IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork_jupyterlite_files_DV0101EN-Exercise-Waffle-Charts-Word-Clouds-and-Regression-Plots.jupyterlite.ipynb

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        "name": "python",
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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  "cells": [
    {
      "cell_type": "markdown",
      "source": "<center>\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%204/logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n</center>\n\n# Waffle Charts, Word Clouds, and Regression Plots\n\nEstimated time needed: **30** minutes\n\n## Objectives\n\nAfter completing this lab you will be able to:\n\n*   Create Word cloud and Waffle charts\n*   Create regression plots with Seaborn library\n",
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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 *p*andas](#0)<br>\n2.  [Downloading and Prepping Data](#2)<br>\n3.  [Visualizing Data using Matplotlib](#4) <br>\n4.  [Waffle Charts](#6) <br>\n5.  [Word Clouds](#8) <br>\n6.  [Regression Plots](#10) <br>\n\n</div>\n",
      "metadata": {
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        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "# Exploring Datasets with *pandas* and Matplotlib<a id=\"0\"></a>\n\nToolkits: The course heavily relies on [*pandas*](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) and [*Numpy*](http://www.numpy.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) for data wrangling, analysis, and visualization. The 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).\n\nDataset: Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) from United Nation's website\n\nThe dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. In this lab, we will focus on the Canadian Immigration data.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "# Downloading and Prepping Data <a id=\"2\"></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\n\nawait piplite.install(['openpyxl==3.0.9', 'seaborn'])",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Import Primary Modules:\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\nfrom PIL import Image # converting images into arrays",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Download the Canadian Immigration dataset and read it into a *pandas* dataframe.\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)\n\nprint('Data downloaded and read into a dataframe!')",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 3,
      "outputs": [
        {
          "name": "stdout",
          "text": "Data downloaded and read into a dataframe!\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Let's take a look at the first five items in our dataset\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "df_can.head()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 4,
      "outputs": [
        {
          "execution_count": 4,
          "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": "Let's find out how many entries there are in our dataset\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# print the dimensions of the dataframe\nprint(df_can.shape)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 5,
      "outputs": [
        {
          "name": "stdout",
          "text": "(195, 43)\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to *Introduction to Matplotlib and Line Plots* and *Area Plots, Histograms, and Bar Plots* for a detailed description of this preprocessing.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# clean up the dataset to remove unnecessary columns (eg. REG) \ndf_can.drop(['AREA','REG','DEV','Type','Coverage'], axis = 1, inplace = True)\n\n# let's rename the columns so that they make sense\ndf_can.rename (columns = {'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace = True)\n\n# for sake of consistency, let's also make all column labels of type string\ndf_can.columns = list(map(str, df_can.columns))\n\n# set the country name as index - useful for quickly looking up countries using .loc method\ndf_can.set_index('Country', inplace = True)\n\n# add total column\ndf_can['Total'] =  df_can.sum (axis = 1)\n\n# years that we will be using in this lesson - useful for plotting later on\nyears = list(map(str, range(1980, 2014)))\nprint ('data dimensions:', df_can.shape)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 6,
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-6-92cc0d23d11f>:14: 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"
        },
        {
          "name": "stdout",
          "text": "data dimensions: (195, 38)\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "# Visualizing Data using Matplotlib<a id=\"4\"></a>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "Import and setup `matplotlib`:\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "%matplotlib inline\n\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches # needed for waffle Charts\n\nmpl.style.use('ggplot') # optional: for ggplot-like style\n\n# check for latest version of Matplotlib\nprint ('Matplotlib version: ', mpl.__version__) # >= 2.0.0",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 7,
      "outputs": [
        {
          "name": "stdout",
