghhD90kabxDWgrjNUAW/suy5RIjP3C7yjqSjDXg12aoRqpl5yrODIS4dbrXCazi7mAL8+revFwC7oOckIKCLjjhOcP+B3KooUyx2ke/jHKuHhWBGVK2DpTlVljV2t7d5yQrRmeaczLfksZXulKXlX+cmgmyPe2t9MzHeJT6/KcHg8ykW6sJN1yVJNH6PHHH+fAgQMkk0n+/u//HoB0Os13vvMdJiYmWLFiBd/4xjeIx+MYY/jJT35CR0cHjuPw9a9/nfXr1wOwbds2NmzYAMAf/MEf8LnPfQ6A7u5uvve971EsFrn33nv5d//u36GUuuQxhBBCCCGE8Ft86nWsUqo8e8rvYK6RAsLZk+Syp3Gjt/odTl2IzO/DqAC2rnxl2cGhCK5WBK3FX7lroxiZC7C7L8pDa+uj6i0xsZFC7E6M3Ribh535TprGnsNYYdBFbEp+h/QBMzmLwyNhPrqqyKGh+t5SdynaKAZnghwfX/hw//JstggvH0sSC2nuX11kZ49D4zVQL081+V34uc99jm9961sXfGzjxo3cddddPProo9x1111s3LgRgI6ODkZHR3n00Uf52te+xg9/+EOgnNB6/vnn+fa3v823v/1tnn/+edLp8l+afvCDH/Dnf/7nPProo4yOjtLZ2XnZYwghhBBCCOGnQK6PyOyuhm/tey8DJEd/Bqb+LtZrTek8Trrc8lVp54ajr0yUODpyde1KCsMbJ5qYzdVJatSUSExs8DuKBQnP7qZp7FmMFUbpAlYdJqcAdvbEACi4jZ2YsZThteNJvAV0yboevHg4yS+PNHNzc4n2mMfuXklONZKavCLdcccdH6hc2rt3L5/97GcB+OxnP8vevXsB2LdvH5/5zGdQSnHbbbeRyWRIpVJ0dnZy9913E4/Hicfj3H333XR2dpJKpcjlctx2220opfjMZz5z/r4udQwhhBBCCCF8o12axl/AqBBWFapr/FLezJYjNvmK36H4zpk/jDIuqgrJuqHZIKPzQVa3ligtYDj6xRgUroaXjzVXOLqrowAnfZhAfsDvUC4rmtpG08RGtBVF6RzK5xlTl5JzFfsGonxohcuJ8auf4VQPtFFMZWwODEYue7vprM33d7VzYDDKfTfnGZ+36J6SeVONxreU+ezsLC0tLQC0tLQwNzcHwPT0NO3t7edv19bWxvT0NNPT07S1tZ3/eGtr60U/fu72lzuGEEIIIYQQfomlthJwJzDKRlVlQpG/orO7sAtjfofhq/D8/vPDsitt70CUoG3om7q2SzljFF1jDl1j9dP+lRx5GkzlB8pfM2OITb5GfOp1PCuGpTN1/dzd0x+l6FlEnUbcDfpBCsObJ5soli7+vZwYd3h8ezuprM2n1+fpGHTIuotZHiDqRd1NCTMXmYCm1MVPRKXURW9/Nd58803efPNNAP72b//2giRZIwsEAkvmexH1Sc4xUQtynolakPNM1IKdHyaaegsTSGCX5v0Op2pax3+Guef/hku8j1+ySlkY3YyV78MEElCqbIKqUFIcHg5z6wqXrtFrrw6xlGHT0WbWt43jBPxNuCjA9uZoL+6HG7/oaywXMBrV8y+ombcb4nlb8mBXb4zVrS6HBpdGBZFBkSkqdvTG+Pytv1kooQ1sORVn2+kEKxMlrk9qdnQ3xhyzxbJte1m8R/EtQZVMJkmlUrS0tJBKpWhqagLKFVCTk5Pnbzc1NUVLSwutra0cO3bs/Menp6e54447aGtrY2pq6oLbt7a2XvYYF/Pwww/z8MMPn///e2NoZO3t7UvmexH1Sc4xUQtynolakPNMVJ3xWDHyI4wKoEoZv6OpGgVQGCd9ZhO55k/5HU5NKC9DdGYHkdmdWLqAZ0WxqvAYHxoOU/QswhVKJmmjmMsrNp9K8K9vr49uEzWwkSn7I+hAwu9QwHg0jT1LOH0Iz4rXfXIK4OBwhHTB5o7rXfqnG7u970KGd87E+cTNWWKOJltUPNvZwulJh9uvLzCZtjm4RBJyF+N53pJ5j7Jq1apLfs63Fr/777+ft956C4C33nqLBx544PzH3377bYwxnDx5kmg0SktLC/fccw8HDx4knU6TTqc5ePAg99xzDy0tLUQiEU6ePIkxhrfffpv777//sscQQgghhBCi1qKpd1DZfowKoqjDNqYKi0++giqlr3zDBqZK88QmX6Wt978RS20FAmjlYOtsVR7jfQNRVsRLHB6uZOLBsKsnyvBsvTTXaJpGf+53EKCLJEeeKien7Di2rv9zWRvY3hPjuniJziWXrFEUPdhyOsHQTJDvbW+nZzrEp9blOT0eZCJdL+evuBY1eRS/+93vcuzYMebn5/mLv/gL/vAP/5Df//3f5zvf+Q5btmyhvb2d//yf/zMA9957LwcOHOA//af/RCgU4utf/zoA8Xicf/tv/y1/9Vd/BcCXvvSl84PX//RP/5THH3+cYrHIPffcw7333gtwyWMIIYQQQghRS5Y7Qyy1uSFahCrHIzn6c2Zu+jO/A6k4qzRLNPU2kbk9YDy0FUUbsHT1KuOGZwMMzYZ4YHWhwhfjCqUMLx5u4T98egLL565MBYTyPQQzJ3BjH/YnBi9PcuSnBPO9eFYM26v/5BTAyXGHiXSQB9fm2d27NFvd9vZH2DcQIRbS3L+6yM4e2dK3lChTqSFOS8zw8LDfIVSEtCuIapNzTNSCnGeiFuQ8E9UUmdlBYvLlcvWUcf0Op6ZmbvgjirHb/Q6jIiw3RSy1jfDcfkCf3eZWxKL6j+lLR5o4MFiuoBqZq07r1u/eMctDa/3fLGkAY4WZXPd/gaptZYzy0jQP/4RAYRRtRbCrmHSstB++28p0NoDWkC4uzSHhljKsbnGxFMtqS19TUxNf/epX/Q6jIi7X4id1cEIIIYQQQlSZk+lCWw6WrvxWt3pmgOTIk7jhNRTid1OIfxQduPRc2HplFyeJprYRnu8AQFsRlC7WLHlRLCkODke4ZYXLibHqXJQrDG+caOKOlXmSEX9bUBWgdJ7Y5KtkVvxezY5rlWZpHvoRtjvdcMmpwZkgvdMOD6zJs7dvaVZPQXlu2kAqiGekamopkgSVEEIIIYQQVaS8PMFcD9qKAMsrQaUAjUWwMEgo30d8chNueC2F+F0Nkayyi2PEprfipA8BCm1HUF6h5omLwyNhCiWLWKh6zS8GhasNLx9r5iv3TVftOIsRnd1FPvkQXqj628vs4iTNwz/C8tJoK9xQySmA7d0xnICmd9K3MdM1I8mppUsSVEIIIYQQQlRRKHsShUbp5dXad46FLpdSAeZ8sqr3Pcmqc5VVdbC17axAYZjo9FaczFHAQttRLC+H7fmTtNg3EKU16nF4qLpb2YxRdI05dI053L6yHpKphqaRp0it/t9AVS8pYRdGaB7+MUoX0SrUcMmp6azN0dEwH7uxSOeQ43c4Qlw1SVAJIYQQQghRRU7mGEYFsEzR71B8V05WldvHysmqgbPJqpdww+veU1nlT7IqkB8oV0xluzAqgLZiWDrrW2IKYGw+wMBMiPtXF9jXX/3kg6UMm442s75tHCfg77hiBQTdcZz5DgpNH6/KMQK5PppHngBjAAtb+z+Da7F29MRQCuYLUlkkGpskqIQQQgghhKgW4xHKnkBbDrZX8juauvLByqp+Qvme3ySrEneRj30UU4NkVTDXS3R6C07uFEYF8Ow4lpfBNv5vb9s3EMVWhtHZ2iQftFHM5RWbTyX417fP1eSYV9I0sZHJ+B0Yq7KzlYLZUzSPPIVRNgawTb6i918LmaLiwECUD6906RqtboWdENUmCSohhBBCCCGqJJjrw9J5vApfWC81701WgUUwfzZZNXG2DTBxN/nYnZVNVhlDMNdNLLWFUK4brYJ4dqycmPL8T0wBuB50DEZY3+5yaqKWyQfDrp4o96zKsipZB4lV45IYf5G5679csbt00kdoGn0GY4VAl7BrsImxGnb3xXC1ImhDueZMiMYlCSohhBBCCCGqxMl2YVDYuvEqM/yi0Bf87zfJqnJlVT5xN4XYnZhA/OoOYAyh7EliqS0E8/1oFcKzYlg6g+XVV5Li6GiEfMkiGTHUNvmgUMrw4uEW/sOnJ7B8znsowEkfIpD/nyiFb7rm+wvP7SMxvuHsNsYCFt61B+kD14PdfVHWtbkcGZbqKdH4JEElhBBCCCFENRhDKNN1dl194821qQcfTFb1Ecp3YyZ+iRtZRz5+N4X4nRh7AckqowlluoilthIsDF2YmKrT+WB7+yO0RKo/HP1itFGMzAXY3RflobX1cf4mR59mas3/DurqN9VFZraTmPzV+fliCn/nbF2LjsEomaJNW6xEz5RUT4nGJwkqIYQQQgghqsB2Jwi4U3hW1O9QloQPJKtyfYRy55JV68nH77p4ssponPQRYqmtBIqjaOXgWbHyprY6TUwBTKRt+lIO991cYP+AP5vZFIY3TjRxx8o8yYi+8hdUNRawS7NEZt4h1/LZxd+BMcSm3ySW2nI+MdnIKR1tysPRr28q0Tko1VNiaZAElRBCCCGEEFXgZLoAsHTO50iWng8mq3oJ5c68J1l1N8XY7YSyp4imthFwJ9BW+D2JqYJvsS/UvoEoljJMpP1LoxgUrja8fKyZr9w37Vsc7xWf+jX5xMcXN4/MaOKTvyI6u/P8nLFGTk4ZA+/2RpnKBnhwbYHRObmsF0uDnMlCCCGEEEJUQShzHG2FsWT+VFVdKlnFxIsAZxNT0YZqsyx55fatdW0lzkz6Wx1jjKJrzKFrzOH2lfWQ2NMkx55h5sY/W9jNjUdifAOR+QN4dgzby1Q3vCqbTNu8dDRJ95TDzc0uh4fkkl4sHVffvCuEEEIIIYS4KOVlCOb7MNh+h7KsKDTq7MBrjY13NkHYSMkpgK6xMFnXoiWmqYfNbJYybDraTKHkfywKCOW6CWZPXvnG2iU5+rOzyal4QyenXA+2nIrzT9tXMDQb5JNr80xlLLKuvMaIpUMSVEIIIYQQQlSYkzmBwqB0/c44WuosvIbdnrh3IEoy7HHEh+HoF6ONYi5vsfnUItrqqsgAydFnwJQueRulCzSP/BQnc6zc2umlaxdghZ2eDPHYOyvYcio9qDOxAAAgAElEQVTBre0uq1s83u0NS3JKLDmSoBJCCCGEEKLCQpkujApi4fodimgwUxmb7imHW1aUyJfq6XLNsKsnyvCs/y1livJst9jk6xf/vJeleehHBHPd5cop3ZiVU+mCxbOdzTyxpw2N4qF1eU5PBjk1UR+JSyEqrZ5e8YQQQgghlhWrNEdy+Cc484f8DkVUkikRyp5Eq5DfkYgGdG44eipbb5dqCqXgxcMtaON3LGXR2R1YxakLPmaV5mgZ+j6BwhDaijZk5ZQ2sKcvynffWsHR0TD33ZxHGc2unjCe9r/NUohqqbdXPSGEEEIsR9olNvU6sclX/I6kpuKTv8LJniQ59nMSY8+hdD0MIBbXKpTtxjJFMJ7foYgGU9JwYDDC2tYSPVP+Vyq9nzaKkbkAu/uifodyliE5+nR5rR1gudM0D/0PbHcKbUUasnJqZC7A93e18dLRJNclPO5a5bJ/wGE6J1VTYumrv1c9IYQQQiwrgcIwTWPPEiiOAeA6N1JMfMznqKovmD1FOH0Iz4ph6Qzh+QOEcmeYvf5/oRS+0e/wxDUIZbswWNimMecfCf+cGAuTKdq0xUt0T9VnpYzC8MaJJu5YmScZ0Vf+gqrGAsHiKM58J6XwKpqHfoylc2gVarjkVKGk2HIqzq7eGOGg4aF1eQ70h+hPSU2JWD4kQSWEEEIIfxhNNPU2sek3Mco+vwY+Of4Ck7GPYCzH7wirR7skJn6JtsJYOnN+R5dVmqNl8Huk236HXPNvgZILk4ZjDE6mq2GrN4S/9g1ESDiao8P1e5lmULja8PKxZr5y37Tf4QDQNPEiRgXBeGgVaLitjcdGHV4+lmQub3PnDQXm8za7esJ+hyVEzcm7HiGEEELUnO1O0Tz0feLTr6PtMBjvNxcUxiU+vsHfAKssOvM2AXcKg3XBAnlFuU0lMfUqzUM/xCrN+ROguGqB4ih2aRaokyE9omGksjanJx1uXeHW/XY2YxRdYw5dY3XyhwTjgilhAFvn/I5mwVI5m6f3tfCzA604AcODawt0jYboT9VvglKIapIElRBCCCFqxxjCs3to6X+UYGEYz4pjexksfjOrRwHh9CECuX7/4qwiuzhJLLWtvPb8In/lP5ekCuZ7aO37B0KZrlqHKK5BKHMMA1gNVsEh/Ld/MALAfKE+W/vez1KGTUebKZT8j1cByhSxTWPM8fM0vNMd49G32zkz5fCJNXnyLuzuddDG/5+nEH6RBJUQQgghakKV5kmOPEnTxIugAhijsPWltyuVB98usSHTxhCffAngigPRz11wNY88SXzil6DdGgQorpWTOY6xIsglplgMT8P+gShrWkucmWiMYdjaKObyFptPJfwOBaBhnnP9qSCP72jn9eNNrG7x+NAKlz19YeYLUjUlhCSohBBCCFF1ofRR2vr/kVD21Nmh4Fksipe8vQJsb57o9NbaBVkDTuYITvYUWjlYlK54e4XBANHZd2kdeBS7MFb9IMVVs0pzBAuDGHmLLRbp5ITDfMHmuoTGNEyqBcCwqyfK8KwkV64kW1RsPJzk+7vaybkWn1qXpz9lc2w05HdoQtQN+e0phBBCiKpROk9i7HmaR58GNEYFFzU4OpbaguXOVC/AGlK6QHzi5UUPzz53qWq7U7QOPEpkZtf5leqivoQyx4ErV8cJ8X77BqLEHc3RkUZL9CiUghcPt6DlZemijIGOoQj/+PYKDgxG+NiNBSJBzc6eMK4nl+NCvJc8I4QQQghRFcFcN639/0h4/gCeFUfpHJbJL/JeTLnVbwmITb2B5c1ddW6pPJvKkJh8ieTIT1GebIirN06mC61CC6qOqyVPw3S2voduL2ezOYuT4w63tLtkio33OGmjGJkLsLsv6ncodWcibfPjPa28cLCZZMRw3+oih4ZCjM03RhunELUmCSohhBBCVJYpEZt8heahH6J0/mzFUPqqmlYUECwMEZo/WOkoaypQGCEyuwttxbHN1W+YOjdAPZQ9QVvfPxDMnqlUiOJa6SKh3GmMVV8XnsbA8web+c62FXRPSStRPdo/GMWgyLuN1Np3IYXhjRNNzObk8hLA9eDNk3Ee276Ckdkgn1ybJ5VV7O1zGqyFU4jaklcQIYQQQlRMoDBC68D3iM28g7aiKF246Ka6xUqOv9C4bVNGk5jYiFF2xTa7KUDpHM3DPyQ2+erSGybfgEK50yhTQun6qp7aNxDh8EgE24JnO1vIFOTtfz3RpjwcfXWLy8nx+kpuLoZB4WrYeKR52bf6nZoI8U/vrGDb6QS3trvc2Fzi3d4wObfxquOEqDX5DSWEEEKIa2c00dRbtAx8D8udxrNi2DpzvuLn2u/fJT6+oTL3VWPhuf0E8/0YFUKhK3a/5362sZm3aRl4DNudqth9i8VzMscx2Fh1tOZ+bD7Ar44lWdPqErA0maLi+UPNMsKsjpyecJjN21zf5DV8ZY0xilMTDm+eqI+tfrU2n7f4RUczP93bBig+uS7P6YkgZyalclGIhZIElRBCCCGuieVO0zz0A+JTr6GtMBhvUUPAF0IB4fQhArn+it5vtSkvQ3zqVbQVrfjPBH4zQD1QHKO1/7uE5w7IAHU/GE0o04W2w35Hcl6xpHimoxknYAjZkC/ZGAOnJhx29sb8Dk+ctW8gSjSk6Rpt3Oqp97KU4e3uOB2DEb9DqRltYHdflO++vYJjY2HuW53HGM27PWE809hJRyFqTRJUQgghhLg6xhCe20tr/z8SLAziWXFsncGieu1mydGnG6qdLT75GkrnwVSucupiFAaMR9P4czSN/RzlLXYYvbgWgcIQtpdG1VFy8OVjTUymA9xzU5FTE+eSHwpLGV4/nmBoZmkkRBrZfN7i+LjDrStc5gtLo/1LG4VtaTYeTtKfWvrn2Fze4vs729h0NMnKhMdHV7ns73dI5Zb+9y5ENUiCSgghhBCLpkppkqNP0TS+AaMCGGNh63R1jwnY3jzR6a1VPU6lBHO9ROb3oa3YVWwvXLxzLX9O+jCt/f9AIN9Y1WaNzMl0YVCoCs0Yu1adQxEODEb5+M0FdnY7F3xOG4VS8ExnS0MP5V4KDgxG0EZRLC2tx8HTFpYF/7K/lVRuaSTeLiZbVDyxp5XxdICH1uUZnbM5OBiCBm/VFMJPkqASQgghxKKE0sdoG/guocyJs1VTWSyKNTt+LLUFy52p2fGuivFITGxEKwerCq19l6MAy0vTMvjfiU5vrnr1loBQpgtjhevisnQybfPSkSZuanY5MRa86FwjTytSWYuXjialI9Qn2pTb+25qbuzh6Jfieop8SfH0vlYKSywBB1D04On9rUxlA3xiTZFdPWGKnlxaC3Gt5FkkhBBCiAVROk9i7AWaR58CozEqVPWqqYsz5Va/OhaZ2UmgOIZRduUGxS9C+ZiK+PSbtAz+c/0n9BqY5aYIFkcxdfC22vXgmY4WbAsSYUO6ePnqlUPDETqGls+soHrSPRUilQuwKumhl+icIk8rxudtnju4tDb7eRp+caCFgVSQh9bm2f6+KkUhxNXz/zepEEIIIepeMNdDa/+jhOf341kxlM7VpG3tYhQQLAwRmj/oy/GvxHJniE2/eXaToX8tX+cSY4HCAK3938FJH/EtlqXMyXQB5QSu317ramJ0PsjHby7SNXqlzWEKWxk2HUkykV66bVj1al9/lEhQc2Is4HcoVWWA42PhJbPZTxt48XCSExNhHlqXZ0d3GGnpE6JyJEElhBBCiEszJWKTr9I89AOUzqGtCLbO1MXb8eT4Cyhd8DuMD4hPvowyHsq4focClC+dlHFJjv4LibHnQdeuHXM5cDJd5VbOKi4HWIijI2F298e456Y8u3oWVtHhGYUGnuloxW2c3QMNL12w6BoLc+sKl9n80k5QnRvM/3Z3fElU6/36eILOoSj3r86zuy980RZaIcTVkwSVEEIIIS7KLozSMvA9YjNvo60oShd8rQj6AOMSH9/gdxQXCGWOE84cRdsRLFM/iSCFwQDh+f209n+XQGHY75CWBKXzBHM9GOVvkmE6a/Pi4SQ3NJXomQgsqmXM04qx+QCvHW+qYoTivTqGIuXk4BJt7Xs/bRQBS7PxUGNv9nunO8b2njh3rSpwaCiEp5fH4ydELUmCSgghhBAXMppo6m1aBx7DdqfPtqplqj5LaWg2QO/0wi9eFBBOHyKQq5NtddolMbEJbYWxPD9mc12eOvvPLs3QMvAYkdR2GaB+jULZUyj8rZYraXi2oxkDrIhrZgtXkywz7O6LcWxUZulUmzGwfyDKqmSJrtHGTdYsVqnBN/sdGIzw+vEmbruuyJmJgAxEF6JK5JklhBBCiPNUKU3z0A+JT72KtsJgNHaVt9B5GjafjPPPO9p5Yk8b09nFXbwkR58G439/Uiy1Fbs0jUHVddPHuURjYupXJEeekCTVNQhlujAq4Gu13BsnEgzOhrh/dYFDw1eaO3UpCltpNhxqZiYnlwfV1DsdYjIT4KaWpTsc/VIadbPf8TGHjYeTrGl1GZ9TZN3GS7AJ0SjkN5AQQgghzotPvUYw349nl6umLEpVPd5UxuYH77ax9XSCD13nYgxsOprELLBYSwG2N090emtV47wSuzhBNPX22WqznK+xLMS5JJWTPUUwe8rnaBqU8XAyx9HKv6qjE+MOO862HL3bG76m+/KMRdFTPNvZiic5y6rZOxDFCWhOjS3Py7ByS6nN8w2y2a93OsgzHS1cl/AolhQz+eVT9SaEH5bnK6MQQgghPsAuThKe70DbYWyvulVT5TaXCN/b3s5kOsCn1ufpmQziGcWpCYfj44u76I+ltmC5M1WK9gqMITHxS1CqLoe2X44BojM7/A6jIQXz/Vg6B/iTzZnNWbxwsJnrEiWGUlZF5uFoo+hPBdl6Ol6BCMX7ZYuKY6Nhbl1RIpVb3omOrrEwb56s781+o3MBnt7XSlNYE3MMI3NLfaC9EP6TBJUQQgghAIimtgAK5eWrepxsUfFMRzMvHm5mZcJjfXuJnd1h3LMX2JYybDqapLioFhBTbvXzgZM+SCh3Bq3CVa84qzQFBPN9fofRkEKZLgzKl4o5T8Oznc24WnFjUjNdwWSHwrDtdJzuqattFxSX0jkUoaRV1ef51b+zm/3OxOms081+qazNT/e2ErThxmaPMxPLO6EoRK1IgkoIIYQQ2MUJwvOdaDuMRfXmOZ2ZDPHY9hV0jYX5xJocUxmLY6MXXghro5jL22w7s/AqDgUEC0OE5g9WOOIrHNfLE598BW1FsHX9DUZfCMsUsYpTfofRcJzMcYzlz8X11tNx+lIOn1hToGOwsokkgyJgwbOdLWQKcqlQKcbAvoEo1zeV6BqT5N+5zX4v1uFmv3TB4ok9rbie4sMrXQ5f9Ww3IcRiyW8dIYQQQhCb3gxYWF51qkFKHrzWleAne9oIWHD/mgJ7+sKXGTZr2N4dYyK9yIHp4y/UtM0uNv1rLG8e0+AVEZHZnX6H0FDs4gQBd8KXR/30ZIi3Tse54/oCe3qrM/+qpBXZouL5Q80LngcnLq8/FWQ8HWR1i1eRdsyloKQtLFXe7Fcvw/kLJcWTe1uYzdt8/GaXff2y2VKIWqqPVwIhhBBC+MYujuGkD6GtCKoK83TG5wP8j13tbO+Jc9cNBQKWZk9vGC676678uV8eWeQFsnGJj2+4lnAXLJAfIjL7LtqKYevqtkVWWzh9xO8QGkoocxwAq8aPe7pg8XxnM60xzWTaOt8WWw3awKkJh529saodYznZNxAlZBvOTMjl13u5urzZ76l9bb5v9it58C/7WxidD/LJdQV29kjllBC1Jq+QQgghxDIXm94MysLS2YrerzHwbm+Ux3e0M5uz+fT6PMfGQoynF9bOoY2idzrE4ZGFbydTQDh9iECu/yqjXiCjSUxsxKjg2UHZjc3y5kAX/Q6jYTiZLrQVrkpC91K0gec6m8mXLNa1lRb8PLp65TlBrx9PMDQrw6GvRc5VHBmJcOt1LlPZ+mpnqwf1sNlPG3juYDPdUw4Prcuz44zD5f+IIoSoBklQCSGEEMuYXRglnD6MVpWtnkoXLJ7a18LLx5Lc1FLiphaPHd3hRbe2WMrwq2NJ8u7ivi45+jSY6s3SCs/tJVgYxKhgTZMU1aIAp8bzuxqV8jIE830YFtd+eq3ePhPjzJTDJ9YW2Ndfm8oObRSWgmc6Whf9HBS/cXAogqsVQUv6JS+nayzMZh82+xkDLx9t4uhohAfX5NnVE8ZIckoIX0iCSgghhFjGYtNvYlSgotVTJ8Yd/umddrqnHD65Ns/orM3J8aurGtBGkSkqNp9a+EWLAmxvnuj01qs65hXvvzRPfOo1PCuKrTNVOYYforPv+h1CQwhlT6LQqBpWnPVOB9l8MsFt1xXZ2xuilpUdJa1IZS1eOpqUeVRX4dxw9JWJEkdHpGXs0soVe2/5sNlv6+k4e/pj3HtTgQODIbSR5JQQfpEElRBCCLFMBQrDhDNH0cqpSBVQ0YNNR5p4al8r0ZDhYzcVebfXIV+69rcb7/ZGGZlbXJtRLLUFy5255mO/X3zqVZQuonT1KrT8EHDHkAzElTmZrnJrJ25NjpctKp7tbKE5oskULVztz9v3Q8MROmqcOFgKhmaDjM4HWd1aoiTD0S/r/Ga/w0kGarTZb3dflC2nEtx+fYGu0SCuJ5fHQvhJnoFCCCHEMhWb3lyx6qmRuQD/fUc7u/tj3HNjgZJn2N9fqRkeCqXKA9MXN5/ElFv9KiiY6yYy34G2oljUbltgLSjjEchXeXZXozMlQpmTaKs2lTDGwAuHmskULW5bWWJoxq9ZUApbGTYdSS56s+Zyt3cgStA29E7KZddClLSFBTxdg81+h0fCvHy0ifVtRQZTdkX+mCKEuDbyLBRCCCGWoUB+CCdzDG05KK6+akYbeKc7xj/vaCfn2nxqfZ5DwyFSucr+9VsbxeBMkI7BhVdwKCBYGCJUqdlKpkRi/Jdoy8HS6crcZ52JzuzwO4S6Fsz1YpkCmNrMHdvZG+PEeJgHVufZ3etve5hnynWWz3S04i6t4sGqKZQUh4fD3NruMpGR9r6FqsVmv9OTIZ7vbObG5hLpgsV8QRYBCFEPJEElhBBCLEOx6TfKbUre1VdPzeYsntjTyuvHm1jX7rIi7rGzO1y1+R2WMrx2vIlsceH3b4Dk+Asofe3VTtGZ7QTccQyBJTs+N5Q77XcIdc3JHMNgYddgc+PgTJBfH09wS3uRjoH62ChW3rYW4LXjTX6H0hAODYcpehbhoLTOLlY1N/sNzQT52f4WWmMaW8F4WpJTQtQLSVAJIYQQy0wg34+TPXF29tTVvfM/MhLmse0rGJgJ8al1OfqnA/RMVXdmiDaKnKv49Ynkgr9GARiX+PiL13Rsy00Rm96CZ8WW1GD091M6hyrN+x1GfTIGJ3McbYWrfqi8q/hFRzMxR1PSikJdzcUx7O6LcWzU8TuQure3P8qKeInDw7WZp7QUVXqz32Ta5qf7WogEDW0xj74azboSQixMPf22E0IIIUQNlDf3Ba+qTa1QUrxwMMkzHeWhzXfc4LKzJ0KxhhfQ+wbCDM4s/KJCAeH0QQL5gas+ZmJyE5jabm7zgwIiss3vouziGHYpVfXjGAMvHk4ym7e58waXvul6u4BW2Eqz4VBz1WcENbLh2QDDcyHWtnqUfBps3/jeu9nv2hPDc3mLJ/a2Aor17R7Hx6TtUoh6I6+WQgghxDISzPXiZE+hrdCiG4YGUkEe295O51CE+27OkynAwcFav8FXWAo2LnpgOiRHngKz+OE5ocwxnEwX2orUbHObn8LznX6HUJecTBcAVpXb+/b2Rzk6GuH+1Xne7anPKiXPWBQ9xbOdrXi1GcfVcPYNRAlYhsEZudy6FtqUB/S/eLiZgUX8YeL9cq7ip3tayRYt7l7l0lHz311CiIWQV0whhBBiGYlNv4lWQSxv4W1qnoYtp+L84N02tFZ8cl2RAwMOcz4NldVGMToXYE9fdMFfowDbmyea2rbIgxVJTGwqJ6eqNBi90vNVrpVdSl1VIm+pO5ekvJalAlcyMhfgla4m1ra6HB5yMHUwd+pStFH0p4JsPR33O5S6k8raHByOcMsKl5G5equAazyeUeXNfvuubrNf0YOn9rUymQ3wwJoi7/bWZ+JXCCEJKiGEEGLZCOZ6COXOYNTCq6emszY/fLeNLacS3HadSyKs2dXj/4WzUoY3TiZIFxb3ViY2vRnLnVnE7bdgl2YwVGdE9fExh2+/sZKOoYVvJ6w2hSGU7vI7jLqiSvMECoOYKr51LpQUv+hoIRw0BGzINcDKe4Vh2+k43VNSjQLlBOOznc18560VlDxF3Kmz7HMDO7fZ7+l9bRQXsdnP0/CLjhYGUkEeWptnR7ckp4SoZ/X/m08IIYQQFVGungphLWDItzHQMRjhe9vbGU8H+PT6PGcmgoua/VRNxiiKJcWri94mZkiOPr2gW9rFMaIz7+DZ8apsbTszGeKZjhaKnuKXh5MMz9bPJqno7E6/Q6grTvYEClORbZCXsuloE1MZm4/dWOT0RH08z67EoAhY8GxnC5lFJouXCmPg9GSIn+xp5XvbV3B8zOGuVUXWtbvs76994i4R+RA3tv3PWKr6w/xrzdOK0Xmb5xa42c8Y2Hg4yYnxMJ9cl2dHd5h62IYphLi05fmbRAghhFhmgtkzhHLdGBW84tvznKv4RWczLxxqZkXc49YVJXZ0h3F1fb2xN8DBoQg9i6jeUECwMERo/uAV7tyQGP8lKBvlVT451Tcd5On9LbREPW5qLgHwswOtZIv18TMOFAb9DqGuOJmucmssparc/4HBCJ1DUe5bXWBng1V4lLQiW1Q8f6gZs4wKhjwNB4fDPL6jnSf2tDE2H+T+1QXaYh4HhxxOT4SodTJEqSAt8XsJBZK0Nd1X02PXUnmz35VbS18/kaBjKMr9q/Ps7Qv7XvkrhLgySVAJIYQQS50xC66eGp8P8E/vrODYaJgH1uRJZSyOjtRr+055w9MvjyYXNajZAE3jL1y2GiY830Eo34O2HCwqO49paDbAk/taSTiGtpihPxWkpBWzOYvnDrbUxUwqy7jYxTG/w6gP2iWUPYVR1XkejKcDbDraxM3NLl2jwYa8iNYGTk047OyN+R1K1RVLil29Ub7z1gqe62yh4Fk8uDaPrTT7+h1fZ04lox/BtsKUvDni4VsIB6/zLZbqObfZL8HBy2z2294dY3t3nLtWFTg0FKJUZ39gEUJcnCSohBBCiCUumDtNKN+7oOqpl4814XqKB9YU2dvnkHHtmsR4tbRRTKYDi7owVoAyLvHxFy/+eS9HfOoVtBXF9io7GH1sPsATe9oIBwyrkh7Hx35zMWsoX+Rvq5Oh05GZHX6HUBdCuTMo44KpfPWU68EvOpoJWhBzIFOs7+fbpZWTBq8fTzBUR62qlZQuWLx5Ms7fbb2OXx1LEgvBg2sLZAuK3b1hZvP+ft+WCpGM3kneHcG24hjj0Z58iKXY0qaNImBpNlxis9+BwQivHW/ituuKnJkIUPTkkleIRiHPViGEEGIpM4b41Bto5Vyxeur0ZIjuqfL8lN29Do1yYaMwbDmVYHYR250UEE4fJJAf+MDnYlOvo7wsmEWUZS3AZMbmJ3taCViwrr3EkQ9UppUv8recinNi3P82Lycjg9Kh/HMw2Nim8vOnXjnWxNh8kHtvLl6QrGxE2igsBc90tJJ3G+O1YyGmMjYvHWni/9t6HW+djrMq6XH/6gJj8za7ex3ydTLMPhm7E6WCaJ1DKQulbIJ2kmT0Dr9Dq4qSti662e/4mMPGw0lWt7pMzCmydf5HFiHEherjFVUIIYQQVRHKniRYGMBYl6+e0gZ+fTxBU9jj5FhjvaE3KEoaftXVvOivTY48BeY3LXyB/ACRuT1oO4pl8hWLMZWz+cnuNrRWfHilS+fgxRNQ5coAeLazmemsv4+D5aWrOhS8IRhDKHMcY1V+4PTh4TB7B2J8/KY8u3r8T0hWQkkrUlmLl44mG34e1eBMkJ8faOa7b61g/2CU265zuXOVS/dkgH39Tl21jFkqTFP0I+TdYSKhtec/boymOf4xbCvqX3BV9P7Nfn3TQZ7paOG6uIfnKVL5xk76CrEcSYJKCCGEWKrOz55yrtiqdnQ0zPBciDuud5lpwDf1xsCxUYdTE4sbmG5780RT287eiSYxsRGjQlhetmKxzectntjdSqGkuPtGl339l09GlLSipBU/29+KW9nxV4uigPDcfv8CqAOBwjC2N4ehstmWqYzNxiNJViVLnJ4IoE39JDsq4dBwhI6hiN9hLJo2cGLc4YfvtvLPO9s5M+nwsZuKrGlxOTYa5MhwqC5nhDXHPorCxuCi1G8u75SyUFi0Nz3oY3TVdW6z39P7W3hqXytNYU08bJZsq6kQS50kqIQQQoglKpQ9TrAwiFGXf6PuaXjzRIIVcY9DQ42XnCort8e9dLR50Umd2PRmLHcGxrYSLAxjVABVoYREpqj4yZ5W5go2995c5N3ehVXKlC+6Arx0xN9KlPDcHv8OXgeczDEMYOnKJSxLZ+dOKaA1ppkrLLULaYWtDJuOJJlIN0Y1ZkmX5xY99k47T+1rZTob4IHVeRJOic5Bh+6p2m/kWyjbipCIfpi8O0QkuOYDn1fKIurcTCR0ow/R1U73lEPQNtzY4nF6olF/jwkhJEElhBBCLEXnqqcsB/sKs6f2D0SZyga4pb3U0PM6tFGksjbvdC92yLiheeQJ1MAv0Vbsij+vhcq5iif2tDGdDfCJNQV29Sy2TczQMRRl34B/lSiB4mTFZ3E1klD2OMaKVDQ18esTCYbnQty3psCR4XrdkHltPKPQlOdR+VkFeCV5V7G9O8Y/bLuODYeaMSgeXJtHa9jbH2YiU/+PT3PsbtTZM1Spi5+pxni0N30SReO+vl+ewlaaVc0eh4fq/zETQlyaJKiEEEKIJbKIAKIAACAASURBVCiUOVauBuLy1Rn/P3t3HiPXdR/4/nvO3Wvvvblvokjtiy2J2mVt9rNe8jyeOPk3doCZSRBhkgyQwZsAA2QwxgQI7AkmCub9ETuDxLZsx3ESJ7YWy7JskdTCVSIl7ms32ftee917z/ujuilKJpvVVbe6ejkfgBDVrDr319213d/9/X6nHMDrpxOsS/scuLj8rzoLFL84k1jQ/CYBmOUhCMsoVYkkjpIv+Lv97QzPmOzaUmLP2XpmDFUrUf71gzT919ipajEIAqzCuZYcu9VkZXL2ORTdx+VjQw57zye4a12Jt89GP9dqKQlCwdCMycvHU60O5VdMFyWvHE/y5z/v5uXjKTJeyP2bSkwVJO+cd8kuk90UTZkg6d1EodyHa62/7u2EMDCNOOn4HYsY3eIKlOTEkE5OadpypxNUmqZpmrbSqHC2esq9YTXQW+fiZEsG69oCKuHy/1igECgF//JBZkGtcQpAWBiq3HAMlQC+faCN/kmLh7YW2X2m/h0RAyUQAr5zsI1cafF/PwrwJncv+nGXAid/HAARRjMsP1uS/PD9DL1Jn4vjkmCFzZ26NsU7F+LsORvnzKjN5SmT8bxBoSIIW9C6Opw1+eH7ab728252n42zsc3n3g0lLk2ZvHvBoRwsr9fATOJOAIQwr1s9NUepkEz8Nixj6SUMNU3T5qy0pndN0zRNW/Wc3AdY5UGCG+zclC8L3jybYEtHhQMXV86V51AJTo3YHBtyuLW3tl3oBEAEiQg/hO8ebOPcmM3D24rsPuPS6OwaPxRkS5LvHm7jy/ePIRcxryEAu3h+8Q64hNi5Y4TCRkaQtAQ40O9RqEhu6fE52L9ynm/zq7ZevXSNKiqBwjEVrhXiWbP/vfL/Ia6l8Mzwyr97Vohrzv7XCrFqLHJSCi5MWOw+m+D4sIspFbf0lvFDODG0NIee18IyUiTcrRTKF/HsX5099UlCSJQK6UztYmDi1UWIUNM0beF0gkrTNE3TVpKPVU/NP9j5l2cSlHxBxgs5N7Y8T9KuRwrFv36Y5qbOEWxzcUo1ghB+cDjDiRGXh7YW2HvWjezkN1RwbszmtZNJnt0xE8matZJhEVmZJLQyi3rcVhJhCTt/htBwIWg8QRWq6qy3dRmfw8t2I4L6BEoSswJidohjgmOBJUEIRaiqCdhKIChUDKYKgnIgKFaqO1nOx5QK15xNZM0mrbxPJLxMqXj/skffpI1nhdy7ocjIjMHRgXpabpeWTOIuFCFSujesnpojhMS1e4k7m8mVzjc3QE3TtDroBJWmaZqmrSBO9ghmeZhAxue93VRB8vaFODt6KhxegdUcoRJMFw1+fjrBZ3c2P6ETKvinI2mODnrs2lzknXMuYaQtXAKJ4pdnEqxPl2uuDIuKO/U2+c7PLeoxW8nOn0IQIMJoZpKdG7Orw/K7SlyaXH0fv/MVo44NGBSWrFZZ2abCkiGOBbYpMCUoFEEo8EOoBJLJgsFwAGVfUPQFavb5l/EC7t9U5MyIwcG+lTH3yzIzJNwt5EpniTtbF3RfpUI6UveTH70U2cw9TdO0qKy+d0hN0zRNW6lUSHz8ZzXNnnr9VBIFCMGybXG5McWec3HuWV+gO+E37ygK/vWDFIcuxbhvY5H9F52mzBcKERgy5Afvt/F7iRE6E4u3PZqXfW91Jahyx1HCjKy9b39fDNcMOTm0PIZvLw2CSiiolIE6fg1zLYSFsuDdCysjMTWnLX43YVjGlMkF31cIicShPXE3YzP7mhCdpmla/ZbXJEBN0zRN067Lyb6HWRm54a5jw1mTg/0et/WWOTa4ktuNqkmiHx1NL2hg+kIoBa8cT/LuxTj3rC9xuN++YWtSI4JQEoTw7YPtlP3FSyxKfwpU85J8S4oKcXLHCUU0bWC5suDDIZebuipMFvW14cWiEBR9SWmZDT6/EdvsIO5upFDuw7G66l4n6e3ENtsjjEzTNK1xK+sVW9M0TdNWKxVcVT01/+yp104ksQzFTFHS6ADvpS5UgvPjNu8PNKeC4uenE+w+l+DOtSU+HLAWZSfEIBSMZA3+8UjzEm+fJFA42aOLc7AWM4t9yDCHUGEk6x2+FCNoYtJSW13aEncThCVMo63uNaozqxSd6QejC0zTNC0COkGlaZqmaSuAO3MYszJ2w+qpvgmLD4dc7lhb5tz4Sq6e+ogUip98mKZYiTZJsPtsnNdPJbm1t8SpEXPRKzWODHi8fWH+nRqj5E3uXbRjtZKTO4ZCIFSh4bWUgv19HmtSPseGVt6sN21xOVY3MWcdhfJFHKux6ichJI7ZQdLbHlF0mqZpjdMJKk3TNE1b7lRAfPx1QunNWz2lFLxyIknMDumbXD2zcEIlyJUFr51a+LyW63nnQoyXj6e4ubvMxXGDwoIHQDdKIIXipWMpzi9SotEqD7BoJVst5OSPoaQXSW3hxQmLkazFhrZAV1FpDatWTxWxjc5I1lMqpD3xKWRE7ayapmmN0gkqTdM0TVvm3JmDGP74DYednxp1OD/ucPvaMsMzq6N66mrvnI8xMN34DKBD/R7/8kGarR1lhqYk2XJr5gqFSiAFvHiwfbZds7mE8jFKA00/TisZlTHM8jBRpeH298WwDcWZEf2RW2uMa6/Bs3splC9iW/W3911NCIkQFh2pT0eynqZpWqP0u6WmaZqmLWfKr6l6KlTw6vEkGS/gxMBqHNQsEAL++WiGsIHswwcDLj98P83G9gozRclUqbU/Sz8UFCqC7x5qI4hmZNK8YlO7m3+QFrJzxwCQYbHhtQoVwdEBj5u6KozlV19CWItWW/xu/DCPba6JfO2Euw3H6o58XU3TtIXSCSpN0zRNW8bc6YMY/uQNKz6OXHYZnLHY2VNpeVKlVUIl6J+0ONjv1XX/E8MO3z+cYW06oOwLRnJL4+cYKsGFCZtXjkfXwng9Tu5E04/RSk7uGKF0EDSe7Xv/skclFFjGym+L1JrLs9fh2l0Uy33YZrTPcyEESgV0pR9kpW+aoWna0qcTVJqmaZq2XCmf+MRc9dT1Bzr7Ibx2Kkl3wue9/tU9qFkKxSvHU+TLCzsROzNq8+LBNjoTAVLAYAStglESKPaeT3DkcnN2K7xynDCP8OffJXK5EkEBq3AeReO/2+pw9BjdCZ+jl1f3c05rXFviHvwgi2ttaMr6QhhYRppU7JamrK9pmlarln+6+td//Vdef/11hBBs2LCB3/u932NycpK/+Iu/IJvNsmXLFp5//nlM06RSqfDCCy9w9uxZkskkf/AHf0B3d7Uc9R//8R95/fXXkVLy5S9/mbvvvhuAw4cP8zd/8zeEYchTTz3FF77whVZ+u5qmaZoWGW96H4Y/RSDn38lt/8UYE3mTXZuLvH2+uQmMpS5UgkIFXjmR5t/cMVnTfS5OWHz7QBsZLyBuh5wdW3oJB4XAEIofHsnQnRylJ+k35TgCcKf3UWh/vCnrt5KdP4EgRISVhte6PGUxMG1x36Yiw9mWf9zWlrGYsxHHaidbOkHC2dG04ygV0pa4m1zxPME87eKapmnN1NIKqvHxcV566SX+7M/+jK997WuEYcjevXv51re+xXPPPcf/+l//i3g8zuuvvw7A66+/Tjwe5y//8i957rnn+Pa3vw1Af38/e/fu5etf/zp/8id/wje+8Q3CMCQMQ77xjW/wX/7Lf+F//s//yZ49e+jv72/lt6xpmqZp0QgrxMbfIJSxeWdPlXzBz08nWJ/xOXBx6SVWWuVAn0vfxI3nAl2eMvnbfe3EHUVHQi3J5NScQAmUgm8faKdYaV6rjjezv2lrt5KTO44SJpJyw2vt6/MwpeLCmG5W0BohaEvcTSWYxrM2NvdIQiKQdKbub+pxNE3T5tPyd80wDCmXywRBQLlcJpPJ8MEHH7Br1y4AnnjiCfbt2wfA/v37eeKJJwDYtWsXR48eRSnFvn37eOihh7Asi+7ubnp7ezl9+jSnT5+mt7eXnp4eTNPkoYceurKWpmmapi1n3vS7GME06gbTp/aei5MrG6xJ+1TClr/tLxHV3e9uNDB9eMbk/+xrxzYV69IBJ4aW/qBrPxRM5CX/8H4G1aTRR0ZlHNQiTGRfTCrAzp8glE7DS5V8wfuXq8PRh7NLN6GpLX1xdzO2maFUGcCQ9c3OWwghJDFnI569tunH0jRNu5aWflJtb2/n137t1/jd3/1d/t2/+3fEYjG2bt1KLBbDMIwrtxkfHweqFVcdHR0AGIZBLBZjZmbmY1+/+j6f/HpHR8eVtTRN0zRt2QrLxCZ+QSBj886eypUku8/F2dZZ5uDFxk+8V5JQCQZnTN69cO32yLGcwd+8244Ugq2dPkcHlk+iQQHHhlx2n403ZX1BiJU/2ZS1W8UqnK/u3KeChtc6MuBSDiSerYeja40QtMXvpuJP4tmbF+2oSgV0pnYhMBbtmJqmaXNa2hSfzWbZt28ff/VXf0UsFuPrX/86hw8fvu7t1TUuBVZ3nrj2B4Dr3f5aXnvtNV577TUA/uzP/ozOzs5avoUlzzTNFfO9aEuTfoxpi0E/zj5h4FVkMIMy5p899Yszccq+IOlW27+0jxNC8eqJFLetKZJ0PqoImixIvvlOO34ouGOtz7sXlltyTyCF4tUTSdamK2zrbLxl7WoKyOT3oTY9Eum6rSTO/wyFxAiLDa+1vy9GRzzg6KWlX3GnLV1J7yYsM0mudJK4efOiHVcIA9NIkI7fzmTuvUU7rqZp8zMMY1V8Fm5pgurIkSN0d3eTSqUAeOCBBzhx4gT5fJ4gCDAMg/Hxcdrb24FqBdTY2BgdHR0EQUA+nyeRSFz5+pyr73P118fGxmhra7tmLE8//TRPP/30lf8fHR2N/Ptthc7OzhXzvWhLk36MaYtBP86uEpbp7H+pWj0VXH/21ETB4J2LcW7prfBevz5RvhalBJVA8dKxNL959wQAMyXJ37zTQdGX3LO+wtvnl1tyqipUAlMqvneojd97ZISMF11LngDC6dMr5zmpFO1jBxHSnXeeWy0Gp036J20+vbHEfl21qNVJIMnE76Tsj+NZmxf9+EqFZOK3ky2exQ9mFv34mqb9qiAIVsz77tq1128jbmmLX2dnJ6dOnaJUKqGU4siRI6xfv57bbruNt99+G4A33niDT3/60wB86lOf4o033gDg7bff5rbbbkMIwac//Wn27t1LpVJheHiYgYEBbrrpJrZt28bAwADDw8P4vs/evXuvrKVpmqZpy1Fs6m1kkL3hDKDXTyaA2cHZ6Oqp61HA+5ddzo7Z5MuC//NOO9Mlg3s3Lt/k1Bw/FJR8wYsH2/Eb71z7GKnKyPLYjW+4DBiVYcxKNCMg9vfFMITi8uTSes5l4newruPXMY1kq0PRapD0bsY04pSDUaRc/PZiISQg6Eo9uOjH1jRtdWtpBdX27dvZtWsX//k//2cMw2Dz5s08/fTT3HvvvfzFX/wF3/3ud9myZQtPPvkkAE8++SQvvPACzz//PIlEgj/4gz8AYMOGDTz44IP80R/9EVJKfud3fgcpq7m3r3zlK3z1q18lDEM+85nPsGHDhpZ9v5qmaZrWCBGWiE384oY79w3NmBy+5HHnujLvXVo+s5Nao9oO96OjGWwjYCxvsmtzid1n3VYHFolACS5NWfzkWIpfv3060rW9qb3kun4t0jVbwckdB0DOM8+tFpUADl/y2NpZ4dTIUqlaFHQk7ycV24FSIWvanuXy+MsEYa7VgWnXITBIx++gVBkhZm9tXRxC4tq9xJ1N5EoXWhaHpmmri1DXG+C0yl2+fLnVIURCt8VozaYfY9pi0I+zqtj4GyTGXyGQDkZYuu7tvrW/jXPjNj3JkIsTLb0WtawYQvHg1hK7zziwgqrOBAqF4N/eOck96xtLwlwtMFKMbfl/I1uvVTL9/x9meag6JL0Bhy95/OC9DJ/aWOLAkmjvk3SnHyHubiZXPINrb0AKAz/McXn8ZcII5m1p0UvHbqM9+SlypVPEne0Lvn9FhUzg0y0avzihVEioSvSN/hNKVRpeT9O0+qVSKX77t3+71WFEYsm2+GmapmmaVhsRFolN/nJ2577rJ6cujFscH3a5Y21ZJ6cWQArFvRtWXnIKQCEwRMg/H00zMB3dY0IG0xBGO4B9sYkgi1W8iIpgx7L9fR4Zb2kMRxfCpDfzFHF3MzPFE3jOJgxpVwdgyzhr2p5BRpDA0KIlhEU6fhulylDd1VO7w2l+FIwzoBp/bgohkcKlLXF3w2tpmqbVQieoNE3TNG0Z8Cb3IMMCYp7ZU0rBqydSJOyQC6N6i/CFCJVg30WXlZacmhOo6ke+7xxop1CJ5nsUgDOzvHf5cnInECjEPEnfWoxkDc6PO2zr9CkFrf14LYXLmrZnce0eZoofknC2I8VHiUkhDCwjTW/bUwihk9hLSTq2E0O6+OEMQiz8NXxC+ZxR1cq4PeEUQUSNMilvJ7Z57Y2mNE3ToqQTVJqmaZq2xImgQGxyN4GMI9X123JODDtcmLC5bU2ZkXzrqzi0pcUPBZNFyd8fbiOMaMBDbOrtaBZqETt3DCUsJH5D6xzoiyGFYjTX2gSnKeOsbf8clpkhWzpOwrllduD1Jwlss5OezGcQ+nRgSZDCJh27jWJloO7qqUNhFgtBmzCYVAFHVeOzxoQQgKJTD0zXNG0R6HckTdM0TVviYpO7kWERoa6/FVuo4KcnkrTFAj4c1Mkp7dqUgpMjDr84nYhkPbMyVF10OQor2PlThA22uvkhHLrksaXD58J46557lplhTfv/hZQO+fLp2eTUtRNmc1/37DV0pR9npVYOLifp+G0IYRGGheskFec3riqcVUW2GC4Ts+8VB8MsM6qx5CtUW/0cq5OEd1PDa2maps2nrgRVuVzG9xt/sdM0TdM0bX4iyONN7SG8QfXUe5c9hrIWO3sqzJR0e592PdVdC392KsHJkcYHeQsVYBYvRhDX4rMLZ5GqDPMkfmtxfMglVzZoiwe0KtHjWN2safssQkCx0kfC2Xnd5NQcIQRKhcTdDXSlHl6kSLVrkcIlFdtJsXIZz95c1xpz1VPT4UfDzENgTzhNFHtiKRXSkfg0UiyFDQA0TVupakpQ/e3f/i2nT58G4ODBg3z5y1/mt3/7t9m/f39Tg9M0TdO01a5aPVWC8Po7KPkB/Oxkgp6kz6E+PfhYm1+oBKaE7x/KMJ5vPJkZm9wTQVSLz84dQyEx5kn81mJ/X4yUG/DBpdY89zx7Pb1tT6NUmZI/QtypvcpFCIlSIQlvKx3JB5oYpTafTPx2BAaKSl3VU2OqwjlVYqvhMnhVxZQC+lWZ86qxGWtQfawIYdGR+lTDa2mapl1PTa+Au3fvZsOGDQD84Ac/4Pnnn+eP//iPefHFF5sanKZpmqatZiLI4U3uqc6e4vo7Mr17McZkwWRLR0DR19372o35oaASCF482E5w/bn7NbELp6MJajEphZM/Tii9hpaZyBucGbXZ1uVTaMFzL+FupSfzBH4wgx/OELM3LXiNapIqIBXbQVvi3iZEqc3HkB7J2A6KlUt41sJ/f1Bt5bMQTAS/+j4hgL3hNOV5NthYiIR7E47VFclamqZpn1TTO2mpVMJxHGZmZhgaGmLXrl3ceeedjI6ONjs+TdM0TVu1YhNvIlQZMU/1VLEieONMgo1tFfZf0NVTWu0CJRiYNjk31tjjRoQFhD8TUVSLwywPYPhTVGtM6negv5rgmi4sfmtfOnYbXelHKPujKBXiWmvrXksIg1AFZOK3k47dHmGU2o1k4ndeaQy9UVvmtYyqChdUiW2GyzC/2q6qgAIhB8NsY4Ey1xYa0JV6ED23TNO0ZqgpQbV27VrefPNNXn75Ze68804ApqensW39QVjTNE3TmkH4WWJTe6uzp+apntpzLk6+bNCTDPGVPmHQFkpx8FK8oRUE4C2z3fyq7X0gw3zdawRhdfe+Te0+Z0cXdzh6W+JTtCc/RaF8CSEcHKuj4TWlMAiVT3vyXpLejgii1G7ElAmS3k0Uyv241vq61jgYZrERjF2jeupqH6g8o+r6FztqJYSBZWZIxXY2vJamadon1ZSg+p3f+R1eeeUVPvjgA37rt34LgPfee+9KskrTNE3TtGjFJ38JykfMc0KRLUn2nIuzvavMAT17SquL4NiQQ6WxOeG4M4ejCWeROLljKOk1VANyasRhpmTQnQxRi1ZNIuhMPUQmfhu50nlsox3bTEe2uhQmYejTmXqAhLs1snW1a8skqudSQph1V09dnK2eGrlG9dTVBLA7nCKMaGB6W+IeDBlreC1N07SrmbXcqLOzk//+3//7x7726KOPcscddzQlKE3TNE1bzWRlHG/qbUIZwwhz173dG6cT+KHAs6uDrzWtHpVAcHzY5Y419Q8LN/yJ6m54YunvICn9KazSJQLZWOXY/r4YcTvkg4GaPk43TGDQnXmMmLOBbPEUMXsTUkafmJbSJAwrdKYeJlQV8qW+yI+hgWWkSLhbKZQv4tUxOwzgwGz11PANqqeguqPfqPI5rgrcKhpLLAkhQUFn6n6GJt9oaC1N07Sr1VRB9R//43+85tf/8A//MNJgNE3TNG3VCyukB78NMO/sqfG8wb6LMW7pLXPk0uK2F2krjeJgf6Ntfgo7+2FE8TSXnTsOgAjrT8hNFyUnhh22d1fIlZuflJPCprftGTx7PTPF48ScrU1JTl05nrRQyqc7/RiuvaZpx1nNMom7UARI6dZVPTWsyvTNVk+N3aB6ao4A9oUz5FWDJZOzq8WcjXh2/bPPNE3TPqmmBJW6RiloPp9HSr1TkKZpmqZFKTH6Y6zSZUJhzzt76mcnkwgBFV8sYnuRtjIJzoxa5MuNPY5iU29FFE9zObljs8+v+k/SD/THUAgKleY/9wzpsabtszhWB9niMRLOzchFqFST0kIR0pP5jN61LWKWmSHhbqFQvohr9da1xsEwi4NgqIbqqTkK8FG8oxrf1GBuYHpnaheitlNKTdO0G5q3Jvl3f/d3ASiXy1f+PiebzfLwww83LzJN0zRNW2WcmUPEpt8hkAmMeXZcGpg2ef+yy13ryxzudxYxQm2lCpXg6KDH/RvrHxpulvojjKg5zMIF7PwpQiMGCzixv1qo4ECfx4a2CieHmlu9aBpJetuewZAO2dJJEu4tdVXb1EsKi1BV6M08xcDEq5T98UU79krWFr+bMCxjylRd9x9SZfpVmVsNjw+DwoLuq4AzYZGbKbFONvb+IYSBaSRIx+9gMvdeQ2tpmqbBDRJUzz//PEop/sf/+B88//zzH/u3TCbD2rW6pFPTNE3TomCUhkgN/yOhjCFvsB34T08kcUzF6Iy+aq1Fpdrm10iCSqoKRnmIwO6JMK7oiCBHeuhFlLSRQf3f55lRm8mCyc09Jfommpcsss12etueAgT58lmS7i1NO9Z8pLAIqdDb9jQD4y9TCaZbEsdKYZsdxN2N5Iqnibs31bXGwTCLi2AgKNV1fwnsUdN8UXViNpjwVCokE7+dbPEsftB4ZZamaavbvAmqW2+9FYBvfOMbOI6+QqtpmqZpzSDCEunBb6OQEFbmbdg7N25zcsTlvk1F9l1wFy1GbaUT9E+aTBQM2rz6W9+8iT1ke74YYVwRUSGpoe8j/RlC4SAJ615qf18Mzwo5MdC8NjvX7qUn/RlCVaYUDJJwdjTtWLWoJqkEvW3PMjD+Ev48mzdo82tL3E0QljCN9rruP6jKXKqzempOCEyrgPdVjntFoq415gghUSqkM7WLwYmfNrSWpmlaTduOGIbBa6+9xvnz5ykWPz5Q8vd///ebEpimaZqmrQpKkRz+IUZllFB6GOr6V8SVglePJ0k4IWdHlv5uadry895ljye2zV/BNx8nf4z67908sclf4uRP3rB99kayJcnxIZdb15Q5crk5F29jzia604/gh1n8YJq4va0px1koKUyEFPS2V5NUQQND5lcrx+om5qwjWzxFwt1e1xoHwywekkt1Vk9d7XCYZZtwSYvGdqIUQuLZa4g5m8iXLjQcl6Zpq1dNvQEvvPACP/7xj3Fdl56eno/90TRN0zStft7U27jZ9wmNOEY4f9vRsSGHvkmb29aUGcvrnfu0qCkOX2ps+3kZZBFh4yfOUbIK54iPvUpgxBtKTgEcuuQRKEGgmtPal/Rupjv9GOVggiAs4NkbmnKceglhYMoYvW3PIkXzdhFcqarVU0Vso7uu+w+oMpdVma2Gy1QDVYBX2xNOX3NDrIWqVlHdj2gw2aVp2upW0yvIe++9xwsvvEA83tgWxJqmaZqmfcQs9pEY/TGBjGME8584hwp+ejJJeyzgg8s6OaU1g2A0azA4bdKb8utcAdzpAxQyD0UbWp2EnyU1+CJKOsg626HmKAUH+mKsTfscH4z+OZiJ30lb4m6KlUGEcHCtzsiPEQUhDCwjVZ1JNfEqStX3WFltXHsNnt1LtniShHvzgu+vlOJAMIOHpC+IpnotBC6rMmdVkW3Ca2gtISQSl/bEPYzN7IskPk3TVp+aKqg6OzupVCrNjkXTNE3TVg0R5EgPfgclLUQNrTKH+j1GshY391TIlnV7n9YsikOXG7sg6U6/G1EsDVIh6aHvIYM8ChANVpycH7cZzZmsbwsII66g6kjeT1vibvLlPgyRwDHbIl0/egLb7KA38yQC/XpUi7b43fhhHttcU9f9B1SZQSpsM1ymI6qegmpS+a1whpKKZs1U7BZcuzeStTRNW31qSlA99thj/Pmf/zm7d+/m6NGjH/ujaZqmadoCXRnYPI1SAsn8Q6krAbx+KklvyufQRd1WozXXe5dcwgY6fszyKER0stuI2MTPsQunCQ0PI4J5Sfv7YjhmyKmhKHfPlHSlHyUV20m2dAbH7MIyGxtavRjE7M5vjtVDd+ZxmHdrB82z1+HaXRTLfdhmcsH3V0pxIMwSQ3I+ouqpK2sDRUL2q8anxwkhCFVAd/pRpNAbbGmatnA15g5jNgAAIABJREFUtfi9/PLLALz44osf+7oQghdeeCH6qDRN0zRtBYtN/GJBA5vfuRBnqmiws7fI4LTeuU9rJkG2JLgwYbOlvVznCgFW4RyVWOuGe1v5M8THf1ZT+2wt8mXBB4MuO3oqfDAQTZJYCJOe9BN4zlqyxZPEnC1IsXzad4UQKBUSc9bTlX6EkandVNMd2ie1Je7BD7K4Vn0zxS6pMkNUuN3wONpgq+r1HAvzbBcu3Q3OFpPCQOHQlX6Qock3oglO07RVo6YE1V/91V81Ow5N0zRNWxWqJ84/rQ5sruHEuVgR/OJMgk3tFQ5c1FektcWgONQfrztBpQBvcnfLElTSnyE19F2UdJE32HigVu9d9vBDgRFRoZAUDr1tT2Gb7cwUjpFwb0aI5dcqJ4REqZCEu4UwrDA283arQ1pyYs5GHKudbPEECXfHgu+vlOJgmCWO5FwEO/ddj6Q6MP3/kR1I0egDXRBzNpJwbyJbPB1FeJqmrRJR1ihrmqZpmjYP6U+TnjtxDmo7cX7zbJxCRdKZCPFD3UajLQbBB4MOfp1degKwi+ejDKh2KiQ19N3qQHSlEBFU9CgF+y/G6En6fBjBcHRTxlnT/jksM0O2eJyEu3NZJqfmVJNUAanYzbQlPtXqcJYYQVvibirBNJ69sa4V+lWZYSpsMVxyEc6e+qQQGFM+H6rGk7rV6rqAjtT9mMbCWxq1pS/l7STubml1GNoKVFMFVT6f5+///u/58MMPmZmZ+dhWpP/7f//vpgWnaZqmaSuGCkgNvogICihh1XTiPFOU7D0f5+buMof69OwpbfGUfMGpEYdbeuqr2JBhEVmZJLQyEUc2v/j4a9iFs7MVirlI1uyftBjKWty/qcjQTE0fna/LMtL0tj2NFBa50mkS7i1X5jktZ0IYKBWQid+GUmUmc0daHdKSkHC3YJsZsqUTJJz6q6cSSM42qbXvagLYH2bZIlziDSZNhTBABfRkHufS2E+gick1bfFI4dCVfpiYsx6lQsqVcSrBVKvDWvEM6WHLta0OY1HUVEH113/915w7d47f+I3fIJvN8pWvfIXOzk6ee+65ZsenaZqmaStCfOxV7OJ5QukiVW1Dbn9+OkEQChyTyHcN07T5KQ72N7abnzf1VkSx1MbOnyQ28cbs3KloklMA+/piWIbi7GhjJ+yGjLGm/XMIISlULpB0d66I5NQcIQxCFdCWuIeUt7PV4SwBgkz8Lir+JJ61ua4V+lSJkdnqqfwizPdSQIDi7XA6kvWEMLDNdtoSd0WyntZattnBuo7n8Ow15EqnAUVX+qFWh7WiufYautOPs6Hz35KwP0WlvPLn/NWUoHr//ff5T//pP3HfffchpeS+++7jD//wD3nzzTebHZ+maZqmLXt29gPik7+snjiHtZ04j+UM9vfFuLW3zJHLy2dwsrZwrtWLWHJTFwQnR2yKlfoTKG72/QjjmZ/0p0gNfj/SuVNQnQF3ZMDlpq4Ko7nGnoep2A6ksCj6A8Sd7RFFuLRIYRCGPh2p+0m4rRuSvxQkvZuwzCQlfwhDLnx+4NXVU6cXoXrqynGBc6pEXxjNvCulQtKx23GtnkjW01oj6e1gbfvnAEGudIaYvQ0hDByri6S3Ml/PWkUKh1TsVtZ3fIE1bc/g2r0UyheoqEEse+Vc1Liemj4NKaWIxWIAuK5LLpcjk8kwODjY1OA0TdM0bbkzKmOkhn9AKGPIGpNTAK+dTGJIKFQEegv3lSsVu5U17c/Sk3kKUdvkhUUThIIPh+rfNVL6U6D8CCO6DhWQGvwuQpUimzs15/0Bj0ogccxG15QkvZso+cPErJU9t0VKkzCs0Jl6iJhT39yl5c42O8jE76TsjxOz6/t9X1QlRvHZYrgUFnl3RAHsCafwVePHFUICId2ZR5EN7hCoLT4hTLrSj9KZeoCSP4Ifzsxu6lD9XKJUSHvy0xjSa3Gky59jddKZepgNXb9BR/LTKCrkiqcIVYWYs4VMcn2rQ1wUNSWoNm3axIcffgjAzp07+cY3vsFf//Vfs2bNmqYGp2mapmnLWlghNfBthApQyq85zXR8yOHIgMfta8ucGdUf6Fcq2+ygPXEvFX8K1+6lt+0phFhKSarG2vwECid7NMJ4ri0+9tNq+6xwam6frdX+ix5dCZ+jDVYxxt1NGNLDD7Mrqq3veqS0UPh0px/Ds1f+3BQpHOLuFrpSD7Ox60us63gOQ7qUgxGkXPhr+Fz1VBJjUaunrhwfyBJyKLzxTrO1EMJACo/O1IORrKctDstIs7b9OeLOJrLFk1hGBtfq/dhthJAIDDpTD7QoyuVNCJOkt5217f83a9s/T9zdSLFyiXz5PJaRIe5uxzISrQ5zUdWUoPr3//7f09XVBcBXvvIVbNsml8vx+7//+00NTtM0TdOWs+Tov2CVBwiFjaHKNd3n8pTJ9w5n6E35XJpYvjt7afMTwqI7/RhBWCBQRUDhWN30tj2DEEulpVNwYdxkplR/+6E3uTfCeH6VnTtOfPIXBDJRc/tsrS5PmVyettncHuCHjbVgprydVIIZPGv1VBRJYaEI6M48QczZsOQqBBsjcKxOMvG7WNv+eTZ2/Sbd6UfxnPVU/AlyxdOUKpPE7ZvrWv2CKjGGz2bDWfTqqasdUTkmIqyCjLubSLhbI1tPa56Eu5W1HZ/HkDa50kniznYMeb2KWkHM2UjMWR0VPlGwjAwdyfvZ2PkbdKYeREqDbPEUFT9HzN5IzN68rHd3bcQN3ynCMOSNN97gi1/8IgCpVIr/8B/+Q9MD0zRN07TlzJ0+iDe9b/bEubar0FMFyd/tb8ezFGlPcWJoqSQqtKh1ph7ENOLkyqeu7O6llMIxO1jT9gyDE68R1pjUbCaF4L3LMR7ZUl8lhVUeAKWgCVVDsjJJauj7s+2z0VR6XG1/XwxTKvonG0tO2WY7rt1FrnRqxc6euh4pLEJVoSfzmeqOX/4kpcpo9Y8/SsWfghYmYBbCkC6evRbPWYdnr8WQDkqFVIJJ8uXzSGFgmz24DVaMzVVPpYTBqRZUT33SnnCK52R7w5V/QgiUCuhIPUCxMoIfzEQUoRYlgaQ9eR+p2A5K/ihhWCThzr/pwdzvtjP1IH2j/4RSlUWKdrmRxN1NpLybce0elAooVgYJVQnP3kDCXV3vD9dzwwSVlJJXXnmFL33pS4sRj6ZpmqYte0ZpkOTIPxEs4MS55Av+bn87JV9w57oK+y8ufKiutjwkve0k3M1kiyeJOx9VWFQ/5FcTGmvan2Fg/DVCFc2g4vopDvV7dSeohPIxSgMEbsRtXsonPfgdhKqglBH5lLayL3jvsse2zgonhhtrs03FdhKqCqbsiCi65UUKCz/IE6gsQpgkvC2kYtXHfRhWKPljHyWtKqMEEQ65b4zAtbpmE1LrcKx2AIKwSNkfJVRFTNmObbZhm+2RHfW8KjGOzx0yxpGgtT+LEBhUFU6pIjeLxmcMCWGAgu70o1wef4nlkpxcLUwjQXf6cRyrg1zxDLbZi2N31nRfIQwkLu2JexibebfJkS4vpkyQjN1M0ttWbfUOsuRKpzFlGs9e1+rwlpyaam0ff/xxfvrTn/LZz3622fFomqZp2rImwhLpwW+jkIiwXNOJcxDC9w5lGM6aPLilxJ6z9Q+m1pY2y8zQnryPUmUYz974K1UJc0kqy2hjTfuzDIy/2uIklWBoxmQ0a9CZCOpaITa1mxn3NyONKjH2Clapr1qhqKKvnjo66FLyJXGnsRPo6myizRQrl4nZq6e975NMI4ZJ7Mr/h6qCH+RQqoRpeLjWrbPDtMEP8pQqI5QqY9X/+mOoxRi2DxgyRmy2Qsqz1yClPVslNUGudBYpbByzp2knlXPVU2lhcHIJVE9BdWD6O+E0G4WDKxrfbbS681snmfidTObeazxALRIxZz2dqUcQwEzxGAlnx5Xn5EIkvR1kC2cp+aPRB7msCGLOOpLezbOvF4qSP0yx0o9rbSTu3NTqAJesmhJUp0+f5uWXX+ZHP/oRHR0dH/sw9ad/+qdNC07TNE3TlhWlSA7/A0ZljFB6GDUkFpSCnxxLcXLE5cEtBfau4uSUIT2EMFds64fApDv9GEpVCFQF5zrzPD5KUqVY0/5ZBideJQijHf69MIpDl+M8c/N0Xfd2cieI8jdqZz8kNrl7Qe2zC7W/L0Z7LGh4OHrC24YUJtVaFG2OFBa2mfnY1/ygSKCyQIhjdxJ3NwHVpE0lmPxYlVXZnySa6huJa3fj2euIOWuxzTYAgjBPyR8mVCUsowPLaMc2m18Bd1YVmVgi1VNzFFBCsS+c4VEjHc2aKiQTv5NCeYBSZTiSNbV6CdoS95CJ307Zn8APp0i6t9S30myrX1f6IfrH/oXVWCFnSJekt52kdzOmEScICxTK5xDCxbXWIETvjRdZ5WpKUD311FM89dRTzY5F0zRN05Y1b+ot3OwRAiOBEdR24vzW+RjvXIhzz/oS+y64qMiblZYH1+qhO/MEQkgGxl+l7I+1OqTIdaTuwzLS5ErHSdzgBKD6QV9iGUnWtH2OgYlXCMLWVVQcvuTy9PbpukZJiTCP8PMoM3bjG9+ArIyTGv77ps2dAhiaMbk4YfOpjUUOXGwsYZzydlD2R/GsDRFFt3KZhovJRz9vpRR+mCMM8whhEHc2kfSqM1pC5VOufLw10K9xSL4pE9W2PWctntVb3XFQBZSDcXKlM0jh4ljdePbiDnwOr6qeOrFEklNXO6EKbFcevaLxnWWFkCgV0J1+lEtjPyLUM4tawpAeXelH8exe8qULmDJDzN7c0JpCGFhmhnTsNqbyzd/FdalwrR6SsR3EnY0IISn5I5RKl3HMdcQcvTHAQtSUoHriiSeaHIamaZqmLW9m8SKJ0Z8QyHjNyaljQw4vHUtxU1eZE0Mmfrg6k1MJdxudqV34YQ6JTW/bU1we/wl+jT/H5SDubibpbSdbPE18dij6jVQr1g1MI86a9s8xMP4qQcQ71dVGMFUw6J+y2JBZ+ImkANzpfRTaH28sjCtzpwJCwtq2oq7DgT4PKRTDU40dwbPXYZlJsqUBbLO2OS7aR4QQ1e3Vr9piPVQ+vp9FUcIwHFLWzis7XflBgZI/SrkySrFS/W+oKggMXLvnyoBz20zP3j5HyR8kpIwtu3DM6p9WOauKTBFwhxHjiL/0ElQS2BNO829kBzKCTQ+EMDBkjM7ULoan3mw8QG1BXKuHrvRjSGkxUzxG3Nk+W+3ZOKVC2hJ3kStdWLEV0VCtBE2420jGbsY2M4RhiULlImDgWeta+nqynNX0KHz99dev+XXLsujo6GD79u1Ylt5pSNM0TVudRJAjPfgdlLSQNVa5XJ4y+f7hDL2pgJmCIF9ZndsJtyXuJRO/fbbNQ2FZKZTy6W17hstjP1kCQ8IbZxpJOpMPUvbHcMzeBc/1EMLAlB5r2z/LwPgrNVeKREtxqD/OhsxkXff2ZvY3nKBKjP4Eq3SpmgRu0s+gEsChSzG2dlY4PdLYZ9tUbAdBWMA19dbrUZHCxLY+3hoYhEWCIIciwDHbiDsfVatV/GkMI4YUJkr5V1VJxapVUsbSqGwLleJQmKVNGJxYgskpqDapTiifoyrPnSIe2bpxdwvxUj+54rnI1tTml47dTlvibvwwS6F8qe6WvuupVsiFdKUfYmD8lUjXXgpss4NU7Gbi7hakMCn742RLp7CNnoYr0LQaE1S//OUvOXnyJOl0mo6ODsbGxpiammLbtm0MD1f7hv/4j/+Ybdu2NTVYTdM0TVtyVEhq6HtIf4ZQOMgaZs1MFiR/t78dz1KkPMWJodV3kUdg0pV+hLi7kXzpPJbRiWVWKyWEMDFlnN62pxgYfwVFfcO5lwZJd/oxICQIsnhOfXNshDAxpFutpJp4pQXVZYIjAw7P3QpGHYVFRmUcVAh1Dll2skeITb1FYMQxguYl6D4ccilUJGlPQQPttqaRwLPXkS+fI67bO5rKkC6G/HhrYBDmq9WGQlCpTKKUj21248z+WWrOLPHqqasdCGfYIlySovGLKnMzizpTuyiVR/Cb1LarVUlh05V+hJiznkL5EoZwmjasWwiJa/WQcLeSLZ5tyjEWm2kk6Uo/gmt1ESqfUmUAhY9nbYx0J8/VrqYE1fr167n//vv5/Oc/f+VrL7/8MpcuXeK//bf/xg9/+EO++c1v8tWvfrVpgWqapmnaUhSb+DlO/lTNVR0lX/Ct/e2UA8Edayvsv+gsQpRLiyFj9GQ+g222kS2eIGZvRcpPJukEttlBd+YxhibfYLkOW21P3ItjdTBTPNbwVWohTKSEtbPtfpWgvqHl9SpUJGfHbLZ3lRd8X0GIlT9JJb5zwfc1yqMkh/6hOneqickpqA5HT3sBHzQ4HD3l7QAUUnjRBKbVTAiBacQxjeiqfJrpo+opk+NLPDkF1Uqqt8JpnjXaIllPCAMUdGce5fL4yyzX1/qlrvp++jim9JgpHidub0XKxueJzUepkI7k/eRLl5Z9NbSULr2Zp5HSJlc6hSk78OylUYG50tR0GWvPnj187nOf+9jXnn32WXbv3o0Qgl//9V+nv7+/KQFqmqZp2lJl5U8TH/9ZzcmpIITvHcownDW5b2N5VSanbLODte2fxzJSZEsnqnMvfiU5NTd/SRFzNtCRfGDxA42AZ68nHb+VfOk8CefmSNaUwkQImzXtn8WKaEet2ikOXkrc+GbXvCfEJvcs/I5hhdTgdxCEoPymbiEwljM4N+awrdOn6Nc/f0pgkPBuolQZxLX0jk3a/E6rAtMErDdslsOocAVcVCUuRLizqBAGjtVFJn5HZGtqH0l6O1jb/jkEglz5NAlnR9OTU1CtohLCojN1f9OP1UxCmPRmnsQwPArl88Sd7TiWrphqlprefdPpNAcOHPjY1w4ePEgqlQKgUqlgmtEMVdM0TdO05UD6U6SHvouSLjK88VVvpeAnx1KcHHHZtbnInrOrLzkVczaypv2zgKJQuUDSvWXeeUxzcyxSsZuX3YmLIWN0pR+m7E9iyrYrg5yjUE1SWdUklZm58R0iIzg+ZFOuo+NSAFbx4oLvlxj9MVZ5gFDYSLXwyq2F2N8XQwrFRL6x4ehxdwuGdAhUYTbRqmnXVq2eytG+TKqn5ghgbzhNRd24pb1WSoVk4nfiWHpDgagIYdKVfpTO1AOU/BH8cIaEs2PRX5fi7hY8e82iHjM6gu70Y9hmO7niKeIRXWzSrq+mrNKXv/xlvv71r7Nx48YrM6guXrzIH/3RHwFw6tSpX6mw0jRN07QVSwWkBl9EhCUUJqKGloS3zsd450Kce9YXefeCi2pqLcjSk47dTnvyXsr+GH5YrHnuhRCSUAW0Je7BD/Jki2eaHGkUBN3pRxFCUgnHidvRzyCSwiREsbbtswxM/JSyPx75Ma6lEgiOD7ncuXbh1RNSlZHlMUK7tjlczsxhYtPvEMgERpNn0wQhHOr32Nzuc3assYuuqdgOKsEUnrUxoui0leqUKjBzZfaU3+pwaqaAHCEHwywPGKlI1px7re9OP0b/2L+g1HKoJ1u6LCNNd+bxarVy8RSeveFjs9oWy0dzxh6if/SfUSyfxzlAZ2oXMWc9M8XjJNyd+qLDIqjpEtFdd93FX/7lX/LMM8+wefNmnn76aV544QXuuuuuK//+pS99qamBapqmadpSkRh7Bbt4oToUXd34RP3YkMNLx1Js7ypzYsjCD1fTBxxJZ+ph2pP3Uij3gzKI2esWtoIwCEOfztSDePbaJsUZnUz8Lly7h1zpdFOSU3OksEBI1rQ9i23WN3x94RQH++uf7eNN7a3pdkZ5hOTwP1bnTi3C4OTjwy7ZskFHIqSR4eiO1YljdVCqDF+zdVXT5gSzs6c6hMmxZVQ9dbWjKs9YhIkkKQwMGV/2LWGtFne3sLbj8xjSJVc6SdzZ1pLk1BwhDEwjTiZxV8tiqEcmfhdJbzvZ4mniznadnFokNdcwp1IpHnvsMb7whS/w+OOPk0wmmxmXpmmapi1JTvYosck3a547dWnK5PuHM/SmAqYKgnwlulavpU4KhzVtz5L0tpEtnsI2u35li/ia15ImioDuzBNLercc1+olE7+DfLmPuLO96cerJqkEa9qeXaTWGMGZMYtcub4P6m726I1vFJZJD35n9u/NnTs1Z3+fR9IJ+eByg9VT3g7CsIxldEUUmbZSnVIFsoSsMaxlVlPyEQHsCadRKtrB5gl3G3Fnc6RrrgYCSUfyAbrTj1IJpigHo7NVP421LUdBKUU6dsuSfv++WtLbTlviLvKli3j2BmSEbfra/K77LvzVr36VP/mTPwHgv/7X/3rdjOGf/umfNicyTdM0TVtijPIoyeEfzFZ13Dg5NVmQfGt/O56lSHqKk0Orp6LCMtL0zA4VnSl+SMJp/EOyFBah8ulte5rLYz9ZcluSS+HSlX4EP5zBlDGkWJz5nNWfS5netmcYnPgZpcpwU4+nlODogMcDmxZe9SGDaQjLMM+A3uTIv2CUBwllDEM1v7JkomBwesThrvVlDvfXPxtOCpe4u5lCuZ+YsynCCLWVZq56qlOYHPcLrQ6nbiEwrCqcUAV2ilgkawohCFVAZ2oXpbER/BreazUwZYLuzGM4Vie50llso3tJzfOqtvqFdKUf4tLYj1nKuzV69jo6kg9QrAxhGe0YcvXNDG2l635yevzxx6/8/cknn1yUYDRN0zRtyZrdTYzZobA3quoo+YJv7W+nHAjuWFtZVTv2efZautOPoQjIl86QcG6JrDReChOFoLf9aS6PvbSktq7uSj+MIR3y5UuLUj11NSnt2STV0wxNvE6xMtjEo1Xb/OpJUAnAmXmPUvq+a/67O30Qb2b/osydmnOwzwMg2+BDKendhBAGugtEu5ETqkCOkC2Gw+gymj11LQJ4N5xhk3DwIqoykcJAAV2ZRxkYf4WlnMxYCjx7PV3phxEIZorHZgeht75q6pOEMLDNdlKxnUznj7U6nGuyzQ66M49RCaaQGFhmfTvXavW7boLqkUceufL3J554YjFi0TRN07QlKzn6I6zyQE2tfUEI3zuUYThr8uDWInvOeIsUZeslvR10JO/DD6bxgykS7o7IjyGEgSkT9LY9ycD4qyjq2FYuYunYbcScdWSLx4k70X/PtZDCJgwr9LY9xdDk6xTKA006kuDSlMlE3qAttvCffWzqrWsmqIzyEMmRf6pWTi1ScipUcKA/xsZ2nzMjjWy7LkjFdlCqjOBaGyKLT1t5fKU4HGbpEhbHlnH11BwFVFC8q2Z4XES3q6gQBq7VTTp2G1P5GlqDVyEpHNLxW8nE76DsT+CHUyTdW1od1ryUCmlL3EO+eHHJVceZRpLetqcIwxJBmMdb4LxMLRo1154fO3aMc+fOUSx+fBjsF7/4xciD0jRN07SlxJ3ejzddW1WHUvCTD1OcHHF5aEuBvWdaN5h0cQnak/eRju2kWBlAYuM1tc1JYJuddGceY2jyDVp5hd2xOmlL3EOhfJmYvbWlg1SlrLb79WSeZGjyDQrlS0071uHLHp+5aeGJJLMyXH2iXP1zCsukB75T3d0yXLzdu06NOEwXDXb0+FwYr//3FnPWYxpxSv4lHKHnT2nXd0LlyROy1XAY8VfGTnUKOBUW2U6JtRG2Q1WTGXdTKA9Q9sciW3dpkxjSxZAepuFhyE/8MTzM2b+L2Yq1fOkCpswQsze3NvQaCCFBQWd6F4MTP2t1OFdI4dKbeQoQlIIh4va2Voe0atWUoPrmN7/JW2+9xc6dO7Htj64u6Un2mqZp2kpnlgZIjvwzQY1VHW+dj/HOxTj3rC/y7gW3esK9wglh0Z1+jJizjlzpLI65BtNobtXY3DyLmLOBjuT9jM2809TjXY8UFl3pRwnCAkKayHlmKy1eTDZhWKYn8xmGp35BvtTXhKMoDl+K8cS27IJb2oQKMIsX8b3ZBKZSJEf+CaMyTCjjGGrxrqrv74sRt0OODTQ+HN0P87jm0queKqiQI2GOMSrcJmJsEI7+DN8ivlK8F+boFhYfroDqqatJYI+a5ouqEyOix5cQEqUCejKP0z/2I5Ravu2QQlhXEktziaa5v5sf+9q1L2oFYYlQlVCqjB9OUwnGUSpECTU7xHtxZh5GQQiJZ68j7mwmVzrf6nAQwqS37UkMI0a+dJqEu7PVIa1qNT2S33zzTb72ta/R3r48pu5rmqZpWhREUCQ1+G0UBiIs3/D2x4YcXjqWYntXmRNDFn648k8CTSNBT+ZJLCPFTPEYcWf7on1Qrp68hKRiO/CDXEvaQDpTD2HKOPnSKeJNaGesl5TVJFV3+nGGp35JvnQx4iMIxnIGgzMma1ILP2mMTe5hejZB5c7sx5s5RGAkMILFG3w/U5ScGHa4Y22Z9y7VX/VhGSk8Zy250hniztK56l5UIe+HOT5UeXwUHpJX1SS9WNxnJOkRrU+mrjbH56qnpMP/z959BclxX4f+//5+3TM9eXY2YZGIHMVMkIIYQImkXLrXt2yXLRfLth4klSMfpLL1YLlMW6Uq21TpwQpXlOxryXTJsvinrGvz0rYsSiQlZooEiUAQaZGBRdy8k7v79/s/zCwCkTbMbDyf4hZ2d6a7D4jdme7T53fO2XBuVE+NMsCQDdlpC9ymGte3RykHRydpS99F7/BrDdtvI0XcFlydvKzC6eJk1JXeF60NCU0Za6sYW8UPi1T82s+F0i5YD0d7uDqGVtE51azbWkNb5i5KvScx9vrnV82j6MxuIeq2ki/vJTWDl0j6DZ6WOVON6Qyyvb2dSGT+TB4SQggxR1mDMpX6RxltyqjzH5X615Xz33Mrp3H8foyO41ynGXfPkMsPtrfQlQkZKimK/twfSexFOlnQ8mFAUajsq0/qm9qknFIaY0Na07cTmiL58qEpO3Y6vpZkbBn58n6S3topO+5YjTZO78xu4dzwKxTKRxp8BMu2niQLM0Pj3jJaOgDWlOoEAAAgAElEQVSAUzlF+twztcqpKUxOAbx9Io6ximowuZ/ZdGId1oZonW5QZJNTrldM7bZFfCw3aI+YUuwPy0RQDBDwH2E/y5THnTpNyyyqvJjNRqunFqgIe8K5VT01SgHbTJ4VKtbwn6t0fDXFyokmJNsnxtEJ0vFVpGKriLiZSx4zpkpYr3YKbZ7AH8TaEJRCEcVRMbSO4ugYrpMEktPzl5hGSmk0Hq3pTdOaeGzPbCbhLWGkvJdUbOrPYcbinPXZafL0jQQ8ZLK4eubF2EhjeuX4wz/8Q/7+7/+ee+65h2w2e8ljGzdubEpgQgghxHnWgvEvSipVLvu89nXlooRTGR2WUbaCCusJqDFMfKvdn3KwSgMa41z/wnmwpPne1lYSEUs6Ztl/du7f1EnFVtKe+RChKVIJz07rXUetHIwJaM/cTWBKlJvWHPyCqJujNX0nZf808egNM/KkFi40Tu/I3IdCNzyBt/NkjI+tH2K858vKlNDVPrKnv4/FATO10xiNhbePJ1jS4rNvEr+vSrmkY6so+6eJRRY1MMLxK1vDLlPgvfOJqShxpdkfls93aPPrn8VR9Ngqx8Je1qk4t+kUyQZNYBNXttsWKWFYpT3OzLHqqVGWWpLqFTPEL+vWhr0uKqUwNqQjczcn+noJzfgniDYkDhwSsaWkY6uJRReilKLinyNfOYCrMmhdq3jSyiMyA5Z7zwbp+GrypYOU/TNTfuyW5C2k42vIlw+Q9NbMqMmH1lpO2Co7bYFTtkoUxTLHww+tJKgADh06xLZt29izZ88lPagAvvWtbzUlMCGEEAIg0f9z1MHn6RxD7wmLBqVrf6LqTZjrb+RKY4idfybWACEac8k+as8OUbY+nSy89oVz2Vf889ZWqqHipkU+W4/NnRL8q8klb6UldTMV/xyWcEY0E9XaxVifBS0f5lT/s1SD/qYdS+HSkd2CtVXAXLVnyEyhdQRjqrRn7gGlyderlyZPka8ojvRHWdk2viUaCsj1/B06LIypQrHRDvdFGSi5rOksc2Jw4gmqVGxlvVKtPG1Jyko9MbWrnphaqj0SSrM/LF11dMDoIyk0+2yJ7rDEjSrJLTpJdAZdpM0Vfn25Zdccrp4aZYDT1mevLbFBJRq2X60cLIrOlvs41f8TpnIwRtRtIx1fTTK2HEd7BGGBUvUwCo9YdKEMRpigWi/JkPbsh+jpfQb7vvOxZkrFV5NL3UKperzew2tmJOiNtRy0ZXaaAgMEJNBsdOKcDqsMx0Pikbn/+jymBNWTTz7Jn/7pn3LzzTc3Ox4hhBDiPB0Mkex/DpwooXVR1jKaXFIEl7UfV5j6Y3VNPn8NDTy1vYVzeZcPrSzz6sHmNgafbgqHjuw9JGPLKVaOEnFaibgzY1kT1BqWGxvQlXuQk30/atoI67bMXUScDIXKzO5XcbFaTyqfjszdKDQjpf0N2nNtmd94E1QATpif8r5To7YeTxBzDfvOTL45uh8MEo82c2LllVWtYZctsssUqGJZqqMklcO+aySm3i9fvyBMKocdtsDesMitOsVGlWhYo2tRq54qY2jTHqfnaPXUxRTwphnhBuU1tDJPKU0ssoBsYiNDxfcatt8r0TpGKraSdHwVUTeHsQFV/wxlWyYWWULCW9nU488XSjlEnAzZ5E0MFnZMyTHj0cW0pzdT9s/g6tyM6O3lW8M+W2KXKZDHkFUON+o4h8Iyu+tJ7QXM/eQUjDFB5XmeLOUTQggx5RIDLwIGTIgzrU00L2ct/NfuDN3nYty9osxrB2d2Fc1kOTrOgpaPEHXbGCnvIxldMSMm1r2fVi4WRVfuIU72/3fDm68mYytIx1dTKB8g6c2cpuhjUauk8mnPbEahGS7tbcBeFbtPe/zqB8Ad53WoUe60JKcKVcXuMzE2LKiy69TEL0xikU6ikRz5Sjcpt6WBEV5b1Rres0XerSemlugoaaXZe9FSvvEarleMekrzCzPCexS4Q6dZrWIzdvnqbFGtV08tnAfVU6MsEGB5zQzzkG5p6M+QtYZc6jZK1VNNqJRVJLwlpGKrSHhLUEpTDfrJl7uJOG3EoosbfDwBtX/TluRNFMpH8MPx9zQcj6jbRmfLFvxwCI1LxG1cQ/+JKNmQ3abIblukgqVTRVjueOwLSuyaJ68X7zemNNzDDz/MP/3TPzE4OIgx5pIPIYQQohl0MEx8+C2MTqJmWHIK4LUjSd48luS2JWXePOphL6vnmjuibiuLWv8nETdLvrKHlLdmRianRinl4DppFuQeQNG4u/euk6Y9vZlK0IsXWTSj+lWM1WiSqi1zF5lEY24+VgLF/nPjT/ToaRoZv/1EgtCoSf/OphPrMaZCVHc2KLJrq1rDNpPnqfAcb5s8HTrCBidGj6myZxLJqYsN27B2caDgRTPEv4d9HDcV7DyZHtUMoxeeOe1O4QKm6WeBo7bCkQYv36297lo6W7agxlZrcV0Rp4XW1B3c0P5xFrR8hFi0g5J/jGL1KBEnSyq2Bi8i0+ybZfS9tCN7T1OP4zppunIP1prYmyLRSFtTj3ctwzbg1XCI/y88xzZbqL+ex+mzPruCEnO/zvLqxvRbPdpn6qc//elljz311FONjUgIIYSgXj1lQ9QU96YZi92nPX68J82ajip7z0QIzNxNTiW8pXRk78VYn1L1COnYbKmoVnhuBx3Zezk79BKTX++p6czej8VgTBEv2t6IIKfF+SRVehMKzVBx1yT3aHmnJ8XGrpn3u/p+1RDePJZgYSZg7+mJ955ydJykdwMl/xiJ6PLGBXgFVWvYXa+YqmBZpKO0aIc9QYmeJhzPAHlrcIEShmfNAAtVlLt0mg419wdANFK1PlFxkY6ydx5WQyjgVTPMQhUl1sCEvlIOrk7TltlE7/AbE9qHVlGSseWk46vxIu1YG1IJzlIKeoi5i5v+ey0upZTGi7STjq9t4BL0C7SK0dXyIKCohGdIRqdniWav9dlhChyxZRSwTHs4Cg6GlSnsqjazjSlB9Y1vfKPZcQghhBDn1aqn3sToBE6T+ghNVM9ghH/d0UJXJmSopCj5M6OxZjNkEx8gl7odPxwgtAWS3prpDmnMas1XDcnYMlrNnfSPvDmp/bWm78CLtDJS3kN6lvSdupZakiqgNX07xeoJ/GBwEntTdJ+NUPYVscjMPcU2Fv7vjhb6iw6bV1Q4NTzx6ot0fA2gULZ5v/9+PTG18+LElHLYG5Y4OQWlOAEQYIii6LM+/y/sY4WKsUmnyKrGVK7Mde/Vq6dalMPJ6Q5mGligjOFNM8IWJ3vd549XOr6WYqWHYuX4GLdQxKNdpOKrSXg3oJWDHw5RqBzA0Rk8t4uYLGmdNtYaWtN3UKwcJzSNS+gqXLpyD+A4CYrVg6SmeHm+tZae+kS+k7ZKBMUaJ0bBhBye4gm2s8GY3l06OmQygRBCiKlzoXpqZi3tGyxp/vntHImIJROzkxpPP7Np2jObScdXU6r2oHWCeGTpdAc1bkpprA3JJtYThoUJN9VNeEvJJjZQqBwm5a1tcJTTR2sXay0tyQ2cG3p9UvsKreK90zHuWDpzq0Re6E7x3uk4H1xW5o3Dk2mKq0jH11INzjalJ41vDXtsiZ0mTxnLQhWhVbvsCUvTkuSo1u/rx1Ecs2WOhGXWqzi36RSJGTL5aiYarZ5arKPzpvfU1ey3JVaZGIsb2IxaKYWxIR2ZuznR98w1ExqukyYdW0UqvgrXSWJMlYrfgyUkFllK0mt88kyMn1IaLLRnPsiZwZ83aq90tmwh6raSL0/tYBNjLYfqE/n63zeRb39YnrI4ZpsxJaiKxSI/+tGPOHLkCOXypf8zH3300aYEJoQQYn46Xz3lJHDCmVM9VfYV/7y1FT9UrF/ks/XY9E99aQatonS2fJh4tIt8+QDx6BIcPXsbwCvlYGxIa/oOAlOkUD48ru0dnaQ9czfVYICI04aaYxfkSikS3gqU2oq1k+l6YXmnJzljE1Q7emL8/ECajV0Vtp2ITqr/VNK7AddJUPZ78CILGhZjYC17bJEdpkAZc0li6tQMmPw2OhswrTR7bYnusMxNKsFNOkl0FvZja7ZdtkgVS0bppizFnE008IoZ4jdUB24DK5S0crAoOrP3cWrgJ5c8ppRL0ltGKr6KeLQLaw3V4ByV4CRRdyHx6A0Ni0M0kiLh3UDCWzqOyrira09vJuEtYaS8l1Rs/ZQMffCtYb8t8e4lE/kSHA5L5yfyiasbU4Lqb//2bzHGcNdddxGNztymqEIIIWa/xMBLteqpGXBBNio08NT2Fs7lXe5eWeaVg/HpDqkpHJ2gK/cQESfNSHk3KW/dnEjIaOVgTEBH5h5CU6JcPT3GLWsXPgpNEA6S8FY0Nc7popVLKraSkdK+SexFcbTfZbisycRmVivo44MR/v3dFpa0+Jwc1FTDySVT0ol1BGGeeLQxVYWBteytJ6ZKGLpUhDbtsXeGJKbeb8TW/n0TSrPNFtgTlrhNJ1mvEjjzeHmUtZYihn4b0I/PLlNgiY6yVyolMMAIhrdNng866YbuWylNLNpFJrGB4eIevEgn6fhqkt4ytI4QhCMUKgfRKkEs0oWnGpdUFo1XW54f0p7ZzPHe05O6cdKSvJl0Yg358gGS3pqmDzYpWcNuUzg/GKFDRVjhxNgXFNkVFpt67LlkTAmq7u5uvvOd7+C6st5cCCFE8+hghPjwL2ZU7ylr4b92Z+g+F+OelWVePTh7q4muJeJk6co9hFYR8pX9pLwNc2q8vNYuxvosaPkIp/p/TDUYuO42ueQtxKKdjJT3ko6tn4Iop4e1lmxywyQTVACKHScT3Lcy35C4GmGwpPmXrTlSniHqWAbLk7vRGnFbiEe7KFQOkPRWT2pfo4mpnaZAsZ6YWqU99oQlTs/AxNT7DdsQgIhSvG5G2EWRTTrFShWbU68dVxJYywAB/davJ6Rqn1/c5jitHFJKS+Pji+yyBVbZGO0NbrZvraE1dQeZ+DoibgZjfSp+LbkRiywh6a1q6PHmO2st+2yJCIpVuvE37JRy0MRoTd1G3wT7R6Ziq8mlbqVUPU48uhTdxJttwzZglymyzxYJgcU6SlY57AtLnAtm/mv5TDOmjNP69evp6elh2bJlzY5HCCHEPJYYrFdPXXTHzA+hr+DiaovjQERbHG1rX2vQTb4Oeu1wkjePJbl9aZlfHPEmPZp+JvIiHSxoeQAwlPyjc6IJ+JVoFcHYgK7cQ/T0/YjwGknQWHQh2eRNFCvHJp2ImOmUUkScDLFIJ2X/7CT2ZNneE58xCapKoPje1lZ8o1jX5fN2A5blZuLrMDbA1RPvWRNYy756xVQRwwIVYeUsSky934gN0YDB8jMzxE4K3KXTDe03NF2stYwQXpKE6rcBw4Tnn+OiyCqHhTqKpzRlQvrDgLwN2RuG19j7/KOAl8wQv6bb0A1MYiqlMdbH2Cr5SjdR3dGwCkdxqao1vGyGOWxrlYFRNEub9Luejq8jXz5Exe8d13bx6GLaM5sp+2dwdQ6nSfH1Wp+dpsDhK0zkm+/LeidjTAmqRx55hMcee4zVq1fT0tJyyWMf//jHmxKYEEKI+UUFI8SHLq+e+sH2HHvOXL1qaTRZNZqwujiB5Tqcf8zV1J9jiTi157oXbetqcJ0L20Y0DJU1P96bZk1HlT2nIwRm7iWn4tEldLZswZgS1bB3Vk3qmwitXCyKhbmHONn/35grNOLXOkZH5l4CM4zjpNHzYGJZrYpqI+XBySSoFGdGXM7lHTpS03thbiz8cEcLZ0Zc7l5VaUjlo1IRUrGVVPzTxKNLxr29tZa9tsQ2k6eIoVNFWKlj7AmLnJmFiamLGaCAIVL/87/NAIttlDt1uuHVMs1SseaSJNRAPSkVvK8qKqMdulQEC/SFAcME9Nnah7g2A/TbgHdtgVtUqqH71iqCF2nHo72h+xUXDNqA58IBhgjZ6CTYHxZ5wQzyq6qNlga/T44u9evI3MOJvmdgjLWIUbeNzpYt+OEQCpeI29ifs6tN5CsaIxP5GmRMP0lPPvkkfX19dHR0UCpdaOw118t3hRBCTJ3k4Mtgg0vqk3oGI+w5E2NDV5WKD46j0Ao0lvNPtLWTXmMV1oIxtc9DqwgtVP3614b6R+3zwNQeD6+TdFqYCRgqaUr+7O/F9H6p2CraMx/CDwcJbZlEdG72WHo/pRxcJ82C3AOc7v8Jlkt7JnVm7kXrCKVqz5yvnhqllDrfED80k+mZY9nWk+SX1g03LLaJeG5fmj1nYmxeXua1g425e56OrULryBWTmmNxxFZ41QzToSKsdDz2BCXOzvLE1Pv5gI8hiuKs9Xk67GOVirFOxYkojUut4shBnf9TM7XXFMZaBqknoC6qjCpc9DrgochqlxtUlAiavKk9f8SGjFipipqst02e5SpGdh4k/+eKI6bMi2YIB7jFTbI9qN1IdLD81AzwK7oNr8E9npRyiLhZsokPMFTcdd3nu06artwDGFMlNIUJ3Ui4liEb8EI4SB8BcTQbnDhnZCJfw43pVeG1117ja1/7GrlcrtnxCCGEmIdUkCc+9MZl1VPPd6eIRwz9BcWZkebdhVeMVk7VKrIcVftaKyhUNMOVuZecyiZupDV9OxX/DAqHeGTRdIc0xRSxSCft2Xs5N/TS+e9mEzcS9xbVJv5466YxvqmnlCaTWMtAfuek9rOjJ8ZH1w4zXfcxt52I89KhFDcurPD2sclN7LtYOrGOatBPPDr+lhfWWnaaAmnlMGKDOd+XpFqvdkiiOGLLHLRXv4BTXJy04nzyykXhqIs+rz9+/nOlLn3uxY+rC9/L2/CSyqhBgvOpKA1klEObdlmsHEJr6DMBQ4ScNT6TqScU1/ayGeKXdasUPMxwxlreNnl22ALtyqVFO+eTUwAhtX50PzOD/JLONXTpJtT6i+VSt1CoHCUIR676PK1idLU8CGgq4SmS0ZUNjaNkQ54NB6hguNFJcDgss0cm8jXFmBJUCxYswHHm3sm5EEKImaHWeypAXXQheXwwwv5zMTbdUGFrA3rHXItFERhFMLOGjzVNa/pOsokNlKoncHSGqJuZ7pCmXG35gCEVW04YFunPb8WLdNSbqvaQjK6adxdO1lrS8XUM5N9lrMspLqcYKjucGIywNDf1SZij/RGe3pXlhpzPsX4H3zTmjn4supCom631t3Fbx739KVvlHD436sS8muZUqP8cpdHElSaiFLqeOKr/B7b202bqtYym/nloLQGWIpYQCK0lpPa9sL7deCTQZLXDShVDA4NhLVk1aEMGpSpqShngtPXZZ0usV4npDkdcRdkafmYG6bFVVuoYvabKgfDypawWOGGrvGVG+KDT2PMJpTTWGjqyd3Oq/9krPweXrtwDOE6CYvVgw28u+dbwk3CQAiEfcBPsDObPa/h0GFOC6r777uPLX/4yH/vYxy7rQXXjjTc2JTAhhBDzgwryJK5QPfVCvXrq2EBzxwLPL5qO7D2kYisoVA4RiyzC0XNzKuFY1E58Q7LJjRjrk46vIjRFlPbQenb0zWkkpRSOipPwllCsHJ/Enizv9CRZmhtsWGxjMVB0+P47ObIxg1YwXGnc8qFMfB2hKeO5Cye0/Q5bII7m8DxdCjKCYcSaiec9r8KhdjEzmvRy6rc5NIqoAlcptFIMhAF5DEVjqC1EFNNNAb8wIyxVHskmTlgTE9NrfZ4LByhhuMVJ8l5YvKQf2/sp4F1bpNVEWNPgyX5KaWKRBaRiq8iXD1525M6WLUTdVvLlvaQaPOTFWMsLZohe/EuWNormGdM797PP1rKVTz755CXfV0rxjW98o/FRCSGEmDcS9d5TXFQ9dWwgQvcUVU/NF0q5LMh+mLi3iHx5P4noinmZhHk/pRyMDcilbsHakEL1wLxb2ncxaw3Z5IZJJqgUu055/K+N4ExRfrnsK763NUdoFMtaA7adiDZs345OkvCWUKoeJeGNv09br/XpsVU2OnF2y5KQhgrrH7XM1/sunq/wLTFzWCDA8poZ5qOOtJGZSfabIq+aYWJoPuAk2BFePyljqS2ZfdkMkVUOnapxr8FQe29qS99JsdKDuWjJcHt6MwlvSW1Zfmx9QyufrbW8aoY5bivc7CTYIcmpKTGmBNXjjz/e7DiEEELMQyrMkxh6/Qq9p9IkooYjvVI91QhaxejKPUDUbWWktIdUbC1K7lifp5VLaKoUq4fmdXIKRu9Ud+E66Wv2+7iekq852OuxtrP5U42MhX/d3sK5gsuHVjZmYt/FMvHaZEvFxC64dpgCURRnwok1VxdirrLAUVvhsCmzYh5X884UobW8bobZa0t0qQhRpdg5jiXJhlpy4afhIL/mtDW0Mk4pDURoz9zJ2aGXAWhJ3kw6sYZ8+QBJb039OY2zzRZqy1B1nPfCouS7p4ic+QshhJg2iYFXwPooc+HC7Wh/hIO9Hh9YWKW3KBU+k+U6KRa1foyI20K+MnqHUZJT7+foKOkG332dray1ZJPrJ7sX3ulJNiSe63l2b5p952J8sIET+y7QpBNrqARniUXHP0hgyAYctmWWOzH6kD5HQryfAl4zw1TsPGkCOUMVbMh/hf3stSXW6ThDNuCYGX9SPQAqGH5qBghs41M6ydgK4tFFpGKr6z0jjxOPLkU3+LxmnynyjsmzXHscMiV59Z5C16yg+su//Mvrnqh98YtfbGhAQggh5gcVFogPvY7Rycuqp5JRw+FzkkSZrKibY0HLgyjlUKweIOVtkASMGJOUt4p+3hltRz0Bir1nolQDRdRt3n3nrcfjvHo4xc2LasuBGzWxb1QytgxHxyn7PSjVNe7td5oCDpA30vdIiCuxQAnDm2aE+5zsdIczL52yVZ4PBwmx3FpvAj6ZdKEBem3Ay2aID+tsw847asNNQjqy96JVlLJ/BlfncHRjb0wcNxVeMcMsVBF6TRWpfZ1a10xQPfDAA1MVhxBCiHkmMfAyylYv6RFyuD/KoT6Pu5aVefOolPtPRiyygAUtH8HgUw5OkPImWxEj5gulFEpFScaWX6Eh7dgFRrH7jMeti5vTGPxwX5T/2JVleavPoV4HP2z8woBMfD1BOEIscsO4ty3akG5bYoWOcdDMz+boQozVPltilYmxqMHJBnF11lp22SJvmhEyymGx47G9gRPqDtoyrdblFpVq2D6VcnCUQzUYQOEScRu3b4Bz1ud5M0hOuYQY8rKwb8pdM0H14Q9/eIrCEEIIMZ9crXrqhf0pUlHDgbNSPTUZCe8GOrP3EZgCQThIMrpqukMSs0xtmd/GSSWowLKtJ9WUBFVfoTaxryVhMBby1cZN7BsVdVuJRTsoVLpJemvGvf0uU+tZYpUsXRLiejTwsh3mN2w7rlT6Np1vDS+bYQ7ZMkt0lKoN2R00doiDBt4yeVqJsLSBiUdrDdZavEhbw/YJMGwDng0HiKHIaM0R6Rs4LaQHlRBCiCmXGHwFZauoi5a9HOqLcrjfY+PCKv0l6T01Uen4Wjqz9+OHg4SmSDw6/soPIZRSRN0cUXcyFwCKQ70uhUpjTzdrE/taAViaCzg20JzXi0xiHcb6uHr8/w8q1rDHFlmqPQ7JRY4Q12WAERvyjslPdyhz3pANeCbs57At8wEnwVlT5axtfJclAzjAC2aQARs0bL9KabxIa8P2B1Cyhh+HA1gsNzieJKemkSSohBBCTCkVFogPvobRSXR9Zb+18EJ3ipRn6JbqqQlrSd5Ce2YzleAMlto0NiEmylpLS2rD5PaBYuepeIMigtDAU9ta6Cs63L60yvYTzVkOpFWUZGwFFf/UhC6E9tgiPpa4VIIIMS7v2gK9Vnq2NctRU+bpsI8iIbe4SXaHxab2WAoBg+WnZmDGNsIPrOUn4QAFQta7cXaHja0kE+MjCSohhBBTKjH4ar166sLdtEN9UY70e2zs8hmQ6qkJULSlN5NL3UKxcgxHpfDc3HQHJWY5pRSJ6DK0ik5iL5ZtPYmGxfTjvRm6e2NsXlHh1UPN61WTiq9GK3dCTeIDa9lliizSUfaH0ntKiPFQwMtmCNOECXDzmbGWreEIPzWDZJTDUifK9qAwJR2WQmrVcS+YwRn372qs5QUzyDl8bnIT7GhgDy4xMVdNUP35n//5+c//9V//dUqCEUIIMbepsFirnnKSaCpArXrq+e40ac+w/4zcNxkvhaYzu4VMYi2F8kG8yIKGNw0V85dSDun46snsgZNDLv3FyVdGvnk0wetHkty6uMybRzxo8MS+i2Xi66gGvcQn0Bx9vy1RxpBTjrTXFWKcDNBnA3ZZSRQ0StkafmIG2G4LrNAeoTUcCCtTGoMFemyVN83IlB73Wqy1vGaGOWYr3ORIcmqmuGpHyZMnT1KtVolGo/znf/4nv/mbv9mUAAqFAn/3d3/H8ePHUUrxR3/0RyxatIivfOUrnDt3jo6ODv74j/+YVCqFtZYnnniCbdu24XkejzzyCCtXrgTg5z//Of/2b/8GwK//+q+fb/B+6NAhHn/8carVKrfddhuf+tSnZMS2EEJMk8TgK2hbwVxUlHCwN8qxgahM7psArSJ0tnyEeLSLfHkvCa9W9SFEo1hrySTWM1TcPan9bD+Z4IHVE78wOdgb5T93Z1jR5tN9LkJgmncuF48uJuKmyVdOEXXbx7WtsZZ3TYF25bJHlokIMSEKeNuMsEx5ZOU9bVL6rM9z4SAFQm52ErwXFidQF9oYCthli7Qal7W6cZW1E7XdFthrS6zTMXaHRbmhMENc9Tf+zjvv5LOf/SydnZ1Uq1W+8IUvXPF5X/ziFycVwBNPPMGtt97K5z73OYIgoFKp8O///u/cdNNN/Nqv/RpPP/00Tz/9NJ/4xCfYtm0bp0+f5utf/zrd3d18+9vf5m/+5m/I5/P88Ic/5Etf+hIAn//859m0aROpVIp/+Id/4A/+4A9Ys2YNjz32GNu3b+e2226bVMxCCCHG73z1lE6i65P7RqunMrGQfaflJOd0iIUAACAASURBVHQ8HB2nq+UhIm6GkfJuUt56lJIKNNFYSilcJ0U8upBS9dQE92LZfiLOR1aNMJF7hL15hyffydGaCPFDRaHa3D51mcQ6QlMi5i4Z97aHbZkRQpY7CXqDxjUFFmI+sfWPV8ww/1PnpLhggrpNiVfMEB6aG50EO8PprRCy1JZvvWKGySqXBZNaPj45+02Rt02e5drjsClPW9JOXO6qZ7KPPPIIn/3sZ3nwwQfRWvORj3zkih+TUSwW2bNnDw888AAAruuSTCZ56623uP/++wG4//77eeuttwDYunUrW7ZsQSnF2rVrKRQKDAwMsH37dm6++WZSqRSpVIqbb76Z7du3MzAwQKlUYu3atSil2LJly/l9CSGEmFqJwVfRtgIX9Z7q7vU4PhhlQ5fPUEUSVGPlOmkWtv4PXCdFvrKPlLdBklOiaaw1ZJMbJ7EHRX/R4dTw+H/HS77in7e2ohUsyhpODDb3daKWjFtM2T+F64zvDr+1lh2mQFY57JOlIkJMigFO2Sr7rVQijldoLa+Fw7xohmhXETq0O+3JqVGGWgLiuXCQQhMmB47FCVPhZTNMl4rQa/ymNokX43fNd/n169ezfv16giA4v2Sukc6ePUsmk+Gb3/wmR48eZeXKlXzyk59kaGiIXK7W3DWXyzE8PAxAf38/7e0XSq3b2tro7++nv7+ftrYLI4BbW1uv+P3R5wshhJhaKiwRH3r1suqpF/anyMZC9pySxuhjFXXb6Mo9CEDRP0Q6NpnEgRDXp5QmFlmEo5OE9d/f8bNsO5lkUXZozFuEBp58J8dg2eGDy6u81sSm6KPS8XWARavxTx48Yav0E3CTTvDuDLkYFGI2U8AvzAhLlUdCyYTfsSjYkOfDQc7is1bHOW7KnJlhjckDwGD4qRngf+k23CmskOu1Ps+ZQVqUi8GQZ2ZOFpzPxnQb6oEHHmDXrl289NJLDAwMkMvl2LJlCzfeeOOkDh6GIYcPH+bTn/40a9as4YknnuDpp5++6vPtFX65rlbyqZS64vOv5rnnnuO5554D4Etf+tIlibDZzHXdOfN3ETOT/IyJsVDH/x/KVLD6wmv2/nMeJ4aibF5e4Y0jzb/wnAvi0UV0Zu/H2ArV8Bwpb910hyTmkWxiHf35dya8/c6eGP9j/RB6DNci1sJ/7c5wqM/jnlVlXj3Y/NcIRa0hfCU4QyzSNe7td5g8STQHpPeUEA1hAR/L62aYBx2ZTHs9p22V58NBfCy3ukl2BoUZm34xQK8NeNkM8WGdnZJlnCM24NlwAA9Fq3Y4OMWN4ifL0XpeXHONKUH1/PPP8+STT/LAAw+wZs0aent7+drXvsbDDz/MQw89NOGDt7W10dbWxpo1awDYvHkzTz/9NNls9nwibGBggEwmc/75vb2957fv6+sjl8vR2trK7t0Xmnf29/ezceNG2tra6Ovru+T5ra2tV4zloYceuuTvcvFxZrP29vY583cRM5P8jInrUWGJtlPPYd9fPdWdoiUe8t4pWdo3FsnYCjoy9xCEwwR2hER0xXSHJOaZVHwN/fntMKFLHkWhqjjcF2VV+/UXVLxxNMGbx5LcvrTMG4ebO7FvVDK2HEd7lP3iuC+Wztgqp/H5QL0JsRCiMSxw2FY4Ysos1zJI5Uqstbxni/zCjJBWDiu0x/ZgotWuU+ugLdNqXW5RzZ0+XLaGH4cDGCwr3Bi7g9l3IyE0Zs5ccy1atOiqj42pYcUzzzzDo48+ym//9m/z0Y9+lN/6rd/i0Ucf5ZlnnplUYC0tLbS1tXHy5EkA3n33XZYsWcKmTZt48cUXAXjxxRe58847Adi0aRMvvfQS1lr2799PIpEgl8tx6623smPHDvL5PPl8nh07dnDrrbeSy+WIx+Ps378fay0vvfQSmzZtmlTMQgghxic+9CralMFe6D2176xHz1CUdQt8RipStn89mcQGOrP3UQ36CPGJR5ZOd0hinlFK4WiPpHfDJPZi2daTvO6z9p/z+NHuDKvaq+w9HSFs4sS+i2US6/HDYeKR8f8dd5gCHoqesNyEyISY3zTwqhmmYmdqPdD0Cazl52aIN8wIi3WUOLB7FlVxauAtk+eYad5rZ2AtPwkHyBOy3o3PyuTUfDKm29YjIyMsWXLpJJNFixaRz+cnHcCnP/1pvv71rxMEAZ2dnTzyyCNYa/nKV77CCy+8QHt7O3/yJ38CwG233cY777zDZz7zGaLRKI888ggAqVSK3/iN3+DP/uzPAPj4xz9OKlXLwv7u7/4u3/zmN6lWq9x6660ywU8IIaaQCkskBl8l1EmcS6qn0rXqqZPSe+p6cqnbaUneSKl6Ekcl8dzsdIck5qlas/QNFCpHJrgHxe7THv6NELlKXvps3uWpbS10pEOKVU3Rn5oEthdpx4u0kS/vJxVbO65tB2zAMVthvRNn7yy6MBRitjBACcNbdoR71fx+D/StoYChYEMKhOwyRfoJ2OjEORCWZl3DbwM4wM/MEL+iXHKqsVX1xlp+Zmo9uW51E2yXARYznrJjaNT05S9/mfb2dn7nd34Hz/Mol8t8//vf5+zZs3z+85+fijin3GhV12wny69Es8nPmLiWRP/zpPqfw6gY2tbuju054/Evb7fyweVlfnFEyvWvTtGe+RDp+GqKlaNE3U5cZ/yNm4VotBO9z+CHgxPe/rduH+ADXZffLS9WFd96rZ1qoFnRFrDr1NSNIO/I3EPCuwE/LOBFWsa17YvhIIdtmTZczhBcfwMhxIT9stPKQjV1rw1Tybe1pt0FG1IkpIAhX09EFayhQEiVSy/dPRQb3Dg7giIzqxX6+DhAUjn8im4j1qCpxNZaXjMj7LFFbnIS7Apn9/+jBakI/+dXV013GA1xrSV+Y0pR/t7v/R5f/epX+eQnP0kqlSKfz7N27Vo++9nPNixIIYQQc4sKyyQGX7mkespYeH5/mlwiZJdUT12VwqGz5X4S3hLy5W4S0WVoPTdPyMXsYq0lm1xP7/AbE90D75xIXpagCgx8/50cI2WHO5dVeP3w1CWvtYqRjC2n5J8gEV02rm3zNuSALbPKiXFAlvcJ0VQaeNkM8eu6fUonvzVC9X2VT5d8fpXkE0AMTUJp4lrTgoOrFBYomZC8NZQI50RVUAiM2JAXzCAf0zl0A/59d9gCe2yRdTrO7lmenJpPxpSgyuVyfPGLX6Svr+988/K2trZmxyaEEGIWG+09ZdSF6Vt7zsQ4PRJh84pKvfGxuJyms+V+4tHFjJT3kvTWoGW8tpghlFIkYyvpG9mKtROpFlJ0n4tQ8hXxSO1ywVr4j11ZjvR73LOyzKuHpva1IR1fjVLOhNqwv1tPvvszbIy7EHORAYZtyDaT504nPd3hnFetJ5gK1pAnrCeezCXJJ/8K6ZE4mvho8kk5RFAYoGBCivXkUxlD2RrmQ3bFAidtlTfNCJudzKT21W1KbDV5lmmPw6ZE2JgQxRQY1yLP0al7QgghxLXUqqdevax66oXuFK2JkJ0nZHLflSk6s1tIeEsYKe8h5a1DNajUXYhG0colFVvJSGn/hLY3VvHe6Ribltb6Nb12OMnbJxLcMYUT+y5QZBJrqQTniI1z+EDZGvbZEsu0xxEzu8aVCzGb7bQFVtoYbWp6KrEDazltq/TYKj22Qv8VlvaOJp+SWpNTDi61yqeCCShYS4mQEobSPEk+jZUCdtkircZlrU5MaB8nTIWXzBBdKkK/8WddX675Tq4QhBBCNFx86DW0KV1SPbX7dIwzIxE2ryjzxhQu35k9FB3Z+0jGbmCkvFeSU2LGqi3z2zDhBNXoMr9NS0vsPePx471p1nRU2X0qSmindtlOwluC66SoVE7huR3j2vY9UyDAEp1lS42EmO0UtaV+v6LbGrIU7HqstfQTnE9InbZVQmpLDttVhLU6hsFSMKEknybJUvv/+ooZJqtcFoyz31iv9XneDNKiXCyGEWTy42wjCSohhBANpcyVe0+90J2iLRmy84T0nrqcoiNzD6nYckbK+0h5ayQ5JWYspRQRJ4sX6aTin53IHjg24NJ9LsoPtrfQmQ4ZLilKwdT/zGfi6whMkZi75PpPvohvDbttkcU6Srf0nhJiShmg1wbsskVuVsmmHKNgQ07aKidshZO2Sqme6Mgqh+VODBfoCauctT5nQ78pMcxXhlqS4rlwkF912kiNsc3BiA14NhwgiqJFOxwOpbJ1NrrumYAxhl27dhEEMpVECCHE9cUHX0ebEuqi/jTvnYpxNh9hTYc/ZWPjZ5P2zGZS8ZWMlPeT9FahpOeUmOGstbQkN05iD4rvvtVK1IW2lOFMfuoT1xEnQ9xbRMU/hTPOCZn7bIkKlqxypEBCiGmggLfNCMMT6oV3Od8ajpsKb4TD/N+glyfDc7xohuixFTp0hA1OnA5chm3IwbDMvrBMXqpzmiYAKhh+agYIxtDjr2wNPw4HCLEsdz1JTs1i101Qaa358pe/jOtKsZUQQohrU6ZCYvBlQp1A29rJgbHwwoEU7cmQHSdkEt37taU/SDq+hnz5AElvBVrJ+62Y+ZRSxKNLcPREl+taHA03LvLZfWp6XhfSiXVYG6L1+Joth9byrinQqSLsC0tNik4IcS22/vGKGcZOYEiBtZZe67PD5PmvsJ9/Ds/yrBlgjy0SVYr1TpwbdJQAyzFTYU9Y4hyBJKSnkAH6bMBLZuia/8aBtfw0HGCEkA1unPcCeV2ezcZUS71hwwb2759onwEhhBDzxYXqqQvzUt49FeNcPsLqDn9alvDMZK2pTWQS6yhUDpKILkNPU8NXISZCKU06sXaiW5OL+7xxeHqSU0q5pGOrKAeniY2z99RBW6KAodOJyGQoIaaRoTb1rduOLSGRtyH7TJEXwkH+JTzL02Efb5k8JQwrnRhrnRgxFGesz96wxDFTvUL7czHVDtkyO2zhio8Za/m5GeQMPje7SXYExSmOTjTamG7TdnR08Nhjj7Fp0yba2tpQFzWje/jhh5sWnBBCiNmjVj310mW9p37WnaYjFbBdqqcukUvdRja5kULlMLHIUrSW5JSYXay1ZOJrGcy/y0Q6AZ8rTN9rQiq2Aq2jGL98yXnt9Vhr2WEK5JTLXrkQEmLaKeANM8IS5ZF43/L4qjWctlVO1JubD9VTynE0C3SUpNKcCav024DBUFJRM5UGtpo8OVyWXVS1a63lDTPCEVvhRifBjuDKSSwxu4wpQVWtVrnzzjsB6O/vb2pAQgghZqf40OuXTe7beTJOb8Fl84oKbxyWpWujWpI305K8iWLlKLHIIhwtyTsx+yilcFSChLeYYuXEdIczLpn4evxgkHh02bi2O1q/yL1JJ3hXLmiFmHYW8LG8bkf4CFl68WvT9kyFM/hYwAE6VISFOkrB1JqfHzXSo2i2MNT+DX9mhvhV5ZCrV5vvtAV22yJrdYy9YUmWX84RY7paeOSRR5odhxBCiFlMmQqJgVrvKcfUqgpCAz/rTtGZCth+XKqDRmUTN5JL3UqxcgwvsgBHe9ffSIgZylpLNrlxViWovEgn0UiOfKWblNsy5u1Gq6fSaLql95QQM4YFDpsyPVSo1tMUOeWySsdQFk6YCqetz2mZtjdrhYCD5Sf1yX4nbIW3TJ4btMdRU5almHPImJuBnDhxgh/+8Id85zvfAeDkyZMcPXq0aYEJIYSYPeJDb6BNES7qyLLzZJy+osuK9oCy9J4CIJPYQGv6dkrVE0Td9kk0mBZiZlBKEYt04TrjazQ+nTKJ9RhTIao7x7XdaXzO4bPMiVGWe/VCzCgOsFBH2eDEacVhwAYcCMt0mzJSWzM3hECekP82/bxkhligIgwaH6mFm1vGdMXw+uuv84UvfIH+/n5eeuklAEqlEt/97nebGpwQQoiZb7R6yugETr1kPjTwswMpOtMB24/L8jWAdHwtbek7KVVP4uoWXCcx3SEJ0RDWWrKJ9dMdxpik4qtJxZZT8k8SjWTHte0OkyeO5khYblJ0QoiJCoGj9Wl7/TK+YM6y1Cb7ZZSDxjCMme6QRIONKUH1gx/8gL/4i7/g93//99G6tsmyZcs4cuRIM2MTQggxC9SqpwpYe+EkYXtPnP6iy4q2kEoo1VOp2GraM5sp+6dwdJqIm5rukIRoqFRsFQrn+k+cRsnYctrTH6LsnybitI1r2z7rc8JWWe545OWCSAghpo0GohZOWUlEzkVjumoYGhpi2bJLm0gqpcY19UQIIcQcZKokBuvVU7ZWVRAa+PmBFF2ZgG3Se4pkbAXtmQ9R9s+gSRB1Z89SKCHGQimF1lGSseXTHcpVJbwldGTupRr2opRH1M2Ma/sdpkAExbmw2qQIhRBCjIUBzkqV3Jw1pgTVypUrzy/tG/Xqq6+yevXqpgQlhBBidogPvYEOL62e2tYTZ6Dksqw1oDrPq6cS3jI6MvdQDXpRKjruJUVCzBa1ZukbpjuMK4pFF9KZvR8/HMSi8NzcuLYftgGHbZkVjkevXBQJIYQQTTOmKX6f+tSn+Ku/+iteeOEFKpUKf/3Xf83Jkyd59NFHmx2fEEKImcpUSQ6+VO89VZvcF9SrpxZmArbN895TCW8pndn7qIYDKBTRcV4UCzGbKKWIuq1E3TaqQd90h3OeF+lgQcuHCcIRjK0Si3SNex87TQEFFIwkp4QQQohmGlOCavHixXz1q1/l7bff5o477qCtrY077riDWEymDwkhxHwVH/oFOiwQqgvvBdtOxBksuaxbUObU8Px9j4hHF9GZ3VKv2LB4kfbpDkmIprPW0pLawNnBV6Y7FACibitdLQ8SmjKBKRCPLh73Poo2pNuWWK5jHDLSHF0IIYRopjElqAA8z2P9+vX09/fT2toqySkhhJjP6tVT4cXVU2G9eiob8M6x+Vs9FYt20dnyYfxwBGN9YpEF0x2SEFNCKUUiugyt3sTY6e3VFHGydOUewlifIOgn7i27/kZX8J4p1lqiKxlTL4QQQjTbmBJUvb29fP3rX6e7u5tkMkmhUGD16tV85jOfoaOjo9kxCiGEmGFq1VN5QhU//723TyQYKrus76pwamjM9z/mFC/SyYKWjxCaAtaWJ7ScSIjZTCmHdHw1Q8Xd0xaD66Toyn0UsFSDsyS8FRPaT9UadtsiS3SUQ2GlsUEKIYQQ4jJj6l77+OOPs3LlSp544gm+/e1v88QTT7Bq1Soef/zxZscnhBBipjFVEqPVU7YE1KqnXjyYYlE24J1j83Nynxdpry8nKhGEeTxJTol5yFpLJrF+2o7v6ARduV9CKYey3zPh5BTAHlvEx5JS83vYgxBCCDFVxvSOe+jQIT7xiU+cX9YXi8X4xCc+waFDh5oanBBCiJknPvwmTpgHe2HJy9bjCYbLDktyAb6ZfxdzUbeVBS0PYWyFIByaUK8bIeYCpRSukyIWXTjlx9YqRlfuozgqSsk/StKb+LTpwFp2mSILVYR9ofSeEkIIIabCmK4i1qxZw4EDBy753sGDB1m7dm1TghJCCDFDGZ/EwKXVU369empJy/zsPRV1c3TlPoq1Pn7QRzy6dLpDEmJaWWtpSW6c0mNqFaUr9xCuk6RQPUTKm9w5arctUcLQqt1aDyohhBBCNN1Vm4Q89dRT5z9fsGABjz32GLfffjttbW309fWxbds27r333ikJUgghxMxQq54auaT31NbjCUYqDhsWVjgxOL96T402YoZwUr1uhJhLlFLEIotwdIKwPkShucdzWdDyIFE3S768n3R8w6T2Z6zlXVOgTbnsDUsNilIIIYQQ13PVK4m+vr5Lvv7gBz8IwPDwMJFIhLvuuotqdXontAghhJhCxicx8OIlk/tGq6eWtvhsm2fVU66TrjdihnJwiqS3apojEmJmySTWMZDf1tRjKBwWtDyAF2ljpLKXdGxyySmAI7bMMCE3Ogn6gqABUQohhBBiLK6aoHrkkUemMg4hhBAz3PnqKR07/723jiXIVxw2dAUcH1TTGN3UcnWKhblfQilN2T8xqV43QsxV6fgaBvI7oGmL5DSdLfcTiywgX9lD2tuAUpN7HbLWssMUyCiH/UHzq7+EEEIIccGY12JUKhVOnz5NuXxpo8h169Y1PCghhBAziwpLJAZexFxUPVUN4aWDKW7I+Ww7Pn+qpxydpKv1o2jlUvKPkfTWTHdIQsw4SikcFSPpLaVQOdqMI9CRvZeEt4SR8h5S3vpJJ6cAemyVPgJu1Al2hZKgEkIIIabSmBJUL774Iv/4j/+I67pEo5dehHzrW99qSmBCCCGmkLUoU8Dx+3H8vvpHP279Tx3mAQj1hd5Tbx5Nkq86bFgYcGxgflRPOTrOwtxHcZRH0T9MypObNEJcjbWGbHJjUxJU7ZkPkYotZ6S8j5S3FqUaMz10hy2QQHNIek8JIYQQU25MCarvfe97fO5zn+Pmm29udjxCCCGaxRp0MHw++XQ+ERXUPtemcuGpACqCVS6gCHUcZQ2OqV20VQPFy4eSLJtH1VNa10fY6zhF/5Akp4S4DqU0XqSDiJPFD4catt/W9F2k46vJl7tJeqtQymnIfs/aKqdslQ84cd6TBJUQQggx5caUoHJdl40bp3ZcsBBCiAmwAY4/cFkllOP34wT9KHuh4a9FYVUElIPFqSWhTIDCRwFYH2X9Kx7mF8cSFOrVU0fnQfWUVh4LWz6K66QoVLob0ohZiPnAWks2tYHeoTcasr9c6jayifUUKodIRJejVeMmh+4wBaIoToYyBEgIIYSYDmN6V3/44Yf57ne/y8c//nEymUyzYxJCCHEtpvq+JXgXKqJ0MIiq1T8BYNEXklAqisUFG6AJUFiUrXLR08ekMlo91To11VPx6GJSsRUY69c+TBVjqxd97te+NrU/7VWSahOlVYSu3ENE3Az5yj5SniSnhBiPpLeCPrUVayc3ES+buJGW5E0UK0eJRRajdaRBEcKADThqK6xz4uyT6ikhhBBiWowpQbVo0SJ+8IMf8Oyzz1722FNPPdXwoIQQQlxO+/20nPwnXP/cJd+3ysGqSC0ZpeNYa8D6aEIUBmUr405CXcsbRxMUqw4dqYCj/c2tnlLKpSNzN0q7YA1KRa7ba8Zaix1jMuuS51z0vdEkl1IRFrQ8RNRtOZ+cakQjZiHmC6UUigip2EpGSvsnvJ9MfD2t6dspVU/guZ042mtglPCuKeAAQ6axCW4hhBBCjN2YElT/+3//b7Zs2cLdd999WZN0IYQQU8BUyZ76Hk4weL4flLLVehVUiLLhlIRRCRSvHEqxom1qqqeyiQ04TpxCpfv8tDxrDcYG9URSUEvI4WMJsRiwFlAo5aCUg1YRHBVDKRdNBKXcMSW5jPUBi1YRRip7GzLCXoj5yFpLNrlhwgmqVGwVbZm7KFdP4eoWHCd+/Y3GIW9DDtgSK50YB8Ly9TcQQgghRFOMKUGVz+d5+OGH5cRcCCGmg7Wkzz1NpPr/s3fn8XFd5eH/P+feuTN3Vs1IsiTvixzb2ffNIcFkoYVQoCxfSunv2xbKUmgIpf1SCgUKtP0B35a0NLQUGtYkJCWQAAkkwQlJnNhOvK+x5N3aLFn77Ms95/vHyEocb5ItaWTpeb9eecVz595znyuPxnOfec5zOvCsMLZOVyyUNQdCZIsW1eEi+3vG998ESwWoCl1IrthByL9oeLtSFrbyY3PmCbJykquINqVykkt7GEqgSoDBDCe5LEreoCSnhDgLSikcu4qAU0e+2DWqY0OB+dTGridf7MRSIRxfZMzj267TGKBkxrDUVAghhBCjNqIE1YoVK3juued4/etfP97xCCGEeI3g4IsEk5sqnpzKFRUv7J/A6qnwRSjlQ5vsmK3SdVQ5yRXAZgTThJyZY3puIaYjYwzx8Pl09o88QRX0z6au6kYKpV6UcvD7qsY8rpzR7DJZ5lkBDrxqJVMhhBBCTLwRJaj27NnD448/zs9+9jPi8fgxz33xi18cl8CEEEKAL3uQyJFHK56cAlhzIDxh1VO2FSIWWkau2EbQWTiu5xJCjD+lFEH/XCzlos3pp9G5Tj118RUUvQGMMgR81eMS18smQwlDQCokhRBCiIobUYLqlltu4ZZbbhnvWIQQQryKKiWpOnw/xnJQunxDly8p8iWFMeW+54ahPw895uh2o4b+zzH/5yTbT7y/Gh5PG3hhf5jG2ompnoqHL+Ho7aJMrRNialDKIhY6j/70tlPuF3BqqY/fjKdTGJPDHacqxqLRbNdpZll+dkvvKSGEEKLiRjzFTwghxAQyHlWdD2B5abTlYONxoNfhB+uqKXqnbvA9XhSGqqBBm/FNGPnsKNHgYrKFQwT988f1XEKIiWOMIRZaSn96OydbWtTvS1AfvwVtcpR0kqAzZ9ziaTZZ8hgSyqZ93M4ihBBCiJEaUYLq6aefPulzN99885gFI4QQoizc8wT+7D48O4LtpejPWvx4Y4JowDCzKo+nLVAMVTgZjvb2LVc9meGqKCjfFA5XWg09BtAaNLyq6mroz+rocbwyriqfblOLM+7XnohcjsHDsgJSPSXEFKKUwlYhgv7ZZAutxz3v2DEaErdi8Ch4vYTGMUGtjWGbTjNDOezysuN2HiGEEEKM3IgSVKtWrTrmcX9/P4cPH2bZsmWSoBJCiDEWSG0j3L8Kzyonp4oe3L8hQdFTLGsoseHQCBp7n6P8vmoi7gLS+b2EA42VDkcIMcaMMcQjF5DtPTZB5bMiNCRuAyBf6iDsH9/f/70mRwrNQjvAkVJxXM8lhBBCiJEZUYLqC1/4wnHbnn76adra2sY8ICGEmM7sQhfRzofQVghLpzAGHtlWRfugn9c15nh+r1vpEMdVInI5ns7js8Z+tS4hROUppXCdBnx2hJKXAsC2gjQkbkMpH7niIcKB88Y1BmMMW3WauLLZVZLqKSGEEGKyOONGJitWrDjl1D8hhBCjo3Seqo57yw90CQWsPhBmS3uIq+flWL1v6lZOQXnVrlBgNtnCIQJObaXDEUKMW52TaQAAIABJREFUE2MMVaFlAFgqQEPiNmzbJVs4MO7JKYBDJk8fJeZaAaR2SgghhJg8RpSg0lof818ul2PlypWEw+Hxjk8IIaYHY4h2PoRd7MYoHxYF9nT7efzlKItnFNjW7h/35uSVlohcQUln8Nt1lQ5FCDHOIu5ibMulIXErPjtCOr+XiLt03M9rjGGLThPBYo/0nhJCCCEmlRFN8Xvve9973Lbq6mo+/OEPj3lAQggxHYX6V+Gmtw83Re/N2Dy4KUFNWJPOW+RKlVm5b6KEAnNx/TNI5Zom5CZVCFE5SimU8jO7+vewLD+pfDORwLIJOXeTydJFkYvtENu8zIScUwghhBAjM6IE1d13333M40AgQCwWG5eAhBBiunEyewn3PI5nhbG9FIWS4v4NCYyBOYkSm1un9tQ+UCQil1HykrjO3EoHI4SYAMYYLMsllXuZiHv+hKzYmTYeL+ok9cqhSZJTQgghxKQzogTVjBkzxjsOIYSYlqzSAFWdP8ZYLpbOYgz8bGsVnUkfyxfleWHf1G6KDhBxF+L3JUjmmohK9ZQQ04JSilzxyIQlp4wxPK8HMUDMsuj0xv2UQgghhBilUyaovvjFL57yYKUUn//858c0ICGEmDZMiaqO+1A6j8GHQvPsvjDbDwe5dn6W1dMgOQUW8cilFEp9hPwLKh2MEGICuc7EfQG6x+RoMXkuskNsl+opIYQQYlI6ZYLqxhtvPOH23t5efv3rX5PP58clKCGEmA4iRx7FybeUp/bpNE1dAVY2RVlaV2BzawDD1G6KDhALLsGxo6RyzUTcJZUORwgxBWWMx1o9yAzlSGN0IYQQYhI7ZYLq5ptvPuZxMpnk4Ycf5qmnnmL58uW8613vGtfghBBiqnIHNxAafBHPDmN7abpTNj/ZHGdG1KMvo8h7U7spOoBSPuLhi8kXjxAKLKx0OEKIKWq1HqSEocayOeIVKx2OEEIIIU5iRD2oMpkMv/jFL3jiiSe44oor+OpXv0pDQ8N4xyaEEFOSL99O9MgjeFYYy0uTKyru25jAUlAf1Wxr91c6xAlRFTof2w6SK7YRmMCpPkKI6WO/znHA5LnQDrFDpvYJIYQQk9opE1SFQoHHHnuMRx99lAsuuIAvfelLzJ0rKywJIcSZUl6Gqo57Mdjl3lMGHtoSpyft4/qF06MpOoClAlSFLiRX7JDqKSHEuMgZzQt6kBrlY59M7RNCCCEmvVMmqD72sY+hteatb30rjY2NDAwMMDAwcMw+F1100bgGKM4tyksR7n2KQGonAzP/iJIrCU0hhhlNrPNBrNIA2nKxTZ6ndkfY1eVy3YLp0hS9rCp8EUo5aJNFKbvS4QghpqA1epACmkY7wI5SqdLhCCGEEOI0Tpmg8vvL00yefPLJEz6vlOLuu+8e+6jEuUcXCQ2sIdT3NEoXAEW87b/pnf9JtK