1. Introduction to Matplotlib and Line Plots.ipynb

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   "source": [
    "<h1 align=center><font size = 5>Introduction to Matplotlib and Line Plots</font></h1>"
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    "## Introduction\n",
    "\n",
    "The aim of these labs is to introduce you to data visualization with Python as concrete and as consistent as possible. \n",
    "Speaking of consistency, because there is no *best* data visualization library avaiblable for Python - up to creating these labs - we have to introduce different libraries and show their benefits when we are discussing new visualization concepts. Doing so, we hope to make students well-rounded with visualization libraries and concepts so that they are able to judge and decide on the best visualization technique and tool for a given problem _and_ audience.\n",
    "\n",
    "Please make sure that you have completed the prerequisites for this course, namely [**Python for Data Science**](https://cognitiveclass.ai/courses/python-for-data-science/).\n",
    "\n",
    "**Note**: The majority of the plots and visualizations will be generated using data stored in *pandas* dataframes. Therefore, in this lab, we provide a brief crash course on *pandas*. However, if you are interested in learning more about the *pandas* library, detailed description and explanation of how to use it and how to clean, munge, and process data stored in a *pandas* dataframe are provided in our course [**Data Analysis with Python**](https://cognitiveclass.ai/courses/data-analysis-python/).\n",
    "\n",
    "------------"
   ]
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    "## 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",
    "1.1 [The Dataset: Immigration to Canada from 1980 to 2013](#2)<br>\n",
    "1.2 [*pandas* Basics](#4) <br>\n",
    "1.3 [*pandas* Intermediate: Indexing and Selection](#6) <br>\n",
    "2. [Visualizing Data using Matplotlib](#8) <br>\n",
    "2.1 [Matplotlib: Standard Python Visualization Library](#10) <br>\n",
    "3. [Line Plots](#12)\n",
    "</div>\n",
    "<hr>"
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    "# Exploring Datasets with *pandas* <a id=\"0\"></a>\n",
    "\n",
    "*pandas* is an essential data analysis toolkit for Python. From their [website](http://pandas.pydata.org/):\n",
    ">*pandas* is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, **real world** data analysis in Python.\n",
    "\n",
    "The course heavily relies on *pandas* for data wrangling, analysis, and visualization. We encourage you to spend some time and  familiarize yourself with the *pandas* API Reference: http://pandas.pydata.org/pandas-docs/stable/api.html."
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   "source": [
    "## The Dataset: Immigration to Canada from 1980 to 2013 <a id=\"2\"></a>"
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   "source": [
    "Dataset Source: [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml).\n",
    "\n",
    "The dataset contains annual data on the flows of international immigrants 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. The current version presents data pertaining to 45 countries.\n",
    "\n",
    "In this lab, we will focus on the Canadian immigration data.\n",
    "\n",
    "<img src = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/coursera/Images/Mod1Fig1-Dataset.png\" align=\"center\" width=900>\n",
    "\n",
    "For sake of simplicity, Canada's immigration data has been extracted and uploaded to one of IBM servers. You can fetch the data from [here](https://ibm.box.com/shared/static/lw190pt9zpy5bd1ptyg2aw15awomz9pu.xlsx).\n",
    "\n",
    "---"
   ]
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   "source": [
    "## *pandas* Basics<a id=\"4\"></a>"
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   "source": [
    "The first thing we'll do is import two key data analysis modules: *pandas* and **Numpy**."
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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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    "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",
    "!conda install -c anaconda xlrd --yes\n",
    "```"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [
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     "text": [
      "Requirement already satisfied: xlrd in c:\\users\\sori-\\anaconda3\\lib\\site-packages (1.2.0)\n",
      "xlrd installed!\n"
     ]
    }
   ],
   "source": [
    "!pip install xlrd\n",
    "print('xlrd installed!')"
   ]
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    "Now we are ready to read in our data."
   ]
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     "text": [
      "excel fetched!\n"
     ]
    }
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    "df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n",
    "                      sheet_name='Canada by Citizenship',\n",
    "                      skiprows=range(20),          # skiprows=0, 1, 2,..., 19행 삭제. 첫째줄을 0행으로 인식첫째줄을 0행으로 인식\n",
    "                      skipfooter=2)                # 엑셀 내 마지막 상관없는 2줄 삭제\n",
    "\n",
    "print('excel fetched!')"
   ]
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    "Let's view the top 5 rows of the dataset using the `head()` function."
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    "df_can.head()\n",
    "# tip: You can specify the number of rows you'd like to see as follows: df_can.head(10) "
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    "df_can.dtypes     # 년도별 이민자 수는 모두 int"
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    "We can also veiw the bottom 5 rows of the dataset using the `tail()` function."
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       "      <td>Western Asia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>124</td>\n",
       "      <td>161</td>\n",
       "      <td>140</td>\n",
       "      <td>122</td>\n",
       "      <td>133</td>\n",
       "      <td>128</td>\n",
       "      <td>211</td>\n",
       "      <td>160</td>\n",
       "      <td>174</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>910</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>91</td>\n",
       "      <td>77</td>\n",
       "      <td>71</td>\n",
       "      <td>64</td>\n",
       "      <td>60</td>\n",
       "      <td>102</td>\n",
       "      <td>69</td>\n",
       "      <td>46</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>910</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>1450</td>\n",
       "      <td>615</td>\n",
       "      <td>454</td>\n",
       "      <td>663</td>\n",
       "      <td>611</td>\n",
       "      <td>508</td>\n",
       "      <td>494</td>\n",
       "      <td>434</td>\n",
       "      <td>437</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Type    Coverage          OdName  AREA AreaName  REG  \\\n",
       "190  Immigrants  Foreigners        Viet Nam   935     Asia  920   \n",
       "191  Immigrants  Foreigners  Western Sahara   903   Africa  912   \n",
       "192  Immigrants  Foreigners           Yemen   935     Asia  922   \n",
       "193  Immigrants  Foreigners          Zambia   903   Africa  910   \n",
       "194  Immigrants  Foreigners        Zimbabwe   903   Africa  910   \n",
       "\n",
       "                RegName  DEV             DevName  1980  ...  2004  2005  2006  \\\n",
       "190  South-Eastern Asia  902  Developing regions  1191  ...  1816  1852  3153   \n",
       "191     Northern Africa  902  Developing regions     0  ...     0     0     1   \n",
       "192        Western Asia  902  Developing regions     1  ...   124   161   140   \n",
       "193      Eastern Africa  902  Developing regions    11  ...    56    91    77   \n",
       "194      Eastern Africa  902  Developing regions    72  ...  1450   615   454   \n",
       "\n",
       "     2007  2008  2009  2010  2011  2012  2013  \n",
       "190  2574  1784  2171  1942  1723  1731  2112  \n",
       "191     0     0     0     0     0     0     0  \n",
       "192   122   133   128   211   160   174   217  \n",
       "193    71    64    60   102    69    46    59  \n",
       "194   663   611   508   494   434   437   407  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "When analyzing a dataset, it's always a good idea to start by getting basic information about your dataframe. We can do this by using the `info()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195 entries, 0 to 194\n",
      "Data columns (total 43 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Type      195 non-null    object\n",
      " 1   Coverage  195 non-null    object\n",
      " 2   OdName    195 non-null    object\n",
      " 3   AREA      195 non-null    int64 \n",
      " 4   AreaName  195 non-null    object\n",
      " 5   REG       195 non-null    int64 \n",
      " 6   RegName   195 non-null    object\n",
      " 7   DEV       195 non-null    int64 \n",
      " 8   DevName   195 non-null    object\n",
      " 9   1980      195 non-null    int64 \n",
      " 10  1981      195 non-null    int64 \n",
      " 11  1982      195 non-null    int64 \n",
      " 12  1983      195 non-null    int64 \n",
      " 13  1984      195 non-null    int64 \n",
      " 14  1985      195 non-null    int64 \n",
      " 15  1986      195 non-null    int64 \n",
      " 16  1987      195 non-null    int64 \n",
      " 17  1988      195 non-null    int64 \n",
      " 18  1989      195 non-null    int64 \n",
      " 19  1990      195 non-null    int64 \n",
      " 20  1991      195 non-null    int64 \n",
      " 21  1992      195 non-null    int64 \n",
      " 22  1993      195 non-null    int64 \n",
      " 23  1994      195 non-null    int64 \n",
      " 24  1995      195 non-null    int64 \n",
      " 25  1996      195 non-null    int64 \n",
      " 26  1997      195 non-null    int64 \n",
      " 27  1998      195 non-null    int64 \n",
      " 28  1999      195 non-null    int64 \n",
      " 29  2000      195 non-null    int64 \n",
      " 30  2001      195 non-null    int64 \n",
      " 31  2002      195 non-null    int64 \n",
      " 32  2003      195 non-null    int64 \n",
      " 33  2004      195 non-null    int64 \n",
      " 34  2005      195 non-null    int64 \n",
      " 35  2006      195 non-null    int64 \n",
      " 36  2007      195 non-null    int64 \n",
      " 37  2008      195 non-null    int64 \n",
      " 38  2009      195 non-null    int64 \n",
      " 39  2010      195 non-null    int64 \n",
      " 40  2011      195 non-null    int64 \n",
      " 41  2012      195 non-null    int64 \n",
      " 42  2013      195 non-null    int64 \n",
      "dtypes: int64(37), object(6)\n",
      "memory usage: 65.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_can.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "To get the list of column headers we can call upon the dataframe's `.columns` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Type', 'Coverage', 'OdName', 'AREA', 'AreaName', 'REG', 'RegName',\n",
       "       'DEV', 'DevName', 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,\n",
       "       1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
       "       1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
       "       2010, 2011, 2012, 2013], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Similarly, to get the list of indicies we use the `.index` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "        39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "       104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "       117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "       130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
       "       143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
       "       156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
       "       169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.index.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Note: The default type of index and columns is NOT list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.indexes.base.Index'>\n",
      "<class 'pandas.core.indexes.range.RangeIndex'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df_can.columns))\n",
    "print(type(df_can.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "To get the index and columns as lists, we can use the `tolist()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "df_can.columns.tolist()\n",
    "df_can.index.tolist()\n",
    "\n",
    "print(type(df_can.columns.tolist()))\n",
    "print(type(df_can.index.tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "To view the dimensions of the dataframe, we use the `.shape` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 43)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# size of dataframe (rows, columns)\n",
    "df_can.shape    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Note: The main dtypes stored in *pandas* objects are *float*, *int*, *bool*, *datetime64[ns]* and *datetime64[ns, tz] (in >= 0.17.0)*, *timedelta[ns]*, *category (in >= 0.15.0)*, and *object* (string). In addition, these dtypes have item sizes, e.g. int64 and int32. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's clean the data set to remove a few unnecessary columns. We can use *pandas* `drop()` method as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>OdName</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>RegName</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": [
       "           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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in pandas axis=0 represents rows (default) and axis=1 represents columns.\n",
    "df_can.drop(columns = ['Type', 'Coverage', 'AREA', 'REG', 'DEV'], inplace=True)\n",
    "df_can.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's rename the columns so that they make sense. We can use `rename()` method by passing in a dictionary of old and new names as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([  'Country', 'Continent',    'Region',   'DevName',        1980,\n",
       "              1981,        1982,        1983,        1984,        1985,\n",
       "              1986,        1987,        1988,        1989,        1990,\n",
       "              1991,        1992,        1993,        1994,        1995,\n",
       "              1996,        1997,        1998,        1999,        2000,\n",
       "              2001,        2002,        2003,        2004,        2005,\n",
       "              2006,        2007,        2008,        2009,        2010,\n",
       "              2011,        2012,        2013],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True)\n",
    "df_can.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We will also add a 'Total' column that sums up the total immigrants by country over the entire period 1980 - 2013, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>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>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",
       "  </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>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>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>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>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>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>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>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>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>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 × 39 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  ...  2005  2006  2007  2008  2009  2010  2011  \\\n",
       "0    39    47    71   340  ...  3436  3009  2652  2111  1746  1758  2203   \n",
       "1     0     0     0     0  ...  1223   856   702   560   716   561   539   \n",
       "2    71    69    63    44  ...  3626  4807  3623  4005  5393  4752  4325   \n",
       "3     0     0     0     0  ...     0     1     0     0     0     0     0   \n",
       "4     0     0     0     0  ...     0     1     1     0     0     0     0   \n",
       "\n",
       "   2012  2013  Total  \n",
       "0  2635  2004  58639  \n",
       "1   620   603  15699  \n",
       "2  3774  4331  69439  \n",
       "3     0     0      6  \n",
       "4     1     1     15  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can['Total'] = df_can.sum(axis=1)\n",
    "df_can.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can check to see how many null objects we have in the dataset as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country      195\n",
       "Continent    195\n",
       "Region       195\n",
       "DevName      195\n",
       "1980         195\n",
       "1981         195\n",
       "1982         195\n",
       "1983         195\n",
       "1984         195\n",
       "1985         195\n",
       "1986         195\n",
       "1987         195\n",
       "1988         195\n",
       "1989         195\n",
       "1990         195\n",
       "1991         195\n",
       "1992         195\n",
       "1993         195\n",
       "1994         195\n",
       "1995         195\n",
       "1996         195\n",
       "1997         195\n",
       "1998         195\n",
       "1999         195\n",
       "2000         195\n",
       "2001         195\n",
       "2002         195\n",
       "2003         195\n",
       "2004         195\n",
       "2005         195\n",
       "2006         195\n",
       "2007         195\n",
       "2008         195\n",
       "2009         195\n",
       "2010         195\n",
       "2011         195\n",
       "2012         195\n",
       "2013         195\n",
       "Total        195\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.isnull()             # Boolean으로 모두 false가 뜬다\n",
    "df_can.isnull().count()     # false의 개수를 세는 것이 무슨 의미?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Finally, let's view a quick summary of each column in our dataframe using the `describe()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>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>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>508.394872</td>\n",
       "      <td>566.989744</td>\n",
       "      <td>534.723077</td>\n",
       "      <td>387.435897</td>\n",
       "      <td>376.497436</td>\n",
       "      <td>358.861538</td>\n",
       "      <td>441.271795</td>\n",
       "      <td>691.133333</td>\n",
       "      <td>714.389744</td>\n",
       "      <td>843.241026</td>\n",
       "      <td>...</td>\n",
       "      <td>1320.292308</td>\n",
       "      <td>1266.958974</td>\n",
       "      <td>1191.820513</td>\n",
       "      <td>1246.394872</td>\n",
       "      <td>1275.733333</td>\n",
       "      <td>1420.287179</td>\n",
       "      <td>1262.533333</td>\n",
       "      <td>1313.958974</td>\n",
       "      <td>1320.702564</td>\n",
       "      <td>32867.451282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1949.588546</td>\n",
       "      <td>2152.643752</td>\n",
       "      <td>1866.997511</td>\n",
       "      <td>1204.333597</td>\n",
       "      <td>1198.246371</td>\n",
       "      <td>1079.309600</td>\n",
       "      <td>1225.576630</td>\n",
       "      <td>2109.205607</td>\n",
       "      <td>2443.606788</td>\n",
       "      <td>2555.048874</td>\n",
       "      <td>...</td>\n",
       "      <td>4425.957828</td>\n",
       "      <td>3926.717747</td>\n",
       "      <td>3443.542409</td>\n",
       "      <td>3694.573544</td>\n",
       "      <td>3829.630424</td>\n",
       "      <td>4462.946328</td>\n",
       "      <td>4030.084313</td>\n",
       "      <td>4247.555161</td>\n",
       "      <td>4237.951988</td>\n",
       "      <td>91785.498686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>42.500000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>952.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>5018.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>251.500000</td>\n",
       "      <td>295.500000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>434.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>508.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>832.000000</td>\n",
       "      <td>842.000000</td>\n",
       "      <td>899.000000</td>\n",
       "      <td>934.500000</td>\n",
       "      <td>888.000000</td>\n",
       "      <td>932.000000</td>\n",
       "      <td>772.000000</td>\n",
       "      <td>783.000000</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>22239.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22045.000000</td>\n",
       "      <td>24796.000000</td>\n",
       "      <td>20620.000000</td>\n",
       "      <td>10015.000000</td>\n",
       "      <td>10170.000000</td>\n",
       "      <td>9564.000000</td>\n",
       "      <td>9470.000000</td>\n",
       "      <td>21337.000000</td>\n",
       "      <td>27359.000000</td>\n",
       "      <td>23795.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>42584.000000</td>\n",
       "      <td>33848.000000</td>\n",
       "      <td>28742.000000</td>\n",
       "      <td>30037.000000</td>\n",
       "      <td>29622.000000</td>\n",
       "      <td>38617.000000</td>\n",
       "      <td>36765.000000</td>\n",
       "      <td>34315.000000</td>\n",
       "      <td>34129.000000</td>\n",
       "      <td>691904.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               1980          1981          1982          1983          1984  \\\n",
       "count    195.000000    195.000000    195.000000    195.000000    195.000000   \n",
       "mean     508.394872    566.989744    534.723077    387.435897    376.497436   \n",
       "std     1949.588546   2152.643752   1866.997511   1204.333597   1198.246371   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%       13.000000     10.000000     11.000000     12.000000     13.000000   \n",
       "75%      251.500000    295.500000    275.000000    173.000000    181.000000   \n",
       "max    22045.000000  24796.000000  20620.000000  10015.000000  10170.000000   \n",
       "\n",
       "              1985         1986          1987          1988          1989  \\\n",
       "count   195.000000   195.000000    195.000000    195.000000    195.000000   \n",
       "mean    358.861538   441.271795    691.133333    714.389744    843.241026   \n",
       "std    1079.309600  1225.576630   2109.205607   2443.606788   2555.048874   \n",
       "min       0.000000     0.000000      0.000000      0.000000      0.000000   \n",
       "25%       0.000000     0.500000      0.500000      1.000000      1.000000   \n",
       "50%      17.000000    18.000000     26.000000     34.000000     44.000000   \n",
       "75%     197.000000   254.000000    434.000000    409.000000    508.500000   \n",
       "max    9564.000000  9470.000000  21337.000000  27359.000000  23795.000000   \n",
       "\n",
       "       ...          2005          2006          2007          2008  \\\n",
       "count  ...    195.000000    195.000000    195.000000    195.000000   \n",
       "mean   ...   1320.292308   1266.958974   1191.820513   1246.394872   \n",
       "std    ...   4425.957828   3926.717747   3443.542409   3694.573544   \n",
       "min    ...      0.000000      0.000000      0.000000      0.000000   \n",
       "25%    ...     28.500000     25.000000     31.000000     31.000000   \n",
       "50%    ...    210.000000    218.000000    198.000000    205.000000   \n",
       "75%    ...    832.000000    842.000000    899.000000    934.500000   \n",
       "max    ...  42584.000000  33848.000000  28742.000000  30037.000000   \n",
       "\n",
       "               2009          2010          2011          2012          2013  \\\n",
       "count    195.000000    195.000000    195.000000    195.000000    195.000000   \n",
       "mean    1275.733333   1420.287179   1262.533333   1313.958974   1320.702564   \n",
       "std     3829.630424   4462.946328   4030.084313   4247.555161   4237.951988   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%       36.000000     40.500000     37.500000     42.500000     45.000000   \n",
       "50%      214.000000    211.000000    179.000000    233.000000    213.000000   \n",
       "75%      888.000000    932.000000    772.000000    783.000000    796.000000   \n",
       "max    29622.000000  38617.000000  36765.000000  34315.000000  34129.000000   \n",
       "\n",
       "               Total  \n",
       "count     195.000000  \n",
       "mean    32867.451282  \n",
       "std     91785.498686  \n",
       "min         1.000000  \n",
       "25%       952.000000  \n",
       "50%      5018.000000  \n",
       "75%     22239.500000  \n",
       "max    691904.000000  \n",
       "\n",
       "[8 rows x 35 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       59\n",
       "1       13\n",
       "5        6\n",
       "2        5\n",
       "4        3\n",
       "        ..\n",
       "1172     1\n",
       "409      1\n",
       "154      1\n",
       "9378     1\n",
       "612      1\n",
       "Name: 1980, Length: 103, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can[1980].unique()           # unique value: 컬럼에 등장한 숫자들 나열(list)\n",
    "df_can[1980].value_counts()     # value_counts: unique value마다 frequency 측정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "---\n",
    "## *pandas* Intermediate: Indexing and Selection (slicing)<a id=\"6\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Select Column\n",
    "**There are two ways to filter on a column name:**\n",
    "\n",
    "Method 1: Quick and easy, but only works if the column name does NOT have spaces or special characters.\n",
    "```python\n",
    "    df.column_name \n",
    "        (returns series)\n",
    "```\n",
    "\n",
    "Method 2: More robust, and can filter on multiple columns.\n",
    "\n",
    "```python\n",
    "    df['column']  \n",
    "        (returns series)\n",
    "```\n",
    "\n",
    "```python \n",
    "    df[['column 1', 'column 2']] \n",
    "        (returns dataframe)\n",
    "```\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Example: Let's try filtering on the list of countries ('Country')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.Country  \n",
    "type(df_can.Country)    # returns a series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's try filtering on the list of countries ('Country') and the data for years: 1980 - 1985."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>1191</td>\n",
       "      <td>1829</td>\n",
       "      <td>2162</td>\n",
       "      <td>3404</td>\n",
       "      <td>7583</td>\n",
       "      <td>5907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Western Sahara</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>192</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>72</td>\n",
       "      <td>114</td>\n",
       "      <td>102</td>\n",
       "      <td>44</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Country  1980  1981  1982  1983  1984  1985\n",
       "0       Afghanistan    16    39    39    47    71   340\n",
       "1           Albania     1     0     0     0     0     0\n",
       "2           Algeria    80    67    71    69    63    44\n",
       "3    American Samoa     0     1     0     0     0     0\n",
       "4           Andorra     0     0     0     0     0     0\n",
       "..              ...   ...   ...   ...   ...   ...   ...\n",
       "190        Viet Nam  1191  1829  2162  3404  7583  5907\n",
       "191  Western Sahara     0     0     0     0     0     0\n",
       "192           Yemen     1     2     1     6     0    18\n",
       "193          Zambia    11    17    11     7    16     9\n",
       "194        Zimbabwe    72   114   102    44    32    29\n",
       "\n",
       "[195 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can[['Country', 1980, 1981, 1982, 1983, 1984, 1985]]          # returns a dataframe\n",
    "# notice that 'Country' is string, and the years are integers. \n",
    "# for the sake of consistency, we will convert all column names to string later on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Select Row\n",
    "\n",
    "There are main 3 ways to select rows:\n",
    "\n",
    "```python\n",
    "    df.loc[label]        \n",
    "        # filters by the labels of the index/column\n",
    "    df.iloc[index]       \n",
    "        # filters by the positions of the index/column\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Before we proceed, notice that the default index of the dataset is a numeric range from 0 to 194. This makes it very difficult to do a query by a specific country. For example to search for data on Japan, we need to know the corressponding index value.\n",
    "\n",
    "This can be fixed very easily by setting the 'Country' column as the index using `set_index()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Continent           Region             DevName  1980  1981  1982  \\\n",
       "Country                                                                        \n",
       "Afghanistan      Asia    Southern Asia  Developing regions    16    39    39   \n",
       "Albania        Europe  Southern Europe   Developed regions     1     0     0   \n",
       "Algeria        Africa  Northern Africa  Developing regions    80    67    71   \n",
       "\n",
       "             1983  1984  1985  1986  ...  2005  2006  2007  2008  2009  2010  \\\n",
       "Country                              ...                                       \n",
       "Afghanistan    47    71   340   496  ...  3436  3009  2652  2111  1746  1758   \n",
       "Albania         0     0     0     1  ...  1223   856   702   560   716   561   \n",
       "Algeria        69    63    44    69  ...  3626  4807  3623  4005  5393  4752   \n",
       "\n",
       "             2011  2012  2013  Total  \n",
       "Country                               \n",
       "Afghanistan  2203  2635  2004  58639  \n",
       "Albania       539   620   603  15699  \n",
       "Algeria      4325  3774  4331  69439  \n",
       "\n",
       "[3 rows x 38 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 숫자 index 대신 나라 이름을 index로 설정\n",
    "df_can.set_index('Country', inplace=True)\n",
    "df_can.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "  </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>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>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>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>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>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>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>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>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>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 × 39 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  ...  2005  2006  2007  2008  2009  2010  2011  \\\n",
       "0    39    47    71   340  ...  3436  3009  2652  2111  1746  1758  2203   \n",
       "1     0     0     0     0  ...  1223   856   702   560   716   561   539   \n",
       "2    71    69    63    44  ...  3626  4807  3623  4005  5393  4752  4325   \n",
       "3     0     0     0     0  ...     0     1     0     0     0     0     0   \n",
       "4     0     0     0     0  ...     0     1     1     0     0     0     0   \n",
       "\n",
       "   2012  2013  Total  \n",
       "0  2635  2004  58639  \n",
       "1   620   603  15699  \n",
       "2  3774  4331  69439  \n",
       "3     0     0      6  \n",
       "4     1     1     15  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tip: The opposite of set is reset. So to reset the index, we can use df_can.reset_index()\n",
    "df_can.reset_index().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>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",
       "  </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",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Continent           Region             DevName  1980  1981  1982  \\\n",
       "Afghanistan      Asia    Southern Asia  Developing regions    16    39    39   \n",
       "Albania        Europe  Southern Europe   Developed regions     1     0     0   \n",
       "Algeria        Africa  Northern Africa  Developing regions    80    67    71   \n",
       "\n",
       "             1983  1984  1985  1986  ...  2005  2006  2007  2008  2009  2010  \\\n",
       "Afghanistan    47    71   340   496  ...  3436  3009  2652  2111  1746  1758   \n",
       "Albania         0     0     0     1  ...  1223   856   702   560   716   561   \n",
       "Algeria        69    63    44    69  ...  3626  4807  3623  4005  5393  4752   \n",
       "\n",
       "             2011  2012  2013  Total  \n",
       "Afghanistan  2203  2635  2004  58639  \n",
       "Albania       539   620   603  15699  \n",
       "Algeria      4325  3774  4331  69439  \n",
       "\n",
       "[3 rows x 38 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# optional: to remove the name of the index column\n",
    "df_can.index.name = None\n",
    "df_can.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Example: Let's view the number of immigrants from Japan (row 87) for the following scenarios:\n",
    "    1. The full row data (all columns)\n",
    "    2. For year 2013\n",
    "    3. For years 1980 to 1985"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Continent                 Asia\n",
       "Region            Eastern Asia\n",
       "DevName      Developed regions\n",
       "1980                       701\n",
       "1981                       756\n",
       "1982                       598\n",
       "1983                       309\n",
       "1984                       246\n",
       "1985                       198\n",
       "1986                       248\n",
       "1987                       422\n",
       "1988                       324\n",
       "1989                       494\n",
       "1990                       379\n",
       "1991                       506\n",
       "1992                       605\n",
       "1993                       907\n",
       "1994                       956\n",
       "1995                       826\n",
       "1996                       994\n",
       "1997                       924\n",
       "1998                       897\n",
       "1999                      1083\n",
       "2000                      1010\n",
       "2001                      1092\n",
       "2002                       806\n",
       "2003                       817\n",
       "2004                       973\n",
       "2005                      1067\n",
       "2006                      1212\n",
       "2007                      1250\n",
       "2008                      1284\n",
       "2009                      1194\n",
       "2010                      1168\n",
       "2011                      1265\n",
       "2012                      1214\n",
       "2013                       982\n",
       "Total                    27707\n",
