IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork_jupyterlite_files_DV0101EN-Exercise-Introduction-to-Matplotlib-and-Line-Plots.jupyterlite.ipynb

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    {
      "cell_type": "markdown",
      "source": "## Introduction\n\nThe aim of these labs is to introduce you to data visualisation with Python as concrete and as consistent as possible.\nSpeaking of consistency, because there is no *best* data visualization library available 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\nPlease make sure that you have completed the prerequisites for this course, namely [**Python Basics for Data Science**](https://www.edx.org/course/python-basics-for-data-science-2?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) and [**Analyzing Data with Python**](https://www.edx.org/course/data-analysis-with-python?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\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 [**Analyzing Data with Python**](https://www.edx.org/course/data-analysis-with-python?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\n\n***\n",
      "metadata": {
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    },
    {
      "cell_type": "markdown",
      "source": "## Table of Contents\n\n<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n\n1.  [Exploring Datasets with *pandas*](#0)<br>\n\n1.1 [The Dataset: Immigration to Canada from 1980 to 2013](#2)<br>\n1.2 [*pandas* Basics](#4) <br>\n1.3 [*pandas* Intermediate: Indexing and Selection](#6) <br>\n2\\. [Visualizing Data using Matplotlib](#8) <br>\n2.1 [Matplotlib: Standard Python Visualization Library](#10) <br>\n3\\. [Line Plots](#12)\n\n</div>\n",
      "metadata": {
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      }
    },
    {
      "cell_type": "markdown",
      "source": "# 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/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01):\n\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\nThe 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](http://pandas.pydata.org/pandas-docs/stable/api.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "## The Dataset: Immigration to Canada from 1980 to 2013 <a id=\"2\"></a>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "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?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).\n\nThe 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\nIn this lab, we will focus on the Canadian immigration data.\n\n![Data Preview](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%201/images/DataSnapshot.png)\n\nThe Canada Immigration dataset can be fetched from <a href=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01\">here</a>.\n\n***\n",
      "metadata": {
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        "new_sheet": false,
        "run_control": {
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        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "## *pandas* Basics<a id=\"4\"></a>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "The first thing we'll do is install **openpyxl** (formerly **xlrd**), a module that *pandas* requires to read Excel files.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['openpyxl==3.0.9'])",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Next, we'll do is import two key data analysis modules: *pandas* and *numpy*.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "import numpy as np  # useful for many scientific computing in Python\nimport pandas as pd # primary data structure library",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Let's download and import our primary Canadian Immigration dataset using *pandas*'s `read_excel()` method.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "from js import fetch\nimport io\n\nURL = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx'\nresp = await fetch(URL)\ntext = io.BytesIO((await resp.arrayBuffer()).to_py())\n\ndf_can = pd.read_excel(\n    text,\n    sheet_name='Canada by Citizenship',\n    skiprows=range(20),\n    skipfooter=2)\nprint('Data downloaded and read into a dataframe!')",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 4,
      "outputs": [
        {
          "name": "stdout",
          "text": "Data downloaded and read into a dataframe!\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Let's view the top 5 rows of the dataset using the `head()` function.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "df_can.head()\n# tip: You can specify the number of rows you'd like to see as follows: df_can.head(10) ",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 5,
      "outputs": [
        {
          "execution_count": 5,
          "output_type": "execute_result",
          "data": {
            "text/plain": "         Type    Coverage          OdName  AREA AreaName   REG  \\\n0  Immigrants  Foreigners     Afghanistan   935     Asia  5501   \n1  Immigrants  Foreigners         Albania   908   Europe   925   \n2  Immigrants  Foreigners         Algeria   903   Africa   912   \n3  Immigrants  Foreigners  American Samoa   909  Oceania   957   \n4  Immigrants  Foreigners         Andorra   908   Europe   925   \n\n           RegName  DEV             DevName  1980  ...  2004  2005  2006  \\\n0    Southern Asia  902  Developing regions    16  ...  2978  3436  3009   \n1  Southern Europe  901   Developed regions     1  ...  1450  1223   856   \n2  Northern Africa  902  Developing regions    80  ...  3616  3626  4807   \n3        Polynesia  902  Developing regions     0  ...     0     0     1   \n4  Southern Europe  901   Developed regions     0  ...     0     0     1   \n\n   2007  2008  2009  2010  2011  2012  2013  \n0  2652  2111  1746  1758  2203  2635  2004  \n1   702   560   716   561   539   620   603  \n2  3623  4005  5393  4752  4325  3774  4331  \n3     0     0     0     0     0     0     0  \n4     1     0     0     0     0     1     1  \n\n[5 rows x 43 columns]",
            "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>Type</th>\n      <th>Coverage</th>\n      <th>OdName</th>\n      <th>AREA</th>\n      <th>AreaName</th>\n      <th>REG</th>\n      <th>RegName</th>\n      <th>DEV</th>\n      <th>DevName</th>\n      <th>1980</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>Immigrants</td>\n      <td>Foreigners</td>\n      <td>Afghanistan</td>\n      <td>935</td>\n      <td>Asia</td>\n      <td>5501</td>\n      <td>Southern Asia</td>\n      <td>902</td>\n      <td>Developing regions</td>\n      <td>16</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>Immigrants</td>\n      <td>Foreigners</td>\n      <td>Albania</td>\n      <td>908</td>\n      <td>Europe</td>\n      <td>925</td>\n      <td>Southern Europe</td>\n      <td>901</td>\n      <td>Developed regions</td>\n      <td>1</td>\n      <td>...</td>\n      <td>1450</td>\n      <td>1223</td>\n      <td>856</td>\n      <td>702</td>\n      <td>560</td>\n      <td>716</td>\n      <td>561</td>\n      <td>539</td>\n      <td>620</td>\n      <td>603</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Immigrants</td>\n      <td>Foreigners</td>\n      <td>Algeria</td>\n      <td>903</td>\n      <td>Africa</td>\n      <td>912</td>\n      <td>Northern Africa</td>\n      <td>902</td>\n      <td>Developing regions</td>\n      <td>80</td>\n      <td>...</td>\n      <td>3616</td>\n      <td>3626</td>\n      <td>4807</td>\n      <td>3623</td>\n      <td>4005</td>\n      <td>5393</td>\n      <td>4752</td>\n      <td>4325</td>\n      <td>3774</td>\n      <td>4331</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Immigrants</td>\n      <td>Foreigners</td>\n      <td>American Samoa</td>\n      <td>909</td>\n      <td>Oceania</td>\n      <td>957</td>\n      <td>Polynesia</td>\n      <td>902</td>\n      <td>Developing regions</td>\n      <td>0</td>\n      <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>Immigrants</td>\n      <td>Foreigners</td>\n      <td>Andorra</td>\n      <td>908</td>\n      <td>Europe</td>\n      <td>925</td>\n      <td>Southern Europe</td>\n      <td>901</td>\n      <td>Developed regions</td>\n      <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 × 43 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can also view the bottom 5 rows of the dataset using the `tail()` function.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "df_can.tail()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 6,
      "outputs": [
        {
          "execution_count": 6,
          "output_type": "execute_result",
          "data": {
            "text/plain": "           Type    Coverage          OdName  AREA AreaName  REG  \\\n190  Immigrants  Foreigners        Viet Nam   935     Asia  920   \n191  Immigrants  Foreigners  Western Sahara   903   Africa  912   \n192  Immigrants  Foreigners           Yemen   935     Asia  922   \n193  Immigrants  Foreigners          Zambia   903   Africa  910   \n194  Immigrants  Foreigners        Zimbabwe   903   Africa  910   \n\n                RegName  DEV             DevName  1980  ...  2004  2005  2006  \\\n190  South-Eastern Asia  902  Developing regions  1191  ...  1816  1852  3153   \n191     Northern Africa  902  Developing regions     0  ...     0     0     1   \n192        Western Asia  902  Developing regions     1  ...   124   161   140   \n193      Eastern Africa  902  Developing regions    11  ...    56    91    77   \n194      Eastern Africa  902  Developing regions    72  ...  1450   615   454   \n\n     2007  2008  2009  2010  2011  2012  2013  \n190  2574  1784  2171  1942  1723  1731  2112  \n191     0     0     0     0     0     0     0  \n192   122   133   128   211   160   174   217  \n193    71    64    60   102    69    46    59  \n194   663   611   508   494   434   437   407  \n\n[5 rows x 43 columns]",
            "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>Type</th>\n      <th>Coverage</th>\n      <th>OdName</th>\n      <th>AREA</th>\n      <th>AreaName</th>\n      <th>REG</th>\n      <th>RegName</th>\n      <th>DEV</th>\n      <th>DevName</th>\n      <th>1980</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>190</th>\n      <td>Immigrants</td>\n      <td>Foreigners</td>\n      <td>Viet Nam</td>\n      <td>935</td>\n      <td>Asia</td>\n      <td>920</td>\n      <td>South-Eastern Asia</td>\n      <td>902</td>\n      <td>Developing regions</td>\n      <td>1191</td>\n      <td>...</td>\n      <td>1816</td>\n      <td>1852</td>\n      <td>3153</td>\n      <td>2574</td>\n      <td>1784</td>\n      <td>2171</td>\n      <td>1942</td>\n      <td>1723</td>\n      <td>1731</td>\n      <td>2112</td>\n    </tr>\n    <tr>\n      <th>191</th>\n      <td>Immigrants</td>\n      <td>Foreigners</td>\n      <td>Western Sahara</td>\n      <td>903</td>\n      <td>Africa</td>\n      <td>912</td>\n      <td>Northern Africa</td>\n      <td>902</td>\n      <td>Developing regions</td>\n      <td>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>192</th>\n      <td>Immigrants</td>\n      <td>Foreigners</td>\n      <td>Yemen</td>\n      <td>935</td>\n      <td>Asia</td>\n      <td>922</td>\n      <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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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.\n\nThis method can be used to get a short summary of the dataframe.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "df_can.info(verbose=False)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 7,
      "outputs": [
        {
          "name": "stdout",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 195 entries, 0 to 194\nColumns: 43 entries, Type to 2013\ndtypes: int64(37), object(6)\nmemory usage: 61.0+ KB\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "To get the list of column headers we can call upon the data frame's `columns` instance variable.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "df_can.columns",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 8,
      "outputs": [
        {
          "execution_count": 8,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Index([    'Type', 'Coverage',   'OdName',     'AREA', 'AreaName',      'REG',\n        'RegName',      'DEV',  'DevName',       1980,       1981,       1982,\n             1983,       1984,       1985,       1986,       1987,       1988,\n             1989,       1990,       1991,       1992,       1993,       1994,\n             1995,       1996,       1997,       1998,       1999,       2000,\n             2001,       2002,       2003,       2004,       2005,       2006,\n             2007,       2008,       2009,       2010,       2011,       2012,\n             2013],\n      dtype='object')"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Similarly, to get the list of indices we use the `.index` instance variables.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "df_can.index",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 9,
      "outputs": [
        {
          "execution_count": 9,
          "output_type": "execute_result",
          "data": {
            "text/plain": "RangeIndex(start=0, stop=195, step=1)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Note: The default type of intance variables `index` and `columns` are **NOT** `list`.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "print(type(df_can.columns))\nprint(type(df_can.index))",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "name": "stdout",
          "text": "<class 'pandas.core.indexes.base.Index'>\n<class 'pandas.core.indexes.range.RangeIndex'>\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "To get the index and columns as lists, we can use the `tolist()` method.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "df_can.columns.tolist()",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%%\n"
        },
        "jupyter": {
          "outputs_hidden": false
        },
        "trusted": true
      },
      "execution_count": 11,
      "outputs": [
        {
          "execution_count": 11,
          "output_type": "execute_result",
          "data": {
            "text/plain": "['Type',\n 'Coverage',\n 'OdName',\n 'AREA',\n 'AreaName',\n 'REG',\n 'RegName',\n 'DEV',\n 'DevName',\n 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]"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "df_can.index.tolist()",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%%\n"
        },
        "jupyter": {
          "outputs_hidden": false
        },
        "trusted": true
      },
      "execution_count": 12,
      "outputs": [
        {
          "execution_count": 12,
          "output_type": "execute_result",
          "data": {
            "text/plain": "[0,\n 1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n 8,\n 9,\n 10,\n 11,\n 12,\n 13,\n 14,\n 15,\n 16,\n 17,\n 18,\n 19,\n 20,\n 21,\n 22,\n 23,\n 24,\n 25,\n 26,\n 27,\n 28,\n 29,\n 30,\n 31,\n 32,\n 33,\n 34,\n 35,\n 36,\n 37,\n 38,\n 39,\n 40,\n 41,\n 42,\n 43,\n 44,\n 45,\n 46,\n 47,\n 48,\n 49,\n 50,\n 51,\n 52,\n 53,\n 54,\n 55,\n 56,\n 57,\n 58,\n 59,\n 60,\n 61,\n 62,\n 63,\n 64,\n 65,\n 66,\n 67,\n 68,\n 69,\n 70,\n 71,\n 72,\n 73,\n 74,\n 75,\n 76,\n 77,\n 78,\n 79,\n 80,\n 81,\n 82,\n 83,\n 84,\n 85,\n 86,\n 87,\n 88,\n 89,\n 90,\n 91,\n 92,\n 93,\n 94,\n 95,\n 96,\n 97,\n 98,\n 99,\n 100,\n 101,\n 102,\n 103,\n 104,\n 105,\n 106,\n 107,\n 108,\n 109,\n 110,\n 111,\n 112,\n 113,\n 114,\n 115,\n 116,\n 117,\n 118,\n 119,\n 120,\n 121,\n 122,\n 123,\n 124,\n 125,\n 126,\n 127,\n 128,\n 129,\n 130,\n 131,\n 132,\n 133,\n 134,\n 135,\n 136,\n 137,\n 138,\n 139,\n 140,\n 141,\n 142,\n 143,\n 144,\n 145,\n 146,\n 147,\n 148,\n 149,\n 150,\n 151,\n 152,\n 153,\n 154,\n 155,\n 156,\n 157,\n 158,\n 159,\n 160,\n 161,\n 162,\n 163,\n 164,\n 165,\n 166,\n 167,\n 168,\n 169,\n 170,\n 171,\n 172,\n 173,\n 174,\n 175,\n 176,\n 177,\n 178,\n 179,\n 180,\n 181,\n 182,\n 183,\n 184,\n 185,\n 186,\n 187,\n 188,\n 189,\n 190,\n 191,\n 192,\n 193,\n 194]"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "print(type(df_can.columns.tolist()))\nprint(type(df_can.index.tolist()))",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 13,
      "outputs": [
        {
          "name": "stdout",
          "text": "<class 'list'>\n<class 'list'>\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "To view the dimensions of the dataframe, we use the `shape` instance variable of it.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# size of dataframe (rows, columns)\ndf_can.shape    ",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 14,
      "outputs": [
        {
          "execution_count": 14,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(195, 43)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "**Note**: The main types stored in *pandas* objects are `float`, `int`, `bool`, `datetime64[ns]`, `datetime64[ns, tz]`, `timedelta[ns]`, `category`, and `object` (string). In addition, these dtypes have item sizes, e.g. `int64` and `int32`.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "Let's clean the data set to remove a few unnecessary columns. We can use *pandas* `drop()` method as follows:\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# in pandas axis=0 represents rows (default) and axis=1 represents columns.\ndf_can.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\ndf_can.head(2)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 15,
      "outputs": [
        {
          "execution_count": 15,
          "output_type": "execute_result",
          "data": {
            "text/plain": "        OdName AreaName          RegName             DevName  1980  1981  \\\n0  Afghanistan     Asia    Southern Asia  Developing regions    16    39   \n1      Albania   Europe  Southern Europe   Developed regions     1     0   \n\n   1982  1983  1984  1985  ...  2004  2005  2006  2007  2008  2009  2010  \\\n0    39    47    71   340  ...  2978  3436  3009  2652  2111  1746  1758   \n1     0     0     0     0  ...  1450  1223   856   702   560   716   561   \n\n   2011  2012  2013  \n0  2203  2635  2004  \n1   539   620   603  \n\n[2 rows x 38 columns]",
            "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  </tbody>\n</table>\n<p>2 rows × 38 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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:\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True)\ndf_can.columns",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 16,
      "outputs": [
        {
          "execution_count": 16,
          "output_type": "execute_result",
          "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')"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We will also add a 'Total' column that sums up the total immigrants by country over the entire period 1980 - 2013, as follows:\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "df_can['Total'] = df_can.sum(axis=1)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 20,
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-20-8cd345a76c6e>:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n  df_can['Total'] = df_can.sum(axis=1)\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can check to see how many null objects we have in the dataset as follows:\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "df_can.isnull().sum()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 21,
      "outputs": [
        {
          "execution_count": 21,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Country      0\nContinent    0\nRegion       0\nDevName      0\n1980         0\n1981         0\n1982         0\n1983         0\n1984         0\n1985         0\n1986         0\n1987         0\n1988         0\n1989         0\n1990         0\n1991         0\n1992         0\n1993         0\n1994         0\n1995         0\n1996         0\n1997         0\n1998         0\n1999         0\n2000         0\n2001         0\n2002         0\n2003         0\n2004         0\n2005         0\n2006         0\n2007         0\n2008         0\n2009         0\n2010         0\n2011         0\n2012         0\n2013         0\nTotal        0\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Finally, let's view a quick summary of each column in our dataframe using the `describe()` method.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "df_can.describe()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 22,
      "outputs": [
        {
          "execution_count": 22,
          "output_type": "execute_result",
          "data": {
            "text/plain": "               1980          1981          1982          1983          1984  \\\ncount    195.000000    195.000000    195.000000    195.000000    195.000000   \nmean     508.394872    566.989744    534.723077    387.435897    376.497436   \nstd     1949.588546   2152.643752   1866.997511   1204.333597   1198.246371   \nmin        0.000000      0.000000      0.000000      0.000000      0.000000   \n25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n50%       13.000000     10.000000     11.000000     12.000000     13.000000   \n75%      251.500000    295.500000    275.000000    173.000000    181.000000   \nmax    22045.000000  24796.000000  20620.000000  10015.000000  10170.000000   \n\n              1985         1986          1987          1988          1989  \\\ncount   195.000000   195.000000    195.000000    195.000000    195.000000   \nmean    358.861538   441.271795    691.133333    714.389744    843.241026   \nstd    1079.309600  1225.576630   2109.205607   2443.606788   2555.048874   \nmin       0.000000     0.000000      0.000000      0.000000      0.000000   \n25%       0.000000     0.500000      0.500000      1.000000      1.000000   \n50%      17.000000    18.000000     26.000000     34.000000     44.000000   \n75%     197.000000   254.000000    434.000000    409.000000    508.500000   \nmax    9564.000000  9470.000000  21337.000000  27359.000000  23795.000000   \n\n       ...          2005          2006          2007          2008  \\\ncount  ...    195.000000    195.000000    195.000000    195.000000   \nmean   ...   1320.292308   1266.958974   1191.820513   1246.394872   \nstd    ...   4425.957828   3926.717747   3443.542409   3694.573544   \nmin    ...      0.000000      0.000000      0.000000      0.000000   \n25%    ...     28.500000     25.000000     31.000000     31.000000   \n50%    ...    210.000000    218.000000    198.000000    205.000000   \n75%    ...    832.000000    842.000000    899.000000    934.500000   \nmax    ...  42584.000000  33848.000000  28742.000000  30037.000000   \n\n               2009          2010          2011          2012          2013  \\\ncount    195.000000    195.000000    195.000000    195.000000    195.000000   \nmean    1275.733333   1420.287179   1262.533333   1313.958974   1320.702564   \nstd     3829.630424   4462.946328   4030.084313   4247.555161   4237.951988   \nmin        0.000000      0.000000      0.000000      0.000000      0.000000   \n25%       36.000000     40.500000     37.500000     42.500000     45.000000   \n50%      214.000000    211.000000    179.000000    233.000000    213.000000   \n75%      888.000000    932.000000    772.000000    783.000000    796.000000   \nmax    29622.000000  38617.000000  36765.000000  34315.000000  34129.000000   \n\n              Total  \ncount  1.950000e+02  \nmean   6.573490e+04  \nstd    1.835710e+05  \nmin    2.000000e+00  \n25%    1.904000e+03  \n50%    1.003600e+04  \n75%    4.447900e+04  \nmax    1.383808e+06  \n\n[8 rows x 35 columns]",
            "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>1.950000e+02</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>6.573490e+04</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>1.835710e+05</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>2.000000e+00</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>1.904000e+03</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>1.003600e+04</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>4.447900e+04</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>1.383808e+06</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 35 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "***\n\n## *pandas* Intermediate: Indexing and Selection (slicing)<a id=\"6\"></a>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "### Select Column\n\n**There are two ways to filter on a column name:**\n\nMethod 1: Quick and easy, but only works if the column name does NOT have spaces or special characters.\n\n```python\n    df.column_name               # returns series\n```\n\nMethod 2: More robust, and can filter on multiple columns.\n\n```python\n    df['column']                  # returns series\n```\n\n```python\n    df[['column 1', 'column 2']]  # returns dataframe\n```\n\n***\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "Example: Let's try filtering on the list of countries ('Country').\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "df_can.Country  # returns a series",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 23,
      "outputs": [
        {
          "execution_count": 23,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0         Afghanistan\n1             Albania\n2             Algeria\n3      American Samoa\n4             Andorra\n            ...      \n190          Viet Nam\n191    Western Sahara\n192             Yemen\n193            Zambia\n194          Zimbabwe\nName: Country, Length: 195, dtype: object"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Let's try filtering on the list of countries ('Country') and the data for years: 1980 - 1985.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "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.",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 24,
      "outputs": [
        {
          "execution_count": 24,
          "output_type": "execute_result",
          "data": {
            "text/plain": "            Country  1980  1981  1982  1983  1984  1985\n0       Afghanistan    16    39    39    47    71   340\n1           Albania     1     0     0     0     0     0\n2           Algeria    80    67    71    69    63    44\n3    American Samoa     0     1     0     0     0     0\n4           Andorra     0     0     0     0     0     0\n..              ...   ...   ...   ...   ...   ...   ...\n190        Viet Nam  1191  1829  2162  3404  7583  5907\n191  Western Sahara     0     0     0     0     0     0\n192           Yemen     1     2     1     6     0    18\n193          Zambia    11    17    11     7    16     9\n194        Zimbabwe    72   114   102    44    32    29\n\n[195 rows x 7 columns]",
            "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>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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Select Row\n\nThere are main 2 ways to select rows:\n\n```python\n    df.loc[label]    # filters by the labels of the index/column\n    df.iloc[index]   # filters by the positions of the index/column\n```\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "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 corresponding index value.\n\nThis can be fixed very easily by setting the 'Country' column as the index using `set_index()` method.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "df_can.set_index('Country', inplace=True)\n# tip: The opposite of set is reset. So to reset the index, we can use df_can.reset_index()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "df_can.head(3)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 26,
      "outputs": [
        {
          "execution_count": 26,
          "output_type": "execute_result",
          "data": {
            "text/plain": "            Continent           Region             DevName  1980  1981  1982  \\\nCountry                                                                        \nAfghanistan      Asia    Southern Asia  Developing regions    16    39    39   \nAlbania        Europe  Southern Europe   Developed regions     1     0     0   \nAlgeria        Africa  Northern Africa  Developing regions    80    67    71   \n\n             1983  1984  1985  1986  ...  2005  2006  2007  2008  2009  2010  \\\nCountry                              ...                                       \nAfghanistan    47    71   340   496  ...  3436  3009  2652  2111  1746  1758   \nAlbania         0     0     0     1  ...  1223   856   702   560   716   561   \nAlgeria        69    63    44    69  ...  3626  4807  3623  4005  5393  4752   \n\n             2011  2012  2013   Total  \nCountry                                \nAfghanistan  2203  2635  2004  117278  \nAlbania       539   620   603   31398  \nAlgeria      4325  3774  4331  138878  \n\n[3 rows x 38 columns]",
            "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    <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>117278</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>31398</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>138878</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows × 38 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "# optional: to remove the name of the index\ndf_can.index.name = None\ndf_can",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 28,
      "outputs": [
        {
          "execution_count": 28,
          "output_type": "execute_result",
          "data": {
            "text/plain": "               Continent              Region             DevName  1980  1981  \\\nAfghanistan         Asia       Southern Asia  Developing regions    16    39   \nAlbania           Europe     Southern Europe   Developed regions     1     0   \nAlgeria           Africa     Northern Africa  Developing regions    80    67   \nAmerican Samoa   Oceania           Polynesia  Developing regions     0     1   \nAndorra           Europe     Southern Europe   Developed regions     0     0   \n...                  ...                 ...                 ...   ...   ...   \nViet Nam            Asia  South-Eastern Asia  Developing regions  1191  1829   \nWestern Sahara    Africa     Northern Africa  Developing regions     0     0   \nYemen               Asia        Western Asia  Developing regions     1     2   \nZambia            Africa      Eastern Africa  Developing regions    11    17   \nZimbabwe          Africa      Eastern Africa  Developing regions    72   114   \n\n                1982  1983  1984  1985  1986  ...  2005  2006  2007  2008  \\\nAfghanistan       39    47    71   340   496  ...  3436  3009  2652  2111   \nAlbania            0     0     0     0     1  ...  1223   856   702   560   \nAlgeria           71    69    63    44    69  ...  3626  4807  3623  4005   \nAmerican Samoa     0     0     0     0     0  ...     0     1     0     0   \nAndorra            0     0     0     0     2  ...     0     1     1     0   \n...              ...   ...   ...   ...   ...  ...   ...   ...   ...   ...   \nViet Nam        2162  3404  7583  5907  2741  ...  1852  3153  2574  1784   \nWestern Sahara     0     0     0     0     0  ...     0     1     0     0   \nYemen              1     6     0    18     7  ...   161   140   122   133   \nZambia            11     7    16     9    15  ...    91    77    71    64   \nZimbabwe         102    44    32    29    43  ...   615   454   663   611   \n\n                2009  2010  2011  2012  2013   Total  \nAfghanistan     1746  1758  2203  2635  2004  117278  \nAlbania          716   561   539   620   603   31398  \nAlgeria         5393  4752  4325  3774  4331  138878  \nAmerican Samoa     0     0     0     0     0      12  \nAndorra            0     0     0     1     1      30  \n...              ...   ...   ...   ...   ...     ...  \nViet Nam        2171  1942  1723  1731  2112  194292  \nWestern Sahara     0     0     0     0     0       4  \nYemen            128   211   160   174   217    5970  \nZambia            60   102    69    46    59    3354  \nZimbabwe         508   494   434   437   407   17196  \n\n[195 rows x 38 columns]",
            "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>117278</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>31398</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>138878</td>\n    </tr>\n    <tr>\n      <th>American Samoa</th>\n      <td>Oceania</td>\n      <td>Polynesia</td>\n      <td>Developing regions</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>Andorra</th>\n      <td>Europe</td>\n      <td>Southern Europe</td>\n      <td>Developed regions</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>Viet Nam</th>\n      <td>Asia</td>\n      <td>South-Eastern Asia</td>\n      <td>Developing regions</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      <td>2741</td>\n      <td>...</td>\n      <td>1852</td>\n      <td>3153</td>\n      <td>2574</td>\n      <td>1784</td>\n      <td>2171</td>\n      <td>1942</td>\n      <td>1723</td>\n      <td>1731</td>\n      <td>2112</td>\n      <td>194292</td>\n    </tr>\n    <tr>\n      <th>Western Sahara</th>\n      <td>Africa</td>\n      <td>Northern Africa</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>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>4</td>\n    </tr>\n    <tr>\n      <th>Yemen</th>\n      <td>Asia</td>\n      <td>Western Asia</td>\n      <td>Developing regions</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      <td>7</td>\n      <td>...</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      <td>5970</td>\n    </tr>\n    <tr>\n      <th>Zambia</th>\n      <td>Africa</td>\n      <td>Eastern Africa</td>\n      <td>Developing regions</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      <td>15</td>\n      <td>...</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      <td>3354</td>\n    </tr>\n    <tr>\n      <th>Zimbabwe</th>\n      <td>Africa</td>\n      <td>Eastern Africa</td>\n      <td>Developing regions</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      <td>43</td>\n      <td>...</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      <td>17196</td>\n    </tr>\n  </tbody>\n</table>\n<p>195 rows × 38 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Example: Let's view the number of immigrants from Japan (row 87) for the following scenarios:\n1\\. The full row data (all columns)\n2\\. For year 2013\n3\\. For years 1980 to 1985\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# 1. the full row data (all columns)\ndf_can.loc['Japan']",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 29,
      "outputs": [
        {
          "execution_count": 29,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Continent                 Asia\nRegion            Eastern Asia\nDevName      Developed regions\n1980                       701\n1981                       756\n1982                       598\n1983                       309\n1984                       246\n1985                       198\n1986                       248\n1987                       422\n1988                       324\n1989                       494\n1990                       379\n1991                       506\n1992                       605\n1993                       907\n1994                       956\n1995                       826\n1996                       994\n1997                       924\n1998                       897\n1999                      1083\n2000                      1010\n2001                      1092\n2002                       806\n2003                       817\n2004                       973\n2005                      1067\n2006                      1212\n2007                      1250\n2008                      1284\n2009                      1194\n2010                      1168\n2011                      1265\n2012                      1214\n2013                       982\nTotal                    55414\nName: Japan, dtype: object"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "# alternate methods\ndf_can.iloc[87]",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%%\n"
        },
        "jupyter": {
          "outputs_hidden": false
        },
        "trusted": true
      },
      "execution_count": 33,
      "outputs": [
        {
          "execution_count": 33,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Continent                 Asia\nRegion            Eastern Asia\nDevName      Developed regions\n1980                       701\n1981                       756\n1982                       598\n1983                       309\n1984                       246\n1985                       198\n1986                       248\n1987                       422\n1988                       324\n1989                       494\n1990                       379\n1991                       506\n1992                       605\n1993                       907\n1994                       956\n1995                       826\n1996                       994\n1997                       924\n1998                       897\n1999                      1083\n2000                      1010\n2001                      1092\n2002                       806\n2003                       817\n2004                       973\n2005                      1067\n2006                      1212\n2007                      1250\n2008                      1284\n2009                      1194\n2010                      1168\n2011                      1265\n2012                      1214\n2013                       982\nTotal                    55414\nName: Japan, dtype: object"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "df_can[df_can.index == 'Japan']",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%%\n"
        },
        "jupyter": {
          "outputs_hidden": false
        },
        "trusted": true
      },
      "execution_count": 37,
      "outputs": [
        {
          "execution_count": 37,
          "output_type": "execute_result",
          "data": {
            "text/plain": "      Continent        Region            DevName  1980  1981  1982  1983  \\\nJapan      Asia  Eastern Asia  Developed regions   701   756   598   309   \n\n       1984  1985  1986  ...  2005  2006  2007  2008  2009  2010  2011  2012  \\\nJapan   246   198   248  ...  1067  1212  1250  1284  1194  1168  1265  1214   \n\n       2013  Total  \nJapan   982  55414  \n\n[1 rows x 38 columns]",
            "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>Japan</th>\n      <td>Asia</td>\n      <td>Eastern Asia</td>\n      <td>Developed regions</td>\n      <td>701</td>\n      <td>756</td>\n      <td>598</td>\n      <td>309</td>\n      <td>246</td>\n      <td>198</td>\n      <td>248</td>\n      <td>...</td>\n      <td>1067</td>\n      <td>1212</td>\n      <td>1250</td>\n      <td>1284</td>\n      <td>1194</td>\n      <td>1168</td>\n      <td>1265</td>\n      <td>1214</td>\n      <td>982</td>\n      <td>55414</td>\n    </tr>\n  </tbody>\n</table>\n<p>1 rows × 38 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "# 2. for year 2013\ndf_can.loc['Japan', 2013]",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 38,
      "outputs": [
        {
          "execution_count": 38,
          "output_type": "execute_result",
          "data": {
            "text/plain": "982"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "# alternate method\n# year 2013 is the last column, with a positional index of 36\ndf_can.iloc[87, 36]",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%%\n"
        },
        "jupyter": {
          "outputs_hidden": false
        },
        "trusted": true
      },
      "execution_count": 39,
      "outputs": [
        {
          "execution_count": 39,
          "output_type": "execute_result",
          "data": {
            "text/plain": "982"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "# 3. for years 1980 to 1985\ndf_can.loc['Japan', [1980, 1981, 1982, 1983, 1984, 1984]]",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 40,
      "outputs": [
        {
          "execution_count": 40,
          "output_type": "execute_result",
          "data": {
            "text/plain": "1980    701\n1981    756\n1982    598\n1983    309\n1984    246\n1984    246\nName: Japan, dtype: object"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "# Alternative Method\ndf_can.iloc[87, [3, 4, 5, 6, 7, 8]]",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%%\n"
        },
        "jupyter": {
          "outputs_hidden": false
        },
        "trusted": true
      },
      "execution_count": 41,
      "outputs": [
        {
          "execution_count": 41,
          "output_type": "execute_result",
          "data": {
            "text/plain": "1980    701\n1981    756\n1982    598\n1983    309\n1984    246\n1985    198\nName: Japan, dtype: object"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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\nTo avoid this ambuigity, let's convert the column names into strings: '1980' to '2013'.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "df_can.columns = list(map(str, df_can.columns))\n[print (type(x)) for x in df_can.columns.values] #<-- uncomment to check type of column headers",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 43,
      "outputs": [
        {
          "name": "stdout",
          "text": "<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n<class 'str'>\n",
          "output_type": "stream"
        },
        {
          "execution_count": 43,
          "output_type": "execute_result",
          "data": {