          "text": "Matplotlib version:  3.5.1\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "# Waffle Charts <a id=\"6\"></a>\n\nA `waffle chart` is an interesting visualization that is normally created to display progress toward goals. It is commonly an effective option when you are trying to add interesting visualization features to a visual that consists mainly of cells, such as an Excel dashboard.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "Let's revisit the previous case study about Denmark, Norway, and Sweden.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# let's create a new dataframe for these three countries \ndf_dsn = df_can.loc[['Denmark', 'Norway', 'Sweden'], :]\n\n# let's take a look at our dataframe\ndf_dsn",
      "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": "        Continent           Region            DevName  1980  1981  1982  1983  \\\nCountry                                                                         \nDenmark    Europe  Northern Europe  Developed regions   272   293   299   106   \nNorway     Europe  Northern Europe  Developed regions   116    77   106    51   \nSweden     Europe  Northern Europe  Developed regions   281   308   222   176   \n\n         1984  1985  1986  ...  2005  2006  2007  2008  2009  2010  2011  \\\nCountry                    ...                                             \nDenmark    93    73    93  ...    62   101    97   108    81    92    93   \nNorway     31    54    56  ...    57    53    73    66    75    46    49   \nSweden    128   158   187  ...   205   139   193   165   167   159   134   \n\n         2012  2013  Total  \nCountry                     \nDenmark    94    81   3901  \nNorway     53    59   2327  \nSweden    140   140   5866  \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>Denmark</th>\n      <td>Europe</td>\n      <td>Northern Europe</td>\n      <td>Developed regions</td>\n      <td>272</td>\n      <td>293</td>\n      <td>299</td>\n      <td>106</td>\n      <td>93</td>\n      <td>73</td>\n      <td>93</td>\n      <td>...</td>\n      <td>62</td>\n      <td>101</td>\n      <td>97</td>\n      <td>108</td>\n      <td>81</td>\n      <td>92</td>\n      <td>93</td>\n      <td>94</td>\n      <td>81</td>\n      <td>3901</td>\n    </tr>\n    <tr>\n      <th>Norway</th>\n      <td>Europe</td>\n      <td>Northern Europe</td>\n      <td>Developed regions</td>\n      <td>116</td>\n      <td>77</td>\n      <td>106</td>\n      <td>51</td>\n      <td>31</td>\n      <td>54</td>\n      <td>56</td>\n      <td>...</td>\n      <td>57</td>\n      <td>53</td>\n      <td>73</td>\n      <td>66</td>\n      <td>75</td>\n      <td>46</td>\n      <td>49</td>\n      <td>53</td>\n      <td>59</td>\n      <td>2327</td>\n    </tr>\n    <tr>\n      <th>Sweden</th>\n      <td>Europe</td>\n      <td>Northern Europe</td>\n      <td>Developed regions</td>\n      <td>281</td>\n      <td>308</td>\n      <td>222</td>\n      <td>176</td>\n      <td>128</td>\n      <td>158</td>\n      <td>187</td>\n      <td>...</td>\n      <td>205</td>\n      <td>139</td>\n      <td>193</td>\n      <td>165</td>\n      <td>167</td>\n      <td>159</td>\n      <td>134</td>\n      <td>140</td>\n      <td>140</td>\n      <td>5866</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows × 38 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Unfortunately, unlike R, `waffle` charts are not built into any of the Python visualization libraries. Therefore, we will learn how to create them from scratch.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "**Step 1.** The first step into creating a waffle chart is determing the proportion of each category with respect to the total.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# compute the proportion of each category with respect to the total\ntotal_values = df_dsn['Total'].sum()\ncategory_proportions = df_dsn['Total'] / total_values\n\n# print out proportions\npd.DataFrame({\"Category Proportion\": category_proportions})",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 9,
      "outputs": [
        {
          "execution_count": 9,
          "output_type": "execute_result",
          "data": {
            "text/plain": "         Category Proportion\nCountry                     \nDenmark             0.322557\nNorway              0.192409\nSweden              0.485034",
            "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>Category Proportion</th>\n    </tr>\n    <tr>\n      <th>Country</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Denmark</th>\n      <td>0.322557</td>\n    </tr>\n    <tr>\n      <th>Norway</th>\n      <td>0.192409</td>\n    </tr>\n    <tr>\n      <th>Sweden</th>\n      <td>0.485034</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "**Step 2.** The second step is defining the overall size of the `waffle` chart.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "width = 40 # width of chart\nheight = 10 # height of chart\n\ntotal_num_tiles = width * height # total number of tiles\n\nprint(f'Total number of tiles is {total_num_tiles}.')",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "name": "stdout",
          "text": "Total number of tiles is 400.\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "**Step 3.** The third step is using the proportion of each category to determe it respective number of tiles\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# compute the number of tiles for each category\ntiles_per_category = (category_proportions * total_num_tiles).round().astype(int)\n\n# print out number of tiles per category\npd.DataFrame({\"Number of tiles\": tiles_per_category})",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 11,