       "Name: Japan, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. the full row data (all columns)\n",
    "# [[--]]이면 dataframe으로 불러옴\n",
    "df_can.loc[['Japan']]     # label로 불러옴\n",
    "df_can.iloc[[87]]         # position으로 불러옴\n",
    "\n",
    "# alternate methods\n",
    "df_can[df_can.index == 'Japan']                # 가로 dataframe\n",
    "df_can[df_can.index == 'Japan'].T.squeeze()    # 가로 dataframe-> T=transpose -> unframe (같은 결과)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "982"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. for year 2013\n",
    "df_can.loc['Japan', 2013]         # 하나의 값이기 때문에 [[--]] 할 수 없음\n",
    "df_can.iloc[87, 36]               # year 2013 is next to last column, with a positional index of 36"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980    701\n",
       "1981    756\n",
       "1982    598\n",
       "1983    309\n",
       "1984    246\n",
       "1985    198\n",
       "Name: Japan, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3. for years 1980 to 1985\n",
    "df_can.loc['Japan', [1980, 1981, 1982, 1983, 1984, 1985]]  # dataframe[[--]]으로 불러올 수 없음\n",
    "df_can.iloc[87, 3:9]\n",
    "df_can.iloc[87, [3,4,5,6,7,8]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Column names that are integers (such as the years) might introduce some confusion. For example, when we are referencing the year 2013, one might confuse that when the 2013th positional index. \n",
    "\n",
    "To avoid this ambuigity, let's convert the <b>column names into strings</b>: '1980' to '2013'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Continent', 'Region', 'DevName', '1980', '1981', '1982', '1983',\n",
       "       '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992',\n",
       "       '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001',\n",
       "       '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010',\n",
       "       '2011', '2012', '2013', 'Total'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for x in df_can.columns.values:           # column header의 type 확인: int64      \n",
    "#     print(type(x))\n",
    "[type(x) for x in df_can.columns.values]    # alternative methods OR [type(x) for x in df_can.columns] \n",
    "\n",
    "df_can.columns = list(map(str, df_can.columns))\n",
    "df_can.columns\n",
    "\n",
    "# [type(x) for x in df_can.columns.values]   # type 변환된 것 확인"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Since we converted the years to string, let's declare a variable that will allow us to easily call upon the full range of years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1980~2013연도 indexing하기 위해 매번 list로 불러오기 어려우므로 years 변수 만듦\n",
    "years = list(map(str, range(1980, 2014)))      # list로 감싸는 이유: map 결과는 출력되지 않음\n",
    "years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Filtering based on a criteria\n",
    "To filter the dataframe based on a condition, we simply pass the condition as a boolean vector. \n",
    "\n",
    "For example, Let's filter the dataframe to show the data on Asian countries (Continent = Asia)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Afghanistan        True\n",
       "Albania           False\n",
       "Algeria           False\n",
       "American Samoa    False\n",
       "Andorra           False\n",
       "                  ...  \n",
       "Viet Nam           True\n",
       "Western Sahara    False\n",
       "Yemen              True\n",
       "Zambia            False\n",
       "Zimbabwe          False\n",
       "Name: Continent, Length: 195, dtype: bool"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. create the condition boolean series\n",
    "condition = df_can['Continent'] =='Asia'\n",
    "condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "button": false,
    "collapsed": true,
    "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>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",
       "  </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>Armenia</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>Developing 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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>224</td>\n",
       "      <td>218</td>\n",
       "      <td>198</td>\n",
       "      <td>205</td>\n",
       "      <td>267</td>\n",
       "      <td>252</td>\n",
       "      <td>236</td>\n",
       "      <td>258</td>\n",
       "      <td>207</td>\n",
       "      <td>3310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Azerbaijan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>Developing 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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>359</td>\n",
       "      <td>236</td>\n",
       "      <td>203</td>\n",
       "      <td>125</td>\n",
       "      <td>165</td>\n",
       "      <td>209</td>\n",
       "      <td>138</td>\n",
       "      <td>161</td>\n",
       "      <td>57</td>\n",
       "      <td>2649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Continent         Region             DevName  1980  1981  1982  \\\n",
       "Afghanistan      Asia  Southern Asia  Developing regions    16    39    39   \n",
       "Armenia          Asia   Western Asia  Developing regions     0     0     0   \n",
       "Azerbaijan       Asia   Western Asia  Developing regions     0     0     0   \n",
       "\n",
       "             1983  1984  1985  1986  ...  2005  2006  2007  2008  2009  2010  \\\n",
       "Afghanistan    47    71   340   496  ...  3436  3009  2652  2111  1746  1758   \n",
       "Armenia         0     0     0     0  ...   224   218   198   205   267   252   \n",
       "Azerbaijan      0     0     0     0  ...   359   236   203   125   165   209   \n",
       "\n",
       "             2011  2012  2013  Total  \n",
       "Afghanistan  2203  2635  2004  58639  \n",
       "Armenia       236   258   207   3310  \n",
       "Azerbaijan    138   161    57   2649  \n",
       "\n",
       "[3 rows x 38 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. pass this condition into the dataFrame\n",
    "df_can[condition].head(3)\n",
    "# df_can[df_can.Continent == 'Asia']    # alternative methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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>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",
       "  </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>Bangladesh</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>83</td>\n",
       "      <td>84</td>\n",
       "      <td>86</td>\n",
       "      <td>81</td>\n",
       "      <td>98</td>\n",
       "      <td>92</td>\n",
       "      <td>486</td>\n",
       "      <td>...</td>\n",
       "      <td>4171</td>\n",
       "      <td>4014</td>\n",
       "      <td>2897</td>\n",
       "      <td>2939</td>\n",
       "      <td>2104</td>\n",
       "      <td>4721</td>\n",
       "      <td>2694</td>\n",
       "      <td>2640</td>\n",
       "      <td>3789</td>\n",
       "      <td>65568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhutan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>36</td>\n",
       "      <td>865</td>\n",
       "      <td>1464</td>\n",
       "      <td>1879</td>\n",
       "      <td>1075</td>\n",
       "      <td>487</td>\n",
       "      <td>5876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Continent         Region             DevName  1980  1981  1982  \\\n",
       "Afghanistan      Asia  Southern Asia  Developing regions    16    39    39   \n",
       "Bangladesh       Asia  Southern Asia  Developing regions    83    84    86   \n",
       "Bhutan           Asia  Southern Asia  Developing regions     0     0     0   \n",
       "\n",
       "             1983  1984  1985  1986  ...  2005  2006  2007  2008  2009  2010  \\\n",
       "Afghanistan    47    71   340   496  ...  3436  3009  2652  2111  1746  1758   \n",
       "Bangladesh     81    98    92   486  ...  4171  4014  2897  2939  2104  4721   \n",
       "Bhutan          0     1     0     0  ...     5    10     7    36   865  1464   \n",
       "\n",
       "             2011  2012  2013  Total  \n",
       "Afghanistan  2203  2635  2004  58639  \n",
       "Bangladesh   2694  2640  3789  65568  \n",
       "Bhutan       1879  1075   487   5876  \n",
       "\n",
       "[3 rows x 38 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can pass mutliple criteria in the same line. \n",
    "# let's filter for AreaNAme = Asia and RegName = Southern Asia\n",
    "\n",
    "df_can[(df_can['Continent']=='Asia') & (df_can['Region']=='Southern Asia')].head(3)\n",
    "\n",
    "# note: When using 'and' and 'or' operators, pandas requires we use '&' and '|' instead of 'and' and 'or'\n",
    "# don't forget to enclose the two conditions in parentheses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Before we proceed: let's review the changes we have made to our dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data demension: (195, 38)\n",
      "Column names: Index(['Continent', 'Region', 'DevName', '1980', '1981', '1982', '1983',\n",
      "       '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992',\n",
      "       '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001',\n",
      "       '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010',\n",
      "       '2011', '2012', '2013', 'Total'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "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>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",
       "  </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",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Continent           Region             DevName  1980  1981  1982  \\\n",
       "Afghanistan      Asia    Southern Asia  Developing regions    16    39    39   \n",
       "Albania        Europe  Southern Europe   Developed regions     1     0     0   \n",
       "\n",
       "             1983  1984  1985  1986  ...  2005  2006  2007  2008  2009  2010  \\\n",
       "Afghanistan    47    71   340   496  ...  3436  3009  2652  2111  1746  1758   \n",
       "Albania         0     0     0     1  ...  1223   856   702   560   716   561   \n",
       "\n",
       "             2011  2012  2013  Total  \n",
       "Afghanistan  2203  2635  2004  58639  \n",
       "Albania       539   620   603  15699  \n",
       "\n",
       "[2 rows x 38 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Data demension:', df_can.shape)\n",
    "print('Column names:', df_can.columns)\n",
    "df_can.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "---\n",
    "# Visualizing Data using Matplotlib<a id=\"8\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Matplotlib: Standard Python Visualization Library<a id=\"10\"></a>\n",
    "\n",
    "The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/).  As mentioned on their website: \n",
    ">Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.\n",
    "\n",
    "If you are aspiring to create impactful visualization with python, Matplotlib is an essential tool to have at your disposal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Matplotlib.Pyplot\n",
    "\n",
    "One of the core aspects of Matplotlib is `matplotlib.pyplot`. It is Matplotlib's scripting layer which we studied in details in the videos about Matplotlib. Recall that it is a collection of command style functions that make Matplotlib work like MATLAB. Each `pyplot` function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. In this lab, we will work with the scripting layer to learn how to generate line plots. In future labs, we will get to work with the Artist layer as well to experiment first hand how it differs from the scripting layer. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's start by importing `Matplotlib` and `Matplotlib.pyplot` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# we are using the inline backend\n",
    "%matplotlib inline \n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "*optional: check if Matplotlib is loaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Matplotlib version:', '3.1.3')"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Matplotlib version:', mpl.__version__     # >= 2.0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*optional: apply a style to Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tableau-colorblind10', '_classic_test']\n"
     ]
    }
   ],
   "source": [
    "print(plt.style.available)\n",
    "mpl.style.use('ggplot')   # optional: ggplot-like style을 사용하고자 할 때 지정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Plotting in *pandas*\n",
    "\n",
    "Fortunately, pandas has a built-in implementation of Matplotlib that we can use. Plotting in *pandas* is as simple as appending a `.plot()` method to a series or dataframe.\n",
    "\n",
    "Documentation:\n",
    "- [Plotting with Series](http://pandas.pydata.org/pandas-docs/stable/api.html#plotting)<br>\n",
    "- [Plotting with Dataframes](http://pandas.pydata.org/pandas-docs/stable/api.html#api-dataframe-plotting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Line Pots (Series/Dataframe) <a id=\"12\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**What is a line plot and why use it?**\n",
    "\n",
    "A line chart or line plot is a type of plot which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields.\n",
    "Use line plot when you have a continuous data set. These are best suited for <b>trend-based visualizations of data over a period of time</b>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Let's start with a case study:**\n",
    "\n",
    "In 2010, Haiti suffered a catastrophic magnitude 7.0 earthquake. The quake caused widespread devastation and loss of life and about three million people were affected by this natural disaster. As part of Canada's humanitarian effort, the Government of Canada stepped up its effort in accepting refugees from Haiti. We can quickly visualize this effort using a `Line` plot:\n",
    "\n",
    "**Question:** Plot a line graph of immigration from Haiti using `df.plot()`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "First, we will extract the data series for Haiti."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980    1666\n",
       "1981    3692\n",
       "1982    3498\n",
       "1983    2860\n",
       "1984    1418\n",
       "1985    1321\n",
       "1986    1753\n",
       "1987    2132\n",
       "1988    1829\n",
       "1989    2377\n",
       "1990    2379\n",
       "1991    2829\n",
       "1992    2399\n",
       "1993    3655\n",
       "1994    2100\n",
       "1995    2014\n",
       "1996    1955\n",
       "1997    1645\n",
       "1998    1295\n",
       "1999    1439\n",
       "2000    1631\n",
       "2001    2433\n",
       "2002    2174\n",
       "2003    1930\n",
       "2004    1652\n",
       "2005    1682\n",
       "2006    1619\n",
       "2007    1598\n",
       "2008    2491\n",
       "2009    2080\n",
       "2010    4744\n",
       "2011    6503\n",
       "2012    5868\n",
       "2013    4152\n",
       "Name: Haiti, dtype: object"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "haiti = df_can.loc['Haiti', years]    # passing in years 1980-2013 to exclude the 'total' column\n",
    "haiti"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Next, we will plot a line plot by appending `.plot()` to the `haiti` series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x285ff4dc048>"
      ]
     },
     "execution_count": 153,
     "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": [
    "type(haiti)    # Series\n",
    "haiti.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<b>*Pandas* automatically populated the x-axis with the index values (years), and the y-axis with the column values (population)</b>. However, notice how the years were not displayed because they are of type *string* (뭔말이지? 이전버전인가???). Therefore, let's <b>change the type of the index values to *integer* for plotting.</b>\n",
    "\n",
    "Also, let's label the x and y axis using `plt.title()`, `plt.ylabel()`, and `plt.xlabel()` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int,\n",
       " int]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1. 앞에서 한 column value와 같은 방법으로 하기\n",
    "[type(x) for x in haiti.index]                # type: string\n",
    "\n",
    "haiti.index = map(int, haiti.index)\n",
    "haiti.index\n",
    "[type(x) for x in haiti.index]                # type: int로 변경됨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2. 더 간단하게 mapping\n",
    "haiti.index = haiti.index.map(int)\n",
    "[type(x) for x in haiti.index]               # type: int\n",
    "\n",
    "haiti.plot(kind='line')\n",
    "\n",
    "plt.title('Immigration from Haiti to Canada')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()    # to show the updates made to the figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can clearly notice how number of immigrants from Haiti spiked up from 2010 as Canada stepped up its efforts to accept refugees from Haiti. Let's <b>annotate this spike in the plot</b> by using the `plt.text()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "haiti.plot(kind='line')\n",
    "\n",
    "plt.title('Immigration from Haiti')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "plt.xlabel('Year')\n",
    "\n",
    "# annotate the 2010 Earthquake \n",
    "# syntax: plt.text(x, y, label)\n",
    "plt.text(2000, 6000, '2010 Earthquake')   # see note below\n",
    "plt.text(2000, 6000, '2010 Earthquake')   # letters gets bold\n",
    "\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "With just a few lines of code, you were able to quickly identify and visualize the spike in immigration!\n",
    "\n",
    "\n",
    "Quick note on x and y values in `plt.text(x, y, label)`:\n",
    "    \n",
    "     Since the x-axis (years) is type 'integer', we specified x as a year. The y axis (number of immigrants) is type 'integer', so we can just specify the value y = 6000.\n",
    "    \n",
    "```python\n",
    "    plt.text(2000, 6000, '2010 Earthquake') # years stored as type int\n",
    "```\n",
    "    If the years were stored as type 'string', we would need to specify x as the index position of the year. Eg, 20th index is year 2000 since it is the 20th year with a base year of 1980.\n",
    "```python\n",
    "    plt.text(20, 6000, '2010 Earthquake') # years stored as type int\n",
    "```\n",
    "    We will cover advanced annotation methods in later modules."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can easily add more countries to line plot to make meaningful comparisons on immigration from different countries. \n",
    "\n",
    "**Question:** Let's compare the number of immigrants from India and China from 1980 to 2013.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the data set for China and India, and display dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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>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>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</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>China</th>\n",
       "      <td>5123</td>\n",
       "      <td>6682</td>\n",
       "      <td>3308</td>\n",
       "      <td>1863</td>\n",
       "      <td>1527</td>\n",
       "      <td>1816</td>\n",
       "      <td>1960</td>\n",
       "      <td>2643</td>\n",
       "      <td>2758</td>\n",
       "      <td>4323</td>\n",
       "      <td>...</td>\n",
       "      <td>36619</td>\n",
       "      <td>42584</td>\n",
       "      <td>33518</td>\n",
       "      <td>27642</td>\n",
       "      <td>30037</td>\n",
       "      <td>29622</td>\n",
       "      <td>30391</td>\n",
       "      <td>28502</td>\n",
       "      <td>33024</td>\n",
       "      <td>34129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>8880</td>\n",
       "      <td>8670</td>\n",
       "      <td>8147</td>\n",
       "      <td>7338</td>\n",
       "      <td>5704</td>\n",
       "      <td>4211</td>\n",
       "      <td>7150</td>\n",
       "      <td>10189</td>\n",
       "      <td>11522</td>\n",
       "      <td>10343</td>\n",
       "      <td>...</td>\n",
       "      <td>28235</td>\n",
       "      <td>36210</td>\n",
       "      <td>33848</td>\n",
       "      <td>28742</td>\n",
       "      <td>28261</td>\n",
       "      <td>29456</td>\n",
       "      <td>34235</td>\n",
       "      <td>27509</td>\n",
       "      <td>30933</td>\n",
       "      <td>33087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       1980  1981  1982  1983  1984  1985  1986   1987   1988   1989  ...  \\\n",
       "China  5123  6682  3308  1863  1527  1816  1960   2643   2758   4323  ...   \n",
       "India  8880  8670  8147  7338  5704  4211  7150  10189  11522  10343  ...   \n",
       "\n",
       "        2004   2005   2006   2007   2008   2009   2010   2011   2012   2013  \n",
       "China  36619  42584  33518  27642  30037  29622  30391  28502  33024  34129  \n",
       "India  28235  36210  33848  28742  28261  29456  34235  27509  30933  33087  \n",
       "\n",
       "[2 rows x 34 columns]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CI = df_can.loc[['China', 'India'], years]     # dataframe으로 얻어짐\n",
    "CI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_CI = df_can.loc[['India', 'China'], years]\n",
    "df_CI.head()\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot graph. We will explicitly specify line plot by passing in `kind` parameter to `plot()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "button": false,
    "collapsed": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x285ff31c548>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fzuWXX84555yTq1fkutqHk+LTb5VUvKHswZfKYEtYT5kyhYULF7Jw4UIA/vVf/5UJEyYM2/ZL5VLPQKSMBlvCOn+Z999/n9/97necddZZw7PxUtHUMxApkWKVsF60aBE7d+7EdV1uuummPoPSIsWgEtZSkVTCWmRwdJhIREQUBiIiojAQEREUBiIigsJARERQGIiICAoDkZIZTAnrVCrFFVdcwbx58zjuuON47rnncsu8+uqrzJs3j6OOOop//ud/RmeDSykoDERKZDAlrP/7v/8bgGeeeYYHH3yQG264gSAIALj22mv5t3/7N/70pz+xbt06VqxYUd6GyIigMBApkcGUsH7rrbeYM2cOAGPHjqWpqYm//OUvbNmyhVgsxic/+Uksy+Lcc8/lf//3f8vbEBkRVI5CKt7KlSvZunVrUdc5btw45s+fP+jlBiphfeihh/L4449z5plnsmHDBl599VU2bNiAbdsFhekmTpzI5s2bi9YOkSz1DETKaKAS1p///OeZOHEin/rUp/j2t7/NJz/5SVzX7Xd8wLKscm+2jADqGUjFG8oefKkMVMLadV2+973v5eY75ZRTmDp1Ks3NzWzatCn3/MaNG2ltbS3vRsuIoJ6BSBkNVMK6s7OTRCIBwNNPP43runz0ox+ltbWVaDTKSy+9hDGGZcuWcfLJJw/b9kvlUs9ApEQGU8J6+/btnHfeedi2zfjx43NnGQH86Ec/4oorriCZTHLCCSdwwgknDEt7pLKphLVUJJWwFhkcHSYSERGFgYiIKAxERASFgYiIoDAQEREUBiIigsJApGSKVcL6Bz/4AYcffjhTpkwpdxNkBFEYiJRIsUpYL1iwgCeeeKK8Gy8jjsJApESKUcIa4Mgjj1Q9Iik5laOQile7+dc43RuKuk6/ehLJ8acPernBlrD+xCc+UdTtFhmIegYiZTTYEtYi5aLfNql4Q9mDL5XBlrAWKRf1DETKaLAlrEXKRT0DkRIpVgnrG2+8kYcffphkMsmMGTNYuHAh11577bC0SSqXSlhLRVIJa5HB0WEiERFRGIiIiMJARERQGIiICAoDERFBYSAiIigMREqmGCWsOzs7+fznP8+sWbOYM2dOwVXKIsWkMBApkWKVsP7617/O888/z/Lly/nzn//M8uXLy9sQGREUBiIlUowS1nV1dRx77LEAVFVV8fGPf5yNGzeWsRUyUqgchVS8l97/L9qS7xR1naNrp3Dkfl8a9HIfpIR1e3s7TzzxBBdffHHR2iGSpZ6BSBkNtYS153ksXryYiy++WF9/KSWhnoFUvKHswZfKUEtYX3PNNUydOpXFixeXd4NlxFDPQKSMhlLC+oc//CEdHR18//vfH56NlhFBVUulIu0LVUvzS1iPHTu23xLWS5cuxbIs1q9fX1DC+rbbbmPy5Mls3LiRww8/nGnTpuXa85WvfIULL7yw3/dU1VIZKoWBVKR9IQyGg8JAhkqHiURERGEgIiIKAxERQWEgIiIoDEREBIWBiIigMBApmWKUsAY477zzOO6445gzZw7f/OY38X2/7G2RyqcwECmRYpWw/ulPf8rTTz/NqlWr2LFjB7/5zW/K2xAZERQGIiVSjBLWAA0NDUBYrC6dTmNZVrmaICOICtVJxatf/x5uZ7Ko6/Tqakl8ZPKglxtqCetzzz2XV155hRNOOIHTTjutqG0RAfUMRMpqqCWsly1bxpo1a+ju7ubZZ58drs2XCqaegVS8oezBl8pQS1gD1NTUcNJJJ/H4449z3HHHlW2bZWRQz0CkjAZbwjoej7NlyxYgHDNYvnw506ZNG56Nl4qmnoFIieSXsJ4xY0a/JawvuOACALZv315Qwjp7llFnZydf+MIX6O7uJggCjj322FyAiBSTSlhLRVIJa5HB0WEiERFRGIiIiMJARERQGIiICAoDERFBYSAiIigMREqmWCWss77whS8UrEukmBQGIiVSrBLWAI899hj19fXl23gZcRQGIiVSrBLW8Xicu+66i6uuuqqMWy8jjcpRSMVL/+IugvXrirpO+yNTiSy8ZNDLDaWE9c0338yll15KbW1tUdsgkk89A5EyGmwJ6zVr1vD2229z6qmnDvOWS6VTz0Aq3lD24EtlsCWsn3/+eV599VVmzpyJ53ls376dM844g0cffXRYtl8ql8JApIy2bdvG2LFj+y1hbYyhvr6+oIT1Rz/6US666CIA1q9fz4UXXqggkJJQGIiUSDFKWIuUi0pYS0VSCWuRwdEAsoiIKAxERERhICIiKAxERASFgYiIoDAQEREUBiIlU6wS1meccQbHHHMM8+fPZ/78+Wzbtq3sbZHKpzAQKZFilrD+yU9+wsqVK1m5ciVjx44tXyNkxFAYiJRIsUpYi5SDylFIxbv7TxtZ19ZV1HVOHV3D4n+cOOjlhlLCGsJDTrZt85nPfIarr74ay7KK2h4R9QxEymiwJawhPET0zDPP8Nvf/pYXXniBZcuWDWcTpEKpZyAVbyh78KUy2BLWABMmTAAgGo3yuc99jldeeYXzzjuvzFsulU49A5Eyyp4J1F8J60QiAVBQwtrzPHbs2AFAOp3mySef5KCDDhqejZeKpp6BSIkUo4R1d3c35513Hul0miAImDt3Ll/4wheGrU1SuVTCWiqSSliLDI4OE4mIiMJAREQUBiIigsJARERQGIiICAoDERFBYSBSMsUqYZ1Kpbjmmms4+uijmTVrFr/97W/L3hapfAoDkRIpVgnrW2+9lZaWFl544QX++Mc/MmvWrPI2REYEhYFIiRSrhPUDDzzAFVdcAYBt24wZM6ZcTZARROUopOK9+lKc9javqOtsGu0y48jooJcbbAnrAw44AICbbrqJ559/nilTpvDDH/6QcePGFbU9IuoZiJTRYEtYe57Hxo0bOeqoo1i+fDlHHnkk//Iv/zK8jZCKpJ6BVLyh7MGXymBLWI8ePZq6ujpOPfVUAD772c/yP//zP+XfcKl46hmIlNFgS1hblsWCBQtyZxc9++yzTJ8+fXg2XiqaqpZKRdoXqpbml7AeO3ZsvyWsly5dimVZrF+/vqCE9W233cbkyZMBeO+99/jGN75Be3s7LS0t3H777ey33379vqeqlspQKQykIu0LYTAcFAYyVDpMJCIiCgMREVEYiIgICgMREUFhICIiKAxERASFgUjJFKOEdTweZ/78+bnbQQcdxNKlS4elPVLZFAYiJVKMEtbRaJSVK1fmbvvtt1+uNIVIMSkMREqkWCWss9atW8f27ds5+uijy7D1MtKoUJ1UvJUrV7J169airnPcuHHMnz9/0MsNtoT1Jz7xidyyDz/8MKeffjqWZRWtHSJZ6hmIlNFgS1jne/TRRznrrLOGY7NlBFDPQCreUPbgS2WwJayzXn/9dTzPY8aMGeXdYBkx1DMQKaPBlrDOeuSRRzjzzDPLv8EyYqhnIFIi+SWsZ8yY0W8J6wsuuACA7du3F5Swzp5llPXrX/+aBx54oOxtkJFDJaylIqmEtcjg6DCRiIgoDERERGEgIiIoDEREBIWBiIigMBARERQGIiVTjBLWENYkmjdvHvPmzeO8885jx44dZW+LVD6FgUiJFKOEted5LF26lIcffphnnnmGQw45hP/8z/8se1uk8ikMREqkGCWsjTEYY+js7MQYQywWY/z48eVtiIwIKkchFa92869xujcUdZ1+9SSS408f9HJDKWH9ox/9iHnz5lFXV8fUqVO5+eabi9oWEVDPQKSsBlvCOp1Oc//997N8+XLWrFnDIYccwu233z7MrZBKpJ6BVLyh7MGXymBLWL/++usA7L///gCcfvrp3HHHHWXeahkJ1DMQKaPBlrCeMGECb775Jtu3bwfCAeZp06YNz8ZLRVPPQKREilHCevz48Xzzm9/k9NNPx3VdJk+erJ6BlIRKWEtFUglrkcHRYSIREVEYiIiIwkBERFAYiIgICgMREUFhICIiKAxESqZYJawfffRR5s2bx5w5c7jxxhvL3g4ZGRQGIiVSjBLWbW1t3HjjjTz00EM8++yzbNu2jVWrVpW9LVL5FAYiJVKMEtbvvvsuU6dOpaWlBYC5c+fmlhEpJpWjkIr30vv/RVvynaKuc3TtFI7c70uDXm6wJaznzJnD2rVrWb9+PRMnTuT3v/896XS6qG0RAfUMRMpqsCWsm5ub+dGPfsTXvvY1TjvtNCZPnozjOMPcCqlE6hlIxRvKHnypDLaENcBJJ53ESSedBMDPfvYzhYGUhHoGImU02BLW+cvs2rWL++67jwsvvHAYtlwqnXoGIiVSjBLWAEuXLuWNN94A4JprruGAAw4of2Ok4qmEtVQklbAWGRwdJhIREYWBiIgoDEREhA/5APLGjRuHtFxLS0vuC8ZHipHW5mQySSQSwfO84d6UsnFdl2QyOeS/iw+jkfZ7DR+szRMnThzwNfUMREREYSAiIgoDkZLZsGEDZ599NvPmzWP+/Pn89Kc/BWDnzp2cf/75zJ49m/PPP59du3YBYIzh29/+NrNnz+bEE09kzZo1uXUtW7aM2bNnM3v2bJYtWzYs7ZHKpjAQKRHXdbnhhht45pln+O1vf8v999/PW2+9xZ133smxxx7Lc889x7HHHpu7wGzFihW8/fbb/PGPf+Tmm2/muuuuA8LwuPXWW3nsscf43e9+x6233poLEJFiURiIlEhrayuHHXYYANFolGnTprF582aeeOIJzjnnHADOOeccHn/8cQCeeOIJzj77bCzLYubMmbS3t7NlyxaeeeYZ5syZw6hRo2hubmbOnDk8/fTTw9UsqVAf6rOJRPZG4/sbiSS7irrOdG0NHfsNfGZGb++99x6vv/46RxxxBNu3b6e1tRUIA2PHjh0AbN68ueBsjwkTJrB58+YBnxcpJvUMREoskUhw8cUXc+ONN9LQ0DDgfP1VhrEsq9/nRYptxPUM6tqWQ3o01V0OgdtA4DbiOw1gj7w6NiPFYPbgiy2dTnPxxRdz5plncsoppwDheeJbtmyhtbWVLVu2MGbMGCDc48+/RmDTpk20trYyYcIEnn/++YLnZ82aVd6GSMUbWWFgDHU7n8Vu66ap10uBXUPgNBK4DfhuI4ETBkWQmc4+hx0Zlk2XDx9jDNdccw0HHnggixcvzj2/YMECHnzwQS677DIefPDB3HcVLFiwgPvvv5/TTz+d1atX09jYSGtrK/PmzeOmm27KDRqvWrUqN7gsUiwjKwwsi+1Tb6BlVB07t7yD7XfgeDFsvwPb68D2Yjh+B1XJt7G9GBZ+n1UEdm1hQOQFh58LkAawRtaPVvp68cUXeeihhzj44IP51Kc+BcC3vvUtvvGNb3DJJZfwwAMPMGnSJO6++24ATjjhBFasWMHs2bOpra3llltuAWDUqFFceeWVnHrqqQBcddVVjBo1angaJRXrQ13CuqTlKIzBCjrDgPA6MoERy93nP2cR9Fk8sOvCgHAbMj2O/ABpyPVCsMrzrVUj7bL9zs5OGhsbR1w5io6ODurq6oZ7U8pmpP1eQ+nKUWj3dSCWhXHq8Z16/OrxA89nAqygM+xh5AIi28sIn3O7t2D78f5Dw6nHzwRDGBiNeYepss9FyxYaIjIyKQw+KMvGOFE8JwrVEwaezwRYfiIXENngCEMk7HG43ZsyoVHYWTNYBE4006PoFRp5vYwwNHSCmIgMnsKgXCwb4zbguQ1QvZuzW4yP7SfyDkl1ZA5JZXseMdzuDdh+YoDQaCgICD8THDj74XYbfKcB49QrNESkgMJgX2M5ubOYYDdfX2h8bD9eMPCdnQ5DZBeR7vXYfvgl62yD0dlFsQt6GX5/Z065DRi7TqEhMkIoDD6sLIfAbSJwe58k24vxsL04o6MWHTveLxwM9zpw0juIJN/BDjr7LoqTC43CQ1KZXkdm2ti1YFklaqiIlIPCoNJZLkGkGRpaSHXvJjiCdK6n4eSFRXZcw01vw06uww6SfRY1lltwLUaQFxz5IWLsGoWGyD5qr8MgCAK+9a1vMXr0aL71rW+xdetWbrvtNuLxOPvvvz+XX345ruuSTqf58Y9/zLp162hoaODKK69k3LhxADzyyCOsWLEC27a56KKLOPzwwwH4y1/+wn333UcQBJxwwgmcccYZpWmtDMyOEOAKPVYAACAASURBVNijCCKj2O3JmEEa28+cWpvXy8gGiJvagp38G3bQ3WfRMDR6D3znn34bBomxqisiNDZs2MCSJUvYtm0btm2zcOFCvvrVr7Jz504uvfRS3nvvPSZPnsxdd91Fc3Mzxhi+853vsGLFCmpra7n11ltzhe4WLlzI6tWr+eQnP8nPfvazYW6ZVKK9DoP//d//ZdKkSSST4Z7hz3/+c0499VRmz57NPffcw4oVK1iwYAErVqygvr6e//iP/+C5557jF7/4BVdddRXvv/8+zz//PLfccgs7d+7ke9/7HrfffjsA//mf/8nSpUsZM2YM1113HUceeST77bdfaVosH4wdIbBHE0RG736+IFU48J2ZdnKD4JuwvTexTarvolZV4VlSbmPP6bf5oWFXl6iRxZEtYX3YYYcRj8f59Kc/zdy5c1m2bBnHHnssl112GT/+8Y+58847uf766wtKWK9evZrrrruOxx57DIBLLrmEZDLJz3/+82FulVSqvQqDHTt2sHr1as466ywee+wxjDG88cYbLFmyBIDjjjuOBx98kAULFvDSSy/lyvMeffTR3HvvvRhjePHFF5k1axaRSIRx48Yxfvx41q5dC8D48eNzVRxnzZrFiy++qDD4sLOr8Kta8GnZ7WxW0N1r4Dv/7KnwzCkn8Vcsk+6zbGBV5x2Syr8ivJGkGQ9BLRgzbIPgra2tud/r3iWsf/WrXwFhCeuzzz6b66+/fsAS1q2trcyZM6egPpFIse1VGNx///1ceOGFuV5BLBajrq4OxwkvhBo9ejRtbW0AtLW15QpvOY5DXV0dsViMtrY2pk2blltn/jLZ+bPTf/vb34rQNPkwMHY1ftVY/Kqxu5nJhKGRdxW4k9fbsL0Yka73qPY7sEx4kKszeipUpXAB76FfEbz/ft6hJwtDZtqygJ7n95Y1eX/s8y/e6/k/SAnr7LwipbTHMHj55Zdpampi6tSpvPHGG3tc4WDL8A40f3+eeuopnnrqKQBuuukmWlp2v9c5ENd1h7zsh9VIaLMxBuN3QmoX7IhDTT0EflhcMPc7ZcILAOl5WCAXDpmbRd50T3DYto3r7t1R1kQiwde+9jW+//3v52oK9V7WdV0sy8JxnNxrlmXhum7useM4uecGUl1dXfH/zvlGwu91b6Vq8x5/m998801eeuklXnnlFVKpFMlkkvvvv5/Ozk5838dxHNra2hg9OjyGPGbMGHbs2MGYMWPwfZ/Ozk6i0Wju+az8ZfKf37Fjx4BFuE488UROPPHE3OOh1udQPZNKV0238al2o2Ftogu+UfjFHcYAQRgKxgf88N4EYPywQKHxsUxA37QgvIAQB9O5GWPZYDkYnPDesiEzjWWRTqe56KKLOOOMMzjppJPwPI+WlhY2bNhQUMLa8zxaW1t57733mDlzJhDW3mppacnVV/J9H2PMgPWWXNelu7t7BP07j7Tf61CpahPt8WDq5z//ee666y7uvPNOrrzySj72sY9xxRVXcOihh/LCCy8A8PTTT3PkkUcCMHPmzNxX8r3wwgsceuihWJbFkUceyfPPP086nWbr1q1s2rSJAw88kAMOOIBNmzaxdetWPM/j+eefz61LpCQsK/ywtiMYpwbj1IdjDZFmgqox+FXj8Ksn4FVPxKuegFc1Dj/Sgh8ZFc5n12aq0gaZMY84jrcLJ70DN7UNN7U5HOvo2sA3r/oG06fux6WLzsX22rG8OAs+dTwPLvslGI8Hly0rKGH9q1/9CmMML7/8cq6EtUg5DPk6g4ULF3Lbbbfxy1/+kv3335/jjz8egOOPP54f//jHXH755USjUa688koAJk+ezDHHHMPVV1+Nbdt85StfwbbDLFq0aBH/+q//ShAEzJ8/n8mTJxehaSIfkGWR3cvP9g/6PdiZ62n09DAsfP705xf51SO/4+CPTuPEk88CDP98zaUsufhcLr78n/nlA//DpInj+X/vvBkntYUFc2aw8qlWZs86Jjy19N9uhiAFlsOZZ53N2rVr6ezsZObMmfz7v/87xx13XJl+EDISqIT1CDHS2rxPlrDuExp+WMk2+xg/79BVP4tbmcNP2Lnp8PBU+Nh1q+mIxairj5azVcNqpP1eg0pYi3z4DaqnkRcMuXGMnse2SYVjHPlSUB17lzHbnh2w7lT2Ij/VnZLeFAYi+xrLAlyw2HNo5AbAAxzb4FV1kbIODE+/Tbd9wLpT2dD48F8NLnumMBD5sLKszEC2G4aF6+LXQGz0gYXzFdSd6vt9Gm56+8B1p3AyvYneX7ikulOVRmEgUumGUncqLzQK606txQ66+izat+5UQ16I5IdGZdSdqkQKAxEJDbXuVOa+sO7UW9imv2KFkcKAyB/X+BDVnapECgMRGZwPUncq1+PYU92pqr5fuNTPlzBJ8SgMREqkWCWsX3/9da677jri8TiO43D55Zdz+umnD3fz9miv606ZwtBwCgKkb92pgsXfrWW0He3zhUuFh6kawpIkslsKA5ESKVYJ69raWm6//XamTp3K5s2bOfnkkznuuONoatrDt9x9GFgWxqrBr6rBrxo38HzGYAVdPd+fkRkAr4+k8WJbsf0YkeS7VHsd4Wm4vQR2bd4XLuWfOZV/+m1DZkB+ZBq5LRcpsWKVsD7ggANy6xw/fnyuzldFhMHesiyMU4vv1OJXtZI9sFTX0kJH/gVYxmAFnQVfuFTwJUxeB1XJ7dhebIDQqOsTEH2/kClakaFReS0S6eWnL23h7Z19z4D5IPYfVcNXj9z7ukHFKmH9yiuvkE6nmTJlSnEaUmksC+PU4zv1+IwfeD4TYAWdOF7hIHj+uIab3JoJjaDP4oFT3+8XLvl504ETzVwx/uEw4sLgV7/6FdFolNraWhobG2lqaqKpqYnGxkYiER1XlOJLJBJcfPHF3HjjjTQ0NAw4357KuW/ZsoUrrriC2267LVfXS4bIsjFOFM+JQvWEgeczAZafKBj47j2u4XZvxvZjWL0uDTRYBNkiiLkzpgq/hCkMk+g+cTX4iAqDIAiIbQto27yJVJAg6JX4tRGXprpamhoaaRw1iqaxY2kaNZrGxkai0eiA37Mg+7bB7MEXWzqd5uKLL+bMM8/klFNOAcLaMtlvMMuWsIawJ5Bfb2vTpk25XkEsFuOLX/wi1157ba7EtZSBZWPcBjy3AaoHruuDCTIX9sX6jGvYuVNuN2L78QFCI1oQEP2NaxinvqShMaLCwBg4qnkyyaCejqCWuF1LynSR9mN4fhw/vYt4rI2du7bQ/d6Ggi++so2h0Qpoch0aq6torK+jqbGRxlGjaRrbStXo0dDQhBWpGr4Gyj7FGMM111zDgQceyOLFi3PPL1iwgAcffJDLLruMBx98sKCE9f3338/pp5/O6tWrcyWsU6kUX/nKVzj77LM57bTThqs5sjuWnfvghkkDz2f83NXgBeMa2dDwdhHpXo/tJ/ouik3gRLG2ToBxXy5+E0Zy1VITGJKdAbEOn0RbksTObhKxgETSJpG28fxO0n4cz48ReLsw6R2kvRjdphuvVyehxkvRmErS5KVotA2NrkNjTTVN9fVEGxuwG0dBQyNWQxNEm6ChMQyP6poi/CT2vs0jxb5QtfTPf/4zZ555JgcffHCuV/mtb32LI444gksuuYQNGzYwadIk7r77bkaNGoUxhuuvv56nn36a2tpabrnlFmbMmMFDDz3E1VdfzfTp03PrvvXWW/nYxz5W8H6u69LR0UFdXV1Z2zmcKvb32njYXryfXkaMmpoatjV9Zkir3V3V0hEdBrvj+4bORBCGQ8wnHgtIxMPprqTBD7rxMkFhTDvGb8fz2un2EnT5qYKOoG0CGlJdNKaSuVtTdhqP6rooNDSF4ZAJiWxghOHRmHudmtohHa6q2D+aAewLYVBuCoORQSWsi6Rh02bseILaVAo/EsGviuBHItBrQM5xLBoaHRoaHaBwYNlLGxJxn0R8VBgSMZ9ELCAeC0inDMYEeEECz49jOQmw4/heBx2pDrZ0xUh5hVdcVmNoMj6NXjeNm7bTuPbvNHbGaUwliaa7cPKjxY1kgqERovnhkelp5L1GYxPU1musQ0T2aGSFgTHUbW/D2bKN3t+y7DsOQSYY8kMiewuqIphMYLgRi6ZRLk39fFVzqjvbg4gSj7VkpgPiMR8/AtSDH6QITAzbTYCTIAjipLwYW7pirEvECBp6Bjwty6KhuorGiEujDU1BmsZ0N41dcRrj7VRv3YgV64DusOJkn26e40K0kR2jRuPX1mPlehmZMGnM9kIyz9VFsXSmisiIM7LCwLLYctghtDQ3s3PzFpx0Oryl0gXTkUQnjt/PBSmO0yso3MzjKoLMdFW1Q1W1zagxhcsaY+juMuFhp7hPPNaQOwSViAdEbKivgzG1AcbqxK3qxHIS+CZG2ovT1RVje6KDrq4U4ch2A0QbqBo9NTw1tiFKU001ja5Dk2VoCDwaUkmcRAfEOnC6k3ht2zHvroVYByTDAao+4WHbUN/Qc9gq/xBVP4exiDZg2R+ec6lFpH8jKwyyXBe/phq/ZjeVEYOgb1Ck09iZx5HOThyvvysY7cJeRVWEIDMdiUSoHRNhzLjC9zWBIZk0PWMTsRoS8WYSsYDOREDEQMQNP4OdMR5uVQLLjROQCAe0u2PsaNvJ2x0dBEHP6bKWZRGNRmlqmsS4A8ZRXV2du7aisa6OGi+FFe+AeDsm1gGx9jAo4u2YzLR5/53w+c54uK29G2xZUB/tOSwVHWDcI3voKtqI5Y7MXzuRfZn+Kgdi2/jV1fjVewoMr6BXYef3MJJdOP0MYAa23Sco6iMRRtVF8Jsi+JFqjGODZRFkB7Lj4aGmRCYs4rFmUsnwo7kKqKqG1v2gurYLy+3EEA5ud6diJDpjvPXWW8Tj8YLtiEQiuQvumpqaaBz7DzQdGF6E19DQgJv3oW08DxKxTGC0Y+K9wqOjHeLtsOk9zFvt4byZcxP6BEhuwLzXuEfucfZsq+YwPHQxoEjJKQw+CNvGr67Cr97NtQV5gWH3c1gq0tWFnfboPcQb2FYuKHKHpsZE8MdnH9eQNjadCZMXEgHxuEMiVkuqu6cmfa0FY8e7VNWkcSKdGCuOF8TpTsXo7Oxg165dvPvuu/i9Do1Fo9E+V2k3NTXR9A/TqK3d/VlNJvAhEe/b28iGRqwjfLxtE2bd/w/iHZDp1fQJj9q6gjOqCsY9Gpr79kSqVAtfZLAUBqW2N4FhTL9B4aQ9nFSa6ngCO53uExjGsnqCoj6C39wzntFFhI4uh1gSEvEALxVhx3ZDx656vHQ90JrdPCaOtpn2EYtIdQpjxQlMjO50nM7ODjo6Oli/fj2JROFFMK7r9hsU2XvXdXvGGjJ2d06TCYJwHCMvMHKHrjK9EBNrhx3besY9/LDX1Sc8qmsIzl+Md8BHw0F/xwHbCe/zp7P3llWSM66KVcL6/fff56tf/Sq+7+N5HhdddBFf/OIXi769su/xfYOXNqTThnQqvO9KJKipL/57jbjrDJ5a910cF3zPYFsOlmVj4WSmHWxsrMzztuXkvWZjk5kns0w4j5v3Ws8yufksp89ruffNe7+e1wof55YzFq4Hrufjpn1cz8sFh50XIgMFhl1XS5cFnhshSYT2lEt7t0MsaRNLmPCsp3hAkNc5cByoj9rUNzjU1AXYTgLfJEilYyQyQdHe3k5HRwfpdOHpsvX19X0CIjtdX//BT3c1xmTCIxsYheMenQfNIDphUnh4K/DD4BjoV92y+wmJ/p7L3Nv2Xm3/li1b2Lp1a0EJ63vvvZdly5bR3NycK2Hd3t7O9ddfz/Lly7nvvvv47//+b1avXs0NN9zAY489RiqVwhhDdXU1iUSC448/nl//+teMH19YiE3XGexbjDF4HqRThR/ouem0wUv1TOe/5mUeB31r5FFT6/Cpzw5c42p3dJ1BntpIM7Zr6A66MPgYE+CbNIHxMQQY42emfQITPjYE4XO518LH/eyTlo2FnQswCxvbcbDcMJRsbGxjhfeZaSdpYweEz5vsKxa2a+M0OTSOcmiyXDxcPBMh7bukfZekZ9OecvDabYyxsYwDxsaxHapGu0yY0MSU6jE4rgGTwg/SdHd1kUx205nYwqbYO6zb2oUxFhgLDNiWS31dlGi0kYZoA9H6RhqijTQ2NtPY0ER1VU2v0A2nC9pvWeHYQ10UWidmfiY97M5O3LyLzowxYALwg0w4+D33vafTqfDe9POXGL45Znc9jcz9uNGjGNfSgjHmA5Wwzq9a2t3dXXCSwL4s3M80ZEeOso/D5zL3hl6Ps/OAydQO61lP5vncv4shkkwTT7Vl1hP0eS+T/36G3OPCefKWNT3LBAF4KfA9Cy9t4XkWftrCS9v4aQvfs/Ey937azt17nk2Qebz7/jBYdoAT8bFdH8f1wvs6n4jrU+962JmbE/GxnDSO69PUXA8c8QH+Zfo34sJg9uQrirY3YUwQBkYmVALj5QVHeB8YLwwRvPBxkJ0OMMbrmYfM8pn19ASSn1tf9nGfwCIgCLyeMMuFWs+2Wa5Nd7orXI/xMUE6vDfhh16QvcfHGIOxAwLbYCIBQa0J1231hF8aKCgKnT/cUJO5jeqZ7E935rYdIJWZ2O0/i4VFuFeevQcbK/ufZWfmsTio6Wxa7akA/H1NDYn23tdOWL2mXXb/52AK7qKNPgcemgwDw6TCF4yBgPDmFb7Hho2bWLPmLxw4bQzbtm2ltr6LjsR6ahsstm/fRkfyfd7f+A5NLcfR0b0JgHGtY/j7+jXUNvts2riZS75yFevffY9vfusK6kb5tHdtKGyRBVs63uHvGx8b8MO274dg3w9O6PnALfyQ7vmA7vfDtM97DSMDBNVYQS34tVh+DQR1WH4Nll8XPhfUZO5rsfzavtNm93XGDAHYXRgnCU4SYyfD6eokpjbznNOFsTtz0zidmOwydhLsvmckDsgPb7Vto9i/RWHwgT2+9jrM373MYGn+3kbvPZb8PZTef0S9/0AG/kP7UNpnL1g2mXDLPRxQ2NvLjikEmL4H0Ia2CZnVBLbBc3rvoff/g+tMdHL5kqVcu/QKapprAfCcng8Bg8EjRWB8fJPCC7IXEPr4pPGCLsaOb+ah393P1i3bWXLpdZzw6Tm0tIwpfFdj4Ztukt4uLCwgOxaSH6J5AWpl7u1soIZnsFmZ17F6zYedObyZnSczbTmFr2ND7tBpz/O25UC2T2pl5+k5XIpl9/QAAwffcwh8l8BzCTyHIO0S+G74fNoh8Bxsq4aupCFIO/ieTeCF977nhD3R3f1T2gbHDXAiAW4kwKkJcCIG1zU4kW7cSDeOG+BGDG7E4ETAjQS4rpV5bGFbZH6eUaABrLDXTuannv35Z3dSwn8Tej3OzpO3rJW/DJkdnXCOljEtdOzq3G3bhmLEhcHo2qlEqhy6u1OZX3go+IfByvxBZP9xwsf9/ePl/79nmcySlp23huw8hb8ohe+T+ce2+tuW3uvZw3vllul5vrGxiVgs1vNeFoXvk2tX7/X0/qXMbIMBOwDH87B9Hyft43g+jufheD52OrwPPIh