            "text/plain": "[None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None]"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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:\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# useful for plotting later on\nyears = list(map(str, range(1980, 2014)))\nyears",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 44,
      "outputs": [
        {
          "execution_count": 44,
          "output_type": "execute_result",
          "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']"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Filtering based on a criteria\n\nTo filter the dataframe based on a condition, we simply pass the condition as a boolean vector.\n\nFor example, Let's filter the dataframe to show the data on Asian countries (AreaName = Asia).\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# 1. create the condition boolean series\ncondition = df_can['Continent'] == 'Asia'\nprint(condition)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 45,
      "outputs": [
        {
          "name": "stdout",
          "text": "Afghanistan        True\nAlbania           False\nAlgeria           False\nAmerican Samoa    False\nAndorra           False\n                  ...  \nViet Nam           True\nWestern Sahara    False\nYemen              True\nZambia            False\nZimbabwe          False\nName: Continent, Length: 195, dtype: bool\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "# 2. pass this condition into the dataFrame\ndf_can[condition]",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 47,
      "outputs": [
        {
          "execution_count": 47,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                                               Continent              Region  \\\nAfghanistan                                         Asia       Southern Asia   \nArmenia                                             Asia        Western Asia   \nAzerbaijan                                          Asia        Western Asia   \nBahrain                                             Asia        Western Asia   \nBangladesh                                          Asia       Southern Asia   \nBhutan                                              Asia       Southern Asia   \nBrunei Darussalam                                   Asia  South-Eastern Asia   \nCambodia                                            Asia  South-Eastern Asia   \nChina                                               Asia        Eastern Asia   \nChina, Hong Kong Special Administrative Region      Asia        Eastern Asia   \nChina, Macao Special Administrative Region          Asia        Eastern Asia   \nCyprus                                              Asia        Western Asia   \nDemocratic People's Republic of Korea               Asia        Eastern Asia   \nGeorgia                                             Asia        Western Asia   \nIndia                                               Asia       Southern Asia   \nIndonesia                                           Asia  South-Eastern Asia   \nIran (Islamic Republic of)                          Asia       Southern Asia   \nIraq                                                Asia        Western Asia   \nIsrael                                              Asia        Western Asia   \nJapan                                               Asia        Eastern Asia   \nJordan                                              Asia        Western Asia   \nKazakhstan                                          Asia        Central Asia   \nKuwait                                              Asia        Western Asia   \nKyrgyzstan                                          Asia        Central Asia   \nLao People's Democratic Republic                    Asia  South-Eastern Asia   \nLebanon                                             Asia        Western Asia   \nMalaysia                                            Asia  South-Eastern Asia   \nMaldives                                            Asia       Southern Asia   \nMongolia                                            Asia        Eastern Asia   \nMyanmar                                             Asia  South-Eastern Asia   \nNepal                                               Asia       Southern Asia   \nOman                                                Asia        Western Asia   \nPakistan                                            Asia       Southern Asia   \nPhilippines                                         Asia  South-Eastern Asia   \nQatar                                               Asia        Western Asia   \nRepublic of Korea                                   Asia        Eastern Asia   \nSaudi Arabia                                        Asia        Western Asia   \nSingapore                                           Asia  South-Eastern Asia   \nSri Lanka                                           Asia       Southern Asia   \nState of Palestine                                  Asia        Western Asia   \nSyrian Arab Republic                                Asia        Western Asia   \nTajikistan                                          Asia        Central Asia   \nThailand                                            Asia  South-Eastern Asia   \nTurkey                                              Asia        Western Asia   \nTurkmenistan                                        Asia        Central Asia   \nUnited Arab Emirates                                Asia        Western Asia   \nUzbekistan                                          Asia        Central Asia   \nViet Nam                                            Asia  South-Eastern Asia   \nYemen                                               Asia        Western Asia   \n\n                                                           DevName  1980  \\\nAfghanistan                                     Developing regions    16   \nArmenia                                         Developing regions     0   \nAzerbaijan                                      Developing regions     0   \nBahrain                                         Developing regions     0   \nBangladesh                                      Developing regions    83   \nBhutan                                          Developing regions     0   \nBrunei Darussalam                               Developing regions    79   \nCambodia                                        Developing regions    12   \nChina                                           Developing regions  5123   \nChina, Hong Kong Special Administrative Region  Developing regions     0   \nChina, Macao Special Administrative Region      Developing regions     0   \nCyprus                                          Developing regions   132   \nDemocratic People's Republic of Korea           Developing regions     1   \nGeorgia                                         Developing regions     0   \nIndia                                           Developing regions  8880   \nIndonesia                                       Developing regions   186   \nIran (Islamic Republic of)                      Developing regions  1172   \nIraq                                            Developing regions   262   \nIsrael                                          Developing regions  1403   \nJapan                                            Developed regions   701   \nJordan                                          Developing regions   177   \nKazakhstan                                      Developing regions     0   \nKuwait                                          Developing regions     1   \nKyrgyzstan                                      Developing regions     0   \nLao People's Democratic Republic                Developing regions    11   \nLebanon                                         Developing regions  1409   \nMalaysia                                        Developing regions   786   \nMaldives                                        Developing regions     0   \nMongolia                                        Developing regions     0   \nMyanmar                                         Developing regions    80   \nNepal                                           Developing regions     1   \nOman                                            Developing regions     0   \nPakistan                                        Developing regions   978   \nPhilippines                                     Developing regions  6051   \nQatar                                           Developing regions     0   \nRepublic of Korea                               Developing regions  1011   \nSaudi Arabia                                    Developing regions     0   \nSingapore                                       Developing regions   241   \nSri Lanka                                       Developing regions   185   \nState of Palestine                              Developing regions     0   \nSyrian Arab Republic                            Developing regions   315   \nTajikistan                                      Developing regions     0   \nThailand                                        Developing regions    56   \nTurkey                                          Developing regions   481   \nTurkmenistan                                    Developing regions     0   \nUnited Arab Emirates                            Developing regions     0   \nUzbekistan                                      Developing regions     0   \nViet Nam                                        Developing regions  1191   \nYemen                                           Developing regions     1   \n\n                                                1981  1982  1983  1984  1985  \\\nAfghanistan                                       39    39    47    71   340   \nArmenia                                            0     0     0     0     0   \nAzerbaijan                                         0     0     0     0     0   \nBahrain                                            2     1     1     1     3   \nBangladesh                                        84    86    81    98    92   \nBhutan                                             0     0     0     1     0   \nBrunei Darussalam                                  6     8     2     2     4   \nCambodia                                          19    26    33    10     7   \nChina                                           6682  3308  1863  1527  1816   \nChina, Hong Kong Special Administrative Region     0     0     0     0     0   \nChina, Macao Special Administrative Region         0     0     0     0     0   \nCyprus                                           128    84    46    46    43   \nDemocratic People's Republic of Korea              1     3     1     4     3   \nGeorgia                                            0     0     0     0     0   \nIndia                                           8670  8147  7338  5704  4211   \nIndonesia                                        178   252   115   123   100   \nIran (Islamic Republic of)                      1429  1822  1592  1977  1648   \nIraq                                             245   260   380   428   231   \nIsrael                                          1711  1334   541   446   680   \nJapan                                            756   598   309   246   198   \nJordan                                           160   155   113   102   179   \nKazakhstan                                         0     0     0     0     0   \nKuwait                                             0     8     2     1     4   \nKyrgyzstan                                         0     0     0     0     0   \nLao People's Democratic Republic                   6    16    16     7    17   \nLebanon                                         1119  1159   789  1253  1683   \nMalaysia                                         816   813   448   384   374   \nMaldives                                           0     0     1     0     0   \nMongolia                                           0     0     0     0     0   \nMyanmar                                           62    46    31    41    23   \nNepal                                              1     6     1     2     4   \nOman                                               0     0     8     0     0   \nPakistan                                         972  1201   900   668   514   \nPhilippines                                     5921  5249  4562  3801  3150   \nQatar                                              0     0     0     0     0   \nRepublic of Korea                               1456  1572  1081   847   962   \nSaudi Arabia                                       0     1     4     1     2   \nSingapore                                        301   337   169   128   139   \nSri Lanka                                        371   290   197  1086   845   \nState of Palestine                                 0     0     0     0     0   \nSyrian Arab Republic                             419   409   269   264   385   \nTajikistan                                         0     0     0     0     0   \nThailand                                          53   113    65    82    66   \nTurkey                                           874   706   280   338   202   \nTurkmenistan                                       0     0     0     0     0   \nUnited Arab Emirates                               2     2     1     2     0   \nUzbekistan                                         0     0     0     0     0   \nViet Nam                                        1829  2162  3404  7583  5907   \nYemen                                              2     1     6     0    18   \n\n                                                1986  ...   2005   2006  \\\nAfghanistan                                      496  ...   3436   3009   \nArmenia                                            0  ...    224    218   \nAzerbaijan                                         0  ...    359    236   \nBahrain                                            0  ...     12     12   \nBangladesh                                       486  ...   4171   4014   \nBhutan                                             0  ...      5     10   \nBrunei Darussalam                                 12  ...      4      5   \nCambodia                                           8  ...    370    529   \nChina                                           1960  ...  42584  33518   \nChina, Hong Kong Special Administrative Region     0  ...    729    712   \nChina, Macao Special Administrative Region         0  ...     21     32   \nCyprus                                            48  ...      7      9   \nDemocratic People's Republic of Korea              0  ...     14     10   \nGeorgia                                            0  ...    114    125   \nIndia                                           7150  ...  36210  33848   \nIndonesia                                        127  ...    632    613   \nIran (Islamic Republic of)                      1794  ...   5837   7480   \nIraq                                             265  ...   2226   1788   \nIsrael                                          1212  ...   2446   2625   \nJapan                                            248  ...   1067   1212   \nJordan                                           181  ...   1940   1827   \nKazakhstan                                         0  ...    506    408   \nKuwait                                             4  ...     66     35   \nKyrgyzstan                                         0  ...    173    161   \nLao People's Democratic Republic                  21  ...     42     74   \nLebanon                                         2576  ...   3709   3802   \nMalaysia                                         425  ...    593    580   \nMaldives                                           0  ...      0      0   \nMongolia                                           0  ...     59     64   \nMyanmar                                           18  ...    210    953   \nNepal                                             13  ...    607    540   \nOman                                               0  ...     14     18   \nPakistan                                         691  ...  14314  13127   \nPhilippines                                     4166  ...  18139  18400   \nQatar                                              1  ...     11      2   \nRepublic of Korea                               1208  ...   5832   6215   \nSaudi Arabia                                       5  ...    198    252   \nSingapore                                        205  ...    392    298   \nSri Lanka                                       1838  ...   4930   4714   \nState of Palestine                                 0  ...    453    627   \nSyrian Arab Republic                             493  ...   1458   1145   \nTajikistan                                         0  ...     85     46   \nThailand                                          78  ...    575    500   \nTurkey                                           257  ...   2065   1638   \nTurkmenistan                                       0  ...     40     26   \nUnited Arab Emirates                               5  ...     31     42   \nUzbekistan                                         0  ...    330    262   \nViet Nam                                        2741  ...   1852   3153   \nYemen                                              7  ...    161    140   \n\n                                                 2007   2008   2009   2010  \\\nAfghanistan                                      2652   2111   1746   1758   \nArmenia                                           198    205    267    252   \nAzerbaijan                                        203    125    165    209   \nBahrain                                            22      9     35     28   \nBangladesh                                       2897   2939   2104   4721   \nBhutan                                              7     36    865   1464   \nBrunei Darussalam                                  11     10      5     12   \nCambodia                                          460    354    203    200   \nChina                                           27642  30037  29622  30391   \nChina, Hong Kong Special Administrative Region    674    897    657    623   \nChina, Macao Special Administrative Region         16     12     21     21   \nCyprus                                              4      7      6     18   \nDemocratic People's Republic of Korea               7     19     11     45   \nGeorgia                                           132    112    128    126   \nIndia                                           28742  28261  29456  34235   \nIndonesia                                         657    661    504    712   \nIran (Islamic Republic of)                       6974   6475   6580   7477   \nIraq                                             2406   3543   5450   5941   \nIsrael                                           2401   2562   2316   2755   \nJapan                                            1250   1284   1194   1168   \nJordan                                           1421   1581   1235   1831   \nKazakhstan                                        436    394    431    377   \nKuwait                                             62     53     68     67   \nKyrgyzstan                                        135    168    173    157   \nLao People's Democratic Republic                   53     32     39     54   \nLebanon                                          3467   3566   3077   3432   \nMalaysia                                          600    658    640    802   \nMaldives                                            2      1      7      4   \nMongolia                                           82     59    118    169   \nMyanmar                                          1887    975   1153    556   \nNepal                                             511    581    561   1392   \nOman                                               16     10      7     14   \nPakistan                                        10124   8994   7217   6811   \nPhilippines                                     19837  24887  28573  38617   \nQatar                                               5      9      6     18   \nRepublic of Korea                                5920   7294   5874   5537   \nSaudi Arabia                                      188    249    246    330   \nSingapore                                         690    734    366    805   \nSri Lanka                                        4123   4756   4547   4422   \nState of Palestine                                441    481    400    654   \nSyrian Arab Republic                             1056    919    917   1039   \nTajikistan                                         44     15     50     52   \nThailand                                          487    519    512    499   \nTurkey                                           1463   1122   1238   1492   \nTurkmenistan                                       37     13     20     30   \nUnited Arab Emirates                               37     33     37     86   \nUzbekistan                                        284    215    288    289   \nViet Nam                                         2574   1784   2171   1942   \nYemen                                             122    133    128    211   \n\n                                                 2011   2012   2013    Total  \nAfghanistan                                      2203   2635   2004   117278  \nArmenia                                           236    258    207     6620  \nAzerbaijan                                        138    161     57     5298  \nBahrain                                            21     39     32      950  \nBangladesh                                       2694   2640   3789   131136  \nBhutan                                           1879   1075    487    11752  \nBrunei Darussalam                                   6      3      6     1200  \nCambodia                                          196    233    288    13076  \nChina                                           28502  33024  34129  1319924  \nChina, Hong Kong Special Administrative Region    591    728    774    18654  \nChina, Macao Special Administrative Region         13     33     29      568  \nCyprus                                              6     12     16     2252  \nDemocratic People's Republic of Korea              97     66     17      776  \nGeorgia                                           139    147    125     4136  \nIndia                                           27509  30933  33087  1383808  \nIndonesia                                         390    395    387    26300  \nIran (Islamic Republic of)                       7479   7534  11291   351846  \nIraq                                             6196   4041   4918   139578  \nIsrael                                           1970   2134   1945   133016  \nJapan                                            1265   1214    982    55414  \nJordan                                           1635   1206   1255    70812  \nKazakhstan                                        381    462    348    16980  \nKuwait                                             58     73     48     4050  \nKyrgyzstan                                        159    278    123     4706  \nLao People's Democratic Republic                   22     25     15     2178  \nLebanon                                          3072   1614   2172   230718  \nMalaysia                                          409    358    204    48834  \nMaldives                                            3      1      1       60  \nMongolia                                          103     68     99     1904  \nMyanmar                                           368    193    262    18490  \nNepal                                            1129   1185   1308    20444  \nOman                                               10     13     11      448  \nPakistan                                         7468  11227  12603   483200  \nPhilippines                                     36765  34315  29544  1022782  \nQatar                                               3     14      6      314  \nRepublic of Korea                                4588   5316   4509   285162  \nSaudi Arabia                                      278    286    267     6850  \nSingapore                                         219    146    141    29158  \nSri Lanka                                        3309   3338   2394   296716  \nState of Palestine                                555    533    462    13024  \nSyrian Arab Republic                             1005    650   1009    62970  \nTajikistan                                         47     34     39     1006  \nThailand                                          396    296    400    18348  \nTurkey                                           1257   1068    729    63562  \nTurkmenistan                                       20     20     14      620  \nUnited Arab Emirates                               60     54     46     1672  \nUzbekistan                                        162    235    167     6736  \nViet Nam                                         1723   1731   2112   194292  \nYemen                                             160    174    217     5970  \n\n[49 rows x 38 columns]",
            "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>117278</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>6620</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>5298</td>\n    </tr>\n    <tr>\n      <th>Bahrain</th>\n      <td>Asia</td>\n      <td>Western Asia</td>\n      <td>Developing regions</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>...</td>\n      <td>12</td>\n      <td>12</td>\n      <td>22</td>\n      <td>9</td>\n      <td>35</td>\n      <td>28</td>\n      <td>21</td>\n      <td>39</td>\n      <td>32</td>\n      <td>950</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>131136</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>11752</td>\n    </tr>\n    <tr>\n      <th>Brunei Darussalam</th>\n      <td>Asia</td>\n      <td>South-Eastern Asia</td>\n      <td>Developing regions</td>\n      <td>79</td>\n      <td>6</td>\n      <td>8</td>\n      <td>2</td>\n      <td>2</td>\n      <td>4</td>\n      <td>12</td>\n      <td>...</td>\n      <td>4</td>\n      <td>5</td>\n      <td>11</td>\n      <td>10</td>\n      <td>5</td>\n      <td>12</td>\n      <td>6</td>\n      <td>3</td>\n      <td>6</td>\n      <td>1200</td>\n    </tr>\n    <tr>\n      <th>Cambodia</th>\n      <td>Asia</td>\n      <td>South-Eastern Asia</td>\n      <td>Developing regions</td>\n      <td>12</td>\n      <td>19</td>\n      <td>26</td>\n      <td>33</td>\n      <td>10</td>\n      <td>7</td>\n      <td>8</td>\n      <td>...</td>\n      <td>370</td>\n      <td>529</td>\n      <td>460</td>\n      <td>354</td>\n      <td>203</td>\n      <td>200</td>\n      <td>196</td>\n      <td>233</td>\n      <td>288</td>\n      <td>13076</td>\n    </tr>\n    <tr>\n      <th>China</th>\n      <td>Asia</td>\n      <td>Eastern Asia</td>\n      <td>Developing regions</td>\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>...</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      <td>1319924</td>\n    </tr>\n    <tr>\n      <th>China, Hong Kong Special Administrative Region</th>\n      <td>Asia</td>\n      <td>Eastern 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>729</td>\n      <td>712</td>\n      <td>674</td>\n      <td>897</td>\n      <td>657</td>\n      <td>623</td>\n      <td>591</td>\n      <td>728</td>\n      <td>774</td>\n      <td>18654</td>\n    </tr>\n    <tr>\n      <th>China, Macao Special Administrative Region</th>\n      <td>Asia</td>\n      <td>Eastern 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>21</td>\n      <td>32</td>\n      <td>16</td>\n      <td>12</td>\n      <td>21</td>\n      <td>21</td>\n      <td>13</td>\n      <td>33</td>\n      <td>29</td>\n      <td>568</td>\n    </tr>\n    <tr>\n      <th>Cyprus</th>\n      <td>Asia</td>\n      <td>Western Asia</td>\n      <td>Developing regions</td>\n      <td>132</td>\n      <td>128</td>\n      <td>84</td>\n      <td>46</td>\n      <td>46</td>\n      <td>43</td>\n      <td>48</td>\n      <td>...</td>\n      <td>7</td>\n      <td>9</td>\n      <td>4</td>\n      <td>7</td>\n      <td>6</td>\n      <td>18</td>\n      <td>6</td>\n      <td>12</td>\n      <td>16</td>\n      <td>2252</td>\n    </tr>\n    <tr>\n      <th>Democratic People's Republic of Korea</th>\n      <td>Asia</td>\n      <td>Eastern Asia</td>\n      <td>Developing regions</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>4</td>\n      <td>3</td>\n      <td>0</td>\n      <td>...</td>\n      <td>14</td>\n      <td>10</td>\n      <td>7</td>\n      <td>19</td>\n      <td>11</td>\n      <td>45</td>\n      <td>97</td>\n      <td>66</td>\n      <td>17</td>\n      <td>776</td>\n    </tr>\n    <tr>\n      <th>Georgia</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>114</td>\n      <td>125</td>\n      <td>132</td>\n      <td>112</td>\n      <td>128</td>\n      <td>126</td>\n      <td>139</td>\n      <td>147</td>\n      <td>125</td>\n      <td>4136</td>\n    </tr>\n    <tr>\n      <th>India</th>\n      <td>Asia</td>\n      <td>Southern Asia</td>\n      <td>Developing regions</td>\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>...</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      <td>1383808</td>\n    </tr>\n    <tr>\n      <th>Indonesia</th>\n      <td>Asia</td>\n      <td>South-Eastern Asia</td>\n      <td>Developing regions</td>\n      <td>186</td>\n      <td>178</td>\n      <td>252</td>\n      <td>115</td>\n      <td>123</td>\n      <td>100</td>\n      <td>127</td>\n      <td>...</td>\n      <td>632</td>\n      <td>613</td>\n      <td>657</td>\n      <td>661</td>\n      <td>504</td>\n      <td>712</td>\n      <td>390</td>\n      <td>395</td>\n      <td>387</td>\n      <td>26300</td>\n    </tr>\n    <tr>\n      <th>Iran (Islamic Republic of)</th>\n      <td>Asia</td>\n      <td>Southern Asia</td>\n      <td>Developing regions</td>\n      <td>1172</td>\n      <td>1429</td>\n      <td>1822</td>\n      <td>1592</td>\n      <td>1977</td>\n      <td>1648</td>\n      <td>1794</td>\n      <td>...</td>\n      <td>5837</td>\n      <td>7480</td>\n      <td>6974</td>\n      <td>6475</td>\n      <td>6580</td>\n      <td>7477</td>\n      <td>7479</td>\n      <td>7534</td>\n      <td>11291</td>\n      <td>351846</td>\n    </tr>\n    <tr>\n      <th>Iraq</th>\n      <td>Asia</td>\n      <td>Western Asia</td>\n      <td>Developing regions</td>\n      <td>262</td>\n      <td>245</td>\n      <td>260</td>\n      <td>380</td>\n      <td>428</td>\n      <td>231</td>\n      <td>265</td>\n      <td>...</td>\n      <td>2226</td>\n      <td>1788</td>\n      <td>2406</td>\n      <td>3543</td>\n      <td>5450</td>\n      <td>5941</td>\n      <td>6196</td>\n      <td>4041</td>\n      <td>4918</td>\n      <td>139578</td>\n    </tr>\n    <tr>\n      <th>Israel</th>\n      <td>Asia</td>\n      <td>Western Asia</td>\n      <td>Developing regions</td>\n      <td>1403</td>\n      <td>1711</td>\n      <td>1334</td>\n      <td>541</td>\n      <td>446</td>\n      <td>680</td>\n      <td>1212</td>\n      <td>...</td>\n      <td>2446</td>\n      <td>2625</td>\n      <td>2401</td>\n      <td>2562</td>\n      <td>2316</td>\n      <td>2755</td>\n      <td>1970</td>\n      <td>2134</td>\n      <td>1945</td>\n      <td>133016</td>\n    </tr>\n    <tr>\n      <th>Japan</th>\n      <td>Asia</td>\n      <td>Eastern Asia</td>\n      <td>Developed regions</td>\n      <td>701</td>\n      <td>756</td>\n      <td>598</td>\n      <td>309</td>\n      <td>246</td>\n      <td>198</td>\n      <td>248</td>\n      <td>...</td>\n      <td>1067</td>\n      <td>1212</td>\n      <td>1250</td>\n      <td>1284</td>\n      <td>1194</td>\n      <td>1168</td>\n      <td>1265</td>\n      <td>1214</td>\n      <td>982</td>\n      <td>55414</td>\n    </tr>\n    <tr>\n      <th>Jordan</th>\n      <td>Asia</td>\n      <td>Western Asia</td>\n      <td>Developing regions</td>\n      <td>177</td>\n      <td>160</td>\n      <td>155</td>\n      <td>113</td>\n      <td>102</td>\n      <td>179</td>\n      <td>181</td>\n      <td>...</td>\n      <td>1940</td>\n      <td>1827</td>\n      <td>1421</td>\n      <td>1581</td>\n      <td>1235</td>\n      <td>1831</td>\n      <td>1635</td>\n      <td>1206</td>\n      <td>1255</td>\n      <td>70812</td>\n    </tr>\n    <tr>\n      <th>Kazakhstan</th>\n      <td>Asia</td>\n      <td>Central 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>506</td>\n      <td>408</td>\n      <td>436</td>\n      <td>394</td>\n      <td>431</td>\n      <td>377</td>\n      <td>381</td>\n      <td>462</td>\n      <td>348</td>\n      <td>16980</td>\n    </tr>\n    <tr>\n      <th>Kuwait</th>\n      <td>Asia</td>\n      <td>Western Asia</td>\n      <td>Developing regions</td>\n      <td>1</td>\n      <td>0</td>\n      <td>8</td>\n      <td>2</td>\n      <td>1</td>\n      <td>4</td>\n      <td>4</td>\n      <td>...</td>\n      <td>66</td>\n      <td>35</td>\n      <td>62</td>\n      <td>53</td>\n      <td>68</td>\n      <td>67</td>\n      <td>58</td>\n      <td>73</td>\n      <td>48</td>\n      <td>4050</td>\n    </tr>\n    <tr>\n      <th>Kyrgyzstan</th>\n      <td>Asia</td>\n      <td>Central 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>173</td>\n      <td>161</td>\n      <td>135</td>\n      <td>168</td>\n      <td>173</td>\n      <td>157</td>\n      <td>159</td>\n      <td>278</td>\n      <td>123</td>\n      <td>4706</td>\n    </tr>\n    <tr>\n      <th>Lao People's Democratic Republic</th>\n      <td>Asia</td>\n      <td>South-Eastern Asia</td>\n      <td>Developing regions</td>\n      <td>11</td>\n      <td>6</td>\n      <td>16</td>\n      <td>16</td>\n      <td>7</td>\n      <td>17</td>\n      <td>21</td>\n      <td>...</td>\n      <td>42</td>\n      <td>74</td>\n      <td>53</td>\n      <td>32</td>\n      <td>39</td>\n      <td>54</td>\n      <td>22</td>\n      <td>25</td>\n      <td>15</td>\n      <td>2178</td>\n    </tr>\n    <tr>\n      <th>Lebanon</th>\n      <td>Asia</td>\n      <td>Western Asia</td>\n      <td>Developing regions</td>\n      <td>1409</td>\n      <td>1119</td>\n      <td>1159</td>\n      <td>789</td>\n      <td>1253</td>\n      <td>1683</td>\n      <td>2576</td>\n      <td>...</td>\n      <td>3709</td>\n      <td>3802</td>\n      <td>3467</td>\n      <td>3566</td>\n      <td>3077</td>\n      <td>3432</td>\n      <td>3072</td>\n      <td>1614</td>\n      <td>2172</td>\n      <td>230718</td>\n    </tr>\n    <tr>\n      <th>Malaysia</th>\n      <td>Asia</td>\n      <td>South-Eastern Asia</td>\n      <td>Developing regions</td>\n      <td>786</td>\n      <td>816</td>\n      <td>813</td>\n      <td>448</td>\n      <td>384</td>\n      <td>374</td>\n      <td>425</td>\n      <td>...</td>\n      <td>593</td>\n      <td>580</td>\n      <td>600</td>\n      <td>658</td>\n      <td>640</td>\n      <td>802</td>\n      <td>409</td>\n      <td>358</td>\n      <td>204</td>\n      <td>48834</td>\n    </tr>\n    <tr>\n      <th>Maldives</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>1</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>2</td>\n      <td>1</td>\n      <td>7</td>\n      <td>4</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>Mongolia</th>\n      <td>Asia</td>\n      <td>Eastern 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>59</td>\n      <td>64</td>\n      <td>82</td>\n      <td>59</td>\n      <td>118</td>\n      <td>169</td>\n      <td>103</td>\n      <td>68</td>\n      <td>99</td>\n      <td>1904</td>\n    </tr>\n    <tr>\n      <th>Myanmar</th>\n      <td>Asia</td>\n      <td>South-Eastern Asia</td>\n      <td>Developing regions</td>\n      <td>80</td>\n      <td>62</td>\n      <td>46</td>\n      <td>31</td>\n      <td>41</td>\n      <td>23</td>\n      <td>18</td>\n      <td>...</td>\n      <td>210</td>\n      <td>953</td>\n      <td>1887</td>\n      <td>975</td>\n      <td>1153</td>\n      <td>556</td>\n      <td>368</td>\n      <td>193</td>\n      <td>262</td>\n      <td>18490</td>\n    </tr>\n    <tr>\n      <th>Nepal</th>\n      <td>Asia</td>\n      <td>Southern Asia</td>\n      <td>Developing regions</td>\n      <td>1</td>\n      <td>1</td>\n      <td>6</td>\n      <td>1</td>\n      <td>2</td>\n      <td>4</td>\n      <td>13</td>\n      <td>...</td>\n      <td>607</td>\n      <td>540</td>\n      <td>511</td>\n      <td>581</td>\n      <td>561</td>\n      <td>1392</td>\n      <td>1129</td>\n      <td>1185</td>\n      <td>1308</td>\n      <td>20444</td>\n    </tr>\n    <tr>\n      <th>Oman</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>8</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>14</td>\n      <td>18</td>\n      <td>16</td>\n      <td>10</td>\n      <td>7</td>\n      <td>14</td>\n      <td>10</td>\n      <td>13</td>\n      <td>11</td>\n      <td>448</td>\n    </tr>\n    <tr>\n      <th>Pakistan</th>\n      <td>Asia</td>\n      <td>Southern Asia</td>\n      <td>Developing regions</td>\n      <td>978</td>\n      <td>972</td>\n      <td>1201</td>\n      <td>900</td>\n      <td>668</td>\n      <td>514</td>\n      <td>691</td>\n      <td>...</td>\n      <td>14314</td>\n      <td>13127</td>\n      <td>10124</td>\n      <td>8994</td>\n      <td>7217</td>\n      <td>6811</td>\n      <td>7468</td>\n      <td>11227</td>\n      <td>12603</td>\n      <td>483200</td>\n    </tr>\n    <tr>\n      <th>Philippines</th>\n      <td>Asia</td>\n      <td>South-Eastern Asia</td>\n      <td>Developing regions</td>\n      <td>6051</td>\n      <td>5921</td>\n      <td>5249</td>\n      <td>4562</td>\n      <td>3801</td>\n      <td>3150</td>\n      <td>4166</td>\n      <td>...</td>\n      <td>18139</td>\n      <td>18400</td>\n      <td>19837</td>\n      <td>24887</td>\n      <td>28573</td>\n      <td>38617</td>\n      <td>36765</td>\n      <td>34315</td>\n      <td>29544</td>\n      <td>1022782</td>\n    </tr>\n    <tr>\n      <th>Qatar</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>1</td>\n      <td>...</td>\n      <td>11</td>\n      <td>2</td>\n      <td>5</td>\n      <td>9</td>\n      <td>6</td>\n      <td>18</td>\n      <td>3</td>\n      <td>14</td>\n      <td>6</td>\n      <td>314</td>\n    </tr>\n    <tr>\n      <th>Republic of Korea</th>\n      <td>Asia</td>\n      <td>Eastern Asia</td>\n      <td>Developing regions</td>\n      <td>1011</td>\n      <td>1456</td>\n      <td>1572</td>\n      <td>1081</td>\n      <td>847</td>\n      <td>962</td>\n      <td>1208</td>\n      <td>...</td>\n      <td>5832</td>\n      <td>6215</td>\n      <td>5920</td>\n      <td>7294</td>\n      <td>5874</td>\n      <td>5537</td>\n      <td>4588</td>\n      <td>5316</td>\n      <td>4509</td>\n      <td>285162</td>\n    </tr>\n    <tr>\n      <th>Saudi Arabia</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>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>2</td>\n      <td>5</td>\n      <td>...</td>\n      <td>198</td>\n      <td>252</td>\n      <td>188</td>\n      <td>249</td>\n      <td>246</td>\n      <td>330</td>\n      <td>278</td>\n      <td>286</td>\n      <td>267</td>\n      <td>6850</td>\n    </tr>\n    <tr>\n      <th>Singapore</th>\n      <td>Asia</td>\n      <td>South-Eastern Asia</td>\n      <td>Developing regions</td>\n      <td>241</td>\n      <td>301</td>\n      <td>337</td>\n      <td>169</td>\n      <td>128</td>\n      <td>139</td>\n      <td>205</td>\n      <td>...</td>\n      <td>392</td>\n      <td>298</td>\n      <td>690</td>\n      <td>734</td>\n      <td>366</td>\n      <td>805</td>\n      <td>219</td>\n      <td>146</td>\n      <td>141</td>\n      <td>29158</td>\n    </tr>\n    <tr>\n      <th>Sri Lanka</th>\n      <td>Asia</td>\n      <td>Southern Asia</td>\n      <td>Developing regions</td>\n      <td>185</td>\n      <td>371</td>\n      <td>290</td>\n      <td>197</td>\n      <td>1086</td>\n      <td>845</td>\n      <td>1838</td>\n      <td>...</td>\n      <td>4930</td>\n      <td>4714</td>\n      <td>4123</td>\n      <td>4756</td>\n      <td>4547</td>\n      <td>4422</td>\n      <td>3309</td>\n      <td>3338</td>\n      <td>2394</td>\n      <td>296716</td>\n    </tr>\n    <tr>\n      <th>State of Palestine</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>453</td>\n      <td>627</td>\n      <td>441</td>\n      <td>481</td>\n      <td>400</td>\n      <td>654</td>\n      <td>555</td>\n      <td>533</td>\n      <td>462</td>\n      <td>13024</td>\n    </tr>\n    <tr>\n      <th>Syrian Arab Republic</th>\n      <td>Asia</td>\n      <td>Western Asia</td>\n      <td>Developing regions</td>\n      <td>315</td>\n      <td>419</td>\n      <td>409</td>\n      <td>269</td>\n      <td>264</td>\n      <td>385</td>\n      <td>493</td>\n      <td>...</td>\n      <td>1458</td>\n      <td>1145</td>\n      <td>1056</td>\n      <td>919</td>\n      <td>917</td>\n      <td>1039</td>\n      <td>1005</td>\n      <td>650</td>\n      <td>1009</td>\n      <td>62970</td>\n    </tr>\n    <tr>\n      <th>Tajikistan</th>\n      <td>Asia</td>\n      <td>Central 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>85</td>\n      <td>46</td>\n      <td>44</td>\n      <td>15</td>\n      <td>50</td>\n      <td>52</td>\n      <td>47</td>\n      <td>34</td>\n      <td>39</td>\n      <td>1006</td>\n    </tr>\n    <tr>\n      <th>Thailand</th>\n      <td>Asia</td>\n      <td>South-Eastern Asia</td>\n      <td>Developing regions</td>\n      <td>56</td>\n      <td>53</td>\n      <td>113</td>\n      <td>65</td>\n      <td>82</td>\n      <td>66</td>\n      <td>78</td>\n      <td>...</td>\n      <td>575</td>\n      <td>500</td>\n      <td>487</td>\n      <td>519</td>\n      <td>512</td>\n      <td>499</td>\n      <td>396</td>\n      <td>296</td>\n      <td>400</td>\n      <td>18348</td>\n    </tr>\n    <tr>\n      <th>Turkey</th>\n      <td>Asia</td>\n      <td>Western Asia</td>\n      <td>Developing regions</td>\n      <td>481</td>\n      <td>874</td>\n      <td>706</td>\n      <td>280</td>\n      <td>338</td>\n      <td>202</td>\n      <td>257</td>\n      <td>...</td>\n      <td>2065</td>\n      <td>1638</td>\n      <td>1463</td>\n      <td>1122</td>\n      <td>1238</td>\n      <td>1492</td>\n      <td>1257</td>\n      <td>1068</td>\n      <td>729</td>\n      <td>63562</td>\n    </tr>\n    <tr>\n      <th>Turkmenistan</th>\n      <td>Asia</td>\n      <td>Central 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>40</td>\n      <td>26</td>\n      <td>37</td>\n      <td>13</td>\n      <td>20</td>\n      <td>30</td>\n      <td>20</td>\n      <td>20</td>\n      <td>14</td>\n      <td>620</td>\n    </tr>\n    <tr>\n      <th>United Arab Emirates</th>\n      <td>Asia</td>\n      <td>Western Asia</td>\n      <td>Developing regions</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>5</td>\n      <td>...</td>\n      <td>31</td>\n      <td>42</td>\n      <td>37</td>\n      <td>33</td>\n      <td>37</td>\n      <td>86</td>\n      <td>60</td>\n      <td>54</td>\n      <td>46</td>\n      <td>1672</td>\n    </tr>\n    <tr>\n      <th>Uzbekistan</th>\n      <td>Asia</td>\n      <td>Central 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>330</td>\n      <td>262</td>\n      <td>284</td>\n      <td>215</td>\n      <td>288</td>\n      <td>289</td>\n      <td>162</td>\n      <td>235</td>\n      <td>167</td>\n      <td>6736</td>\n    </tr>\n    <tr>\n      <th>Viet Nam</th>\n      <td>Asia</td>\n      <td>South-Eastern Asia</td>\n      <td>Developing regions</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      <td>2741</td>\n      <td>...</td>\n      <td>1852</td>\n      <td>3153</td>\n      <td>2574</td>\n      <td>1784</td>\n      <td>2171</td>\n      <td>1942</td>\n      <td>1723</td>\n      <td>1731</td>\n      <td>2112</td>\n      <td>194292</td>\n    </tr>\n    <tr>\n      <th>Yemen</th>\n      <td>Asia</td>\n      <td>Western Asia</td>\n      <td>Developing regions</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      <td>7</td>\n      <td>...</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      <td>5970</td>\n    </tr>\n  </tbody>\n</table>\n<p>49 rows × 38 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "# we can pass multiple criteria in the same line.\n# let's filter for AreaNAme = Asia and RegName = Southern Asia\n\ndf_can[(df_can['Continent']=='Asia') & (df_can['Region']=='Southern Asia')]\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",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 49,