+q0tEJMSmEe58mkGkeboq+83CA3+6JckFDng2HpkdTdADbChELLSVXbCXoSPWUEGLsHdQ59poc51tBdpSkekoIIYQ4F5wyQfXNb35zouIQ5ypjCKS3E+n+NXapD88KA2CRB1Mg3vpteufdCdb06KkjxMn407sI9z01nJzqTPp4aEucmbESXUmbop76TdGPiocvQQ0l45SaHkk5IcTEyQ9N7Ysrm0NaVpwWQgghzhXT545IjDlf7hDxtv+i6vD9KJ3Ds0LYOl1OTgEKsEu9VHXcC8ZUNlghKsgu9hDrfBBthbB0mmxRcf+GBI4NibCmOz2i9SqmBJ8dJRpcTLbQguvMqXQ4Qogp6EWdJItmjhUgja50OEIIIYQYIUlQiVGzin3EDj9Adet/4st34NlhLJ3F1hmMga3tLg9uipPKWyggkN1NuOeJSoctRGXoArGO+1DGw5gSxsD/bIrTn7W5dE6RnR3Tq7owEbkcg4dlBaR6Sggx5lp1nmaTZYnlsl1W7RNCCCHOKdPna3tx1pTOEep7llD/82AMnhXB0mksrwDAoT6HX78co6W/fMM9kPPx/mu78VkQ7n+WUmAW+egllbwEISaWMUSPPIKv0IEemtr3RFOU3d0uyxfmWL0vUOkIJ5TfV03EXUA6v5dwoLHS4Qghppii0TyvB4gpm8O6UOlwhBBCCDFKUkElTs94uAMvUnPwXwj3PYNWLgYLW6dQGPoyNg9uivPtNbX0ZX1ctzCH39Yc6nN4bGesPAQQ63wQX769stcixAQKDq4lmNyEtiPYOs3WdpdV+yJcPDPPukN+mCZN0Y9KRC7H03l8liycIIQYe+t0ihSaBZafAZnaJ4QQQpxzpIJKnJI/3Uyk51f4Cp1oK4Rnudg6BUCuqHh2b4Q1B8qN0a+cm6elz2LtfhcAhWHdoTCzYkWunpfFoIm3fYfeeZ9E+6IVuyYhJoIve5DIkcfKTdG9FB2DPh7eGmdOvEhLn0XRm17fD7hOPaHAbFK53UTc8yodjhBiijlsCuw0Gc6zXLZ5smqfEEIIcS6SBJU4ITvfSaTnVwQyzWjLHV55DMDTsKE1xFPNEdIFm/PrC2gUG1qOna5kUNjK8MsdVdRFSsyvLoLOEW/7Nr1zPw6WU4lLE2LcqVKSqsP3YywHS2dJFxT3bUjgOoawH1r7p99rPxG5Ak9n8Nt1lQ5FCDHFlIzhOW+ACBa9uogsyyKEEEKcm6bXV/jitFQpRaTrEapb/g1/dj+eFUHp3HByavcRP998vpZfbK8iETJcNT9PU5dDU+eJb7g9o7AU3L+xmoFsuWm6r9hNrPMBWdlPTE3Go+rwj7G8NAbQWvPApgSpvM2Fs4o0dU2/5FQoMBfXP4NsoQW/I9P7hBBja4NOMojHIp9LD16lwxFCCCHEGZIElSjTRUJ9z1Bz8J8JDr6EtsNgvKE+U9CZ9PGDdQl+sK6Gomdx/cIcXUmL9QcDaHPqPjolrcgVFfdtrKY49LnRTe8k1Pv0+F+XEBMs0vME/tx+tB3E1jke3xVjf0+AaxfkWLt/eq3YV6ZIRC6j5CVxnbmVDkYIMcV0mQLbTYZGy2VbSVbtE0IIIc5lkqCa7owhkNxCzaGvE+l5AmP5MDjYXgqFJpW3+MX2GHevqqWlz88183NobViz36Uwih46nlG0Dzj8Ynt8uHAq3LcSf2rnOF2YEBMvkNpGqH8VnhXB9lJsbA2y5kCYS2fnePGAy3Rrig4QcRfi9yXIFtvx2aFKhyOEmEI8Y3jOGySExaBM7RNCCCHOeZKgmsZ82YMkWv+Tqs4HUDqPZ4WwvTQWBYoePLc3zF3PzmB9S4iLZxWoj2leOugykD+z1mUKw6a2IGsPvnKTWnX4Puz84bG6JCEqxi50Ee18CG2FsHSK1n6HX2yvYl6iyL5uHyU9/ZJTYBGPXEqh1EfIv6DSwQghpphNOkU/JRb7XI7I1L5pY+v//4es/9QtDO7dXOlQTqjtyR+w/lO3sP/Br1Y6FCGEOOdIk/RpyCr2Eel5HDe1Fa38xzRANwa2d7g80RSlP+ujsbZINGDY3ObnbKs/jjZN/9XLMeoiJRprCxg0ibZv0zP/rzB2eAyuToiJp3SOqo57yw90iVTe4v6NCcIBjc8yJPPTr+8UQCy4BMeOkso1E3GXVDocIcQU0m2KbDFpFliBKTO1r2/78+z94RcAiJ13JUs++LUKRzR+Bvdupvm//uq47crn4EQShOcuY8Z1byF23pUTFk9y7xZCsxpJXPS6k+7X9uQPAKi/8Z34gpEJiU0IIaYTqaCaRpSXI9z9ODWHvk4gtaPcAN0UhpNTLX0O315Tw4ObE/htuG5hnoO9vjFJTh3lGYWt4IFNCfoyNgpQOku87TtgSmNyDiEmlDFEOx/CLnZjlA+tC/x4Y4Js0WJpXYl9PdOx7xQo5SMevph88QihwMJKhyOEmEK0MazyBnCxyBsPXemAxkjPhieH/zy4ZxOF/iMVjGbi+MJV+CIJfJEEoCj0d9G37Tmav/MpWn75nxMSQ3LvFjpW/pD+HS+ccr+OlT+kY+UP8bKpk+7jhKtwZ8zFidWMdZhCCDHlSQXVdGA8goPrCPesxNJpPCuMMnlsXf7HtS9r85tdUbZ2BIn4NdcvyLGlzWHt/sC4hFPSCgPcu6GaD1/fjd9ncAqdxA7/D4MN7wU1HadCiXNVqP853HQ54WvrFD/fEeNQn5/XLcrx/D630uFVTFXofGw7SK7YRsCZUelwhBBTyBaTpocSl/pCbJki1VOl9AADu17EclziF95A7+an6Nm0kplveG+lQxt359/xHwSqGwAwWpM9vJ9Dj3yD1IHtdK56iMjCi09Z1TTZ1N3wdupueHulwxBCiHOSVFBNZcZA3zaqD32D6JGfAwatXGydxqJErqh4sinKvz07g52dLlfMzRMKeKw54JIp2uMamqcVnUmbn217pWm6m95GqP+5cT2vEGPJyewl3PMEnh3G1ileOhRiXUuYy+fkWDNOCd5zgaUCVIUuJFc8LNVTQpxGyRi26BQbvCTbdZo9OkurznPEFEmaEgWjMUbafx/VZ4ps0inmWQG2T5HkFEDP5qcxXon4hcuZcd1bytteVVE1XSjLIjSrkcV/8g/4wlUAHFn7ywpHJYQQYqJIBdUUZZUGiXY+hJXdDZZbboCuyx/kPA0bW4OsbI6SLtgsqy8Aio0tE39Dvb3DZVYszE2N5WmG4Z7HKfkbKISXTngsQoyGVeynqvPHGMvF8rIc6HV4bEeMhTVFmjsdPDN9KwGrwhehlIM2GZQa32S3EOe6DTrJNjOUaDlJHsoCAli4WARQuMp65bGycFGv+nP5uQAKNcUqkvXQqn1+FBozpdqi96wvJ6OqL7+FyMKL8cfryHUdInVoF5F5y054TNuTP6Bj5Q+pufKNLHj3/+HIml/QvfFJ8kda8XJpLvjEfxGatXh4/2Kqn85VDzHw8lryvR1gwF/dQPzC5TTc9G58odhx5yhlBunfuYb+nWvIdR6gMNANxuBP1FG19Grqb/pf+Ktqx/zn4QtFiS25it5NT5FpbR7VscVkL4efeZCBXS9S6O9C2T4CM+ZQfckK6m54O5bvlan3+d7DbPvK+4Yf92x48rjE4MWfvo/23/zgmO2vPgag5so3svA9fwMc+/dydNtR6z91y/CYKEXHU/cy0PQSpdQATqyGxMU3MuvW/w/bPXFfVqM9ul54hO51vybX3YYdCBKeu4yGm/+Q6IKLjhn/aFWaEEKcSyRBNUVpy8XyBjFODFUcHC6V233Ez+Mvx+hMOcyJl7hwZp71h/zoitxMKyxleLIpSkO0xJK6PABVHT+id96deH6ZFiQmKVOi6vB9KJ3H4GMwCw9sTFAV1Hga0uNcgTiZ2VaIWGgpuWIbQUeqp4Q4lcOmwDaTodFy2atzQ4kohQ8LPwrXsvAN9YDUGIoYSsbQS7myKo/hZMVVCspjDCWsXklqvZLgCmARUzY16txYyGGHyXCEIpfaYbZ46UqHM2ayhw+QaWvGF4oRW3IVSimqL7uZw888QM+GJ06aoHqFYe+PvkD/jtVgWdiB0HF7JPdvY88PPoeXSQKgbAdlWeQ6D3C48wC9G1ey5M++hls395jjOp6+n87nfjL82HbDeIUsua5D5LoO0bPxKZZ86GuEZjae9c/htY4mvrz8yCvlUod2sfu7nx6+TisQQpeKZFqayLQ00bNxJUs++FWcSAIoV2z5Igl0IYsu5FA+/3HJIWVZ2G4YXyRBKdUHlPtmoV6ZiHKyhNLJZDr2cuAn/xcvk8QKhMBoCn2H6XzuJyT3bWXZx76BZR97m6a9Enu+/zkGm14aisvGaI+BXS8y2LyeRe/7u1HFIIQQk5EkqKYqy0//rD+jtu1uFNCV9PH4rijNR1ziQY/rF+bYcMhPa39lXwLaKHyW4cHNcf78hm5qwx4Gj3jrf9E7/68wdrCi8QlxIpHuX+HkW/GsMLqY5v6NNRQ8xdKGIhsOTd+pfQDx8CUoFGCmXPWGEGOpaDTPegNEsBjQRQA0kMXA0dqgEXT/PpqIcrBwgKBl4aBQKDRQwlA0hkFKFIwhj8Z7TVKrUblca0UJTeKKxwFTYr1OMsfys2MKJacAujc8AUDi0hXDSYnqy2/h8DMP0Lvlt8z9vY9i+U6eROzb9jxGl5j3+3dSc+Ubsf0uxVTfcKVQvq+TPd//O7xsitpr3kzDTf+LQO1sUIpc50FaHvsWg03r2POjL3DhX34HZb3yOnBitTS84b1UX/J6ArVzsANBjPbItO+l7fHvMti8jn33/xMXfvK/x/w9v9DXBYDtjmy1vFImyd4ffB4vkyTYsJAF7/5rwnOXYbRH3/bnOfjTu8h27GXfj/+JpR/8vwD443Vc9vmHhqueqi9dcVzVE8C8t/0F8972F8MVSq/um3UmDvzP1wjNPo95b/0YwYaF6FKB3k1PcfDhfyPT2kT3i49Rt/xtxxzT8dS95eSUZTH3LR9hxrVvwXIC5Ps6OfTINzjw0L+ccTxCCDFZSA+qqUwp0nn4xfYYdz9fy8E+P9fMz4HRrNnvUvAmx19/SSs8rbh3fTW5YvnW1tJp4m33gJlKBfxiKlBeiuDAi3hWGMtL84vtVbQN+LlmQX7aJ6d8dpRocDHZQguuM6fS4Qgxqb2kUyTxWOQL0H0Wk9UMkMeQwqMPj3Zd5KAucEDnOaTztOsCR0yRfuORQQ+fyQ+EsYhgsd/k+KnXTZPOTMp+V2Zo1T4bhU/BVFrz12iP3o1PAVB92c3D20MzFxFsWIiXSTLw8ppTjqELWea99S+ou/6t2P7y4hxOJDFc1dP2+D142RR1N/w+C971V7h1c1GWhVKKYMMCFv/xlwnObCTXeZC+7c8fM3bDTe9izpv+jNDs87AD5S8NlWUTnrOExX/yJdz6+eQ6D5Dat3XMfiYAhcEe+ne9CED4tBVkZV2rH6GY7MEORljywa8RnrtsON7qS14/XGGU3L2RwT2bxjTe0XJitZz3/n8i2FCuNLZ8fmqvfhMzrrkdgL5tx/Zk9fLZ4Uq22W/8E+pf904sp/yZI5Cop/F/fxF/vH4Cr0AIIcbH5MhQiDFXKpVYv3Erd60Msr4lxIUzC8yKebx00KU/N/lK+Uta0ZO2eWhrHG3K3wg7hTaiXQ9XOjQhjuEmt6DQKJ1n7cEQm9pCXDUvx+ppvGLfUYnI5Rg8LCsg1VNCnEKrzvOyybDEctlWylYkhgKQRpNCowEfilV6kMe8XvrN5EoBvWyyHKbIUjvIAa9Q6XDG1GDzeorJHvyJeiILLjrmuerLy9U63etP3SzdF4pRe/WbTvicLubp21pOdtTf9O4T7mP5HBKX3FSOZ/eGEcdu+fzEzrsSgNTB7SM+7lRKmSQDzetp/s6n0ENT++qWj2xFvKNJndqr34wTrT7u+aolVxGef0F53y3PjEm8Z6rhpncd0wvrqPiFNwDlaZ+vNti8bngKYt0N7zjuOMv2UX/Tu8YlViGEmEgyxW+KymazrH1pIwsTRSIubG71A5P7htEAuzpdfrs7wi1LUgAEkxsoBmaSi99Q2eCEGOImN6Etl/1dml+/HKOxtsCODqdCfdwmD7+vmoi7gHR+L+HA2PciEWKqKBjNKj1AlbLp1IWT9UWfcGk0PqCbEj/zurnMinCpCmNXONmcNCVe0klmKodd3tRZte+o7qHG29WX3XxcYr/6sptpe/weBpteopjqx4nETzhGaM5SlH3i6Znp1iaMV55C+vK/f+ykcZhSOfFX6D9y3HPZrkN0rX6E1L6t5Ps60YUsr21+VhjsOenYp/PahuPDlMXs3/lTqpZefdoxdKk4nNSJNl520v1ijZeTPriTdPvuMwl1zITmnHgxIOdo361s8pjtmbY95eNmNQ5Xsr1WdOHFYxihEEJUhiSopqhoNMq73/1uHn30l6S6z5VeDeWm6b/dE2VmrMgFDeWm6dHuR/H8DRRDctMrKsvOd+Lk20h5YR7cVEV1SJMrWWQnsCl62F1AvthNyUtN2DlHIhG5HE/n8VlVlQ5FiEltjU6SQXOhFWL7JEu4lOumDGFlsVGn2EeW19lVNKjjKz0mgjGG5/UgCghbFh1TbNZ/KZsqNzbn2Ol9RwUS9UQWXExq/1Z6Nz9F/eveecJxfJGTv+8WB3tfOd9Qg+9T0cX8MY97Nz/N/ge/ivGGqupUuWG4ssvV+Eebi+tC7rRjn8yrG45bPgdfJEF43jJmXP0mQrPPG9EYXjYJpty07VSrCjpV5QV4SqmBM453LJyokT0wXFVl9LEv9lK6HK8TqznpmKd6TgghzhWSoJrCotEok71q6rWONk1/aEuCD4e7qY+WPxDFO75Pz7y/RDvHl2wLMVHc5CYMip9u9KMNzK322NQycTduNdFriYWWYowhkz/EQGYH+WL3hJ3/ZFynnlBgNqncbiLuyG4mhJiODuocu02WZXZw0iWnXi1tNBaQQ/Oo18syFeRqK0pATWxniGaTpc0UuNgOsW0S/7zOVN+WZ4Yrl3be9cFT7tuz/smTJqjUqf5ehpI2djDK5V98ZFTxFVP9HHjo6xivROLSFTS8/j0EZy46ZnW5tie+R8dT9x5XUTUaZ9tw/LV0qThmY00WZkS1lufWZ34hhDgR6UElJp2SVmgD922oJjvUNB1TItH6LZQ+82/ohDgrRuMmN5H1XHYfcblkdmFCk1OJyBXEQktJ5/dT8gYJBmYxq/rNzEz8LqHAPCr5wTQRuQJPZ/DbdRWLQYjJLmc0z+tBEspHq5c//QEVpoEcBhfFLpPlIa+bfTo7YU3U08bjRZ2kXjk0TcHkFLwyvW8kMu17yHTsG/U5fJEEUK4wKiZ7T7P3sQaaXkIXsrj181n03s8SnrPkmOQUQDF5+qqsiWAHo8NVWIX+zpPuVxwoT2E8VdXZZOSEy9M7i6eYSnmq54QQ4lwhFVRiUippRV/W4sHNCf73Vb1YCiwvSVXb9+mf86HhDyFCTBQnuxfbG+Tljii2MrT2Tdy0vqrQRcTDF5HJH8B1ZmNb5cRY0cvg+KLUx1dQLA0ykHmZVHYvZgLXuAoF5uD6Z5DKNRFxT9xTQwgBL+hB8mgW2QF2VKgx+pnIDVVuKAVP6wHmqhzLrRhRNX7vgUen9nkYqiybTm/qVcTkuttIH9wBwAWf+Db+xMlXYNv/wFcYeHkNPRueJPSWj4zqPOE5S1GWjdEefdtWUbf8bSM+9mgyJ9SwCGUd/7nLGENyb2VXwzvK8jkEGxaQ7dhHcu9mqi95/Qn3GxyKNzzr2GrfV/p/nSYBq9RQtdjEdo8LzV4MQKZ9L14+e8I+VKn92yY0JiGEGA+T4i5fa82nPvUpvvKVrwDQ1dXFZz7zGT7+8Y9z1113USqVb7aKxSJ33XUXd9xxB5/5zGfo6uoaHuPhhx/mjjvu4M4772Tz5s3D2zdv3sydd97JHXfcwSOPjK60WVSWMbDnSIDfNEWBcn2IP3+QyJFfVjYwMS0Fkxsx+FjZ7LKwtkj74MTk92PBZVRHryBTaCHg1A8npwAcO4RtBfF0HqUUtbFrmTvjnSTCl2FbJ26iOrYUicjllLwkrjN3As4nxLlpr86y3+RYZgfPqeTUq2WMxkHRbvL81Otmm06jx6maaq/J0WLynG+HaPamZuV0z1D1VHBmI6FZjfiCkZP+dzTZ0rvpqeN6E52O7YaIX3wjAB1P3XfKKirjeXj5V16fthsGINt54ISVc90vPka+p31U8YynxMXllQh71j9xwqbtA83rSR/cWd730hXHPHf0Wr3sqfu2Hu0d5WUntg9k7LyrsPwuplSga/Xx9zPG8+hc9dCExiSEEONhUiSofvWrXzF79uzhx/feey+333473/jGNwiHwzz99NMAPP3004TDYf793/+d22+/nfvuuw+A1tZWVq9ezde//nU++9nPcs8996C1RmvNPffcw2c+8xnuuusuXnjhBVpbWytyjeJMlJumr9oXYWu7O7w1NLgWd+ClCsYlphul8wRSO+jN+UnmfMRDmomYUhdxF1ETu4ZcoQPHSpw06WRbAXx2FG1KaJOjKnwxc2vfQW1sOY594lWfxia+hfh9CbLFdnz2iRu+CjHdZYzHaj1IrfKx/xxPthQxeICrFC/qJL/weug2Y1vdlDEea4Z+Xnu8czOZdzrGGHo2/gaAxEWvO+3+VRdcj7J9FJO9DDStH/X55rzpg9ihKMVkD7u++XH6tj+PHup9BeVqrs5VP2X7P/8p6dam4e2xxVeCUmQP76fl53dTGkrKeLk0h595kEOPfANfKDbqeMZL3fK340Rr0MU8u+/5NOmW8rWUq8eeY999/wBA9LwriC2+/Jhj3fr5ACQPbCN35OT3Cm79AgC6N/xm1MnCs2G7IepvfBcA7U98j84XHh5uaJ/v62TvvX9Pvu/whMUjhBDjpeJT/Hp6eti4cSPveMc7ePTRRzHGsGPHDu68804AVqxYwU9+8hPe+MY3sn79et797ncDcN111/Hd734XYwzr1q1j+fLlOI5DXV0dDQ0N7NlTXo61oaGB+vpy2fTy5ctZt24dc+bMqczFilHTRmFbmp9tjTMj0s3MWAkDRI88QslfRym4oNIhimkgkNqOMkXWHQgR9mt2tjvjfs5QYB61seXki11YKoTji5z2GEv5sOwqjDGUdIqIu5BocDGZfBsDmZ3kCh1jGKFFPHIphVIfIf+CMRxXiKnDGMMqPUgJQ63lo9ubuOm34yk11ER9EI+fez1cpEJcYUVwxmD6/RqdpIhhhuVMmZ/XayX3bqbQV+6TdLTq51R8wQjRxssZbF5Hz4YniZ9/7ajOF6huYMkHvsKeH3yefG8He3/4BZRlY7thvEIW86qm4upVX764dXOpf9076Fz1U7pWP0LX6kewg1G8fBq0JrbkasJzltDx9H2jime8+EJRGv/4S+y+59NkO/bx8r9/FCsQwnil4Wb0wZmLWPTezxx3bLTxMgI1s8j3tLP9n/8EX6gKyx8AYNmf/xv+eHn1vxnXvJnBcVZlAAAgAElEQVT0wR10Pf9Tjqz9JU4kDkqRuPgm5o5y+uVozbzlj0i37GKweT0tP7+b1ke/heUP4mWTKNvHovd9jr0//AIAyjf+n1OEEGI8VLyC6vvf/z5/9Ed/NDz3O5lMEgqFsO1yb4Pq6mp6e8vlyL29vdTUlJdQtW2bUChEMpk8Zvurj3nt9pqamuGxxPhQykcicjmza96G3zc2K+55uvwyvXd9Nem8NfTRyZBo/y5WsX9MziHEqbjJjXj4Wb3fpXFGkUxxfPtPBf2zqKu6kaLXh1I2fmd0zVyVUjh2FUrZlLwUrlPLzMRtzKp+CxF3EWPx1h8NnodjRymUjmBbgbMeT4ipaLfJDk9V23WOV0+9lgYKGIIotpkMP/W6adFn1/x9v84NTYUM8fIUrZ6CV6b3BWrnEGxYMKJjEkPT9Pp3vjBcyTQa4bnLuOivv8/sN3+Q8PwLsQIhSrkUlhMgNGcpDSv+gPPv+A+ijZcec9zc3/so89/5SUKzFqN8DkZ7hGYuZu5bP8p57/9HsCauH+NIROYt46K//i71N76TQO0cjFdCWTahOUuZc/uHOf8vvokz1Dj+1Szbx5IP/TM1V9yGE6vFyyYp9HVS6Os8plKq9urfZf47P0l47jKUZVMYOEKhr5NSemDcr83yOZz3p//EnLd8hGDDQlAKZVlUnX89Sz9yF9HGy4b39QVP/6WWEEJMRhWtoNqwYQNVVVUsWrSIHTt2nHb/E81/V0qddEWZk+1/IitXrmTlypUAfOUrX6G2tva08Ux2mUzmpNc7HiLuIhKRK/DZIbQp0pC4hfaeX1HSp57PPxIlrUjmLR7YnOBPru7BtsCYIjUd38Fc+kWw3dMPIsaFz+ebEr8vJ5Xvwcru49BABG0sPD2+v1MBp466+ApKXhKDR8B3divj+ezyh9SSzuGzA8yoeh2JyBUMZl4mmW1Gn8H0HKV8JMKXkC91EwosPKv4hJiqUsZjzdAqdLuncLIlM9Qs2gOe0H0sMi7XWVFCo2yinjOa1XqQGuVj/xT+eQEsfM/fsPA9fzOqY2Zcezszrr39mG2z3/jHzH7jH494DNsNMXPFHzBzxR+c9bnPNIajYo2XcdXXnhr1cQCX/O39p3zeiVYz9/c+ytzf++ioxg0k6ln4B58+7X6n+nnAqX8mp7vmQHXDKfdRtk3DTe+m4aZ3H/fc4O6NAPgT9ViOfHEkxFRjW9bUvucaUtEEVVNTE+vXr2fTpk0UCgWy2Szf//73yWQyeJ6Hbdv09vZSXV2uxKmpqaGnp4eamho8zyOTyRCJRIa3H/XqY169vaenh0Ti+G9NAG699VZuvfXW4cfd3d3jcckTKpudmOWg/b4aaqLX4PpnUCj1ks61EQo0opRFQ+JW2nt/dUY3wa+lDezv8fPrXTHecsEgCjCFXorb72Jg1gdkZb8Kqa2tnRK/LycT6v0tEeC3TX4aYiV2HR6/snm/r4aG+M14OkPJZAg6s8ZsbJ9VTuJ6uoihRHX0SuLhS0hmdzOYeXlUieRY6HxsO0gu10bAN/X/oRRitIwxPKcHMECVZdHpTeyKX5WQReNHccDkaPXyXGNFWaqCI/6i7EU9SG54lcOpObVPiPF0+Nn/ASB23pUVjkQIMR48rafMPdesWSe/x6noHf0f/uEf8q1vfYtvfvObfOITn+Ciiy7i4x//OBdeeCFr164F4JlnnuGqq64C4Morr+SZZ54BYO3atVx44YUopbjqqqtYvXo1xWKRrq4uOjo6WLx4MY2NjXR0dNDV1UWpVGL16tXDY4mzZ1tBamPLmV1zO44vQirXhG2FCLvnoZSFUjY+O0p9/A2MzUtNoTCsPRBmY2twaAsEsvsI9/x6DMYX4jWMwU1uIqeD7O4OMDdRwjPjU0Hl2FU0JG5BmyIlr39Mk1OvZlsOjh3DGA/PZIiFljGn9veZUXUTfl/NaY+3VIB46EJyxcNSPSXESbxssrSbAufbQZq9s5v2di4pYNCAg+J5PchjXi/95vTJphadZ7fJscw6d1c5FGK8Ge2x90d/z0DTS8dM88wePsDeH/09g83rULaPuht+v4JRCiHE2al4k/QTed/73se//uu/8sADD7Bw4UJuvvlmAG6++Wbuvvtu7rjjDiKRCJ/4xCcAmDt3Ltdffz2f/OQnsSyLD3zgA1hWOSHy/ve/n3/8x39Ea80b3vAG5s6VpdDPnkVV6Hzi4UtQyiKd34djVRFxl55gX4Xrb2BG7HqODL5w1mc2lJum/3x7FXWREnPi5cqscP/zlAKzyEcvP80IQoycL9+Kr3iE7Ycj2MrQ2jc+vTZ8doSGxG2AoeB1EfKPf+JHKRvnaEN1L0koMJuIu4BcoZOBzE4y+Vbg+KqPqvBFKOWgTQY1yik8QkwHg6bESzrJTOWwy8tUOpyKSKPxAT2U+JnXzWVWhEtVGPsE1VQFo3leDxBX9ln3sBJiSjPQt20VfdtWAWC7YYznoYtD/e2Uxby33UFo5qIKBimEEGdHmYmYA3YOam9vr3QIZy2bzfLjH/+YVGr0zTRPJuifTU30ahxfjFzxMFrnCPrnn7KE3xiNUhZ9qa30pzePSRw+y+A6ho+97gjRgB66jVb0zf5zSkFJQk6kqTzFL3Lk5wQHXuLrv62hJqLZc8QBxraCyraCzKz+XSzlJ1dsIRxYPKbjj0bJS6OUhW0FKZYGGcjsJJXdO7SwPNhWiDm1bydfPIzrzJ7QHndCnAu0MTzm9dJHidm2n/3TqHrqZMLKIm00VdjcaFfRoPzHPP+8N0CTyXKRHWLbNE3oCTESxhiOrP0lg83ryHbsp5jqx+gSTrSa6MJLqHvdOwjPWVLpMIUQ46Q+4vDttzVWOowxcaopfpOygkpMPo4dozp6NaHAbIreIKlcE6FAI5Y6/UtIKQtjPBKRSyjpFKnsnrOOp6QVmQLcv7GaD1zTjc8GgyHe/t/0zv8rtC921ucQ05wp4Sa3MJB36cv6aKzLMdbJKUsFaEjchq1cMoV9J6lCnDg+OwyApwsoZVEbu45E5DIGM00MZpvKVZPl7m+SnBLiBLabDJ0UudQOsUWSLQCkjcYC8mge9XpZqoJcY0UJKIs2nWeXybLUciU5JcRpKKWou/6t1F3/1kqHIoQQ40a6SotTUsqhOnIVs2veiuvMIJVrBmMRcZeOKDn1yjg2WpeojV5H0D82vXW0UbT0OTy6s5yMUoAyBeJt3wEpDBRnyZ9uwtJZNhxyiAQ0O9vHtjm6Ug4NiVvx2RHShT0VT069mm358dkRtCmhTZ54+BLm1b6TaHAx2UILrjOn0iEKMen0mRIbdJI5lp8dkmw5hgZyGFwUzSbLQ143u3WW5/UgMWXTqQuVDlEIIYQQk4AkqMRJKCLBxcytfTux0Plki60UvB4i7hIcX+SMRrQsHwaPuvjr8ftOvJri6KM0rG8J89LB0NBj8BW7CSQ3jcn4YvoKJjfi4bBqr0tjbZFMcez6LSl8NMRvwe+Lk843EwksG7Oxx5KlfDh2FWAo6RQlL4lluVI9JcRraGN41uvHh8ICZA26E8thMIBS8KweIInHAttPP7rSoQkhhBBiEpAElThOwJnBrOo3MyO2HE+nyRT2EHTm4Dozz3psS5V7+DQkbsW2wmc9nkFhK8OjO2Mc6H2lwiXa/SgY+cArzozy0vjTTbQP+ilpi4I3lgkZi/r4CgJOLalcE5HA+ZM+4aOUhWNX4fiqcJ2GSocjxKSzxaTppsQyX5BDUg10WhmjcVAssV22yap9QgghhBgiCSoxzLZCzIjdyKzqN2FbQVK5JmwrSjhwHkqN3UvFUj4s5achcQtKnf20Kc8oLAU/3lhNf7Ycp6WzuIMbznpsMT25yS0oPFbt9jMzVqK5c6ym9ynqqm4kGJhVTk65yyZ9ckoIcWrdpshGnWK+FWB7Sab2jVQRQ7OXO8FaoUIIIYSYriRBJVDYxMMXM6f2bYTdeaTze/F0noi7FNsKjM85lY1jx6iPr2AsXoYlrcgVFfdtqKZYXnCMSM+vpIpKnBE3uYm8CbCz02VOooRnxiaJVBtbTtidTzLbRMRdOqaJXyHExPOM4VlvABeLInpovUshhBBCCHEm5O5omgsF5jG79q0kIpdTKB4hV2gl5F9EwBmbHlGnpgj6Z1Ibu35MRvOMomPQ4RfbqwCwdA534KUxGVtMH3ahCyffyu4uB9syHOoZm7fJmug1RIONpHK7CbuLJTklxBSwUafoo8RSn0u7LlY6HCGEEEKIc5rcIU1Tji9OQ/y2oQomTSrXRMCpJxiYP2FTjpRSGKOJBhuJhy8do1ENm9pCtPSXVxiM9DwORr7TFiPnJjdhUKzc5bKopkhnyn/WYyYilxMLLSOd30vIvwBLjV3DdSFEZXSZAltNmkVWgK0ytU8IIYQQ4qxJgmqasZSfmug1zK5+C36nmlSuCYUzNN1o4m+albLQxiMRuZSI2zgWI2Ipw5NNQ1VUJk+wf80YjCumBaNxk5sYLLh0px2qgmffHaUqdBHx8MVk8gdwnTlY1lj1sxJCVEppaGpfCIuULskadEIIIYQQY0ASVNOGIhpcypza3ycaXEK2cIiC10/EXYrPDlU0MkvZaF2iNnY9Qf/ZrxSojWJ/j599PeVEQLj3N1JFJUbEye7DLg2wpdVHJKDZ0eE7q/GiwaVUR68gW2gl4NSPW083IcTEWqeTDOCx2A7SJZ2nhBBCCCHGhCSopgHXqWd29VuojV1LyRskU9hP0D+foFNf6dCGWZYPg6YuvgK/7+z7Xx2tojIGLFMg2P/8GEQppjo3uRGNzTO7QzTWFskWz7yqMOIuojZ2LbliBz4rjm0FxzBSIUSltOs8O0yGxZbLVi9d6XCEEEIIIaYMSVBNYdmMIRa4lpnVv4Nl+UnmduHYVYQDjZNyaXtL+QBFQ+IWbOvsqrq0UbT2OzQfKfcPivQ+BaY0BlGKqUrpPIHUDg6nAhQ8i0LpzH9HQoF51MaWky92YRHC8UXGMFIhRKUUjOY5PUhU2fTpImc/CVgIIcbWVd07+czW7/I7bWuoKiQrHY4QQozK2c1fEZNWOumx5ukCjl1PKrcbvz2DqLus0mGdlqV8GBQNiVtp7/01xpz5qkhKGZ5oquK8GUewKBLqfZZMzS1jGK2YSgKpHVimwAt7Q8yMlWjqOrNeUUH/LOqqbqTo9aGUjd9XNcaRCiEq5SWdJI3HxXZIGqMLISadZQMH+Osd96KVxVW9u/jg7kfYHm9k9YyLeXHGxQz45QszIcTkJhVUU1QoYrFoqYNWvUTc8/A78UqHNGJK2Th2bGiFwTOvYjFG0ZX0seNwue9PuP8ZkGXAxUm4yY0UjZ8tbS5zEiW0Gf1rL+DUURdfQclLYvDw+2rGIVIhRCW06Dy7TJYlVpBtkpwSQkwys9Nd/O2279HrVrE7Pp+CsmmJzGRWrpuP7H6Y/179Zb6w+dvc1r6WWCFV6XCFEOKEJEE1RSmlaFwaJBo6+6bjlaEI+mdSG7v+7EZRht80VeFpUKZEuO/pMYpPTCVWsR8nu4+93Q62BYd6Rv/W6PdV0xC/GU9nKJkMAV/dOEQqhKiEvNGs0gPElU2HzsvUPiHEpBLPJ/nc1nvwLJvdiYVc3Lsbv/GYn2qnNtdfTlZFZzIn182fN/+Me1b/A1/Y8h1ubX+RaEF66QkhJg9JUIlJSSmFMZpocDHx8CVnPI4xit6MzZb2coPqUN8q0IWxClNMEW5qMwrDU00ui2qKdKb8ozresatoSNyKNkVKXj9BZ9Y4RSqEqIQ1epAsmjl2gEF0pcMRQohhbinH3227h1gpzYszL+fG9vXH7eM3HguS7dTk+ikoHy3RBuZkuvho80/57uov8/mhZFWkKMkqIURlSYJKTFpKWWjjkYhcRsRddObjYFjZHKXkgcIj3LNyDKMU5zxjcAc3kSq6dAz6qQqNrjbCZ0doSNwGGAqlLoL+eeMTpxCiIvbrHHtMjmVWkO0ytU8IMYnY2uP/7LiX+anD/GbuDfzOwVWnPcZvSuVkVX5gKFk1k7lDyarvvfBlPrflv7ml4yVJVgkhKkKapItJzVI2WpeojS2npLPkCh2jHsOgGMzZrG8Jcd2CDKGB1WRqbsFYgXGIWJxrfPlWfMUutrVHiQQ0O9pG3hzdtoI0xG9DKZtcsYVwYPE4RiqEmGhZo3lBD1KtfBzUuUqHI4QQrzCGP2/+KZf3NfPo/Ndz+4FnRj1EOVnVBkBe+WiP1DM/c5jLm5r5cPPP2BZfzAt1l/JS7YWknLNbYVsIIUZCKqjEpGdZPgya+vgKHN+ZNXtXGH67J0pxqIoq1PPkGEcpzlVuchMGi982B2msLZItjext0VIBGhK3YVsu2cIBSU4JMcUYY3hBD1BA02A5ZKTzlBBiEnnPgd9w8+H1rJx9Hb9z6Hmss3yPCpgSC5NtVOcHySsfLZGZLMgc5i+afsJ3V3+Jv9t6Dzd3rCNclEpSIcT4kQSVOCdYygcoGuK3YlvBUR9vUKQLFmsOhAEIDaxFybfhwpRwk1s4kgmQKdrkSyNbuU8ph4bErfjsCOnCXiLu0nEOVAgx0faaHAdMnvPtEDu9bKXDEUKIYbe2v8h7Dq5kbd0lXH94E47xxnT8o8mqRH6QnFVOVi1MdQwlq77MZ7d+lzd0rJdk1TTmlvIEPOnrK8aeTPET5wxL+VCWoiFxK+29j2NMcVTHKwzP7Y1w7bw0AUcT7n6cVN3bxylacS7wp5uxdIY1+6qYWVWiuWtk0/vqql6P3xcnlW8iEjh/nKMUQky0tPFYrQeZoXzsleSUEGISuaLnZT7c/DA7Eo009h8g7OXH9XyuLierAHKWQ3uknsZUG1f27qLYbLMlcR6r6y7lpZoLyDij/xJZnHuuPbKNjzT/jKwd4O8v/RBdwepKhySmEKmgEucUpWwcO059/PXAyKpdjjIociWLVfujAAQH16HkxmNac5MbKRkfG1qCzI2X0Ob0r6mAM4NQYBbp/F4igfNRanSvQyHE5GaMYZUewMNQY/nIydQ+IcQk0TjYwl/vuJfWSD1uKc+MwuCEnt/VRRYNthIvJMlZDi3RmSxOtfHxXQ/yvdVf4m+3fY/Le3ZNaExi4oSLGe7c+WP+ZsePGPBHqSqm+afN/8GsTFelQxNTiCSoxDkp6J9Fbey6MzjSsHp/mGwRFJpI92NjHps4NygvTSC9iwO9ASwFB3pG9nZYFboArfP47TpJTgkxBTWZLK2mwPl2iF2eTAUXQkwO9dkePrvteyT9YXqC1TQmWysaz4mSVUuSLXxu23f5UPPD+L3RzXQQk9sVPS/zb+v+hRuObOGp2dfhFrMEvTyhUo5/3PSfzE+NfiErIU5EElTinKOUwhhNNHge8fDFoz2aggfP7IkB5Qoa5ckyutORm9qKwuOZ5gCLaot0pfynPcZnRwkF5pEttuF3zqxhvxBi8kqaEi/qJPXKodmT3ipCiMkhWkjzua334DMeO2qWcsWRHZUO6RhHk1VVhRQp2+V329fwtQ3fYF7qcKVDE2cpWMrx0V0/4e+2fY+sE+S3c67j5ra11BUGAHC9An5d4subv8XiwUMVjlZMBZKgEuckpSy08UhELifsLhz18WsPhkjlFWCIHPnl2AcoJj13cBPZUoADfQGi7sim8FSFLgA0tiVLLQsx1RhjeE6Xp8tELQtp/SqEmAz8XoHPbP8etfl+npt9DSvaXqx0SKcU8XJ4KGoLA3xt4zf43bbVYGSq9Lno4r7d/Ou6r3Pz4fU8M/NqVMnjtpbVxzVZCXp5LDRf3PIdLujfV5FYpzrHKzJ3oKXSYUwISVCJc5albLQuMSN2A67TMIojFZ6Gp3bHUAxV0pRS4xWmmITswhGcfAvbOxyiAc3O9tM3R7dUgEiwkVyxnYCvfgKiFEJMBGMMGeOx2aTpMAXOt4PsGeemw0IIMRKW0fzlzh9z3mALj8+7iTcdfLbSIY2IjSFUypH1uXxo9yN8evsPiBRlxsK5IuAV+LPdj/DFLd/h/7F35+FxnvW9/9/PMvPMPtplSZblfd8Txw5ZnA0CBCgJtBBKW6DQngOHtoHT5Vz9tYee3yn016sQ4DRsLdAeyhK2FBI7dhLHie04cbxvkiXZsrUvI82MZp9nnue5f39IdgLYlmxLGkm+X9eVK7Z0z8xnZGk0853v975tVeO5hju5u/cQtfmhK17GZ+URisLfnPg266PNU5h29gqaae7pO8Sfn/q//Nsrf8dn9v8zojD73z6Tp/hJM5qq6jjCorrkHnqiz1Kwh8d92cOdXrYuShL2OgQjvyRR86FJTCpNJ57kEQSwu9XLwooCx7uNMS8T8i1DVXQEttx7SpJmICEEKWziwiaORUxYxIVFHAtzdCP0OtVNkxztkyRpOhCCP2z9BZuHTrN93l08dGH3NR4PVHxhM0VOdXFL9AxfPvg4X17xQU6VLi52LOkqlg9f4L+deZI52Sh752xkfqKHt7fvHddlfVaOjO7hf5z8N7648nd5vXL1JKedfWoyEW4bbOTWoUaWD19AQxBzhzhZsZy822CrMvv7i2SBSprxVEXHwWJO6QP0RLdjO+M5mU9BCMHzLWF+e10MI30K1Uri6MFJzysVmXDwJI8SzXpI5HTyBWfMiyhohHzLyBX68brmTUFISZKulyMECexLxaeLhahhbKw3ncjnQSGo6NQqbgxFJS8cBhwTua2vJEnTwcOdL/GOnld5qeZW7u96DW2GnijqcQoIQBc2nzv+L/x83r08Of+t2KpW7GjSm7jsAo9e2Ml7Ovcy5ClhZ8NdvK1jH5oY+3nym10sUv356e/x1RUfZG/1hklKPDuowmFpooNNg6fZNNjI3GwEgE5/NftrNmI4BVZHznDbwAmomIOiz/7yzey/h9JNQVV0FFVhTun99ER3IIQ15mUEcKLb4L7FGmV+m+DAUwzX/v7kh5WKypU9j2YN83p7mNqwRUtk7PG+gHchmuolW+jGo8jxPkmaDiwhGGa0C+pSMcomgcWbn077UAmpGvMUNy5UMsIm7likcMiJApGZ+ZpPkqRZ7O7+I/xe27McrljJxshpDGdml84VIFRIk3D5eH/Hi6yNneVLKz/EgLes2NEkYHGik0+feZL6zACvVq+jKjPIO9r3XPf1+awcGc3gT5t+iGGbvFC7eQLTznweK8+6WCubhhq5ZaiJcCGNpWicC8/jxbJFzEn1syzRTn26v9hRi0IWqKRZQ1E0XFopVeG76Y+/OJ5LgCLY2RzmQxujuDNNqIU4jjydbVbzJI9iC40DF7xsqM/TMzz2w2DIt5KCFcfnapiChJIkvZkpHOKjhajY6HheXFgksS+tUYCAohFUNSoUDxqQdCyGhU0ah4xzbe8AS5IkFcuaWCufOvMTmsPzmZPqJ2SNZzJgZggVMhQUlXnpPr506HG+sfQR9skOm6LRHYvfbt/FI+27GTaCbGvYyts69uES9tgXHoPPzpPVDD7Z8jM8tskz9XdNQOKZqzQ/zK1DTdw22Mia2FncwiKte2gpWUDMKGFptJWl8fMsi58vdtSikwUqadbxGXPxuuvImt1jrhVCobHPoD+hURWyCUaeYrj2o1OQUioKx8RInaRjeGTPqfahsee4ve65uPUwqVwLAc/SyU4oSTcNIQQFRv7L42AiMIVDGmdkLG+0EJV5Uz+UCoQUjbCqMUdxoQBx2yKBTVKM/CdJ0hQRAo+dp9RMUmKmKDGTlJpJSs0EJWby0t9LzCTdvkq+ufQRenxVxU49rTWkevmLU99jwFeOUBTqclfelHqmcgkHlzAZdgf4TNMPWR9t4V+X/BY53VPsaDeVhlQvf9L0Ixake3m9cjUhM8lDE7wJv9fOk1XdfOzc0xiOyc8a7p/Q65/WhKAh3cemwUY2DTWyJDlyAl/EU8rhqtVYms6agSY2DDYVOej0IwtU0qyiKApCOJSHNtE12APjmNdXFMGzzSV8ZNMQ7kwLqhnDcZdOflhpyhnp06jCZE+rn4UVBVoG3GNeJuxfiWWn8bjmTkFCSZo5hBAjRaXR4lJeOKPFpjc+ZorRz/3a3y9+/kqP0DoKIUWjQnXhUVQcIRhyLFJYI5ucy0KUJE0azbEJF95UcMonfqUA9UbxKYHnMqNntqKScPlJuf1kdC+dnhBLY+f50qEv8/0Fb2fb3DtxboKNfq9VeS7O/3PiO+R1N12BGjb3Hy92pEkVNlNkVDf39B9hReICX1z5u7QF5XOtyaY6Ng93vszvXHiejMvLtoatvLXzFdzO2NujXA+vY5JTXfzu+Z14bJPvL3g7zNLDhjTHZuVw26WiVHUuhoNCe7CWl2tvI2gmWTXUwu39x4oddVqTBSpp1lEUFZcWIuhdTDLbOuZ6IRTORtx0xXXqSixCkZ8Rr/v4FCSVppo3cYS87aI1YrBx3tjHtLr1crzuOaRyrQQ8S6YgoSQVX044nBVZcr9WTHpzcSk/2vk0FhcKLhTcioKOgltV8KGioaOhoCogEFhiZJQvJxxsIIlNVFhEx7GfoCRJ4+MrZCk1E6NdTsnf6HK6+P9wIX3Zy6d1D0mXn7TLx6C/nK5QLabqQsPBZ2YI5hMErCwhM3XpNt4sZoT46Lln2BI5xT8v/216fZVTcbdnBF8hy9+c+DZeO8f+2lt4oHN/sSNNCZ9jYjMy+vcPR57g+wvezi/r70LIAuakqEsP8OkzT7I02cmRihW4HGvCu6Yux+MUyKs67+vYjcc2+c7id8+af2NfIcuGaPPoflJn8Ns5TFWnNdzAqYplzBvuYlGymwXJsSd7pBGyQCXNSkI4lAY2kMqeRzD2CxxFEWxvKuETWwZxZc+hmkM47vIpSCpNFdUaxpU9x/F+HwFDcLpn7M3Rw/5VOI6JS5PfC9LNISNsttsx4qOPm+7RApNLUXErCh5FwY+OriioYqTA5AD2aHEpL8DCwRrtpLo4wpe5WMuSG5JL0pQKFDLcOY24x4QAACAASURBVHCM+/oOsTjZ9RufLyg6CbeflMtH2u1nyFdGTjMQioLHyo8UncwMwUKagJXDb+Uge31jZ6X5BKaisyDdw5cOfZn/WPgOtte9Zda8UL1eumPxl6f/ndrsIDvn3cVD7S8VO9KU0gC/lWXYHeAP2raxNtbK/1n+AeKGPFl7oqjC4aGufXzo/A5Mzc22hq3c37n/sh2Qk8VwLExF56HuV/DYeb6+7P0ztpOyMhtl01AjmwYbWTXchi4cEi4/TWWLSbgDrBxsZlXsHKti54oddUaSBSppVlIUFU3xEPavJJ4+MeZ6IRQ6Yi4uRF3MLy8QGvgp8bl/PAVJpaniSR5DQbC7xcvCigLHu42rrtdVP35jHhmzHb+xYIpSSlLxpITNdjtKBod1mp+Tdnq0Y0rAxWOmZYFJkqY91bFZH2vhvr5DbBpsxCVsOv1z2FN7K4ZTwJdPEcqnCBYyhAopyvPDlOeHpySbW1hgj3RT/eHZX7IlcpJ/Xv479HtvzjeCFOHw6TNPsibextMN9/Ku9t3FjlQ0YTNFXtFZHT/H44e+xFeXf4Cj5cuLHWvGq84O8ekzT7Jy+AIny5ZgqfqUdE1djltYFBSN+/sOYdgFvrLig9iqVpQs10p3LO7pO8w7uvezIN0LQK+vkgPV61FxWBM5w62RU0VOOTvIApU0awkhCPtXk8i24Di5Mddf7KL65B0RXLkLaPkBbENu5jkrCIEneYR43mAo46IyMPZ4X8i3AgCVqxeyJGk2SAiL7XYME4dVupfj1uVHfCRJmr7mpvu5t+8Q9/QfodRMknT5OFi9BkUIbomcpj7dV+yIl5TmE+QVncXJLr588Ev8+6KH2Fm75abrpvpw27PcNXCcnfV38I6Ol5mdO/OMnyEsBCN7yv7Nye/w9Nw7+d7Cd2Kp8iXrtVKEw4M9r/H757Zhqxrb523lnq5X8TljPweeTC5hYykqd0aOYzgm/7TywxS0sacaisVtF7iv7yAPd7xEZT5OR6CG3XWbKc9GWRFroyYTKXbEWUcRQsj3Qy+jp6en2BFumJl32PNcmmzm5t1MVghBKtfKYOK1cV/m9zcNsaTSpGDUE6//5CSmmx0qKioYHBwsdoyr0nPdlHX9M7taQzQPGPQlNBxx5aeBquKmvuJ95K1+PK46lFm6maMkAcSFxXY7io1gqe7lpJUpdiRJksbJX8hw58Bx7us7xJJkJ5aicqZ0EX2+Cjb0n6K8kBz7SoosZoQozSc4WbKQJ5b9DgPesmJHmhLv7HqFj5/9Ba9Ub2Bj5DTeIhcOppuk7iVoZWkL1PL4ig/R7ZdvGo9XRS7Gp5p/yrpYK00lC0kaAW7rH3uiZCrZKGgIjpcu4R9W/wF5beyDi6aSYZs82PMqv9W5h1IzyblQPedK5nFn18HiFfkq5qB94VvFue0JVltbe8XPyQLVFcgC1eww8u0t6B76JQU7MeZ6RRFU+G3+5K4IKBCt/1NsY87kB53BZkKBKhB5Gs/wa3z+uUrW1pm83n71o4zDvtWUBTeSMc/jc8vxPmn2iooC2+0YAIt0g9NWtsiJJEkai+rYrIu1cl/fIW4bPI1L2HT5q2kuXcDcRC9LE+0zrhMnr+qAglAU/m3hQzxXu2XWnvQFsDlykj8//R+cLFtCfbKHskKq2JGmJQuVguZCFQ7/uuS32DVn06z+vrhhQnBf3yE+evZpNBx2123mru6DBOyxJ0mKwRl9pGoJNfC/136UjO4tciLwWVne0f0q7+7cQ8jK0ByeT0eojru6DuAp9sEtN0mBSvZLSrOaoigIISgL3Up/7MUx1wuhEEnpnO4zWFWTJ9T/U2Lz/tsUJJUmjbDxJI/TkzCwHIULQ2OND6iEfMvJFwbwuhqmJKIkFcOgKPCsHUVDYb4sTknStFeX7ufevsNs7T9CuZkgqfs4VLUGR1W4tf8kc9P9xY543YzRI+5jRoj/0voUtw+e4mvL3k/EU1rkZBNv+fAF/qzph5wP1VGST8ri1FXoOOh2nrg7yKeaf8r6aAtfX/o+Mq7iFzKmm9J8gv/a/FNujZ6hJdzAoLeMd3TsLXasq1IROMCSRDt/d/xb/N3aj5Ny+YuSJVDI8FDXPh7qfoWAleV06WL6/JXc1XOQZcMXipLpZiU7qK5AdlDNPj3RHeQLA2OuUxCUeB0eu2cAVYGhuZ/G9ly5ynuzm+4dVO50IyW93+NHR0so2BrNA1dvIQ54FlIZvpN0vhW/sWSKUkrS1OoXJjvtGG4U6jQ3zdP03VVJutn5CxnuGDjOfX2HWZrswFZUmkoX0uevYn3/KSrMsbvDZ5qc6kIBHEXlu4vexQs1t82arpnazABfOPI10i4vEV8Za4Zaix1pxsiqLtzCZsgI8/iKR2kOzy92pOlBCO4cOMYnWv8Twymwu24zW3qPEp5B4/oCsBWVHl8Vn1v7iSk9wTFsJnlP517e3rMfr21yonwpQ55S7uo5iH7xgJjpQnZQSdLsIYRDRWgT3UPbxl6LQiyrcazbw4a5OUIDPyE270+nIKU0GTyJoxQcncY+Dxvqx54ZD/tWUbCHZfeUNGv1CpPn7BgeRaVa1WVxSpKmGVU4rI21cl/vyAifW1h0+6p4sW4ztak+VkXPsjp6ttgxJ43HKQAj3VSfbPkZt0dO8rVl72fIU1LkZDemJJ/kb058G0dROFu6gDt7DhU70ozidQo4gM/O87+PfoMfz3+AnzXch3OTbax/kc/KUp/u591de3lL5CRtwbl0BWt4W+crM27EVwE04VCTHeTvj32d/7nuEwxOcvdkWX6Y93a8zFt7D+ByLI5VLCft9vOW3iNo060wdZORBSrppqAoKm69HJ/RQCbfPvZ6BM83h1lXm8Nl9qHnOrE89VOQVJpIip3BSDfRFDEIGILTPVc/JcTrrsXtKiWVbyFgLJ2ilJI0dbqcPM87MYKKRqmqcdbOFzuSJEmjajMD3Nt3mHv6DlNuJkjpXo5UrcJWNG7tP0FdZuwu8NmkNJ8gp7pYNdzGVw5+ke8sfg8vzrl1RnZTeawcf33yO4TNFC/Xb+HB9uk9ejVdqYyMYiVcfh698BxrY618ecWjM754eTUeK8/cTD/z0v3Up/uZl+6jPtNPRX4YgIKi8fzct3BL/wkWJruKnPb6KYDLsSjPD/P5o1/nb9f9EX2+igm/ncpslEc6dnNf3yEUBEcrVlJQdbb0H0dFDpZNB3LE7wrkiN/sI4SD7WToHPxPYHyV8XevjHPb/Cy2q4pow2OTG3CGms4jfp7hA4Qi/8nX9pVTFRIc6zKuun5OyQO4XaUI4aBrxZmBl6TJ0u7k2OXECSs6QUWlQ54YJUlF57Oy3DFwnHv7DrM80Y6tqJwpWUBvoHrWjvBdj4sn/R0uW87Xl72PqBEudqRx0xyb/3Hq31gXbWX7/K28+8LuYkeaFUxFA0XFVHWeWPZ+DlSuKXakG+K2C9RlBkYKUOl+6jMjxajqXOzSmoKi0+8rZ8hTStLtR3cs5iT7acj0zbiuqavJqy4yuof/ue6P6PJXT8h11mQivK9jN1v7juAoCoerVqM7NrdETs2cr50c8ZOk2UVRVHQtQMi7lET2zNjrEexqDXFLfRadAfTsBSzv/MkPKk0Yb/IIKdNNT8JF0Fu46lq3XorXqCWdP4vfWDxFCSVparQ5OXY7ccoVHUNRZHFKkopIFQ5rYme5t+8QmwdPYTgWPb5KdtdtYU6qj5Wxc6yKnSt2zGmlNJ8gq7pZGz/LVw5+kW8v/i1eqt44/buphOC/tPyMjdFmtjVs5aELLxU70azhFjYIm6xu8Jenv8fOms3sqHsLKd1LyuUjr7qm5feH7ljUZiKXuqHmZfqpT/dRnY2ijXbwWIrGgLeMfn8lraUL0ISgMjlATXaQuen+GX0gwngYTgFhK/z9sa/zd2s/Tltw7nVf17xUH+/r2MVbBk5gqTqvzVmHv5Blc//xmVOYusnIDqorkB1Us5MQDkIU6Bj8OUJcvWBx0YPLEty5KI3tqiDa8NlJTjjzTNcOKs2MUN7xJfa2BTnV66E3oeGIK/8qqgzdgc8zD9NK4HGVT2FSSZpcrU6WPc4wlYoLBYd+IX8nSFIx6I7F+9pf5P6+g1Tkh0npXk6VL8VSdTb1Hcco9hHmM8TFbqqD5Sv4xtL3ETNCxY50RR84/xwfaH+BXXWbubvnEC75+DspUpqHwK/tp1hQNNKjxaqU7r1UuErpXtK6l6TL92uf95FyjXzOUm+8h0N1bGqygyOjeaPdUPXpfmozg2ijkxw2KhFvKYPeMobdAUChKj3AnMwgQbk/JFnNQCgK/++aj13zpvgLk128v/1FtgyeIqu5OVi1lvJcnFWxGbx/n+ygkqTZR1FUFMWgxL+aWOroOC4heOlcgC3z07gYRM+2YXkXTnpO6cZ5kkcRwP7zXpbPsegevvLDnab68HsWkDU78Rlyc3Rp9jjjZNjnJJijuLCEwyDyxZEkFcuH257lPV17aSpdxNGqVazrP82W/uPFjjXjXOymWh9t4SsHv8i/LHkve6vWT7tumft7X+cD7S9woGoNm/uOy+LUJArYOWwU2oJzybp9FEYLTLpj4XYs3LZJTT6KJ92L18rhG2P/xazqJjVawEq6vKT10SLW6J+Tupe0a7SopXsxNRc1mUHqM2/sE1WXiVz6N3dQGPSUMugt5UCoDkfRqEhHmJOJMCc7xJzs0KR/jWYir50fGfU7/q98fs1HOFU69oTD0uF2frt9F7dEz5DWPbxUexs1qT7u7pWHEswUskAl3XSEEIR9K0lkmrGdsY5gVchbCnvOBbhvaYpQ/0+Jzv+LKckp3QDh4EkeZSDlIVtQuTB49SetId/ykT8o2hSEk6SpccpJ85qTpFZ1k3Es4uPce0+SpIl3y1AT7+nay745G9jcf4IVcoTvhngdEwGkNBePNf2Q2yMn+ebShxl2T93x9AAIQZmZoC4zQF0mQm0mMvL/bITqXIzGkoUsiHf8RnePNPE0BEuSneNaKwBT0Um7PORV98ieR24fpmZgqxoKApdj4bYLBO08lWYCj5XHZ2VxO1fvdBw0Shj0lXE4uBpTdVGeiVKTjlCZi1KVi07APb25+KwcGd3D35z4Dv+4+vc4XL7iNxcJwer4Od7f/iJr42dJuHy8WLeF+YlO7ul5fepDSzdEFqikm46iKAihUhbcQGT4lXFcQrCvLcCdC9MYxHClWyj45Qlv05krdwHNirO/LcyiigLNA+4rrlUUFyHvUnKFXryuuilMKU13eeHwvB1DVxQ2qAGqlSt/H003x50UB50U9aqbuGORlMUpSSqasvwwn256kk7/HObHO2UnzQRRGOmmymhubh1qYtXBNr615L28UrV+wm/LbZvUZgapy/5mIcprv7GnX051E/GW0e+vorl0EfWpXqrM4QnPI90YBTCEhWGm3vjgWO9Zj3KAnGaQ0QzympucbpBx+UEI5qQHqMjHqcjHJyP2TetikeqvTv07X1rxIV6tWjvyCSHYEG3h/e27WJG4QNwd5Pm5t7N86Bz3db9W3NDSdZMFKumm5TcWEtdPU7DG+iWiUHAEu1qDvGNFglDk5wz5/nLatZJLb/AkjmALlRM9XtbMvfpeY0HvYlTVjSOyKPLfVBplC8ELTpx+CriEwtN2lFrFzQY1QM00LlQJITjipDgq0jSoBhHHJCOPTZakolGFw581/hC3sGgraWBr94FiR5p1fPZIN5Wlany28QfcHjnJt5Y8TMIduKbrUYRDeX74jQJU9o1CVOWbCg4OClEjTNRbwqnyZeR0A18hQ1UqQlUuSn26j/p03wTfS2m6UAGfnR9zTFCaWCNFKoPPNn6fJ2yTlMvLb7fvYnGyiyEjzHP1d7BmoJG3dr1a7KjSDZIFKummNNJF5VAR3ERv7PlxXebABR/3LkriZRhXppmCf/kkp5Sui2NipE5ydsjA63Zo7Lnaw5xC2LcS0xrE514wZRGl6U0IwUvOML3CZJ3m47idwYvCkCiwzY4yBxcbRwtV06moKYTgdSfFSZFmgeqh18mTk8UpSSqq97XvYvVwG9vmbeWhjpeLHWfWGummSpLRDG4bPM2qeBvfXPoIr1Wu+Y21HitP7ZuKTxc7oWozETzOG29qZTWDiLeMnkA1zWULUQVUpAepyEUpl10ykjTlfFaejGbw6eYfAzDgKWPnvDu5pe8Eb+scz1SMNBPIApV001IUFY+7Bo+7hpzZO9ZqHCF4riXEe1YPExp4iqH5fyW7qKYhI92IKkz2ni1lYYXFsS7jimv9nvnomp9Uvhu3XjGFKaXpSgjBa06S8yLHas3HCXuk5z87WujxoBDDYrsToxoXG9QAddOgUCWE4FUnSaPIsFj10OnkkO/tSlJxrYy38TsXXuBQ5Sruly+epoTPziMAoaj8xenvsa9yHWfCDZcKUXWZCOVvGrlzUBjylDDkKeFE5QpymptgPk1lJkJVLsa8VC/zUmM9R5Qkaar47DwJ3ceBOeu5tf84D3bsK3YkaYLJApV0UxPCoTx4K91DT4+9FjjU4eX+JQkCRgJ3+jRmYPXkh5SuiSd5hKylcyFqsFS/+nhf2LeSgp3E65In90kjTog0p0WGJaqHJvs3h+MudiQZKAxjscOJUTnaUTW3SIUqRwhecRI0iyxLVS/nnSxX/86XJGmyBQppHmv8IRFvGSX5BB5x9Y2VpYmjACVmkrRmsGXwJHdGjpPWPQx6y+gM1dDoWowmHCrSg1Rlo1TkYlTmYsWOLUnSOIWsDG/t2l/sGNIkUYsdQJKKSVFU3HopAc/C8awG4NkzYQCCkV+AkOMz04lqJXBnznK826A2bNMacV1xrcdVjeEqJ1/oRVOv3GUl3TxanSwHnRTzVIMOJ8fVtjHOI8ghMFBIYbPTifELe4h2J4eYwscFRwhedoZpFllWqF7aZHFKkopPCD595seECilOVKxgcWJ8J4tJE8tv59GEw4BRgtsyaUj2sH7wDHf1HuYtfUdZmuykxEoje+ElSZKmD1mgkm56QjiUBjeioI29FoXj3R6GcyqancJInZiChNJ4eZLHUBC8esFHbYmNI678tDPsX4Xt5HDrc6YwoTRddTl59jjDVCsu4k5h3ONxeQRZHNwopHF43onzlD3E+SkoVNlC8KIT55zIsVLz0urkkD0aklR8D3W/wqahJnbNvZ23dsrxk2JSgKp8HJc8yVSSJGlGkAUq6aanKCq66iPkG9+m54oCz5y+2EX1S9lFNV0IgSd5hKGsQSKncX7wyg9vLi2Mz5hLrtCNWw9NYUhpOoqIAi84cUoUHXBIXMcLGXO0UOVCIYfDLifOz+0hzjlZnEl4jLBGTxm8IPKs1nw021ksuSG6JBXdwmQXv39uGyfLlnBnzyHZnSNJkiRJ10AWqCSJkQ2GSwJrUZWxR72EUGjq9xBNa6hOBiN5bAoSSmPRzV50s58D5w0WlhcYTF95vC/sX4kjLDQ1OIUJpekoISx22jE8KJSoKv3iaoN9YysgyIwWqkwcdjvD/Mwe5OwEFqoKwuE5J0anyLNW89FoZ646jihJ0tTwWDk+0/h9km4/ioCAnSt2JEmSJEmaUWSBSpIARVFQ0CkNrB3nesEvTo12UQ0+DUK2jhebJ3EERygc7fbiN65cCNBUDwHPQnKFHjyuqilMKE03WWGzw44hENRrbs7b5oRddwFBGgcXYCuCl5xhfmoP0nKDhSpTOOy0Y/QKk3Wan5N2Rg6uSNJ0IAR/3PIUc7JRDsxZz+pYa7ETSZIkSdKMIwtUkvQmQe8ydC0w5johFM4NGQwkNVQniydxeArSSVckbDypY3TEDVya4HTPlbunQt7lyIc+qTBa6Eljs0z30jRJnQ4FICUcdMBRBHucYX5iD9LsZLCvsVCVFw7P2jH6KbBO83PCTsuhPkmaJu7pP8zWgaPsrtvMg+17ix1HkiRJkmYk+SpNkkZdPB6+PHjLONcLnjpZghAQGNouu6iKyJ1pQbXTvHLOw8IKi7x9+Yc2BZ2gbxl5qw+vq36KU0rThSMEu5w4Q1is0f2csDKTfpsWbxSqUGCvk+AndoSmcRaqssJhux1liALrdT/HZHFKkqaN2swAf9TyFC3hBjYOnESVP52SJEmSdF1kgUqS3kRRVHxGA269fMy1Qih0xt30JnRUJ4dn+PUpSChdjid5FNPWaIl4yJhX3pI24F2EphrYTvpSQVK6uQgh2OsM0yVM1mg+jlnpKb19C0gKG42RovgrToIf2xEanTTWFQpVGWGzzY4Sx2Kt7uPoFGeWJOnKXHaBz57+PqbmYtgIUlqQP5+SJEmSdL1kgUqSfo0QDhWhTeNaqyiCn5+42EW1A25wg2Xp2il2FiPdxOk+g5qwzdnIlcb7FMK+lZjWEF73/KmMKE0jh5wUrSLHSs3LKXvyO6euxGakUKUCmqKw30nyYzvCqV8rVKWEzTN2lBQ2a3Q/x6ag20uSpPH7g7ZtLEj3srf2NjYNnCp2HEmSJEma0WSBSpJ+jaKoGK4qvO65Y64VQqEv6aIz5kIVefxDz4GwpiCldJGROoEiLF4976MmbOOIy3dG+Yx6XHqQgj2EquhTnFKaDk47aY6LNAtVDy12dlpsLu4AidFClUtReM1J8qQd4YSTJioKPGNHyeKwSvdMebeXJElXtzlyind272dPzS082LGn2HEkSZIkacaTBSpJugwhHMpDtwJjj4EpCH42uheVP76HivNfwD/4LJoZmfygEp7kURJ5F5G0xvnIlR/Swr5VWHYKj2veFKaTpovzTo5XnSRzVTe9Tp7pVkZ2gOHRQpVbUXjdSfJze4gCDis0L8etbLEjSpL0JpW5GJ9q/gntgRqWRM+jy30oJUmSJOmGyQKVJF2Goqi4tBBB7+Ix1woUhtI6x7o9I5d1THzxPZR3fImSrm9hJI/JrqpJopmDuHPtHGw3WFhuMZi5/Hif4arE464kZ/WiqZ4pTikVW68w2e3EqVRcZByL7DTewPjNhaoqRWeR7uFEEUcRJUn6Tapj81jjD9CEQ0eojpp8tNiRJEmSJGlWkAUqSboCIRxKAxtQxjEOpiB4oTWE5YCChcJI4cqVayfc/yQV5z9PYHAbmjkw+cFvIp7kUYSAw10+/MaViw5h3yocJ49brZzCdNJ0EBUFnrdjBBUNtwLRaTHYNzYHGBAWjbJzSpKmnQ9ceJ7liXZenLuFu3oOFTuOJEmSJM0aciMWSboCRVHRFA9h3yri6eNXXStQGM5qPN8c5O6FafyGg4KA0U4NxTHxxvfhi+/D9MwnG76NvH81qFfa0Fsak3DwJI/SkzQAON1z+a+lrgXxGfVkzPP4jYVTmVAqspSw2WHH0FGoVHXO2vliR5Kk37A5cpLfa9tOzB2iOdRAc7iB5lADCXeg2NGky1gTa+V9Hbs5ULWGBzpfKXYcSZIkSZpVZIFKkq5CCEHYv4pkthnbyV11rYLglfMBXr3gZ2llnvV1WZZV5XBpoDByut9IV1Un7twFHPWX5IK3kA1vwnZXT8XdmVVcuXY0K8b+tjCLKiyOdhmXXRf2rQQcVNU3tQGlosoJhx12lAKCZbqHU7ITSZpmdMfi99q28+6ufXT5qwlbGd7TtQe9c6TLr8dbTnNo/qWCVae/GkeRje/FFDZT/FnTj+jzVVCdGcRw5Pi+JEmSJE0kWaCSpKtQFAWERmlgPYOJ1666VqCgKYKKgE1H3M2ZAQ8e3WF1TZb1dVkaSgsoioDRYhWOhXf4FXzDr2B6GsiFbiMXWCO7qsbBlTlLMPJLLEelsd/D/LLCZdepikHAu4hsoQeva+xTGaXZwRKC5+0YCWzWaH6Oy9PvpGmmPBfns43fZ3minX1zNrIoduHSPkZxl5+IrwIFwa3RJu7tPwxARjNoCc271GXVEpxHxuUt5t24qSjC4dNnnsRvZTlSvYb7O/cXO5IkSZIkzTqyQCVJ4xDwLGY43UjBTlx1nS0U+pMjP1aG5lDqdzjW7eNQp59Sn8X62pFiVbnfRh09R2ykq6oLd66dwOBoV1VoE7YxZ9Lv10yjFuIEhrbjSZ3EUQyeOhWiOuhwNuK+7PqQbxmqogP2SLFRmvUcIdjtxOmnwHrdzzFZnJKmmQ1Dzfxp0w9xCZtnGu7hwY69uIR96fMlhTQlw2983+YVjQFvOSkjyJx8jLXtZ1EROCh0+qtpDs2jOTSfM+EGer0VIB/rJsV7OvewMdrMs/Pu4u0de4sdR5IkSZJmJVmgkqQxKIqCEIKy0K30x14c9+Xytkrv8Mg4RsBt4dIUdp8NsPtskPoSkw11WVbXZPG5f72raj++4f0UjHqy4c2jXVWXL8DcNJwCvvg+/LHdIBxsLUDXYI6jXV5ua8jTFf/NhzIFjZBvGblCP17XvCKElqaaEIL9ToJ2kWe15pOdU9K0ogqHD1x4nve1v0i3v4rz4Xm8q/2lMS9nCJv6zABkRg7ZEEDUFSTir8DlFLhr4Dhv630dgITuuzQSeCbcwNlgPaZ2k//+mABLEh387vkdHCtfxt1dryNLgJIkSZI0OWSBSpLGQVFUfO65GK4q8oVrP4kvZeqkTABBud8mmdf45ekw25pCLKvMsb4uy9KqPLr6xl5Ver6H0MBPCUSeJhfaQDZ0G7ZRM7F3bAZwp88QGHwavRDFVv0ookBbn8m2xlJcmuBc5PJ7sgS8C9FUL7lCN4oi9/i6GRwTac6ILMtUD012hiuf6yhJUytsJnms8YesjZ/lQNUaalMR7u45eF3XpQBlhSRl8eSlj2VVF/3eCnJuL/PTfWwaagLAUlQu+GtoDs/nzOho4KBRIrusroGvkOUzjd8n7g7hsU38jjxsQZIkSZImiyxQSdI4CeFQEdpE99C2G7gWhaG0BoCmCCoDNuejBo39Xrwuh7Wj+1XNLSmgKBdHPiy8w6/hG36NgjF3tKtq7azvqtIKQwQiz2BkzuCoHmzVx1Aiz46mLy4nOgAAIABJREFUIC0RD6Vem00Nefa3eS57+ZBvJaYVw+tqmOLkUjE0OxkOOynmqwbnnRz22BeRpCmxMt7GZxq/j9/Ksa1hKw907sdwLr9v3vXyOgXmp3thtGnQAQaNEqK+MrxWjrf2vs5D3SMnzg25QzSHGzgTaqAl1EBbsA5LlU8HL0sI/mvLz6jID/PcvDt5R/ueYieSJEmSpFlNPiORpHFSFBW3Xo7faCCdb7/h67OFQl9idL8q3abEKzjU5eNAh59yn8X6uizr6rKU+d7cVdVLaOBnI11VwQ3kwrdhGbU3nGVacUz8sZfwxfaAomCrAXK5FLtbQ7zeEcKlCTY15Gjp165YnPIZc3HrYVK5Ftye0im+A9JU63By7HMS1CguBh0Ts9iBJImRTbXf2/EyHzq/g4i3jKNVq3ln+8tTMh6mAlX5OFX5+KWPpTUP/b4KCpqLFYl23hI5CUBS9/KtJQ/zSvX6KUg2szzQ+zp3RE7w/NzbebBd7jslSZIkSZNNFqgk6RoI4VAWvIV0vpOR96gnRt7S6B3dfz1kWGiqwq7WILtagzSU5tkwN8uqOTm8rpFilSJsvInX8SUOUDDqyIZuIx9ch1CNCcs05YTASJ8iMLgNzRrGVv3Ydp5DFxxebK0mbymsqjFJ5xUOtl++MHVR2LcKy07jkSf3zXoDwmSXE6dM0SkIh5Qc7JOmgUAhw6fPPMmmoSaOVKwgZKa4v+vVomby2zkWJrsu/d1GIeIpRUPw2aYfsDHazL8u+S2y+tUfX28W9ek+/vDsL2gqWcCmvuOo8rFFkiRJkiadLFBJ0jVQFBVdCxDyLiWRPTMpt5HI6yTyAIKKgE0sq/OfJ0t45rRgeXWO9bVZllTm0dSRzXL1fB+hyFOIwafJ+1eTDW2k4F0EyuX3ZpqONLOfYORp3NlzOKoXS/Fyttfi2aZShjI6DWUFyvyCY51uxBj9B269HI+7mlSulYBnyRTdA6kY4sJipx3Dh0ZAVWm3Ze+UVHyLEx3899Pfp8xM8Oy8u9jadQCfM/2+NzUEc3JRADKqm639R1g5fJ7HVzxKS/jmHo122yafPf19srqHrO6hxMoUO5IkSZIk3RRkgUqSrpEQDqWBdaRy53DExO4j8qsUBlMjP6Ka6lAZEJyNGJzq9eJ326ytHSlW1YYLI7vmCoGROo4ndQxbC5ILbiQX2ojtrprEjDdGcXL4o7vwxveDomGrASLxLNubwrQNGZT7bbYsyHGo3U17dHwFt7B/FY5j4tLKJzm9VEwZYbPDjqEAtZqLZjtX7EizRn26j7f1HGB/5VqawvPlhtrjJQTv6N7PR849w7A7yK76t/D2jr0z4sQ3n2PiAH4rx98f/To/nn8/P5t3H46qFTtaUXzs7C+Zl+ln2/x7eOjCS8WOI0mSJEk3DUUIIXuWL6Onp6fYEW6YmXfY81yabEZuFzwZ4ulTxFJHpvx2vbpNiQ/6kyqOUKgMFFhfm2VtXY5S78X9qnTAQgEKxtyRYlVwLULzT3ieiooKBgcHr+1CQuBJHsU/tAPVTuKofjLZPLta/Bzu9OJxCdbWmTT2ukjmx/8CSVcDzK14LxmzHb+x4BrviTRTmMLhGTtKApuVmpcTtuxumCiV2ShfOPoEZebICXEdvmp21m7h5TkbyejeIqebvjxWjk81/5Q7Iic4VboYFcHK2Llix7ouCZefUCFNU2g+X17xQSLesmJHmlJ3DBzjs40/YHftJu7uPYwmJm6cX5IkSZKuW8UctC98q9gpJkRt7ZX3UJYFqiuQBSrpakZ+bBw6B5/Cdor34jhsWLhcCoOpkSLOvFKTtTVZVtfkCBjO6DicioKNQCXvX0EutBHTtwyUiXln/FoLVHq+h0Dkl7hz7Tiqj4LjcOCcxsvnAhRshdU1JrGMSmfcdc1ZyoKbCHmXkSv04XXPss3jJQBsIdjpxOgVJut0H8fk6M2ECZppPn/0CUoKKQ5XrWVNpBFTN6jODpFTXeyt3sDO2i20BeXebm/WkOrlv5/+HnOyUV6cu4UtvUcJ2tlix7ohpqIhFBVbUfnW0ofZU72x2JGmRHV2iH869BX6fRUE8wkqzUSxI0mSJEnSiJukQCVH/CTpOiiKghAKZcGNRIb3FS3HcF6HPCij+1UlchrPNIbZ3hRiUXmeNbU5Vlbn8LgAFIx0E570aRzVRy64nlzoFix3zZSM8Ch2Bv/Qc3gTryMUF5bi50y3xc4zIWJZnYUVBQKG4ES3G65jKEZV3AQ9i8kVevC46ib+DkhFJ4RgjzNMjzBZr8ni1EQybJO/PvkdKvNxXph3B++88PLIJwopTEVjwFfB1v4jvLX3dVqD9eys3cK+qnWYmru4wYvsvt6DfKL1KTK6l+ca7uTB9r2zYjNtt7BB2Ay7g/xZ04/YOHSGby19eFZ30emOxWONPwAF+v2Vv7KhvCRJkiRJU6OoHVSDg4M88cQTxONxFEXhgQce4J3vfCepVIrHH3+cSCRCZWUljz32GIFAACEE3/3udzl69CiGYfDJT36ShQsXAvDSSy/x85//HIBHHnmEe+65B4C2tjaeeOIJTNNkw4YNfPSjH0UZx4tx2UEljeXij05P9BlMK1bkNG/QFEFl0CaVV0nlVXRVsKwqx9qaHEurcri0Xx0BtNzVZIO3kA+ux9GD13x7Y3ZQCQdP4hCBoZ0oThZH89MXNdneGKA9ZlAZsFhUaXOo3Y3lXH+hLOxbTVlwIxnzPD63HO+bjV6zE5wSGVZpPprszASeo3lz0xybvzr1b2yItrBt/j2868LuK5aII0YJigIVuThpzcPuObfwXO0WuvzVU5q52Ny2ySda/5P7+w7RHJ5PxuVlw2BTsWNNioxmYNgmQ0YJX175KGfC84sdaVL83rltPNz5MtsatvJQ+8vFjiNJkiRJv+om6aDSPve5z31u6qL8qnw+z9KlS3n00Ue5++67+eY3v8maNWvYsWMH9fX1PPbYY8RiMU6cOMHatWs5evQox44d4/Of/zwLFizgO9/5Dvfffz+pVIqvfvWrfOELX+D+++/nq1/9KnfffTdut5t//Md/5OMf/zgf/vCH2bFjB8FgkJqamjGzJZPJKfgKTC7bFrSfK2AVZv67udPRSKFT4HaFSWXbih3nEoFCKq9i2gouzaEy6NCbcHG028dr7X4GUzouzabEa6MooNp5jGwz3vg+XLlOQMV2lY17BNDn85HJXL6TRc91EO77D3yjXVMpU2f7CRdPnw5TcFQ21ufpjmmcH3LhiBvp4lKpCt9FwY7hcdWOqwgtzSwnnDRHRZolqoezThZZdp8gQvCp5p9wx+BJts/byjvaX+ZqxxH47Rw+K0dedRHxlbMheoaHuvezKnYOU9Xp9VXgzKATRK9HTSbC/zzxr6yPtbK7bjOL4u0sTM38N7WuxCVG9zZUVd7e/SqKEDSF5yNm0b/zhqFm/rj1KfZXr+Muue+UJEmSNB35AqgPvLvYKSZEMHjlpoiijviVlpZSWloKgNfrpa6ujmg0ysGDB7lYN9u6dSuf+9zn+PCHP8yhQ4e4++67URSFpUuXkk6nicVinD59mrVr1xIIBABYu3Ytx44dY9WqVWSzWZYuXQrA3XffzcGDB9mwYUNR7q80+yiKisdVg9ddQ9bsLXac31CwVXqHR15EGLpNuV9wus/L0W4ffrfN6poca2uy1JeOFNzcmVaMTDOOYpAPriMb3IjlmXfNI4CKlSQwtBNv8jCO4iaPn1eaYW9bEEcorJ+bZyChcuCCZ0LuZ8CzAF3zkbe6MZTpe2qhdH2anQyvO0nqVTedTh6r2IFmkQ+3Pct9/Yd5oW4Lb+vchzbO8TTDKTAv1YsA+r1l1GUjfLbpBwyf9fNCzW08X7OZgVm4ufZbBo7zqeafYKk6Oxru5sGOvTdFMUMFgoUMCZefD7S/wPpYC19e8Sj93pl/WmppPsGfnPkRXf5q6pN9uB35CCNJkiRJxTJt9qAaGBjg/PnzLF68mOHh4UuFq9LSUhKJkU0qo9EoFRUVly5TXl5ONBolGo1SXv7Gk6SysrLLfvziekmaSEI4lAc30TX0NEzjvUfylkbP8MiffS6bkFdwqNPHgXY/YY/F2toca2szzAkCWHgSB/EmXsdylZEL3kIuuAHHVXr1GxE23uHX8EefR3EK2Kqf010OO874SeQ0llSaeFxwrOv69pm6krB/JQV7GK+rYcKuUyo+WwhecxI0iSxzFBdJxyI3jX/GZpqHuvbxSOdL7K9ez+29Ry91ylwLBajOjvxezahuEkaQhzte4uGOlzhatpTnardwuGw5jjoxhzIUi+5Y/MG5bTzU/QrnQnMZ9JbxzptwDCxUSFNQNBpSfXzp0Jf5lyXv5aXqjVOyj+FkUIXDnzb9CMM2OVK6kHu7Xi12JEmSJEm6qU2LAlUul+OLX/wiH/nIR/D5fFdcd7ntsq40yjOyifX4X8i88MILvPDCCwD8wz/8w68UwmaqXM4G5CbCk01RVFx6CQHPAlK56TPqdzWZgkZmtFgVdFt43Qr7zvvZ2xagMlAYKVbVZCjzCzQrSSD6PIHo84jQMkTlW6BsI2gj3U+6ro/8vAw3o1z4IUq2G6EH6BnWePqEh65hN9VBi5U1eQ62u7FvYJ+py/G6a3HrpaTzLfiNpRN63VLxJIXNi3acCAWWaV467BxZWZyaMHcMHOOjZ5/mePkylkfP4nfyN3ydPsfEl+pDAL2+SpYku7jl1L8zaIR5vmYzL9RsImaEbzz8FKvMxfjs6f9gabKTvTW3sDTaxqLEzbuBtkvYuIRN3B3kT848ycahM3xj6SNkXDNvA/WHO3azNn6W7Q1bb8qCoyRJkjRzqJo6K2oUYyl6gcqyLL74xS9y1113sXnzZgDC4TCxWIzS0lJisRihUAgY6YB682bMQ0NDlJaWUlZWRmNj46WPR6NRVq5cSXl5OUNDQ7+yvqzs8iMHDzzwAA888MClv1910+cZwsw7TOeOntlECIey4EbSuXbEDNsdJ2nqJE0AQanXAlR2tQTZ1RKkLmyyrjbL2loLn6GgJM6iJpoRbd8jF1hDLriRcPUSzNYf4EmdwFENkgU/zx7VONkbJGA43L4gx7EuF6+dNyYlf9i3CtvJYujy5L7ZosvJs9uJ4wDrdR8nLLkh+kRaE2vlT5qe5FyonrJsjLJCakKvXwFqMxEAUrqHnO7h0QvP8TsXXuD1ilXsrN3CydJFM2IPo1uGmviTph+hCYdnGu7hwY6919VpNhuVmEmympvbIydYlmjnKys+SGPJwmLHGrfl8fN88PzzHK5Yyb2d+4sdR5IkSZKuyrGdWVGjgKtvkl7UZ4dCCL7xjW9QV1fHu971rksfv/XWW3n55ZF3sl5++WU2bdp06eN79uxBCEFLSws+n4/S0lLWr1/P8ePHSaVSpFIpjh8/zvr16yktLcXr9dLS0jJyPPmePdx6661Fua/S7KYoKprqozJ8Jz5jHqoyOcWYyaUQy+pEUhogqPBbZAsa25vC/H+7qvnugRIOd7rIW4AQeJLHKO35NurRv8JIncZUAuw+4+JLu4I09XvZWJ/D57Z59byHbGFyxnvceileo4ZcoRtd80/KbUhTRwjBESfFDieGT1FZpBkck8WpCbUg2c1fnvq/DPjKsVSd+szApN5ewMoxN92PDfT6K1kbb+VzJ/6F//P6P/GezpcJFNKTevvXS3VsfrftWf765HeJecIcrl7Du9pfksWpX+O1TRQEHsfkfx37Jo+27UBzpv/XKFDI8FjTDxnyhAmbKbxOodiRJEmSJEkCFHEtc3AT7MyZM/zt3/4t8+bNuzSq9+ijj7JkyRIef/xxBgcHqaio4DOf+QyBQAAhBN/+9rc5fvw4brebT37ykyxatAiAF198kaeeegqARx55hHvvvReAc+fO8bWvfQ3TNFm/fj0f+9jHxnXCV0/PzD+Rx8w77HkuTTYz/Z8szgZCOAgcVEVHCIFpxciZvWTNPnKFAYSYmU+AVQSVQZtsQSWRU9EUwZLKPOtqM6yozqO6PJzq1tje6CeV11hWZaIAZwbck56tMnQHPs88TCuBxzXzN+u9meWEw0tOnC5hMl81SDgForI0NaGqs0N8/sgT2KrO+ZJ6NvWfLEqOYd1H2u2nNhPBVHT2V61lZ+0WmkMN02Ivo9J8gscaf8Dq4TZerV5LfbKPuZNcyJsNEi4foUKGlmA9X17xKH2+6TeGUJ6Ls2q4jbf2HGBZooNd9bfzYMe+YseSJEmSpLFVzEH7wreKnWJCXK2DqqgFqulMFqik62XbeSwniapo6FoIRdEQwiFfGCJX6CNr9pE3B2bcKCCApjpUBRyGcyoZU8WlOYQMh6GMTk3YYm6JxeEOA0dM/otMTfVRX/EIWbMDnzF/0m9PmjwRUWCXHSODwyrNR6OdmYE/HdNb2Ezx90efIPT/t3fnwXFd99nnv/fe3huNnQSJhYS4L6JEKZRkS7YkxpLj2LJfRxMncZJJZMWVOHKsee1ybDnRK6ecqF4lFUV+UyWW5YyiyWuXnUx5pCSTsZO89CJam02KpLjv4AICxL703n3vPfNHN1qkRFICCaBB4PlUodD3dvftc6iDFvrBOb9TzLBj0Y3c3f1atZtEEZvemoUszA4T8QqcjC/iP1rfy+tNayjaQVzLxrMcPMvGsx38GVgSeP3IMT5/4DtEvTw/7Lide868TFi7ur1rRcvGs0vVI/7PFf+FHy3aVL3Q0RgW5kZYP3qC9WMnWDd6gkW5ckF/J8KLbbfyodPbpnC7DhERkWmkgGp+U0AlU8XzsrgmhW2FCNgJLMvGGI9ccYBcoRxYFQfhGpstEnJ8mmt8PGOxrKnIztMh8t5MrBq2iIXbqYtdTzjYRLbYQyzUPgOvK1PNGMMhk+VVf5wYNtc5EfZ62thhqkXcPF974xk60n38qOO9/PKpbdVu0tsMhxIUnWBlR8CL8bFKYVU5uHJtp3Lbs2xcy8G72LlywHW5x7qWTcQrsPncDvpjTRxqXMHmWRDiXatGQwnqC0leXnAD31h1P+ngpTfAmTLGsDg7yPrRE6wb62L96AkW5EcBSAWinKptYzDSSG1hnJUjJ6nxctPfJhERkamigGp+U0Al08EYg+dn8Uwa2wqXAysL3xTJFQYqSwIL7jAqcH8h2wqTiK4gEVtN0KnB8zNkC2eIh1e9q2W7Mru4xvCyP8ZRk6PVCmJZcFZ1YKZcwHf5yt7/ixtGjvH9zru47+SPZ/WMkYLlcLy+k0wwil9u6fnvhBYGyxjs877bxj/vnI9tSucm7nvrl/OW247vYxsPx/jsbVpNY26UZcn5u0vfVMnaIULGZSSU4H+s+Q32Nyyf2hcwhvZMf2mG1OgJ1o2doLGQBGA8GOdUoo2haD2N2RFWjp4i6hem9vVFRERmkgKq+U0BlcwEYwyun8E3aRwrRsCpAcDzC+QKfaXAqniOojta5ZZWTyjQSG1sNfHIddhWgLw7iOsNEw60E3Bm4K/yMuXGjMtWb5QRXNY5UY57OfIKZKecZXwePvhP3NW/i39behcfPrUNW//OMoMMkAzGqSlmeGHJ3fxT57249pVtIG0ZnyXpvgsCqbpykf3RUIJTiVaGo/UsSA2yYuwUEaOlmSIiMofMk4Dqyn5LEJEpYVkWQScOlHagM8an6KUw5ImEmohHOgDw/Fyp2HrhHNlCL66XrGKrZ4JFPLyU2tgaIqGF+MYlV+wFPKLBDsKB2Vd8V96dk36OF/0xbODGQIw9bkaRyTT5nePf567+Xfyv9lIhaIVTMtMsoLaYZjwY5387/WNuHDnKU2s/SW9swTs+1zY+nameSiC1dqyLhJsFYChcR1fdEkbDtbSk+lg+foYbhw5Pc29ERERkuimgEplFLMsmFKitHJcCqySGAtHQImoinQC4XroSWOUK53D92blV+2Q5doREdBWJ6CoCTgzXS5HKHyNgNxALdVS7eXIVfGPY7qfYa9I0WQEabIc3XNWbmi4fO/Mi/6V7Gy8tuon39bxO0GgmrVRPbTFN0bJpz/TzNzu+zrMrPsbWxbdeUEDd8T2Wpc5WAqk1YyeJl+tE9UcaONJwHclQDa3JXq5L9rBx8GC1uiMiIiLTRAGVyCxWCqzqKse+8XC9cQwe8XA7iWippkfRTZZmVxV7yRXO4fnXVvHXcLCZ2uha4pElWJZDvthPrniWaKiDmvCKajdPrlLGePzIG+UcRZbbEQb8Asc8Lb+ZLnf27eSB4/8fu5rWcP3gEdXekVkhaHyCXoHRUIKHjvw/3DR8mB+03c7q8dOsKwdSE2P1XLSJg00rSAXjtI+doTN9joW5kSr3QERERKabAiqRa4htOYQCDZVj3y/i+knAEI92koitBKDgjlZ2CMwV+vBNvkotvjQLm3jkOmpjawgHm/D9AtliN2ARDbYTDi6sdhNlCpwzBX7ojVLA50Ynxj4vg+byTJ8bh4/wR4f+b47ULWVheoB6d27MrpS5o76QJGsHuXXoAO8d3AdAT2wBe5tXkwlE6Rw9TUemj0XZoSq3VERERGaaAiqRa5htBwnZjZVjzy/g+UnAoia6gtrYGowxFNyRyg6BuWI/xlRvtzTHjlMbW0UiuhLHjlD0xknljhB0FhILLa1au2RqGWPYazJs95MkLIdlTlRL+qbZ8vEzfGnf/6Q3thBjQVtOH/Bldor6RQxwoGEZ8XyKjswArZmBajdLREREqkwBlcgc4tghHLupcux5eVw/iW051MbWUBdfjzE++eIQuWJphlW+0I+ZgTktkeAiamOriYVLtaTybh/ZYppYcAk1kVXT/voycwrGZ5s/xkmTp8MOkTce+8vFjWV6LM4M8OjevycVitEXX8Cm/r3VbpLIZVnAupET1W6GiIiIzCIKqETmMMcJ4zjhyrHnZXFNGscJUhdcT318A8Z45IoD5Ap95Aq95IqDgD8lr29ZAWoiy6iNrSEUqMfz82QLp7AIEQm1EjmvQK7MDcOmyFZvlCQe650oR7ws1ZuvNz/U55P8tz3PYgEHmldxV/fPq90kEREREZFJU0AlMo84ThSHKFBaguV6GTyTJuDEqI/fgFVzI75xyRf6K7sE5t0hmOT29AEnQW10NYnoCmw7RNEdJZU/SshpIRa+bhp6JrPBMT/LS/44QSw2BGLs0ZK+aRd1czy691nqC0le7HgPv3Tqp9VukoiIiIjIFVFAJTJPWZZFwIkRIAaUAquil8I3GYKBBNFwKwC+XyBX7CdbKO0QWHAvvZNSNNRGbWwNsXBbaWaWew6/mCMaWkpNoH5G+iUzzzOG1/xxDposC60gUdtSODUDAr7Ll/b9T5akz/HvS+/kvpM/qXaTRERERESumAIqEQFKgVXQqQFqgHJg5aYwZAkFG4iF2wHw/Px5OwSew/Oz1ESXUxtdTTBQi+dnSeePY9txIoFWLC3jm9NSxuOH3igDFFltRznt5+j3JjfjTibPMj4PH/wnbhw9xr8tvYsPn3yx2k0SEREREbkqCqhE5KIsyyIUTAAJAIzxKXpJDHkioYXEI0vL5w2WZVFwh0jljxAJtBMPL69iy2WmdPt5fuyP4gMby0v6pqZ6mVyWMXzq2L/xvoE3+M+OO/jQ6Z9iT3IZroiIiIjIbKOASq6aMYYkHkPGZcgUGcIlazzW2TFWWFFszaCZEyzLJhSoqxwb41H0kvgmje/niYaWEgo0XeYKMlcYY9hl0uz0U9RbDovsILu1pG/G/MqZn3Df2Zf46aJf4P1nf07AKBYUERERkWufAiqZFN8YRnErYdSgKTKMS6H813sLqLMcjAXb/HH2keFWO0G7Hb78heWaY1kOoUA9oNpS80nO+PzEH6XbFOi0w4z7RQ55uWo3a964+9wO/vcTP+D15rXcMHiQqK89EkVERERkblBAJZfkGsMwRQYnZkaZIiO4eOX7HaDeCtBmhQhbNknfY9gUGTVe5f4sPv/uj9BuQtxiJ2iygtXqjohcpQFT5IfeCBl8NjgxDnoZ3Go3ah65eeggnz30PQ7VX0drso86zVoTERERkTlEAZUApVkRE8vzJmZGjeNVqpqEsKi3A3RaEQLAiOcxQrE8k+riH1E9SgFVCIs+U+QFb4hVVpRfsGuIW85MdU1EynxjKFL6KuCXvhtDEZ/CxHlTPs/EY/3yYwxjuESxWe/E2OspHJlJK8dP88X93+ZszUIc47E4P1ztJomIiIiITCkFVPOMMYY0fiWEmgik0ueVNo5hU28HWEgQLBj0iozh0e8X6Wfyy0kmlv/FsThmshz3smyw4txgxwlZ9pT1TWSu800pJMphKJrzQiV8imYiVHrzfOncm4GT+y4LaQexCGIRsN78HrNsGqwweWMUTs2w1kw/f7rn7xkP1TAYaeTmwQPVbpKIiIiIyJRTQDWHecYw7BU56+cvmB2VP+9Daq3l0GgHaLccisYw6BdI4pPxC1PennT5dWsth90mzSEvyy/YNaxWIXWRtzHGMIbHQDlMHjBFhihWlti+lcWFwVIAi6BtkcDBwcIGbMvCAgyl94eC8ckZHw9wMbj4FKAyy+qCPEubxFVFQ36Mx954FmNZHG5cwft7tle7SSIiIiIi00IB1Rw1lCnyh/96grxX+lRpU6oXtcgOEbEsssZnyC8ybjzGzaU+8k6PidcLWhYv++PsJ82tdoIOK4yloErmIWMMqXIYNWCKDOIyaIqlkAgIYNFgBei0wwSwGPc98uVgqXhesFQoz5SqhEkKla5pMTfLf9vz99S6aba13coHT79U7SaJiIiIiEwbBVRzVEM0wAeW1XG8K8+YW9ppb9iUvmaLpPFwKH2o/k9/lMVWiNvsBM0qpC5zXNp4lVlRg5S+T8xstIEGK0C7Xdp8YMQr/fwOmCIDM5slz1sRN8fq8dOsG+tizVgXcTeHwcJYFj4WvmVjsPAt683zlo1fvj1xn499wXHlfPn5b15v4jpvXsO3LNaMnaIfhpgEAAAgAElEQVQ908e/L72T+07+pNr/LCIiIiIi00oB1RxlWxafvHEB//XsSYbc2RNKvZUHZPAJYjFkivyzN8RyK8ImO0FChdRlDsgZ/4IgatAUyZRrvllAveWwuDyzMeV7DJQ3HrjU5gMy9RKFNGvHulhX/rou2YODj2fZnI23kAlEKEVIYBlDAINlfCxD6bxvSt/NxHcoRU+cd+6txwb7gucY7Inb5WPXdviBwikRERERmScUUMmsMLGUKYZFl8lx0suxzoqz0Y4TViF1uUYUjF+aGcVE3SiX1HlVo2oth2Y7QMxyyOAx4BUZMR4jM7zMdr5ryo2ybqyrFEqNdrEk0wdA0QpwOrGY1xZvBAPLR0/SkeqlaguPPfiowikRERERmScUUMmskikHVQnLZq9Jc8TLcJNdw1orhqP6VDKLuMYwyJsFzAdNabfLCTXl3TDbrBB54zHou1Wp+TbvGcPi7CDrR0+wduwk68ZO0JIbASDrhDmVaGVb/SYibp4VIydZPn6G5eNnqtxoEREREZH5RwGVzEpJU1oCFbJsXvOTHCDDLXaCThVSlyrr8fPs9FP0Veb9QawcRrVYQXxj6PeLJPFJTcNumHJ5tvFZmupl7VgX60dLs6TqiykAxoNxTidaOdi0kvrcOMtHT7JmtIs1o11VbrWIiIiIiCigklktaTxswLMMP/RHWUiQ25wELVao2k2TeWbQFNnuJzlrCsSwWWlHMBYMeKWZUxmFUVUR8F1WJLtZO9rFurETrBk7RdzLATAUruNkXQcj4VpaUv0sGz/D9cNHq9xiERERERG5GAVUMuv5QNqUCqmP4fL/esN0WmFusRPUWRrCMr1GjcvrfpIukyeMxXonymkvzxE/V+2mzUsRN8/q8VOV+lErk6cJ+6WC8r3RZg42rSAVjNE2dpbO9Dk2Dh6scotFREREROTd0Kd7uWZMLKiKYnHG5Dnt5VlrxbjJriGiQuoyxVLGY6ef4qjJEsBijRNlwCuw38tWu2nzSk0xXaodNVraYW9Z8iwOPj4W3TWL2LVgPQUnxNLRU3RkBlicHax2k0VERERE5AoooJJrTrYcVNVgc8BkOOpludGOs96KE1B9KrlKWePzhp/igMkAsMKJkPZdDimYmhGN+THWjnaxvrzL3tL0OQCKdoDTNW/fYW9JqrfKLRYRERERkamggEquWSlKhdQjls12P8VBMmyyEyy3IiqkLpNWMD57/TT7TAYXQ6cdBguOerNnKZ9tfBbkRhgNJcg7c6AOmzEsyg6xbqw0O2rd6AkW5YaB0g57pxOtbGvVDnsiIiIiIvOBAiq55o2XC6ljwU/8MXaTZpkdodMK00BAYZVclmsMB02G3X6KPIYOO0TUsjlS5WAq5BVYmj7HdcmzXJfq4bpUD0vT5wj7RQD6Ig2cibVwJr6IM/GFnIm10B1vmdXBlWV8lqT7yjvsnWDtWBeNhSQAyWCMU4k2DjeuoLYwzoqRk6we7WK1dtgTEREREZkXFFDJnOADKeOXBrRl2Omn2EmKWhw6rQiddpgFBBVWSYVvDEdMll1+ijQ+i60gjU6AA+7EItKZU1tIVUKozlQPy1I9LM4M4JRbkg5E6Y0vYPeCtRTtIPWFJI7n0p4bZOPIUQLGK/UJi/5IA2fiLXTHWjgTb+F0vIXu2EIKVQiuHN9jWeos60ZPsG6sizVjJ0m4paWSw6FaztS2sSdcS3NmiOVjp7XDnoiIiIjIPKaASuYUFxgtf1ivxSZoWew1afZ4aWLYpbDKCrPICmErrJqXjDF0mRyv+ynG8Gi2AlznRDjoZuh1i9P62pbxWZgbYVnybCWI6kz10FQYrzxmKFxHX6yZk3XtWAZakz20ZgZYMXaaFWOnL3rdtBNmOFTHWLSOgO/RkR3gpuEjFw2uzpSDqzPTEFyFvAKrxk+zbqyLtaNdrB4/RWRixle0iSMNy0iG4rSN99KZPMuNg4em7LVFREREROTapoBK5qxxfDClOlVxbGKWzWGT4YDJEMZiaTmsarXCKq4+Dxhj6DYFdvhJhnCptxw2ODEOuhkGXXfKXy/guyxJn6Mz1VueHXWWzlQvMS8PgIdNX6yZM7Wt7A+tJlrM0p7sYWFuhKb82KReK+7liWf76cj2X3A+7YQZCtcxHqkrtSfTz03DhwmUfy7OD65OxxeVlwkupDvWQsEJvuPrxopZ1o6fZG15h73lyW6CxsPHoie+kD0L1pJ1IuUd9vppyQ5Nql8iIiIiIjJ/KKCSeSGNT7r8oTyKTcJ26PJzHDFZglh0WGE6rTDtVpiQZVe5tTLV+kyB7V6ScxSpwWaDE+OIl2Gvm5mS68eK2coSvYmv9kxfJQjKOSF64wvZ37SKTCBCY3aE9lQvrZl+WjP973D1Kxf38sQz/ZC5VHBVT8AvXjS46os0VmZaTcy6Gg/G35whNdZFZ6oXG4Nr2ZypWcz2lhvwLZvrRk/Slu6jPd03bX0TEREREZG5xTLGzHS5lWtCT09PtZtw1cbzHv/1+ycZykzvsqVrWRiLOjvAmO+Sx+AArVaY66wwS6wIEYVV17QhU2SHn+KMyRPFZrkT5oSXI3M1VaaMYf3oCdaNnags0WvJjVTuHg3VcC62gOFIPZ7tsGj8HO2ZfqJ+YQp6NL3SToShcG0luGrMj9GUG8UpB1cT8naQ04lW+mLNBHyXZSMnWViY3KwvERERERF5l5oX4fz3b1a7FVOitbX1kvdpBpXMa3kM/eUaOUGgwQowaIqcMXksxllshVhqhem0IsQtp7qNlXdt3Li87qc4bnKEsFjnROnx8uzzsld8zbBX4K5zr/Phs6+wJNOHj8VAtJH++AKONC4n6BVpS/awODvEmmt057m4lyOeyV1kxlWEwXAdQ/FmfGDFSBcrx06xcuxUdRoqIiIiIiJzjgIqkbIi0G9KtYgcoNEKkMTlVb/AqyRZQJBOuxRW1Vn60ZmNMsZjl5/ikMliA6udCCNekQNXEUwtyA7zyz2vcE/vdmrcLKdrFvOfHe9jw8BBFmWH5kVdpYngamlGS/ZERERERGR66FO2yEV4wEA5rLIohVUFfLb7KbaTooEAnVaYTjtCIwEsFVmvqpzx2eOn2W/S+MAyO0Le+Bz2cld2wfIyvo+cfZlbBveDZbG3cRUjkXre27ODJaneKW2/iIiIiIjIfKeASuQdGGDIvLnLW73lYAG7TZpdXpoETiWsWkhQYdUMKhqf/SbDHj9NAcNSO0zAguNXGEyFvCLv79/FR7pfpjPdSzIQ4+XFN1ObT3Pj0CH0X1ZERERERGR6KKASmaRR41VuJ7AJWxb7TYa9XoYINm1WiHYrTJsVIqa6VdMib3wOmyx7/TRZfNrsELWWwyEve0Xlz5tyo3yo51U+2PMzEm6G7ngL/9lxBxv6D3Bn7+tT3n4RERERERG5kAKqOcrkc4T//n/wYF+epG/j2gEKdoCiHaRoBSjapa+C/ebt0nGQou1c8LiCHTjv+aUvo93tAEjikyzvcBbDImE7dPt5jpvSDJ4mArRbYdrtMC0EsTW76qqMGZf9foYjJouLocUKstyOcMDLcHayFzOGNWMn+cjZl3nPwD4sDPsaVzIQa+L2ntf54JmXp6MLIiIiIiIichEKqOYqt4h9totVo2kCxiPguwR9l4DvMhXRUtFycG2nHGgFLgi9inaAvB0kFYyRDMZIBkrfU8H4BcfJYJx0IDJnwq4Mhkx5R8AA0GAH8AzsMWne8NIEsWgtz65qt0IkVGj9XTHG0GMK7DMZzpg8NrDEDhO2LI56Ofq84qSuF/SK3DHwBvd1v8SyVA/pQIRXF99ErJhh4+Ah7OEj09MRERERERERuSTLGHMlK2LmvJ6enmo34aqZ1Dh87f/AH7lwlzEfcC2Hoh3ALQdNrhXAs2w828GzSjOuinYIN+Dg4eDbNr5lY7AwgIXBNgbb+DjGr3x3jIdjfAK+S9TNEXVzxIpZnEssvPKwSAeilcAqFYiSnAiyKmFWvBxwvXlccILT/w84hWLY1NoO475HhtKMqzqcclgVZrEVIqDZVRdwjeGYybLfzzCCSwSLTifMmO/Se15NsHerIT/Gh3pe44M9r1FXTNMTW8C+ppWsHzhKW25gGnogIiIiIiIyBZoX4fz3b1a7FVOitbX1kvdpCsc8ZAMh4xHyvHd87FQpWA6ZQJSsEyLnhMmEYhScEL5l4xiPkFck4hVozQ8TTfcQK2YJ+5eeGZO3g28LsCaOx0I1DIXrGYjUMxSuZywYhyqHPxl8Mn4pmLIpFVp3sDhkMuw3GRyg5bzZVQ3zeGfAtPE46Gc4aDLkMTRYAa63o3R5eQ5dQfHzVWOn+PDZl7l9YA+2MRxoXM722ELe2/M6HzzzyjT0QERERERERCZLAZXMiJDxCBVT1E9kTul3fo4PZJ0ImUCYnB0iG4ySC0Yp2qVd9IKeS9gvUO+mWZQfJlbMEnNz2G+ZrVWwAgxG6kqhVbieoUg9g+E6BsMNDEbqGAzXkw1EprrLl+3X8HmF1iNY1NkBksbj536Sn1OacTUxu6rNChGeI8sgL2fAFNnnpzlhchigvVz4/LCXZcSb3IypgO9y+8AePtz9MquSZ8g4YX7WciMhv8jN/fu5fvjY9HRCRERERERErogCKpm1bCDu5YhPzJrJvrvn5S2ntFwwGCMZrsWzbcJegbpihvZMP3WF1NtCrLQTYbASXNWXb9dXbg+F63Dt6flxyWHInTdbrA6HiG3T5ec4YrJYwAKCtNul2VXNc6jYum8Mp0yefX6aPooEsVjuRPDw6fIKk75eXSHJL/W8xi/1vEZDIcm5aBNbO25n5dAx7ji3axp6ICIiIiIiIlNBAZXMOWHjES6M01wYh/S5iz4ma4cYC8ZJhmrIhONgDFEvz6L8CKvHT1Pjvj0NGwnWMBQpz8IK1zMYqWMg3MBQuI7BSD2joQT+FMx0GsNjzC/NsAoCDVaAIj47/RQ7gTAWbeWlgG1WmLjlXPVrzrS88Tlsshzw06TwSWCzzonS6xU4dgXL+JaPn+EjZ1/mjv43CBqPAw3L2LFwA+/t3ck9WsYnIiIiIiIy6ymgknkp6heI5gssyo9A8u33GyA5UbA9nCAXiOAYj5ibY1m6l5tGjhB5ywwf17Lpji3kUF0nh+o6OVjbyUCk4arqXxWB/vMKgsexSdgOPX6eE6YU5DQQoN0KscAK0mAFqcOZtTOsRo3Lfj/DUZPFxdBiBel0Ihx1Mxzw3uUUuTLH93jP4F4+0v0ya8ZPkXVCbG/ZgGM8NvXtY93IiWnqhYiIiIiIiEw1BVQiF2EBtW6WWjcL2cGLPsbFYjxYQyoYJRmppWgHqCukuLtvJx/qeQ2AoVBtKawqh1Yn44vx7Suf8ZTGJ10utu4A9VbpR3i/yeCXVy3aQB0BGqzyV/l2okrBlTGGs6bAfpPhjMljA0vsMCHL4piXo8+9dDH8i2nIj3Nv78+4t+dnNBXG6Ys2srXjdpaPnOT2c7unpxMiIiIiIiIyrRRQiVyhAIbGYpLGYhIy/Rfcl3HCnIs2UwwEWT/WxR0De4DS0sIjtUsqs6wO1y4hd4UF2j1g6LzZVWEs4tjEbIcihj5T4IT/5nI5ByphVYMVrNyOY0/LjoGuMRwzWfb5GUZxiWKzxoky5hc56ecndzFjWDd2gl8++yq3De7DNoZDDdexs+V6buvdpWV8IiIiIiIi1zgFVCLTIOblWZY6WzkuYtMfbWQ8UkdLfpQNp36IjcHD4lTN4sqSwEN1nQxF6q/oNfMY8ngM+94F5yNYJCyHsGWTNz7dpsBR82ZwFcQ6L7h6c8ZV9AqDq7TxOOBnOGQy5DE0WAGut2N0eVkOTXIZX8TNlWaknX2VJZk+UoEoP2vZSNgvcFP/fi3jExERERERmSMUUInMgCA+bdlB2srLBQ0wFEowGGsm7BX4QO92Pny2NAtoIFxfWRJ4qLaT0zWLrqr4eg5Dzri8ZeNCYtgkLJugZZMzPl0mx2Hz5oPC5eCq0QpSbwVotALUEyByibb0mwL7/QwnTA4DtNshai2Hw16WEc+96HMupSN9jg+dfZW7+14n6hU4VbOY/9VxB+sHDnLHuZ2T/ScQERERERGRWU4BlUgVWEBTIUlT4c0K7WknQl+sGdcOsHHkKHf2l+opZZwwh2uXcqhuKQfrruNo7RLyTuiq25DBJ2P8C4Iri1JwVWs72Fhkjc9Rk6V4XnAVw75gppWNxUE/Qz9FgliscCIU8Tn5liLy78TxPW4d3M+Hel5hw+gJCnaAvU2rSQbjvLd3J0tTvVfdZxEREREREZmdFFCJzBJxL8eyZHfluIhNX6yJZDhBa26IG0eOYAMeNl01izlUd115ptVSRsJ1U9IGw4WF2CfYTOwgGAAMaeNxzhTwyrlVAof1TpQer8BRL/fWy17WW4ueD0bq+VHbbbQlz3HzwH5m536EIiIiIiIiMpUUUInMUkF82jMDkBkASuHRYKiWwVgTUTfPB3tf476zLwHQF2ngSO0SjiaWcLS2g66aNgpOcMra4gNJfJL+hbOiAkANDhk89k+mvtRlip7f2rubXzz7sylru4iIiIiIiMx+CqhErhEW0FwYp7kwXjmXdCL0xZsxlsOG0eO8v/8NAFzL5nR8EUcTHRytLYVWZ2MLr6qW1cW4wCjeOz5ugoqei4iIiIiIyMUooBK5hiW8HInxN5cFulgMRuoZjjYSdXPc2b+LX+otzUbKOiGOJdo5lljCkdoOjiU6GArXwRXs1DdZ7em+StHzmJcvFz2/nfUDh1T0XERERERERBRQicwlAQyLciMsyo1UzuXsIP3RRpKhBI2FcT7avY2AKdWYGg4lOJpYwrHaDo4mOjhW204mEJ2StqjouYiIiIiIiLxbCqhE5riIX2RJug/SfZVzY4EYg7Emik6Q5ekebhvaX7mvO7awvDSwNMvqZM1iXPvdv1Wo6LmIiIiIiIhMlgIqkXmozs1QN56pHPvAcKiOwXgTAd9l0/BBNve9DkDRcuiqaeNoeZbV0doOeqPNFy4NvEzR89t6d6nouYiIiIiIiFyWAioRwQaaC2M0F8Yq54rY9EcaGY3WU1PMcG/vz/nI2ZcBSAWi5SWBHaQCUT7Qu11Fz0VEREREROSKKaASkYsK4tOWG6QtN1g5l3HC9EebyASjLMoPc8OpozgYTpeLnl/ff1BFz0VERERERGTSFFCJyLsW8/J0pnoqxwYYCiVoSQ+wREXPRURERERE5AopoBKRK2YBTYVktZshIiIiIiIi1zi72g0QEREREREREZH5TQGViIiIiIiIiIhUlQIqERERERERERGpKgVUIiIiIiIiIiJSVQqoRERERERERESkqhRQiYiIiIiIiIhIVSmgEhERERERERGRqlJAJSIiIiIiIiIiVaWASkREREREREREqkoBlYiIiIiIiIiIVJUCKhERERERERERqSoFVCIiIiIiIiIiUlWBajdgJuzevZvnnnsO3/f5wAc+wMc//vFqN0lERERERERERMrm/Awq3/d59tln+ZM/+ROeeuopXn75Zbq7u6vdLBERERERERERKZvzAdWxY8dYtGgRLS0tBAIBbr/9drZv317tZomIiIiIiIiISNmcD6iGh4dpamqqHDc1NTE8PFzFFomIiIiIiIiIyPnmfA0qY8zbzlmW9bZzW7duZevWrQA88cQTtLa2Tnvbpl8rfPs/qt0IEREREREREZHLmvMzqJqamhgaGqocDw0N0dDQ8LbH3XPPPTzxxBM88cQTM9m8affII49Uuwkyx2mMyUzQOJOZoHEmM0HjTKabxpjMBI0zmQ5zPqBavnw5vb299Pf347our7zyCps2bap2s0REREREREREpGzOL/FzHIcHH3yQxx9/HN/32bx5Mx0dHdVuloiIiIiIiIiIlM35gArg5ptv5uabb652M6rinnvuqXYTZI7TGJOZoHEmM0HjTGaCxplMN40xmQkaZzIdLHOxKuIiIiIiIiIiIiIzZM7XoBIRERERERERkdltXizxm0u2bNnCzp07qaur48knnwTg5MmT/N3f/R25XI4FCxbw8MMPE4vFcF2Xb3zjG3R1deH7PnfeeSe/8iu/AsDu3bt57rnn8H2fD3zgA3z84x+vZrdklpmqcfbZz36WSCSCbds4jjPndsmUqzPZcfbNb36T48ePY9s2DzzwAOvXrwfgxIkTPP300xQKBW666SY+9alPYVlWNbsms8RUjbE/+7M/Y2RkhFAoBMCjjz5KXV1d1fols8vg4CBPP/00o6OjWJbFPffcw4c//GFSqRRPPfUUAwMDLFiwgM9//vPU1NRgjOG5555j165dhMNhHnroIZYtWwbAT37yE55//nkA7r//fu6+++4q9kxmi6kcY7/+67/OkiVLAGhububLX/5yNbsms8hkx9nZs2fZsmULXV1d/MZv/AYf+9jHKtfSZ025YkauKfv37zfHjx83X/jCFyrnHnnkEbN//35jjDE//OEPzXe/+11jjDE//elPzVNPPWWMMSaXy5mHHnrI9PX1Gc/zzB/90R+Zc+fOmWKxaL74xS+aM2fOzHxnZNaainFmjDEPPfSQGRsbm+HWy7ViMuPsBz/4gXn66aeNMcaMjo6aL33pS8bzvMpzDh8+bHzfN48//rjZuXPnDPdEZqupGmNf/epXzbFjx2a49XKtGB4eNsePHzfGGJPJZMzDDz9szpw5Y771rW+ZF154wRhjzAsvvGC+9a1vGWOMef31183jjz9ufN83hw8fNl/5yleMMcYkk0nz2c9+1iSTyQtui0zVGDPGmN/+7d+e+Q7INWGy42x0dNQcPXrUfOc73zH/8i//UrmOPmvK1dASv2vMunXrqKmpueBcT08Pa9euBeCGG27gZz/7WeW+XC6H53kUCgUCgQCxWIxjx46xaNEiWlpaCAQC3H777Wzfvn1G+yGz21SMM5F3Mplx1t3dzfXXXw9AXV0d8XicEydOMDIyQjabZdWqVViWxZ133qn3M6mYijEm8k4aGhoqs1Oi0ShtbW0MDw+zfft27rrrLgDuuuuuynvTjh07uPPOO7Esi1WrVpFOpxkZGWH37t3ccMMN1NTUUFNTww033MDu3bur1i+ZPaZqjIlczmTHWV1dHStWrMBxnAuuo8+acjUUUM0BHR0d7NixA4DXXnuNoaEhAN7znvcQiUT4/d//fR566CE++tGPUlNTw/DwME1NTZXnNzU1MTw8XJW2y7VjsuNswuOPP86Xv/xltm7dWpV2y7XlUuOss7OTHTt24Hke/f39nDhxgsHBQb2fyaRNdoxN2LJlC3/8x3/M9773PYz2l5FL6O/vp6urixUrVjA2NkZDQwNQ+uA3Pj4OwPDwMM3NzZXnTLxvvfX9rLGxUe9n8jZXM8YAisUijzzyCH/6p3/Kz3/+85nvgFwT3s04uxT9biZXQzWo5oA//MM/5LnnnuN73/semzZtIhAo/Wc9duwYtm3zzDPPkE6neeyxx9iwYcNFf7FWvRZ5J5MdZy0tLfz5n/85jY2NjI2N8Rd/8Re0traybt26KvdEZrNLjbPNmzfT3d3NI488woIFC1i9ejWO4ygokEmb7BgDePjhh2lsbCSbzfLkk0+ybdu2yl+TRSbkcjmefPJJHnjggcvOJJ7M72H6/UzONxVjbMuWLTQ2NtLX18fXvvY1lixZwqJFi6atzXLtebfj7FL0WVOuhgKqOaCtrY1HH30UKC1d2LlzJwAvvfQSGzduJBAIUFdXx+rVqzl+/DjNzc2VvxgDDA0NVVJxkUuZ7DhraWmhsbERKE0BvuWWWzh27JgCKrmsS40zx3F44IEHKo979NFHWbx4MfF4/G3vZxPjTuRiJjvGgMqYikajvO997+PYsWMKqOQCruvy5JNP8v73v5/bbrsNKP2/b2RkhIaGBkZGRqitrQVKswnOn5038XtYY2MjBw4cqJwfHh7W/zOlYirGGLz5ftbS0sK6des4efKkAiqpmMw4u5SmpiZ91pQrpiV+c8DY2BgAvu/z/PPPc++99wKlnTn27duHMYZcLsfRo0dpa2tj+fLl9Pb20t/fj+u6vPLKK2zatKmaXZBrwGTHWS6XI5vNAqW/xOzZs6eya4zIpVxqnOXzeXK5HAB79uzBcRza29tpaGggGo1y5MgRjDFs27ZN72dyWZMdY57nVZYzuK7L66+/TkdHR3UaL7OSMYZvfOMbtLW1cd9991XOb9q0iRdffBGAF198kVtuuaVyftu2bRhjOHLkCLFYjIaGBjZu3Mgbb7xBKpUilUrxxhtvsHHjxqr0SWaXqRpjqVSKYrEIwPj4OIcPH6a9vX3mOySz0mTH2aXos6ZcDctofcQ15etf/zoHDhwgmUxSV1fHr/3ar5HL5fiP//gPAG699VZ+8zd/E8uyyOVybNmyhe7ubowxbN68ubL9586dO/mHf/gHfN9n8+bN3H///dXslswyUzHO+vr6+Ou//msAPM/jfe97n8aZXGAy46y/v5/HH38c27ZpbGzkM5/5DAsWLADg+PHjbNmyhUKhwMaNG3nwwQc1lVyAqRljuVyOr371q3ieh+/7bNiwgd/93d/FtvU3Pik5dOgQjz32GEuWLKm893zyk59k5cqVPPXUUwwODtLc3MwXvvAFampqMMbw7LPP8sYbbxAKhXjooYdYvnw5AD/60Y944YUXALj//vvZvHlz1fols8dUjbHDhw/zzW9+E9u28X2fj3zkI/ziL/5ilXsns8Vkx9no6CiPPPII2WwWy7KIRCL8zd/8DbFYTJ815YopoBIRERERERERkarSn/9ERERERERERKSqFFCJiIiIiIiIiEhVKaASEREREREREZGqUkAlIiIiIiIiIiJVpYBKRERERERERESqSgGViIiIiIiIiIhUlQIqERERkSr527/9W7Zs2XLBuQMHDvDggw8yMjJSpVaJiIiIzDwFVCIiIiJV8qlPfZFY4HQAAANXSURBVIpdu3axZ88eAAqFAs888wy/8zu/Q0NDw5S9ju/7U3YtERERkekQqHYDREREROarRCLBgw8+yDPPPMOTTz7J888/T0tLC3fffTe+7/PP//zP/PjHPyaTybBhwwY+/elPU1NTg+/7PPXUUxw6dIhisUhnZyef/vSnaW9vB0ozs2KxGH19fRw6dIhHHnmEXC7Ht7/9bYaGhojFYtx3333cd999Vf4XEBERESmxjDGm2o0QERERmc+efPJJXNfl8OHD/NVf/RXNzc3867/+K9u3b+fzn/88NTU1PPvss7iuy+c+9zl832fbtm3cdtttOI7Dt771LY4ePcoTTzwBlAKqXbt28ZWvfIUVK1bgeR6f+cxn+NKXvsTq1atJpVL09/ezbNmyKvdcREREpERL/ERERESq7Pd+7/fYt28fv/qrv0pzczMAW7du5ZOf/CSNjY2EQiE+8YlP8Oqrr+L7PrZtc/fddxONRiv3nThxglwuV7nmLbfcwqpVq7Btm2AwSCAQoLu7m2w2S01NjcIpERERmVW0xE9ERESkyurr66mtra0s0QMYHBzkL//yL7Esq3LOsizGx8epra3lO9/5Dq+99hrJZLLymGQySSQSAagEXRO++MUv8vzzz/Ptb3+bpUuX8lu/9VusXLlyBnonIiIi8s4UUImIiIjMQk1NTTz88MMXDZF+/OMfs2vXLh577DEWLFhAMpnk05/+NJer3LBy5Uq+/OUv47ou3//+9/n617/O008/PZ1dEBEREXnXtMRPREREZBa69957+e53v8vg4CAAY2Nj7NixA4BsNksgECCRSJDP5/nHf/zHy16rUCjw0ksvkclkCAQCRKNRbFu/BoqIiMjsoRlUIiIiIrPQxA57X/va1xgdHaWuro477riDTZs2sXnzZvbs2cMf/MEfkEgk+MQnPsHWrVsve70XX3yRZ599Ft/3aW1t5XOf+9xMdENERETkXdEufiIiIiIiIiIiUlWa2y0iIiIiIiIiIlWlgEpERERERERERKpKAZWIiIiIiIiIiFSVAioREREREREREakqBVQiIiIiIiIiIlJVCqhERERERERERKSqFFCJiIiIiIiIiEhVKaASEREREREREZGqUkAlIiIiIiIiIiJV9f8DEWz8DY0z4r4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting - xlabel이 string type인 경우\n",
    "df_top5.plot(kind='area', alpha=1,               # alpha: 투명도, default=0.5\n",
    "             stacked=True, figsize=(20, 10))     # unstacked = line plot\n",
    "\n",
    "plt.title('Immigration Trend of Top 5 Countries')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.text(20, 40000, 'Area Plotting', fontsize=24)    \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The unstacked plot has a default transparency (alpha value) at 0.5. We can modify this value by passing in the `alpha` parameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting - xlabel이 integar type인 경우\n",
    "df_top5.index = df_top5.index.map(int)\n",
    "\n",
    "df_top5.plot(kind='area', alpha=0.5, stacked=True, figsize=(20, 10))\n",
    "\n",
    "plt.title('Immigration')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Immigration #')\n",
    "\n",
    "plt.text(2000, 110000, 'Area Plot', fontsize=24)        # xlabel이 integar type인 경우\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Two types of plotting\n",
    "\n",
    "As we discussed in the video lectures, there are two styles/options of ploting with `matplotlib`. Plotting using the Artist layer and plotting using the scripting layer.\n",
    "\n",
    "**Option 1: Scripting layer (procedural method) - using matplotlib.pyplot as 'plt' **\n",
    "\n",
    "You can use `plt` i.e. `matplotlib.pyplot` and add more elements by calling different methods procedurally; for example, `plt.title(...)` to add title or `plt.xlabel(...)` to add label to the x-axis.\n",
    "\n",
    "```python\n",
    "    # Option 1: This is what we have been using so far\n",
    "    df_top5.plot(kind='area', alpha=0.35, figsize=(20, 10)) \n",
    "    plt.title('Immigration trend of top 5 countries')\n",
    "    plt.ylabel('Number of immigrants')\n",
    "    plt.xlabel('Years')\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Option 2: Artist layer (Object oriented method) - using an `Axes` instance from Matplotlib (preferred) **\n",
    "\n",
    "You can use an `Axes` instance of your current plot and store it in a variable (eg. `ax`). You can add more elements by calling methods with a little change in syntax (by adding \"_set__\" to the previous methods). For example, use `ax.set_title()` instead of `plt.title()` to add title,  or `ax.set_xlabel()` instead of `plt.xlabel()` to add label to the x-axis. \n",
    "\n",
    "This option sometimes is more transparent and flexible to use for advanced plots (in particular when having multiple plots, as you will see later). \n",
    "\n",
    "In this course, we will stick to the **scripting layer**, except for some advanced visualizations where we will need to use the **artist layer** to manipulate advanced aspects of the plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '# of Immigrants')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# option 2: preferred option with more flexibility\n",
    "ax = df_top5.plot(kind='area', alpha=0.4, figsize=(20,10))\n",
    "\n",
    "ax.set_title('Immigration Trend')\n",
    "ax.set_xlabel('Years')\n",
    "ax.set_ylabel('# of Immigrants')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question**: Use the scripting layer to create a stacked area plot of the 5 countries that contributed the least to immigration to Canada **from** 1980 to 2013. Use a transparency value of 0.45.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total이 낮은 순서대로 다시 정렬\n",
    "df_can.sort_values('Total', axis=0, ascending=True, inplace=True)\n",
    "df_bot5 = df_can.head()\n",
    "\n",
    "# 1980~2013년 값만 추려서 transpose\n",
    "df_bot5 = df_bot5[years].transpose()\n",
    "\n",
    "# dataframe --> plotting\n",
    "df_bot5.index = map(int, df_bot5.index)     # index type: str -> int for plotting\n",
    "\n",
    "df_bot5.plot(kind='area', alpha=0.7, figsize=(15,8))\n",
    "\n",
    "plt.title('Immigration Trend')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('# of Immigrants')\n",
    "\n",
    "plt.text(1995, 1, 'Bottom 5 Countries')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    # get the 5 countries with the least contribution\n",
    "    df_least5 = df_can.tail(5)\n",
    "     \n",
    "    # transpose the dataframe\n",
    "    df_least5 = df_least5[years].transpose() \n",
    "    df_least5.head()\n",
    "\n",
    "    df_least5.index = df_least5.index.map(int) # let's change the index values of df_least5 to type integer for plotting\n",
    "    df_least5.plot(kind='area', alpha=0.45, figsize=(20, 10)) \n",
    "\n",
    "    plt.title('Immigration Trend of 5 Countries with Least Contribution to Immigration')\n",
    "    plt.ylabel('Number of Immigrants')\n",
    "    plt.xlabel('Years')\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question**: Use the artist layer to create an unstacked area plot of the 5 countries that contributed the least to immigration to Canada **from** 1980 to 2013. Use a transparency value of 0.55.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(1995, 1, 'Bottom 5')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_bot5.plot(kind='area', stacked=False, figsize=(15,8))\n",
    "\n",
    "ax.set_title('Immigration Trend')\n",
    "ax.set_xlabel('Years')\n",
    "ax.set_ylabel('# of Immigrants')\n",
    "\n",
    "ax.text(1995, 1, 'Bottom 5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    \n",
    "    # get the 5 countries with the least contribution\n",
    "    df_least5 = df_can.tail(5)\n",
    "\n",
    "    # transpose the dataframe\n",
    "    df_least5 = df_least5[years].transpose() \n",
    "    \n",
    "    df_least5.head()\n",
    "\n",
    "    df_least5.index = df_least5.index.map(int) # let's change the index values of df_least5 to type integer for plotting\n",
    "    \n",
    "    ax = df_least5.plot(kind='area', alpha=0.55, stacked=False, figsize=(20, 10))\n",
    "    \n",
    "    ax.set_title('Immigration Trend of 5 Countries with Least Contribution to Immigration')\n",
    "    ax.set_ylabel('Number of Immigrants')\n",
    "    ax.set_xlabel('Years')\n",
    "\n",
    "    \n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Histograms<a id=\"8\"></a>\n",
    "\n",
    "A histogram is a way of representing the _frequency_ distribution of numeric dataset. The way it works is it partitions the x-axis into _bins_, assigns each data point in our dataset to a bin, and then counts the number of data points that have been assigned to each bin. So the y-axis is the frequency or the number of data points in each bin. Note that we can change the bin size and usually one needs to tweak it so that the distribution is displayed nicely.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question:** What is the frequency distribution of the number (population) of new immigrants from the various countries to Canada in 2013?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Before we proceed with creating the histogram plot, let's first examine the data split into intervals. To do this, we will us **Numpy**'s `histrogram` method to get the bin ranges and frequency counts as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "Palau               0\n",