7LrGUSzwdIZZ2iXkRYimXdJDtRofrr6u1qKu3qYtGqG9wqIs61NaB53URi8eIdYS3jo5YZryig1QqldsegJqaGpoam3E/8Q9UN7diWzYfn+HgOA72Xh7rz2fykqcn4PMPOfR+jcyhhvBxOp3m65dfzFlnfY4zTjsXjKFlbAuJNsPY1rFs3byFljEt1NHIpNZJtG3YRe3hNRgTsHXTdiY3j6O6y870QAImN4xm+v5TeO25v/Dpk44L38UKf2+wLCJJQ9OOAOM6BI6FcW2MbRM4YGwLY0Fghb3HIOjv0Ogg9lRLwdiQORQJ4bSVfQ6753XLxqrKjK1VZcfZwsCxLQfXtnEsF9t2cBw7vLddHMfBdRwcOzzUaVlurzHA7Nhh3rhe9lBs5jVw8C0bEziZoMsbczTh31bvMcD85bPvlx07HKwqtw5QGHxgx9VNp66+nkRnCmO5GDsClouxIuFjK/PYDh+Te94Nv+ruQ1rnp6Wlhe1W+QbaDOGREsv3aUylGZUuHOy2U2nSXR6JuKGj22ZXyqUj5bKrzWXTZhfP9PyROHYd0dpaovWttEZt9j/ApbY5QrTRJTDdxGIx2tvbc4PZ7e3tdHd1k4gnCr4wxrIsbNvGcZx+b/19YUxhQOa/sBc/A2O45tp/Yvq0j/L1Sy7LPX/Sgk/z60d+z2WXXcZvHv0Zn/70ydTUtnDyKZ/l/vvv55zzvsTq1atpah7NlGkz2bhxI6NGjaKmpoZdbW288upfufRr36C+alzPeIfnYxmDtaOGKU+44RlY8djA4x519YXVc/PqW/nRBry6ZtI19XRX1ZGO1JIODOm0T9rzSad9vLQXTnvhGU6e7+N5mZvvAQHGCsDygcy9FYAVHrq0bB/bNThOgO0E2G7m3g6wnPBm2wHYfuY+wLLCx5YVHosz+LgRl+5UMm8srzsXbj4BXvZwaeAT+D4m1XsMMFzP8LH6npCSCw6774kkODRsGsMxE5YUf0tG2tlEY//+bSwz9EqWJi84sCIYu9fjAYNkb+eNgN07nJwP/KUW+/JZF5bvF1zhbafSdCfCMh3xhCHWadHR7dCeDgMjyPskjtgBjTUBDXUQrbeob7Cpa3ShEZpGN5NKpfB9v99b74HY/oIi/7nB9iqKVcJ61apVfPe7382t98tf/jIXXnhhn/dzXZf29nZqquvCs1O6PdIdSdKxBF48STrRTTqZJt3tZc5igbRv4RmXNFV4djVptxbfrd1j21yTwrU8InaA60KkysKtdojURojUVROpccPnIhaRzM2t6pm2HQb98+xPMX6vTWbQOBzDyx8D7Okt5Y/Z9ZxY0vNakFmmvxNQdntCSn7PLO9xuE4vbzt6lonWNvOJcV8dUltVwjqf8WkZ3cyO7ZuxjIcVpMF4WCbd63H4XO61oNfj/pbNvh6Ejwtf+2CllA1O3+DYQ68m99hyqWtoJtHZved5ez3eF76OD8Dyg/Bai+4U3TGPRLtHIhEQS0AsadHRZRNPO2R32acfBhPGR3Fsg20ZHDt7xqiF7Vhgh99s6wXBgEHR+09joB5FNiiK8eFmTDjom3+jn+cK5gvIjaNs3hjjzTV7+FlaYbHFSFWvD2qX8AM+6Mb1k0TSCdxUnEiyHTe5i0i8DSe+A7djG3ZsV3jx4EAlwquq+6mmm9cTyeuV0NAI1SrNvrdUwrpYLAecaoxTX97hXROA8QuDI+gJC/ICpk+Q9AqdvvOmsYOuXGj1CSiANhjKmckGe4DeSn+hE/Z+8ns52cNrez1vLvAKi98Zx8Z3qqGmGqcJGveDRiD/22v9tE/XrhSdu9KYGp+qKvB8i3Rg0e0TngKVYVsBjgWOZXAsiyrHxY642NXhsXdjgW8MnjH4foAf9ARFf6d3WpaVCQY306PI3KxwsLS/D3IG+HDfEys3jBUORlo22BY4jk1Dk81BH68KP+R77ZlnHztF2is3xkBXMleiJHdhYF6pEhPPPL9xffhcOhUu23tlbiRX26rfL4HKL8sebYLaOpVmL7KRFwbDJXP2hCFS5hAygE/LqEbatm8pCJL+ezX9h06feTPz2UHnwD2iD9DSMIQKg2Sg4MgPnUY7ghnjstOaSn19Nb5vwg93A4GxCMJMJggs/ADSgUUqsDBedmutsCdhgU1mnAEbx7LBrsLYFoEbfpj5vffgsSBzKUPhUei+x+0ty4S9CRts28r0LMCyrew5Ank3q8/j/riuS2BcJkwqz7fnWZYVlgqprYNxYSzv9ipzY6C7q9dV5R2Z8iR5JUriHZjNG8J5usMTmPv8JjkuNDSyo3kMfm1dT89jgJ4ItfUqzb4HIy4Mnnm7nfptQc+ZNbv57S04E303MxbON/Brvf9SCs90t/ZyvoHffMD1AU3xKjo6LDJl7QZe/+5+Hnkv7u7okWUBxmAR4OBhGw+HNDY+tklj4+EYD8cKX8s+tuk1nX2NdDhtsuvysOnMrSv3XG4dAbHoqdAdod/i2pkTVQZisMBYmGyc5aat3Ie+yZ7ZlQ2BzE/SZH5G4bzhWfnGGPzAEJiAwM9cm2IoWJ9lO9iOg5098yXXu7CzG5X7RBwoZH0CEimfNq87M1/hP+Ze/zvv9Xy91j/AvIXPu1j1Y6B+TLYiSq95837HAJPuhngcK9ERhkQ8DomO8BaPEenuIr1zB6x/DxL/H3QlC34+uUtjbDtTXbcR6huwoo1Y0Uyp9mj4mPqwJDsNjVh19bnS7Hv7M9jt3/6AP4/e8w38M81OekFpdidHXBjc+afNdPubhnszBCdz6ykqF8EigkU1NlVYVFmZeyyqsKmyMveZx9W9HrtW9kTagI8eZhhX1RDugQNY2Y92A1bPtSKW1XONCJnp8AhMdtpgW2BnnretbMj1rNOysn2KD/ZHmhkeyIRR2IMxHgSZ0AgyIeRjhb0c7Nxz2dc3b9/K83//G92BQ8o44X3g0B24mXuH7rznU4FDsLtk3KfUZ26ZFMleJ/hB6vTEMrecZOa27xpdt477zjyg6OsdcWFwx6n70zxqFDt37ix4vvexWlMwPfCLpv+ndztf+H79f3D0na/f2fq+d8F79V2ouamZXe27dtvO3b2v2U1jDGACQ+BlznT0wukgHdaACry85zwI/H6e25vxdQtsN3uzsDNDC7YLVuZx+JxDpMknUh8hMEHP3tXAna++7c+77+/kTEP4Qnj6v8lNZ45HhYdzMgFjZW52bjrIhEsYLNk1http+tyygeMQhIeQILd+yypsS3N0Gycc8Nxe/DB7+Nj4RAhw8HH3cHPwiWTu+5/HG2C69zoCUxhC+b9je/t7X19fTzyeGGC+Pa/PBAGmuys8HNXdBV3Jgsd9plPduQ0ovKbNCgfNq6uhqgZTXYNVXQPVtZjqasg8NlU1UF0Tzmc7vT4jCjfSDPBgHIVLRwAAIABJREFUdNPQ6hLtyYgLg/ENVbQ011Lj9f8LVKlaWhrY7nb3+5oxJlOSJ6+IlldYRMtL0TOdV0ExO93PF8P14UYoPHulLm+AM3Nmi7ubacfZ+wHDzs5OGusjudpE5WRMWNcm8MOfSxAYAh/Sft/CY3ZmzMC2s4PZ2XsTXjeQuWU/DYwBzwSkA4NvAjxjwjOigvDw08bNtfzPi2NxHYNrQ0NdFU0NdTREa2ioryVaV0V9bRV1NRGqIzYW2TPlMmNAxsPpM1bUjWUS4RhSwZiS98HGhSybwtOue40L7cXJCPUNzcQbuvdyTCmzTpzdH/vZ3TZnS7PnSrHnV9dth9jOTGn2DujYtftrPWrre8Y0oo19vr/civY6AysSHt4t1RlUI+/UUirvdLQg6FvtsHAaXLeaWEeyT9XE7OM9/RZYNn3OSunv/PGC6dyHObiuFQ6OlklnZyeNeYXqhkN/Jay/sugr7Gjbyde//nXef/89Jk3aj1tv+X9oiDYRBIYf3vQvrHp2JbU1tfzgB//OYYcdFp4Wa0NnIsYJC47n5JNO4oc3fjcMiV6Bkdi+A/udd2nr6qKtK8mO7hQ7upPsTCZpTxYe/nAcp09l2fz7qqrd1+bJnpxgmbAbaOWdZNDn8YAnIwwwb7+ncuetp9/+2t4xWLlTrvsLnfyz3fYUULs7U85YEYyxMV3dmHgS4vFw3KO/0ux53/sx4J5VdS00NFI1cTL+5d8ZUtt1auk+zJjMXuMeStrurvStvxefd5FIN0724qCIRXWNRbTR7vnQ7v0h3+sDv1gXCY0krutyww03FJSwnjt3LsuWLWPu3GNzJaz/62d3cf311/PUU0/x/oZ3eObpZ3n55Vf47veuZ9kvf0PKC8P6ph/9GzM/8Y90p2BX0s5cOxH2KmwXqiMuXlOaUTM+TmM6zQF534lhp9OY7i46kknaksmesOjqom37Dt58/326ewVnXU0NTY1NNDQ10tTcXFCKvL6+PjO4nfnAtGvKfJZcGEJjMmfJ7dU1QHk9oN0GVBD2hPoNqA9ytbINpsnFNPcOknEYa1JPQAUWxjMYL4CUR5DyoTuFSaYwXd04VjWlOK6hMPiATGDwvPwvn2CAvfOBP9gH6kVmWRZ9PpyrMx/kvT+43Qj9XExkMXbc2IrqDX0YtLa25spP700J6yeffJJzzz2H6hqHWbOPJB7voLNrO+PGjePVV1+jvX0Hxx03j9fWvIZjh6ewptM9vzwJUmzdYnhxvU20oYb6hjrqow7RZpv6BpuqKgvLD2hOpxnT66ta7VSKVGcn7R0xdiU7M4ERhsb2995j7dq1BHndR9u2aaqvp6mxkcamJhqbm2lsbs71Kqp393WxxWA5GMuBSANBpP/DnyVhgj2HTt4p2ru9aLXPtUYedpDMhE4aHA+rJo1V7WFFe4LaRNzhCYNUKsUNN9yA54WVPo8++mjOPfdctm7dym233UY8Hmf//ffn8ssvx3Vd0uk0P/7xj1m3bh0NDQ1ceeWVjBs3DoBHHnmEFStWYNs2F110EYcffjgAf/nLX7jvvvsIgoATTjiBM844owRN7V/um4RS/e91D/TBnn3spff8Ho5LwR74/5+9N4+zpCrv/9/nVNWtuvvtbXpW9hkFHATBqKCggIDLFxTBCOg36A9UYH5O1BhBDGhcQhKFrwj+1GCUmGgCihjJ7xUSGBbBgOCAgriMOsJsPdM9vd61tvP9o+rWrXv79jbTPcNM38/rdbqqzjlVt+p23ed5zrOaliCTlW314wsZJLRY8fDDDzM4ODiv1+zr6+PUU0+d9fwtW7bw7LPPcsIJJzA0NBQxif7+fnbv3g3AwMBA0zJ+2bJlDAwM0NfXx2c/+xm+9KUv8cgjj6DrgnQ2cHuM7BO+AiVJlwRWUjA64rFjq9Ok/jMSwXsXFCwyyGRN0llJuldDN4L3Swf6PI+lLTW9qdUojgcFjUaLRUbKIcMYG2fTzp1UWlYVyUSCQiZDPpsNamoXCuS6u8kVCmQymbZ5oA4ICAkigSKxn4JWXXq68jBuz/tHzMgMDMPg+uuvx7IsXNfluuuu4/jjj+eee+7hLW95C6eccgpf//rX2bBhA2eddRYbNmwgnU7z5S9/mUcffZR/+Zd/4cMf/jBbt27lJz/5CTfeeCMjIyN85jPByw3wjW98g09+8pP09PRwzTXXcNJJJ7Fy5cp5f1iAR+6bwPNKVCsurtO+klAcUeh+jGCnM3KSFD6d4VPuQ115By8+lEolLr/8cj796U+TzU7tCdLOfCeE4Pbbb+f0009nxYoVbceDGjsCXdfp6jFYsSqodOZ7inLJpzjhUyo26mQP7XLZ+nyzFFMXUNJZjXRWkslqpDMGqYJsMtznwnaoF2amDZmGXSoxMTbK2Ng4YxMTjJYChjG4cxe/feGF5lWFEOSTSQrpDPlshnwuXFl0Fch0d2OmF6Cm44GOWNAqiRwzFP7YI8zIDIQQWFYQ0VgPxxdC8Mtf/pL164PMea9//eu58847Oeuss3jyySe58MILAXj1q1/NP/7jP6KU4oknnuDkk0/GMAyWLFnC0qVL+d3vfgfA0qVLI0np5JNP5oknnlgwZiAYIZtJk88bJEwdw5QYCdlWIjcMgaZ3pPIDHXOR4OcbjuNw+eWX8/a3v503v/nNQODAUK9gtnPnTnp6groEy5Yta3KK2LFjB/39/fzsZz/j8ccf5/bbb6dUKuE4Dul0mk984hPTfrbUBJmcRianAUbTmOsqykWf4kSDSRQnPAa2Odi1GFMSkEoFaqZ6CdSAaUhSqQSeFaqDerpIsZIUjRQhwg/zSVVrlMfGGB8dZXxsLGAWxSIj5RKbRkcoOX9oujdL1+lOpiik08HKIpclny+QLRRId3eBaaK0tuGEHewFZmUz8H2fj3/84wwMDHD22WfT399PKpVCC/8h3d3dDA8PAzA8PBy93JqmkUqlmJiYYHh4mNWrV0fXjJ9Tn1/f37Rp0/w8XetzKJ+T7lqH3qLa8XUNdB1hmIH7lm5EzTeMuk8k6AZCbz6OtlPsi3jfTOcZRhT12MGBD6UUH/3oRznqqKP4wAc+EPWfddZZ3Hnnnaxbt44777yTs88+O+r/1re+xXnnncfGjRvJ5XL09/dzyy23ROf+27/9G7/4xS9mZAQzQdcFuYJGrjD5fXPsOnNorCiKEz4jQ3ZTXjohIZ0OGUWdSdRrZicFSIlnBr73iXyO3kNW0dv6Yb6PWypTHN7NxEjALEYnJhibmGDn+Di/2TmAF09DDhRMi65kkq50mp58nkwqFdktEpkMfiKBZxgoTe6xC+lixKyYgZSSv//7v6dUKvGFL3yBbdu2TTl3qqXulEFWU8xvh/vuu4/77rsPgBtuuIHe3kmv1rRQyufXF11OpTxIpTRIpTqCUx3FdypoXlCBLqEEFgYmFiYJEr6O7ktEzYNyFeXYYNsI1wn2HSdIvqX8wMWv8WmNJAVNL3NsTmx+1C+1gCkYRsCYjERjXw+2IhHbN0Imoof94T5G81zbsshE1zbD88Jr6+F+ItHYr48foD+mnTt3AoFHz/7C448/zve//32OPvpozjrrLAA+8YlPsH79ei6//HL+9V//lRUrVnDbbbeh6zpnn302Dz74IKeccgrJZJIvfelLk+6/nlZ7qucyTXPOv4vZQilFteIxNuowPuowPmYzPuowNuowtNMO8kCF0HVBLm+QKyTIFYyg5Q3yhQSm1abI0OGHtf1M3/eZGBtjeNcuRgcHGdk9zPDoCKNj4/xmaIjitq1N801No9tK0m1ZdKdSdGWzdOfzdHV1UejqRkungiAwywq2xoFXo0TX9QX5H8/pl5JOpznmmGPYtGkT5XIZz/PQNI3h4WG6u7uBQLLfvXs3PT09eJ5HuVwmk8lE/XXEz4n37969m66urraff+aZZ3LmmWdGx3viHXPVtjXUvMYKBZN4RoQXDURYTaue6Sboa055UGcywlXggqiopnPr+41zFEIFlYcnXbeFYdX7gzlRjbXoh1P//TSqxRGWOAwGG1G/9frE8XNFOKdRArBx3Aixjc+jXl0uFnEbnUt0SoTzjkxxeH9h+lDWxgPPGJHcesK088PBZWvW8uSvN08aLgH/57ZvR8dFoDhSRgDrPn4d6z7e8CHfOlJuuuZrzzmX151zLtvalD2UQvLC4CiP/nRX25w2wXc3OZdOay6ipv9zfDz+3RP+HzWgB+gJnCkiJwtb4ZQVzmgjjqV+rqYFqtiEKUjEtqYpg/TiU35uLyLfB4WgOw/0myYjIyNUqxVqlQqlSuAuW61VqQ7Z+IP1fBNbEQQqqLRukDKCbTphkDQtkkmLRCKBr+soXUNpjUZshdH87rV8F22+79nMaf/9iknfd/2gt6vAkek9c3HdqziD8fFxNE0jnU5j2zbPPPMM5513HsceeyyPPfYYp5xyCg8++CAnnXQSACeeeCIPPvgga9as4bHHHuPYY49FCMFJJ53EzTffzFvf+lZGRkbYsWMHRx11FEopduzYwa5du+ju7uYnP/kJH/rQh/boQWeD97+yHyuVplgsRnQiIpUxuuErn5pXpOKMUnFHo23NHY/mC3RMPUdSL2DpBUwtj2XkMWSyIf+rRpi5in1YLO/YpDlNqSVa7jHoUy3HzeO+UoQuJqggFBbTMKhWKlGf8r3AN1HVj+PzG9tgXAUF0qMx1bhO/LPCfVVPsh/uN6fKCFmMiK+dwqQLsV+Fis8XsTkyMKQhJUoEP9KoT8iwtKNEX3EY0kk2sbX6tZspeZixqN0vsvVcUZ8/A4Np8z+b5SmzHlHthoRipOry9EApmqSa5sfesdiFWuc0Hzf2m9/FRkR0u7QoU81RCgi8KBegcmNMstOA1DRTfcAOW7He6dCU5/xFiu7UrgXJTTRjBPLzzz/PrbfeGhX7eM1rXsMFF1zAzp07J7mWGoaBbdvccsstbN68mUwmw5//+Z9HxuG77rqLBx54ACkll156KSeccAIAGzdu5Pbbb8f3fd7whjdw/vnnz+rm90cEsufbjNe2M1rbylh1C2PhtuI2ch0ltDR5c2XQrFVBM1eS0KZ7OxcW+zPqWvk+odhIGIjR2LbpV619U50X7qs216qccS6ZQw4PGVNLmy+nwNiKpSXHdHObaXw+roHAMAzGx8dJpfbfezZXeK4KKtpNuEyEhuzSRFDlrlaNsxRIpgSprEY6I0llJam0ZOWqbqr2eKCKbpOLaGoBTGE7bqO2djGoqz0+PsHERNBc12vKP5ROmBQsi0LCpGCaFEyLvGmSNy3SiQSebuAZOp6u4+s6rmHgGzqu3uiLP89kZhu7/9gzNJ2joKe7QJeo7tH33al01oKFIIw1txgxhrHaVkarWxirbcH1G/+0lNFD3lxFwVpJ3gyYRDaxDE0uvF77YEvBMROmSkcRve7tmMTkijNTjPlTz5npGu3G5wtCUvrj70j+8J+ndVKY0RFirvPb9Il58PZxbNVkwG7se03xPUJAKiNDA3bdNbZhyN4Tu5dSikqlMqm2dn2/WCw2zdc1ja5Uiq5kkh7Toidh0m1Z9FhJuiyLhKahAM8InFK8RMg4jERsP3RYmeF+O5XOXuQw9QxL9JeyJP3SqE8pRdnZzVhtS8gcAmYxUHyGehFugUbOXBatHgrWKvLmKlJGzwFrvH0xI/pOXyTfrZozI5p6XAoBfUsRr3hNY9XUuqqqVVv63cABor6qmiLwZs4sS8rpPehaveja9GuGQU4PWtSfNiCv4yqdqqODnmH3mE25qlHeLtlV1XDR8WUCX+qg6yRzJpmcTjojyNSZRlqQSIRlLVSoLKzneArF8oSCfC6HyGaDObFcUJ7nUa1UqJbLVMtlatUq1UoFu1bDrtYo2jXKjs324gSakJiGTjKRIKUbJHUdS9NISo2kpmHIhkFdQaD6DFvdqBCpVAVIy4IjDpvzuzYTFh0z+I//+A80TcN1XaSUyPAfUd9vd9zaN9389mO95OUSurRXIjICMoqKt5uiu4uSM0DR3sFA6Q9s9p4McuMLha6Z5K3l5JMr6bJWUUgGTMLUM/v7K+xgHtEwju89NF1HGhbymJfP/eQ6U/E8cByEU4s85YQTqOKEYxOmrw3muA7KthFeTFXXVg3oNhhQeKzcIKo56HfBC/s9tzE+TdItjUYZg1mFqEkNoekILdgS7gdbPRprPY7PFbExQ9NITjXX0hCaGY0pGRiifU2G6btBoXB9j5rn4iuFEHVaIZBCokmBISSGEGgt74daoOSLi44ZVCoVlFI4joPv+00tCO1vf7ywMIBDwtZAUIKnBPwa+BVBbnwQss5wNDSpoUmjifnUC7TH+0zTxPO8PWRmCzc2W+a731ZJ7dQ4U6l26hJmm/52snVUhat1rE2/mKK/6VDYJMbH6d26PZRgg3ttSL1ERL+e5bQxZ7YwQBiNgnl7YZ6IS8FAJAmrSBoGpXyU5wXNd/E9D3wv2HouuhTYtSrKc1Fhn++5EB4H/eGx4+A5Lp7t4jsOfn3ruCg7YD7CryG8IFeQ9IMm/IBZCX8vktTFIQRK01Gajq9peEISZiPCUQoHgS8knpB44QpLS5jopomRsEgWuigc97L5uZcYFh0zuOCCC/ZI5zYdo9jTsdnM9TwP2ytTsceoOmNUnHFqbpGaWwq8epTAUwIhUkiZQhdJNJFEx0KgR9f0PA/btvf4/vc3hBBIEUhNgfQ01XHQ1p54IstC/WiTe15wsUl9dcfBye6WdXfZFre/Kfri52/fMcCHr/44g0NDSCm5+J3v5LL//WeMjo5yxUc+zNZt21i5YgVf+z9foiufR6H4q89+lvsfeohkMsnNf/u3HHds8KNf9pI1HP2SlwCwYtlyvv31r7feaHQnfpDMagoiG1M7xAlvfE7ds6vlvCbiHbt+cx8t12m+fvw687Ei6u3tpTTH33K9xl4cnhdEZE/EI7JDG0XdkI3ykb5LynLJJBUZyyNluaQtn6TpYeke0mvjCNFmxSTCFZLm2BgtKj3fruFVq3h2FWXbKKcM5TGE5yJ8D8dYGF/4RccM9hR1iVubjWEsZqRs6BlpkdIauslJUlqT9NbQVTaf52G7E1SdwN3VdsaxvSKeV0XiIHDRMTC1LJbMkzbzSD9BQibR0JslRaa7TyIXUhUWUFG+wqszijbNi/b9GcbbH085RrwvcKH1qI/RdI4hJJKYFwY0XHJVzO9EtbpSqsY+MYPzHmDMsVm3fj1HH300pVKJSy65hJe94hX8+7//O8efeCJfuuUWvvnNb/K3t97C+vXreeSRR/jV73/P93/wA5555hk+/MlP8k//9E9AEEz2z9/5TvA+CMGO4kS0X4cQgh1Dg9zxi1/MaZXWbiW50CvAuY4tNDRNkM1rZPNtIrIdFXk41Q3YpQmf3SM+jhNbvQlIxSOyuxr7ydTsV7eTk4c04Ps+3ckktdr8Z2pddMwgt20H2tAw+Up58rJ5kiFpMnFsMiS1LL8b/fsSCaAX6A395QVKKHx8fDw818NVNn6thMcEFXyUACkNhDTQZAJNM9GkhZD6nKXJSGJvJ022uc6+kibns7hNk8tfnVm06Wvd7+rqYs2aNQAUCgXWrFlDqVTixz/+Md/5znfI5/NcdNFFXHLJJfzVX/0Vjz76KBdccAG5XI6TTz6ZUqlEuVymr68PIQSpVGrazxdCkEgkyGazk1Z7rutOqxbdfyrS2WEqRqHrwep3XzIoLSnJpwSFpQLfFzi2wK4FFTFrVdg5CLUXQPkChEQgkJokmdJIpTRSaT30ejLIZDVMS5u1ilRKSTab7TCD+UBydBSJwFIqIjrTESJfNhO1aXWcEbGaefndjtBNIo4txHdaghleYyqkchrPb/95IzaitpWx6lY83w6L/AoyiSWhy+tKCmGMRCbRjxQHdr6kzOCP0Gs75vWarrmMYt//mvX8LVu28Nxzz/GqV72K3bt3c8ghgX3o0EMPZXh4mGQyydDQEIcffngUJ7B8+XLGx8c5/PDDqdVqXHjhhWiaxrp16zjnnHMmfYau6+i6HjGg+cL+UpFONRbvSyQSlMvlGT8rzgwPSBVpjDnkcjkuvvjief+cRccMdh579KLzuQdIJbpYmnkZSzMNw5OvfEr2IGO1LYxVtzBa28JYdSvbJ34WBfBIYZAzl1MImUTeWkXBXIWlF/afUfcAw96msAb46U9/ytKlS3n++ed55zvfyUtf+lIOO+ywhbrlSfcwaxXpPsZC/5aDCHq110zQ87yY/c6nWvWoloNWqbjUqkGfXav7GgXnaZrCSKjIs1YzFF1dwcpvvn9/i44ZdNCAFJKs2U/W7Gdl7qSo3w2jrOtMYqy6lYHSs/xx7JFoTkLLRBHWwSoiCKQztOT+eJRpMRcJfr4xHymsIUjzDsFK4jWveQ3PPvvsPmMGixl1NY2U+6YYjxfWoKhHYhdjEdnVisKrwYS7MHaUDjPoYBJ0maA7eRjdycOa+mvuRKReGg0ZxR9Hf9wSZd0bqZjqgXQ5cxlSLL5Xbb5SWI+OjpJMJjFNk+HhYZ544gmuvPLK/fVYHSwgNE2QzWlk29WgcIKI7KSVYwESO3WYQQezh6lnWaIfzZL00VFfEGU9FKmY6uqmHbEoayk0sonloS2iwSQO9ijrJ554Ikph/cY3vhGAq6++mquuuooPfvCDfPe732XFihV87WtfA+CMM85gw4YNUQrrG2+8EYBNmzZx9dVXU08Fv27dunm3C3Tw4oduCPJdOr29KYaG5p8ZdHITLRLs62f2fJcJe3ssDcdWxmpbKDuNdOWGTIXqpUZSv4K1ioS292UP59Ob6ECBrusHXKK6vUXntzw3dHITdbDPoUmdgnUIBas5qtr2yo2EfiGjeGHsMRy/Iekk9a6YLSJIw5Ezl6PJqbyvO+igg71Fhxl0sE+R0FL0pdbQl2qoOZRSVNyRJo+msdoWflt6Dl8Fkr1AkjWXxlxfA3VT2uhFiH1j3Ougg4MZHWbQwX6HEIKU0U3K6GZZtpFkzVcuE7WdsdTgWxiubGbL+OPRHF2a5MwVYWrwVZG6qfNqd9DB3ND5xXTwooUUOnlrBXlrBeRfFfU7XpXx2tZYgaEtbJ/YyObRh6I5x3f/P6zQX4oUBppIBJHWwuisIjroYAp0mEEHBxwMzaIndRQ9qaOiPqUUVXcsjI3YiqUK+MrH9iYIQ6wBgRQ6esQcEkHGV2Ec1F5NHXQwG3SYQQcHBYQQJI0CSaPA0szawJsonQtSlSsXT9l4vh1tbbdMIx2dRBNGxCB0GawmhNA6TKKDRYMOM+jgoIYQIiD0GBBzWVXKx1NOE4Nw/Qq2mmicixaplxoriQRylqqmbdu2sX79egYHB5FScskll3DZZZcxMjLCFVdcwZYtW1i1ahVf/epXKRQKKKW47rrr2LBhA8lkkptuuom1a9dG1/qLv/gLtm/fjhCCb3/726xatWp+v6wOFjU6zKCDRQkhJLow0WVzbnhfeU0MwlMOtldEeY2EZYEdoplBaG1UTbquc/3117N27VqKxSLnnHMOp556KnfccQevfe1rWbduHbfccgu33nor1157LRs2bGDz5s088sgjbNy4kWuuuYZ77rkHgPXr1/OhD32IU089lVKptM/SI3SweNBhBh10EIMUGlJLYtDIsRTUbairmpyIUThuhViZsUkMorevhyVLlgCQyWRYvXo1AwMD3HvvvXzve98D4MILL+SCCy7g2muv5d577+WCCy5ACMGJJ57I2NgYO3fuZGxsDNd1OfXUUwFIp/c+KK+DDlrRYQYdHPTYuOOfGa0+P6/XLFiHcsLSi0NVkxNTNVWxVTGaF6iaDHZsG+SZZ5/hZccdzdDQUJSArr+/n927g6jsgYGBpgjRZcuWMTAwwI4dO8jlclx22WW88MILvO51r+MTn/jEizKLaAcHLjrMoIMO9hDTq5oaDGK8OMq6D36Yv7z2KjAnUMpntPpCtIJQSuH6tSlTWLuuy09/+lPuvfdeVqxYwRVXXMEdd9zBRRddtK8etYNFgA4z6OCgxyuWvXuffl6gatIwsHAch4+u+xAXvONPece578FTNr19vQwPTdDTm2f79i109+QZr22je0mG3/3xGY45/jA0kWD7ju309vVg2zYve9nLOPTQQwE4++yz2bhxY4cZLAJ4nsKxg2aH2+JYkUx+/j+rwww66GCB0C6FtYbO2Wedw/9/9wOsW7eOf/r3H3D22W8ibSzhrLPO5tu3/wtv+l9v5ImnHiedsbAKNQ7LdjE8MsSWHb+hr28pP37kYV7+8uP389N1MFsExW0ICHpN4dh+RNjjRD7qqzXGPW/y9axklTeeO3WRpD1Fhxl00MECYS4prE09w5vPOo9HH/opbznjXViWxd9/4QZSRi+eZvOxa9bznksuRynFMS9bw1vOPyVUNTWM1kok8f021KODeYFSCtehiZg3bWv1fb+ZuNuK6apnSglGQpBICAxTkMxI8gnZ6IttjYSgf2kPNXts3p+vk8J6kWCxPfPBmMI6KMHo4SkbN2aT8JRDPcp6YOT3/LH0X1HNiEKY9TWdWDLr+IgDCXvyXitf4ThTEfKpJXfHVkxHLTU9RtQTsi0hT5j1Phn1aRpzCm7spLDuoINFjqAEo45Ex4g5EgWurw5KeFTMGnl3JaPVLWwdf5K666smEkFCvyjja1Cm1NLzB2yUte8pKmWXiXEPpxYn7v5kFUytQdAdZ3r51zCaiXcyLTGMOCEPiH0rode0A/N7rKPDDDro4ABHEGWdQNd1upKHsqJnPQCuX2O8to3RMCX4WHULA8Vf8MfRH0fnmlo2Ygz1GhI5cyWGZu2Te4/r05tULLWpdOoxfboLMIW6RNAkmZuWIJOTk6R2w2xWxeiGQMoDm6jvKTrMoIMODlLo0qQ7eQTdySOa+qvueFRYqF6JbvPoQ7h+LZqTNpZQsBoV6PLmKrLmUqRoH9uglMJ1Q33cCk/KAAAgAElEQVT6lIQ8JrXHJPnp9OlCNhP1ZEqSL8iIkHd3Z6nZpRaVjEQ35qZ66aDDDDroYNHB0nNYmWPpzxwb9SnlU3KGGK28wHB5C6PVrYyWt7J94mlUaI8Q6Jj+UgxvBbq9HFlbjiovw6vkcW2m16drcSOpJJ2TdLXTqdcld3N2+vTe3jxDQ868fTeLGR1m0EEHByF8f7Lxc2a3RhPHPgpopAa3hIMyd+Jb2/HN7TjJ7djWb/DTj0Ma6AapUiRZQVquIKOvDAsNrSRlpQ8affpsEai9vGmb67ozzplubiaT4bTTTpv3e+8wgw46eBEj8CAC5QeSt99yHI0rQPns3GHz22dtZnKiapXI0xkZ83ZpdWvsxkgcg5Fo6NNtrxSpmBoqp8cZdB4EByhCyuiJqZkCz6ZsYjmanF+y044AT0dwZyLGezs+X9A0bVLTdb1tpPp8oMMMOuhggdCawvqiiy7mfe+7jJGRYa666iq2bt3CypWruPlLXyGXy+N7is9+7noeeugBrGSSz33mCxxzzFoe/+lP+Nu/+0x03c2bf88X/u7LnHHG2QgBQgqEAE1CwhKsOiIxya2x7sNuJASGIfZan66LJF2JI8lph7HcDIig4ziUnd2M29uYcLZTcncwWhpgoPhMpGpCSRKqi4Tfg+51o7tdSKcAThLPnTsB9n1/3giwEGJKAhzfN01zyvGZ2kxz6+NSyin/RwvlJt6JM1gkWGzPvBBxBnEpPJLKfZr7fBVK8LBz104Gd+3k6JeupVQqcuGfvpWbv/R17v7h98jnClx+2ZX8w21fYXx8jI/9xTU8/PAD/PN3vsVtX7+dnz/zFJ/77Ke4664fBQQ/bKNjI5x22ut4/PGfkkxasftSaJrG7t27KRaLc1ZLzFZ1UZ83J7IhfLRUFSNTQc9U0NNl9EwFLWk3vltXw6+kUZUM1HKIWg7NKaCRnJaIZrNZarXarInsTAT4QEBvVxdDIyN7dG4nzqCDDmJoIup+GyLvK/wm4h54vAQEMCCCKkpdrcIuBSLY1gW63p4cvT05EBXSWY0jjjicXYPP88AD9/Ktb90Ossjb3vYmLn3vpXz4I1fx3/ffw1vecg5Ve5SXvORwRsdG+MPmX9Pb2xsR3+9///ucfPLJlMslyuXSpGfbunUrjz766LTPL6WckUjWpd/5ILJTNR+bCWc7Y9UtsXrWW7G9hpBn6IVGXETo1ZQ3l6PJRPAdH8xCjlJotoNRqWBUquiVKka1EmSrfcnqef+4DjPo4EWFVv3vdBKr47jUqi627WLbHrbt4tgujuNx+JHL6F/i4SvF0uFhLLsuhU4n0ao2e+1R0XW25WefH2bHjh386lfPcfTRL2X37t10dRXwPI+e3h52796NEILBwUGWL1+OYQSFcpYtW8bo6CirVq0KA84E999/P+9973vJ5XJAw9NGCBER31WrVk1LhF88LpcGprGa3lSDsAW1rEcZDeMixqpbGa1tYdfwffgq8BoSCDKJpeStlSwvvgTD7yFvriKT6EMcoFHWwvdDYl/FqFSC/UoVGfrdKsA1TZxUCnp7Akllnv+Pi44ZdG25FbHDoOBr+FoSJS18aaFizdcslGwdMwOn54MQdb3ri8EA50/ndD4H9PW/lkIhjRCglMck8i5CQZ7YD0qI2JFot4nGTNOkUCg0EeOp9kulEldffTWf+cxnOPzwwxFC0NvbG11NSklXV1ek9sjng5SUmqaRyWTIZgOms3PnTn77299y9tlnYxjGpGfWdZ1UKkUqlZrDN/XiQlDLuouk0cWyzHFRv688ivbOmLF6K6PVF9j6/OQo60JosM5bqyhYQZT1iwZKIV0Xo1Jtkvj1Wi16x3wpcZIWle4CTjKJY1m4SQsVqrF6e3thAVZDi44ZeEYPuuYiquPo7hjCryL9KkLN7KvsSzNkDMkmBhIwj3A/ZCJKmhGzqTMVhAFChGqHuRHgPdHrtno42Lbdds58mY2klKEKQkdKDSlkWFQ+bEhAAklQEpSG9CVSaCQMiTA0RP0cgn0pA3WDYWgYCQ3D0EkkwmZpmKYeNCtoSUvHShq4XpV8Po/rujhLlrAQnujmzFNwHIf3v//9vP3tb+fNb34zEPyYd+7cSX9/Pzt37qSnpwcIitnE7WA7duyIiuAA/OhHP+JNb3pTW0ZwsEMKjZy5nJy5nFW8KurPd2X4w/ZfhKuIgFFsn3iazaMPR3OCKOtVkctrEG29Al0ucJS1UujVakj4qyHhr6DFDN6uYeAkk1QLeZykhZO08BKJeZf6Z4MZmcHQ0BC33noro6OjCCE488wzefOb30yxWOSmm25icHCQvr4+PvzhD5PJZFBK8c1vfpOnnnoK0zS58sorOeKIIALywQcf5K677gLg/PPP5/Wvfz0Af/jDH7j11luxbZsTTjiB9773vQu2lB3sejv5fJ7BwcFmidS1UW4ZvArCqyL8CtKvIlUN6dfQqKGpGho2Oja6GEcXuzGEgyFdEtJlpih2z4eqK6k6gqorqYXb6gzbmiuoOMHWV80fMlv9byqVwvO82RnRlAQ0lC9RSsP3JL4vUb7EcwWeK/E8Dc8VuI4MmyAg9O3RGnQ0VUbGuQYdTfldl/e/KqRdCmuAs846izvvvJN169Zx5513cvbZZ0f93/rWtzjvvPPYuHEjuVyuiRncfffdXHPNNfv8OV7MMDSLnuQR9EyKsh6LVEx1JvGHkQfwVF1dKMgk+prScOStVWQS/VNGWU8HEUn7ocRfraJXa4hQ0FJC4FgW1XwON2lFEr/SXzzV6mZkBpqm8Z73vIcjjjiCSqXC1VdfzXHHHceDDz7I2rVredvb3sbdd9/N3Xffzbvf/W6eeuopBgYGuPnmm9m0aRO33XYbn//85ykWi3zve9/jhhtuAIJUvieddBKZTIZ/+Id/4AMf+ACrV6/mb/7mb3j66ac54YQTFuSBb7vttr12RdM0A02zWgiwxDQEqQQkDbAMRdJQWIbC0n0Smoepe5iah6F5JAyXrnTARHThoFNDFzN7vvjCiK1Kkqh2qxLNxBMWjmdiexY1zyRh9TK426bm6Ng2TUFHdklRjAUiTQfdIMrtYlmCRK6VkAsS5sGXxGtPMJcU1gBnnHEGGzZs4JRTTiGZTHLjjTdG19qyZQs7duzgNa95zX55lgMNlp7HyuTbRFkPBvERYb6m0epWtk9sjBwCpDDImcubMr7mrZUk9a5AKFEKzbab1DxGpYrmNNaenq7jJC2qfRncuprHMveLtD8XzMgMurq66OrqAiCZTLJixQqGh4d54okn+NSnPgXAaaedxqc+9Sne/e538+STT3LqqacihGDNmjWUSiVGRkb45S9/yXHHHUcmkwHguOOO4+mnn+bYY4+lUqmwZs0aAE499VSeeOKJBWMGr33ta8lkMlQqlTn7/s7k/ztXuGGLoHyEX4tWJcKrotwKyq7gO1VwKyg3WLlIp4pUVaQqojOEJmpYsoompmB0E7AS8KXAMUxszcQ1LVxl4mLiCwtfWCFzSaJ0C6EnEYaFlkgiE0k0Kwl6EsSi0y7uEf7kT/6Ebdu2tR274447JvUJIfj85z/fdv6qVav42c9+Nq/3t9gghCST6CeT6Gdl7qSo3/NtxmvbYx5NW9hVeo7nxxpeWSZJesQSer0e+lQvvaqPlOpFs3LUMilcKxmpefwDVI03p1/1rl272Lx5M0cddRRjY2MRk+jq6mJ8fByA4eHhJuNYT08Pw8PDDA8PR7pRgO7u7rb99fkLhZe//OX73B1NqSDD4rS50mvxrIw6jp3GtlNMV6tECCJ1SpSdMeGRMm2sRA3LqGEaNRJ6jUJa4tijGKKKThVd1Uh4VYRfRfgTSH8w3K8hUEF6fDtsLR6MSuhtjO7N9pHFaozv4MCDJgx6tOUsFd0YrMHwKxh+FccZY7cYZEgMMSiGGNJ28yv5S35BI6FfSvaSZyUFVpL3V5H3VpHVls17lPW+wKzvuFqt8sUvfpFLL710Wm+FqYp6t4MIjamzxX333cd9990HwA033NDEdOYCXdf36FylFHbNp1bzqVU97JpHrepTa9m261fTOMlousA0JaalYZoG6ayGaWqYlmzaJlqO9TlEkuq6PqsALKV8lFcDL7CfENpRgv0KeGWEW0FOGhuM9oVvz/w5WhLqTa9vU2FfClXv01JtxpMgpzey7dy5M3ruxQTTNPf4d3EgYs6/Zd9HFEuIYhExUQy2xSLCafw2VNJC5fIY2eX0Z17JkmwGLCty/pio7WK49Ed2x9pvdj+DrwLJTQqdQmolPenDotadPoysuWRetAp7Sr9mvO5sJrmuyxe/+EVe97rX8apXBZb8fD7PyMgIXV1djIyMRH7PPT09TVJ34FPdRXd3N88991zUPzw8zDHHHENPT+BnHZ/f3d3d9j7OPPNMzjzzzOh4T6X77u4edmwfbF9/NJaCd5IE76hpHdD1sNJR3QCayggK3RpGQm8xksbywBgCTZ/uBfHCFugkFVCtBW0u2LPVkBW2EHrYZnKjUW7gpeVVm9VeYZOhgV74NaRfQbhVhL0b6W1rzGF6F1OFDLy6NLPNysPC1Y/GzC/DU4rAsC1QIvRmEiK2PXig6zq1Wu3gDcJqg+nea+m6ob9+Q7evV6sNF04hAmNuLotjJUPDroXSWoy6pVLQImhkOJJM6kgOTQF94PkuE/aOSM00Wt3KtpFn2LTrwegsQybJRbaIhutrQkvP2zPPhL2KQFZK8dWvfpUVK1bw1re+Neo/6aSTeOihh3jb297GQw89xCtf+cqo/z//8z855ZRT2LRpE6lUiq6uLo4//ni++93vUiwWAfj5z3/OxRdfTCaTIZlM8tvf/pbVq1fz8MMPc8455+zRg87mWe69exzHHp12XqtBtJ7Eq523S9RnCOQiNJK2hdBRWgZPy+zZ+UqBcgJGEdpOmpiJX0V4lcZ+2HRnKJxfQU8XwDaZ3ldjMoNQdUaBBCFRiFClJcLjxn6w7fzP9zuUQq/VokCtujePFpP2PUMPvHlyfTjJwG/fNefPqKtJnUJI3OOwvTLjYVxEvcDQlvHH+b23IZqT1LvaFBhqRFnvK8yYm+jXv/411113HYcccki0xLnoootYvXo1N910E0NDQ/T29vKRj3wkci39xje+wc9//nMSiQRXXnklRx55JAAbNmzgBz/4ARC4lr7hDW8A4Pe//z1f+cpXsG2b448/nve9732zWk7tSW6iX/2iQjabxnEr7d0bDYE4CCsdHdRh+21QLhXJ5bK4TlAfOHDx84OmFAI/yEVBkJNCEN9XjbGZ0IZBxBmMopXZxOfO7+pE13XGx8cP6KCzmSA8ryk1Q9JxYaKIrLtwAq5lRcZcJxlI/P6LSF2olKLijsQ8mgLX1/Ha9liUtSSTWBqLiwhWFGmjj76+JQuyMugkqlskWGzPPC+J6mLMAhRCNZgJhAykvt80N85QZoJoYRAhA2llKJFqK1ytRHMaq5ODihnU8/JUg0CtSM1jN+xRvqZBLktZ1xqRupYJB0jCuVYEUdYDwSoipm4qObuiObo06cscxetWfnyP7A+dRHUddLAnqHs8hUFIrVLTTFJUI4X1LqSUvPvid3HZ+/6MkdERPnjlerZu3cqqlSv42q1foJDPgu/zyU/fwP0PPkLSsrj5767juJe9BIC/vuHL3Pfgo/i+4tRT/oTPXfeRGDEI1Vu2hlHaTn706Sk8vJJBZLy0ULFULPXI+P0G38eo1ppy8hiVSlNeHs9M4KSSlLu7IonfN3R6+/oYP0iEnCDKegU5cwXkG1HWjlcNa1m/wFhxM5pwFyQot8MMOuhggaDrOtdffz1r166lWCxyzjnn8LrTTueOO+7gta87jXXr1nHLLbfw5a/9C9deey33338/f3hhJ488+hgbN27kL6+/nnt+9COeePKnPP7Ur/jv//5vhPJ52zveySM/28TJr35lbAXih/RcIL0JpD0Y2VLErIzx8bQqySnydcXTrQQxKfV5zDJqVzpOU2qGKFI3HPelxLUsKl2FhprHMicbdQ8SKLsGE2MwPgbjo6iJUbzxMUrjJUrFMsVylWLVoWx7FB1FSTORyTQv/9j830uHGXTQwQKhv78/SieRyWRYvXo1AwMD3HvvvXzve98D4MILL+SCCy7g2muv5d577+WCCy5ACMGJJ57I2NgYO3ftQgiNWs3GdgEkjuvT278KpWebVidS13HSFiN9L290zoMxXqpZuAoLozkvl0wCBsLXkZ6OcCWaI5CeBiqBUAaensI1s1RzeZxkDieV3G95eeYLyvdRxQns0VFKo2NMjBcpTpQplSqUKjWKVZeS41NyFEVfUpIGJT1JUU9S0pOU9D4qemiEToatBQVdcf4C3HuHGXRw0MP/139Abdk8r9cUqw5HvuvyWc/fsmULzz77LCeccAJDQ0MRk+jv749cqwcGBpp0usuWLWNgYICTTjqJk08+mVe84hUopbj00ktZvXqW+eyFAJHAlwlgD7N3tomMD5hHyETcCppdRHNKwb5fQfhDIBzAAWGD8MEAvyU4V1egl8EqC9SI2bwq0VoCGKN0K7EVScVFuJUgbmUeI+M9X1FxfIq2x0S5Sml0PJTWK8Fx1aZU8yg6PiVPUPQlZWFQlAlKehJX6kAC6A4bgZNaaM6xlEtaeKSlImNAf0IjbRmkkybptEU2ZZJOaKQTkowRbk2NtKGxcmlfkzv+fKHDDDroYIFRKpW4/PLL+fSnPx2lo26HqQI2N2/ezKZNm3jyyScBeNe73sVjjz3Gq1/96gW75+abkJGNQdg2uh1T81Sq6I4DBMzN07UgNUPGalLzoNyWFUkQZ9JuVSLrzMYZQY/6w8j4VmyFvnBXCb0p3sQTFjYmNUyqfoKKn6DoJSi6BuNVwWgZRiswXJHsrmkMVQ0mfElJaZSndOsMYm+k8ki7VdK+QxqHtO7Tp7lkEj5p0yFtJcikLTKZFOlchnQuTdY0SCck6YSGvhceiwuVxLPDDDo46DEXCX6+4TgOl19++V6lsL7rrrt4xSteQTodBCedfvrpbNy4cUGZQVRsJXThnKrYip1OUa5n4ay7cLYjVsLAlwYw+4JAvlKUHZ+S7VGqeVTsCo5dxnEqeG4Zz6mS0Fyc6gTSq6CrCoaqYYoapnRIaWNkdZu0ZtOn2VhamNtFA9Jha0HVkdRcDcfTcD0dT+n4JEBaSD2JZmbQU3n0dBeY2RefMX4v0GEGHXSwQJivFNbLly/nO9/5Dq7ropTif/7nf7jsssvm6yaRjotRjblvzlRsJWnhWo1iK9PB8XxKdqBuKTk+xZrX2Le9xpgdEP34vLLjT+OxZSJUgrRfI+0I0o5OxtVJuToZR5JxIeW6ZNBJGwZpK08maVDI6mTyJumsSSJrIlMJhKUjTQ2h+0hVI+HXsOo2lmilMhiuTnyoAVNkAJiLMX5K1dcepNCeD3SYQQcdLBDmK4X1W9/6Vh599FHOOOMMhBC8/vWv56yzzpr7Dfk+eq3WlHdfr1Sbi60kDByrUWzFtiyKUqNoK0pOQKyLuz1K9niMkDeIetH2w3kBcbe96R1wE/iBmsW3SbtVCk6JldUJ0tUJ0k6ZjFshHbUqGa9K2tRJp0yS6SSpvn5qmSTk8pDNI7JLIVcIjjN5hDl17pQ5R6DsU2N8zBW4tUiW0w/GK+Z69zOiE3S2SLDYnnlegs4OMMSDzuLFViiVqRarVCo2RRfGPZjwBaPSYEzqjKExriTjnqDk+hEhL4VSuj8NhRBASicwhAqXlO+Q8auknQppu0SmMk66Mka6NEy6MkHGLQe69nCb8F2wkpDNB0Q8W0CEhJ1sAXItx+kMIrYiOeDea+WFxvhqW2N8EyOJmE2dwQQ2FhJZBg/5+B59fCforIMODmAopfBVoEOvbz0Fvh8e+wpf+fiqxvM7xvnRcy8w4SqKXkD4q1F1vNafe5CnXJeCTEKSSWikdUFeeiw3XTJGjbRXDYh6rUi6Mk66PEKqOExmbJDM2BCWW0FrVeZIGRLvgMCLrjwcsjyQ1nMFREToQ2k+MZsCogcJhIbSUigtNUP0x9To7c7D8Ni83hZ0mEEHHewT1Am6pxoE3IsTdqXw/fpxjOiHfTNBApqAiqcQumRZKnBVTFo6KekH6hWnQtoukq5OkCmPki6NkpoYwhwfhvHRIPjJnkIZbiVDyT0k6CuPbhzXpfn6capZeu9gniEXpnhOhxl00MEsoFRQGNHzm6V0r4mIT90/G22sJgVSCKQICLshghrQGqApFWwFSBRS+QRZinyk8oM8/Sh6als55Tc/bBD34niYP6kFUgbSeTYPuTxiybJITUMuH0jv9eNsbnFJ74sUHWbQwaJBO+k8fjxVfyDFz0zMhRABsRYCKcHQ6sS9uV8LCX7UrxSa5yJcFzwXXBd8D3w/iGgNib0K+1oT4NXvTEkNShPg2NC/HHHUMQ1VTIzQd6T3Dtqhwww6OGCglML2VIsrot/wcon1vXqpzipPx/VVpJKZjXQuhUCTRETckCB12Z6Iy7oUH2zrwUBKqUAa97yAuPteuO81CL7nge+h/IDQt3WiFAKkBpoGuhZUd9M00PRgWx8L941EAtm7FO2U0+f7q+9gEaDDDDrYp4iH+Td8zZsJedxVMe6mWLR93OlcWwBLF6QNjeN7cigFuhRoWlz9EkjnTdJ62C/E1NGdSqkmol4n5hFh97xAcq+PTZG+WgkZSORSA91ARMRdR+nhfp3IS7lg0aYddNCKDjPoYM6wPT9GoKcJHGozNn0gEUgBaSMI2c+EuVl6U0EYfyYR5GaJ9qOtRiYhSRkaRlhtLnAtTU3pWhpI736olvEiQq+aCH1csp+i2E1depcaQkqEkYBEQPC379zF/3vtJxncvRshJe+++GIuu+wyhsfGuOLKK9myZQurVq3iq1/9KoV8AZTiuuuuY8OGDSSTSW666SbWrl0LwOc+9znuv/9+ANavX8955523N//CDjqYhA4zWIRoCvOPE+u6uqXmT5LI437nMwYSaYJMSKDTCY3elM6heY20qZE2mgl5M1GXJPU9l4aV56HGxmFiFCUNfKFQjtMkvTML6b0hmWtgJCJVjBANaV0IgUQ03atCoKREyWArHI/r/vqvWXvccY0U1m94Q5DC+rWvjVJY33rrrVx77bVs2LCBzZs388gjj7Bx40auueYa7rnnHu677z6eeeYZ/uu//gvbtnnHO97B6aefPm2eow46mCs6zOAAheOpSeH7rWqXuEReU1sYLduRdD5TIFE9oVadoHcnzWi/sW3sx7MrGtr8GCaVUlCrRLnemRhDTYwGxxP1/O+NMUoTkeeMetcH8I5cEz6QaNav6w0C3073LoRA+GGpTN8P9n3VlChNiYDo+1JG+6GeKZrTv2wZ/cuWAXuRwnrnTjZt2sSrX/1qdF1H13WOOeYYHnjgAc4999x5+Z476AA6zGC/IaiD6sek8GYpvTXMv1XtMhvpPK5u6c6YLE21UbG0UbskDYlcIF218rzA3XF8NJDgY4R98vFY4BnTDsl0w6992UrEmmObIljFisPQlyzFVXDbU0P8caRKmF6NSYkIVPhH0T4zJgSVxICgzCQc3mVx2Un9s37uvUlhfcwxx3DjjTfygQ98gEqlwk9+8pPZp7DuoINZosMM9gKur1qk8GYDaDsvl9mG+UNDd14n0itziYYuvWWsVd2SaJHOFypsXykF1QrEJHYVEvboOEbsKU60v5CmRz7vZPOIpSsbOWayeUSYmqAe2SqM6QNvRLmMSJgI16WJrdU9iqYg/Cok9sFF5och7m0K69NOO42nn36ac889l56eHk488UT0F1GB9w4ODizqN0opRdVV00jhk9UtcS+Xqjs9Na+H+ddVKwVLY0U2MUkFM0ntYgTSubYXOc/3Bsp1A+l9YiyU1mOqmbr0Xpfcp5PeU+lGYNOyVYiXvCwWsVqIEf8CpNLz5jkjXA/hOFCz0VyXD74sj/BzNAzAItDri4Z+X0m5IKmH5yOFNQRG4/Xr1wNw1VVXcfjhh8/7vXawuLGomIFSio/f+wdKzh8Yr7mUbJ8ZtC2kDNlk9FyaNcgkrLbqlkxCNhlJE5p4UbgGKqXwyyXUru2xWqtjDek9Oh6bpfQeRqkuXxULZKonFJu99D4PD4Zm25OycOqOw7be7uDzRUD0fV02E/198H+ZrxTWnucxNjZGd3c3zz33HL/61a847bTTFvz+O1hcWFTMQAjBcvV7dMMjY9lk9RpZzSar22TCbU6vkdEcsnqNtOagi2ZuoRBQbyJICIAvwA5bMSA0KjaH2DmB7rk+FvYho/34WOtnBXPDMU+hyjZ+yUaVanjh1i/Wgm2piipV8YvBdtCdwnPGSiCzKWQ6iehJIg45BJlNIzIpRDoF2RQyExyTtBD1Z255ruB+XWA3iGGoCKjI5udteub6fuM60343Pug1H73qodcaTYa6NgV4po6TNKh0JaklTYx0Etfz2hB+1ZKiITY+j0xivlJYO47D+ecHVW8zmQw333xzR03Uwbxj0aWwtsZ/RiaTojgxAWFul4AwhE3VvUZUrD/wMRct80JnwtBFceZriKbj5mso30fV7ICAl6qoYjUi7gFht/FLdUJvoypO+weUApk2kOkEMmUg0zoybaBlEpDU0FI6MqUj0xoypSE0McX9NnvP7DMoQCURfhf4BYTfhfC6QGURIdFW2CBHUdoISo6i5AjIMRCNvPw7Mm/BLBy2FzfSrlpXc59qN2dSn2hzOMOcKa6jJg01z5FCUh7bQY/36xizDRnuJOGiVUCRNDPhyfMi4ae1v4mRSyYz/FahQU6+h+nud9JnNQSI7p4ehodHJp/fKlC1FUoOTOyN/a+TwjqGau5EMr29VOXC50APdO9jUxtWI7172OdOQeBTmVAVsxR6C4hsmAY4F8s5U9e/JwPduwK8sMEevkBxxjWJATYzyikZShtGGV3D921yl9YAACAASURBVNFrbtBiEr9s0HQ8A5yUwDMFrgmuCb5mIkQ/sCS83mRm7fhLMM0evFjhlvChoj3Rpq/pWLXpm+uxin/WTOfE+2LbpmvQJvFc41h64yRKz8X+RzP8f/YHw59PPA+9e3BaO8Y4mXlNzZTUHBhPnHm1v8bkz/JROMrHVT4OXtDC49JgmnTfn+3xVzYVFh0z2BsopaBSbu8KWTe0TjSIP6UpdO+6HiPgBcTyQxqG1FyD0EcZI/UF1r1PhehlrR8Hm6nIx7SRxa4b1tGNlVes1UICHrhuOpZFJR8UUnfD8opK27MSgH65DEYWJaYubnOAk8FJ0HUd28uze8mr5nZinGHHmfVcGMpU12haCfuN8ycJDbMTLloFjEw6TbE40fazJj9Hm8+a9BxTf1brNZTy8fBwfQdHubjKxcXF8YN9BxfX94Ixwq3ycJQXHof7IcF3lR/0MUWuqhDJCYNzO8xg/tGQ3gPpvGFYjR/HXCOnqpyVyjRcIVcc0uQKKWKEnmwekqkXhWF5QaAUWs0OiH61YdjVnMb35uk6TtKimsviJkPib5oH9NL9gIaIuSHvBcPfH8j09lLVpl/xKuXj+rVGU1Vcv4YX9VUbW2XH5laDOarW1BcfU3P4RqTQ0KWFJkx0aaJrFrpMoEsLS5rBmDQxpIkmTXQR9OkyvjXp61mGX9nbb24yFhUzUEqhvvZ3DFdKeMODgQRfLrafrOuxfO75UHqPec7EvGrI7EfpfT9CeB56RPBDb55qtcmo61omtUwmlPYtHCuJbyyq166DWcLzXTzVhkD7tckEOtzXhwXF8lhbQl0/x5tF3eE4NJGICG+dQOvSJGVkGv0thFqL5pstxDvYasJEk/Pz3nenexmqzL+ae1H9KoUQ+MODgWS+4jA4uuEKKWKE/qCX3ucKpdAcZ7Kax278yHxN4iSTlLu7G2oeywyKqHRw0EAphRdJz60SdquUHSPIkwi83Uy8VQ1ftdp3poZAoEmThJ5CYkREN6GlSOldIXG2piXQeiSBNx9LsTjf2UXFDAC0T3yB7gOtiPa+hO9jVGvoMaJvVCtIr+Ga6iYSOEmLcncBJ5nETVp4oU9/By8O+MqfQg0SJ9DVNkS8+TiQ1JvH5qIskkJvSM+iQZgtPY8ul0wi1pOIeBtViSZNNGEghFiwyPrFiEXHDDpoQDpupNppqHlqkcnYlwLXsqgUCjh13b6150bdxYZt27axfv16BgcHkVJyySWXcNlllzEyMsIVV1wRpbD+yv93K/l8nt/9bhMf/ejH+OWzz/LRj32Ey9//PkCh8HnwgYf57F/fgOd5XPinb+f9V7wXpfxQZ+2HqSwUO8Z+x7N//DaemsIzbQoEqhFrkhRtyWwLgbZCAj0DEQ+bFB0Sc6Cg859aDFAKiiWSI6NNqh4tZgz3DCMw6uZzOJaFk0zimYmOtA8RoVUh0VUEniTNfYFnisKPxmtqjI99Yh3HHPtSisUJ3nHuezjhVWv4wffv4cRXHctXvvk33PbVb3PjzTfwkb+8Apkq85efvIIN//1jbG+CkrMLAM/zuP6v/pqv334Ty5b286fnX8app7+G1auPDDKsooVV1zTSRi+ru984LYFu0nULE10mEItUNdJBAx1mcJBBeF5TaoZAzVNFKEWCwIXTNU1q2QxO6L7pJC3UQRDR6isv0k87nofjJXA9ty2hbiXkKnShrI+r0IUwvj97SASC7t4MPb05FB7pTIYjjzqCoV2jPHjfI3z7X/8RS8/zp++8iEvedSl/de11pJcu4dBlR/PYw89gahly5koEgo0bn+LII47iuDWnIITg/LddwP88+HNOWntq06fqug5OilWptfP3pXawz6GUwnVdarVa25bJZDjyyCPn/XMPfAqwWKEUmu1EUn6g6qmg2w31gKdpuEmLUm8PVl8fw54TuHDuR6NuUJTeaXH1i3uAtNddT/ISaaPL9mOqkbVd/xtPD34wf3g2RWmsnWpLhH8lTfEUiHBB1NIX288WBMccnwglcxFcQwTbdo4HW7Zs4dfPbeLkP3kDu3d/nMNXvhSAQ1d0M7x7BFNvZDPVpIEmdXSZAGDnwC6WL18RXXfZsmU89dRTs/7OO9i3UErheR7VapVarYZt21MS9nprneP7U6SPAVKpVIcZLFr4fpPPfl3il+ELowDXNHFSKco9gaQfuXCGBCTR24s7B0Nb4JtttxgQWwn1FES8DaGOz1FM/aK3QiAneX8Y0iKhZUgZPS167IaBMSePIJ9ciu8pEtKlJlXTVRt/9wya0DA0a1ZzZ5vCeipMldq6g4VBnZjPhXi3tumIOYCmaZimGTXLssjn8019pmmSSCQm9fUvXcbE+Ni8P3eHGbyYoBTSdScRfb0WN+pK3KRFpbuAYyWxLYOKCQ51abuM6w/j1Wq4lQZBfqGsMTYxHPMmqU7tErhHvtlGWx11Su+aRn/dbLCMAm5ihF0KfY8IX7lcxtQzuLgcd+KcT583zCWF9VSYLrV1B+3RTs0yEwG3bTuS5veGmLcj4K0EfqZEg1XXZ2DCZseEw46izcCAw47iBAMTuzH0XXzlrYfN47cVoMMM9gEC32ynWYL2As8ds+Zg1XySNUjbGgm/oc4oyxqD2gTD1hi75TBDYpARNRxI68Uq7ngNX02dbmEyRBMxbhBdC0svtDU0tnf1aw24SSBFx8OoFXNNYT0Vjj/+eDZv3swLL7zA0qVL+eEPf8itt9660Le/X+G67qyIN8D4+Piksck5qZohpcSyrCbincvlpiTerX3zkTV2ouaxY8JmoOiE24D4D0zY/N/23j1Ikqu+8/2ck6+q6qrurq7ql2YkhKQRkpBkjRitQWDBgswVXtgr2whdHNJdhLRGKxArYbgWxgvy5WIrTPAwYTC+2MBubAS7GHNlx40AwhIrgS9+SCEEqxEgISFrJM1Mv7u6uiqf59w/Mqsq69GP6emZ6ZnOryKVmaeysjJruj7nl7/zO7/fott9/cOOwVTR4uLxAhdMjaK13vanw12XtRTWTtq2/rT1zbhKBrlIXMwIqnqccT3BuJ6gqieoUMFM+uKQkHnmmBUzzIoZ5sQ8S0aNyGCgNd0FaDHI0u6fETk5voelhZVd415oNBoMDw8TrpU+5CTon//5n/n1X/91Lr744vb3fs8997B//35uv/12XnzxxXYK63K5zMzMDG95y1uo1+tIKSkUCjz00EOUSiUefPBBPvaxj6GU4sYbb2wXuknLNE1qtRqFQuFk32qf1nKzbMZ/vlmYO45DoVDANM1NAXy7Yb6RtNYsNMMO7FtWfrJe9bufPioFk+mixVTJZrpoM1WymC7ZTBUthuyOsXWispbuus7g/3v+T9BGQNOrb+u09VYukTIVKnqMsajMaFRiNBwir+z2e3ypaDga1xF4jomfd4hy+SRPSW5bp62ntdsm5+yEzuBkazs7gxbMNwvvXthv9L23YL5ZePcuhmHsiElnkdLMNYLYnZO28lcCjtR9vFT1LClgYqgD+OlSB/iTQxaOubnAjiyF9TbJjWpYwsSWBfJmeQ3/dfdgZO8ApSkdTG1htxKytXz7bs+gbs4hGM5RS4VwqlT1LytZMmXabkVRtCbI077xtY7ZDMx7AV4sFgcCvtcd07LMT5enVD9SHK33w/5w3WemHnRVS7QNwVRi3V8xXYit/AT+40MW5ikqZbsZbdgZfOELX+Cxxx5jZGSET33qUwDU63U+85nPMDs7y/j4OHfffTfFYhGtNV/5ylf44Q9/iOM43HHHHZx33nkAPPTQQ3zzm98E4Dd+4zd4wxveAMCzzz7L5z//eXzfZ//+/dxyyy0n9I/kTS//T8fWs2rdnqkbx+zX4iycnt81qBunZyi3s3AGuVyWlyfTlqW1jsNwlerbXqsN4hDWH/zgBwTB+jOQhRB91nahUNi0ZX46wXwzagRRG/Atv/3heryeb4Rds0wKlmS6ZHFeOcdrzxlmumQxVbSZLlmU8ybyBHwvUajxXIXravxmAzu/7R+xcWfwhje8geuuu65rwOr+++/nsssu4/rrr+f+++/n/vvv56abbuKHP/whR44c4XOf+xxPP/00f/EXf8Ef/uEfUq/X+cY3vsF9990HxH7TAwcOUCwW+dKXvsR73vMe9u3bxx/90R/x+OOPs3///u2/081IKUzXS8I4Oxa/TPkv23l5yqOErZm6dpaXJ1O3tgLz3raNJGU8p0EIgZQSwzAoFotceuml60ayOI6DZVlnFMw3ktaamhd1W/Yp4C973WMUIzmD6aLNpZOFLrfOdNGi5Bjb8t1prfE9jedqXFfhNTvA91wVtzcVXjMiDDuf54gF3vyOieP+/F5t2BlccsklzMzMdLU98sgj3HvvvQC8/vWv59577+Wmm27i0Ucf5ZprrkEIwYUXXsjq6iqLi4scPHiQyy+/nGKxCMDll1/O448/zitf+UqazSYXXnghANdccw2PPPLISekMNlVsJZ+jOTLcTsZ2PMVWMp1eOhUwby2t/fRrg7Z7gWSaJqZpnpAJSaeDVDJgm4b9kZR7pxF0BmwFUC2YTJVsfvnsYtuyn0oGbgvW1n/naSveSyDvJnD3XIXb7MB+0J+JoQOcsI7jLVJqzFF1l3D8JRxvGcdfplAwgD/Y8vWtpS2NGSwvL1MulwEol8vUajUAFhYWqFY7RegqlQoLCwssLCx0xVKPjY0NbG8dv5YeeOABHnjgAQDuu+++rs/alJTC/NH/RPzkKaY8r92sbRtdKqKmJtDFIqpUhEIBhDhj/PqmaR7793Ua68iRI20gp4HdC+/etl63y3pKg3sQyHvbBoF+u+U4zhn97xxGiiMrHi8uu7yw1OTwT5/j+YUGLy67vLTs4qey65pSMD3ssGekwP5zcuwdybFnJM/e0RxTw7lND9hCbBx4rqLRCGmuRjQbIc1GFO83onhZjbd9v/9vR6BxZEhON8gFNYabC9grR3FWjuJ4LdAv4YR17NESRnUKY3wSuWcKY/wsjPEr4/3qJHbpxARGbOsA8rHMlBRCbMp6Suvaa6/l2muvbe9vZUR9zPexyqPUpWhb/Ko3zKzZjJczSKc66uJYpbXeVJz5Wq9dddVV7N27d93PGATuli98LYt8Pct8s9pMR7MVmaaJ53mn1b/zIHlh/4Bty50zsxqgUtjImZLJosVU0eKKydGOO6dkUS1YGH0DtgpUg5WlBitsgxVvaBwjJCc8CmGdsreE3ZjDqR3BWXyR3Oosjr+M7dfispq2A5UJqIwjquNw4SRULkOMjcfto2MIw0DFV9qv1QbVfGHnRBONjIywuLhIuVxmcXGR4eFhILbs0xc5Pz9PuVxmbGyMJ598st2+sLDAJZdcQqVSYX5+vuv4sbGxrVzSprVw/supVqusnuY/mJ2utWC+2Vmgvr9xmG9vNEt60lC1WmVkZKQ9OWc7Yb5ZbTaF9Re/+EVGR0f5+c9/zt13380TTzzB7/7u73L77be3z/WBD3yABx54gGq1yne/+90Tet0nQ3U/aodf9kbpzDe7rd6iLZku2eyr5Ljm3OFUWKbNvr2TXQyBji9+taYG++Kbne1w0Di7AMcROA440mfYaWKLGo67QK4+i738Es78IZy5f8EMe4zG0kgM9bFxxNl7oXJlAvpxGBuHodKOHavZUmdw4MABHn74Ya6//noefvhhrrrqqnb7t7/9bV772tfy9NNPUygUKJfLXHHFFXzta1+jXo9LTP7oRz/it37rtygWi+TzeZ566in27dvH9773Pa677rrtu7tMW1YL5sdinbcg3trfSGmY27ZNqVTacOAzvS/XidZqNBoUi8VTOs/ANE0+9rGPcdlll1Gv17nuuuu45ppr+PrXv87rXvc63ve+9/Gnf/qnfP7zn+cjH/kIo6OjfPzjH+fb3/5237ne8Y53cMsttwycbLYTpbVm2Y3aVn0L+K0onZWeAdtyPp5w9UvTQ52JV4kPv+TE/vteK772QsQPX1pgcaGxOSvehFxO4uQFwyMSpxyRi+rY/jJOY75tzdtzzyMWjkJjtf8EY9UY9C8/Cw5cAWNVRAJ/xqoI2zlRX+kJ14adwWc/+1mefPJJVlZWuP3223nHO97B9ddfz2c+8xm++93vUq1W+cAHPgDA/v37eeyxx3j/+9+PbdvccccdABSLRX7zN3+TD3/4wwC8/e1vbw8m33bbbXzhC1/A932uuOKKUxdJdIZJa00QBG0w1+t1ZmZmjimL4kZuPMuyugDdC/P1JhJtBPMzQZOTk+0cQsVikX379nHkyBG+853v8I1vfAOAG264gbe//e185CMfoVqtUq1WefDBB/vO9epXv5pDhw6d1OvfSJHSzDfCThqFLis/wA07jg4poFqwmC5ZXH12KQZ9yWZqyKTiWBCIbit+UVM7rJhtNta14oVoYjsCJyfJ5QXDoxZOTuDYGidcIecuYq/O4iy9hLl4BP3cLMzPwOIc9BoK+aEY9pUJxL6LYsBXJjqW/fAoQp78ABIdBtBsQHMVmg38+SNQmdr2z9mwM7jrrrsGtn/0ox/taxNCcNtttw08/o1vfCNvfOMb+9rPP//89vyFTB2lYb7VWaDHCvOhoSHGxsY2FWd+OsH8e9/7HrOzs9t6zvHxca655pqND0x06NAhnnjiCfbv38/c3Fy7k5ic7Hdz7CQFkWZmtT93zuF6wNF6QJhy4JsymXBVtLi0WmAyZ1G1LEqGQR5JkIRReq7CfUFTdxUHXQ+t+58iu6z4UYNczsTJyRj0OYGDh9OYoyJc6s89Awuz8IsZ9MJsvL282H1CIdAj5Rju5+6DK6/uBv3YOKIwtO3fn44icBvxU0YCc5qr6EYj2a4n6/gYnRwTNRv4nk/g+fiRJpAmvjQJpIUoDnPh/3nftl/rrpuBfLK0UYGKzfjSNwPztPU9COat1ycmJvA8r71v2zZGFiZ7UnS8KaxPtNoZMgfk0JlrdA/YjpgGZxdsLs4V+JVhkxHDpIDE1gIdEMN+UREm0ehzRMyRuIRavvgeK74FfScnyeUEtqMx64uwMIueT+D+7Ax6YS626udnwYt99SutC7PsjqvmsgMdwLdcOOUKwtx8XGCkNH4Y4a+uEjSa+I1GvG66+M1mDGnXI/ACfD8gCAL8IMQPFUEQ4UeKQGl8JWKQG1Yb5h2w5/BlkUBa+IZFkLcIhkx8YaDWqTw3Zgu+cgz/vptV1hmsoV6Yt2C92YIVvu9vGDXSm2CrNcaSnsa/lqvlWGF+ukUTbaeOxYLfbm1HCuvt0IoX9Vj2nVj8mhuRx6CApCAkZdNk0rZ5hVlgaERiK4mIQPlxBVUayQJEQMNUqLWs+HwMeCcncRyBkALteTHg5xNLPgG+XkhAvzjX99tRQyXC0SpheRLv3FfiDVdoDlcRU2dzVDg0ciV8FT/J+JHCj+Ixr2A2wH9xhSBYxA9CgkDFoE6OCTTx+7TARxJgEAiDcKA7yACGkiUlCTjJ0mrSGlsoLKGxhcaSAtsQWIbANg1sU1K0TCzLxDaNuN0Q2IbsHGcILNm9bxuSyUoZOLYa15vRruoMtNY89dRTPPvss8zPz29onW8W5i1g5/N5RkdHN+ViaSXbynTmartSWG/2s8JIsdQM+IfDSxyuBczWfBZXIlYbCkIoYJAXkgKSYcNiWji8Wkuk2RPdogEfHCGwHIFVEBg2GLZAWCAs0KZGmxAZmgAIIkUt0gSRgnoN+8U57OU5crV58ivzDK3MU1yZp7S6SMGrd31cJCRLzghz+TKzub3MnH0pR51R5pwyM7lR5p1RXLNnYNZNlpmIrt4pkdAKS4XYKkzWAVZq21YBwzqKQS3AljFwLVNimxJbGDGobQvLtrAdG9uxsXI57JyDVchjF/LYdpxgzjJEco4Y3v0hrZv7N4xC8H2F7+nO4mv8ZtwW+JraTBP2bz+6d13W0j/7sz9r523pLVCx2UiWVtvJSIO7XdptTwY7IWvpdqSwfvC7/4NiqcT73nsH//gP/8ji4gKVSpXb77yb63/jRpTW7YB0qQVHjq7w5I81eeTAHDkRmkAqfKnxhMJF0USxqhWrKmJFR6yoiJUoGlj12VARFW+ZqrfIhLtE1V1i3Ftk3F1MtpdwVLfV6ho2C7lRFnOjLOeGqTnD1J0idXuIhpnHMyzMKMQOXKygie27WKHfBfEY4Cmwa4VtGTg5B9MysBwHy7FxcjmsnINZKMRjAPkC5IcQ+QIUhuJB4qQN2z5hYZ6xZwECL4a452sCT+N7KoZ7GvSeIkja1rM/LVtgO4LRco4rX2OvfeA6ylJYp7S0tMTU1BT1ev20gvnxKusMjl1xeonYUFZao6G9n34t5nH3vk4dr+jej49LnxtE8isUyXZSVTleRGtbrFmqM56kpDl0uMaDP/ZoEoM+kJrAUCipiQwwzJQLouWSkB0XRCFoMLoyx8jKUYZr8xTrcwytLFBYXSS3uozt1vuuITJMlDTRgFARQkVIrVjT6y0E5PLdYM634N3dRmEIMaANJ3fSUlhrrQmDHos9gXjLWu9t832NXgvsAmxbxIsTP305tsRy4v24Xaa2BZYVu9ggS2G9bRodHaVYLOK67qm+lEwbKFI6HoRL3A/xOrWvdJePuP2aUlw4LJiWDlGk+mHeA+P0vkrB/nglAEMIJAJDxDBvQ13HYG9Bv++NJB0F8XUrAVpohAApBYYBhozdEULGoZumaWIJg4srAsvzkG4TmvV25Ipu1GFxEZbmobYMqytxhIvvQRCAWr+gTFumBU4uhnJxGGOohNkL6xbgB7SRyyNOUSRaDPY0vFvb/aAPUses9ecgRMditx3BUNGgXBHdILdl+3XbFlj2iZ/wuBXtus4g0+aldAzYINIJeLuh2w1hlQJ3z2sD3tve74G9n3o9Og4e//YvjWDn/HimMTEshYjhG6/jfUkM085xov1avO4cL0WL0yK25FOLTq216jw5tJXablnxkQaFAkEb6IYEU2gsobBQGFqBUjGolYpPrtJLd7vxzFNY/+3P25+zoaQBjgOjYzBUimfQjo5BuYoYn4zDLwvFDtBzBcQOGeuK8wVF1FeilIWu1gV94K8P9hjeCdiHDcr2OtZ6y2LfJrBrrfE8j0ajQbPZXHPtOA5ve9vbtuUz08o6gx0srXUfYDvWb/f+eoANIo20lqitNtZ8vd/a1l0x5FuVvYZLorWft1qDbzIVMZE6PhVF0RWRYchUxEXPviFQnsvISHHD8onp77rNVK3bzNWq0661Js6DNvh7UcRPGFEb9B3LXqAwUBg6xNIRTuL/tqIAqcL4kWSN87Ylkl5DiE7K9HYvNPANUBqGcjUOtRyfRlQnEa3UCJWJ2Je+A6R1AvM0xBP/epDse75qb/vJsejlgedrgb0F79Kw0WXB22lrPdk3rbVzqW1VYRiuCfb0duB6GFFI3jAZsiyKlsWQZTNkWVRsi2EnR7FcYWjCAmn0DJdvj7LOYB1prQkVBGoNqzaxeruhnAJ2y0JWa793IyAfr1oAdSwjtjjTUDYlJVv2+5CTyIhewK4H5EEhcabc/h/XZtUI4s9VSvcDXqfbY+CvaS0mpr/WOrbm0UQIAuKwyk5CMd2OVjFVSE5H7X1Lh/EThZTJI4ARbxsSrFynHVJ+rJTlH0UQhcnFpzo3IcE048WwkLYNSOQHP7Gl2PrtklYaP3HFBAN86f6AgdQgWKtDi7+eDsglpZEOyMtjJfygEc9CtkXid5eY5on529Na47puH9x7AR+6LjIMsREJ2DtwL9sWRduhVBphaKxKwTCx1nCbaUCZZrIYRKaJMzKcdQbboc/+4CWUnKXedDdlUR8vjk1JB5qy3+LNWwYjue5BvC4Ay34g25sEtClFO6LkTBpAjsJOZkm3HuCteHh1H7cZxZOefMFZF9pUpwZHXAitETpC6AipYqSrBPIREAhJICR+T6y5QGNphaUjcsRx5KYAS8azb4WUIE2Qdjf0Wz/0MIQoiNdhCGGQLGEM+94eyTDAsOIJVflC7Ks3zE4HILuLrBimiTRtxDbUQG5JqV6Lff1B09Zra0lKutwuw6Oy7XaxnfR2x2I31gF7tTrK3NzxRYyFYdhnqfeulechghBTRRTMDtyLlsWUZTFk25ScAkOlEQqGibkG3JUQKMNAWWYb8p5p0DQ7+5FpxNuGiTZkX+GsarUKJ+C3vOs6g+eWPIQMkVphG4KcKRh2zBjSsj/Sog/Qa1nMbdh3gG3KrcUb7xbpMOyanu/Xm3grPm4jjBOOeQIvkLihiacdPHJ4skBo5AaczMTxGzj+cpwf/uwpHC+GPsQ/wlBKQiEJRGtikSSU3ZazFLTjzoeSvwPLjDtcQw72D2utYwu+BXnfj+He2o+C2LpPS4gO2J1cDPqUlY9pbvsgaxvsvb70FOiDnrDHdcFu0AXykULHYu8dNG2Bfj2wb999KlzXXdvv3migfR/hBxgqwhEycc3ElvtEC/S2TXGsSH7CxFjjmiMhUKaBNq024N3EgldGbM2nLXst++G+U7TrOoPP/trLzygr+VRJqyip+7Ca5F1JoN5KqNVo5Vhx8TxwPYEXGbjKwcfBE3lccxjPGcGzR/DtUbTsn4lrhC5OuIIT1SnpZarCJWcEOKbCsVWc3qBg0HQcZswih3WeZ9VZmKPDiEIJP9JxLH76nDLu1PNJ524lT1fWGpOFtFYQRhAEcQfWsurTwO+NI5SSl+YW+I9/8HFm5xeQUvJbN97Ibbe+m8V6nTved+emU1gPSoV96623drxJuhUlE1FbivjFz5p90TFByxWzhqRBly99ZGiwxW6lBlLN3slqJ1BBEAy03rXWLC4soD0PGYTIMMQChkyz43O3LM6xbIZsi+JwmfzY+Jp1isME7sq00JaFMg0aKZj3Wu9nUp3zXTfPAM4sl8lmlb5nrRS4za5MiLF1vtrXFifPSrU1VtHNJr4y8JxRPHsktR7Bs0fbgPecUUJzgMtCKxzhEY2ZWQAAGy5JREFU40gfxwhxbEXOASdv4AyZ5Io2zkgeZ6SA5cT2itJxhsyugiftTJn9GTLfd+UoF+8ZwxTJzNIW+KVA9gBfp636PtC3rPpeF07LijcHWvVIyczMDDMzM10prL/85S/z9a9/ndHR0XYK66WlJX7v9z7C7Mwch144xHe+8x1Ghke47bbb0RqOHjnC0ZkZLrnkMuordX7zhn/D5/7k/+aC8y/s+2qPHqnzzE9oW+OWLXDWtNY7x5xMsENsvad97em122h04B5FmFFE3jDacE/734u2TX6N+UKaGO6RYaAtE2y7z//eC/mdarWnlc0zyAQkLgnP7YI1zUY72+Falvq87xGt1OJ2t9HvnwYiaXXAnh/DLU7g58/DzZfxRkfwzBKeUcQXOfSAKUWGVDhOnG1yuGCQKxjr5qjpVRBpZlcDnlvxObIYcPj5+Tbsj9YDgp4MmZNFi+mixSsnCu3c99Mlm4khi8BrMjxciGebRxGEPgQx4HXaTx+G/fH1bReOlfjqU5BPOoC1XDjtyWgRjI2NUy6P47kK0yhw3nkX8NwvXuRb3/oO/+U//3dqSxHXvfnX+Xe3/G+87457cOwyF5xX5jv6QYIgLoYuBIyNTVCpTCIEDI8UueCCC1hamiFfuCgVYCSwLBPT9njFJduffXMjtbLsDnLLeI1G7DrzfYwwwlAKB9pAH7EszmrDPY9TGJzMT9GCu0SZFtgW9vAwtTDoQN7odsucDnDfKco6g5MorTUEfhes17PK+9oaqzHINyqbaBjtGZs6P4Q/VMWtXkjdKMawt2Kou6IQ++GVhReZhNEAwPVkmhxJ4N6badLJSUxr4x+eGyr+ZdlrlzFMW/e9GTJzpmC6ZHP2iM2/2ttdtLxSMDGkQAd+kujsMPrZGUgyW3qXXUV41l4IA4rhP2DpVG3tVmjmoIVU2GYYL+l+s7Xtm1PMD/+b1LyCAXMLUnrxxUMcPHiQS1+5n/n5OIW1ELBnzxQLC/PkC7IdNdrKzz9SNvr864cOHeInPznIL7/6VTi57n8vw+h/6jkeRVE00HoPmk206yGCABGGmJHC0pqCabQHVSfb1ruNPZofeH6FJhCC0DDQpom2bQLbxuux2KOWv93oh7tVrVI/HZ/ytUZoHxE1EaqJVE1E1LNWLrLndaGaiEN5OPsD235JWWdwDNJB0OdCWcsq77Q1oJHKWR5tEPkgJORbU/WHoFCIU/HmCwngi1AoEOWG8KwRPLOIJ4fwRB5P2biRheeLdt74gVWfwjj/YjtffArom7Xi11Pdizjck/u+tV7sKWlYcgymihYXjeeZKg7H9WuTsobDjkQ0V5O0xS+gn+/OdBktzEJtqf/7Gx2DSw8gnBy6MATNAoR1QKBbwCcNed0O12+9sJHvNFLxP6VIokINAULKTr+SgF0KQaO5yu986D/w8Y//AWftHUUIKJY6kUpC0AV2mbiyejuC402F3ape1z2Y2iRsNlCuB4GPDEIMpbC1xhGibb2Pp1wz5tBwX+JOgFDHcI8tdxNtWbiOg2vbnQiZFOR38mDqpqQ1QnsDID4I7E1k5HaDfZ1pgRqBljm0zKOMPFrmiZwRtMyRK02ekNvZdZ2Bnj1CsDyPPvxiB9o9Fnh3WwryAwum9iiX7556PzyKmNwTQz09ZT8/1JVIi3wBnS/gk8P36K/d2lSd7VVFuDTgswU4jhqYL746MYIf1I/Jil/zO9SaRTdKLPtU/dpkXfe7/8jH8ibTJYsrp4e63DmTBUmxsZwA/jl4IbbsW6mM9cIs2utJG2Lb6PI4enSc6KKXEZTG8YeqeIUKzXyFhlnGjwwq1RDsEkoparn/dV24xwAXKYCDkKIH7CJp7xw/vInvKggC3vOe3z7uFNaDUmG3/i2UUiilCMOQ5aUlnv3pT1GuC56PDEOMKMLSGltDIfG5VxK4FywTwymA0z+2EyRwD6WIIW5Z1HMOwnZSoZEdyOvTcTBVK4TyElivZ6V3Xu9su8k8lDVOjUTLXBvmSubRVjlet9tyaCN5LQV+LZ3O3JMeOVlo6fZIffS9LKwFdSfXgXNhCIolxPhU0pZqXzfnSv9U/TDsWOluU3Ws9qbGXUztuwFa91/bulWfNmnFV6tF5uY2n48pUpq5RtDlxknnw/dSE+KkgImhuMLVr7xsmKmSxXTRZtLRTHmLOMtzcZGSI7NwMJXDfmm+L014VBgmLFUJilN41Utx81UaToW6XaVulFmliFI995jKYGzZGtuOGJtohfiLPrDLVoqJFNhPhLaSwjp2NWmiKGrXxNBRxAc/9CFe/rKX8b+/8524tVqSzC7+7qUQWEJgCMFkpHmVtKBgQQ/ffa3xgVBKlGngWxaB4yByTjK4asRJ51r+9tMF7loNgLm7SbB7GwO9C9ZDaKvaDfjU6/F2DHgtnNPqyWfXRROpR77P8FiFlTDqt9SPIeeK1skUeVd3W/Ep2K9Xu7XXF+8cpy9+Iw2KQAgixdF60InOSfnxZ1Z9UgE6WK0B25KdwN5iyvCZ9muMN2YxFmZRszOo+diVI5bmkI2Vrs/TwsAvjOHmKzRyFVbtCk2nQjNXpZmr0MxVUEYnb71l90S+2Ok49lTYYyoBWMtnvpNSWF900UVIKdFa88EPfpBfuvxy7rzzTl46fJizpqf5/Gc+Q3l0lNnZWf7tDTe0U1gPFQr8/be+xcGf/Yx/+853cvErXtEOify93/kd3vT6N8Tzo5NkSkJKVpeXGVV6oPW+o8Gko26g94A97TNPg10mbpp1Ty3MHsu72/0yEOzJOi7gsLO+txMVTbTrOgNY/8tc14p31fq+eLqteGebffFbUTNQHKn71Mnx9EvzXRWu5hphl02UN2XixrGYzJtUoybjbo1qfY7y4hHk4izG0ixGbQ6rPoeMunu50MglUE/gno+33VyFoDSOHiljO0YfxNOx7O00vtbxfT8nsjNoWe8qipK0Fqoziqx0bGm2LHeSFNTtZY1zkkqPLWKfccdnlcxuFsmYRwtOPZAyTZNarUZhG2cgH5N0uK4V3nKtDPSva3/9UwurzwpXRo7c0BgNj3XBjjz5KTlOpLLQ0m2Q1prZoyGLczXmZ90tW/Fr1W7dLiv+WO9pxVdJ7Vq/L0pnye0OmyxZkmrO4hzb4EoZUvVqTKzMM1E7Qrl2GHt1jlxjHsdb6nt8du0RmvkqXuEcgvH9hMPjRCNVKE+gx6qYw8UE8pJRRzAxIBf7TlUH7iqGu0qHCcWpKFpwFwjMBO5dPBYiHkkmVceADuBDkQK7lIgN4N51fSfmtvulgk34yvsHQ6VqIga4OLtOLeyUrzxPZI0lYM+tCfNWG2IwqpxqldXTMZpoB2pXdQYAj/79KlG0CmyPL/5kSGnNYjOecPXCks9Ly0ns/WrATCOgGXX73UtSUgZeFnpc2awz6S5w1upR9tRfYKxxlHxzDqvn0VpJE3+oSliq4E39Es3ROLOlrE4gq+OYk+PYBZvCDs3F3iulFFEUEQZBAvd2/GcSBdICfGytG8R+9z64izTck5oICCIRpyJoDT4IKZKEc2vDvfdbOyGA1xqUv6GvvON26Qlf1Os/SSnpdME6tKpoJ+1eyfUNhrba1wL6mS6lIyLlE2mfUHmpbT/Z9lLb8TrUPlFybJi0Rcoj0gGFI8P8q6k7tv06d9W/jhCC1/zrIpOTYzSayyfdik+rN2Wv6yqOrMQunCOrAbPNgDk3YCEIWQoj0g4dAQwrTSUKuDRYZdJdZLoxyzmNF9m7/Byl5mySgC31efki0UgVffYUonI5anwcY3wiTmdcmUCWRtbMnLhTFPg+/kqdYHWVyHXRrgtB0ImYUXE4ZE5K7D1nYUjZ/weewFolBW1a1nuISAoYxIUFhJRt10wX4FunOZE32k5LHedEFToucwOxO0qQdGwohO6042mclWeZOPz/rn1qBLoF9ATWoT0xOKKlD+w5EDujlsHxSmuN0gGRDtqADrVHpIIEur1QDpLXE4h3Abq7LUwA34K70pssGtQjQ9iY0sGQNoaw22uxRpTR8WpXdQYA5YrJ8KiNH27fz7kN9jVS9AZJLvamq2LIeyELQUhNR6zQWnfXnDW1pqJDpqIGVwQ1ppuz7K2/xJ6lf2Fy6VBc9KT1+UIiRstxfvq9r4DKa2EsAf3YBFSqjO89Z8el4FBhSFBfJWw0CJMUBPidcEgzgbsjJAXTID8oHbNhoaSJG0W4KsLTmqYQeIAlkkjuxHLvA3yizsyDbVQf0FV7X6TA3gX7LtBvVNtAxrPARey40tJCSJPQnqBeuW6Af33jkMWdIKVVl4UcW82DAR0qn+cbFrX6Yryvg7Y1nQZ0mMA8DehQ+Wzl2Uwg+wBtSgdDWNhGEdNMoC1tDOFgSgtDOMlxrfc4qe3kHEl73AHYSGGtk6n1xKTT2XWdwUbadC72dMreVC52XytqRNR0RI2QFWLQ1wip94RR5lFM4nJJUGPanWd65SWmFp7nrNphyn6tAyjbTqA+nsD+V+KJaK0iJaMVxE6o5xxGKNfFX60TNd041t33EUGAGSlMpbABR0oKhoFjDLhmYRCZkiYBTRSe1tTQLCiFUiHashCOg5HPYxTyWENDSKc7hM8EjEYDq1Q6vgHkNqR7rXCdQLtVzWAA7NcsgNu+0XhAuA10iZZGez/dHk+WS7YT+A8aX5CmSZSzaBT2bf2eByi2oqMeQPvdro8ExL1WcxrQbct7gFukBWi1wbjDWjKElcDU6oNtwSz0ALof5P2AdhJA2ykL3UKewa6uM/fOBkhrzfPP+rz0/CJLC82BaXw3ysVu2RBZmlVDUTMiavmQJTtiIQiZ9wLqPf77UXymojoXugtMrRxmavEFphtzTDXnKYaNGPilkaTy1DjinIug8jpEAn/GJuL5DifbT681Qinw/Ngt02y24U4QYIQRZjKRKScFecPAGjDHAiAUBqsoXK3xtKauFEeVIlIKbVtg2xj5HGahgDU0hJXv1MiVgJMsW72PjhWuu9wqaXdLt8WuNw103YZzC+hmAvJu0MeAF2wE9GO7taTwTlJ8B6XwwjqNxgsD3BaDfNDdgO49Nn0Ovbkimj3fjlgT0LYsYJijKdj2AtpOWdMxiAcBemJ8mqWF2glznewm7arOQAjBwcebRGEzlYs9SdmbysVuWoKGUCyGIQtBwLwXMtuM4/GP1H2a9bT/XlNVDabcRS6sH2Vq5QjTzXkmm/NMufPkieKygy1L/vx9UHktIoE/5XGEs2XUbV5aI8IQGYREbpOwkcA9Kdohw7BjuQtBThprFujwFaxGimYUUdeKReKJTJEh0VYMd5HLYRby2MUiTqGAkTy5mGzhj05H64Qq9udzEVGTMPdqMPZibhrordQIcc55nQZ4F9Bbx60NdK1jQL/00gvc9R/vZiZJPf3Od97ILbf+O5YWF3nfHXfxwgsvsHfvHj73+U8xPDLMMz9/hns+9J84ePAn3P077+Pd//4mQON6Ljff+Nv4vk8YRfzq//Kvee9dt9JX5diDI/Vn+J+L/2XdO5bCHAhbUzjkzOFua3gjq7nt4khZ3cmxUpgn3IixjBxC1E/oZ+wW7bp5Bs2GYnq6ytziPDP1zqzaw3WfwzWPI8suRxsRgU65HXTEhL/M1OosU41ZppvzTCXLBE2ssUrKbTMBY1VEZSJx4ZQHzko+bmmNDENkGIHvEzYaKNdtJxCLU/8qLHQb7mvlcHfDkNUgoBmFNJWKZ6kK0c4nIxwH4TiYiVsmNzSEbdvH9kNvhSwmsN4U2FvRLscag27kmbUPUBjdmwwUt1wtcfyQbhezFO34IJ24eHR7Ox5e7mx3hptbsAeV2m61t4akYXZmjtmZeS659BWs1hvceP2t/Mmf/SF/881vMTwyzG2338RffPG/Uqut8IH/4z8wP7/M4ReP8D/+7vsMjwzz7n9/c/wda2g0XIrFImEQ8s4bbuH3P/a7XHnlfqAV3SUxDYPF5TlCcwlTON1Wdwrm8gwZBIYsHf2xKptnkCgMI+576OccbTzFUU+jUsOGuchnsjnH3uY8B1KwnzJDKsM5zLEqTI/DWByN03brFIrbc3FKIcMoAXwYu2WaLtr3wA/alrulO4Oqa8G4EQSsBgGrYYCXwF2ZZrI20LaNTCx3s1AgPzRELpfDMAyGGJiDLAnJbMWgLyLdnnjzQbHoXTHog333WmtCNL6w8KWNL2wCaRMKE98cIRRjcVUyZJwnhzhnTigg1IoQRaTDxK3hEUUrRJ7PueI8JoJSXJiGDrCPXSL+T8j2dgvAgjiraLwdA7mzLXjZnirn7om3i2OCfftewcq85uEH/5H//vX/xogzxc3vvI133HAjH//oHzO2V7BvLzzy90+St4YYye1pX8VwUtytGTVREeStUfJWuetKTdNkyIZC4Zwt3Gem3a5d1RkYhkQ+81MuCBq8rjnPlL/ElBUxVTQpj5YQ03GYpRi7ECoTUK4irC3OXlQKI7HcZRiiPR/lNlEtyz0IMBK42wictWqmat2Gey3wY8tdawIZl3HUVlyRSeYcZC6PNVQgXyhQKBSwLAs76TCq1Spzs7MD0ubOIqPnEbXutLk6WiVSq0Rhg0g1UFGTkIgATaAVIbqzaE2AJhQGAUYCbUkgkizQunWsItQRkY4IdUioA9QGce1rSQqr23WRCsOzzAK2UcA28mgNB2f/H2pe+ilS9P0/3uxtWfvJZzT3Mq6cvmnT13vo0CGePPgTrjrwy8zNzXPW9F4ApqfOYn5+fsOnrCiKuO6663juued417vexZVXXrnpz86UaTPaVZ2BEIKPvmEPo3vOYcmwoDSyuTqzyWBq2nKXYYhquijPjS33IMRQcay7nSQOG6RIqdhqDwKWAp9mGOJBDE5pxAnCbBvh2Bj5PFahA3fHcTAJUdEKKqyhohWicAWtakTRarI0aNSa1JdcQuWiVBzt8ZOfBfGAIJoAlQJ0B+ghmgCIUFsYLgQQXeFzBt0uioK0uwcUu/zOzgAfdH8YXhx2ZyM3GDBsNBoM5+N0FKbMn9IokONNPQ1gGAZ/93d/x/LyMrfeeis//elPueiii7b5SjPtZu2qzgBAXHYAq1JBHp1B+kFivcfl9fD8TjhkGGKESepfWLMgdhBFiUvGby8eIb6MCAxFYCgiK15CK0LnNYYFhhEiDR+tPZRyiZRLqDyU8uJoDx0QqpCoHhLWI0LdPfHsWCQRmMLEEGayzsdQlTamzOHIHIbMI2Uusa47A4p9cdBd0R/JcUn0x8kYMNyKjsWC324NSj29lRTWLY2MjHD11Vfz0EMPZZ1Bpm3VruoMtFKUf/wjTAym13AB+CqgoVxWtUtDN2nqJk1cXNnEFS5No4krGriyTkPU8U0XnTuGGYZJ9SwAE9FZRLy2ENgITGkmE1YKbctZSgcjAbdh5JGygGEMYRhDSGMIwywhjWJfeJ4Uxq4caDvV2koK60Gan5/HNE1GRkZoNpt8//vf5447tj8dQabdrV3VGQgp+QmPE8mQJg2aokGDJk3RoEm8rUSEgZFY0RJLGAmwwSL+woooylpjCYFJoQ/oZmKJx5Z2DmnkMWQBQxaQ5hCGUUQYRTAKp03a3EzHrkceeYS//uu/5uKLL+ZXf/VXAbjnnnt473vfy+23387XvvY19uzZw5//+Z8DMDMzw1ve8pZ2CusvfelLPPTQQxw9epS77rqrXcTmbW97W/t8mTJtl3ZXaKnW1J/9v7BEgBl52DrqBjgiDkJsJSdbI22u7k3EdRqkzd1tTwY7oZ7BydYpT2F9CrTb/q4hCy3dHgnBWcULcXIFmr7oJOoy8gTHkDY3U6ZMmc407Tra1SZvpFqtUt9l1kSmTJkyracsoUemTJkyZco6g0xnpk7jobDj0m6970zHrx3jJnr88cf5yle+glKKN73pTVx//fWn+pIyncaSUhIEwY6c93CiFAQBcocXKMq0c7UjOgOlFH/5l3/J7//+71OpVPjwhz/MgQMH2Lt376m+tEynqXK5HFJK6vX6rugQtNYUi0VyudypvpRMp6l2RGfw85//nKmpKSYnJwG4+uqreeSRR7LOINOWJYSgUqnsKrdJpVLZdWGWmbZPO6IzWFhY6JqSX6lUePrpp/uOe+CBB3jggQcAuO+++6hWq1v6PNM0t/ze01XZPZ/52m33C9k9b+t5t/2MW9Ag623Qo/21117Ltdde297fqhWUTVTZHdpt97zb7heyez5WrTfpbEeMNlUqFebn59v78/PzlMvldd6RKVOmTJm2UzviyeD888/n8OHDzMzMMDY2xg9+8APe//73b/i+9Xq5E/ne01XZPZ/52m33C9k9b5d2xJOBYRi8+93v5hOf+AR33303r3nNazj77LNP2Ofdc889J+zcO1XZPZ/52m33C9k9b6d2xJMBwJVXXplVb8qUKVOmU6Qd8WSQKVOmTJlOrYx777333lN9EadC55133qm+hJOu7J7PfO22+4XsnrdLp3U9g0yZMmXKtD3K3ESZMmXKlGnnDCBvVUtLS3z1q1/lmWeewTRNJiYmuOqqq3j00UcHjrp/8Ytf5K1vfWuW6iJTpkyZUjqtOwOtNZ/85Cd5/etfz1133QXAc889x6OPPrrme26//faTdXmZMmXKdNrotO4MDh48iGmavPnNb263nXvuuayurvLEE0/wqU99ikOHDnHeeedx5513IoTg3nvv5eabb+b888/n5ptv5td+7dd47LHHsG2bD33oQ4yOjvLoo4/yzW9+kzAMKZVK3HnnnYyOjp7CO82UKVOmE6vTeszg+eef5+Uvf/nA137xi1/wrne9i09/+tMcPXqUn/3sZ33HeJ7Hvn37+OQnP8nFF1/Mgw8+CMBFF13EJz7xCf74j/+Yq6++mr/92789ofeRKVOmTKdap/WTwXq64IIL2plQzz33XGZmZrjooou6jjFNk1e96lVAHKr14x//GIizqH72s59lcXGRMAyZmJg4uRefKVOmTCdZp3VncPbZZ/NP//RPA1+zLKu9LaVEKdV3jGEY7eyoUkqiKALgy1/+Mm9961s5cOAABw8e5K/+6q9OwNVnypQp087Rae0muvTSSwmCoF3jAOJCOU8++eRxnbfRaDA2NgbAww8/fFznypQpU6bTQaf1k4EQgg9+8IN89atf5W/+5m+wLIvx8XGuuuqq4zrvDTfcwKc//WnGxsbYt28fMzMz23TFmTJlyrQzlc1AzpQpU6ZMp7ebKFOmTJkybY+yziBTpkyZMmWdQaZMmTJlyjqDTJkyZcpE1hlkypQpUyayziBTpkyZMpF1BpkyZcqUiawzyJQpU6ZMwP8PadUBsh8A7OoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CI.plot(kind='line')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_CI.plot(kind='line')\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "That doesn't look right...\n",
    "\n",
    "Recall that *pandas* plots the indices on the x-axis and the columns as individual lines on the y-axis. Since `df_CI` is a dataframe with the `country` as the index and `years` as the columns, we must first transpose the dataframe using `transpose()` method to swap the row and columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "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>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>5123</td>\n",
       "      <td>8880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>6682</td>\n",
       "      <td>8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>3308</td>\n",
       "      <td>8147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>1863</td>\n",
       "      <td>7338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1527</td>\n",
       "      <td>5704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>1816</td>\n",
       "      <td>4211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>1960</td>\n",
       "      <td>7150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>2643</td>\n",
       "      <td>10189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>2758</td>\n",
       "      <td>11522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>4323</td>\n",
       "      <td>10343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>8076</td>\n",
       "      <td>12041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>14255</td>\n",
       "      <td>13734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>10846</td>\n",
       "      <td>13673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>9817</td>\n",
       "      <td>21496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>13128</td>\n",
       "      <td>18620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>14398</td>\n",
       "      <td>18489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>19415</td>\n",
       "      <td>23859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>20475</td>\n",
       "      <td>22268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>21049</td>\n",
       "      <td>17241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>30069</td>\n",
       "      <td>18974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>35529</td>\n",
       "      <td>28572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>36434</td>\n",
       "      <td>31223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>31961</td>\n",
       "      <td>31889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>36439</td>\n",
       "      <td>27155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>36619</td>\n",
       "      <td>28235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>42584</td>\n",
       "      <td>36210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>33518</td>\n",
       "      <td>33848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>27642</td>\n",
       "      <td>28742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>30037</td>\n",
       "      <td>28261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>29622</td>\n",
       "      <td>29456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>30391</td>\n",
       "      <td>34235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>28502</td>\n",
       "      <td>27509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>33024</td>\n",
       "      <td>30933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>34129</td>\n",
       "      <td>33087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      China  India\n",
       "1980   5123   8880\n",
       "1981   6682   8670\n",
       "1982   3308   8147\n",
       "1983   1863   7338\n",
       "1984   1527   5704\n",
       "1985   1816   4211\n",
       "1986   1960   7150\n",
       "1987   2643  10189\n",
       "1988   2758  11522\n",
       "1989   4323  10343\n",
       "1990   8076  12041\n",
       "1991  14255  13734\n",
       "1992  10846  13673\n",
       "1993   9817  21496\n",
       "1994  13128  18620\n",
       "1995  14398  18489\n",
       "1996  19415  23859\n",
       "1997  20475  22268\n",
       "1998  21049  17241\n",
       "1999  30069  18974\n",
       "2000  35529  28572\n",
       "2001  36434  31223\n",
       "2002  31961  31889\n",
       "2003  36439  27155\n",
       "2004  36619  28235\n",
       "2005  42584  36210\n",
       "2006  33518  33848\n",
       "2007  27642  28742\n",
       "2008  30037  28261\n",
       "2009  29622  29456\n",
       "2010  30391  34235\n",
       "2011  28502  27509\n",
       "2012  33024  30933\n",
       "2013  34129  33087"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CI = CI.transpose()          # Or CI = CI.T\n",
    "CI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "*Pandas* will auomatically graph the two countries on the same graph. Go ahead and plot the new transposed dataframe. Make sure to add a title to the plot and label the axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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atGlMmjTJ6DYpRZj6waJEWloa/v7+7N27l5EjR7Z3cSQzGTt2LC4uLtdlsJUkc7suRziQJFP7/fffiYmJYfjw4Wi1Wv773/+ya9cufv755/YumiRdk2TwkSQjKIrCBx98wGOPPYZerycoKIhvvvmGyZMnt3fRJOmaJB+7SZIkSW3uukw4kCRJkjo2GXwkSZKkNveXb/O5cOFCi7Zzd3dvsD9GRybL3bZkuduWLLf5mWo+JFnzkSRJktqcDD6SJElSm5PBR5IkSWpzf/k2H0mSpIYIISgrK0Ov15t1+vBLly5RXl5utv03lxAClUqFtbW12c5bBh9JkqQGlJWVYWlpiVpt3q9KtVqNhYWFWY/RXJWVlZSVlTV7QFRjycdukiRJDdDr9WYPPB2VWq1u0RxQxpLBR5IkqQHmfNR2LTDn+cvgI0nSdUGUFFG680eTzwAqmYcMPpIkXRfEju8pWPUKnDNuJs1rRWZmJo8++ihhYWGMHj2amTNnsnHjRu67775613/qqadISkpq41I231/zYaYkSdcdEVM11bZITUbxC2jn0piGEII5c+Zw55138sEHHwAQFxfHr7/+2uA2b775ZlsVr1VkzUeSpGueuHgezp+p+iO14//qN9b+/fuxtLSsVcvp06cPQ4cOpaSkhIceeojw8HDmzZtneNx4xx13cPz4cQB69OjB8uXLGT9+PFOnTuXy5csAbN++nalTpzJhwgTuvvtuw/K2JGs+kiRd88SxgwCouwZQmZZslmPov/gYcS7VpPtUuvij+vtDDb6emJhI3759630tLi6OyMhIvL29ueWWWzh8+DBDhgyptU5JSQkDBgxg4cKFvPzyy2zatIkFCxYwZMgQfvjhBxRF4bPPPuP9999n6dKlJj23psjgI0nSNU8cOwBdA7EaHkHl/z5BlJWgWNu2d7HMKjQ01DDIZ3BwMOfOnasTfDQaDTfeeCMAffv2Ze/evQBkZGTw6KOPkpmZiVarxc/Pr20Ljww+kiRd40TOZUhNQrl1JpaBvUEIOHMaevUx6XEaq6GYS8+ePfnpp5/qfU2j0Rj+b2FhQWVlZZ111Gq1IV265jr/+te/+L//+z8mTJhAdHQ0b731lhlK3zjZ5iNJ0jWt+pGbMiAMyx43VC1Luz7afUaOHIlWq2XTpk2GZbGxsRw8eLBV+y0oKMDb2xuAzZs3t2pfLSWDjyRJ1zQRcwB8/FC8fVE5OoOHN+I6STpQFIU1a9YQFRVFWFgYY8aMYcWKFXh5ebVqv//85z95+OGHufXWW3F1dTVRaZtHEX/xHllyMrlrgyx327pWyi0K8tA/NQtlyp2obpmBu7s7ma88izj1BxavrW31/ktKSrC1NX/bkVqtrvexWXur7/zlZHKSJP3lieO/gdCj9B/+50L/npBzGZGf234Fk5rUpgkHer2ehQsX4urqysKFC8nMzGTlypUUFRXh7+/P/PnzUavVVFRUsGrVKk6fPo2DgwMLFizA09MTgG+++YbIyEhUKhUPPPAAoaGhQNVz0HXr1qHX6xk3bhzTpk1ry1OTJKkdiJgD4OENXfwNyxT/Hgio6u8TOrTdyiY1rk1rPj///DO+vr6Gvzdu3MiUKVN49913sbOzIzIyEoDIyEjs7Ox47733mDJliqGxLT093ZCZsXjxYtauXYter0ev17N27VoWLVrE22+/zf79+0lPT2/LU5MkqY2JkiI4eRyl//DaA2B2CQCVCpFqnv4+kmm0WfDJzs4mJiaGcePGAVXDRsTHxzNs2DAARo8ezeHDhwE4cuQIo0ePBmDYsGHExcUhhODw4cOEhYVhaWmJp6cn3t7epKSkkJKSgre3N15eXqjVasLCwgz7kiTp+iROHAFdJcqA4bWWK1ZW4Nv1usl4u1612WO39evXc++991JaWgpAYWEhtra2hgmUXF1dycnJASAnJwc3NzegKjfd1taWwsJCcnJy6NGjh2GfNbepXr/6/8nJ9f/q2bFjBzt27ABg+fLluLu7t+h81Gp1i7dtT7LcbUuW23zy4mOocHHHfXAYiqrqd3R1uQtuCKFsfyRurq6G11ri0qVLbTafT0ecN8jKyspsn4M2OdujR4/i5ORE9+7diY+Pb3L9+hLwFEVpcKj0htavz/jx4xk/frzh75Zm9Fwr2UBXk+VuW7Lc5iHKy9HHRKOEjSf7yg9Q+LPc+k5+iOJCshJ+R/H2bWRPjSsvL2+TGUY7arZbeXl5nc/BNZXtlpiYyJEjR/jHP/7BypUriYuLY/369ZSUlKDT6YCq2k51vrmbmxvZ2dkA6HQ6SkpKsLe3r7W85jZXL8/OzsbFxaUtTk2SpPYQfxS02jqP3Kop/j2B66Ozac2nPcaIjo42DES6fft2Vq1aZY5itVqbBJ977rmHDz/8kNWrV7NgwQL69OnDY489RnBwsKGn7u7duxk0aBAAAwcOZPfu3QAcPHiQ4OBgFEVh0KBBREdHU1FRQWZmJhkZGQQGBhIQEEBGRgaZmZlUVlYSHR1t2JckSdcfEXMA7BygZwND6HTqDFbW8BdPOpgwYQLz5s1r72LUq10fMs6YMYOVK1fyxRdf4O/vz9ixYwEYO3Ysq1atYv78+djb27NgwQIAunTpwvDhw3nyySdRqVTMmTMH1ZXnubNnz2bZsmXo9XrGjBlDly5d2u28JEkyH1FZgThxGGXAcJQGHokpKgvoGnjdjHQAGDJ9XVxcSExMJCQkhPfeew9FUdi1axdLly7F1dW11ijY//vf/zhx4gTLli1j+/btvPvuu2i1WlxcXFi1ahUeHh7tdj5tHnyCg4MJDg4GwMvLi1dffbXOOhqNhieffLLe7W+77TZuu+22OssHDBjAgAEDTFtYSZI6nj9OQGkJSv+wRldT/Hsgdv6AqKxAUVu2+rBrjlwiNbes1fupyd/FmgcHGT9UTn3TKISEhPD000/z5Zdf4u/vzyOPPFLvth1hGoWaOl56hSRJUiNEzAGwtoHe/RpdT/HviaishHNp4N+8dpOOqr5pFGxtbfHz86N79+4A3H777WzcuLHOth1hGoWaZPCRJOmaIfQ6ROwhlL6DUCw1ja/c7c+kA8UEwac5NRRzaWgahYaye2vqCNMo1CTHdpMk6dqRfBIK8xvMcqvF1R0cna+rabXrExgYyNmzZ0lLSwPg22+/rXe9jjCNQk0y+EiSdM0Qxw6A2hL6DGxyXUVRwL/ndT/MjrW1Na+//jr33Xcf06ZNo3PnzvWu1xGmUahJTqkgp1S4Jshyt62OWG4hBPpn54BfdyzmLal3navLrf/xf4jvNqF653MUW7tmH1NOqSCnVJAk6a8uLQVys1AGNJ7lVlN1Z1POpJipUFJLyeAjSdI1QcREg4UFSr/Bxm/ULbBq2+u83edaJIOPJEkdnhCiKvj06oti52D0doqdA3j6tLjd5y/eKmHW85fBR5Kkju/CWcjMqD1jqZGU7j2hhWO8qVSqDtkW0xYqKysNI8iYg+znI0lSuxNCQHEh5OdBQW7VFNgFuX/+fS4VFAWl/7Dm77xbTzi4G5GbjeLi1vT6NVhbW1NWVkZ5eblRfWlaysrKivLycrPtv7mEEKhUKqytrc12DBl8JElqFlGYj9jxAyLuKKpHnkXx8G7V/vTr30Uc3A26emoYlpqqvjpOLihT7kJxav5o9bWm1XZpXs1JURRsbGyafczm6ojZheYmg48kSUYR2ZmI7d8i9m2HigpAQez4HmX6/7V8n+dSEft3QP9hKD37VAUZRxdwcgZHF7CxNbrGcTqnjOX743l4gBsuNjW+2rr4g4UakZpkXOdUqU3I4CNJUqPEhbOIrV8jfosCQBk2GmXi7YifNyP270RMuxfFpmV9YUTkj6DRoLp/frMSCeoTlVbAgbRccovKeHl8FywtqtorFEsNdO4mM946mBYFH61Wi0ql6pDTvkqSZBoiNQn9z19B7EHQWKGMvgllwjQU1yvD8I+diji4CxEdiTJuavP3X1SAOLQHZfiYVgcegPjMEpxtLPkjq5QPD19i3lBvQ61J8e9ZVVa9rmq6BandGZXKsGHDBlJSqjppxcTE8MADDzBr1iyOHDli1sJJktT2xOWL5D4/H/0rT0FSHMrUv6NavhbV3x/6M/BQ1ZZC916IyB8Ren3zj7P3V6jQooyZ0uoyl1XqOZVTxtRgL+4MdmPHqXx+Tsr7cwX/HlBWChfPt/pYkmkYVXXZt28fd999NwBfffUV8+fPx9bWlk8//VTOGCpJ1xnx7SYqkhNQ7nwAJXwiinXDj9SUsVMRa1ZA/DHo2/R4a4Zj6HSI3T9X9dvp3K3VZU7MKkUnINTXkQA7O9Lyyll79BJ+zhr6etlV1XwAkZqM4tO+UwlIVYyq+ZSXl2NlZUVhYSGXLl1i2LBhhISE/OWyMyTpeifKyxCxB7EOn4Bqwq2NBh4AZWAYOLmij/yxeQc6fghyLqMa2/zHdfVJyCxBAfp2ckSlKDw5ohOdHDS8vvcCl4q04OVbNQdQC/v7SKZnVPDx8fFh7969bN26lZCQEKBqeO6ac0tIknTtE7GHQFuO9agJRq2vqC1RIiZB3FFEMx5p6SN/AjdP6DekpUWtJT6zFH8XK+ytqh7m2FpasCiiMzq94NWo85TrgW49rvsRrq8lRgWfOXPmsG3bNuLj4w2P344fP24IRJIkXR/E4b3g7IZlE7OE1qRETKxKZd71k3HHSE+FxN9RRk9GsWh943+FTpCYVUpvz9q1NF9HDU+N9OFMXjnvHsiAbj0gPRVRoW31MaXWM6rNx93dnZdffrnWslGjRtG3b1+zFEqSpLYnigog7ijKuJtRmjGsiuLogjJ4JCLauLRrEfkTaDQoRtaumnIqpwytThDsWbcz6AAfe2b28+DT2Mv4e4Rwm+5rOHsaAoJMcmyp5Yz6hD3++OP1Ln/iiSdMWhhJktqPiIkGnQ5laHizt1XG/g3KShHRkY0fo7gQcWg3ytDRJkmvhqr2HoDeHvUHvVt7uxLe1ZFNl2044hqESOtYj96EEJy8WNimg5i2JDvR1IwKPvVdlJKSErMOOidJUtsSh6LA2xe6dG/2toa0610/NfrFJvb9ClotytjWp1dXi88swddRg7NN/Q9yFEVh3jBvurtasTJ4BulpHSvdet+ZQh7833GOXywx+7FEaQn6r9ahX7m03UfsbvSx26OPPgpUdSqt/n+1oqIiRowYYb6SSZLUZkROFiTHo/xteosH0DSkXSccq3eaa6HXIXZVp1f7t7bIAOj0gpOXSwnza7wWZaVW8Vx4Z57cEs+rSghvanXYaTpGZ9OtKVX9kQ6fLyK0U/NnWzWG0OuqRqP45r9QVIASNhYqtKCxMsvxjNFo8Jk/fz5CCF599VXmz59f6zVnZ2eTTacqSVL7Ekf2ghAoQ5r/yK2aMjAMsfkT9Dt/xKKe4MPxw5Cdiequ2a0oaW1n88sprtAT7Nn08D4edpY843SBJYXd+Tn+Enf2b//vr/SCcuIulWChUoi5UAR4mfwYIjkB/Rcfw9lTEBCE6rHnUbr1MPlxmqvR4NO7d28A1q5di5VV+0VISZLMSxyKgm49ULxa/oWsqC1RwichfvgccelCnX3pd/4Arh7Qb2hri2uQkFkKQO96kg3q06eHLzdEprLrVFfuCO1k1mkSjLE9OQ8LBe4d2JlPD58jo1BLJwfTdGER2ZcRX6+vymB0cUd58J8oQ8Lb/ZyrGZXtZmFhwY4dO0hLS6OsrKzWa/PmzTNLwSRJahsiIx3OnkK5e06r96VETKoacHTXTyh/f+jPY5w/U5Vefdv9JkmvrhafWYK7rRpPO0vjNugexJgvf+Z9Z3+Sssvo5W7+6RIaotXpiUwtYGgXB27q7cmnh88Rc6GYKb1aF3xEeRli6xbEti0AKH/7O8rE21Cs/pybR6cXWKjaNwgZFXxWrVrFmTNnGDhwIE5OTuYukyRJbUj8FlU1Udugka3el+J0Je16/w7EtBmGERJE5I9gqUEZdWOrj1FNCEFCZgl9ve2M/jWvWFkR5mnJGn0FO0/ltWvwOXC2kMJyHRMDnensbEMnB0uOXihiSq/mz1lUTfx+BP1/34fcLJTBo1Buvx/FzbPWOt//kUNsRjHPjvLFSt1+SWNGBZ/jxxO5ORoAACAASURBVI+zatUq7OzM0xgmSVL7EEIgfttTlQTg3LxZPhuijJ2KOLi7arTrsVMRxUWIg7tQhkag2Dua5BgAGYUV5Jbp6u3f0xi7AYMZvu8Eey0HMGegV7t9AW9PycPb3pIQ76oAPcDHnl9T8tDq9Ggsml8mUVaK/uMV4OyK6ulXUXoG11ln/5kCPjmaybAu9qjbueZj1Bm6u7tTUVFh7rJIktTW0lIgM6NViQZXU/x7gn9PRGRV2vWf6dWmGcetWsLlK/17jEg2qFW+kMGMvRxLiU7hUHqRSctkrPT8cuIyS5kQ6IzqSq1tYCc7tDpB3KWWpVyLg7uhtBjVffPqDTxxl0p4KzqDIA8bngjzaffHbkYFn/DwcN544w327dtHXFxcrX+SJF27xG9RoFajDAgz6X6VsVPh0nmIj6kadqdnMEoX06RXV4vPLMHRyoIujs1rI1FsbAnu7IS7Np/I03lNb2AG21KqEg3Gdf+zGaOPly0aC4WYC8XN3p8Qouo6+3Wvd/SGs3nlvBKVjre9JYsjOrfr47ZqRj1227p1KwCff/55reWKorBq1SrTl0qSJLMTel1VJlSfQSh29ibdtzJoBOKrdeg/fQ/yc1Hdabr06moJmaXc4GHTouwtiwFhjNl1mK8148kuqcDN1siEBRPQ6vTsOp3PsC4OtTrGWqlV9PG05eiFYh5s7k6T4uDCWZT759e5HlklFbyw6xwaCxVLx3TBwapj9G8yKvisXr3a3OWQJKmtJcZBfo5JH7lVq5l2jas7hJouvRogu6SCi0UV3NSzZY3zSr8hjPlyE5u7jWdXagF3BJumvcsY0WcLKdTqmdjDuc5rA3zsWHM0k4uFWrybkXKtj/wR7BzqvJdFWh3/jkynRKvn1Ql+eNq3XZBtSvvXvSRJahfityiwskEJGWzS/VboBIfTi3jbYTgzR77InhEzTZpeDVVTKIDx/XuuptjZ4+3fhd7F6USezm/ToWa2JVclGvT1qttWNcCnqgYak2H8ozeRfRmOHUIZeSNKjRELKnR6Xo06z/nCcp6L8MXfxbqRvbQ9o2o+JSUlbN68mYSEBAoLaw+A98EHH5itcJIkmYeoqEAcjUbpPwzFBB3IdXpBfGYJe88UVKUQa/XYa1RY2tnxrYUTY4QwaefGhMwSrNUqurfiC1UZEMaY7dGstuvcZn1+zuWXk3C5lPtDPQyJBjX5OFjibW9JzIUio2t1Ys8vACijJxuW6YXgnQMZxF0q4YmwTvTz7niZykbVfNasWUNqaip33HEHRUVFzJ49G3d3d6ZMMd3ggJIktaH4o1Ba3KIRrKsJIUjKKmXN0UvM+fYU/9p5jqi0Avr72LMkojPrb+vBPf08OZOn5Y+sUhMWvqq9J8jDplUZW0roMMKy47BCx85T+SYsXcO2peShVsHYgPr7SyqKwgAfO05cLEGra3rkaVGhRezdDv0Go7j/OTTPp8cus/dMIfeHejDav2P2zTSq5nPixAnefvttHBwcUKlUDB48mICAAF577TWmTjVt+qQkSeYnDkWBgxMEGT9pXE3n8st5Zc95LhRqUasUBvrYMaqrI0M629fKpBrVzYF1MZlsS87jhgamPGiuwnIdZ/LLGdmtdVMyKA6O2AT0ZFh+EnvPBDNnoKdZs8CqEw2GdnbA2brhr96BPvb8nJRHQmZpkwONisN7oaig1nTk3/+Rw7cnc5jSy4Vbe7uarPymZvSUCra2VR8ca2triouLcXZ25uLFi2YtnCRJpifKShDHf0MZOAJFbdTvz1q0Oj1v7LtAsVbH/GHefHp7IIsiOjOqm2OdL29bSwtG+zuy70xVb35TqO7fE2yCYKYMDGNsahQlFXqz9/mJPltIkVbPpHoSDWrq62WLpUrh6IXGyyOEqJqYr1MXCKqaVbq6E+nwLg7MGeDZYcZxq49Rwadr164kJCQAEBQUxNq1a1mzZg2dOnUya+EkSTI9cewQVGhb/MhtfUwmZ/LKeXx4J8YHOGPfxNQEE3s4U6EX7Eo1zaOthMxS1CqFHu6tb0BX+g8nOD8VD0XLztPmffS2LTmPTg6W9Kkn0aAmK7WKYC/bpvv7nE6EMykoY6agKAr5ZZWsOnSRXu42PDmiU7t3Im2KUcHn4YcfxsPDA4DZs2ej0WgoLi6Wg4pK0jVI/LYH3Dyhe/Onkv4tvZCfkvL4W5ALA32N6xvk72JNL3drtibnmSSrLD6zhJ5u1i0aguZqipMLqsAgRmcd53hGMVkl5hnJ5eyVRIOaIxo0ZqCPHekFWi4VaRtcR+z6CaxtUIaPBuDzE1mUVeqZN8zbJNfG3Jqsc+v1enbv3s1tt90GgKOjI4888kizDqLValm6dCmVlZXodDqGDRvGXXfdRWZmJitXrqSoqAh/f3/mz5+PWq2moqKCVatWcfr0aRwcHFiwYAGenlWD433zzTdERkaiUql44IEHCA0NBSA2NpZ169ah1+sZN24c06ZNa+61kKTrnijMh4RYlIm3ojRzJuLskgrePXgRfxcr7g/1aNa2EwOdeffgReIyS+jr1fLMq9IKPadzyri1t+n65SgDwhjz3RY2uw1m9+kC7uhj+j4/25OvJBp0r934LwpyEft3knvqJOKWew2jQAzwsWPtUYi5UMzknnX7+4j8XMSR/SijJ6NY23Iuv5xtKXlMDHSmi9O1Mf1Nk58+lUrFtm3bsGhFnr6lpSVLly7ljTfe4PXXXyc2NpakpCQ2btzIlClTePfdd7GzsyMysmr+98jISOzs7HjvvfeYMmUKmzZtAiA9PZ3o6GjeeustFi9ezNq1a9Hr9ej1etauXcuiRYt4++232b9/P+np6S0uryRdr8SR/aDXowyJaNZ2eiFYeSADbaWep0b4YNnMX9Yjuzpip1GxLbl1w9kkZpWiEzR7MNHGKAOG412WQ291MTub0edH6HSIioZrJtXKK/VEpl4Z0cBaXTXeXUIsug+Xo39mNmLLBioS49CvWII4cwoAXwcNXvaWHG3g0ZvYuw10lYb06k+PZWKtVjE9xN3Is25/Rn2CIiIi+PXXX1t8EEVRsLauej6r0+nQ6XQoikJ8fDzDhg0DYPTo0Rw+fBiAI0eOMHr0aACGDRtGXFwcQggOHz5MWFgYlpaWeHp64u3tTUpKCikpKXh7e+Pl5YVarSYsLMywL0mS/iSOHYBOXVA6d2vWdt8k5HDiYgkPDvKicwt+WVupVYz1d+LAuULyyiqbvX21hMslqBQI8jBh8HH1AP+ejMk4woVCLYlZZU1uIyoq0L+5CP28u9C99AT6TR+gj96JuJiO0NdOkY4+W0ixVs9EHzX6rV+jX/II+refhz9+RxkzFdW/38f17Q1gZY3+rSWItOSqlOtOdvx+qZiKq1KuRWUlYs9W6N0fxbszJy4Wc/h8MXcEu+HUSBZdR2NUSVNSUti6dSvff/89bm5utTIoXnzxRaMOpNfrefbZZ7l48SITJ07Ey8sLW1tbQ43K1dWVnJwcAHJycnBzq6r6WlhYYGtrS2FhITk5OfTo8ef0rzW3qV6/+v/Jycn1lmPHjh3s2LEDgOXLl+Pu3rJfCmq1usXbtidZ7rbVkcot9Houn0nBOnwCjk2UqWa5Ey4WsulEFmMC3Zg+NKDFGVR/H2LLD4m5HLpYyYxB3i3aR1JuBj087PDrVP900y293sWjbiRs0xrWeo8m+kI5I2/o0uj6BR+8RmnKSWxuvBndpQtUHNqD2P0LAlDsHFD37I1lj2Ase/ZmR7zARxTT+41FiMpKLHv3w2bGw1gPH20YkUCtVuP+6ofk/Gse4u2lOC9dyZggH35JziO9XMNgvz8z5Mr27yQ/Lwfnuc+hdnVjw/ZzeDtYMWtEjw4xYKixjAo+48aNY9y4ca06kEql4o033qC4uJg333yT8+fPN7hufdVeRVEarA43tH59xo8fz/jx4w1/Z2VlNVX0erm7u7d42/Yky922OlK5RcY5REkxZd5+aJsoU3W5Syp0/OvnNFytLXgw1JXs7OwWH9+eqsdlW46f58auVkY1vNdUoRPEZxQwsYdzg9e0pddbBPXDRlfOMHUuvyYqzAiumzZeTR+1FbH9O5TJd6C97T4AFL0OJeM84vQfkJqENjUJbexhzth6EDf4n9x3LhpVxGSU8InoffwoBooLCoFCQ7lzVZbw5EuIFUvIeeEx/Oa9gFqlsOvkefxt/6wt6r77HNy9KOgayM7Dp0i+XMw/R/hQmJdzZW/m5ePT8qnWazIq+FQ/AjMFOzs7evfuTXJyMiUlJeh0OiwsLMjJycHVtapDlJubG9nZ2bi5uaHT6SgpKcHe3t6wvFrNbWouz87OxsWl5bMBStL1SKQmAaB072n0Nv85fInM4gqWjffD3gSjIU/q4cKK/Rc4cbGkyQ6UV0vJKUWrEybp33M1xcMb/Loz5mw0u71u4lB6EeHd6k58J1JOIj77CPoMRJk248/tVRbg64fi6wejJiCEYF9KNmuPXcaqUjBu3hxUDk2XW3HzRPXUMvRvLkbz3lKCJ7zA0QvFzB545fjnUiE5AeXOByjXK2w8nkVPN2tGdW1dh9v2YFQdLTIyst5/e/fuJSEhocmJ5goKCigurmo402q1/P777/j6+hIcHMzBgwcB2L17N4MGDQJg4MCB7N69G4CDBw8SHByMoigMGjSI6OhoKioqyMzMJCMjg8DAQAICAsjIyCAzM5PKykqio6MN+5Ik6YrUJLC2AS9fo1bfnZrP7tQC7urj1uwJ2xoyvIs9jlYWbE3Obfa2Ca0cTLQpyoAwgk/uwcNGVW+fH5GXjf7D5eDqjurBf1YFnHpcKtLy713pvPlbFq4ONrw6yR9nIwKPoRyuHqiefhUcXeh/YhvpBVoyi6q+Y8Wun0CjQRkxnm8TcsgtrWT2wI7dmbQhRtV8oqKiSEpKwsnJyVD7yM/PJyAggMzMTACeeeYZAgIC6t0+NzeX1atXo9frEUIwfPhwBg4cSOfOnVm5ciVffPEF/v7+jB07FoCxY8eyatUq5s+fj729PQsWLACgS5cuDB8+nCeffBKVSsWcOXNQXUkXnT17NsuWLUOv1zNmzBi6dGn8ma0k/dWI1GTo1sOoFOvz+WV8+NslbvCw4a4+pmuzsrRQMa67E9/9kdPseXTiM0vo7KgxW6O6MjAM1bcbGaO6zOYMN7JKKnC/Uj5RUYH+g+VQVorqiX/XO/9RpV7w3ckcvvg9C5Wi8OBAT27q6dKizp6Kixuqp19hwHtvsh6IiU1iYn8/xKHdKENHk6NYsyXhAmF+DiYbtqitKcKIvMI1a9bg4+PDTTfdZFi2detWzp8/z+zZs9myZQsxMTEsW7bMrIU1hwsXLrRou470LL85ZLnbVkcpt6jQop9/N8qEW1FdaadoSKVe8K/I85zJKWHlTf4mnwMmo1DLI9+fZkaIO3f1NS6w6fSCmV8lM7KrI3OHNpys0NrrrXthPhcdOzHX6zZ6uFkzrIsDfb1s6f7LOlRRW1E98izKwBF1tjt5uYQPDl3iTH45w7rY89AgL0PgMkZD5dbn5fB/36bgX5DOc1YpiCP7UC19h1Xp1uxOLWDVVH86NWPeH1MwVZuPUY/d9u/fz6RJk2otmzBhAvv27UNRFG6++WbZr0aSOrKzp0GnQ/Fvur1nW3IeCZeK+MdQb7NMPtbJQUM/b1u2peSh0xvXp+ZMXjnFFXqzPXKrpgwYjvcfh3igtz3llXr+G3uZZ7ad4V79CF4au4hvrINIyio1lLuoXMf7hy6ycPtZiit0LIrw5bnwzs0KPI1RObsyMNCLEy6BaI8egJ7BpNp2YuepfKb2cmnzwGNKRtVfnZycOHr0KIMH/znpVExMDI6OVQ1yFRUVqFswQKEkSW2jOtkA/x6Nrwj8fqkEHydrRnSt2+BuKpN6OPPa3gscyyhmkBHD9Px+6cpgoiZqe2qIMnAE4ocvuDn/d6ZNnUzuyZPEbfqcuG6DiXcN5tPYywDYqFX09rThVE4ZBeU6bglyYXqIBzaWpk91HtDNla1nSvljyN8IiRjOumOZ2GtU3NmGs6+ag1ER44EHHuCtt97Cz8/P0OZz9uxZnnzySQCSk5Pr1IwkSepAUpPAxR3FuekvrFM5ZfTxMe8cMEM6O+BiXZV40FjwyS2tZNPxy+w4lU83Zys87Mw8DbSPH3j5ImKiEaFDcFy7nDCNFSOnj0GxsyevtJK4zBLiLpVUBWkHDUvHeNHd1XyzhIZ42aFWKRwbMJVKW1tOXEznwYGeJsk+bE9GBZ9+/frx3nvvERsbS05ODv3792fAgAE4ODgYXu/Xr2XzgkiSZH4iNcmoWk9BuY7M4gp6eRo3aGhLqVUK4wOc+Tohm8vFFXWCSnmlnu//yOGr+Bwq9XpuDnIxaeJDQxRFQRkwHLFtC/pVy+okGDjbqBnZ1ZGRZqwVXs3GsqqWdeR8EUfOF+HjoGGykbOcdmRGPytzdHQkPLzlsx5KktQ+RFEBXL6IEj6xyXVP51QNLVMVfFo+DI4xJgQ681V8NttT8pjRr2qgUiEEUWkFbIi9TFZJJcO72HN/f882bdtQBo5A/PIVnEmpSjDw7dpmx27IQB871sVUPfJbFO6LuoNPl2CMBoPPsmXLWLx4MQDPP/98g3nkxg6vI0lSO0mtGmrKmGSDFEPwsUNbZN75bTztLRngY8evp/K5u687ydmlfHI0k6TsMgJcrXgizKfJuW/Mwq87hA5F6R5Ub2ZbexjgY8+6mMv08bRhSGfz1krbSoPBJyLiz1Fvq/vfSJJ07RGpSaAo0LX+fng1ncopw8veEkdrS7LMO7EnUJV4sGzPeRb9eobErDJcbdQ8PrwTo/0dmz38jqkoioLFPxa3y7Eb0sVRw4MDPRnsa39NdiitT4PBZ+TIkYb/m3J4HUmS2pZITQIfPxTrpmsRKdll9HAzX+P51Qb62ONppyYtt5zpfd2Z1tsV62tocMy2oigKfwtybe9imJTRbT4nT54kNTWVsrLaw41XTzInSVLHI4SAtCSUfkObXLc62WBSD+cm1zUVC5XC8gldsVApOF9D0wFIrWfUu/3JJ59w4MABgoKC0Gj+bPi7Xqp/knTdyroERYVgxGCi1ckGAWZMG65Pc4bYka4fRgWfvXv3smLFCsMI0pIkXRvE6UQAlG7GJxu0dfCR/pqMerjq7u6OpaX8dSJJ15y0ZNBowIh04epkA4drvPOidG0wqubzyCOP8J///IcRI0bg5FS753Pv3r3NUjBJklpPpCaBXyCKRdMB5VROmaz1SG3GqOBz+vRpjh07xsmTJ2u1+QB88MEHZimYJEmtIyor4explNGTm1y3sFzHpaIKJgS2XbKB9NdmVPD5/PPPefbZZwkJCTF3eSRJMpXzZ6BCC0Z0Lj11pb0nUNZ8pDZiVJuPlZWVfLwmSdcYkXol2aAZwcecA2RKUk1GBZ+7776b9evXk5eXh16vr/VPkqQOKjUZHJzAzbPJVU/llOFpZ4mjTDaQ2ohRj92q23V+/fXXOq/973//M22JJEkyiaqRrHsa1R/vVE4ZgW04soEkGRV8Vq1aZe5ySJJkQqK0BC6mowwZ1eS6ReU6LhZVcKNMNpDakFHBx8PDw9zlkCTJlNKSQQiM6Vx6KlcmG0htz6jgU1JSws8//0xaWlqdsd2WLFliloJJktRyzZk2OyVbJhtIbc+o4PPWW2+h1+sZMmRInX4+kiR1PCI1GTx9UOwcmlxXJhtI7cGo4JOcnMzatWtRq+Wos5JkSpV6QVJWKTd42Jh2oN60JJRefY1aVY5sILUHo1Ktg4KCOH/+vLnLIkl/Od+dzOG5X8+y8kAGWp1pui6InCzIywH/Xk2uW51sINt7pLZmVFVm7ty5vPrqqwQGBuLsXDsj5o477jBLwSTpeieEYFdqPo5WFuxOLSCjUMtz4Z1xsWnlE4a0qvYexYj2nupkgwCZZi21MaOH18nOzsbDw4PS0lLDcjmfjyS1XGpuOefytTwy2AsnawtWRmfw1NY0Fkd0blXjv0hNBgs1dOne5LqnsuU0ClL7MCr4REdH88477+Di4mLu8kjSX8aetAIsFBjh54CjtRpvew0v70ln4fYzPBHmw3C/ppMF6iNSk6CLP4oR06Ck5JThaaeWyQZSmzOqzcfLywsLI4ZklyTJODq9ICqtgAE+9jhemT66u6s1KyZ1o6uzFcv3nufL37OqpsFuBqHXQVqKUY/cQCYbSO3HqJrPqFGjeP3115k0aVKdNp8+ffqYpWCSdD2Lzywhp7SS2d0cay13sVGz7EY/Vh28yKYTWZzL1zJvmDdWaqN+J0JGOpSXGpdsoL0yskGAHNlAantGBZ9t27YBVW0/NSmKIofekaQW2JNWgLVaxZDO9nVe01ioeCKsE37OVmyMvUxGkZbnwn1xs236MVp151Jjaj6nc2SygdR+jAo+q1evNnc5JOkvQ6vTE322kDA/+wZrNIqicEewG10cNbwVfYGntp5h+QQ/vOyb6OSdmgw2duDp02Q5UnJksoHUfoysy0uSZCqHzxdRUqEnoptTk+sO7eLA8gldKSjX8UNibpPri9RE8O+Bomr61j4lkw2kdtRozef5559vMp36xRdfNGmBJOl6tye1ABdrC/p62Rq1vr+LNUM627MntYD7Qz2xtKj/nhTl5XD+DMok4/reyWQDqT01GnzGjh3bVuWQpL+EwnIdRy8Uc1NPZyxUxveTG9/dieizhRw5X9RwCva5U6DXo3RveiTrIq2OjMIKxnVvuvYlSebQaPAZPXp0GxVDkjqG8ko9T/6Sxm29XRlnhiyw6LOFVOqFUY/cagrtZIerjZodp/IaDD7i9JWRrLs1I9lA1nykdiLbfCSphj+ySkkv0PLRkUtcKtKafP970vLxddQQ4GrVrO0sVApjuzsRk1FMdklF/SulJYOrB4pT053Bq5MN5JhuUnuRwUeSaojPLEGlgILCqoMX0Tezk2djLhdXEJ9Zyuhuji0ammpsdyf0AnanFhiWCSEQ58+i/+UrRPwxFP+mH7lBVXuPh63a0MFVktqa/ORJUg3xl0rwd7FmUg9nVh+6yLbkPCb3NM2wUnvSqoJG+FUdS43l66iht4cNO0/lcStn4cRhxInDkHWpagW/AJSJtxm1r1M5ZbJ/j9SuGqz5LF682PD/zZs3t0lhJKk9aXV6ErPK6ONpw40BToR627L+2GWTPX6LSi2gl7sN3g7Nn5BRFOajj97JmLP7OV9YwclPPkHs3Q4+figz56J6fR0W/3rbqM6lxVeSDeQjN6k9NVjzuXDhAlqtFo1Gw48//sidd97Z4oNkZWWxevVq8vLyUBSF8ePHc9NNN1FUVMTbb7/N5cuX8fDw4IknnsDe3h4hBOvWrePYsWNYWVkxd+5cunevGqF39+7dbNmyBYDbbrvNkBRx+vRpVq9ejVarpX///jzwwANy1G2pWZKzy6jQC4I9bVEUhXnDOjH/x1RWHbzIv8d1adXnKS23jDP55Tw82KtZ24nCAsSPXyD2/AI6HWGu3qztG8KucQ/Re1wvFKvmtR1BVa0HZLKB1L4aDD6DBw/m8ccfx9PTE61Wy9KlS+tdz5h+PhYWFsycOZPu3btTWlrKwoULCQkJYffu3fTt25dp06bx7bff8u2333Lvvfdy7NgxLl68yLvvvktycjJr1qzhlVdeoaioiK+++orly5cDsHDhQgYNGoS9vT0ff/wxDz/8MD169ODVV18lNjaW/v37t/CySH9F8ZdKUIDenlX9bzzsLJk90JPVhy6ytZWP33anVo1gPdLIkapFhRax8wfEz5uhvAxl5ASU8InY+XUn7OBF9p4t5EELS1oSPk7JZAOpA2gw+MydO5c//viDzMxMUlJSGDNmTIsP4uLiYpiOwcbGBl9fX3Jycjh8+DAvvPACABEREbzwwgvce++9HDlyhPDwcBRFoWfPnhQXF5Obm0t8fDwhISHY21eNhxUSEkJsbCzBwcGUlpbSs2dVY2t4eDiHDx+WwUdqlrjMEro6W+FQo8f/jQFO7D9TwPpjlxngY9f08Db10AtB1JkC+neya7KBX+j1iN+iEN/8F3IuQ78hqG6/H6VTF8M64wOciDydT/TZQsa2oJ+OTDaQOoJGP31BQUEEBQVRWVlpsj4/mZmZpKamEhgYSH5+viEoubi4UFBQ1SCbk5ODu7u7YRs3NzdycnLIycnBzc3NsNzV1bXe5dXr12fHjh3s2LEDgOXLl9c6TnOo1eoWb9ueZLnrV6nTk5iVxJRgrzrH+ddkB2ZuOsZ/jmbzzm19mvX4Ta1Wk15uSXZJJfPDAxo9B21cDIXr3qPydCLq7r1wWPA8mr4D66wX7ibo/FsmUWeLuWtIgPEneUVafho3dHJqtCzyc9K2rtVyt4ZRP33Gjh1LXFwcUVFR5Obm4uLiQnh4eLOnUygrK2PFihXMmjULW9uGhxapbw6Thm54RVGaNefJ+PHjGT9+vOHvrKwso7etyd3dvcXbtidZ7volZpVSVqknwFGpcxw1MCvUg/d/u8jGAynNevzm7u7Od7HnsFar6O0k6j0HkXEO/defwvHfqvrpzHkC/ZAIClQqaOCcR3ezZ+PxLH5PvUCnZiQwFGt1nMsrI7yrfaPXU35O2ta1VG4fn6YHrTWGUf18du7cycqVK3F2dmbIkCG4uLjwzjvvGGoQxqisrGTFihWMGjWKoUOHAuDk5ERubtVgibm5uTg6VqWgurm51XojsrOzcXFxwdXVlezsbMPynJwcXFxccHNzq7U8OzsbV1dXo8smSXGXSgAI9qz/R9GEQCf6tSD7rbxSz4GzhQzrUv8I1iLhGPoX5kNSHMpt96N66X1Uw8Y0OTDomO5OqBSIPJ1vdFkATufK9h6pYzAq+Hz//fcsWbKEe+65hxtvvJHp06ezZMkSvv/+e6MOIoTgww8/xNfXl6lTpxqWDxo0iD179gCwZ88eBg8ebFge3Xf2wwAAIABJREFUFRWFEIKkpCRsbW1xcXEhNDSU48ePU1RURFFREcePHyc0NBQXFxdsbGxISkpCCEFUVBSDBg1q7rWQ/sLiM0vo7KjBuYF2EEVRmDe0Ewqw6tBFo2vbB9JyKK7QE9FA3x79r9+DkyuqZf9BNfl2FI1x2WvutpaEetux83Q+Or3xNX+Z6SZ1FEY9dissLKRz5861lvn4+FBUVGTUQRITE4mKisLPz4+nn34agOnTpzNt2jTefvttIiMjcXd358knnwSgf//+xMTE8Nhjj6HRaJg7dy4A9vb23H777Tz33HMA3HHHHYbkgwcffJD3338frVZLaGioTDaQjKbTCxIyS5vs/Olpb8kDAzx5/7eLbEvJY1KPph+/bf/jMs7WFvTztqvzmsjNhvhjKDfdgeLQ/MSB8QFOvL7vAiculdC/U939X02nFxw9X4y7rRonmWwgtTOjPoFBQUFs2LCBGTNmYGVlRVlZGZ999pkhu8yY7b/88st6X3v++efrLFMUhQcffLDe9ceOHVvvaNsBAQGsWLHCqPJIUk2pueWUVurpY8QUBxMCndh/toB1MZcJcrfB11GDpUX9DxCKynVEp+UwKbD+EazFgUgQepSwcS0q95DO9jhoVOw4lddk8Cmv1PPGlUA1e4Bni44nSaZkVPB56KGHWLlyJbNmzcLe3p6ioiJ69uzJ448/bu7ySZLZxWdWt/fYNLlu9eO3x35K5fGf0wCwslCwt7LAXmOBg0aFncYCBysLCst1VOgEEf51a1RCCMT+HdCrL4pnpxaV29JCRbi/E9uT8ygs19VKEa+psFzHy7vTScwq5ZHBXiYbLkiSWsOo4OPi4sKLL75Idna2IdutZmqzJF3L4jNL8La3xM3W0qj1Pe0teX1iV+IzSyjS6ijS6iks1135v46LRRWkZJdRpNXRp5ND/Y37yQmQmYEy5e5WlX18dyd+SswlKq2AKb3qBpXLxRW8uOscGYUVPDPKhzC/lo0rJ0mm1qwHv25ubjLoSNcVvRAkZJYwtItxIw9U83O2ws+56eSAhlJoRfQOsLZBGRjWrONerburNf4uVuw8nV8n+JzNL+eFyHOUVuh5cWwXox4rSlJbkVMqSH9pZ/PKKdTqG0yxNgdRVoI4sh9l8CgUq9ZnnY3r7sSpnDLSrqRRA5y8XMJz28+g1wteudFPBh6pw5HBR/pLi88sBYxr7zEVcWR/1XhtI8Y3vbIRIvydUKsUdlzp8/NbeiHP7zyHo5UFr03sir+LTKuWOp4mH7vp9XoSEhIICgpCrZbpmdL1JS6zBA9bdYvGbGspsX/H/7d35+FRlff//5/3ZLJPEjKTEAgBIkuACDFgAEUMYXGpK6KiVFRAKootv4qtYKVAUSr6kWKxpK5Fi3VFQFwoGlniwioJ/ABZQgAJkH3fl3N//whGUwJMwixJfD+uywsyOXPOK+OQ95z73Od9Q6cI6NHHIfsL9PZgaISFTUeLiQj04uUdWfS0+vDnhAiZUi1arQue+ZhMJp577jkpPKLd0VqzL7ucS104JKUzT0La96jhYxy65MfoHkGUVNXxz+1ZxHTy56nR3aTwiFbNrmG3fv36cejQIWdnEcKlThZXU1RZ59rrPd8mgcmEuqLlXeKbEtvZn942H0b3CGLOiAh8PWVEXbRudn00Cg0N5ZlnniEuLg6bzdboE9tdd13cVFEh3GXvmft7+ruo+Oi6OvS3G2FAHCrIsffaeJgUz18f6dB9CuFMdhWf6urqhr5r51qqQIi2Zl9WBcG+ZjoH2Hd/z8UfcBcU5WNy0EQDIdoyu4rPj73VhGgvGq73dPR12XLrxjdfQkAQDJCmt0LYfUUyIyODrVu3UlRUxAMPPMCpU6eoqamhe/fuzswnhFNkltaQV1HruiG3kiLYvR01+iaUTN4Rwr4JB1u2bGHevHnk5+eTnJwMQEVFBf/+97+dGk4IZ2no5+aimW562yaoq0UNkyE3IcDOM5/333+fP//5z0RGRrJlyxYAunfvzrFjx5yZTQin2ZddTqC3B10DnX9/j9Ya/XUSXBKF6tLN6ccToi2w68ynqKjorOE1pZTLxsqFcLS9WRWuu97zwxE4edxhHQ2EaA/sKj49evRoGG770TfffEOvXr2cEkoIZ8opqyG7rMZl9/for5PA0ws1+GqXHE+ItsCuYbfJkyfz9NNPs2HDBqqqqli4cCGnTp1izpw5zs4nhMPtzfpx/R7nFx9dVYXevhl1+TCU34VXGxXil8Ku4tOlSxdeeOEFvvvuOy6//HJsNhuXX345Pj7SsFC0Pfuyy/H3MtHdjiURLlbl9s1QXtbi1UqFaK/snvPp7e1N3759yc/Px2q1SuERbda+7HKiQ/2aXNra0Sq//BRsHaHPAKcfS4i2xK7ik5uby9KlSzl8+DD+/v6UlZXRq1cvZsyYQWhoqLMzCuEw+RW1nCqp4dpeHZx+LJ2XTfWenaibJ6BM0mtNiJ+z61/EsmXL6NGjB8uXL+e1115j+fLl9OzZk2XLljk7nxB20Vqjq6suuN2+M9d7nL24mq6pRn/0HwDUsFFOPZYQbZFdxSc9PZ2JEyc2DLX5+PgwceJE0tPTnRpOCHvpz9dgzJqCLi4473b7ssvxMZvo4cQF1nT6QYynHkVv2YjfLRNQto5OO5YQbZVdxad3796kpaU1euzIkSNERUU5JZQQzaW3bYLSEvRH75x3u73Z5fQL9XXK9R5dXYWxcjnGollQVYHp/5tPwKTfOvw4QrQH57zm89577zX8PSwsjGeeeYZBgwZhs9nIy8sjJSWF4cOHuySkEOej87I5WFTHpkvvxuNUBd7JaXh1CMLLZMLTQ+HlofD0UCjgRFE1CZFBjs+Q9j3GG0sh6yQq/jrUHZNRvq5bJ0iItuacxScvL6/R10OHDgWguLgYT09PhgwZQnV1tXPTCWGHb7fuY0nsNDzMnnhUV1DzQyXVJ2rPuX1sZ8fdb6OrqtAfvYVOWgvWUEyPLkBFxzps/0K0V+csPrKMgmgLPj6Qz+sFnYiqOs2c8SOwJH+Cfv911Ix51EUPpLpOn/nPoKZO42FSdA5wTD83fWgfxptLIfs0KuEG1O33oXzkbEcIe9h9n09VVRWZmZlUVlY2erxPnz4ODyXEhRha88aubD46UMDQvH08GlGOr7cHeuQN6I2folcuxzw3Fk8vD5zRV8BY+w76k3fB1hHTY0+j+sY44ShCtF92