      "outputs": [
        {
          "execution_count": 49,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                           Continent         Region             DevName  1980  \\\nAfghanistan                     Asia  Southern Asia  Developing regions    16   \nBangladesh                      Asia  Southern Asia  Developing regions    83   \nBhutan                          Asia  Southern Asia  Developing regions     0   \nIndia                           Asia  Southern Asia  Developing regions  8880   \nIran (Islamic Republic of)      Asia  Southern Asia  Developing regions  1172   \nMaldives                        Asia  Southern Asia  Developing regions     0   \nNepal                           Asia  Southern Asia  Developing regions     1   \nPakistan                        Asia  Southern Asia  Developing regions   978   \nSri Lanka                       Asia  Southern Asia  Developing regions   185   \n\n                            1981  1982  1983  1984  1985  1986  ...   2005  \\\nAfghanistan                   39    39    47    71   340   496  ...   3436   \nBangladesh                    84    86    81    98    92   486  ...   4171   \nBhutan                         0     0     0     1     0     0  ...      5   \nIndia                       8670  8147  7338  5704  4211  7150  ...  36210   \nIran (Islamic Republic of)  1429  1822  1592  1977  1648  1794  ...   5837   \nMaldives                       0     0     1     0     0     0  ...      0   \nNepal                          1     6     1     2     4    13  ...    607   \nPakistan                     972  1201   900   668   514   691  ...  14314   \nSri Lanka                    371   290   197  1086   845  1838  ...   4930   \n\n                             2006   2007   2008   2009   2010   2011   2012  \\\nAfghanistan                  3009   2652   2111   1746   1758   2203   2635   \nBangladesh                   4014   2897   2939   2104   4721   2694   2640   \nBhutan                         10      7     36    865   1464   1879   1075   \nIndia                       33848  28742  28261  29456  34235  27509  30933   \nIran (Islamic Republic of)   7480   6974   6475   6580   7477   7479   7534   \nMaldives                        0      2      1      7      4      3      1   \nNepal                         540    511    581    561   1392   1129   1185   \nPakistan                    13127  10124   8994   7217   6811   7468  11227   \nSri Lanka                    4714   4123   4756   4547   4422   3309   3338   \n\n                             2013    Total  \nAfghanistan                  2004   117278  \nBangladesh                   3789   131136  \nBhutan                        487    11752  \nIndia                       33087  1383808  \nIran (Islamic Republic of)  11291   351846  \nMaldives                        1       60  \nNepal                        1308    20444  \nPakistan                    12603   483200  \nSri Lanka                    2394   296716  \n\n[9 rows x 38 columns]",
            "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>117278</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>131136</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>11752</td>\n    </tr>\n    <tr>\n      <th>India</th>\n      <td>Asia</td>\n      <td>Southern Asia</td>\n      <td>Developing regions</td>\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>...</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      <td>1383808</td>\n    </tr>\n    <tr>\n      <th>Iran (Islamic Republic of)</th>\n      <td>Asia</td>\n      <td>Southern Asia</td>\n      <td>Developing regions</td>\n      <td>1172</td>\n      <td>1429</td>\n      <td>1822</td>\n      <td>1592</td>\n      <td>1977</td>\n      <td>1648</td>\n      <td>1794</td>\n      <td>...</td>\n      <td>5837</td>\n      <td>7480</td>\n      <td>6974</td>\n      <td>6475</td>\n      <td>6580</td>\n      <td>7477</td>\n      <td>7479</td>\n      <td>7534</td>\n      <td>11291</td>\n      <td>351846</td>\n    </tr>\n    <tr>\n      <th>Maldives</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>1</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>2</td>\n      <td>1</td>\n      <td>7</td>\n      <td>4</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>Nepal</th>\n      <td>Asia</td>\n      <td>Southern Asia</td>\n      <td>Developing regions</td>\n      <td>1</td>\n      <td>1</td>\n      <td>6</td>\n      <td>1</td>\n      <td>2</td>\n      <td>4</td>\n      <td>13</td>\n      <td>...</td>\n      <td>607</td>\n      <td>540</td>\n      <td>511</td>\n      <td>581</td>\n      <td>561</td>\n      <td>1392</td>\n      <td>1129</td>\n      <td>1185</td>\n      <td>1308</td>\n      <td>20444</td>\n    </tr>\n    <tr>\n      <th>Pakistan</th>\n      <td>Asia</td>\n      <td>Southern Asia</td>\n      <td>Developing regions</td>\n      <td>978</td>\n      <td>972</td>\n      <td>1201</td>\n      <td>900</td>\n      <td>668</td>\n      <td>514</td>\n      <td>691</td>\n      <td>...</td>\n      <td>14314</td>\n      <td>13127</td>\n      <td>10124</td>\n      <td>8994</td>\n      <td>7217</td>\n      <td>6811</td>\n      <td>7468</td>\n      <td>11227</td>\n      <td>12603</td>\n      <td>483200</td>\n    </tr>\n    <tr>\n      <th>Sri Lanka</th>\n      <td>Asia</td>\n      <td>Southern Asia</td>\n      <td>Developing regions</td>\n      <td>185</td>\n      <td>371</td>\n      <td>290</td>\n      <td>197</td>\n      <td>1086</td>\n      <td>845</td>\n      <td>1838</td>\n      <td>...</td>\n      <td>4930</td>\n      <td>4714</td>\n      <td>4123</td>\n      <td>4756</td>\n      <td>4547</td>\n      <td>4422</td>\n      <td>3309</td>\n      <td>3338</td>\n      <td>2394</td>\n      <td>296716</td>\n    </tr>\n  </tbody>\n</table>\n<p>9 rows × 38 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Before we proceed: let's review the changes we have made to our dataframe.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "print('data dimensions:', df_can.shape)\nprint(df_can.columns)\ndf_can.head(2)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 50,
      "outputs": [
        {
          "name": "stdout",
          "text": "data dimensions: (195, 38)\nIndex(['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",
          "output_type": "stream"
        },
        {
          "execution_count": 50,
          "output_type": "execute_result",
          "data": {
            "text/plain": "            Continent           Region             DevName  1980  1981  1982  \\\nAfghanistan      Asia    Southern Asia  Developing regions    16    39    39   \nAlbania        Europe  Southern Europe   Developed regions     1     0     0   \n\n             1983  1984  1985  1986  ...  2005  2006  2007  2008  2009  2010  \\\nAfghanistan    47    71   340   496  ...  3436  3009  2652  2111  1746  1758   \nAlbania         0     0     0     1  ...  1223   856   702   560   716   561   \n\n             2011  2012  2013   Total  \nAfghanistan  2203  2635  2004  117278  \nAlbania       539   620   603   31398  \n\n[2 rows x 38 columns]",
            "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>117278</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>31398</td>\n    </tr>\n  </tbody>\n</table>\n<p>2 rows × 38 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "***\n\n# Visualizing Data using Matplotlib<a id=\"8\"></a>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "## Matplotlib: Standard Python Visualization Library<a id=\"10\"></a>\n\nThe primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01).  As mentioned on their website:\n\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\nIf you are aspiring to create impactful visualization with python, Matplotlib is an essential tool to have at your disposal.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "### Matplotlib.Pyplot\n\nOne 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",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "Let's start by importing `matplotlib` and `matplotlib.pyplot` as follows:\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# we are using the inline backend\n%matplotlib inline \n\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "\\*optional: check if Matplotlib is loaded.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "print('Matplotlib version: ', mpl.__version__)  # >= 2.0.0",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 52,
      "outputs": [
        {
          "name": "stdout",
          "text": "Matplotlib version:  3.5.1\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "\\*optional: apply a style to Matplotlib.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "print(plt.style.available)\nmpl.style.use(['ggplot']) # optional: for ggplot-like style",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 53,
      "outputs": [
        {
          "name": "stdout",
          "text": "['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### Plotting in *pandas*\n\nFortunately, 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\nDocumentation:\n\n*   [Plotting with Series](http://pandas.pydata.org/pandas-docs/stable/api.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01#plotting)<br>\n*   [Plotting with Dataframes](http://pandas.pydata.org/pandas-docs/stable/api.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01#api-dataframe-plotting)\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "# Line Pots (Series/Dataframe) <a id=\"12\"></a>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "**What is a line plot and why use it?**\n\nA 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.\nUse line plot when you have a continuous data set. These are best suited for trend-based visualizations of data over a period of time.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "**Let's start with a case study:**\n\nIn 2010, Haiti suffered a catastrophic magnitude 7.0 earthquake. The quake caused widespread devastation and loss of life and aout 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",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "First, we will extract the data series for Haiti.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "haiti = df_can.loc['Haiti', years] # passing in years 1980 - 2013 to exclude the 'total' column\nhaiti.head()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 54,
      "outputs": [
        {
          "execution_count": 54,
          "output_type": "execute_result",
          "data": {
            "text/plain": "1980    1666\n1981    3692\n1982    3498\n1983    2860\n1984    1418\nName: Haiti, dtype: object"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Next, we will plot a line plot by appending `.plot()` to the `haiti` dataframe.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "haiti.plot()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 55,
      "outputs": [
        {
          "execution_count": 55,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "*pandas* automatically populated the x-axis with the index values (years), and the y-axis with the column values (population). However, notice how the years were not displayed because they are of type *string*. Therefore, let's change the type of the index values to *integer* for plotting.\n\nAlso, let's label the x and y axis using `plt.title()`, `plt.ylabel()`, and `plt.xlabel()` as follows:\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "haiti.index = haiti.index.map(int) # let's change the index values of Haiti to type integer for plotting\nhaiti.plot(kind='line')\n\nplt.title('Immigration from Haiti')\nplt.ylabel('Number of immigrants')\nplt.xlabel('Years')\n\nplt.show() # need this line to show the updates made to the figure",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x5778ff0>",
            "image/png": 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"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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 annotate this spike in the plot by using the `plt.text()` method.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "haiti.plot(kind='line')\n\nplt.title('Immigration from Haiti')\nplt.ylabel('Number of Immigrants')\nplt.xlabel('Years')\n\n# annotate the 2010 Earthquake. \n# syntax: plt.text(x, y, label)\nplt.text(2000, 6000, '2010 Earthquake') # see note below\n\nplt.show() ",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x5bf4f10>",
            "image/png": 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"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "With just a few lines of code, you were able to quickly identify and visualize the spike in immigration!\n\nQuick note on x and y values in `plt.text(x, y, label)`:\n\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\n```python\n    plt.text(2000, 6000, '2010 Earthquake') # years stored as type int\n```\n\n```\nIf 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```\n\n```python\n    plt.text(20, 6000, '2010 Earthquake') # years stored as type int\n```\n\n```\nWe will cover advanced annotation methods in later modules.\n```\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "We can easily add more countries to line plot to make meaningful comparisons immigration from different countries.\n\n**Question:** Let's compare the number of immigrants from India and China from 1980 to 2013.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "Step 1: Get the data set for China and India, and display the dataframe.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "### type your answer here\ndf_CI = df_can.loc[['India', 'China'], years]\ndf_CI\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 59,
      "outputs": [
        {
          "execution_count": 59,
          "output_type": "execute_result",
          "data": {
            "text/plain": "       1980  1981  1982  1983  1984  1985  1986   1987   1988   1989  ...  \\\nIndia  8880  8670  8147  7338  5704  4211  7150  10189  11522  10343  ...   \nChina  5123  6682  3308  1863  1527  1816  1960   2643   2758   4323  ...   \n\n        2004   2005   2006   2007   2008   2009   2010   2011   2012   2013  \nIndia  28235  36210  33848  28742  28261  29456  34235  27509  30933  33087  \nChina  36619  42584  33518  27642  30037  29622  30391  28502  33024  34129  \n\n[2 rows x 34 columns]",
            "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>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    <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  </tbody>\n</table>\n<p>2 rows × 34 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for a sample python solution</summary>\n\n```python\n    #The correct answer is:\n    df_CI = df_can.loc[['India', 'China'], years]\n    df_CI\n```\n\n</details>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "Step 2: Plot graph. We will explicitly specify line plot by passing in `kind` parameter to `plot()`.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "### type your answer here\n# df_CI. ...\ndf_CI.plot(kind='line')",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 60,
      "outputs": [
        {
          "execution_count": 60,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for a sample python solution</summary>\n\n```python\n    #The correct answer is:\n    df_CI.plot(kind='line')\n```\n\n</details>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "That doesn't look right...\n\nRecall 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.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "df_CI = df_CI.transpose()\ndf_CI.head()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 61,
      "outputs": [
        {
          "execution_count": 61,
          "output_type": "execute_result",
          "data": {
            "text/plain": "      India  China\n1980   8880   5123\n1981   8670   6682\n1982   8147   3308\n1983   7338   1863\n1984   5704   1527",
            "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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1980</th>\n      <td>8880</td>\n      <td>5123</td>\n    </tr>\n    <tr>\n      <th>1981</th>\n      <td>8670</td>\n      <td>6682</td>\n    </tr>\n    <tr>\n      <th>1982</th>\n      <td>8147</td>\n      <td>3308</td>\n    </tr>\n    <tr>\n      <th>1983</th>\n      <td>7338</td>\n      <td>1863</td>\n    </tr>\n    <tr>\n      <th>1984</th>\n      <td>5704</td>\n      <td>1527</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "### type your answer here\ndf_CI.index = df_CI.index.map(int) # let's change the index values of df_CI to type integer for plotting\ndf_CI.plot(kind='line')\n\nplt.title('Immigrants from China and India')\nplt.ylabel('Number of Immigrants')\nplt.xlabel('Years')\n\nplt.show()\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x5b3d8b0>",
            "image/png": 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fDlhnA58swe8orTiuej8VFBRQXFzMqFGj6iV/5eXlbN68ubWhEhAQQI8ePcjOzubgwYP1trfXsngpKSn4+vqydetWp0MhmhOXOz7fQpxIEkAh2sBf/vIXLBYLjz76qEOtvcOHD/Paa6+16JwJCQkUFhayfft2h+fnzZvH0qVLWxOuXUhICJqmkZmZWW9bTU0Nq1evrve80WiksLAQAF9fX5fE4YynpydTpkyhrKyMJ554wmHbwYMHeeONN/Dw8OCmm25qsxgaMnr0aKZPn86xY8cYP348+/fvr7ePxWLhf//7X7vE54yr3k+RkZH4+vqyadMmysvL7c8bjUbuu+8+p2P/WuKWW27BYrHw8MMPO3QPp6en88Ybb7jkGs3l4eFhf0/+eb3fjRs3NmulG3d8voU4kXQBC9EGHnroIb7++msWLlzIvn37uPDCCykpKeHTTz/lnHPO4euvv0ana973rxkzZrB06VLOOussew2+jRs38vvvv3P11Ve3ankxG39/f0aOHMlvv/3GlClTSE5ORq/Xc/nllxMXF8dZZ51Fjx49GDp0KPHx8VRXV/Pzzz+zZ88eLr/8cnr37t3qGBrz4osv8ttvvzFnzhw2bNjAueeea68DWFZWxpw5c+zFrN3trbfeQq/X8+6779K7d2/Gjh3LwIED8fLyIjs7m+XLl5OVlcXVV1/dLvH9maveTzqdjnvvvZcXX3yR/v37M3HiRGpra1mxYgWFhYWce+65TguyN9cDDzzA119/zRdffMGQIUO46KKLKC4utn+mWrvCSks9//zzLFu2jNdee42NGzfa6wAuWrSICRMmNDkud3y+hTiRtAAK0QZ8fHxYsWIF99xzD7m5ubz66qusWLGCxx57jEcffRTA6WzNxowfP57vvvuOPn36sGjRIubNm4eXlxcrVqzgkksucVnsH3/8MZdccglLlixh1qxZPPHEE2zevBk/Pz9eeuklevTowZo1a3j99df55JNPCAwM5J133uGzzz5zWQwNCQ0NZe3atTz00EMcO3aMV155hc8++4wRI0awZMkS/va3v7V5DA3x8PDgnXfe4Y8//mDq1KlkZmYyd+5cZs+ezfLlyxk2bBiLFi1q0oop7uDK99MzzzzD7Nmz8fHxYe7cuXz55ZcMGzaM9evXu2zZMy8vL3755Rf+/ve/k5+fz+uvv86qVauYOXMmr776qkuu0RLh4eGsXr2aW265hb179/Laa6+xdetW3nnnHf7+9783+Tzu+nwLYaOpE/unhBBt7r333uP222/n3XffdbpqhRBCCNHWJAEUoo0cOXLEXmvOJjMz095FdOjQoXrbhRBCCHeQMYBCtJFJkyZhNBoZOnQowcHBZGRk8P3331NZWckLL7wgyZ8QQoh2Iy2AQrSRt99+m48//pj9+/dTUlKCv78/gwcP5u677+aqq65q7/CEEEJ0YpIACiGEEEJ0MjILWAghhBCik5EEUAghhBCik5EEUAghhBCik5EEUAghhBCik5EEUAghhBCik5E6gC5QVFSEyWRy+XkjIiLIz893+XlPVXI/HMn9qE/uiSO5H47kfjjqzPfDYDAQEhLS3mG0K0kAXcBkMmE0Gl16Tk3T7OeWSj1yP/5M7kd9ck8cyf1wJPfDkdwPIV3AQgghhBCdjCSAQgghhBCdjCSAQgghhBCdjCSAQgghhBCdjEwCEUIIIdpBTU0NNTU17Xb9qqoqamtr2+36bc3LywsvL6/2DqPDkgRQCCGEcLOKigo0TSMgIMA+I9fdPDw8XF7BoqNQSlFVVUVFRQV+fn7tHU6HJF3AQgghhJuZTCZ8fX3bLfk73Wmahq+vb5vU6D1dSAIohBBCuJkkfu4h97lhkgAKIYQQQnQykgAKIYQQwm26du3KkiVLADh8+DBdu3Zl586d7RxV5yOTQIQQQgjRJDNmzKC0tJT58+e75HwxMTFs2bKF0NBQl5xPNJ0kgEIIIYRoF3q9nsjIyPYOo1OSLmAhhBCnvU3Z5Xyy8TBKqfYO5bRx9dVX88QTT/Dss8/St29fBg0axOzZsx32SUtL46qrriIpKYmxY8fy66+/Omz/cxew2WzmgQce4IwzzqB79+6cffbZvP/++257TZ2JtAAKIYQ4rRnNipd/z6bKaKHr+AR6hnm3d0j1KKWg1r1FoZXFjDIawdOrxbNlP/vsM26//Xa+++47Nm3axN///neGDx/OOeecg8ViYfr06YSHh/Pdd99RVlbGk08+2ej5LBYL0dHRzJ07l5CQEDZu3MhDDz1EZGQkl19+eYtiFM5JAiiEEOK0tq+giiqjBYCDhdUdMgGktgbL3de69ZK2dFM351Pwatk96d27N/fffz8ASUlJfPjhh/z++++cc845/Pbbbxw4cID//ve/REVFAfDII49w4403Nng+Dw8PHnzwQfvjuLg4Nm3axHfffScJoItJAiiEEOK0tvlIuf3f6UXV7RjJ6ad3794OjyMjIykoKABg//79xMTE2JM/gKFDh570nB9++CELFy4kOzub6upqjEYjffv2dW3gQhJAIYQQp7fNORX2f2cUtd/au43y9LK2xLmRfSk4z5avl2swOKYRmqZhsVhafL5vvvmGZ555hieeeIJhw4bh5+fHO++8w5YtW1p8TuGcJIBCCCFOW0VVJtJPSPoyiquxKIWug60QoWlai7thW3xNDw80nb7Nzt+zZ0+OHDnC0aNH6dKlCwCbN29u9JgNGzYwdOhQbr75Zvtzhw4darMYOzOZBSyEEOK0taWu9S8xxAsvg45qk+JoubGdo+oczj77bJKSkpgxYwa7du1i3bp1vPTSS40ek5iYyPbt21m5ciUHDx7k5ZdfZtu2bW6KuHORBFAIIcRpa8sRawI4vKs/SWF+gIwDdBedTsf7779PdXU1l156KQ8++CAPP/xwo8fceOONXHzxxdx5551cdtllFBUVMXXqVDdF3LloSooitVp+fr51HIULaZpGdHQ0OTk5UrcKuR9/JvejPrknjuR+gNmimPrlAcpqzLx4YTxrcmr5dkcO1/YLY8rAiHaNrbS0lMDAwHaNwT4G8DTW0H328PAgIqJ93wPtTcYACiGEOC2lFVVTVmPG10NHcrgPBWZPAIcxgUJ0VtIFLIQQ4rS0ua77d2CULwadRs8IfwAypAtYCEkAhRBCnJ5sE0CGxFgTP1sCmF9porzG3G5xCdERSAIohBDitFNeY2ZfQRUAg6Otkz8CvD2I9PMAIKNYuoFF5yYJoBBCiNPOtqMVWBTEBnoSUZf0ASSEWIsey0xg0dlJAiiEEOK0Yyv/MjjGz+H5xBBrsWWZCCI6O0kAhRBCnFaUUvbl34ZE/zkBtLYAZhRLC6Do3CQBFEIIcVo5XFLLsUoTnnqNvpG+DtsS6loAM4trMVs6Z31EIUASQCGEEKcZ2+zfvpG+eBkc/8xF+XvgbdBhtCiyS2vbIzwhOoR2LQT99ddf88knnzBhwgT7ws9KKT799FOWLVtGRUUFKSkpTJs2jejoaPtxtbW1fPTRR6xZswaj0cjAgQOZNm0awcHB9n3Ky8uZP38+mzZtQtM0Ro4cyS233IK39/HFtgsKCnjvvffYtWsX3t7ejBkzhhtuuAG9vu0WxxZCCNG2Nh8pB2DIn8b/Aeg0jYRgL/YWVJFeVE1csJe7wzvtde3alXnz5jF+/Hin29esWcM111zD7t27CQoKcnN0wqbdWgAPHDjAzz//THx8vMPz33zzDYsXL2b69Ok8//zzeHl58dxzz1Fbe/yb2oIFC9i0aRP3338/s2bNoqioiNmzZzuc54033uDw4cPMnDmTRx55hD179jB37lz7dovFwgsvvIDJZOLZZ5/lrrvuYuXKlSxatKhtX7gQQog2U2OysCvPWv7lz+P/bI6PA5SJIC2Rl5fHzJkzOfPMM0lMTGTYsGFMnTqV3377rUnHDxs2jC1btrT7UnidXbskgNXV1bz55pv89a9/xc/v+AdUKcWPP/7IVVddxfDhw4mPj+fuu++mqKiIDRs2AFBZWcny5cuZOnUq/fr1Iykpib/97W/s27eP1NRUALKysti6dSt33HEHPXv2JCUlhVtvvZU1a9ZQWFgIwLZt28jKyuKee+4hISGBwYMHM3nyZJYuXYrJZHL/TRFCCNFqO49WYrQoInwNdA30dLrP8VIwkgA21+HDh7n44otZvXo1M2fO5JdffuG///0vo0aN4vHHH2/SOTw9PYmMjETTtDaOVjSmXRLA999/n8GDBzNgwACH5/Py8iguLnZ43tfXlx49etiTu7S0NMxmM/3797fv07VrV8LDw+37pKam4ufnR/fu3e379O/fH03TOHDggH2fuLg4h27jQYMGUVVVxeHDh13+moUQQrS9zSes/tFQgnG8FIzMBG6uxx57DIAffviBSy65hO7du9OrVy/++te/8t1339n3Kyws5LbbbqN79+6MHj2an376yb5tzZo1dO3alZKSEgAWLVpE7969WblyJWPGjKFnz55MmTKFo0eP2o/ZunUr1113Hf369SMlJYVJkyaxY8cON73q05PbxwCuXr2a9PR0XnjhhXrbiouLAeqNCQgKCrJvKy4uxmAwOLQcOtvnz03Ler0ef39/h31OTP5OvK5tnz8zGo0YjUb7Y03T8PHxsf/blWznk29IVnI/HMn9qE/uiaPOej+OL//m5/DaT7wfCSHeaEBxtZniajMhPu06HB6w9oDVmN07K9mMBaPJgpdea9L7pKioiBUrVvDwww/j6+tbb/uJf7tfeeUVZs6cycyZM/nggw+4++67WbduHSEhIU7PXVVVxbvvvssbb7yBTqfjnnvu4ZlnnmHOnDmAdVz/Nddcw7PPPotSirlz53LTTTfx+++/4+/v32jcne0z0FRufdcXFBTw4YcfMnPmTDw9nTfNd2RfffUVn3/+uf1xYmIiL730EhEREW12zaioqDY796lI7ocjuR/1yT1x1JnuR3ZxFdmlteg1jQsHdcffq/6fONv96BaSSWZRFaWaL32iw9wdKlVVVXh4HF+hpNpoYfKiXW6PA+DLKX3x9jh5h2BWVhZKKXr16uUQuzPXX38911xzDQAzZ85k3rx57Ny5k3HjxmEwWH8uHh4eeHh4YDAYMBqN/Otf/yIxMRGA2267jdmzZ9uvc+655zqc/9VXX6VHjx5s2LCBCy+8sME4PD09HSaRiuPcmgCmpaVRUlLCww8/bH/OYrGwZ88elixZwmuvvQZASUmJw7eEkpISEhISAAgODsZkMlFRUeHQClhSUmJv0QsODqa0tNTh2mazmfLycod9bN3BJ57Dts2ZK6+8kksvvdT+2PatIj8/3+XjBjVNIyoqitzcXJSSWlVyPxzJ/ahP7omjzng/lqYWAdArwpuywnzKTtj25/vRLcBAZhFsOphDvLf7y8HU1tY69CgZTRa3x3D82kb0TRgRZovXbDY7xO5McnKyfR9PT08CAgLIzc3FaDTa/17aetVMJhM+Pj7ExsbajwkPD6egoMD+OD8/n5dffpk1a9Zw7NgxzGYzVVVVHDp0qNFYamtrycnJqfe8wWBo08abU4FbE8D+/fvzr3/9y+G5d955h5iYGCZOnEiXLl0IDg5mx44d9oSvsrKSAwcO2DP8pKQk9Ho9O3bs4IwzzgDgyJEjFBQUkJycDFjfeBUVFaSlpZGUlATAzp07UUrRo0cP+z5ffvklJSUl9mbr7du329+Ezti+rTjTVr9glVKd5pd3U8j9cCT3oz65J4460/2wl3+J9mvwNdvuR0KIF6szy0gvqu4Q98dLr7FocrJbr+lh8MBoMuKlb1oXaWJiosNY+kbP/ae/lZqmYbE0nOQ62//En8uMGTMoKiri6aefJjY2Fk9PTy6//PKTJqLQdn+fT3VuTQB9fHyIi4tzeM7Ly4uAgAD78xMmTODLL78kOjqayMhIFi5cSEhICMOHDwesk0LGjRvHRx99hL+/P76+vsyfP5/k5GR7AhgbG8ugQYOYO3cu06dPx2QyMX/+fEaNGkVoaCgAAwcOJDY2ljlz5jBlyhSKi4tZuHAhF1100UmbtoUQQnQsRrNie24lAIOjGx8TBpAYbJ0IktFBZgJrmoa3wb1j1Tw8dE1q+bMJCQlh7NixfPjhh9x22231xgGe2KDiahs2bOD555/nvPPOAyA7O9te1UO0TPuPfP2TiRMnUlNTw9y5c6msrCQlJYXHHnvMYczg1KlT0TSN2bNnYzKZ7IWgT3Tvvfcyb948nn76aXsh6FtvvdW+XafT8cgjj/D+++8zc+ZMvLy8GDNmDJMnT3bbaxVCCOEa+wqqqDJZCPLSkxR68uLOtlIwWaU11JoteOplYaymeO6557jiiiu45JJLePDBB+nduzdms5lff/2Vjz76iFWrVrXJdRMTE/niiy8YOHAgZWVlPPvssw4LO4jma/cE8KmnnnJ4rGkakydPbjQR8/T0ZNq0afWSvhP5+/tz3333NXrtiIgIHn300WbFK4QQouOxzf4dFO2HrgmzPsN9Dfh76iivtXC4pJbuoZJMNEV8fDxLlizhjTfe4OmnnyYvL4/Q0FAGDBjgtLqHq8yePZuHHnqI8ePHEx0dzSOPPMIzzzzTZtfrDDQlneOtlp+f36RxCM2haRrR0dHk5OTI+AXkfvyZ3I/65J446mz34+8/ppNWVMPfR0UzNrF+N6Sz+/H4L5nsPFrJPWdEcX73YLfGW1pa2u4rYXh4eLj8b1dH09B99vDw6PSTQKTNWwghxCmtuMpEWt1YvkENLP/mTGLdOsAdZRygEO4kCaAQQohTmq37t3uoF8HeTR/ZZFsTOF3WBBadkCSAQgghTmm25d+aMvv3RLYl4TI6SCkYIdxJEkAhhBCnLItSbD1h+bfm6BbkiV6D8loLBZWuLeYvREcnCaAQQohT1sHCakprzPgYdPQK92nWsR56HbGBdd3ARdVtEZ4QHZYkgEIIIU5ZW45YW/8GRvti0DW/kLKtHqBMBBGdjSSAQgghTlm28X9Dmjn+zyahHSeCNLY0mmg9ub+NkwRQCCHEKam81sy+gioABjej/MuJTpwI4k6+vr6UlZVJktJGLBYLZWVl9ZarE8e1+0ogQgghREtsz63AoiA20JNI/5at4W6rBZhTZqTaZMHb4J52EYPBgJ+fH+Xl5W65njOenp7U1ta22/Xbmp+fHwaDpDkNkTsjhBDilLS5bvzf4GbO/j1RsI+BEG89RdVmDhXXNHsiSWsYDIZ2Ww2ks60UI+qTLmAhhBCnHKWUvQD0kBZ2/9ok1HUDy0xg0ZlIAiiEEOKUc7i0loJKE556jb6RrRvnZV8RRGYCi05EEkAhhBCnHFv5lz6Rvni1ctxeQrAkgKLzkQRQCCHEKWezi7p/4fhM4EPF1VhkPJzoJCQBFEIIcUqpMVnYdbQSaP7yb850DfTEQ6dRbVIcLTe2+nxCnAokARRCCHFK2ZVXidGiCPc1EBvo2erz6XUaccGyJJzoXCQBFEIIcUqxlX8ZEuOHpjV/+TdnZCKI6GwkARRCCHFKae3yb87IRBDR2UgCKIQQ4pRxtLyW7NJadBoMiHLdMl/ttSScEO1FVgIRQgjRoSilKK0xc6zSxLFKEwWVRgqrTBRUmjhUbG2hSwn3wc9T77Jr2loA8ytNlNeY8fdy3bmF6IgkARRCCAFYZ9dmFNfQM8wbnYvG1jVm19FKDhZV1yV6Ruv/q0wUVpowWhovx3JmXIBLY/H30hPhayC/0kRGcQ39uriudVGIjkgSQCGE6OTMFsWK9BI+2VbAsSoTNw2M4Op+YW16zX0FVTz2S2aj+wR76wnz9SDM10CYj4FwXw9CfQ1E+XuQEuH6NXsTQrzJrywnvahaEkBx2pMEUAghOimlFBuyy/l4az6ZJbX25xfvL+KqvqFt2gr4y8FiAOKDvRgY5WtN7nwMhPsaCPP1IMTHgIfedddXh9OpytiHik9ucJ/EEC82ZJeTUSwTQcTpTxJAIYTohPYVVPHh5jx251cB4O+pY1LfMD7fdYyCShM7jlYyMKr1RZadqTFZ+P1QGQDTh0XSv0vbXMdGKYXlzWcoKMxHd8t9aKPOc7qflIIRnYkkgEII0Ylkl9by8dZ81h62JmCeeo1Le4UwqW8Y/p56csuMLD1QzPK0kjZLANdllVNptBDpZ6BvpBu6WgsLoDAfAMsn/0bXsy9aRFS93WwzgTOLazBbFHpd24+DFKK9SBkYIYToBIqqTLyzPpe7v09j7eEydBqc3z2Ity9LYurgSPzrZtSe1z0IgLWZZVQazW0Sy/K0EgDOTQpyy2QTMlKP/7umCsu8V1Dm+q+ti78H3gYdRosiu7S23nYhTieSAAohxGms0mjmk+353PHtQZbsL8aiYHhXP16bkMg9Z0QT4efhsH9ymDcxAZ7UmBVrMstcHs+xSiPbcq2FnM9NDHL5+Z1R6fsB8B42Gnx84eBe1I+f1dtPp2nEy5JwopOQBFAIIU5DRrPi081Z/PXrgyzacYxqkyI5zJvnz49j5thu9kTnzzRN47wka2Jma6lzpZXppVgU9InwITqg9ev4NoXKsCaAPqPHoZtyp/W57xeiDu6tt69tHKBMBBGnO0kAhRDiNFNpNHP/4nT+uSyVkhozMQEePHx2DC9fFE/fJpQ3GZsUiAbsyqsit8x1XaFKKXtSOS7JTa1/FjNkHADAM7kvujPGoo04BywWa1dwdaXD/rIknOgsJAEUQojTzJrMMg4V1xDkbeCOEVG8eWkSo+IC0Zo43i7c14OBdcusrUh3XSvg/mPVZJXW4qnXGB3v2kLODcrNhpoq8PLGo1siANqUOyA0AvJzUQvfd9g9KVSWhBOdgySAQghxmlmfVQ7A5CHdmJAcgqEFs1nPrWuhW5FeikU1vipHU9la/87sFoCvh3uWWrON/yO+O5reek3N1x/drX8HTUOt/gW1eY19//hgLzSgqNpMcZXJLTEK0R4kARRCiNNIjcnC1hzrJItzeoS3+DxndgvAx6DjaLmR3XlVrY7LaLbw26FSwH3dv4B9BrCW6FgAWuvVD238VQBYPnoLVXQMAG+DjugA68SYdBkHKE5jkgAKIcRpZHtuJTVmRbivgeRI/xafx8ug46y6btplLpgMsj67nPJaC2G+Bvq7cZk1WwugltCz3jbt8hsgrjtUlGH54DWUxQJYl4QDmQksTm+SAAohxGlkfba1dMuI2IAmj/lriK2lbk1mKVVGS6vOtfxgXe2/xCC3FVhWRiNkZQD1WwABNIMHumkPgKcn7NmGWvYdAIl1E0EyZCKIOI1JAiiEEKcJi1JsqBv/N7Jby1v/bHpH+BDl70G1SdlXDmmJoioTm+u6pc9NCmx1XE2WlQ5mEwQEQVik01206Fi0a24DQH25AJWVTkKIJIDi9CcJoBBCnCb2H6umqNqMj0FHv8jWL+OmaZq9FXBFK7qBV2WUYFHQK9yb2EDn9QfbgkqvWwEkoWejraHamPEwcASYTFjef4UEf+tkkazSGozm1rV8CtFRSQIohBCnCdvs3yExfnjoXdPNalutY/vRSvLKjc0+XinF8oOlDudym0bG/51I0zR0f7nb2lKYfYiwxf/F31OHWcHhElkSTpyeJAEUQojThK37d0Rs67t/bSL9PeyTNla2oCZgWlENh0pq8NBpnB3vxu5fjq8AoiU2ngACaIHB6G65z/pg+XckeFqT3TSZCCJOU5IACiHEaSC3rJZDJTXoNBgW47oEEI5PBlmWVoJqZk1AW+2/EbH++Hu5p/YfgKqsgNws64OTtADaaP2HoZ07AYD4tM3A6TsOUJnN1OzdYZ/5LDofSQCFEOI0sD7b2vrXN9LX5YnWqLgAvA06csuN7Mlvek1Ao1mxKsPa/XueO2v/ARyyLv9GWCRaQNOvrV19C0R3I6EwHTh9S8FYvvkveQ/cglq5uL1DaVBhlYnX1+ZQWi0FuduCJIBCCHEaWN8G3b823gYdo+KsNQGXN2MyyKYj5ZTVmAnx1jMouvWTUprjePdv/fIvjdE8vdBNe4CEyjwAMgrKm93q2dEpixm1+hfrv7f+0c7R1GdbM/ru79NYnlbCvM157R3SaUkSQCGEOMWV15jZlVcJwIiurk8A4XgL3u+HyqgxNa3b0JYsjnVj7T8b+wzgJoz/+zMtLom4C85Dp8yUW/TkZ2a7OLp2tm8nlBQBoA7sQZk6TgvbsUojz67M4vW1OVTUWuge6s2VvUPbO6zTkiSAQghxitt4pByLgvggL6ICPNvkGn0ifYj086DKZOGPJtQELKk2sbGuW9qtS7/ZZFi7gE82A7ghXhdOpKvJ+jrTv/vOZWF1BGrdquMPamuOd5e3I6UUvxws5p7v09l4pAKDTuOmgRH886J4+8oswrUkARRCiFOcrft3eBt0/9roNI1xdUWcm9IN/GtGKWYFPUK9iQt2X+0/AFVcCEUFoOmsS721gKbTkRRnLR6dUVRjXyv4VKeMRtTmtQDoI7pYn0vd2Z4hkV9hZNaKLN78I5cKo4WeYd68OiGBq/uFub3luDORBFAIIU5hRrNi8xHrKhsj2zABhON1/LblVlJQ2XhNQFuS2D6tf9bxf8R0Q/P2afFpEqKssaf7x9iTplPerk1QVQEhYQRcMQUAtW9Hu4SilOKnA9ZWvy05FXjoNKYOjuClC+OJC3Lvl4bOSBJAIYQ4he3Kq6TKZCHEW0+PsLbtKosK8KRvpA8KWJlW2uB+GUXVpBXVYNDB2Qnurf0HoJpYAPpkkuq6Hg8GxGLZvLrVcXUEat2vAGjDz8Zr4HDrkwf2un0cYF65kSeXH+atdblUmSz0CvfhtQkJXNVHWv3cRRJAIYQ4ha3Lso5TGx7rj66R5c5cxdaitzy94ZqAtta/4V39CXRj7T8blWGbANK8GcB/lhzujUGDPJ9QcrLyUHUTJ05VqroStX09ALoRY/CI7w5+AVBTBZkH3RKDRSkWpxZxzw/pbMutxFOvceuQSF64II5YafVzK6cJYEFBAXv37nV4LiMjgzlz5vDqq6+yfv16twQnhBCiYUqp4+Vfuga45Zqj4gLw0mtkl9aSeqx+jTyz5Xjtv3PboftXKWXvAm5tC6Cvh54+kdZVUDaHJKO2dLySKc2htq6D2lro0hXiu6PpdGjJfa3b9rX9OMDcslr+seww7244SrXJQp8IH16fkMjE3qENtvqpY1ICpq04TQDnz5/PZ599Zn9cXFzMrFmzWLduHXv27GH27NmsW7fObUEKIYSoL72ohoJKE156jQFRvm65pq+HnjPragIuO1h/MsiWnAqKq80EeekZ6uIVSZokLwcqK8DgAV3jW326ITHW+oWbw1JQm9e0+nztSa3/DQBtxNloda3FWq/+1m1tPBFkcWoR9/6Qzo6jlXjpNaYNjeS5C+KICWx41rrlt5+wzLwTy+8/t2lsnZXTBPDgwYP079/f/vjXX3+ltraWf/7zn7z77rv079+f706zafFCCHGqsbX+DYr2w8vgvhE94+w1AUupNTvWBFxW1/17TmIghnYYy2UrAE1cEprB0Orz2ZLY3UFJ1OzfgypreOxjR6bKSmH3FgC0EefYn9eS+1n/sX83ymxuk2tvz63g3Q1HqTEr+nXx5fVLErksJbTRIQvq0EHUJ3PBZLTXLBSu5fQ3Rnl5OUFBx5vuN23aRJ8+fYiKikKn0zFixAiys0+zwphCCHGKWZ9tHf/XFqt/NKZ/F1/CfQ1UGC2sO1xuf76sxmxPSt2+9JtNXQHo5q4A0pBuQZ6E+Rqo1XuwKzCxQ66c0RRq02owmyGuO1pU7PENsQng6183DjCtTa79y0FbQfBAnjmvG9EnqVWpKsqwvPOCNfkbOALt4qvbJK7OzmkCGBgYSH5+PgAVFRXs37+fgQMH2rdbLBYssoC0EEK0m4JKIwcLa9CwTrZwJ52m2UvCrEg/3g3826FSTBZFYogXie1UvNfeApjQwyXn0zSNobZu4NBep2w3sFpvLf6sjTzH4XlNp4Oefaz7pLq+HEyl0czausLhlySHnHSikrJYsMx7FY7lQUQUultmWGMULuf0rvbv35/Fixfz/fffM2fOHJRSjBgxwr49KyuLsLAwtwUphBDC0Ya6lraUCB+CvFvf1dlctm7gLTkVHKurCdiutf/AWsqkrhVLS3BNCyDAkLpu4C2hvWDPNlRF+UmO6FhUYT7s3w2ahjbs7Hrb7eMA22AiyJrMMmrNithAT3o2oUyR+vEz2LERPDzR3fEIml87jCPtJJwmgDfccAOxsbF8/PHHbN++nZtuuonISGtFdKPRyNq1a+nXr59bAxVCCHHcOvvs3/b5AxkT6ElKuA8WBasySjlcUsP+Y9XoNTinHWr/AXDkEBhrwdcPIqNddtqBUb7oNcjxjSDXIwi17dSaBKk2/G79R88+aKHh9bbbxwEe2I2yuHYc4Iq6LwXnJgbZJ540GOeuLahvP7HGNOVOtLgkl8YiHDn92hgcHMwzzzxDZWUlnp6eGE4YSKuU4oknniA8vP6bSAghRNurNJrZcdS6+oe7x/+d6LzuQewtqGJ5WgllNdbEYUiMP8Ht0CIJxwtAE9/Dpd2Gvh56ekf4sDOvis1hKUzYvBZGneey87c1e/fviDHOd+iWAD5+1hVCDqdDvGu6z4+W17IzrwoNGJPY+JcCdSwfy/v/AqXQzr4Q3ehT5/6eqpx+Qj7//HMyMzPx9fV1SP4APD090ev1LFmyxC0BCiGEcLQlpwKTBWICPNu1eO7ouAA89RqHS2r5YZ91pma7Tf6A4/X/XDQB5ES2buDNob1g12ZUVaXLr9EWVE6WtVtcr0cbOsrpPppOf3wcoAuXhVuZbp0x3T/Klwg/j4ZjNBqxzH0Jysusyfv1t7ssBtEwpwngZ599RmZmZoMHHT582KFOoBBCCPdZXzfztj1b/wD8PPWcEWutCVhjVgR46hjW1a/d4lG2GcCtLADtjG0iyM6QHtRaQG3f4PJrtAW13rr0G30Go/k33Aqn9bJ2A6vUXa65rlL2CULjEhv/UqA+fd86e9vXH90dD6N5ND5LWLhGi9rIy8vL67UMCiGEaHtmi2LjkY6RAAKM6378j/s5CYF46NtnxqaqqYYjh60PEl2fAMYHexHqY6BW58HuoMRTYjawUsqeAJ5Y+88Z20QQUne5ZBzg3oIqcsqMeBs0zujW8Co1lrUrUCsXg6ahm/YAWniXVl9bNI09i9u9eze7d++2b1i3bh25ubn1DqioqGDNmjXExcW5J0IhhBB2e/KrKK+1EOClJyXcp11jUbnZ9PvtJ7pUp5DvFcR5ke4v/Gx36CAoCwSHoQW7vkqFpmkMifHjl4MlbA5NYdCOpajqKjTv9v0ZNOrQAcg7Ap6eaINGNr5vt0Tw8a0bB5gB8d1bdekVadbu31FxAfh4OP9SoLLSUf95CwDt0slo/Ye26pqieewJ4K5du/j888/tG9avX9/gmr+xsbHceuutbR+dEEIIB+uzrDXVhnf1a3D91LakjEbUlrWoX5fCvh3ogKe9gin29Ccx4nyIv8rtMQGoDGv3L23Q/WtjSwC3RPaBg9/Bzk0w7Kw2u15r2Vv/Bo48aaKq6fTQow/s2IhK3YnWigSw1mzh98y69aAb6P5VlRVY3nnRujZx38Fol05u8fVEy9gTwIkTJzJ+/HiUUkyfPp3p06czcqTjNwZN0/D09MTTU/rnhRDC3ZRSJ5R/abhbrU2unZuF+u0n1Jpl1sH6AJoO+g0hMiSciF+XoNatgovaJwEk44A1pDbo/rUZGOWHToNsrzDyvEPosmkNWgdNAJXFjNpgW/u38e5fG61XP9SOjdaJIBdMbPG1N2SVU1FrIdzXQL8u9deoVkph+eB167rNoRHWrl+dvsXXEy1jTwBPTOzmzJlDYGAgXl7tN7tMCCGEo8OlteSWG/HQaQyKbvvJFspYi9pc19qXekKR4OAwtLMvQBt9AVpYBKqiDLX6FzicjjqSiRbj/iFCbTkBxMbf09rtvju/is2hvRi/YyOqtgbNswP+rdy/G4oLrTUR+w5p0iFacn9U3bHKYmlxKR1bQfCxiUFOV/5QS7+ErX+AwWAt9tzI5BTRdpzO5IiIiHB3HEIIIU7Cts7ugCjfBsdVuYLKOYz69SfU2uVQcUJrX/+h6M4ZD/2GoOmPt9hofgHQbwhsW49a9yvalTe2WWxO4y0rhYKj1gcuWgKuIUNi/KwJYNQAxh/5A3ZtgcFntOk1W8Le/TtkFJpHwyVYHMQlgbcPVJZDVob1cTMVV5nYnGOtUXluUv3ETu3bgfryY2ts19/epi22onFOE0ClFL/88gvLly8nLy+P8vL6y95omsbChQvbPEAhhBBWtvF/bTX7V+VmY/l4DpxYCiQkHO2sC9DOOh8ttOHGAW3kGNS29aj1q1BXTDnpqg8uZVv/N6ormm/bzoweGuPPf7YVsDMwEaOmx3PTarQOlgAqkxG1cTXQ9O5fwJrU9+gDOzdZxwG2IAFclVGKRUFymDexgY4to6roGJa5L4OyoJ05Du3si5p9fuE6ThPA//znP3z//fckJCRw9tln4+fXfnWdhBBCQFGVidSCagCGt8Hyb8pksv5xzkq3tvYNGIbunIusrX1NGJ+lDRiB8vK2tsSl7YPuKS6PsSHu6P61SQzxIsRbT1E17AlKZMD2DSijsemtbO6wa6u1FS8oFHo1b9lWLbkfaucm67rA51/e7Evba//9qSC49f31EpSVQGyCdak3d35JEPU4TQBXrVrFyJEjuf/++90djxBCCCc2ZpejgJ5h3oT5uj7ZUMu+tSZ/vv7oZr6CFhHVrOM1Ly+0wWeg/liJWrcKzZ0JoK0FMMH1K4D8maZpDI7xY3laKZtjBjJg9wHYsxUGDG/zazeVfem34Wc1e3KF1qtf3TjAXc0eB5hRVE16UQ0GncZZ8Y7dv+rzD+DgXvDxQ3fnI2gyx6DdOf3J1tbWMmDAAHfHIoQQogHrs22zf9ug9S8/F/XtJwBo197a7OTPRhtpXWtWbfwdZW59MeGmUEodXwKujcf/2QyJtv4MtkTWrZ6xqeMUhVY11ait64Dmdf/axXUHLx/r2M8jh5p1qG3yx/Cu/gR4HU88VepO1LLvANDdOgMtMqb5cQmXc5oA9uvXjwMHDrg7FiGEEE7UmCxsrRtY7+rxf0opLJ+8a63H1qs/2qjzWn6ylIEQEGTt5tuzzXVBNqYw33o9vb5FkxZaYlC0tRzMYfwo8ApCbV2HMpnccu2TUVvXQW0NRES1qCaiZjBAD2vrrdrX9GXhzBbFqoy62n9/mvxh+c46X0A756KTF6QWbuM0AZw2bRr79+/nyy+/pKyszN0xCSGEOMHW3ApqzYpIPw/ig13bdaY2/AY7N1tLctzYunFZmsGANmy09bzrVrkqxMbVjf+ja4Lb1pAN8NKTHGYtrLw5ZrB1vN2+HW659smcWPuvpT9L27JwKrXpr2lLTgXF1WYCvfQMjTn+JUUd2AN7t4PegDbh2hbFI9qG0zGAM2bMQCnFokWLWLRoEZ6enuicjANYsGBBmwcohBCdna38y4hYf5cOnFcVZaiF7wGgTbgWLSq21efURo5FrfgRteUPVE1Nm4/1Uul13b9uLicyJMaPvQVVbIkbzoXpK1GbVqP1HezWGP5MVZRZk3la2P1bR0uuGweYurPJ4wBtkz/OSQjEcMIKNZYfPrWec9Q4tDApMdeROE0AR44c2Sazc3766Sd++ukn8vPzAeuScldffTWDB1s/NEopPv30U5YtW0ZFRQUpKSlMmzaN6Oho+zlqa2v56KOPWLNmDUajkYEDBzJt2jSCg4Pt+5SXlzN//nw2bdqEpmmMHDmSW265BW9vb/s+BQUFvPfee+zatQtvb2/GjBnDDTfcgF4v1ciFEB2H2aLYkH08AXQl9cUCa/dpVCza+EmuOWlSLwiLhGN5qO0b0Ia37UoZ9gkgiW0/AeREQ2L8+GR7Adv14Rg1PR5b/kBNudOhPqK7qU1rwGyC2MRGi3EXVZn4fl8Re5dlc13fYPr/ebWO+B7g6WVd8SXnMHSNb/S65bVm1h22vkdPnP2rMvZbl8vT6Vz3/hIu4zQBvOuuu9rkYqGhodxwww1ER0ejlGLVqlW8/PLLvPzyy3Tr1o1vvvmGxYsXc9dddxEZGcmiRYt47rnneOWVV+yrlCxYsIDNmzdz//334+vry7x585g9ezbPPPOM/TpvvPEGRUVFzJw5E7PZzNtvv83cuXO57777ALBYLLzwwgsEBwfz7LPPUlRUxJw5c9Dr9dxwww1t8tqFEKIl9h+rpqTajJ+Hjr6R9ZfVaimVugv1208A6G66y2VlTDRNs9YE/PEz1LqV0IYJoLKY4VDdEnBuKAFzou6h3gR56ympNrMvqjf9cnbC/l2Q0n4TKO3Fnxto/csqreGbPYUsTyvFZFEAPFdQzvPnx5EUeryBxDoOsDfs3oratwPtJAngmswyjBZFXJAnSSHHW3ztrX8jxqBFRjd0uGgnbVdK3olhw4YxZMgQoqOjiYmJ4frrr8fb25v9+/ejlOLHH3/kqquuYvjw4cTHx3P33XdTVFTEhg0bAKisrGT58uVMnTqVfv36kZSUxN/+9jf27dtHaqp1HEhWVhZbt27ljjvuoGfPnqSkpHDrrbeyZs0aCgsLAdi2bRtZWVncc889JCQkMHjwYCZPnszSpUsxdZCBvEIIAceLPw+N8XfoWmsNZTRi+fgtALSzL0RL7uuS89poI6yzgdm52dot2VZysqGmGry8Ibr13dfNodM0Btctx7el59lA+84GVkXH7Mv1aSPOdti2r6CKF37N4u7v0vnpQAkmiyIl3If+MYFUGS08vTKL/AqjwzFact0M5xOXAGyAbfbvuUlB9t5DlZUOW9eBpqFNuKbVr0+4ntMWQJtjx46Rnp5OZWWldar9n4wZM6bFF7ZYLKxdu5aamhqSk5PJy8ujuLjYofyMr68vPXr0IDU1ldGjR5OWlobZbKZ///72fbp27Up4eDipqakkJyeTmpqKn58f3bt3t+/Tv39/NE3jwIEDjBgxgtTUVOLi4hy6jQcNGsT777/P4cOHSUxMdBqz0WjEaDz+IdE0DR8fH/u/Xcl2PimUaSX3w5Hcj/pO13uy8Ujd7N9uzRv/19j9UEu+gNwsCAxGd/Utrv/9FRuPpVsiHE6HzWvRzmmbFR/s3b/x3dHpG/1z1ibvj6Ex/qxML2Wzbzw3AWrLWrjhry1aQ9eiFK+uPsKuvEp6R/jSr4sv/bv40jXQs0kxq42/g1LQoze68C5YlGJTdjlf7D7G7rwq+34jYv25qk8Yfbv44Rccxi0fr+dQcQ2zVhzmpQsT8K8r36JLGYAZ7KvCNBRDTlkte/Kr0GnWtX9t+9lb/4adhS6mW7Pvh2h7Tj8xtbW1vPXWW6xbt85p4mfTkgQwMzOTxx9/HKPRiLe3Nw8++CCxsbHs27cPgKAgx+rhQUFBFBcXA1BcXIzBYKi3Msmf9wkMdJyCrtfr8ff3d9jnxOTvxOva9nHmq6++4vPPP7c/TkxM5KWXXmrTtZOjolpWj+t0JffDkdyP+k6ne1JtNHO4ZA8A5/VPJNy/+RMq/nw/jIczyP3xMwDC7vg/fHu0Tddp6fmXUvLBm3hsWUvk5Jvb5BqFeVlUAAH9hhAc3bQuRle+P8YHhfHK6iMcqtZRGBxNaHEOYcX5ePUd1OxzLdx02F5G5bdDpfx2yPrvUF9PhnQLZki3YIZ2CyExzNdpMpa7ZS0WwO/8y1l9DD5ef5j0Y9YvDwadxoS+Udw4PI7EMMe/n29dN5Rb/7OJwyU1/HNtHm9eMxAvgx4VHk62lxeqrIQIUzUeDZTY+fZgGgAj4kPp19067tCYmU5uXWto5M134dnEn41wL6cJ4P/+9z/Wr1/PddddR3JyMrNmzeKuu+4iODiYH3/8kaKiohaPE4yJieGf//wnlZWV/PHHH7z11lvMmjWrVS/CXa688kouvfRS+2PbhzA/P9/lXceaphEVFUVubm6jSXhnIffDkdyP+k7He5JaUIVFQbC3HmNZITnN6E11dj+UUphfeQpMRrR+Qyju2Z+SnJw2iV2lDAKgZudmjuzagRYa7vJrmHZZaw1WRMZQdZLX0Vbvj57h3qQWVLOl7/mct/pj8n/6Fn1ol2adI7O4hjdXpQNwdd8wPPUaO45Wsq+gisLKWn7Zl8cv+/IACPLS07eudbB/Fz+6BXlCXg6laWn8HDeW7zMiKNxr/dLg66FjfM8QLksJsa4eU1tKTk6pw/1QFcXMHBPDIz8dYktWMY98uZkHz+qKTtMgqTfs2Ure78vRnetTL26LUny7PRuA0V29yan7GZgXvAVKoQ0+k2NeftBG77HWMBgMbdp4cypwmgD+8ccfjB07liuuuMJeBzA0NJR+/foxYMAAZs2axdKlS5k+fXrzL2gw2L+BJSUlcfDgQX788UcmTpwIQElJCSEhIfb9S0pKSEhIACA4OBiTyURFRYVDK2BJSYm9RS84OJjS0lKHa5rNZsrLyx32+XOh65KSEvu2hnh4eODRwEDptvqDo5Q6bf6YuYLcD0dyP+o7ne5JWqF17d/EEO8Wv6YT74fl95+t48Q8vdBuuMO+vU2ERkDPPrB/N5b1v6K76EqXnl4Za61L1wHE92jy63D1+2NotD+pBdVsDkvhPKzjAC3X3NrkbmCjWfHK6mxqzYoh0X7cODAcTdOY3B+MZgupx6rZebSSnUcr2VtQRUmNmTWZZazJtP5tDvTS09NcyJ4zH6XS4APVFkJ8DFyeEsJFPYLx89TbX7czSinig7149JyuzFpxmN8PlRHum8ctQyIhuS/s2YratxM1dkK9Y3cfrSSvwoiPQcfIWH/rvc07glpXNxnlkmtOm8/i6cjpO7S0tJQePaxL6thm31ZXV9u3jxw5kvXr17skAIvFgtFoJDIykuDgYHbsOF54srKykgMHDpCcbJ3en5SUhF6vd9jnyJEjFBQU2PdJTk6moqKCtLQ0+z47d+5EKWV/TcnJyWRmZtqTPoDt27fj4+NDbKx7BxILIURD0otsCWDra+mp0mLUZx8AoF1+Q4uXe2sObeRY67XXt0FR6Mw0MJutK4+ERbr+/E00OMbaGLGtyhOTtx8UFRwvTt0EC3cUkFZUQ4CXnnvOjHbo3vXQW2d+T+4fzjPnx/HJNT154YI4pgwMZ2CUL556jdIaM5tMQVQafIj1MHLPGVG8NzGJq/qE2ZO/phgQ5ce9Z1i7ar/eU8h3ewuPF4Tet8NpIre8rvbf6PgAvAzWdEL9+DkoC/QfhhbvnqX5RMs4bQEMCgqyt/x5eXnh5+fHkSNH7Nurqqqora1t9sU++eQTBg0aRHh4ONXV1fz+++/s3r2bxx9/HE3TmDBhAl9++SXR0dFERkaycOFCQkJCGD7cusi2r68v48aN46OPPsLf3x9fX1/mz59PcnKyPQGMjY1l0KBBzJ07l+nTp2MymZg/fz6jRo0iNDQUgIEDBxIbG8ucOXOYMmUKxcXFLFy4kIsuuqjBFj4hhHC39KIaABJcsPqHWjTPumJFt0S08y9v9fmaQhs6CvW/uZCZhso5jBbtuskAKqOuFyehZ7tO/OkR6k2Al56yGjP7B11A7z++Rm1ei9Y95aTH7smr5MvdxwC4a0QUoT6NT2Tx0OvoE+lLn0hfru0HtTVGDnz7Ham7DxJdW8Swxx9H7+vX6DkaMyYxiGOVJhZszWfepjxCz4zmDA9Pa63I3GyHmdY1JgurD1nzhHGJ1jH0quAo6o8VAOgukVU/Ojqn77YePXqwd+9e++OhQ4fy3XffERISglKKH374wZ5wNUdJSQlvvfUWRUVF+Pr6Eh8fz+OPP26f+Ttx4kRqamqYO3culZWVpKSk8Nhjj9lbIQGmTp2KpmnMnj0bk8lkLwR9onvvvZd58+bx9NNP2wtB33rrrfbtOp2ORx55hPfff5+ZM2fi5eXFmDFjmDx5crNfkxBCtAWLUmQUWxPAxBNqtLWE2rnZ2gqn6dDddLfbihVr/oHQdwhs34Batwrtihtdd/IMayubu+v//ZleZy0H82tGKZu7DqY3X6M2rUZdfXOjiWml0cyra3OwKBiXFMiZcQHNuq7KO4L+/VfolZ5KL0C79Dp0rUj+bK7sE0p+pZEfU4t5dV0eT6WcTe8dy6z1AE9IANdllVNlshDp50HvSOv4QLXkC2urbO+BTUqARftymgBOmDCBtWvXYjQa8fDwYPLkyaSmpjJnzhwAunTpwi233NLsi915552Nbtc0jcmTJzeaiHl6ejJt2rR6Sd+J/P397UWfGxIREcGjjz7aeMBCCNFOjpYbqTZZ8NBpdA1o+Rq3qqYay3/fAUAbd4nbl0zTRo5Bbd+AWv8rauIUl7XW2UrAaG5eAcSZoTF1CaA5iCmeXnAsDzIPWlfUaMC8TXkcLTcS6Wdg2tCmTxpRSqHWLkd98m+oqQJfP2sh72GuKbitaRrThnbhWKWJdVnlvBB2Hs/77qBb6k4Ye7F9v+O1/wLRaRqq6Bhq9S8A6C6VxpRTgdMEMCUlhZSU49l7eHg4r776KpmZmeh0Orp27SpLpgkhRBuyjf+LC/ZC34oC0JbvFkLBUQgJR7tiiqvCazJt4AiUlzfk50LaPnBBy5CqLLd2SQK0cwsgwKC6gtDpxbUUDhhF6MYVqE1rGhwD98fhMn45WIIGzDgzpslj9VRlOeo/76A2/GZ9Irkvulvvd/kau3qdxgOjY3hi2WH2FVTxzIBbeWH/fwhXCk3TOFZpZFuutcTMubbu36VfgskEyX3tRaRFx1ZvEkhNTQ3/+te/+O233xx31OlISEggLi5Okj8hhGhjtvF/rZkAUpuWivrpKwB0N/wVzdt1S8k1lebljTZoJHB8qbJWs43/C++CFhDY+L5uEOxtoEddN/227qMBrN3ATiZOFFWZeGtdLmDtbu3753V4G6BSd2GZdZ81+dPp0K64Ed0Dz7o8+bPxMuiYOaYrMf4e5HuH8lzCJKqyswBYlVGKRUHvCB+iAzxRpUWoX5cCMvbvVFIvAfTy8mLHjh3U1NS0RzxCCCFofQKoLGaK3nwOLBYYcqY9CWsP2kjrogFqw28os7nV57N3/3aA1j+bIXWzgTd7RIHBA/Jy4OBeh32UUsz5I4fSGjOJIV7cMODktRGV2Yzlm/9i+dfjUJgPEVHoHnkZ3SXXounatjEm0NvAk+O6EWSuIj2gKy+vK8BkUaywdf/aWv9++hqMtZCYDL0HtWlMwnWcloFJSUmxr60rhBDC/TKKjtcAbAm1cjG1qbvA2wfddbe7MrTm6z0I/AOts0n3bm/16VR63RJwbh7P2JihMf4AbMurxtxvKACWlx7G/NQ9WL5YgNq3k6X7Ctl4pAIPncbfR8XgoW+8VqDKz8Xy8iOo7xeBsqCNOg/dP15z67jHqABPHg/IxMtcy5ZqH2YtP0xmSS0eOo3R8QGoslLUysWAdezf6bYU4+nM6bvv1ltvZe/evSxcuJBjx465OyYhhOjUymvM5FdaVxdqSQkYVVaK5cuPANBdNRUtJMyl8TWXZjDYJymodStbf8IO2ALYM8wbf08d5bUWDo651jrWUdNB9iHUki/IeusV5q+3jlu8MaiIOCoaPZ/ljxVYnr7POm7Sxw/t9v9Dd8t97dKNn9wniQd3/QedsrD9aCVgXVPY31OP+uVbqKmGuO7Qf5jbYxMt53QSyP/93/9hNpv56quv+Oqrr9Dr9U7r4y1YsKDNAxRCiM4mvdja+hfp59GsYr52B3ZDdRWGrnGoseNdHF3LaCPPQa38EbX5D9SNNWieLezaLjoGxcesyVVcdxdH2XJ6ncagaD9+P1TGZnMQvR95GVVeitq1BfPOLbxu6keN3pP+Rfu5ZOX7WL5SEJeE1m8YWv+hkJSMptOjKitQ/333ePHsHn3QTbsfrR2LXZPUi6GlB/lr6pe80+tqAMYlBVknpaz4HrCO/ZPWv1OL0wRw5MiR8oMUQoh2ktHa8X/ZhwDw7NWPWp2+YyzHlZRiXbHjWB5s3wAtLVtS1/pHTDc07/rr07anoTH+/H6ojE1HKpgyMALNPxBt5Bg+9e3L/u0F+Orh3thqdCU94NABa4HszDTUj5+Crz9a38GotH3We6TToV12PdqEq9t8rN/JaB6ekJTMBanr0Z15LvkxyQyJ8bN2TVdVQtd4aMcxpqJlnCaAd911l7vjEEIIUSettTOA6xJAj/juNH/Nprah6XRoI85BLf4cy7pV6FuQAKojmVh++NR6vg7U/WszuK