       "Western Sahara      0\n",
       "Marshall Islands    0\n",
       "New Caledonia       2\n",
       "San Marino          0\n",
       "Name: 2013, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's quickly view the 2013 data\n",
    "df_can['2013'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[178  11   1   2   0   0   0   0   1   2]\n",
      "[    0.   3412.9  6825.8 10238.7 13651.6 17064.5 20477.4 23890.3 27303.2\n",
      " 30716.1 34129. ]\n"
     ]
    }
   ],
   "source": [
    "# numpy histogram\n",
    "count, bin_edges = np.histogram(df_can['2013'], bins=10)    # bins = 10 by default\n",
    "\n",
    "print(count)        # 각 bin의 frequency count\n",
    "print(bin_edges)    # 나눠진 bin range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "By default, the `histrogram` method breaks up the dataset into 10 bins. The figure below summarizes the bin ranges and the frequency distribution of immigration in 2013. We can see that in 2013:\n",
    "\n",
    "-   178 countries contributed between 0 to 3412.9 immigrants \n",
    "-   11 countries contributed between 3412.9 to 6825.8 immigrants\n",
    "-   1 country contributed between 6285.8 to 10238.7 immigrants, and so on..\n",
    "\n",
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%202/images/Mod2Fig1-Histogram.JPG\" align=\"center\" width=800>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can easily graph this distribution by passing `kind=hist` to `plot()`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fvn0RFRWlGTZt2jTN402bNsHGxkbz3MXFBcuWLauO0oiIiCSjWr7T9/b2hlwuL/c1IQSOHj2Kbt26VUcpREREkmX07/QvXrwIOzs7NGzYUDMsPT0ds2bNgrW1NYYNG4bWrVuX+97Y2FjExsYCAMLDw6FUKvVaW5pep6Z/+uzXzMxM78uvJpNSv+y17pJSv1LqFTBcv0YP/cOHD2tt5SsUCqxZswb169fHtWvXsGzZMkRERGjt/n8sODgYwcHBmucZGRnVUnNNoc9+lUqlpJaflPplr3WXlPqVUq+A7v0+Pv5NV0Y9Za+0tBTHjx+Hn99fR8mbm5ujfv36AABPT084OzsjNTXVWCUSERHVGUYN/XPnzsHV1RWOjo6aYbm5uVCr1QCAtLQ0pKamwtnZ2VglEhER1RnVsnt/5cqVSExMRF5eHkJDQzFkyBD07NmzzK59AEhMTMS2bdtgamoKExMTjBs3rsKDAImIiEh31RL6U6dOLXf4xIkTywzr0qULunTpYuiSiIiIJIeX4SUiIpIIhj4REZFEMPSJiIgkgqFPREQkEQx9IiIiiWDoExERSQRDn4iISCIY+kRERBLB0CciIpIIhj4REZFEMPSJiIgkgqFPREQkEQx9IiIiiWDoExERSQRDn4iISCIY+kRERBLB0CciIpIIhj4REZFEMPSJiIgkgqFPREQkEQx9IiIiiWDoExERSQRDn4iISCIY+kRERBLB0CciIpIIhj4REZFEMPSJiIgkwqw6ZrJmzRokJCTAzs4OERERAIBt27Zh3759sLW1BQAMHz4c7du3BwDExMQgLi4OJiYmGDNmDNq1a1cdZRIREdVp1RL6QUFB6Nu3L6KiorSGv/766xg4cKDWsNu3b+PIkSNYvnw5srKysHDhQkRGRsLEhDsliIiIqqJaktTb2xtyuVyncU+cOAE/Pz+Ym5ujQYMGcHFxQXJysoErJCIiqvuqZUu/Inv37kV8fDw8PT0xcuRIyOVyqFQqeHl5acZxcHCASqUyYpVERER1g9FCv3fv3ggJCQEAbN26FZs2bUJYWBiEEDpPIzY2FrGxsQCA8PBwKJVKvdaYptep6Z8++zUzM9P78qvJpNQve627pNSvlHoFDNev0ULf3t5e87hXr15YunQpAMDR0RGZmZma11QqFRwcHMqdRnBwMIKDgzXPMzIyDFRtzaTPfpVKpaSWn5T6Za91l5T6lVKvgO79urq6Ptd0jXZ0XFZWlubx8ePH0bhxYwBAx44dceTIETx8+BDp6elITU1F8+bNjVUmERFRnVEtW/orV65EYmIi8vLyEBoaiiFDhuDChQtISUmBTCaDk5MTxo8fDwBo3LgxunbtiunTp8PExATvvvsuj9wnIiLSg2oJ/alTp5YZ1rNnzwrHHzx4MAYPHmzIkoiIiCSHm9BEREQSwdAnIiKSCIY+ERGRRDD0iYiIJIKhT0REJBEMfSIiIolg6BMREUkEQ5+IiEgiGPpEREQSwdAnIiKSCIY+ERGRRDD0iYiIJIKhT0REJBEMfSIiIolg6BMREUkEQ5+IiEgizHQZ6fbt25DL5bC3t0dhYSF2794NExMTDBgwAJaWloaukYiIiPRApy39yMhIPHjwAACwadMmXLx4EUlJSfjqq68MWhwRERHpj05b+vfu3YOrqyuEEDhx4gQiIiJgYWGBSZMmGbo+IiIi0hOdQt/c3BwFBQW4ffs2HB0dYWtri9LSUjx8+NDQ9REREZGe6BT63bp1wz//+U8UFBSgb9++AIDr16+jQYMGBi2OiIiI9Een0B89ejTOnDkDU1NTtGnTBgAgk8kwatQogxZHRERE+qNT6ANA27ZtkZGRgaSkJLRo0QLNmjUzZF1ERESkZzqFfkZGBiIjI5GSkgIA2Lx5M44dO4bTp08jNDTUkPURERGRnuh0yt5XX32FV155BRs3boSZ2aO/E3x8fHD27FmDFkdERET6o1PoJycnY9CgQTAx+Wt0Gxsbzbn7REREVPPpFPp2dna4e/eu1rDbt29DqVQapCgiIiLSP52+0x8wYACWLl2KQYMGQa1W49ChQ4iJicGgQYN0msmaNWuQkJAAOzs7REREAHh0XMDJkydhZmYGZ2dnhIWFoV69ekhPT8e0adPg6uoKAPDy8sL48eNfsD0iIiJ6TKfQ79mzJ+RyOfbt2wdHR0ccOHAAQ4cORadOnXSaSVBQEPr27YuoqCjNMB8fH7z11lswNTXFli1bEBMTgxEjRgAAXFxcsGzZshdoh4iIiCqi8yl7nTp10jnkn+bt7Y309HStYW3bttU8btGiBY4dO/ZC0yYiIiLdVBj68fHxCAgIAADExcVVOIGePXtWuYi4uDj4+flpnqenp2PWrFmwtrbGsGHD0Lp16yrPg4iISOoqDP3Dhw9rQv/gwYMVTqCqob9jxw6YmprC398fAKBQKLBmzRrUr18f165dw7JlyxAREQEbG5sy742NjUVsbCwAIDw8XO8HFqbpdWr6p89+zczMJHVgppT6Za91l5T6lVKvgOH6rTD058yZAwAQQiA0NBRKpRKmpqZ6nfn+/ftx8uRJfPTRR5DJZAAe3dzH3NwcAODp6QlnZ2ekpqaWewXA4OBgBAcHa55nZGTotb6aTp/9KpVKSS0/KfXLXusuKfUrpV4B3ft9fNC7rp55yp5MJsMHH3ygCWV9OX36NHbt2oV//OMfsLS01AzPzc2FWq0GAKSlpSE1NRXOzs56nTcREZEU6XQgX9OmTZGamopGjRq90ExWrlyJxMRE5OXlITQ0FEOGDEFMTAxKSkqwcOFCAH+dmpeYmIht27bB1NQUJiYmGDduHORy+QvNl4iIiP6iU+i/9NJL+PTTTxEYGFjmOwZdvtOfOnVqmWEVva9Lly7o0qWLLmURERHRc9Ap9C9fvowGDRrg4sWLZV7Tx9H7REREZHg6hf6CBQsMXQcREREZmE7X3p81a1a5w2fPnq3XYoiIiMhwdAr9p2+2Azw6lS8traafyU5ERESPVbp7f/Xq1QCAkpISzePH7t27h8aNGxuuMiIiItKrSkP/yfPjn3wsk8nQsmVLdO3a1XCVERERkV5VGvpvvvkmgEfn0Ldr165aCiIiIiLD0Ono/Xbt2uHOnTtISUlBYWGh1ms8ZY+IiKh20Cn0d+zYgZ9++gnu7u5al8wFGPpERES1hU6h/+uvv+LTTz+Fu7u7oeshIiIiA9HplD0LC4sXvu4+ERER1Qw6hf7QoUMRHR2NrKwsqNVqrX9ERERUO+i0e3/NmjUAgH379pV5bevWrfqtiIiIiAxCp9B/+sI8REREVPvoFPpOTk6GroOIiIgMTKfQ//zzzyGTycp9bdKkSXotiIiIiAxDp9B3cXHRep6dnY1jx47B39/fIEURERGR/ukU+o8vx/uknj17Yvv27XoviIiIiAxDp1P2ytO0aVNcvHhRn7UQERGRAem0pX/+/Hmt50VFRTh8+DDc3NwMUhQRERHpn06h/8UXX2g9t7Kygru7O6ZMmWKQooiIiEj/dAr9qKgoQ9dBREREBqZT6ANAfn4+Tp48CZVKBQcHB3To0AFyudyQtREREZEe6XQgX1JSEiZPnoz//e9/uHHjBmJjYzF58mQkJSUZuj4iIiLSE5229L/55hv8/e9/R7du3TTDjhw5gg0bNmDJkiUGK46IiIj0R6ct/dTUVHTt2lVrWJcuXXD37l2DFEVERET6p1Pou7i44MiRI1rDjh49CmdnZ4MURURERPqn0+790aNHIzw8HHv27IFSqcS9e/eQmpqK2bNnG7o+IiIi0hOdQr9ly5b4/PPPkZCQgKysLHTo0AHt27d/rqP316xZg4SEBNjZ2SEiIgLAozMCVqxYgXv37sHJyQnTpk2DXC6HEAIbNmzAqVOnYGlpibCwMHh6er5Yh0RERATgGbv3i4uLcfPmTQCAXC5HQEAA/va3vyEgIAAqlQrFxcU6zygoKAhz587VGrZz5068/PLLWLVqFV5++WXs3LkTAHDq1CncvXsXq1atwvjx47Fu3brn7YuIiIieUmno79q1C3FxceW+tn//fuzevVvnGXl7e5fZM3DixAkEBgYCAAIDA3HixAkAwB9//IGAgADIZDK0aNEC9+/fR1ZWls7zIiIiorIq3b1/5MgRzJ8/v9zX+vfvj4ULFyIkJOSFZ56TkwOFQgEAUCgUyM3NBQCoVCoolUrNeI6OjlCpVJpxH4uNjUVsbCwAIDw8XOs9+pCm16npnz77NTMz0/vyq8mk1C97rbuk1K+UegUM12+lof/46nvlcXBwgEql0ntBACCEKDNMJpOVGRYcHIzg4GDN84yMDIPUU1Pps1+lUimp5Selftlr3SWlfqXUK6B7v66urs813Up371tZWVU404yMDFhaWj7XzJ5mZ2en2W2flZUFW1tbAI+27J+cb2ZmZpmtfCIiIno+lYb+K6+8gu+//77c13744Qe0b9++SjPv2LEjDhw4AAA4cOAAfH19NcPj4+MhhEBSUhJsbGwY+kRERFVU6e79YcOG4cMPP8TMmTPRqVMnKBQKZGVl4fjx4ygoKMCiRYt0ntHKlSuRmJiIvLw8hIaGYsiQIRg0aBBWrFiBuLg4KJVKTJ8+HcCjPzYSEhLw/vvvw8LCAmFhYVXrkoiIiCAT5X2B/oT8/Hz88ssvOHfuHPLz8yGXy/Hyyy+jf//+Ne4ue3fu3NHr9ErHDdTr9PTN9Gvdz554Fn5fVnex17pLSv1KqVfAcN/pP/PiPHK5HMOGDcOwYcOea8JERERUs+h07X0iIiKq/Rj6REREEsHQJyIikogKQ//DDz/UPN6+fXu1FENERESGU2Ho37lzR3NDnV9++aXaCiIiIiLDqPDofV9fX0yZMgUNGjRAcXExFixYUO54n3zyicGKIyIiIv2pMPTDwsJw6dIlpKenIzk5GT169KjOuoiIiEjPKj1Pv1WrVmjVqhVKSkoQFBRUTSURERGRITzz4jwA0LNnT5w/fx7x8fHIysqCQqFAQEAA2rRpY+j6iIiISE90OmVv3759WLlyJezt7TXX4I+MjNTcy56IiIhqPp229Hfv3o158+ahadOmmmF+fn6IiIjQup89ERER1Vw6benn5eXBzc1Na5irqyvy8/MNUhQRERHpn06h36pVK2zatAlFRUUAgMLCQmzevBktWrQwaHFERESkPzrt3h83bhxWrlyJ0aNHQy6XIz8/Hy1atMCUKVMMXR8RERHpiU6hr1Ao8MknnyAzM1Nz9L6jo6OhayMiIiI90in0H3N0dGTYExER1VK8yx4REZFEMPSJiIgk4pmhr1arcf78eZSUlFRHPURERGQgzwx9ExMT/Otf/4KZ2XN9/U9EREQ1jE6791u3bo2kpCRD10JEREQGpNPmu5OTE5YsWYKOHTvC0dERMplM89rQoUMNVhwRERHpj06hX1xcDF9fXwCASqUyaEFERERkGDqFflhYmKHrICIiIgPT+ei827dv49ixY8jJycG7776LO3fu4OHDh3B3dzdkfURERKQnOh3Id/ToUSxYsAAqlQrx8fEAgIKCAmzatMmgxREREZH+6LSlv23bNsyfPx9NmzbF0aNHAQDu7u5ISUmp0szv3LmDFStWaJ6np6djyJAhuH//Pvbt2wdbW1sAwPDhw9G+ffsqzYuIiEjqdAr9nJycMrvxZTKZ1lH8L8LV1RXLli0D8OgiQBMmTECnTp3w22+/4fXXX8fAgQOrNH0iIiL6i0679z09PTW79R87fPgwmjdvrrdCzp07BxcXFzg5OeltmkRERPQXnbb0x4wZg0WLFiEuLg5FRUVYvHgx7ty5g3nz5umtkMOHD6Nbt26a53v37kV8fDw8PT0xcuRIyOVyvc2LiIhIimRCCKHLiEVFRTh58iQyMjLg6OiIDh06wMrKSi9FlJSUYMKECYiIiIC9vT2ys7M13+dv3boVWVlZ5Z42GBsbi9jYWABAeHg4iouL9VLPY2lv+Ol1evrmHHNEb9MyMzOT1P0VpNQve627pNSvlHoFdO/XwsLi+aar64iWlpZo1aoVVCoVHBwc9Bb4AHDq1Cl4eHjA3t4eADT/A0CvXr2wdOnSct8XHByM4OBgzfOMjAy91VQb6LNfpVIpqeUnpRRAonYAABlgSURBVH7Za90lpX6l1Cuge7+urq7PNV2dQj8jIwOrVq3ClStXUK9ePdy/fx/NmzfH+++/r5fv4J/etZ+VlQWFQgEAOH78OBo3blzleRAREUmdTqEfFRUFT09PzJ07F1ZWVigsLMQPP/yAqKgofPzxx1UqoKioCGfPnsX48eM1w7Zs2YKUlBTIZDI4OTlpvUZEREQvRqfQv3btGj788EPN7XWtrKwwYsQIjB07tsoFWFpaIjo6WmvY5MmTqzxdIiIi0qbTKXteXl5ITk7WGnb16lW0aNHCIEURERGR/lW4pb9161bNY2dnZyxZsgTt27eHo6MjMjMzcerUKXTv3r1aiiQiIqKqqzD0MzMztZ537twZAJCbmwtzc3N06tRJ76fIERERkeFUGPq8nS4REVHdovN5+kVFRbh79y4KCwu1hrds2VLvRREREZH+6RT6Bw4cQHR0NMzMzMpc/eeLL74wSGFERESkXzqF/pYtWzBjxgz4+PgYuh4iIiIyEJ1O2TMzM4O3t7ehayEiIiID0in0hw4dik2bNiE3N9fQ9RAREZGB6LR739XVFdu2bcPevXvLvPbk+fxERERUc+kU+p9//jkCAgLg5+f33LfxIyIioppBp9DPz8/H0KFDIZPJDF0PERERGYhO3+kHBQUhPj7e0LUQERGRAem0pZ+cnIz//Oc/2LFjB+zt7bVe++STTwxSGBEREemXTqHfq1cv9OrVy9C1EBERkQHpFPpBQUEGLoOIiIgMTafQj4uLq/C1nj176q0YIiIiMhydQv/gwYNaz7Ozs3H37l20atWKoU9ERFRL6BT6CxYsKDMsLi4Of/75p94LIiIiIsPQ6ZS98gQFBVW625+IiIhqFp229NVqtdbz4uJixMfHo169egYpioiIiPRPp9AfPnx4mWEODg6YMGGC3gsiIiIiw9Ap9FevXq313NLSEra2tgYpiIiIiAxDp9B3cnIydB1ERERkYJWG/rMusSuTyfDRRx/ptSAiIiIyjEpD39/fv9zhKpUKe/bsQVFRkUGKIiIiIv2rNPSfvvBOXl4eYmJisG/fPvj5+SEkJMSgxREREZH+6PSd/oMHD7B7927s3bsX7du3x9KlS+Hi4qK3IiZOnAgrKyuYmJjA1NQU4eHhyM/Px4oVK3Dv3j04OTlh2rRpkMvlepsnERGR1FQa+sXFxfj3v/+NX375Bd7e3vjnP/+Jxo0bG6SQBQsWaJ0RsHPnTrz88ssYNGgQdu7ciZ07d2LEiBEGmTcREZEUVHpFvokTJ+KXX37BwIED0adPH+Tk5OD8+fNa/wzlxIkTCAwMBAAEBgbixIkTBpsXERGRFFS6pW9hYQEA+O9//1vu6zKZrMw5/C9q8eLFAIBXX30VwcHByMnJgUKhAAAoFArk5ubqZT5ERERSVWnoR0VFVUsRCxcuhIODA3JycrBo0SK4urrq9L7Y2FjExsYCAMLDw6FUKvVaV5pep6Z/+uzXzMxM78uvJpNSv+y17pJSv1LqFTBcvzodyGdoDg4OAAA7Ozv4+voiOTkZdnZ2yMrKgkKhQFZWVrlXAAwODkZwcLDmeUZGRrXVXBPos1+lUimp5Selftlr3SWlfqXUK6B7v7puJD/2wnfZ05fCwkIUFBRoHp89exZNmjRBx44dceDAAQDAgQMH4Ovra8wyiYiIaj2jb+nn5OTgs88+AwCUlpaie/fuaNeuHZo1a4YVK1YgLi4OSqUS06dPN3KlREREtZvRQ9/Z2RnLli0rM7x+/fq8xC8REZEeGX33PhEREVUPhj4REZFEMPSJiIgkgqFPREQkEQx9IiIiiWDoExERSQRDn4iISCIY+kRERBLB0CciIpIIhj4REZFEMPSJiIgkgqFPREQkEQx9IiIiiWDoExERSQRDn4iISCIY+kRERBLB0CciIpIIhj4REZFEMPSJiIgkgqFPREQkEQx9IiIiiWDoExERSQRDn4iISCIY+kRERBLB0CciIpIIhj4REZFEMPSJiIgkgqFPREQkEWbGnHlGRgaioqKQnZ0NmUyG4OBgvPbaa9i2bRv27dsHW1tbAMDw4cPRvn17Y5ZKRERU6xk19E1NTfHOO+/A09MTBQUFmD17Nnx8fAAAr7/+OgYOHGjM8oiIiOoUo4a+QqGAQqEAAFhbW6NRo0ZQqVTGLImIiKjOMmroPyk9PR3Xr19H8+bNcenSJezduxfx8fHw9PTEyJEjIZfLy7wnNjYWsbGxAIDw8HAolUq91pSm16npnz77NTMz0/vyq8mk1C97rbuk1K+UegUM169MCCH0PtXnVFhYiAULFmDw4MHo3LkzsrOzNd/nb926FVlZWQgLC3vmdO7cuaPXukrH1eyvF0y/3q23aSmVSmRkZOhtejWdlPplr3WXlPqVUq+A7v26uro+13SNfvR+SUkJIiIi4O/vj86dOwMA7O3tYWJiAhMTE/Tq1QtXr141cpVERES1n1FDXwiBL7/8Eo0aNUL//v01w7OysjSPjx8/jsaNGxujPCIiojrFqN/pX758GfHx8WjSpAlmzpwJ4NHpeYcPH0ZKSgpkMhmcnJwwfvx4Y5ZJRERUJxg19Fu1aoVt27aVGc5z8omIiPTP6N/pExERUfVg6BMREUkEQ5+IiEgiGPpEREQSwdAnIiKSCIY+ERGRRDD0iYiIJIKhT0REJBEMfSIiIolg6BMREUkEQ5+IiEgiGPpEREQSwdAnIiKSCIY+ERGRRDD0iYiIJMLM2AXQiysdN1Bv00rT25S0mX6920BTJiKi58UtfSIiIolg6BMREUkEQ5+IiEgiGPpEREQSwdAnIiKSCIY+ERGRRDD0iYiIJIKhT0REJBEMfSIiIolg6BMREUkEQ5+IiEgiavS190+fPo0NGzZArVajV69eGDRokLFLIiIiqrVqbOir1WqsX78e8+bNg6OjI+bMmYOOHTvCzc3N2KVRHaLPmxYZCm9aRKSbmv7zXBN+lmts6CcnJ8PFxQXOzs4AAD8/P5w4cYKhX8vU1B9CQ91VkKiuMvbPMn9m9aPGfqevUqng6Oioee7o6AiVSmXEioiIiGq3GrulL4QoM0wmk2k9j42NRWxsLAAgPDwcrq6u+i3i33/od3pERqL3n40aTEq9AtXYL38fVjtDrNsau6Xv6OiIzMxMzfPMzEwoFAqtcYKDgxEeHo7w8HCD1DB79myDTLcmklKvgLT6Za91l5T6lVKvgOH6rbGh36xZM6SmpiI9PR0lJSU4cuQIOnbsaOyyiIiIaq0au3vf1NQUY8eOxeLFi6FWq9GjRw80btzY2GURERHVWjU29AGgffv2aN++vdHmHxwcbLR5Vzcp9QpIq1/2WndJqV8p9QoYrl+ZKO+IOSIiIqpzaux3+kRERKRfNXr3vrHUlcv/Tpw4EVZWVjAxMYGpqSnCw8ORn5+PFStW4N69e3BycsK0adMgl8shhMCGDRtw6tQpWFpaIiwsDJ6engCA/fv3Y8eOHQCAwYMHIygoyIhd/WXNmjVISEiAnZ0dIiIiAECv/V27dg1RUVEoLi7GK6+8gjFjxpQ5bdSYvW7btg379u2Dra0tAGD48OGar8NiYmIQFxcHExMTjBkzBu3atQNQ8Wc7PT0dK1euRH5+Pjw8PDB58mSYmRnn10NGRgaioqKQnZ0NmUyG4OBgvPbaa3V23VbUb11cv8XFxViwYAFKSkpQWlqKLl26YMiQIRXW9/DhQ6xevRrXrl1D/fr1MXXqVDRo0OCFloExVNRvVFQUEhMTYWNjA+DR7+qmTZtWz2dZkJbS0lIxadIkcffuXfHw4UPxwQcfiFu3bhm7rBcSFhYmcnJytIZt3rxZxMTECCGEiImJEZs3bxZCCHHy5EmxePFioVarxeXLl8WcOXOEEELk5eWJiRMniry8PK3HNcGFCxfE1atXxfTp0zXD9Nnf7NmzxeXLl4VarRaLFy8WCQkJ1dzhX8rrdevWrWLXrl1lxr1165b44IMPRHFxsUhLSxOTJk0SpaWllX62IyIixKFDh4QQQqxdu1bs3bu3ehorh0qlElevXhVCCPHgwQPx/vvvi1u3btXZdVtRv3Vx/arValFQUCCEEOLhw4dizpw54vLlyxXW95///EesXbtWCCHEoUOHxPLly4UQL7YMjKGiflevXi2OHj1aZvzq+Cxz9/5Tnrz8r5mZmebyv3XFiRMnEBgYCAAIDAzU9PbHH38gICAAMpkMLVq0wP3795GVlYXTp0/Dx8cHcrkccrkcPj4+OH36tDFb0PD29oZcLtcapq/+srKyUFBQgBYtWkAmkyEgIMCon4Pyeq3IiRMn4OfnB3NzczRo0AAuLi5ITk6u8LMthMCFCxfQpUsXAEBQUJBRe1UoFJqtG2trazRq1AgqlarOrtuK+q1IbV6/MpkMVlZWAIDS0lKUlpZCJpNVWN8ff/yh2aLt0qULzp8/DyHEcy8DY6mo34pUx2eZu/efUt7lf69cuWLEiqpm8eLFAIBXX30VwcHByMnJ0VzkSKFQIDc3F8CjvpVKpeZ9jy97/PTycHBwqNGXQ9ZXf7XlMtB79+5FfHw8PD09MXLkSMjlcqhUKnh5eWnGeXKdlffZzsvLg42NDUxNTcuMb2zp6em4fv06mjdvLol1+2S/ly5dqpPrV61W4x//+Afu3r2LPn36wNnZucL6nlxXpqamsLGxQV5e3nMvA2N6ul8vLy/897//xffff48ff/wRbdq0wdtvvw1zc/Nq+Swz9J8idLj8b22xcOFCODg4ICcnB4sWLar0ko7P03dtXB7P219549c0vXv3RkhICABg69at2LRpE8LCwiqsvbZ9tgsLCxEREYHRo0drvvssT11Zt0/3W1fXr4mJCZYtW4b79+/js88+w59//lnhuBX1VJuWwdP93rx5E2+99Rbs7e1RUlKCtWvXYteuXQgJCamWzzJ37z9Fl8v/1hYODg4AADs7O/j6+iI5ORl2dnbIysoCAGRlZWkOEnJ0dERGRobmvY/7dnBw0FoeKpWqRi8PffVX3ufg8fKsKezt7WFiYgITExP06tULV69eBVD2M6xSqeDg4FDhZ7t+/fp48OABSktLtcY3ppKSEkRERMDf3x+dO3cGULfXbXn91uX1CwD16tWDt7c3rly5UmF9T/ZUWlqKBw8eQC6XP/cyqAke93v69GkoFArIZDKYm5ujR48eSE5OBlA9n2WG/lPqyuV/CwsLUVBQoHl89uxZNGnSBB07dsSBAwcAAAcOHICvry8AoGPHjoiPj4cQAklJSbCxsYFCoUC7du1w5swZ5OfnIz8/H2fOnNEcJVsT6as/hUIBa2trJCUlQQiB+Pj4Gvc5eByAAHD8+HHNFSs7duyII0eO4OHDh0hPT0dqaiqaN29e4WdbJpPhpZdewrFjxwA8OkrYmL0KIfDll1+iUaNG6N+/v2Z4XV23FfVbF9dvbm4u7t+/D+DRke3nzp1Do0aNKqyvQ4cO2L9/PwDg2LFjeOmllyCTyZ57GRhLRf0+XrePj094ct0a+rPMi/OUIyEhARs3btRc/nfw4MHGLum5paWl4bPPPgPw6C/k7t27Y/DgwcjLy8OKFSuQkZEBpVKJ6dOna057Wr9+Pc6cOQMLCwuEhYWhWbNmAIC4uDjExMQAeHSqSI8ePYzW15NWrlyJxMRE5OXlwc7ODkOGDIGvr6/e+rt69SrWrFmD4uJitGvXDmPHjjXarsLyer1w4QJSUlIgk8ng5OSE8ePHa7ZqduzYgd9++w0mJiYYPXo0XnnlFQAVf7bT0tLKnDJlbm5ulF4vXbqEjz76CE2aNNEs7+HDh8PLy6tOrtuK+j18+HCdW783btxAVFQU1Go1hBDo2rUrQkJCKqyvuLgYq1evxvXr1yGXyzF16lQ4Ozu/0DIwhor6/eSTTzTHpLi7u2P8+PGwsrKqls8yQ5+IiEgiuHufiIhIIhj6REREEsHQJyIikgiGPhERkUQw9ImIiCSCoU9UC0RFReGHH34wyryFEFizZg3GjBmDOXPmGKWGxw4ePIhFixYZtQai2oyhT/QCJk6ciHHjxqGwsFAzbN++ffj444+NV5SBXLp0CWfPnsUXX3yBJUuWlHl9//79mD9/frXU4u/vj3nz5lXLvJ524cIFhIaGGmXeRPrC0Cd6QaWlpfj111+NXcZzU6vVzzX+4/vXP75bWG31+DKvRFLGG+4QvaCBAwdi165d6NOnD+rVq6f1Wnp6OiZNmoTvv/9ec/ewjz/+GP7+/ujVqxf279+Pffv2oVmzZti/fz/kcjkmT56M1NRUbN26FQ8fPsSIESM0txUFHl3Sc+HChbhy5Qo8PDwwadIkODk5AQD+/PNPREdH49q1a7C1tcXQoUPh5+cH4NFXAxYWFsjIyEBiYiJmzpwJHx8frXpVKhW+/vprXLp0CXK5HH/7298QHByMuLg4rF+/HiUlJXjnnXcwYMAADBkypNLlMnHiRPTp0wfx8fFIS0uDn58fhg8fjjVr1uDSpUvw8vLCtGnTIJfLNcvpvffew7Zt21BYWIjhw4fD09MTX375JTIyMuDv7493330XADTLbeHChQCAM2fOIDo6GtnZ2fD398etW7cQEBBQZhkfOHAAffr0QVBQENauXYsbN25AJpOhbdu2ePfddzXr78na7927h3bt2mHixIlQq9X49NNPNcsBACIjI6FSqbBu3TqkpqbCwsIC3bt3x6hRo17k40RULbilT/SCPD098dJLL+Hnn39+ofdfuXIF7u7uiI6ORvfu3bFy5UokJydj1apVmDx5MqKjo7W+Pjh06BD+7//+D+vXr0fTpk2xatUqAI/urbBo0SJ0794d69atw5QpU7B+/XrcunVL671vvPEGNm7ciFatWpWpJTIyEo6Ojli7di1mzJiB77//HufOnUPPnj0xbtw4tGjRAps3b35m4D/2+++/Y968eYiMjMTJkyexZMkSDB8+HOvXr4darcaePXvKLIvIyEhMnToVGzduxI4dOzB//nwsX74cR48eRWJiYpl55ObmYvny5XjrrbcQHR0NV1dXJCUllZmus7Mz1q1bp7kc6xtvvIG1a9dixYoVyMzMxPbt27Xec/ToUcydOxdRUVG4efMm9u/fDysrK8ydOxcKhQKbN2/G5s2b4eDggA0bNuC1117Dxo0b8fnnn6Nr1646LR8iY2HoE1XBkCFDsGfPHs11tJ9HgwYN0KNHD5iYmMDPzw+ZmZkICQmBubk52rZtCzMzM9y9e1czfvv27eHt7Q1zc3MMHz4cSUlJyMjIQEJCApycnNCjRw+YmprC09MTnTt31tzABAB8fX3RqlUrmJiYwMLCQquOjIwMXLp0CW+//TYsLCzQtGlT9OrVC/Hx8S+8XPr27Qt7e3s4ODigVatWaN68OTw8PGBubo5OnTrh+vXrWuOHhITAwsICbdu2haWlJbp37w47OzvN+58eHwBOnToFNzc3dO7cGaampujXrx/s7e21xlEoFOjXrx9MTU1hYWEBFxcX+Pj4wNzcHLa2tnj99dfL/EHRr18/ODg4QC6Xo0OHDkhJSamwz8frKDc3F1ZWVmjRosULLzOi6sDd+0RV0KRJE3To0AE7d+5Eo0aNnuu9dnZ2msePg/jJ0LKwsNDa0nd0dNQ8trKyglwuR1ZWFu7du4crV65g9OjRmtdLS0sREBBQ7nuflpWVBblcDmtra80wpVKpuZXri3i6t6efFxUVPdf4Ty6HJ+t+si+ZTFbmtqJKpVLreU5ODjZs2ICLFy+isLAQarUacrlca5yn14FKpaqwz9DQUGzduhXTpk1DgwYNEBISgg4dOlQ4PpGxMfSJqmjIkCH4xz/+oXVb1McHvRUVFcHGxgYAkJ2dXaX5PHnf7MLCQuTn52vuqe3t7V3pEfSV3XVLoVAgPz8fBQUFmuDPyMioEfdcr4y9vb1WIAshKg1oAPjuu+8AAJ999hnq16+P48ePIzo6Wqf5lbcMGzZsiKlTp0KtVuP48eNYvnw51q9fX+sPeqS6i7v3iarIxcUFXbt21fqe2tbWFg4ODjh48CDUajXi4uKQlpZWpfmcOnUKly5dQklJCX744Qd4eXlBqVSiQ4cOSE1NRXx8PEpKSlBSUoLk5GTcvn1bp+kqlUq0bNkS3333HYqLi3Hjxg389ttv8Pf3r1K9hta+fXvcvHkTx48fR2lpKfbu3fvMP6wKCgpgZWWFevXqQaVSPdfxGHZ2dsjLy8ODBw80w+Lj45GbmwsTExPNH3cmJvy1SjUXt/SJ9CAkJAQHDx7UGjZhwgSsW7cO33//PXr27Fnl73u7deuG7du3IykpCZ6ennj//fcBANbW1pg3bx42btyIjRs3QggBd3f35zqKfMqUKfj6668xYcIEyOVyvPnmm2WO8K9pbG1tMX36dGzYsAFRUVHw9/eHp6dnpfeJf/PNN7F69WqMGjUKLi4uCAgIwL///W+d5teoUSN069YNkyZNglqtxvLly3H69Gls2rQJRUVFcHJywpQpU8ocM0FUk8iEEMLYRRARVZVarcZ7772HyZMno02bNsYuh6hG4n4oIqq1Tp8+jfv37+Phw4eIiYmBEIJH0BNVgrv3iajWSkpKwqpVq1BSUgI3NzfMnDmTu9eJKsHd+0RERBLB3ftEREQSwdAnIiKSCIY+ERGRRDD0iYiIJIKhT0REJBEMfSIiIon4f3fA5Efb5j8qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_can['2013'].plot(kind='hist', figsize=(8, 5))   # bins=10 by default이고, 설정할 수 있음!\n",
    "\n",
    "plt.title('Histogram of Immigration in 2013')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "plt.ylabel('Number of Countries')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "In the above plot, the x-axis represents the population range of immigrants in intervals of 3412.9. The y-axis represents the number of countries that contributed to the aforementioned population. \n",
    "\n",
    "Notice that the x-axis ticks do not match with the bin size. This can be fixed by passing in a `xticks` keyword that contains the list of the bin sizes, as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bin range와 xtick 맞추기\n",
    "count, bin_edges = np.histogram(df_can['2013'], bins=6)     \n",
    "\n",
    "df_can['2013'].plot(kind='hist', figsize=(8, 5), xticks=bin_edges, bins=6)    \n",
    "\n",
    "plt.title('Histogram Immigration in 2013')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "plt.ylabel('Number of Countries')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "_Side Note:_ We could use `df_can['2013'].plot.hist()`, instead. In fact, throughout this lesson, using `some_data.plot(kind='type_plot', ...)` is equivalent to `some_data.plot.type_plot(...)`. That is, passing the type of the plot as argument or method behaves the same. \n",
    "\n",
    "See the _pandas_ documentation for more info  [http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.html](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.html?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fcb2ba7508>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Side Note\n",
    "df_can['2013'].plot.hist(bins=6, xticks=bin_edges)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can also plot multiple histograms on the same plot. For example, let's try to answer the following questions using a histogram.\n",
    "\n",
    "**Question**: What is the immigration distribution for Denmark, Norway, and Sweden for years 1980 - 2013?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fcb0f79288>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# multiple histograms\n",
    "df_can.loc[['Denmark', 'Norway', 'Sweden'], years].plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "That does not look right! \n",
    "\n",
    "Don't worry, you'll often come across situations like this when creating plots. The solution often lies in how the underlying dataset is structured.\n",
    "\n",
    "Instead of plotting the frequency distribution of the population for the 3 countries, _pandas_ instead plotted the frequency distribution for the `years`. **Histogram은 세로축 값을 이용하여 bin range를 설정!!!**\n",
    "\n",
    "This can be easily fixed by first transposing the dataset, and then plotting as shown below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Country</th>\n",
       "      <th>Denmark</th>\n",
       "      <th>Norway</th>\n",
       "      <th>Sweden</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>272</td>\n",
       "      <td>116</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>293</td>\n",
       "      <td>77</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>299</td>\n",
       "      <td>106</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>106</td>\n",
       "      <td>51</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>93</td>\n",
       "      <td>31</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Denmark  Norway  Sweden\n",
       "1980         272     116     281\n",