FZ/Nmzfzr3/9C7PZjJdX40+N//znP50STIhzqa4zeOHb03zzQwk3BpUxadMKPG9+DgBl9sR0xySMfy5Cf/0FasT1Dj++PnoI/fE7qCEjUPdOR/n4OvwYQrR3dhWft956i8cee4yYGPl0J9yrtKqOvyZnsC+7gkkDQ7k5eQ0qKBgie/+00cAroXc0+qP/oIfEO/TCvzYMjHdegaBg1L0PS+ERooXsmmptNpuJjo52dhYhziunrIZZnx/nYG4lj10VztjeAai9u1CXDWnUQUAphWn8A1BShF73gUMz6O2b4egh1Di5viPExbCr+Nx11138+9//pri42Nl5hGjSoZxS/rj+OAUVtcwfFUF8ZCAc2ANVlajYoWdtryJ7o64Yif5iLTo3yyEZdGUF+sM34cy+hRAtZ9ewW3h4OO+//z7r168/63s/vx9ICGdIPV3Gs18fxteseOba7g3dqHXqNvD2hXNc7Fe3TUTv+ga9egXqN3+46Bx63YdQmI/podnSq02Ii2RX8XnxxReJj49n2LBhZ004EMKZNqYX8eLW00Ra/XgyvjM2P0+g/tqL3r0d1X8QytOzyecqayjqmrHoT99Hj7oJ1bNvi3PonEz056tRV4y8qP0IIerZVXxKS0u56667ZNls4TJaaz7cl8+K3TnEhPnxf7fFUFlS+NMGRw9BUQE0MeT2c+r629Fff4Hxwb8wzXq2xe9hY+Vy8PBAjbuvRc8XQjRm19hBQkLCWctoC3EuWms2pBfxmzVprEjNoarWaNbz6wzNyzuyWLE7h/jIQOaO7IrFu/HnJL17G5hMqAFx592X8vFF3XoPHDmA3vlNs38WAP39bti1BfWrO1DBthbtQwjRmF1nPmlpafz3v/9l1apVdOjQeB2Uv/zlL04JJtqm4spaErdnsuVEKZ0snqzcl8dXx4t5MC6MuC6WCz6/qtbg+W9OsT2jlHHRVu6NDcXUxNmKTtkGfQag/C+8T3XVaPSGT9AfvoGOHYLytH/oWNfVYbz3Gtg6oq4da/fzhBDnZ1fxGT16NKNHyzLA4vx2nizlxa2nKa02mDQwlFv6WtmfU85L27N4alMGV3YNYGpcR0L8mr5GU1xZy9ObMziUW8mDcWHc2Ce4ye105knIzEAl3GBXLmXywHTnFIwlc9Fffoy6/na7fyb91Xo4eRzTw7ObVbSEEOdnV/FJSEhwcgzRllXWGizflc1/DxfSPcibv4zqTOSZ9XIGhPnzwg2XsOb7PN7fm0fKx2X8OiaEm/oEN1rW4HRJNQs2niC3vJZZV3fhym4B5zye3r0NABU7xO6MKjoWBsShP/sAfUkUyo5lrXVZSf2CcH0G1N+4KoRwGLuKz4YNG875vVGjZJXGX7KDuRW88O0pTpfUMLaflXsuC8HLo/GlRE8PxZ39Q4iPDOTlHVn8a1c2G48W8fCQTvQJ8eVwXgVPbcrAMDQLRnWlX8fz37ypU7ZCtx7NXqTNdNdUjL/NwXj+Seg/CNO4+1FdLzn3cT5+F8rKMN09VSbbCOFgdhWfr776qtHXhYWFZGZm0rdvX7uKT2JiIrt27SIoKIjFixcD9TPolixZQk5ODqGhoTz66KNYLBa01ixfvpyUlBS8vb2ZPn06PXr0AGDTpk2sWrUKgHHjxjWckaWnp7Ns2TKqq6sZOHAgkydPll8WTlZraN7fm8sHe/Ow+Zp5akxXBoSdv4VnmMWLPydEsPVEKa9+l8Ws9ce5qnsAOzJKCfIxM29MBBFB3ufdhy4ugPSDqJvubnZmFRaO6al/ojd+hv7sA4ynfo8aEo+69R5UaKfGxzn1A3rjp6gR16Eizl2ghBAtY1fxmTdv3lmPbdiwgZMnT9p1kISEBK6//nqWLVvW8NiaNWsYMGAAY8eOZc2aNaxZs4aJEyeSkpJCZmYmS5cu5fDhw7z22mv89a9/pbS0lJUrV7Jo0SIAZs+eTVxcHBaLhVdffZVp06bRu3dvnnnmGVJTUxk4cKBd2UTz5ZXX8EzySQ7nVZJwSSAPxoXh7+Vh13OVUlzZLYDLOvvx7p5cPj5YQGQHb+aO7Eqw74Xfjnr3DtAaNfCKFmVXXt6o625DX30N+r8for/8GL3zG1TCr1A3jkcFBKG1rp9k4OOLuuWeFh1HCHF+Lb5NOyEh4bzDcT8XHR2NxdJ4VtKOHTsYMWIEACNGjGDHjh0A7Ny5k/j4eJRSREVFUVZWRkFBAampqcTExGCxWLBYLMTExJCamkpBQQEVFRVERUWhlCI+Pr5hX8LxtNa8uDWTE0VVPD48nEeHhdtdeH7Oz9ODKZeH8cqtPXn2uu52FR4409XA1hEiIpt9zJ9TfhZM4+7H9PTLqGGj0Bs+xfjTgxgfv4vengz7U1G3/BoVEHhRxxFCNM2uf/GG0fg+jerqapKTk/H3b/lKKUVFRQQH189mCg4Obugbl5+fT0hISMN2NpuN/Px88vPzsdl+usfCarU2+fiP259LUlISSUlJACxatKjRsZrDbDa3+LnudLG5vziYQ8rpMn4/oge3xoZfdB57o5jNZqz+fuR8vxvfa28lMDT0oo/dEGDmfGrHT6L0P69QtfZtADwiIrHdfi/KbPeSV036pb5P3EVytx12/cuaMGHCWY9ZrVamTZvm8EBa67MeO9f1G6VUk9ufz5gxYxgzZkzD17m5uc0LeEZISEiLn+tOF5O7pKqOJZvS6W3zIT7c06U/f0hICHlfJUFNNVV9L3P8sX0s8MBMTAk31A/Fjb6ZvMLCCz7tQn6J7xN3ktzOFx5+8R86wc7i849//KPR197e3gQGXtxwRFBQEAUFBQQHB1NQUNCwP5vN1uh/Ql5eHsHBwVitVvbv39/weH5+PtHR0dhsNvLy8hptb7VaLyqbaNobKdmUVNUxf2TXRtOkXUWnbgM/C/S+1GnHUD37Su82IVzArms+oaGhjf672MIDEBcXx+bNm4H6lVIHDx7c8HhycjJaaw4dOoSfnx/BwcHExsaye/duSktLKS0tZffu3cTGxhIcHIyvry+HDh1Ca01ycjJxcedvuSKab29WOUlHiri1r5UeVh+XH1/X1aL37ETFxKE8mn+NSQjRupz3zOdCrXOUUsydO/eCB3nhhRfYv38/JSUlPPTQQ4wfP56xY8eyZMkSNmzYQEhICDNnzgRg4MCB7Nq1ixkzZuDl5cX06dMBsFgs3H777TzxxBMA3HHHHQ2TGKZOnUpiYiLV1dXExsY6fabbvuxyuqlyPGrq8PNs/78Ia+oMErdn0tHfk7tj3DMuXfP9HigrQcW2bJabEKJ1Ufo8F03ONZstPz+fdevWUVVVxVtvveW0cK5w6tSpZm2vtWb8e4eorqt/2XzNJmx+ZkL8zNj8PAnxNxPi54nN10zXIG86WppuJeMuLRlbfmdPDu/+/3nMGxnBoPAL91JzBu+1/6F83SpMS95qU0tXt6Wx/J+T3K7VlnK75JrP/95AWlJSwurVq/nyyy8ZNmwYd9xxh0NCtCUamD+qK9UevhzNyievvJa88hpyy2v54XQZBRW1/Lyahwd4EdvZj9jO/gwI82tzZ0oniqpYuS+f+O6Bbis8Wmuqtn8F/S5rU4VHCHFudk04KC8vZ+3ataxfv55Bgwbx7LPP0qlTpws/sR0yKcWlHf3qP6nYzr7oXmtoCipqyS2vIS2vkpTTZXx5pIjPDhXioaBPiC8DO/sT29mfnlYft1y4t5ehNYnbMvExKx64vHmtbBzq5DHqsk6hrr3NfRmEEA513uJTXV3Np59+yieffEJ0dDQLFiyga9eursrWJplNilB/T0L9PekX6sfNfa3U1Bl8n1NB6ukyUjPL+M+eXP6zJxeLl4lB4Rau79WB6I6+ra4lUNKRIvbnVPDboZ3oYOdNoM6gU7eBUqjL7G8kKoRo3c77G+WRRx7BMAxuueUWevbsSVFREUVFRY226d+/v1MDtgeeHiZiOvkT08mf+4Ciylp2Z5aTcrqM7RklJB8rpnsHb26MCmbEJYH4mFvceMJhCitqeSMlm0s7+jKmZ5DbcujaWvS3G/DsMwAjqOklFoQQbc95i4+XV/36JZ9//nmT31dKnXUPkLiwIB8z8ZGBxEcGUlUbxuZjxXx2qIDE7Zm8mZrNmB5B/CoqmM4B7ls/5rXvsqiq1Uwf0smtZ2R6ywbIycT/N49R4rYUQghHO2/x+XkjUOEc3mYT1/bqwDU9g/g+p4JPDxXwycEC1h4oYFC4PzdGBTMw3L/J1Tyd5buTpXx1vIQJA0Iu2GXamXRtDfqT9+CSKLzihsHPbiYWQrRt7hvIF40opYju6Ed0Rz/yymtYn1bI+sOFLNiUQXiAJzOvCqe3zfkzvSprDV7akUlEoBe3X+reThH66y8gPwfTfb9tddfDhBAXx/0XF8RZbH6e/DomlNfG9uKxq8KpNTR/+uIHtvzg/IGnd/bkkl1Wy/ShnfD0cN/bQ9dUoz/9AHr1g+hYt+UQQjiHFJ9WzNNDER8ZyP9dF0lkB28WfXWSVfvymt1M1V6H8ypYeyCf63p14NILrCbqbHrzf6EwD9Ot98hZjxDtkBQfJ9BaYySvx9i6ySH76+Br5ukx3biqWwBvpuawbFsmtYZjC1BNnebFLZkE+5i5f6CDlitoIV1VhV63EvoMQPWNcWsWIYRzyDUfB9PVVegVy9BbN4HZjO7RB9Wx80Xv19ts4g/Dw+m8O5eV+/LIKqth1tVdsLRgIbemrNyXy/GiKuaMiGjR4nCOpDd9BsWFmB6a7dYcQgjnkTMfB9L5uRjPPYHeugl13TjwMGN8+IbD9m9SintjQ5lxRSf2Z5cza/1xMkuqL3q/xwoq+WBvHvGRgQyOcE8LnR/pynL0fz+E6IGo3tFuzSKEcB4pPg6i077HWDgTsk5ieuRJTHdMQl1/O+zagj6416HHGt2zA/NHdaWwspbH1x/n+5zyFu+rzqhfFtvi5cFv3NlC5wy94VMoLcZ066/dHUUI4URSfBzA+PoLjMVPgrcPpif+DxU7FAB17ViwhmC8/xraqHPoMQeE+fPcdZH4eZn4c9IJko8Vt2g/aw/kk5ZfyW/iwgj0ce8orC4vQ69fDQPiUD36uDWLEMK5pPhcBF1bi/HOK+g3X4So/pieXIwK79bwfeXljRp3P/yQjt6y0eHH7xLoxXPXdqe3zYfF35zirdQc6poxEeFUcTVv78llaISF4d0DHJ6vuXTSWigvxXTrPe6OIoRwMik+LWQUF2H8fT56wyeoa27FNGMeyv/sX+BqSDz06INevQJdWeHwHIE+ZhaM7sqYnkF8sC+PBRtPUFxZe+H8WvOPbafxNCmmDQ5z+3RmXVaCTvoIYq9Ade/p1ixCCOeT4tMCOuMY+Y8/AGnfoyb/HtP4B865tLNSCtP4B6CoAL3uQ6fk8fQw8bsrOvPI0E7sza5g5rpjHM47f6Fbf7iQfdkVTB7UEZuf+xe8059/BBXlmG6d4O4oQggXkOLTTLq0GOO52ejqakx//CumYaMu+BzVsy9qSDz6izXovBynZbu2VwcWXVs/7Df78x/4PK2wyRtSc8pqeCMlh8s6+bm1Y/WPdEkx+suPUXHDURGXuDuOEMIFpPg0k7IEou55GOvzrzfrorgadz8AetWbzooGQG+bL3/7VST9w/xYti2Tf2zLpKrWaPi+1pp/bs9Ea80jQ93bsboh0/oPoboSdfPd7o4ihHARKT4tYBo6Ag9r87oAKFso6tqx6O3J6CMHnJSsXqCPmbkJEYzvbyPpSBGzPz9OVmn9/UDrD+Tw3aky7o0NJcziviUbfqSLC9AbP0UNiW80WUMI0b5J8XEhdf3tEGTFeO81tGFc+AkXwcOkuOeyUOaMiCCrrIaZ646xMb2Ivyen0zfElxv7tI6F2fS6D6G2FnWzXOsR4pdEio8LKR9f1G33wtFD6B1fueSYgyMsLL4+klB/T17YcpqKmjp+d0Unl64P1BStNcb6VfXXeq4ciQoLd2seIYRrSW83F1NXjkRv+AT94Zvo2CtQ3s5frK1zgBfPXtudt/fk0r+rjYggN/du+1n/O3X5VagJ09yaRwjhenLm42LKZMJ01wNQkIv+YrXLjuttNjF5UEd+1S/MZcdsii7I+6n/3a33oKY9jvL2cWsmIYTrSfFxAxXVHwYNQ6/7EF3wy1kaWh85UN//LvMkpul/wnTTXa1itp0QwvWk+LiJ6Y5JYNShV73ptMXhWhPj2y8xnv8TeHpheuI51MAr3B1JCOFGcs3HTVRoJ9Q1Y+sXTTMMuHc6yse9q4c6g66rQ698o751Tt8YTNMeR1kC3R1LCOFmUnzcSI2dCN4+6I/eRh8/gumhx9vVHf66rBTjledgfypq1E2oO6egzPKWE0JI8XErZTKhbhyP7tUP49XnMf76R9SEB1HDr2nV10J0bQ38kA7lpeiKcigvg4qyM3+WQ0UZurwMMo5BcSHqvt9iuvpad8cWQrQiUnxaAdVnAKa5L2C89jf0v/8Bh/bBxIdb1SwwbRiQ9j162yb0zm+gvPTsjUwm8PUHP3/w9YPOXTE9+AdUL1mRVAjRmBSfVkIFBmP6/Xz0J++jP3kXfTwN07RZqC7ubTmjTx6vLzjbkiE/B7y8UbFXoAZdCUHBZwrNmWLj7dOqz9iEEK2HFJ9WRJk8ULdMQPeOPjMM9xjqnocwDRvt0hw6Pxe9Ixm9dTNkHK0/o4keiLrtXlTsUJSPr0vzCCHaHyk+rZDqdxmmuX/HeG0xevnfMQ7sQQ2Ohy7dIdjWorMLXV0F2aepOpGGceoklJZAaTGUFUNpCbqs5KfHCnJBa7gkCnX3g6jBw1GBHZzwkwohfqmk+LRSqoMV06ML0B+/g/5s5U/LcPv5Q5fuqC6RZ/7sXv+nn3/9dZmCXMg6ic48CZkn0VmnIOtk/ZCZ1hT+74H8LGAJAP8ACAquH+brGI4afLX0WxNCOI0Un1ZMeXigxk5EX3MrnDyOPnkcMo6jTx5Db90IlRU03J7awVo/CaC6+qcd+PhCWBdUz35w1RgIC6fDJb0oqjXqC46f5ZwrsAohhDNJ8WkDlH8ARPWvb8tzhta6/mzmTDEiMwMsgdCpCyosAsLC689k/meIziskBJWb6+KfQAghGpPi00YppcDWEWwdUZcNdnccIYRoFuntJoQQwuWk+AghhHC5djXslpqayvLlyzEMg9GjRzN27Fh3RxJCCNGEdnPmYxgGr7/+On/6059YsmQJ33zzDRkZGe6OJYQQogntpvikpaXRqVMnwsLCMJvNDBs2jB07drg7lhBCiCa0m2G3/Px8bDZbw9c2m43Dhw+ftV1SUhJJSUkALFq0iJCQkBYdz2w2t/i57iS5XUtyu5bkbjvaTfFpajXQptrQjBkzhjFjxjR8ndvCe15CQkJa/Fx3ktyuJbldS3I7X3i4YzqftJthN5vNRl5eXsPXeXl5BAcHuzGREEKIc2k3Zz49e/bk9OnTZGdnY7Va+fbbb5kxY8YFn3cxVdxRnwBcTXK7luR2LcndNrSbMx8PDw+mTJnCwoULefTRR7nyyivp2rWr0443e/Zsp+3bmSS3a0lu15LcbUe7OfMBGDRoEIMGDXJ3DCGEEBfQbs58hBBCtB0e8+fPn+/uEG1Vjx493B2hRSS3a0lu15LcbYPSTc1RFkIIIZxIht2EEEK4nBQfIYQQLteuZrtdjMTERHbt2kVQUBCLFy8G4NixY7z66qtUVlYSGhrKjBkz8PPzo7a2lpdeeom74HVvAAAJbklEQVSjR49iGAbx8fHcdtttgOs7azsq9yOPPIKPjw8mkwkPDw8WLVrUqnK/8sorHDlyBJPJxKRJk7j00ksBSE9PZ9myZVRXVzNw4EAmT57cZGeL1ph9/vz5FBQU4OXlBcCcOXMICgpySubc3FyWLVtGYWEhSinGjBnDDTfcQGlpKUuWLCEnJ4fQ0FAeffRRLBYLWmuWL19OSkoK3t7eTJ8+veGaxKZNm1i1ahUA48aNIyEhwSmZHZ37rrvuolu3bkB9R4FZs2Y5LXdLsp88eZLExESOHj3K3XffzS233NKwr3bZsV8LrbXW+/bt00eOHNEzZ85seGz27Nl63759Wmutv/zyS/3OO+9orbX+6quv9JIlS7TWWldWVurp06frrKwsXVdXp3/729/qzMxMXVNTo//whz/oEydOtPrcWms9ffp0XVRU5NSsLc29bt06vWzZMq211oWFhfrxxx/XdXV1Dc85ePCgNgxDL1y4UO/atavNZJ83b55OS0tzel6ttc7Pz9dHjhzRWmtdXl6uZ8yYoU+cOKFXrFihV69erbXWevXq1XrFihVaa62/++47vXDhQm0Yhj548KB+4okntNZal5SU6EceeUSXlJQ0+ntrz6211hMnTnRaTkdkLyws1IcPH9Zvv/22/uijjxr2447fK64gw25nREdHY7FYGj126tQp+vXrB0BMTAzbtm1r+F5lZSV1dXVUV1djNpvx8/NzS2dtR+R2h+bkzsjIoH///gAEBQXh7+9Peno6BQUFVFRUEBUVhVKK+Ph4l3Qyd0R2VwsODm44A/D19aVLly7k5+ezY8cORowYAcCIESMaXr+dO3cSHx+PUoqoqCjKysooKCggNTWVmJgYLBYLFouFmJgYUlNTW31ud2hu9qCgIHr16oWHh0ej/bTXjv1SfM6ja9eu7Ny5E4CtW7c29I674oor8PHx4cEHH2T69OncfPPNWCyWJjtr5+fnt/rcP1q4cCGzZs1q6PrdWnJHRkayc+dO6urqyM7OJj09ndzc3Fbzerck+48SExP54x//yMqVK5tsjusM2dnZHD16lF69elFUVNTQAzE4OJji4mKgvkv8z7ss//ja/u9rbrVaXfaaX0xugJqaGmbPns2TTz7J9u3bXZK5OdnPpTW9zx1Jrvmcx8MPP8zy5ctZuXIlcXFxmM31L1daWhomk4mXX36ZsrIy5s6dy4ABA+zurO1szc0dFhbGU089hdVqpaioiKeffprw8HCio6NbRe6RI0eSkZHB7NmzCQ0NpU+fPnh4eLjsl7U9mpsdYMaMGVitVioqKli8eDHJyckNn4idpbKyksWLFzNp0qTznvU2573sive4I3InJiZitVrJyspiwYIFdOvWjU6dOjkt84/szX4ureX3iqNJ8TmPLl26MGfOHKB+WGXXrl0AfP3118TGxmI2mwkKCqJPnz4cOXKEkJCQVtFZu7m5w8LCsFqtQP2p/+DBg0lLS3N58TlXbg8PDyZNmtSw3Zw5c+jcuTP+/v5nvd4//hyu1tzsQENWX19fhg8fTlpamlOLT21tLYsXL+bqq69m6NChQP3/74KCAoKDgykoKCAwMBCo/3T98zO0H9/LVquV/fv3Nzyen5/v9PeJI3LDT693WFgY0dHRHDt2zOnFpznZz6W9duyXYbfzKCoqAuqX6F61ahXXXHMNUD9TZu/evWitqays5PDhw3Tp0qVRZ+3a2lq+/fZb4uLiWn3uyspKKioqgPpPaXv27GmYFdQacldVVVFZWQnAnj178PDwICIiguDgYHx9fTl06BBaa5KTk93yercke11dXcNwS21tLd99951TG+FqrXnppZfo0qULN910U8PjcXFxbN68GYDNmzczePDghseTk5PRWnPo0CH8/PwIDg4mNjaW3bt3U1paSmlpKbt37yY2NrbV5y4tLaWmpgaA4uJiDh48SEREhNNytyT7ubSW3yuOJh0OznjhhRfYv38/JSUlBAUFMX78eCorK1m/fj0AQ4YM4de//jVKKSorK0lMTCQjIwOtNSNHjmyYFrlr1y7efPNNDMNg5MiRjBs3rtXnzsrK4vnnnwegrq6O4cOHt6rc2dnZLFy4EJPJhNVq5aGHHiI0NBSAI0eOkJiYSHV1NbGxsUyZMsXpQxKOyF5ZWcm8efOoq6vDMAwGDBjA/fffj8nknM+DBw4cYO7cuXTr1q3h9ZkwYQK9e/dmyZIl5ObmEhISwsyZMxumLL/++uvs3r0bLy8vpk+fTs+ePQHYsGEDq1evBuqnWo8cOdIpmR2Z++DBg7zyyiuYTCYMw+DGG29k1KhRTsvdkuyFhYXMnj2biooKlFL4+Pjwt7/9DT8/P5f/XnEFKT5CCCFcTobdhBBCuJwUHyGEEC4nxUcIIYTLSfERQgjhclJ8hBBCuJwUHyGEEC4nxUcIJ1q6dCmJiYmNHtu/fz9TpkxxW8NLIVoDKT5CONHkyZNJSUlhz549AFRXV/Pyyy9z3333ObRFimEYDtuXEK4gvd2EcKKAgACmTJnCyy+/zOLFi1m1ahVhYWEkJCRgGAZr1qxh48aNlJeXM2DAAKZOnYrFYsEwDJYsWcKBAweoqakhMjKSqVOnNrSEWbp0KX5+fmRlZXHgwAFmz57dsEidEG2BdDgQwgUWL15MbW0tBw8e5LnnniMkJIS1a9eyY8eOhpUsX3/9dWpra/nd736HYRgkJyczdOhQPDw8WLFiBYcPH25YYXbp0qWkpKTwxBNP0KtXL+rq6vD09HTzTymE/WTYTQgXeOCBB9i7dy933HFHw3ozSUlJTJgwAavVipeXF3feeSdbtmzBMAxMJhMJCQn4+vo2fC89Pb2hSSnA4MGDiYqKwmQySeERbY4MuwnhAh06dCAwMLBRJ+Xc3FyeffbZRo1QlVIUFxcTGBjI22+/zdatWykpKWnYpqSkBB8fH4BGi6YJ0dZI8RHCTWw2GzNmzKB3795nfW/jxo2kpKQwd+5cQkNDKSkpYerUqa1qAT0hLoYMuwnhJtdccw3vvPNOw+JnRUVFDUtxV1RUYDabCQgIoKqqinfffdedUYVwODnzEcJNflxgbMGCBRQWFhIUFMRVV11FXFwcI0eOZM+ePUybNo2AgADuvPNOkpKS3JxYCMeR2W5CCCFcTobdhBBCuJwUHyGEEC4nxUcIIYTLSfERQgjhclJ8hBBCuJwUHyGEEC4nxUcIIYTLSfERQgjhcv8PtM9NiM94qLwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CI.plot(kind='line')\n",
    "\n",
    "plt.title('Comparison on Immigration from China and India')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n"
     ]
    }
   ],
   "source": [
    "# for x in CI.index:\n",
    "#     print(type(x))                 # type: string\n",
    "\n",
    "CI.index = CI.index.map(int)\n",
    "CI.index\n",
    "\n",
    "for x in CI.index:           \n",
    "    print(type(x))                   # type: int"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "df_CI.index = df_CI.index.map(int) # let's change the index values of df_CI to type integer for plotting\n",
    "df_CI.plot(kind='line')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.title('Immigrants from China and India')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "plt.xlabel('Years')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.show()\n",
    "--> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "From the above plot, we can observe that the China and India have very similar immigration trends through the years. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "***Note***: How come we didn't need to transpose Haiti's dataframe before plotting (like we did for df_CI)?\n",
    "\n",
    "That's because `haiti` is a series as opposed to a dataframe, and has the years as its indices as shown below. \n",
    "```python\n",
    "print(type(haiti))\n",
    "print(haiti.head(5))\n",
    "```\n",
    ">class 'pandas.core.series.Series' <br>\n",
    ">1980    1666 <br>\n",
    ">1981    3692 <br>\n",
    ">1982    3498 <br>\n",
    ">1983    2860 <br>\n",
    ">1984    1418 <br>\n",
    ">Name: Haiti, dtype: int64 <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<b>Line plot is a handy tool to display several dependent variables against one independent variable.</b> However, it is recommended that no more than 5-10 lines on a single graph; any more than that and it becomes difficult to interpret."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question:** Compare the trend of top 5 countries that contributed the most to immigration to Canada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "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>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",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>4211</td>\n",
       "      <td>1816</td>\n",
       "      <td>9564</td>\n",
       "      <td>3150</td>\n",
       "      <td>514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>7150</td>\n",
       "      <td>1960</td>\n",
       "      <td>9470</td>\n",
       "      <td>4166</td>\n",
       "      <td>691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>10189</td>\n",
       "      <td>2643</td>\n",
       "      <td>21337</td>\n",
       "      <td>7360</td>\n",
       "      <td>1072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>11522</td>\n",
       "      <td>2758</td>\n",
       "      <td>27359</td>\n",
       "      <td>8639</td>\n",
       "      <td>1334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>10343</td>\n",
       "      <td>4323</td>\n",
       "      <td>23795</td>\n",
       "      <td>11865</td>\n",
       "      <td>2261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>12041</td>\n",
       "      <td>8076</td>\n",
       "      <td>31668</td>\n",
       "      <td>12509</td>\n",
       "      <td>2470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>13734</td>\n",
       "      <td>14255</td>\n",
       "      <td>23380</td>\n",
       "      <td>12718</td>\n",
       "      <td>3079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>13673</td>\n",
       "      <td>10846</td>\n",
       "      <td>34123</td>\n",
       "      <td>13670</td>\n",
       "      <td>4071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>21496</td>\n",
       "      <td>9817</td>\n",
       "      <td>33720</td>\n",
       "      <td>20479</td>\n",
       "      <td>4777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>18620</td>\n",
       "      <td>13128</td>\n",
       "      <td>39231</td>\n",
       "      <td>19532</td>\n",
       "      <td>4666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>18489</td>\n",
       "      <td>14398</td>\n",
       "      <td>30145</td>\n",
       "      <td>15864</td>\n",
       "      <td>4994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>23859</td>\n",
       "      <td>19415</td>\n",
       "      <td>29322</td>\n",
       "      <td>13692</td>\n",
       "      <td>9125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>22268</td>\n",
       "      <td>20475</td>\n",
       "      <td>22965</td>\n",
       "      <td>11549</td>\n",
       "      <td>13073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>17241</td>\n",
       "      <td>21049</td>\n",
       "      <td>10367</td>\n",
       "      <td>8735</td>\n",
       "      <td>9068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>18974</td>\n",
       "      <td>30069</td>\n",
       "      <td>7045</td>\n",
       "      <td>9734</td>\n",
       "      <td>9979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>28572</td>\n",
       "      <td>35529</td>\n",
       "      <td>8840</td>\n",
       "      <td>10763</td>\n",
       "      <td>15400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>31223</td>\n",
       "      <td>36434</td>\n",
       "      <td>11728</td>\n",
       "      <td>13836</td>\n",
       "      <td>16708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>31889</td>\n",
       "      <td>31961</td>\n",
       "      <td>8046</td>\n",
       "      <td>11707</td>\n",
       "      <td>15110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>27155</td>\n",
       "      <td>36439</td>\n",
       "      <td>6797</td>\n",
       "      <td>12758</td>\n",
       "      <td>13205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>28235</td>\n",
       "      <td>36619</td>\n",
       "      <td>7533</td>\n",
       "      <td>14004</td>\n",
       "      <td>13399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>36210</td>\n",
       "      <td>42584</td>\n",
       "      <td>7258</td>\n",
       "      <td>18139</td>\n",
       "      <td>14314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>33848</td>\n",
       "      <td>33518</td>\n",
       "      <td>7140</td>\n",
       "      <td>18400</td>\n",
       "      <td>13127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>28742</td>\n",
       "      <td>27642</td>\n",
       "      <td>8216</td>\n",
       "      <td>19837</td>\n",
       "      <td>10124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>28261</td>\n",
       "      <td>30037</td>\n",
       "      <td>8979</td>\n",
       "      <td>24887</td>\n",
       "      <td>8994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>29456</td>\n",
       "      <td>29622</td>\n",
       "      <td>8876</td>\n",
       "      <td>28573</td>\n",
       "      <td>7217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>34235</td>\n",
       "      <td>30391</td>\n",
       "      <td>8724</td>\n",
       "      <td>38617</td>\n",
       "      <td>6811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>27509</td>\n",
       "      <td>28502</td>\n",
       "      <td>6204</td>\n",
       "      <td>36765</td>\n",
       "      <td>7468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>30933</td>\n",
       "      <td>33024</td>\n",
       "      <td>6195</td>\n",
       "      <td>34315</td>\n",
       "      <td>11227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>33087</td>\n",
       "      <td>34129</td>\n",
       "      <td>5827</td>\n",
       "      <td>29544</td>\n",
       "      <td>12603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      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",
       "1985   4211   1816                                               9564      \n",
       "1986   7150   1960                                               9470      \n",
       "1987  10189   2643                                              21337      \n",
       "1988  11522   2758                                              27359      \n",
       "1989  10343   4323                                              23795      \n",
       "1990  12041   8076                                              31668      \n",
       "1991  13734  14255                                              23380      \n",
       "1992  13673  10846                                              34123      \n",
       "1993  21496   9817                                              33720      \n",
       "1994  18620  13128                                              39231      \n",
       "1995  18489  14398                                              30145      \n",
       "1996  23859  19415                                              29322      \n",
       "1997  22268  20475                                              22965      \n",
       "1998  17241  21049                                              10367      \n",
       "1999  18974  30069                                               7045      \n",
       "2000  28572  35529                                               8840      \n",
       "2001  31223  36434                                              11728      \n",
       "2002  31889  31961                                               8046      \n",
       "2003  27155  36439                                               6797      \n",
       "2004  28235  36619                                               7533      \n",
       "2005  36210  42584                                               7258      \n",
       "2006  33848  33518                                               7140      \n",
       "2007  28742  27642                                               8216      \n",
       "2008  28261  30037                                               8979      \n",
       "2009  29456  29622                                               8876      \n",
       "2010  34235  30391                                               8724      \n",
       "2011  27509  28502                                               6204      \n",
       "2012  30933  33024                                               6195      \n",
       "2013  33087  34129                                               5827      \n",
       "\n",
       "      Philippines  Pakistan  \n",
       "1980         6051       978  \n",
       "1981         5921       972  \n",
       "1982         5249      1201  \n",
       "1983         4562       900  \n",
       "1984         3801       668  \n",
       "1985         3150       514  \n",
       "1986         4166       691  \n",
       "1987         7360      1072  \n",
       "1988         8639      1334  \n",
       "1989        11865      2261  \n",
       "1990        12509      2470  \n",
       "1991        12718      3079  \n",
       "1992        13670      4071  \n",
       "1993        20479      4777  \n",
       "1994        19532      4666  \n",
       "1995        15864      4994  \n",
       "1996        13692      9125  \n",
       "1997        11549     13073  \n",
       "1998         8735      9068  \n",
       "1999         9734      9979  \n",
       "2000        10763     15400  \n",
       "2001        13836     16708  \n",
       "2002        11707     15110  \n",
       "2003        12758     13205  \n",
       "2004        14004     13399  \n",
       "2005        18139     14314  \n",
       "2006        18400     13127  \n",
       "2007        19837     10124  \n",
       "2008        24887      8994  \n",
       "2009        28573      7217  \n",
       "2010        38617      6811  \n",
       "2011        36765      7468  \n",
       "2012        34315     11227  \n",
       "2013        29544     12603  "
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# line up values in Total column\n",
    "df_can_sorted = df_can.sort_values(by='Total', axis=0, ascending=False)\n",
    "top5 = df_can_sorted.head(5)             # top 5\n",
    "\n",
    "# transpose for plotting\n",
    "top5 = top5[years].T\n",
    "top5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z008/YWFhQfny5XMdqxDi9SYNKSGEKAC+/vprWrZsyfvvv4+3tzcPHz7kgw8+yHYokVarJSwsjMuXL1O1alU++OADBg8eTLFixQzShYaGcvToUdzc3AzmN6Xl4uLC1q1buXHjBrVq1SI4OBgPDw9+/fXXF1bP3NJqtWzevJmgoCA++ugjKlWqRHBwMFu3bs12flNaJiYmrFu3DgsLC+rVq8e7775Lhw4dsu1VKlmyJFu2bOHixYvUqFGDNm3aULNmTVauXPm8VTNqxYoV1K5dm3bt2vHOO+9w7tw5tmzZkqH37ZtvvmHq1Kl4eXmxfv16Vq9eneVDZ+Pj4xk6dCheXl74+vryyy+/sHDhQsaMGZNlPJUqVeL48eM0btyYTz75hKpVq9KgQQOWLl3KhAkT1DlxM2bMoGPHjoSEhODp6cmGDRtYs2aNQWNz9uzZlCxZkkaNGtG6dWvat29PxYoVc32OxowZg1arpUqVKjg5OXHq1Kkc57W1tWXfvn24u7vTuXNnKlasSMeOHTlz5gwlS5YEUm5sTJo0iVq1alG9enV2797N+vXrX/qDq4UQrx6N8iwDq4UQQrx0jRs3xs7OLl8bMqJgOX78ONWrV+fUqVMGy34LIYTIe7LYhBBCFACnTp3i2LFj+Pr6kpiYyJIlS9i1axebNm3K79CEEEIIYYQ0pIQQogDQaDR8//33fPzxx+j1eipXrsxvv/1G8+bN8zs0IYQQQhghQ/uEEEIIIYQQIpdksQkhhBBCCCGEyCVpSAkhhBBCCCFELklDSgghhBBCCCFy6Y1fbCLtU9bzk6OjY6YPyRQFj1yvV4dcq1eLXK9Xh1yrV4tcr1eLXK+CJbPnyEmPlBBCCCGEEELkkjSkhBBCCCGEECKXpCElhBBCCCGEELn0xs+RSk9RFBISEtDr9Wg0mjw77p07d3j69GmeHU88H7ler4434VopioJWq8XS0jJPP7eEEEKIN5k0pNJJSEjAzMwMU9O8PTWmpqaYmJjk6THFs5Pr9ep4U65VcnIyCQkJWFlZ5XcoQgghxBtBhvalo9fr87wRJYQQz8vU1BS9Xp/fYQghhBBvDGlIpSPDYoQQryr5/BJCCCHyjjSkCqAKFSrkKn14eDjdunUDYOvWrcyaNetlhCWEEEIIIYT4fzKG7TUTGBhIYGBgfochhBBCCCHEa00aUgVYeHg406ZNw87OjvPnz1O1alW+++47NBoNu3bt4vPPP8fe3h5PT081z6pVqzh58iSTJk1i69atzJw5k8TEROzs7Jg1axZOTk75WCMhhBBCCCFeD9KQyoJ+5XyUiKsvtExNKTe07/XJcfrTp0+zc+dOXF1deffddzl8+DBVq1ZlxIgRrF69Gjc3N/r162c0r7e3N+vXr0ej0bB8+XLmzJnD559//qKqIoQQQgghxBtLGlIFXLVq1ShevDgAVapUISIiAmtra0qXLk25cuUAaNeuHUuXLs2QNzIykv79+xMVFUViYiKlS5fO09iFEEIIIYR4XUlDKgu56Tl6WczNzdWfTUxMSE5OBnK2Otenn35KSEgIgYGB6jBBIYQQQgghxPOTVfteQe7u7vz7779cu3YNgLVr1xpN9+jRI1xdXQEICwvLq/CEEEIIIYR47UlD6hVkaWnJlClT6NatG61bt6ZkyZJG0w0bNoy+ffvSpk0b7O3t8zhKIYQQQgghXl8aRVGU/A4iP926dcvg97i4OKytrfM8DlNTU3XYnij45Hq9Ot6ka5Vfn18vkqOjI/fu3cvvMEQOyLV6tcj1erXI9SpYUtcrSE96pIQQQgghXmOKovCG3zcX4qWQhpQQQgghxGts3pE7DPjlVH6HIcRrR1btE0IIIYR4TcUm6th+OYZEnUJ0gjNFLeWrnxAvivRICSGEEEK8pvZef0yiLmVY35k7cfkcjRCvF2lICSGEEEK8prZdjqaUrTlWZiackoaUEC+UNKSEEEIIIV5D16OfcvF+AoHuRfEqXkQaUkK8YNKQKoCioqLo378/derUoVGjRnTt2pWlS5fSrVs3o+mHDx/OhQsX8jhKIYQQQhRkOy5HY6qFhmWL8E5JW248SuRh/JvxOAgh8oLMOCxgFEWhV69edOjQge+//x6A06dPs23btkzzTJ06Na/CE0IIIcQrIEmn8OfVR9QqURhbS1PeKWkDwKk7cTQoWySfoxPi9SA9UgXMvn37MDMzM+h98vDwwMfHh7i4OPr06UODBg0YOHCg+kyI9u3bc+LECQAqVKjAV199hb+/P8HBwdy9exeArVu3EhwcTGBgIJ06dVK3CyGEEOL1c+TmE2Ke6vAvbwtABedCWJtpOS3D+4R4YaRHKgsLjtzh6sOEF1qmm50lvWu6ZLr//PnzeHp6Gt13+vRpdu7ciaurK++++y6HDx/G29vbIE1cXBzvvPMOo0aNYuLEiSxbtozBgwfj7e3N+vXr0Wg0LF++nDlz5vD555+/0LoJIYQQomDYfjkaBytTqhdL6Yky1Wp428lK5kkJ8QJJQ+oVUq1aNYoXLw5AlSpViIiIyNCQMjc3JyAgAABPT0/27NkDQGRkJP379ycqKorExERKly6dt8ELIYQQIk/cj0viWGQsbd92wESrUbd7ulpz5NZd7scl4WBtlo8RCvF6kIZUFrLqOXpZKlasyMaNG43uMzc3V382MTEhOTnjhFFTU1M0Gk2GNJ9++ikhISEEBgYSHh7OtGnTXkL0QgghhMhvu648Qq+gDutL5eliA9zl9J04GrrZGs8shMgxmSNVwNSrV4/ExESWLVumbjt+/DgHDhx4rnIfPXqEq6srAGFhYc9VlhBCCCEKJkVR2H4lGg9nK4oVNjfYV7aoBTbmWhneJ8QLIg2pAkaj0bBgwQL++usv6tSpg5+fH6Ghobi4PF/v2LBhw+jbty9t2rTB3t7+BUUrhBBCiILk7N14Ih8n0aR80Qz7TLQaqjhbS0NKiBdEo6Qu/faGunXrlsHvcXFxWFtb53kcpqamRofqiYJJrter4026Vnn9+dW+fXvs7OyYP39+hn3NmzenUqVKfPvttwAMHjyY8+fPs3nzZjVNfHw83bp14+zZs6xatQoPDw8cHR25d++emiY8PJwOHTqwY8cOKleu/PIrlYms6prfli5diqOjI82aNcvT46a/Vi9KaGgoP/74I6dPn37hZefWP//8Q5MmTQgLC6NOnTr5HU6OzNgfyf5/H7O4nTuWpv+7X556vdb984D/jv8C7eVwjhw6lI+Riqy8rPeXeDapaxSkJ3OkhBBC5LnExER69+7NqVOnWLFiBR4eHkbTeXp6sm7dOsqUKZPHERqaPHkypqYF80/msmXLqFSpUp43pETBE5ekY9/1RzR0K2LQiErLwznlZkuS7o2+jy7EC1Ew/yoIIYR4bSUlJdG3b18OHTrE8uXLqV69eqZpCxcuTI0aNfIwOuMqVqz4QsqJj4/HysrqhZQlck+n06HT6QwWb3peBema7r3+mKc6BX8jw/pSlbWzwNxEQ4w0pIR4bjJHSgghRJ7R6XQMHDiQv/76i8WLF1OrVq0s04eHh1OiRAn++ecfdVuJEiWYN28e48ePp0qVKnh4ePDDDz8AsHr1anx9fXnrrbcYOnQoCQn/exbgqlWrKFGiBKdOnaJ9+/aUL1+egIAATp06RVxcHEOGDKFy5cr4+vqydu1agzjat29Pnz59DLatX7+eunXrUr58edq3b8/p06cpUaIEq1atUtP4+Pgwfvx4pk+fTo0aNdThiUeOHOGDDz7gnXfewd3dnYCAANasWWNQfmq8586d47333sPd3Z0GDRqwadMmg7hOnjxJWFgYJUqUMDj+1q1badasGe7u7rz99tsEBwezf//+bM91eHg4ISEhVKhQAV9fXxYvXpztuUh/nSIiIihRogS///47Q4YMoVKlStSoUYNff/0VgDlz5vDOO+/g6enJpEmT0Ov1GeI5fPgwTZs2pVy5cgQEBHDIyDC05cuX4+fnh5ubG97e3syZM8dg/+DBg2nevDl//PEHfn5+lCtXjr///jvTc7B48WJq1qyJu7s73bt3JyoqKkOaEiVKMHfuXD777DM8PT3x9/dX9/3444/UrVsXNzc36taty7x58wzyhoaG4uHhkW3dfHx8+OKLL5g+fTrVqlWjQoUKDBw4kEePHhmke/jwISNHjsTLy4ty5coxomcnbB5cpKKDpZomJiaGAQMGYGdnR/Xq1flu5kwcrE2lR0qIF0B6pIQQQuQJvV7PkCFD2LZtm/qF81nNnTuXJk2aMGfOHLZv386ECRO4d+8ex48f54svvuDmzZuMHz+ecuXKMXDgQIO8gwcP5oMPPuDDDz9k8uTJhISEUK1aNcqUKcO8efNYuXIlgwYNwtvbO9Nx8SdOnODDDz8kKCiIiRMncvHiRfr162c07dq1a6lYsSJffvmlOl/v5s2b1KpVi65du2JhYcHhw4cZOnQoWq2W1q1bG+QfOHAgXbp0oX///ixatIgPP/yQ8PBwihcvzuTJk+nTpw+lS5dm8ODBAJQtW5Zr164REhJCr169GDduHE+fPuXkyZNER0dne25HjBhBhw4d+M9//sPatWsZO3YsXl5eWfYcZmbSpEm0adOG+fPns3LlSgYPHszp06e5ceMGoaGhnDx5kilTpuDh4cG7776r5ouPj+ejjz5i4MCBODs7M2/ePP7zn/+wd+9enJ2dAfj+++/56quv6N+/P3Xq1OHkyZN88803WFlZ0aNHD7WsiIgIJk6cyJAhQ3BycqJUqVJGY92yZQtjx46la9euNGvWjP379zN06FCjaX/44Qd8fHyYOXOm2ghctmwZ48aNIyQkhEaNGhEeHs4XX3xBYmKiwWswJ3UD+P333ylbtixTpkwhKiqKSZMmMXz4cLVx9vTpU9577z0ePXrEuHHjUKxsGT9jPgdmDOVuJx+1rKFDh7J//36mTp2KlZUVc+fO5dqlK+gUDVFPknAuJM+TEuJZSUNKCCFEnjh9+jSnT5/miy++oGHDhs9VlpubG1OmTAGgfv36bNiwgeXLl3Pw4EEKFy4MwP79+/njjz8yNKT69u1Lx44dgZSlort164avry+jRo0CUh5+vnHjRrZt20b37t2NHn/27NlUqFCB77//Ho1Gg5+fH8nJyUyaNMlo+p9++glLy//1EqRtNCiKQu3atYmMjGTZsmUZGlJ9+vThvffeA1LmjFWrVo3t27fTrVs3KlasiLW1NQ4ODgZDIPfv34+NjQ2ffvqpuq1JkyZZnNH/ad26tdoo8/X1Zdu2bWzevPmZGlJ169Zl9OjRAFSvXp2NGzeydetW/vrrL0xMTPDz82Pr1q1s3rzZ4JwkJCTwySef0KZNG7Ucb29vFixYwJgxY3j8+DHTpk1j0KBBamOnQYMGxMfHM2PGDLp164aJiQmQ0muzcuXKTOfhpZo5cyZ+fn589dVXADRq1IgHDx6wfPnyDGmdnJzUXlBIuUkwbdo0OnbsyOeffw5Aw4YNefToEbNmzaJ3797q9c+ubmnPwc8//4yNjQ0A1tbWfPzxx1y8eJEKFSqwZs0azp8/z86dOylXrhw/HouiYrf/EjmnN3PnzuXTTz/l/Pnz/PHHH8yZM4devXpx79496tatS42atcDEktNRcTQuJM+TEuJZydA+IYQQeaJMmTKULVuW77//nps3bz5XWfXq1VN/1mq1lCpVCk9PT7URBSk9M5GRkVnmLVu2LIBB71iRIkVwcHDg9u3bmR7/xIkTBAQEqA9ABwgMDMw01rSNKIDo6Gg+/fRTvL29KVOmDGXKlGHZsmVcuXIlQ/4GDRqoP9vb2+Po6Gi0XmlVrlyZx48fM2jQIHbv3k1cXM6Xu07byDUzM8PNzS3b42Um7bkuXLgwDg4O+Pr6qo0cSLkGxs512sUzbGxsqF+/vjos7+jRo8TFxREcHExycrL6r27duty9e9cgXldX12wbUTqdjtOnT2e4hs2bNzeaPn2jNDIyktu3bxMcHGywvVWrVjx+/NhgaGp2dUtVv359tRGVGouiKBw/fhyAPXv24OnpSenSpUlITGLHpfvULGlDHV9fTpw4AaCmTVsvGxsbGjZsgFYDp+7EZn5ShBDZkoZUARMREUHjxo0NttzH4voAACAASURBVIWGhhrc+TLmxIkT6p3H8PBwDh8+nOtj+/j48ODBgyy3nzx5ktq1a3P69Gm2bt3KrFmzcn0cY8LDw+nWrdsLKSsnLl26REBAAIGBgVy7ds1gX2xsLKNGjaJOnToEBgbSrFkzgwckP6/58+cTHx9vdF/79u2pX78+AQEBNGzYkKVLl2ZazvDhw7lw4QKQcic1J7p27UpMTEzug35Gmb12Q0NDKV++vMHSrhUqVMh1+Wnrbey987Jk9l7JSvv27dUvN/lx/JfBxMTE6NwWSPlimn6VO1tbW5YvX45Op+P9999/rjrY2hreRTc3N6dIkSIZtj19+jTLvKmLDqTPa2ZmZjRvqrt372Z4Jl9mz+hzdHTMsG3IkCGsW7eO/v37s3z5cjZt2sR7772Xbbw5iQ3A3d2dRYsW8e+//9K1a1c8PT0ZMGAA9+/fzzIfZDwX5ubmBnPNcsPYec3JubaxscmwgIOjo6M6Zyn1tePn56c2RMuUKUOHDh0Aw0ebODk5ZRvn/fv3SU5OznCtjF07Y2XeuXPH6PbU39MOqcyubpkd28rKChsbG4NzcOzYMcqUKUN5t7LsGuLPnP/UZdWqVWpD8u7duxQqVCjD8ZwcHTHTajl1O443/Ck4QjyXPB3ap9frGTVqFPb29owaNYqoqCi+/fZbnjx5gpubGx999BGmpqYkJSUxa9Ysrly5QuHChRk8eLA61ve3335j586daLVaevToQbVq1YCUuy4//vgjer2eJk2aZBga8brz8vLCy8sL+N+QjuwmcefW2bNnCQkJ4fvvv8fDwwMPD49M78AWdH/88QdNmzZl+PDhGfYNHz6c0qVLs3fvXrRaLffv32flypUZ0ul0OoO7qjm1YMEC2rVrl+kqT7NmzcLLy4uHDx9St25dOnbsmGGFKZ1Ox9SpU9Xfv/vuOz7++ONsj71kyZJcx/uy2NvbM3fuXMaOHZvrvIqioChKjuudE8nJyc+1vHVqTFrtm3N/ysHBgYiICKP7oqKicHBwyLC9TJkyLFmyhPbt29O9e3dWr15dYFY8yw0nJ6cMDcHMGoZpe60gZcjWjh07mDhxosENpMwapc/K398ff39/Hj16xI4dO/j8888ZN24c33///XOVa2FhQVJSksG2nMy9yo3Y2NgMq+Hdu3dP/S5QtGjKqnQ//fST0YZS+fLl1Z/Tn39jHBwcMDU1zfDcnsye45O+TBcXF6Pp7969axAvZF+3zI4dHx9PbGyswTnw8vLiyy+/ZOHRKCIePeXThiUx0WrUvxlOTk48efIkw827e/fuYW6i4W5cMlGxSbgUenGrGArxJsnTv/ibNm2iRIkS6u9Lly4lKCiImTNnYmNjw86dOwHYuXMnNjY2fPfddwQFBam9ATdu3CA8PJxp06YxduxYFi5ciF6vR6/Xs3DhQsaMGcP06dPZt28fN27cyMuq5Zn27dszadIkgoKCqFevHgcPHgT+16MTERHBkiVLmD9/PgEBARw8eJD79+/Tp08fWrRoQYsWLdTeqgcPHtC5c2cCAwMZOXJklnelLl68SK9evZg5c6Y6Tn7VqlXql+DBgwfz6aef0qpVK3x9fdmwYQOQ8sVg9OjR+Pn50a1bN7p27aru27VrFw0aNKB169YGD+l8+PAhPXv2xN/fn+DgYM6ePQuk9GIMGjSIzp074+Pjw6ZNm5g4cSJNmjShS5cuGf6wQ8qcjODgYPz9/enVqxfR0dHs2LGDBQsWsGLFCtq3b2+Q/tq1axw/fpxPPvlE/ULs4ODAgAED1PPcvn17+vXrpw7t+PXXXwkKCiIgIICRI0ei0+kAGDVqFM2bN8fPz09t9CxcuJA7d+7QoUOHDMdOLy4uDisrK7WxVqFCBb755huCg4M5evSo2sMxefJkEhISCAgIUOeC9OzZk2bNmuHn52fQq5XakxEREUHDhg0ZMWIEfn5+dO7c2Wgv2datWwkODiYwMJBOnTqpXwpCQ0MZOnQo7du3x9fXl4ULF6p5ZsyYQf369enUqROXL1/OtH7vvfce69at4+HDhxn2zZ07l8aNG9O4cWP1AaipMY8ePZqmTZsybNiwDPXW6XRG63Tt2jW6dOlCs2bNaNOmDZcuXQJSXrf//e9/1fdVVvUyJn1Mt27dYvfu3bRs2ZKmTZsSEhJCbGzGoTPGXhup12fq1Kk0bdqUJk2aqHHm5r2al7y9vTl16lSGYV/Hjh3j7t27+Pj4GM3n4eHBokWLOHPmDCEhIa/kA5O9vLzYtm2bwbXYunVrjvImJiai0+mwsLBQtz158iTH+dPLroeqSJEitGnThmbNmnHx4sVnOkZaxYoVU1+bqf7666/nLje9P/74Q/05NjaWPXv2qH9/atSogaWlJXfu3FFvJKb9V6hQoVwdy8TEhCpVqmS4Bmn/NmWlWLFiuLq6qn/fUq1fv57ChQtneJh0VnVLtWfPHoPPj82bN6PRaNSbpvXq1ePatWtYO7gQYVmKNg29ead6Nby8vHjrrbcA1JvNaeuVejwzk5TG4Kk7OR/2KYQwlGc9Uvfv3+fYsWO0bduWDRs2oCgKZ86cYdCgQUDKpM6wsDACAwM5cuSI2j1fu3ZtFi1ahKIoHD58mDp16mBmZoazszOurq7qh7mrq6t6R6hOnTocPnyYkiVLPlfMp4/F8Sha91xlpFekqAke71g/VxnJycls3LiRHTt2MG3aNIOldkuVKkXXrl2xsbFRV5AaMGAAffr0wdvbm5s3b/L++++ze/dupk+fjre3N0OGDGH79u1ZDl/r2bMnM2fOxNvbO9M0d+7cYe3atVy6dIkePXoQHBzMpk2buHHjBjt27ODevXs0atSITp06kZCQwIgRI1i9ejVubm4Gq12lLg+7aNEi9u7dy6BBg9i2bRsA169fJywsjAsXLtCqVSvmz5/PuHHj6NWrFzt27MjwQMrBgwczYcIEfH19+eabb5g2bRpffPFFhnOU6sKFC7z99ttZ9iocP36c3bt3U6JECS5evMi6detYu3YtZmZmjB49mjVr1tChQwc++eQT7Ozs0Ol0dOrUibNnz9KrVy/mzZtHWFhYpsOABg4ciIWFBVevXuW///2v2pCKi4ujUqVKjBgxwiD9mDFj+PHHH9VzlHoO7ezsiI+PJygoiBYtWmQ43tWrV5k9ezbffPMNffv2ZdOmTbRr184gjbe3N+vXr0ej0bB8+XLmzJmjTqS+dOkSYWFhxMbGUr9+fbp168a5c+dYt24dW7duJTk5mWbNmlG1alWj9bSxseG9995j4cKFBj2DJ0+eZPXq1ernRHBwML6+vtja2nL58mWmTZvGl19+CcCGDRvUekdERBitU6dOnRg5ciRfffUV5cqV49ixY4wePZqwsDAArly5wqpVqzAxMSE0NNRovczMMl/VKm1MDx48YMaMGaxatQpra2tmz57NvHnzGDJkiEEeY6+Nt99+G0jpqduyZQuLFy/mhx9+YOrUqbl6r+alDh06MH/+fNq1a8egQYMoUaIEly5dYtq0adSsWZNGjRplmrdOnTp899139OvXj6FDhzJjxowc9RwUFAMGDCA4OJj+/fvTqVMnLl68qF6X7HolixQpQrVq1Zg+fTqFChVCq9Uye/ZsihQpwuPHj3Mdi7u7O7t37+bPP//Ezs6OUqVKsXHjRo4ePYqfnx8uLi5cvXqVjRs3ZniPP4tmzZqxYsUKhg8fTp06dQgPD2f37t3PXW5alpaWfP3118TGxuLi4sLcuXNJTEykV69eQMpwx2HDhvHZZ59x48YNateujV6v58qVK4SHh2d7E8SYjz76iN69e6s3Ovbv38+uXbtylFer1TJ06FD1vd2gQQP279/Pzz//zKhRowzmyGVXt7TpunXrRr9+/YiKimLixIk0b95cfaZZ+/btWbJkCe916IClT1uKOtdg48bDHD9+HCcnJ0JCQqhUqRKBgYHqIhbW1tb88MMPWFpaYqrVYGtpwqnbcVk+d0oIkbk8a0gtXryY//znP+od4sePH2Ntba1+SbS3t1eHRTx48EAdEmJiYoK1tTWPHz/mwYMHBvMo0uZJO4TEwcHhhdx1yw85+SLRokULAKpWrZqjnrc9e/aoc2kg5c7nkydPOHDgAAsWLABShoCkHXqQXr169VixYgWNGjXKdDhbs2bN0Gq1VKxYUe25OHToEMHBwWi1WpydnalTpw6Q8iW8dOnSlCtXDoB27dqpPSeHDh1SeyHq1avHw4cP1Wdn+Pn5YWZmxltvvYVer8fPzw9ImVidfojRo0ePiImJwdfXF0j50te3b99sz1daM2bMYMOGDeqNAEBdJjk5OZm9e/dy6tQp9ZokJCSo49rXr1/PsmXL0Ol03Llzh4sXL6pflrOSOrTv/v37tGrVCj8/P0qWLImJiQlBQUE5invRokXqndRbt25x9erVDA2pUqVKqROwq1atanSIVmRkJP379ycqKorExERKly6t7mvSpAkWFhZYWFjg6OjI3bt3OXjwIM2aNVOHrAQEBGQZZ8+ePQkMDDS4LocOHaJZs2ZYW6fccGjevDkHDx4kMDCQkiVLZvlwVmN1io2N5ejRowbHSExMVH8ODg42eE0bq1dmS2ADBjEdPXqUCxcuqKuPJSUlGY03q9dG6uT2qlWrqtcwN+/VvGRjY8Ovv/7KV199xeTJk4mOjsbR0ZFWrVoZ9OpmJigoiEmTJjF69GicnJwMVpgr6Ly8vJg9ezZff/01W7dupWrVqnz55Zd07tzZYMGLzMyaNYuRI0cyaNAg7Ozs6NGjB/Hx8fz444+5jmXQoEHcvHmTfv36qavZvfXWW2zdupXx48cTHR2Ns7Mz77//vtHhzLnl7+/PqFGjWLp0KYsWLaJp06aMHz/eYMnx52VlZcWMGTMYN24cly5donz58ixZskS9YQrw4Ycf4uLiwvz585k3bx4WFhaUK1eOli1bPtMxmzdvzsSJE5k1axZhYWHUqVOH0NBQ3n///Rzl79KlC4mJiSxYsICFCxdSrFgxPvvsM0JCQnJdN0hZqKJQoUIMHz6c2NhYAgMD1ZtIkNLQWr16NUEDPuPO9p/5eO0MHBwcqF69usGw++nTpzN69GiGDx+OtbU1H3zwAV5eXmzcuBEPZ2tORaXMk3qVbmQIUVDkSUPq6NGj2NraUq5cOc6cOZNtemPDVjQaTabDWTJLb8z27dvZvn07AF999VWGyZx37txR50lU8y6SIf/LZGpqipOTEzExMQZzNWJiYihbtiympqZoNBqsrKwwNTXF3NxcndBtYmKCRqPB1NQUrVaLVqtVy1AUhU2bNmWYh5CaPu2xTExMMswT0Wg0fPXVV4wcOZKxY8eqQ5FMTEzU42i1WjWu1GOmxps2Fo1Gox4j7XatVqvGkz4OjUaDmZlZhmOYmpqqPQWmpqbqMdOez7Rlpv09/TlK9dZbb3H27Fl1/7Bhwxg2bBhubm7qeU5dRSm1vI4dOzJu3DiDcq5fv87cuXPZsmULRYsW5eOPPyYpKUnNY+w8pz8/Li4uVK1alRMnTlC2bFn1y72xtKnxAOzbt4+9e/eyadMmrK2tadOmjTr/JzWPiYkJFhYWah4zMzMSExMzxPTZZ5/Rt29fmjVrxr59+5g6darR653aENFqtQYxZXaeU7c7ODjQtm1bde6WsddMatrUmyrpy0obg7E66fV6ihQpYvTOslarpXDhwgbHMlYvY++J1POYNiatVkvDhg2ZO3duptf25s2bWb42UsszNzdHr9cbvG6ze6+mNv7ykqOjY5aLoqTKLM3QoUMNntVjampqUIdWrVplGLZmbBjbn3/+mWHbl19+afDFc8CAAeow3bTxGysv/bBUY+X37NmTnj17qr+nLpNdt25dtQ6ZDW91dHQ0+prMLl5jZTo6OrJjx44M6dL30GfH2LkG43UfP348kyZNMhiWmbbBkdPzChlfG2mvW3Y3j/r27ZvlDbKcvDbTGjFiRIZe/5y8/rLKn5a1tTVarZagoKAs65b62ZL+NZzerURzHJr3JzR0GkFVXIymcXR0JCwsDFNTU4PrNXPmTH47Gcm+fy/z1KwQJYu+enMVX2fpPwtFwZQnDanz589z5MgR/v77bxITE4mPj2fx4sXExcWpE/YfPHig3jF3cHDg/v37ODg4oNPpiIuLo1ChQur2