4czMHCaoqrTQR7G0gtqGLRjgIA7hgZTWRiClxxjXVpvl1bYMdG6/8ry4+Xdwnvgm7aAx2qmLKW3B+Vuovzs/9Ad/FoVFUlll++tW6bcE2T1z4WHUfj684IIYRwO9sEkISWTgCxJYAJHWstVm3kGNTiz2HHJlRFOZqff5OOUyYjaskXqB8+tdaa8/ZBO6EocUcR4mMgKcSLtKIathyp4My4AF5dcwSLgrPjAxhTN2sWQAsMRjvzXDjzXOvM6PRU1M5NYDCgnXc5mo/7x/o1Rkvui8JajkYpZZ34UVkOUV3Rho1u7/BECzhNAFetanzhbk3T8PDwICwsjMTERFk/VwghXMRkUWSWWNvtklrQAqiMRjhqnWzgEd8dTB2g+7eO1jXe2l2YfQi1eQ3a2Ree9BiVvh/LgjfsrZr0H4buxjvRQtum/l1rDYnxJ62ohs1HKthXUMWRMiNhPgbuGB7V4DGaXg89eqP16O3GSJspKQUMBigqgOwM1M9fA6BdfE27d1GLlnGaAL799ttNPoGvry9XXHEFEydOdFlQQgjRWWWV1GCyKHw9dET6teDLdW6Wtfafjx/68C6Qm+v6IFtBGzkW9eUC1LpV0EgCqGpqUN/+F/Xzt6As4B+Idt10tBHndOgx6kNj/Ph81zH+yCqj1mxNvu89Mxp/r1M7SdK8vKx1/vbvxvLBG9aSPhFR9hqP4tTjNAF8+eWXeeuttwgICOCiiy4iKsr6zSUnJ4elS5dSUVHBbbfdRnFxMUuWLOGTTz7Bx8eHCy88+bc5IYQQDcsoto7/Swj2alGiY+v+JTa+QyZK2oizUV8ugNSdqMICtND6xZDVnm1YPn7LunwcoI0Yg3bdNLSAoHr7djS9wn3w89BRYbQAcFmvEPtScac6Lbkfav9u6zrHgHbx1dbWS3FKcjpq84cffiAoKIgnnniCESNGEBcXR1xcHCNHjuSJJ54gMDCQ5cuXM2LECGbOnElycjJLly51d+xCCHHaafUScHUJoBYT76qQXEoLi4QefUAp1EbHJUdVZTmWBW9ieeUJa/IXEo7unifQTX/glEj+wFoOZmBdwtctyJObBnXMruqW0Hr1P/4gNNw6hlGcspwmgBs2bGD48OFOD9A0jWHDhrFu3TrrCXQ6Ro4cSW4H62YQQohTUbqLJoBosR0zAQRrTUAAte742sBqyx9Y/nE36vefrfuMnYBu1hy0Ac7/FnVk1/cP59zEQB45pytehtNodmxSCuitHYfa+EloBhn/fypz2gVssVg4cuRIgwdlZ2c71JUyGAx4enq6PjohhOhElFKtrgFobwHs2oETwKFnoRa+B5kHUft2YFnxA2xaY93YpSu6v9yNlty3fYNshbhgL2aMimnvMFxO8/JCmzQVsjPQzrqgvcMRreQ0ARw2bBhLly4lKiqKcePG2ZO72tpali1bxs8//8yoUaPs+6emptrHCQohhGiZwioTJTVmdBrEBbVgBnBVJRTmWx900C5gAC0gEPoMhh0bsfzrceuTOh3aRVehXXadtfCw6JB0F8iEz9OF0wTwlltu4ejRo3zwwQd8/PHHBAcHA1BcXIzJZKJHjx7ccsstgDUp9PT05JJLLnFb0EIIcTqytf51DfRsWdehbQJIcCiaf4ALI3M9beQY1I6N1gdxSeim3oPWgZZ2E+J05zQB9Pf355lnnmH9+vVs27aN/HzrN8qBAwcycOBAhg8fjq6u6renpyd33HGH+yIWQojTlH0CSHALx/8dqUsAO3D3r402dLQ1YQ0KQRs7QWaTCuFmDa4EomkaI0eOZORIWd9PCCHcIb3YNgGkheP/sjr++D8bzWBAu+ov7R2GEJ3WaTQ9SQghTm2tnQCijmRa/3EKJIBCiPZlALjrrrvQ6XS8+uqrGAwG7rrrrpMWENU0jTfffNMtQQohxOmuxmThSJl1CbjEFpSAUUpBdgZwarQACiHalwGgT58+aJpmH9dneyyEEMI9DhXXYFEQ5K0nxKfB0TkNKy2G8jLQNIju5vL4hBCnF3sL4In+/FgIIUTbsi0B15LWP8De+kdENJpnC8cQCiE6DRkDKIQQHUBaoXUCSGJwC8f/ZdeN/+vAK4AIITqORvsZCgoKOHr0KBUVFQ4rf9jIDGEhhHCN4y2ALV0BJAPouGsACyE6FqcJYEFBAe+88w47d+5s9OBFixa1SVBCCNGZWJQ6XgOwpWsAZ3X8NYCFEB2H0wTwrbfeIjU1lSuuuIKePXvi6+vr7riEEKLTOFpupNpkwUOn0TWw+cugKYsFcqQEjBCi6ZwmgKmpqUycOJFrr73W3fEIIUSnY6v/FxfshV7XggoMBblQWwsGD4iIdnF0QojTkdNJIGFhYfj5+bk7FiGE6JTSiuomgLR4/F9d619MN1lSTQjRJE4TwMsuu4zly5dTU1Pj7niEEKLTsU0ASWjxDOAMQCaACCGazmkX8AUXXIDFYuHee+/ljDPOICwszF4k+kSXXnppmwcohBCnu/S6EjBJLa4BKCVghBDN4zQBzMzM5Ntvv6W4uJglS5Y0eLAkgEII0TrlNWbyK00AxLd0DeDsuhnA0gIohGgipwngv//9byorK5k+fbrMAhZCiDZk6/6N9PPA37P54/eU0QhHs60PZAawEKKJnCaAGRkZXHvttZx//vnujkcIITqV9NZOAMnNAosFfPwgJMyFkQkhTmdOJ4FERka6Ow4hhOiUbAWgE1rZ/UvXeDStBSVkhBCdktME8Nprr2Xp0qUUFBS4Ox4hhOhUjrcAtnQCSN34v65xrgpJCNEJOO0C3r17N76+vsyYMYP+/fs7nQWsaRq33HKLW4IUQojTkcmiyCypBSCxxSVgbC2ACS6KSgjRGThNAJcuXWr/9+bNmxs8WBJAIYRouezSWkwWha+Hjkh/jxaeRFoAhRDN5zQBXLRokbvjEEKITsfW/ZsQ7IWuBeP3VFUlFOZbH8gMYCFEMzgdAyiEEKLttXYCiK31j+BQNL8AF0UlhOgMnLYAnshisVBZWel0m7+/v8sDEkKIzqK1E0BOnAEshBDN4TQBNJlMfPPNN6xYsYJjx45hsVicHixdxUII0TJKKTLqWgBbXAPQPv4vwUVRCSE6iwZXAlm1ahXJyckMHz5cVgIRQpyWVF4OHMuDlAFur6FXVG2mpMaMToO4oNbOAJYJIEKI5nGaAP7xxx+cc8453HXXXe6ORwgh3EIpheX1pyAvB238JLjqL25NAtMLrd2/MQGeeBmaPxxbKQVHpAVQCNEyTn/reHl50bNnT3fHIoQQ7pOZBnk5AKglX6D+929UA8Nd2kJ6cSu7f0uKoLwMNB1Ex7owMiFEZ+A0ARw9enSj9f+EEOJUp7aus/4jLBI0DbXiB9SCN1EWs1uun9HaFUDqWv+IjEbzbGESKYTotJx2Ad944428/fbbvPjii5x77rlOVwIBSEpKavMAhRCiLaitfwCgXX4D6DTUB6+j1iyD2hq47e9ohhYWZm6i9FZOAFFZMv5PCNFyThNAo9GIUootW7awZcuWBg+WWcBCiFORys+FrAzQ6dAGDEPzD0R5emP59z9RG39H1dagu+NhNA/PNrl+jcnCkTLrEnAJrWwB1KQEjBCiBZwmgO+88w7r169n9OjR9OjRQ2YBCyFOK2pbXfdvz75o/oEAaEPORHf341jefgG2b8Dy5jPo/vYYmrePy69/qLgGi4Igbz0h3voWncPWAigJoBCiJZwmgNu2bWP8+PHcfPPNbg5HCCHantpiTQC1QSMdntf6DUV331NY3nwG9mzD8tqT6O79B5qva4veZ9gmgAR7tWjmsbJYICfT+kASQCFECzidBOLj40NUVJS7YxFCiDanykth/26gfgIIoPXqh+7+p8HXDw7uxTL7CVRZqUtjaO0KIBTkQm0tGDwgItqFkQkhOgunCeB5553H6tWrG1wBRAghTlVq+wZQFohNRAvv4nQfLakXugefh4AgyDyI5Z+PoooLXRZD69cArmv9i+mGpm9ZF7IQonNz2gUcGxvLxo0befjhhxkzZkyDs4BHjqz/7VkIIToyW/kXZ61/J9K6JaL7vxewvPIE5BzG8vIj6B54Fi0sslXXtzgsAdfSNYAzrDHGSPevEKJlnCaAr732mv3fH3/8cYMHyyxgIcSpRNXWwC5rZQNt8Mm/wGrRsegeqksC83OtSeD9z6J1iWlxDEfLjVSZLHjoNLoGtnCWsa0FMFYSQCFEyzhNAJ988kl3xyGEEG1vzzZrnb/QCOjWtDqmWkQUuodetCaBuVl1SeAzLZ59a2v9iwv2xKBr2dJztjWAZQawEKKlnCaAffr0cXccQgjR5tSWuuLPg0Y2a/atFhKG7v+ex/Lqk5CVjuWfj6Gb8RRaQvOXzEwvtk4ASQhuYfev0QhHs60PpAtYCNFCzV+BXAghTkHKYrZOAOHk4/+c0QKD0T34HCQmQ0WZtWi0Us0+T2tXACE3CywW6yzlkLCWnUMI0enZWwDnz5/frAM1TeOWW25xeUBCCNEmDu6DshJr4tSzb4tOofn5o/v701j+72bIz4WMA5DYvFbA1q4BbJsAQkx8i2oICiEEnJAALl26tNkHSwIohDhV2Nf+HTAczeB09EuTaD6+aAOGozb8htr0O1ozEsDyGjN5FSag9SVgNJkAIoRoBftvQZnRK4Q4XSmlHMb/tZY2dLQ1Ady4GjXp5ia3xNlWAIn0M+Dv2cIl4OomgMj4PyFEa8gYQCHE6e/IYWuXrcED+g5u/fn6DQVPLziWB4cONPkw2wogCS1dAQRAZgALIVxAEkAhRJtpySSJtmDr/qX3QDRv31afT/PyQhsw3HrujaubfFxrJ4CoygoozLc+kARQCNEKkgAKIdqEZf6rWB66FZWf296hNHn1j+bQho22nnvT6iYnuhl1JWASW1gChiN1BaCDw9D8/Ft2DiGEQBJAIUQbUMZa1PrfoPgYlk+bV2HA5bEUHYOM/aBpaANHuO7E/YZZu4ELjkLmwZPubrIoDhXXAi2fAGIf/9c1rkXHCyGEjSSAQgjXy0wDs3W2K1v/QO3e2m6hqG3W1j+SeqEFhbjsvJqXF1r/YdZrbPj9pPtnl9Zisih8DDq6+Hu07KL28X8JLTteCCHq6AB+/PFHjhw50t6xCCFOEypjv/UfdbNjLYveR5nN7RPLFtd3/9o0pxs4taAKsLb+6VpYv09aAIUQrqIDWLBgAWlpafYnJ0+ezO+/n/wbrRBCOJWWCoB23mXgHwBHMlErF7s9DFVZAft2WGNpgwSQ/sPA07PBbuBjlUa+2VPIg0symLPOOhayxRNAlIIj0gIohHANA4C/vz/FxcXtHIoQ4nSh0vcBoPUdAl26ov77Durb/6JGnIMWEOi+OHZusnZFR8WiRcW6/Pyal7c1Cdy0BrVxNVp8D0qqTazJLOO3Q6XszqvC1i6o06BfF18u6xXasouVFEF5GWg6iHb9axFCdC4GgD59+vDZZ5+RkZGBr6+1RMKqVatITU1t8EBZCk4I4YwqL7XW3ANI7InWZyBq1RLISkd9+1+0KXe6Lxjb7N/BbdD6V0cbehbl2zaz7mARq5dnsi23EssJvcG9I3w4Oz6QUXEBhPi0fAUSW+sfkdFoni1cRUQIIeoYAKZNm8aHH37I9u3bKSkpAWD79u1s37690YMlARRC1JNeN/4vMgbNLwAA3XXTsfzrMdSqpagx49FiE9s8DGU0onZsBEAb6PoEsNpkYX1WOb+Vd2PzqH9g0hkgpxKA7qHenB0fwFnxgUT4tXDCx5+oLNv4P6n/J4RoPQNAUFAQ9913n/3JyZMnc88993DWWWe1W2BCiFOTvfs3Kdn+nNarn3X5tE2rsSx8H90DzzZ5+bQW27cDqqsgKAQSk0++fxPlVxj5z9Z81h4uo8Zc19SnM9CtIpezg4ycffEYYgI9XXY9O/sMYJkAIoRoPaf9EXfeeSfJya77hWnz1VdfsX79erKzs/H09CQ5OZkbb7yRmJgY+z5KKT799FOWLVtGRUUFKSkpTJs2jejoaPs+tbW1fPTRR6xZswaj0cjAgQOZNm0awcHB9n3Ky8uZP38+mzZtQtM0Ro4cyS233IK39/ECrAUFBbz33nvs2rULb29vxowZww033IBe37I1OoUQoGwtgH9KurRrbkFt32BNzDavgaGj2zaOuvIv2sARaLrWV7wyWxSL9xfx8dYCqk0WAKL8PTg7PpDR5fuJW/AKREShu/b8Vl/LGSUlYIQQLuT0t+LYsWOJjIwEICsriy1btrBlyxaysrJadbHdu3dz0UUX8dxzzzFz5kzMZjPPPvss1dXV9n2++eYbFi9ezPTp03n++efx8vLiueeeo7a21r7PggUL2LRpE/fffz+zZs2iqKiI2bNnO1zrjTfe4PDhw8ycOZNHHnmEPXv2MHfuXPt2i8XCCy+8gMlk4tlnn+Wuu+5i5cqVLFq0qFWvUYjOTCkFGXUzgP+cAIZFol10FQCWzz5A1da0XRwWywmrf5zR6vNlFFXzyE+HeG9jHtUmC73CfXj5onjevTyJGwdFkDBsMHh4Wsc+Hk47+QmbSVnMkFO3Coi0AAohXKDBr8UbNmzgnnvu4YEHHuDFF1/kxRdf5IEHHuCee+5h48aNLbrY448/ztixY+nWrRsJCQncddddFBQU2EvQKKX48ccfueqqqxg+fDjx8fHcfffdFBUVsWHDBgAqKytZvnw5U6dOpV+/fiQlJfG3v/2Nffv22SetZGVlsXXrVu644w569uxJSkoKt956K2vWrKGwsBCAbdu2kZWVxT333ENCQgKDBw9m8uTJLF26FJPJ1KLXJ0Snl59jnalqMICTcX7a+EkQEg7H8lA/fdV2cRw6CMWF4OUDKQNafJoak4WPt+Zz/+IMUo9V42PQccfwLrx4YRy9wn3s3diatw/0Hwo0b23gJis4CrW1YPCAyOiT7y+EECfhtAt48+bNzJ49m4iICK6//npiY60lB7Kysli2bBn/+te/eOSRRxg0aFCrLl5ZaR0w7e9vXdMyLy+P4uJiBgw4/gvb19eXHj16kJqayujRo0lLS8NsNtO/f3/7Pl27diU8PJzU1FSSk5NJTU3Fz8+P7t272/fp378/mqZx4MABRowYQWpqKnFxcQ7dxoMGDeL999/n8OHDJCbW/+NlNBoxGo32x5qm4ePjY/+3K9n/sLT1OKlThNwPRx31ftgLQMd1R+dZfxyc5u0N19yC5d//RC3+HEafjxYa4ZJrn3hP1NY/rP/uP8RpHE2xPbeCt9blkFNm/cyf0c2fvw6PIszX+aQO3bCzsGxei9q0Gq76i0t/Niq7rvUvphs6fdNmEnfU90h7kfvhSO6HcPqb5IsvviA+Pp5Zs2Y5jJkbNmwY48eP5x//+AefffZZqxJAi8XChx9+SK9evYiLs3Zp2GoRBgUFOewbFBRk31ZcXIzBYMDPz6/RfQIDHWuN6fV6h3qHxcXFDsnfiddtqCbiV199xeeff25/nJiYyEsvvUREhGv+gDkTFRXVZuc+Fcn9cNTR7kfR0WzKAf++gwiJdt5SpS6/lvzVv1CzawtePywi7KHnXBpDVFQUOTs3YQJCzr0YvwbiaEhJlZHXVx7gu505AIT7efLQ+b04N7nxz7nlwss48uEbqLwcwqvK8Ozeq6UvoX5MpYWUAr49exPWzNfT0d4j7U3uhyO5H52X0wQwMzOT66+/3iH5s/H29mbs2LH873//a9WF582bx+HDh3n66adbdR53uvLKK7n00kvtj23fnPLz813ebaxpGlFRUeTm5p50ianOQO6Ho456P0w7twBQ2SWW6pycBvdTk6bC7q1UrlpKzRnnovXs2+pr2+5JzrZNmDLTQK+npFt3ShuJwyEmpfjtUCnvbTxKSbV12bqLk4P5y6BI/DxN5DTlPH2HwJa15C35Gv1Vf2nNy3Fg3mtdzaQ6JLJpcdBx3yPtRe6Ho85+PwwGQ5s23pwKnCaAHh4elJeXN3hQeXk5Hh4tr201b948Nm/ezKxZswgLC7M/b2uRKykpISTk+KLtJSUlJCQk2PcxmUxUVFQ4tAKWlJTYjw8ODqa0tNThmmazmfLycod9Dhw44LCPrQbin1sGbTw8PBp83W31AVJKdcoPZ0PkfjjqSPdDmYyQWTcBIrFn43F1S0I76wLUbz9h/t+/0T0+G03nmtn3li3W7l+S+4GPX5PuT165kXc35LLpSAUAsYGe3D0yit6R1sL4Tb7HQ0fBlrWojauxXHGjy7rX7F3AXeOa/fPuSO+RjkDuhyO5H52X00kg/fr148cff3S6Esj+/ftZvHixwxi8plJKMW/ePNavX88//vEP+0xjm8jISIKDg9mxY4f9ucrKSg4cOGAvS5OUlIRer3fY58iRIxQUFNj3SU5OpqKiwmF94507d6KUokePHvZ9MjMz7UkfWItf+/j42Mc8CiGa4XAGmIzgFwARJ++m1K68CXz8IDMNtXqZy8Kw2Gf/nrz4s9mi+GZPIXd/n8amIxUYdBrXDwjntQkJ9uSvObSBw60TNfKOQFZGs493RhmNcDTb+iBGikALIVzDaQvgjTfeyOOPP84TTzxBjx497HX6jhw5woEDBwgKCmLKlCnNvti8efP4/fffeeihh/Dx8bGPtfP19cXT0xNN05gwYQJffvkl0dHRREZGsnDhQkJCQhg+fLh933HjxvHRRx/h7++Pr68v8+fPJzk52Z4AxsbGMmjQIObOncv06dMxmUzMnz+fUaNGERpqXYdz4MCBxMbGMmfOHKZMmUJxcTELFy7koosualXrphCdlaor/0Jizya1fGkBQWiXXYf6dB7qq49RQ0ej+fqd9LjGmIsL4cAe6/mbkAC+uuYIvx0qA6BPhA93jYwiNqjly6xp3r7Qbyhs/cO6NnA3F6x4kpsFFgv4+kFI2Mn3F0KIJnCaAEZGRvKvf/2Lr776iq1bt7JmzRoAIiIimDBhAldccUW9iRpN8dNPPwHw1FNPOTz/t7/9jbFjxwIwceJEampqmDt3LpWVlaSkpPDYY4/hecJMvqlTp6JpGrNnz8ZkMtkLQZ/o3nvvZd68eTz99NP2QtC33nqrfbtOp+ORRx7h/fffZ+bMmXh5eTFmzBgmT57c7NclhADS6ur/JTS9iLx27iWoX5dCbhbq+4Vo197WqhCq1v8GSkF8j5POLj5WabQnf3eO6MKFPYLRuaDLVhs2GrX1D9Sm1agrprS6G1hlZ1j/ERMvMzaFEC7TYD2BoKAgbr75Zpde7NNPPz3pPpqmMXny5EYTMU9PT6ZNm1Yv6TuRv7+/w/J2zkRERPDoo4+eNCYhxMmp9LoEMKkZCaDBgG7ybVhen4Va/j3q7IvQols+BKNq7UrreQeNOOm+azKtyV9KuA/je4acZO+m0wYORxk8rN222RlO6yE2S934Py1Wun+FEK7T+vWRhBCdnqooPz5OrRktgABav6EwYDiYzVg+ndfyGGqqqdm63nrOJqz+YUsAR8cHtPiazli7gYdYY3JBUWjbEnAy/k8I4UqSAAohWs9WADoiCi0gsPF9ndBdexvoDbBzk3W94BZQu7ZYl5cL7wJdG0+WjlUa2ZNfBcCoONcmgABa3TrHatPqVs2wVAf2wL7t1nPGJrgiNCGEACQBFEK4gL37N7F5rX82WpcYtPMvA8CyaJ61pExzY6gr/6INPuOkY+XWZJahsHb/hjewskdraANHWGcD52aDrQWvmVTGfixvzLIuAddnMHRPcXGUQojOTBJAIUSr2RJAWpgAAmiXTIbAYMg7glowB8uqJahNa1D7dqKOZKJKi1Fms/Prm832lkNdE2b/tlX3r43m4wt9B1tj29T8bmCVlY7l1SehqhKS+6L722NoOvl1LYRwnaYtKimEEA1QSkErWwDBmjRpV/0F9eEbqD9WwB8rcNp56usP/oEQEAj+gWj+AaCAijJ0gUHQo0+j12nr7l8bbdho1Lb1qI2rUZff0OQZvConC8sr/4DKckjqhe6eJ9C8Wl6aRgghnKmXANbU1PCPf/yD8847jwsvvLA9YhJCnEqO5UFZiXUMX1xSq06lnTnO2uqVfQhVXmo9b3kZlJdChbXVjspy6395RwAckkTvEedQq9c3Ou5u7WFr92+vNur+tb+WASNQBoO1jt+RzJOOSwRQeTlYXplpfd1xSejue9I6qUQIIVysXgLo5eVFXl6e1JsSQjSJvfs3NgHNw7PxnU9C0+nQzr/c+XXMZmviV14KZaVQXmpNEm3/mc0ETfkr+ebGJ12srqv9N7oNW/8Aa1HrvkOgrhVQO0kCqI7lY5k9E4oLoWs8uhlPo/n6t2mMQojOy2kX8KBBg9i2bRsXXHCBu+MRQpxq0lrf/dsUml4PAUHW/+pWmjvxa6qmaRgioyAnp8FzuKv71x7T0Lpu4E2rYeINDe6nio9hmf04FOZDl67o/v50i2ZTCyFEUzkdVTxp0iRycnJ488032bt3L4WFhZSXl9f7Twghji8B17YJoCuc2P0b4df2Sz5aZwMbIOcwqq6g85+p0mIss5+A/FwI74Lu/mfQglxXmFoIIZxx2gL4wAMPAJCVlcXvv//e4MGLFi1qm6iEEKcEZTLBoYNA27cAuoK7un9tNF8/awmX7RtQm35H6+rYCqgqyrC8+g/rOMGQcHQPPIsWGu6W2IQQnZvTBHDSpEkyBlAIcXLZh8BYCz5+0CWmvaNpVGGVya3dvzba0NGo7Rusq4JcfjwBVJUV1lIvWRkQFGJN/sK7uC0uIUTn5jQBvPbaa90dhxDiFHS8/l/PDl+nbm2mrfvX2y3dvzbaoBEofV038JFMtJg4VHWVtcjzoQPgH4ju78+gdfAEWghxemnSb+zKykosFktbxyKEONXY6v81c/3f9vD7oVIARse5d3KF5usPfQYB1rWBVW0NljnPwsG94OtnnfDRNc6tMQkhRIOFoA8ePMjChQvZs2cPJpOJmTNn0q9fP0pLS3n33Xe55JJL6Nu3rztjFUJ0MPYl4JI6dgLYXt2/Ntqw0agdG1Ebf0el74N9O8DbB92MWWitrJ0ohBAt4bQFcN++ffzjH/8gNzeXs88+26GoamBgIJWVlfz8889uC1II0fGoqkrr5AWAxJ7tG8xJtFf3r402aKS1UHbOYdi5GTy90N3zj1Ni4owQ4vTkNAH83//+R9euXXnllVe4/vrr623v27cvBw4caPPghBAdWMZ+UArCItECO3bZktWZ7dP9a3NiNzAGD3R3PY6WLD0oQoj247QL+ODBg1x//fV4eHhQXV1db3toaCjFxcVtHZsQogNTLlj/1x0Kq0zszmu/7l8b3SXXYqmuRDfhWjRbMiiEEO3EaQKoP8lamoWFhXh7e7dZUEKIju/4DOCOnQC2d/evjdY9Bf1DL7bb9YUQ4kROu4B79uzJH3/84fSA6upqVq5cSZ8+fdo0MCFEx6WUOj4DuIMngO3d/SuEEB2R0wTw2muvJS0tjRdeeIEtW7YAkJGRwbJly3jkkUcoLS1l0qRJbg1UCNGBFBVASRHodBDXvb2jaVBH6f4VQoiOxmkXcM+ePXn00Ud57733eOuttwD4+OOPAejSpQuPPvoo8fHx7otSCNGx2Lp/YxPQvLzaN5ZG2Lp/k8Pat/tXCCE6mgbrAPbr14/XX3+d9PR0cnNzUUrRpUsXkpKSZJk4ITo5lXZqFIBeY+v+jZfWPyGEOFGDCaBNYmIiiYmJ7ohFCHGKUBl1LYAduAB0UZWJXbbu324y/k8IIU7UYAJoNBpZtmwZW7ZsIS8vD4DIyEgGDx7MuHHj8PT0dFuQQoiOQ5nNkGGtA9qRWwDXnND9G+kv3b9CCHEipwngsWPHePbZZzly5AjBwcFERUUB1okgW7duZcmSJTzxxBOEhYW5NVghRAeQkwm1NeDtA9Fd2zuaBkn3rxBCNMxpAjhv3jzy8/P5+9//zhlnnOGwbe3atbz11lvMmzePhx56yC1BCiE6Dtv4PxJ6oun07RtMA6T7VwghGuc0AdyxYweXXHJJveQP4MwzzyQ9PZ3Fixe3eXBCiA7IXv+v467/u/awtfu3p3T/CiGEU07rAPr4+BAUFNTgQcHBwfj4+LRZUEKIjuv4EnC92jmShq3OLAPgLOn+FUIIp5wmgGPHjmXlypXU1NTU21ZdXc2KFSsYN25cmwcnhOhYVHUVHDlsfdBBVwApqjKx62glIN2/QgjREAPAunXrHJ5MTExky5YtzJgxgzFjxtgngeTm5rJq1Sr8/f2Ji4tzf7RCiPZ16AAoC4SEowWHtnc0Tq3NLJXuXyGEOAkDwCuvvNLgDl999VW95woLC3n99dcZNWpU20UmhOhwbN2/HbX1D453/46Wpd+EEKJBBoAnn3yyveMQQpwC7OP/OmgB6GMVtezKq+v+lQRQCCEaZADo06dPe8chhDgVdPAl4Fak5mFR1u7fLv5SrF4IIRridBKIEEL8mSo6BsXHQNNBfPf2DsepX/ZZVy2S7l8hhGhcg0vB7d27l+XLl5OXl0dFRQVKKYftmqbxz3/+s80DFEJ0ELbxfzHd0Lw7XhmooioTW7KKAen+FUKIk3GaAH7//fd8/PHHeHp6EhMTg7+/v7vjEkJ0MCrDNv6vY9b/W3u4TLp/hRCiiZwmgN9++y0pKSk8/PDD+Pr6ujsmIUQHZF8CroPOAF59qG7t3zip/SeEECfjdAxgTU0NZ511liR/QggAlMUMGQeAjrkEXHGVyT77d7Ss/iGEECflNAHs27cvmZmZ7o5FCNFR5WRDTRV4eUNMxyoCb1GKz3cfw6KgT1SAdP8KIUQTOE0Ab731Vnbu3Mm3335LeXm5u2MSQrQBo1nx+tocvt1b2OxjVfo+6z/ie6Dp9C6OrOVKq008syKL7/YWATBpUNd2jkgIIU4NTscAhoeHc/755/Pxxx/z3//+F09PT3S6+rniggUL2jxAIYRrbM4pZ3laCToNhsT4ERvo1fSDbQWgO1D37978Kl7+PZtjlSY89Rp3joji8v4x5OTktHdoQgjR4TlNABctWsSXX35JaGgo3bt3l7GAQpwGtuVUAGBR8PnOY8wYFdPkY+0rgCS2/wxgpRTf7Sviw815mBXEBHjy8NkxJIZ2vNI0QgjRUTlNAH/++WeGDBnC//3f/zlt+RNCnHq25lba/70qo5TJ/cOJDjj5eDlVUwPZh6wP2nkGcEWtmTf/yGHtYevQlLPiA7hrZBS+Hh2nW1oIIU4FThNAk8nEkCFDJPkT4jSRX2Eku7QWHYpeunL2WAL4/KfN3BVWCB5e4OmJ5ukFHp7g6QmeXvbnyToEFgsEhUJIWLu9hrTCal76LZvcciMGHdw6pAsTkoPRNK3dYhJCiFOV0wRwyJAh7NmzhwsuuMDd8Qgh2sCWI9YWsx4lmdx08HseG3IXKyoDuHrF20TWFAOgGjkegMTkdkm2lFL8dKCE9zYexWhRRPoZeOjsrvQMky5fIYRoKadNfNdccw3Z2dm8//77pKWlUVpaSnl5eb3/hBAdn7JY2PbHNgAGFh+g98BeDFDHMOv0fDX0Oug3BJL7Wbt3u8ZDRBQEh4KvHxg8rCfRG9CNPMftsVebLLy2Noe31+ditCiGd/XjlYsTJfkTQohWctoCOGPGDAAyMjL4+eefGzx40aJFbRKUEMI1lFKYP3mX7aYR4AmDRg1GN3YUk49Wsv2XTJZ5d2fy5AsI8/Vo+BwWM1gUmqHBpcPbxOGSGl76LZvDJbXoNLhpYARX9AlFJ12+QgjRak5/o0+aNEnG1QhxilNKof43l7RNOygdNg5vzULKOWcC0K+LL30ifNidX8WXuwuZPqxLg+fRdPoG+grazqr0Et5en0u1SRHiY+D/RsfQt4tUIxBCCFdxmgBee+217o5DCOFCSinUovdRK35kW9xYAPrHBGLQHf9iN7l/OE8uP8xPB4q5um8YIT7ubeFzxqIU/95wlMX7iwEY0MWXB0bHENwBYhNCiNOJTPMV4jSjlEJ9Nh+17DsAtvUeB8CgaMcWtIFRvvQK96bWrPh6T/NXB2kLv2WUsnh/MRpwbb8wnhrXTZI/IYRoA05/s37++edNOvjqq692aTBCiNZRSqG+WID6+RsAaqfcxZ4cH0AxKMrPYV9N05jcL5ynV2axOLWIq/qEEuTdfsmW2aJYtPMYANcNCOe6/uHtFosQQpzunP62/+yzz5p0sCSAQnQcSinUVx+jln4JgDblDvYkj8aUnUWYr4GugfWLPg+J8aNHqDcHCqv5Zk8hfxkc6e6w7X47VEp2aS0BnjouTwlptziEEKIzaHApuD+zWCwUFBSwZMkS9uzZw2OPPdbmwQkhmk59+wlqsbX1Xrv+dnRjJ7Btcx4Ag6P9nE7s0jSNa/uF8fyv2fyQWsyVfcII8HL/qhpmi2LRjgIAJvYOlZU9hBCijTV5DKBOpyMyMpK//OUvREdHM3/+/LaMSwjRDJbvFqK+t35x0ybfhm7cpQBsrVv/d+Cfun9PNCLWn8QQL6pNFr7b1z5jAVdllHKkzEiAl55LeknrnxBCtLUWTQLp3bs3W7ZscXUsQogWsPzwKerbTwDQrrkF3fkTASiqMpFRXANYJ3w0RNM0rulnXeLt+71FVNSa2zhiR2aL4tOd1ta/K6T1Twgh3KJFCeDBgwelTqAQHYDlx89QX/8HAG3SVHQXXmnfti3X2vqXFOJ10skdZ3YLoFuQJxVGCz/sK2q7gJ1YmV5CTpmRQC89lyRL658QQriD078Kq1atcrpzRUUFe/bsYf369YwbN65NAxNCNK70i4+xfPkRANoVN6IbP8lhu637d1B0w92/NjpN49p+4cxefYRv9xZyaUqIW1riTBbFp3Uzf6/sE4qPh1SmEkIId3CaAL799tsNHhAQEMDEiRNlBrAQ7cjy09eUfDoPAG3iDegucSzerpRia24l0Pj4vxONjgvgf9s9OVJWy+LUYib1DXNt0E6sTC8ht9xIkLeeCdL6J4QQbuM0AZwzZ0695zRNw8/PDx8fWYRdiPZkWfY9ypb8XXYdukuvq7fP4ZJaiqpMeOo1+kQ27TOr11nHAr6+Nodv9hRySa8QvA1t1yJ3YuvfVX1C2/RaQgghHDlNACMiItwdhxCiCSwrf0Qt/DcAAdfeQuUFVzrdb2vd+L8+kb546pueWI1JCGTRjgJyy40s3V/MxN6hrQ+6AcvTSjhabiTYW8/FPaX1Twgh3Em+cgtxirD8uhT133cB0MZPIugvf2twMpZ9/F8js3+d0es0e9fvV7uPUWOytCLihhnNik/r6v5d1ScML2n9E0IIt7K3AD744IPNOlDTNP75z3+6PCAhTkVKKdi+AULC0eKSXH5+y+8/oz5+CwDtgonoJk1tMPkzmi3sPGod/9eUCSB/dm5iEJ/uKCC/0sQvB0vapC7fsrRi8itNhHjrGd8z2OXnF0II0Th7Aujv79+k0i7FxcUcOXKkTYMS4lSiampQH72JWv8raDq0CVejXXodmsE16+pa1ixHfWQdl6uddxnaNbc2+lndW1BFjVkR5K0nPtir2dfz0FtbAd/dcJQvdh3jwh5BeDSjG/lkjGYLn9WN/ZvUV1r/hBCiPdj/Qj311FON7lhcXMzXX3/N/v370el0nH322W0dmxAdnsrPxfL2C5CVDpoGyoL64VPU7q3obrsfrUtMq85vWbcK9eEboBTa2Alok6ed9Iva1pzjs391LazXeX73ID7beYxjVSaWpZUw3oVj9H45WEJBpYkQHwMX9gh22XmFEEI03UmbKGyJ37JlyzCZTJx99tlcddVVREVFuSM+ITostXsLln//CyrKICAI3R0Po0qKUf95C9JTsTx9nzVhO/vCFhVOt2z4DTXvVVAWtHMuQrv+9iadx1YAenALun9tPPQ6ruwTyvub8vhi1zHO7x6MQdf64u9Gs4XPdllb/66R1j8hhGg3DSaAzhK/SZMm0aVLF3fGJ0SHo5RC/fQV6ouPQFkgoSe6Ox9FCw1HA1T3FCwfvAZ7t6M+fgu1YyO6v9yDFhDY9GtsWoN6f7Y1+Rt9PtqUO9F0J0+WSmvMHDhWDTS+/FtTXNgjmM93HSOvwsTK9BLO7x7cqvMB/HSghGOVJsJ8DFzQI6jV5xNCCNEy9RLAPyd+55xzDpMmTSIyMrI94hOiQ1E11agFb6I2/AaANvo8a3Lm4WnfRwsNR/f3p1E/f4P66mPYug5Leiq6m+9F6zf05NfY+geW9/4JFgvameei/eXuJiV/ADtyK1BAtyBPwnw9WvQabbwM1lbADzbn89nOY5ybGIS+Fa2AtWYLn9e1/l3dL6xZ5WmEEEK4lj0BLCoqsid+ZrOZMWPGcNVVV0niJ0QdlZ+L5a3nIPsQ6PVok6ejjb3YabesptOhXXQlqvdALO/PhpzDWF6fhTbuUrRJU9E8nU/OUNs2YHn3ZTCb0UaMQbv53iYnf3C8/t+gJq7+cTLje4bwxa5CcsuNfLw1nykDw1s8IeSnA8UUVpkI9zVwQXdp/RNCiPZkTwDvuecejEYjCQkJXHnllURGRlJeXk55eXmDByclub7chRAdkdq1Bcu//wmV5RAYjO6OR9B69jnpcVpcErqZr6C+WIBa/r31v73b0U17AK1bouM1dmzC8u4LYDahDT8b7dYZaLqmr8erlLJPAGlJ+RdnvA06rukXxrxNeXy1p5AN2eXcMaIL/bs07/w1Jguf7yoE4Oq+YS6dVSyEEKL57Amg0WgEICMjg1dffbVJBy9atKhtohKig1BKoZZ8ae3KVRZITLaO9wtp+jq5mqcX2vW3o/oNxfLh63AkE8vzD6BdcRPaBRPRdDprgvn282AywZBRaLfdj6ZvevIHkFtuJK/CiEEHfSNbN/7vRJf1CiHIS8+8zXlkldYy85fDjE0M5JYhkQR7N63UzU8HiimqMhHha3DJWEIhhBCtY//tfeedd7ZnHEJ0OKq6CvXhG6hNqwGss3mv/yuaR8vG1mn9h6J76k0sH82BretQn3+A2rkJ7awLUAveBJMRBp2BbvqDzU7+4PjqHynhPvh4uK6FTdM0xiQGMTTGn/9sy2fJ/mJWppeyIbucqYMiuaBHUKPlZmpMFr6wzfztF46HvvWziYUQQrSOPQEcO3Zsm19s9+7dfPvtt6Snp1NUVMSDDz7IiBEj7NuVUnz66acsW7aMiooKUlJSmDZtGtHR0fZ9amtr+eijj1izZg1Go5GBAwcybdo0goOD7fuUl5czf/58Nm3ahKZpjBw5kltuuQVvb2/7PgUFBbz33nvs2rULb29vxowZww033IC+BX94xelH5eVYW+SyD4HegHb97ejGjG/1ebWAIHR/ewz120+oRe9bZwrv3W7dOHAEur/+X4sLSG+pSwAHuqj798/8vfTcMSKKc5OCeGd9LulFNby9PpdlacXcOSKKxBBvp8ct2V9MUbWZSD8D45Jk7J8QQnQEbh2IU1NTQ0JCArfddpvT7d988w2LFy9m+vTpPP/883h5efHcc89RW1tr32fBggVs2rSJ+++/n1mzZlFUVMTs2bMdzvPGG29w+PBhZs6cySOPPMKePXuYO3eufbvFYuGFF17AZDLx7LPPctddd7Fy5Urp0hYAqD3bsDx3vzX5CwpB9+BzLkn+bDRNQ3fOReieeA0Selqf7DcU3V8fRjO0rHXRbFHssC3/5qIJIA3pFe7D7PEJTBsaibdBx76Cau5fnMH8TUepNJod9q02Wfhit7X171pp/RNCiA7DrQng4MGDue666xxa/WyUUvz4449cddVVDB8+nPj4eO6++26KiorYsGEDAJWVlSxfvpypU6fSr18/kpKS+Nvf/sa+fftITU0FICsri61bt3LHHXfQs2dPUlJSuPXWW1mzZg2FhdZB6Nu2bSMrK4t77rmHhIQEBg8ezOTJk1m6dCkmk8l9N0R0OKqiHMvcl6GyArqnoJv5ClqP3m1yLS2qK7qHX0L3+Gx0d89scdcywP5j1VQaLfh76uge6rwlzpX0Oo3LUkJ5+7JERsUFYFHwzd4i7v4+nbWHy6xrIwOLU4soqTYT5e/BudL6J4QQHYZrFit1gby8PIqLixkwYID9OV9fX3r06EFqaiqjR48mLS0Ns9lM//797ft07dqV8PBwUlNTSU5OJjU1FT8/P7p3727fp3///miaxoEDBxgxYgSpqanExcU5dBsPGjSI999/n8OHD5OY6Dg708ZoNNony4C1JcfHx8f+b1eync/V5z1Vuet+WBZ/bl3Zo2s8+gefb1VS1hSahwckJjf/uD/dD9vqHwOi/DC4cYZtuJ8nj5wTy6bsct7dkMvRciMv/prNsK7+TB0cwVe7rV+6ru3f8vIxTSWfGUdyPxzJ/XAk90N0mASwuLgYgKAgx1aCoKAg+7bi4mIMBgN+fn6N7hMY6Ljigl6vx9/f32GfE5O/E69r28eZr776is8//9z+ODExkZdeeomIiIgmvMKWkSX3HLXl/TDl5ZCz/HsAwm+/H5+4uDa7lqvY7sfuFUcAGJMS4zBm1l0ujYbzBybxwR8ZfLQ+k43Z5WzMtpaQig324fpRvTA0o55ha8hnxpHcD0dyPxzJ/ei8OkwCeCq48sorufTSS+2Pbd+c8vPzXd51rGkaUVFR5Obm2rvTOjN33A/z+7PBWIuWMoCimESKc3La5DqucOL9qKgxsf1ICQCJviZy2jHuK3r4MjQikXfW5bIzzzomcVLvYPKPHm3za8tnxpHcD0dyPxx19vthMBjatPHmVNBhEkBbi1xJSQkhISH250tKSkhISLDvYzKZqKiocGgFLCkpsR8fHBxMaWmpw7nNZjPl5eUO+xw4cMBhn5KSEoc4nPHw8MCjgS7BtvoAKaU65YezIW11P9Shg6g/VgKgXX2L/VodnVKK7UcrsCiI8vegi59Hu8cdG+jJs+d3Y01mGUXVJsYmBro1JvnMOJL74UjuhyO5H51XhynHHxkZSXBwMDt27LA/V1lZyYEDB0hOto6RSkpKQq/XO+xz5MgRCgoK7PskJydTUVFBWlqafZ+dO3eilKJHjx72fTIzM+1JH8D27dvx8fEhNja2TV+n6HiUUlg+/wAA7YyxaPHdT3JEx7KtrvyLq1b/cAVN0xgdH8ilvUIbrREohBCifbi1BbC6uprc3Fz747y8PDIyMvD39yc8PJwJEybw5ZdfEh0dTWRkJAsXLiQkJIThw4cD1kkh48aN46OPPsLf3x9fX1/mz59PcnKyPQGMjY1l0KBBzJ07l+nTp2MymZg/fz6jRo0iNDQUgIEDBxIbG8ucOXOYMmUKxcXFLFy4kIsuuqjBFj5xGtu5GfZuB4MH2hU3tnc0zbY11z3lX4QQQpw+3JoAHjx4kFmzZtkff/TRRwCMGTOGu+66i4kTJ1JTU8PcuXOprKwkJSWFxx57DE9PT/sxU6dO5f/bu/P4qOp7/+OvM5ksJJMFCEkgAcKOIgasoGhlERGsoHJlsVx7vYBwb/XXerWolIIKCoqUWmtrr1Woy4WK4AIiokbRyqLgAggIYTUBAoHAZF9m+f7+GBidgsqSyWQy7+fj4SPMmTPnfM6Hb/Tjd76LZVnMnTsXt9vtXwj6u379618zb948ZsyY4V8Iety4cf73bTYbkydP5rnnnmPq1KnExsbSr18/Ro8eHeQMSENjvB68rz4PgDVwKFbztNAGdJaOVLg4UFqLzYLuGXW3/ZuIiDRultGX/+ftyJEjAcvD1AXLsmjZsiWFhYUan0Hw8uH9+F3Mi3+GeAe2WX/DSnDU2bWD6WQ+Xvz4a576pJAuqXE8Pjg71GGFlH5nAikfgZSPQJGej+jo6IifBNJgxgCK1DdTU41ZthAAa+josCn+vuvk/r85+vpXRETOQoOZBSzf8n78LiZ3KUdatcGTmAzN07FS06B5GjRPB0eiFu+sA+a9peA8BqnpWP1/FupwzprXGP8C0A1pAoiIiDR8KgAbosMH4GAB1QcL/IcCOuhjm8CJgtBqngapaVjN033H0lphNdFYsB9jSo9jVr4GgDX8F0Hf8SMY8orKKa3xEGe30SW1SajDERGRMKICsAGyrrkB68KeJLtrcO7Og6NFmOLDUFzk67GqqYID38CBb/yFob9AtNmgUzesHpdh9bw87CY11Bfz5iJfHrM7YfW6KtThnJNP9/m2Weue3gS7TT3CIiJy5lQANkBWSnOspqk4Wrak7F8G6BpXLRQfgeITReHRIjh6GFPs+0mpE3Z8hdnxFWbRc9CmPVaPy7F6XgaZ2frqGDCH9mP+uRIA24ixYZuTkwWgxv+JiMjZUgEYZqzoGMjIhIxMTle2mCOHMJs+xXz5KezcBvl7MPl7fJMdWmT4egZ7XA4du2LZouo9/obA++qL4PVCTm+sLheFOpxzUuP2svGAE4CeGv8nIiJnSQVgI2O1yMC65ka45kZMWQlm8wbMl5/Ato1w5BDmvaW+yQ+JyVgX98Lq2QcuuBgrJjbUodcLs3MbbPwEbDZsN98W6nDO2daiSlweQ2q8ncykmB//gIiIyHeoAGzErMRkrCuvgSuvwdRUw9YvMV9+gtm8AcpKMGtyMWtyITYO65I+WNeNwGrZOtRhB03Alm8/vTasn9W//EvLhLD9CltEREJHBWCEsGLj4JI+WJf0wbjdsHMrZuOnmI2fwLGjmHWrMJ98iHXJFVjXj8Jq3S7UIde9z9fAnh2+gveGn4c6mnNmjOGzA+WAtn8TEZFzowIwAll2O1yQg3VBDuaWCbBnB96Vr8HGTzCfr8F8vgZyemO7fhRWu86hDrdOGLcL72u+rQeta4djJTcNcUTnbtOhSvaX1tIkOoqfZIbf4tUiIhJ6KgAjnGVZ0KErUXdOwezfh1mxGPPZati0Hu+m9XBhD18h2Dk8J0ucZD5aCUcOQXJTrGtvCnU452Xp177Zvzd0b4kjJioit3ESEZHzowJQ/KysbKyJ92Ju+Dnm7Vcxn6yCbRvxbtsInS7ENnQ0XNAj7MacmcoKzPKXAbBu+DlWXPgumpxfUsMXhRVYwC2XZEF1SahDEhGRMKS9gOUUVkYWtrF3YXvkf7H6DQG7HXZuw/vEg3gfvdc3djCMep3MyiVQXgYtW2NdOSjU4ZyXN7f7ev8ua51IVlPt+CIiIudGBaB8L6tFBrZb78A261msgcMgJgb25uH9y0y8M+7Cu2E1xusNdZg/yBw7gsl9EwDbzbdhRYXv2ocl1W5W7SkF4KYLmoU4GhERCWcqAOVHWU2bY7tlArZHn8UacrNvL+L9+zB/exzz3FzfrOIGyryxAFy10PkiuLhXqMM5L2/vdOLyGjo1j+OCFuH7NbaIiISeCkA5Y1ZSU2w334Zt9nNYw26BKDtmw8d4n52DcbtCHd4pTMFe3zhGwnvLN4Baj5cVeccBuKFrs7B+FhERCT0VgHLWrIREbDeMwXbHb33jA79Yh/d/Z2NcDacINMbgfWUeGIPV6yqsdp1CHdJ5+ee+UkqqPaTG27miTWKowxERkTCnAlDOmXVxL2x3ToXoGN+yMX99FOOqDXVYPpvWw/bNYI/G+rf/CHU058UY41/6ZWiXptht6v0TEZHzowJQzot10SXYfjXNN0Hkq8/w/vkRTE1NSGMybhfexfN98Q26ESs1PaTxnK+NhyrJL6klzm5jUMeUUIcjIiKNgArARsIYw5KtxcxZfYDyWk+93tu6IAfbrx+C2DjfuoFPzfDtPRwiZtUKKCqEpBSsn40IWRx1ZdmJ3r9rOiTjiAnfWcwiItJwqABsBDxew58/PcRLG4+w+psy3th2rN5jsLpchO1/HoK4JrDjK7xPPoSprqz3OExZ6beLPt90K1ZceK+Vl+/8duHnYV3Cd/s6ERFpWFQAhjmXx8uc1QfI3f3tjhDLdxynrKZ+ewEBrI4XYvuf6dAkwbdw9B8fwlRW1GsM5s2FUFkBrdthXTmwXu8dDMtOLPx8eWsHGYkxIY5GREQaCxWAYaza7eWRD/ezrqAcu81i8lWZtE2Jpcrt5c0d9d8LCGB16IrtnhkQ74Dd2/H+8UFMZXm93NsczPft+QvYRt+OZQvvr0ud1W4+3Otb+PnGrlr4WURE6o4KwDBVXuPhgfcL2Hiokji7xbT+WfRpk8jo7s0BWL79eL2PBTzJyu6E7TcPgyPRt3PI3GmYirKg39e7eD54vdDjcqwu3YN+v2Bbmfftws9dtfCziIjUIRWAYeh4lZvf5eaz42gVjhgbMwa2oUfLBAD6tE6kTXIMFS4vy7cfD1mMVpsO2H4zExKTIX833t9PxZSV/PgHz5H56nPY8gVE2bGN/M+g3ae+fHfh5xu18LOIiNQxFYBh5nB5Lb997xv2OWtoGhfFzGva0CX1294hm2Ux6qJUAJbtOEZFiHoBAaysbGyTZkJSCuzfi/f3v8OU1n1Ratzub5d9GTgUK61Vnd+jvn20t5SSGg8ttPCziIgEgQrAMJJfUsPkd/MpLHOR7ojm0Wvbkt007pTzrmiTSFZSDBW1Xt7aEbpeQACrVRts986ClGZwMB/vnN9hnHU7PtF8/A4UFoAjCev6UXV67VAwxvgnfwzt2pQoLfwsIiJ1TAVgmNhZXMWU9/I5VuWmdXIMjw5qQ8vvmRUaZbMY3d3XC7h0+zEqXaHrBQSwMrJ8RWCzVDi0H++cKZijh+vk2qaiHLN0oe8+N47BinfUyXVDKWDh5w4poQ5HREQaIRWAYeCrwxVMyy2grMZDp+ZxzBrUlubx0T/4mSvbJJKZFEN5rZcVO5z1E+gPsNJaYZs0C5qnQdFBvI/djynYe97XNcsXQUUZtGqDddXgOog09E5u+zaoQzIJWvhZRESCQAVgA7d+fxnTP9hPldtL9/R4ZgxsTVLsjxcFUTaLURf5ZgS/sf0YVS5vsEP9UVaLDGz3PQaZbaHkGN7HJ2O+3nTO1zOHDmBWLQfANmo8VlT4F0vfOGv4srACm+Xb91dERCQYVAA2YG9vO8Ssj/bj8houy3LwwIAs4qPPvMi5qm0SrRKjKavx+GeUhprVLBXbfY9C54ugugrvk9Pxrv/nOV3Lu+Tv4PFA90uxuvWs40hD4+TYv8uyErXws4iIBI0KwAbqrR3HeOCtbXgN9G+XxP1XZRITdXZ/XVE2i5EnZgS/8fUxqt2h7wUEsOId2P7nIayfXAkeN+bZ3+N9942zuob5ehNsWg9RUdhGjgtOoPXMWeXmo5MLP1+g3j8REQkeFYAN0JItxTyzwTdJ4vouTbmrT8tzngnaLzuJDEc0pTUe3m4gvYAAVnQM1sR7sQYOA8Asno/3lXkY748XqcbrwfvKPN91+l2H1TIrqLHWl7d3HsflNXRuHkfXVC38LCIiwaMCsAFKivN9zTu+TzYTL03Hdh6LAPt6AX1jAV//+hg1DaQXEMCy2bBG34414j8BMO8txcz7A8bl+sHPmdW5sH8fxDuwht0S/EDrQa3Hy9t5TgBuvEALP4uISHCpAGyAru2Ywh+uy+a/f9q+TgqB/u2SSXdEU1LtYeVO5/kHWIcsy8I2+N+wxt8NUVGY9f/E+6fpmKrK055vqioxb/yf77PDbsFyJNVnuEHz3YWf+7TWws8iIhJcKgAbqI7N6+4rQLvNYkS3E72A24obVC/gSbbLB2D71QMQ2wS2b8b7+G8xzuJTzvO+9QqUlUBGJlb/n9V/oEFgjGHpickfw7o208LPIiISdCoAI8SAdsmkJdg5Xu3h3V3OUIdzWla3nr4Fo09uHffY/ZjC/f733YX7MblLAbCNGIdlt4co0rr1ZWEFBSW1NLHbuKZDcqjDERGRCKACMEJER1mM6OabEfzqtmPUehpeLyCA1bYDtsmPQ1orKC7CO/t+zO7tADj//hS43XBhD7j40tAGWoeWbvdNzrmmoxZ+FhGR+qECMIJc3T6Z1Hg7x6vcvLerJNThfC+rRQa2ybOhXWeoKMM7dyqeN/6PqjXvg2XzLfrcSCZJ7DtezcYTCz8P08LPIiJST1QARhBfL6BvLOCrW4txNdBeQAArMRnbbx6B7peCq9a35Rtg9b0WK7NtiKOrG4fLa3n0nwcA38LP6Q4t/CwiIvVDBWCEuaZDMs3j7RRXucnd3XB7AQGs2Dhsd/4O68prfK/jE7Dd+O8hjqpu5JfUMPndfA6Vu0h3RDPukrRQhyQiIhFEBWCEiY6ycfOFvl7AJQ28FxDAiorCuu1X2H41jbQ587CSUkId0nnbWVzFlPfyOVblpk1yDI8OakOaIzrUYYmISARRARiBBnVMplkTO0cr3by/p2H3AsKJtQJzehOT3THUoZy3LYcrmZZbQFmNh07N45g5qC3N41X8iYhI/VIBGIFiomz824XNAN+2cy6PCXFEkeGzA+VMX1VAldvLRenxzBjYmqRYzfoVEZH6pwIwQl3bMYWmcVEcqXSzam/D7wUMd//cV8qsj/ZT6zH0ynTwQP8s4qNV/ImISGioAIxQsXYbw0+MBVy8pRi3V72AwfLOTid/WHMQj4G+2UlM7ptJrF2/eiIiEjr6r1AEG9IphZS4KIoqXHyoXsCgeG1bMU+vP4TBl++7r2iJXVu9iYhIiKkAjGC+XkDfWMAXNx5h97HqEEfUeBhjeGnjEV748ggAI7o15797pWNrJAtYi4hIeFMBGOGGdGpKdkosJdUeprz3DZ8dKA91SGHPawzPbDjMkq3FAPxHjxb8okeLRrN7iYiIhD8VgBEuzm5j1qA25GTEU+02zPxoPyvyjoc6rLDl9hr+uLaQt3c6sYBf9k7n5hO7r4iIiDQUKgCFhJgoHhjQmms6JOM18MyGw8z//DBeo4khZ6PW42X2xwf4aF8pURbcc2UrhnTS/r4iItLw2EMdgDQMdpvF/7ssgwxHNP+36ShLtx+nqMLF3Ve0ivgZqx6voaLWQ3mtl7JaD+U1HspPvC6v9fiP7T1ewz5nDTFRFvf9NJNeWY5Qhy4iInJaKgDFz7IsRl6USrojhifXFbKuoJzi3Hx+1z+LlLjIaCrfOGt4Z+dxth+toqzGV+BVus58u7w4u42p/TPpnp4QxChFRETOT2T8V13OSt/sJJrH23n0o/3kFVdz3zvf8ED/LLKSY0MdWlC4PF7W5pexcqeTbUeqvve8+GgbjhgbjpgoHLFROGKiSIyJIiHGRuKJYzkZ8aQ7YuoxehERkbOnAlBOq1taPLMHZzNjVQGHyl3c9+43TOmbxUXp8aEOrc4cKqvlnV1OcneXUFrjAcBmwWVZifTLTqJZvN1X7MXYSIiJ0vp9IiLSaKgAlO+VmRTD44PbMvOjA+w4WsWDH+Tzq8tb0r9dcqhDO2cer+GzA+Ws3Onky8IKTk5zad7EzrWdUhjUIZnm8dEhjVFERCTYVADKD0qOs/PwwNY8ua6QNfllPLG2kEPlLkZf1Dys1rU7VuXmvV1O3tnlpLjS7T/es2UC13VK4dJMB1Hq4RMRkQihAlB+VKzdxqSftiJ94xFe23aMf2w+yuFyF3f0ziA6qmEWTS6PobTGTX5JLe/sdLJ+fxmeE919ibFRXNM+mcGdUmiZqPF6IiISeVQAyhmxWRa39Uwj3RHNMxsO88GeEo5WuPj3nBa0axob9KVijDGUVLnYX1KDs8qNs8ZNSbWHkmrfT+fJP9f4fpbXnjpz94IWTRjSKYUr2iQSExXZS9uIiEhkUwEoZ2VIp6a0iI/m8dUH2Xy4ks3vfoPNgtbJsXRsFkeHZnF0bB5Hdsq5FYXGGI5Xe8h31vDNiX/yS2rId9ZQ49l+VteyWZASZ+eyLAdDOqWQ3TTurOMRERFpjFQAyln7SaaD2de2YcHmo+QdrcJZ7fEXa+/vKQF8xVfblFhfQXiiKGybEhvQ81Ze66HA6Vs8+WSR901JLWUnZuSeTkK0jeS4KJLj7L6fsb6fKSdf+/9sxxFjwxZG4xRFRETqiwpAOSfZTeP4Xb8sjDEUV7nZXVzNrmPV7D5Wza7iakpO7Iyx93gNubt9RWGUBW1SYkmOs1NQUhMwGeO7bBZkOGJomxJL25QY2qTEkp0Sx8UdW3PsSBFGW9SJiIicFxWAcl4syyI1PprU+Ggua50I+L7GPVrp9hWE3ykMS08UhVDj/3xqvJ22KbG0SY49UejFkpkUc8rXx5ZlEWuPqs9HExERabRUAEqdsyyLFgnRtEiIps93isIjFW52HauivNZL66QYWqfE4ohRUSciIlLfVABKvbAsizRHNGkOLbIsIiISaloLQ0RERCTCqAAUERERiTAqAEVEREQijApAERERkQijAlBEREQkwkT8LOCVK1fy5ptv4nQ6adu2LePGjaNjx46hDktEREQkaCK6B3Dt2rW8+OKLjBgxgtmzZ9O2bVtmzpxJSUlJqEMTERERCZqILgCXL1/OwIEDGTBgAFlZWUyYMIGYmBhWrVoV6tBEREREgiZiC0C3282ePXvo3r27/5jNZqN79+7k5eWFMDIRERGR4IrYMYClpaV4vV5SUlICjqekpHDw4MHTfsblcuFyufyvLcuiSZMm/j/XpZPXq+vrhivlI5DycSrlJJDyEUj5CKR8SMQWgOfi9ddfZ8mSJf7X7dq1Y/bs2bRo0SJo98zIyAjatcOR8hFI+TiVchJI+QikfARSPiJXxBaASUlJ2Gw2nE5nwHGn03lKr+BJw4cPZ+jQof7XJ//P6ciRI7jd7jqNz7IsMjIyOHToEMaYOr12OFI+Aikfp1JOAikfgZSPQJGeD7vdHtTOm3AQsQWg3W6nffv2bNmyhd69ewPg9XrZsmULQ4YMOe1noqOjiY6OPu17wfoFMsZE5C/n91E+Aikfp1JOAikfgZSPQMpH5IrYAhBg6NCh/OUvf6F9+/Z07NiRFStWUFNTQ//+/c/qOnZ78NIYzGuHI+UjkPJxKuUkkPIRSPkIFKn5iNTn/i7LRHjpv3LlSpYtW4bT6SQ7O5uxY8fSqVOnUIclIiIiEjQRuwzMSUOGDOHpp59m4cKFzJo1q8EUf1VVVdx///1UVVWFOpQGQfkIpHycSjkJpHwEUj4CKR8S8QVgQ2WMYe/evRqbcYLyEUj5OJVyEkj5CKR8BFI+RAWgiIiISIRRASgiIiISYVQANlDR0dGMGDHie5ediTTKRyDl41TKSSDlI5DyEUj5kIifBSwiIiISadQDKCIiIhJhVACKiIiIRBgVgCIiIiIRRgWgiIiISITRZnhBsm3bNpYtW8bevXs5fvw4kyZNonfv3v73nU4nCxYsYPPmzVRUVHDBBRcwbtw4WrZsGXDOSy+9xObNm6murqZVq1YMHz6cyy+/3H9OeXk58+fP5/PPP8eyLC677DLGjh1LXFxcvT7vmaivnNx5550cOXIk4N5jxozhpptuCvozno26yMehQ4d46aWX2L59O263m5ycHMaNG0dKSor/nHBpI/WVj3BpH6+//jrr16/nwIEDxMTE0LlzZ2699VZatWrlP8cYwyuvvML7779PRUUFXbt25fbbbw/ISW1tLS+++CJr167F5XKRk5PD7bffHnZtpD7zEWltJDc3l9WrV7N3716qqqr4+9//TkJCQsC9wqGNyNlRD2CQ1NTUkJ2dzfjx4095zxjDnDlzKCoq4t577+Xxxx+nRYsWPPzww1RXV/vP+/Of/8zBgwe5//77+f3vf0/v3r154okn2Lt3r/+cP/3pTxQUFDB16lQmT57M119/zTPPPFMvz3i26isnAKNGjeJvf/ub/58hQ4YE/fnO1vnmo7q6mpkzZ2JZFg8++CAPP/wwbreb2bNn4/V6/dcKlzZSX/mA8Ggf27ZtY/DgwcycOZOpU6fi8Xh45JFHAn4fli5dyttvv82ECROYNWsWsbGxzJw5k9raWv85L7zwAp9//jn33HMP06dP5/jx48ydOzfgXuHQRuozHxBZbaSmpoYePXowfPjw771XOLQROUtGgm7kyJHm008/9b8+cOCAGTlypMnPz/cf83g8Zvz48SY3N9d/7NZbbzUfffRRwLXGjh3rP6egoMCMHDnS7Nq1y//+l19+aUaNGmWKi4uD9Th1Ilg5McaYO+64wyxfvjyI0de9c8nHxo0bzahRo0xFRYX/nIqKCjNq1CizadMmY0z4tpFg5cOY8GwfxhhTUlJiRo4cabZu3WqMMcbr9ZoJEyaYpUuX+s+pqKgwY8aMMatXr/a/vuWWW8y6dev85+zfv9+MHDnS7NixwxgTvm0kWPkwJrLayHdt2bLFjBw50pSXlwccD9c2Ij9MPYAh4Ha7AQIW4LTZbERHR7N9+3b/sS5durB27VrKy8vxer2sWbMGl8tFt27dAMjLyyMhIYEOHTr4P9O9e3csy2LXrl319DR1o65yctIbb7zBuHHjuO+++1i2bBkej6d+HqSOnEk+XC4XlmUFnBMdHY1lWf5zGksbqat8nBSO7aOyshIAh8MBQFFREU6nk4svvth/Tnx8PB07diQvLw+APXv24PF46N69u/+czMxMUlNT/eeEaxsJVj5OipQ2cibCtY3ID9MYwBBo1aoVqampLFy4kIkTJxIXF8fy5cspLi7G6XT6z7v77rv54x//yLhx44iKiiImJoZJkyaRkZEB+MZEJSUlBVw7KioKh8MRcJ1wUFc5Abjuuuto164dDoeDHTt28I9//IPjx49z2223heDJzs2Z5KNz587ExsayYMECfv7zn2OMYeHChXi9Xv85jaWN1FU+IDzbh9fr5fnnn6dLly60adMGwP9MycnJAecmJycH/P3b7fZTxnP96znh1kaCmQ+IrDZyJsKxjciPUwEYAna7nUmTJvHXv/6VcePGYbPZ6N69Oz179sR8Z2OWRYsWUVFRwbRp00hMTGTDhg088cQTzJgxw/8L3ljUZU6GDh3qP79t27bY7XaeffZZxowZEzbbHp1JPpKSkrjnnnt47rnnePvtt7EsiyuvvJJ27dphWVaIn6Bu1WU+wrF9zJs3j4KCAmbMmBHqUBqEYOdDbUQigQrAEGnfvj1z5syhsrISt9tNUlISU6ZMoX379oBvNuPKlSuZO3curVu3BiA7O5vt27ezcuVKJk6cSEpKCqWlpQHX9Xg8lJeXB8xoCxd1kZPT6dSpEx6PhyNHjgTMjmvofiwfADk5OTz11FOUlpYSFRVFQkICEyZMID09HaBRtZG6yMfpNPT2MW/ePL744gumT59O8+bN/cdP/v2VlJTQtGlT//GSkhKys7P957jdbioqKgJ6vUpKSvyfD7c2Eux8nE5jbiNnItzaiJwZjQEMsfj4eJKSkigsLGT37t306tULwD9D6197cmw2m7/Ho3PnzlRUVLBnzx7/+1u2bMEYQ8eOHevpCere+eTkdPbt24dlWad8hREuvi8f35WUlERCQgJbtmyhtLSUSy+9FGicbeR88nE6DbV9GGOYN28e69ev54EHHiAtLS3g/bS0NFJSUvjqq6/8xyorK9m1axedO3cGfEVzVFRUwDkHDx7k6NGj/nPCpY3UVz5OpzG3kTMRLm1Ezo56AIOkurqaQ4cO+V8XFRWxb98+HA4HqamprFu3jqSkJFJTU8nPz+f555+nV69e5OTkAL4xTxkZGTz77LP84he/wOFwsGHDBjZv3sz9998PQFZWFj169OCZZ55hwoQJuN1u5s+fzxVXXEGzZs1C8tw/pD5ykpeXx86dO+nWrRtNmjQhLy+PF154gauuuso/MLqhON98AKxatYrMzEySkpLIy8vj+eef5/rrr/f3UoRTG6mPfIRT+5g3bx6rV6/mvvvuo0mTJv6xVvHx8cTExGBZFj/72c947bXXaNmyJWlpabz88ss0bdrUXxTHx8dz9dVX8+KLL+JwOIiPj2f+/Pl07tzZXwCESxupr3xEWhsB3xg/p9Pp//3Lz8+nSZMmpKam4nA4wqaNyNmxzA91ncg527p1K9OnTz/leL9+/bjzzjtZsWIFb775Jk6nk6ZNm9K3b19GjBiB3f5tTV5YWMiCBQvYsWMH1dXVZGRkMGzYMPr27es/p7y8nHnz5gUszjlu3LgGuThnfeRkz549zJs3jwMHDuByuUhLS6Nv374MHTq0wY3dqYt8LFiwgA8//JDy8nLS0tIYNGgQ119/fUAvabi0kfrIRzi1j1GjRp32+B133EH//v2Bbxf5zc3NpbKykq5duzJ+/PiArylPLny8Zs0a/+LYp1sIuqG3kfrKRyS2kVdeeYUlS5b84HXCoY3I2VEBKCIiIhJhNAZQREREJMKoABQRERGJMCoARURERCKMCkARERGRCKMCUERERCTCqAAUERERiTAqAEVEREQijApAERERkQijAlBEGoVZs2YxduxY/3ZY31VZWcnEiROZMmUKXq+3/oMTEWlgVACKSKNw++2343a7eeGFF055b+HChZSVlTFx4kRsNv1rT0RE/yYUkUYhLS2NESNGsGbNGjZt2uQ/vmvXLt577z2uv/56srOzgxpDbW2tehhFJCxoL2ARaTQ8Hg+TJ0+murqauXPnYrfb+e1vf0tFRQVz587l6NGjvPzyy2zZsoXa2lpat27NiBEjuPTSS/3XKC8v57XXXmPTpk0UFRVhs9no0qULY8aMCSggt27dyvTp07nrrrsoKChg1apVOJ1O5s+fT2xsLK+//joff/wxxcXFxMbGkpmZyciRI7n44otDkBkRkUD2UAcgIlJXoqKimDhxIlOnTuXVV18lOTmZvXv3MmXKFIqKipg2bRrNmjXjpptuIjY2lnXr1jFnzhx+85vf0Lt3bwAOHz7Mhg0b6NOnD2lpaTidTnJzc3nooYf4wx/+QLNmzQLu+eqrr2K32xk2bBhutxu73c7ixYt54403uPrqq+nYsSNVVVXs3r2bPXv2qAAUkQZBBaCINCqdOnVi8ODBLFu2jOjoaK688kp69OjBww8/TGpqKo8++ijR0dEADB48mAceeIAFCxb4C8A2bdrw5JNPBowV7Nu3L3fffTcffPABI0aMCLify+XiscceIyYmxn/siy++oGfPnvzXf/1XPTyxiMjZ0xhAEWl0brnlFhITE7Esi9tuu43y8nK2bNlCnz59qKqqorS0lNLSUsrKysjJyaGwsJBjx44BEB0d7S/+vF4vZWVlxMXF0apVK/bu3XvKvfr16xdQ/AEkJCRQUFBAYWFh8B9WROQcqAdQRBqd+Ph4WrVqRVlZGSkpKezatQtjDIsWLWLRokWn/UxJSQnNmjXD6/WyYsUK3n33XYqKigImdTgcjlM+l5aWdsqxUaNGMWfOHO666y5at25Njx496Nu3L23btq27hxQROQ8qAEWk0TtZxA0bNoycnJzTnpORkQHA66+/zqJFixgwYACjR4/G4XBgWRYvvPACp5sz96+9fwAXXnghTz31FBs2bGDTpk188MEHvPXWW0yYMIGBAwfW4ZOJiJwbFYAi0uilp6cDvkkiPzYJ45NPPqFbt2788pe/DDheUVFBYmLiGd/T4XAwYMAABgwYQHV1NQ8++CCLFy9WASgiDYLGAIpIo5ecnEy3bt3Izc3l+PHjp7xfWlrq//PpFopet26df4zgmSgrKwt4HRcXR3p6Oi6X6yyiFhEJHvUAikhEGD9+PNOmTWPSpEkMHDiQtLQ0SkpKyMvL49ixY8yZMweAn/zkJyxZsoSnn36azp07k5+fz+rVq/29iGfinnvu4cILL6R9+/Y4HA52797Np59+yuDBg4P1eCIiZ0UFoIhEhKysLB577DEWL17Mhx9+SFlZGcnJyWRnZ3PzzTf7zxs+fDjV1dWsWbOGtWvX0q5dOyZPnszChQvP+F7XXXcdn332GZs3b8blctGiRQtGjx7NDTfcEIxHExE5a9oJRERERCTCaAygiIiISIRRASgiIiISYVQAioiIiEQYFYAiIiIiEUYFoIiIiEiEUQEoIiIiEmFUAIqIiIhEGBWAIiIiIhFGBaCIiIhIhPn/ZziLK/N3isQAAAAASUVORK5CYII="
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for a sample python solution</summary>\n\n```python\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    plt.title('Immigrants from China and India')\n    plt.ylabel('Number of Immigrants')\n    plt.xlabel('Years')\n\n    plt.show()\n```\n\n</details>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "<br>From the above plot, we can observe that the China and India have very similar immigration trends through the years.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "*Note*: How come we didn't need to transpose Haiti's dataframe before plotting (like we did for df_CI)?\n\nThat's because `haiti` is a series as opposed to a dataframe, and has the years as its indices as shown below.\n\n```python\nprint(type(haiti))\nprint(haiti.head(5))\n```\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>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "Line plot is a handy tool to display several dependent variables against one independent variable. 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.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "**Question:** Compare the trend of top 5 countries that contributed the most to immigration to Canada.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "### type your answer here\n\n#The correct answer is:    \n#Step 1: Get the dataset. Recall that we created a Total column that calculates cumulative immigration by country. \n#We will sort on this column to get our top 5 countries using pandas sort_values() method.\n\ninplace = True # paramemter saves the changes to the original df_can dataframe\ndf_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n\n# get the top 5 entries\ndf_top5 = df_can.head(5)\n\n# transpose the dataframe\ndf_top5 = df_top5[years].transpose() \n\nprint(df_top5)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 65,
      "outputs": [
        {
          "name": "stdout",
          "text": "      India  China  United Kingdom of Great Britain and Northern Ireland  \\\n1980   8880   5123                                              22045      \n1981   8670   6682                                              24796      \n1982   8147   3308                                              20620      \n1983   7338   1863                                              10015      \n1984   5704   1527                                              10170      \n1985   4211   1816                                               9564      \n1986   7150   1960                                               9470      \n1987  10189   2643                                              21337      \n1988  11522   2758                                              27359      \n1989  10343   4323                                              23795      \n1990  12041   8076                                              31668      \n1991  13734  14255                                              23380      \n1992  13673  10846                                              34123      \n1993  21496   9817                                              33720      \n1994  18620  13128                                              39231      \n1995  18489  14398                                              30145      \n1996  23859  19415                                              29322      \n1997  22268  20475                                              22965      \n1998  17241  21049                                              10367      \n1999  18974  30069                                               7045      \n2000  28572  35529                                               8840      \n2001  31223  36434                                              11728      \n2002  31889  31961                                               8046      \n2003  27155  36439                                               6797      \n2004  28235  36619                                               7533      \n2005  36210  42584                                               7258      \n2006  33848  33518                                               7140      \n2007  28742  27642                                               8216      \n2008  28261  30037                                               8979      \n2009  29456  29622                                               8876      \n2010  34235  30391                                               8724      \n2011  27509  28502                                               6204      \n2012  30933  33024                                               6195      \n2013  33087  34129                                               5827      \n\n      Philippines  Pakistan  \n1980         6051       978  \n1981         5921       972  \n1982         5249      1201  \n1983         4562       900  \n1984         3801       668  \n1985         3150       514  \n1986         4166       691  \n1987         7360      1072  \n1988         8639      1334  \n1989        11865      2261  \n1990        12509      2470  \n1991        12718      3079  \n1992        13670      4071  \n1993        20479      4777  \n1994        19532      4666  \n1995        15864      4994  \n1996        13692      9125  \n1997        11549     13073  \n1998         8735      9068  \n1999         9734      9979  \n2000        10763     15400  \n2001        13836     16708  \n2002        11707     15110  \n2003        12758     13205  \n2004        14004     13399  \n2005        18139     14314  \n2006        18400     13127  \n2007        19837     10124  \n2008        24887      8994  \n2009        28573      7217  \n2010        38617      6811  \n2011        36765      7468  \n2012        34315     11227  \n2013        29544     12603  \n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "#Step 2: Plot the dataframe. To make the plot more readeable, we will change the size using the `figsize` parameter.\ndf_top5.index = df_top5.index.map(int) # let's change the index values of df_top5 to type integer for plotting\ndf_top5.plot(kind='line', figsize=(14, 8)) # pass a tuple (x, y) size\n\n\n\nplt.title('Immigration Trend of Top 5 Countries')\nplt.ylabel('Number of Immigrants')\nplt.xlabel('Years')\n\n\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x5c3bf58>",