       "1981         293      77     308\n",
       "1982         299     106     222\n",
       "1983         106      51     176\n",
       "1984          93      31     128"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transpose dataframe\n",
    "df_t = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
    "df_t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate histogram\n",
    "df_t.plot(kind='hist', figsize=(10, 6))\n",
    "\n",
    "plt.title('Histogram of Immigration from Denmark, Norway, and Sweden')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "plt.ylabel('Number of Years')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's make a few modifications to improve the impact and aesthetics of the previous plot:\n",
    "\n",
    "-   increase the bin size to 15 by passing in `bins` parameter\n",
    "-   set transparency to 60% by passing in `alpha` paramemter   **--> 막대가 겹치는 부분 확인 가능**\n",
    "-   re-label the x-ticks by passing in `xticks` paramater\n",
    "-   change the colors of the plots by passing in `color` parameter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5 13 21 14  9 10  5  9  7  3  1  0  0  2  3] [ 31.          49.46666667  67.93333333  86.4        104.86666667\n",
      " 123.33333333 141.8        160.26666667 178.73333333 197.2\n",
      " 215.66666667 234.13333333 252.6        271.06666667 289.53333333\n",
      " 308.        ]\n",
      "<class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# x-tick values\n",
    "count, bin_edges = np.histogram(df_t, bins=15)\n",
    "print(count, type(count))                 # count: 15개의 bin마다 몇개가 속하는지\n",
    "print(bin_edges, type(bin_edges))         # bin_edges: 양끝 포함하여 16개의 ticks\n",
    "\n",
    "# # unstacked histogram\n",
    "# df_t.plot(kind='hist', figsize=(10, 6), bins=15, alpha=0.5, xticks=bin_edges, color=['coral', 'darkslateblue', 'mediumseagreen'])\n",
    "\n",
    "# plt.title('Histogram of 3 Northen European Contries')\n",
    "# plt.xlabel('# of Immigrants')\n",
    "# plt.ylabel('# of Years')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Tip:\n",
    "For a full listing of colors available in Matplotlib, run the following code in your python shell:\n",
    "\n",
    "```python\n",
    "import matplotlib\n",
    "for name, hex in matplotlib.colors.cnames.items():\n",
    "    print(name, hex)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aliceblue #F0F8FF\n",
      "antiquewhite #FAEBD7\n",
      "aqua #00FFFF\n",
      "aquamarine #7FFFD4\n",
      "azure #F0FFFF\n",
      "beige #F5F5DC\n",
      "bisque #FFE4C4\n",
      "black #000000\n",
      "blanchedalmond #FFEBCD\n",
      "blue #0000FF\n",
      "blueviolet #8A2BE2\n",
      "brown #A52A2A\n",
      "burlywood #DEB887\n",
      "cadetblue #5F9EA0\n",
      "chartreuse #7FFF00\n",
      "chocolate #D2691E\n",
      "coral #FF7F50\n",
      "cornflowerblue #6495ED\n",
      "cornsilk #FFF8DC\n",
      "crimson #DC143C\n",
      "cyan #00FFFF\n",
      "darkblue #00008B\n",
      "darkcyan #008B8B\n",
      "darkgoldenrod #B8860B\n",
      "darkgray #A9A9A9\n",
      "darkgreen #006400\n",
      "darkgrey #A9A9A9\n",
      "darkkhaki #BDB76B\n",
      "darkmagenta #8B008B\n",
      "darkolivegreen #556B2F\n",
      "darkorange #FF8C00\n",
      "darkorchid #9932CC\n",
      "darkred #8B0000\n",
      "darksalmon #E9967A\n",
      "darkseagreen #8FBC8F\n",
      "darkslateblue #483D8B\n",
      "darkslategray #2F4F4F\n",
      "darkslategrey #2F4F4F\n",
      "darkturquoise #00CED1\n",
      "darkviolet #9400D3\n",
      "deeppink #FF1493\n",
      "deepskyblue #00BFFF\n",
      "dimgray #696969\n",
      "dimgrey #696969\n",
      "dodgerblue #1E90FF\n",
      "firebrick #B22222\n",
      "floralwhite #FFFAF0\n",
      "forestgreen #228B22\n",
      "fuchsia #FF00FF\n",
      "gainsboro #DCDCDC\n",
      "ghostwhite #F8F8FF\n",
      "gold #FFD700\n",
      "goldenrod #DAA520\n",
      "gray #808080\n",
      "green #008000\n",
      "greenyellow #ADFF2F\n",
      "grey #808080\n",
      "honeydew #F0FFF0\n",
      "hotpink #FF69B4\n",
      "indianred #CD5C5C\n",
      "indigo #4B0082\n",
      "ivory #FFFFF0\n",
      "khaki #F0E68C\n",
      "lavender #E6E6FA\n",
      "lavenderblush #FFF0F5\n",
      "lawngreen #7CFC00\n",
      "lemonchiffon #FFFACD\n",
      "lightblue #ADD8E6\n",
      "lightcoral #F08080\n",
      "lightcyan #E0FFFF\n",
      "lightgoldenrodyellow #FAFAD2\n",
      "lightgray #D3D3D3\n",
      "lightgreen #90EE90\n",
      "lightgrey #D3D3D3\n",
      "lightpink #FFB6C1\n",
      "lightsalmon #FFA07A\n",
      "lightseagreen #20B2AA\n",
      "lightskyblue #87CEFA\n",
      "lightslategray #778899\n",
      "lightslategrey #778899\n",
      "lightsteelblue #B0C4DE\n",
      "lightyellow #FFFFE0\n",
      "lime #00FF00\n",
      "limegreen #32CD32\n",
      "linen #FAF0E6\n",
      "magenta #FF00FF\n",
      "maroon #800000\n",
      "mediumaquamarine #66CDAA\n",
      "mediumblue #0000CD\n",
      "mediumorchid #BA55D3\n",
      "mediumpurple #9370DB\n",
      "mediumseagreen #3CB371\n",
      "mediumslateblue #7B68EE\n",
      "mediumspringgreen #00FA9A\n",
      "mediumturquoise #48D1CC\n",
      "mediumvioletred #C71585\n",
      "midnightblue #191970\n",
      "mintcream #F5FFFA\n",
      "mistyrose #FFE4E1\n",
      "moccasin #FFE4B5\n",
      "navajowhite #FFDEAD\n",
      "navy #000080\n",
      "oldlace #FDF5E6\n",
      "olive #808000\n",
      "olivedrab #6B8E23\n",
      "orange #FFA500\n",
      "orangered #FF4500\n",
      "orchid #DA70D6\n",
      "palegoldenrod #EEE8AA\n",
      "palegreen #98FB98\n",
      "paleturquoise #AFEEEE\n",
      "palevioletred #DB7093\n",
      "papayawhip #FFEFD5\n",
      "peachpuff #FFDAB9\n",
      "peru #CD853F\n",
      "pink #FFC0CB\n",
      "plum #DDA0DD\n",
      "powderblue #B0E0E6\n",
      "purple #800080\n",
      "rebeccapurple #663399\n",
      "red #FF0000\n",
      "rosybrown #BC8F8F\n",
      "royalblue #4169E1\n",
      "saddlebrown #8B4513\n",
      "salmon #FA8072\n",
      "sandybrown #F4A460\n",
      "seagreen #2E8B57\n",
      "seashell #FFF5EE\n",
      "sienna #A0522D\n",
      "silver #C0C0C0\n",
      "skyblue #87CEEB\n",
      "slateblue #6A5ACD\n",
      "slategray #708090\n",
      "slategrey #708090\n",
      "snow #FFFAFA\n",
      "springgreen #00FF7F\n",
      "steelblue #4682B4\n",
      "tan #D2B48C\n",
      "teal #008080\n",
      "thistle #D8BFD8\n",
      "tomato #FF6347\n",
      "turquoise #40E0D0\n",
      "violet #EE82EE\n",
      "wheat #F5DEB3\n",
      "white #FFFFFF\n",
      "whitesmoke #F5F5F5\n",
      "yellow #FFFF00\n",
      "yellowgreen #9ACD32\n"
     ]
    }
   ],
   "source": [
    "for name, hex in mpl.colors.cnames.items() :\n",
    "    print(name, hex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "If we do no want the plots to overlap each other, we can stack them using the `stacked` paramemter. Let's also adjust the min and max x-axis ticks to remove the extra gap on the edges of the plot. We can pass a tuple (min, max) using the `xlim` paramater, as show below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count, bin_edges = np.histogram(df_t, 15)\n",
    "\n",
    "# get buffer ± 10 on each end\n",
    "xmin = bin_edges[0] - 10\n",
    "xmax = bin_edges[-1] + 10          # 가장 마지막 값의 index=-1\n",
    "\n",
    "# stacked histogram\n",
    "df_t.plot(kind='hist', figsize=(10, 6), bins=15, alpha=0.7, xticks=bin_edges, \n",
    "          color=['coral', 'darkslateblue', 'mediumseagreen'],\n",
    "          stacked=True, xlim=(xmin, xmax))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question**: Use the scripting layer to display the immigration distribution for Greece, Albania, and Bulgaria for years 1980 - 2013. Use an overlapping plot with 15 bins and a transparency value of 0.35.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Greece, Albania, and Bulgaria 1980-2013년만 추출\n",
    "gab = df_can.loc[['Greece', 'Albania', 'Bulgaria'], years].T\n",
    "\n",
    "# Numpy histogram\n",
    "count, bin_edges = np.histogram(gab, bins=20)\n",
    "\n",
    "# Stacked histogram\n",
    "gab.plot(kind='hist', figsize=(15, 8), stacked=True, \n",
    "         bins=20, xticks=bin_edges, xlim=(bin_edges[0] - 15, bin_edges[-1] + 15),\n",
    "         color=['aliceblue', 'bisque', 'thistle'], alpha=1) \n",
    "\n",
    "plt.title('Immigration Histogram of Greece, Albania, and Bulgaria')\n",
    "plt.xlabel('# of Immigrants')\n",
    "plt.ylabel('# of Years')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    \n",
    "    # create a dataframe of the countries of interest (cof)\n",
    "    df_cof = df_can.loc[['Greece', 'Albania', 'Bulgaria'], years]\n",
    "\n",
    "    # transpose the dataframe\n",
    "    df_cof = df_cof.transpose() \n",
    "\n",
    "    # let's get the x-tick values\n",
    "    count, bin_edges = np.histogram(df_cof, 15)\n",
    "\n",
    "    # Un-stacked Histogram\n",
    "    df_cof.plot(kind ='hist',\n",
    "                figsize=(10, 6),\n",
    "                bins=15,\n",
    "                alpha=0.35,\n",
    "                xticks=bin_edges,\n",
    "                color=['coral', 'darkslateblue', 'mediumseagreen']\n",
    "                )\n",
    "\n",
    "    plt.title('Histogram of Immigration from Greece, Albania, and Bulgaria from 1980 - 2013')\n",
    "    plt.ylabel('Number of Years')\n",
    "    plt.xlabel('Number of Immigrants')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Bar Charts (Dataframe) <a id=\"10\"></a>\n",
    "\n",
    "A bar plot is a way of representing data where the _length_ of the bars represents the magnitude/size of the feature/variable. Bar graphs usually represent numerical and categorical variables grouped in intervals. \n",
    "\n",
    "To create a bar plot, we can pass one of two arguments via `kind` parameter in `plot()`:\n",
    "\n",
    "-   `kind=bar` creates a _vertical_ bar plot\n",
    "-   `kind=barh` creates a _horizontal_ bar plot\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Vertical bar plot\n",
    "\n",
    "In vertical bar graphs, the x-axis is used for labelling, and the length of bars on the y-axis corresponds to the magnitude of the variable being measured. Vertical bar graphs are particuarly useful in analyzing time series data. One disadvantage is that they lack space for text labelling at the foot of each bar. \n",
    "\n",
    "**Let's start off by analyzing the effect of Iceland's Financial Crisis:**\n",
    "\n",
    "The 2008 - 2011 Icelandic Financial Crisis was a major economic and political event in Iceland. Relative to the size of its economy, Iceland's systemic banking collapse was the largest experienced by any country in economic history. The crisis led to a severe economic depression in 2008 - 2011 and significant political unrest.\n",
    "\n",
    "**Question:** Let's compare the number of Icelandic immigrants (country = 'Iceland') to Canada from year 1980 to 2013. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980    17\n",
       "1981    33\n",
       "1982    10\n",
       "1983     9\n",
       "1984    13\n",
       "Name: Iceland, dtype: object"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# step 1: get the data\n",
    "df_ice = df_can.loc['Iceland', years]     # 하나의 열과 행을 추출 시, series를 반환\n",
    "df_ice.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# step 2: plot data\n",
    "df_ice.plot(kind='bar', figsize=(10, 6))\n",
    "\n",
    "plt.title('Icelandic Immigrants to Canada')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('# of Immigrants')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The bar plot above shows the total number of immigrants broken down by each year. We can clearly see the impact of the financial crisis; the number of immigrants to Canada started increasing rapidly after 2008. \n",
    "\n",
    "Let's annotate this on the plot using the `annotate` method of the **scripting layer** or the **pyplot interface**. We will pass in the following parameters:\n",
    "\n",
    "-   `s`: str, the text of annotation.\n",
    "-   `xy`: Tuple specifying the (x,y) point to annotate (in this case, end point of arrow).\n",
    "-   `xytext`: Tuple specifying the (x,y) point to place the text (in this case, start point of arrow).\n",
    "-   `xycoords`: The coordinate system that xy is given in - 'data' uses the coordinate system of the object being annotated (default).\n",
    "-   `arrowprops`: Takes a dictionary of properties to draw the arrow:\n",
    "    -   `arrowstyle`: Specifies the arrow style, `'->'` is standard arrow.\n",
    "    -   `connectionstyle`: Specifies the connection type. `arc3` is a straight line.\n",
    "    -   `color`: Specifes color of arror.\n",
    "    -   `lw`: Specifies the line width.\n",
    "\n",
    "I encourage you to read the Matplotlib documentation for more details on annotations: \n",
    "[http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.annotate](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.annotate?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ice.plot(kind='bar', figsize=(10, 6), rot=90)    # rot: 반시계 방향으로 회전(여기서는 xticks 회전)\n",
    "\n",
    "plt.title('Icelandic Immigrants')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('# of Immigrants')\n",
    "\n",
    "# Annotate arrow\n",
    "plt.annotate('Increase',           # text 넣지 않아도 됨\n",
    "             xy=(32, 70),          # annotating 하는 부분. 화살표 있는 경우, 화살표 머리가 가르키는 부분 (year 2012, pop 70)\n",
    "             xytext=(28, 20),      # text가 들어가는 부분. 화살표 있는 경우, 화살표 시작 부분 (year 2008, pop 20)\n",
    "             xycoords='data',      # data가 있는 좌표 이용\n",
    "             arrowprops=dict(arrowstyle='->', connectionstyle='arc3', color='burlywood', lw=2))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's also annotate a text to go over the arrow.  We will pass in the following additional parameters:\n",
    "\n",
    "-   `rotation`: rotation angle of text in degrees (counter clockwise)\n",
    "-   `va`: vertical alignment of text [‘top’ | 'center'| ‘bottom’ | ‘baseline’]\n",
    "-   `ha`: horizontal alignment of text [‘left’ | ‘center’ | ‘right’]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ice.plot(kind='bar', figsize=(10, 6), rot=60)\n",
    "\n",
    "plt.title('Icelandic Immigrants')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('# of Immigrants')\n",
    "\n",
    "# Annotate arrow only\n",
    "plt.annotate('', xy=(32, 70), xytext=(28, 20), xycoords='data',\n",
    "             arrowprops=dict(arrowstyle='-|>', connectionstyle='arc3', color='coral', lw=2))\n",
    "\n",
    "# Annotate text only\n",
    "plt.annotate('2008-2011 Financial Crisis', \n",
    "             xy=(28, 30),         # text 시작점 (year 2008, pop 30)\n",
    "             rotation=72.5,       # 반시계 방향으로 글자 회전\n",
    "             va='baseline',       # xy 위치가 글자의 top/bottom에 위치\n",
    "             ha='left')           # xy 위치가 글자의 left에 위치\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Horizontal Bar Plot\n",
    "\n",
    "Sometimes it is more practical to represent the data horizontally, especially if you need more room for labelling the bars. In horizontal bar graphs, the y-axis is used for labelling, and the length of bars on the x-axis corresponds to the magnitude of the variable being measured. As you will see, there is more room on the y-axis to  label categetorical variables.\n",
    "\n",
    "**Question:** Using the scripting layer and the `df_can` dataset, create a _horizontal_ bar plot showing the _total_ number of immigrants to Canada from the top 15 countries, for the period 1980 - 2013. Label each country with the total immigrant count.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the data pertaining to the top 15 countries.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "button": false,
    "collapsed": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "India                                                   691904\n",
       "China                                                   659962\n",
       "United Kingdom of Great Britain and Northern Ireland    551500\n",
       "Philippines                                             511391\n",
       "Pakistan                                                241600\n",
       "United States of America                                241122\n",
       "Iran (Islamic Republic of)                              175923\n",
       "Sri Lanka                                               148358\n",
       "Republic of Korea                                       142581\n",
       "Poland                                                  139241\n",
       "Lebanon                                                 115359\n",
       "France                                                  109091\n",
       "Jamaica                                                 106431\n",
       "Viet Nam                                                 97146\n",
       "Romania                                                  93585\n",
       "Name: Total, dtype: int64"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top15 = df_can['Total'].head(15)\n",
    "top15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    \n",
    "    # sort dataframe on 'Total' column (descending)\n",
    "    df_can.sort_values(by='Total', ascending=True, inplace=True)\n",
    "\n",
    "    # get top 15 countries\n",
    "    df_top15 = df_can['Total'].tail(15)\n",
    "    df_top15\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot data:\n",
    "\n",
    "1.  Use `kind='barh'` to generate a bar chart with horizontal bars.\n",
    "2.  Make sure to choose a good size for the plot and to label your axes and to give the plot a title.\n",
    "3.  Loop through the countries and annotate the immigrant population using the anotate function of the scripting interface.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top15.plot(kind='barh', figsize=(20,10), color='teal', alpha=0.8,\n",
    "           xlim=(0, 800000))\n",
    "\n",
    "plt.title('Immigration to Canada of Top 15 Countires')\n",
    "plt.xlabel('# of Total Immigrants')\n",
    "plt.ylabel('Countires')\n",
    "\n",
    "# Annotation\n",
    "for index, value in enumerate(top15) :\n",
    "    text = format(value, ',')\n",
    "    \n",
    "    plt.annotate(text, xy=(value-60000, index-0.2), color='cornsilk', fontsize=18)    # x좌표: value, y좌표: index\n",
    "   \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    \n",
    "    # generate plot\n",
    "    df_top15.plot(kind='barh', figsize=(12, 12), color='steelblue')\n",
    "    plt.xlabel('Number of Immigrants')\n",
    "    plt.title('Top 15 Conuntries Contributing to the Immigration to Canada between 1980 - 2013')\n",
    "\n",
    "    # annotate value labels to each country\n",
    "    for index, value in enumerate(df_top15): \n",
    "        label = format(int(value), ',')         # format int with commas\n",
    "    \n",
    "    # place text at the end of bar (subtracting 47000 from x, and 0.1 from y to make it fit within the bar)\n",
    "        plt.annotate(label, xy=(value - 47000, index - 0.10), color='white')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "## Author\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/aklson/\" target=\"_blank\">Alex Aklson</a>\n",
    "\n",
    "### Other Contributors\n",
    "\n",
    "[Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n",
    "[Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n",
    "[Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork-20297740&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description                 |\n",
    "| ----------------- | ------- | ------------- | ---------------------------------- |\n",
    "| 2021-01-20        | 2.3     | Lakshmi Holla | Changed TOC cell markdown          |\n",
    "| 2021-01-05        | 2.2     | Lakshmi Holla | Changed solution code for annotate |\n",
    "| 2020-11-03        | 2.1     | Lakshmi Holla | Changed the URL of excel file      |\n",
    "| 2020-08-27        | 2.0     | Lavanya       | Moved lab to course repo in GitLab |\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
 ],
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