VGnzpN1+//597OzsjMaSOvk2VfrJnE+fPn2mBQSeV+oHnIWFBc7OzuzatYv69evz8OFDdu7cSc+ePUlOTkZRFHQ6HcnJyeh0OhRFyfCzlZUVjx49Uj8wGzRowIIFC+jfvz+QMm/Iw8MDHx8fwsLCGDx4MDt37iQ6OlotO63USfSzZs2iS5cufPnll4wYMQKdToderyc5ORm9Xp8hb3JyMjVr1iQsLIx27dpx//59wsPDeffddylbtizXr1/n0qVLlC1bljVr1qjxp8Y1ZMgQwsPDsbOzw8rKSp0Pl/4YgNF91tbWFClShH379uHj48OqVavw8fFR402fHlJ6NKpWrcqkSZMYOXIkJiYmJCQkZDjPqceuU6cOPXr0oHfv3jg6OvLw4UNiY2OJiYnBysoKa2trIiMj2bFjh3psGxsbYmJiMqzElXquU89jfHw8p06don///mqcaeNNm9bMzIz4+HjMzMyIjo6mSJEimJub888//3D06FE1XWqe1HlcWZ0/SGnEOzs7k5yczMqVK9XzYCy9TqdTh5/1798fnU7Hli1b6Nq1a4Zy0+ZPnb+XGmNqGR9++KF6E2DmzJkZYgYM6p1ZnQoXLkypUqX47bffaNmyJYqicPbsWapUqZLhdZtZvYy9J4ydx2rVqjFq1CguXryIm5sb8fHx3Lp1i/Lly6t5oqOjM31tZPb+zul79enTp5lOjn9VODo6vjJ1GDVqFA0aNMDW1pZTp04xc+ZMmjRpQuHChV+ZOjyPV+laFRRxcXHo9fpsz5tOpyM+Pj7bdL8ei8TSVEtVe022aY1dLzeblAVO/vrnJoHuBaOnW6SQ91fBktnIlDxpSL3//vvqnaozZ86wfv16Pv74Y6ZNm8aBAweoW7cuf/75JzVr1gRSJpH++eefVKxYkQMHDlClShU0Gg01a9Zk5syZBAcH8/DhQyIjI3F3d0dRFCIjI4mKisLe3p7w8PAXtpJXfpgxYwZjxozhiy++AFLu2KY+6yQnAgIC6Nu3L1u2bGHixIlMmDCBMWPG4O/vr34p+/rrrxkyZAgDBgygadOm1K5d22AhEGMsLCxYtGgR7dq1w8nJKUcrbQUFBbF3714aN25MuXLlqF69OkWKFMHS0pIpU6bQrVs37O3t8fb2Vp+zkXqH2t/fH0tLS7799tsc1z29b7/9llGjRpGQkEDp0qWZNm1atnmmTp3KhAkTqFu3LkWLFsXS0jLTleUqVqzIyJEj6dy5s9ojNmnSJGrUqIGHhwd+fn6ULl3aYAXFLl268J///AdnZ2d++eWXDGUOHDgQS0tLEhMT6dixY6ZzjNLq0qUL/v7+eHp6EhoaypIlS/D396dcuXK888472ebPzLBhw+jbty+urq688847ma7QlsrT05OWLVuqw/AyW2wgLXt7e5o1a6YO5/T09KRDhw7q3drOnTvj4eFh9Nhp6/3JJ59keoxZs2YxevRoZsyYQXJyMu+++y5VqlTJNrbccnBwYPr06QwYMEAdPjhy5EiDFcSqVKmS6WsjM7l9r4q88fDhQ8aMGcPDhw+xs7OjZcuWGXqnhXhZ4pP07L3+iHplimBl9mxrh5UoYo6dpQmn7sRJQ0qIZ6BR8nj5p9SG1KhRo7hz506G5c9Th+PMmjWLq1evUqhQIQYPHqyOHV6zZg27du1Cq9XywQcfqKvcHDt2jJ9++kmdN9O2bdscxZP2WROQcrcodW5GXkrf5f46iY2NxcbGhgcPHhAcHMzatWszLPP6qnmdr9fr5k26Vvn1+fUiyV3YV4dcq/y1/XI03x24zVeBpXnLKfv3fWbXK3TvLU5FxfFjm/IyT6oAkfdXwZJZj1SeN6QKGmlIvXzt27cnJiaGpKQkdYWrV93rfL1eN2/StZKGlMhLcq3y16it13n8VMesYLccNYAyu15bLkYz59BtZrd0o2QRCyM5RX6Q91fBkq9D+8SbzdjwNSGEEEI8mxuPnnLubjzdqzs9dy+Sp0vKzZfTd+KkISVELuXpA3mFEEK8+p4m6Lly4WmBeTCwEG+aHZdj0GrAzy3jokW5VaywGQ5WpvJgXiGegTSkhBBC5MrFswmc+Tueh/df7APLhRDZS9Yr7LwSQ80ShbCzev6BRRqNBg8Xa07fiZObI0LkkjSkhBBC5FhykkLEtZQVCe9HvRlzz4QoSI7dekJ0gg7/8s/fG5XK08Wa6AQdNx4lZp9YCKGShpQQQogcu/lvIslJYGoG96QhJUSe2345hqKWJtQoXuiFlZk6T0qG9wmRO9KQKoBKlSpFQEAAjRs3JiQkhPj4eCIiImjcuLHR9N988w1//fUXkLJC3okTJwDo2rUrMTExzxTDzz//TFhY2LNVQAjxWlIUhWsXn1KkqJZSZc15eC8ZvU6GAgmRV6Ljkzly8wl+braYal/cUuUuhcxwtJZ5UkLklqzaVwBZWlqybds2IOXhrD///DMtWrTINP2IESOMbl+yZMkzx9CtW7dnziuEeD09vKfjUYyeqjWtMLfQcPViItEPdNg7yZ8SIfLCrqsx6BRe6LA+SJkn5elizdFbsSiKIs+TEiKHpEeqgPP29ubatWsA6HQ6RowYgZ+fH507dyY+Ph6AwYMHs2HDhgx5fXx8ePDgARERETRo0IBBgwbh7+9Pnz591Lw+Pj5MmjSJoKAggoKCuHr1KgChoaH88MMPQEovV2qaevXqcfDgQTWeCRMm0KJFC/z9/dWG2507d2jbtq3aq5aaXgjxart26SmmZlCijDkO/994kuF9QuQNRVHYfjmGyo5WlLR98cuUe7pY8+ipjn9jZJ5UftLr9ezYsYOTJ0/mdygiB+Q2Yhb++usv7t69+0LLdHJyokGDBjlKm5yczK5du2jUqBEAV69eZfbs2XzzzTf07duXTZs20a5duxyVdfnyZUJDQ6lVqxZDhw7lp59+ol+/fgAUKlSIjRs3EhYWxueff87PP/9sNJaNGzeyY8cOpk2bxqpVq1ixYgWFCxdm06ZNPH36lNatW9OwYUM2bdpEw4YNGTRoEDqdTm20CSFeXU8T9Ny6kUTZ8uaYmmrAVEORolru35WGlBB54fy9BG48SmSgj+tLKd9DnScVS5mi8jyp/HL//n3OnDnDmTNnqF69OnXr1kWrlX6PgkquTAGUkJBAQEAAzZs3p0SJEnTu3BlImTvl4eEBQNWqVYmIiMhxmcWLF6dWrVoAtG3blkOHDqn7Wrdurf5/9OhRo/lThxZWrVqVGzduALB7925++eUXAgICCA4O5uHDh1y9epVq1aqxevVqQkNDOXfuHIUKvbgJsUKI/PHvlUQUPZR1/98XLAcnUx7cS0Yn86SEeOm2X47G0lRD3TKFX0r5LoXMcbYx47TMk8pXt27dAsDT05O///6b33//nYSEhHyOSmRGeqSykNOeoxct7RyptCws/vcFxsTEJFdvrPTjndP+ntnPaZmbm6vHTU7+3x3oiRMnqj1maf3666/s2LGDQYMG0a9fPzp06JDjWIUQBYuiV7h2+SmOLqYUKmKibnd0MVPnSTnIPCkhXpqEZD17rj+mbukiWJuZZJ/hGXm6WHPoxmP0ioJW5knli9u3b2NjY0P79u1xdnZm165drFy5kpYtW+Lg4JDf4Yl0pEfqDXHz5k2OHDkCwO+//672TgGsW7dO/b9GjRo5LrNhw4b8/PPPJCUlASnDB+Pi4rhx4waOjo506dKF9957j1OnTr3Amggh8tqdyGQS4hTKupsbbLd3SvlCJ8+TEuLlCv/3MQnJ+he+yER6Hi7WPE7Ucz366Us9jsjcrVu3KFasGBqNhipVqtCuXTt0Oh2rV6/m8uXL+R2eSEduIb4hKlSoQFhYGKNGjcLNzY3u3bur+xITEwkODkav1zN79uwcl/n+++8TERFBs2bNUBQFe3t7Fi1aRHh4OD/88AOmpqbY2NgwY8aMl1ElIUQeuXbpKZZWGlyKmxlsNzfXUqSoCfeikqlYJZ+CE+INsO1SNMULm/GWk9VLPU7q86RO34nDzc7ypR5LZPTkyRMeP35MtWrV1G3FihWjU6dObNy4kY0bN+Lt7Y2Pj4+srFhAaBRFeaMHt6eORU0VFxeHtbV1nsdhampqMGTuRYqIiKB79+7s3Lkzwz4fHx82b96Mvb39Szn26+plXi/xYr1J1+plfH49eaxj16bHVPKwpGKVjF+szvwdz7VLT2nW1hYTk+f/w+7o6Mi9e/eeuxzx8sm1yhs3HyXy4fordK3mRPsqzz60K6fXq+/vlylT1IIxDUs+87HEs7l48SKbN2+mY8eOeHh4GFyv1AXIzp07R7ly5QgICDCY8iFeruLFixvdLkP7hBBCZOr6pUQ0GihdztzofgdnU/R6eHhfl8eRCfFm2HklBq0GGpd7ucP6Unm4WHM6Kg6d/o2+z54vIiMjMTU1xcnJKcM+U1NT/P39adCgAVevXmX16tVER0fnQ5QiLWlIvQFKlSpltDcK4ODBg9IbJYQwKjlZIeJqIsVKmmFpZfzPhYOTCWjgflRSHkcnxOtPp1fYcSWGGsVtsLfKm9kYni7WxMo8qXwRGRmJs7MzJibGFxTRaDRUq1aN1q1bEx8fz6pVq9RnjYr8IQ0pIYQQRt36N5GkJMVgyfP0zMy12P7/PCkhxIv1d2QsD+OTaVK+aJ4d01N9npQsg56XkpOTuXv3LsWKFcs2balSpejUqROFCxdm/fr1HD16lDd8pk6+kYaUEEKIDBRF4erFRArbatXV+TLj4GxK9H0dumT5Qy7Ei7T9cjS2libUKpF3z2N0sDajeGEzTt2JeuRDVQAAIABJREFUzbNjCrhz5w56vT7TuTjp2dra0qFDB8qXL8++ffvYsmWLuoqyyDvSkBJCCJFB9H0dj6J1lHW3yHZ1KEd1npT0SgnxosQkJHPoxhP83Gwx1ebtCm2eLjaciYqXeVJ5KDIyEgBXV9cc5zEzM6N58+b4+vpy4cIFfvnlFx4/fvyyQhRGSENKCCFEBtcuPcXUFEqWMb7IRFr2TqagQYb3CfEC/Xn1EToFmuTRIhNpebhYE5ek58rDhDw/9pvq1q1b2NnZYWWVuyXuNRoNtWrVomXLlsTExLBy5Upu3rz5kqIU6UlDqgAqVaoUAQEBNG7cmJCQEOLj47NMX6FChQzbbt++TZ8+fTLNExMTw+LFi583VCHEa+jpUz23IpIoWdYcU7Ps74SbmWkoamciD+YV4gVRFIVtl6Op6GBJ6aJ5v8S1h8yTylOKonD79u0czY/KjJubG506dcLS0pLffvuNkydPyrypPCANqQLI0tKSbdu2sXPnTszNzfn5559zXYarqyvz58/PdP+jR4+eqVwhxOsv4koiej1ZLjKRnoOzKQ8f6EiWeVJCPLeL9xOIiEkkwD3vFplIy97KlJJFzDktDak8ER0dTUJCwnM1pADs7Ozo2LEjpUuX5s8//2Tnzp1vzHMU84s0pAo4b29vdWnLnj170qxZM/z8/Fi6dGmGtA8ePKBly5Zs376diIgIGjduDMD58+cJCgoiICAAf39/rly5wuTJk7l+/ToBAQFMmDCB2NhYOnbsSNOmTWnSpAlbtmwBUh7m27BhQ0aMGIGfnx+dO3fOtodMCPHqUvQK1y4n4uBsSmHbrBeZSMvB2RRFDw/vyR9tIZ7X9ssxmJtoqFemcL7F4OlizVmZJ5Unbt26BfDcDSkACwsLgoODqVWrFmfOnGHNmjXExsrCIS9L3jyU4BVV6O56TJ9GvtAyky2K8cSpZc7S/v9TrBs1agRAaGgodnZ2xMfHExQURIsWLdRnQN29e5cePXowcuRIGjRoQEREhFrOkiVL6NWrF23btiUxMRGdTseYMWM4f/4827ZtU4+1cOFCChcurDbIAgMDAbh69SqzZ8/mm2++oW/fvmzatIl27dq9wLMihCgoom4nEx+r520vy1zlc3A0RaOB+3eTcXI1e0nRCfH6e5qs569rj6hbujDWZjm/mfGiebhYs/liNJceJFDJMXfzdkTuREZGYmlpiZ2d3QspT6vV4uvri6OjI9u2bWPlypUEBQXlaiELkTPSkCqAEhISCAgIAMDHx4fOnTsDsGjRov9j777Do7rOxI9/z52m3kYaFQQCREcCA8KAMTbdNriAe+IkdoqT/cVx6iabjZM468Qbr504ie3sZpN1nGyyjrtxAWxT3AAXig0STQiEEGhUZqRRG0295/fHIIxBAklomnQ+z5MnZmbuva80ozv3ve8572H9+vVA6O5FdXU1WVlZBAIBbrnlFu6//37mzZt31v5mzZrFI488gt1u56qrrmLs2LFnvUZKyQMPPMAHH3yAEIL6+nqampqA0JytkpISAKZNm/apJE1RlKHlaJUXS4Igb0T/kiGjSZCeacDREIDSMAWnKMPAtmPtdAV0lkVw7aiedM+Tqmhwq0QqzOx2O3l5eeftkNpf48ePJyMjg7Vr1/L888+zePFiJk+ePKjHGO5UInUOfa0cDbbuOVKn27ZtG++++y6vvPIKiYmJ3HjjjXi9oVXHDQYDpaWlvPXWWz0mUqtXr2bGjBls2rSJ2267jYceeoiioqJPveaFF17A6XSyfv16TCYTc+bMObV/i+WTeRIGgwGPR3XxUZShqLMjSKM9wISpFrQBtFvOzjVy+ICXQEBiNEa2XbOiDBUbj7SSn2piii26yUtGgpFR6WbKG9zcMNUa1ViGMo/HQ0tLC5MmTQrL/nNycrjllltYv349GzZsQErJlClTwnKs4UglUnGivb2d9PR0EhMTqaqqYteuXaeeE0Lw8MMP87WvfY3HHnuMb3zjG5/atqamhqKiIr785S9TU1PD/v37mTJlCh0dHZ/af3Z2NiaTia1bt3L8+PGI/WyKosSGmiofQsCosQPrEmbNMVK130uzI4BNDe9TFKSUdPp12r1B2rxB2jxBWr0B2rzBTx47+XjosQDtPp3PTc8e9OrEQJTkJrH5SCsBXUZ8Lavhonv9qMGYH9WbxMRErr32Wl555RU2bdqE2Wxm3LhxYTvecKISqTixcOFC/va3v7F06VLGjh3LzJkzP/W8wWDgP//zP7njjjtISUlhyZIlp557+eWXeeGFFzAajdhsNr7zne+QmZnJ7NmzWbx4MYsWLeKuu+7i9ttv56qrrmLq1KnqD0xRhplgQHKs2kfeCBOJSQPrQ5TVPU+qUSVSysA0dfoxaYKMxNi/PGnzBtlV1/HphOhUYvRJshTspVeDUROkWwykJRhItRgYm2whzZJEZqKRlRMHZ67MhSrNTWJdpYsqp4dJOWp4XzjY7XY0TSM3NzesxzEajVx99dW8+OKLvPbaa1x77bWMGjUqrMccDoQc5k3muzuldHO73SQlJUU8DqPRqFpUxhH1fsWP4fReXcj5q7bax8cfupm3MJns3IEnQVs2tgNw6dKBdRvLzs7G4XAM+PhK5AzWe+UPSj483s4bVS4+rndTkGrisavHYojhCoguJd9df5TqltAQeE1AqjmUEKVZDKQnGEizGE/9+9T/Err/20iCUUS06jSQ96vNE+Dzz1fxuenZ3FSSHabIhrfnn3/+1Fz304XrXOjxeHjhhRdwuVysXr06rJWwoaSgoKDHx2P/lo+iKIoSdkervKSkalhtF/a1YLWdnCfll31azFcZvk60+XijysWbR1pp9QbJSTKyaEwab1a38WZ1K0uj3GzhXN471k51i5d/mp3LpUVpJJs1tBgYitcbGfAj/b5+b5eWYKQow0J5g5ubSsIQ2DAXDAZpaGg41dArEhISErjuuut47rnnePnll7nhhhvIzlZJ8kCpdaQURVGGOZczgKs5yOhxlgu+Q55tMyIlNKv1pJQe+II6b1W38qMNNXz9lSO8cqCZKbZE7l1UyH9fV8y35uUzLiuBp8sd+HsbExdlQV3y5B4HI9PNLB+XQarFENNJFIB88r9xfPV6pL3/859Lc5PY39QVs+9HPHM4HAQCgYi3JU9OTmb16tUYjUbWrFmDy+WK6PGHEpVIKYqiDHNHq3wYjFA42nzB+8rMNiK00DwpRelW4/Lypx0NfPGFKn6zzY7THeDzF+Xw+Opx/PCyQmYWpGDQQkPdPjstm8bOABsPx+bF3bs1bRxv8/GZ0uyYHn7YTQaDyJ1b0V3N6L/+MbKx7vwbnaY0NwlfUHLI2RWmCIevSDSa6E1aWhqrV69G13VefPFF2tvbIx7DUKASqTMM8yljiqLEsYGcv3xenRO1PgqLzJjMF35RaDQKMrIMOFQiNex5AjobD7v4wetH+ebaal475GJGfjI/XzKS/7p2LDdOtZLZQ1OJmQXJTMxO5NkKJ76gHoXIexfUJU+XOxidYWHeqIHNA4y4IwfB3UnyLV+CoB/91z9BOhv7vPlUWxICKG9why/GYcput5OamkpqanQ+S1lZWaxatQqPx8OaNWvo6lLJcn+pROoMmqYNm4npiqIMHYFAAE3r/ym9ttqHHoTR4wbW8rwn2TYjrS1B/H51Y2o4Otzs4b8+rOeO56t49P16On06X5pp44nVxfzzpSOYlpd8zqFwQgg+Nz0bZ1eA1w/FVlXqzepW6tr9fHZadswP5+smK3aCppF0zS1o37kPPG70X92DbO5bI4NUi4HRmRYqVCI1qKSU1NXVRb3Zg81m45prrqGtrY2XXnrp1BqiSt+oZhNnSEhIwOPx4PV6I9pNx2KxqA9vHFHvV/wYDu+VlBJN00hISOj3dkcP+8jKNpCWYRi0eKw2I4f2eWluCpBboNqgDwduf5C3q9vYcNjF4WYvZoNg/qhUlo/LYHJOYr+/T6flJVOSm8Rze50sH5eBxRj9+77+oOTpcifjshK4uDAl2uH0mSzfAcWT0JJTEaOK0b79b+gP/wT91z9G+/6/IzKyzruP0twkXjvkwh/UMRmi/14MBR0dHXR2dkY9kQIoLCxkxYoVrF27lldeeYVVq1ZhNKoUoS/Ub+kMQggSEyO/VoJq+Rtf1PsVP9R71bum+gDuDp1JJYO75EOm1YimgVMlUkOalJKDji7eqHLx7tE2vEHJmEwLXy3L5fIxaaSYLyw5v21aNv+64RhrK1u4fop1kKIeuE1HXDR2+vmn2bkxsVhuX0iXE2qrEdd/4dRjYswEtG/di/7bn6E//JNQMpWafs79lOQm8fKBFg46PJTkRn6JmKEomvOjejJmzBiWLVvG66+/zrp161i5ciUGw+DdYBuq1G0FRVGUYepolRezRZBfOLjJjtEoyLAacDSoYdJDldPt54v/+JgfvF7Dlpo2Lh+Txq+uLOI3V41m5cTMC06iAKbYkrgoP5kX9jXj9gcHIeqB8wd1nqlwMjE7gZkFyVGNpT9kxS4ARMmsTz0uxk1Bu/sn4GhAf/inyM5zNxqYaktCE6jhfYOorq4Ok8kUU63HJ06cyKJFizh69CgbN25UfQP6QCVSiqIow5C7M0hDXYCiYjOaYfDvrmfbjLS6gvh96ot4KHr1YAtHHJ18/eI8nrh+HHfNyWe8tf9D+M7ntmnZtHuDvHqwZVD3219vVLXidAf47LScuKlGwcn5URlZUDj6rOfExFK0u+6B+lr039yLdHf2up8Us4ExmQmUN/T+GqV/7HY7ubm5A5rbGk6lpaVccsklHDx4kLfeekslU+cRW++eoiiKEhE1h30gYNTYwWsycTprjhHUelJDkjegs6HKxaVjrVwxPoMkU/iG/0zITmT2iGTW7G+mwxedqpQ3oPPsXidTbYlMz4ufYW0yEIB9HyNKZvWa/ImpM9D+6V/heDX6I/+G9PRecSrNTeKgwxNznRTjkc/nw+FwxMywvjOVlZUxa9YsysvLee+996IdTkxTiZSiKMowEwxKjh3xkVdgIik5PF8DmdmheVKqDfrQs6WmjXafzg3TI3MR+NlpOXT6dF4+0ByR453ptUMuWroC3BZn1SiOHIAuN6J01jlfJqbPRrvz+1Bdif7oL5C9NOcpzU3Cr0sONKkW2ReqoaEBKWXMJlIAl1xyCSUlJezYsYOdO3dGO5yYpRIpRVGUYcZe68fnlYwed+EL8PbGYBBkZhvVwrxDjJSStZUuRqabmVl47gYFg2VsVgLzRqbw8v4W2ryRrUp1+XWe3+tkel4SU+OsyYIs3wkGA0y+6LyvFbMuQXzpO3BoH/rvf4H0+856zRRbIppQ60kNhlhrNNETIQQLFy5k/PjxbN26lYqKimiHFJNUIqUoijLMHK3ykpyqkZ0b3sat1pyT60n51FCgoaLS6eFws4erxmdGtDrzmWk5eAI6a/Y5I3ZMgLWVLbR6g9w2PSeixx0MsmInjJuCSOxbAqjNuRxxx92wfzf6fz2ADPg/9XySyUBxVoJqODEI7HY7VqsViyU8Q6sHi6ZpLF++nKKiIjZv3kxlZWW0Q4o5KpFSFEUZRlpbArQ4g4wuNof9QjjbFkrUnE3R7bimDJ51lS0kGjUWjU2L6HGLMixcWpTKqwdbcHkiU+V0+4Os2edkVkEyE7MjvyzKhZDNDjh+FFEys1/baZcsQXz+61C+A/2PD4XmWZ2mNDeJSmcX3oC6OTJQUkrsdntMV6NOZzAYWLFiBQUFBbzxxhscPXo02iHFFJVIKYqiDCNHq3xoBigcE75hfd0yrAY0A2p43xDR6gmwpaadRWPTwtpgoje3TsvGr0te2BuZqtTLB1po9+l8dlocVqP2nmx7XlrW7221y65E3HonfPQ+8s+/Qeqf3AgpzU0ioMN+NU9qwJqbm/H5fHGTSAGYTCauueYarFYr69at48SJE9EOKWaoREpRFGWY8Pt0jtf4KCwyYzaH//RvMAiyrEbVcGKI2FDVSkCXrJiQGZXjF6ZZuHx0GusPuXC6/eff4AJ0eIO8vL+ZOYUpjLMmhPVY4SDLd0BmNhSMGtD22pJrEDfegdz+LvIvjyD1UAVqUk6iWk/qAsXD/KieWCwWrrvuOlJTU3nllVdobGyMdkgxQSVSiqIow0RttQ89CEXF4a9GdbPajLS5gvi8aihQPAvqktcOtTAtN4mR6dGb13FLaTZBXfJ8mKtSa/Y30+nX+ey02Fksta9kwA/7dyNKe2973hfaFdcjrvss8r03kX//T6Suk2QyMN6aoBpOXAC73U5iYiLp6ZFp1jKYkpKSWLVqFRaLhZdeeomWluiu7xYLVCKlKIoyDEgpOVrlI9NqICMrvE0mTmc9NU9KVaXi2fYTHTS5A1GrRnXLTzWzpDid16taaeoMT1WqzRPglYMtzB+VyujM+KtGUbUfPF2IknO3Pe8L7epbEStuRr77BvKpPyGlpDQ3mUPOLrr86ubIQNTV1ZGfnx9frfRPk5qayurVqwF48cUXaW9vj3JE0aUSKUVRlGHA0RCgs0Nn9LjIVhMystQ8qaFgXWUL1iQjFxemRDsUbi4JVYmerQhPVeqFfc14AzqficNqFJzs1mcwwuRpg7I/seo2xPJVyDfXIp97gqm2RIISDjjUPKn+crvdtLa2xt2wvjNlZGSwatUqfD4fL774Im738K1QqkRKURRlGDha5cNsEeSPNEX0uAaDIEutJxXXjrd62V3v5srxGRi06N9Fz0k2sXxcOhsPu6hvP3u9owvR0hVgbWULl41Oi+oQxgshy3fC+CmIhMFZ90oIgbjxi4hFK5BvrGHSBy9j1KC8vnNQ9j+c9GV+lJQS/bXnCdiPRyqsAcnJyeHaa6+lvb2dLVu2RDucqFGJlKIoSgzwdOlU7vVw/KiP1pYgwaActH13uXXq6/yMGmvGYIj8hbDVZqStVcer5knFpXWHXBg1WF6cEe1QTrlxqhWDJni6wjGo+31+n5OALrm1NE6rUc4mqDuGKL3wYX2nE0Igbv0qYsFyLOueYpzoVPOkBsBut6NpGjabrfcX7dmOfP6vdD7318gFNkAFBQWUlpZy8OBBWltbB2WfupRsO9bGI+/ZkXLwvgfDJXID5RVFUZRe1Rz2UrnXe+rfQkByqkZauoHUDANp6QbS0jUSk7V+j62vOewFGdkmE6fLthk5CDQ3BcgvjE4MysB0+XXePNLK/FFpZCTGziWDNcnEleMzePVgCzdMtVKYduHVI6fbz2uVLhaNSacgLT4/p7JiJ8CgzI86k9A0+NzXwe+n5Mj7vFC0GLc/GJVW+PHKbrdjs9kwGnv+W5JSoq99BgDvB+8gbvoSwhjZUQT9NWPGDPbs2cOuXbtYtGjRgPcT1CXv1rTx3F4nta0+ClJNtHqCMXXe6YmqSCmKosSAFmeQ1DSNhVemMnNeEuMmW0hO0WhpDnKw3MP2LZ1sWtvO+hda2bKxnd3b3VRXenE0+s/ZEU8PSmoO+8gtMJKUHJ0LnoxMAwZDaJ6WEl/eqm7F7dej3mSiJzdMtWLSBE/vGZy5Us9WONGl5JZS66DsLxpkxU6w2iB/ZFj2LzQNccc3KbUloSPYu+HtsBxnKAoEAjQ2Np57ftSBPVBdCRfNQXa2w96PIxfgAKWmpjJ58mT27t1LZ2f/h3v6gzpvVLn4+itH+M02O5oQfG9+AY9dPTbmkyhQFSlFUZSok1Liag6SX2giNd1AavqnE56AX9LWGqS9NUibK/T/9uN+jh35ZH6IJUGQlhHaNi3dQGq6Rmq6gfrjfnxeGfEmE6fTDIKsHKPq3BdnpJSsq2xhbKaFidmx170uI8HIyomZvLivmRtLrBRlDPwz3tjhZ8NhF0uLM8hNidNqlP9k2/O5C8PaEU4YDEz63G0YnztExe5KykamD/pQwqGoqamJYDBIQUFBr6/R1z0L6VloX/4O8l/vRG5/BzF9dgSjHJhZs2axb98+du3axYIFC/q0jTcQSqBe3N+M0x1gvDWBL82yMXtEClocdTRUiZSiKEqUuTt0/D5JRlbPFSOjKdSwISv7k1O2lBKvR55KrNpag7S5dJyNXvTuApUAgwZJyRo5edE93VtzjBwo9+D16FgS1GCIeLC3sYtjrT7unpsXs62aV0+xsr7SxT/2OPjhZSMGvJ9nKhyA4KaS+K1GUbUPvB5EaVnYD5WQYGZCTjIVXRPR1z2LQSVS59XdaCIvL6/H5+XhA3BgT2g4X0IS5rkL8by7EenzIsyx3fgkIyODCRMmUFFRQVlZGYmJib2+1u0Psr7SxUsHmmn1BJlqS+TuuflclJcUs+eZc1GJlKIoSpS1OIMAZFr7fkoWQpCQKEhI1LDlfzKGXtclnR36qepVR5vOiCJT1L+gsk9bT6pgZHze8R9u1la2kGLWWFCUFu1QepVmMXDNpEyeqXBypNnD2Kz+V87s7T42HWnlqgmZ5CTH9nyUc5HlO8BohEmD0/b8fErzk3nWkUfnziOkVlcixkyIyHHjld1uJy0tjeTk5B6f19c9C8mpiMuuACBh/hI8G1+Bip0w85JIhjogZWVlHDx4kN27dzN37tyznm/3Bnn1YDOvHmyhw6czIz+Zm0qsTLUNTnfJaFG3BRVFUaLM1RzAYICUtAs/JWuaIDXNQMFIM5NKEymbnxwTDR7SswwYjGo9qXjhdPt5v7adpcUZWIyxfalw3eQsks0aT+4ZWAe/p8sdGDXBjVPjuBoFyIpdMKEEYYnMMMzS3JPzpGyTkW+sicgx45WUErvd3uuwPllbDXu2I5Zeg0gIVXPMpTMhNR25PT5ai1utVoqLi9m9ezde7yeNk1xdAf76USNfWXOYp8qdTLUl8asri/jZ4pFxn0SBSqQURVGirsUZJD3LgBYDa/SEi6aFhic6VCIVF16vciElXDU+dlqe9ybFbGDVpCy2n+igsp+LxB5v9fL20TZWTMgkKw4mtvdGOhrAXhvRuUqTshNJtRh4Z/Jy5M5toRiUHrW1teF2u3ttNCHXPwcJiYhFV596TBiMiFmXIPdsR3o9kQr1gsyePRuv10t5eTlNnX7+uKOBO186zJr9zVw8IoXfrRjNjy4vZLy196F/8UYlUoqiKFEUDIbmOfVnWF+8yrYZ6WjT8XrUelKxzB+UvH7IxcyCZPJSo1/N7IurJ2WSajH0uyr1VLkDs0Fw/ZSsMEUWGeFse94bk0Fj4Zg0PpRWWi0pyE2vROzY8aaurg7oeSFeWX8CuWMLYuEKRHLKp54TZQvA50Xu2R6ROC+UzWYjd8RItn24k6+/VMlrJxe3/v3VY/nepQWMzoy9pjUXSiVSiqIoUdTuCqLr9NpoYiixds+TUlWpmPZebTsuT5CVMdjyvDdJJgPXT8niI3sn+xv7tlBsjcvLlpp2rp6YRXpCfN/IkOU7IScPcgfecGMglhVnEJDwzszrke9uQLo7Inr8eFFfX4/ZbCYr6+yEXb72PBhNiGXXnr3h+MmQnoXc/m4Eorwwx1q9PLy1jvUdeRDwsjDFyR+uLebuufkDWpdNBuLje0IlUoqiKFHU0tz/RhPxKj3TgNGIGt4X49ZXtpCXYmJGQc+T4mPVygmZZCQY+L8+VqX+saeJBKPGdZPjvBrl94W6vZXMjHhTmaKMUGv8jRlTkN4u5LsbInr8eGG328nLy0PTPn3ZLZ1NyPffRCxYjkg7+8aF0AyIsvlQvhPZ1bcbBJFW3eLhgXeOc/er1XxwvJ3LS4vJyc0n0VGFNXFgaYb+7hvo//690FpaMU4lUoqiKFHkcgawJIQ68A11mnZyPSmVSMWs6hYP+5q6uGpCRlyt5QJgMWrcONVKeYObPfXnXhj0SLOH92o7uHZyJmmWOK8GV+4Fnzcibc97sqw4g1o3VJYsQm56JW4qCZHi9XpxOBw9D+t7/QUAxPLVvW4vyi6FgB/58Qdhi3Ggmjr9/OD1GvbUu7m5xMqfVo3jizNtzJszm46ODg4cONDvfeobXkL+72OQngnG2B9arBIpRVGUKGppDpJhNUS9PXmkZNuMdLTreLrUPKlYtK6yBbNBsGRs7DeZ6MkV4zOwJhp5co8DKWWvr3tyj4Nks8a1k+K7GgUn50cZTTChNCrHv7QojQSjxsbxS6DFgdy5NSpxxKr6+nrg7PlRsq0FuWUDYu4ihDWn9x2MnQhZ2TE5vO/ZCie6lPxmxWhum55z6qZEUVERNpuNHTt2oOt9O9dLKdFffhL5zOMw6xK0u+5BWGJ7/SxQiZSiKErU+Hw6ne06mVlDf1hfN+tp60kpsaXDG+Tt6jYuG51GapxWacwGjZtKrOxv6uIje89VqUpHF9tPdLBqchYp5vj8OU8ny3fCpNKoXXQmmjQuLUpla7uFroIxyDfWnDOJHW7sdjtCiLMW4pUbXoZAAHHVjefcXmhaqOnEvo+RnbEzB62hw8emIy6WFWeQm/LpypEQgrKyMlpbWzl06NB59yWlRD7zOPKVpxDzl6Dd+X2EMT7WdFOJlKIoSpS4Ts6PyrDG/8VcX6VnGDCawNGgEqlYs+lIK96gjKsmEz1ZWpyBLbn3qtSTexykWgxcPTG+f04A2WiHhhMR7dbXk+XjMvAEJFvn3ATHDkNlRVTjiSV2ux2r1YrZ/EmyITs7kG+tQ5TNR+T2vLbU6cTsSyEYQH70XjhD7ZdnK5yA4MaSntdfKy4uJisrix07dpwzsZZ6EPnXR5EbX0YsuQbxhbsRhvj5TlSJlKIoSpS4nCcTqczhU5ESmsCaY1QVqRijS8n6Qy1Myk5kbFZ8tyg2GQQ3l2RzyOnhwxOfvoO/v9HNR/ZOrp+SRZIpfi7WenOq7XkE14/qyQRrAqPSzWwUIyA1HV0t0AuAruvU19efPazvzVfB04VYce5q1ClF4yAnL2YW561v97HpSCtXjEsnO6nnylF3VcrpdHLkyJEeXyMDfuQff4XcuhFxza3drainAAAgAElEQVSIW76C0OIrNYmvaBVFUYYQV3OAlDQNk3l4zI/qZrUZ6VTzpGLKx/ZO7O1+VkyIz7lRZ1o0Np28FBP/2ONAP+1u+P/tcZCRYIj7qls3WbELbPkI2/mrGuEkhGDZuAwqm70cW7Aa9mxH1h+PakyxwOl04vf7P5VISU8XcuMrMP1iROGYPu1HCBFqOnFgN7K9NVzh9tkzFU4MQnDD1J6rUd0mTJhAWlpaj1Up6fWi//5+5M6tiJu/jHbtZ+NyrrBKpBRFUaJASkmLMzis5kd1s+aEfmbVBj12rKtsIT3BwCWjUqMdyqAwaoJbS7OpbvHyXm2ohfKe+k7KG9zcONWKxRj/lz/S5w21PY9St74zLRydhlGDjbllYDSF5gANc3a7HYCCgk8SXfnOa9DZjnaeuVFnErMXgK4jd24b1Bj7q67Nx5vVrVw5IQNrL9WobpqmUVZWRkNDA7W1tacel+5O9N/dC3s/QnzhG2jLrgt32GET/2cSRVGUONTl1vF55bCaH9UtPcOAySRUG/QY0dDhY8eJTq4Yl4HJMHQuCy4bnUZhmpl/7HEQ1CVP7nFgTTRyxfihUXWjsgL8PkTJzGhHAkBagpE5ham8fcJDYN5i5HubY6J6Ek12u53k5GRSU0M3KKTfh3zjJZg0DVE8qX87KxwNeSOQO6I7vO/pCgdGTXDDlHNXo7pNmjSJ5ORktm/fDoBsb0P/9Y/hyEHEnd9HW7A8nOGG3dA5YyqKosSRlu75UVnDL5ESmiArx6ASqRixvtKFEAydBOMkw8mqVG2rj99/UM/+pi5uKrFiHiLJoizfCWYzTCiJdiinLB+XQbtP58NpK8DvQ761PtohRZXdbic/P//UkDW5dRO0NqOtuKnf+xJChKpSlRVIV/Ngh9onx9u8vHO0jRUTMslM7NtoCqPRyMyZMzlx4gR1Bw+gP/SvYK9Fu+setNmXhjni8BsaZxNFUZQ443IG0QyQljH8EikIrSfV2aHT5VbzpKLJG9DZeNjFnMLUXieNx7P5RakUZVjYdKQVW7KRpcVDJ1mUFTth4jSEOXbW2pmWl4Qt2cgGpwFKy5BvrkX6fdEOKyo6Ojpoa2s7NT9KBgLI156HMRNg0rQB7VPMXgBSRm1439PlTkyaYPWU/q2/VlJSQoLFzPaXnoMWB9q3fhYzQ1IvlEqkFEVRoqClOUB6hgFNi7/JtYPh1HpSqioVVVtq2mj36UOmycSZhM/HZ9NdANw8IRWTYWj8vcmGOmi0R71b35k0IVhSnMHuejdNl10H7a3I99+KdlhRceZCvHL7u+BsRFt584CbKoj8kTCiCLkj8ovzHmv18u7RNlZOzCQjoX9ze42NdUxvOEpNQhrOr3wfMTF2qqgXSiVSiqIoEabrktaWIJnW4ddooltahgGTWaiGE1EkpWRtpYuR6WZKc5OiHc6gkY4G9DfXEnzkPvTv3Mbsv97Lbz/8NYsPvBHt0AbNqbbnUV4/qidLxqYjgI0UwMgxyA0vIfXhV3muq6vDYDCQk5OD1HXk+udC85ymzb6g/YrZC6BqP7K5aXAC7aOnyx1YjBqrJ/evGiWrD6E/9CNK25swG43sONEYpgijQyVSiqIMWV6PztHDsbMSfLf21iB6cHgtxHsmIU6uJ6USqaipdHo43OxhxYTMuGw73E0G/MgDe9CffYLgT+9C/9c7kU/+N9QfR1x2Bdq3/41Rk8bCm68iO2PvfDAQsnwH5I1A5ORFO5Sz5CSbmFmQzOYjrejLVoG9FvZ+FO2wIs5ut5Obm4vBYICP3gd7LeKqGy/4b02cnFcUyaYTNS4vW2vauXpiJmn9qEbJg+WhxhKJSST+4H6mXXQRVVVVNDdHZ45XOAzf26GKogx5h/Z5qD7UxuKVqSSnxE7S0t1oInMYNpo4ndVmpP6EH3enTlKyuq8XaesOtpBo1Fg4Ji3aofSbdDUjK3aGGi7s+wg8XWAwwoSpiMuWI0rKEHkjTr1eS8tA3/Ue8s1XEVffGsXIL5z0euFgBWLhVdEOpVfLijN44N0TfDxiBjMzrOgb1mCIsWGI4RQIBGhqamLGjBlIKdHXPQu2AkTZ/Avet7AVwKji0OK8y1cPQrTn91S5gwSjxqp+VKPknu3of/gPyM5F+859iEwrFyWn8fHHH7Njxw6WL4/vbn3dVCKlKMqQ1WgPVTua6gMkj4udpMXVHMRsESQO8+Qh+7R5UkljzFGOZnhxeQJsOdbO8nHpJJli52+jN1IPQvWhUPK0ZwccOxx6IsOKmL0AMa0MJk1HJCT2uL0YOQamX4zc+Apy6bWIhDgeynhwDwT8MTc/6nRlI1JItxjYeLSDWYuvRr7wV+SxI4hRY6MdWkQ0NDSg63poftTeXXDsMOL2uxHa4PytiYsXIJ/7C7KpPuxVyeoWD9uOtXNziZVUS9/i1z98B/nn30DhmFBjidTQzZqkpCRKSkrYvXs3c+bMIT09PZyhR8Tw/hZXFGXI6mgP0tkRGpffVB9bw8danAEysgxxPZxqMKSma5jMaj2paNhY1UpAl6yYkBntUHolO9vRP3gb/fGH0b93O/oDP0CufRbMZsTqz6P99HdoD/4Z7QvfQFw0t9ckqpu24ibobEe+/VqEfoLwCLU9t8D42J2wbzIIFo1NZ/vxDlovXgqWBOSGl6IdVsR0L8Sbn5+PvvZZyMpGzF04aPsXZZEb3vePPQ6STRrXTepbNUp/5zXk//waiiehfe8Xp5KobjNmzEAIwa5du8IRbsSpipSiKENSdzWqYGQijfYudF3GRIc8v1/S0aYzYpSqwAghsNqMOBr90Q5lWAnqkvWHWpiWm8TI9NhpnQ0gnU3ID94KzQE6fBCkDimpoaYKpWWIqTMQyakD2rcYOxGmXIR8Yw1y0cqYahveV1LK0O9m8nSEKbbb1S8rTmfN/mbebAiw6tJlyLfWIa//AiKzbwu5xjO73U5mZiYJtUfQq/Yhbv0qwjh475ew2mDsxFAnwKtuHLT9nulws4cPjnfwmdJsUvpQjdJffxH53BNQMgvtn36IsJz9N5aamsrkyZPZu3cvF198McnJyeEIPWJURUpRlCGp0e4nJVVj4tR0AoFP5iVFW2tzKMEbjgvx9iTbZqTLLXF3xsb7MxxsP9GBwx2IuWqUdDai//KfkS/+DXw+xMqb0H74INqv/xfty99Fu/iyASdR3bQVN0ObC7llwyBFHWH1J8DZiCiZGe1Izqsw3cKUnEQ2VLXC4qtBl8jNr0Y7rLCTUp5aiFdf9wykpiMWLBv044jZl0JtNbL++KDvu9s/9jhINmtcM+nc5wopJfqavyOfewJRdinaXT/qMYnqNmvWLKSUQ6IqpRIpRVGGnIBf4mwMYMs3UVCYiBDQVB8bVY/uhE4lUiHWnNDACEeDGt4XKesqW7AmGbm4MCXaoZwi3Z3oj9wHPh/aT3+H4Se/QbvuNkTxpEGbVwLAhKkwbgrytReQgdg4J/RHLLc978mycRnUtfvYTzrMnIt85zWkpyvaYYWVy+XC4/GQZ9Jg70eIZdeFpfopZl0KQoSaToTBIWcX2090sGpSFsnm3v8Gpa4jn/oTcu0ziAXLEXd+77zVt4yMDCZMmEBFRQVdXfH9eVCJlKIoQ46jMYCuQ26BEbPFQIbVEDPzpFzNQZJTNMwWdfqF0Dwps0XgbIqN92eoO97qZXe9myvHZ2CIgaGuADIQQP/DA9BwAu3//TDUGCJMhBBoK2+GFgfyvTfDdpxwkeU7IH8kIjs32qH0ySWjUkk0amyocqEtWwXuTuTWTdEOK6y650fl7v0QkpIRC1eE5Tgi0wrjJoeG94XBP/Y4SDVrXH2OapSUEvm/jyE3vxpKGD9/V59vfJSVleH3+9m9e/dghRwV6ptcUZQhp6HOj9EIWdmhaoctz4SrOYjXG91FIaWUoUYTw3j9qDN1ryflaAwgpYx2OEPeukMujJpg+biMaIcCnLwQ+/t/wv7diM9/AzF5evgPOnUGFI1Drn8OGYyfIaXS0wWH9sZ0t74zJRg1LhudxtZj7bhHjofiSchNL4e6MA5Rdrsdi8lE5kfbEIuvRiSGr0OkmH0Z2GuRJ2oGdb8HHV3srOtk1RTrObt6ym2bkFs3IlbcjLjpS/1qoGS1WikuLmb37t14vd7BCDsqVCKlKMqQIqWk0e4nO8+EZgid1HPyYmP4mKdL4vVIMrNUn5/TZduMeNwSd2d0E92hzu0P8uaRVuaPSiWjH4tqhpNc92zoQmzlzWjzl0TkmEIItKtvhqZ65PZ3InLMQXFgDwQCcTOsr9uycen4gpJ3jraFqlJN9fDRB9EOK2zsdjt5uhdhtiAWXxPWY4lZ80Bog16V+sceB2kWAyvPMY9SNjuQT/9PaO226z47oC60s2fPxuv1Ul5efiHhRpVKpBRFGVLaW3U8XZLc/E8uFDMyDZjMIurD+1qcJxtNqIrUp1hPW09KCZ+3q9tw+/WYaTKhf/A2cs3fERdfjrjutsgefNrFMKIIue45pB4fCbys2AmWRBg3Jdqh9Mu4rATGZFrYcLgVZsyBnDz0DWuiHVZYeDwempubyaurRlx+5VmtvwebSMuESaXI7VsGraK/v9HNR/ZOVk/JItHUc5ogpUT/22MQDKLd/k2ENrB0wmazMWrUKD766CP8/vibswgqkVIUZYhpsIdOxrb8Tya7Ck2Qk2ukqd4f1eFjruYgmgZpGSqROl1KmoYlQeBQiVTYSClZV9lCcZaFidkJ0Q4HeWgf8i+/g/FTEHd8M+JrqglNQ6y4Cey18NH7ET32QITanu+EydNivu35mYQQLC1O53Czh2qXH7HkWjh8AHn4QLRDG3T19fUA5HvaEctXReSYouxSaKyDY0cGZX9PljtITzCc84aL3LIBKnYhbrgdYcu/oOPNnj2brq4u9u7de0H7iRaVSCmKMqQ01vlJyzCQkPjp01tOnhFPl6S9NXp3n13OAGkZBgyG2JjkHyu650k51TypsKlodHOs1ceKCZlRXwhaNtSh//5+sOaiff1HUUsMRNl8sBWgr3069j939lpoboqr+VGnu3x0OiZNsOGwCzF/CSQlD8mqVN2RwwgpyZ0+C5ExsPWyGjp8bDzsor7N06fXi5nzwGAYlOF9exvc7Kl3c8MUKwnGXqpRzU3IZ/8ME0sHpZHGiBEjKCgoYNeuXQTjaM5it4gMkvb5fNx7770EAgGCwSBz587l5ptvprGxkd/+9rd0dHQwZswY7r77boxGI36/n8cee4wjR46QmprKt7/9bWw2GwAvvvgimzdvRtM0vvjFL3LRRRcB8PHHH/PEE0+g6zpLlixh1arI3AlQFCV2+Hw6zc4g4yef3Wo2J88EdNHU4I9KRUjqEldLkFFj1EK8PbHajNTV+mlvjc/hHbFuXaWLFLPGgqLwDjU6H9nehv7Iv4EQaN/8KSIlevEIzYBYcVOoMla+A6bNjlos5yPL46vt+ZlSLQbmjUrl7aNt3DHDhumyK5Gvv4hsqkfk5EU7vEFjr9xPjqcd81Vf6dd2TZ1+th5rY0tNO4ecoQTqb7sdfH9+ASW5525WIVLSYPJ05I4tyBtuv6AbJU+WO8hMMHDl+J6b0Ugp0f/3MdB1tNvvHvCQvjOVlZXx8ssvc+DAAaZOnToo+4yUiFSkTCYT9957Lw899BAPPvggH3/8MZWVlfz9739n5cqVPPLIIyQnJ7N582YANm/eTHJyMo8++igrV67k//7v/wA4fvw427Zt4+GHH+aee+7h8ccfR9d1dF3n8ccf50c/+hG/+c1v2Lp1K8ePh2+BMkVRYlNTfQDkp4f1dUtM0khJ06I2T6q9TScYgAzVaKJH2SfnSdlPxPeaIrHI6fbzfm07S4szsPRylzkSpN+H/vtfQLMD7a57LnhI0GAQcy4Hqw391diuSsmKnTCiCJGVE+1QBmxZcTqdPp33atsRi68GTSA3vRLtsAZNoNVFg8dPXmpKn5LDpk4/L+1v5gevH+Uraw7zxK4mdAlfuCiHXywdSVqCiZ9uOsbrh1zn3ZeYvQCcjVBdOeD499R3UtHg5oap1l7PE3LLhtDaWDfcPqgJcFFRETabjR07dqDHyZzFbhE5owohSEgIjckOBoMEg0GEEOzdu5e5c+cCsHDhQrZv3w7Ajh07WLhwIQBz586loqICKSXbt2/nkksuwWQyYbPZyMvLo6qqiqqqKvLy8sjNzcVoNHLJJZec2peiKMNHY50fk1mQ2ctitzl5JpxNAYKByF8wqUYT55acqpGQJPhwi4Nd73fSaPej67F7YRtPXjvkQkq4qpe7zJEgdR35xO/g8AG0L38HMW5y1GI5nTAaEVfeELoAPbAn2uH0SHrccGgfomRmtEO5ICW5SeSlmNhwuBWRaUXMvgy5ZQOysyMix+/0BfEFw3eR3vTaGgKagYJZc3p9jdPt55UDzfzL6zV8Zc1h/ryrEX9Q8vmLcvjDtWN5+KrR3DDVSmluMn+8ZTrT85L5zw/r+eP2eoLnOB+Ki+aC0TjgxXmllPxjj4OsRCNX9FaNcjYhn3k8NKTv8qsGdJzeCCEoKyujtbWVQ4cODeq+wy1it0Z1Xedf/uVfqK+v54orriA3N5ekpCQMhtBFRVZWFs3NzQA0NzdjtYbGlhoMBpKSkmhvb6e5uZnx48ef2ufp23S/vvu/4+2NUBTlwkgpaawPYMs3InpZaNSWZ6S60ouzKdBj1SqcXM1BTGZBcoqamtoTIQRzL0vBXis4cqidEzV+zBbBiFEmCkebSc80RH1uT7zaUtPG9Lwk8lKjN6xUrvk7cvu7iOtvD02OjyFi/hLk2qfR1z6DIRLrWPXX/j0QDCBKy6IdyQXRTjad+PtuB3VtPvKXr0K+/ybyndcRV90Q1mN/cLydB989gS6hINXMmEwLozMSGJ1pYXSmBWui8YLOL9Ldib1iN+SMIX9Kyaeec7r9vFfbztaadvY1hSruYzItfG56NvNHpVGQ1vPfZarFyI8XFvLXjxp56UALtW0+fnDpCFItZ9+ME0nJMHVmaHjfTV/s95C73fVu9jV18dWyXMyGs7cNDel7FKQc1CF9pysuLiYrK4sdO3YwYcKEuDnfRyyR0jSNhx56iM7OTn71q19x4sSJXl/bU3ldCNFr2b231/dk48aNbNy4EYAHHniA7OzsvoQfdkajMWZiUc5PvV+xp6neg8/bSvGELLKzU089fvp7lZGus2NrNR2tJqaURvb9a289hi0vkZyc+B2aE27Z2TB+opE5C/wcr+nkcGU7NYc7qT7kIz3DxNiJqRRPSCU1Lb66lkVTU4eXunY/N1xUOOjnrL6eB90bXqZ9/XMkLruW1M99NSYvkDpXf46OJx4hrakO8+Rp0Q7nU9oOVeBJTCJ7zgKEceCXbbHwvXVjWSpP7nGw1e7j/82fTcu0MgJvrcV665fC1nTkg5oWHtpSx/icFOYUZVLl6KTK0cm7Ne2nXpOWYKQ4O5lx2UkUW5MZl5PMWGsSFmPfRhB0Pr+OelMiaclJjBkzBmenj7eqHGw+5GD3iTYkUGxN4s55o1g0PpuizPMv0ms0Gsm15fCDK3KYOrKBhzZX8cMNtfzHtVMYnXX29l2LV9D2m5+R7qjDPOWiPv9+pJQ8u/kEthQzn5lTjLmHYX3uN9bQvu9jUr/2zyRNLulhL4Nj0aJFPP/88zgcDiZPjo2q9flEfLB+cnIyU6ZM4dChQ7jdboLBIAaDgebmZrKysoBQRcnpdGK1WgkGg7jdblJSUk493u30bU5/3Ol0kpnZc9vGpUuXsnTp0lP/djgc4fgx+y07OztmYlHOT71fsafyQBcISEzuwuH4ZJX0M9+rzGwDx6rbGDspcrEFApKWZh85eUJ9bs4jOzublhYnyWkwrczE5Glp1NX6OVHj46MPmvnog2Yysw0UFpkpGGnCbFEVvnN5u7oVgLEpctA/e305D8p9H6H/4UGYOgPv9XfgO+27OpbIWZfCs3+h5ck/YfjWvdEO5xQpJfqObTB5Ok7X+efKnEssfG8JYFZBCmsr7Fw/Phlt4Qr0R+6j6fU1aHMXDfrx9ja4+dmbtRSmmfnxgnxSLAYYnwyEhvrVuLwcdXk52uLlqMvDKxVteE4O/dYE5KeaGZ0RqlqNzrAwJjOB7KRPV6+k14v+0j+oK5qFlmrja0/tYm+DGwmMSjdz67Rs5o9KZWT6ySZIQTcOh/u8sZ/+fs2xGfj5kpH88p0T3PnUx/zz/AJmjUj51Ovl2ElgMuPa+CqarbDPv6NddR1U2Nv5p9m5tLmaz3peOhvR//woTJpG58xLcYfxM5Sfn09aWhqbN28mOzs7pm66FBQU9Ph4RBKptrY2DAYDycnJ+Hw+ysvLue6665g6dSrvv/8+8+fP56233qKsLFS2njVrFm+99RYTJkzg/fffZ+rUqafGTz7yyCNcffXVtLS0YLfbGTduHFJK7HY7jY2NZGVlsW3bNr75zW9G4kdTFCVGNNQFyMwynPfC2pZnZN9uD11uncSkyFyEtzYHQapGEwNhMmsUFVsoKrbg7tQ5cczH8aM+ynd2UfFRF7Z8I4VFZnILTKqtfA/KG9ykmDVGZ57dyTLc5Ika9D/8B+SPRPvav1xQNSXchCUBsew65It/Q9ZUIYrGRTukkBM10OJAXHNrtCMZNMvGpbP9RAc76jq4eOpMyB+JfGMNcs7CQb1wrnR08fO3jmNLNvGzxSNDSdRpks0GptiSmGL7pLqjS0lDh5+jLV6qXR6Otng53Oxh67H207bTQslVhoXRmQkE9u/hvTE3kCOPcbAtARID3FJqZf6oNEZlDN7f3eScJH595Wjuf/s4v3j7OLfPyOG6SVmnfmciIQlKy5A7tyFvvROhnb+aJqXkyT0OcpKMLC0+e26UlBL9r48ChG1I3+k0TaOsrIzNmzdTW1vLqFGjwnq8wRCRs1pLSwu///3v0XUdKSXz5s1j1qxZFBYW8tvf/pannnqKMWPGsHjxYgAWL17MY489xt13301KSgrf/va3ARg5ciTz5s3ju9/9Lpqm8eUvfxnt5Jv6pS99ifvvvx9d11m0aBEjR46MxI+mKEoM8HTptLYEmVR6/oVGc/JMsNtDU72fUWMjc3HZ0nyy0UQvTTCUvklK1hg/OYFxkyy0uYIcrwlVqhpOuDGaoKDQzIjRJqw5FzbfYSgpb3Az1ZaEFuHfh3Q5Q23OLQmhNueJ5x/KFG1i0Urk6y+E5kp9/UfRDgc42a2P+G173pOyghQyEwxsqGplTmFqKIH938dCzT4GaY7a0RYP//ZmLekJBu5bMpKMhL5d7mpCkJ9qJj/VzLxRnwwRd/tPVq9aQhWs6hYvm4604Qm4ABuTEjqgC766aArTxo4I2/knJ9nEA8uL+O02O0/saqLG5ePrF+diOjmvSbt4AfqubXCwok+/y511nRxyerhrTh6mHm5Eybdfg/27EZ/7OiI7d9B/np5MmjSJDz74gO3bt6tEqltRUREPPvjgWY/n5ubyy1/+8qzHzWYz3/3ud3vc1/XXX8/1119/1uMzZ85k5sz47mijKMrANNpDaw/Z8s9/SktN10hIFDTVByKWSLmcQZKSNSwJahjaYBBCkJ5pJD3TyJRpCTgaAxyv8XGi1sexah8JSYLCUWZGFJmjsmZYrGjs8NPQ4eeaiT0PdQ8X6elCf/QX0NmB9oNfxk3LbpGYhFh8DfLVp5AnahAjiqIdUmj9qMIxiMyBLe4aiwyaYPHYdF7c34zT7Sdr7kLki39D3/DSoDT7ON7q5aebarEYNe5bMhJr0oXPvUoyGZick8TknE9uCAR9XhpeehH/lo1UX3YF+xqMlI4pCPtNnASjxg8WFPB0uYOnyp2caPPxr5eNIDPRCCVlYElA7tiCOM/vsrsalZtiYvHY9LOfdzQgn/sLTJ6OuOyKMP00ZzMajcycOZMdO3bgdrtJSortmzDqW11RlLjXaA+QkCj6dNEshCAn10RTQwAZofbaLc0B1fY8TIQmyMkzMWNOMsuvS2fmvCTS0g0cPujl7dfbefv1NqoPeWN6jaBwKW/oBKD0PAt6DiapB9H/59dQW4321e8jRhVH7NiDQSy5GiyJyHXPRTsUpLsTDu9HlA69m8RLizPQJbx5pA1hMiMWrYTyHUh77QXtt77dx0821SIE/HzJKHJTBr9TpQwE0N95HX78/8h940kKJ4+j3h8kLy/v1CipcNOE4DPTcvjBpQVUt3j43mtHOdLsQVgsiOkXI3dtQwbOvWbihyc6ONzs4eYSK8YzOt1KXQ8N6RMnh/RFuKJdWlrKHXfcEfNJFKhESlGUOKfrkqYGP7Z8U59P9jl5Rvw+iaslGOboQsMOPW6phvVFgNEoGDHKzJzLUlh2bRolMxMRQlCxqytqCzFHU3mDmzSLYVDnaZyPfPpx2P0h4jN3IqbNjthxB4tISUMsvBK5/V1kQ110g9m/G4JBREl8tz3vSUGamZLcJDYcdqFLiVh4FZjMyA0vDXifTZ1+frKpFn9Q574loxjRS1vxgZK6jv7B2+j33oX82+8h04r23Z8T/NJ3aWpqIj8/8gtMzy9K4z+WFyGAf3mjhq01bYjZl0JH+znXReteNyovxcSiMT1Uo955DQ7sQdz0RYTVFsafoGdGoxFTmLo4DjaVSCmKEteaHQEC/r4N6+uWnRd6bSQurl3NoWQt0xq7E+2HIkuCxpjxFuYvScFkEhyv8UU7pIiSUlLe4KYkN3Lzo/SNLyM3v4pYdh3aopUROWY4iGWrQoubro9uVUpW7ITEZCiOYIvRCFpWnE59h5+KBjciNR0xbxHyvTeRbf3vTujqCvDTTbV0+IL8bPEoigbx5oGUEvnxB+j3fQv5P78GswXtGz9B++GDiMnTaWhoQEoZlUQKYGxWAr++cjRjMhN4cEsd/5Bj0BOTkdvf7XWb9493UN3i5ZbSbAxnVqOa6kND+qZchFgQuSF98WCNe/IAACAASURBVEolUoqixLXGugBCg5zcvt+9slg00jMNNNX7wxhZSIszgBCQPozn6kSTwSDIH2mi/rifgH/4DO+r7/DjcAciNqxPfvw+8pnHYcZcxI1fjMgxw0WkZyIWLA8tFutsjEoMUkpkxU7ElIsQhqF57pg3MpVkk8aGw6EW/WLpdRDwI99a16/9tHmD/HRzLU63n58uLGSc9fxNh/pK7t+N/svvo//+fvD7EV/9PtpPfouYPvvUCIj6+noA8vLyBu24/ZWRaOT+pSNZPDadp/e18Ouyr9G1eyfSf/Z3nH6yGlWQauby0Wmfeu6TIX0C7QuRH9IXjwaUSFVUVLBv377BjkVRFKXfGux+rDlGjKb+nfBt+UZanEH8vvBeXLuag6RlGDAY1RdStBSONhMMQv2J8CfOsaK8IbROTSQSKVl9CP1Pv4LR49G+/L2wt0iOBHHFakAgX3shOgEcPwquZigdOt36zmQxalw+Jo33jrXT4Q0i8gth2mzkm+uQPu/5d0BoLah/21xLXZuPexYWMtk2OJ93efgAwV/dg/7wT6C1GfGFb6Dd93u02QvO+nzX1dWRlZVFQsLgJXADYTJofHNuHl+cmcMHhlzumXQ7TR9/fNbr3qttp8bl5ZZS69nVqLfXw8FyxM1fRljjo0lMtPXpbHfvvfdy4MABANasWcPvfvc7fve73/HCC1E6wSiKogDuziAdbXq/hvV1y8k1ISU4GsN3cS2lxNUcUPOjoiwr20Bi0vAa3lfe4CYzwUDhIM8TOZN0NKA/9nNIzUD7xj0IS+TXqwoHkZWDuGQxcssGZA+LlIabLN8RimPq0Gs0cbplxRn4dcnbR9sA0Javgo425PtvnndbT0Dn528dp7rFww8vG8H0vOQLjkfWVhN89OfoD/wA6o4hbr0T7Rf/jbZgeY+VQSkl9fX1URvWdyYhBKsmW7nnsgIaEq3880Ez+xs/WfxXl5Kn9jgoTDOzoOiMalT3kL6pMxCXLotw5PGrT4lUbW0tEyZMAGDTpk3ce++93H///WzYsCGswSmKopxLY11ojlNuQf8npWZmGzAawztPqqNdJ+CHTNWxL6qEEIwoMtPUEMDTpUc7nLA7fX5UOIfm6J3t6I/cB35/aK2otMi2WQ83cdWNoAeRG9ZE/NiyYieMGovIyIr4sSNpbFYCxVkW3qhyhTprTiiBUcXIZ/5M8Lf3or/0f8jd25FtLZ/azhfU+fe3j3PQ0cX35hdQNiLlguKQ9SfQ//gQ+n3fgqp9iNWfR/vln9CWXIM4R9OD5uZmvF5vzCRS3cpGpvOA2EWSp4MfbzzGxsOheWdba9o51urj1jPmRkldR//LI2AwoH3hG2pIXz/06TZud9vY7nGghYWFAHR2doYpLEVRlPNrsPtJStFITun/UCJNE1hzjTTWB5BShuWLw+U8uRCvajQRdYWjzVTt93LimI/iidEdghNuJ9p9tHQFKM298Dv0vZGBAK0P3geNdWjf+hmiIPYXzuwvkZOHuPgy5FvrkVfeiEhNO/9Gg0C6O+DwAcSVN0bkeNG2rDiDP2xvoKrZw3hrItpXvovc8BKyuhK59lmkPHnzIysHxownOHoC/xGYxO5WjW/Ny2d+0cDfF+lsCq0btm0TmMyIFTcjrliFSOpbYma32wEoKCgYcAzhMqpsJg88+ksevuLHPPp+PTUuL7vqOhmVbmZ+UeqnXivfXAeVFYjb746bdd9iRZ++3SdOnMif//zn/8/efYfHfZWJHv+e3xTNqI+6ZFmSZRXLsmTZcYmT2I4d44SQhMQEvCGFwLKw6yzZhb2wgRt24cICC5twExKWFkIuLBBIIwmBNJc43bZcVGxLslWt3sto6u/cP8ZW4riNpflpRtL5PI+fR8+MNOeVZzTze88573sYGBhg5cpAO9POzk7i4uIu8JOKoijG8Pskvd0+cvOtk06C0jIsdJ0YZ2xUJzYu9KtGA31+zBaIjZv5NSMzXVy8iQSHiRPN3lmfSFV1TkN9VPU+PIf2Im6/64IHf85k4sM3I9/ZhXzlWcRNt03PoLUHQNdn5flRZ7M2L55fVnbzcsMQhcl2ROZ8xB3/CIB0u6D5GLKpHprq8TXW88PxBexN0/hc3dOsrzuBvqAQ8ooQCwphXi7CfOEdCnJ4APnCE4GaIEBsvA7x4ZsR8YlBx63rOjU1NcTExJCQcGYL8bArLiPObuXrnX/h0RWf4tkjgVW9r6zNOq2Tp+zuQD71GCy5BHH5pnBFO2MFlUjdddddPPfcc8THx3PDDTcAgeK6a6+91tDgFEVRzqW3x4fuh7TMyZ81kfq+NuhGJFKD/X4Sk8xqm0SEyM61UHPAxciQn7iE2bvdsqrLSbLdTGacceewyPqawAz+mo2GjREJRFYOLF+D3PE88iJWKqZCVu2D6FhYUGz4WJEg1mri8pw4Xmsa5jOXpGEzvzfxJKJsUFSKKCpFl5L/fruDN48Pc2emmw/H5SGbvMiDe+CNV5EAZktgS+SCIsgrQOQVQVrmRIMIOTaKfPEp5KvPgc+LuHwT4rqtk1qFqaqqoquri82bN0fke7wwmRCXXIb25qv83Z1fIN8RRUOfizXz31sECXTpexBMZrTb74rI3yPSBZVIVVdX88lPfvK025YvX87bb79tSFCKoigX0t3uxWSC5LTJb5uLiTURHavR0+llQWFoi+T9PsnwoJ+Fi2ZH8f1skJVjpeagi7ZmDyXl9nCHYwgpJdXdTpZlxBh6USTra7EUlqDPkEMzp0K79hPo+95Ebv8z4rqtho4ldR1ZU4koXTZr256fzYcWJrKjcZg3W0bYmH+WA2Kl5Gd7uth+fJhbylO4qSwFWDpxH33dyMZ6aKoLbAnc/RK8+lwguYqOgbxCRGoG8t3d4HIiVq5F3PBJRPrktuSNjo7y1ltvMX/+fIqLIzfhFSvXBramHtrDplXr2LTw9Pvljj9DXQ3izrsRSSnhCXKGC+oK5Cc/+Qlr1qw54/af/vSnXHrppSEPSlEU5XyklHR1+EhJN2MyTe1iMS3DTGuTB90v0ab4WO83NOhHSnUQbySx2TVS082caPawqMw2K2dfW4c8DLn8lGUYt61Pul3QcgzrjbfiMmyUyCFy8gNtuV99FrnpBoQt9Em41HXY/zb6X56AoQFYuirkY0SyxWl2suIsvNwweEYiJaXkV/t7+Ev9IFsWJ7F1SfJp9wshICUdkZIOK68I/IzfDx2tyMY6aKpHNtUj66qhdDnajbcishdMKd7du3fj9/vZsGFDZL+PFCyGxCTkntdh1brT7pLd7YEtfWUrEJddFaYAZ77zfsJ3dXUBgX2g3d3dE00nTt1ntRrbVlVRFOVsRkd0xsd0CkKw2pOaYaGpwUN/n4+UtNDNrk80mlCtzyNKdq6V/e846e/xT2k1M1JNy/lRx4+C349lccWcSKQAtGs/jv69ryBf+yti800he1zp8wZqsP76JHSeCGxDu+MfER+46J3thBB8aGEijx3ooW3ITXbCe+/tj1f18czhfq4tSuSOitSgEhdhMkF2HiI7D9ZuBghZU6Gmpibq6+tZvXo1iYnB11SFg9A0xCWXI3f9FTnuRNgD7wtS19EffRBMFrWlb4rO+yly9913T3z9hS984bT7EhMT+fjHP25MVIqiKOfR3R44+2kq9VGnJKeZESJQJxXKRGqg348tWmCzq0YTkSQj24JpH7Q1e2ZpIjVGWoyZ9FjjJjplfQ0IDcuiMnCOGzZOJBELF0HJUuRLzyCvvBZhndokjnS7kLtfRL70JxjohfkLEJ/7CuKSNQhtbk6+bMhP4DcHe3j52BCfXp4GwNO1ffyuqpeN+Qn83Yr0KV3whyJZ8Hq97Ny5E4fDwSWXzIzDksXKtchXn0MeeAexZgMAcvtzgTbvn/4nhCP5Ao+gnM95P0Uef/xxIHAg7ze/+c1pCUhRFOVCujp8xCVoRMdMPUmxWASOFBPdHT5KykMQ3EmDfX4cSbPvQn2mM5sFGfMstLd6WLLcPuWtoZFEl5LqLicrs43tqCvramB+Hlp0zJxJpAC0j3wC/b/+N/KNVxAbPjKpx5BjI8jtfw5cyI6OQNEStDvugtLlc35VwGE3szI7lh2NQ9y2NJWXjw3yq/09XJEbxz+uzjit01y47Nmzh+HhYbZs2YLZPEPe3/OLISkVuWc3rNmA7GpHPv3rwJa+Wd4sZjoE9SpQSZSiKJHC65X09/hYWBy6Jg5pGRaOVLlwu3SibFNPztwuHeeYTm6B2v4cibJzrZxo9tLd4SUze/Y8R82DbkY8uqHb+qTPC41HEWuvNmyMiFW0BApKkH99Erl2c1Bttk+RA33Il59BvvYiuF2wdBXah28OrHQpEz60MJG3W0d58O0OXmsaZlV2LF+8LOu0w2PDpa+vj8rKSkpKSibOU50JhBCIFVcEavxGhtF/9QCYLWh3qC19oRBUItXd3c3vfvc7mpqacLlO3xH93//934YEpiiKcjY9nV6kDM22vlNSM8wcqQps78vOm/qF9WC/H0CtSEWolHQzUTZBW9PsSqSmpT6q+Rh4PIjCUuPGiFBCiMCq1APfRL61A3Gy9uZ8ZOcJ5EtPI9/cDlJHrFqHuHpLoHZHOcOyzBiS7WZeaxqmIiOaL1+RhTkCkigpJTt27MBqtXLFFVeEO5yLJlatRb70NPrD3w4c9vyZLyIS1Za+UAjqU/6BBx4gPT2dO+64g6go1cpXUZTw6e7wYbaAIyV0dQQJDhPWKEFPpzdEiZQPBCSoRhMRSdME83KsNDa48bh1rFGzo46tqstJRqyF1BgDz49qqA18UVhi2BgRrXQ55BYEVqUuu+qcLcpl8zHkX55AVr4JZgti7WbE5hsRqRnTHPDMYtIEt1Wksr9jjLtWZ2A1RcbfZm1tLe3t7Vx11VXY7TPw6ISchZCaAceOwNJViEuvDHdEs0ZQiVRbWxvf+ta30LTIeEErijI3SSnp7vCSlmFBC+EspRCC1HQzPV2+kHR2GujzEx+vYTaHfyZVObt5uRaO17lpb/WSVzDzJwj9uqSmy8llOQbXR9XXQvo8RLzD0HEi1cSq1I+/g9z7OmL1+on7pJRQV43+whNQux/s0YhrPobYdP2c/f+ajI35CWc9SypcnE4nb7zxBllZWSxevDjc4UyKEAJx+Sbkq8+h3bZNbekLoaASqZKSEpqamsjPzzc6HkVRIlzzMTfH69xcvjF22mfyhwb8uF0ypNv6TknNsHCixcvwoE6CY/IrSVJKBvv9ZGbP/oNKZ7IEh4nYeI0TzZ5ZkUg1DrgZ8xpcH6XrUF+LWH7muZJzytJVkJWD/PMfkCvXBm47tCdwBtTxoxCXgNhyB2L9hxHRMeGNVZmy119/HY/HE/lnRl2AuPbjiM03IebAIdrTKahEKjU1lf/4j/9g1apVZ/TM37rV2FO+FUWJHFJKjh91MzqiU3vQRcUqA2sxzqK7I3A2U1pm6GuPUjMCj9nT6Z1SIjU2quP1SHV+VIQTQpCda+VIlQvnqJ/o2Jn9fFV1jQFQlmHghXtHKzhHoXBmzsqHitA0xLUfR/7iPuQfHkEePgjtLYFDYW/9e8RlV025PboSGVpbWzly5AgrVqwgOXlm1xQJIUAlUSEX1HSy2+3mkksuwe/309fXd9o/RVHmjsE+P6MjOnHxGq2NHnq6vNM6fneHl8QkU0g6632Qza4Rl6DR0+mb0uMM9p1sNJGsGk1Eunm5gXq4tubpfR0boarLybx4K0l24153sr4GYE42mvggsfIKSMtCvvocCIH47L+gffsnaCE4Y0qJDD6fjx07dhAfH8+qVavCHY4SoYJ6x922bZvRcSiKMgO0NHowmWDNhljeeHWUQ3vGWX+NeVpqgdxunYE+P0WlNsPGSMuw0FjvxueTk/6dBvt9mMwQF69qSiNddIxGUqqJtmYPhYujZuy2Hb8uqe0eZ/2CeGMHqq+FxCRISTd2nBlAaCa0f7wXBnqgpGLGvnaUc9u3bx+Dg4N89KMfnTlnRinT7qI+6cfHx+nu7qarq2vin6Ioc4PPJ2lv8ZA530KUTaN8ZTTOMZ26ateFfzgEek5u60vPMu4DLTXDjK5DX/fkV6UG+vwkOkyICGjZq1xYdq6VsRGdoQF/uEOZtIZ+F+M+g+ujpETW1yIKS1XScJLIzEYsXqb+P2ahwcFB9u7dS2FhIbm5ueEOR4lgQXfte/DBB2lubj7jvscffzzkQSmKEnk627z4fDB/QWA7VEqamZx8K8fq3GTlWEg0+Myk7g4vUTYxpfqlC0lKNaOZAnVS6VkXv5fc75cMD/pZUKS29swUmfMtVFeO09bkMfw1bJRT50ctMfL8qL5uGOid8/VRyux36swok8nEunXrwh2OEuGCWpH6xS9+QWlpKb/85S+Jjo7m0Ucf5UMf+hB33XWX0fEpihIhWhs9RMdoJKe+d7G5eKmNqCjBwT1OdF0aNrauS7o7faRlWAyd/TWZBMmpZronWSc1POhH11GNJmYQq1UjPSvQsdHI17CRqrqc5CRYSbQZWB9Vd6o+SiVSyux29OhRWltbueyyy4iJUV0XlfMLKpFqbm7m1ltvJSYmBikl0dHR3HbbbWo1SlHmCOeon95uH/MXWE9LZCxWjbJL7AwP6hw76jZs/ME+P16PJM3AbX2npGWYGRvRcY7pF/2zg/2q0cRMNC/Xgsct6emaWqORcPD6JYe7nYZu6wOgoRaiYyBLbXNSZi+Xy8Xu3btJT09nyZIl4Q5HmQGCSqQsFgt+f+ACIS4ujt7eXqSUjI6OGhqcoiiRobXJA0B2nvWM+zKzrWRmW6irdjE6YkydSVeHFyEgNd341q2pJ8+o6um8+E5uA30+omwCm13VTMwkaZkWLFbBiZOv85mkoW8ct19Slm7szLmsr4GFJQhNNVFRZq8333wTl8vFxo0b0dRrXQlCUK+SRYsW8dZbbwFw6aWX8p3vfIdvfOMblJaqFqiKMttJKWlt9JCSbiY65uxvGUuW2zGZBIf2OJEy9Nujuju8JKWYsFiNT1Bi4zRsdjGpNuiDfX4cyWZVfD7DmEyCrPkWOk548Xln1va+qi4nAig1stHE8CB0nlBtz5VZraOjg+rqaioqKkhNTQ13OMoMEdT+ky996UsTX99yyy3Mnz8fl8ulivAUZQ7o7fYx7pSUlJ+5GnWKza6xuMLGwT3jtBz3kLswdM0Wxp06w4M6JUuNa3v+fkII0jIstLd50HWJFmT3PY9bZ2xUZ37+uf+flMg1L9dK8zEPHSe8zD/LymukqupykueIIj7KwLq8hloARJFKpJTZye/3s337dmJjY1m9enW4w1FmkAuuSOm6zje+8Q283sA2F03TWLduHZs3b8Zmm54LG0VRwqe10YPZAhnzzr+tbv4CK8lpZmoPjuMav/j6onPp7gi896RnTt+J7KmZZnze92qegjFRH6UaTcxISSkm7DEaJ5pnzvY+r1/nSO+4sd36AFlfC1Yr5C40dBxFCZcDBw7Q19fHlVdeidU6cyZSlPC7YCKlaRrd3d2GbNdRFCWyeT2SjjYv83KsmC5wQK0QgqUr7Og6VO0bD9l7RneHD3u0IHYaD7hNSTODuLg6qVOJVMIMbaE91wkhyM610NPlC+lEgJGO9rrw+KXhjSZkfS0sKEaYp28yQ1Gmy/DwMO+88w75+fnk5+eHOxxlhgnqyuTmm2/m5z//OT09Pei6fto/RVFmrxMtHnQ/5CwIboYuJs5EcamNzhNeOtouvlnDB/n9kp4uL2mZxrY9/yBrlEaiw3RRdVIDfT5i4zUsFlUfNVPNy7WCDLzuZ4KqrjE0AaVpBtZHuZzQcly1PVdmJSklO3fuRAjB+vXrwx2OMgMFNXX605/+FIDXXnvtjPtUC3RFmb1aGz3ExWskXMR2tfziKE60eKmuHCcl3YzVOvmVpP4eH34fkzocd6rSMs3U1brxePQL/g5SSgb7/WGJUwmduHgTCQ4TbU1eFhZH/tb1qi4nCxw2Yq0Gbic9dhSkrhIpZVY6duwYTU1NrF27lri4uHCHo8xAQSVSDz30kNFxKIoSYUaG/Az2+1lcYbuo1SBNE1SssrP75VEOH3CxdNXkZ8u7OnxoGiSnTf92udQMC3U1bnq7fGTNP/+K3PiYjsct1UG8s0B2npWa/eOMDPmJS4jc59Pt0zna6+K6Yoeh48j6GtA0yF9k6DiKMt3cbje7du0iJSWFpUuXhjscZYYK6upEtYFUlLmntdGDEJCde/GFtwkOMwuLo2g44mZeroWUSZ7/1N3hJTnNjPkC9VlGSEwyYbZAT+eFE6mBiYN4I/fCWwnOvBwLtQfGaWv2UFJuD3c453SkdxyfPk31UfPzEbbI/b9QlMl4++23GRsb4yMf+Yg6M0qZtKASqR/96EdnnZE2m80kJyezcuVK8vLyQh2boihhouuStmYPaVlmomyT+4ApKrXR0ebl4N5x1l998cnQ2IifsRGdBYWha6V+MTRNkJJuobvTi5TyvKtyg31+NBMRvYKhBCfKppGaYaat2cOisotbjZ1OVZ1ONAGL04xLcKTXC8ePIq681rAxFCUcuru7OXToEOXl5WRkZIQ7HGUGC+oKKTo6mj179iClJCkpCSkle/fuRdM0Tpw4wb333suuXbuMjlVRlGnS3eHD7ZLkLJh8EmMyC8pX2HGO6tTVuC7657s6Ao0e0jLD1wUvLcOMyykZHTl/Y52BPh8JDlPQZ04pkW1erhWXU9LXE3z7++lW1eWkIMlGtMXA5L25HnxeVR+lzCq6rrN9+3aio6NZs2ZNuMNRZrigrlA6Ojr46le/yqJF7+2Rrqur4/HHH+frX/86Bw4c4Fe/+pXqeKIos0RrowdrlJhyEpOSbiEn38qxo26y5ltIvIjW4N0dXmLjNGJiw7fKk5oRiLenw0tc/Nnj0HXJ0KCfvBAeQqyEV8Y8CyYznGj2BFrhR5hxr0593zg3liQZOo6sDxzEi0qklFnk0KFDdHd3c8011xAVpd63lakJakWqvr6ewsLC027Lz8+noaEBgKVLl9LX1xf66BRFmXZul05Xu5fsPGtIVlgWL7URFSU4uGccXQ/ubCmfT9LX7SNtGg/hPZvoGBMxcRo9Xedugz486Ef3Q6Kqj5o1zGZBZraF9lYPfn/knaF4uMeJX0JZRoyh48j6WsjIRsQlGDqOokyX0dFR3nrrLXJzc8+4rlWUyQgqkcrLy+N3v/sdHk/gbA2Px8Pjjz8+URfV3d1NbGysYUEqijJ92po9SBn82VEXYrFqlF1iZ3jQz/Gj7qB+prfLh65Delb4VwPSMsz0dvvOeUE9qBpNzErZuVZ8Xuhqn/p5aKFW1eXErEFJqoH1UbofGg6rbX3KrLJr1y50XefKK6+M2PpHZWYJ6irlrrvu4sEHH+RTn/oUsbGxjI6OsnDhQu6++24gkOF/9rOfNTRQRVGMJ6WktdFDYpIppI0TMrOtZGR7OVrjIiPbQmzc+R+7u8OL2QxJKeFPpFIzLDTWe+jv8ZGaceYK2WCfH2uUwB6tuj7NJilpZqJsgrZmzwW7Nk63qi4nhcl2bGYDX3MnWmB8DIpKjRtDUaZRY2Mjx44dY82aNSQkqFVWJTSCukpJS0vj29/+Nr29vQwMDOBwOEhJSZm4f+HChYYFqCjK9Bka8DMypFN2SehnusuW29nxFy+H9jhZsyH2nLOBUkq62r2kZFjQTOGfMUxOMyO0QBv0syVSA/0+HMkmNbs5ywhNMC/HSmODG49bxxoVGYmy0+vnWL+Lm0uTDR1H1tcAIApVIqXMfD6fj507d5KUlMTy5cvDHY4yi5zzk0HK97ax6LqOruskJSWxcOFCkpKSJm5TItPosJ/XXxmhv+fctR2K8kEtxz1oJpiXE/oZeJtdY/FSO309flqOe875fSNDOq5xSXoYu/W9n9ksSEox09N55hYvr1cyOqxfVBMNZebIzrMgdWhvjZztfbXd4+gSw8+Por4WklIQyWnGjqMo0+DIkSOMjIywbt06TCa1DVsJnXN++t9555089thjANxyyy3nfIDHH3889FEpU+Jx67y7e4yxUZ2qSifrNsep2XLlgvx+SXuLl8x5FixWY14vOflWTrR4qT04TnqWBZv9zLmc7o7ARWu4G028X1qGmcOHXLjG9dNiHuwPTFSoRhOzU3yiibh4jbZmD3kFkdHdK1AfJShOMbA+SkpkfS2iuMywMRRlukgp2b9/P6mpqcyfPz/c4SizzDkTqfvuu2/i64ceemhaglGmTtcl+95yMu7UyS+K4nidm7YmL/ND1DhAmb06T3jxeiXz8417rQgROFtq14sjVO0bZ+UVZ3Yd6+rwEp9oOmuSFS6pGWYOHwps73v/39JgX6DRRGKSSqRmIyEE8/KsHDnkYmzUH9ZW/KdUdY2xKMVGlJH1UT2dMNSv2p4rs0JjYyMDAwNcffXValJZCblzvhO/vwYqNTX1nP+UyFJdOU5vl4/yFdEsrrCR4DBxpHocvy/yWvgqkaXluAd7tDD83JzYOBPFpTY6T3hpbz19i5/HozPQ64+Ibn3vF59oIsomztjeN9DvIyZOw2qNnKRPCa1T21xPNId/e9+o28/xfjdl6dPQ9hxVH6XMDpWVlcTFxVFQUBDuUJRZKKirFafTyQsvvEBTUxMul+u0++69915DAlMuXmO9m+ZjHhYuipqYNV+81MZbO8dorHdTUGILc4RKpHKO6fR2+SgqjZqWGbv84ihOtHiprhwnJd08kYj0dPqQMrK29UFgZSIl3XwyPokQAiklg31+UtMjK+lTQis6RiM51URbs4fCxdPz93EuNd1OJNNRH1UNMXGQmW3sOIpisM7OTtrb21m7dq2qjVIMEdQVwP3334+u66xatQqrVW0Ri0Q9nV5q9o+TnmWmpOy9hCkl3UJappn6wy5y8q0R03lKiSxtTYGVofl50/P3rWmCpSvtvP7K/myLfQAAIABJREFUKIcPuFi6KnBh2N3hxWIVOCJwq1xahoUTzV6GBvwkJplxjUvcLkliskqkZrvsPCsH94wz1O8P6/Nd1eXEahIUpRg7KSbra6GgBKGpzwtlZtu/fz9Wq5XSUrW6qhgjqE+E+vp6HnnkEcxmdcEQiUZH/Ox700lsvMbyS2MQ2ukzpouX2tn54gh1tW6WLDOuQFmZmU6dHZWcZiZ6GmtAEpPM5BdHceyIm3m5FpLTzHR3+EjLNJ/xGo4EqRmB97+eTh+JSWYG+gKNJiIx6VNCKzPbQtW+cdqaPWFPpBal2rGYjEtw5NAAdHcg1l1j2BiKMh2GhoZoaGhg+fLlahFAMUxQ78aLFi3ixIkTRseiTILHE+jQJzRYtTYGs+XMC9C4BBM5C6w0NbgZG/WHIUolkvX1+HGO6WFpSFJUaiM6VuPg3nH6enx43DLitvWdEmXTiE800X2yTmqw34+mBeqnlNnNYtVIz7JwosWLroen3nTY5aNp0D0N2/pOnR+lGk0oM9uBAwcQQrB06dJwh6LMYkFNrW3bto3vfve7FBQUkJiYeNp9N998syGBKRem65J9bzpxjumsuTKW6JhzX9AVL7FxotnDkUMuLrnM2EJlZWZpbXRjNgdm3aeb2SxYusLOWzvHqHzLCSLQajxSpWWYOXbUjc8rGejzEZ9oiohDg5Xzkz4fYoo7KrLzrHS0eenp9JGeNf1/K9XdTsD4+ihZXwvWKMhZaOg4imIkl8tFbW0txcXFxMbGhjscZRYLakXqd7/7HX19fQwNDdHR0THxr7Oz0+j4lPOo2R/o0Ld0hZ3k1PNfJNjsGvnFUbS3eie2JCmKzyvpaPWSlWPFbA5PQpCSbiFngRW3S+JIMkV0HV9qhhkpoafLy1C/H4c6PyriyYPvov/jJ9Cf+Q3SN/n3vrQMMxar4ETzuQ+TNlJVlxObWVCYbOz2bFlfA/nFU048FSWcqqqq8Hq9LFu2LNyhKLNcUO+Ub775Jg888AAOh8PoeJQgNTW4aWrwsLA4ivkLgjsosmCRjeZjHmoPjnPZhlh1noJCe6sHv5+wnzNWUmGjv9cX9jguxJFixmSG43Vu/P5AnZcS2fS/PgUmDfnnPyCrK9H+9kuISXSj00yCrPkWWps8+LzyrNuojVTV5aQkNRqzgfWD0jkGbU2I67YaNoaiGM3n83Hw4EFycnJOO8pHUYwQ1NRvenq6ahsZQXq6Am2j07PMlJQH373JbBEUL7HR3+Onq12tSinQ0ughNk4L+8qK1aqx4dp4chcGNykQLiaTIDnVTH9PoNYw3P9vyvnJlmPQUIu48Xa0v78H+rrQv/XP6K8+j9T1i3687Dwruh862qb3TKnBcR+tQx7j66OOHQEp1flRyox29OhRnE4ny5cvD3coyhwQ1HTq2rVr+f73v88111xzRo3UkiVLDAlMObuJDn1xGsvO0qHvQnLyrRyvc3P40DhpmWa0COyOpkyP0WE/A71+SsptanXyIqRlWOju8GGxCqJjI3cbogJy+/NgjUJcfhUiOhZt4SL0//cQ8vc/Qx56F+3Of0I4koN+PEeyiegYjbZmz7SunlZ1TVd9VA2YTJC/yNBxFMUoUkoqKytJSUlh/vz54Q5HmQOCSqRefPFFIFAr9X5CCB566KHQR6Wc1USHPhHo0GeZxNYSTROUlNvY+4aT1kZPxK8AKMZpbfIgRGCWXQleaqYZ9kNikkkloBFMjgwj33kNccUmRHSg2FwkJqF94evI115E/uER9G98AXHbP6CtXBvUYwohmJdrob7WjWtcx2afnkS6qsuJ3ayxMGkazo/KWYiIUp8LyszU3NzMwMAAmzdvVu/PyrQIKpF6+OGHjY5DuYAzOvRN4byfjHkWHCkmjla7mJdjnfa9/kr46bqkrclDWqZ52i4GZ4uYWI2MbAuZ8yKzTbsSIF9/CXxexIaPnHa7EAKx/hrkonL0R+5H/uwH6AfeRXzy84iYC3f3ys6zUl/r5kSzh4WLjE1sTqnqclKaZsdkZH2U1wNNdYiN1xs2hqIYrbKyktjYWAoLC8MdijJHqCuoGeJUh77ySy7coe9ChBAsXmrH7ZIcr3OHKEJlJunp9OEalxHf3CESCSFYeXmMWsmLYNLvR+58AUqWIrJyzvo9Ij0L7V//E/HRTyL37kb/5t3Iwwcv+NixcSYSk0y0NU9PnVSf00v7iIeyDIProxrrwOdT50cpM1Z3dzdtbW1UVFSoun5l2gR1Rd7U1MRjjz1GU1MTLpfrtPs+uN1PCb1THfryi6PIyQ/NloukFDOZ2RYajrjIybeqVYk5prXRgzVKkB6hh98qypQceAf6e9Fu+dx5v02YTIjr/ga55BL0R+5Hv//riKuuR2y5A2E993ttdq6V6v3jDA/6DT+Q+b36KGPP/5P1tYEvCkoMHUdRjFJZWYnVaqW0VDVLUaZPUInUAw88wOrVq/n0pz+N1apmYadT78kOfWmZZhZfRIe+YCwqt9F5wktdjYvyFQbPdioRw+3W6Wz3klcQpQ6TVWYlffvzkJwG5SuD+n6RV4h27/9FPvUY8tXnkLUHAm3Sc89+KG1WjoWaA+OcaPYQn2jsuU5VXU5irBp5icbWLcn6GsjKQcTGGzqOohhheHiY+vp6li1bRpSq8VOmUVCJ1ODgIFu3blWFe9NsdMTP3pMd+pavufgOfRcSG2cid6GV5mMeFhRFERevlsLnghPNXqQOOWpbnzILybZGqKtG3PxphBb8e5qIikLc8jlk+Ur0Xz2A/t3/hbj+FsQ1H0N8YJtQlE3DkWKir8f4YySqu5wsSYs2tj5K98OxI4jV6w0bQ1GMdODAAYQQLF26NNyhKHNMUPu51q9fz+uvv250LMr7eE926ANYOckOfcEoKrVhMsHhQ+OGPL4SeVobPSQ4TIZvSVKUcJDb/wxWK+KKTZP6eVG6DO0bP0IsW4N85jfoP/gqsrvjjO9LSDQxPORHSjnVkM+pZ8xL56jX+POjWpvANQ7q/ChlBnK73dTU1FBYWEhcXFy4w1HmmKBWpG688Ubuvfdenn76aRISEk6779///d8NCWwu03XJvrdOduhbH0vMFDr0XUiUTaOgxMaRKhd9Pb4pN7JQItvQgI/hQT9Llhu7HUlRwkGOjSDf2YlYfSUiZvIXVCImDj73ZahYjfyfn6D/n39CfOJvEWvfa6kcn2jC7wPnqE5MnDHv0dN6fhSoRhPKjFRdXY3X61UH8CphEdRV8/33309aWhqrVq1SNVLToPbAOD2dPpautJOcZnxis6AoiqYGN7UHxrliU6zawjmLtTZ60DSYl6OaTCizj3z9ZfB4EBuvm/JjCSEQq9cjCxej/+pB5K8fRh58F+1T/4iIdxCfEEiehof8BiZSY8RFmcgxvD6qFpLTEEmpho6jKKHm9/s5cOAA8+fPJzVVvX6V6Rd0175f/vKXmM1qtcJozcfcNNZ7yC8KXYe+CzGbBcVLbBzcM05Hm5es+SpZno38fklbs5eMeRasUapLozK7SN2P3PECFC1BZOeF7HFFUiraP38Tuf155JOPoX/jbrQ77iJuyWoQMDzoJzM7ZMNNkFJS1Rmoj9IMnNySUkJ9DaJ0mWFjKIpR6urqGBsbY9OmyW3lVZSpCupqqqSkhLa2NqNjmfN6u7xU7TvZoW/p9Bz0eMr8PCtxCRqHD7nQ/cbt+VfCp6vdi9ejzo5SZqlDe6CvGy0Eq1EfJDQNbdMNaF//ITiS0R/+DuI3PyI2BoYG/SEfD6Br1EuP02d8fVRXO4wMgdrWp8wwUkoqKytJTk4mJ+fs58UpitGCWmJKTU3l29/+NqtWrTqjRmrr1q2GBDbXDA962Pumk5g4jeWXhr5D34UILXBI7zuvjdF0LLAipswurY0ebHZBarpaWVZmH337nyEpBSpWGzaGyMpB++oPkM89jvzLE8RdvoYhWWzIWBP1UQYfxPtefdQSQ8dRlFBraWmhr6+PD33oQ6okQQmboK6oPB4Py5cvx+fz0dfXZ3RMc47Xo/PaS4GuUKvWxmCxhucNITXDTEq6mboaF/PzrGGLQwm9cadOd6ePwpKoaU/SFcVosr0FDh8MHKRrMrYbpTBbEDfdhm4xE3fwAB22QrxeGfLOqlVdThJtJubHG7yCXF8LcQmQMc/YcRQlxCorK4mJiaGoqCjcoShzWFCJ1LZt24yOY07b/66T4SEflxrcoe9ChBCUlNvY/fIoDUdclJSrzm6zRVuTB2RgC6eizDZy+/NgtiCu2DxtY4orryXunQcAGBn0kxTCjqdSSqq6nCxJjzZ8pl3W10BBiZrRV2aUnp4eWltbufzyyzEZPHmiKOdz3hopXdcv+E+ZuoJFNtZuTCdlGjr0XUhikpl5uRaO17kZd6rndzaQUtLa6CEp1WRYdzFFCRfpHEW+tQOxeh0iLn7axhWx8SQszgNgqH04pI/dPuKlf9z4+ig50Ae9XQh1fpQyw1RWVmKxWFiyRG1JVcLrvFfut9xyywUf4PHHHw9ZMHNVUoqZlJQ4envd4Q4FgEVldjpavRytclGx2uBCZ8VwA71+xkZ1CherFUZl9pFvvAoed0hanl8s+1UfwvzSGMNHu2FpUsget6orcBh7WXpMyB7zbNT5UcpMNDIyQn19PeXl5URFqXpuJbzOm0g99NBD0xWHEkGiYzQWFEZx7KibBUVRJDjUKsZM1tLowWSGzGy1rU+ZXaSuI3f8ObA1LWfhtI+vpWUQL2oZHpJIlxNhC83EU1WXkyS7maw4g897q6+FKDvMzzd2HEUJoYMHDyKlpKKiItyhKMr5Eyl1uNncVbA4ipZGD4cPjXPp+thwh6NMks8naW/1kDXfijnExfCKEnbV+6CnE3HTHWELIX5eIq0nNPRdL2G6+sYpP56UkuouJ0szYqanPmphseENOhQlVNxuN1VVVRQWFhIfP31beRXlXNSpnMpZWa0ahYuj6On00dPpDXc4yiR1tHrx+1BnR81h+s9+gP7CH8MdhiH07c9DYjJi2aVhiyEhJxm/2cbYG28ifb4pP17rsIdBl9/4+qixUWhvUdv6lBmlpqYGr9fL8uXLwx2KogAqkVLOI68gCnuMRu1BF1KqQ3pnopZGNzGxGkkpasZ5LpJ93cg9u5FP/xq5741whxNSsrMNavYj1l+DMIevUU98QuBva8QXi9z7+pQfr6rz5PlRRh/E23AYpFSNJpQZw+/3s3//frKzs0lLSwt3OIoCnCeRGh0dnc44lAhkMglKymwMD/ppa1arUjONx6PT3+NnXq5VtTaeo+ShvYEvMuahP/oA8kRLeAMKIbn9z2A2I9ZdHdY44k4mUsOZS5AvPj3lSaeqLiep0WbSY42tj5L1NWAywwJ1Bo8yM9TX1zM2NqZWo5SIcs5E6q677pr4+lvf+ta0BKNEnqwcCwkOE0eqxvH71arUTDI84AdQq1FzmKzaC6kZaP/ybbDZ0X/8HaRz5k+SyXEn8s3tiBVrEfGJYY3FZBbExmmM5CyDtkY4fHDSj6VLSXW3k7KMaTg/qqEW8goQVtX1TIl8UkoqKytJSkoiNzc33OEoyoRzJlJWq5WWlhZ0XaehoQEppTpHag4SQrB4qQ2XU9JYFxnt2ZXgDJ1MpOJV18U5SbrdcOQQomwFIjEZ7e//Ffq60H9xP3KGv3fLN7eDezwsLc/PJj7RxIiWBAkO9Befvuif9/olh3uc/M/BXkbcfuPbnnvc0NSAKFD1UcrM0NraSm9vL8uWLVM7LJSIcs6N5R//+Mf52te+htcb2NL1N3/zN2f9PnWO1OyXkm4hLdNM/WEXOflWrFGqtG4mGBrwY4sWRKnna246egi8HkTZCgBEwWLE3/wd8n9+gnzu94iPfjLMAU7ORMvz/GLEgsJwhwMEEqn2Vi++DR/F/MyvkG2NiOwF5/x+ny5p6HNR3eWkqtvJ4W4n7pMr/otS7KycZ3Cn1MY68PtUfZQyY1RWVhIdHU1xcXG4Q1GU05wzkdq8eTNXXXUVg4OD/PM//zP3338/Uko1EzBHLV5qZ+eLI9TVulmyTB3sOhMMDfhJSFSrUXOVrNoL1igoXjJxm1j/YWiqRz7/e2RuPqIifN3uJq12P3SdQHz2X8IdyYT4k39no+UfIvEvv0e++Azib784cb9Plxzrd1HVGUicjvQ4cfkCiVNuYhSbChIpS4umNM1OvM34xhmyvgaEgIISw8dSlKnq7e2lpaWFNWvWYA5jYxlFOZvzviJNJhPJycl8//vfV2dKzXFxCSZyFlhpanCzoNBKTKy6QI9kPp9kdEQnK8fgAz2ViCSlRFbtg5KlCMt7re+FEHDrPyDbmtEf+SHa1+5DZGaHMdKLp2//MyQ4EJdcFu5QJpxKpEZcVhxrN+Pd+Rcar9xKtSuK6i4nh9+fOCVEcVV+AmXpMdOWOH2QrKuBebmIGHVGoBL59u/fj8VioaysLNyhKMoZgnoHT01N5Q9/+AO7du1icHAQh8PBunXr2LJli5odmEOKl9g40ezhaJWL5WuM3cOvTM3wYKA+KsGh/j7npPZW6OtGXHvzGXcJixVt21fRv/0l9B//RyCZshvcajtEZHc7VO9DXLcVYY6cSQJLFGhmONA4xmOJG6i9dCWut4cByEmwsjE/gbL0aErTokkIQ+L0ftLvh+NHEZdtDGscihKM0dFRjh49SllZGTabLdzhKMoZgnpH/81vfsOxY8f43Oc+R2pqKj09PTz55JM4nU7uvPNOg0NUIoXNrjF/gZWWRg+6LtE0tc0zUp1qNJGgGk3MSbJqDwBiyQr6+/sxmUwkJCRM3C+SUtE+/6/o99+L/sv/i/YP9yC0yK+lkzteAE1DrLsm3KHQMuRmb9soVV1OanvGuUomovVDb6xgg+ygtO4dlvzzF3E44sMd6ulajoPbBao+SpkBDh48iJSSioqKcIeiKGcVVCL19ttv84Mf/IC4uDgAsrKyWLBgAV/+8pdVIjXHJKWaaWrwMDzoJzFJrXZEquEBP9Yogc2ukt25SFbthewF+OMT+eMjj+B2u8nNzaWsrIy8vDw0TUMUL0F84m+Rv/858oU/Iq7bGu6wz0u6xpFvvIK45ApEYlJ4YjjZnvzp2n72tY8BMD/ByoYF8eSNW/H2wN9+ZAG0SPQ3fox49xW4ektYYj0XWV8DgChUHfuUyOZ2u6mqqqKgoOC0iSBFiSRBXQlP9YDB3t5eHn74YQYHBxFCsGnTJq699lpGR0f54Q9/SE9PD6mpqXzxi18kNjYWKSWPPvoo+/fvJyoqim3btpGfnw/Azp07eeqppwDYsmULV155JQDHjx/n4YcfxuPxsGzZMj796U+rxhgGcCQHVjgG+1QiFckGB/zEJ5rU38AcJMdGoeEw4pqP0dTcjNvtpri4mLa2Np5//nni4uJYsmQJpaWl2DdeB00NyGd/i8zJR5SvDHf45yTf2gHjTsTGj0z72H5d8k7bCE/V9lPf5yIhysStS1P40MJEHPbA+2DLcTcHO8dxjurE5C6EReXIV55DXnV9RG1DlPW1kJqBSEwOdyiKcl61tbV4PB51AK8S0YK6El6zZg3/+Z//yc0330xKSgq9vb08+eSTrFmzJqhBTCYTt99+O/n5+YyPj3PPPfdQXl7Ozp07KSsr48Ybb+SZZ57hmWee4bbbbmP//v10dnby4IMPUl9fzy9+8Qu+853vMDo6yhNPPMH3vvc9AO655x5WrFhBbGwsP//5z/n85z9PYWEh3/3udzlw4ADLli2b/P+Mclb2aA1rlGCg30ce6iDHSKT7JSPDfvKL1PMzF8na/aDriLIVNDQ0YLPZ2LRpEwCNjY1UVVXx1ltv8c4771BQUEDZpptIb29G/8X9aP/7PkR6Vph/gzNJKQMtz3MLIH/62h97/Do7jg/zzOE+2ke8ZMRa+PuV6WzMTyDKfPpWyPiEwCTT8JCfmDgT2tU3oT/wTeS7uyOmHklKCQ21Ey3xFSVS+f1+Dhw4QFZWFunp6eEOR1HOKahN8bfddhtlZWU88sgj3HPPPfzyl7+ktLSUW2+9NahBHA7HxIqS3W5n3rx59Pf3s2fPHtavXw/A+vXr2bMnsK9/7969rFu3DiEERUVFjI2NMTAwwIEDBygvLyc2NpbY2FjKy8s5cOAAAwMDjI+PU1RUhBCCdevWTTyWElpCCBzJJgb6/OEORTmHkWE/Ulf1UXPWob0QG4cvJ5/jx4+zcOFCTCYTJpOJgoICbrrpJm6//XbKy8tpamriyT89y+O5y6hOzMD14+8gXc5w/wZnOnwQOloRG6+bllXWUY+fJ6r7+LtnjvHjdzuxW0x85Yosfnx9Ph8ucpyRRAHEJphAvNfohdLlMC8X+dLTU97VETKdbTA6DGpbnxLhGhoaGBkZUatRSsQLakXKbDazdetWtm6d+h767u5uGhsbKSgoYGhoCIfDAQSSreHhQJej/v5+UlJSJn4mOTmZ/v5++vv7SU5+bztCUlLSWW8/9f2KMRzJZrraXXg8OlZr5BeozzWq0cTcJXU/snofonQ5La1teL1eCgvPPLT2VOfVNWvWUFdXx6FDh9iVsoA3/T6Kf/HflP/Np057Dw43ffvzEJeAWLnW0HH6nF6ePTLAX+sHcfl0lmXGsGVxEmXp0RdM4MxmQUysxvCgDgQmncTmG5GPPgA1+2FJ+C8I36uPUo0mlMglpaSyshKHw8GCBec+2FpRIsG0Frm4XC7uu+8+7rzzTqKjz91u92yzd+f6EBNCXNRs3yuvvMIrr7wCwPe+972IuVgwm80RE8uFeBY4OVLVjvTFkJI1N9ugR/Lz1VDbg9niIjcvTdVIEdnPVah5jlYzMDpM3OUbaGlpITo6mqVLl2IynTupzszMZN26dbS1tfHWk49zuHeA6t/+ltzcXFatWkVJScm0HnPxwefL39VO76E9xHzsDmIzMw0Zs7HPyW/3tfHS0R6klGwsSuWTl8yjKPXizllKTffR2+2aiF9+eAu9f/ofzDuex3HlZiNCvyhDLcfwJDhIKS0PyXvDXPrbmg1myvN1/Phxenp6uOGGG+b0GaYz5fma66bt09Hn83Hfffexdu1aVq9eDUBCQgIDAwM4HA4GBgaIjw+0iU1OTqa3t3fiZ/v6+nA4HCQlJVFbWztxe39/P4sXLyY5OZm+vr7Tvj8p6exdnTZt2jRRLwCcNk44nao9mwmEOZC4NjcOEBU9HuZowiOSn6/O9lHiE7TT/ibmskh+rkJN3/0KCI2BrAUc2fl7iouLGRgYCOpn7XY7G279FKt/9l/UNh+nxmbjj3/8I9HR0ZSWlrJkyZKJzq1G+uDzpT/1GxCC8VVX4grx83i428mTtf3sOTGK1SS4ujCRjy5ykB5rBVz09rou6vFsdh+jwz46OnqwWAKJitzwETxPPkZP5TuInIUhjf9i+av3w8KSkL03zKW/rdkg0p8vKSVVVVW8+eabxMTEkJ2dHdHxGi3Sn6+5Jivr7PXD07IvS0rJT37yE+bNm8d11103cfuKFSvYtWsXALt27WLlypUTt7/22mtIKamrqyM6OhqHw0FFRQUHDx5kdHSU0dFRDh48SEVFBQ6HA7vdTl1dHVJKXnvtNVasUMW0RrFYBLHxGgN9vnCHonyA1CXDQ361rW+OklV7YWExLT1959zWdz5CCKLv/ALLowS31ezkhvVrSU9PZ8+ePfzqV7/i+eefp7m5edpqfqTbhXz9ZcTyyxCO0HSZ02WgA9+/vtjMPS+3cKR3nFvKUnjkxoV8bkX6ySRqcuITA393I4Pv1ZCKddeAzY588Zkpxz4Vsq8ncEizqo9SIlBvby9//OMf2blzJ+np6XzsYx+b1pVwRZmsaXmVHj16lNdee42cnBy+/OUvA3DLLbdw44038sMf/pDt27eTkpLCl770JQCWLVtGZWUld999N1arlW3btgEQGxvLxz72Mb761a8CcPPNNxMbG9h68dnPfpYf//jHeDweKioqVMc+gzmSzXSe8CKlVNvHIsjoqI7fp+qj5iI52ActxxE33U59ff1EY5+LJaJsaNu+hv7tLzH/mUfJvef7jLg9VFdXU1NTw/Hjx0lISKCiooLy8tBsETsX+c5OcI4hNl53we+9EK9fsqtpiKdr+2kb9pAWY+FzK9LZtPDMDnyTdSqRGh7yk5Qa+HgV0TGItZuRrz6H3HI7IjktJGNdLFUfpUQir9fLu+++y/79+7FarWzevJni4mJ1XaHMGEEnUjt27OC1116jv7+fpKQk1q1bx4YNG4L62UWLFvGHP/zhrPf927/92xm3CSH47Gc/e9bv37hxIxs3ntlKduHChdx3331BxaNMnSPZRGujJ3BmSpy6aI8UwycbTcQnqpm8uUZW7QPAt7iCxhdepqSkBE2bXIIgUjPQ/u5/oT/4TeRjPyLu7/4Xl112GatWreLYsWOB5hS7dmG1WikpKQnlrzFBSonc/meYvwAKJj+GX5c8d7SfPx0eoH/cxwJHFP9yeRaX58Rh0kJ7sWazCywW8V7nvpPEphuQ259HvvIcYuvfhnTMoNXXgs0O8/PCM76ifEBzczM7duxgeHiYxYsXc/nll2O328MdlqJclKCutp566il27drF9ddfP7Fn89lnn2VgYIAtWyLr1HZlepw6jHegz68SqQgyNOhH0yAuQXVTnGvkob3gSKHJI/H5fBe9re+DxJLliJtuRz71/yCvALH5JsxmM8XFxRQVFfHEE0+we/du8vLyjLn4qauGE82IT31hSrPTzx3t59HKHsozorl7TSYVGRfuwDdZQgjiE7UzE6mkVMSKK5C7X0JevxURfXFNLEJB1tdAQQlCU+/XSng5nU52797N0aNHcTgcbNmyhezs7HCHpSiTEtTV1quvvsq9997Lpk2bqKioYNOmTXzta1+b6H6nzD1xCRomM6pOKsIMDfiJSzChhXimXYls0uuFwwcmDuGNjo4+Z2HsxRBitOXCAAAgAElEQVTXfAwuuQz5xGPIwwffu10INm7ciMfj4Y033pjyOGejb38eYuMQq9ZN+jG8fsmzhwcoS4/mW1flsCwzxvAtQ/GJJoaH/GfUkYmrt4B7HLnrr4aOfzZydDhwDleBqo9SwkdKSXV1Nb/+9a+pr69n9erV3HLLLSqJUma0oBIpt9s90VHvlLi4ODwejyFBKZFP0wSJDhOD/epg3kghpWRoQDWamJPqq8Htwlu6jKamJgoKCia9re/9hBBod/4TZGaj/+z7yL7uifuSk5NZvnw5tbW1tLW1TXms95N93bD/HcTazQhr1KQfZ3fzMH3jPm4qOXsXVyPEJ5rw+8A5qp92u5i/ABZXIF99PpD4TqeGQLdbVR+lhEt/fz9PPvnkRE38Jz/5SVavXq0aSigzXlCftBUVFTz44IO0t7fj8Xg4ceIEDz30EEuXLjU6PiWCJSabGRr04/dPTwcv5fzGnRKvR6pEag6Sh/aC2UJTVFxItvW9n7DZ0bZ9Dfw6+o+/g/S4J+5buXIl8fHx7NixA58vdKvTcudfAmOvv3byjyElzxzuJzchiuXTeN5dfMJ7DSc+SNt8Ewz1I9/dNW3xAMj6WjCbYUHoXheKEgyfz8fbb7/Nb3/7W/r7+9m0aRNbtmw55xE1ijLTBJVIfeYzn8Fut/PlL3+Z22+/na985SvYbDY+85nPGB2fEsEcySak/l6DAyW8hgYCF7IJiSqRmmtk1V5YVEZDUzMxMTFkhvjgWpGehfbZL0FrI/LXD09sW7NYLGzYsIGBgQH27dsXkrGk243c/RIsW41InvxhnPs7xmgedHPj4qRp7QAWm2ACwRl1UgAsroDsBcgXn56+FvLV+5BvvAr5ixCWybd2V5SL1draym9/+1veffddioqKuO2221i8eLHqyKfMKhdcU9V1nePHj/P5z3+ebdu2MTIyQlxcXEi2jSgzmyP5VMMJH44UtTwfbsODfhAQpxKpOUV2tUN3B94rP0JTXRNLliwx5P1ZlK9E3HAL8k+/hbxCxFXXA5Cbm0tRURF79uyhqKgIh8MxpXFcu1+GsRG0KbY8f7q2nyS7mbW58Rf+5hAymwUxsRrDg/oZ9wkhEFffiHzkh1C9D8qMO+9Q+v3IZ3+H/MsfISsH7fa7DBtLUd5vfHyc119/ncOHD5OQkMCNN95ITk5OuMNSFENc8NNW0zS+//3vY7FY0DSNhIQElUQpANjsGja7YEDVSUWEoQE/sXEaZrOa7ZtLZNUeAJoS0/H7/SHd1vdB4tpPQMVq5B8eQR6tnrh97dq1mM1mduzYMaWVFiklzhf+CPNyoWjJpB/nWL+LQ11Ori92YDFN/99DQqLp7CtSgFixFhwp6C8+bdj4crAf/Yf/hnzhD4jLrkL76n8hMi7+TDFFuRhSSg4fPsyvf/1rjh49ysqVK7n11ltVEqXMakEtI5SUlFBXV0dRUZHR8SgzjCPZzGCfSqQiwdCAn+Q0tTI418hDeyFzPg1dPYZs63s/oWlon/ki+nf+Bf1H/wfSs8Aeg81m5zJzLDvb2jj8P4+wKMUB9hiw2RHRMWCLBnt04Da7PXD72dpw19fia6xH3H7XlLb/PFPbj92scXVh4hR+28mLTzTR3urF65VYLKf/HsJsRmy6HvnHR5FN9Yi80Ca+8vBB9F/cBy4n4s5/Qrv8qpA+vqKczcDAADt27KCtrY3MzEw2btxIcnJyuMNSFMMFddWVmprKd7/7XVasWEFycvJpH3Bbt241LDgl8jmSTXS0eXG7dKJsaqUyXNwuHde4ajQx10iXE+pq8G64jqamJsrLyw2vPxD2aLQvBFY75MgwjI9BXw+Lx5s4krSA17s95L7+F2z+QGe6c65PRdlPJlfv+9fXjYiNQ6y+ctLxdY96eb1lmBsWJRFjDc/fQ/zJ7bUjg36SUs/8mBVrr0Y+/zjypWcQn/tySMaUuh54Tp79PaRnoX3pW4h5uSF5bEU5F5/PR2VlJXv27MFkMrFx40ZKS0tVHZQyZwSVSHk8HlauXAkEWlgqyimJye8dzJsxTyVS4TJ0suGHajQxx9QeBL+PxrT56F2HDd3W934iLRNx5z+dcfvG3l5+//vf89ZNn+Oq1Sth3BlItFxOGHcinWPgGg/cNn7ytnFn4H7nGCCI+cRnGI+afMvzZ4/0I4DriqdWqzUVpxKp4aFzJFL2aMS6q5Ev/Ql50+2I1IwpjSdHhtB/cT/U7kesWo+4fRvCZsAhycqcpOs6Y2NjjI6OMjIywujo6MTXPT09DA8PU1hYyLp164iJmb4OmYoSCYJKpLZt22Z0HMoMleAwIQQM9vvImGcJdzhz1tDJegy1IjW3yKq9YI+hYWSc2NhYMjKmdkE+VSkpKSxbtox9+/axqLT0jIM2g5mjjklJYby3d1Ljj7r9vHxskLV58aTG/H/27js8qvNM+P/3nOl91CUkRO8d05upxhib7sROvMnrZOMkTjbebPbdGm822d13s9nNxnbizeYXZ51kU0xssMCYFtMthOlGYLowTQWV6X3mnN8fQgRCGyGNZkZ6PtfFZSPNnHMLmDPnnud+7jt91yOjSUKnk+66TwpAmrcE9b31qNveQXrqCw98LvXsRyj/37+D39uaQM1cKFYDhKSpqkooFLolSfrj//r9/tv2Pup0OqxWKzk5OcyePZu+ffum5wcQhDRLekPFlStX2LdvHx6Ph89//vPU1tYSi8Xo00eUDvRkWq2EzaHBJfZJpZXHlcBskdHpxapgT6GqKmr1IaLDxnLx0iXGjBmTETfQkyZN4uzZs+zYsYOnn366SwdubjrrIhxXu3QA751IkoTdKd87kcrJQ5o0C3XPVtQnnkKy2Np1DlVRULe+jfr2/0J+EfLffg+pfEBHQxe6qWAwyMGDB6mrq7uRJLUlSonErf9OZVnGZrNhtVopLS3FarXe+L3NZsNms6HX6zPieiMI6ZbUO1xVVRWvvfYakydPprKyks9//vOEQiF+85vf8OKLL6Y6RiHD5eRpuHopiqqq4sKaJl5XArtYjepZLtWAp4ULpQNRPr7aZWV996PT6Zg9ezbr16/n8OHDTJo0qUvOG00obDjtYmyJhb45xi45573YnRouXbj3dVF6ZDlq1Q7UnZuQFn8i6WOrAR/K/7wExw7AQ9OQP/NnrU09BOEOVFVl3bp1NDY2IkkSFosFq9VKYWEhAwYMuC1RMplM4r1cEJKUVCL1u9/9jhdffJG+fftSVVUFtM4O+fjjj1MZm5AlcvI0XDwPfq+CzSFu5rtaLKoS8CuU9RPDNnsStfoASBJn42Cz2SgqKkp3SDf07duXQYMG3Zgt5XSmvnvezgte3OFE2lej2tidGhJxCPoVLLY7Xxelsr4wYhzq9g2ojyxLamCuWnMa5SffA48L6annkOYuFje9wj2dOnWKxsZGli5dSu/evcUIG0HoREm9mjwez20lfJIkiYu3ANzccCKe5kh6prbyIdFoomdRjx0k3Hcwl2vrGDRoUMZdj2fNmoVGo+nwbKlkKKrKupMt9MsxMKbYnNJzJcvu+EPDiXuRF64Arxt13857Pk5VVZT31qN8729BkpD/+rvI8x7PuL93IbPE43GqqqooLCxk3LhxIokShE6W1Cuqf//+7N69+5avVVZWMnDgwJQEJWQXq01GqwO3GMybFqLRRM+j+jzw8Vku9B2OoigZU9Z3M4vFwvTp07l8+TKnT59O6bkOXvVzxRtl+bDcjEksrA4NSNxznxQAQ0dDeX/UrRWoinLHh6jBAMp/fxd19Wswcjzyiz9A6ifmOgr3d+TIEfx+PzNmzBBJlCCkQFKvqmeffZY33niDb33rW0QiEf7lX/6F1atX89nPfjbV8QlZQJIknLlasSKVJh5XHINRwmgSb5I9hVp9CFSVc7IRu91OYWFhukO6o5EjR1JcXMzu3bsJhUIpO8/bH7VQYNYyvY89ZedoL61WwmKV8brvnBy1kSQJ6ZHlUH8Fqg/e9n314nmUf/46HP0A6clnkb/y9+1uTCH0TG0NJvr3739bB01BEDpHUndepaWlvPTSSyxcuJCnnnqK2bNn8/3vf5+SkpJUxydkiZw8DV6PQjye2hIe4XYeV0KsRvU01QcJO/O40tSckWV9bSRJYu7cuUQiESorK1NyjtNNIT5qDLFkWC5aObP+HBxOzf1XpADpoemQW4CyZe2Nr6mqirJzE8p3/wpiMeT/+/+QH1mesX/XQubZv38/8Xic6dOnpzsUQei2kv4I22AwMHToUIYPH86wYcMwGtPfFUnIHDl5WlDBI8r7ulQiruL3KiKR6kHUeBz1xBEuDBqbsWV9N8vPz2f8+PF89NFHXL16tdOP//ZHLVh0MvMHODr92B1lc2oIBhRisXt/wCRptUjzl8DZj1BrTqOGg6ivfR/11z+GISOR/+ElpIHDuyhqoTtwuVxUV1czcuRIcnLSN5xaELq7pLr2NTU18corr3D27FksFguBQICBAwfyta99jYKCglTHKGQBZ27rjbyrOU5eYdfNjenpfJ4EqtraIUzoIc6fglCAc0YHDoOUFdfgttlS27dv79TZUnW+KPsu+1gxPBezLvNeA20NYHzuBLkF9/6ZpZkLUN95A2XNL8DrgoY6pGXPIC1ahST2tgjtVFlZiVarZfLkyekORRC6taSuzq+++ir9+/fn9ddf57XXXuP1119nwIABvPrqq6mOT8gSBqOM2SLjEitSXaqt0YRTrEj1GGr1AUJ6E5c9vowu67tZ22wpl8vF4cOHO+246062oJElHh+aGS3P/1jbBxz369wHIBnNSLMfhTPHIRRE/sY/IS/+hEiihHa7evUqNTU1TJgwAbM5M7pYCkJ3ldQVuqamhmeeeeZGOZ/RaOSZZ56hpqYmpcEJ2SUnT4NbNJzoUh5XAp1OwmQRN1s9hXrsIDUDRqGqasaX9d3s5tlSbre7w8fzhONsq/Ewu5+dXFNmroIbTRI6nZTUPikA6dGVSEs/jfziS0hDRqU4OqE7UlWV999/H6vVyrhx49IdjiB0e0ndfQ0aNIhz587d8rXz588zeLBovyr8gTNPSzikEgreu0uV0Hk8rgT2HE1WrEoIHac2NUDdZc7bC3E6neTn56c7pHbpzNlSm864iSZUlmXIAN47kSQJu1NOPpEyW5Ef/ySSQ+xpER7MmTNnaGhoYOrUqZ1WQisIwt3d9VW2evXqG/9fVFTEv/7rvzJ+/Hjy8vJobm7myJEjzJgxo0uCFLJDTt4f9kmZzPo0R9P9KYqK15Og7wBDukMRuohafYigRseVUJgJI0ZlXQJtsViYNm0aO3fu5PTp0wwdOvSBjhOJK7x7xsXEUgu9HZn979/u1HDpQhRVVbPu70vILvF4nL1791JQUPDAry1BENrnritSzc3NN37FYjEmT56MTqfD6/Wi0+mYNGkS0Wi0K2MVMpzdqUGWxWDeruL3KigJMYi3J1GrD1JTOhBVJavK+m42cuRIioqK2LNnD+Fw+IGOsb3GgzeSYNmwvE6OrvPZnRoScQgGxEq9kFoffvghPp+PGTNmiKRdELrIXVeknn/++a6MQ+gGNBoJu1MjBvN2kbZGEyKR6hnUSAROHePcqNnkWB3k5WV+EnEnsiwzd+5c3njjDSorK5k3b167np9QVCpOtjAoz8iIQlOKouw8dsf1hhPuBBareK3eiybagLVpC0jgK1iGos2cAcuZLhQKceDAAfr27Uvv3r3THY4g9BhJF9BGIhHq6+tv+wRxyJAhnR6UkL1y8jRcqomiKCpyhg3H7G48rgSyBqw20WiiRzh9jIAKtTGFiVnSre9uCgoKGDduHIcPH2bo0KGUlpYm/dwPrvio98f4zNiCrPgzsDo0ILUmUiVl6Y4mM0mJAJaWbZg8H6DKeiQ1Qe6ll/AVLidiFU03knHgwAFisZgYvisIXSypRGrXrl38z//8D1qtFr3+1r0vP/7xj1MSmJCdnHlaLpyN4vMkcOSIja6p5HXFsTs0SCJh7RHU6oPU5PZCJXvL+m42efLkG7OlPvWpT6HR3H+1RlVV3v6ohWKrjim9bV0QZcdptRIWq4zXLUr7bqMmMHn2YWl5D0mJEnJMIpA7HzkRxN7wOxz1vyFkG4c/fwmqxpjuaDOW2+3m2LFjjBgxImtXqgUhWyV1p/urX/2Kb3zjG4wePTrV8QhZ7g8NJ0QilUqqquJxJygtF009egJVVVGPHeRc6Qhyc3O7xc2STqdjzpw5rF+/nsOHDzNx4sT7PudkY4gzzWGem1CEJos+QHA4NWLv6M1UFX3wNNamjWhjjURNA/HlP07CUARAQmPBVfYlLC07MLt2oA9dwFv0JDFT/zQHnpkqKyvRaDRi+K4gpEFSNUFarZbhw4enOhahGzBbZPQGSdw0pFgwoBCPif1RPUbtJQJeD7Vou8VqVJu+ffsycOBA9u/fn9RsqbdPtmAzaJg/wNEF0XUem1NDMKAQi3Ws5Xt3oIk04Kh9HWfdLwAVd8lncff63I0k6gZJQyBvPq6yL6JKGpxXX8PStAlUsQf3ZrW1tZw/f56HHnoIi8WS7nAEocdJKpH65Cc/yS9/+Uu8Xm+q4xGynCRJOHNFw4lU87hEo4meRD12kPOOQgAGDhyY5mg616xZs5Bl+b6zpa54Iuy/4uexwU4M2uzaF+hwtr5OfUnOk+qOpEQAa+M6ci+/gi5yGV/+47SUv0DUMhTusdctbiynpffXCNsnYnHvJufyf6GJ1Hdh5JmrbfiuxWIRw3cFIU2Sqr3q1asXv/vd79iyZctt37t53pQgAOTkablWFyYWVdHps6f8Jpt4XAkkCWwOkUj1BGr1Ac4V9iEvL69blPXdzGq1Mm3aNHbt2sWZM2coKCi44+MqTrag10g8Njj7htXarydSXk+C3IIeVvJ8l31QqqYdqyeyvrXxhGUY9mtryL38I/x5Cwk5p4OUXUl1Zzp37hz19fXMnz8fnU6X7nAEoUdK6or+wx/+kFmzZjFt2rTbmk0Iwh9r2yflbolTUCwu7qngcSWw2WU0GpGodndqwI//Yg11Q2YwpRuV9d1s1KhRnDp1it27d9/xk3VXKM6OC17m9XfgNGZfImI0Seh0Et6etCL1R/ugIqZB+PMX317C1w5Ry1Cay1/Afu1tbM0bMQRP4S18EkXn7MTAs0M8HqeyspL8/HwxfFcQ0iipj3L8fj+f/OQnKS8vp7i4+JZfgvDHnLnXG06IfVIpoaoqHpdo5tFTqCcOc97WukrT3cr62rTNlgqHw1RUVJBI3Hrt2HDaRUJRWTYsN00RdowkSdidco9JpO60D8rT69kOJVFtVI0VT/EzeAtXog1fIffyyxh8RzsedJaprq7G6/Uyffp0ZLnnrsoJQrol9eqbPXs2u3fvTnUsQjeh08tYbTJusU8qJSJhlWhExS72R/UM1Qc5l9uLvLw8cnOzM5FIRkFBATNnzuTUqVO8++67xOOt149QTGHzWReTe1vpZc/eigi7U4PXk7jnPrBsd+d9UH9+331Q7T+RRNg+gZbyF4jri3A0rMZe/1ukRKjzzpHBwuEw+/fvp7y8nD59+qQ7HEHo0ZL6SPvcuXNs3ryZtWvX4nTeuoT+7W9/OyWBCdnNmafhWl0cVVWzYmhmNhGNJnoOVUngPVlNffk4pg4enO5wUm7s2LE4nU7Wr1/PunXreOKJJ3ivxo8/qrB8WHbvDbM7NSTirR03LdZu9tpV49f3QW27vg9qMoHcee3bB/UAFF0u7tLnMLt2YWl5D13o49Y26ebuuXLb5sCBA0SjUWbMmJHuUAShx0sqkZo3bx7z5s1LdSxCN5KTp+XKxzFCAQVzd7tpSLMbiZRT/Ll2exfOcl7bejPandqe38uECROIRCJs3bqVtW+/zU7taIYVmBlaYEp3aB1iv94YxutOdJ9ESlXRB05e3wfVRMR8fR+UvuMlfEmTZIK5c4iaB2NvWE1O7c8IOqbhz3sU5O63R9fj8fDhhx8ybNgw8vPz0x2OIPR4SSVSs2fPTnEYQndz82BekUh1Lo8rgcUqo9WJlb7uTj12kHPOIgrycm+rBujOhgwZglarZeOmTfSWqpg1+vF0h9RhVocGpNZEqqQs3dF0nJQII516GafnBHFdAe6SzxI1D+ncEr52iBtLaen9VazNmzF79qIPncNb9Enihl5piSdV9u7diyzLTJkyJd2hCIJAkonU9u3b7/q9uXPndlowQvdhc2iQNeBqjlPaJ3v3NWQijytOTp5oNNETeI4focHRh2lDel5Xrv79+1NXPInCugNcqNrM2OLl2Gy2dIf1wLRaCYtVxutW0h1KpzB59iF5TuDLX0zIMRWkDPjATNbjL1hC1DwU27W3yLn8XwTyFhB0zuwWbdLr6uo4e/YskyZNwmq1pjscQRBIMpHas2fPLb93u93U19czdOhQkUgJdyTLrYN53aJzX6eKRhRCQZW+AzPgpkVIKdXVzPlgBBzdt1vfvVQ3BDkRsTNx0nwajuzgrbfeYvny5Vm9MudwdpNroprA5N2Hah9KyJl5+3SilsG0lL+ArXEd1ubN6AOn8BZ9AkWXfTPI2rQN3zWbzYwfPz7d4QiCcF1SidS3vvWt2762fft2rl692ukBCd1HTq6WC2cjJBKqmHfUSdraJ4tGE92fWn2Qc44iCp2OrE4eHtTbH7XgMGpYPGEA7n45VFRU3EimsnUosc2pofZyjFhMRZfFpbn6wCk0cQ9K8achQxfYVI0Fb9HTRM1DsTatJ/fSy3iLniRqHZHu0B5ITU0NdXV1zJ07V8zzFIQM8sBr3bNnz75nyZ8gOPM0KAo9ZnZKV2hrNCFan3d/nmOHuWZ2MHB4dt74dcRFd4TDdQEeH5yDXiNTWFjIypUrkSSJNWvWcO3atXSH+EDaGsT4svyaaPZUkdA6IWd0ukO5N0kibB9PS+8XSOgLcNb/CnPLTsiyFvSJRIL333+f3Nxchg8fnu5wBEG4SVKJlKIot/wKh8O89957WCypbW0qZLe2fTzu5uy+acgkHlcCo0nCYMj+en/h7tRYjHMNrclCT+nWd7OKk80YNBKPDv5DKVZeXh4rV65Ep9Oxdu1aamtr0xjhg7G1de7zZO81URNtQB86T8gxOTP2RSVB0eXgKn2OsHUM1pYt2Bt+B0os3WEl7fjx43g8HmbMmCGG7wpChkmqtO/pp5++7Wu5ubl88Ytf7PSAhO7DaJIwmiRcLXH6YUh3ON2Cx5UQZX09wdnjnLfkUmiz4HA40h1Nl2r0R9j9sZeFA53YDbf+W3c6naxatYqKigoqKip4/PHHKS8vT1Ok7WcyS+h0Ulav0ps8+1AlLSH7BMzpDqY9ZF1rFz99EdaWrWhizXhK/gRFm9kNTCKRCB988AG9e/cWw3cFIQMllUj96Ec/uuX3BoMBu92ekoCE7kOSJJy5WlxiRapTxOMqfp9Cr/LuNxtFuJX7yH6umR1MHzEq3aF0ud8drUVRYemw3Dt+32azsXLlSioqKli/fj2LFi1iwIABXRzlg5EkCbtTztpESkqEMXoPE7aORtVkYdc4SSKYO4e4vhBHw2pyrryKp+QzGd0i/eDBg4TDYWbMmCGG2wtCBkpqjbigoOCWXyKJEpKVk6ch6FeIRDJ0R3IW+UOjCdH6vLs7+/ElAAYN7Vltz4OxBOuq65na20aR9e4b6s1mMytWrKCgoICNGzdy6tSpLoyyY+xODV5PAjXL9ukAGH2HkNVoa7vzLBa1jsBV9iUAcq78Nwb/8TRHdGder5ejR48ybNgwCgoK0h2OIAh3cM87sm9/+9v3fLIkSfzDP/xDpwYkdC/Om/ZJFfUStd0d0dZoQpT2dW9q/VXOaU0UmXreyv+Ws24C0QTLh995NepmRqOR5cuX884777B161ZisRijRmX+Cp7dqSERh2BAwZJNw8pVBZNnHzFDb+LG7J8oHDf0wlX2FRx1/4uj/tf4cxcQzJmTtoHCd1JVVQUghu8KQga7ZyI1c+bMO369paWFTZs2EYlEUhKU0H04czQggbslTlEvUZLWEV5XAp2+dd+Z0H25DlXRZLIzY9iwdIfSZQLRBGtONLP+lIvxZQ4G5ZmSep5er2fp0qVs3LiRHTt2EIvFMn7Gjr2t4YQ7kVWJlD50Dm2sCU/RJ9IdSqdRtDZcpV/A3rgWa8vv0Uav4S1cCXL636saGho4ffo0EyZM6PggalVFG7kM3mZQczMqWRSEbHfPROqPh+36fD7efvtttm3bxrRp01i1alVKgxOyn1YnYbfLYp9UJ3BfbzQh6uTTS1VV/rOyjjElZuYP6Pz5TmfPnAWNhUFjxnX6sTNNXFHZctbNG9VNeCMJZvez8/V5QyDkTfoYWq2WxYsXs2XLFt5//32i0SiTJ0/O2NeJ1dH64ZLXnaAkixZ2TO59KBorEWvmr/q1i6zDW/gJ4voiLM1bybnRhCJ9q8Ftw3dNJhMPPfTQAx9HSoQw+o5g8h5AG60HwGnsQyB3ATFzduwrFIRMl9Rmi2AwyPr169myZQvjx4/n3/7t3yguLk51bEI34czTUnc5hqqqGXtzk+mUhIrPm6D/YNH9MN3ONofZfdHLvis+hhaYKLN33t+JGg5yLqZSbJQ7/il0BlNVlf1X/Pz8SCO1vigji8x8bnwhA3KN5Fv0NIXadzyNRsOjjz7K9u3b2b9/P9FolJkzZ2bk9UarlbBYZbzu7Nk3Ksda0AdPEcyZDVI33KMpSQRzZhPXFWJvWE3O5etNKIylaQnnwoULXL16ldmzZ2MwtPP6oqpow5cwefdj9FcjqTFihlK8BcuxWi1oLq0np/Y1oqYB+PMeIW7Mnq6XgpCJ7nlFjEajvPvuu2zYsIHhw4fzne98h969e3dVbEI3kZOn4VJNlIBPwWrPnlKWTOLzJlAVsT8qE2yv8aDXSOg1Eq9U1fGvC/qgkTvnht11cB/NRiszs6QL3XHL2V8AACAASURBVIM42xzi9cPXOHEtRJldzzcfLmNCqaXDSY8sy8ybNw+dTsfRo0eJxWLMmTMnI+fuOJwa3C3Zs0pv8nwASK2zo7qxqHU4bt2XcNT9kpyrP8Fb9GSXr8CFQiEqKyvJyclhxIjkh3H/YfVpP9poA4qkJ2wbR8g+6UZCaM3Pp1kajMn7ARbXTnKv/JiIeSiBvAUZ3blQEDLZPROpr3zlKyiKwpIlSxgwYAAejwePx3PLY0aOHJnSAIXs1zaY19WcEInUAxKNJjJDLKGw56KXKWU2JpRa+M+9daw71cKK4XmdcvyzJ1q7hw2clN1d0e6kwR/lV0eb2H3Ri8Oo4UsTi3hkoLPTklBobYA0a9YsdDodBw8eJBaLsWDBAjSazHrd2Jwaai/HiMVUdLrMWzW7hRLF5D1AxDIcRdv9Z5rFDSW0lD2Po/7XOOp/gz93PsGcuSnfVxSPxzl69OiNf7dPPPHE/f/d3nX1aQUR22hU+Q6rWbKOkHMGYftETO69mN27yb38Q8LWUQRy55PQF6bmBxSEbuqeiZRe39p+duvWrXf8viRJt82YEoQ/ZrXJaLXgao7Tu9/dWxoLd+dxJdBowWLNvE/Xe5IDV/34owpz+tsZV2Jh72Ufv/mwiQmlVsodHSvxUxWFc74gJQYjNmfn771KF//1RhLvnHIhSfDkiDxWjMjFrEtNciNJEtOmTUOn01FVVUUsFmPRokVotZlTkuZwtv7sPk+C3PzMietOjP4PkZUQIWfqk3tVVdm5cyeRSOTG6mI6qFob7tI/xXZtLdaW99BGG/AWrgK589+/FEXh1KlT7Nu3D7/fT9++fZk+fTp5eXf/cOb21ScDIdt4wvaJSZcjqrKBYO4cQo4pmN17MLkrMfiPE7aNI5A7D0V3/86ZgiDcJ5F69dVXuyoOoRuTZAlnnjarSlkyjceVwOEUjSbSbXuNl1w9jPzp36O0XOM5g4MXRjzHy29W8a+X16DRakGnA40WtLrW/9dqkbS61t9rr3/9xvf+8PUmt4dmvZmZZSXp/jE7RSyhsvmsi9XHm/FHEszpb+fTYwrIN3fNzfHEiRPR6/Xs2rWLd955h8WLF9/4cDDdbDd17svoREpVMbmriOuLiBn7pfx01dXVVFdXA63NrZYsWdL+PUKdRdLiK3yShL4IS/MWcmIt15tQdM6qnKqqXLx4kcrKSpqbmykqKuKRRx6hrOwuHUhuWX06hqTGiRnK7r36lEwcGhOBvEcIOqdhce3G5KnC6DtKyD6RYO6cHrEKKQgdkcFXcKE7ceZqOH8qQjyuotWKZKA9VEXF60lQLlbz0soVinH4qo8nruxG4/ciTZ5NTiLOFxNn+A/TWCrKZ7MycALisdZfoQD4YhCPo8ZjELv+9UT8xv+rQJ3ZyfG8Ms7bC9GiMmjajHT/qB2iqir7Lvv5xdFr1PlijC4y8+z4QvrnGrs8ljFjxqDT6di2bRsVFRUsWbIEo7Hr4/hjJrOETifdGLKdqXThi+iidXgLlqe8tK2xsZE9e/bQp08fhg8fzpYtW1izZg3Lli3DbDan9Nx3JUkEcx4mri/AXn9zE4qOtVtsaGigsrKSK1eu4HA4ePTRRxk0aNAdPyhrXX06fL3zXtvq00OEHZM6dV+TqrHiz3+MoHM65pYdmLwHMPkOEXJMIZDzMKrG2mnnEoTuRCRSQpfIydOiqhE8rgR5BeKfXXv4/QqJuNgflU5qwMfu32wkYR7DHGsI+fmXkWyt7ZFnAlV7rrL6yggmrVhM35z736hHo1FOnTpF9bFjNLe0oNfrGD1gAKNGjsBalL0rUqebWhtJnGwM0duh58XZZTzUq+ONJDpi+PDh6PV6Nm/ezNq1a1m6dCkWiyVt8UBr+aHdKWd8ImXyVKHIRsK2sSk9TzQaZdOmTRiNRhYsWIDZbMZgMLBhwwbefPNNli9fntbh1FHLcFxlX8ZZ94vWJhSFTxKxjW73cdxuN1VVVZw9exaj0cjDDz/MyJEjb98Lparowhcx3tj7dH31qXAFEeuDrz4lQ9E68BcuI5gzC0vLdkzuSoye/YSc0wk6Z6JqkpvxJgg9hbijFbpETl7rG4W7OS4SqXbyXm80YXeKP7d0UM+dRPnpv7Oj/6cZqA3R5/kXbksMvjixiOMNQV7ZV8f3FvZFe5cGCs3NzVRXV3Py5ElisRgFBQXMmzePwYMHp20/SGdo8Ef55dFG3r/ow2nU8PykYuYPcHRqI4mOGDhwIEuWLGHDhg289dZbab8xB7A7NVy6EM3YsRBy3IvBf5yQY2pK9ga1UVWVHTt24PF4WLFixY3Vp/LycpYvX8769et58803WbZs2T33DaVawlBMS9lXcNT/CkfDbwlEGwjkzgPp/vtWg8EgBw4coLq6GoMWZk0ewZhh/TBoosiBo8gJP3IigHT9v5pYM9pYc8pWn5Kh6HLxFa26nlC9h8W1A5OniqBzFiHntJQmc4KQTcSdmdAlDEYZk0XGJfZJtZvHnUCWweYQjSa6kqooqJvXoK77NRdLhnHBWspzY4vueNPrMGr50qQi/m1PLWtONPPJUfk3vpdIJKipqaG6uporV64gyzKDBg1i9OjRFBcXZ+RNdLL8kQRvnmhmw2kXsgSfGJnH8uGpayTREX98Y758+XJyc9O3od7u1JCIQzCgYLFm3p+X0bsfCYWQY0pKz3PixAlOnz7NlClTKC29tVFCSUkJK1eupKKigjVr1rB06VKKiopSGs+9qFrr9SYUFVhc29FEr+ErXIGkRpETAeS4HznhR0oEkBMBiHnxu+rQhlqYn5Ng2SOgkxPAVbj2R8eWdCgaC4rGSkJXSDDn4ZSvPiUjoS/EW/wpgpE6LM2/x9qyFbOnkkDObEL2ySBn7wdAgtAZRCIldJmcXA2u5ni6w8g6HlcCm0ODnCGf7vcEqteF8rMfwEdHkSbMYOfYT6Ot8TGzz92H5E4rtzOrj5/V1U1MLLVSqI9z4sQJjh8/TiAQwGazMW3aNIYPH56+PR+dJKGovHvGxerqJgJRhbn9HXx6TD55XdRI4kGVlJSwYsUKKioqeOutt1i2bBmFhelp92y/qeFExiVSahyTZz8R82AS+vz7P/4BNTU1sWvXLnr37s2ECRPu+Jj8/HyefPJJ3n77bdauXcvjjz+e3nmWkhZf4Uri+iKszZswXjh+x4cpqkwgKpEIS6CxIjt7ETPlENFaUTTW60lTa+Kkaiyokj7l+9A6Im4owdPrM2jDl7A2b8XW9C5m1x4CuXMJ2ycmtTInCN2RSKSELuPMa52dEg4pGE3iopsMVVXxuBKUlGX2DWp3on50FOVn/wmhINKfPI8y/RF2VZznoV5W7MZ7XzL/dEIhNZcu8UbFO9iDDSiKQp8+fZg7dy59+vTJyOGwD+KXRxupONnCmOLWRhL9ktgXlikKCgpu3JivWbOGJUuW3LYSkgyTaw+K1kbkAfcPWR0akFoTqZKO9S7odAb/CTQJHz7HipSdo21flMFgYOHChfd8bTgcDlatWsW6detYt24dixYtYkA6h1ZLEqGcmcQNpejCH99IiBTZwqW6Fir3V1PX6Ka4uIQZM2bQq1cvIkAkfRF3mrixHHfpn6ILnsfashV7YwXa6DX8BU+kOzRBSAuRSAld5g+DeeOUlIkOdMkIBVViUVU0mugCaiKBuv63qJvehKJS5K9/B6msL4ev+nGHE8ztf/c2wJFIpLV5RHU1Q1paiEk6DGWDeXLOJJzdaCYUwPGGIOtOtrBwoJMvT7pzqWOmczqdrFq1ioqKCioqKli8eDF9+/ZN+vna0EVszRtRJS3Nxr4ouvb/HWu1EharjNettPu5qWbyVBHX5RI1D07ZOXbt2oXL5WL58uVJrdBarVZWrlzJ+vXr2bhxI/PmzWP48OEpiy8ZMXN/Yub+ANTX1/P+++9TW1uL0+nksccWM2DAgKx8fSQjZh6Ay/QlrE3vYPbsJWbqS8Q6Kt1hCUKXE4mU0GUcORokGdwtmfcJbKbyuFpLIdsGeAqpobY0ovz0+3DuI6Tp85Ce/iKSoXWVZXuNB5tBw0O9bm//29zczLFjxzh16hSxWIzCwkLmz5/PlhYbGy8FmKsY6U5pVDCW4OWqWoptOj73UGFW3yTabDZWrlzJunXr2LBhA4888giDByeROKgKtqZ3SGhsyEoIa/NmvMVPPVAMDqcm4+braSO16MMX8eUvTlm51smTJzl58iSTJk1qV5me0Whk2bJlvPvuu7z33ntEIhHGjRuXkhiT5XK52Lt3L+fPn8dsNjN79mxGjBhxeye+7kiS8Oc/hi58BVvDGuL6kpSWggpCJhKJlNBlNBoJu0ODqzmzbhwymdedAAlsIpFKGfXD/SivvwzxONLn/wJ5yuwb3/NHEuy/4mfBABvhoB9XKEQwGMTn83H69Glqa2vRaDQMHjyY0aNH39gIXx5J8OG1C7y8t47vL+qDTtM9Svp+evAaTcE4332kD0Zt9v9MZrOZFStW8M4777B582ai0SgjR46853OMvsPoIlfxFH0CbbQRi2sHwdBU4qY+7T6/zdla7hyLqeh0mZGUmjxVqJKOsO2hlBy/ubmZHTt2UFpayqRJk9r9fL1ezxNPPMGWLVvYs2cPkUiEyZMnd3lS73K5OHz4MCdPnkSj0TB58mTGjRuXMUOfu4ykxVP8KXIvv4K9/te4yp4XDSiEHkUkUkKXysnTcPnjKKqiIonmCfflcSWw2mQxxDgF4pEwwTW/JFi1i2BJOeE5jxPUGgju3k0wGCQYDHLN7WNqIEikMcbr+259vt1uZ8aMGQwbNgyT6dbZKlaDhq9OLuafdl7hjepm/mRsQRf+ZKmx77KP7TUenhyRx5D87jNLxmAwsHTpUjZt2sT27duJRCI89NCdkwhJCWNp3kLMWE7EOpaoGsXoPYStaQOusi+3ewWnbaXZ50mQm5/+t2MpEcToO0rYNj4l84JisRibNm1Cp9Pdd1/UvWi1WhYtWsT27dvZv38/4XCYhx9+uEuSqbq6Og4dOkRNTQ0ajYYRI0YwadKktM8mSydF58Rb9EmcdT/H1rgeX9HKdIckZDkpEUQfPEvENibdodxX+q/cQo+Sk6fl43NRfF4Fu1hluS+PK0FeoXiZPohEIkFdXR0nT56ksbGRUChEIBBoTZICfsKRaOsDB01u/e+BQwDodDrMZjNmsxkPZiI2JwuHFd/4Wtsvu91+zxu3CaVW5vV3sPajZqb0tjIoL3uTD3cozqsf1NM/x3BLa/fuQqfTsXjxYrZu3UplZSXhcJhp06bd9vdrbtmBJuHHU/IZkCRUyUAgbyH2a2+2JiD28e06r+2mzn2ZkEgZvQeR1DjBFLU83717Ny0tLSxduhSr9fZS2faQZZl58+ZhNBo5fPgwkUiE+fPnp6SkTlVVLly4wKFDh6irq8NgMDBx4kTGjBmT9R04O0vUMoRAzmwsrp3ETH0J21Ozoil0c6qC0XsIa/NmJCVCs6kfija9M//uJ/1XbqFHcV4fzOtqjotE6j4iYYVwSDSaaA+v18vFixe5ePEily9fJhaLAbcmR041Qa+Gi5iUOJaJM7AMHXlLgtQ2GLfWG+VX79Tw2XEFTB7+YINAP/dQIUfrA7y0t44fPNYXfRaW+Kmqyo8+qCcUU/j69F7oNN1zdVSj0bBw4UIMBgOHDh0iGo0ye/bsG8mUJtqE2V1JyPYQceMf9vWEbWMxeaqwNG8hbB3ZruG1JrOETie1lvCmm6pg9uwjauxHwlDS6Yc/ffo0J06cYMKECfTp0/4yyDuRJInp06djMBioqqoiEonw2GOPodV2zq1NPB7n9OnTHD58GJfLhc1mY9asWQwfPrznlfAlIZA7H134ErbGdcQMpSQMxekOScgi2vBVbI3r0EUuEzX2xVewJOOTKBCJlNDFLFYZnV7C3ZygTxq712YDj6v15ko0mri7eDzOlStXuHTpEhcvXsTlcgGtjQSGDh1KeXk5o0ePJhAIoEYiqKt/irpnK/Qfgvzc/0XKu/sMoR0XPMgSPNz3wS/kVn1rid+3d1zht8ea+Oy49Mws6oj3zns4cNXP58YXUu5I73DQVJNlmTlz5tySTLWtclib3kWVtATyFt76JEnGl/84uVf/G4trF4G8BUmfT5IkbE45IxIpffA0mrgLf/6iTj+2y+Vi+/bt9OrViylTOne1S5IkJk6ciNFoZMeOHVRUVPDEE09gMDz4v9VwOEx1dTUffvghwWCQ/Px8Fi5cyKBBg7rNCIOUkDR4i54i5/IPcdT/Clfvr6LK2TMaQUgPKRHE0rwVk3c/isaCt/BJwrZxGT1X7WYikRK6lCRJ5ORpcLWIwbz347l+c2UXK1I3qKqKy+W6sep09epVEokEGo2GsrIyRo0aRZ8+fXA6nTdWEkwmE/6zJ1F+8j2ovYT06EqkpZ9Gusen1oqqsqPGw5hiS4eHzI7vZeWRgQ4qTrYwuczG0ILsKfGr90V57dA1RhWZeWJoTrrD6RI3r3Ls3buXSCTC8tmDMARP4c9bhKK9fShz3NSHsHU0ZvduQvaJ7WqH7nBquHQhiqqqae2CaHJXkdDYiVg6t6V4PB5n06ZNN1b8UpWIjBo1CoPBwNatW1m7di1Lly5td9mdz+fj6NGjHD9+nFgsRu/evXnkkUfo3bt3Vneo7EqK1oa3+CmcV1/Ddm0t3qKns+aGWOhiqoLRdwhr0xYkJUjIMZVA7gJUTXYl3yKRErqcM1fLtbow8ZiKNkM6VWUijyuB2SKj1/fsT0AjkQhXrly5kTz5fD4AcnJybiROpaWldyznUVWV0HsbUH76H2AwIb/wj0gj77+P5XhDkMZgnM900grSs+MLOVIb4OWqOl56rC+GLOh4l1BUXq6qQ5bghaklyD3sZmjChAno9Xp279qBfOkIMWsuQee0uz7en7cIQ+AjrM2b8BY/nfR57E4NiTgEAwoWa3o+NNFEGzGEzuLPXQBS58awZ88empqaWLJkCTbb7UloZxo8eDB6vZ6NGzfy1ltvsXz58qTO2dTUxOHDhzlz5gyqqjJ48GDGjx9PQUH2N4lJh5ipP4G8hVibNxMz9iV0j9eN0DPdqYwvFSXFXUEkUkKXy7m+T8rdEie/SLRJvRuvK9EjV6NUVaWpqelG4lRXV4eiKOh0Onr37n1jj4Xdfu+SOzUcRP3Vj/F+sAuGjkb+/F8gOXOTimHHBQ9mnczkso5tiG9j1mn46pQSvrX9Mr/+sJHPPVTUKcdNpYqTLXzUGOLPp5ZQYOmZr9PRo0fTz3iOHLme9SdLGFMUw2S689umonMSdM7C4tpOMDQt6Xbo9psaTqQrkTJ59qGiIWSf2KnHPXPmDNXV1YwfP75dA487om/fvixbtoz169fz5ptvsmzZMnJzb3/dq6rKlStXOHz4MBcvXkSn0zF69GjGjh1732uLcH9B50x0oY+xNm0kZiwjbixPd0hCBpASISwtWzF5PkDNwjK+OxGJlNDlnLltDScSIpG6i1hUJeBXKOvXczY0X7x4kTNnznDx4kWCwSAA+fn5jBs3jj59+lBSUpJ0Ry710nmUn/w7NNZjefoLhGY/hiQn99xQTGHvJR8z+9g7deVobImFRYOcrD/lYkpvG8MLM7fb1wVXmN8ca2Rqbxuz+/Xcm0op4aefthq3Usahmihnm1pXOe7WcS6Q8zBG78F2tUO3OjQgtSZS6RhULikRjL5DRKyjUO9Qtvig3G4327Zto7i4mKlTp3bacZPRq1cvVq5cSUVFBW+99RbLli2jsLB1dVlRFM6dO8fhw4e5du0aJpOJqVOnMmrUKIzG7CopymiSjLfoSXIv/xBH/W9p6f1VVE3PbRHf46kKRt9hrE2bbyrjm5+SMQtdTSRSQpfTG2QsNlnsk7qHts3nPaXRxOXLl1m3bh0Gg4Hy8nL69OlDeXl5u1skq6qKunMj6u9+BlY78l/+M9Zpswk3NSV9jKrLPsJxlbn9He39Me7rs+MKOVQb4JV9dbz0WL+MHGobSyj8YG8dVr2G5ycV9ei9Idbm3yMpURLlT7J0aZR33nnnxo2503mHfVCyvt3t0LVaCYtVxutWUvAT3J/RdwRZiRB0dl6y07YvSpZlHn300Xa1JFdVFVUFuYNzBgsKCnjyySd5++23WbNmDYsWLcLj8XDkyBG8Xi9Op5O5c+cydOjQTuvyJ9xK1ZjxFH+KnCs/wd7w5vWxAZl3zRNS69Yyvj74C5YSz9IyvjsR/6KFtMjJ1eBuTqCqarpDyUhtjSZ6QuvzcDjM1q1bycnJ4XOf+xyLFi1i+PDh7U+ign6U//4u6m9+AkPHIP/Dy0iDR7Y7nh0XPBRbdQxLQVMIk07ma1OLqfPF+N+jjZ1+/M7w6w+buOiO8GdTSrAbe+4NpjZSi9F7gJBjKgl9IWVlZaxYsYJoNMpbb71F012S87BtLDFDGZbmLUhKJKlzOZya9HTuU1VMnipihlLiht73f3ySKisraWxsZP78+e0qk0skVCq3+dm7w4+qdPy9wel0smrVKqxWK+vXr2fXrl2YzWYWL17MM888w8iRI0USlWJxY2/8BYsxBE9jdu1KdzhCF5ISIayN68i58iqauAtv4Srcpc91qyQKRCIlpIkzT0skrBIKikTqTjyuOAajhNGUeS9RNZFADQc751iqyrZt2wiFQixcuPDGDKd2H6fmNMp3/hw+3I+06lnkP3sRydb+FaXGQIzq+iBz+jtSthIzqsjC4iE5bDjt4nhD5/w5dpYTDUEqTrawcKCTCaWdsz8sK6kq1sZ3UDVmArnzbny5qKiIlStXIkkSa9asob6+/vbnXm+Hrkl4k75xtDk1BAMKsVjXXg91oRq00WuEHFM7bY/CuXPn+PDDDxk7diwDBiQ/40JVVaoPhXA1J3A1JbhwLtop8dhsNlatWsWECRNYtWoVn/jEJxgwYIBoY96FQvYphK2jsbT8Hl3wfLrDEVJNVTB6D5J36fuYPB8QckyhufwvWoc0d8MVye73EwlZ4UbDiWZR3ncnHlciI1ej1I+OoHz7aygvfArl56+gNjV06HgnT57k/PnzTJ069cYehnbFoygoW95G+d7fACD/1XeRFy5HesCbpJ0XPKjAnBTvC/rM2AKKrTpe2VdHKJaekq4/FowleKmqjiKrjmfHZ9+8q85k8FejD3+MP/eR22r48/LyWLVqFQaDgfXr1+P1em97fms79DGY3XuQY677nq+thNfn6dpVKZOnCkU2E7aO7pTjeTwe3nvvPYqKipg+fXq7nnvxfJTLF6IMGm6goFjL6eoQoWDnvDZMJhPTpk2jV69enXI8oZ0kCV/hChK6fOwNbyDHb3/NCN2DNlJLztWfYL+2hrguH1fvr+IvWNIt9kLdjUikhLSwOzXImtaGE8KtEnEVv1fJqERKvVZH4tV/QfnBtyAeQ5o+H/WDXSjf/DLKr/8b1dXc7mO63W527dpFWVkZ48fffy/JbTH5vCg/+mfUt16HMZOQX3wJqf+Qdh/nxvFUle01XkYUmiiyprbJh1Er87WpJVzzx/jFkWspPVeyfnboGk3BGH8+rQSTrge/NShRrM0biRl6EbZPuONDHA4HS5cuRVEUNmzYQCwWu+0x/rxHAQlr8+b7ntJ2U+e+riLH3BgCH7V26pM73vQnkUiweXPrz9refVEtTXGOHwlRWKJlyAgjox4yoahw/Eiow3EJmUGVDXiKP4WsRLDXvwGqeO/vTlrL+NaTc/lHaGLNN5Xxdf8PL3rwu6WQTrIs4XBqcIkVqdv4PAlUtTXZTDc1HEJZ+0uUb30FTn6ItOIzyN9+FfkzX0X+l58gzZiPumcLyt89h7L6Z6hed1LHTSQSbNmyBVmWWbBgQbvL6NQzx1G+8zU4eRTpU19E/tLfIFk6Vop2pjlMrS+akiYTdzKisHXI7aazbo7VB7rknHfzwWUf7533sGJ4HsMKMrebYFewuHajiXvw5z9+zzKUnJwcHn30UZqbm/n9739/237P1nboMzH6j6ELfXzPc5rMEjqd1KWJlMn7AQAhx+ROOd7evXtpaGhg/vz5OBzJv4bCIYWDlQFMZplxU8xIsoTFqmHwcCP1V2I01N6epArZKWEoxlewDH34Apbm36c7HKGTGHxHr5fx7btexveNblvGdyc946cUMlJOnhaPO4HSCZuKu5NMaDShqirKvh0oL34ZddNbSBNmIP/zj5EXrUK6vo9Jys1HfuZ55H/6MdLkWajb3mlNqNb+EjXgu+fxDxw4QENDA3Pnzm3XkE5VSaBsWI3yH98EvRH5b/8dec7iTtnPtL3Gg14jMa08tUNDb/bMmAJ62fT8cF8dwVh6PqF1h+O8+kE9/XIMPDUqPy0xZAo55sbs3kXYOpqYqd99H9+3b1+mTZvGuXPnOHjw4G3fD+Q8TEJjx9q0AdS7l6lJkoTNKXddIqXEMHkOELUMQ9HldPhwNTU1HDlyhNGjRzNw4MDkw0ioHKwMEI+pTJxuuWX4+IAhBqx2mepDQeJx8R7RXYTt4wnZJ2Jx70IfOJnucIQO0oYu4mhYTUKb2yPK+O5EJFJC2uTkaVASXVvOkg08rgRaHZgt6Xl5qh+fRfm3v0b92Q/AkYv8N9+7Psw2746PlwqKkf/PC8jf+RHSmEmom9eg/O0XUNb/FjV0ezOF2tpaDhw4wLBhwxg0aFDycXlcKC/9I+q6XyNNnIn84n8ilSe/mf1eogmFPRe9TO1tw6zrugTWoJV5YWoJTcE4Pz/c9V38VFXlvz6oJxBT+Pq0Xug0PbfVOYC1eSMg4c9blPRzxo8fz5AhQ6iqqqKmpubWb8p6/PmPootcxeg7cs/jOJwavJ6u6WRq9FcjKwGCjo63PPf5fPz+97+noKCAGTNmtOu5J462NpcYM8l82wq8rJEYPcFMKKhy5kS4w3EKmcOX/wQxfQn2hjeT2kMoZCg1ga2xgoTWgbv08z2iYXyQHAAAIABJREFUjO9ORCIlpI0zr7XtrFvsk7pFa6MJbZfP71G9LpSfv4Ly//4SrtUhffbPkP/uP5AGDE3q+VJxGfIX/hL5W6/AsDGo7/y2NaHatAY10nojFIlE2Lp1KzabjYcffjj52K43ueD8SaTP/hnSn/4FkrHzStAOXPETiCpdVtZ3s6EFJpYOzWXLOTdH6rq2xG9bjYcPrvj5k7H59HEauvTcmUYXuoDRX00gZxaK7g4zou5CkiTmzZtHYWEhW7Zsobn51v2CEeuYpNqh250aEnEIBlLffMTkqSKuKyBm6tgHEW37ohRFYdGiRe1qJX75QoSPz0XpP8RAafmd9yTmFWgp76en5nQEj0u8T3Qbsg5vyacBBUf9b0AVJf7ZyOSpQhetx5f/BKrcc98/RCIlpI3JLGEwSmKf1E0URcXrSXTpIF41HkPZ+jbKN7+Mum8H0oKlyP/838gzFjxQ9zuptA+aL/8t8jf/E/oPQV37i9aE6r117NyxHZ/Px8KFC9Hr79/QQU0kUN7+X5SX/hFsDuS/+8/WuDo5ydxe4yHPpGVUUXr2B31qTD5ldj0vV9Wx84KHeBeUuzb4o7x28BojC00sGZqb8vNlNFXB2vgOCa2ToHNWu5+u1WpZvHgxOp2ODRs2EA7ftIIiyfgKnkCT8N2zHbq9ixpOaMOX0EWudErL823btlFXV8e8efPuPKD4LtwtcY4dDJFfqGXYaOM9HztsjBGdXqL6UFDMHexGEro8vIVPootcwdr0brrDEdpJjnuwNP+eiHkIUcvwdIeTViKREtJGkiSceRpcLeKTxjZ+r4KS6Lr9UWr1IZRvfw31zddhwDDkf/wh8pOfQzJbOnxsqc9ANF/7B+S//jco7cPpzRs4feYsEwtzKC64/14ctaUR5T/+DnXjm0gzFiD/3feRSss7HNcfc4fiHK4LMLufHY2cntI2vUbmL2f0wqKT+cHeOp6rOM/aE834o6l5bSQUlZf21gHwwtReyF28+plpjN6D6KJ1rSV98oN1bLTZbDz22GP4fL4bqzRt4sby+7ZDtzo0IKU+kTJ7qlAkA2F7+ztltmloaGDTpk28//77jBw5ksGDByf93EhY4UBlAINRYvxUM/J9XnN6g8zwsSZczQkunu+c2VJCZohaRxB0zsDs2YfBdyzd4QjtYG3aiISCr2BJp82gy1ZipLeQVjm5WhquholGlVs2GvdUXdVoQm2oRVn9GlQfhMJerQNsR09MybmkgcMIfOGv2PWr/6U4EuSh995EObIT6YmnkKbMQbpDm2T1w/0or78M8TjSn34DeXLyZYDttetjL4oKc9JQ1nezfjlGfvh4Pw7XBlh3soVfHG1k9fFmFgxw8MTQnE5tyb7uVAsfNYZ4YWoJhdaOt77OZlIihLV5K1FjPyLWUR06Vq9evZgzZw7btm2jsrKSmTNn3vieP+9RDIGPsDZvxlv89G3P1WolLFYZrzt1pX1S3IfBV03IMandpTiKonDhwgWOHDlCbW0ter2eGTNmMHp08jOoFEXlcFWQaFhl+jwrBmNy1/yyPjquXNBy8liI4lJdRg4qFx6MP+9RtOHL2K6tIW4oIaEvSHdIwn3ogmcx+o/hz52Pouvh1QyIREpIsz8M5k1QWCLeHD2uBLIGLLbU/FmooSDqu6tR33sHtDqkVf8Had4TSNrU3UwrisLWrVtRJZlHnv0C2itzUSp+jfrzV1A3voW05GmkiTORZBk1HkNd80vU99ZBeX/k5/4KqSi1G1h3XPAwKM9Ib0f6a7xlSWJCqZUJpVZqWsKsO9nCxjMu3j3jYmpvG0uH5TIkv2MdkT52hfn1h01M6W1N+eDhbGBp2YakBPEXPN4pn6yOGDGCxsZGjhw5Qn5+PsOGDQPa2qHPwuLaRig0lZip723PdTg1uFO4Qm/yHkQiQcgxJennxGIxTp48ydGjR3G73dhsNmbOnMmIESPo1asXTU1NSR/r5LEwTdfijJ1kwpmb/O2HJEmMesjEri0+PjoaYvzUjq+YCxlC0uAtfprcSz/EUf9rWsqef+BVYaELqHFsjeuI6/IeqAy6OxKJlJBWbW+mruYEhSU9+5NxAK8rjt2huW+5S3upioK6bwfq2l+Cx4U0bR7Sis8gOTre+vh+Dh06RG1tLQsWLGjdR+F8CHnEePjwg9aE6rXvt5bvPbIMdecm+Pgs0tzHkVY9e6PVeqpccIW54Irw3ISilJ7nQfTPNfL16b34k3EFvHvaxZazbiov+RhWYGLpsFwmlVrbXYoYSyj8YG8dVr3M85OKu7yhSabRRK9h8lQRtk/s1I5TM2fOpKWlhe3bt5OTk0NxcTEAgZxZGL0HsDZtwFX2/G1zVmxODbWXY8RiKjpdJ//dqAlMnn1ETQNJ6Avv+/BAIMCxY8eorq4mHA5TVFTEo48+ysCBA5EfYO/k1UtRak5H6DtQT+9+7f/QwmrXMHCYgTMnIpT1i1FYLN4vugtF68Bb/AkctT/H1liBr/DJHl8ulqnMrt1oY824ez3bKYO8u4MuSaT+67/+i8OHD+NwOPj+978PgN/v5wc/+AGNjY0UFBTw9a9/HavViqqqvP766xw5cgSDwcDzzz9P//79Adi5cydr164FYMWKFcyePRtonWHx6quvEo1GGTduHM8++2yPv0HIFlqdhM0h424RDSdUVcXjTty1g9UDH7fmNMobP4ULZ6DfYOSv/D1Sv+T3NHREfX09H3zwAYMHD2bo0D90/5MkCcZOQR49CfVQJer636D+/BUwW5C//DdI46d1SXzbazxoZZjZN3NXZvLNOj47rpAnR+ax7byH9adcfHf3VYqtOpYMzWXeAAdGbXI3tr851sTH7gjffLgMh7GHf46mqlgbN6DKevx5Czr10BqNhkWLFrF69WreffddnnrqKSwWy4126I6G32H0HWkdWnkTZ27rCv3Vj6P0HdS5K6SGwEk0CS8+59J7Pq65uZkjR45w6tQpFEWhf//+jB8/npKSkgd+X/W6E3y4P0hOvoYRYx98RXXgMCNXL8WoPvj/s/fe4W2d593/5znYgyRAgpsUSYnUoBapvaenbCeq7TqJnTRJ48RumtW0v7xJ+7Z1rvZt0rRNHKfNTpM0y0rjRI4dx5JlydqDkkgtalASqc29sYHz/P6AREmWZEsiSADk87kuXAAODs+5wQMcPN/z3Pf39rPsASMGo/qdHymE7OPxuVfg6HqTsLWMQMbQpJor7h4t3ImjaxMB51RC9uEZQ6QCw/JLumzZMh544AH+67/+a2DZ2rVrmTp1KqtXr2bt2rWsXbuWD37wg9TW1tLc3MwLL7xAQ0MDP/zhD/mXf/kX+vv7+c1vfsNXv/pVAL74xS8ya9YsnE4nP/jBD3jmmWeoqKjgK1/5CnV1dVRXVw/HW1PEAXemkUsXwkgp7+qHWtcl4bAkHLp6C12+NxggK8eIw5m45ra3i8+rEwnHrz5K6lHkz7+D3LoeMtyIj34OMW/ZXTnx3Q2hUIh169bhcDhYvnz5TY+t0DTE7MXIGQvgYA2MGYfIGp4c+Ygu2dzUy+xCJ+mW5P982E0GHpmYyarxbnad62Pt0U6+v7eFXx1s4/4KNw9NcJNpu/Upvb7Vx+/qO7l3XAazi5zDGHlyYvYdw+JvoM/zMNIQ//+HzWbj4Ycf5te//jV/+MMfePTRRzEajTE79O6dODrWEXROua5WKTvXiCfXSP0BP9l5Rhxp8ftc2np2EjW6CNlvbGcgpeTcuXPU1tZy5swZjEYjkydPprq6+o7c+G5GKKRTs82L0SSYtcCBNoheZQaDYNpMGzvf8tJwNMDEqaOr8edIx5u5AlPgDGntvydiLRy1fYmSEilJa/s9Eo1+z0OJjiapGBYhVVlZSWtr63XLampqeO655wBYunQpzz33HB/84AfZu3cvS5YsQQjB+PHj8Xq9dHV1ceTIEaZNm4bTGfvBmzZtGnV1dUyePBm/3z/gGrRkyRJqamqUkEohXFkGzjaG6OmKYjKLGwXRLURSOKQTDkkitzGZZXdoeC4PUjw5xtsuch5OrvRJiZuQemUNcut6xD3vQbz3ybj2XbodtmzZQk9PD4899hgWyztfXRcGA1Tfft1GPKi96KUnEE24ycSdYtAEC0vSWTAmjWNtftYe6+SlIx2sPdrJktJ03jvRTan7ektpXzjK8zsvkeM08ecz3z2ta8QjIzjb/0DElHNH9UJ3isfj4b777uO1117jrbfeYuXKlQih0Zf9MJnnv4O9azPerPsG1hdCUDXHzubX+6jd7WPBCmdc0nwNwWbM/tP0Zz1wXTphNBrlxIkT1NbW0t7ejt1uZ/78+UyZMgWbbfAiReqS2l0+/H6dBcudcTGJ8OSaKCoxcfJYkMISM2npyX8RRHGbCI2e3PeRee5buC7+N17XUvwZc1XNVBJg9tZj8R2nz/MQujG1fjOHmoTldvT09OB2x+oz3G43vb29AHR2duLxXLVGzsrKorOzk87OTrKysgaWZ2Zm3nT5lfUVqYP7cmPerW/033IdTQOTWWAyC8xmgc0uSHcZMZk1zGaBySQGXr+yjsksCIUk7S0R2lsiXDwX4uzpmH1uusuAJ9dIdq6RzGwjxiRIEenpiiIEpGUMfmAg62uRf1iDmL8C7X1PxyG6O+PkyZPU19cza9YsCgsLh33/t8PGxh4yLAZmFqTm7IwQgkk5dibl2LnUF+L3xzp581QPG0/3UJXvYPWkTKry7Agh+O99rbT2h/mXe8dgN6mBp717eyzPP/+jIIb2/1FeXs6cOXPYs2cPHo+Hqqqqy3boVdi7t+JPn41uulqraLNrTJ1pY/8uH6eOBamofOc+S++GIdSOs+OPSGHEnz4LgEAgwOHDhzlw4ABer5fMzEzuuecexo8ff0dNdd+N40cCtF6KMHWmjUxP/LZbWWWj5VKEg3t9LFjuVKn8IwhpdNJd8FHS2l8lreM1HN1b8LkW48+YO6qbviYUPURa+yuEzXmx/nOK60i6JPmbNdy71UlSCHHHDfo2bNjAhg0bAPjqV796nWhLJEajMWliGW6ysiThZVYiYYnZomGxGmL31zw23mYNyM0oi5XYoeuSjrYgF8/5uHjeT1ODn9PHg2ga5ORZyS+yU1Bsw5NjfderwENxvPzei7gyzeTmDi61LdrZRsePvoGhqJSsz/wdwjq86S+9vb1s2rSJgoICVq1aFdeB2d1ws2PVGwhTc+E4q6fmk5eT+na7Hg9MLSvg04EwvzvYzEsHLvLcxnOMy7Izr9TNG6d6+ODMIpZUxr8PV7wZ8nNhqAdxehPSPZ30kuGpxbvSX2rbtm2UlZUxbtw4SPsAHPh7svo2Isc/c936WVmSzvYWThzpZ/ykbLKy73AAqYehsw7RugXRewyJhix6BGFKY9fOnezfv59wOMy4ceNYsGAB5eXldyVG3ulYnTndT0N9NxUT05g5NyfuYmfOQgvbN7XR1WZhfGXy1jgmE6kzzvBA4RT03gbEhVdxdvwRR89WZP59kLccDIO7uJAqJMvxEmdfQkR6EJOfxZOWfMZMiSZhI5yMjAy6urpwu910dXWRnh47EWZlZV1np9rR0YHb7SYzM5P6+vqB5Z2dnVRWVpKVlUVHR8d162dm3trX/p577uGee+4ZeH4n1q1DicfjSZpYEkHWTb6bUQk+f+wWL4QBCkuhsNRCJGKmqz1CW0uE9pYwtXs6qd0DRhNkZRvJzjXhyTXiTNduGATE+3hJKWlr8ZObbxrUdmU0iv71/wsBP/Kv/5mOfi/0e+MW57vuX0rWrl1LOBxm5cqVdHd3D9u+b8XNjtUfT3QRjkoW5JtH3PfuobE27ispY0tTLy8f6+IX+y5Q6rKwusKeEu91qM+FaS2/wapH6Ey7h+gw/j+WLl1KS0sLa9as4X3vex8ZGRk4Mhbj6HyT7nMzb7BDnzBF49J5waZ1F1h8bxqG26gtMoTasPbWYOvdj6Z7iRpd+DPv5Yy/mD1vnuD06W8ihGDChAlUV1cPDNKu/Q29E251rPp6o2x7o48Mt4GKKdpdb/+dcGdLMj0G9mxvw5EeSMp07WQj9cYZbsj+EEbnGRxdG7Gc+y36hT/icy3Cn7EAOcIFVTIcL0OohcyL6/GnzaIv6IJgKn1+4ktBwc1r9hImpGbNmsXmzZtZvXo1mzdvZvbs2QPLX3/9dRYuXEhDQwN2ux23201VVRW/+tWv6O+PpX8dOHCAJ598EqfTic1m48SJE1RUVLBlyxYeeOCBRL0tRQphNAqy80xkX7bRDQV12ltjaYBtLRFaLsYUnMUqLheBx4SVzR7/H+xgQBIKStIHWR8lf/9LOHEkZixRMPyzD7W1tZw7d44VK1YMpO4mI5saeyhxWShzj8xUEZNBY+U4FyvGZnC0zU9emhmTQQ00jYFz2Pr24XUtIWoe3iu9ZrOZhx9+mDVr1vDKK6/wxBNPgHsJ1r69N7VDN1s0ps+xs2eLl+OHAlTeyu1OD2PxHsHWW4PZfxqJRtAxCa9zFvUXoe6NA7S0HMZisTBz5szrao2HgnBYsnebF80gmLXQcVsC8G4QQjBtlj3WW+qAn+q5qrfUSCViK6HH9lGMgXM4Ojfi7HwDe/dWfK6F+DMWIg3KdGRIkJK01peRmoV+jxpX34phEVLPP/889fX19PX18eyzz/LEE0+wevVqvvGNb7Bx40Y8Hg+f//znAaiurmb//v185jOfwWw288lPfhIAp9PJY489xpe+9CUAHn/88YEfg6effppvf/vbhEIhqqqqlNGE4q4wWzQKis0UFMcKW33e6ICoam2OcP5MGIg1y83ICBLVwxiNAoNBYDCC4drHBnH5Obdcx2gQaIbYgCAeRhPy8L5YP6aF96AtWDH4f8gd0tbWxo4dOxg7diyTJ08e9v3fLud7gxxvD/CR6uwRX1shhKAyZ3hNRpIWqZPW9gpRQxq+zOH/fgC4XC4efPBBXn75ZdavX89DDz1Ef9b9t7RDz803UTLOzKnjQXILTGTlXP3JjvXA2oO1bz+a7idqzKQ/6366TJM5eLSJgwe34/V6cbvdLFu2jIkTJ2I2D23RvpSSut0+vP0685Y6sDuGVrynZRgYN9HCyaNBisvCeHJUX5uRTMRaTE/BhzEGLuDo2oiz803s3dvwZyzE51qINKhzXTyx9NdhDjTSm/0nSIO6UHErhLzTIqMRxsWLFxMdApAcU7iKWyOlpK9Hp60lTEdrBKkbCQTCRCOSaFQSjUA0KtH1O9+24fLYKBqBBx/NwHgXjThlZzv6P30WMjLRvvTviHdxyYs34XCYF198kVAoxJNPPhkXx6948fbv1s/q2vhtfQc/+pPyd7QLVySGoToXWvpqyWj5Nb05j98gWIaburo6tmzZwpw5c5g3dy7u899Bi3TTWfLXNxTUR8KSzev7kBKW3mvFGTqCrWcP5kATEgNBZyX+9Dk0+1zUHTjAsWPHiEajjBkzhqqqKkpKSobsgsHbj1VDfYBjhwJUVlkZN2F40q4iEcnm1/sQGiy9//ZSIEcrI22cYQxexN65Cav3MLow43ctwOdaNGIG/Yk8XiLqJ+vs14ka3XQVPXtD8/DRSNKl9ikUqYQQgnSXgXSXgXETbn2C0/WroiomsmI/9NeKrWgk9jhyzeNoVOJI0+5OREUi6D/4NwiH0Z75P8MuogC2b99OV1cXq1evTioR9XZ0KXmrsYfqfIcSUaMIoQdxtr9O2FJEIC3xGQvTp0+nra1twMlvYtEVO/S38Gbdf926RpNg7qw+/Kd2knPmCCYtQMSURX/Wg/ic1TSeb6duZx3nzp3DaDQyadIkpk+ffp2b7XDQeinMsUMBCsaYGDt++M5BRqNg6kwbu7d4OXUsyPjJI7tuRnGViKWA3vyn8AabcXRtxN61GVv3DvwZ8/C5FiONqenImgw4Otcjol76Cj6qRNS7oEYSCkUc0TSBZgYTw3dVVK79OZw8inj6rxH5RcO23ys0NjZy8OBBqqqqGDMmuV3hDrX4aPdF+Ei16qU0mrB3vYUh2ktP/lNJMSgQQrB8+XI6Ozt54403cP3pn2J3VsXSlNLnxOzQ9RDW/kNYe2vICZxBzzbQ2DUe8uZiyS3l6LFj1NX9lp6eHpxOJwsWLGDy5MkJuZDh7Y+yf5eP9AyN6bPtw54ym5NvoqDYREN9TMg549jIWJH8RC159OY9iSHYgqNrE/burdh7duLPmIvPtQTdmJboEFMKY+ACtp7d+DPmq6bIt4ESUgpFCiMP1CDX/Rax5H60uUuHff8+n48NGzbg8XhYsGB4rKQHw8bTPThMGnOL1ZXK0YIW7owJlLRqItbkEfpGo5GHH36YF198kVdffZUnH3+QQu8R0trWEjVlYe2rRdMDREzZ9GWtwueoZuexHlqOHMUb2EQoHCIvL4/58+czbtw4DIbEiIdIJGYugYRZixwJ68k3udpGa3OYQ/v8zFvqGPH1j4obiVpy6c17P4bQCuxdb2Hr3o6tZxf+9Dn43EvRjcom/12ROmlta9ENTryZ9yY6mpRACSmFIkWRHW3oP34eisoQCWi6K6Vkw4YNhEIhHn300YT3i3o3fOEoO8/2sawsA7NysBs1ODteB8QNKXPJgMPh4KGHHuKll17i1fXbeWrJYtK6NyKFkaBjCv6MOYQsJVy8dIm6HZs53XQaKSHLXcaKe2aSn5+f0PillBys8dHbozN3iQOHM3EzQVabxqSpNg7t93PhTJii0qE11lAkL1FzDn25T+BzXxZUPbuw9e6h2zyTdvNyMMcEv8EoLptBgXiX3pGjBWtvDabgeXpy3zfi7eXjRXKPfEYJsqGewGmBRIMMN6S7ECb1I6C4NQN1UdEI2rP/B2Ee/rqogwcP0tTUxNKlS4e9HuNu2Hm2j2BUsnysuio5WjD6z2DtP4TXvRLdmJHocG5KXl4eK1as4I033mDdkancO/MpQraxhKWZhoYGamtfpL29HavVysyZM0m3j6fxuJFIIPEOZfUHerhwNsyEqVZy8hPvmFcyzsy5phBH6vzk5BsxW9QFk9FMxJhFI++lq3c+OZGtjHPXYOqr581Tq2n3XZ+ypl1x2DUKjAauiizj1eWxZVwnwIym2GOT+fLNFLvXUlCYiUg/zo51hGxjCTqnJzqclEEJqSRAf2MtPbW7rl9oc0C6CzJciDTXgMAi3YVId0OG6+pzY+J/wBTDi/zd/8CpY4hPfAGRO/w5zB0dHWzbto2SkhKmTZs27Pu/GzY19pKfZmKiJ3nNMBRxREqcHX+M2Z27Fyc6mndk0qRJtLe3U1tbizNtEeHwIQ4ePIjf7yczM5MVK1YwYcIETCYTUpf0dPZzuNZHVo5xyC3Gb8XZ00EO7u0mr9BExaTk6McmNMG0WTa2vtHP0YMBps9OvNhUDC+6Lulsi3DxXJhL58OEghKDwUFvwSNETXMZq/2aRyb9kvPagzTrM2PGTxGIRmTMGCoiiUSvPg/5LptDDbx+e3EYjGAyC8ymKyJLu05oXXszv21ZokSYs+N1hB6iL/u9oFJjbxslpJIA7am/wPWBp+k+ewbZ2wW93bFbTxeyrxt5vgnq68DvBeAGv3q78+pM1mVxRYYbMtyIMWOhYAxCU8W3IwVZtxu5fi1i2Sq02YuGff+RSIR169ZhNpu55557UqIWobU/zKEWH09O86REvIrBY/EewRw4Q2/2ozdYiicjCxcupL29nW3btgFQWlpKVVUVxcXF131mhSaonmPnrXV91O3xMX/Z8NYDRaOSw/v9nD0dIr/IxvQ55qT6TmW4jZSNt3D6eJDiUjOZ2WqYM9K5uXiC3AIT+cUmcvJNl2v3yuiJfor0ll8zxvcq2WkX6MteDdrtZwBJGXPjjV4RXZdFVjh0zS0sCYUk4ZA+8NzbHx14PRp9530YjGA2C2YvsJAxTMkeJn9jrFm5exlRszJjuhPUGSYJEBluTB4Pwp3zjl5vMhwaEFj0dl8numTP5fszJ2PLAv7Y3wDY7FA2ATFuIqJ8Eowdj7CqK3WpiGxvidVFjRmHeOJjw77/5uZmtm7dSnt7O4888ggOR2r063irsQeAZWUqrW9UICM4Ov5IxJyb8J5Rt4umaaxatYojR45QVlaG2+2+5bp2p4Ep1TYO1PhpbAgNm924z6uzd7uXnq4o5ZMsLFxWQGdnx7Ds+06YMNnKxXMhDu7zseS+tJRMs1K8M7cvnq5HGhz05H8YR+dG7F0bMQYv0Zv3FFGz57b2K0QsvW8wpip69Fqxdb0Au/K4oy3C1jdbmLXQQV7hEGcdyShpbS8TNbrwupcP7b5GIEpIpRDCZIasnNgN3ll0BYPQ1Y5sOgGnjiFPHkW++iJSypj9b1EJYtwkKJ+EGDcRsnKS6qqi4kZkJIz+/X8DKdGe+QLCNHwpnb29vezYsYMTJ05gs9m49957KSsrG7b9DwYpJZsae5iSayfXqWoPRwO2nt0Yw510538kKezObxeLxcKMGTNua93iMjPNF8IcPegnO89IWvrQZh20tYTZv9OHHpXMWmgnv8ictALFaBJMnWGnZpuX08eDlE9SRfMjgbsVTzcgNLxZ9xC2FpPesgb3+f+kN+cJQs7KoX8TgGYQWAwCyzt8LCNhSc22APt2epm/zEmmZ+iG67buHRhDLXTnf+iOZucUMZSQGqEIiwXyChF5hTAvdoVB+n1w+jjy1NGYsNq5Cd56LTZr5cqEcRMR4ybFZq2Ky1TtVZIhf/MTaDyB9uwXETnD49YVDAbZu3cvdXV1AMyePZuZM2diNqfOyfbwpT4u9oV5fHLyG2IoBo+I+nF0vknIVk7IPj7R4QwZQgimz7bz1ut91O7ysege55AIGyklJ48FOXYogDNNY/ZCJ84hFm3xIK/QRF6hieNHAhQUm7An0FFQcffETTzdhJBjAp3Fnyaj+Re4mn+G17UUb9a9IBL/WTGaBPc+XMDvf32GPVu9LFzRFDQqAAAgAElEQVThJC0j/nFpkR4cnRsI2icRcgyPkBxpKCE1ihA2O0yuRkyuBkDqUTh/BnnqGJw8GhNY+3bEhJXJDGUVsXTAcZUwbgLCqdKiEoXcvwP55iuIlY8gZg59v6ZoNMrhw4fZvXs3gUCAiRMnMn/+fNLSUq+x4R+PtmIxCOaPSb3YFXeOvWsTQg/Q71k14gumLVaNabNs7N3uo6E+wIQp8TVSCYcldbt9NF8IU1BsYvpsO0ZT6vxPp8ywsemPYQ7t9zNnseotlSpIPZbaNhTi6e3oJjddhc+Q1v4Kju7Nl62/3480Jr7XoNVmYN4yB9s29LNrcz8LV6bF3VzG2fYqAklf9sNx3e5oQgmpUYzQDDBmbMyQYvkqAGR3x9VUwFPHkOvXIqMvxf4gryiWBjhxGqJqjqqzGiZkWzP6T74FpRWIxz4ytPuSksbGRrZv305XVxeFhYUsXryYnJzULD4NRnTePNHG/DFp2E2Jv8qoGFq0cBf27h0E0mYQsSS2x9JwkV9kpqg0TEN9kJx8E+6s+Pys9/VEqdnuxdevU1llZex4S8oJEZtdY+IUK0fqAlw6H6agOHVm0kcj0ajkXGOIU8eC+Lz6kImnG9BM9OU8Stg6hrS2l8k89y168p9KigbedoeBeUudbN/Yd1lMObHEydbf7D2O1XuY/sz70E2ZcdnmaEQJKcV1CFcWzFyImLkQuFxrdabhqrCq2w3bNyDNZsT0uYi5S2OzXCoNcEiQ4TD6974GArRP/H9DWhfV2trKtm3bOH/+PC6Xi4ceeoixY8em3ODpWnae66M/FGXF2OTsIaSIL86OdZfrH+5NdCjDypRqO+2tvdTujpkrDHbAefFciLo9PgwGwbxlTjw5qTtUKK2wcK4pzOH9flyZibOLV9yacEhy5lSQ0yeCBAMSV6aBidPs5BYMoXi6CYH0WUQsBWRc+jnu89+n37MKf8b8hM9sp7sMzFnkZNfmfvZs8TJ/uXPw/xc9jLP990RM2UnfHiLZSd2zo2JYEBYLjJ+CGD8FAKnrsTqr3ZuRe7cha7aCIw0xcyFi7hIor0Ro6ocqXsj//RGcOYn2yb9FZOcNyT76+/vZuXMnR48exWq1snTpUqZMmYLBkNozOIGIzi8OtDE2y87UXDV7OtIxBs5h7T+A1708aZvvDhUmc8wSfedbXo4d9DNlxt193nVdcvRggNPHg7izDMxc4MBmT+3zuaYJps+2sX1jP5te66VsvIWKSRZM5tR+XyOBYEDn9IkgTSeDRMKQnWekfJKFrGxjwi7gRSwFdBZ/mvSWNaS1v4IpcJbenEcTbsKQlWNkxnw7e3f42Lvdy5zFjkHVRNq7N2MMd9JV8DEQSgoMBvXfU9wRQtNiTn/lk5DvexqO1iF3bUbu2oTc8jpkehCzlyDmLYXC0pSezUg0es025KbXEPe8F1E9L+7bD4VC7N+/n/3796PrOjNmzGD27NlYLMnfc+d2+N/DHbR6Izz34CQ0EU50OIqhREqc7a+hG5z43EsTHU1C8OSaKKsw09gQIrfARHbenc1eBwM6+3Z46WiLUlpuZnKVDc0wMs7frkwjyx9M5/hhP6eOBTl7OkRFpYXScguGEfIeUwlvf5RTx4Kcawyh65BfbKJ8ogVXZnIMSaXBRk/+n2Hv2oyj8w0yQ8305D1F1Jyd0Ljyi8xMmyk5uNdP3R4f1XPtdzXGMoTacXRtJuCcTthePgSRji6S41OrSEmE0QhTZyGmzkIGA7FGsbs3Ize8jFz321gj4LlLEXOWIDy5iQ43pZCtF5H/8y0oG4947M/ium1d16mvr2fXrl34fD4qKipYsGABGRkj5yr+uZ4ga492sGJsBtMLM2hvb090SIohxOytxxxoojd7dUo03x0qJk2z0dYcoW6Pj6UPpGG+zVmXrvYIe3d4CYUkVXPsFJeNvFoiu0Ojeq6DseMjHD0YoL4uQGNDiIlTrBSWmNRFv2GgtzvKyaMBLpwLowkoKjUzbqIFZ1oSZj8IDV/mcsLWIjKaX8R97r/oy32coHNKQsMqGWchGJQcPxTAYglQWWW9s8+ulDjbf4/EEDPkUQwaJaQUcUFYrLF6qblLkX29yH3bkLu3IH/3M+TvfhazVp+7DDFrISJt5AzYhwIZDqF/919BM8T6RcWx/uzMmTNs27aNjo4O8vLyeOihh8jPH1lF+VJKvlfTgtWo8ZHqxF5BVAwDMoqz449ETDkE0mclOpqEYjAKqufZ2bahn8P7/cyY984Ns6WUnDkV4nCtH5tNY9FKBxnukT0syHAbmbfUSVtzmPoDAWp3+zh13EBllZXsXFXrOxR0tEU4eTRA66UIBiOMG29h7AQLVlvyp1eG7RUDFukZzb/A61qMN+v+hFqkV0yyEPTH0iItVnFHfdIs3iNYfA30eR5GNyon5ngwss+YioQg0tIRy1bBslXI9hZkzdbYTNUvv4t88fsweUZslqpqLsIaX7vekYBc80M414j2qb9HZMXHLa+jo4Nt27Zx5swZ0tPTeeCBB6ioqBiRV2E3N/VyqMXHX8zJJcOqTnEjnVjz3Q668z+cFP1fEo0r08j4yVaOHw6QVxi6pVNdNCI5uM/H+aYwOflGqufZb3sGaySQnWdiSa6RC2fCHDvkZ9dbXrLzjFROt5HuSsznKBTUabkYob0ljNEksDk07HYNm0PDZtewWEXKnLOllLReitBwNEBXexSzRTBhqpXScnPKfc50k4uuomdwtr2Ko3srpsB5evM+gG5MTEsNIQRTZtgIBWM1jRaroLjs3WfihR7E2fYKYXM+/oz4lwuMVtQoQzGkCE8u4sHH4cHHkeebYoJqzxbkj76ONFtiYmrOFec/9XHUd29Gbn4dcf+fIKbPHvT2fD4fu3bt4siRI5hMJhYtWsS0adMwjtD/dX8oyo/3t1KRZeW+cleiw1EMMSIauNx8dxwh+4REh5M0lE+y0HIxzMG9fjI9xhuu/Hv7o+zd7qO3O8r4yVbGT049a/N4IISgqNRMfrGJpoYgDUeDbF7XR1GpiQlTbMPi8Bfw6zRfiPVL6miNICWYLQJdl0TeVtqpaTFL9yvCyu7QsNnF1WU2LeF1bbouuXg2zMljAfp6dGz22KC/uMw8rA58cUcY6c9ZTcQ6hrS2tbjPfYvevCcJ20oTE44QVM21Ewp5OVDjx2zRyC24cUZV6AFMvlNYfMcxe49jiPbSk/+UuugUR0bmaEqRlIiiUkRRKfJPPhTrVbX7LeTe7cg9W8CZhnjvU4ilD47KH3QA2Xwe+bNvx9IgV39o0Nvr6+vjV7/6FaFQiGnTpjFnzhxstpE9A/jLA230BqP8w/JitFH6ORpN2LveQuj+UdF8907QtNgga8v6Pg7U+K5rRttyKUztLh9ImLPYcdPB12jDYBCMm2ileKyZk0eDNJ4IcvFseMgc/nzeKJfOx8RTV3sUAIdTY9xEC/lFJjLcBoQQhEMSv0/H79PxeWP3/sv3rZfCBAPyhm1bbeJ6sXXNY6czih6VQyK2ohHJ2cYQp44H8Xt10tI1qufaKRhjGpS7XLIRSJ9B2JJPRvMvcF34Af2eB/FnLEzI+cdgEMxe6GDHpn727vAyf5mTzCwDhlALFt8JzL7jmPxNCHR0zULIVkFfWnVS9McaSSghpRh2hKZBRSWiohL5/o/DkTr0DS8jf/Fd5OH9aB/+9Kiro5KhYKxflMmI9okvDHp2Ttd11q1bRzQa5f3vfz8ejydOkSYvJzsC/LGhmwfHuxmXefs544rURAt3Ye/ZTiCtioilINHhJB1p6QYqp9k4XOvn7OkQY8aaaagPcvxwgHSXxqyFDhxOdVX6WsxmjcrpNkrLLXF3+OvrjdJ8WTz1dMXEU7pLY8IUK/lFJpzp2g0XEU1mgclsuGWqYTQqCfh0fNcILL9X4vPpdHfGxJrUr4sCiM1sGYwCo0lgNHL5Xly9v7Js4Pnlxybetp5Al5IzJ0OcPhEkFJS4swxMqXaQW5A4C/OhJmrJp6voU6S3/i9p7X/A4j1GyFZGxJxHxJKPbnSBGJ70RaNJMG+Rkcaa42inTuHubcSk9wAQMefhcy0m5BhP2FqiZqGGCCWkFAlFGE0wfTba1JnIja8gX/op+pc/g/bRzyEmVyc6vGFD/vJ7cL4J7TP/iMgcvOjZu3cvFy9e5N577x0VIiqqS75b00y6xcBT00b++1WAs2M9AN7M+xIcSfJSWmGm+WKYI3V+Lp0P09YcoajExNRZ9tROsxpi4uHwJ6Wkt/vqzFN/b0zRuLMMVE63kldkGrSQNRgEjjQDjlu43kkpCQbkwGyWyWCnu6efaFgSDksikdgtGo41xfV79diysCQSubNYcvKNlE+0kpltGLEC6lqkwUpP3lPYurdh79mN039q4DVdmImac4lY8i6Lq9i9NMSpn6GUGMKtmL0nsFyedSoqjBKKmrnUXYq9eDm4J466fnqJQgkpRVIgNA1xz3uRE6ah/+Df0Z//R8Q970E8+mGEaWSnnuhb1yO3b0CsegIxdeagt3fp0iV2797NhAkTmDhxYhwiTH7Wn+ymoSPA5xfk4zCrq24jHWPgPNb+OrzuZegmVQt3K4QQVM2x89brvbS3Rpg6w0ZJuXlUDHTjwS0d/qZbb9qnS0pJV/tl8XQhjN+rgwBPtpHScgt5haZhbXAshMBqEwM1ch6Pi/b221NIUkqiEa4RVlcF1rXPdR1yC2LpiKMOoeF3L8HvXoLQgxhCLRiDzRhDzRhDLVj6D2PTawZWjxrSrxNXUXMeEXP27TXE1UOY/acwe49j8R3HEOkGIGLOxedaSMg+nvZAETuO+LE2ayxc6cSsRvjDgvo3K5IKUVyG9n+/jvzNj5Ebfo88dhDt43+DKBiZOb2yqQH5y+9CZRXivR8Y9PaCwSDr1q0jLS2NZcuWjYoBU3cgws8OtDEt186SUmXnOuIZaL7rGLXNd+8Em11j4YqYu1ii3OhSnRsc/jbHHP4mTbORlqHR0Rrh0vkwzRdidUuaBp5cI+MrLeQWmrBYUsulDmIizGiKpY4xsktr44LULESsY66vP5ISLdo3IK4MoWaMwWbsvlMIYumdEo2oOZuIOZeIOZ+IJZeIOS+WHuhvxta9C4v3OCZ/I4IoujATtpfjdS8nZB9/3YWkDDvMWWRg1+Z+dm/xMn+5U808DwNKSCmSDmG2IJ58Fjl5JvpPX0D/588j/vTPEctGlhGF7OtF/85XId2N9vTfILTBD3Leeust+vr6ePzxx7FYRkdj0p/WthKM6DwzO3dEfT4UN8fsO4Y50Ehf9nuQmqqFux2UgBo8N3P427K+D5NJEA5LDAbIyTeRX2QiJ9+EyazORaMeIdCN6YSM6YQc468ul1EM4fars1fBZkyBs1j7D15dRRgRMkIaEDHl4HctIGgfH3MJfIcZrKwcIzPm29m7w8e+HV5mL3KMKLOPZEQJqRHKpb4QLx/t5L2TMslPS80u9WL6bLR/fAH9J9+M9aA6vC9mRJGe+qk8Uo+i//A/oLcL7Qv/ikgb/EzKsWPHOH78OHPnzh1xTXZvxZEWHxtP9/L45CyKMkaHcBzVyCjO9j8SMWXjT5+T6GgUo5BrHf5OHQsS9EtyC43k5JkwqKv/ittBGIiac4macwky/eriaODyzFULxlAr1swyOvVCdJP7jjafX2Rm2kzJwb1+6vb4qJ5rVxcZhxAlpEYYUkpeb+jmx/tbCUYlJzsD/Ot9JRhS9IqEyHCjffofkJv+gPzNT64aUUyZkejQBoV85UWor0V86JOIsopBb6+np4dNmzaRn5/P7NmD7z+VCkQuG0zkOIw8MSUr0eEohgFbbw3GcBvd+R9SDlSKhGI2a0yapnLeFPFDGqyEbaUDvamsHg96e/tdbatknIVgQHL8cACLNcDkKvVZHSpSL3FXcUvavGGe23iO79a0MCnbxtMzc2joCPBSfUeiQxsUQtPQVj6C9nf/Ac509G8+h77mh8hwKNGh3RXyQA3y1TWIhSsRi+8f9PauWJ0LIbj//vvRtNHxtf79sU7O9oT4+KxcLMbR8Z5HM0IP4OjcQMhaRsg+KdHhKBQKRVITs+03c/p4kJPHAokOZ8SiZqRGAFJKNjX28sO9LUR0ybOzc3mgwoUQgmPtftYcamdWgZOxKd5bRxSVov3dfyB/85OrRhRP/w2iMHWMKGRbM/p/fx3GjEU8+Wxcptv37NlDc3MzDzzwAOnpo8Nsoc0b5sWD7cwpcjKnKC3R4SiGAXvXZrSol/581XxXoVAo3g0hBFOqbYSCkqMHAlgsGsVlqVnqkcwoIZXidPsjfHtPM7vP91OZbeMz8/Ovq4l6ZnYeh1t8fHPnJf79gVJMQ9DRfDiJGVE8g5w6E/3H30T/f59HPP4RxPKHkj4HWAaD6N/+CiDQnv0iwjz4mp4LFy5QU1PDpEmTGD9+/Lv/wQjhR/takMDHZ+YmOhTFMKCFu7F3byPgrCJiLUp0OAqFQpESCE1QNddOKOTlQI0PISAtQwPEddejBh6La+7e9li87YUrfyMEmC1i1JpaKCGVwuw428t39rTgD+t8dEY2j0zIvKEWKt1i4C/n5vH/Nl9gzaF2PliVnaBo44uYOgvtuRfQf/wC8lffRx7ej/aRzyStEYWUEvmL78CFJrRP/z0iO2/Q2wwGg6xfv5709HSWLh09NtB7L/Sz81w/H6rKJsc5snuMKWI4OmPNd/uzVPNdhUKhuBMMBsHshQ52bOqndrdvSPYhNHA4NdLSDTjTNZzpBtLSNRxphhFvwa6EVBKwa9cu2tvbEUJgsVgwm8033K5dHhFGfn64my1nfYzLsvG5+fmMcd16dmNOURorx2bwUn0Hs4ucTPCMjKJDke5G+8w/IDf+AfmbH182ovgsYsrgm9rGG7llHXLnRsQj70dMnTX47UnJxo0b8Xq9PP7445jNo2O6PhjR+cHeForSzbx3Ymaiw1EMA8bABax9dfhci+/YvUqhUCgUsX5gC5Y7aW+NIKUE4PJdDAny7Y/lwNPrXrz276SM3fw+nb7eKL09UZovhK9bx+bQSLtGXDkviy2zeWTUNishlSSEw2G8Xi+hUIhgMEg4HH7H9U3ASgTmfhNvNltuKrosFgv5+fkUFxfzsZk5HGj28s2dl/jGg6UjpjhfCIFY+TBy4lT0H/w7+je/jFj5COKxDyNMySEuZOMJ5IvfhykzEQ+/Py7bPHr0KA0NDcyfP5+8vMHPbqUKvznSQXN/mH9aWZzyaaqK20BKnB2vITUbPveyREejUCgUKYvRJMgrHPosjmhU4uuPCav+3iv3UdpbI+jRq+tZrOIGcZWWbsBiFUlfqnEtSkglAfPmzcPj8dB+jc2llJJQKDRw6/X5+UN9Kwcu9JJjlSwttpNm1AdeDwaDhEIh/H4/PT09BINBgsEg+/btw2AwUFRUxKOeAv6n0cDPD7TxsRFWWyIKS2JGFC/9FPnmKzEjio//DaKwJKFxyb4e9O9+FTIy0Z7+PCIOjnrd3d1s3ryZwsJCZs5Mvtm3oeJCb4jf1neytDSdaXmORIejGAbMvuOY/afp8zyCNIyMmXSFQqEYyRgMgrQMA2kZ17eokLrE59OvEVc6/b1Rzp8JEblm7sBoAmeagbR0A1Nm2DCakltUKSGVpFxJ87NYLDGziH3dtHnt/El1ER+Y5sFsePcBeSQS4eLFizQ1NdHU1ET3mTMsBFp3OVjbPpZZkyvIz8/HYBgZ/ViEyYx4/8eRk2eg//h59H++xogiAZbgUo+i/+DfobcH7YtfQzgG7y4XjUZ5/fXX0TSN++67b9RYnUsp+V5NMxaD4KMzchIdjmI4GGi+m4U/QzXfVSgUilRGaAKH04DDaSC34OrMmJSSYEDS3xul77K46uvV6WiLYEgBlZICIY5eghGdnx1o45VjXeSnmfjKfWOYlG2/7b83Go2MGTOGMWPGsGTJErq7uzlx8jQb9h+l6fhhzh4/hNlspqSkhNLSUkpKSrDbb3/7yYqYOhPtuW+h/+QF5Is/QO7djvbhTyPyCoc1DvnyL+HoAcSHP40oGReXbe7atYvW1lZWrVpFWtrosf3efraPA80+PjErF7dNnbZGA9bevRjDrXTnfRCEOuYKhUIxEhFCYLUJrDYNTwomS6lfpyTlRLuf53de4kJviIfGu/iz6hysg6xrcrlczJk1A+eYifz9+tOs8AQoN3bT1NREQ0MDALm5uZSVlVFaWkp2dnZK5alei0h3oX3675E7NiJ//UP0L38G8Z4nEfetRgzDDJys24V87X8Ri+9DW3RvXLZ57tw59u3bx+TJkykvL4/LNlMBXzjKD/e1Mi7TwgMVyenKqIgvQg/i7NxAyFpKyFGZ6HAUCoVCobgpSkglGeGoZM2hdl6q7yDTZuTLK4qpyo9vPUhljp2HK3NYe7STBSsmsXLlStra2mhsbKSpqYldu3axa9cuHA4HpaWllJaWUlxcnHLOcEIIxMKVyMnV6L/8LvK3P0Xuuzw7VVw2ZPuVLRfR//t5KClHfOATcdmm3+9n/fr1uFwulixZEpdtpgq/PNhOtz/C3y4pvMHeXzEysXdtQYv205//IdV8V6FQKBRJixJSSURTV4Dnd16isSvIyrEZfGxmDg7z0MyePDXdw94L/Xxr1yVeeKiMnJwccnJymDt3Lj6fb6CuqqGhgSNHjqBpGoWFhQOzVS5X6swMCFcm2l98CfZtR//l92JNfB98HLHqCYQpvg42MhhA/85XQDOg/cUX4+IceMXq3O/388gjj2CKc8zJzOnOAH843sX9FS7GjxDbfsW7EOrC3r2VgHMaEeuYREejUCgUCsUtUUIqCYjqkv+pOcePdp3BaTbwt0sLmVs0tPUvZoPG5xbk84V1Z/jRvhY+O79g4DW73U5lZSWVlZVEo9HrDCu2bNnCli1bcLvdlJWVUVZWRn5+ftKbHgghYNYitInTkGt+hHx1DXLfjlgT37ET4rIPKSXy59+Gi2fRPvscIis+pghHjhzh1KlTLFq0iJyc0WO0oEvJd2taSDMb+ND0kdFIWvHuiHMvg9Tpz7o/0aEoFAqFQvGOKCGVBHxt2wV2netn4Zg0np2dS7p1eA5LRZaNxydn8evDHcwrTrupeDMYDBQXF1NcXMzixYvp7o7VVDU2NlJXV8f+/fuxWq2UlJRQVlZGSUkJFsutmwMnGuFMR3zsr5BzFqP/7NvoX/0CYuV7EKs/iBhk3PKt15C73kK89ynE5Oq4xNvZ2cmWLVsoLi6mujo+20wV3jzVw/F2P5+dn4/TMjKcJRXvjDF4Cdp24HctQjephssKhUKhSG6UkEoC7i938eDkQqqyhr8W4IkpHmou9PPt3c1M8tjeVcS5XC6qqqqoqqoiGAxy9uzZgdqq48ePo2kaBQUFA7NVyZoCKKbOQvvyfyJf+glyw8vIA7vR/uxTiInT7mp78tQx5JofwbTZiFV/GpcYI5EI69atw2g0cu+996as8cfd0BuI8NPaViqzbSwvS090OIohQEQDGMIdGMLtl28dmP1NYLDhdS9PdHgKhUKhULwrQkopEx1EIrl48WKiQwC4oSHvcNLUFeCvX29iblEaX1h8dxbhuq7T3NxMY2MjjY2NdHZ2AqRECqA8fgj9p9+CtmbEkvsRj30EYX9ng49rj5fs7Ub/p78Ckwnt776OcDjjEte2bdvYv38/Dz/8MGPHjo3LNlOFb+26xKbTPXxjVRklrsHNFCbyuzXaEXoAQ6jjbYKpE2O4HS3qvW7dqCGdqNmDccxDtEcLbrFFRTKhvluphTpeqYU6XslFQcHNf5fUjJSCUreV90/18PMD7Wxt6mVx6Z3PAFyZiSooKGDhwoX09PQMiKpkTwEUE6ai/eO3kL//JfKNl5EH96J96JOIabPf9W9lNIr+/X8Db9/lprvxEVFnz55l//79TJ06ddSJqKNtPjac6uFPJmUOWkSNZrRIL2bvUSze4wg9gNTMSM0Su4krj80Dz/Urr2lmpLjmNc38jn2chB7EEGq/LJY6BmaXjOEOtGj/detGDelETVkEHZVETVlETVlETB6ipkzQYsYsHrcH1OBBoVAoFCmAElIKAB6tzGL3+X6+V9PMlFz7oJueZmRkpFQKoLBYEH/6UeSsheg/eQH9W/+EmLsU8b6PI9JuLSzl734Gxw8hPvo5xJj4CB6fz8f69evJzMxk0aJFcdlmqhDVJd/d04LHbuR9Uz2JDie1kBJDqBWLtx6Ltx5T8DwAUaObqNGFFu1DhDsQehChhxAyhOD2EhIkhhuEGIAW6cJwS7E0kajJc1ksxUTTFbGkUCgUCsVIQAkpBQAGTfC5+fn81R+b+K/dl/i7pUVxq8mxWCxUVFRQUVFxQwrg1q1b2bp1K263m/LycsrLy/F4PAmrBxJl49H+/hvI1/43dquvQ3zgGcSshTfEJPfvQK77LWLpA2gLVsRl/1JK3nzzTQKBAKtXrx5VVucAfzjRRVN3kC8uKcRmSr400KRDRjEFzmLx1mP21mMMx1Jqw5Yi+jPvI+iYRNSce/NeTFIHGUHTgzFxJUNXRdZNn1/zWA8h0Ak5Jl6eWfIosaRQKBSKUYcSUooBijIsfKgqmx/ta2Xj6R5Wjov/LNGtUgBPnz7N3r17qampweVyUV5eTkVFRUJElTCaEO95EjljAfpPXkB+/2vIPfPQnnoG4coCIHLhDPqPvwll4xHv+3jc9n3o0CEaGxtZsmQJHs/ompHp8IX5xYF2ZhY4mFcUnxTJEYkewuxruDzzdAxN9yExELKPw+daQsgxEd2Y8e7bERoIM7pmBoa23YJCoVAoFCMRJaQU1/HwBDe7zvXxw32tTMtzkO0Y2hmRa1MAfT4fp0+fpqGhgX379rF3714yMjKoqKigvLyc7OzsYRVVoqgU7Uv/FnP1e/mX6P/wKcQTf46YtYjur/0tGE1oz/6fuDX17ejoYOvWrZSUlEpn4iEAAB9SSURBVDB9+vS4bDNViOqS7+9tQZeST8zKHVUOhbeDFum7XO9Uj9l/CiEj6JqVkH0iQWclIft4pKbqyRQKhUKhGE6Ua59y7buB5r4Qn32tkQkeG19eUZyQQa3f7+fUqVM0NDRw/vx5pJRkZGQMzFQNt6iSzRfQ/+db0FAPaRkxc4nPfRkxKT6CJxKJsGbNGnw+H0899RR2uz0u200FzvcEeX7nJRo6Any4KptHJ2fFdfvJ9N26baTEEG6LzTr112MMnkcgiRpdBB2VBB2VhG2lIEZef62UPF6jFHWsUgt1vFILdbySC+Xap7ht8tLMfHRGDt/Z08LrDd08ON497DHYbDamTJnClClTBkTVyZMn2b9/P/v27Rt2USXyCtH+5l+QW15H/u5nOP/sk/jjIKKi0ejA++ro6OA973nPqBFRupS8eryLn9W1YTFqfGFRAQtLRnHPKKljCpzFfNkswhjuACBsKcSbuZKQo5KIOe/m9U4KhUKhUCiGHSWkFDfl/nIXO8/18+P9rVTlO8hPS1wB+buJqvT09AEzi6EUVULTEMtWIZc+iCM7G/8grhQFAgEOHz7MgQMH8Hq9uN1u7rvvPkpLS+MXcBLT0h/ihZ2XONzqZ3ahk7+cmzdop8hURehBrL37sPXsxBhuj9U72cbidy0k6Ki8vXonhUKhUCgUw87oHLko3hUhBJ+el8dnXm3khZ2X+Od7xmDQEn8l/O2i6kpNVW1t7XWiqry8nJycnCERVYPZZnd3N3V1ddTX1xOJRCguLmblypWUlJSMirogKSUbTvXww32tCODT8/JYOTZjVLz3t6OFO7F378DatxdNDxK2FNGb86cEHZVIgzXR4SkUCoVCoXgXlJBS3BKP3cTTs3L55s5LvHK8k9WT4lu7MlhsNhuTJ09m8uTJBAKBgZmqa0XVuHHjKC0tJT8/H6MxMR93KSUXLlygtraWxsZGNE1jwoQJVFdXjypnvk5/hP/adYm9F71MzbXzmXn55DhHl707UmLyn8beswOz9yggCDqn4HMtJGIdk+joFAqFQqFQ3AFKSCnekeVl6ew618fP69qZWeCkOCM5ncGsVut1ourKTNWBAweora3FZDJRVFREaWkpJSUlpKcPfS1ONBodmC1ra2vDarUyZ84cpk6disPhGPL9JxNbm3r5Xk0zwajk6Zk5PDTBjTaaZqH0MNa+Ouw9OzCGmtE1Bz73MvwZc1XqnkKhUCgUKYoSUop3RAjBJ+fk8ak/NPL8jkt87f6SpEjxeyesViuVlZVUVlYSCoU4f/48Z86c4cyZMzQ2NgLgdrspKSmhtLSUgoKCuM5WBQIBDh06xMGDBwfqn1asWMHEiRMTNiuWKHqDUb5X08y2M31UZFn53IJ8itKTU4wPBVqkB1vPLmw9e9B0H2FzHr05jxFwTgdtlM3GKRQKhUIxwhhdozrFXeGyGfmL2bl8bdtFXjrSwRNTUycdzWw2M3bsWMaOHYuUkq6urgFRdejQIerq6jAajRQVFQ0Iq4yMu5sh6Orqoq6ujqNHj47K+qe3s/dCP/+56xJ9oShPTffwWGVW0ovwuCAlxsBZ7D07sPQfBiQhxyR8GQsJ28qU655CoVAoFCMEJaQUt8XCknQWn+tjzeF2ZhU6GZuZesXwQggyMzPJzMykurqacDg8MFvV1NREU1MTmzdvxuVyUVJSQklJCUVFRe84iySl5Pz589TW1tLU1ISmaUycOJGqqqpRVf90Lb5wlP/e18obp3oocVn4h+XFKfl5uWNkBEv/IezdOzAFz6NrVvyuBfgy5qObMhMdnUKhUCgUijijhJTitvnE7DwOt/j4ypYLfHZ+PlNyU7vfkclkoqysjLKyMqSU9PT00NTUxJkzZwasyQ0Gw3WzVS6XC4g10D169Ci1tbW0t7djs9mYM2cO06ZNGzV9oG7G4RYf39x5iXZfmMcqM/nANA8mg5bosIYUEenH1rsbW89uDNE+IiYPfZ73EEifgdRGTxqjQqFQKBSjDSGllIkOIpFcvHgx0SEAqdPB+ni7n69vv0hzf5h7xmXwkeoc0iyGRIcVdyKRyHW1Vd3d3QBkZGRQUFDA+fPn6evrG5jdmjBhwqirf7qWYETn5wfaeOVYF3lpJj47P59J2ckhKIfqu2UMXsTWvQNrXx2CKEH7ePwZCwjZK0CMbPE4lKTKuVChjlWqoY5XaqGOV3JRUFBw0+Wjd+SnuCsmeGy88FAZaw6187ujndRc6OdjM3JYUpo+ouqAjEYjpaWlAw1yu7u7B0TVqVOnGDNmDCtWrGDMmDEj6n3fDQ0dfp7fcYnzvSFWjXfx4eocrMYRKCRkFFPgHGbfScy+E5iC59CFGX/GbPwZ84macxIdoUKhUCgUimFECSnFHWMxavxZdUw8/efuZr6+4xKbGnv5izm55DrNiQ5vSHC5XLhcLqZPnw6oK0UAEV2y5lA7vznSgdtm5MsriqnKH0G27lJiCLVg9p/E7DuJyd+IJkNIBBFLIX1Zqwikz0IabImOVKFQKBQKRQJQQkpx15S6rfzrfSW83tDN/9S18alXG/nANA/vnZg5OtzZRjFnuoM8v+Mip7uCLC9L5+lZuTjNqZ/iqUV6Ls84ncTkP4Uh2gdAxOQhkD6DkK2csG2sEk8KhUKhUCiUkFIMDoMmeGiCm7nFTr5f08JPa9vY0tTLX87NoyJLDTaTlYgu8Yd1AhEdf1jHf+X+2se3eC0Q1jnZGcBh0vjSkkLmFacl+u3cNSIawOQ/PTDrZAy3AaAbnIRs4/DaywnZytFNrgRHqlAoFAqFItlQQkoRFzx2E3+7tIid5/r4fk0LX1h3hlXj3Tw13YPdlPozFalGpz/C3gv91F7y0uWP3CCIwvrtecxoAmwmDZtRw2rUYo9NGivHZvDk9P+/vXuPjeq++zz+PufMjGc8Yw+2x9hgoGkgsCGFktSQNk0JzkXPKk2jNGrapuq2hGZ7IS2rRG3jtCzJpkVKq7p0K+EnSYVQlVXS1ZOFttrdqhJtEtqmCSbcnsADwUC2QMCO8WXGl/HMnPPbP8YMdoCU6WN7POPPS7LOZS78fnw19vnM7ze/iTEjWGS/QkwGf/JvuVEn3/ApLAzG8pMKfZCh6ApSoQW4gTp935OIiIi8ryK7CpKp7mNzK1haV87/2P8u/+dID389meDry+tYMad4Ry2KgWcMx7uH2X26n12n+znWnQSgttzHrIoAlRE/oVFBKOSzCY5sQ6O3/lGByWcTcKziXkzDS8PA3wj1vJEddRo6gWXSGGwywTkMVjWRKl9AOjgXLP06FBERkSunKwcZd+GAw9eW17Pqg1E2v3aWja+c5mNzK/jPjTOpKfcXunklI5nx2H92gN2n+2k7nR15ssiurPifPlzL8jkR5kUDxR2ELsVLY7v9Iz8J7Mxl9t1+bG8YgAog45/JUGXjSHC6GuNMgy8JFhERkQmjICUTZlEsxE/vvIpf/1s3//Nfu9j/vwf40rJa/umaGdildnE/Sd4dSI8Ep34OnB0k7RlCPpsbZodpbIjwkdlhosU23W6E5Q7gpLuzAei9gSiTuBCevOQlH+/ZQTynAs+JkCmbndsvr55Ld6YWzxed5B6JiIhIKSvOKy4pGj7b4jPX1fDxeRX8866zPN3Wwcsn4qy9sZ4PzCgrdPOmPNcztHcnaTuVDU9v92ZHWOojfv7jwhksb4iwuLYcv1OkwdR4BAbbCcZ3UTbwb1h4Y24eG45m4TmR7LEvMrJ/4fhyU/PKYzG8ab5UvYiIiIw/BSmZFLMqAvy3W+fy8ok4W/Z08vD/PcG9i2v47JIaAk4Jfnnrv8Ng2mXfmQHaTg/wxul++oZdbAsW14ZYfX0tyxsiNFQW95Q9K5MglHiDUF8bTqYbzwkzOONm0qGrLoQjJwy2poKKiIjI1KQgJZPGsiyaro7ykdlhtu7t5F8OnuPPf4uzdkU9S+tL6Itcr0DGMwymXAbSHv0pl4GUx8m+YdpO93Owc5CMB+GAzUdmRVg+J8L1s8JUlBX56ofG4B86Tii+i7L+g1i4pEJX01/zTwxHFmuxBxERESkqunKRSVcZ9PFfPjabVR+M8s+7zvJf/3CSW6+u5BMfqGR2RYDasH/Kf6Gv6xkSwy4DI2FoYCQM9adcBtLZ/fPnLhxfuD2ZufTy43MqA9y1qJoVDRH+Q21oyv8/XAnLHSAY30Mo3oYv/S6eHWIo+lGGoitwAzML3TwRERGRf4iClBTMh+vD/Pc7P8i/vHmObYfO8cfjcSD7uar6iJ/ZlQFmV4z8VPqZXRGgOuSblCltg2mXzv40HQPpMdvOgezPQMp738fbFpT7bcIBh7DfJhJwmF3pJxwIEj5/PmAT9o9sAw6xch91kcCE921SGIM/+f8IxncR7P9XLJMhFfwA8ar7SEaWaMqeiIiIFD0FKSmoMp/NF5fVcve11ZzsG+ZMIsXpeIp3EinOxNPsfWdgzJfHBn0Ws0bC1ayKAA25sOWnosy54pA1lPZ4dyBNx6hw1NGfyu73p0m8JygFfRYzw37qIn6urQ0xq6oSK5PMBaLIqEAUDmS/i2k6rkxouUmCiT2E4rvwpTrwrDKGKhsZqlyBWzar0M0TERERGTcKUjIlVJY5XDeznOtmlo857xnDucEMp+OpbMhKpDgTT3GiJ8lrJxO4o2bIRQJ2NlxVBJg1ErDK/fYlAlOa+LA75t8JOBeC0sKaEDMjfurC/tz2vSEtFovRpZXgcnzJU4TirxNM7McyadJlDcRr72W4YinG1uqMIiIiUnoUpGRKsy2L2rCf2rCfZbPGLkiR8Qyd/WneSWRHsN4ZGck62DnIK2/HGf0pJL+dfZ6ZET8fmxtkZsSfC051YT/R4JWPZkmW5Q1TlthPKP46/uF3MJafZMUyhipXkAnOKXTzRERERCaUgpQULZ9tZT9HVXnx54qGMx5n+9MMpT1qwz6qQr5pOdVu3BgXO5PAyfRiZ/rwD50gmNiHbYbJBOpJ1N5NMnI9xgkWuqUiIiIik0JBSq6cyWBn+gEPzxcFa+oux13ms/WFv1fKGCx3ACfTh53pzYUlJ9M7cq4POxPHGjXGZywfycgShipvJBOcBwqpIiIiMs0oSEkuINluPDvqMLK1M3FsN7t13AS2O3DhIdh4viiuvxrXX4Xrq76w76/G2GFdXE8RlpscCUh9uYB04bgXJxPHMpkxjzGWD9cXxfPNIBWaj+ebMXIcxfXPwPVVgV0iKwyKiIiI/AMUpKYAy0tCOoGVCyrWyA+jjs9vLpw3l7oPXAgwxmC7A3kFpAvPbeM5ETxfBZ6/ikxwHq6vEs+pBMvCSXfnfgIDh3Hc/jGP96wAnn8kXPmqRkLWhWMtfz3OjMF24zipTnypDnypzty+7SXH3hUbz1eB65tBpmwOw+EZ2YDkG9n6owrCIiIiIn+HgtQUUNHxv7CPv0ntJPxb7xeQPF8Fnq8Sz6nAc8Jg2Vf+xF4qG6wyPaNCVg9O+hyBwaNYJj3m7q5Tgeuvxhs9muWLZtvgVGLsoC7kL2UkMPlSHSNB6dKBybPDZAIzSUY+nP1/Pj+i5J+B50Sm9LRMERERkWKgIDUFJCsbCdR+iIH+ARiz1tyofTP2vHWp+2BGHRrAwnPKs+HoHw1IV8oO4JbV45bVX3ybMVhu/0jQOh+wsmHLP/Q2ZZn9Yz5/A2AsP+5IqDrfdjfXjwuhr2SX1r5EYDq/f8nAVLEM1z+TTKCOTGAmxhcpYONFRERESp+C1BSQCi+CWIyhUv1eIsvC+CrI+CrI8IGLbzcZnHTvyBTE0dMQs/u+4dMjn+NJX/RQzyobCYrZ0OWO2s+GrgieExoZ4ZpCozAmg+0OYrmD2emX3iB2pm/MtLwxgckJkwnUZQNTYCYZfx2ZspkYR4FJREREpBAUpKTwLB9uIIZL7PL3MQbLDOfCle3GcTLxUZ/3SuBP/o0y9+KFE87zrDKME8KzQ+/ZlmPs0EjgCmGcYPbc+fvYwfcfxTPuSCgawHYHsb1R++4AljcSls4fu4PYZvjSbXxvYDo/wqTAJCIiIjKlKEhJcbAsjBXEDQRxAzMvfz9jsLzkSMCKY7v92O4Qljc0sh3EdpNY3hC+dBdWchDbG7ps+ILsREpjl40KW0GsM4aa4b7s871nMYfRPCuAccLZKZZOGDcQw7MvHBun/MKxrwLjhC/7XCIiIiIydShISWmxLIwTwnVCuNRd+eO8dDZQ5QLX6O1gdusNYblD2VUWfWHSViXeSEgyThjPLh977JSDpZeYiIiISCkqqau8ffv2sXXrVjzP47bbbuOee+4pdJOkWNh+PNsPVOJewd1jsRjxUv1Mm4iIiIj8XROwfFtheJ7Hli1b+N73vsemTZv4y1/+wqlTpwrdLBERERERKUElE6Ta29upr6+nrq4On8/HTTfdRFtbW6GbJSIiIiIiJahkglR3dzc1NTW545qaGrq7uwvYIhERERERKVUl8xkpY8xF5yzLuujcjh072LFjBwBPPfUUsdj7LLk9iXw+35Rpi/x9qlfxUK2Ki+pVPFSr4qJ6FRfVqziUTJCqqanh3LlzueNz585RVVV10f1uv/12br/99txx1xRZMCAWi02Ztsjfp3oVD9WquKhexUO1Ki6qV3FRvaaW2bNnX/J8yUztmz9/PmfOnKGzs5NMJsOrr75KY2NjoZslIiIiIiIlqGRGpBzHYc2aNWzcuBHP82hqamLu3LmFbpaIiIiIiJSgkglSADfccAM33HBDoZshIiIiIiIlrmSm9omIiIiIiEwWBSkREREREZE8KUiJiIiIiIjkSUFKREREREQkTwpSIiIiIiIieVKQEhERERERyZOClIiIiIiISJ4UpERERERERPKkICUiIiIiIpInBSkREREREZE8WcYYU+hGiIiIiIiIFBONSE0Rzc3NhW6C5EH1Kh6qVXFRvYqHalVcVK/ionoVBwUpERERERGRPClIiYiIiIiI5Ml54oknnih0IyTr6quvLnQTJA+qV/FQrYqL6lU8VKvionoVF9Vr6tNiEyIiIiIiInnS1D4REREREZE8+QrdgFLW2trKnj17iEajtLS0APD222/zi1/8gmQySW1tLevWraO8vJxMJsPTTz/NiRMn8DyPlStX8ulPfxqAffv2sXXrVjzP47bbbuOee+4pZLdK0njV6qGHHiIYDGLbNo7j8NRTTxWyWyUr33o9++yzHDt2DNu2Wb16Nddddx0Ax48fZ/PmzaRSKa6//noeeOABLMsqZNdKznjV6oknnqCnp4dAIADA+vXriUajBetXqerq6mLz5s309vZiWRa33347d955J/39/WzatIl3332X2tpaHn74YSKRCMYYtm7dyt69eykrK2Pt2rW56Ugvv/wy27ZtA+Dee+9l1apVBexZ6RnPWn3uc59j3rx5AMRiMR599NFCdq0k5Vuv06dP09rayokTJ/j85z/P3XffnXsuXRdOIUYmzMGDB82xY8fMI488kjvX3NxsDh48aIwx5g9/+IN54YUXjDHG/OlPfzKbNm0yxhiTTCbN2rVrTUdHh3Fd13zzm980Z8+eNel02nz72982J0+enPzOlLjxqJUxxqxdu9b09fVNcuunn3zq9bvf/c5s3rzZGGNMb2+v+e53v2tc18095siRI8bzPLNx40azZ8+eSe5J6RuvWj3++OOmvb19kls//XR3d5tjx44ZY4wZHBw069atMydPnjTPPfec2b59uzHGmO3bt5vnnnvOGGPMG2+8YTZu3Gg8zzNHjhwxjz32mDHGmEQiYR566CGTSCTG7Mv4Ga9aGWPMF7/4xcnvwDSTb716e3vN0aNHzfPPP29+85vf5J5H14VTi6b2TaDFixcTiUTGnHvnnXe49tprAVi6dCmvv/567rZkMonruqRSKXw+H+Xl5bS3t1NfX09dXR0+n4+bbrqJtra2Se3HdDAetZLJk0+9Tp06xYc+9CEAotEo4XCY48eP09PTw9DQEAsXLsSyLFauXKnX1gQYj1rJ5KmqqsqNUoRCIRoaGuju7qatrY1bbrkFgFtuuSX3Wtm9ezcrV67EsiwWLlzIwMAAPT097Nu3j6VLlxKJRIhEIixdupR9+/YVrF+laLxqJZMj33pFo1EWLFiA4zhjnkfXhVOLgtQkmzt3Lrt37wbgtdde49y5cwB89KMfJRgM8tWvfpW1a9fyqU99ikgkQnd3NzU1NbnH19TU0N3dXZC2Tzf51uq8jRs38uijj7Jjx46CtHu6uly9rrrqKnbv3o3runR2dnL8+HG6urr02iqgfGt1XmtrK9/5znd48cUXMVonacJ1dnZy4sQJFixYQF9fH1VVVUD2gjAejwPQ3d1NLBbLPeb86+i9r6/q6mq9vibQv6dWAOl0mubmZr7//e+za9euye/ANHMl9boc/e2aWvQZqUn2jW98g61bt/Liiy/S2NiIz5ctQXt7O7Zt88wzzzAwMMCGDRtYsmTJJS8W9BmOyZFvrerq6vjBD35AdXU1fX19/PCHP2T27NksXry4wD2ZHi5Xr6amJk6dOkVzczO1tbUsWrQIx3F0IV5A+dYKYN26dVRXVzM0NERLSws7d+7MvYsr4y+ZTNLS0sLq1avfd8Q9n79R+ts1McajVq2trVRXV9PR0cGTTz7JvHnzqK+vn7A2T2dXWq/L0XXh1KIgNckaGhpYv349kJ3esmfPHgD+/Oc/s2zZMnw+H9FolEWLFnHs2DFisVju3VqAc+fO5d65kImVb63q6uqorq4GskPyy5cvp729XUFqklyuXo7jsHr16tz91q9fz6xZswiHwxe9ts7XTyZWvrUCcrUJhULcfPPNtLe3K0hNkEwmQ0tLC5/4xCe48cYbgezvtJ6eHqqqqujp6aGyshLIvhs+etTw/N+o6upqDh06lDvf3d2t34UTYDxqBRdeX3V1dSxevJi3335bQWoC5FOvy6mpqdF14RSiqX2TrK+vDwDP89i2bRt33HEHkF0l580338QYQzKZ5OjRozQ0NDB//nzOnDlDZ2cnmUyGV199lcbGxkJ2YdrIt1bJZJKhoSEg+47TgQMHcqsgycS7XL2Gh4dJJpMAHDhwAMdxmDNnDlVVVYRCId566y2MMezcuVOvrUmSb61c181Nd8lkMrzxxhvMnTu3MI0vccYYnn76aRoaGrjrrrty5xsbG3nllVcAeOWVV1i+fHnu/M6dOzHG8NZbb1FeXk5VVRXLli1j//799Pf309/fz/79+1m2bFlB+lSqxqtW/f39pNNpAOLxOEeOHGHOnDmT36ESl2+9LkfXhVOLvpB3Av3sZz/j0KFDJBIJotEon/3sZ0kmk/z+978HYMWKFXzhC1/AsiySySStra2cOnUKYwxNTU25pS737NnDL3/5SzzPo6mpiXvvvbeQ3SpJ41Grjo4OfvKTnwDgui4333yzajVB8qlXZ2cnGzduxLZtqqur+frXv05tbS0Ax44do7W1lVQqxbJly1izZo2mSIyz8ahVMpnk8ccfx3VdPM9jyZIlfPnLX8a29V7geDt8+DAbNmxg3rx5udfC/fffzzXXXMOmTZvo6uoiFovxyCOP5JbU3rJlC/v37ycQCLB27Vrmz58PwB//+Ee2b98OZJc/b2pqKli/StF41erIkSM8++yz2LaN53l88pOf5NZbby1w70pPvvXq7e2lubmZoaEhLMsiGAzy05/+lPLycl0XTiEKUiIiIiIiInnS23kiIiIiIiJ5UpASERERERHJk4KUiIiIiIhInhSkRERERERE8qQgJSIiIiIikicFKRERERERkTwpSImISEn6+c9/Tmtr65hzhw4dYs2aNfT09BSoVSIiUioUpEREpCQ98MAD7N27lwMHDgCQSqV45pln+NKXvkRVVdW4/Tue543bc4mISPHwFboBIiIiE6GiooI1a9bwzDPP0NLSwrZt26irq2PVqlV4nsevf/1rXnrpJQYHB1myZAkPPvggkUgEz/PYtGkThw8fJp1Oc9VVV/Hggw8yZ84cIDvSVV5eTkdHB4cPH6a5uZnrrruuwL0VEZHJZhljTKEbISIiMlFaWlrIZDIcOXKEH//4x8RiMX7729/S1tbGww8/TCQSYcuWLWQyGb71rW/heR47d+7kxhtvxHEcnnvuOY4ePcpTTz0FZIPU3r17eeyxx1iwYAGu6+L3+wvcSxERmWya2iciIiXtK1/5Cm+++Saf+cxniMViAOzYsYP777+f6upqAoEA9913H3/961/xPA/btlm1ahWhUCh32/Hjx0kmk7nnXL58OQsXLsS2bYUoEZFpSlP7RESkpM2YMYPKysrc1DyArq4ufvSjH2FZVu6cZVnE43EqKyt5/vnnee2110gkErn7JBIJgsEgQC6QiYjI9KUgJSIi005NTQ3r1q3jmmuuuei2l156ib1797JhwwZqa2tJJBI8+OCDaCa8iIiMpql9IiIy7dxxxx288MILdHV1AdDX18fu3bsBGBoawufzUVFRwfDwML/61a8K2VQREZmiNCIlIiLTzl133QXAk08+SW9vL9FolI9//OM0NjbS1NTEgQMH+NrXvkZFRQX33XcfO3bsKHCLRURkqtGqfSIiIiIiInnS1D4REREREZE8KUiJiIiIiIjkSUFKREREREQkTwpSIiIiIiIieVKQEhERERERyZOClIiIiIiISJ4UpERERERERPKkICUiIiIiIpInBSkREREREZE8/X+koosor9BahgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# index value type\n",
    "[type(x) for x in top5.index]      # str\n",
    "\n",
    "top5.index = top5.index.map(int)   \n",
    "[type(x) for x in top5.index]      # changed into int\n",
    "\n",
    "# plotting\n",
    "top5.plot(kind='line', figsize=(14, 8))\n",
    "\n",
    "plt.title('Immigration Trend of Top 5 Countries')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Numbero of Immigrants')\n",
    "plt.text(1994, 40000, 'UK immigrants number dropped', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "<!-- The correct answer is:\n",
    "\\\\ # Step 1: Get the dataset. Recall that we created a Total column that calculates the cumulative immigration by country. \\\\ We will sort on this column to get our top 5 countries using pandas sort_values() method.\n",
    "\\\\ 'inplace = True' paramemter saves the changes to the original df_can dataframe\n",
    "df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
    "-->\n",
    "\n",
    "<!--\n",
    "# get the top 5 entries\n",
    "df_top5 = df_can.head(5)\n",
    "-->\n",
    "\n",
    "<!--\n",
    "# transpose the dataframe\n",
    "df_top5 = df_top5[years].transpose() \n",
    "-->\n",
    "\n",
    "<!--\n",
    "print(df_top5)\n",
    "-->\n",
    "\n",
    "<!--\n",
    "\\\\ # Step 2: Plot the dataframe. To make the plot more readeable, we will change the size using the `figsize` parameter.\n",
    "df_top5.index = df_top5.index.map(int)    # let's change the index values of df_top5 to type integer for plotting\n",
    "df_top5.plot(kind='line', figsize=(14, 8))    # pass a tuple (x, y) size\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.title('Immigration Trend of Top 5 Countries')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "plt.xlabel('Years')\n",
    "-->\n",
    "\n",
    "<!--\n",
    "plt.show()\n",
    "-->"
   ]
  },
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   "source": [
    "### Other Plots\n",
    "\n",
    "Congratulations! you have learned how to wrangle data with python and create a line plot with Matplotlib. There are many other plotting styles available other than the default Line plot, all of which can be accessed by passing `kind` keyword to `plot()`. The full list of available plots are as follows:\n",
    "\n",
    "* `bar` for vertical bar plots\n",
    "* `barh` for horizontal bar plots\n",
    "* `hist` for histogram\n",
    "* `box` for boxplot\n",
    "* `kde` or `density` for density plots\n",
    "* `area` for area plots\n",
    "* `pie` for pie plots\n",
    "* `scatter` for scatter plots\n",
    "* `hexbin` for hexbin plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "This notebook was originally created by [Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan) with contributions from [Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani), and [Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic).\n",
    "\n",
    "This notebook was recently revised by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab session interesting. Feel free to contact me if you have any questions!"
   ]
  },
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   "source": [
    "This notebook is part of the free course on **Cognitive Class** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this free self-paced course online by clicking [here](https://cocl.us/DV0101EN_Lab1)."
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "<hr>\n",
    "\n",
    "Copyright &copy; 2019 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
   ]
  }
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