            "image/png": 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4Yvr16+daZ7FYuPrqqwHYuHHjSWP94IMPsFgsTJs2DcMwXMtbtGhRppq2Ivfff3+FFZK+vr40b9683PLQ0FCuu+460tPTK7ykujoKCwv56KOPCAoKKhd7q1atuPPOOyksLOTf//53uX179+5dru3Eddddh9Vq5X//+99pxVWZp59+2lXl6DR//nx27NjBHXfcUeYxBGjWrBn3338/Bw8erHByvDvvvJNOnTqVWXbjjTcClLkNa9as4c8//+T8889nxIgRZba//fbbq9WXed++fSxevJiWLVty//33l1nXq1cvrrzyStLS0vjiiy+qPOapmjt3LmlpaYwdO5a+ffuWWTdlyhTi4uL49ttv2bNnT7l977rrLmJjY12/WywW13Pyvffeq9Lxo6KieP755/ntt9/Izs4mNTWVhQsX0q1bNz7//HOGDx+O3W4/6ThpaWkUFhYCVPicOZH33nsPwzCYPn06VutfFytGR0fz2GOPAfDOO+9Ua8zKjBs3joEDB5ZZdtNNNwGc8nOmoudEZU71ueLv719uLIvFQnh4+CnFLCIiIu6nFg0iIiJSJ7p27epqV+Dk7KvZunVrgoODy6zz8vKicePG7Nu3r8LxevToUW6Zc7yKLjOOiYkBqHQ8p6ysLHbs2EGLFi0qvGy+T58+J9z/7LPPrnTd5s2befHFF1m1ahUHDhwgPz+/zPrKesVW1Z9//klubi69e/cmIiKi3PoBAwbwzDPPsH79+nLrKro/vb29ady4Menp6acVV2Uquq+cPXB3795dYV/Ubdu2AfD777+Xa9NQ0W1o0aIFQJnb8MsvvwCUS4qB47zr06cPO3bsqNJtcN6Xffv2xdvbu9z6AQMG8NFHH7F+/Xr+9re/VWnMU+W8XRW1mLBarZx//vkkJyezfv36cm1PKrovEhISaNGiBcnJyWRkZFTYUqC0Dh060KFDB9fvQUFBDBkyhF69etG1a1dWr17NvHnzyiXVa0p2djbbt28nJiamwgkInfdLRef/qajq+VYdJ3r9OF51nyvt27ena9eu/Pe//2X37t2MGDGCPn360KNHD3x8fE4pXhEREakflOAVERGROlHRDPDOCrvKZoe3Wq0UFRXVyHjOdZWN5+ScVKpx48YVrq9suVOTJk0qXP7jjz8yYMAAiouLGThwIJdeeikhISFYLBY2bNjA3LlzXRPEnarMzEwAmjZtWuF65/KMjIxy6ypL3lmtVmw222nFVZmK7ivnRHizZ88+4b7Hjh0rt6yi2+B83EvfBuf9VNljWdljWJHTuc9r2unEcqL7Yvfu3WRmZp40wVuZkJAQrrrqKp599llWrVp10gRvREQEPj4+FBYWkpKSUuWK6rp+LKp6vlVHdc696j5XvLy8WLZsGVOnTuWzzz7jgQceACA4OJgJEyYwbdo0goKCTiluERERcS8leEVERERKCQkJAeDQoUMVrq9suVPptgilPfPMM+Tl5bF8+XL69+9fZt20adOYO3du9YM9jjOxffDgwQrXHzhwoMx27lbRfeWMbe7cuVx66aW1clznMSp7LCu7/040Vn24z08nlkOHDlU4kZ5zrNONPyoqCoCcnJyTbmu1Wjn33HNZtWoVS5curXKC91Ruv7MtTHFxcYX7VKVyuSZV9vpRkVN5roSHh/PKK6/wyiuvsH37dlauXMnMmTN57bXXyMjI4MMPPzyluEVERMS91INXREREpJSQkBASEhJISUkhOTm53Prvv//+lMbdvn07ERER5ZK7ACtXrqxwHy8vr2pVArZp04aAgAA2btxYYZXi8uXLATjrrLOqPGZdO/fccwH47rvvau0Yzttf0f1us9mq9Rh369YNcJwXFSUJ6/I+d8ayYsWKcuuKi4td92lFsVR0X+zcuZO9e/cSFxd32knOH3/8EXC0fagKZy/bl156idzc3BNu66x8Dw4OJjExkZSUFFd7gtIqeiycfWf37t1bbvvt27e7qoJPl7M9TU1Ww5/ucyUpKYnrr7+elStXEhQUVCNfMomIiIh7KMErIiIicpy//e1v2O12HnroIUzTdC3fu3cvM2bMOKUx4+LiSEtL49dffy2z/N1332XRokUV7hMZGUlqaip5eXlVOoaPjw/jx48nOzvbNaGU044dO3j11Vfx9vbmmmuuOaXbUBdGjBhBYmIi//znP1mwYEGF2/zwww8nTfqdSK9evWjTpg2rVq0ql9R67bXXqtx/FxyTgF144YUkJyeXOzd++uknPv74Y8LDw7nssstOOd6qGjlyJBEREfz3v/91JVSdZsyYwa5duxg0aFC5/rsA//jHP9i9e7frd7vdzn333Yfdbufaa6+t0vF//vnnCpd/9NFHzJo1Cx8fH8aMGVOlsa688koGDx7Mtm3bGDFihKv6trTCwkL++c9/MmXKFNey6667DtM0ue+++8okU48cOcLTTz/t2sapbdu2hISEMHfuXA4fPuxanpeXx5133lmlWKsiMjISoMIJ7k5VdZ8ru3btYufOneW2SU9Pp6CgoMLJ10RERMQzqEWDiIiIyHHuv/9+5syZwyeffMKff/7JRRddRGZmJp9++innn38+c+bMcV3aXVWTJ09m0aJF9OnThzFjxhAaGsrPP//M999/z+jRo/nss8/K7TNw4EDWrl3LkCFDOP/88/H19aVLly4MHz680uM8//zzfPfdd7z22musXbuWCy64gCNHjvDpp5+SnZ3Na6+9Rnx8fLXvk7ri7e3NF198weDBg7n44otdE3QFBASwd+9e1q5dy86dOzlw4AABAQGndAzDMHj33Xe58MILufzyyxk1ahRJSUls2LCBpUuXMmTIEL755psqj/fmm2/Su3dv7rvvPhYvXkyPHj3Yu3cvs2fPxmKx8K9//avcJIK1ISgoiPfee48rrriCfv36ccUVV9CyZUvWrVvH4sWLadKkCTNnzqxw3969e9O1a1fGjh1LaGgoixYtYuPGjXTv3p3777+/SscfPXo0VquVHj160Lx5c/Lz81m7di3/+9//sFqtzJw5s8KJCytisViYPXs211xzDXPnziUhIYGBAwfSrl07vLy8SE5OZtmyZaSmpnLvvfe69rv33ntZuHAhc+fOpUuXLgwbNozc3Fxmz57N4cOHuf/++8tMlOjt7c1dd93F008/Tbdu3bjssssoLi7m22+/pVmzZq6JG0/XwIEDefHFF7nxxhu5/PLLCQ4OJiwsjNtvv/2Ux6zuc2Xjxo2MGjWKnj170q5dO5o1a0Zqaipz586lqKjI1ZNXREREPI8SvCIiIiLH8ff3Z/ny5Tz++ON89tlnvPLKK8THx/Pwww/Tt29f5syZ4+rVW1VDhgxh3rx5PPPMM8yaNQsvLy/OPvtsli9fzs6dOytM8D766KNkZGQwb948Vq9ejc1mY8KECSdM8EZERPDDDz8wbdo0vvjiC6ZPn46/vz9nn3029913HxdddFG174+61rlzZzZu3Mj06dP5+uuv+de//oXFYqFp06Z069aNp556ikaNGp3WMXr37s13333HI488wsKFCwE455xzWLFiBYsWLapWgjchIYGff/6ZZ555hgULFrBixQpCQkIYMmQIjzzyCD179jytWKtjxIgRrF69mueee45FixaRmZlJkyZNmDRpEo899lilCctXXnmFL7/8krfffpvk5GQiIyO56667mDp1Kn5+flU69i233MKSJUtYvXo1R44cwTRNYmJimDhxIpMnT6ZLly7Vui3BwcHMmTOHxYsX8/777/PDDz+wdOlSTNOkWbNmDBo0iL/97W8MGTLEtY+Pjw/ffvst06dP5+OPP+b//u//sFqtdOnShRkzZnDllVeWO85TTz1FQEAAb7/9Nm+99RZNmjRh3LhxPPnkk7Rv375aMVdm8ODBvPzyy7z99tvMmDGDwsJCYmNjTyvBC9V7rvTo0YMHH3yQlStX8s0335Cenk5UVBTdu3fnzjvvZOjQoTVxU0VERMQNDLP0dYciIiIickJvv/02N910E2+++SY333yzu8MROS0TJ07kgw8+YNeuXVWurhURERGR+kU9eEVEREQqsH///nLL9uzZw9NPP43Vaj1hFa2IiIiIiEhdUYsGERERkQpcfvnlFBUV0b17d8LCwkhOTubrr78mNzeXadOm1VhvThERERERkdOhBK+IiIhIBa655ho+/PBDPv/8czIzMwkKCuKcc87h9ttvZ9SoUe4OT0REREREBFAPXhERERERERERERGPpR68IiIiIiIiIiIiIh5KCV4RERERERERERERD6UEr4iIiIiIiIiIiIiHUoJXRERERERERERExENZ3R2AyOlIT0+nuLjY3WGcsqioKFJTU90dhpxhdF5JTdM5JbVB55XUNJ1TUht0XklN0zklVWG1WgkPD3d3GOJBlOAVj1ZcXExRUZG7wzglhmEAjttgmqabo5Ezhc4rqWk6p6Q26LySmqZzSmqDziupaTqnRKS2qEWDiIiIiIiIiIiIiIdSgldERERERERERETEQynBKyIiIiIiIiIiIuKhlOAVERERERERERER8VCaZE3OWMXFxeTm5ro7jBPKy8ujsLDQ3WHIGUbnldQ0nVOnJyAgAKtVb7lERERERKR26NOGnJGKi4vJyckhODgYi6X+Fqp7e3tTVFTk7jDkDKPzSmqazqlTZ7fbyc7OJjAwUEleERERERGpFfU38yVyGnJzc+t9cldERM58FouF4ODgen9FiYiIiIiIeC5lv+SMpeSuiIjUB/p7JCIiIiIitUmfOEREREREREREREQ8lBK8IiIiIiIiIiIiIh5KCV6RM0xMTAzffPMNAHv37iUmJoZNmza5OSoREREREREREakNms5ZpB6ZPHkyWVlZvPfeezUyXrNmzVi/fj0RERE1Mp6IiIiIiIiIiNQvSvCKnMG8vLyIjo52dxgiIiIiIiIiIlJL1KJBpJ4aPXo0jz32GM888wwdOnSga9euvPzyy2W22blzJ6NGjSIhIYH+/fuzatWqMuuPb9Fgs9mYMmUK5557LomJifTt25d33nmnzm6TiIiIiIiIiIjULFXwSoNgmiYUFtT9gX18MQzjlHefPXs2N910E/PmzWPdunXcfffd9OzZk/PPPx+73c6NN95Io0aNmDdvHtnZ2TzxxBMnHM9ut9O0aVNmzpxJeHg4P//8M/fffz/R0dFceumlpxyniIiIiIiIiIi4hxK80jAUFmC/fUydH9by2qfg63fK+7dr14577rkHgISEBN5//32+//57zj//fL777ju2b9/Of/7zH5o0aQLAgw8+yNVXX13peN7e3tx7772u31u2bMm6deuYN2+eErwiIiIiIiIiIh5ICV6Reqxdu3Zlfo+OjubIkSMAbNu2jWbNmrmSuwDdu3c/6Zjvv/8+n3zyCSkpKeTn51NUVESHDh1qNnAREREREREREakTSvBKw+Dj66imdcNxT4fVWvYpahgGdrv9lMebO3cuTz/9NI899hg9evQgMDCQN954g/Xr159WnCIiIiIiIiIi4h5K8EqDYBjGabVKqI9atWrF/v37OXToEI0bNwbgl19+OeE+a9eupXv37kycONG1bPfu3bUZpoiIiIiIiIiI1CKLuwMQkVPTt29fEhISmDx5Mps3b+ann37i73//+wn3iY+P59dff2XFihXs2LGDF154gY0bN9ZRxCIiIiIiIiIiUtOU4BXxUBaLhXfeeYf8/HwuueQS7r33Xh544IET7nP11VczdOhQbrnlFoYPH056ejoTJkyoo4hFRERERERERKSmGaZpmu4OQuRUpaamUlRUVG55VlYWISEhboioery9vSuMX+R06LySmqZz6vR5yt+lumIYBk2bNuXAgQPorajUBJ1TUht0XklN0zklVeXt7U1UVJS7wxAPogpeEREREREREREREQ+lBK+IiIiIiIiIiIiIh1KCV0RERERERKSUjQdzuHLWnyzYfMDdoYiIiJyUErwiIiIiIiIipXy26Sg5RXbm/Lrf3aGIiIiclBK8IiIiIiIiIiUOHSvk10O5AGw6kEVBsd3NEYmIiJyYErwiIiIiIiIiJZbvynL9v8hm8ueRPDdGIyIicnJK8IqIiIiIiIgAdtNk2c5MAIJ9vADYVFLNKyIiUl8pwSsiIiIiIiICbDmcx6FjRfhbLYzp1AhQgldEROo/JXhFPExMTAzffPNNpevXrFlDTEwMmZmZdRiViIiIiIjnW1pSvdsnNpgeMYEA/Hkkj0Kb+vCKiEj9pQSvSD1z+PBhHn30Uc477zzi4+Pp0aMHEyZM4LvvvqvS/j169GD9+vWEhITUcqQiIiIiImeOvCI7a/Y4+u8OTAilWbAPkYE+FNlNth7Jd3N0IiIilbO6OwAR+cvevXsZOXIkISEhPProo7Rt25bi4mJWrFjBI488wqpVq046ho+PD9HR0XUQrYiIiIjImWPNnizyi02aBXvTNsofwzDo3iKMxX8cZtPhXDo2DnB3iCIiIhVSBa9IPfLwww8DMH/+fC6++GISExNp06YNN998M/PmzXNtl5aWxvXXX09iYiK9e/dm8eLFrnXHt2iYNWsW7dq1Y8WKFfTr149WrVoxfvx4Dh065Npnw4YNjBs3jo4dO9K2bVsuv/xyfvvttzq61SIiIiIi7uecXG1AQiiGYQBwVotwQH14RUSkflOCV6SeSE9PZ/ny5UycOJGAgPLVAaGhoa7/T58+neHDh7NkyRIGDhzI7bffTnp6eqVj5+Xl8eabb/Lqq6/yxRdfkJKSwtNPP+1af+zYMa644grmzJnDvHnziI+P55prruHYsWM1eyNFREREROqhg9mFbDqchwH0j//rffdZLcIARx/eIvXhFRGRekotGqRBME2TAptZ58f19TJc3/6fTHJyMqZpkpSUdNJtx4wZw8iRIwF48MEHeffdd9mwYQMXXHBBhdsXFRXx/PPPExcXB8DEiROZMWOGa32fPn3KbP/CCy/Qrl07fvjhBy688MIqxS8iIiIi4qmW7XJU73ZpGkhUoLdreVxEAKF+XmTm29h2NJ/20WrTICIi9Y8SvNIgFNhMxs7aWufHnTW2NX7WqiV4TbPqCeh27dq5/h8QEEBwcDBHjhypdHt/f39XchegcePGZbZPTU3lhRdeYM2aNRw9ehSbzUZeXh4pKSlVjklERERExBPZTZPlJe0ZBiaElllnGAYdowNYvSebTYdyleAVEZF6SQlekXoiPj4ewzDYvn37Sbf19vYu87thGNjtlV8yVtH2pRPKkydPJj09nalTp9K8eXN8fHy49NJLKSoqquatEBERERHxLJsO5XI4p5hAbwvnNA8qt75T45IE7+FcxrghPhERkZNRglcaBF8vg1ljW7vluFUVHh5O//79ef/997n++uvL9eHNzMws04e3Jq1du5bnnnuOgQMHApCSkkJaWlqtHEtEREREpD5ZWlK92yc2BF9r+WlqOjYOBOD31DyKbCbe1XiPLyIiUhc0yZo0CIZh4Ge11PlPVfvvOj377LPY7XYuvvhi5s+fz86dO9m2bRvvvvsul156aS3dO47q4c8//5xt27bxyy+/cMcdd+Dn51drxxMRERERqQ9yi2ys2ZMNwMDEiospWoT6EOLrRaHNZHtaXl2GJyIiUiVK8IrUI7GxsXzzzTf06tWLqVOnMnDgQMaNG8f333/PtGnTau24L7/8MpmZmQwZMoQ777yT6667jkaNGtXa8URERERE6oPVu7MptJnEhPjQOrLiAgfDMOhQ0nt38yEleEVEpP4xzOrM7CRSz6SmplbYJzYrK4uQkBA3RFQ93t7e6nMrNU7nldQ0nVOnz1P+LtUVwzBo2rQpBw4cqNYkoyKV0Tklp+qhxbvZkprH37pGcXmHyDLrSp9X8/44yts/H6Zr00CeGtDCTdGKp9NrlVSVt7c3UVFR7g5DPIgqeEVERERERKTB2Z9VyJbUPCwG9I8/8ZdwHUsqeP9IzaXYrsSciIjUL0rwioiIiIiISIOzrGRytW5NA4kM8D7hti3DfAn2sZBfbLIjLb8uwhMREakyJXhFRERERESkQbHZTZbtciR4ByRUPLlaaRbDoH1JFe+mQ7m1GpuIiEh1KcErIiIiIiIiDcpvh3I5mltMoI+Fs5sHVWmfTo1LJlo7rASviIjUL0rwioiIiIiISIOytKQ9w/mxIfh4Ve1jcYdoZ4I3D5v68IqISD2iBK+IiIiIiIg0GMcKbfy4NxuAgYknb8/gFBfuS5CPhfxiOzvT1YdXRETqDyV4RUREREREpMFYvTubQptJi1AfkiL8qrxf6T68v6kPr4iI1CNK8IqIiIiIiEiD4WzPMDAhFMMwqrVvR2ebBiV4RUSkHlGCV0RERERERBqEfZkF/HkkD4sB/eOr3p7BqWPJRGtbUtWHV0RE6g8leEXOQOeccw5vv/12rYwdExPDN998c1pjjB49mscff7yGIqrcmjVriImJITMzs9aPdSo++ugjevToQfPmzWvt8fIUL7/8MhdeeGG9Gac27N27l5iYGDZt2uTuUMqpq+dkXavN18LSJk+ezHXXXVfrxxERkdO3rKR6t3uzQML9rdXePy7Ml0BvC7lFdnalF9R0eCIiIqdECV6ReqKyBMusWbNo165dtcZasGABV199tev3mkjKVlVFiY6vv/6ahIQE3nzzTQDefvtt7r///jqJp77Kzs7m0Ucf5bbbbmPdunVlHq/jrV69mr/97W906tSJhIQEevXqxaRJk/jxxx/rJNaqJv9Gjx5NTEyM66dLly7cdNNN7Nu376T7Tpo0iVmzZrl+P9WE2fHjeBrnfTh37twyy99++23OOeec0x6/vn/p4XSmJpxFRMS9bHaT5buyABiQUP3qXQAvi0H7aH8ANh9WmwYREakflOAVOQNFRkbi7+/v7jAA+Pjjj7njjjuYNm0akyZNAiA8PJygoCA3R+ZeKSkpFBUVMXDgQBo3blzp4/X+++8zduxYwsPDeeONN1i1ahXvvPMOPXr04Mknn6x0fJvNht1ur6XoKzd+/HjWr1/PL7/8wnvvvcf+/fu54447Kt3eNE2Ki4sJDAwkIiLitI9fU+O4k5+fHy+88AJFRUU1Om5Nj3cyhYWFHjm2iIicuTYezCEtr5hgHws9Y079vWgHTbQmIiL1jBK8Ih7GWdn45ptv0q1bNzp06MDDDz9cJnlT+rJkZ9Xf9ddfT0xMTJkqwEWLFjF48GASEhI477zzmD59OsXFxa71O3fuZNSoUSQkJNC/f39WrVpVrVhff/11HnvsMV5//XXGjh3rWn58dd4555zDq6++yj333EPr1q3p2bMnH330UZmx1q5dy4UXXkhCQgJDhw7lm2++KXe5+9KlS+nTpw+JiYmMHj2avXv3lotp/vz5XHDBBcTHx3POOee4qopLxzJjxgzuvPNOWrVqxdlnn83ixYs5evQo1157La1atWLQoEFs3LjxhLc9JSXFtX2bNm24+eabSU1NBRxV2QMHDgTgvPPOIyYmpsJYU1JSePLJJ7nhhhv4xz/+QZ8+fWjevDnt27fnhhtuYOHCha5tnZXeixcvpk+fPsTHx5OSkkJBQQFTp06le/fuJCUlcckll7BmzRrXfmlpadx66610796dxMREBg4cyJw5c1zrJ0+ezA8//MC7777rqsytKFYnPz8/oqOjady4Md27d+faa6/lt99+c613VpAuW7aMIUOGEB8fz//+978yrRVefvllZs+ezaJFi1zHdMb87LPPuh7j8847r1wi9PgWDVV5vhwvOTmZa6+9li5dutCqVSuGDRtW7tyvyjm7fv16LrroItc5W9XWDCNGjCAzM5P//Oc/J9zugw8+oFevXsTFxdG3b18+++yzMutjYmL44IMPmDhxIklJSdx3331cccUVALRv356YmBgmT57s2t40TZ555hk6dOhA165defnll8uMl5mZyb333kunTp1o06YNV1xxBZs3b3atd973H3/8Meeeey4JCQmuOD7++GOuv/56EhMT6d27N4sXL67SfeF0zjnn8Morr3DnnXfSpk0b1xUA//vf/7jssstITEykR48ePPbYY+TmVv5he+bMmQwcOJCkpCR69OjBQw89RE5Ojmu983m0YsUK+vXrR6tWrRg/fjyHDh1ybWOz2XjyySdp164dHTp04JlnnsE01YNRRMQTOCdXOz8+FG+vU/8o/Fcf3lz14RURkXpBCd4Gas6cOYwZM4b333/ftcw0TWbNmsVNN93E+PHjefrppzlw4ECZ/QoLC3nnnXe47rrruOaaa3jppZfIyMgos82xY8d49dVXmTBhAhMnTuSNN94gPz+/zDZHjhxh2rRpXH311dxwww18+OGH2Gy22rq5Z5w1a9aQnJzM7NmzmTFjBp9++imffvpphdsuWLAAgOnTp7N+/XrX7z/99BN33XUX119/PcuXL+fvf/87n376Ka+++ioAdrudG2+8EW9vb+bNm8fzzz/Ps88+W+UYn332WWbMmMH777/P0KFDT7r9zJkz6dy5M4sWLWLChAk89NBDbN++HXC0M5g4cSJt27blm2++4b777isXS0pKCjfeeCMXXnghixYt4qqrrmLatGlltvn111+ZNGkSl156KUuWLOGee+7hxRdfLHdJ/9tvv03Pnj1ZtGgRAwcO5M477+Suu+5i1KhRfPPNN8TGxnLXXXdVmtSx2+1ce+21ZGRk8Pnnn/Pf//6XPXv2cMsttwBw6aWX8sknnwCOhPP69etp1qxZuXHmz59PUVERt956a4XHOX7W57y8PP75z38yffp0li1bRqNGjXj00UdZt24dr7/+OkuWLOGSSy7h6quvZufOnQAUFBTQuXNnPvjgA5YtW8b48eO58847Wb9+PYArOeyszK0s1oqkp6czb948unXrVm7dc889x8MPP8yKFSvKtSCZNGkSw4cP54ILLnAds0ePHoCjQveVV15hxYoVPPXUU3z88ccn7bFanecLQE5ODgMGDGDWrFksWrSI/v37c+2115KSklJmuxOdszk5OUyYMIHWrVuzcOFC7rnnHp5++ukq3W9BQUHceeedzJgxo9Jk5cKFC3niiSe46aabWLp0KVdffTX33HMPq1evLrPd9OnTGTp0KEuXLuXee+913VerVq1i/fr1TJ061bXt7NmzCQgIYN68eTzyyCO88sorZRLbN998M0eOHOGjjz5i4cKFdOrUibFjx5Kenu7aJjk5mQULFvDOO++USeJOnz6d4cOHs2TJEgYOHMjtt99eZr+qmDlzJu3bt2fRokVMnjyZ5ORkxo8fz7Bhw/j222954403+N///scjjzxS6RgWi4WpU6eyfPlyZsyYwerVq3nmmWfKbJOXl8ebb77Jq6++yhdffEFKSkqZx27mzJnMnj2bl19+mTlz5pCRkVFnLXBEROTUHSuw8dPeYwAMPMX2DE4J4X74Wy3kFNrZnaE+vCIi4n5K8DZA27dv59tvvyU2NrbM8rlz57Jw4UJuvPFGnnvuOXx9fXn22WfLXAr7wQcfsG7dOu655x6eeuop0tPTy1V5vfrqq+zdu5dHH32UBx98kN9//52ZM2e61tvtdqZNm0ZxcTHPPPMMt912GytWrKjVvpmOy8Dr/qe2qrpCQ0N59tlnSUpK4sILL2TgwIF8//33FW4bGRnp2ic6Otr1+/Tp07ntttsYM2YMsbGxnH/++dx3332uKsTvvvuO7du3849//IMOHTpw7rnn8uCDD1YpvuXLl/P666/z3nvv0bdv3yrtM2DAACZOnEh8fDy33XYbERERrqrNL7/8EsMwePHFF2ndujUDBgxwJUud/v3vfxMbG8sTTzxBUlISo0aNYsyYMWW2eeutt+jTpw933303iYmJjB07lmuvvbZcFe+AAQO45pprSEhI4O677yY7O5suXbowfPhwEhMTufXWW9m2bZurIvd433//PX/88QevvfYanTt35qyzzuIf//gHP/zwAxs2bMDf35/w8HDA8fhER0fj5eVVbpydO3cSHBxMdHS0a9n8+fNp1aqV6+f33393rSsqKuK5557j7LPPJikpibS0NGbNmsXMmTM555xziIuLY9KkSfTs2dP1fGvatCmTJk2iY8eOxMbGct1119G/f3/mzZsHQEhICD4+Pq7K3MpiLf04tGrViqSkJDp27MiOHTuYPn16ue3uu+8+zj//fOLi4lz3hVNgYCB+fn74+Pi4junj4wM4KnJ79uxJixYtuOiii5g0aZIr1spU5/kC0KFDB6655hratm1LQkIC999/P7GxseWqTk92ztrtdl566SXatGnDhRde6GpRUhUTJkzA19e3zGtnaW+++SZjxoxh4sSJJCYmcvPNNzN06NBy5/LIkSMZO3YssbGxNG/enLCwMAAaNWpEdHQ0ISEhrm3btWvHPffcQ0JCAldccQVdunRx3U8//vgjGzZsYObMmXTp0oWEhAQef/xxQkNDmT9/vmuMoqIi/vGPf9CxY0fat2/vWj5mzBhGjhxJfHw8Dz74IDk5OWzYsKHK9wdA7969mTRpEnFxccTFxfHaa69x2WWXceONN5KQkEDPnj15+umn+eyzz8p9qeh044030rt3b1q0aEGfPn24//77y50/RUVFPP/883Tp0oVOnToxceLEMufLO++8w+23386wYcNo1aoVzz//PMHBwdW6LSIiUve+251Fkd0kNsyXhHDf0xqrdB/eTerDKyIi9UD1pw0Vj5afn8///d//cfPNN/PFF1+4lpumyYIFCxg1ahQ9e/YE4Pbbb+fGG29k7dq19O7dm9zcXJYtW8Zdd91Fx44dAbj11lu5++672bp1K61bt2bfvn1s2LCBadOmkZiYCMB1113HtGnTuOaaa4iIiGDjxo3s27ePxx57jLCwMOLi4hg7diz/+c9/GDNmDFZrzZ+WNhss/LzuJxUaenkotXBzaN26dZkkW+PGjcsk+qpiy5Yt/Pzzz66KXXAk3/Pz88nLy2Pbtm00a9aMJk2auNZ37969SmO3a9eOtLQ0Xn75Zbp160ZgYOBJ9ymdDDIMg6ioKI4ePQrAjh07aNeuHX5+fq5tunbtWmb/7du3l6sUPT7ebdu2MXjw4DLLevbsyTvvvIPNZnPdp6VjiYqKAqBt27bllh05cqRM8rX0cZo1a0ZMTIxrWevWrQkNDWXbtm3lYj+R46t0+/fvz+LFizl48CCjR48uU/nu4+NTJvbff/8dm81WLsleWFjoSqrabDZeffVVvv76aw4ePEhhYSGFhYWn3MP5sssu48477wQc98+rr77KlVdeyTfffFOm73Lnzp1Pafy5c+fy3nvvsXv3bnJycrDZbCft51zd50tOTg4vv/wyS5cu5fDhwxQXF5Ofn1+ugvdE5+y2bdvKnbNVff4A+Pr6cu+99/LYY48xYcKEcuu3b9/O+PHjyyzr2bMn7777bpllXbp0qfIxj6+kjo6O5siRIwBs3ryZnJwc12u/U35+Prt373b9HhMT4/oSqbKxAwICCA4Odo1dVcefM1u2bOH333/nyy+/dC0zTRO73c7evXtp1apVuTFWrVrFa6+9xo4dO8jOzsZms7le85znvL+/P3Fxca59Gjdu7Io1KyuLQ4cOlXmtsVqtdOnSRW0aRETqOWd7hoEJoeXeX52KjtEBrNufw6ZDuVza1rP7/4uIiOdTgreBeeedd+jWrRudO3cuk+A9fPgwGRkZZT5ABwQEkJSUxNatW+nduzc7d+7EZrPRqVMn1zYxMTE0atTIleDdunUrgYGBruQuQKdOnTAMg+3bt3P22WezdetWWrZs6aokA0ey7p133mHv3r3Ex8eXi7uoqKhMz0zDMFwfxmviDVp9EBQURHZ2drnlWVlZ5arDvL29y21X3eRCbm4uU6ZMqbB9gq/v6VU1NGnShLfeeosrrriC8ePH89FHH500CXd8Yt8wDLdMEnZ8LM7zq/R97lxW2/HFx8eTlZXF4cOHXYnkwMBA4uPjK/wixM/Pr8zzIScnBy8vLxYuXFiu6taZdH/jjTd49913eeqpp2jbti0BAQE88cQTpzwhV3BwsOs5HB8f70ryf/XVV1x11VWu7QICAqo99s8//8wdd9zBlClT6N+/P8HBwcydO5e33nrrhPtV9/kydepUvvvuOx577DHi4uLw8/PjpptuKjexV22fs5dffjkzZ85kxowZtGjR4pTGqM79fKLbk5OTQ3R0dLk+v+CokD7Z8Y5/DE7lvjp+7JycHK6++mquu+66ctuW/nLFae/evUycOJFrrrmGBx54gLCwMNauXcuUKVPKfKlRUaw1lbw9U/5e1QTnfaH7RGqKzik5kT0ZBWw7mo+XAf2rkeA90XnVsUkgkMqWw3mYgEXnnlSBXqtEpLYowduArF69ml27dpXrSwq4+uiW/qDu/N25LiMjA6vVWq4a8/htSl/yC+Dl5UVQUFCZbUond0sf9/h+vk5ffvllmcRCfHw8f//7312VlMfLy8sr8yHdajW5dFyjCretTV5eJ//j7YyzVatWrFixolxyYfPmzSQmJrqWWywWDMMos52Xl1eZZYZh4OXl5fr9+H/BkXjftWsXrVu3rjCutm3bsn//ftLS0mjcuDGAa2Kx0mMfzxlffHw8c+fO5bLLLuPqq69m1qxZriTv8fEd/7tzmcViwdvbm9atW7sueXcmn50TVlmtVry9vWnTpg2LFi0qM4bzEnBvb2/XOD///HOZbdatW0diYqKr0rKiWI6/zc5EmPPYld13hw8fdiWa/vzzTzIzM2nfvj3e3t4nHQMcl9dPmzaNN998s1z/1uP3d/5e+rHu1q0bNpuNzMxMzj333AqPsW7dOoYOHcq4ceMAR9LaeV44x3K2R6gsTqeK7jvn41VUVFQuztLbHX9e+/n5kZmZWWab9evX07x5c+69917XMmef8MqeH1V5vlR0n4wbN45LL70UcPQV37dvX7XO2bZt2/LFF19gs9lc55bz+XOix/z4cR999FGuvfZaJk6cWCbmVq1a8csvv5Sp4l23bh1t2rQpd1tL/+5MYjrjrOy4x9+ezp07k5qaip+fHy1btqww9oru68riqO79UFF8nTt3Ztu2bZW+hh2/3+bNm7Hb7TzzzDNYLI4OVc6e5M7z8fjnkTN257LIyEgaN27Mxo0bXZXxxcXF/Pbbb3Tq1OmEzxEfHx+aNm1a6fqGqvRVIiI1QeeUVGT2n44e+X0SG9EuvvpfmlZ0XkVF2/FfupfsQhu51hBaRZ+4mEGkNL1WiUhNU4K3gThy5Ajvv/8+jz76qCtZ40kuu+wyLrnkEtfvzqRpamoqxcXF5bYvLCw85QrEmlRBaGV4e3u74rz66qt57733ePDBB7nyyivx8fFh6dKlfPnll7z//vuu7ex2O6Zplrl9NputzDLTNLHZbK7fmzdvzsqVKznrrLPw8fEhLCyMyZMnM2HCBJo2bcrFF1+MxWJhy5Yt/PHHHzzwwAP06tWLhIQEbrvtNh599FGOHTvGc8895zpeZfdv6fiio6OZPXs2V1xxBVdccQX/+c9/CA4OLhff8b87l9ntdoqKirj00kt57rnnuPvuu7n99ttJSUnhn//8Z5lYxo8fzxtvvMHjjz/OlVdeyW+//eaayMxZAX7TTTcxbNgwXnjhBS699FLWrVvHu+++y3PPPXfCWI6/zc5zrri4uML7oVevXrRt25ZJkybx1FNPUVxczMMPP8x5551Hhw4dKCoqOukY4Lg0/PHHH+fxxx/n6NGjjBkzhpYtW7omb3PGW3o8ZyK1qKiIli1bMmrUKG677TYef/xxOnbsyNGjR/n+++9p164dgwYNIjY2lvnz57NmzRrCwsJ46623SE1NpVWrVmXOn3Xr1rFz504CAwMJCwtzJchKM02TnJwcVyuD1NRUZsyYgZ+fH7179y4XZ+nbffx53axZM5YtW8bvv/9OREQEwcHBxMbGkpKSwmeffUaXLl1YunSpq/9rZc+PqjxfjhcXF8fXX3/NgAEDXL2f7Xb7KZ2zkydP5o477mDv3r2uc/ZEj/nx4/bv359u3brx73//m0aNGrmWT5o0iUmTJtGuXTv69u3Lt99+y/z58/nkk0/K3dbSvzdp0gTDMFi4cCEDBw7Ez8+PwMDAk96efv360b17d/72t7/x6KOPkpCQwMGDB1m6dClDhw6lS5cuFd7XlcVR3fuhovhuueUWhg8fzv3338+VV15JQEAA27ZtY9WqVa5JGEvv16JFC4qKipg5cyYXXngha9eudU006jwfS5+fpWMvvez666/n1VdfJTY2lqSkJN566y0yMzNPeE6B4+/S8ROXNmSGYdCkSRMOHjyo9hZSI3ROSWVsdpOvf3O8N+kd41et1+KTnVdtG/mx/kAOyzfvJsimNg1ycnqtkqqyWq2VFrSJVESTrDUQO3fuJDMzkwceeIBx48Yxbtw4tmzZwsKFCxk3bpyrgjYzs2yf2szMTFe1bVhYGMXFxeTk5Jxwm6ysrDLrbTYbx44dK7PN8ZW6zuMeX9nr5O3tTUBAgOundH9Q0zTL/Xii2NhYPv/8c7Zv3864ceMYPnw48+bNY+bMmVxwwQWnNfbjjz/OqlWr6Nmzp6sHbf/+/fnggw9YuXIlw4YNY/jw4bz99ts0b94ccFTjvfPOO+Tn53PJJZdw77338sADD1T72M2aNeOzzz4jLS2Nq666qsI2FCcTHBzM+++/z+bNm7nooov4+9//zt133w38VSEaExPDW2+9xTfffMNFF13Ehx9+WG5SuE6dOvHmm2/y1VdfMXDgQF566SXuu+8+xo4dW+2YTsQwDP71r38RGhrKqFGjGDduHC1btuSNN96o9ljXXXcdH3/8MWlpadx000306dOHa665hr179/Kf//ynXN/U402fPp3Ro0czdepUzj//fK6//no2btzoqiy+66676NSpE+PHj2f06NFERUWV61N88803Y7FY6N+/P506dSrXi7a0//znP3Tr1o1u3boxZswY0tPT+fe//01SUlK1bvf48eNJTExk2LBhdOrUibVr13LRRRdx44038sgjj3DRRRfx888/M3ny5GqNWxVPPPEEoaGhjBgxgokTJ7pud3UEBgby/vvv88cffzB48GD+/ve/88gjj5xSPA8//HC5ScOGDBnCU089xcyZMxkwYAAfffQR06dPp1evXiccq2nTpkyZMoVp06bRpUuXKsdkGAYffvgh5557Lvfccw99+/bl1ltvJSUlhUaN6v7qCHD0QP7888/ZuXMno0aNYvDgwbz44ouuKw6O16FDB5544glef/11BgwYwJdffslDDz1U7ePefPPNXH755UyePJlLL72UwMBAhgwZUqV9K/p71ZB/dJ/op6Z/dE7pp6KfX/YfIz3fRoivF2c1Daz2/ic6rzpGO9oHbTqU4/bbqR/P+dFrlX6q8iNSXYapM6dByMvLIzU1tcyyN954g2bNmjFixAhatGjBzTffzPDhwxk+fDjg6NF64403cuutt7omWbv++uu56667XJd779+/n8mTJ/PMM8+4Jlm75557eP7550lISAAclyU/99xzvPHGG0RERLB+/Xqef/553nrrLVdiecmSJXz44Ye88847J70MvLTU1NQKK6aysrLKtYqoj0pX8Er1fPHFF9xzzz38/vvvpzwh2JlK55XUNJ1Tp89T/i7VFcMwaNq0KQcOHNCHGKkROqekMn//LoU1e7IZ3jacG7pX/CVgZU52Xv2RmscDi3cT4uvFB5cnqQ+vnJReq6SqvL29VcEr1aIWDQ2Ev79/ub6Jvr6+BAcHu5YPGzaML774gqZNmxIdHc0nn3xCeHg4PXv2BBwT3AwYMIB///vfBAUFERAQwHvvvUfr1q1dPRCbN29O165dmTlzJjfeeCPFxcW899579OrVi4gIx2VLXbp0oXnz5rz22muMHz+ejIwMPvnkEwYPHlyt5K40LLNnzyY2NpYmTZqwZcsWnn32WYYPH67kroiIiIhUKKvAxv/2HQNgYELoSbauvqRIP3y9DLIKbOzNLCQ27PQmKhYRETlVSvCKy4gRIygoKGDmzJnk5ubStm1bHn744TI9eydMmIBhGLz88ssUFxfTpUsXbrjhhjLj3Hnnnbz77rtMnToVwzA455xzysxybrFYePDBB3nnnXd49NFH8fX1pV+/fjV+mbycWVJTU3nppZdITU0lOjqaSy65pFwLBhERERERp++Ssyi2m8SH+xIf7lfj41stBm2j/Nl4MJdNh3KV4BUREbdRiwbxaGrRIFKeziupaTqnTp+n/F2qK7pEVWqazimpyD0Ld7EjrYAbukczvG31J0Grynn16aYj/GfjEXq1DOaBvjGnG7Kc4fRaJVWlFg1SXZpkTURERERERM4oyen57EgrwGqBfnG19wWbc6K1zYdzlbATERG3UYJXREREREREzijLdmYC0DMmiBC/2utM2CrSDx8vg8x8G/uyCmvtOCIiIieiBK+IiIhIA3XkUBF5uXZ3hyEiUqOK7SYrkrMAGFALk6uV5u1loW0jx6S/mw7l1uqxREREKqMEr4iIiEgDdPhAET+syGHdmhx3hyIiUqPW7T9GZr6NUD8vzmoWVOvH69DY0aZh02EleEVExD2U4BURERFpgHZtKwAg/aiNwgJV8YrImcPZnuGC+FCsFqPWj9eppA/vpkPqwysiIu6hBK+IiIhIA5ObY+PwgWLX72lHbG6MRkSk5mTmF7N23zEALoivvcnVSmvVyA9vi0FGvo2UbPXhFRGRuqcEr4iHmDVrFu3atTvhNpMnT+a6665z/T569Ggef/xx1+/nnHMOb7/9do3GtWbNGmJiYsjMzKzRcUVEpPbs3lE2AXH0cHElW4qIeJZVyVnYTEiM8CMu3K9OjunjZaFNlKMP7+ZDeXVyTBERkdJqbzpREam2yZMnM3v2bAC8vb2JiYlh9OjR3HHHHVXaf+rUqSe8LGzBggUEBATUSKxOPXr0YP369YSE1E2FhIiInB67zWTvLkeCt2kLbw7sLeJoqhK8InJmWFrSnmFgLU+udryO0f5sOpTLpkO5DG4VVqfHFhERUYJXpJ654IILmD59OoWFhSxdupRHHnkEq9VKdHT0Sfc9WZI1MjKypsJ08fHxqVJsIiJSPxxMKaIg38TXz6B9F38O7C0iM91GUaEdbx9d3CUinmtnWj670guwWgzOj6vb4oMO0QHAUTYddvThNYza7/0rIiLipHfxIvWMM2HavHlzJkyYQN++fVm8eLFr/YoVK+jXrx+tWrVi/PjxHDp0yLXu+BYNxzu+RUNMTAwffPABV199NYmJiZx33nl8/fXXrvV79+4lJiaGuXPncumll5KQkMCAAQP44YcfXNsc36LB2UriRHECfPzxx/Tr14+EhATOP/983n//fde6wsJCHnnkEbp160ZCQgJnn302//d//1f9O1NERMpJLmnP0DLBh4BAC4FBjreD6sMrIp7OObnaOc2DCPb1qtNjt2nkj9VikJZXzIHsojo9toiIiBK80iCYpklRUVGd/9TELLp+fn4UFTneJObl5fHmm2/y6quv8sUXX5CSksLTTz99WuO/+OKLDBs2jMWLF3PZZZdx6623sm3btjLbPP3009x8880sWrSI7t27M3HiRNLS0iod82RxfvHFF7z00ks88MADrFixggcffJAXX3yRTz/9FID33nuPxYsX8+abb7Jq1Spee+01WrRocVq3U0REIDvL5ui3a0Bsoi8AkdGOC7rUh1dEPFmRzWRlchYAA+q4PQOAr9VC60hHz99Nh3Pr/PjiGfLy8jh27Ji7wxCRM5BaNEiDUFxczBtvvFHnx73lllvw9vY+pX1N0+S7775j5cqVXHvttQAUFRXx/PPPExcXB8DEiROZMWPGacV4ySWXcNVVVwFw//33s2rVKt577z2mTZvm2ubaa6/l4osvBmDatGksX76cTz75hFtvvbXCMU8W58svv8zjjz/OsGHDAGjZsiVbt27lo48+YsyYMaSkpBAfH8/ZZ5+NYRg0b978tG6jiIg47Cmp3m3c1Ip/gON7/sgoK3t2FqoPr4h4tJ/3HyOrwEa4v5VuTQPdEkPHxgFsSc1j06FcLkoKc0sMUn/ZbDY++ugjioqKuPDCC0lKSnJ3SCJyBlEFr0g9s2TJElq1akVCQgLXXHMNw4cPZ8qUKQD4+/u7kqYAjRs35siRI6d1vO7du5f7/fgK3tLbWK1WunTpUm6b0k4UZ25uLsnJyUyZMoVWrVq5fl599VV2794NwJgxY9i8eTN9+/blscceY+XKlad1G0VEBGzFJnuTHQne2CRf13JnBW9Guo2iotO/8kRExB2c7RkuiA/By+Ke/rcdGzsmM3b24RUpLTU1ldzcXIqKiliwYAE//fSTzhMRqTGq4JUGwWq1csstt7jluNXVq1cvpk2bho+PD40bNy4zxvHVwIZh1Ms3BSeKMycnB3C0hujWrVuZ7by8HL3SOnXqxI8//siyZcv4/vvvmTRpEn369CnTP1hERKpn/94iigpN/AMtRDf562+Lf4CFgEALuTl20o4U07jpqV15IiLiLhl5xfyc4rjs/QI3tGdwatvIH6sFjuYWc+hYEU2CfdwWi9Q/Bw4cABwt+PLz8/npp584evQoF1544Slf9Ski4qQKXmkQDMPA29u7zn9OZfbcgIAA4uPjiYmJOaUEcXX98ssv5X5v1apVpdsUFxfz66+/ltumqqKiomjSpAm7d+8mPj6+zE/Lli1d2wUHBzNixAhefPFF3njjDRYsWEB6evopHVNERGD3jgIAYhN9yv19clbxpqkPr4h4oJXJWdhNaBXpR8tQ35PvUEt8rRZaRfoD6sMr5R08eBCAfv36MWjQICwWC9u3b2f27NlkZ2e7OToR8XSq4BVp4L7++mu6dOlCz549+fLLL9mwYQMvv/xymW3ef/994uPjadWqFW+99RaZmZmMGzfulI85ZcoUHnvsMUJCQujfvz+FhYX8+uuvZGRkcPPNNzNz5kwaN25Mx44dMQyDr7/+mujoaEJD3VeRISLiyTLTi0k/asOwQMv48hVlkVFW9u4q5IgSvCLiYUzTZOkOR3uGgW6s3nXqEB3A7yV9eAclhrk7HKlHnBW8LVu2JCAggLCwMObPn8+RI0f45JNPuOSSS2jatKmboxQRT6UEr0gDN2XKFObOncvDDz9MdHQ0//znP2ndunWZbR5++GH++c9/snnzZuLi4vjXv/5FRETEKR/zqquuwt/fnzfeeINnnnmGgIAA2rZtyw033ABAUFAQr7/+Ort27cLLy4suXbrw4YcfYrHoogMRkVOxu2RytaYx3vj6lX8tdVbwZqbbKC4ysXq7p3+liEh17UgrYHdmAd4Wg76xIe4Oh06NA/hs81E2HVIFr/wlOzubY8eOuSaQTktLo1mzZowdO5avv/6aI0eO8PnnnzNgwADat2/v7nBFxAMZZn1s4ClSRampqRQVFZVbnpWVRUiI+9/gnYy3t3eF8deVmJgY3n33XYYMGVLh+r1793LuueeyaNEiOnbsWMfRyaly93klZx6dU6fPnX+XiotMFn+Via0YzrsgiEbRFX+/v2ReJnm5Juf0CyS6Se32AjQMg6ZNm3LgwIF62UtePI/OqYbrrbUHmb81gz6xwdzXJ6ZGxz6V8yq/2M5Vn27FZsJbIxJoHKQ+vALbtm1j4cKFREVFMWXKlDLnVGFhId9++y07duwAoFu3bvTu3VvFLQ2ct7c3UVFR7g5DPIheMURERETOYPt2F2IrhqBgC5FRXpVu56ziPao2DSLiIYpsdlYlZwH1oz0DgJ/VQlKkHwCbD+e5ORqpL5ztGSpqweDj48OwYcM4++yzAVi/fj3z5s2joKCgTmMUEc+mBK+IiIjIGco0TZK3l0yuluR7wsk/I6OU4BURz/K/lGNkF9qJ9LfSpUmgu8Nx6RgdAMBvatMgJZwTrFXWY9cwDM4991yGDBmC1Wpl9+7dfPrpp2RkZNRhlCLiyZTgFWnAUlJSKm3PANCiRQtSUlLUnkFExEOlH7WRnWnH4gXN407cdsFZwZuRbqO4WJe4i0j9t6xkcrULEkLxstSf3uEdGzsSvJsPK8ErUFxczOHDhwFo0qTJCbdt3bo1o0ePJigoiPT0dGbNmsWePXvqIkwR8XBK8IqIiIicoXaXVO/GtPTBx+fEb/sCAi34+RuYdkg/qipeEanf0vKK+eVADgAXJNSvuTfaRvljMeDQsSJSc9TDvqFLTU3Fbrfj7+9PaOjJW4lER0czduxYmjRpQkFBAXPnzmXjxo3qLy4iJ6QEr4iIiMgZqLDAzv69jsRCbOLJJ/kxDEN9eEXEY6zYlYndhDaN/Gke4uvucMoI8PYiMcLRh3eT2jQ0eM7+u02aNDlhq6TSAgMDGTVqFG3btsU0TVauXMmyZcuw2Wy1GaqIeDAleEVERETOQHuTC7HbISTMi7CIyidXK83VhzdVCV4Rqb9M02RpSXuGQYn1Y3K14zn78G5Sm4YG70QTrJ2I1WrlwgsvpE+fPgBs3ryZL7/8ktxcnVMiUp4SvCIiIiJnGNM02b29EIC4JJ8qVwy5+vAetWFTH14Rqae2Hc1nX1YhPl4GvVsGuzucCjn78KqCt2EzTfOkE6ydiGEYnHXWWQwfPhxvb2/279/Pp59+ypEjR2o6VBHxcErwioiIiJxhjhwuJueYHavV0X+3qgKDLPj6GdjtkJ6mKl4RqZ+W7XRU757bIphAn6pdoVDX2kc7+vAePFbEkVz14W2osrOzycnJwWKxEB0dfcrjxMfHM2bMGEJDQ8nKymL27Nns2LGjBiMVEU+nBK+IiIjIGcZZvds8zgerd9VnljcMg0auPrzq8yci9U+hzc6q3VkADEyon+0ZwNGHNyFcfXgbOmf1bqNGjfD29j6tsSIjIxkzZgzNmzenqKiI+fPns3btWk2+JiKAErwiZ4xZs2bRrl27StevWbOGmJgYMjMz6zAqERGpa/l5dg6mOCdXq/7EQxHqwysi9dhPe4+RU2inUYCVTiVtEOorZ5uGzerD22Cdav/dyvj7+zNixAg6d+4MwA8//MCiRYsoLtbfbJGGTglekXpk8uTJxMTEEBMTQ1xcHL179+aVV16pkT/YPXr0YP369YSEhJx0WyWDRUQ8156dhZgmhDfyIiSs+pcuO/vwph8txmZTVZCI1C9LS9ozDEgIxctS9SsU3KFDtD8Amw7luTkScRdnBW+TJk1qbEwvLy/69+/PBRdcgMViYevWrXz22WccO3asxo4hIp5HCV6ReuaCCy5g/fr1fP/999x00028/PLLvPHGG6c9ro+PD9HR0VWeaEdERDyPaTfZvbMAgLhTqN4FCAou6cNrg4w0tWkQkfrjaG4RGw/mAI4Eb33XPjoAA9ifXUhaniosG5ri4mJSU1OBmqvgLa1Tp06MHDkSPz8/Dh8+zKxZs1wJZRFpeJTgFalnnInY5s2bM2HCBPr27cvixYuZOXMmAwcOJCkpiR49evDQQw+Rk5NT6ThHjx5l6NChXH/99RQUFJSryt23bx8TJkygffv2JCUlccEFF7B06VL27t3LFVdcAUD79u2JiYlh8uTJACxfvpyRI0fSrl07OnTowN/+9jeSk5Ndx9y7dy8xMTEsWLCA0aNHk5iYyKBBg/j5559r7f4SEZG/HD5YTH6uibePQdMWp9brzzAMIp1tGg4rISEi9cfyXVnYTWgf5U/T4KpPIOkuQT5exIc7vmxTH96G59ChQ9jtdgICAggODq6VYzRv3pyxY8cSERFBTk4On3/+OX/++WetHEtE6jcleKVhME2wF9b9Tw00vPfz86OoqAiLxcLUqVNZvnw5M2bMYPXq1TzzzDMV7pOSksJll11GmzZteOutt/D1LV/F9fDDD1NYWMjnn3/O0qVLefjhhwkMDKRZs2a8/fbbAKxatYr169czdepUAHJzc7nppptYsGABs2bNwmKxcMMNN2C328uM/fe//51JkyaxePFiEhISuO2229QXSkSkDiRvd1Tvtoz3wcvr1K/YiFQfXhGpZ0zTZOkOR6HCwMT6X73r5OzDqwRvw+Ospm3atGmtXkUZGhrKFVdcQXx8PDabjUWLFrF69WpNvibSwFjdHYBInTCLiN75RJ0f9nDCU2CcWnWBaZp89913rFy5kmuvvZYbb7zRta5Fixbcf//9PPjgg0ybNq3Mftu3b+fKK69k6NChPPXUU5W+mdi/fz/Dhg1zTcwWGxvrWhcWFgY4ZnsNDf3rDfTFF19cZozp06fTqVMntm7dStu2bV3LJ02axKBBgwC49957ueCCC0hOTiYpKekU7gkREamK3Bwbhw84ErItE0+vss3ZhzftSDF2m4nlNJLFIiI14c8j+ezPLsTXy6BXy9qphqwNHaMD+OqPdE201gDV9ARrJ+Lr68vFF1/MDz/8wLp161i3bh1paWkMHjwYH5/6X+0uIqdPCV6RembJkiW0atWK4uJi7HY7I0eOZMqUKaxatYrXXnuNHTt2kJ2djc1mIz8/n7y8PPz9HRM45OfnM2rUKEaOHOmquq3Mddddx0MPPcTKlSvp27cvw4YNo3379ifcZ+fOnbz00kusX7+etLQ0V+VuSkpKmQSvM2kMEB0dDcCRI0eU4BURqUW7dxQC0KixlaDg6k+uVlpQiAUfX4PCApOMdBsRjfSWUUTca1nJ5GrntQwmwPv0XuPqkrMP776sQjLyignz1+tpQ2CapivBW5MTrJ2IxWKhd+/eREZGsnTpUnbt2sWnn37K8OHDyxTtiMiZSX9dpGEwvB3VtG44bnX16tWLadOm4ePjQ+PGjbFarezdu5eJEydyzTXX8MADDxAWFsbatWuZMmUKhYWFrgSvj48Pffv2ZenSpdxyyy0n/Lb4qquuol+/fixdutSVPH788ce57rrrKt1n4sSJNG/enBdeeIEmTZpgt9sZMGAARUVFZbazWv96aXFWEB/fxkFERGqO3WayZ6cjwRuXdPqVOs4+vAf2FXH0cLESvCLiVgXFdr7bnQXAQA+YXK20YF8v4sJ92ZVewKbDufSJDXF3SFIHsrKyyMvLw2KxuApe6krbtm0JCwtj/vz5pKWlMWvWLIYNG0bz5s3rNA4RqVvqwSsNg2GAxafuf06h11JAQADx8fHExMS4EqW//vordrudJ554gu7du5OYmFjhDKkWi4VXX32VTp06ccUVV5x0FtWYmBj+9re/8c4773DzzTfz8ccfA+Dt7UhM22x/zZ6elpbGjh07uOuuu+jbty+tWrVyTdgmIiLudTCliMICE18/g8bNTm1yteOpD6+I1Bc/7s0mt8hOdKC3q6etJ+kQrT68DY2zejcqKqpM8UtdadKkCWPHjiU6Opr8/HzmzJnDpk2b6jwOEak7SvCKeIC4uDiKiop477332L17N5999hkffvhhhdt6eXnx2muv0b59e8aMGcPhw4cr3O7xxx9nxYoV7Nmzh99++43Vq1e7Wig0b94cwzBYsmQJR48eJScnh7CwMMLDw/noo4/YtWsX33//PU895YaqaBERKSe5pD1DywQfLJaa6Zdbpg+vXRO1iIj7LC1pzzAgIQRLLU5WVVtcE62pD2+DUXqCNXcJCgpi9OjRtG7dGrvdzrJly/j999/dFo+I1C4leEU8QIcOHXjiiSd4/fXXGTBgAF9++SUPPfRQpdtbrVZef/112rRpw5gxYzhy5Ei5bex2O4888gj9+/dn/PjxJCQk8NxzzwGONyJTpkxh2rRpdOnShUceeQSLxcLrr7/Ob7/9xsCBA3nyySd59NFHa+02i4hI1WRn2Th6uBgMiE30rbFxg0MtePsY2IohM9128h1ERE6R3TTJKrCxL6uALYdz+XFvNou3Z/DZpqO8s+4Qvx50JEYviPes9gxOHaIc7dT2ZhaSka+rIhqCuu6/Wxmr1crgwYPp2rUr4JjvZdeuXW6NSURqh2GapkoyxGOlpqaW6/8Kjp5HISH1v7+Vt7d3hfGLnA6dV1LTdE6dvtr8u7RpfR67thbQuJmVs/sG1ejYa7/P4WBKEe06+5HUzq/GxjUMg6ZNm3LgwAH0VlRqgs6p+qXQZicz30Z2gY3MAhtZ+cVkFdjIKrCRmW8r+X/Jsnwb2YU2TnahQKfGATwzqGXd3IASNXle3fn1LnZnFnB/32b0bln/P6fIqSsqKuLNN9/ENE2uvfZagoODXevc9VplmiaLFy/mzz//xGq1MnLkSJo1a1Znx5fq8/b2Jioqyt1hiAfRjBkiIiIiHspWbLJvl6M9Q2xSzVXvOkVGeXEwpYijqcUktavx4UU8Xm6Ro7o9wNvLzZHUjX2ZBaRkFTqStsclbksnb/OLT21y3UBvC8G+XoT6eRHi60WIr5UQXy/C/L08fnKyjo392Z1ZwOZDuUrwnuEOHTqEaZoEBgaWSe66k2EYDBo0iIKCApKTk5k3bx6XX345jRo1cndoIlJDlOAVERER8VD79xZSVGTiH2ghuknNv61z9eFNdfThran+viKezDRNfjuUy6LtGfy4N5swPyuvXhxPoM+ZneT9PTWXhxbvoao1h14GjiStn7UkWev4cSRvraX+79gm2McLb68z9zWmQ+MA5m/NYNPhPHeHIrXM2Z7Bnf13K+Ll5cXQoUOZM2cOBw4cYO7cuVxxxRUeceWriJycErwiIiIiHip5e0n1bqIPRi1MPBQS6oXVG4qLICvdRlik3jpKw5VVYGPZzgwWbctkf3aha/mR3GK+/jOdsZ3O7Eq4jzcewQSaBHnTPMSHkOMStcG+XoT6Wl3/D/S21MrrkqfqEO2YaG13RgFZ+cWE+On19ExVHyZYq4y3tzfDhw/n888/5+jRo3z55ZdcccUVBAQEuDs0ETlN+qsiIiIi4oEy04vJSLNhWKBlvE+tHMOwGERGWTm0v5ijqcVK8EqDY5omW1LzWLQtgzV7sikqaRTrZ7XQPz6ERgFWPtp4hLl/pHFJm/Aztop386Fcfj2Ui9UCTw9sSXSQt7tDqnWmaUL6EcwamiQrzM9Ki1Af9mYWsjk1j/Na1I9L96VmmaZZbyZYq4yfnx8jRoxg9uzZZGZmMnfuXEaNGoWvb823ehKRumNxdwAiIiIiUn3O6t2mMd74+tXeW7rIKEdS92iqZn6XhuNYgY15f6Rxx/xdPPztHlYmZ1FkN0mM8OW2c5rw/qgkbjm7CaPaR9Ii1IecQjvz/kx3d9i15pPfjgAwMCGsQSR3AcyfVmK7/zrSXngE026rkTE7llTxbjqUWyPjSf2TmZlJfn4+FoulXk+QFRQUxGWXXYa/vz+pqanMnz+f4mL9nRfxZErwioiIiHiYoiKTlD21N7laac4+vEdTizFPNs29iAczTZM/UvP4xw/7ufbL7byz7jB7Mwvx9TK4MDGUl4bEMn1oPBclheHv7fgY5WUxGNvR0Zrhqz/SOFZYM4nA+qR09e4VHSPdHU6dMf+3CoDcVYuxf/Aapv3UJo4rrWNjR4J382EleM9Uzurdxo0bY7XW76tewsLCGDFiBN7e3uzbt49FixZhr4HzXETco36/4oicBrvdjsWi7zBERMS9auPDUsruQmzFEBRsITKqdi8JDwn7qw9vZoaNsAi9fZQzS06hjRW7sli0PYPdGQWu5XFhvgxuFUa/uJATtl7oHRvMrE2OS++//iOdcZ3PrF68/y2p3h2UGEZUYAOp3i0qhD9/++v31UvA1w/G3XhafYWdfXiT0wvILrAR7HtmtvRoyJz9d+tre4bjRUdHc8kllzB37lx27NjB8uXLGTBggPpni3ggZb/kjBQQEEB2dra+gRQREbey2+1kZ2fX6OQlpmmSvN2RhIpN8q31D2EWi0FEI7VpkDOLaZpsO5rH//14gGu/2M5bPx9id0YBPl4GAxJCeWFwLDOGxTGs9cn76loMg3Gdzswq3k2HcvmtpHp3dIeGU73Lti1QWAChEURMeQoMA3PZ15hzPjqtYcP9rTQP8cEEtqiK94zkrOCtjxOsVaZFixYMGTIEwzDYvHkzP/zwg7tDEpFToBIMOSNZrVYCAwM5duyYu0M5IR8fHwoLC0++oUg16LySmqZz6vQEBgbW6GWa6UdtZGfasXhB87i6qaaLjLJy+IBjorXENnVySJFakVtkY1VyFou2ZbAz/a9q3ZahPgxuFUb/+FCCTmGitF4tg4kN9WV3ZgHz/kjjys71t/dmdTTE6l0Ac/MvABgdzyJwwMVkHD6E/cPXMRfMxu7rh2XYFac8dofoAPZlFbLpcC7naKK1M0phYSFHjx4FPKeC1ykpKYkLLriAZcuW8fPPP+Pv70+3bt3cHZaIVIMSvHLGslqthISEuDuMShmGQdOmTTlw4IBjll6RGqDzSmqazqn6Z3dJ9W5MSx98fOrmYixnH960VBumaerSTfE4O9Py+WZbBiuTs8gvdlzh5W0x6N0ymMGtwmgX5X9a57XFMBjbOZIXvtvPvD/SGd424pQSxfXJb4dy2NQQq3cBc1NJgrdTdwAs/YZi5uVhfvYvzC8/xO7rj2XgJac0dsfGASzanqGJ1s5Ahw4dwjRNgoODCQoKcnc41daxY0fy8vL44Ycf+O677/D396dt27buDktEqkgJXhEREREPUVhgZ//eIgBiE33q7Lih4V54WaGo0CQrw05ouGcnrqRhyC+2812yo7futqP5ruUxIT4MTgrjgoRQQmqwB+p5LYKJDfNld0YBX/2RxlUeXsX7ya+O6t0LG1r1bloq7N8DhgWjXVfXcsvgy7AX5GHO+wTzk7ew+/lh6T2o2uM7J1rblV7AsUKbx38RIH/xxPYMx+vRowd5eXls2LCBJUuW4OfnR1xcnLvDEpEqUA9eEREREQ+xd1chdrsj4RoWUXdJAfXhFU+SnJ7PzLUHufaL7bz200G2Hc3HaoG+scE8M6gF/7wknhHtImo0uQvOXryOStd5f6RzrMBze/H+diiHTYfzsFoMLm+g1bsktMYILFuFaQy/EuPCEY7tPngN+9rvqz1+hL+VZsHemMDvh/NON1ypRzxtgrWKGIZB3759adOmDXa7nQULFrgS1yJSvynBKyIiIuIBTNNk9w5HL+TYRJ86b5MQGaUEr9RfBcV2vt50gPu/SeauBcks2JpBbpGdpsHeTOgWxXuXJXFvnxg6NQ6s1efOuS2CiQvzJbfIztw/0mrtOLXtr+rd0AZVvQtl++8ezzAMjCuuwzh/MJh2zHdfxty4ttrHcFbxbtJEa2cM0zTPiApecJzngwYNIi4ujuLiYr766itXb2ERqb+U4BURERHxAEcOF5NzzI7V6ui/W9f+6sNbrH7MUq+YpskTS/fw1MLf+eNIHl4G9G4ZzNSBLXh9eAKj2kcS6lc3nekcVbyNAEcVb7YHVvH+evCv6t3RHRtY9W5xMfy+EQCjQ/cKtzEMA2P8JIxz+oHNhv3N5zFL9qmqDtElCV714T1jpKenU1BQgJeXF40aNXJ3OKfNy8uLoUOH0rRpUwoKCpgzZw5ZWVnuDktETkAJXhEREREPsHu7o3q3eZwPVu+6n+QsLNwLLy8oLDDJzrTX+fFFKvPboVy2pObh523hmq5RvHtZEvf3jaFLk0AsbpgQ8JwWQcSF+ZJXbOcrD6viNU2TT35zVO9elBRKo4CGVb3Lzj8hLxeCQiA2sdLNDIsXxrWToeu5UFyE/Z/PYu74o8qHcVbw7kzPJ6fQ874EkPKc7RkaN26Ml9eZ0VfZ29ub4cOHExERQU5ODnPmzCE3V19KiNRXSvCKiIiI1HP5eXYOpjgnV/N1SwwWL4Nw9eGVemjhtgwALu7QlCs6NiLc373zSFsMg3GdPbOK97dDuWxuoL13oVR7hvbdMCwn/qhseHlhuek+aN8NCvKx/+MpzD07qnScRgHeNAnyxm7C76nqw3smOFPaMxzPz8+PkSNHEhwcTEZGBnPnzqWgoMDdYYlIBZTgFREREann9uwsxDQhvJEXIWHuqwxSH16pb9LyivlpbzYAo7vGuDmav5zTPIj4cEcV79zfPaOK1zRN/lvSe3dwQ6zepdQEaxX0362I4e2N5daHoVV7yMvB/soTmPv3VGlfZxXvZvXhPSM4E7yePMFaZYKCghg5ciT+/v6kpqYyf/58iov1PkCkvlGCV0RERKQeM+0mu3c6qmXi3FS96+Tsw3v0sPrwSv3w7fYMbCa0j/InKSrI3eG4lOnF+2c6WR5QxetsddFgq3ez0qGkAtfo0LXK+xm+vljueBxik+BYFvZXHsdMPXjS/TqW9OH9TX14PV5BQQFpaY4vcs60Cl6n8PBwRowYgbe3N/v27WPRokXY7WrXJFKfKMErIiIiUo8dOlBMfq6Jt49B0xburagLi/DCUtKH91i2PtiJe9nsJotK2jMMbR3u3mAq4KzizfeAKt7jq3cjG2L17uYNjv+0TMQIqd75ZPgHYJn8JMTEQkYa9pcfxUw7csJ9nBW8O9LyyS2q/18ASOWc/XdDQkIICAhwczS1Jzo6mksuuQSLxcKOHTtYsWKFvuwVqUeU4BURERGpx3bvcFTvtoz3wcur7ieMKs3LyyA88q8qXhF3WptyjKN5xYT6etGrZbC7wynHMAyuLKni/frPdLLy6+9z5teS6l3vBlq9C0BJewajiu0ZjmcEhWC5eypEN4Ojh7G/8hhmVkal20cFetO4pA/vH+rD69GcCd4ztXq3tBYtWjBkyBAMw2DTpk38+OOP7g5JREoowSsiIiJST+Ues3H4gCMp1DLRx83ROKgPr9QXzsnVBiWG4u1VPz/WnN08iARnFe8f6e4Op0KmafJJSfXuRa3CGmb1rt2GuaUkwdvh1BK8AEZoOJZ7noaIKDiY4ujJm3Os0u07lLRp2KQ2DR7tTO6/W5GkpCQuuOACANauXcuGDRvcG5CIAErwioiIiNRbu3cWAtCosZWgYPdNrlZaZLQjDvXhFXfan1XIhgM5GMDgVmHuDqdShmEwrnP9ruLdeLBU9W77CHeH4x67d8CxbPAPhMS2pzWUERmFZcrTEBoO+3Zh/8eTmPkVJ3A7RvsDsOmwKng9lWmaDaqC16ljx46cd955AKxatYo//vjDzRGJiBK8IiIiIvWQ3WaypyTBG5dUP6p3AcIjrVgsUJBvknNMfXjFPRZtzwDgrGaBNA6qP8+PipwdE0RihKOKd04968Vrmiaf/FbSe7eBVu8CmCXtGWjXBcPr9L9MM6KbOdo1BAbDrq3YX3sWs7Cg3HbOPrzbj+aRX6zXU0+UlpZGYWEhVquVRo0auTucOtWjRw+6du0KwJIlS0hOTnZrPCINnRK8IiIiIvXQgZQiCgtMfP0MGjerP0kXLy+DsMi/qnhF6lpBsZ2lOzIAGNqq/k2udjzDMBhX0ot3/tZ0MutRFe/Gg7n8npqHj5fBqIZavQuYm0+v/25FjJhYLHc/Bf4B8Odv2N94HrO4qMw2jYN8iAqwYlMfXo/lbM/QuHFjLJaGlV4xDIO+ffvSpk0b7HY7CxYscN0fIlL3GtYrkIiIiIiH2L3DUb3bMsEHi8W9k6sdT314xZ1W78kmu9BOdKCVs5oFujucKukZE0RihB/5xWa9qeI1TZP/lvTeHZzUgKt3c7Jh51YAjA7danRsIzYJyx2Pg48PbFqH/e2XMW22Mts4q3h/Ux9ej9QQ2zOUZhgGgwYNIjY2luLiYr766iuOHj3q7rBEGiQleEVERETqmewsm6M61oDYRF93h1NOo+iSBK/68IobLNzqmKxscFI4XvXsy4/KGIbBlSVVvAvqSRXvxoO5/HGkpHq3Q6S7w3Ebc8tGMO3QrCVGRFSNj2+0ao/ltkfAaoVf1mB+8Cqm/a92DM4E7+bDSvB6ooY2wVpFvLy8GDZsGE2aNKGgoIA5c+aQlZXl7rBEGhwleEVERETqGWf1buOmVvwD6t/btbBIK4YF8vNMcnPUN1Lqzs60fLYezcdqgUFJoe4Op1p6xASSVE+qeI+v3o3wt7o1HrfavA6o2fYMxzPad8Ny8/1gsWD+sBzzv2+5vhzrEO1I8G47mkeB+vB6lPz8fNLTHV84NeQEL4C3tzeXXnopERER5OTkMGfOHHJz9aWFSF2qf58YRERERBqw4mKTfbscCd7YpPpXvQtgtRqERagPr9S9hdscyZTzWgQT5udZSUnDMLiyc0kv3j/TyXBjFe8GVe8CjkS3uWk9AEaH2kvwAhhdz8W47m4wDMwVCzA//wDTNGkS5E1kgJViO/xxRH14PYmzPUNYWBgBAQFujsb9/Pz8GDlyJMHBwWRkZPDVV19RWFjo7rBEGgwleEVERETqkQN7CykqMvEPtBDdpP4msFx9eJXglTqSU2hj5S7HZb9DW9f/ydUq0r1ZIK0i/SiwmczZ4p4q3jLVu60aePVuSjJkpoGPL7RqX+uHs5zTD+Oa2wAwF32BOf9TDMOgY0kV7yb14fUozgRvQ6/eLS0oKIiRI0fi7+/P4cOH+frrryku1vsEkbqgBK+IiIhIPZK8vaR6N9EHw6i//UVdfXg10ZrUkeW7MimwmbQM9aF9lL+7wzklhmEwrlQvXndU8W44mMufzurd9g23ehfA3PSL4z9tOmF4+9TJMS19L8IYe73j+HP/g33JXFcfXiV4PYuz/25DnWCtMuHh4YwYMQJvb2/27dvH8uXL3R2SSIOgBK+IiIhIPZGZXkxGmg3DAi3j6ybZcKrCG1kxDMjLNcnNsZ18B5HTYJomC7dmADCkVXi9/vLjZEpX8X5Zx1W8jurdVACGNPTqXf5K8NZm/92KWAaNwBgx3hHDrHdpv8cRx9aj+erD6yHsdnu1K3hNux3b2y9x9IVHyky0dyaKjo7mkksuAeD3338nLc29fcdFGgIleEVERERKZKQVk7KnkOxMG3a7WefHd1bvNo3xxtevfr9NK9uHVwleqV2bD+exL6sQP6vBBQkh7g7ntBiGwZWlq3jz6q6Kd/2BHP48kq/qXcDMz4XtvwN1n+AFMC4egzF4FABNPvkH4V42iu0mf6oPr0dIS0ujqKgIb29vIiOr+Fza+D/Mn1aSu3IR7PijdgOsB1q0aEFCQgIAv/zyS50dNzO/mH+vP8xP+7Lr7Jgi9UHD/spWREREpERxkcma5cewleRaLBYICvEiJMxCSJgXIaFehIR51VritajIJGVP/Z5c7XiRUVbSj9o4eriYFvW84lg824KtjsnV+sWFEuDt5eZoTt9ZzQJpHenH1qP5fPl7GteeFV3rxyzde3doqzDCG3j1Ln/8CrZiiGqCEd2szg9vGAZcPgEK8mHFAjoe+I3voruy+XAunZsE1nk8Uj3O9gxNmjTBYjn5+wLTNLEv/Mz1u/3n77Ektau1+OqL7t27s3PnTv744w/OOeccgoODa+1YGXnFfPl7Ggu3plNgM4kL86VnTBAWD77iQ6Q6GvhfdRERERGHjLRibMWOxK5hcXzuz8qwkZVhA4pc2/n6GQSXJHsdSV8LQSFeeHmd3geIlORCbMUQFGwhMsozEliR0Va2/1GgPrxSq9Lzivlxr6MSa2jrMPcGU0MMw+DKzo14avk+FmxNZ2S7iFpPuK4/kMPWo47q3csaePUulG7P0N1tMRiGAVfeBAV5tE/ewXfRXdm06zB0jnJbTFI1pRO8VbJ1E+za6vrVXLcac8z1GFVIDnuypk2bEhMTQ0pKCuvXr+f888+v8WMczS3iy9/TWLQtg0Kb4+qrpAg/xnaKRKldaUiU4BUREREBMtIcbQYaN/Ome68A8nLsZGXaXUnerEwbOdl2CvJNCvKLOXLor6SmYUBgcNlK35AwL/z8jSr1CjVNk+QdBYCjetdT+otGlPThzc2xk5drxz/gzP6gKu7x7Y4MbCa0aeRPfLifu8OpMd2aBtKmkR9/Hsnnyy1Hua5741o7lqp3yzJN0239d49nWCww4U46vvMGAH9m2Sn4cwu+bdq7NS45MWf/3apOsGZf4KjeNfpcCL+swcxIc7RpaHXmP849evQgJSWFTZs20bNnT/z9a2aSzCO5RXyx+SiLt2dSVNJWq3WkH+M6NeKsZoEe815KpKY07L/sIiIiIiWcCd6wSC8MwyAgyIuAIC+axHi7tikuNjmW6Uj2OpK+jgRwUaHJsSw7x7Ls7C9V7evtbRAcZimT9A0O9cJqLfuhI/2ojexMOxYvaB7njaewehuEhnuRkeZo09A8Tm0apGbZ7CaLtmUAMOwMqd51MgyDcZ0cVbwLt2VwWfvIWku8/rL/r+rdht57F4BDKXD0MFit0KaTu6PB8PKi+XU3EfbJZjIs/vz573/R6dZbMWJi3R2aVCAvL4+MjAygahW85u4dsGU9WCxYLh6Dr483ucsWYK5bjdEAErwtW7YkKiqK1NRUNm7cyLnnnnta4x0+VsTnW46yZEcmxSWJ3XZR/ozr1IguTQKU2JUGSwleERERERwtGgDXxGEVsVoNwiKthEX+9RbKNE3y80yyMm1kl6r2PZZlp6jIJC3VRlpq2UnIAoMsBJdq8bBvtyMpHNPSBx8fz6qCjYyyKsErtebn/cc4kltMsK8XvVrWXu9Gd3FU8frz55E8Pt9ylBtqoYrXNE3++5ujendY63DCGnj1LvzVnoFWHTB860dVuMXbhw4tG7F6Xw6b/WPoMOc/eN32sLvDkgo4q3fDw8Px8zv5+WN+8zkARs++GFFNCOh7oSvB2xDaNBiGQffu3fnmm2/49ddfOeuss/Dxqf77hYPZhXy2+SjLdmZS0omBjo0DGNsxkk6NldgV0V93ERERafAK8u3k5To+LYSFV+/tkWEY+AcY+AdYaNz0r+pbm81R1etK/JZU/Rbkm+Qcs5NzzM7BfUVlxopN9LwEaWS0lR1/qg+v1I5vtmYAMCghFB+vMy8J4uzF++SyvSzalsGo9pFE1HAC9pf9OWxz9d6NqNGxPZW5uX60Zzhep6ZBjgRvWDxsfAfz8AGM6Kq1AJC64+y/W5X2DOah/Zjr1gBgDLkcAL9u54B/ADSgNg1JSUmEhoaSmZnJ5s2b6datW5X33Z9VyOzNR1ixK4uSgl26NAlgbMdGdGgcUEsRi3geJXhFRESkwXO2ZwgKsWD1rpkKEC8vR/uC0PCyFcEF+Xayj2vxkJ1lI6qx9YTVw/VVRCMrGJBzzE5+nh0//zMvCSfucSC7kPUHcgAY3CrMvcHUoq5NAmjbyJ8/juTxxeaj3NCj5qp4y1Xv+unjn1lYAH9uAsDo4L4J1iriTFb9GRZPERZ8ln2NMe5GN0clx6vOBGvmoi/AtEPnnhjN4wAwvH0wup6L+cOyBtOmwWKx0L17d5YtW8b69evp3LkzXl4nfs+zL7OA2ZuOsmr3X4nds5oGMqZTJO2ilNgVOZ7egYuIiEiDl37UUX0aHlH7yQ9fPwuNGnuT0MaPrmcHcP5FwQy7PJSz+wZ55OWF3j4GoWGOD2lHD6uKV2rOom0ZmDg+0DcN9rzq9qpyVvECLNqeQVpezT2P1pVU7/qqevcvWzdDUSGEN4JmLdwdTRktQnwI9fWi0LCyPaQF5vdLMHNz3B2WlGK32zl06BBw8gpeM+Mo5g/LALAMvbzMOqNHb8c261Zj2u21EGn907ZtWwIDAzl27Bh//vlnpdvtySjgpe9TuP3rXaxIdiR3e8YE8uLgWJ4Y0ELJXZFKKMErIiIiDZ5rgjU3VdB6YmK3tMgoR2L8iBK8UkMKbXaW7MwEYMgZNrlaRbo0CaBdlD+FNpPPNx+tkTFN0+QTVe+WU7o9Q3177TUMw1XFu6FFDyjIw1y9xM1RSWlHjhyhuLgYHx8fIiJO/KWJueQrKC6GpPYYSWWrdI323cq2aWgArFarqzXDunXrsB+X2N6Vns/zq1K4Y/4uvtudjQmc0zyI6UPjeLR/C1o38ndD1CKeQwleERERadBM0/wrwRvpeS0S6oPIaEfiSH14paas2ZNNdoGNRgFWejQLcnc4tc4wDMZ1Kqni3ZbB0dyik+xxcqWrd0eqetfFOcGa0aF+9d91Ore543xf0fgs7BiYS+dh2m0n2UvqinOCtSZNmpzwCwIz5xjmim+A8tW7AIa3N0bXcxzb/vx9LURaP3Xs2BFfX1/S09PZuXMnANuP5vPcyn1MXpDMD3uzAejVMpgZw+J4uF9zEiPqx0SIIvWdErwiIiLSoOXm2CkqNLFYICRUCd5TERHluN9ysh19eEVO14KSydUGtwrDy1K/qixrS5cmAbSP8qfIbvL5lrTTGss0Tf77q6p3j2ceOQQH94HFAu06uzucCp3bIphAbwupNiubmnaEo4dh/U/uDktKVLX/rrliARTkQUwsdOpR4TZG9z6ObdetaTBtGnx8fOjc2fHc+/7H//H0sj1M+SaZn/YdwwD6xgbz6sXxPNA3hvhwJXZFqkMJXhEREWnQnNW7IWFeWLwaRiKppvn4WAgJc7ytVBWvnK5d6fn8eSQPLwMuTAxzdzh1xjAMxpX04l18mlW8P6fksD1NvXePZ25e7/hPQluMgPpZGe5rtXB+XAgAy9oPA8C+5Ct3hiSlOBO8J+q/axYUYC6dB4AxdHTllb7tu4J/IGSmwfbfazrUeiugeWtMw4ustCPs2LMXiwH940J47ZJ47u0TQ2yYr7tDFPFISvCKiIhIg5Zx1L39d88Uzj68mmhNTtfCkurdc1sEE+7fsCpPOzcuVcV7ir14TdPkvyW9dy9uE06oqnddzE3rAEf/3fpsYGIoAD+YkRzzDYLtWzCTt7k5KsnNzSUrKws4cQWvufpbyM6ERo0xevSpdDtHm4azHfusW12zwdZDmw/l8tjSPTy+KpV9vjEAdGMv/7wkgbt7N6N5aM0ldk3TrLGxRDyFErwiIiLSoGWkORKSYRFKgpwO9eGVmpBbZGNlsmNytaENYHK14xmGwZUlVbyLtmeeUhXvzyk57EjLx89qMLKdqnedzOIi+P1XAIyO3d0czYklRfgRG+ZLkR1W93D0bzVVxet2zurdiIgIfH0rTkaaxcWYi+cAYAy+DMPrxF8eOxPAZ2qbBtM0+fVgDo98u5uHl+zh14O5eBnQsk1HDMOCkXUYr7z0mj1mdhb2vz/g6rct0lAowSsiIiINlt1ukpmuCdZqQkRJBe+xLDsF+Wfeh1SpG8t3ZpFfbNI8xIeO0QHuDsctOjUOoEO0P8V2k8+qWcVbunp3WGtV75ax4w9HT9TgUGgR7+5oTsgwDAaVVPEuDe8IOCbiMtNPrapbaoZzgrUTtmf4+TtH3+TgUIxeA08+6BncpiG3yMbjS/fy2NK9bDqch9UCQ1qF8ealidzRL4k2bVoD8PPPP9fYMc3MdOwvPQw7/sD+4T8xi05/wkoRT6EEr4iIiDRYx7Ls2GxgtUJQsN4WnQ5fXwvBoerDK6fONE2+2eao5BraOuyEM9SfyQzDYFynkl682zM5Uo0q3rUpx1zVu5epercMZzWf0eEsDEv9f73vFxeClwHbj5nsbt8bbDbHxF3iNiebYM202zEXfg6AMehSDJ+TtxwwrN4YXc9x7P/z9zUUaf3w1e/p/HooF2+LwcWtHYndW85uQnSQNwDduzsq6Xfs2EFa2ulNLAlgpqVif+Eh2L8HQiOwTH4Sw9v7tMcV8RT1/y+biIiISC1xtmcIjbA22GRSTVIfXjkdW1Lz2JNZiK+XwQXxoe4Ox606Nwmko7OKd1PVqjZN0+QTZ+/d1uGEqHq3DNfl2vW8/65TqJ+Vs5s7JoJb3nYwAObKbzALCtwZVoNls9k4fPgwcIIK3t9+diQX/fwx+g+t8thGj94AmL/8gGm3nW6o9UJOoY2v/nQkbSf3aspNPZsQFVg22RoZGUlCQgIAv/xyeu0UzNSDjuTu4f0QEYXl/ucwmrY4rTFFPI0SvCIiItJgpZdMsBauCdZqhPrwyulYuNVRvXt+XAiBPnpOjivpxfvtjkxSc05exfu/lGPsSCtQ790KmBlHYd8uMAyM9l3dHU6VDUoMA2BFTiBF0c0gJxvzx+XuDaqBOnLkCMXFxfj6+hIeHl5uvWma2Bd+BoDRfxhGQFDVBz8D2zR8/Wc6OYV2WoT60KtlcKXbOat4//jjD7Kzs0/pWOaBfdhfeNDRGiO6KZb7n8eIbnZKY4l4MiV4RUREpMHKSHMkeEOV4K0Rzgre7Ew7hQXqwytVl5FXzA97HR/uh7YunzxpiDo1DqRj4wCK7Safn6QXr2mafPKrqncrY27e4PhPbBJGsOdUh3drGki4v5WsAhvrzhsDOCZbOxMn46rvnP13mzRpUvEVP9u2OPo8W70xBg6v1thl2zSsPu1Y3S23yMZXfziqd8d0bITlBFdINW3alJiYGOx2O+vXr6/2scx9u7C/+BBkpEHTFljum4YRGXXKsYt4MiV4RUREpEGyFZtkZ5ZU8EYqGVITfP0sBIWoD69U35IdmRTboXWkH4kRfu4Op964spOzijfjhFW8/0s5xs70AvysFlXvVmRzSf9dD2nP4ORlMRgQHwLAMv8E8POHg/tgS/UTYXJ6nP13K2vP4Kre7TUQI6z6z0GjZx8AzF/WeHybhq//TOdYoZ3mIT70PkH1rlOPHj0A2LRpE3l5eVU+jpm8DfuLj0B2JrSIx3Lfc6d034ucKZTgFRERkQYpM8OGaYKvn4Gfv/rv1hT14ZXqstlLT66m6t3SOjYOoFPjAIrt8FklVbylq3cvaaPq3eOZdhvmZkdC1OjgWQlegIElbRp+OZRPeu9LALAv+cqNETVMJ5pgzdy7CzatA8OCMfiyUztAuy4QEAiZ6R7dpiG3yMZXvzuqd8d2aoSX5eTvr1q2bElUVBTFxcVs3LixSscxt2/BPv0xyD0G8a2xTHnWo6rzRWqDErwiIiLSIDnbM4RFeGmCtRr0Vx9ez65Akrrzy/4cUnOLCfaxVKnaq6FxVvEuqaSK93/7/qreHaHq3fJ2bXMkgQICIb61u6OptpgQH9pF+WM3YWVCXzAssHk95v497g6twcjJySE7OxvDMGjcuHG59eY3nwOOydKM6EomYDsJR5uGcx3j/fz9qQfrZvP/TCe7GtW7AIZhuKp4f/31VwoLC0+4vfn7RuyvPAF5udC6I5Z7pmIEVqPnscgZSgleERERaZAyjjoqTMMiVO1Wk5wVvFkZNgoL1SdSTm5hSfXuwMQwfK36eHK8Do0D6FxSxTt7U9kqXtM0+e9vpap3fdVP/Himsz1Du64YXp55/wxKdFQmLjlox+xW0qt16Tx3htSgOKt3IyMj8fX1LbPOTD2IudaRkDWGXH5axzF69HaM+csPHtmmIbfIxtyS6t0rOkZWqXrXKTExkdDQUPLz89m8eXOl25m/rsX+6lQoLID23bDc+QSGX8Bpxy5yJtA7KBEREWmQSlfwSs3x87cQGOx4i5mmKl45iUPHCvllfw4Ag5PC3BtMPTaus6OKd+nODA4f+6uK96d9x9iVXoC/qncrZW5yJHjxsP67pfVqGYyf1WB/diFbz74UAPOH5ZjZWW6OrGE4YXuGRV+AaYeOZ2G0TDi9A3l4m4YFWzPILrTTLNiHvrEh1drXYrHQvXt3AH755RdstvLvH8x1a7C/Pg2Ki6DrOVhufxTjuIS7SEOmBK+IiIg0OIWFdnKOOapLleCteerDK1X1zbYMTKBrkwCahfi4O5x6q0N0AJ2blO3Fa5omn6h694TM7CxI3gZ4Zv9dpwBvL3q3dCTMlhRGQGwSFBVirvrGzZE1DAcPHgTKT7BmZqZjrl4KgGXo6NM+jie3acgrsjOnpHp3TDWrd53atm1LYGAgOTk5/PHHH2XW2X9cjn3mC2ArxujZF8vND2B4e9dI7CJnCiV4RUREpMHJLKneDQiy4OOrt0M17a8+vErwSuWKbHaW7MgENLlaVZTuxXv4WBE/qnr3pMwt68E0ISYWIzzS3eGcFmebhtV7ssgfMAIAc/kCzOLyfZml5thsNg4fPgxUkOBd+pWjmjSxLbTqUCPH89Q2DQu3ppNdYKNpsDfnx1WvetfJarXSrVs3wFHFa7c7voi3r1qE+d4MMO0YvQZi3HAPhlXttUSOp080IiIi0uCkqz1DrXJW8GZm2CgqNN0cjdRXa/Zkk1VgIzLASs8YTZBzMu2jA+jSJACbCZ9uOsKskurd4W3DCVb1bsWc/Xc7dndzIKevXZQ/zYK9yS82+aFRRwiNgMw0j6v09DSpqanYbDb8/PwIDQ11LTdzczBXLATAMuTympustXSbhm2e0aYhv7h09W6jU6rederYsSO+vr6kp6ezc+dO7Eu+wvzwn2CaGP2HYUy4A8Oi1zuRiijBKyIiIg1ORppzgjV9SKgN/gEWAoMsYELaEVXxSsUWbssA4KL/Z+++4+Oo7/yPv76zRavem1XdZVuSbWxjum16C4SEhAu5O0IO7nIh/Xe54wIpXOolIblLQu6SXLhAcgkEAoEkgCkGgzHgXiRXSbZ673XbfH9/jFa2wd2SZlb6PB8PHljS7s5n7d3VzGc+8/7OSTmnhsB0Epnifam6d2x696YSmd49Hm2a6MrtAKgozt+NUEpxxawUAF45PIBacz0A+qVn0VpOpE2Uo/N3j27i6vXPw/AQ5BZA+Ypx294xMQ1bo6N5//yBbnr9YXISPKw6y+ndCK/XS3l5OQBbXnkJ8/H/AUBdfQvq9n9AGdLCEuJE5N0hhBBCiGmnpzMywSuX+E2UsRxeiWkQx3G4e4S97cMYCq6anXzqOwgAFmTFsSTnyIrxMr17Eg2HoK8HYnwwZ4Hd1YyLNbOSMBTsaR+m6bwrweOFumo4uMfu0qasSIP36HgGHfCjX34WAHXdrePedFTLL7G2s3Wj42MaRkImT59j9u67LV68GLeCNn+QhvhU1Pv+CnXrx8ZvSlqIKUoavEIIIYSYVoaHTPwjGqUgOVUaIxMlLUsWWhMn9sLo9O4FBYmkx8lCOadLjwzzV75WAOIMU6Z3T0JXWPEMlJSj3FPjNZYe52FpbjwAr7aGUReuAcB8+Rk7y5rSjrfAmt64zjp5kJaJWnHp+G90QbkV09DX4/iYhhcOdtM7Yk3vrp557ifrtNb4/vQ7FrTXAbCt9CKMm26X5q4Qp0EavEIIIYSYViLxDIlJBm63HDBMlLEc3u4woaBcPiyOGAqGefVQHwDXzU2xt5gooNtbMF/5M+H/+Crm5z/KvF99nQd2/IxvbP4RCX3tdpfnWHosfzf64xmOFllsbV1NL+aaG61v7ngH3d5iY1VTU39/PwMDAyilyM7OBkCHw+i1TwGjsQETsNiXcntQS0djGhycsewPmTy1x5re/dA4TO9q00T/33+hX36GJR11GEDDkJ/W1tZxqFaIqU8avEIIIYSYVnoiC6ylSzzDRIqLN4iLN9CSwyveZf2hPkZCJnlJXsqy4059h2lGh8PoA5WYT/6K8FfuwfzS36Mf+zlUbodQCDJzKPMOUdzfhH7xabvLdSQ9NAjV+wBQi6ZWg3dFXiJJMS66hkPsUOmwaClojV73Z7tLm3Ii07sZGRl4PNYUuN6yATpaISEJdclVE7ZttWw0pmGbc2MaXjjYQ+9ImOxxmN7V4TD6Vz9Cr38BlCL59ruZV1ICwJYtW8ajXCGmPDmyEUIIIcQJNdUHqN7XzMy5GjVFTguPNXhlgbUJl57pZmgwQGd7iKzcqXGJtDg3WuuxxdWum5sil92O0oMD6IqtsGuL9f+hgSM/NAyYsxBVvgJVvgJy8uBAJeb3v4Te8DL6xr9CJafaV7wT7dsF4TBk56Eyc+yuZlx5XIpVM5P4075uXq7uZdmVN2NWbkdveAl90+2oWDlpMl6OXmANrM8v/cIfAFBXvA8VEzNxG19QDnEJozENe2B+2cRt6yz4QyZP7+kE4NZF6bjPYXpXh0LoX/7Aap4bBurjn8dYuYplnZ3s27eP6upqurq6SEuTSBohTkYavEIIIYQ4Lq01u7YMEfAPEuOLJ68o+ht0WuuxiAZp8E689CwX9Yclh1ccsa99mNoeP16XYs2s6bu4mtYaWhrQuzajd22Gqr1gmkduEJ+IKlsGZctRi85DxScce/95i2B2CVTvQ7/0DOrWj03uE3C4qRrPEHHlrGT+tK+bzY399K0oIyG3AJrr0W++hLryZrvLmzLes8BaxVZoOAwxsag1N0zotq2YhpXoN19Bb3kT5bAG74tVPXSPhMmKd7PmHKZ3dTCA+bPvws5N4HJj/P0XUeddCEB6ejqzZs2ipqaGbdu2ceWVV45X+UJMSVNkFkcIIYQQ462nK0zAb2WntrcGba5mfAz2m4SCYLggMVkavBMtksPb0xUmFJIcXgHPjU7vXlacRIJ3er0HdTCI3rMd87FfYN73D5hfuQf95K/gQKXV3M0rQl33QYx/+Q7GDx7F+LsvYJx/2XuauwBKKYzrPmQ97vrn0YMD77nNdKW1tqagmXrxDBHFqT5mp/kImfB6bT/qyvcBoF/5s2Mv5482oVCI9nYr4zrS4DWffxIAteqa474vx5ta7syYhkDY5A+j2bu3LsrA4zq76V3t92P+5BtWc9fjxbjnvrHmbsSyZcsA2LdvH/39/edWuBBTnEzwCiGEEOK42pqPTF22twTRWkf95dSReIbkFBfGOS4GIk4tNt4gNk4xPKTp7giRmRP9U+Di7PWOhNhYZx2gXztNFlfTvd3o3VvQu7dA5Q7wDx/5odsNJeVW9ELZclRG9pk9ePlyyCuCxlr0q39B3XjbuNYetZrroasD3B6YV2p3NRPmytnJVHeN8FJ1LzdcsRqe+rWVDbtjE7yrSSbOXHt7O6ZpEhsbS1JSErpqjxWV4HajrpqkKemSxY6MaXixqofu4RCZcW4uP8srMfTwEOaP/816XjE+jE/djyopf8/tcnNzycvLo7Gxke3bt3PZZZeda/lCTFkywSuEEEKI42prPjK1OzKsGeg3T3Lr6DAWzyALrE0KpdTYFG9nu8Q0THcvV/cSMjVz033MTY+1u5wJobVG11Zj/ukxwt/8f5j/dAf6kR/Dtres5m5yGurSqzHu+RLGD/8P12e/hrHmhjNv7mK9v9R1t1rbfeVPaP/IeD+dqKQrrHgG5pVObEaqzS4rSsJjKGp7/NQMglp1LQDmy8/YXNnUcHQ8g1IK84WnAFAXXo5KSZ+UGpTbjVp6ATC6uJsDBMImf6i0pnc/uCj9rKZ39WA/5g+/YjV3Y+MwPvfAcZu7EcuXLwegoqKC4eHhE95OiOlOGrxCCCGEeA+/3xybdk3LsA6Q21uiv0EnC6xNvvQsq8HbITm801rY1Kyt6gGm3vSu9o+gd7yD+ehPMP/5TsxvfB797G/h8EHrBsVzUTfdjnH/DzC++zDG334KteQClO/cm9xq+SWQmQMDfeg3Xjznx5sKpnr+bkRCjIsLCxIBeLm6B7XmenC54OAedG2VzdVFv6MXWNONtVaMgFKoq2+Z1DrU8osB0FudEdPwUlUvXcMhMuLcXDn7zKd3dX8v5vfvh0MHID4R4/99AzVnwUnvU1hYSGZmJqFQiJ07d55t6UJMedLgFUIIIcR7RJq5SSku5i5IHv1edOfwmmFNb7d1cJQqDd5JIzm8AmB78yCtA0HivQaXFiXZXc640KEQ5iM/xvzcRzEf+qbVYO3pghgfLL0AdcenMb73K1z3PYjxvr9CFc1BGeN7+KVcLtQ1H7DqefGP6FB0f06fK+0fgQMVAKjSZTZXM/GuGG2wvV7bRzAx9Uhm68vP2llW1NNa09LSAlgTvPqFPwCgzrsIlZM3ucVEYhr6e628bhsFwyZ/qOwE4NZF6XhcZ/Z5pns6Mb/3JWg4BEkpGF/8Fqpozinvp5Qam+LdtWsXgUDgzIsXYhqQBq8QQggh3iMSz5CV6ya/0FpIpLM9hBmO3gZdX28Y0wSPVxGXILtAkyUuwcAXq9Am9HTKFO909cLBbgAun5VMjDv6339aa/Tvfo7e8BKEgpCehbr8RozPPWBFL3zySxiXXIVKSZvwWtRFl0NyGnR3oN9ZP+Hbc7QDFRAKQXoWTHYjzgblOXFkxrkZDJi8XT8wlg2rN29A93TaXF306u/vZ3BwEMMwyHKB3vQ6AOq6D056LcfENGx9c9K3f7SXqnvpHA6RfhbTu7qzDfO7/2plZKdmYHzx26i8otO+/+zZs0lJSWFkZITKSnsb3UI4VfTvXQkhhBBiXGmtxyZ4s3I9pGfG4I1RhEPQ1Wn/5YFn6+h4hmhfLC6aSA6vaB0IsKVxEJg68Qz65WfRr78ASmF84l6Mb/8C4yN/j1q0FOWZ3MUElcd7pLH3/B8ccRm3XSL5u2rRedPic95QistHG22vVPdY05BzFkI4hH71eZuri16ReIaMjAxc6/4MpgkLl5zWtOlEcEJMQzBs8uTo9O4HF57Z9K5ua8L87r3Q3gIZ2dbk7hmegDEMg/POs2JXtm3bRjg8fT/nhDgRafAKIYQQ4hi9XWECfo3bA2kZbpRSZOZYDYuO1ui9/Ffyd+0jObzT24tVvWisacP8pOhf9Erv3IR+4mEA1Ic+jlp2ke3NRLXqGusy7tZG2P62rbXYaazBO8Xzd492xSyrwbuzZYi2gSDGVTcBoF9/Hh3w21la1IrEM+Skp1lT+oBx7eRP745xQEzDy9W9dA6FSIt1c9Wc05/e1U11mN/9EnR1QHaeNbmbmXNWNZSUlBAfH8/g4CD79u07q8cQYiqTBq8QQgghjtE2Or2bke3BMKymRWa21aCL5oXWerqs2lPS3DZXMv2M5fB2hglHccyHOHPBsOal6h4ArpsC07u6/hDmL74PWqMuuxZ15U12lwSA8sWhLr8RAPO5J9B6+r3PdFsztDVZC42VlNtdzqTJTvBSnh2HBtYd6oUlK62IioF+9Nuv2V1eVBpbYK2tHoIBmDnP1teU3TENwbAey9794KI0vKc5vas726zM3d4uyCvC+OdvodIyzroOt9vN0qVLAWuK1zTNs34sIaYiafAKIYQQ4hhj+bs5RxqhkQnenq4wAX/07VCHgpr+PqtumeCdfPGJBjE+hWlCdxTHfIgz91Z9P70jYdJi3Zyfn2h3OedE93Rh/vjr4B+BBYtRH/l72yd3j6auuNFa4K2uBiq3213OpNOR5zx7ASo2zt5iJtkVYzENvWhloK54HzAaJTINm/3nIhgM0tHRAUD2Nit717j2g7a/18cW0LMhpmFdTS/tQyFSY91cPSfltO6jtcZ89Ccw0AcFMzH+6ZuopNRzrqW0tJSYmBi6u7upqak558cTYiqRBq8QQgghxgT85lgDLiv3SI5kbJxBQpK12xCNl9n3dodBgy9W4YuV3Z/JdnQOb5fk8E4rkcXVrp6TjNtwTjP0TGm/H/Mn34DuDsjJx/jEv6DczroaQCUkoS69BgDz+Sdsrmby6crpF88QcWFBIvEeg7bBIBWtQ6hLrgJfrLWg1Z4ddpcXVdra2jBNkziXQWJft7VY35KVdpdlTRDHJ056TEMwrHmiwmp4f3DhGUzvvvmy9drzeDH+/p9RCUnjUo/X62Xx4sUAbNmyRU5gCHEUOcIRQgghxJhIBENiskFs3LG7CdEc0yDxDPaTHN7pp7bHT2XbMIbitKe+nEibJubDP4TaKkhIxPj0l1FxCXaXdVzqqpvB5YYDleiqvXaXM2l0MAj7dgHWAmvTTYzb4JIiq4H2SnUvKjYOdfGVAJgvP2NnaVFnLJ6hvxMFqGs/iDLsb5scE9OwZcOkbffVQ6PTuz7X6U/v9nSifz+aU37zR894QbVTKS8vx+1209bWRkNDw7g+thDRzP5PKiGEEEI4xlg8Q+57V4GPxDS0t4aibmJibIG1dIlnsEukwdvdGZIc3mkiMr17fn4C6XHv/UyJFvqPv4FtG8HtxvjkfaisXLtLOiGVloG66HIAzOeftLmaSVS1x4rOSE6Fgpl2V2OLK0djGjbW9zMYCFsxDUpBxTZ0c73N1UWPsQXWetshNQO1cpXNFR2hll0MgN72Fjo88TENIVPzRIWVvfuBRenEuE/dPtJaY/7mv2B4EGbOQ101/jnlcXFxLFq0CLCmeIUQFmnwCiGEEAKwdsojC6xl5b530jU9y40yYHjQZGggunJ4xxq8kr9rm4REA2+Mwgwf+fcQU9dw0OTVmj4Arpt77rmLdjHffAU92ihVd3waNXehzRWdmrrmA6AM2LUZ3XDI7nImha4YjWdYuNT2rFS7zE33UZjsJRDWvFHbh8rMgcVWtIB++U82VxcdtNZHJniHelFX34xyO+jk1DExDRUTvrlXa3ppGwyS4nNxzelO7256HXZuApcb447PoIyJ2e8677zzMAyD+vp6WltbJ2QbQkQbafAKIYQQArByagN+jdsNaRnvbfC63Wrs+9EU0+AfMRkaHF1gLVUiGuxydA5vZ1vQ5mrERFt/uJfhkMmMRA/lOdG54JU+UIH+9UMAqBs+jHHBGpsrOj0qewZq+eik3/N/sLmayRHJ32Ua5u9GKKW4cnYKAC9X9wJgjE5P6rfXoQf67CotavT19TE8PIxhmmQaGnXJ1XaXdIxjYhq2vjmh2wqZmicqR6d3F57m9G5fD/qxnwOgbrwNlVc4YfUlJiYyf/58QKZ4hYiQBq8QQgghAGhrtpq2GdkejBMshjSWw9saPQ3enm5rWjQ+0cDjnZ6TXU4hObzTg9aaFw72AHDt3FSMKJyo1G1NmD/9NoRDqGUXo2663e6Szoi69oMA6M0b0G1NNlczsXRXBzTWgjJQC5fYXY6tVs1MwqXgYOcIdT1+mLsICmdDIIB+fa3d5TleU5P1Xskc6cez5gaUL3bcHjtkarY2DvCjt5r44bqDBMJndyWUWn4JMPExDa8d6qV1IEiyz8W1c1NO6z76dz+HgX7Inzn2GTSRzjvPOqFTXV1NV1fXhG9PCKeTBq8QQgghgKPzd0885Rpp8Ha0BTHN6MhR7em0DoBSJZ7BdpEJ3u4OyeGdyvZ3jHCo24/Xpbh8VrLd5ZwxPTiA+aOvw2C/lSH58c85YpGlM6EKZ0HZctAmeu3Tdpczocamd2fORSUk2VuMzVJ8blbkWwsAvlLTi1IKdeXoFO+rf0GH5OqJk2nZY8Ue5IwMoC6/4ZwfL2xqdrUM8tN3WvjYU1X822sNvFzdy2+31vOll2rpHj6Lk53zyyY8piF8VPbuLQvSTm96d9tGa/E3w8D42GdQ7om/Yio9PZ1Zs2YBsG3btgnfnhBOJ9cpThMvvvgiL774Iu3t7QDk5+dz6623snTpUsCatPj973/PK6+8wuDgICUlJdx1113k5h5ZRCIQCPDoo4+yceNGgsEgixcv5q677iIlJWXsNgMDAzz88MNs3boVpRQrV67kzjvvxOfzjd2mo6ODX/ziF1RWVuLz+Vi1ahW33347LpcceAshhF0CfpPu0VzU4y2wFpGc6sLjVQQDmp6u8HGjHJymp8s6gEpJc36tU11isjH2+mlvHYboG+wUp+H50cXVLilKIjEmuvbvdCiE+d/fgdZGSMvEuOc+lDfG7rLOinHdrZi7t6A3voJ+31+hUtLtLmlCRBq8atH0jWc42hWzknm7foBXa3r5myWZuFZcgv7Dr6CnC711o6MWDXOa5vo6MDzkzpx51icLTK3Z3z7MG3X9bKzto3vkyJRtss/FyvxE3m4Y4EDHCF984TD3r86nONV3kkc8lnK7UeddiH7jRfSWN1ELFp9VnSez/nAfLQNBkmNcXDfv1BnqerAf8//+26rv2g+iimaPe00nsmzZMmpqati3bx8rV64kMTFx0rYthNNE16locdbS0tK4/fbb+c53vsO3v/1tSktL+e53v0t9vbWi6jPPPMPzzz/P3Xffzbe+9S1iYmL45je/SSAQGHuMRx55hK1bt/KFL3yBBx54gO7ubh588MFjtvOjH/2I+vp67r//fu6991727t3Lz372s7Gfm6bJt7/9bUKhEN/4xje45557eO2113j88ccn5y9CCCHEcbW3hkBbDbjYuBPvHihDkZEdPTm8WusjC6ylR1ejaSo6Ooe3tqYfrWWKd6rpGwmxobYfgOtO87Jep9Bao3/737BvF8TEYnz6flRy9C4Qp+YuhDkLIRRCv/SM3eVMCB0Ow56dAKhpnL97tGUzEkj1uej1h9nSOIBye1CrrwdAv/SMfO6egH9/BZ3K2k/Ivfy6M7qv1pqDncP877Y27v5jNfe+VMdf9nfTPRImwWtw1exk/u2KAv73ljl86oJcHv7ocmYkemkfCvEvL9axuWHgjLanlo1mbG8f/5iGsKl5fHcHAO9fkIbvdKZ3H/8f6OuB3ALUjbeNaz2nkpubS15eHqZpsn379kndthBOIw3eaWL58uWcd9555ObmMmPGDD7ykY/g8/k4ePAgWmuee+45PvCBD7BixQqKior41Kc+RXd3N5s3bwZgaGiIdevWcccdd1BaWsqsWbP45Cc/yf79+zlw4AAADQ0N7Nixg0984hPMnTuXkpISPv7xj7Nx48axTJydO3fS0NDApz/9aYqLi1m6dCm33XYba9euJRRyfqNACCGmqiPxDKdeLXosh7fF+Zd6Dg9pAn6NUpCUIg1eJ8gYzeHdsbmTV/7Sx/6KEQYHJi5HUEyul2t6CZma2WkxzE0//ak0J9Av/hH9xougDIx/+CIqf6bdJZ0z4/oPAaDXv4Ae7Le5mglwaD8MD1qXrBfPsbsaR3AZijWj0SiRxdbUqmvB44XaKqjea2d5x+WEpnPLS39BK4MEpUksKDrl7bXWHO4e4Tc72vnEszX80wu1/HFvFx1DIWLdBqtnJvHl1fn86gNz+dQFuSzOicc1ur5BUVoc37u2mLLsOEZCJt9c38Aze7tO/+9hAmMaItO7Sac7vbt7C/qtV0EpjDs+jfJ4x7We07F8+XIAKioqGB4envTtC+EU0uCdhkzT5M0338Tv9zNv3jza2tro6emhvLx87DZxcXHMmTNnrHlbU1NDOBymrKxs7DZ5eXlkZGSM3ebAgQPEx8cze/aRSzLKyspQSlFVVTV2m8LCwmNiHZYsWcLw8PDYNLEQQojJpbUeW2AtK+fUMQaZo7fp6QoTDNh/UHYykXiGpBQXLpfkAThB/kwvhbO8eDwGQwMmBypHWPeXft58pZ/aaj/BwNktPCOc4bWaPsBaXE1F0eJqesfb1mXsgLrt71Bly+0taLyUngcFM8E/gn7lz3ZXM+50xWg8w8IlKENO4kVcMdrg3do0QNdwCJWYjLpgNQDmS8/aWNmxQqbm399o5NbHDvC55w7xn2818czeLna1DNLnn7wTf7q5npamRgBy8vJPetvGvgCP7+7g0385xGefO8wTlZ20DATxuhQXFyZy72V5PPLBOXz+ohksz0vAc4J9j8QYF1+7vICr5ySjgYe3tfHQOy0ETyOfPhLTAKC3vHlmT/YkrOzdI9O7sZ6Tt4v08BDmr39q1XTlTajZJeNWy5koLCwkMzOTUCjEzp07balBCCeQMLpppK6ujvvuu49gMIjP5+Of/umfyM/PZ//+/QAkJx+7CEZycjI9PT0A9PT04Ha7iY+PP+ltkpKOzSpyuVwkJCQcc5ujm7tHbzdym+MJBoMEg0cmxZRSxMbGjv05GkXqjtb6hTPJ60qcjd7uMAG/xuWG9EzPMa+f472m4hPcxCcaDPabdLaHyM2f/GmN03V0PIO8L5zB61UsXZnAlddnsWNrPfWH/LS3hujqCNPVMUzFtmFy8jzkF3vJyvVgGPLvFi16RkLU9voBuLAgcVLfc+fy+0/XVWP+4kHQGrX6Oowr3jdlPi+UUnD9hzB/9l30uj/DNbegfLF2lzVuxvJ3S5dNyL9ZtO5XFaT4KMmMZV/7MK8d6uODi9IxrryJ8Bsvwva3oaMVlZlja41hU/OfbzWzsc6aLD/U7edQtx/oG7tNRpyb4lQfM1NjmDn6/5wE79gk7HgxX/gDrXHWMemMWbPf8+/dOhBgQ20/bxzupabbP/Z9t6FYNiOeS4uTWJGXeMqGKBz7mvK4DO5ZmUtBcgwPb23jpepeWgaC3HtZ/inzy9Xyi60c3m0b4aOfQI3DejZv1PbR1B8kMcbFDfPTTvm6N//wK+jugMxcjPf/tW3vE6UUK1as4LnnnmPXrl0sW7YMr9e5+6ZCTBRp8E4jM2bM4Hvf+x5DQ0O8/fbbPPTQQzzwwAN2l3Vann76aZ588smxr2fOnMm///u/k5mZaWNV4yMnx96dKzE1yetKnInmunagn4KiRPLyZxz3Nu9+TRXNgj07uxns9x6zIKfTbN5wGIDiWRnk5qbYWot4rxUXFLHiAhgcCHJwXy8H9vTS3emnqT5IU30QX6yLOSXJzFuQTEaWL+qaLNPNnv1tAMzOiGf+zAJbajjT33+hjjbaHvoWBPz4zruAjC98FeWaWodI+oYP0vKnxwg11ZG4YyOJt/y13SWNi3BPF02HrasEcy6/FldaxoRtKxr3q249T/ONtftYXzvAPVcsQuXm0n7eBYxse5vYTa+RetfnbatNa82/v3SA1w/34TIUX7tuAbEeFwfaBjjQPsDBtn4ae0foGArRMTTAlsYjGbU+j8GcjATmZSUwNzOBuVmJzM2MJ857du/bUFsLTe+sp2WelWtbWlpKbm4u7QN+Xt7fxkt7W9ndfKTp7FKK84tTubokm1VzMkj0nTra6niOfk19YsYMFhV1cP+fKtndOsS/vlzPDz6wmKK0uBPeX2deQ9Mvf4DZ10taRxO+JeefVR0RYVPz1HO1APzN+UXMKsw76e1Hdm6mff0LAGT+v6/hK7Y30iY7O5tNmzbR0dFBXV0dl156qa31CGGHqbX3Ik7K7XaP/SKZNWsW1dXVPPfcc9x8880A9Pb2kpp6JGent7eX4uJiAFJSUgiFQgwODh4zxdvb2zs2kZuSkkJf35FffgDhcJiBgYFjbhOJazj6MSI/O5FbbrmFG2+8cezryAFee3t71Gb3KqXIycmhpaXFEblTYmqQ15U4G1UHrM/upNQwzc3Nx/zsRK+p+ERrEc7aml7mLJi8Ws+ENjVtLUMAKGOA5mbJZXOK472usvMga0YsfT1e6g8HaDgcYGQ4TMX2Liq2d5GYZJA/M4b8Iu9JFwIU9nljfwsAJene93yWTLSz+f2n/SOE//1e6GyDGQUEP/Y5WtraJ7hSe5hX3QyP/JieJx+lf9llKM/ZNaWcxHzrVesPBbNo8wdhAl5z0bxfVZpiEuNSHO4a4rXdNZRkxmFeeg1se5uBF55m+IqbUL4TNxAn0iPb2/hDZScK+MJFM1icqoEQ8xJ8MMsHZDAUCHO4x8+h7pHR6d4Ranv8jARNKpr7qDiq6aqAnETP6JTvkYnfjDj3KU8Mhh/7Bb2uGEbcXgyXi5eqe9nw6ttUtg0R+RdXQGl2HJcWJ3FRQSJJPquNMtDdwZktj3bi19ScOPj21YV847V66rqH+divN/Evl+WzOCf+hI+lF6+EN16k48VncWWf20m19Yd6qe0aItHr4rIZ7pN+hmv/COEffM16PquvoztjxoS8/87U4sWLeeWVV1i/fj0zZ87ENQ5TzXZyu91TYqBNTB5p8E5jpmkSDAbJysoiJSWF3bt3jzV0h4aGqKqq4uqrrwashrDL5WL37t1ccMEFADQ1NdHR0cG8efMAmDdvHoODg9TU1DBr1izACjrXWjNnzpyx2zz11FP09vaORTPs2rWL2NhY8vNPnHfk8XjwnGBHNNp2tt5Nax31z0E4j7yuxOkK+E26O60Yg6xc9wlfN+9+TaVnulEKBvtNBgdCxMU7bye6vy9MOAQuNyQkKnlPONDxPquSUlwsWhLLgnIf7S0hGg4HaGkM0t9nsnfnMHt3DpOR7Sa/2Etunge3R6Z6naJi9IRKaVasbe+30/39p03TimWoq4bEZIxPfRli46bu58QFq+GZ30JPJ+bGVzAuu8buis6ZrtgKgCpdOuH/btG4XxXrNri4KJF1NX28VNXD/IxYWLgUcvKhpQFzw8sYV7xv0ut6srKTP1R2AvDJlTlcUpR43L/bWI/BgsxYFmQeiRQJm5rm/gA13X4Od4+MNoD9dA2HaO4P0twfHIt8AEjwGlbEQ0oMxakxzEr1UZDsxeOyThLq/j4G3lzPutzFAHSrRP57y5GTPCUZsVxanMhFhUmkxR5pnYzHa+F4r6nilBi+f00x33q9gf0dI3ztlTr+YUUO18xNOe5jjMU0bN2I+ZF/OOuYhrCpeXy3lb1704JUYt3GSZ+j+dSj0NEKaZmoD97hmPfG/PnzefvttxkYGGDv3r0sWrTI7pKEmFTS4J0mfvvb37JkyRIyMjIYGRlhw4YN7Nmzh/vuuw+lFNdffz1PPfUUubm5ZGVl8dhjj5GamsqKFSsAa9G1yy+/nEcffZSEhATi4uJ4+OGHmTdv3liDNz8/nyVLlvCzn/2Mu+++m1AoxMMPP8xFF11EWloaYJ1Vy8/P5yc/+Qkf/ehH6enp4bHHHuOaa645YQNXCCHExGlvDYGGxCTjjKYiPV5FSpqL7s4w7S0himY7r8EbWWAtOdWFkhzXqGMYiuwZHrJneAgGTJrqgzTUBuhqD9PRGqKjNcRuN+Tme8gv8pKR5ZZ/Zxv1HZW/uyjLnqnAM6GfehR2vA1uD8Ynv2R7HulEU24P6ur3o3//S/Tap9CXXBnVi5Jp00RXbgdALVpmczXOdeWsFNbV9PFGbT93Lc/G5zZQV7wP/X//hX7lT+g110/q6+D5A938eofVQP3Y0kyunpNyRvd3GYr85Bjyk2O4rPjI2i+9IyEOdfs53DPCoS4/h3r8NPT6GQiYVLQOUdE6dOQxFOQnWQ3fofo6tq/4F2YPHSDf30CPO5nZaT4uLUrkkqIkMuMn//g0JdbNN64s5MdvtfB6bR8/3dRCQ5+fjy3Nem/28PxySEiEgT44UAELFp/VNt+s66ehL0CC1+DG+aknva2u2mPleQPG39xj2xT48bjdbpYuXcqGDRvYtm0bCxYswDDkih8xfUiDd5ro7e3loYceoru7m7i4OIqKirjvvvsoLy8H4Oabb8bv9/Ozn/2MoaEhSkpK+NKXvnRMOPkdd9yBUooHH3yQUCjE4sWLueuuu47Zzmc+8xl++ctf8m//9m8opVi5ciUf//jHx35uGAb33nsv//M//8P9999PTEwMq1at4rbbbpucvwghhBDHaGu2FrDMyj3zg5jMHI/V4G0NUTQ7ZrxLO2eRyeTUNNndiXYer0HR7BiKZscwNBCmoTZIw+EAgwMmDYeDNBwO4otV5Bd5yS/2kpgcvY2raFXZZkWgFCZ7SfY5+z1nbngJvfYpANTHPoNyas7MOFOXXo1+7vfQ1ozeuhG1IoozKuuqraaWLxZmz7e7GsdamBVLbqJnbLL18lnJqAsvRz/9a2hvgZ2bYekFk1LL+kO9/GxzKwAfWpTOLQvTx+2xk31uluS6WZJ7JM4gGDap7w1YEQ89fg6PxjwMBExqe/2jJ6RSwIDMcA8Af3XhfFaUFY9bXWfL6zL4wsW55Cd7+e2uDp7d101TX4D/d8kM4jxHfr8plwu19EJrinfLBtRZNHiPmd4tSTvm8d9NB/yYv/qxtSDlxVegSs878yc3wUpLS9m8eTPd3d3U1NSMXUksxHTg7L0vMW7+8R//8aQ/V0px2223nbTR6vV6ueuuu97T1D1aQkICn/3sZ0+6rczMTP71X//15AULIYSYcFpr2lusKdes3DPfJcjMcXOgEjpaQ2hTO256sqfLavCmpEmzbyqJS3Axb5GLuQtj6O4M03A4QFN9kJFhTdU+P1X7/CSnusgv9pJX6CHGJ9M7k2F322g8Q7ZzprmOR+/fjf7NTwFQ7/srjJWrbK5o8ihfLOry96Gf/S36uSfRyy+J2oULdcU26w8li1FuuQrwRJRSXDErmd/s7ODl6h6rwRsTg1p1Dfr5P2C+/CyuSWjwbmro5z/eakYD189L4aOLJ25BvAiPy2BWmo9Zab6x72mt6RgKcbjbT82W7bB7K0t1O2tTc9DAwln2LA55PEopbivLIC/Jy3++1cyWpkHuXVvHfavzyE44MoQ1FtOw7S307Z8445iGjaPTu/GnM737p8egtRGS01Af+ruzel4Tzev1snjxYjZt2sSWLVuYPXt21H7OCXGmZI9XCCGEmKZ6u8P4RzQuN6RlnHmDNyXNhdsDwYCmtzs8ARWevXBY09c72uBNlwbvVKSUIi3DTfnyOK66KYnlF8eRnWdlQ/d2h6ncPsxLz/ax6Y0BmuoDhMPOyAicqiKXQDu5watbGjF/+m0Ih1ErLkW97yN2lzTp1OU3QEwsNByC0QzbaKQrrQavKnPeBKHTrJmVjKGsKfvmfmuBVLXmRnC54EAFuq56Qre/u3WQ777RhKlhdXESdy/Ptq3hppQiIzzAsjd+y60v/JBb69YRs2w5WmsSExOPWUzcKS4pSuKbVxaS6nNR2+vni2tr2dd+1KKxR8c07N99Ro9tas3vK0and+enEe89yfTu4YPotU8DYPz1P6LiE878yUyS8vJycnJyOO88+XwQ04s0eIUQQohpqq3Zmt7NyHZjuM78YMswFBlZ1uRUe2toXGs7V309YbQJ3hh1RtnCIjq5XIrcfC/nX5LAVTcnUXpeLClpLrSG1qYQWzcO8eIzvezcPEQgYNpd7pTTNxKitsfZ+bt6sB/zx1+HoQGYNR9152en5VSXik9ErboWAPO5J2yu5uzowQGo3g+AWiQNnFPJiPOwJMdqXL5S3QuASk1HLbsEAP3ysxO27YOdw3zjtUaCpmZlfgKfuTAXw6b3nR4ewnzmt5hf+gcrQzYcgsXn05Jm5W/n5ubaUtfpmJcRy/euLWZmagy9I2Huf7mO9YdG/y1HYxoA9NY3z+hx36rrp643QLzH4MaSE0/v6lAQ81c/Am2izr8MtWTl2T+ZSRAXF8eHP/xh5s2bNy0/58X0JUc8QgghxDQ1lr+bc/aXt2ZmW5O/TmvwHh3PIDv300tMjMHMuTFcelUiq69LZM6CGHxxilAQ6moCVGwbPvWDiDNSOTpNVpDsJcWB+bs6FLQmd9uaID0L454voTzeU99xilJX3QxuN1TtRR+otLucM7dvJ2gTcgtQ6Vl2VxMVrpydDMC6ml7CpnU1g7ryJgD0pjfQPV3jvs26Hj8PrKtnJGRSnh3HP10y472LhE0CHQxgvvQM5pfuRv/5MfCPQPFcjC98Hden7qelrQ1wdoMXIDPew7evKuL8/ASCpuYHG5v5v53tmFqjlo8267e9hQ6f3hVVptY8vrsTgPeVpJJwsund556ExlpITEb91d+f+5MRQkwIafAKIYQQ01DAb9I92gQ9mwXWIjJzrGZOV0eIUNA5l8D3dFkNZ8nfnd4Sk1wsKI/lyhuTOO9Ca7K0pSHoqNfqVDAWz+DA6V2ttZW5e6ACfLEYn/4yKunkOZNTnUpJQ110JQDm89E3xRvJ35Xp3dN3fn4CiV6DzuEQO1sGAVAz58KcBRAOodc/P67ba+kP8JV19fQHTOal+/jXVXl4XZPbetBmGPPNVzDv/0f0738JA/2Qk4fxiXsxvvR91ILFaK1paWkBICcnZ1LrOxuxHoN7L83jlgVpAPy+opPvb2jCP3sRJCSdUUzD2/X91Pb6ifMYvG9+2glvpxsOWYszAuojf49KTDr3JyKEmBDS4BVCCCGmofbWEGhISDKIiz/73YG4BIPYeANtQme7c6Z4ezojE7zOmyYUk08pxYwCD3EJBuEwtDQG7S5pSok0eMscmL+rX3gK/eYroAyMf/hnVF6R3SU5grrmFlAGVGyb8AzW8aS1PtLgLZUG7+nyuAxWzbSmeF8ejWkAMCJTvK89jw74x2VbnUNBvrKunu7hEEXJMXxlTQFxnsk72aq1Rm9/G/Nrn0H/6j+hqx1S0lF/+ymMr/0EteyisSt7uru78fv9uN1uMjImfuG38eAyFB87L4tPX5CD24A36/q5/9VGus+zFozUWzac8jGOnt69cX4qCTHH//fR4TDmr34M4TAsuWBsUlgI4UzS4BVCCCGmobF4hnOY3gWrcTYW09DijKZZMKAZ6LdyVmWCV0Qopcgvsl7vjXUBm6uZOvr8YQ5H8ncd1uDV2zain3oEAPWRu1Gly2yuyDlUVi5qxaXA6OXX0aKpDno6weuFeYvsriaqRGIa3mkYoM8/ehn/kgsgPQsG+tDvrD/nbfT5w3x1XT2tA0FyEjx87YoCEk/QPJwIen8F5nf+GfOn34LmeohPRH3oToxv/jfGpVejXMfW0tzcDEBWVhYuV3TtL1w5O4UHLi8k0WtwsHOEf465hJqEGejtp45peKdhgMM9fmLdBjeVnGR698U/Qm0VxMVjfPQTEnklhMNJg1cIIYSYZrTWtLdY07ZZuec+4RqJaXBKDm9vt1VHbLxBjE92dcQRMwqt3NX2lhD+EVlsbTzsabOmd/OTnJW/q2urMH/5AwDUmhsw1txgc0XOo677IDDaCG9ptLma06Mrtlp/mFc2rXOUz8bMVB+z02IImZrXDx+1QNflNwLWYmtan318zVAwzAPr6qnvDZAe6+bfriggLXZyPhN0XTXh//wa5ve/BDX7wRuDuv7DGN/6OcbVt6C8Mce9XySewen5uydSmh3H964tJj/JS2dQcd/ST/KOrxD27zrhfazp3Q7Amt49UQNeNzegn/0tAOq2u1ApJ24ECyGcQY56hBBCiGmmtzuMf0TjckNaxrkffGVkWY8x0GcyPGR/0+zoBdaEOFpikovkVBdaQ3O9MybOo50T4xl0Vwfmj78BgQCULkPddpfdJTmSyi+GxeeD1ui1T9ldzmmReIZzc8WsFODYmAZ1yVUQE2tNR+/dcVaP6w+ZfPO1Bqq6RkiMcfHAFQVkJ0x8A163NWH+/HuYX/88VGwDlwu15nqrsXvLX6Pi4k96/8gEbzTk755IbqKXf7+miCU5cfhdXr676G94amvDCZv1mxoGONTtx+c2uGnB8Zu22gxjPvIjCAWh9DzUhZdP5FMQQowTafAKIYQQ00zb6PRuRpYbl+vcL7fzxhhjzdSOVvubZpHF41KlwSuOI280pqGhVmIaxkPF6ARvqUMavHpkGPMnX4feLsgrwvj7L77nsmxxhHHdrQDot15Fd3XYXM3J6ZFhOLgHkAXWztZlxUm4DcWhbj81XSMAqLh41CWji+69/KczfsyQqfnehkYq2oaJdRt8bU0BBcnHn5gdL7qnC/M3P8X8yj3ozW8AoM5fhfFvP8W4/ROo5FMvpOj3++nq6gKid4I3IsHr4strCrg2I4RWBo8ac/nxW00Ew8c2efW7pneTTjS9u+4vUL3PWpjyr++RaAYhooQ0eIUQQohpZrzyd482FtPQYn9MQ0+XVUOyLLAmjmNGgTVV1t0ZZmjg5DmF4uT6/WEOd1v5u6VZ9jd4dTiM+T8PQv0hSEzG+PSXUbH21+VkanYJzCuFcAj90h/tLufk9u+GcAgysiF7ht3VRKXEGBcXFCQA8HJ1z9j31eU3glKwewvm736O+c56dFvzKSMbwqbmPzc2s7lxEK9L8eXV+cxJ901Y/XpoAPOpRzHv+3v0+hesxb/KlmN8+T8w7v5/qKzTb9RG4hmSkpKIi4v+zwm3ofiHK0r4u7oXMLTJK4f6+eq6OvpGjuyXbWocoKbbj8+tuLnk+E1w3d6CfvrXAKhb70SlZ05K/UKIcydHPkIIIcQ0EgiYdHdaTa3xbPBmZHs4uMdPe2sIrbVt0x4jwyYjQxoUpKTK1J54r9g4g4wsNx1tIRrrgsxdKK+Ts7WnbQjNaP7uJGVtnkzv//4YveMdcHsw7rkPlZ5ld0lRwbj+Q5gHKtCvr0Vf/2FUYpLdJR3X0fEMMlF49q6cncKG2n7WH+7jY+dl4XUZVmN06YWwbSN63Z9h3Z/RAAmJUDwPNXMeatY8mDkPFZ8IWNOgP9/Syuu1fbgU/MuleRO20KIO+NHr/ox+/g8wNGB9c3YJxgf+FjWv9KweMxLPEO3Tu0cz3G5uzPeQu/t/ebDsDirbhvni2lruX51PfpJ3bHr3hnmpJB0nM11rjfnIjyHgh/llqEuvnuynIIQ4B/bviQkhhBBi0nS0hEBDQpJBXPz4XciTlu7C5YaAX9PXEyY51Z5djEj+bmKigdsjDQBxfHlFHjraQjTUBpizIEaaRWdpt4PiGczX19L/9G8AUB//nDWZKk7PwiVQOBvqqtHr/oS6+aN2V/QeWuuxBdYknuHclGfHkRHnpmMoxDv1A1xabDX0jTs/iz7vQjh0AH3oANRVw0A/VGxFV2xlbJY3awZq1jx+nX4hLwymooDPXzSD5XkJ416rDofRb76M/tPvoMeKU2BGIcYtfwOLzz+nz+5oX2DtRNTySzjv9bV8u/J/+NbKT9EyEORf1tZyzdwUqrus6d33nyh794211qS814vxt59CGXLBtxDRRBq8QgghxDTS1mxdqpeVM37TuwCGS5Ge6aatOUR7a8jGBq/1/FLSZRdHnFhuvofdW4cZ6DPp7zVJSpEp3rMRWWDN7ngGbYYxn/xfAIybb0etuNTWeqKNUsqa4v3v71hTklff4rxoi7Zm6GgFlxtKyu2uJqq5DMXls5L5fUUnr9T0jjV4lS8WtXIVrFwFgA4Fof4w+tB+q+lbcwDamqCtiT/45vGUz7rE/xMHn+Ki2i7MWfOtCd+ZcyEz95yar1pr2Pom5h//D1obrW+mZ6Fuuh11wSqUcW6f2b29vTQ2Wo871Rq8zCuFhCQKO2r43sx+vtOaxt72YZ7aYzXIrz/R9G5XO/oJ63NU3fI3ZxR3IYRwBjn6cZiOjg46OjooKTkydXD48GH+/Oc/EwwGufjiizn//PNtrFAIIUS00lrT1hLJ3x3/XYDMHI/V4G0JMcem4bnIBG+KLLAmTsLjNcjK9dDSGKShNsDClFi7S4o6A0fn79o9wdtQC0OD1mJRN3zY3lqi1dILICcPWhrRr7+AuuYDdld0jEg8A3MXonzyfj1XV4w2eHc0D9I+GCQz/r0nfZXbAzPnWg3bUXqwnxc2H+I3jVaW+d/Wv8RVjW9bPzt0wPo/nDTa4VT0nh2YTz0KtVXWNxKSUDd8GLXqOpTn3E9Oa61Zv3494XCYgoICMjIyzvkxnUS5XKjzLkK//gJJO9/k6x/9JD95p4XXDvWdcHpXa43565/CyDDMLrEymYUQUUcavA7z8MMP4/f7+fKXvwxAT08PDzzwAKFQiNjYWN5++22+8IUvsHLlSpsrFUIIEW36esL4RzQuN6RlTkSD13rMrvYQ4ZDG5Z7cy9611tLgFactr8hq8DbWBVhQ7pOYhjNU2W7l7+YleUm1OX9XV+0BIGbBYkKG65QLQ4n3UoaBuvZW9K/+E/3SM+jLb0R5vHaXNUZXHsnfFecuJ9FLaXYcFa1DvFrTy4fLTq/J+Xqbyc9Gm7sfWpTOB27/FLTfajV3Dx1A1+yH+poTRDvkombOg5nzraZv/sxjGrb60EHMpx+FvTutb8TEoq5+P+rqm1G+8TuJVFNTw+HDhzEMg9WrV0/Jz361/GL06y+gt7+F+/ZP8LkLc7m4MJH0OA/Jx5vefetVqNhq5Zff8ZlznpAWQthDGrwOU11dzXXXXTf29euvv04gEODBBx8kKyuLb33rW/zpT3+SBq8QQogzFolnyMhy43KN/wFNQqKBL1YxMqzp6giROc4xEKcyNGgSDGgMA5KS5eBEnFx2rge3B0aGNF3tYdKzZLf4TDglngGAqr0AeBeWEzrFTcWJqZWXoZ/9P+jqQL/5Cmr1dae8z2TQwQDs3wVI/u54unJWMhWtQ7xS08utpekYp2h0bmro5z/eakYD189L4aOLM6zmaFaudTn/KaMdmtFtzfDOeqvp63ZDwSzUzHnoni7YttHakNttTete/yFUUsq4PudAIMD69esBWLZsGampqeP6+I4xrxQSk6G/F/bvRi1ayvn5x5+g1j1d6Md/AYC66SOo3PzJrFQIMY5kT9ZhBgYGSE5OHvt669atLFy4kJycHADOP/98fve739lVnhBCiCjW1hyJZ5iYxqtSisxsD/WHA7S3Tn6Dt6fTmt5NSnFhTEADW0wtLrciN89L/eEAjXUBafCeobEGr83xDFpr9MHRCd6FSxiytZroptwe1NW3oB/7BXrtU+hLr0a57D1ZpttbrAW2AgFISYO8IlvrmUouKkzkZ5tbaRkIUtk2RFl2/Alvu7t1kO++0YSpYXVxEncvzz7h5OuJoh2sxdsOjk777remfCMLugEohbpgjdVkzMge1+casXnzZgYGBkhKSmLFihUTsg0nUC4XaumF1hTv1jdRi5Ye93Zaa8zf/jcMDULRHNTVt0xypUKI8SR7sg6TlJREe3s7AIODgxw8eJDbb7997OemaWKapl3lCSGEiFLBgEn3aAN0IvJ3IzJz3FaDtyUIiyc3J1HiGcSZyiuyTkg01QcpXarlxMBpGgiEOeSU/N2udujpBJcL77xS6O62t54opy65Gv3nx6GjFb35DdQFq22pQzfXo597Er1pPYwe+6g1N0zJy+ntEuM2uKw4ibVVPbxc3XvCBu/BzmG+8VojQVOzMj+Bz1yYe8pp33dT8YlQugxVugwYXUStvWUs2oFgEHX5DagJbOB3dnayfft2AFatWoXbPbVbIWMxDdveQt/+CdTxnu/WN2H72+ByYXzs07af0BFCnJup/akWhcrKynj++eeJi4ujsrISrfUxi6o1NDSQnp5uY4VCCCGiUXtrCK2tGIW4+Inbgc/ItnYt+npM/CMmMT5jwrb1bt1d1sXZKemyeyNOT0aWmxifwj+iaWsJkZM3uVPn0WpPm5W/OyPRS5rd+buj07sUzcHw+WytZSpQMTGoK29C//E36Bf+gD7/MpQxeZ/juq4a8y9PwPa3IJKlvHApxg0fQs0rnbQ6posrZieztqqHjXX9/MOKMHGeY/cP6nr8PLCunpGQSXl2HP90yQxcxrk32Y8X7TCRtNa8+uqrmKbJrFmzmDlz5oRv03bvimngXVO8ur8P87c/A7DiMPKnwd+JEFPc5P22Fqfl9ttvJz8/n1//+tfs2rWLv/mbvyErKwuAYDDIW2+9RWmp7NwIIYQ4M5H83YmKZ4iI8RkkpVgHiO0tk5eGaZqa3m6Z4BVnRhmKGYXWgkGNdQGbq4kekXiGMrundwFGF1hTcxbYXMjUodZcD75YaKyFXZsnZZu6ag/h/3wA8+uft7JYtYYlF2B86UFcn39AmrsTZF66j/wkL4GwZkNt/zE/a+kP8JV19fQHTOal+/jXVXl4XdHZPti3bx9NTU243W4uu+wyu8uZFMrlQp13IQB6y4b3/Fw/9gur+ZtXhLr+Q5NdnhBiAsiIi8OkpKTw9a9/naGhIbxe7zGXjmit+fKXv0xGxumtciqEEEKA9fvjSP7uxP/qz8xx09cTpr01SH7x5KzC3t9rYobB7bGmlIU4XfmFHg4d8NPSGCQU1Lg9cgn4qVS0OSN/F0CPLrCm5i60uZKpQ8UloFZfj37hD5jPP4mx+PwJiUbQWsPenZjPPWFNGAIoA7XiUtT1t07o5frCopTiytnJ/Gp7Oy9X93D1nBQAOoeCfGVdPd3DIYqSY/jKmoL3TPdGi5GRETZssBqc559/PklJSTZXNHnUsovR619Ab38b/dF/HItp0DveseJPlIFxx2es3GQhRNSTIyCHefLJJ6mrqyMuLu49uUBerxeXy8ULL7xgU3VCCCGiUV9PGP+IxuWCtMxJaPCOxjR0tIasA/hJ0BOJZ0h1S0ajOCPJaS7iEwzMMLQ0Bu0ux/GOzt9dlDW5OdvvpgcHrClTQM2RBu94UlfdBB4v1OyHAxXj+thaa/SOdzC//UXMH37Fau663KhLr8b4xk8x7v5/0tydRGtmJmMo2N8xQl2vnz5/mK+uq6d1IEhOgoevXVFAYkx0NncB3nrrLYaHh0lNTWXp0uMvNjZlRWIaBvth3y4A9NAA5m/+CwB19fuPWQxPCBHdpMHrME888QR1dXUn/Hl9fT1PPPHEJFYkhBAi2kXiGTKy3bgmYRGptEw3hgtGhjUDfZOzMOjYAmvp0XsQKuyhlCKvyJpeaqiVmIZT2ds2jKlhRqKH9Dibp76qreldcvJQicn21jLFqKRU1MVXAlgTtuNAm2HMTa9jPvAZzIe+aS2u5fGiLr8R41s/w/jbT6GyZozLtsTpS4l1syIvAYA/7+vm316tp743QHqsm3+7osD2nO1z0dbWxu7d1nT46tWrcU2zRcSOiWnY+qb1/98/DL1dkJ2HuukjdpYnhBhn0ftpPU0NDAxM+RU/hRBCjK+2ltF4hpzJaca4XIr0TDftLSHaW4IkJk/8AdXYBK/k74qzkFfk5UCln47W0KQvDhhtnBXPEMnflendiaCuuQX9+guwZwf68EFU8dlN+ulQEP32a+jn/wBtTdY3fbGo1dejrroJlZQ6fkWLs3LF7GTeaRhgbVUPAIkxLh64ooDshMmJWZoIpmny6quvAjB//nwKCgpsrsgex8Q0LL0A/ebLoBTGxz6N8sbYXZ4QYhxJp9AB9uzZw549e8a+fuedd2hpaXnP7QYHB9m4cSOFhYWTWZ4QQogoFgyYdHdY062Tkb8bkZk92uBtDTFr/sRuKxTS9Pdak8IpabJrI85cQqKL5FQXvd1hmuqDzJwrB70nsnt0gbXSLAc0eA+OTvDKAmsTQmVko85fhX77Vcznn8T1j/96RvfXAT/6zZfRLzwFXe3WN+MTUVe8D3X5jaj4hAmoWpyNZTMSSPG56BkJE+s2+NqaAgqSo/tzsLKyktbWVrxeL5dccond5dgnEtPQ34v5s+8CWO8/OTEmxJQjR0EOUFlZyZNPPjn29aZNm9i0adNxb5ufn8/HP/7xySpNCCFElGtvDaE1xCcaxCVM3nRrRrYHGKGzLUQ4rCc0GqKvO4zWEONT+GIlf1ecnbwiD73dYRprA9LgPYHBQJhD3SOA/RO8OhiEwwcBmeCdSOraD6LffhW2v41urkflnnoKUo8MWRODL/4R+nqsbyaloK6+BbXqGpTP/pMD4lhuQ3F7eSbP7uvikytzmJPus7ukczI0NMTGjRsBuPDCC4mPj7e5IvtEYhr0+hcg4IeMbNQtf2N3WUKICSANXge4+eabufbaa9Fac/fdd3P33XezcuXKY26jlMLr9eL1Ru9lMkIIISZfJH83K3dyszKTUgxifAr/iKa7M0RG1sRt/+h4BllgTZytvEIve3aM0N0ZZnAgTPwknhCJFnvbrfzdXCfk79ZWQShoTaZl5dpbyxSm8gphyQWw4230839AffxzJ7ytHhxAr/sz+uVnYWjA+mZaJuraD6AuvlIuB3e4a+amcM3cFLvLGBdvvvkmfr+fzMxMysrK7C7Hdmr5JVaDF6ys65jobuALIY5PGrwOcHTj9ic/+QlJSUnExMgOkBBCOFEopOlsC5GV40YZzm4maq1pj+TvTmI8A1gnJjOy3TTWBmlvmegGb2SBNdmtEWfPF2uQke2mozVEY12QeQulwftujopnGM3fZe5CObEzwYzrb8Xc8TZ603r0zbej0rOO+bnu60a/9Cz6tedgZNj6ZtYM1PW3olauRsn6IWISNTY2snevFd+yZs0aDEMy1ZlXirrxNkhOQy1YbHc1QogJIr9tHSYzM9PuEoQQQpzEnh3D1FYHmDnXS+l59jc5Tqavx2RkWONyQXrm5P/Kz8z2jDV4F5RP3HbGGryywJo4R3mFHqvBWxtg7oIYaRy+S0WrkxZYsxo4Es8w8dTMebBgMezdiV77NOr2fwBAd7Wj1z6NfuNFCAasG+cXo67/EGrZRShDPpPF5AqHw2MLq5WWlpKTk2NzRc6gDAN180ftLkMIMcGkweswWmtefvll1q1bR1tbGwMDA++5jVKKxx57zIbqhBBieguFNA211kHsoYMB8ou8jp4abRud3k3Pck9oBu6JZOZYfze93WECfhNvzPhP0QT8JoMDowuspUozQZyb3Hwvu7cOM9Bn0tcTJjnVue/vyTYUDFPjlPxd0wRp8E4q47pbMffuRG94CX3+ZdbiaW+9CmErIoeZ8zBu+DCUr5ATI8I2O3fupKurC5/Px0UXXWR3OUIIMalkr9VhfvOb3/DnP/+Z4uJiLr300mkdCC+EEE7T3BAcO5YF2LllmEuvSsBwaFRDW3MknsGerExfrEFikkF/n0lHa4gZheOfI9/TbU3vxicYE9JAFtOLx6vImuGhpSFIY11QGrxH2dNm5e/mJHjIsDt/t6UBBvvBGwMFM+2tZbooKYfiuXD4IOa//8uR788vw7j+Q7BgsTR2ha36+/t55513ALjkkkvw+SRnVggxvcheq8OsX7+elStX8oUvfMHuUoQQQrxLwyFrerd4jpfGuiB9PWEOHfAzu8R5BxHBgKa7w2p+Tnb+7tEyczz09/lpn6gGb6fEM4jxlV802uCtDbCg3CdNq1HOimcYzd+dNV/yXSeJUgrjhg9jPvRN6xtlyzGu/xBqzgJ7CxNi1Ouvv04wGGTGjBksWCCvSyHE9CN7RA4TCAQoL5/AoEIhhBBnZWjQpKPNGt+dXRJDcqqLnZuH2VcxQm6+h7gEZzUY21uDaA3xiQbxNtaWkeOm5oCf9pYgWutxb5b1dFn/JtLgFeMlK9eD2wMjw5rO9jAZWbK7DFDR5pwF1jgo8Qx2UEtWYnz+AUhKQeXL5LRwjsOHD1NdXY1SitWrV8uJOSHEtCTXMjpMaWkpVVVVdpchhBDiXSLZu+lZbuLiXRTM9JKe6cIMw66tw2itba7wWO3NVuMzK8fe5lR6phvDgOEhPZaVO1601kcWWHNwFrKILi6XIjffmjZvHH3fT3dDwTDVXc7I34UjE7wyPTr51MKl0twVjhIKhXjttdcAWLJkCRkZGfYWJIQQNpEGr8PcddddHDx4kKeeeor+/n67yxFCCIHVSIzEMxQUW40fpRTly+MwDGhvCdFUH7SzxGNorccWWLMrfzfC7VakZljN1/aW0ClufWZGhjX+EY1SkJQiE7xi/OQVWe+b5oYgZthZJ2/ssPeo/N3MeHs/U3R3J3S0gjJg1nxbaxFC2G/Lli309fURHx/PypUr7S5HCCFsI+MuDvO5z30OrTWPP/44jz/+OF6vF8N4bx/+kUcesaE6IYSYnro7wwwOmLjckJt/pLmRkORi7kIf+ytGqNg2TGa22xELffX3mowMawyXNXFst8xsN51tIdpbg8ycGzNujxuJZ0hMduF2y+WYYvxkZLqJ8Sn8I5q2lhA5eTYvKmazsXgGR0zvWvEMFBSjYu2vRwhhn56eHrZu3QrAZZddhtc7/ln/QggRLew/6hPHWLlypWQGCSGEw9SPTu/m5ntwe479jJ5dEkNjbYCBfpO9u0ZYvML+hkNbszW9m5HlxuWy/3dKZo6bfbuhszWEaWoMY3xqGotnkPxdMc6Uocgr9FJzwE9jbUAavKMLrC1yQv5uteTvCiGsq5Vee+01wuEwhYWFzJkzx+6ShBDCVtLgdZh77rnH7hKEEEIcJRzSNNWPxjPMfO9kiMulKF8Rx8Z1A9TVBMgv9pKeae+v10iD1+54hojkFBceryIY0PR0hkkbp7+fnk5p8IqJk1fkoeaAn5amIKGgfs/JneliKBimKpK/64AGrz5o5e8iDV4hprWqqirq6upwuVyysJoQQiAZvEIIIcRJtTQGCQUhNk6dsHGbnummcJbV/N21eYiwjZmdwaCmq8NqfGblOuM8rjIUmdmjObyt45NVrLWmp9uKaEhJc8bzFFNLcqqL+EQDMwzNjc7J2J5s+9qt/N3sBA9ZCTbn744MQf0hQBZYE2I6CwQCvP766wAsW7aMlJQUewsSQggHkCMih+rs7OTQoUMMDQ0dd2X2VatW2VCVEEJMP/WHj0zvnmw6ZMFiH61NQQb6Tar2+plf6pusEo/R0RpEa4hPMIhPcM5ka2aOm6b6IO0tIeaXnvvjDfSbhIJguCAxWc5Xi/GnlBXTcKByhMbawNgCi9PN7tF4BidM71KzH7QJGdmo1HS7qxFC2OSdd95hcHCQ5ORkli9fbnc5QgjhCNLgdZhAIMBDDz3EO++8c9zGboQ0eIUQYuIND5m0t1pTovmnaO54vQaLlsay7a0hqvaOMKPQQ2LS5DdY25qtep0yvRuRke0BhunpChMMaDzec7uUMpK/m5zqGrdMXyHeLa/Iw4HKETpaQ/hHTGJ80+9kQqWTFlg7KPm7Qkx3nZ2d7NixA7COid1uZ+3vCCGEXabfXqrD/e53v2PTpk381V/9FV/96lcBK5f3vvvuY+nSpRQXF/O9733P5iqFEGJ6aKwNgIa0DNdpTcPOKPCQlevGNGHXluNfgTGRtNaOy9+NiIs3iE800Bo62s79cveeTquRnSrxDGICJSS6SElzoTU01U+/mIbhoMnBTit/d1FWrM3VgK4azd+dK/EMQkxHWmteffVVtNbMnj2b4uJiu0sSQgjHkAavw7z99tusXr2a97///RQUFACQlpZGeXk59957L3Fxcaxdu9bmKoUQYurTWlN/6MSLqx2PUoqyZbG4XNDVHh67/2Tp7zUZGdYYLmxf6O14xnJ4W0Ln/FiRCV5ZYE1MtLxC62RJY+3kvp+dYG/7EKaGrHgP2Qn2RlToUMiKaADUbJngFWI62rdvH01NTXg8Hi677DK7yxFCCEeRBq/D9PX1MWfOHAC8XmtHemRkZOznK1euZNOmTbbUJoQQ00lPV5iBfhPDBbkFp9/YiIt3jeXv7tk5gn/EnKgS3yMyvZuR5cbldl5sQWaO1SjraD23Bq8Z1vT1jDZ406XBKybWjEIvKOjuDDM4ELa7nElV0eqceAbqD0HAD3EJkJtvdzVCiEk2MjLChg0bADj//PNJTEy0uSIhhHAWafA6THJyMv39/QDExMQQHx9PU1PT2M+Hh4cJBKbfBIkQQky2yPRtbr4Hj+fMmqUz58WQlOIiGNBUbh+eiPKOq60lkr/rrHiGiPQsN0rB4IDJ0Dk0yvp6w5gmeLyKuHjZlRETyxdrkJFlTZ831k6vmIaKNuvzq9RJ8QxzFqAMed8LMd289dZbDA8Pk5aWxpIlS+wuRwghHEf2jhxmzpw57Nu3b+zrZcuW8ac//Yk33niD119/nb/85S/MmzfPxgqFEGLqC4f1WN5mwSkWVzsew1AsXhELChrrgmOTtRMpGNR0tTtzgbUIj0eROjpx234OU7xHxzMo5bxJZTH15BcdiWmY7GxtuwwHTao6Rxu8Dpjg1VWywJoQ01VLSwu7d+8GYM2aNbhccvWOEEK8mzR4Heb6668nOzubYNBqBtx2223ExcXxk5/8hIceeoi4uDjuvPNOm6sUQoiprbUpSDCg8cWqscm9M5WS5mbmHKs5vHvrMKHQxDaFOlqDaA3xCcZpLQhnl0hMw7nk8Er+rphsOXleDAMG+s2xeJCpbl/HMGENWfFu+/N3tYbRCV4lC6wJMa2Ypsmrr74KQElJCXl5eTZXJIQQzuTMEZ9prKSkhJKSkrGvMzIy+OEPf0hdXR2GYZCXlydnLIUQYoJF4hnyi70o4+wnREvKYmluDDI0aHKgcoSFiyfuMue2ZmdP70ZkZLvZXwEdbSG0qc/q77en03quKWnOfq5i6vB4FdkzPDQ3BGmsDZKcOvVfe47K321vhr4ecHugaK7d1QghJtHu3btpb28nJiaGSy65xO5yhBDCsWSC10H8fj/f//73eeONN475vmEYFBcXU1hYKM1dIYSYYCPD5th06dnEMxzN7VGUnWc1R2r2++ntnpjJP631WAyEU/N3I1LSXLg9EAxoes7i7yMU1PT3mWOPJcRkyYvENNRNj5iGSIN3UZb9DV590IpnoHguyuPszzghxPgZHBzkrbfeAuDCCy8kLs7+zyMhhHAqafA6SExMDLt378bv99tdihBCTFtWxiakprtISDr3BmJOnofcfA9aw64tQ2hz/BtD/b0mI8MawwXpmc6eLDQMRUbWaEzDWeTwRprCvjiFL1Z2Y8Tkycr14PbAyLCms/3sI0aiwUjI5OBo/m6ZEyZ4JZ5BiGlpw4YNBAIBsrKyKC0ttbscIYRwNDkycpiSkhIOHDhgdxlCCDEtaa2pP3wknmG8lJ4Xi9tjZccergqM2+NGtLVY07sZWW5cbucvOpaZYzWh21vOfPG53i6JZxD2cLkUM/Ktz4XG2olfONFO+9qt/N3MODdZ8fZPzOpIg1cWWBNi2mhoaGD//v2AtbCaYUjrQgghTkY+JR3m4x//OPv27eOxxx6js7PT7nKEEGJa6e0O099rYhiQVzh+TQ1frMGCcit/d+/uYYaHzHF7bDgqfzfH/kbM6Yg0eLs7w4SCZzbR3D26wFqqxDMIG0RiGprrg4TDUzemYSyeITsOpew9aaT7e6Gl0fpidsnJbyyEmBLC4fDYwmplZWVkZ2fbXJEQQjifjL84zBe/+EXC4TBPP/00Tz/9NC6XC89xssYeeeQRG6oTQoiprWF0ejcnz4PHO77nQItme2k4HKC7M8zubUOcf0nCuDxuKKjp6oiOBdYi4hNcxMUbDA2adLaHyJ5x+o3pntEGr+TvCjukZ7rxxSpGhjXtLSFy8qLjpMqZqmizGrzOiGcYzd+dUYiKT7S3FiHEpNi+fTvd3d3ExsZy4YUX2l2OEEJEheg4EpxGVq5cafukhBBCTEdmWNMwetl1/szxi2eIUEpRvjyO11/sp7UxRHNDgNz8c99Oe2sQbUJ8gkF8YvQ0PTNz3NRWB2hvCZ52g9c/YjI8aE0/J6fKLoyYfMpQzCjwUnPAT2NtYEo2eI/O3y11wgJrEs8gxLTS19fHpk2bALjkkkvw+Xw2VySEENFBjo4c5p577rG7BCGEmJZam4MEA5oYnyIze2J+PSaluJhdEkPVXj8V24bJyPbg8ZzbSb2xeIYomd6NyMgebfCewUJrkendhEQDj1dOhgp75BV5qDngp6UpSDCoz/k97DT72ocJmZAR5yY7wf4Gtj5oNXiRBdaEmBZef/11QqEQM2bMoKREYlmEEOJ0SQavEEIIAccsrmYYE9ewmbfQR3yCwciwZt+u4XN6LK312AJrmbn2N2LOREa2GxQM9JmnnUncE1lgLT16JpXF1JOc6iI+0cAMQ0vD1FtsrXI0nqE0ywH5u34/1NUAMsErxHRw6NAhampqMAyDNWvW2P4ZJIQQ0SS6xn2mgfXr15/050opPB4P6enpzJw587j5vEIIIc6Mf8SkrclqHhYUj388w9FcbkXZ8ljefm2Qw1UB8ou8pGac3a/jgT6TkSGN4YKMzOj6le71GqSkuujpCtPeEqRwVswp73Mkfze6nquYWpRS5Bd52V8xQmNdgIIJiHSxU2SBtVIn5O8ePgjhEKSkQ3qW3dUIISZQKBQaOxZeunQp6enpNlckhBDRRY6QHOanP/3pad82Li6O97///dx8880TWJEQQkx9jXVBtLYm8xKTJ346NDPbQ36xh4bDQXZuGeKyqxPPamq4rdmaHkzPdONyR9+US2aOm56uMB2toVM2eLXWssCacIy8Qg/7K0Zobw3hHzGJ8U2Ni+L8IZMDkfxdBzR4x/J35y6UST4hprgtW7bQ19dHQkICK1assLscIYSIOtLgdZjvfve7PPTQQyQmJnLNNdeQk5MDQHNzM2vXrmVwcJC/+7u/o6enhxdeeIHf/va3xMbGcvXVV9tcuRBCRK/6Q1Y8w2RO4i1cEktrU4j+XpOa/X7mLDjzRUSO5O9G59UcmdkeDu7x094aQmt90gbO8JBJwK9RhpVlLISd4hNdpKRZE+iNdUFmzTv1BHo02Ndh5e+mx7nJcUL+7miDlzmSvyvEVNbd3c2WLVsAWLVqFV7v1LoyQgghJsPUGDeYQv7yl7+QnJzMl7/8Zc4//3wKCwspLCxk5cqVfPnLXyYpKYl169Zx/vnnc//99zNv3jzWrl1rd9lCCBG1+nrC9PWEUYY1lTdZYmIMFi2JBWB/5QiDA+Ezun8oqOnsiM4F1iJS01243BDwa/p6Tv78ezqtnyclu3C5ZJJP2C+vyGpANNYGbK5k/IzFMzghf9cMQ/U+QPJ3hZjKtNa89tprmKZJcXExs2bNsrskIYSIStLgdZjNmzef8JIUpRTLly/nnXfeAcAwDFauXElLS8tkliiEEFNKZHo3e4YHb8zk/lrML/aQkeXGDMOuLcNorU/7vh1tIbQJcQkGCYnROdFquBQZWVZzur0ldNLbSjyDcJoZBR5Q1mvzTE/QOJWj8ncb62B4CHyxkF9kdzVCiAly8OBB6uvrcblcrFq1yvaTS0IIEa2kweswpmnS1NR0wp83NjYe0wBwu91yCYs4I8GgZqB/ahyICnGuTFPTWDcazzDBi6sdj1LWgmuGAR2tIRprg6d930j+blZOdE7vRmRkW1PT7a0nb/B2d1k/T02XBq9wBl+sQWa29f47k/euU1n5uyMAlDmgwTsWzzCrBGXI+16Iqcg0TTZu3AjAihUrSE5OtrkiIYSIXtLgdZjly5ezdu1aXnjhBQKBI5f8BQIBnn/+eV566SWWLVs29v0DBw6M5fQKcSrBgMkbL/Xz6vP9dLafvJkixHTQ3hLCP6LxxijbYg4SEl3MXWTl71buGCbgN095H631kQZvlObvRmSONqi72kOEQ8efYNamprc7MsEb3Q1tMbXkFVonhhpqA2c0ge9E+zuGCZma9Fhn5O9yMLLAmuTvCjFV1dTU0NfXh8/nY+nSpXaXI4QQUU2OkhzmzjvvpLW1lf/93//l17/+NSkpKQD09PQQCoWYM2cOd955J2A1fb1eLzfccIONFYtooU3N1reGGOy3mkd7dw5z8RUJchmUmNYi8Qx5RV4Mw773wpz5MTTVBujvM9mzc4Ql5598em6gz2R4SGMYkJ4V3b/KExINfLGKkWErUzgr572Npf4+k3AIXG7r9kI4RU6+B2MrDPab9HaHo/oEREWbFc+wKNsB+btaoyMNXsnfFWLK2r59OwBlZWV4PA44sSSEEFEsevdCp6iEhAS+/vWvs2nTJnbu3El7ezsAixcvZvHixaxYsQLDsA5uvV4vn/jEJ+wsV0SRvbtGaG8J4XKBBro7w7Q0BsnNl4gPMT0F/CatTdYUrB3xDEczXIryFXG8+coA9YcCo9m8Jz7QiUzvpme5cbuj+ySNUorMHA/1hwK0txy/wdszGs+QkupC2diIF+LdPB5F9gwPzfVBGuuC0d3gHc3fdUI8A13t0NMJLhfMnGd3NUKICdDS0kJzczOGYVBeXm53OUIIEfWidy90ClNKsXLlSlauXGl3KWKKqD8coHq/H4AlK+Po6wlzcI+ffbtGyJ7hsXVyUQi7NNUFMU1ISjFITrU/3zEtw03RbC+11QF2bRlm1TVuXK7jvzfbRhcki/Z4hojMHDf1hwJ0tASB2Pf8fGyBtXTZbRHOk1doNXib6gIsLPdF5UkIf8hkf4eVv1uaZX+DV1fttf5QOBsV47O3GCHEhIhM786fP5/4+HibqxFCiOgn1zkKMcV1d4bYtdmaypm7MIYZBV5mz/fh8SoG+s2xS9SFmG7qD9u3uNqJLCj3EeNTDPabHNwzctzbhIKarvZIg3dqNDwzRmMm+npNRobfm0E81uBNs78RL8S7ZeV68HhGY0aiNN/+QKeVv5sW6yY30QEnjqoi8QySvyvEVNTX10dVVRWAZO8KIcQ4mRpHhlHsnnvuwTAMfvjDH+J2u7nnnntOmXumlOLHP/7xJFUootnIsMmWNwcxTcjOczO/1JqC8XgVcxfGsGfHCAcqR8gr8kb9Zd5CnIn+3jA9XWGUsvJ3ncLjNSg9L5atG4eo2ucnr9BLYvKxTc2OthCmCXHxBvEJU+M8bYzPmqLu7Q7T0Roi/6imezis6euRBdaEc7lcitwCD3U1ARrrgmRkO6BBeoYi8QylWfbn7wKSvyvEFLdz50601hQUFJCRkWF3OUIIMSXIkZLNFi5ciFJqLFc38rUQ5yoc1mzeMMjIsCYxyeC8lfHHvLaK58Rw6ICf4SHN4YN+5iyQSyDF9BGZ3s3KdRPjc1aTNDffQ/YMN61NIXZtGeKiy49dDDGSv5uV655Svy8ys930dodpbw0e0+Dt6wmjNXhjFLFxU+f5iqklr8hq8DbXByk9T58wXsWpxhq8Dsjf1YMD0FRnfSETvEJMOX6/n8rKSkCmd4UQYjxJg9dm99xzz0m/FuJsaK3ZtWWInq4wHq9ixaXxuD3HHmy6XIr5ZbHseGeIg3tHKJzlxRvjrEaXEBNBm5rG2tF4hpnOmd6NUEpRel4cHW19dHWEqasJUDQ7BrDe20cavNE3JXgyGTluqvb5aW8JobUea173dB6JZ5hKDW0xtaRnuPHFWjENbc3RtYBpIHxU/q4DGrzU7AOtITsPlZRidzVCiHG2Z88eAoEAqampFBUV2V2OEEJMGdLNEWIKqjngp+FwEKVg2UVxxCccP7cyv9BDYrJBKAhVe/2TXKUQ9mhvDTEyrPF4FdkObZLGxRuUjEaq7Nk5PJZLO9BnMjykMQxIz5pa52jTMtwYLvCPaPp7j+Tw9nRZmaYSzyCcTBmKGYVWU7exNmhzNWfmQMcIQVOTGutmhgPyd4/EM5TYXIkQYryZpsnOnTsBa3pXTtwKIcT4kaMlh+ro6KC1tZXBwUG01u/5+cqVK22oSkSDtpYge3ZakzgLl8SSeZIsQGUoFpTHsumNQQ4d9DNzXgyxcXLeR0xtkXiGvEIPhoMvo545N4aG2iC93WEqtw+z7KL4send9Cz3lMvNdrkU6Zlu2ltCtLcGSUqxTkx1RxZYS5cF1oSz5Rd5qNnvp7UpSDCo8Xii4z16JH831hHNFj26wBqSvyvElFNTU0NfXx8+n4+SEjmJI4QQ40kavA7T0dHBf/3Xf1FRUXHS2z3++OOTVJGIJgP9YbZtHAJtXXo+c+6pLxHNynWTnumisz3M/ooRlpzvgMszhZggwYBJS6PVJHViPMPRlKEoXx7LGy8P0FQfJL8pSFuLNc2alTM1f31nZo82eFtCzJ4PwYBmsN+a5k1JkwavcLakFBcJiQYD/SYtDUHHf8ZE7G5zUP5uMAiHDgKywJoQU9H27dsBKC8vx+2emvsyQghhF/lUdZiHHnqIAwcO8P73v5+5c+cSF2f/zraIDsGgtahaMKhJTXdRtuz0JnGUsqZ4N7wyQP3hALPnx5CYLI0UMTU11Qcxw5CYZJCc6vzXeUqam1lzY6g54Gf31iH8I9YVHVMtfzciM8cDO0fobA8RDmt6uq2Gdly8QYxkhAuHU0qRV+Rlf8UIDbWBqGjwBsIm+9uHAWc0eKmtglAQEpMhe4bd1QghxlFzczPNzc0YhkFZWZnd5QghxJQjDV6HOXDgADfffDMf/vCH7S5FRBFtara/PchAn4kvVrH84vgzWsE7NcNNTr6HloYge3cNc/6lCRNYrRD2qT9kxTPkz/Q64lLk0zG/1EdzQ4DhIau5GxdvEJ84NZudickGMT6Ff0TT3RGip+vIAmtCnCvd1oT5h0dQ512EsXLVhGwjr8jD/ooROtpCjAyb+GKd/V49OJq/m+JzkZdof0NaV++1/jBnQdR8RgshTs+OHTsAKCkpIT4+3t5ihBBiCnL2Xuc0lJ6eLr/wxBnbVzFCa1MIwwUrLok/qwPKBWU+lILWphCd7aEJqFIIew30h+nuDIOC/CL7Gxmny+1RlC07MlmXleueso0PpRQZ2da55/bWED2d0uAV48d84lew7S30/zyI+Yvvo4cGxn0b8QkuUtNdoK0rBpzu6HgGJ3yuHFlgTeIZhJhK+vr6qKqqAmDJkiX2FiOEEFOUNHgd5n3vex/r1q3D7/fbXYqIEo11Aar2Wq+XxSviznql+YQk19jlpHt3DR93cT8holnD6OJqWTlux0/VvVv2DI/1/oyy5vTZyMyx4ifaW0L0dFknm1LS5YIjcW50Zzvs3GR9YRjoTa9jPvBZ9IGTr3lwNvIKrfdoY21g3B97vB1ZYM3+eAZtmjA6wavmSoNXiKlk586daK0pKCggIyPD7nKEEGJKkiMmh7nqqqswTZPPfOYzXHDBBaSnp2MY721E3HjjjTZUJ5ympyvEjk3WwdnskphzbvzMW+SjoTZAd0eY1qYQOXlTM+dTTD9aa+pHG7wFxdHZIF28IpZFS2PxeOyfsptImaMTvL3d1vQuiqjISxbOpl9/AbQJ88swPvC3mP/zILS3YH7/PtS1H0Td9BGUe3x+580o9FC5Y5ierjCD/WHiE535+g2GTfZ3WPm7ZU7I321thIF+8HqhYJbd1Qghxonf76eyshKApUuX2lyNEEJMXdLgdZi6ujqeffZZenp6eOGFF054O2nwCv+IyeYNg5hh65LtBWW+c37M2DiDWfNiqNrrZ++uYbJz3ShjajeTxPTQ0RZiZEjj8Siyo/TEhVIKT3SWfkZ8sQaJyQb9vSZgLYjndsvnkDh7OhhEv/EiAMaaG1Cz5mN85T/Qj/0P+s2X0c8/id6zA+OuL6By8s95ezE+g4xsN+0tIRrrgsxb5MwG74HOEQLh0fzdJPtPfEXiGZg5H+WWQxQhpoo9e/YQCARITU2lqKjI7nKEEGLKkr0nh/n5z3/O0NAQd999N3PnziUuzgETFcJxwmHN5jcHGRnWxCcanHdB/Lg1YueUxFBbHWCgz6T+cIDCWTHj8rhC2CmyuNqMQs8ZLUAo7JGZ7aG/14qeOdvYGSEi9NY3ob8XUtJhyUoAlC8O9bHPoMuWYz76E6itwvz651G33YW69OpzzqPNK/TS3hKioTbA3IUxjsi3fbdIPMOiLGfk71IVyd9dYHMhQojxYpomO3fuBKzpXUd81gghxBQVXSGE08Dhw4e55ZZbuPLKKykqKiIzM/O4/4npS2tNxbZhujvCuD1w/qXxeLzjt7Pk8RrMXWA1dfdXjBAOSRaviG6hoKalwVrsKFrjGaabzJwjTV1ZYE2cK/3acwCoVdegXMe+ntSyizC+9mNYsBgCfvSvH8L86bfQ/X3ntM3cfA+GCwb7zSNxIw4TafA6Ip4B0FWj+buywJoQU0Z1dTV9fX34fD5KSkrsLkcIIaY0afA6TFZWlt0lCIc7XBWgriYACpZdGE/CBGT7Fc+NwRenGBnWHKqSBf9EdGuqDxAOQ3yiQUq6NAujQVqmm0j8fKr8m4lzoOuqoXofuNyoS6857m1UajrG5x5AfehOcLthxzuYD3waXbHtrLfr9ihyZliZKo21wbN+nIkSDJvsG83fLXVAg1f3dEJ7CygDZksTSIipYvv27QCUl5fjlugVIYSYUNLgdZgPf/jDrF27lo6ODrtLEQ7U0Rqkcrt1QLaw3EdW7sQEcrpcipJSK9O3aq+fQMCckO0IMRmOXlxNLg2MDm63YukFcSxc4iM5VQ4IxdnTr45O7553ISo59YS3U4aBcfUtGF96EHILoLcb8z+/hvnYL9DBwFltO2904dOm+gDadNbVMAdH83eTfS7yHZC/y+j0LvlFqFj7G85CiHPX3NxMS0sLhmFQVlZmdzlCCDHlyVGTw+zZs4e4uDg+97nPUVZWRnp6OoZxbB9eKcWdd95pU4XCLoMDYbZsHEJryCvyMGv+xGbj5hd5qd7np7/PpHqvnwWLYyd0e0JMhMGBMF3t1uXR+RLPEFVmFMi/lzg3enAAvWk9AGrNDad1H1UwE+P+H6Cf/BX61b+gX/kTet8ujLv+Hyq/+Iy2n5XjxuO1robpbA+Rke2cVRIj8QylDsnflXgGIaaeHTt2AFBSUkJ8fLy9xQghxDQgDV6HWbt27dift2078aWB0uCdXkJBzeYNgwQDmpQ0F4uXT/wBmTIUCxbHsumNQWoO+imeG0NsnAz9i+jSMDq9m5HtltevENOMfvNlCAQgvxjOYOEu5Y1B3f4P6LJlmP/7n9BYi/nNL6A+eAfq8vehjNP7LDFcitx8D3U1ARprg45q8O5uG23wOiCeAY40eJkrDV4hpoK+vj6qqqoAWLJkib3FCCHENCENXod5/PHH7S5BOIzWmu3vDNHfaxLjUyy/OB6Xe3KmbbJy3aRluOjqCHOgcoTFK5xxICjE6dBa03BYFlcTYjrSpnlkcbU115/VSVFVthzjaz/GfOTHsGsz+vFfondvxbjzs6iU9NN6jLwiL3U1AZoaApQui8Xlsn9aNhjW7Gt3UP7uyBDU1QCgZp9+I14I4Vw7d+5Ea01BQQEZGRl2lyOEENOCjDMJ4XAHKkdoaQxiGLDi4vhJnUJUSo1FM9QdCtDf58yVwIU4ns72MEODJm435OQ7Z3JOCDEJ9my3Fu2KjUetXH3WD6OSUjA+dT/qo/8IXi/s2YH5wGfQ298+rfunZ7rwxSpCQWhrdsZia1Wdw1b+boyLAifk79YcAG1CehYqTRpBQkQ7v99PRUUFAEuXLrW5GiGEmD6kwetgpmkyMDBw3P/E9NBUH+BApR+A8uVxpGZM/tB9WoabnDwPaNi3a2TSty/E2YrEM8wo8OKepKl3IYQzmJHF1S66HBXjO6fHUkphrL4O4/7/gMJZMNCP+dNvYT76E/TI8Cnvmzt6gqmzLXROdYyXSDzDomyn5O/uAUBJPIMQU8KePXsIBoOkpqZSVFRkdzlCCDFtSESDw4RCIZ555hleffVVOjs7MU3zuLeTKIepr68nzI53rIOwWfNiKJhp35RNSbmPlqYgLY1BujpCpNnQaBbiTIRCmqZ6q8Gbb+N7Rwgx+XR7C+zeAoBaff24Pa7Kzcf41++h//h/6BefRr/xInp/hbUA28y5J7xfcqoLgL7e4+/TTbajF1hzgrH8XYlnECLqmabJzp07AWt61wknkYQQYrqQLo3D/PznP2f9+vXMmzePFStWEBfnjJ1vMbn8fpNNGwYJh63FoRYsPrfpo3OVmOSisNhL3aEAe3cNc9GaBNlhE47W3BAkHIK4BIO0DJfd5QghJpFe/wJoDQuXoHLyxvWxlduDuvVj6NLzMB/+D2hrwvz3f0a97yOo6z6IMt77eZOYPNrg7Qmjtbb19+fR+btlTsjfDYWgZj8gE7xCTAXV1dX09fXh8/koKSmxuxwhhJhWpMHrMG+//TaXXXYZ99xzj92lCJuYpmbrxiGGB03iEwyWXRSHYdjfTJ1X6qOhLkBXe5i25hDZMyTTVDhXwyFrereg2CsnI4SYRnTAj97wEgDGmvGb3n03VVKO8dUfoX/zU/SWDeg//gZduQ3j776ASs865raJSS5QEAxo/CMaX6x9n0lVXcP4w5qkGBcFyQ64uqHhEPhHIC4ecgvsrkYIcY62b98OQHl5OW63tBqEEGIySQavw8TExDB37okv8xNTX+X2YTrbQrjdsOKSeLxeZ7xNY+MMZs6NAWDvrmG0qW2uSIjjGxo06RjNuswvlhMRQkwnevMGGOyHtEwoXzGh21LxCai//yLqzs9BTCwc3IP5wGcw337tmNu53IqEBOt3eV+vvYuVRuIZFmU5K3+X2QtQhjP2d4QQZ6e5uZmWlhYMw6C8vNzucoQQYtqRPSmHufjii9m2bZvdZQib1Fb7OVxlTR4uvSB+7LJOp5izIAaPR9Hfa9JQ64zVwIV4t8jiaulZbuLinfUeEkJMLP3qXwBQq649blzCeFNKYVx0OcZX/xNml8DwEPqXP8D8xYPooSOL4iamWLX09zijweuEeAYAfdDK35V4BiGiX2R6t6SkRGIGhRDCBtLgdZi//uu/Ji4uju985zu88847VFVVUVNT857/xNTT2R5i91YrF6+kzEdOnvMmD71egzkLrSnefRXDhMMyxSucRWs91uAtKHbA5cdCiEmjDx2A2ipwu1GXXj2p21aZORhf/DbqfR8Bw0BvWo/5wGfRByoBSDoqh9cuIVOzdzR/d1FWrG11RGitYXSCV82RBq8Q0ayvr4/q6moAlixZYm8xQggxTUkwjsMEg0G01mzfvn3sLOjxPP7445NYlZhoQ4MmW94cRGuYUeBhzoIYu0s6oZlzYjh0wM/IkOZwlZ/Z8+1dAE6Io3V3hhkcMHG5ITffeSdJhBATZ2x6d/klqMTkSd++crlQN30EvWgp5i9/AO0tmN+/D3XdB0lc9iEA+nrNSa8roqpzBH9YkxjjojDFAfsZ7S3Q1wNuNxTPsbsaIcQ52LlzJ1prCgoKyMjIsLscIYSYlqTB6zD/9V//xaZNm7j44ouZM2eOXN4yDYRCms0bBgn4NUkpLhaf74xcvBNxuRXzS33s3DzMwT1+Cmd68TgkJ1iI+tHF1XLzPbg9zn0fCSHGl+7vs/J3AbV64hZXOx1qdgnGV/4D/btfoDe+gn7uCRL2H4KZn2GgL4xpalsWT43EM5RmxWI4YD9jLH+3eC7KI1dcCBGt/H4/FRUVACxdutTmaoQQYvqSBq/D7Ny5k2uvvZaPfexjdpciJoHWmh3vDNLXE8Ybo1hxSTxut/0HXaeSX+yler+fgT6Tqn1+FpTbf6mnEOGQpqle4hmEmI70hpcgFISiOTBrvt3loHxxqDs/iy5fjvnoQ8RWb8VdHCBkehnsN23J2K9oG23wOiR/l6rR/F2JZxAiqu3Zs4dgMEhaWhpFRUV2lyOEENOWjN05TGxsLDk5OXaXISbJ9k0dNNUHUQYsvzieuPjoeEsahhpr6tYc8DMybN8lp0JEtDQGCQUhNk6RniXnL4WYLrQZRq9/HgC15npHXQWjll2M8ekvo9Ak9NUB0Nc7+Tm8Vv5uZILXGQ1efTCSv7vA5kqEEGfLNE127NgBWNm7Tvr8FUKI6SY6uknTyBVXXMGbb76JaUrDbKpraQyweWM7AGXnxZKeGV0NqewZblLTXZhh2F8xYnc5QlA/urhafrFXDjCEmE52b4XONohPRK241O5q3kPNWQCzS0jsH23w2rDQWnXXCCMhTaLXcET+ru7vhZYG64vZJfYWI4Q4a9XV1fT39xMbG0tJibyXhRDCTtHVUZoG8vPz2bJlC//yL//CqlWrSE9PxzDe24dfuXKlDdWJ8WKGNbu3WitZF8+NoWi2/QdbZ0opxYLFsWxcN0D9oQCz58eQkDT5l5wKATA8ZNLeGgKgYKbEMwgxnZiRxdUuvhLldebvU+Oqm0n68xYA+rqDwORGG+0ezd9dlB3niPxdqq14BnILUAlJ9tYihDhrkUXBy8rKcLultSCEEHaST2GH+Y//+I+xP//6178+4e0ef/zxSahGTBTDpbhwTQJNtQbRHD2Xnukme4ab1qYQ+3aPsPzieLtLEtNUY20ANKRluIhPkBMNQkwXurUJKreDUqjV19ldzoktvYDE59cD0N82DExuU7Oy1WnxDKP5u3OjeCdIiGmuubmZlpYWDMOgvLzc7nKEEGLakwavw3z1q1+1uwQxSRISXay6Kpfm5ma01naXc9ZKymJpbe6nuSFId0eI1Az5WBGTS2tN/aHRxdVkeleIaUW/ZmXvUroMlencNQyU4SLp/MXQDcNmDIGRMF7f5JyMCpmaPe3WVUNOWWBNV1n5u1F9lluIaS4yvVtSUkJcnDM+W4QQYjqTTozDLFwoO7oiuiSluCgo9lJ/KMDeXcNcuCZB8k/FpOrpCjPQb2K4ILdAGrxCTBfa70dvfBkAY831Nldzat7L1uB7vJkRXzr92/eQfmHZpGzXyt81SfAaFDkhf9fvh9pqQBZYEyJa9fX1UV1tvY+XLl1qczVCCCFAFlkTQoyD+aU+DAM628O0tYTsLkdMM5Hp3dx8Dx6PnFwQYrrQm9bD0CBk5sCi8+wu55SUL44kXxCA3p37Jm27FZH83SyH5O8ePgjhEKSkQUa23dUIIc7Cjh070FpTWFhIenq63eUIIYRAJngd4eGHHz6j2yuluPPOOyeoGiHOXGycwcy5MVTv97N35zBZ2W6U4YCDSDHlhcOapnqrYVJQLNO7QkwXWmt0ZHG1VdehjrMgrRMlFWfSdhj6Bl3oumpU4ewJ32Zlm9XgLXNYPIOas1Cu+BEiCvn9fiorKwGZ3hVCCCeRBq8DrF279ozvIw1e4TRzFsRQW+Onv9ekoS4ozTYxKVqbggQDGl+sIiNLfqWJ6KUbDkNcAiotw+5SokP1Pqg/BB4v6pIr7a7mtCXlJMLhIQYS8tEvPYP6uy9M6PbCpqayzcrfXeSUBdaqrAXWJH9XiOhUWVlJMBgkLS2NwsJCu8sRQggxSo6GHeDxxx+3uwQhzpk3xmDOAh/7do2wf/cwMwo8uFwymSMmViSeIb/YK1PjImrp9hbMb3wBfLEY9/8AJZetn5J+9TkA1PmXouITba7m9CWlWAur9ScUYL6xAfWBO1CpE3d589H5u8WpDsjfNcNWcx7J3xUiGpmmyc6dOwFYsmSJTOELIYSDRMf1bEKIqDBzbgy+WMXwkKa2ym93OWKKGxk2aR/NfJaJcRHN9K7NVibpYD/mf30HHQzYXZKj6b5u9NY3AVBrbrC5mjMTn2hgGBByxzLsSUG/+ucJ3Z7j8neb6mB4EGJiIb/Y7mqEEGeourqa/v5+YmNjKSkpsbscIYQQR5EGrxBi3LjdinmLfAAc2OMnGNA2VySmsub6IFpDarqLhCSX3eUIcdb07i1HvqirRv/2Z/YVEwX0Gy9ZDfGZ81BFc+wu54wYhiIhydr97k8oQK9/AT0yPGHbqxjN3y11Sv7uwdF4htnzUS753BYi2mzfvh2AsrIy3G65GFgIIZxEGrxCiHFVMNNLQqJBMKCp3j9idzliCuvutKZ3s2Z4bK5EiLOn/X7YXwGAuu0uUAZ6w0uYb7xoc2XOpMNh9PoXgOib3o1ISh6NachZCEOD6I2vTMh2wqZmz2j+bqlD8nc5aoE1IUR0aW5upqWlBZfLRXl5ud3lCCGEeBdp8AohxpVhKErKrSnemv1+RoZNmysSU1VvTxiA5BSZAhNRbP8uCAUhLRN1xftQ7/8oAPq3P0MfPmhzcQ60cxN0d0BCEmr5xXZXc1bGcniLlgGgX37WyqYdZzXdIwyHTOK9BkUp9ufvAuixBq/k7woRbSLTu/PnzycuziEnjYQQQoyRBq8QYtzl5HlITXcRDsOBSpniFeMvFNIM9FsnD5JTpcEropeu2AqAKl2GUgp17QdhyUoIBa083v4+myt0FvO10cXVLr0K5YnO7O3EyASvJxPiEqC9BXZsGtdthE3NO/UDgJW/63LAIpS6sx26OsAwYNZ8u8sRQpyB3t5eqqurAVi6dKnN1QghhDgeafDa7LnnnqOpqcnuMoQYV0opFpTHAlBXE2Cgf/wnk8T01t8bBg3eGEWMz/7GhRBnQ2uN3j3a4C2zpjmVYWDc+TnIyoWudsz/eXBCpjujkW6uh707QRmoVdfZXc5Zi0zwDgxqzFVWzIT50jPn9Jim1tR0jfDM3i6+8Vo9f/3kQZ6o7AScE88Qmd6lcDYqxmdvMUKIM7Jz50601hQWFpKenm53OUIIIY5DGrw2e+SRR6ipqRn7+rbbbmPDhg02ViTE+EjPcpOV60Zr2LdbpnjF+OrtHo1nSHWhnLAyvBBno7UROlrB7YaSI3mGKi4e4x//Fbxe2LMd/ezvbCzSOfSr1vQu5ctR6Vn2FnMOYnwKj1eBhsHl14LLDVV70IcOnPZjaK2p6/Xzl/3dfOf1Bv72yYN8/vnDPLytjc2NgwwFrWiGiwsTWTMreQKfzRmoshZYk/xdIaKL3++nsrISkOldIYRwMln60mYJCQn09PTYXYYQE2JBeSxtzf001wfp6QyRki4fOWJ89En+rpgCItO7zF2E8sUe8zOVX4z6m0+hf/kD9F9+j545D7X4fBuqdAY9MoR+ax0AxuXRubhahFKKpBQXnW0h+sMJJJ1/KfqtV9EvPYP6+y8e9z5aa5r7g+xuHWJ36yC7W4foGTl2stvnNliUFUtZdhxl2fHMTI1xRDRDxFj+7lzJ3xUimlRWVhIMBklLS6OwsNDucoQQQpyAdFtstnDhQp544gkOHz48Fla/fv16Dhw48RSHUoo777xzskoU4qwlpbjIL/bQcDjInl0jXLg6XqYtxbiITPAmSf6uiGJH5+8ej3HBasxDB9Dr/oz5yx9i3P8gKmvGZJboGPrt12BkGLLzoGSx3eWcs6Rkg8426Osxyb/q/VaDd+ub6M47xqaT2waC7G4dZFfrELtbh+gcCh3zGF6XYkHmkYbunHQfbgc1dI+mhwagsdb6QhZYEyJqmKbJzp07AWt6V/bjhRDCuaTBa7O77rqLX/3qV+zatYve3l4Adu3axa5du056P2nwimgxvzSWprognW0h2ltCZOV67C5JRDltavp6ZYJXRDc9MgwHKgBQZctPeDv1oTvRtVVQvQ/zv76Dce/3UDExk1WmI2itx+IZ1OrrUEb0J4xFcnj7esOopTNhwWK6qg9R8dLbVBSvoKJ1iJaB4DH3cRswP8Nq6JZnxzMvw4fHFSV/F9X7QWvIykUlpdpdjRDiNFVVVdHf309sbCzz58viiEII4WTS4LVZcnIyn/3sZ8e+vu222/j0pz/NJZdcYmNVQoyfuHiD4jkx1Bzws3fXMJk5bjn7L87JwICJGQaXC+IToqS5IcS77d8NoRCkZ0FO3glvptwejE/8C+bXPw8Nh9G/eQg+/vnp9Tl6oBKa6sAbg7rocrurGRdJyVaDt7MrxH9vamFX4UdozDYgDFRbJ/wNBXPTfZRlx1OWHceCzFhi3NH5mTcWzyD5u0JElR07dgBQVlaG2y2tAyGEcDL5lHaYf/zHf2TevHl2lyHEuJqzMIbaGj99PSZ9PWGSU+WjR5y9vkg8Q4oL5dDLCQpz4QABAABJREFUkYU4lbF4hrLlp2zWqpR0jL//Z8wf3G9FFcwqQa25fhKqdAb96l8AUBesRsUl2FzN2Rvwh6lsG2JX6xCVLUNcrlMgqHjtYB/DGCitmTnQaE3onl/OwqxY4jxT4yqFSINX4hmEiB7Nzc20tLTgcrkoLy8/9R2EEELYSrosDrN69eqxPzc0NNDe3g5AZmYm+fn5NlUlxLmJiTFITXfT0Rqiu1MavOLc9PYcafAKEY201mMLrKnSZYTDYdavX49pmpSWlpKTk/Oe+6j5pagP3oF+4n/Rj/8PunAWanbJZJc+6XRPJ3rH2wBR2dT2h0zW1fTyUnUvNV0j6KN+1ucKk4yb6wpSmD8zloXVbxP/fz+G9CyMm36Gck2NzzgdDMKhgwCouTLBK0S02L59OwDz588fWytGCCGEc0mXxYE2b97Mo48+Sltb2zHfz8rK4o477mD58hNn9QnhVKnpLjpaQ/R0hU99YyFOIrLAWrIssCaiVUsDdLaB2w0lZdTW1lJRYeXx7tmzh+zsbMrLy5k7d+4xl8Sqq96PrtkPWzdi/vd3ML78wymfZ6pfXwvhMMxZiMqfaXc5p20wEOb5gz38aV8XPSNHfu/lJXlHM3TjULWKzuYwl2QnMbvAh86+DPPZR6GzDb3tLdSKKRLXVVcNwQAkJFmL5AkhHK+3t5fq6mrAWlxNCCGE80mD12G2bdvGgw8+SGZmJh/5yEfGpnYbGhp45ZVX+P73v8+9997LkiVL7C1UiDOUkuYG/PR0hU55WyFORGtNX48ssCaim969xfrDvDJUjI+qqirAyuXv7++ntbWVl156iTfeeIPS0lLKyspITExEKYXxsc9gNtZBSwPmz7+P8fl/mzKTnu+mQyH06y8C0TO92zMc4k/7u3nuQDdDQROAzDg3Ny9I46LCRNLjjiw0ur9/hM7mMP091u2UNwa1+jr0nx5Dv/RH9PKLp0TW8pF4hoVT4vkIMR3s3LkTrTWFhYWkp6fbXY4QQojTIA1eh/nDH/5AUVERDzzwAD6fb+z7y5cv59prr+UrX/kKTzzxxBk3eJ9++mk2bdpEY2MjXq+XefPm8dd//dfMmDFj7DZaa37/+9/zyiuvMDg4SElJCXfddRe5ubljtwkEAjz66KNs3LiRYDDI4sWLueuuu0hJSRm7zcDAAA8//DBbt25FKcXKlSu58847j3k+HR0d/OIXv6CyshKfz8eqVau4/fbbcU3Rg1QBKWnWv21/r0koqHF75CBPnLmRYU3Ar1EKEpPl80JEJ12xDQBVdh6hUIiamhoArrrqKlJSUqisrGT37t0MDAywZcsWtm7dyqxZsygvLyc/Px/jk/+K+c1/gv270U8/irr1TjufzoTR29+G3i5ISkGdd6Hd5ZxU60CAp/d08UpNL4GwFcRQkOzlAwvTuaw4Cfdx8sKTUqwF0/p6j0z4qtXXo5//Axw6ANV7YQosSqYPji6wNlfyd4WIBn6/n8rKSkCmd4UQIppE51K8U1hdXR2rVq06phka4fP5WL16NXV1dWf8uHv27OGaa67hm9/8Jvfffz/hcJhvfOMbjIyMjN3mmWee4fnnn+fuu+/mW9/6FjExMXzzm98kEAiM3eaRRx5h69atfOELX+CBBx6gu7ubBx988Jht/ehHP6K+vp7777+fe++9l7179/Kzn/1s7OemafLtb3+bUCjEN77xDe655x5ee+01Hn/88TN+XiJ6+GINfHHWAW5Pt0zxirMTmd5NSDRwueUkgYg+emQIDlgHzqp0OQ0NDQQCAeLj48nNzSUuLo4VK1bwsY99jBtuuIH8/Hy01lRXV/P000/zf//3f+xu7yb0N/dYj7f2afTWjXY+pQmjXxtdXO2ya1BuzylubY+6Hj8/fLOJTzxbw/MHewiENfPSfXzpsjx+dMNMLp+VfNzmLhzJEe/vDWOaVlNYJaWgLlwDgPnSM5PzJCaQ1tpqVANqCjSrhZgOKisrCQaDpKWlUVhYaHc5QgghTpM0eB3G4/EwMDBwwp8PDAzg8Zz5Qc59993H6tWrKSgooLi4mHvuuYeOjo6xqSGtNc899xwf+MAHWLFiBUVFRXzqU5+iu7ubzZs3AzA0NMS6deu44447KC0tZdasWXzyk59k//79HDhwALCiJHbs2MEnPvEJ5s6dS0lJCR//+MfZuHEjXV1dgHXJT0NDA5/+9KcpLi5m6dKl3Hbbbaxdu5ZQSBp/U1lqmnXRQE+n5PCKsxPJ302S/F0RrfbtgnAIMnMge8ZYPMPs2bOPuXzdMAxmz57NBz7wAT760Y9SVlaGx+Ohq6uL1157jYe3VfLGyuvo9sZh/uo/0c0Ndj2jCaEbDluNcMNAXXat3eW8x/6OYb65voFP/+UQrx3uw9SwJCeOr19RwHevKWJlQSLGKeII4uINXG4wTRgcMMe+r664yfrD9rfRbc0T+TQmXksjDPSDxwuFs+yuRghxCqZpsnPnTsCa3pVYFSGEiB7S4HWY0tJSnnvuubGG6dEOHjzI888/T1lZ2TlvZ2hoCICEhAQA2tra6Onpoby8fOw2cXFxzJkzZ6yWmpoawuHwMdvPy8sj4/+zd99hUt3X4f/f907v21l6770jiigSSEIFIRS5xkUucYmdxEnsOOUbx4l/jtNsxyVucpVtyZaEQEJCIASI3nvfpcP23el97uf3x7ArkJBouzszu+f1PPsAOzP3nllm79w593zOKStru8/JkydxuVwMHjy47T5jx45F07S2D7EnT56kX79+17R1mDBhArFYjAsXLtzxcxP5q6g0m5STQWvidkn/XVHo1KE9AGhjJmMYRtsQmyFDhrzrY0pLS5k/fz5PPvkkd999N0VFRaRSKQ5GU/xu+ExWVo6g+qn/JRONdMpz6AxqwyvZv0yYgVacH/0flVLsvRzmH9ae48uvnWPnxTAaMLOfh/+6vz//ck8/xlW6bjohomkaHu+VKl7/VW0aeveDMZNAKdS6lzriqXSatv67A4flbRW2EOItVVVVhEIhHA4Hw4cPz3U4QgghboH04M0zH/7wh/mHf/gH/umf/okhQ4a09ci9fPkyVVVV+Hw+PvShD93RPgzD4Je//CXDhw9vW3bj9/uB7ICXq/l8vrbb/H4/ZrMZl8v1nvfxer3X3G4ymXC73dfc5+rk7tX7bb3P26VSKVKpVNu/NU3D4XC0/b0QtcZdqPHfjuLSKxW8zelu9bw7U1d/XQVaE7zF5i77HPNNV39NdSalFOrwlQTv2ClcvHSJRCKB0+mkd+/eN/wZ2+12Jk6cyIQJEzh//jwHDx7k9OnTXPCUcgHw/OxnjJs1m9GjR7e9R+ar93pdqWgEtX0DAPqCB3P+2ssYim0XQjx/pInq5mxrK7MO8wb6eGxUKX18ttvetq/IhL85QzBg0PvqCu5FSzEO70VteR2WfAjN5b7j55ETVa39dzt+wJocq0RH6E6vK6UU+/btA2DcuHG3tWpU3Fh3ek0JITqXJHjzTEVFBf/1X//F8uXL2b9/P1u3ZvvqlZeXs3jxYh599NF3JGFv1VNPPcWFCxf4+te/3h4hd4rly5fz3HPPtf174MCBfOtb36K8vDyHUbWPysrKXIfQacpKDbatP04sqvB6ynC55cSxo3TF11UikSEabgFg2Ihe2B3yFtaZuuJrqrMlz1ZR19yIZrXRc+5Ctr38MpD9IN27d+9b2lavXr2YMWMGzc3NbFn1ErsPHCRktrBlyxZ27NjB+PHjmTlz5i1vt7Nd73UVWvkM/kQcc79BVM5blLMPwcm0watHa/n1zvOcb8mufLJbdJaO680Hp/Sl0vvOeQm3qqlfM+dO15KMm68ZaqsqK6l74Vekzlbh3r8V7+MfveN95ULNmZOkgdJps3Fc9fw6khyrREfoDq+rc+fOUVdXh9lsZuHChW0rPUXH6A6vKSFE55JPx3nI5/PxsY99rEO2/dRTT7F3717+5V/+hdLSt5Y8tlbUBgIBiouL274fCAQYMGBA233S6TSRSOSaKt5AIND2+KKiIoLB4DX7zGQyhMPha+7T2q7h6m1cHcfbLV26lIceeqjt360f9hoaGgq2b6+maVRWVlJbW5sdQtJNuL06oYDBiWOX6dnHmutwupyu/Lpqqs9W8TucGi3+BvDnNp7uoiu/pjqbsf617F+GjeZyYyOHDx8Gssnamprb77U65d5FjCPOidde5mBpPxodHvbs2cOePXvo2bMn48aNY+jQoZhM+dPa5N1eV0opMiueAcCYvYja2tpOjy2WMnjtVAsrjjfTFM2eY7itOg8NL+Gh4cV47WZUpIWaduiIofTsca2hLvqO14Ax/0H4xXcJvPg7wtPnF1yLA+VvJlNzETSNluIK/HfwGr8ZcqwSHaG7vK7OnTvHunXrABg+fDihUIhQKJTjqLqm7vKaEnfObDZ3iYI20XkkwdtNKKX4+c9/zs6dO/na175GRUXFNbdXVFRQVFTEoUOH2hK60WiUqqoqFi1aBMCgQYMwmUwcOnSIGTNmANnWEY2NjQwbNgyAYcOGEYlEOH36NIMGZYdpHD58GKVUW3/BYcOG8cILLxAIBNqqkQ8ePIjD4aBPnz7Xjd9isbzrMqFCf2NUShX8c7gVRSVmQoEkLU1pKnsX1ofVQtIVX1f+1gFrRaYu99wKQVd8TXU249Du7F/GTOHixYvE43EcDge9evW645+tecFDjDpzkhE7NlJX3odDd91P1bnz1NTUUFNTw6ZNmxgzZgxjxozJq6qst7+u1LEDUHsRbA6YMa9TX3PBRIZVJ5pZdaKFUDI79KzEYebRkSUsGlKEw6K3xdxePN7sNqMRg2TSwGK5qlp56t3wwq+hpQlj12b0GfPabb+doa3/bu8B4HB22v+lHKtER+iqr6tYLMamTZs4fvw4AB6Ph6lTp3bJ55pvuuprSgiRO5Lg7SaeeuopNm/ezJe//GUcDkdbr1un04nVakXTNBYvXswLL7xAz549qaio4JlnnqG4uJipU6e23XfBggX8+te/xu1243Q6+fnPf86wYcPaErx9+vRhwoQJ/PjHP+ZTn/oU6XSan//858ycOZOSkhIAxo8fT58+ffj+97/Phz70Ifx+P8888wz33Xef9HrqBopLTVw4I4PWxK1rG7BWnD9ViELcLBWLvtWPdOwkqg5nP0wPHjwYXb/zmbeapsGffh7t4lkqL52j8ugWZn/2Hzh6/DiHDh0iEomwc+dOdu3axeDBgxk/fjy9evXKux6AxvpVAGh3zUdzODtln43RFCuONbOmyk88nf2w3ctjYemoUuYP9GIxddxMYqtNx+7QiMcUoUCGkrK3Ts01iwVt/oOoF59GrX0RNX1u3v1/vRd1qrX/7sgcRyKEeDulFCdPnmTjxo3E49ne4uPHj+euu+7CapUVdkIIUYgkwdtNrFmzBoCvfe1r13z/c5/7HPPmzQNgyZIlJBIJfvzjHxONRhkxYgR///d/f82b/Ec/+lE0TeO///u/SafTjB8/nk9+8pPXbPOLX/wiTz31FF//+tfRNI3p06fz5JNPtt2u6zp/93d/x89+9jP+8R//EZvNxty5c3nf+97XMU9e5JWikmxyzt+cRilVUB9WRW4FrqrgFaLgHNsPmQxU9EKVVVJdnU1ktq5uaQ+azY7+ua9i/NtfQ9UxnK88y7T3f4rJkydz+vRpDhw40Da0taqqitLSUsaPH8+IESMwm3N/SqiaG2D/TgC0eYs7fH+XgkleONrEhjMB0tmCXQYV23h8dCkz+now6Z3z/uQtMhGPpQn6r03wAmhz70e98gc4fxpOHobhYzslpvagqo5l/zJkVG4DEUJcIxgMsn79es6dOwdAaWkpCxYsuKYPuBBCiMKT+7N50Sn+8Ic/3PA+mqbxvve97z0TrVarlU9+8pPvSOpeze128xd/8Rfvua/y8nK++tWv3jAm0fV4fCZ0E6RTEA4ZeLySrBM3ZmQUoaBU8IrCpQ7vBUAbO5lLly4Ri8Ww2+3tPgRNq+iF/uRfYvzgG6h1L2EMHIZp+lyGDh3K0KFDaWxs5ODBgxw/fpympibeeOMNjh8/zrJly3J+wU1tfA2UAcPHovXu12H7qW6O89yRJradD9G6OHZMhYNlo0uZ2NPV6T8Hj89EfU2aUOCdK1s0txdt5j2oDa9irF2BqUASvCoegwunAdAkwStEXjAMg4MHD7Jt2zZSqRS6rjNt2jQmT56cVz3ahRBC3B5J8AohOpWua/iKTbQ0ZvA3ZSTBK25KKGigDLBYNBzOjlsuLURHUEqhDu0BQBszuW3QaGtv+/amTZiOtvhPUK/8EfXr76P6DEDr3R+AsrIyFixYwMyZMzl27Bjbt2/n8uXLHDlyhDFjxrR7LDdLpVKoTdkhdPr8BztkH/5Ymu9sq2HfVdPRpvZ28/joUkaUOzpknzfD68u+Blrb0Lydds8jqA2vwoGdqNqLaJXXn1eQV86cBMOA0gq0krJcRyNEt9fY2Mi6deuoq6sDssM9FyxY0NZCTwghROGTT8l5JJFI8JWvfKWtnYIQXVVxSfbakr85neNIRKEI+rOvFW+xKedVhkLcsktnwd8EVivG0FFUV1cDMHTo0A7bpbbkgzByPCQTGD/8JioaueZ2u93OxIkT24ambtmyhWg02mHx3IjaswVCASgqhQnTO2Qfv9xXz76aCLoG8wZ4+d8HB/KP8/rkNLkLb7WdCQYy1x24o1X2hvHTAFCvr+zU2G5XW//dwdJ/V4hcSqfTbNu2jWeeeYa6ujqsVivz589n2bJlktwVQoguRhK8ecRms1FfXy/JC9HlFZW29uGVQWvi5kj/XVHI1KFsewaGj6OmoZFoNIrNZqNPn46rxNR0E/qn/hZKyqH+MsYvvnvd5OH48eMpKysjkUiwZcuWDovnRtSGVwDQ5t6H1gFVzU3RFJvOBQH4xr39+KtZvehfZGv3/dwOt0dH07Kti2LR609U1xc+CoDa9gYqFOzE6G6PujJQEBmwJkTOXLp0id///vfs2rULwzAYNGgQH/7whxk7dqx83hRCiC5IErx5ZsKECRw4cCDXYQjRoYqvDFoL+DNkMtf/MCvE1QJXli77JMErCpA6vBvI9t9tbc8wePDgDu95qHm86J/5OzCbYf921OoX3nEfXdeZP38+AMeOHePSpUsdGtP1qPPVUH0cTGa0Ofd1yD5eOeknbcCocgejKpwdso/bpZs03N7sKfn1+vACMGw09BsMySRq46udGN2tU5kMnD4BSP9dIXIhkUjwxhtv8Pzzz9PS0oLT6WTx4sU8+OCDuN3uXIcnhBCig0iCN88sW7aMmpoavve973H8+HGam5sJh8Pv+BKikDlcOlabhjLeveegEK2UUm2vExmwJgqNikag6lj2H6MntbVnGDJkSKfsXxs4FO0Df5aNZflvUMfeeRG5Z8+ejB49GoANGzaQyXTucVmtv1K9O+kuNF9xu28/njZYfaoFgEdH5ueS5LY2De/Wh1fT0BYuAUCtX4VKpTottlt28Qwk4uBwQa+OG5YnhHin6upqnn76aQ4fPgzA6NGj+fCHP8yQIUOkalcIIbo4GbKWZ/76r/8agIsXL7J58+Z3vd+zzz7bWSEJ0e40TaOoJDs13N+UobhUDkXi3UUjBukU6DptVW5CFIxj+7PDpip7U5tWRCIRrFYrffv27bQQtDmL4PQJ1JbXMX7yn+j/9G20kvJr7jNz5kyqq6tpampi//79TJ48uVNiU5EwaufGbJwdNFxtXXWAcNKgp8fClN75Wb3m9Zm4RIrgu1XwAtqU2ajnfwX+JtTON9Fm3dOJEd681v67DBmJpssxW4jOEIlE2LBhQ9tFxKKiIhYsWNChrYCEEELkF8mq5Jlly5bJ1VXRLRSVmKmvSdPSnGYg+dEHUeSn1oo2j8+ErsvxURQWdWgPANqYKZw6dQqAQYMGdXh7hqtpmgYf/DPUhTNwvhrjR99C/9tvolksbfdxOBzMnj2b119/nR07djBs2DA8Hk+Hx6a2vA7JJPQZAEPav19rxlCsPN4MwCMjSjDl6THEc4MKXgDNbEa75yHU879CrX0RNXNB3p0zKsNAHb7ymu+A/08hxLWUUhw5coTNmzeTTCbRNI3Jkyczbdo0zGb5qC+EEN2JHPXzzBNPPJHrEIToFMUyaE3cJBmwJgqVUgp1+MqAtTETqdq+D+i89gxX06w29M98BePfvgRnTqKe/Snahz93zX1GjhzJ0aNHuXz5Mm+++SYPPtgxFbWtlGFgtLZnmL+4Q5KVOy+FqQ2ncFt1Fgzytfv224vXlz2+RUIGmYzCZLr+z0K7+z7Uy8/CpXPZ6vBREzsxyvemIiGMp74NR7Kvc210/sQmRFfU0tLCG2+80dY7vaKignvuuYfy8vIbPFIIIURXJOum8lw0GsUwjFyHIUS7Kyp568NsMimvcfHupP+uKFgXzkCgGaw2ar3lRCIRLBYL/frlpi+pVl6J/sm/Bk1DbVyNsXXdtbdrGvPnz0fTNKqrqzlz5kyHxhPfux0aasDhQps+r0P2sfJYtnr3/qHF2M35e9prd2hYrBpKQTj4HlW8Tjfa7IUAGGtXdFZ4N6TOVWH861/Bod1gsaJ97C/Q+nf+hQwhuoNMJsOuXbv43e9+x6VLlzCbzcyZM4cnnnhCkrtCCNGN5e+ZbjdWXV3NN77xDT784Q/z5JNPcvRotpdZMBjkP/7jPzhy5EiOIxTizlltOk539hAkVbzivUgFryhU6tDu7F9GjqfqSrJ00KBBOV02q42djPbwBwBQT/8f6vzpa24vLS1l4sRs5eXGjRtJdeAwr/CqP2ZjmrkAzWZv9+2fbIxxtCGGWYfFw4raffvtSdM0vL7se2LQ/94XPbV7HgZNh8N7UZfOd0Z470ophfHmaox//zI01UN5Jfrf/Qd6nvYHFqLQ1dXV8eyzz7Jt2zYymQz9+vXjQx/6EBMnTkSXntdCCNGtybtAnjlx4gT/7//9P2pra5kzZw5KqbbbvF4v0WiUtWvX5jBCIdpP8ZUqXn+TJHjF9SUSBvFY9jjokwSvKDBt7RlGT6KqqgrITXuGt9MefALGToFUEuP/vokKB6+5fdq0abjdboLBILt37+6QGFRDLfFd2WGy2rzFHbKPF69U7949wEup03KDe+ee50qbhtB7DFqDbCU2E2cAoF7PXRWvSiRQv/gu6jc/hHQaxk9D/8f/Qes3KGcxCdFVpVIpNm3axB/+8AcaGxux2+0sWrSIJUuW4PPlb/sZIYQQnUd68OaZ3//+9/Tu3ZtvfOMbxGIx3njjjWtuHz16NBs3bsxRdEK0r6ISE5fOp/A3p3MdishTwSvVuy63jtmSX8OEhHgvKhKG6uMA1PcaSPhQFRaLhf79++c4MtB0Hf0TX8L4xpegoRbjrz4MVhs4XOBwYnI4meMo5lWThz27djHszGGKPR5wOLPtFBzO7N+drrbv4XCimW8+iWpsXA1KoY2agFbZu92fY104ybYLIQCWjChp9+13hNZVCsEbJHgB9IVLMPZuRW1fj1r6YTRvcUeHdw1VdxnjR/8OF8+CpqMt/VO0+5aiSQWhEO3u3LlzrF+/nmAwezFu+PDhzJkzB6fTmePIhBBC5BNJ8OaZ6upqPvCBD2CxWIjH4++4vaSkBL/f3/mBCdEBikqzhyB/cwalVN5NAxe5F/BLewZRmNTR/aAM6NmX6oYmAAYOHJg3U801lxv9M3+H8f1/g5ZGSCayX4Fs1etAoH//8ZzzlrPh7CWWnNlL6xFavdtGLVawO9oSvq0JYO2qJHDrn2rzmmwc8ztmkNvLJ1owFIyvdDKguP3bP3SEtgSv/yZWtQweAQOHZQfmrX8VbckHOzi6t6h92zF+8R2IRcHjQ/+zL6MNH9tp+xeiu4jFYmzatInjx7MXCz0eD/Pnz2fAgAG5DUwIIUReyo9PGaKNyWS6pi3D2zU3N2O3F8YHFSFuxFdkQtMgEVfEogqnSxK84lqtFbwyYE0UnNb+u2MmcerUKSA/2jNcTes3CP3ffwrRSDZZF3vrTxWNcncwwO9OXeCSu4RT0xcy3IijWu/T9pgoJGLZDaaS2a9Q4Jr9vNtZjam8EsZPbffnFUlmWFuVjeHRkYVRvQvg8WaPc4m4IhE3sNnfvRpW0zT0RY9i/Pg/UBteQT2wDM1q69D4VCaDWv5r1GvLs98YMjKb3C0q7dD9CtHdpNNpqqqqePPNN9sKfiZMmMCMGTOwWq05jk4IIUS+kgRvnhk6dCjbt2/nwQffWdESj8fZsGEDo0aNykFkQrQ/k1nDW2Qi0JLB35zG6ZKTVnEtqeAVhUgZBupItv9uQ7/hhHbuxWw250V7hrfTdBO4vdmvq78PFAPTSnexbds2tuBg0Ec/jc32ziSiMjIQi12TICYWvX4yOB5FxaJoqSTFT3wMv/7eF7Zvx9pqP7G0QV+flYk9Xe267Y5ktmg43TrRsEEokHnPBC8AE++C0gpoqkdtX4929/0dFpsKtGD85D/gZHbQr7ZwCdpjH0XLk4p0IQqFUopoNEooFCIUChEOh9v+3vrvaDTadv/S0lLuueceKisrcxi1EEKIQiBnZXnmiSee4Gtf+xrf/OY3mTVrFgBnz56lrq6Ol156iWAwyLJly3IcpRDtp6jkSoK3KUOvvrmORuSTdFoRDmWnyUsFrygoF05D0A82B1WpbPJy4MCBWCz5P+jr7SZOnMjx48dpaWlh27ZtzJs37x330XQTuNzZr6u//x7b1TQNR8+e+Gtq2jXejKF46XgLkO29W2itf7w+E9GwQTBgUNbjve+rmUxo9zyM+sNTqLUrUbMXdUgPXHXySDa5G2gBuwP9Y19Emzyr3fcjRFeQTCbfNXnb+j3DMG64HavVyqRJk5g8eTImk5wDCSGEuDFJ8OaZoUOH8tWvfpWf/vSn/OAHPwDgN7/5DQA9evTgq1/9al5WAAlxu4pKTJyrhhYZtCbeJhTIgAKrTcNmL6wkjeje1KE92T9HjKequhrIv/YMN8tsNjNv3jyWL1/OwYMHGTlyJD163CDzmENbzodojKbx2U3MHei98QPyjLdIp/bSTfbhBbTZC1Ev/R5qL8KRvTB2SrvFopRCrXkR9cKvwDCgVz/0z/4dWmWfdtuHEIXEMAwikcg7krZXJ3MTicQNt6NpGk6nE4/Hc82X2+1u+7vdbi+4C1RCCCFySxK8eWjMmDF897vf5cyZM9TW1qKUokePHgwaNEje6EWXU3xl0FqgOYNhKHRdXuMiK3BV/1059olCog5nE7xNQ0YRPHkes9lc0ENx+vbty/Dhwzlx4gTr16/niSeeQO+AStE7pZRixbHskLjFw4qxmvIvxhvx+G5h0BqgOZxocxah1ryIseZFTO2U4FXRCMav/hf2bsvuZ8Y8tA9/Ds0mcyBE9xEKhdi+fTvRaJSmpiYikchNtZSx2WzXJGvfnrx1uVxSlSuEEKLdSYI3jw0cOJCBAwfmOgwhOpTbo2M2QzoN4aAhvVZFm9YEh09eE6KAqEgITp8EoMqSbVnQv3//gmzPcLXZs2dz5swZ6uvrOXz4MOPGjct1SO9wtCFGVXMcq0njgaFFuQ7ntrS+B4aCGZSh0G7ioqe24GHU6yvh+EHU+dNo/QbdUQzq4hmM//t3qK8Bkxnt/Z9Em/uAXGgT3c7GjRs5ffr0Nd/Tdf0dydurE7hut/u6vcqFEEKIjiYJ3jyUSqVYt24d+/bto76+HoCKigomTpzIggULZHqq6FI0XcNXYqapPk1LU1oSvKJNawWvV/rvigKijuwDZaB696fq4iUg236p0LlcLu666y42btzI1q1bGTx4MC5Xfg0wa63enT/Qh89emKe4LpeObgIjA5GIgdtz4+OfVlqONnkWatcm1Osr0J78q9vev7FtPerpH0AyCSXl6J/5CtrAYbe9PSEK1eXLlzl9+jSaprFs2TJMJhNutxun0ykXO4QQQuSlwlu71sU1NTXx5S9/mV/84hecPXsWr9eL1+vl7Nmz/OIXv+DLX/4yTU1NuQ5TiHZVXJL9AOtvvrklqaLrU4YiGJAKXlGArvTfbR4+gUAggMlkKuj2DFcbO3YsFRUVJJNJNm/enOtwrnE5mGTnxTAAj4woznE0t0/TNby32KYBQFu4BAC1cxPKf+vniSqVxPjND1E//3Y2uTt6Ivo/fluSu6JbUkqxZcsWAEaNGsWUKVOorKzE5XJJclcIIUTeKszyhi7sqaeeoqGhgb/6q79ixowZ19y2bds2fvCDH/DUU0/x5S9/OUcRCtH+ikpbE7wyaE1khcMGRgZMJnC55VqkKAzKMFBH9gJQ7SmDhgj9+/fvMitvdF1n/vz5PPvss5w4cYJRo0bRt2/fXIcFwMrjzShgSi8XfXyFvTza4zPhb85kB03e5I9XGzgMhoyCqqOoN1ahPfaRm96faqzD+NG34FwVaBraQ+9He+gJNF0uronu6fTp09TU1GA2m9/xeUwIIYTIV/KpOc8cOnSIBx988LonE3fddReLFy/m0KFDOYhMiI5TVJK91hQKGKTTNx5eIbq+YGt7hiLTTfWgFCIvnKuGUABld3CqOQB0jfYMV+vRowdjx44FYMOGDWQyuV95EUxkWHc6+/NeMrIkx9HcudZWRUG/cUuP0xc9CoDauBqViN/UY9ShPRj/9qVsctflQf/iP6M/8gFJ7opuyzAMtm7dCsCECRNwu905jkgIIYS4OZLgzTMOhwOfz/eutxcVFeFwODoxIiE6nsOpY3doKPVW31XRvQX8byV4hSgU6vCV9gwjJ+L3+7tUe4arzZw5E4fDQUtLC3v37s11OLx2qoVkRjGw2MbYHs5ch3PHvL7s6Xlrm5qbNn4qlFdCNIzauu4976qMDMaK32J87+sQCcGAoej/9G20MZNuN2whuoSjR4/S0tKC3W5n8uTJuQ5HCCGEuGmS4M0z8+bNY8OGDSQSiXfcFo/HWb9+PQsWLMhBZEJ0rNYqXn+TtGkQbyX6fTJgTRQQdWg3ANVl2XX1/fr165LT1G02G3PmzAFg586dBAKBnMWSyhisOtECwKMjS7pEf0zPlQtb0bBBOnXzq1o03YR27yMAqNdXoozrJ4hVKIjx3X9BvfwsKIU2bzH6l/8drbTijmMXopClUim2b98OwLRp07rk8VsIIUTXJT14c2zHjh3X/HvgwIHs27ePv/zLv2Tu3LlUVlYCUFtby8aNG3G73fTr1y8XoQrRoYpKTNReSsmgNYFSqm24kAxYE4VChYJw9hQA1bHshaohQ4bkMqQONXz4cI4ePcrFixfZuHEjDz/8cE6Sq5vOhWiJZyh1mJnVz9vp++8INpuOza6RiCtCgQzFZTd/uq7Nuhe14ndQXwMHd8GEa1t+qdMnMH78LWhuBKsV7U8/jz5jfns/BSEK0r59+4hGo3i9XsaMGZPrcIQQQohbIgneHPuf//mfd71t+fLl7/hec3Mz3/3ud5k5c2ZHhiVEpyu+MmitRRK83V48pkgmFJqWHTYkRCFQR/aCUjT3G0pzIICu6wwaNCjXYXUYTdOYN28ev/vd7zh79iynT59m8ODBnRqDUooXjzUD8ODwYiymwq/ebeUtMtFQmyZ4qwlemx1t7n2oV5/HWPMipisJXqUUasMrqGefgkwaevRG/+zfofXu31FPQYiCEo1G2bMn22bnrrvuwmyWj8lCCCEKi7xz5dg///M/5zoEIfKCrzh7OIpFDBJxA5tdOsh0V63Vu26PjsncdRI2oou70n/3dN9hEEx02fYMVyspKWHSpEns3r2bjRs30rdvX6xWa6ft/0BtlHP+BHazxn1Dijptv53B67uS4PXf+kVPbf5DqDUvwqmjqDOnoGcf1G9+gNr5ZvYOk2aif+yLaI7C71csRHvZtWsXqVSKiooKhg0blutwhBBCiFsmCd4cGzVqVK5DECIvWKwabq9OOGjgb87Qo5ckeLur1v67Xum/KwqEMjLZCl6gyjADiS7dnuFqU6dO5eTJkwSDQXbt2sWsWbM6bd8rrlTv3jO4CLetax0vWvvw3vKgNUArLkWbejdq+3qM5b8GfzPUXABdR3v842j3PtIlehUL0V78fj+HDh0CYNasWfL7IYQQoiBJBkUIkTeKSq60aZBBa92a9N8VBedsFYRDtHhLaQpHunx7hqtZLBbmzp0LZPtXNjU1dcp+z/sT7K2JoAGPDC/ulH12Jq8ve4oeChgodfOD1lppC5dk/3LsQDa56ytB/+tvoC9cIskrId5m27ZtGIZBv3796Nu3b67DEUIIIW6LVPDmoePHj/PGG29QX19PJBJ5x4m9pmn853/+Z46iE6LjFJeYuXhWBq11d1LBKwqNOpRtz1A9aDRkoG/fvtjt9hxH1XkGDhzIoEGDOH36NOvXr2fZsmUdnkRccTxbvTujr5tKT+e1hegsbq8JTYNUUhGPKRzOW/t5av0GweiJcGQfDB+L/qm/QfN1vUS4EHeqrq6OU6eyAzI7cwWCEEII0d4kwZtnXn75ZX7zm99gtVrp1asXbrc71yEJ0WmKrgxa8zdnUEpJlVE3lEoqohEDkApeUTjUlf671VYvxOLdpj3D1ebOncv58+e5fPkyx44d69AWVP5Ymg1nggAsGVnSYfvJJZNJw+3RCQUNgoEMDuetL7rTP/MVOH0Sho9FM8nxVIi3U0qxefNmAEaMGEF5eXmOIxJCCCFunyR488zKlSsZMWIEX/nKV3A6ZfiF6F68PhO6fiXJFzZweeQDaXfT2p7B7tSw2qSLkMh/KuiHs6fwWx00xuJomtZt2jNczePxMH36dLZs2cLmzZsZNGhQh1Uxv3KqhbShGF5mZ2R51z1X8hSZCAUNQv4MPXpabvnxmt0Joya0f2BCdBHnzp3j0qVL6LrOjBkzch2OEEIIcUfk03OeSSQSzJ49W5K7olvSTRq+4tY+vNKmoTsKSP9dUWDUkX0AVPcfAUCfPn1wOBy5DClnJkyYQElJCfF4nK1bt3bIPhJpg1dP+gFYMqJrVu+28vquDFrzy/uhEO3NMAy2bNkCwPjx4/F6vTmOSAghhLgzkuDNM6NHj+b8+fO5DkOInGkdtOZvlkFr3VHbgDXpvysKxaHdAFR7KgAYOnRoLqPJKZPJxPz58wE4fPgwNTU17b6PDWeCBBMZKlwWZvT1tPv284n3yoWuYEASvEK0t+PHj9PU1ITNZmPq1Km5DkcIIYS4Y5LgzTNPPvkkhw8fZuXKlYTD4VyHI0SnKyrJdo6RQWvdU9uANangFQVAGRnUkX0ELA4aUplu257har1792bkyJEArF+/HsMw2m3bhlJtw9UeHlGMSe/afdo9Vyp4w0EDI6NucG8hxM1Kp9Ns374dgClTpnSroZhCCCG6LunBm2fKysq49957+c1vfsNvf/tbrFYruv7OPPyvfvWrHEQnRMcrvjJoLdCSwcgodFPX/gAv3mJkFKGgVPCKAnL6JETDVPfKVu326dNHWiyRnUR/+vRpGhsbOXjwIBMmTGiX7e65FOFSMInTonPvYF+7bDOfOZwaZgukUxAOGXLhKx8ZKRzBnYBGzDcdNPk/KgQHDhwgHA7jdrsZP358rsMRQggh2oUkePPMs88+ywsvvEBJSQmDBw+WD4qi23G6dSxWjVRSEQxk2ip6RdcXCmZQBlgs2m1NjBeis6nDewA4XdYXFAwZMiTHEeUHp9PJrFmzeOONN9i2bRtDhgzB7Xbf8XZbq3cXDSnCaen6iTRN0/D6TDQ3Zgj6M5LgzSdKYY0cxdP4CqZ09nVpCx8k2ON9GJbiHAcn3ks8Hmf37mxrnRkzZmA2y3mmEEKIrkHe0fLM2rVrmTRpEn/7t3973cpdIbo6TdMoKjHRUJvG3yQJ3u6ktf+ut9iEpknltsh/6vBeghY7dUpH0zQGDx6c65DyxujRozly5Ah1dXVs2rSJBx544I62d7o5zqG6KCYNHhrefRJo3qIrCV7pw5s3TMk6PA0vY41VAZAxedGMBNb4OUoufJdQ+RISnok5jlK8m127dpFIJCgtLWXEiBG5DkcIIYRoN5JBzDPpdJpJkyZJcld0a62D1lpk0Fq3Iv13RSFRgRY4V8VpX3a4Wq9evWTVzVU0TWP+/PlomsapU6c4d+7cHW1vxbFsleSs/l7KXZb2CLEgtPbhDUmCN+e0TAx3w0uUnP9frLEqFCYixfNo7v8lmvt9kaS9P7qRwFf3B7y1z6Jl4rkOWbxNMBjkwIEDQLaVjHzeEkII0ZXIu1qemTRpEseOHct1GELkVHGpDFrrjgJXKnh9kuAVBUAd2QtAVXk/QNozXE9FRUVbf8sNGzaQTt/eRbvGaIpN54IALBlR0m7xFYLWC16tKxxEDigDe2Anpef+G2dgKxoGCdcomvp/iUjpfSjdhmEpwd/7U4RL7kWhYw/vp+TCd7HEzuY6enGV7du3YxgGffr0oX///rkORwghhGhXkuDNM3/yJ3/CpUuX+NnPfsbp06cJBoOEw+F3fAnRlbVW8IaDBqmkTA7vDpRSbQkMGbAmCsKhPYQsNurM2enrkuC9vunTp+NyuQgEAuzZs+e2trHqRAsZBWMqHAwp7V7T7lsreOMxRTJh5Dia7scSO0PxxR/gbViObkRIWyto6fUJAj3/FMPytosNmoloyT209P40GXMxprSfoks/wdW0FpQk6HOtoaGB48ePA9nqXWkFJYQQoquR5pZ55i//8i8BOHv2LGvXrn3X+z377LOdFJEQnc9m13G4dGIRg0BLmrIe3Wc5bncVjRikU6Dr4PbKtUeR31Qmgzq6j9Pet9ozuFyuHEeVn2w2G3PmzGH16tXs3r2b4cOHU1RUdNOPj6UMXjvlB+CRkd2reheuDJ288n4YDBiUVcjxsTPoKT/uplexhw8CYOh2IiX3EvPNAO29L0KmHf1p7vdF3A0v4QjtxdXyBtboKYKV7yNjKe2M8MV1bNmyBYChQ4fSo0ePHEcjhBBCtD9J8OaZZcuWyRVlIYDiEhOxiEFLU0YSvN1Aa/Wux2dC1+UYKPLc6RMQjVDdewyQTRiIdzd06FCOHDnChQsX2LBhA0uWLLnpc53Xq/1EUga9PFam9nZ3cKT5yevLJnhD/gxlFXLq3qGMFE7/m7haNqKpFAqNuHcq4dKFKNPNv/6UbifU409IOofjaViOJXGB4vP/S7j8EeKeSSDn+p3qwoULnD9/Hl3Xueuuu3IdjhBCCNEh5CwxzzzxxBO5DkGIvFBUYuLyhZT04e0mWgesSf9dUQjU4T2EzTZq7B4ABg8enOOI8lvrwLWnn36a8+fPU1VVxbBhw274uIyheOlECwCPjChG76ZJMW+RibrLaYIyaK3jKIUtcgR34yuY0tnXXNI+gHD5w6RtvW57swnPOFL2fnjr/oA1fgZv/XNYoycIlS9FmRztFb14D0opNm/eDMDYsWNvaQWBEEIIUUhknZcQIi8VtQ1au72hPKKwtFbweqX/rigA6tBuTvuy7Rl69uyJ2909K0tvRVFREVOmTAHgzTffJJFI3PAxOy6GqAun8NhMLBjk6+gQ85bXJ4PWOpIpUUvR5afw1f4WU7qFjNlHoMf78ff+9B0ld1sZliL8vT9JuOS+KwPYDmUHsEVPt0P04kZOnjxJQ0MDFouFqVOn5jocIYQQosNIBW+eee65527qfo8//ngHRyJEbvmKTWhadrBMLGrgcMr1qK5MKnhFoVD+JrhwhupBkwEZrnYrpkyZwokTJwgEAuzYsYMBAwa85/1XHMtWUj4wtAibufu+B3iuHBdDgQxKKWnl1U60TBRX8+s4AtvRUCjNTLTobiLFc0G3tvPOdKIl80g6B+OtexZzqomiyz/L7q/0XtDkI1lHSKfTbNu2DYDJkyfjdDpzHJEQQgjRceRsIs/88Y9/vKn7SYJXdHVms4bHpxP0G/ib0zic7fxhS+SNRNwgHlNAdimyEPlMHd5LxGzlsrMIkATvrTCbzcydO5eVK1eyf/9+5syZ867JyhONMY43xjDrGouHFXdypPnF5dbRTZDJQDRs4PLIcfKOKANHcCeuprXoRhSAuGsM4bLFGJaOfa2l7X1p6fsF3I0v4wjuxuXfiDVWRbDH+8hYyzt0393R4cOHCQaDuFwuJk6cmOtwhBBCiA4lCd488+yzz77je4Zh0NjYyOrVqzl27Bh///d/n4PIhOh8RSVmgv4k/qYMPfvkOhrRUVqXHbvcOmaLVKaJ/KYO7+G0twI0jcrKSjweT65DKigDBgxgyJAhVFVV8fTTT7NkyZLr/gxfPNYMwNwBXood3ft0Vdc1PF4TgZYMwUBGErx3wBI7jbvhJSzJWgDS1h6Eyh4m5ey8PtpKtxGqWJYdwFb/ApbEJUoufI9Q2cPEvVNkAFs7SSQS7Ny5E4Dp06djscjAXiGEEF1b913vVkB0XaeiooKPfOQj9OzZk5///Oe5DkmITlFUkv0Q2yKD1rq0gPTfFQVCpdNwdD/VV/rvSvXu7Zk7dy4+n4/m5mb++Mc/4vf7r7m9Lpxk+4UQkB2uJq7uw2vkOJLCpKda8Nb+juJLP8WSrMXQHYTKHqa57xc6Nbl7tYR7DM39/oKkYzCaSuFteAFv7W/RMpGcxNPV7Nmzh3g8TnFxMaNGjcp1OEIIIUSHkwRvgRk5ciT79u3LdRhCdIriK4PWAs1plKFyHI3oKEHpvysKxenjRJMpLruySUdJ8N4el8vF448/Tnl5OeFwmOeff57m5ua221863oKhYEJPFwOK7TmMNH94irKn7KGAXPC8JUYSZ/PrlJ7/H+zhQyg0ot7pNPX/a2JFM0HL7fuOYfbh7/Uk4dIHUJiwR45Qcv5/sUSrchpXoQuHw+zfvx+AmTNnouvykVcIIUTXJ+92Baa6ulqGa4huw+PVMZkhnYZwSKqWuiqp4BWFQh3aw2lfBUrT6NGjB16vN9chFSy3282nP/1pSktLiUQiPP/88zQ2NhJOZlhbHQDg0ZElOY4yf7xVwSsJ3puiFLbwIUrPfxt38zo0lSZpH0hL3y8QrngUZXLlOsK3aDrR4rtp6fNZ0pZyTJkgxZefwtX4Cqh0rqMrSDt27CCdTtOzZ08GDRqU63CEEEKITtG9m5rloY0bN173+5FIhGPHjrFz504WLFjQyVEJkRuaruErNtHckMHfnMbjkwRgV5NOq7bkvVTwinynDu+R9gztyOPxsGzZMpYvX05DQwMvvPAC3nHziKcN+vtsTKiUifetWgdQRsIG6bTCbJaL/e/GlKjBXb8Sa/wMABmzj3DpYhLusXnd3zZt701z3z/H07gqOwTOvwlrtIpg5fvJWCtyHV7BaGpq4ujRowDMnj1bCmOEEEJ0G5LgzTM//OEP3/U2j8fDkiVLePzxxzsxIiFyq7jETHNDhpamDH0H5joa0d5CgQwosNk17A5ZVCLyl2puJFZziUsjs9VgkuBtHw6Hg6VLl7JixQrq6uqI7Hodr2cSj4wcIYmZq9jsOlabRjKhCAcyFJXKKfzbaZkImVO/orhmPRoKpZmJFs0lUnw36NZch3dzdCuhiqUknMPx1j+PJVlDyYXvES57kJh3el4nqPPF1q1bUUoxaNAgevbsmetwhBBCiE4jZ4d55vvf//47vqdpGi6XC4fDkYOIhMitotJs1ZJfBq11SYEr/Xe9Ur0r8pw6spfT3nKUplNeXo7P58t1SF2G3W7n0Ucf5ek/LifSXM+k0B6GWnsDRbkOLa94i0w01qUJSoL3nZRB0cWfopK1aEDcPZZw6QMYlsIc0pd0j6LZ3gdv3R+xxqrwNKzAGjlJsMdjKJM71+HlrcuXL3PmzBk0TWPmzJm5DkcIIYToVFIulWfKy8vf8VVWVibJXdFtFZVkP8QG/RkyaRm01tW09pP0Sf9dkeey7Rl6ADB06NAcR9P1WK1WjhZPptlcgkllWPXSS5w/fz7XYeUV6cP77qzRU5iTtWBy4u/9KYKVHyzY5G4rw+zF3+vjhMoeRGHCFj1GyfnvYo2cyHVoeUkpxebNmwEYPXo0JSXSw1sIIUT3IgleIUReczg1bHYNpd4axiW6DqngFYVApVPEjx/mojubMJL2DO3vSH2MKn+GY8WT6N23H+l0mpdeeomzZ8/mOrS84S3KnrYHAzJ09O0cge0AaJVzSDkH5ziadqTpxIpm09L386StFZgyYYpqfom74SUwUrmOLq9UV1dTW1uL2Wxm+vTpuQ5HCCGE6HSyvisP/M3f/M0t3V/TNP7zP/+zg6IRIr9omkZRiYm6y2n8zRlKyuSw1VUoQxEMXKnglQSvyGfVxzlt86A0nbKyMoqKinIdUZfz4rFmAOYNLmHJpIdYvXo1p0+f5uWXX+aBBx5g8OAulLS7TZ6rKniVUtKj+Ao91YI1mq1q1XsugEDXW+2TtvWkuc+f4256FWdgG87AVqyxagI93k/GVpnr8HIuk8mwdetWACZOnIjL5cpxREIIIUTnkwrePOB2u/F4PDf8ymQyXLhwQZYsim6ntU2Dvymd40hEewqHDYwMmMzgcsvbkchf6tBuqn3ZKfbSnqH9XQom2XUpDMAjI0owm8088MADDB06FMMweOWVVzh58mSOo8w9j88EGqSSikS86yUxb5cjsAMNRdI5FM3ZhZOduoVw+SP4e34Uw+TGnKyj5OIPsUaO5jqynDt69Ch+vx+73c6kSZNyHY4QQgiRE1IKlwe+9rWvveftfr+fF198kVOnTqHrOnPmzOmcwITIE62D1lpk0FqX0taewWdC06USTeSv2JH9XPQNBKQ9Q0dYeTxbvTu1t5veXisAJpOJ++67D5PJxPHjx3nttdfIZDKMHDkyl6HmlMmk4XbrhEMGQX8Gu0MujKHSOIK7AYj5ZuDMcTidIekaQVPfL+KrexZrrBpfzdOESx8gVjQbumFVdzKZZMeOHQBMnz4dm82W44iEEEKI3JAEbx5rTeyuW7eOdDrNnDlzeOyxx6is7MLVCUJcR1FJNsEbDRskEwZWm3yo7QpkwJooBKq5gbPhOEaxTmlxMcXFhT24Kd8E42neOB0A4NGR1w5F0nWdhQsXYjKZOHLkCGvXriWTyTBmzJhchJoXvEWmbII3kKGipyXX4eScLXwI3YiQMftIukbkOpxOo8we/L0+jqdhJY7gTjxNr2BONRIqfwS07vWeum/fPqLRKD6fr1sfG4QQQghJ8Oah6yV2ly1bRo8ePXIdmhA5YbXquDw6kZCBvzlDRU9J8HYFMmBNFAJ1eE9be4Yhw4blOJqu59VTfpIZxeASG6MrHO+4XdM0FixYgNls5sCBA7zxxhuk02kmTJjQ+cHmAY/PBBdShGToKJBtzwAQ807rdolNNBOh8kdJW8txN76CI7gTU6qJQOWHUKZ3/i51RdFolL179wJw1113YTJ1s9eAEEIIcRVJ8OaRtyd27777bpYtW0ZFRUWuQxMi54pKTFcleKVqqdAppaSCVxSE+ME9nHeXAtJ/t70l0hlWnci2Z1gyouRdh4Zpmsbdd9+NyWRi7969vPnmm2QyGSZPntyZ4eaF1gtirQMquzNz4jLW+DkUOnHv1FyHkxuaRqxoNhlLCd7abMuG4ov/R6DXR8lYSnMdXYfbuXMnqVSKiooKOT4LIYTo9iTBmwdaWlraEruZTIa5c+fy2GOPSWJXiKsUl5i5dC5Fiwxa6xLiMUUyodA08HglwSvyk0qlOHupBqPnMEo8bkpKSm78IHHTXjtWhz+eodRpZlZ/73veV9M0Zs2ahdlsZufOnWzZsoV0Os20adPeNTHcFXmLsitYQkEDw1Do3bh/eWv1bsI9BsPsofv+JCDpGoW/z5/hu/wrzKkGii/8kEDPPyXlGJDr0DqM3+/n8OHDAMyaNatbHQeEEEKI65EEbx74whe+QCqVYsCAASxdupSKigrC4TDhcPhdHzNo0KBOjFCI3GsdtOZvzqCUkhP5Atdavev26JjM8n8p8lTVUapc2Z67g0d03+FeHUEpxe92XwDgoeHFmG8iUalpGjNmzMBkMrFt2zZ27NhBJpPhrrvu6jbvCQ6njtkM6TREQka2ZUM3pGXi2EP7gOxwNQFpWy9a+n4eX82vsSQuUXTpZwR7LCPhmZjr0DrE1q1bMQyD/v3707dv31yHI4QQQuScJHjzQCqVAuDs2bN8+9vfvqnHPPvssx0ZkhB5x1tkQtMhmVDEIgZOd/f8UNtVtPXflfYMIo8lDu7mgjtbtTtM+u+2q/01EaobIzjMOouGFN3SY6dOnYrZbGbTpk3s3r27bV5Bd0jyapqGx2eipSlD0J/ptglee2gvmkqRtvYgZR+Q63DyhmH20tL703jr/oA9cgRf3R+IJBuJlNwLXej3o7a2lqqqKiBbvSuEEEIISfDmhc9+9rO5DkGIvGcyafiKTPibM7Q0ZyTBW+ACrf13ZcCayGNnTp0i4+1FkcMm7Rna2YvHsr137x3iw2299ePAxIkTMZlMbNiwgf3795PJZJg3b163SPJ6i64keAMZeuc6mFxQCkdgOwAx3/QulbhsF7qVYOUHyTStweXfiKvlDUypRoIVj4Ne+DMMlFJs2bIFgJEjR1JWVpbjiIQQQoj8IAnePDBv3rxchyBEQSgqySZ4/U0ZevfLdTTiTgRbZMCayG+qsY5qlT1NGjp8RLdIHHYGQyk2nAmyryaCrsHDI24/cT5u3DhMJhPr1q3j0KFDZDIZFixYgK7r7Rhx/vFeqdptbXXT3VhipzGnGjA0K/Eu2n7gjmk6kbL7yVjL8NQvxx4+iCnVgr/nn6LMnlxHd0fOnj3LpUuXMJlMzJgh7TmEEEKIVpLgFUIUjKISM5CkpVkGrRWyVFIRjRjAWxPhhcg3iQO7OOfJTqEfMnJUjqPpGo41RHlqTz2nmuIA3D+ykkq3FaXUbW9z9OjRmEwm1q5dy9GjR8lkMixcuLBLJ3k9V46bwUD3TPC2Vu/GPRNRuj3H0eS3uHcKGXMJvtqnsSQuUHLxh/h7fpSMrTLXod0WwzDaqnfHjx+Px9OByWojiT10AGv8DIY2CdQgoOseV4QQQhQ+SfAKIQpG66C1QEum208PL2StVWcOp4bVJh+WxPV9f3sNFwJJ/mleH9y2zr8QcPboYTK6E5/FJEuA71BdOMmv9zew+VwIALtZ5/ExpXxm/nCaG+rvePsjRozAZDLx2muvceLECdLpNPfffz8mU9e8gOT1ZY+b8agilTSwWLvPcVRPB7FFjgIyXO1mpZyDaOnzWXw1v8KcaqL44o8IVn6ApGt4rkO7ZceOHaO5uRmbzcaUKVM6ZB+mRB2O4A7sob3oRgIAI7SPYmsFkZKFJFyjpS2IEEKIvNR9zgiFEAXP7dExW8DIQKibVi51Ba39d6V6V7ybi8EEa6sDHG+M8dTeO08A3iqVSlIdjAAwZOBAac9wm6KpDL/eV8/nXzrD5nMhNODewT5+9MggnhhThs3cfseAoUOHsnjxYnRdp7q6mlWrVpFOd83VHharjsOZfU0GA0aOo+lc9uBONAyS9gEFW4WaCxlrOS19PkvSPhBdJfDV/AqHf1uuw7olqVSK7duz1dtTp07Fbm/H6m2VxhbaT9HFH1N64Ts4A9vQjQRpSwlR30wwuzAn6/HV/pbiiz/EEq1qv30LIYQQ7UQqeIUQBUPTNIpKzDTWpfE3Z/AVyyGsEEn/XXEjG04H2/7+xukAs/t5mNzb3Wn7Tx0/yDlXtjfskImTO22/XUXGUKw7HeDpAw0E4tnf93E9nDw5uYKBxR23pH7QoEE8/PDDvPzyy5w9e5aXXnqJhx56CIul8AdLvZ23yEQsmiboz1Ba3k3eC1UGR2AXINW7t0OZXPh7P4mn/kUcoT14GldiSjUQLnsQtPx/Pz5w4ACRSASPx8O4cePaZZumVBP2wE4cwT3oRvainkIn4RpJzDedlGMwmm7CU/6nBI//EUfLZiyJixRffoqkYzDh0vtI2/u2SyxCCCHEneomZ4RCiK6iqMREY12alqYM/QfnOhpxO6SCV7wXQyk2ng0AMLDYxpmWBD/YWcv3HxqI09I5r5mze/eQ1k14daioqOiUfXYV+2si/HxvPef82aXNvTwWPjapgmm93Z1SCd2/f3+WLFnCSy+9xIULF1i5ciUPP/wwVqu1w/fdmTw+E3WX091qNYstcgxTJohhcpNwj851OIVJMxOqWEbGWoa76TWcgW2YUk0EKz+Q1/2M/X4/u3fvBmDGjBmYzXfwEVZlsEaO4wjswBY71fbtjNlHzDuVuHcKhtl3zUM0s5No6SKi3rtwtazHEdiBNVZNycUfEneNJlK6kIy1x+3HJIQQQrQDSfAKIQpKcakZSOCXQWsFycgoQkGp4BXv7lh9jPpIGqdF5+v39ONvV5+lNpzil3sb+Nz0zlmSXVVXD3YfQ3pWSnuGm3QxmOCXe+vZdSlbBeey6rx/bBkPDC3GYurcn2GfPn1YsmQJK1as4NKlS7z44ossWbIEm83WqXF0pNYLZK09zbuD1uFqMe9U0OQjzG3TNKLF88hYSvHW/QFb9CTFF3+Mv+dHMSxFuY7uGvF4nJ07d3Lw4EEMw6CsrIzhw2+vd7CeDuAI7MIe3IUpk10lotBIOocS800n6Rx+w0pmZXYTLn+YaNFsXM3rsIf2Yo8cwRY5StwziUjJPRiW4tuKTwghhLhTcnYkhCgoRSXZk+9QwCCdUpgtknwpJKFgBmWAxaLhcEobePFO689kq3dn9vPgtZn48xmV/OPrF3itys+s/h7GV7o6dP+pmouctWT3MWTKtA7dV1cQSmR45lAjr55sIaPApMEDw4p539gyvDkYjteqV69ePPbYY7z44ovU1tayfPlyHn300fbt25lDXt+VBG8gg1Kqy1+IMCXrscaqUWjEvPn1e9nS0oJSipKSklyHcksS7rG0mIvw1fwGc7KW4os/INDzI3nRciCdTnPw4EF27dpFIpFdDdCvXz/mz5+Prt/CuYMysEarcAR3YI0cRyPbs9owuYh5phDzTcOw3Pr/m2EpJtTjcaLFc3A1rcUeOYIjtAd7aD8x33QixfNR5s5rKySEEEKAJHiFEAXG7tCxOzXiUYW/JUNZhRzGCkngSv9db7GpyyckxK1LZgy2ng8BMH+gD2UYjO3h4oGhRbx6ys/3t9fyvw8OxGHpuIsDZ3dsIW0y41FpevTr32H7KXSpjOLVUy08e6iRcDKbNJna283HJpXTx5sflbI9evTgscceY/ny5dTX1/PCCy/w6KOP4nQ6cx3aHXN5dHQdMmmIRQyc7q69IsIR2AFA0jUyr6pM6+vr+cMf/oBSioULFzJixIhch3RL0va+tPT5HL6aX2FJ1lJ86ScEezxBwj02J/EopTh16hRbt24lGMxW2ZaWljJ79mz697/547GWCeMI7sER2Ikp3dz2/aR9IDHf9GyLj3aoAs9YexDs+WGi8fO4m9ZgjVXjDGzFHtxNrGg20eI5ed36QgghRNcimREhRMEpLjFTE03hb05LgrfAtC4n9kn/XXEduy6GiaQMyu0aw3/5LxhVx8Bk4sNWF7sn/Dn1FPGrHz7Dpxo2gdkMZgtYLNk/r3xpFstbt13ndizm69z/ra+qM2fB5GBISZFchLgOpRQ7L4X55d56LodSAPQvsvHkpAom9OzY6urbUV5ezrJly1i+fDmNjY08//zzPPbYY7hc+RfrrdB1DbfXRNCfIRjo4gleI4k9tAeAmG96joN5SzKZZPXq1RhG9gLHmjVrSKVSjB2bm+To7TIsRfj7fAZv7e+xRU/gq/0d4ZJFRIvnQSceAy9fvsymTZuoq6sDwOVyMWPGDEaOHHlzVbtKYYmfzfbWDR9GI3u+Yeh24p5JxHzTyVg7pqd62t4Pf+9PYolW4W5ajSVxCVfLGzgC24kUz8sOBdS73rBHIYQQ+UUyI0KIglNUYqLmYgp/U/fpPdhVyIA18V5a2zPcXf0metWx7DczGRyxIJ89/ke+Pv5TvFo2iZkXdjA6cOa621C3uM/W+zfYPRws7Ut1cbbP75DxE2/jGXRtZ1ri/HxPPQfrogD47CY+PL6cewb5MOn5mwwvLS1tS/K2tLTw3HPPsXTpUrxeb65DuyPeIj2b4PVnqOzddZNH9tB+dCNB2lJK0jEk1+G02bhxI36/H7fbzYABAzh8+DDr168nmUwyefLkXId3S5RuI9DzI7gbX8EZ2IK7eQ2mVCOhiqUd3u/Y7/ezZcsWqqurAbBYLEyaNIlJkyZhsdz4da1l4thDe3EEd2BO1rd9P2XrQ8w3nbh7HOidM2Qx5RxCi+Pz2CJHcDWtwZxqwNP0Ck7/ZiIl9xL3Trphn18hhBDidkmCVwhRcIpKsyfHLTJoraAopd6q4JUBa+Jt/OE4ey+FAJ25l7bD4BHoH/0i2B2QTjEpnWLh0Rhraw1+OOMzfGdEHFsmDekUKp2CdApSV/5s+0q/9edVt6kr982kU1QbZg6ZnNSa3morMMik6DFmfO5+GHmmJZbm6QMNrKsOoACLrrFkZAnLRpfgtBTG73JxcXFbkjcQCPDcc8/x2GOPUVRUlOvQblu2D2+KYKALX+xU6qrhatNBy4/e7cePH+fYsWNomsZ9991Hr169sNvt7N69my1btpBKpZg+fXphrQLQdMLlD5GxluFueAlHaC+mVDOBnh9Gmdq/4j0Wi7Fr1662AWqapjFq1ChmzJhxUxX25vhFHMEd2EMH0FR2NYHSLMQ9E4h5p5O29273mG+KppFwjyHhGok9tA9X8+uY0gG8DS/g9L9JpHQRCdfovHktCyGE6DokwSuEKDhFxdlDVzyqiMcM7A45SS4E0YhBOgW6Dm6v/J+Jt6jGOt78/SoyJTMZHLxA3zmz0R79UzTztacpHy/NsPflM9TE0vwu0YsnJ/cA4FZTKOFwmMOHD3P48GGi0Ww1qq7rDB06lPHjx9OjR4/CSsx0kETaYOXxZp470kw8nV2GPru/h49MKKeHu3Mq4tqTz+dj2bJlvPjii9dU8paWluY6tNviubISovXCWVdkjp/HkqxBaWbi3vyoivX7/axfvx6AadOm0bt3NpE4c+ZMLBYL27ZtY+fOnSSTSebMmVNwx5KYbwYZcwneut9hjZ+l+OIPCfT8GBlrebtsv3WAWuvPCKB///7Mnj37xr+LRhJ7+ACOwA4siUtvbdPag5h3OnHPRJQpT3reaibi3inE3eNxBHfgat6AOdWIr/Z3pGy9iJTcR9I5tFPbYAghhOjaJMErhCg4ZouGx6cTChj4mzNU9pZkYSFoHbDm8ZnQ83g5t+hcav8OjF98h40jPg7A/OHl6AsWXve+LquJz02v5F83XGTl8RZm9fcyvMxxc/tRipqaGg4ePEhVVVVb30yXy8XYsWMZPXp0wfdlbS9KKTadC/HrffU0RLMrJYaW2vnE5ApGlhf2gDKPx9OW5G3tyfvoo49SUdExvTk7UraCFyJhg0xaYTJ3veOq80r1btw9HmXK/Wsvk8mwevVqUqkUvXr1YurUqdfcPnXqVKxWKxs3bmT//v2kUinmz59/cz1k80jSNYyW3p+lqOZXmFPN2SRv5YdJOQff9javN0CtrKyM2bNn069fv7fuaKTQMxH0TPjKV/bvplQTtvBBdCOR3R4mEu6xxHzTSdn752+iVLcQK5pN3DsVh38TzpbNWBKXKar5BUn7QMKl95F2yEBPIYQQd04SvEKIglRUYiYUSOJvTnfp3oNdiQxYE1dT6TRq+a9Ra17kkqOcKm8/dA3mzBzzno+b0tvN/IFe1p8J8r/bavj24gFYTe+ePEmn05w4cYKDBw/S0NDQ9v1evXoxbtw4Bg8ejMkkr8lWJxpjPLWnjhONcQDKnGY+MqGcOQO86PmaQLlFTqeTxx57jBUrVlBXV8cLL7zAkiVL6NmzZ65DuyU2u4bVppFMKELBDEUlXeu0XkuHsYUPAdmq0nywdetW6uvrsdvt3HfffddN3I4fPx6LxcK6des4cuQIqVSKhQsXFtxxJmPrQXOfz1FU+xss8fMUXf45oYpHiXun3vjBV1MGtZdOc3DPFhKhevq5DEp6mBk2qCcVRXZMxka0i1cldK8kcN9N2lJC3DudmHdyh7SO6ChKtxEtuZeYbwau5g04gjuwxs9QculHJFwjCZcsImOrzHWYQgghCljXOhMUQnQbRSUmLpyBFhm0VjBaK3i90n+321PNDRg/+U+oPg7AxhnvBwWTeroost/41OQTk3uwvybCxWCSZw428pGJ76y+DAaDHDp0iCNHjhCPZ5OVJpOJ4cOHM378eMrL22e5cVfREEnx630NvHkuW1lnN2ssG1XKkpEl2MyFVX14M+x2O48++igvvfQSly9fZvny5Tz88MP07ds316HdNE3T8PpMNNanCfq7XoLXEdqNRoaUrQ9pe59ch8PZs2fZt28fAPfccw8ej+dd7ztq1CgsFguvvfYaJ0+eJJVK8cADD2A2F9b/kTK7aen1Sbz1z2MPH8Bb/wKmZCPRkgVobVW2V/5MR9AzoauqbyOQCqEbESo0GDf67VtvhNC77BcThsmFYXZn/zS5MUxuUs4h2UF7Bdy/VpnchMsfIlo0G1fLOuzBPdgix7BGjhP3TCBSci+GpSTXYQohhChAhXWWIYQQVxRfGbTmb06jlCq4HnfdkVTwCgB1aDfGU9+GSAgcLvjYX/Dm+TKIpJk30HdT2/DYTHxmWiXffPMSy481c1c/D0NLHSiluHjxIgcOHODMmTMopQDwer2MHTuWUaNG4XDcXEuH7iKayvD8kWZWHm8mmVFowIJBPj40voxSZ9deHWGz2ViyZAmrVq3i/PnzrFy5ksWLFzNw4MBch3bTPD6dxnoIBYxch9K+lIEjsAPIj+rdSCTC2rVrAdoq/29k6NChWCwWVq1axZkzZ3jppZd48MEHsVoLrH+1biHY431kLGW4Wtbh8r+Jy//mzT/+yulZImNBs3rB4n1b8taDakviZv9Uuj1/Wy60E8NSRKhiGdGiObia1mKPHMYR2oc9dJCYbyrR4gUY5ne/iCCEEEK8nSR4hRAFyeMzoZsgnYJIyMDtlaRhPkvEDeKxK8k2SfB2SyqTQb34NGr189lv9B+C/mdf5qjhpf7YeZwWnWl93De9vRl9Pczu72HzuRDf33qRj/WNcvjQQVpaWtru07dvX8aPH8+AAQMKrgdmZ6gLJ/nqmvM0xbJ9dsf0cPKJSRUMKsmTIUWdwGKx8NBDD7F69WpOnz7NqlWruO+++xg6dGj77UQpTOlmMpb2H+bWejwNBrrWahZr9ASmtB9DdxB3j8tpLEop1qxZQywWa+sZe7MGDBjAkiVLeOmll7hw4QIrVqzgkUcewWazdWDEHUDTiJTeS9pahqd+ObpKojRzW2Vta2I2rTm5WBfgePUlAlFFOKFT3KM/k6bdTWlZ4fW57gwZawXBnh8iGr+Eq+k1bLFTOAPbcQT3EKj8EEnX8FyHKIQQokBIglcIUZB0XcNXbKKlMUNLc0YSvHmutXrX5dYxW7p2VY54J9XSlG3JUHUUAG3BQ2iPfxzNYmH99hoAZvbz3HIrgPcNseE/sZvS5ktsOpNNUlosFkaOHMm4ceMoKZFlru/GUIrvbK2hKZam0m3h45MqmN7H3S1XQ5jNZh544AHWrl3LyZMnWb16Nel0mpEjR7bL9l1Nr+LybyJU9jCxopntss1WbQlef9dK8DquDFeLeaeAnttK8t27d3PhwgXMZjP333//LbdZ6NOnD0uXLmXFihXU1NS09Xx2OnM/NO5WJTwTSLhGo2GgNGtbla1SipMnT7J161ZCoRBgpaysjDn3zimotie5lLb3JtD7SSzRatxNq7EkLuKte5bmvl/AsBTnOjwhhBAFQBK8QoiCVVxipqUxg78pTd8BBbbksZsJ+KX/bnelDu/FeOp/IBwEhxP9o19AmzwLgGTGYOv5bBPGeQO9N7U9wzA4d+4cBw4c4Pz58/S48v2oycm0SROYNWls4VXH5cBLx1s42hDDbtb5+j196eHu3sdQk8nEokWLsFgsHDlyhLVr15JKpRg37s6qR03Jepz+LQC4mtcS94xv18FQrRc3kwlFIm5gsxd+pbop1YQ1egqAuG96TmOpqalh+/ZssnnevHm3fdGosrKSZcuWsXz5choaGnj++edZunQpbvfNr1rIG7oFddU/L126xObNm6mrqwPA5XIxc+ZMRowY0S0vGN2plHMwLY4/o/jij7EkLuKr/T0tff4MNDl/EkII8d4kwSuEKFhFbX14u1blUlcUbJH+u92NymRQK3+PevWPoBT0HYj+ma+gVfRqu8+ui2EiKYNyp5nRFe9dzRaPxzl69CgHDx4kGAy2fX/AgAEc13uzze+krsXO3Zbunai8GRcDCZ4+0ADAk5Mqun1yt5Wu6yxYsACLxcL+/fvZsGED6XSaSZMm3fY23Y2r0Mj2x9WNOK7mdYTLH2mvkDGbNVwenUjIIOjPUF5Z+Alee2AnGoqEc1iHtLW4WYlEgtWrV6OUYtiwYXdc0V1WVsbjjz/O8uXLaWlp4bnnnmPp0qX4fDfXezzftLS0sGXLFk6fPg1kV09MmTKFCRMmYLF07f7dHU4zE6j8ACUXvoclcQFX02tEyhbnOiohhBB5ThK8QoiCVVSSTRYG/BkyGYXJJJUi+UoqeLsX5W/G+Ol/wcnDAGjzHkB74hNob0u+rj+TTdTOHehDf5dKr8bGRg4cOMCJEydIp7NtGGw2G6NGjWLcuHH4fD7mxNIcfPk0p1sSLD/axJ+MKeu4J1fgMobiO9tqSGYUE3u6WDSkMJNLHUXTNObMmYPZbGb37t1s3ryZZDLJ9OnTb7ka0Ro5gS16EoWJUPkjeBuW4wjsIOabQcbafv1IvT5TNsEbyFBeWeCJNSOFI7gLyO1wNaUU69atIxQK4fV6mT9/frtUoxYXF7cleQOBQFuSt5DayUSjUXbu3Mnhw4cxDANN0xgzZgzTp08vyLYT+cqwlBCseJyi2qdx+TeRcgwk6WqftjFCCCG6JknwCiEKltOlY7VpJBOKoD9Dcakc0vJROq0Ih7IVbFLB2/WpYweyyd1QAGwOtI98Hn3a3e+4XyCeZu/lMABzB3iIx+NEIhGi0SjRaJRIJMLp06e5fPly22NKS0sZP348w4cPv6ZCrNhh5pNTevDtrTU8c6iJ6X089CuSNg3Xs/xoM6ea4rgsOn8+o1KWUF+HpmnMnDkTi8XCtm3b2LlzJ6lUitmzZ9/8z0tlcDe+AkCs6C7ivmnYIsexRY/hblxNoNdH2i1eb5GJmoupLtGH1x4+hG7EyJiLSDpzN1zqyJEjVFVVoes6999/f7u2ffF6vW1J3ubmZp577jkeffRRKiryewhZJBJh//79HDx4kFQqBWRXUMyaNYvS0txVWndlSfdoor6ZOANb8db9kea+X8SwFOU6LCGEEHlKsiFCiIKlaRpFJSbqa9L4myXBm69CgQwosNk17I7CXz4srk8ZGdTLz6JefjbbkqHPANJPfomYp4jo5cttSdvWBO6Zej8TW0K4tBQrn34dwzCuu11N0xg8eDDjx4+nV69e75pgmzvAy6azQXZfjvC/22v41qL+mHRJXl7tbEuc3x/Ktmb41JQelDkLvNqzg02dOhWLxcKbb77Jvn37SKfTzJs376aSvI7ATsypegzdSaR4AQDhsgewnj+BLXoMS7SalHNwu8Tp8WWPq0H/9X+HCslbw9Wmg5ab94umpibefPNNAO666y4qKyvbfR8ul4tly5axYsUK6uvr2wav9ezZs933daf8fj979+7l6NGjbcfp8vJyZs+eLQPUOkG47AEs8XNYEpfw1j2Dv/enpB+vEDdBT4dQuhWlywV/0X1INkQIUdCKSszZBG9TGobKG3g+Clzpv+uV6t0uIZPJXFNlG41GibY0EdmznWgoSHTgZKKeIqK6mdTyle+5rdbmAK1pKZvNhtPpxOl04nK5KC4uZtSoUXg8nhvGpWkan5teyZ+/fIZTTXFWHm9m6SipKmuVymRbM6QNmN7HfdND7bq71n6i69at49ChQ6RSKe699150/d2Tj1omhqv5dQAipfeiTA4AMtZyYr7pOAPbcDeuoqXvn7dLErP12BoOZjAMhV6gFzbM8UtYEhdQmIh5p+QkhnQ6zauvvko6naZfv3531H/5RhwOB0uXLuWll17i8uXLvPjiizz00EN5kzStr69n9+7dVFdXo1R2rFrPnj2ZMmUKAwYMkOr/zqKZCVR+kJIL/4s1fg5X0xoiZQ/kOioh8pcycAR24GpeQ9w7lbD0rxbdiCR4hRAFrXXQWosMWstbrcuGfdJ/t6Ck02lqamo4d+4cgUCAlpYWotEo8Xj8+g/QHeDLJrIwACPbL9dsNrclbFuTt2mzjRdORknpVv7unsH0KPbicDgwm+/stKTUaeHJSRV8f0ctvzvYyLQ+Hnp7ZYAYwB8ON3KmJYHHZuJz06Q1w60YPXo0ZrOZNWvWcPz4cdLpNPfddx8m0/WPaa7mdehGlLS1gph32jW3RUruwR7ahyVZgz20l3g7JDKdLh2TGTJpiIQNPN7CPNY6gtnq3YR7DMrszkkMmzZtorm5GafTyaJFizr898Rms7FkyRJWrVrF+fPnWblyJQ888ACDBg3q0P2+G6UUFy9eZPfu3Vy4cKHt+wMGDGDKlCn06tXrPR4tOophKSFU8Ti+2t/i8r95pR/viFyHJUTeMccv4ml4EUviEgCW+DlQGal6F92GJHiFEAWtddBaJGSQTBpYrdICIN9IBW/hCIVCnD17lnPnznHhwoW2Potvp+t6NlmbiuNsqMWZTuC023HNWoCzstc1yVyr9Z0J1t8eaOCyvYnJvVwM6de7XZ/DvYN9bD4fYn9NhO9tr+Eb9/br9q0aTjXFeO5IEwCfndaDIoec/t2q4cOHYzabefXVV6mqqiKdTrN48eJ3XJQwJRtwBLYBEC598B0fKpXJRaR4AZ6mV3A1rSHuHgf6nV2E0DQNr89ES1OGoD9TkAleLRPDHjoA5G64WlVVFYcOHQJg4cKFnTYwzGKx8NBDD/Haa69RXV3NK6+8wqJFixg2bFin7B/AMAxOnz7Nnj17qKurA7Kvq2HDhjF58mTKymRwZa4l3GOI+u7CGdiW7cfb74sYZhmSKQS0rpxZgyOwAw2FoduJlC7KabsfIXJBzvCFEAXNZtNxunSiEYNAc4bySnkTzyfKUAQDUsGbrzKZDJcvX+bcuXOcPXuW5ubma253Op3079+fMWPGkMlkcDgcuFwurMk46qlvw9F9AGgz70H74J+h2ew33KehFBvOBAGYN7D9P5xqmsbnp1XyhVVnONYQ45WTLTw8onAm1Le3ZMbgO1trMBTM6e9hVj9pzXC7Bg8ezMMPP8yqVas4e/YsK1eu5KGHHrrmIoa76VU0DBLO4SRd10/QxYruwhnYjindjKvlTSKl995xbJ4rCd5QoDBXs9hDe9BUipS1kpS9f6fvPxgMsm7dOgAmT55M//6dG4PZbOaBBx5g7dq1nDhxgtWrV5NMJhkzZkyH7jedTnPixAn27NmD3+8HwGQyMXr0aCZNmoTXK8eLfBIuW3ylH+9lvLW/l368QiiFLbwfT+Mr6Jns4N64ZwLh0sUY5hu39xKiq5EErxCi4BWXmohGDFqaM5RXytCgfBIOGxgZMJnB5Zbkez4IhUKcO3eOc+fOcf78+WuqdDVNo7KykgEDBtC/f3/Ky8vRdZ2ePXtSU1ODUgp18gjGT/8T/M1gtaJ98LPos+656f0fa4hRH0nhMOtM79Mxy7Ar3BY+NrGcH+2q4zf7G5jS201PT/ds1fDbA41cDCYptpv49NT2HxbV3fTv358lS5awcuVKLl68yIsvvsiSJUuw2WxYolXYIsdQ6O/d808zEy67H1/t73D63yTmm3rHlXitKyRaW+IUFGW8NVzNNwM6uX2IYRi89tprJBIJevTowYwZuakg1nWdRYsWYbVaOXToEG+88QapVIqJEye2+76SySSHDx9m3759RCIRINsuYty4cYwfP77TqpfFLdLMBHt8gOIL37/Sj3ctkbL7cx2VEDlhStbjaViBNXYagLSlnFD5knYbYCpEIZIErxCi4BWVmLh0PpUdtCbySlt7Bp9Jen7mSCaTaeule/bsWZqamq653eFwtCV0+/Xrh91+/SpcZRgYq59Hvfg0GAZU9kH/zFfQet9apduGMwEAZvbzYDN3XNL/vqFFbD4f4nBdlB/sqOXr9/RF72avwaP1UVYcy1Zlf356T7w2qfRqD71792bp0qWsWLGC2tpaXnjhBR5d8ggljasAiPmmk7FWvOc2Eq4xJO3925I0oR6P31FMXt+VBG/AuME9848ldhpzqglDs5HwTOj0/e/YsYOamhqsViv333//u/ZW7gyapjFv3jwsFgt79+5l06ZNpFIppk6d2i7vodFolAMHDnDw4EESiQQALpeLiRMnMmbMmOu21BH5JWMtI1TxGL663+Pyb7zSj3d4rsMSovMYSVwt63G2bEIjg9LMRIoXEC2eA5qkt0T3Jr8BQoiCV1SaPZT5mzMopSSRmEdkwFpuhMPhtoTuhQsXSCaT19zeWqU7YMAAysvLb/g7kwn6Mb73r6hDuwHQZsxD+9Bn0eyOW4ormTHYci4EwLyBHbv0V9c0/nx6JX+x6gyH6qK8dsrPA8OKO3Sf+SSeNvjuthoUcM8gH1M7qFq6u6qsrGTZsmUsX76choYGTm5+iv6DajF0B5GSm6ho1zTCZYspufh/2EN7iRXNJG27/QFWnqLsxZJYxCCVVFishfM+2Fq9G/dOQum2Tt33hQsX2LVrFwALFizA58t9T1NN05g1axZWq5Xt27ezfft2kskks2bNuu3zm2AwyN69ezl69CjpdPZieFFREZMnT27rLy0KR8IzjmjsNM7gjiv9eL8g/XhFt2CNHMfTsBJTugWAhHM4ofJHMCzdtxWXEFeTd3MhRMHzFZnQNEjEFbGowukqnA+2XZ0MWOscmUyG2tratgFpjY2N19xut9uvqdJ1OG4+MauqjlH3s/9GNdaBxYr2gU+jzV54W4mGXZfCRFIGZU4zY3p0/BLgnh4rfzqhnJ/tqeeX+xqY3MtNhbt7tHH51b56asMpypxmPjH5vatJxe0pKyvj8ccfZ9WKP3JX73MANDlnoUyum3p82t6PuHs89vAB3I2r8Pf65G23J7BadewOjXhMEQpkKCkvjFN8PR3AFjkKZCufO1MsFmPNmjUAjBo1qlOHmt2IpmlMmzYNi8XCpk2b2Lt3L6lUinnz5t3SsbexsZE9e/Zw8uRJlFIA9OjRg8mTJzNo0CB0XVonFapw2YNY4uexJGvw1j6Dv/cnpR+v6LL0lB9348vYI0cAyJh9hMoeJuka1eltfYTIZ4Vx9ieEEO/BZNbwFpkItGTwN6dxumSJYT5QSkkFbweKRCJtVbrnz59/R5Vujx492qp0Kyoqbjkhq5RCrX0R9cKvIZOBHr3RP/NltD4Dbzvmq4erdVa7hAeHF7PlfIhjDTF+sLOWr83v0+Wr/A/URnjlpB+AL8zoicsqv38dpbi4mI8trMAdO0ND2MQvNp3l0aV+ioqKburx4dL7sEWOYI2dxho9lv2wepu8RSbisTTBAkrwOgI70VAkHYPIWHt02n6VUqxdu5ZIJEJxcTFz587ttH3fiokTJ2KxWHjjjTc4dOgQyWSShQsX3jAxe/nyZXbv3s3Zs2fbvtevXz8mT55Mnz5d/xjYLegWgpUfpPjC97DGz+JqXkekdFGuoxKifakMTv8WnM3r0FUShU60aDbRkgWdvuJDiEJQGGd/QghxA0UlrQneDL365joaARCPKZIJhaZlJ7yLO6OUoqamhrNnz3L27NnrVun279+/rUr3TobkqEgY4xffgQM7AXDcvZDkE58E2621ZLhaMJ5mz6XshOOObs9wNV3T+PMZlfzVK2fZXxNh3ekA9w4u6rT9d7ZIMsP3ttUA8MDQIib0vLlqUnF79FQzxbFs65LNF3oRDEV47rnnWLp0KaWlpTd8vGEpJuqbhcu/EXfjqzQ7h992FZ7XZ6K+Jl04g9ZUGnsw2x4h5uvcwWYHDhzg7NmzmEwm7r//fiyW/K3sb+2Nu2bNGk6cOEE6nea+++57R1sFpRRnzpxhz5491NRkjwGapjFkyBAmT55MRYVU8nc1b/XjfQZnywZSjgEknflTiS7EnbDEzuJpeBFzsg6ApH0AofIlZGwyMFaIdyMJXiFEl1BUYuJcNTJoLY+0JhncXh2TSaqF7kRrtdnx48ev+X6PHj3o379/W5Vueyy3VWdOYfz4W9BUD2Yz+vs+RekHnqS2trZtie/t2HQuREbB4BI7fX2dW3XRx2vjA+PK+NW+Bn6+p56JPV2UOvM3oXMnfr63noZomkq3hY9OlIROR3M3vopGhqRjCBMXvJ9zK1bQ2NjI888/z6OPPnpTSbVoyTwcod2YU404AjuIFc28rVg8Ra2D1gojwWsLH8WUCZExeUjcQeXyraqvr2fz5s0AzJ49m/Ly8k7b9+0aNmwYZrOZV199lerqal5++WUefPBBLBYLmUyGkydPsmfPHpqbs0MVdV1n1KhRTJo06aaryUVhSnjGE4udxhHcibfuDzT3/SKGufMuogrR3rRMGHfjahyhPQAYupNw2QPEPZNAk7YyQrwXSfAKIbqE4tZBay0ZlKHQdEko5pr0320/J06c4Pjx4+i6zpAhQ9oqde+kSvftlFKoN1ah/vhzyKShvBL9z76CPmBIuyznXX8mAMD8TqzevdqSESVsPR/iVFOc/9tZyz/M7XrLlHdfCvN6dQAN+OJdPXFY5INQR7LETmOPHEahESp7EKfNxWOPPcaKFSuoq6vjhRde4JFHHqFXr/cenqZ0O+GShXgbXsTVvI64ZyLKdOvV8t4rKyVCgcIYOPrWcLWpndY7NJlMsnr1agzDYNCgQYwbN+6OtxkJZ9/rXO6OfQ6DBg3ikUce4aWXXuL8+fOsWLGCIUOGsG/fPkKh7PBKi8XCuHHjmDBhAi6XVO93F6GyhzDHz2NJ1l7px/sJ6ccrCo8ysAf34G56Fd2IARDzTiVcet9N97YXoruTM38hRJfg9uiYzNm8VCho5DocAQSk/267CIVCbNiwAYBp06Zx//33M3LkyPZN7kYjGD/+FuqZn2R/iSbdhf6P30brP7hdtn8xmOBUUxxdgzkDcpPgNekaX5zRE7OusetShI1ngzmJo6MEExm+vz27LHvJyBJGV3T8ELtuTRm4G1cBEPNOa1syarfbWbp0Kb169SKZTPLiiy9y4cKFG24u7p1C2lqBbkRxtay/rZDcHh1Nh3QKYtHbr7bvDKZELdb4GRQ6Md+0Ttvvxo0b8fv9uN1u7r333jtOggf9GTauDrHxtRDRSMdXTvft25elS5ditVq5fPkyb775JqFQCIfDwcyZM3nyySeZNWuWJHe7myv9eA3NijV+BlfzG7mOSIhbYk7UUHzpx3gbXkA3YqSslTT3/gyhisckuSvELZAErxCiS9B0jaKSK1W8zdKmIR8Er1Tw+qSC97YppVizZg3JZJLKykqmTJnS/vs4X43xb38Fe7aCyYz2vk+if+bv0Jztd0K98cpwtYk9XRTZc7d4qF+RjfeNzfZF/enuOlpiXedY8dNddbTEM/TxWvnguLJch9Pl2UN7sSQuY+g2IqX3XnOb1WplyZIl9OvXj3Q6zcqVKzlz5sx7b1AzES5dDIDDvxVTqumWY9JNGh5P9tQ+lOdtGhzBHQAkXCMxzL5O2efx48c5duwYmqZx3333Ybfb72h7qaTBri0RMpnsdbHDe2PtFOl769mzJ4899hgulwufz8f8+fP5+Mc/zpQpU7DZZOhQd5WxlhOqWAqAs2U9lmhVjiMS4sY0I4G74WWKL3wfS/w8hmYlVPYgLX3/nLSjf67DE6LgSIJXCNFlFJdkE4ktTfn9wbY7SCUV0Ui2krqrtWhQ56oxVvwWdfl8h+9r3759XLp0CYvFwqJFi9qlx24rpRTGxtUY3/wyNNRCSTn6l7+Jfu8j7bq021CKDVcSvPMGdk4i5708NqqUQcU2wkmDH+26s77C+WLL+SBvnguia/AXd/XEZpbTu46kGQlcTWsAiBQvQJnc77iPxWLhoYceYtCgQWQyGVatWsXZs2ffc7tJ13ASjqFoZHA1rr6t2Nr68ObxoDXNSGAP7gU6b7ia3+9n/fpsZfS0adPo3bv3HW1PKcW+HVGiYQO7Q0PToO5ymtpLqfYI94YqKip48skn+ehHP8rYsWPfMXBNdE8JzwRi3qloKHx1z6Knu9ZKFdGFKIUtfIiSc/+DM7AFDYO4awzN/b9ErGi2tBgR4jbJJwAhRJdRVJo9GZAK3txrTS44nBpWW9d4q1HhIMZvfojxjS+hXn4W42tfxPj5d1CNdR2yv8bGRrZu3QrAnDlz2nVQjorHUD/7H9TTP8yu5x43Ff3/fQdt0PB220erYw0x6iMpHGad6X3emQjrbGZd44t39cSkwfYLYbacD+U6pDvij6f50c7sa/Dx0aUMK7v13q3i1jhbNmLKhEhbSt5zIJrZbOaBBx5g2LBhGIbB6tWraWlpec9th8sWo9CwRw5jiZ295dha+/Dmc4LXHtqHrpKkLeWkHO3TBua9ZDIZVq9eTSqVolevXkydOvWOt1l1LEHd5TS6DlNnuxg8PFs5e3hvlHS6cy4a5XuPZZEbobKHSVsr0TNhvHXPgpK2ZSK/mFJN+Gp+ia/2d5gyQTLmEvw9P0aw54c6bUWHEF1V1/jULYQQ0NaiIRQwOu0Dlri+1v673i7Qf1cZGYwNr2L842dRb64GpaDfIFAGatsbGP/4WYzf/QgVeO/Eza1Ip9OsWbMGwzAYMGAAo0ePbrdtq0vnML7x16idG0HX0R7/GPrn/wHN5Wm3fVxtw5XhajP7efKmsnRgsZ3Hx2RbNfxkVx2BeGFeFFJK8cMdtQQTGQYW23hijLRm6Gh6qgWnfxNAtqWC9t6VkyaTiYULF7b15H355ZdJJpPvev+MrZK4N9uKxd246paTM20VvPnaokGptuFqMd906IQk5datW6mvr8dut3Pffffd8UqIhroUxw/HARgzyUFRiZmho+04nBqxqOLU0Xh7hC3E7dEtBCo/kO3HGzst/XhF/lBpnM3rKDn/HWzRkyhMRIoX0NTvL0m62r/AQIjuKD8+aQkhRDuwOzRsdg2lINCSpx9uu4mu0n9XVR3LJkN/+38QCUGfAeh/+/9h+qfvoP/9f8HI8ZBJo9a/gvH3n8J44VeoSPiO97tjxw4aGxux2+3tMgiolbFlHcb/99dQexGKStH/5v9Dv+8xtHZs/XC1ZMZgy7lshey8gbkZrvZu/mR0Gf19NgKJDD/d3TFV2B1t49kgOy6GMevZ1gwWk1T0dTR302o0lSbpGETSNeqmHmMymXjggQdwuVy0tLSwdu3a92wNEilZiKFZsSQuYgsfvKX4Wit4IyGDTCb/LnRa4mcxJ+tQmoW4Z1KH7+/s2bPs27cPgHvuuQeP584uZMWiBnu3RUFB34FW+g/OVu6azRpjJmUHG1afSBAKyjmIyJ2MtYJQxaMAOFvewBKtzm1AotuzRKsoOf8d3M2vX3kPHUxzv78gUroQdEuuwxOiy5AErxCiy9A0jeJSGbSWDwL+7M+/UPvvqkALxs+/g/Gtr8D50+BwoX3g0+j/+G20YWMA0AYOw/Slf0X/0r/CwGGQTKJefR7jq5/CWPUHVPz2Bu5cunSJPXv2ANmEhNPpvPPnk0hg/OK7qF9+F5JJGD0x25Jh6M0lqG7XrkthIimDMqeZMT3u/Hm0J4tJ4wt3VaJrsOlciO0XCqtVQ1M0xU92ZRPT7xtbxsDiOxsYJW7MHDuHPXwQhUa47MFbqj51uVw8+OCD6LpOdXU1u3fvftf7GmYP0eJ5ALibXgPj5vu62h0aFmv2Qmc4D5OMrdW7cc8ElKlj24lEIhHWrl0LwLhx4xg8+M7aQWQyit1bIiQTCm+RibGTro2/Ry8zPXqZUQYc2hPrEv29ReFKeCYS80xBQ+GtewY9XVjvcaKLUGm8dc9SfPkpzKkmMiYPgR7vw9/rE2Ss5bmOToguRxK8QogupejKoDW/DFrLGSOjCAWzy4p9xYU1+EWl0xivr8D4p8+itmWXNWqzF6L/2/+hL3gIzfTOhLU2cjz6V/8T/fP/AL37QyyCevFpjL//NMa6l1Gpm0/OJBIJ1qzJDm8aNWrUHSckAFTNRYxv/g1q6zrQdLRHP4z+xX9G83R8n7PW4WpzB3jR87Bf5NBSB0tHlgDwo521hBKFcdxQSvH97bVEUgZDS+0sG1Wa65C6PmXgaXwZgLh3Mmlbr1veRGVlJfPnzwdg27Zt7zl0LVo0m4zZhyntx+nfctP70DQNry97eh/051fvTT0dwhY+AkDMO71D96WUYs2aNcRiMcrKypg9e/Ydb/Po/hj+5gwWq8bUWU5M5muPaZqmMWaiA90ETfVpLp3vnIFrQrybUPnDpK09MEk/XpEjzpY3sYf2o9CI+u6iud+XSHgmdEp7HiG6I0nwCiG6lNZBay3NhZGo6YpCwQzKAItVw+EsnBM4dfwgxr/+JerZpyAWhf5Dsonbj34BzVv0no/VNA1twvRsVewnvgTllRAKoJ75CcY/fRZjyzpU5savyTfffJNQKITX6+Xuu+++4+dk7NiI8Y0vwaVz4CtG/9LX0R98osNaMlwtGE+z51K2XcW8Qfk7NOP948ro47XSEs/w1J7CaNWwtjrA3poIVpPGX97VE5NeOL9nhcoWPoAlcRFDsxIpWXTb2xk9ejRjx44FYPXq1fj9/uvfUbcQLr0PAGfLBrRbqL7z5mkfXntwNxoZUra+pO29O3Rfu3fv5sKFC5jNZu6//37M5ju72HjxbJKzVdneyROnO3G6r786xek2MXRUtpr+6P4YqaRU8Yoc0q0EKj+I0ixYY9U4W9bnOiLRjeipFlwtGwAI9vgTwuWPoEyy2kiIjiQJXiFEl1J0pWI0FjFIxKVSIRda+x97i0wFMeVbNTdi/OQ/Mf77H+HyeXB70D7y5+h//19og25t6IOmm9BnzEP/+g/RPvRZ8JVAUz3ql9/F+NoXUHu2vuuy3aqqKo4dO4amaSxatAir1Xr7zymVxPjND1A/+29IxGH4WPR/+g7aiHG3vc1btelciIyCwSU2+vlsnbbfW2U16XxhRk80YP2ZILsv3XkP5Y5UF07y1J56AD48vpw+efyz7TKMJO7G1QBES+ZjmO+sj+vdd99Nz549bzh0LeEeT8rWG10lcDe/ftPb91zpwxv051GCV2VwBHcAEC26q0N3VVNTw/bt2VYQc+fOpaSk5I62F/RnOLA7CsCw0TZ69HrvfpGDh9tweXQSccWJw7fXqkeI9pKxVhAqfxQAV/M6LNHTuQ1IdBvuxlVoKkXSPpCEe0KuwxGiW5AErxCiS7FYNdye7KHNL1W8OdGaVMj3AWsqlcJ49blsO4Zdm7LtC+YvRv+3H6HPWXRHVa6a2Yw+7wH0b/wY7fGPgcsDtRcxfvTv2aFtR/Zdk+iNRCK88Ua2JcTkyZPp1evWl3+3Pa/6yxjf/FvUm6+BpqE99D70L30dzVd829u8HRvOBACYNzB/q3dbjSh38PCI7M/nhztqiSTz89hhKMX/bq8lnjYYVe7goeGd+3/aXTn9b2LKBMmYi4n6Zt3x9kwmE4sXL8blctHc3PzuQ9c0PdvrF7AHd2FK3FyFeWsFbyiPKnitkROY0gEM3UXCNabD9hOLxXj11VdRSjFs2DBGjbqzPuOppMHuLRGMDJRXmhk26sbVZyaT1taf90xVUmYCiJyLeycR80xu68d7KysChLgd1shJ7JEjKHRC5Y9ISwYhOokkeIUQXU5rmwb5UJUbgSsJXm9x/iZ41eE92YraF34NyQQMGYn+j/+D/sHPoLnurDrvaprNhn7fY+j/30/QHno/2BxwrgrjO/+M8V//gKo6hlKK119/nXg8TllZGdOn335vSrVnC8a//hVcOANuL/pffA19yYfQ9M79v7gUTHKyKY6uwd39vZ2679v14fHl9PRYaIql+b+dtURT+ZMca7XqRAuH66LYzRpflNYMnUJPB3C1vAlAuOyBdpv27XK5WLx4cdvQtdbBim+Xcgwk7hqNhsLd9MpNbdvjzf6+J+Iqb1aytA5Xi3mndNjEdKUUL7zwQlubm/nz59/RKhKlFPt2RomEDRxOjUkznGg3+TtXXmmhVz8LqCsD1wxp1SByK1T+CGlrBaZMCF/dH6Qfr+g4Ko27cSUAMd9dZGyVOQ5IiO5DErxCiC6nuCTbpqFFBq11OqUUwZb8reBVDbVkfvANjO/+C9RfBl8x2if+Cv3L/47Wb1CH7VdzutCXfBD9mz9Bu3cJmC1w8jDGt77Cwe/9B+fOncNkMnHfffdhus4gtxtRqRTG73+C8aNvQTwGQ0ah/7/voo2e2AHP5sZaq3cn9nRR5CiMQXs2s84XpvcEsu0lPrm8ml/tq6cpmh+Dki4Fk/x6fwMAH5tYQU/P7bfwEDfP3bT6yhLTAe1eedqzZ0/mzZsHwNatW9916Fqk9H4UJmzRk1gjJ2+4XbNFw+nOnuLnQxWvKdmILXYKhUbMN61D9qGUYt++fRw6dAhd17n//vux2e6sfUnV8QR1l9LoOkyZ5cJqu7WPTaMnODCbs6uJzp2+fhsOITqNbiXQo7UfbxXOlo25jkh0Uc6WzZhTTWRMHiKl9+Y6HCG6FUnwCiG6nLcqeDPv2u9UdIxoxCCdBl0Htzd/3mJUMoGx8ncY/+/zsH8HmExoix5F/9f/Q59xZ1Vet0Lz+NDf9wn0b/wIbc4i/HYXW4xsom6mnqAknbjlbaqGWoz/+DvUGy9n93H/MvS/+QZacWm7xn7T8SjFxrNBoDDaM1xtdA8nX57Ti95eK5GUwQtHm/n0imq+t72GC4Fb/79pLxlD8d1tl0lmFBMqndw/tChnsXQn5vgF7KH9ANlWCR1wnBgzZgyjR48G4LXXXrvu0LWMtYyYbwZAtor3JirvvHnUh7e1927SOQzDcmf9cN8unU5z9OhRnn76aTZt2gTAXXfdRWXlnVWMNdSlOH4oDsCYSQ6KSm79QpXdoTN8bLZVw/FD8bypphbdV8bWI7tcHnA1r8USk368on3pKT+ulmzLsXDZAyhdhqoJ0ZkKo6xGCCFugddnQtchlVREwwYuT/5VknZVrQPWPD4Teh4sH1dKwf4dGM/+DJqyg6kYOR79/Z9C69UvZ3FpJeUYH/osa3Uf6eYW+oSbGHtmH8b+N9Fm3Yv20PvRSspuuB21fwfGL74D0Qg43ehP/hXa+Kkd/wTew7GGGHXhFA6zzvQ+7pzGcjtm9fNyV18Puy6FWX60mWMNMV6vDvB6dYCpvV0sHVXKqHJHpw4QfPFYMyca4zgtOn8+o2dBDC8seErhblwFQMwzibS9T4ftau7cuTQ1NVFbW8uqVav4kz/5k3cMWYyU3IM9tBdzsg57cDfxG1TCeot0ai9BMJDjpKKRxB7Mtp9oTVK3h0QiwaFDhzhw4ACRSAQAq9XK7NmzGTPmziqtY1GDvduioKDvQCv9Bt1+tfyAIVYunEkS9Gc4diDOhOnOO4pNiDsV90zGEjuNI7QPb+2zNPf9AspceO/VIj+9NVhtgAxWEyIHJMErhOhydJOGt8iEvzlDS3NGErydKJ8GrKnaixjP/BSO7Mt+o6QM/YlPwKSZeZEg2717N/XNLdhsNhbe9wTaayY4tBu1aQ1q23q0+YvRHvgTNM87e9iqdBq1/NeoNS9mvzFoOPqnv4xWWt65T+I6NpzJVu/e1c+DzZw/Vdy3Qtc0pvfxML2Ph+MNMZYfa2LHhTC7LkXYdSnCsFI7S0eVML2Pp8P74J7zJ/jdwUYAPjm5gnJXx/QvFdeyhQ9hjZ9DaRYipfd16L7MZjOLFy/mmWeeoampiXXr1nH//fdfc5xSJgeRknvwNL6Mu3ktCc94lP7uLQg8eVLBaw8fRDdiZMzFJJ3D7nh7oVCI/fv3c/jwYVKpbPsUl8vFhAkTGDt2LAMGDKCmpua2V+9kMordWyIkEwpvkYmxk+7sYo6ua4yb7GDzujAXzibpO8hKabl8/BI5pGmEy5dgiV/EnGrAW/cHAr0+Blphvl+L/GGJVmGPHJbBakLkkJxhCCG6pOLSbILX35SmT3/pVdlZWit4czlgTcVjqJefRb2+EjJpMJvRFj2GtvhxNFt+LBWrra1l586dAMybNw/P8OEwfDTq1FGM5b+GU0dRa1eg3lyDtmgJ2sJH0RzZyi/V3IDxk/+E6uMAaPcuQVv2ETRz7hN/yYzB5vPZBO/8gYUxXO1GRpQ7+Gp5Hy4Fk6w41swbpwOcbIrzrU2X6emxsGRECQsG+TokmZ02FN/Zepm0oZja282CQYXV8qJgGSncTa8CECmei2Hu+Ney2+1m8eLFvPDCC5w6dYqKigomT558zX1ivuk4Atswp5pwtmwkUrroXbfnvXKRLRTMoAx108PB2lvbcDXf9DtKIDU2NrJ3715OnjyJYWSrkktKSpg8eTLDhg3DZDK1y4W7o/tj+JszWKwaU2c5MZnvfJvFZWb6DbJy/nSSQ3ui3L3IkxcrXET3pXQbgcoPUnLxh9hip3C2vEm0ZF6uwxKFTKXxNLQOVptBxtYzxwEJ0T1JglcI0SVl++Ul8Tfnvv9gd5LLCl6lFGrXJtQffw7+5uw3x05Bf/8n0Sp6dXo87yaVSrFmzRqUUgwbNozhw4e33aYNHYX+t9+EI3sxlv8Gzp9GvfQMav0qtAceR6voifGr70E4BA4X+se+iDbprhw+m2vtvhQmkjQodZoZ06NrLUXu7bXyuemVfHBcGatOtvDKyRZqQil+tKuO3x9sZPHwYhYPK8Zra7/X/nOHmzjdksBj1fn89Mq8qDzvDpz+zZjSfjJmH9GiOZ223169enH33XezYcMGtm7dSllZGf3793/rDpqZcOkDFNU+jdO/iZh3Goal6Lrbcrl0TCbIZCASNnB7O/+YbI5fwJK4hNLMxLxTbvnxSikuXrzI3r17OXfuXNv3e/fuzeTJk+nfv3+7/k5cPJvkbFV2GNrE6U6c7vb7mY0cZ6fmYopQwODMyQSDR+THxUbRfWVslYTKH8Zb/wKu5rWkHP1JOQbmOixRoJz+rZhTDRgmN5ESGawmRK5IglcI0SW1DloLtGQwMgrdJImRjpaIG8Rj2WWx3k5O8KqLZzF+/xM4eTj7jfJK9Pd9Kuf9aK9n8+bN+P1+XC4X8+bNe8ftmqbBmMnooybC3q0YK34LtZdQf/wFbYuO+w1G/8xX0MrvbJBQe2ttzzB3gBe9iyYjixxmPjS+nMdGlfJ6tZ+Vx5upj6T5/cFGXjjSxL2DfSwZWUIP952tHKhqivOHw9nWDH82tZJih5yydQY9HcTZsgGAcOn9oHfuCpCxY8dSX1/P0aNHWb16Ne9///vx+d6q3E66RpG0D8QaP4O76TWCle+77nY0XcPjy65kaaxP5yTB6whkh6vF3WNRJtdNP84wDKqqqti7dy/19dne6ZqmMXjwYCZPnkyPHj3aPdagP8OB3VEAho220aNX+66IsNp0Ro23c2BXjBNH4vTqZ8XhlCXxIrfinilYY6exh/bjrX2G5n5fvKXfVSEA9HQAZ/M6IPu+qUyOHEckRPclnxa6iaNHj7Jy5UrOnDlDS0sLf/M3f8O0aW8N6FBK8Yc//IF169YRiUQYMWIEn/zkJ+nZ863lFclkkl//+tds3bqVVCrF+PHj+eQnP0lRUVHbfcLhMD//+c/Zs2cPmqYxffp0Pv7xj2O3v1Wp0NjYyE9/+lOOHDmC3W5n7ty5fPCDH8Rkyn3PTtF1uNw6FotGKqUIBjK3NQFb3JrW6l2XW8ds6ZzknoqGUSt/j1q/CgwDrNZs39r7lqJZ8q81x9mzZzl06BAACxcuvObY+HaarsOU2egT70JtX49a+XtobkCbtxjtiSfz7vkFExn2XA4DMH9g128l4LDoPDyihMXDitlyPsTyo9lq21Un/bx6ys/Mfh6WjixlSOmtV+qlMgbf3XaZjIJZ/TzMGdA12l0UAlfTGnSVJGXrS8I9vtP3r2ka8+bNo6mpibq6urahaxaLpfUOhMsepOTi97GH9xONzyRt73vdbfXsa8HfnOH4wTiVvS3YHZ2XUNQyEezhA8DND1dLpVIcPXqUffv2EQxmLxaZzWZGjhzJxIkTrznfbE+ppMHuLRGMDJRXmhk2qmOqa/sOtHL+zP/P3n3HyXWW99//3GfO9NnZ3qSVdle9SyurS+6OZYMJccCGUIPBQIAkQEjCk4f88gAh+dFCcEgxvbcAxjT3KlnV6r2upO29TG/nfv6Y1WjXVrV2d2Z2r/frNa9pZ+bcszo6M/Od61x3nL7uFIf2RFixXoI0kWVKESj/E8xoy4V+vNXvln684pr4uv+Qft90TSda0JDt4Qgxqcnee5KIxWLU1dXx3ve+96L3P/roozz22GM8+OCD/Mu//AtOp5PPfe5zxOPxzDLf+9732LVrFx//+Mf59Kc/TV9fH1/+8pdHPM9DDz1EU1MTn/rUp/jkJz/JkSNHePjhhzP3W5bFv/7rv5JMJvnnf/5nPvzhD/P888/zs5/9bGxeuJi0lFKZKt6xaNOgLU08ZhEKpvv8drYlaDkX58yJGMcPRTm0N8KpY1EG+1OvebKXfHO+/27hOPXf1aeOYn3qL9DP/DYd7i5fh/GZ/8K45y05F34CRCIRnn76aQCWLVvG9OnTr+pxymbDWH8Hxuf+B+P/fhPj7R/Myde3+ewgSQtmFDuZXnTpyZ8mGpuhuKnOz7/dXcdnbp9GQ7UXS8PmswH+5vEzfOrpc+xqCV7TfuDH+7s5NxCn0GXjgytHv1pRXJwZbcEV2A1AoPyerE0Qc37SNbfbTXd3N88888yI7Sfpmkpk6Eu0r/sPcIlta8YcJ4XFNhIJzb6d4XF9L3IN7kbpJAnnFJLOiwfQ54XDYbZt28Z3vvMdXnjhBQYHB3G5XJkigVtvvXXMwl2tNXt2hAkFLdwexfI1njHrV6yUYskNHpSCtuYEHW2JMVmPENci3Y/3z9DKxBk+jqd/U7aHJPKIPXwKV3A/GkWg/I3y44AQWSYlbZNEQ0MDDQ0X/0VNa80f/vAH/vRP/5SVK9OHM3/kIx/hwQcfZOfOnaxfv55wOMyzzz7LX//1X7No0SIAPvShD/Gxj32M48ePM2fOHJqbm9m7dy//+q//ysyZMwF44IEH+Nd//Vfe+c53UlJSwr59+2hubuYf//EfKSoqoq6ujre85S386Ec/4v7778c0ZZMUo6eoxEZXe5L+nhTMuvgyqaQmkdDEY5pE/Pxla9hlfdHLifjVflGO4nQpyipNyivtlFeZ41pFNZ7OV/COR3sGHRzEevgLEBiAqhqMP3s/asGyMV/va6W15tlnnyUcDlNcXMy6deuu+TmUaYfSijEY3eh4vnEAgFsmQfXuxSilWFrlZWmVl8a+KL8+3Mums4Mc6AhzoCNMbZGTP5lfwo21fuyXaRlztCvCr4+ke0h/eHUVfpe8L44LrfF1/x6FJupbStJ1dT/AjJWCggJe97rX8cgjj3D8+HEqKipYvnx55v5Q6UZcwYM4omdwhg4R8y161XMYhqJhtYcXnwzQ2ZakqTHO9Bnj8OOLtnAPDk2u5l9zyaC8v7+fPXv2cPjwYVKpofcPv5/ly5czf/78C1XLY+jk0RgdLUkMA1as9+Jwju37s7/IRv1sJ6ePxzi4K0LZXeaoTOQmxPVIOasJlL0Bf9cjeHueRFkxwkU3yqH24vJ0ioLu8xOrrSbpzJ35LoSYrORbg6Czs5P+/n6WLFmSuc3j8TBr1iyOHz/O+vXrOX36NKlUisWLF2eWmTp1KmVlZZmA9/jx43i93ky4C+leckopTp48yapVqzh+/DjTp08fUYmxbNkyvvnNb9LU1ER9vTT3F6OnuNQEYnS2J9i7PUw8MRTcxoYC27jGus7iXpsJdofC4VDYHUbmsmlXBAZS9HQliUU1LWcTtJxNV+sU+A3KquyUV5qUVpiYE+TL3fkKXv8YV/BqrbG+81Xo64bKqRj/75dRrtz+EnL06FFOnTqFYRhs3Lhxwv2Y1TIY51h3FEPBTdJOgPpiFx9bP4V3LCvnN0d7efLkAGf7Y3x1axs/3NvFH88v5s5ZRXjsI/+vxJLp1gyWhlvr/ayuKcjSK5h8nKFDOKKNaGVP997NAVOnTuXGG2/khRde4KWXXqKsrCxT+W8NTQDn7XsWb89jxLzzQL16v1JQaGPuYhdH9kU5tCdCWaUdj3dsQ0xH+ARmohfLcBEteHWbi/b2dnbt2sWpU6cyt1VWVrJ8+XJmzpyJYYzPj6BdHQmOHogCsGi5e9xaOc1d5KK1KU44ZHHyaJS5i3L7/UtMDlH/SuzRs7gDu/H2PYd7YAuRwvWEizZI0Csuyt2/BTPeiWV4CZXcme3hCCGQgFeQrqAARkzicf76+fv6+/sxTROv13vZZfz+kV/sbTYbPp9vxDKvPMzu/HrPL3MxiUSCROLCoWxKKdxud+ZyPjo/7nwdfz4oLjVBQSyqaToTv+RySqVD2gtBbTqsdbzqNoXDaVy4bFdXnLwtldL0difpak/S1Z5goC9FYNAiMBij8XgMZUBJmUl5pUl5lZ2iYtt1HR6are0qmdQEAxYARcXmmK5fP/Uo7N8Jph3bB/8e5faM2bpGw+DgIC+88AIAq1evHpMJgsbS1WxTL5xJ98tcVu2lxDP2VXf5osLn4H0rqnjr4nIeP9HHb4/20hNJ8p3dXfzsQA93zS7mDfOKKR36m/1gbxetgQSlHpMHV1ZN6PeHnHoPtJL4uh8DIFx8I9pRTA6MCoClS5fS2dnJkSNHXjXpWrjkZlyDOzETvXgGthEpvvGizzFrrov2lgR93Sn27Qyz9hbfmPzdVSqKM7AHT1/6EO+o/waULV0xrLWmsbGR3bt309LSknlMXV0dN9xwA1OnTr3uMV3LNhUJW+zeGgad7o1bO9M5btui3aFY1ODh5S0hTh6JUVPnxFcg81DkqpzaV40lpQhWvpm4dz7e3qcx4x14+55NB71FG4gUbUDbxqY/9WQzEbYpIzmIty89sVqo7C4wPTnzvinEZCYBr8gLjzzyCL/4xS8y1+vr6/n85z9PeXl5Fkc1OqqqqrI9hAntjrt9dHVEcbpsw04GTqcN19B1u8MY0w9ZNTUXLkcjSVrOhWgeOgUHE/R0JunpTHL0QHqcU6d5mDrdR02tF3/ha+u1Ot7bVUdbGOjH7bFRP2PqmK0nfuIwHb/6HgBFD36cglXX3upgPFmWxaOPPko8Hqe2tpY3vOEN41adNtoutU1prdn020YA7m2opbpa9mkXM7uuhg/cavHY4XZ+uPMcZ3rD/OpwD7852svdC6pYVlPIb4/1AfD/vW4hs2tLszzi8ZEL74FW0++xkr3gKMI//y0U5liI8ba3vY2HH36Y5uZmnnjiCf7iL/4ChyP93mAZ92Gd+Da+/uconP06lN130efYeE+MX/zwNN0dSfq6XCxcWjJq49OBRqy2Z9Gd28Aa+jHVXkDB7Hvx2IvZs2cPmzZtorOzE0j/+L9s2TJuuummMfnB60rbVCql+c3/niEe05RVuLjznjpMc3z3y1VVmvaWczSfDXH8YIrX3Xv9AbcYW7mwrxofU9H6DnT3y1hnf40RbsHb+zTewS0YU+9CTb0TZUpF72jI520qdfRRtBWDgpkUzb0HJb13hcgJEvCKTEXtwMAAxcXFmdsHBgaoq6vLLJNMJgmFQiOqeAcGBjKPLyoqysx6fF4qlSIYDI5Y5uTJkyOWGRgYGDGOi7n33nu55557MtfPfwju6uoimUxe9WvNJUopqqqqaG9vnzSTcGWDxw+1fgALuFAFnrQgGE6fsjGmOYsUsxd6CQUtutoTdLUn6e5MEIumOH0iwOkTgfSyPoPyKpOKKjtlFSZ2x+U/QGVruzpzMgZAQaGira1tTNahwyFSn/s7SCZRN6wjsHw9wTFa12jZtWsXZ86cwW63c+utt9LR0ZHtIV2zK21ThzvDtA5EcZsG8wpSY/bvP1GsKlesuHs6L7cE+dWhHg53RfjtwTZ+ezD9d7trdhG1rviE/zvmynugSgYoOfsoBjBY/EfEOvuyNpbLufPOO/nJT35CW1sbP/rRj9i4cWP6s5CeRbGjCjPeTuDIjwmVv+GSzzFviYuDuyNsfaEdpzuM93qqRq04rsBeXAPbsccuVOQmHRVEC1cz4FzI/qe3sXfvXkKhEAAOh4PFixezbNkyfD4flmWN6nZ+tdvU/l1hOtti2O2KpascdHVlZ788Z5GN1iZoPhti985zTJmWe5NnitzZV42/aTDlwziDB/D0PpM+FP/sr7CaHiNSfCORwnVS0fsa5fs2ZY+cpqhzKxpFf9HdJNvz77NtvjBNc0IUtInxIwGvoKKigqKiIg4cOJAJdMPhMCdPnuTOO9P9dGbMmIHNZuPAgQOsWbMGgNbWVrq7u5kzZw4Ac+bMIRQKcfr0aWbMmAHAwYMH0Voza9aszDK/+tWvGBgYyBxiuH//ftxuNzXDyxxfwW63X3KyjXx8YxxOa533r0G8dl6fgXeWk7pZTixL09+borsj3c6hrydFOGhx9mScsyfjoKC4xJaesK3KTnGpDeMS7RzGe7sa6Ev/0OIvso3JerXW6B/8J3S1Q2kF6l0fydyeq7q6utiyZQsAN910E36/P6fHeyWX2qaeO53+kW7tdB8Om8rr1zheFLByqo+VU30c647wyOEetjUFqS5w8O6G8kn1N8z2e6Cv5ykMK0bCOZWobxnk6N/e5/Nx991388gjj3Ds2DEqKiqGJs9VBMpeT3Hrt3D3byXiX03KcfEvg3WzHLQ1p48a2bMjxLpbr71Vgy3WjntwO67AHgwr/cOexkbMt4hI4Wp64iXs27+fgwd/nGmt5fV6aWhoYOHChTidF1o2jJXLbVPNZ+KcOZEed8MaDx6vkbXtz+szmDXfyfFDMQ7uDlNeaWLapYo3V2V7X5UdiqhvCVHvIpzB/Xh7n8VMdOHteRJ33ybCRTcSKVqHNsZh8sYJKC+3KZ3C15meWC3qX0nCOTVn3zeFmIwk4J0kotEo7e3tmeudnZ2cOXMGn89HWVkZr3vd6/jVr35FdXU1FRUV/PSnP6W4uJiVK1cC6UnXbrvtNr7//e/j8/nweDx8+9vfZs6cOZmAt6amhmXLlvHwww/z4IMPkkwm+fa3v826desoKUkfCrh06VJqamr42te+xtvf/nb6+/v56U9/ysaNG8dltmQhcplhKErKTErKTOYsdJFIaHo602FvV0eSUMCirydFX0+KE4dj2EwoqzApr7RTVmXiKxjbVhOXc36CtcKisekjqDc9id65CWw2jPf/Lcpz8cOQc0UymeTJJ5/Esizq6+tZsGBBtoc0JuIpi83n0kdu3FJfeIWlxcXMLXPzyZtq6I8kcZoGbrsc5jhebLE2XIM7AQiW3QM5fohpTU0NN954Iy+++CKbN2+mrKyMadOmkfDMIuaZizN8DF/P4wxUv/Oij1dKsWyVm+cfD9DbleL08Rgz515FBZ6VwBU8gGtwB47o2czNSXsJUf9qIv7ltHUF2fP8Hk6ePJkJLEpKSrjhhhuYM2cONlv2e8wO9qfY93L6sJ3ZC5xUTsn+585Z81w0n00QDlocOxhlYYMc+i5ykDKIFSwj5lsyIuj19T6Jp38z4eIbiRSulaB3EnAPbMOMt2MZboKlMrGaELlGAt5J4tSpU3z605/OXP/+978PwM0338yHP/xh3vjGNxKLxXj44YcJh8PMmzePf/iHf8j0eAN497vfjVKKL3/5yySTSZYuXcr73ve+Eev5q7/6K771rW/xmc98BqUUq1ev5oEHHsjcbxgGn/zkJ/nmN7/Jpz71KZxOJzfffDNvectbxvgvIET+sdsVVVPtVE1NfwmNhK1M2NvdkSQe03S0JuloTVfPutyK8io7bdVdhCNRbDaw2RQ2E0xTYTNV+jZTpa8PXb5UFfDVsizN4EA64PUXj/6XeN1yFv3TbwCg7n0nasbcUV/HaNu6dSs9PT243W5uv/32Cdtb8eWWIKG4RanbZFFFbk92l+uK3PKRbFxpTUH371Foor7FJNx12R7RVTk/6drRo0d57LHHeOtb34rf7ydY9joc507gDB3GHjlNwj3joo/3eG0sXOZm/8sRjh6IUlltx+e/+H7bFu/CPbgD1+AuDCsCgMYg5l1ApHA1MWcdpxvPsOfpx0a0Wpg2bRoNDQ3U1tbmzL4vEbd4+aUQVgrKq0zmLsyNQ8ttpmLRcjc7XgzReCLGtHoH/jH6oVSI6zYi6N2Ht/cZzEQPvp4n8PSlg95w4VowpN3IRGQkA3h7nwIgWHoX2ua9wiOEEONN6bw7LkCIC7q6ujKHAOYbpRTV1dW0tbXl3+E5Iuu01gz2p+hqT9LVkaS3K4llvbbnUgbYbEMhsO3SQfD5218ZFicTmgO7IthMuPtPC0f1C72ORbE+9zfQ1gSLbsD4y39E5fgkZU1NTTzyyCMA3HPPPZmWNfnqcvuqf3mhme3NQf50QQnvbqjI0ghFPsr2e6AjdJiith+glUnP9I9h2Udv0rGxlkwm+d///V+6urooLy/nvvvuwzRNfF2P4hnYRsI5hb6aD1+yIllrzfYXQ3S1JykqsbH+dt+FH/p0EmfoCO6B7TgipzKPSZmFRPyriPpXEEk5OXz4MPv27cvMvWAYBvPmzWPZsmWUlZWN+d/gYi61TWmt2flSiI6WJG6P4qY7C3A4c+t95OWXQrQ1JyguTf975EowLrK/r8ppOoUrsA9P37OYiR4ALJuXcNFNhAvXSNB7Cfm6TRV0/C/uwG4Szqn01Xwo5496mQjsdrv04BXXRMpFhBAiDymlKCw2KSw2mTUfUklNb3eSns4UNpuLwGCIZFKTSqXvSw1dTt+mSSUvtMzSVnrSuWRCA6/9g6a/0DbqX0r1T76eDncLSzAe+GjOh7uxWIynnkpXNyxcuDDvw93LGYyl2NUaBKQ9g8gzOomv+w8AhIs25FW4C+lJV17/+tfz05/+lK6uLp555hnuvPNOQiW34wrswR5rxRXYS9S//KKPV0qxdKWH5x8fpL83xaljMebOCuMe3Il78GWMVPr/tUYR98wlUriauGcOgWCIvVv3cujQIeLxOAAul4slS5awePHiEZPw5pKTR2N0tCQxDFixzptz4S7AwgY3nUO995sa40yfIYe6izygbET9y4kWLMUV2DsU9Pbi63kMT/8mQkU3ESlcLUHvBGBGzuIO7AYgUP7HEu4KkaMk4BVCiAnAZqbbM1RUO66qKkBrnQ52h8LedOirSQ67nEqODIRH3J66EBqnkhrLglnzR/eQV2v7C+iXngalMN73cVRB7oeIL7zwAsFgEL/fz4033pjt4YypzWcHSVpQX+yktkjCCJE/3APbMBM9pGw+wsW3ZHs4r4nf7+fuu+/m17/+NceOHaOyspJly5YRLr4VX8/jeHueIOpbdMlgxe0xWLTMSe/xg9SG91J6thE19ANfylaQrtT1r8SyF9Pe3s6eF58Y0V+3uLiYhoYG5s2bh2nm7teJro4ERw9EAVi03E1RaW6O1e0xmLvQxeF9UQ7vi1I11Z6TQbQQF6VsRP03EC1YhiuwB2/vs9iSfRT0/AFP/4uEi28m4l8NRvb7XovXQFsUdD0KQMS/gqRrepYHJIS4lNz8lCOEEGJMKaVQNnDYFORgYYXuaEX/4L8AUK9/C2rekiyP6MpOnDjB0aNHUUqxcePGET3MJ6LnGwcAuFWqd0UeUakQ3t5nAAiV3pnXkwJNmzaNDRs2sGnTJjZt2kRZWRk1U9bhHtiGLdmPp38T4ZLbX/U4IzmAa/Bllqmd2GYNZG6PuWcRLVxNzDsfSytOnz7Nnj1P53x/3UuJhC12bw2Dhml1DqbPyO19cv0cJ02NcQKDFkf2R1m6UvqaizyjbET9K4gWNIwMert/j6fvfNC7SoLePOMe2I493oZluAiWbsz2cIQQlyEBrxBCiJyiEwmsr38RYhGYswj1htyfhDEYDPLcc88BsGLFCqqrq7M8orHVOhjnWHcUQ8GNdf5sD0eIq+btfRrDipJwVBMtuCHbw7luy5Yto7Ozk2PHjmUmXXOW3k1hx0/w9L1I1L8Sy/SDtnCET+Ie3I4jdBRFuml7yvBwpGMxhzuWUj2rmvpyg8P7DrB3794R/XXnzp3LsmXL8qYXoJXS7NoSIh7T+ItsLL7BnfOBtGEoFq/wsOXZIOdOx5le76C4TL6qiTw0POgd3I2371lsyX4Kun+Hp+8FCXrziEoF8fY+CQz9KGrzZXlEQojLkU8NQgghcor+5Xfh3Cnw+THe9zcoI7dnFNda8/TTTxONRikvL2fVqlXZHtKYe/5MuupvaZWXErd8lBD5wRbvwD2wA4Bg2esnRA9BpRS33XYbPT09dHd38/vf/543v+lNeJzTsMea8HX/gaSzGvfADmzJ3szj4q46IoWriXkXkjA1fY2dnN22mWc3nSSRuNBfd/HixSxZsiRn++teyqG9Efp6UtjtihXrPdjM3A53zystN6mps9N8JsH+XRFu/KNhE+AJkW+UjWjhSqL+80Hvc8OC3vMVvSsl6M1hvu4n0j+KOqek22wIIXKafCsTQgiRM/SebehnfguQnlStuDTLI7qy/fv3c+7cOWw2Gxs3bsRmy+1A+npprXmhMV3Zd0u9VO+K/OHrfhyFRcy7gIRnZraHM2rsdjv33HMPP/3pT+ns7OS555/n7g2vp6Tlf3AF90FwHwCW4SJa0EDEv5qUsxKAjo4O9h/aTVP3Sc5PsllUVJTpr2u351/w0nwmxpmT6ZC6YY0Hry+/9skLlrrpaE0y2J/i7Mk49XPyt42IEAAok2jhKqL+5bgGdw0FvQMUdP82XdFbcks66FUSTeQSM3oOd+BlAAJlMrGaEPlA9qJCCCFygu7pwvruQwCoO/8EtXhFlkd0Zb29vbz00ksAbNiwgZKSkiyPaOwd7YrQHkzgMg3WTCvI9nCEuCr2yGmc4aNoDIKld2V7OKPO7/dz11138eijj3LkyBEqKirYUHUD7sAuEs4aIoWrifqWgOHAsiwaT51iz549tLa2Zp7D46yiwL2AxUtnsGBxfvZ/7emOsm9nGIDZC5xUTsm/gNrpMpi32MWBXRGOHoxQPc2Oyy3BipgAlEm0cDVR/w24B1/G0/s8ttQABV2/wdb2PPsC9xKwpmMzwbQrbKbCtKUnEjbtCtNU2Gykz19x3bBJpfuoGj6xWsENJN21WR6QEOJqSMArRJZYe7cT3KextAH+IigsAn8Rysy/LyNCXC+dTGJ944sQDkL9HNS978z2kK4olUrx5JNPkkwmmTZtGkuW5P5EcKPhuaHq3XXTfbhMCR1EHtAaX/djAET8K0k58qOP7LWaPn0669evZ/PmzelJ1/7kjUyr24hlpn+IicfjHDmyjz179ozorztnzhwaGhpIxgp5+aUwp47FqZ6af/1fE3HN8483k0pBeZXJ3IWubA/pNaud4aCpMU5/b4rDeyMsX5tfLTKEuJzBAcXRpqW0N82hxrWXpVVb8ToGWVX4A7Y3386Rrgbg2gJbZTAyADYVpjkUDg+dbBe5btoVdofCblc4nAq73cC0k/M9u8eaa3An9ljr0MRqE+9HUSEmqvz65CbEBKKf+wN9h3a/+g5vwVDgW4zyF6eD38Ji8BejCovAX5y+7i1AGRKuiIlB/+bHcOoouD0YD34iL37o2LlzJ52dnTidTv7oj/5oUnwZSKQsXjp3vj1DYZZHI8TVcYYOYo81YykHoZLbsz2cMdXQ0EBnZyfHjx/nD489zlvf+lYgwL59+zh48CDx+IX+uosWLWLJkiX4fBcmzZlam6DlbII9O8LcfGdB3vSuTSY0L28JMdifxO0xaFjjQeVx71plKBbf4GbT00FaziWYNiNBeWXuvy8KcSmBwRSt5xK0NsUJDlpDtxocCy8n4GpgRcXvKTUOsW76U8yY0snRyOtIJE1SSU0yqUkmyVxODbtuDT2VttI/8iTS165vsIp04OsYCn8drwyBh99ujFjOZsv/cFilQvh6ngAgVPJHaFMmVhMiX0jAK0SWqFnzcXq9RDvbYKAfBvsglYJQIH1qa3rVx5MR142hyt+hwFcNhcL4i9OVwMPDYFfuzx4tJi99eA/68V8CYLzrI6jyqiyP6Mra2trYuXMnALfeeuuIgGQie7klRDBuUeo2WVSRn4dwi0lGp/AOfVGNFN2INid2WxGlFLfffju9vb10d3fz85//nHA4jNZX11930XI33R1JQgGLoweiLGxwj/dLuGbBQIqdm0MEBy1sNsWK9V6czvz/AbyoxKRupoMzJ+Mc2BXh5o0mNjkMXeSRUHAo1D0XZ3DAytxuGOkq+ynTHVRNsWPaFSn9dgL9m/D1PE6VuY/Ssm4Gqt6OZS++7Dosa2TgOzwMvlI4nExqkgmdDoYTmnhcY6UAPRQWx689KFYG6cDXPjIcdrwiEPa449f83OPF1/MEhhUh4agiUigTqwmRTyTgFSJLjDe8lfLqatra2tBaoy0rfXj6UNirB/rSoe+I6/3pU2AALAv6e9MnXv1b9YjrDseFsNdfhCqtgBlzUbMW5MUkVmLi0gN9WN/8N9AadfNdqBUbsj2kK4rH4zz55JNorZk7dy5z5szJ9pDGzXONAwDcXO/HlsfVcWLycA/swEz0kLL5CBffmO3hjAu73c7rX/96fvaznxEKhQCoqamhoaGBurq6y/7g63AYLF3pYcemEKePx6iqsVNanrtfFzraEuzeGiKZAKdLcdcba9FqIBNo57t5i120NScIBSxOH4sxe0H+tp0Qk0M4ZNHaFKf1XIKBvlTmdqWGQt1pDqqm2rE7XrEfUopI8U0knVMobP8J9lgLJU1fY6Dqz0h4Zl1yfYahMBwKu2N0xp9K6Uy4G49fuJyIW+kQOJYOgy+2jNbpSuJYVBOLXn4ftO/lU6y52Ztz+1cz2oRrMD2xWrD8jaDya5JKISa73NqjCDGJKcMAnz99mjr9sp2ndDIJwYGR4e/5AHigDz0sGCYagXgcujvSJy6EvxqgtAI1az7MWoCavQCqp0nrBzEutJXC+ta/pX+wqKlD3f/ebA/pipqamti0aRMDAwP4fD5uueWWbA9p3AzGkuxqDQLSnkHkB2XF8PY9A0C45Ha04czyiMZPYWEhf/Inf8KJEyeYPXs2FRUVV/3Yyil2ptWne8Du3R7m5o0FmPbc+kFHa83JIzGOHogCUFxqY+UGH1VTPLS1DWR5dKPH7jBYsNTNnu1hjh+OMnW6HY9PAheRWyJhi7amOK1NCfp6LoS6KCirMJkyzU51jR3HVVTWJzyz6J32EQrbf4Q91kJR67cJld5FuOjGdEo8xmw2hc2tcF3jwQtaa1JJhoXA1rBweGQQHAxYDPan2LEpxPrbfPiLcuT/tLYo6PoNCk2koIGEuy7bIxJCXCMJeIXIQ8o0oag0feLy0xDoWDQT/KbD4P50+4dTR6DpDPR0ons6YfsL6cDX44WZ81Gz0ifqZqMck+dLsRg/+rFfwpF94HBivP/vcno76+/vZ/PmzZw+fRoAh8PBxo0bcTpzd8yjbfPZAEkL6oud1BZNntct8pen70WMVIikvYyIf2W2hzPuKioqrinYHW7hMjddHQnCIYsj+yMsviF3WrIkE5o9O8K0N6e7bdbOdLCowY1tgk76OLXWzrlGk57OJAf3RFh14+RoCSRyWyxq0dqU7qnb25UacV9puY0p0x1U19hxuq79/6VlL6Zv6gco6HoUd2AXvp7HMGPNBCrelLM/1CmlMO3pSdvcHoBLh7ZWCnZtidHeGmH7i0E23FGA25P9/ZdrcNdQv3onodK7sz0cIcRrIAGvEBOccrqgvCp9YmQYrKNhOH0MfeII+uRhOH0MwiE48DL6wMvpwNdmQt0s1Mz5qNnz0+FvgVTvieujTxxGP/pjANTbPoiqrsnyiC4uFouxc+dO9u7di2VZKKVYvHgxq1evxu3O/d6Uo+n50+mquFulelfkASM5iKd/EwCh0o1ymOk1sjsUy1Z62PZCiDMn41TV2HNikq/h/XaVAYuXu6mdmZuBz2hRKj3h2gtPBOhoTdLekqBqavb/LcTkE4tZtDcnaD2XoLsrOaIfXHFpOtSdMs2Oyz0KYaVhJ1DxJpKuGnxdv8MVPIAZ72Cg6p2kHGXX//xZZDMVG984nV/+6ATBQYvtLwRZf7sPuyN7Ia9KhfH1PA5AqPQOrAner16IiUoCXiEmMeXywIIG1IIGYKj1Q3Mj+uRQ4HvySLry99RR9Kmj6CcfST+waipq1gKYNT99XlEtk7iJq6aDg1jf+BJoC7XmFtS627I9pFexLItDhw6xdetWotH0IcDTp0/nxhtvpLR08vWtbuoLc7Q7gqHgxjp/tocjxBV5e59B6QQJ5zRi3oXZHk5eKq+yUzvTwdlTcfbtCHPzXX7sWWzV0NGaYPe2C/12V6z3UlI2Ob7KFPhtzJzr5OSRGAd3hymr9GOa8rlLjL1E3KKtOUFrU4LujiTD21sXldiYMs3OlOmOsalAVYpI4RoSjmoK23+EGe+kuPlrDFa+hbh3/uivbxy5XDbW3FzA5qcHCQxa7NwcYvXNvqxNpOjteRLDCpN0VBIpXJuVMQghrt/k+FQkhLgqyjTTLRnqZsMdf5yepKS7A33iMJw8jD55BNqaoL0F3d4Cm59K/3hfUJgJe9XsBTBtRvq5hHgFrTXWdx+Cvm6onIp6+wdz7seBc+fOsWnTJnp6egAoLi7mxhtvpLa2NufGOl7+cKgdgKVVXkrc8n9b5DZbvOvCJDFld49L38aJasFSN13tScIhi8N7IixdNf6tGrTWnDgS49iwfrsr1ntHp0owj8xe4KLlbJxIWHPicJT5SybXUSRi/CQSmo6WdPuFzvYk2rpwn7/IxpTpdqZMs+Mdp37QSXctfdM+gr/9xziiZylq+z6h4tsIldwOKn/3Ax6vweqbfLz0bICerhR7toe5Ya1n3D9rmtEW3IM7AAiU/7Ec8SJEHpNvaUKIS1LpKW9R5VUwVGWpg4Ppit7zVb5nTqQnydqzDb1nWzrwdTigfu6FPr4z5qE83qy+FpEb9DO/hX07wLRjvP9v01XkOaKvr4/NmzfT2NgIgNPpZM2aNSxatAibbfJ+2NVa89jhdMB7S71U74rc5+15AoVFzDOfhLs+28PJa6ZdsWyVhy3PBTnXmG7VUDll/NoDXKrfrpGlKrdsMk3FouUedm4OcepojJpaBwWFk/e9SYwurTVdHUnOnozT2ZbAGhbqFhQaTJnmYMp0O76C7Gxzlumnf+r78HX/Ac/AVrx9z2LGWhisfAvalr8/dviLbKxc72XbiyHamhIcckVY2OAev5BXWxR0PYpCE/UtJeGeMT7rFUKMCQl4hRDXRPn8sHQVaukqAHQiDmdPpvv4njqSbusQCsCxA+hjB9KBr1IwtRY1bwlq1U3pKmGpqJp09NmT6F98FwB1/wOo6bnxITIajbJjxw7279+PZVkYhpHps+tyubI9vKw70hWhZSCKy1SsmSY92URuMyNncYUOoVEEyzZmezgTQmmFyYw5Tk4fj7H/5TA331WAYxx6RQ7vt2sYsGgS9Nu9kqqpdiqnmHS0JjmwO8LaW7zyeUpcF6017S0JThyOMdB3YbI0b4HBlGl2pk7PoR8SlEmw/I9JOqdS0PVrnOFjFDd/Ld2X11mV7dG9ZmWVdhpWedi9LUzjiThuj8HMeePz+dMV2I091oSlHATLXjcu6xRCjB0JeIUQ10XZHTBrQboXL6AtC9qb0+0czrd16GqH5jPo5jPop3+TPjR/zc2o1bekq4PFhKcjYayHvwCpJDSsQd2S/Q+RlmVx4MABtm/fnumzW1dXx4YNGygpKcny6HLH843pydXWTvfjmqCz1IsJQmt8PY8BEPWvIOWozPKAJo55i110tCUIBSwO7o6wfM3YHpUzvN+uy61Ysc5L8STpt3slixrcdHUE6OlM0nI2QU2dI9tDEnnIsjSt5xKcOBIlOJgu1zVsUDvDwbR6B/4iW87+eBD130DSUUVh+w8xE72UNP8XgxVvJlawJNtDe82m1jqIRiwO74tyeF8Ul9tgau3Y/t9WqciFidVKbscy5SgtIfKdfFISQowqZRgwZTpqynS4KV09pft702Hvnm3ovdugowX96I/Rj/443bt39S2oFevT1cFiwtFao3/wn+mgv7QC491/lfUvDWfPnmXTpk309vYCUFJSkumzKy5oD8R55lQ64L2tvjDLoxHi8hyhwziiZ9HKTqjkjmwPZ0KxmYqG1R42PxOk5WyC6po41TWjHz5orTlxOMaxg0P9dstsrFg3+frtXo7HZ2POAhdHD0TZuzNMf1+KOQucOJzyNxJXlkppmhrjnDoaIxxKB7umHepnO6mf7cTpyo/tKOmaSu+0j1DY/lMckZMUdvyEcKyZYOnGvO0hO3Oei2hEc/p4jD07wjhdirLKsWuJ4+19CiMVImmvIFK0fszWI4QYPxLwCiHGnCoqgRUbUCs2oKNh9O6t6G3Pw9H9cPJIup/vT78Bi2/AWHMLLFmZrgwWE4Le/BR65yYwDIwHP4Hy+rI2lt7eXjZt2sTZs2cBcLlcmT67hpEfX2rG0zd3dZKwNCunF7OkKnf6JQvxKjqFr+cJAMJF66USaQwUl5rMmufk5JEY+1+OUFJu4hzFUDGZ0OzZHqa9RfrtXsnMuU76epN0tCRpPB6juTHO7IVO6mY5scnfS1xEMqk5eyrG6WMxohENgMOpmDEnvd3YHfm33Wibl/4pf4635ym8/S/g6d+EGWtloOqtaFv2PmtejwXLXEQjFq1NCXZuDrHutgIKi0c/sDZjrbgHtgEysZoQE4kEvEKIcaVcHtS622Hd7ei+HvTOF9Nhb1Mj7N2OtXc7uL3pit7Vt8DsBemqYJGXdMs59E+/DoC6952omfOyMo5IJJLps6u1xjAMli5dysqVK6XP7iXsbA6ysyWITcHf3jEHFR9Ea53tYQlxUa7BXZiJLizDQ7jo5mwPZ8Kas9BFR2uCwIDFgV0RVqwbnVYNr+y3u/gGN9NnTO5+u5dj2BSrNvjobE9weG+EwIDF4b1RzpyIM3+pi+oae9aPlBG5IRG3aDwR5/TxGIl4+j3c5VbMnOdi+gwHppnn24myESq7i6RrKgUdv8AROUVJ09cYqHoHSVdNtkd3zZRSLFvtIRYL0dOZZPuLQTbcUYDHO4rfhbTG1/WboYnVFpPwzBy95xZCZJUEvEKIrFHFpag774U770W3nEVvex69/QXo60ZvehK96UkoKUetvhm15pZ02weRN3QshvXw5yEeh4UN6X/rcZZKpTJ9dmOxGAD19fVs2LCB4uLicR9PvoinLL65qwOAP55fQn2pl7a2wSyPSohLsOJ4e58GIFRyG9omP9qMFZtNsWyVh81PB2lrStByLs7U6dd3xM2r+u2u91JcKl9RrkZFlZ3yO02azsQ5eiBKOGSxa0uY4lIbC5a5KZG+xZNWLGpx+niMMydiJJPp2zw+g1nznNTUOSZcpXfMt5ikvYLC9h9gJnoobnmYQPkbifpXZHto18xmU6xc7+GlZ4MEBiy2vxBk/e2+UWvD4grswRE9OzSx2utH5TmFELlB3vWFEDlBTa1Fvend6HvfCScOobc+h969BXq70I/9Av3YL2D6jHS/3lU3pds+iJymf/YNaGuCwhKMBz42rpXYWmvOnDnD5s2b6evrA6C0tJSbbrqJadOmjds48tWvDvfSHkxQ4jZ5y+KybA9HiMvy9G/GlgqQMouJFK7O9nAmvKISk9kLnBw/FOPArgil5eZr6pEr/XZHhzIU02c4mTLNwaljMU4djdLXk+KlZ4JUT7Mzf4kLr08Ov54sImGLU0ejnD0dx0qlbysoNJg930X1NDuGMbGC3eFSzkr6aj6Cv+PnOMNH8Hf+EjPaTLD8HlD5FXvYHQarb/Kx+ekAwYDFjs0h1t7sw3adFdcqFcU7NBlpuOQ2LFPmVxBiIsmvPZ0QYsJThgFzF6PmLka/7QOwfyfWtufh4C44dxp97jT6F9+F+UvTVb0Na1Aud7aHLV7B2vFiugJbKYz3fgzlLxq3dff09LBp0ybOnTsHgNvtZu3atSxYsED67F6FjmCcXx7qAeCB5RV47BIMiNylUkE8fS8CECy9M+++xOer2QtctLckGexPsf/lMCs3eK+pJYD02x19pl0xd5GL2pkOjh2Icq4xTltTgvaWBPWznMyWidgmtFAgxckjMZrOxtHpudMoKrExe4GLyinmpGnZoW0uBqrfgafvOby9z+AZ3I493sZA1dvzrje722Ow5mYfLz0TpK87xe5tYVas86CuI6T39j6NLRUkaS8nLBOrCTHhyKdgIUTOUg4nrNiAbcUGdGAQvWtzul/vqaNweA/68B60w5kOedfcAvOXoWwSRmWb7mxF/+A/AVCvvx81f+m4rDcSibBt2zYOHjyY6bPb0NDAihUrcDqll+PV+tauTuIpzZJKDxtqC7I9HCEuy9v7LIaOkXBOJeZbku3hTBqGoWhY7eHFpwJ0tCZpPpNgWv3VtWqQfrtjy+U2WLrKQ/0cJ4f3RehqT3L6eIymM3FmL3BSP8uZd0F6MJCiqz2JzQZur4HHa+B2G3n3OsbCYH+KE0eitDYlYKhNfmmFyez5TsoqJ0+wO4IyCJfcTtI5FX/Hz7BHz1Hc9B8MVr2dhLsu26O7JgWFNlZu8LLthSDtLQkO7omwaLn7Nf272mLtuAe2AuRlVbMQ4srkf7UQIi+oAj/qltfBLa9Dd7aht7+QDns7W9OXt78ABYXp9g1rboHaWZPzQ22W6UQC6+EvQjSSniDvnreO+TpTqRT79u1jx44dxONxAGbOnMn69espKioa8/VPJC+3BNnenJ5Y7cGVlfJ/SOQ0W7wb98B2AIKld4OS6sTx5C+yMXehi6MHohzcE6as0sTtufy/gfTbHT/+IhtrbvbR2Zbg8L5hE7GdjDN/Se5PxBYYSNHWnKC1KU5gwLroMi63wu0ZCny9xqsu5/0EYpfR15PkxOEoHa3JzG0V1SazF7ik9/KQuHcefTUfprD9h5jxDopavkGw7PVECtdCDm/7r1RaYdKwxsOuLWHOnIzj8qRbblw1ncIePYev+w8oLKLehcQ9c8ZuwEKIrJG9vxAi76iKatQb3oq+5y1w5kS6X+/OTRAYQD/zW/Qzv4WqmnQLh9U3o8oqsz3kSUP/8rtw7hT4CjDe94kxrajWWtPY2MimTZsYGBgAoKysjJtuuomamvybOTnb4imLb7ycnljtDfNKmF4oFXUit3l7n0RhEfPMkVnAs2TmPCftLQn6e1Ps2xlm9U0Xb9Xwyn67JWU2bpB+u+OiotpOeaXJucY4xw5GCQcvTMS2cJmb4hwJA7XWBAYsWpvitDUnCA5eCHWVSodcSkE4ZBEJW1gpiEY00UiKvp7URZ/T4VTpwPcSIbDdnj8hH6T/Rj2dSU4cidHdcSHYnTLNzqz5TgqLc+PfMpekHGX01fwFBZ2/whXcT0H3bzFjzQTK7wXDnu3hXbUp0xxEGzSH9kQ4uj+Ky2Vc9qgJlQriDB3HET6KI3wCw0rve7WyEyy7Z7yGLYQYZ/IuIITIW0opqJ+Dqp+Dvv+96bYN255H790O7c3oX/8Q/esfwuIVGO/8MKq4NNtDntD03u3pcB0w3vNRVMnYTs61Z88eNm/eDIDH42Ht2rXMnz9f+uy+Rr8emlit2G3ylsXyf0XkNjPahCt4AI0iWHpXtoczaRmGYtlqDy8+GaCrPcm503FqZ478cSiR0Owd1m+3bpaDhcuk3+54UoaidqaTqdMdnDoW5dTRGH09KTY/E2TK0ERsnixMxKa1ZqAvXanb1pQgFBwW6hpQXmlSXWOnaqp9RP9grTXxmM6EveGQReQVl5NJiMc08ViK/t6LB8B2u0qHvl6Fx2NcaP8wFALbHSonqpy11nS2pSt2z4fZSkFNnYNZ85z4/NKe7HK04WSw8q0knDX4eh7DHdiDGe9goOodWPbibA/vqs2Y4yQasTh1NMa+nWGcLkVF9VBIrTVmrBVH+CjO0DHMWDPqfM8OwDI8xL1zCBeuxbIXZecFCCHGnAS8QogJQZkmLFmJWrISHQmjd29Fb38eju6HAy9jfeavMP78o6ilK7M91AlJ93ZhffchANQfvRG1ZGz/zp2dnWzZsgWAZcuWsWbNGhyOq+v/KF6tM5jgf4cmVntPQ7lMrCZym9b4eh4HIFqwjJSzOssDmtwK/DbmLXZxeG+UQ3sjlFeZeLzpfUhwcKjfbkD67eaC9ERs6X+DYwejNDXGaR2aiK1u9tBEbI6x/ZFUa01/b4q2pgRtzQnCoQuhrmFAebXJlBoHlVNM7JcYi1IKp0vhdBlc7Ld7rTWJhCYSGgp8wzoT/J4PhRPx9DKJ/hSD/Rcfq81MT3Tl80WxdALTVJimwmZX2O2kr9vVK84ZcZth8JpDYm1pWpsTnDwcZXCoTYVhwPQZDmbOc+Hxyg/aV00pIsU3knROobD9J9hjrZQ0/QeDFW8i7pkNRn58hpy/xEU0YtFyNsHebb3ctraDYuMEjtAxbKnAiGUTjmri3nnEPHNJuqZJGyMhJgEJeIUQE45ye1Drb4f1t6Pbm7G+8SU4dxrra59F3f4G1JvejbLnxwe5fKBTqfTfOBSAutmoP33XmK4vkUjwxBNPYFkWM2fO5MYbb8yJCpt89q3dHcRTmkUVbm6qy69ZpsXk4wgfwxE5jVYmoZI7sz0cAcyY7aS9OUFvd4q9OyKsvcVLZ1tS+u3mKLfHYNkqD/Wz0xOxdXckOX0sRlNjnDkLXdTNdIxqhbXWmr7uFK3NCdqa40TDFyoLDRtUVtupnmanstqOOQptE5RSOBwKh8Og8BIFmsnEhQrgSMgiHB4ZAMeimlQSgoMWwcHIdYzlfOA7Mvg17Qr7UFhsmulqYpupsA/dd75S83xVs82EullOZsxxSmuT65DwzKR32kcobP8h9lgLRe0/RKNI2UtIOqpIOqsz55ZZlHOhqJnoZt2co8R8hylxnMM2rOrdUg4SnlnEPPOIe+dgmYVZHKkQIhvkU5YQYkJTVTUYn/wi+pHvo596NN2j99hBjPf/Lapa+rSOBv3oD+HkEXB70n9Xc2x7mr300kv09fXh9Xq57bbbJNy9Trtbg2xrCmIoeP/KKvl7itymrUz1rhxqmjvUUKuGFx4P0NOZZPuLIbra0z1CS8ptrFjnxenKraBEQGGxjTU3e+lsT3J4b4TgoMWhPREaT8SueyI2bWl6ulO0DfXUjUUvhLo2Eyqn2KmusVNRbc/KZGimXeEvsuEvuvgRK6mkJhKxiIY1Pl8RXZ29JBOaRFKTSqSrf5NJTTKhSSYZOteZ89RQi1ytSVcLx4Fhh8xfLbtDMWOOk7rZjjGvrp4sLHsRfVM/gK/ncZzBA9hSAcxED2aiB0KHLiynnCSdVSQdVaSGzpPOKrRxDROcXS+dxB5pxBk6iiN8LD1GgKEhDESLaQ/PpHjuYvDPACXxjhCTmewBhBATnrLbUfe/Fz1/KdZ3vgrNjVj//DHUWx9EbfgjCbSug963E/3YLwEw3vURVHnVmK7vzJkz7N+/H4A77rgDt9s9puub6BLDJla7Z24xtUVy6LTIba6h3omW4SZcfGu2hyOG8fpszF/q5uDuSCbclX67uU8pReXQRGxNr5yIrWxoIrarrLy2LE1PVzLTfiEeuxBomvZ0qDtlmoPyShNbFkLda2EzFb4CGwV+RXW1H7cvhNZXH9Bqayj4HRb6Xjh/dSB8ITxO960GmDrdTu1M56hUNYtXMOwEy99AsPwNqGQQM96OGWtLn8fbMWMdGDqGI3oWR/TsiIemzOIRlb5JRxUpe8moVfsayQEcoWM4w8ewh09i6HjmPo2NhLuemHcuAWMOz7/gJBLWFA3YWHurDVPSHSEmNdkFCCEmDbV4Bcb/+SrWt78CR/ahv/81OLIP3vEhlMeb7eHlHd3VjvXtfwNIt75YsWFM1xcOh3n66aeBdN/d2traMV3fZPDrI720BhIUu2z82ZKxnRRPiOtmJfD2PgVAqPgWtE1+4Mk1dbMcdHck6epIsKhB+u3mE2PYRGwnj0Y5dSxGX3eKzU8HmTLdzvzFF5+IzUppujuHQt2WBIn4hRDUbldUTU23XyirNLFNoqBfGQq7I12BK3KbNn0kzFkkPLOG3ZjCFu/GjLdhxtozAbAtNYgt2Yct2YczdPjC4sqeqfAdHvxe1fuUtjCjTTjDx3CEjmKPt424O2UrIO6dS8wzj4RnFtpI71dNYPXNKV56Jkh/b4rdW0OsWO/FMGSbE2KykoBXCDGpqKISjI9+Gv3EI+hHf4jeuQl9+hjGg59AzZyX7eHlDZ1IYD38BQiHoH4O6s1/Prbr05pnn32WcDhMSUkJ69atG9P1TQZdoQQ/P5g+1O/Pl1fIxGoi53kGtmBLDpAyi4gUrs32cMRFKKVYsd6DtpCq3Txl2hXzFrupnenk2IEoTWfitJ5L0N6coH6Ok9nznRg2RXdHktamOB0tyUzFKYDDORTq1tgpqzBlOxD5SdlIOStJOSuJFQy7ORXCjHUMC37bMOMdKJ3AHmvCHmsa8TQps2hY8FtN0lmF5ShHJ4I4A3txBI/iCB/HsMKZx2gUSWcNMe9c4p55JJ3Vl6wOLvDbWLXBy9YXgnS0JjmwK8KSFW45OlGISUoCXiHEpKMMA3X3m9BzF6UnB+vuwPrCJ1FvfDvqrj9FGRJ0XYn++bfg7EnwFWB84O/HvO/u4cOHOX36NIZhsHHjRkw5Bu26fWtXJ/GUZkG5m5tlYjWR41QqhKfveQBCJX8Extjuc8Rrp5RCydto3nN7DJat9lA/x8HhfVG6O5KcOhrj3Ok4WqfbDJzndKVD3SnT7JSUm1JBKCYsbfOS8Mwg4Zkx7EYLW6J7WKVvOvi1JfszJ2f46IXFlUnqZAr/sJ7MluEi7plN3DOPmGcO2vRd9ZhKyk2Wr/Hw8pYw507HcXsM5iwcxz7BQoicId+QhZhkLK2JJTVuu0zUoGbMxfjHf0f/6L/RO15EP/ID9JF9GO/9GKqoNNvDy1nW9hfQz/8BlMJ478dRpeVjur7+/n5efPFFANauXUt5+diubzLY2xZia1MAQ8EHVlZKpYfIed6+5zGsKAlHFdGCZdkejhCTRmGxmZ6IrS3J4X3pidgAXG5FdY2d6hoHJWU2lIS6YrJSBilHBSlHBTGWXLg5FRkR+J4PgJVO/zqSdFQS88wl7p1LwlXL9fwyVl3jYPFyzYFdEY4djOJyK2mRI8QkJAGvEJPIiZ4IX9nSRm84yWfvmMbsUulfqDxeeN/fwIIG9E8ehqP7sT79Vxh//lHU0pXZHl7O0W1N6B/8JwDq9fejFt0wpuuzLIsnnniCRCLB1KlTaWhoGNP1TQaJlObrQxOrvX5OMXXFUuUhcpuR6MPdvxWAUOldozaRjRDi6iilqJxip7zKpLsjiWlXFJfa5MdBIS5D29wk3PUk3PXDbrSwJfuoqJxCV1/8mibuu5K6WU4iYYuTR2LsfzmC02VQOUWOdhFiMpFPyEJMAilL89MD3fzdE2dpGYwTSVp8dWsb8ZSV7aHlBKUUxvrbMT71bzB9BgQDWF/7LNZPv4FOJK78BJOEjkaw/vv/QiwK85ei3vDWMV/nzp076ejowOFwcOedd2IY8rZ1vX5ztJeWwThFMrGayBO+3idRpIi7ZxL3zMn2cISYtAxDUVFtp6TMlHBXiNdCGViOMpRrbI4UnLfYRU2dHa1h15YQfT3JMVmPECI3yTdlISa4lsE4f//kWX6yvxtLw/rpBRS5bDQNxPnZgZ5sDy+nqKoajE9+EXXHHwOgn/kt1r9+At3enOWRZZ/WGv3D/4K2JigqwXjf34x5r+K2tjZ27NgBwK233kpBQcEVHiGuJD2xWjcA726owOuQRpkit5mxVpyBfQAES+8GCZWEEEKIi1JKsXSlh/Iqk1QKdmwKEQqksj0sIcQ4kYBXiAlKa81jx/v46B8aOdETxesw+Jv1U/i7G6fyF6uqAPjV4R5O9ESyPNLcoux2jLe8D+Mv/xF8fmhqxPrsx7BeenpUD6PKN/rFJ9DbXwDDwHj/36H8RWO6vng8zpNPPonWmrlz5zJ37twxXd9k8Z3dnUSTmvnlbm6tl4nVRO7zdT+GQhP1LSXpmprt4QghhBA5zTAUK9Z5KSy2EY9ptr0QIhaVozaFmAwk4BViAuoJJ/jMc838z84O4inNkioPX31dPTfVpQOdNdMKuKnWj6Xhoa1tJKRVw6uoJSsx/ukhmL8U4jH0dx9Cf+NL6HAo20Mbd/rsSfRPvw6A+tN3o2YvGPN1btq0iYGBAXw+H7fccsuYr28y2NsW4qVzMrGayB/28AkckZNobARL78z2cIQQQoi8YNoVq2/y4vEahEMW218MkUxM3kIVISYLCXiFmGBeOjfIX/++kd1tIRw2xftuqODTt02j3Duyyf6DKyoodNk4NxDnp9Kq4aJUUQnGRz+N+tN3gWGgd27C+sxfo08dzfbQxo0OBdN9d5NJWLYGdeefjPk6T506xaFDhwC48847cTplFuDrlUhpvjE0sdrdc4qpl4nVRK7TFr6exwGIFK7GspdkeUBCCCFE/nC6DFbf7MXhVAz0pXh5SwjLkpBXiInMzPYAhBCjIxRP8fWXO3i+cRCAmSVOPrpuCtMLLx6O+V0mf7Gqiv/7Ygu/OtzDmmk+Zpe6x3PIeUEZBuruN6PnLML6xpegpxPrC59EvfHtqLvehJrAk35py8L6zr9DTyeUV2G856/GvOozFArxzDPPAHDDDTdQU1MzpuubLH57rJfmwTiFLhtvk4nVRB5wBvdhj7ViGU5CJbdlezhCCCFE3vEV2Fh1o5ctzwXpak/y/OMB7HY1sp29AjV0njkbWmD47cNuHnH51c+lhl1On7tcioJCGz6/jQK/gcM5cb8/CZFNEvAKMQHsbw/x1a1tdIeTGAretKCUtywuw267fBi3dloBN9YWsOlsgIe2tvFvd9dht8kb7sWomfMw/s9X0T/8L/TOTehHfoA+uh/jgY+iisZmJtxs008+Avt2gGnH+MDfozy+sV2f1jz99NNEo1HKyspYvXr1mK5vsugJJ/jZgaGJ1ZaV45OJ1USu00l8PU8BEC66GW3zZnlAQgghRH4qLjVZsc7Lzs0hQoHcaMvndKlM2Fvgt+ErTJ87nEpaiAlxHSTgFSJLNm/eTF9fH0opHA4HDocDp9OZuTz8+vDb7XZ75o0vnrL4wd4ufnO0D4Aqn52PrZvCvPKrr8R9/4pK9neEOTcQ52cHenjHsvIxeb0TgfJ44cFPwMIG9I8fhiP7sD791xjv+WvUkpXZHt6o0scPoh/5AQDqzx5E1c4c83Xu37+fs2fPYrPZ2LhxI6Ypb1Gj4fzEanPL3Nw6ozDbwxHiitwD27Al+0jZ/ISL1md7OEIIIUReq5xi59bXFRAYSAe8wyeOHjGHtAb9yss6c3XEncMfd/7yxZ5La4iELAKDKYKDKSJhTSyqiUWT9HSOHKfdoSgYCnvPB8A+vw2XW4JfIa6GfHsWIks6Ojpobm6+5sedD4QN00F/QhG2bCxRNir8Hub7Cuk90cHLZ0eGwhcLis+/SfpdJn+xsor/u6mFXx7uYbW0argspRRq/R3omfOwvv5FaGrE+o/Pom5/A+pNf46y26/8JDlOD/SlX5tlodbcirpx45ivs7e3l82bNwOwYcMGSksnZlX0eNvfHmLT2fTEah9cWYkhH45FjlOpCN7eZwEIld4BhiPLIxJCCCHyn9dnw+vL/lFcyYQmOJgiMGgNnacvh4MWibimtytFb1dqxGNMOyND30IbBX4bbo8Ev0IMJwGvEFmyZs0aTNOko6ODWCxGPB5/1fn50/nrWmu01sRiMYjFcALnO+zqHjh8lXOl2e12pk+fTn19PXV1daydXsCG2gI2nw3wH1vb+fLdtdKq4QpUVQ3G//Ml9C+/i37mt+nT8YMY7/9bVFX+9o3VVirda3igD6ZMR73jL8b8g1MqleKJJ54glUoxffp0lixZMqbrmyySlubrQxOrbZxVxIwSmVhN5D5P3wsYVoSko4JowfJsD0cIIYQQo8i0K4pKTV7Z4S6V1AQDw4LfgXTVbzhokUxAX0+Kvp6Rwa/NTPcZHlH1W2jg8RgoQ4JfMflIwCtElkydOpXq6mra2tpGHCZzKVprmvvC/PfWJhq7g5g6yeIyOxvrvdh08lVh8KUC41QqRSKR4NSpU5w6dQqAyspK1kyr5aRh52y/5ucHe3j7UmnVcCXKbke99UH0/GVY3/33dDXvZz+GetsHUOtuz8tflPWjP4FjB8DpxvjgJ1HOsQ8Ft23bRldXFy6XizvuuCMv/2656HfHemkaiON32niH/H8WecBI9OMZeAmAYOldoLJfaSSEEEKIsWczFYXFJoXFI29PpTShwMhq3+BgimDAIpWEgb4UA30pIJF5jGFLB79llSYLl8mRqWLykIBXiDygtebpUwN8c1cn0SS4XX7es7KSW+v91xyGJZNJent7OX36NGfOnKGzs5OOjg46OjpYCMwwXOzbXs4MYxEr59dLH9SroJauxPinh7C++W9w7AD6uw/B4b3w9r9I9+3NE/rAy+g//BwA9a4Po6rHvhK5paWFXbt2AXDbbbfh843tRG6TRU84wU/2p0v6391Qjs8pQZnIfd7ep1E6SdxVR9wzL9vDEUIIIUSW2WwKf5ENf9HIz7KWpQkHrQuh78DQeSCFlYLB/hRujxSNiMlFkhshclx/NMl/bm9nR3MQgAXlbj66rppK32vrS2iaJhUVFVRUVLBmzRqCwSBnzpyhsbGRpqYm3MkoNdEmdj7XxJ5NdqZPn5Zp5eD15k9YOd5UUSnGxz+DfvxX6Ed/hN7xIvrkEYx3/yVqwbJsD++KdE8n1re+AoC69XUYq24a83XGYjGefPJJABYsWMCsWbPGfJ2TxXf3dBFNWswpdXGbTKwm8oAt1o4rsBuAYNndIJX8QgghhLgEw1D4htoyVA+7XVuacNgiMGBh5v/UKEJcEwl4hchh25sD/Oe2dgZiKUxD8falZbxxXgm2Uewp5PP5WLRoEYsWLSKRSHDs9Fl+uukA/mgnrmSM06dPc/r0aSDdyqG+vp76+nrKysrkUPpXUIYN9br70HMXY33zy9DdgfWV/4O66S7Um/8c5fZke4gXpRMJrIe/AKEA1M1G3ffecVnv888/TyAQwO/3c9NNYx8oTxYHO8K8eGYQBXxgZZVMrCbygq/ncRSaqHcRSdf0bA9HCCGEEHlIGSpnJpQTYrxJwCtEDgonUnxrVydPnxoAoLbIycfXVVNXPLb9UO12O4vmzuJudwVfeLEFvxXg7dMT9Lc3jWjlsG3bNnw+XybsrampkVYOw6iZ8zD+6SH0L7+Hfv4P6BcfRx/chfHuj6AWNGR7eK+i//fb0HgcPD6MD/49yj72P3cfP36cY8eOoZRi48aNOByvrSJdjJS0NA/vbAdg4+wiZpXKxGoi99nDp3GGj6ExCJXeme3hCCGEEEIIkXckkREixxzuDPPvW9voCCZQwJ/ML+HtS8uw24xxG8P66X7W1wZ46ZziDyEnX75vHfFomMbGRs6cOcO5c+cIBoMcOHCAAwcOYJom06ZNywS+0soBlMuNevsH0Tesw/refwxV8/4T6sY7Ufc9kDPVvNbOTejnfg+A8d6PoUorxnydgUCA5557DoCVK1dSXV19hUeIq/X7Y32cG4hTIBOriXyhNb6exwCIFK4i5ZDtVgghhBBCiGslAa8QOSKR0vxkfxe/OtyLBiq8Jh9dO4WFldkJAj+wspIDHWHO9sf4+cFu3r60PNPKIZlM0tzcTGNjI42NjQSDwcxlgIqKikzYW15ePqlbOah5S9LVvL/6Pvq536M3PYk+tBvjXX+JWpjdal7d3oz+3tfS43zdfaglK8d+nVrz1FNPEYvFqKysZOXKsV/nZNEbSfKT/d0AvGtZOQUysZrIA87gAeyxZizlIFR8W7aHI4QQQgghRF6SgFeIHHC2P8ZXtrTS2BcD4LYZhTy4ogKPPXsBTaHL5IMrK/nC5lZ+caiHNdMKmFmSPtzbNE3q6uqoq6vjlltuobu7OxPwdnR00NnZSWdnJ9u3b8fr9WbC3mnTpk3KVg7K5Ua97QMXqnm72rH+faia983vQXnGv+JZx6JY//1/IRaBuYtRf/y2cVnvnj17aG5uxjRNNm7ciM0mIeRo+d7uTiJJi9mlLu6YKROridynrSTenicACBffiDYLsjwiIYQQQggh8tPkS1qEyCGW1vz6cA8/2NtFwtL4nTY+tLqKtdNy40vu+lo/688FeOlcgIe2tvGlu+qw20ZW4yqlKC8vp7y8nFWrVhEKhThz5gyNjY00NTURCoU4ePAgBw8exDRNpk+fngl8PZ7caFMwXtTcxReqeZ/9Xbqa9+BujHd9BLVo+biNQ2uN/uF/Q+s5KCzGePATqHEIWru6utiyZQsAN910E0VFRWO+zsniUGeY5zMTq1XKxGoiL+i257AlekjZfESKbsz2cIQQQgghhMhbEvAKkSVdoQSf+fkeXj7XD8CKKV4+sqaaYndu/bd8/1CrhjP9Mf73UDdvW3L5/oher5eFCxeycOHCTCuH84FvIBDg9OnTnD59GoDq6mrq6+uZMWMGxcXFk6KVg3K6UH/2fvTydVjfeyhdzfvV/w+14Y/SvXnHoZpXb3oSve05UAbGg3+LKiwe83Umk0meeOIJLMuivr6ehQsXjvk6J4uUpXl4ZwcAd84qYnapO8sjEuLKVCqK1fQoAOGS29GGM8sjEkIIIYQQIn/lVpIkxCSRtDSffPIMXaEkLlPxwPJK7pxVmJMBZ5HL5AMrK/ni5lZ+cbCHNTUFzBhq1XAlw1s53HzzzZlWDqdPn6azs5O2tjba2trYsmULRUVFmbC3uroawxi/SeWyQc1dlK7mfeQH6Gd+i978FPrQHox3fhi1+IYxW68+dwr9k6+nx3DvO1FzF43ZuobbsmULvb29eDwebr/99pzc1vPVH473cbY/RoHD4B3LZIIqkR/c/S9CIkDSXkbEL724hRBCCCGEuB4S8AqRBaaheMfSCp5uDPLhVeVU++zZHtJlbaj189K5AFvOBfjqJVo1XMkrWzkEAgHOnDnD6dOnaWpqor+/nz179rBnzx5cLhd1dXXMmDGD6dOn43A4xuiVZZdyulBvfRC9fC3Wd4eqeR/6NGr97aj734vy+EZ1fTocxPqfz0MyAUtXoTbeO6rPfynnzp1j7969ANxxxx2TrjXHWOqLJPnx0MRq71xWgV8mVhO5SGuUFcIW78FMdGNLdOPpfwmAUOlGULLdCiGEEEIIcT0k4BUiS26p9/OWtXPp7GhHa53t4VzRB4a1avjFoW7+7AqtGq6koKCAxYsXs3jxYuLxOOfOneP06dOcOXOGaDTK0aNHOXr0KIZhMG3aNGbMmEF9fT0+3+iGnrlAzVmE8U//gf71UDXvS8+gD+1N9+YdpWperTXWd9IhMqUVGO/5KGocqqQjkQhPPfUUAIsXL6aurm7M1zmZfG9PJ+GExawSmVhNZJ9KRbAlurElerAlujHjFy4bVvTVD/DPIu4bn6MIhBBCCCGEmMgk4BUiS5RS2Iz8OUy9yGXygRWVfOmlVv73YA+rr6FVw5U4HA5mzZrFrFmzsCyLtra2TK/egYEBzp49y9mzZ3nuueeoqKjIhL1lZWUT5lB/5XSi3vK+dG/e7z4Ena2jWs2rn/o17N0Gponxwb9Hecc+KNda8+yzzxIKhSguLmbDhg1jvs7J5EhnmOcaL0yslk/7E5HHrDjmUGhriw+dJ7oxE90YqdBlH5oyC0nZy0jay7Ac5RTOuQe6+iEPfuQUQgghhBAil0nAK4S4ahtqC3jpXAFbmwI8tK2NL2689lYNV2IYBlOnTmXq1Kls2LCBvr6+TNjb3t5OZ2cnnZ2dbNu2jYKCgkzYO3XqVGy2/D/MV81egPF/vop+9Ifop38zVM27Z6iad8Vrek594jD6l99LP/9b3oeqmz2aQ76ko0ePcurUKQzDYOPGjdjtud2KJJ+kLM3DL6cnVrtjZiFzymRitXxlJAdxhI7gDB1FWVG04UAbTrRyXrhsDF1WTqxXXB9+/6i1OtBJbIneoRC3e1ig240tNXjZh6ZsBaTspaTsZaQcZaTspSTt6XOMC/sApRRFphvoH50xCyGEEEIIMYlJwCuEuGpKKT64spKDnWEa+2L88lAPb11SNqbrKykpoaSkhBUrVhAOhzOTtDU1NREIBNi3bx/79u3D4XBQV1dHfX09dXV1OJ35OyO7cjpR97/3QjVvRwvWQ59Brb0tHdBeQ/WtHuzH+voXwLJQq25G3Xz3GI78goGBAZ5//nkAVq9eTUVFxbisd7J4/EQ/jX0xfA6Dd8nEannHFu/CGTqMM3gIe6xp1J5XKxOtRobClhoWCJ+/ffgyyo6RCqRbKcSHKnGT/SguXVVrGe50Je5QgJuyl6fPHaVoY3SO7BBCCCGEEEJcPQl4hRDXpMh9oVXDzw92s3qaj/ri8flC7/F4WLhwIQsXLiSRSNDU1MTp06dpbGwkEolw/Phxjh8/nqkCrq+vZ8aMGfj9/nEZ32hTs+Zj/J9/Rz/6I/RTj6K3Pos+shfjnR9GLbnyrPPaSmF988vQ3wvV01Dv/NC4tLSwLIsnn3ySRCLBlClTuOGG0ekjLNL6I0l+tK8LgHcsLcfvkrfynKc1ZqwFZ+gQztBhzHjniLsTzmnEfAtJ2UtQVgxlxYfOYyg97LIVx9CxVy9DCgClkyidBCt83UO2lJOUY6gSd6gC93ygq23e635+IYQQQgghxOiRb4VCiGuWbtXgY2tTkK9ubeNLd9VhjnP/T7vdzowZM5gxYwZaa9rb2zNhb29vL01NTTQ1NfHiiy9SVlbG7NmzmT17NkVFReM6zuulHE7UfQ+gG9ZeqOb9j8+i1t6KesuDl63m1b/9GRzZBw5nuu+ua3wO49+1axdtbW3Y7XbuvPNOjHGYzG0y+d7eLkIJi5klTu6cVZTt4YhL0SnskTOZUNeWHLhwFwZx90zivgXEvAuwzOv8EUonhwLfodBXXwiAjVdcz4TDw4Jiy+YdaqlwIdC1bD6YID3OhRBCCCGEmOgk4BVCXLN0q4YqDnY20tgX4xeHenjr4rFr1XA146murqa6upr169fT39+fCXtbW1vp7u6mu7ubrVu3UlFRkQl786my90I174+HqnmfQx/eh/HOD6GWrnrV8tbB3ejf/yz92Hd9BDVl+riMs6Ojg+3btwNwyy235NXfOB8c7Yrw7Ol0UPiBlVUysVquseI4wieGQt2jGFbkwl3KQdwzh5hvIXHPXLRtFH9wUSbaZqJtntF7TiGEEEIIIUTekIBXCPGaFLlN3r+iki+/1MrPD3Szumb8WjVcSVFREcuXL2f58uVEIhFOnTrFiRMnaG5uzkzS9tJLL1FZWcmcOXOYNWsWBQUF2R72FaWred+DXr4W67tfhfYWrK/9M2rNrai3vg/lTb+GZFc71je/BFqjbr4LY/XN4zK+RCLBE088gWVZzJ49m3nz5o3LeieLlKV5eGc7kJ5Yba5MrJYTVCqEM3QUZ+gwjvAJlE5k7rNsXmLe+cS8C4i7Z42YZEwIIYQQQgghRosEvEKI1+zGoVYN27LYquFK3G43ixYtYtGiRYTDYU6dOsXx48dpaWmho6ODjo4ONm3axJQpU5g9ezazZs3C683t/pJq5jyMf/x39G9+gn7y1+htz6V7877jQ7D4Bnq+9E8QDEDtLNRb3jdu49q8eTP9/f14vV5uvfXWcen3O1lorfnFoR5O98XwysRqWWck+tKTpIUOY4+cQWFl7kuZxcS8C4j5FpJw1YKSFiVCCCGEEEKIsSUBrxDiNTvfquFQx2ka+2L88lAPb8liq4Yr8Xg8LF68mMWLFxMKhTh58iTHjx+nra2N1tZWWltbeeGFF5g6dSpz5sxh5syZeDy5ecizcjhRb/7zdDXvd74K7c1Y//k5qJ5Gqq0JPF6MD/wdyu4Yl/E0NjZy4MABAO68805crtyo5p4I+iJJvratjZdbQ0B6YrVCmVhtfGmNLd6R6adrj7WOuDvhqCbmW0Dcu5Cko0p61wohhBBCCCHGlXxDFEJcl2K3yftXVqVbNRxMt2qoy5FWDZfj9XpZunQpS5cuJRAIZMLejo4OWlpaaGlp4fnnn6empiZT2ZuLoaWaMTfdm/c3P0E/8Qi0NQFgPPAxVHnVmK9fa01bWxtPP/00AA0NDUybNm3M1ztZbGsK8J/b2xmMpTANxbuWlXP37KJsD2ty0Bb26DkcocM4Q4cwE70X7kKRcNUROz9Jmr0kiwMVQgghhBBCTHYS8AohrtuNtQVsPutje3O6VcMXc7BVw+UUFBTQ0NBAQ0MDg4ODnDhxghMnTtDZ2UlTUxNNTU08//zzTJs2jdmzZzNz5kycTme2h52h7A7Um96NXr4W/ZsfU7j+NoLLVqO1HrN1WpbFqVOn2L17Nx0dHQCUlpaydu3aMVvnZBJOpPjWrk6ePpWeUK2uyMnH10+htih3trsJSWvskVO4gvtxhI5gSwUv3KVM4u5ZxHwLiXnnoW2+LA5UCCGEEEIIIS6QgFcIcd2UUvzFqioOd57mdF+MXx3q4f4cbtVwOX6/nxtuuIEbbriB/v7+TNjb3d3N2bNnOXv2LM8++yy1tbXMnj2bGTNm4HCMTxuEK1H1czA++mkKqqsJtrWNyTri8ThHjhxhz549DA4OAmCz2Zg/fz6rVq3CNOVt5Xod6Qzzla1tdAQTKODeBSW8bUkZdpv0ch0zVgJXYC+egZcw4x0XbjZcxD3z0u0XPHPQhgTsQgghhBBCiNwj38SFEKOi2G3y4IpK/m1LGz872M2qPGnVcDlFRUWsXLmSlStX0tvbmwl7e3t7aWxspLGxEZvNRl1dHbNnz6a+vh673Z7tYY+JUCjEvn37OHDgALFYDACXy8WSJUtYsmRJzvYqzieJlOanB7r51eEeLA0VXpOPrp3Cwkr5244VlQzgGdiGe3A7Rird49hSDmIFy4j6FpFw14OSj0pCCCGEEEKI3CbfWoQQo+amOj8vnQuwvTnIQ9va+MLG/GrVcDklJSWsXr2a1atX09PTw4kTJzh+/Dj9/f2cOnWKU6dOYZom9fX1zJ49m7q6uglRzdrT08OePXs4evQolmUBUFhYSENDA/Pnz5+wgfZ4OzcQ4ysvtXK6Lx2e3zbDz/tuqMTrsGV5ZBOTLdaGp/8lXIG9KFIApMwiwoXriPpXoG3uLI9QCCGEEEIIIa5e/qcPQoiccb5Vw6HO05zqze9WDZdTWlpKaWkpq1evpru7m+PHj3PixIkR/Xvtdjt1dXXU1dVRW1ubVxWuWmuam5vZvXs3Z8+ezdxeXV3N8uXLqa+vxzCkXcBosLTm98f6+P7eLuIpTYHD4EOrq1g33Z/toU082sIRPoan/yUckVOZmxPOaYSLNhDzLQQlgboQQgghhBAi/0jAK4QYVedbNXxlArVquBSlFOXl5ZSXl7Nu3To6OzszAW8gEMhcBqisrKS2tpb6+noqKipQKvcqm1OpFCdOnGDPnj10dXUB6dc4c+ZMGhoaqK6uzvIIJ5aecIKHtraxtz0MwPJqL3+5tpoSt7w1jyorjjuwC3f/FsxENwAag5hvIeGiDSRd07M8QCGEEEIIIYS4PvItUggx6m4eatWwYwK2argUpRSVlZVUVlayfv16Ojo6aGxs5MyZM3R1ddHR0UFHRwc7duzA7XZTW1tLXV0d06dPx+XKbgAei8U4dOgQe/fuJRgMAmCaJgsWLGDZsmUUFRVldXwT0aYzg/zPznaCcQuHTfGe5RXcPbsoJ4P/fGUkB3D3b8U9uAPDigDpSdMi/pVECtdh2YuyO0AhhBBCCCGEGCUS8AohRt35Vg2Hz7dqONzD/YsmXquGS1FKUVVVRVVVFWvXriUUCnHmzBnOnDnDuXPniEQiHD16lKNHj6KUorq6OtPOobS0dNxCvkAgwL59+zh48CDxeBwAj8fD0qVLWbRoEW639CEdbcF4iod3dvDimUEAZpW4+Nj6amr8ziyPbOIwo814+jfjDB5Ake4bnbSXEClcT9R/A9qQv7UQQgghhBBiYpGAVwgxJkqGt2o40M3qmgJqiyZnsOL1elm4cCELFy4klUrR1taWCXx7e3tpbW2ltbWVLVu24PV6M2HvtGnTcDgcoz6erq4udu/ezYkTJzITpxUXF7N8+XLmzp07ISaHy0X720P8+9Y2esJJDAX3LSrl/kVlE766fVxoC2foMO7+l3BEz2RujrvqCRdtIO6dB0r6RgshhBBCCCEmJvkWL4QYM8NbNXx1axtf3FiLbZKHWTabjZqaGmpqatiwYQODg4OZsLe5uZlQKMShQ4c4dOgQhmEwderUTOBbVPTaD+HXWnP27Fn27NlDU1NT5vaamhqWL19ObW2ttAcYI/GUxQ/3dvHo0T4AqgvsfGzdFOaWSYX09VJWFNfgy3j6t2BLpv++GoNYwRLChRtIuqZmeYRCCCGEEEIIMfYk4BVCjJnzrRoOdZ7mVG+UXx3u4b5J1Krhavj9fpYsWcKSJUtIJpM0Nzdz9uxZGhsbGRwcpKmpiaamJjZt2kRhYSF1dXXU1tZSU1NzVZW2yWSS48ePs2fPHnp6eoD0v8vs2bNZvnw5FRUVY/0SJ7XGvij/9lIr5wbSLTA2ziriPcsrcNulmvR6GIk+PP0v4Qq8jGHFALAMN5HC1UQK12KZ/iyPUAghhBBCCCHGjwS8QogxVeI2efCGSv59axs/3t9NOGHx1sVlOE0JuF7JNM1Mte5NN91Ef39/prq3paWFgYEB9u3bx759+zBNk5qamszyfv/IQCsajbJ//3727dtHOBwGwG63s2jRIpYtW0ZBQUE2XuKkkbI0jx7p5Uf7u0haUOiy8Zerq1lZ48v20PKX1pjRc+n+uqFDKDQASXs54aL1RAsawBj9liZCCCGEEEIIkesk4BVCjLlb6v0c7grz5MkBfnW4l21NAT68uppFlZ5sDy1nKaUoLi6muLiYhoYG4vE4TU1NmcB3+MRtACUlJdTV1VFTU8POnTvZuXMniUQCSPcAXrZsGYsWLcLpnJx9kMdTRzDOV7e2cagzAsDqGh8fXl1FoUvecl8TncIZPIinfzP2WHPm5rh7Vrq/rme29NcVQgghhBBCTGrybVMIMeaUUnx4dTUrpvj4n50dtAYS/L9Pn2PjrCLe1VCOz2HL9hBznsPhYObMmcycOROtNT09PTQ2NnL27Fna2tro7e2lt7eX3bt3Zx5TVlZGQ0MDc+bMwWaTv/FY01rzXOMgX9/ZQSRp4TINHlxRwe0zCqW/8WugUiHcgy/jHtiKLTkAgFYm0YJlhAvXk3JWZXmEQgghhBBCCJEbJOAVQoyb1dMKWFTp4Xt7unjiZD9PnOxnR0uQD66sZM00aRlwtZRSlJWVUVZWxsqVK4lGo5w7d44zZ87Q2tpKVVUVCxcupKamRoLFcTIYTfJfOzrY2hQAYH65m4+uraaqQFoGXDWdxB45hyNyAkf4BGasNdOGwbL5CBeuJuJfgzalzYUQQgghhBBCDCcBrxBiXHkdNj60uoqb6/x8bXs7rYE4//piC+umF/D+FZUUu2W3dK1cLhdz5sxhzpw5KKWorq6mra0NrXW2hzYp7G4N8tDWNvqiKWwK3raknHsXlGAzJFy/LK2xJTpxhE/iCJ/AHmnE0PERiyScU4gUriXqWwqGPUsDFUIIIYQQQojcJkmKECIrFlZ6+Orr6/jZgR5+dbiHLecC7G8P8Z7lcki7yA+xpMV3dnfy2Il+AGr8Dj6+fgozS1zZHVgOU8kgjsjJTKhrSw2OuN+y+Yi7ZxH3pE+WWZilkQohhBBCCCFE/pCAVwiRNQ6bwTuXlbOhtoD/2NbOqd4o/7GtnRfODPLhVVVyeLvIWSd6IvzbS220BtIVp2+YW8w7l5XjNGWyrxGsBPboWRzhdNsFe7xtxN1amSRcdcQ9s4l7ZpN0VMqEaUIIIYQQQghxjSTgFUJkXX2xiy9urOU3R3v58f5u9reH+cvfN/L2pWW8Ya4c6i5Gj6U10aRFJGEROX9+rZeTFl2hBJaGErfJX6+tZlm1N9svLTdojS3ePhTonsQRbUTp5IhFEo7qoUB3FglXnbReEEIIIYQQQojrJAGvECIn2AzFvQtKWTOtgP/a3s7+jjDf2d3FpjMBPrKmivpiOexdjNQVSnCwI0wwnhoRwIYvE9JGkxaj1Zl4Q20BH1xZRYHTNkrPmJ+M5GA6zI2cwB4+iS0VHHF/yubPtFyIu2ehTZlQUQghhBBCCCFGkwS8QoicUl3g4DO3T+OZ0wN8e3cnJ3uj/M1jZ7h3QSlvWVyKwyaHb09WWmtO9cbY0RJgR3OQxr7Ya34uQ4HbNHDZDdymgds+dDJHnnteebvdhts0KHLZJm8LESuOI9KYrtKNnMSMd4y4Wys7cXd9ukrXPZuUowKkp7YQQgghhBBCjBkJeIUQOUcpxR0zi1g+xcfXd3awtSnALw71sLUpwIdXV7GwwpPtIYpxEk9Z7G8Ps6M5yM6WIL2RC4f7Gwpml7op95oXCWRtrwhmR1522pRM5He1dBIdOIO7d2u6j27kDIrUhbtRJJ1ThgLdWSTctaDk44UQQgghhBBCjBf5BiaEyFklbpNP3jSVrU0BHt7ZQctgnH946hx3zS7i3Q3leOyT+9D4iWogmuTlliA7WoLsbQsRTV5oquAyFQ3VXlbVFHDDFC+FLnkbe020hUqFsKWCGKkARvL8eQAjFRw6DV23IqQA37CHp8yioZYLs4l7ZqJt0oNYCCGEEEIIIbJFvhkLIXLe2mkFLK708L09nTx5coDHT/SzsznIB1dVsqpG+nnmO601zYNxdjQH2dEc5Fh3ZESf3FK3yaoaHyun+lhc5ZE2HZeiNcqKjgxsR1wOjghw1bV0I7a5iLnqibtnEffMJmUvk7YLQgghhBBCCJEjJOAVQuQFn8PGh1dXc2Otn//a0U5bIMHnXmhh/fQC3r+ikiK37M7yScrSHOmKsKM5wI6WIG2BxIj7ZxQ7WVXjY1VNATOKndJOAVCpEPbIGWypwYuHtsnAiNYJV6JRaJsHy1aAZfORMgsyly1z6NxWgLYXUDl1BoPtHWg9WlPUCSGEEEIIIYQYLZKICCHyypIqL199XT0/PdDNr4/08tK5APvaQ7z3hkpurfdLEJjDwokUu1tD7GgOsqs1SDBuZe4zDcWSSg+ranysmOqj3GvP4khziNaYsSbcA9twBQ+gdPKKD7EM10WDWss8f37+Ni+oK7c5UUqhlFRNCyGEEEIIIUSukoBXCJF3nKbBuxsq2FDr52vb2jjdF+OrW9t4oXGAD62uotLnyPYQxZDOYIKdLUF2NAc42BkmeSHTpcBpY8UUL6tqfCyr9kpP5eGsOK7AXtyD27HHWjM3Jx0VpOxlpIYHtiMu+8CQcFwIIYQQQgghJhMJeIUQeWtmiYsv3VXHo0d6+cmBbva2h/nL3zXy9qXl3DO3GJsh1bzjzdKaU71RdjQH2dkSpLEvNuL+KQWOodYLPuaVueXf6BVs8U7cA9txBXZjWFEAtDKJ+pYQKVxN0jlNet8KIYQQQgghhBhBAl4hRF6zGYo/XVjKmmkF/OeOdg52hPn27k42nR3kI6urqCt2ZXuIE1IiZRGMW4TiKYJxi75Ikj1tIXa0BOmLXGgjYCiYV+ZOT5JW46PG78ziqHOUTuEMHcY9sA1H5HTm5qS9hIh/DVH/crTNm8UBCiGEEEIIIYTIZRLwCiEmhCl+B/98+zSeOjXAd3d3cqInyscfO8ObFpZy/6JS7DbpITqcpTXhuEUokRoW1KYInb8t9sr70ueheIpQwiKeuvRkWy5T0VCdrtJdMcWL3yVvNRdjJAdwD+zANbgTWyoApCc+i3vnE/GvJu6ZBdL7VgghhBBCCCHEFci3biHEhKGU4s5ZRdwwxcvXX+5gW1OQnx/s4XfH+pjqdzC1wMFUv4MpfgdTCtLnLjP/AzStNf3RFB3BBF3hBKotRUtX74VgNvGKgDZuEU5YXDqivToK8DgMvHYbPofB3KFK3UWVHhwSqF+ctrBHTuMe2IYzdARFuilxyuYj6l9JxL8Ky16U3TEKIYQQQgghhMgrEvAKISacUo+d/+emGracG+TrL3fSF0lyoifKiZ7oRZY1mToU9k4dCn6n+h1UeO051R82GE/RGUzQEUzQEYqnz4dOnaHEZStqL8dhU/gcNrzDglqvY/j5hfu8DuPCdYcNj93AkH6wV0WlIrgCu3APbMdMdGduj7vqiRSuIeZbAErekoUQQgghhBBCXDv5NimEmLDWTfezcmoBrYE4LYMxWgcTtATitAzGaQ3ECcRS9IST9IST7O8Ij3isaUCVbyj4LRh5XuSyoUY52IwlLTpCiUyI2xlK0BEcCnJDCUJx67KPV6TD6kqfg+piH0Yqng5p7RcC2fOhrddh4Bu6XVpXjC0z2oJ7YBuu4D6UTgBgKSdRfwMR/2pSzqosj1AIIYQQQgghRL6TgFcIMaHZbYraIie1Ra+e3GswlqJ1KOxtGXbeFogTT2maB+M0D8Zf9TiP3ci0eBhe9TulwIHbfvHANGlpukPpsDZTeTusGrc/mrriayl02qjw2an02an02qn0OdKXfXbKPHbsNoVSiurqatra2tD6epswiNfESuAK7sc9sA17rDlzc8JRla7WLViGNmSyOSGEEEIIIYQQo0MCXiHEpOV32vCXu5lX7h5xu6U1PeEkLYMjg9/WQJzOYIJwwuJkb5STva9u+VDiNjPVvklLD1XlxukOJ7GukLd67AaVPjsVXvuI80pfumXEpcJjkRts8W7cg9txDe7CsCIAaGzEfIuJFK4m4aoFaWkhhBBCCCGEEGKUScArhBCvYChFuddOudfOsmrviPviKYv2YCId+A4PfwfjDMRS9EaS9EaSHHxFywcAu6HSFbjnA9xMNW66EtfnMEa99YMYYzqFI3QU98B2nJETmZtTZhGRwtVEClagTV8WByiEEEIIIYQQYqKTgFcIIa6Bw2YwvdDJ9MJXH2IfjKVoCVwIfk1DZdopVPjsFLtNmZRsgjCSAVyDO3EP7sCWHABAo4h75hApXEPcMweUVFwLIYQQQgghhBh7EvAKIcQo8TltzHW6mVvmvvLCIrdZcWzJfmzJfozEQPr8/PVkP7ZEP4r0xHeW4SXiX0GkcBWWvSTLAxdCCCGEEEIIMdlIwCuEGHtWAiMVQGmLlL0IlOx6RBZpCyMVwEj0Y0u+IrxNDJ0P9dC9nLirlkjhamLeRWDYx2HgQgghhBBCCCHEq0nKIoR47XQKIxnASA1iJAPYkoOZy0ZyEFtqECM5OCIs0ygss5CUvYSUvZSUWTJ0OX3ShlsmohLXRaWiw6pt0wGuLTG8AncwU317OZZyYtmLSJmFWGYRKbOIlL1o6HIxlr1o7F+MEEIIIYQQQghxBRLwCiFeTVsYqVA6nH1VeDuYrn5MDmKkQij01T2lSlc4Kp3IHPpO5PSrlrMMVzrszQS/pZnw1zILQdlG85WKfKQtbIlebPEOzHhXJrg9X4lrWLErPwUGluknZRalf3Awi4bC3KKh24rQNtc4vBghhBBCCCGEEOL6SMArRJbYYh3owRBmpAedCUmHV66qkdfVyPv0iOWGnauLPc8rLuvkyGrb1GD6eia8DVxVhSOAxoZlFmDZCkiZfqzzJ5t/6HoBls2PNtJhmUoF0+Fcsjd9nujBluhLX04NYlhRjFgr9ljrRdZlpKsn7SOrftNhcKkEchONtjCSA5jxDsx4x1Cg24EZ70Tp5GUfahnuYaHtsArcodssW4FMgiaEEEIIIYQQYkKQgFeILPF1/47UuRMUZ3sgl6BRWDZfJqy1zKEA13YhxE2ZBWjDc01BmTYLSJoFJKl99Z1WHFtyKOx95SnZi9LJdDCc7IWLtEi1DPfIqt9MFXBxOtCTPqm5SWuM1GA6xI2lA1wz3o4t3omh4xd/iDJJ2stJOSpJ2YszVbfpFgqFaMM5zi9CCCGEEEIIIYTIDgl4hcgSy1YArnJSyRSgh07nDbusL3E7elhtrh5212WeK3PZfEVgW5AObIddtmy+8W+HYDjSgZ2j8tX3aQsjFRyq+O0dUQVsJHqxpYIYVgQj1oI91nLRp7cM97DXOSyozrzuwqHXLZWdY0Ulg5mK3OFVuYYVvejyGhspRxlJR2XmlA51S+TfSQghhBBCCCGEQAJeIbImUHU/vupqutra0Prq+thOasrIBLIJd/2r77firwh+hwfBAyidSAfA8QgmHZdcTbpyuSAT+KZsBRdpO+GXyeCuQKXCQwFuuhrXjHdixjowrNBFl9cYpOylQwFuBUlnFUlHBSl7mfRdFkIIIYQQQgghLkMCXiHExGA4SDmrSDmrXn2f1igrmpk07kK/4WGXk4MYqSAKC1tqEFtqEC5RCQzpFgHDA990D+LCYZXABVimD62cEy8I1hbKimCkwumJ9lIhjFQQM96Vqcq1pQIXfygq3VLhFRW5SXuZtNAQQgghhBBCCCFeAwl4hRATn1Jom5uUzU2KShKXWm6oDUR64rkBjGQAW3Igc92WDGAkBzCsyMh+wJehMdCGG8vmfsW5Z9h1zyvuT19HjcMuWmuUjg+FtKGRoa11/nJ46L70/coKj5jm71JSZtGwELeCpKOKpKMcDMfYvy4hhBBCCCGEEGKSkIBXCCHOG9YGAqZeejkrgZEKjKgENpKvrgxWOoHCQlmhS7YmuByt7BeCYMONtrmxRoTBrw6JsbnRsV5ssVZUclhga70ywB0Kba0wSidf05/LMlxDY/Bi2bzpFgvOC4GuNlyv6XmFEEIIIYQQQghx9STgFUKIa2XYsYwSLHvJ5ZezEukANRVJV/2mIq+4Hh52ewRlhTFSEZQVRaFROoEtOQAMXNPwUqfhCiN7lXTLiXRQmw6LvUMnD/r85aHb09fHqcJYCCGEEEIIIYQQlyXfzoUQYqwYdiyjEMxCUtfyOG2hrFimz236PPKq6yPC4vP36QRgYNk8I0LZdDDruRDaGt4RIa60TRBCCCGEEEIIIfKTBLxCCJFrlIG2DbVkuFKV8CsfSoqq6ql0tXeg9ZX75AohhBBCCCGEECK/ScArsubxxx/nt7/9Lf39/dTW1vLAAw8wa9asbA9LiPymTJQysj0KIYQQQgghhBBCjBNJAURWbNmyhe9///u8+c1v5vOf/zy1tbV87nOfY2Dg2nqNCiGEEEIIIYQQQggxmUnAK7Lid7/7Hbfffju33norNTU1PPjggzgcDp577rlsD00IIYQQQgghhBBCiLwhAa8Yd8lkktOnT7N48eLMbYZhsHjxYo4fP57FkQkhhBBCCCGEEEIIkV+kB68Yd4ODg1iWRVFR0Yjbi4qKaG1tvehjEokEiUQic10phdvtzlzOR+fHna/jF7lJtisx2mSbEmNBtisx2mSbEmNBtisx2mSbEkKMFQl4RV545JFH+MUvfpG5Xl9fz+c//3nKy8uzOKrRUVVVle0hiAlItisx2mSbEmNBtisx2mSbEmNBtisx2mSbEkKMNgl4xbjz+/0YhkF/f/+I2/v7+19V1Xvevffeyz333JO5fv4Xz66uLpLJ5FgNdUwppaiqqqK9vR2tdbaHIyYI2a7EaJNtSowF2a7EaJNtSowF2a7EaJNtSlwt0zQnREGbGD8S8IpxZ5omM2bM4ODBg6xatQoAy7I4ePAgd91110UfY7fbsdvtF70v398YtdZ5/xpE7pHtSow22abEWJDtSow22abEWJDtSow22aaEEKNNAl6RFffccw//+Z//yYwZM5g1axZ/+MMfiMVi3HLLLdkemhBCCCGEEEIIIYQQeUMCXpEV69atY3BwkJ///Of09/dTV1fHP/zDP1yyRYMQQgghhBBCCCGEEOLVJOAVWXPXXXddsiWDEEIIIYQQQgghhBDiyoxsD0AIIYQQQgghhBBCCCHEayMBrxBCCCGEEEIIIYQQQuQpCXiFEEIIIYQQQgghhBAiT0nAK4QQQgghhBBCCCGEEHlKAl4hhBBCCCGEEEIIIYTIUxLwCiGEEEIIIYQQQgghRJ6SgFcIIYQQQgghhBBCCCHylAS8QgghhBBCCCGEEEIIkack4BVCCCGEEEIIIYQQQog8JQGvEEIIIYQQQgghhBBC5CkJeIUQQgghhBBCCCGEECJPScArhBBCCCGEEEIIIYQQeUoCXiGEEEIIIYQQQgghhMhTEvAKIYQQQgghhBBCCCFEnjKzPQAhrodp5v8mPBFeg8g9sl2J0SbblBgLsl2J0SbblBgLsl2J0SbblLgS2UbEtVJaa53tQQghhBBCCCGEEEIIIYS4dtKiQYgsiUQi/P3f/z2RSCTbQxETiGxXYrTJNiXGgmxXYrTJNiXGgmxXYrTJNiWEGCsS8AqRJVprGhsbkSJ6MZpkuxKjTbYpMRZkuxKjTbYpMRZkuxKjTbYpIcRYkYBXCCGEEEIIIYQQQggh8pQEvEIIIYQQQgghhBBCCJGnJOAVIkvsdjtvfvObsdvt2R6KmEBkuxKjTbYpMRZkuxKjTbYpMRZkuxKjTbYpIcRYUVqavwghhBBCCCGEEEIIIURekgpeIYQQQgghhBBCCCGEyFMS8AohhBBCCCGEEEIIIUSekoBXCCGEEEIIIYQQQggh8pQEvEIIIYQQQgghhBBCCJGnzGwPQIh8dvjwYX7zm9/Q2NhIX18fn/jEJ1i1alXm/v7+fn70ox+xf/9+QqEQ8+fP54EHHqC6unrEMj/4wQ/Yv38/0WiUKVOmcO+997JmzZrMMsFgkG9/+9vs2rULpRSrV6/mPe95Dy6Xa1xfrxgf47VdffjDH6arq2vEut/2trfxJ3/yJ2P+GsX4Go1tqr29nR/84AccPXqUZDLJ0qVLeeCBBygqKsosI/uqyWO8tinZT00ejzzyCDt27KClpQWHw8GcOXN4xzvewZQpUzLLaK35+c9/zjPPPEMoFGLevHm8733vG7FdxeNxvv/977NlyxYSiQRLly7lfe97n+yrJqnx3K5kfzU5jNY29fTTT7N582YaGxuJRCJ85zvfwev1jliX7KuEENdCKniFuA6xWIy6ujre+973vuo+rTVf/OIX6ezs5G//9m/5whe+QHl5OZ/97GeJRqOZ5b72ta/R2trK3//93/OlL32JVatW8ZWvfIXGxsbMMg899BBNTU186lOf4pOf/CRHjhzh4YcfHpfXKMbfeG1XAPfffz9f//rXM6e77rprzF+fGH/Xu01Fo72RQysAAA79SURBVFE+97nPoZTin/7pn/jsZz9LMpnk85/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S8AIAAAAAFJSAFwAAAACgoAS8AAAAAAAFJeAFAAAAACgoAS8AAAAAQEEJeAEAAAAACkrACwAAAABQUP8CsrmAzmSz47kAAAAASUVORK5CYII="
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1400x800 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for a sample python solution</summary>\n\n```python\n    #The correct answer is:    \n    #Step 1: Get the dataset. Recall that we created a Total column that calculates cumulative immigration by country. \n    #We will sort on this column to get our top 5 countries using pandas sort_values() method.\n    \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    # get the top 5 entries\n    df_top5 = df_can.head(5)\n\n    # transpose the dataframe\n    df_top5 = df_top5[years].transpose() \n\n    print(df_top5)\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    plt.show()\n\n```\n\n</details>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "### Other Plots\n\nCongratulations! 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\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    }
  ]
}