      "outputs": [
        {
          "execution_count": 11,
          "output_type": "execute_result",
          "data": {
            "text/plain": "         Number of tiles\nCountry                 \nDenmark              129\nNorway                77\nSweden               194",
            "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>Number of tiles</th>\n    </tr>\n    <tr>\n      <th>Country</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Denmark</th>\n      <td>129</td>\n    </tr>\n    <tr>\n      <th>Norway</th>\n      <td>77</td>\n    </tr>\n    <tr>\n      <th>Sweden</th>\n      <td>194</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Based on the calculated proportions, Denmark will occupy 129 tiles of the `waffle` chart, Norway will occupy 77 tiles, and Sweden will occupy 194 tiles.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "**Step 4.** The fourth step is creating a matrix that resembles the `waffle` chart and populating it.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# initialize the waffle chart as an empty matrix\nwaffle_chart = np.zeros((height, width), dtype = np.uint)\n\n# define indices to loop through waffle chart\ncategory_index = 0\ntile_index = 0\n\n# populate the waffle chart\nfor col in range(width):\n    for row in range(height):\n        tile_index += 1\n\n        # if the number of tiles populated for the current category is equal to its corresponding allocated tiles...\n        if tile_index > sum(tiles_per_category[0:category_index]):\n            # ...proceed to the next category\n            category_index += 1       \n            \n        # set the class value to an integer, which increases with class\n        waffle_chart[row, col] = category_index\n        \nprint ('Waffle chart populated!')",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 12,
      "outputs": [
        {
          "name": "stdout",
          "text": "Waffle chart populated!\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Let's take a peek at how the matrix looks like.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "waffle_chart",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 13,
      "outputs": [
        {
          "execution_count": 13,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3,\n        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3,\n        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3,\n        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3,\n        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3,\n        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3,\n        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3,\n        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3,\n        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3,\n        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3,\n        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]],\n      dtype=uint32)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "As expected, the matrix consists of three categories and the total number of each category's instances matches the total number of tiles allocated to each category.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "**Step 5.** Map the `waffle` chart matrix into a visual.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# instantiate a new figure object\nfig = plt.figure()\n\n# use matshow to display the waffle chart\ncolormap = plt.cm.coolwarm\nplt.matshow(waffle_chart, cmap=colormap)\nplt.colorbar()\nplt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 14,
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-14-2ce014475eca>:7: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n  plt.colorbar()\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x45fd0b8>",
            "image/png": 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"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1600x400 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "**Step 6.** Prettify the chart.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# instantiate a new figure object\nfig = plt.figure()\n\n# use matshow to display the waffle chart\ncolormap = plt.cm.coolwarm\nplt.matshow(waffle_chart, cmap=colormap)\nplt.colorbar()\n\n# get the axis\nax = plt.gca()\n\n# set minor ticks\nax.set_xticks(np.arange(-.5, (width), 1), minor=True)\nax.set_yticks(np.arange(-.5, (height), 1), minor=True)\n    \n# add gridlines based on minor ticks\nax.grid(which='minor', color='w', linestyle='-', linewidth=2)\n\nplt.xticks([])\nplt.yticks([])\nplt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 15,
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-15-b89dfe51edf5>:7: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n  plt.colorbar()\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x48b6b68>",
            "image/png": 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+WdfQTwigBo7AggazYMGCWLBgQf/3Rv719nBklGENMopTX0axMuwAOb4yyrCGYc9o4F/blu0XwzJGtr6MYmWUYQ0yilN/uDOOW01JJLmOwHIHCDDyNECgwe3706G4+Iqn61L7oR+dHZMmnhC7ew82bEYZ1iCjOPVlFCvjaP1KpRLbft895PUjIqZNnxEtLS0yCpBRhjUMd0bfjp54fMa5dck4v/vJaO+YXLeMeteXUayMMqxBRnHqyyhWRhnWEBHxie1PxagPT6xL7UbgEnSgkWmAAAAAAADZkpyXoCcuQQdGnn8TAQAAAAAApWMHCAAAAACQKWnKd5yVDSBAEWiAAAAAAACZkqQpkhzdjDxjAIaaBggAAAAAkC1JIvJcaJ64BB0YeRogAAAAAECmpCmJJMcl6HmOzQIYahogAAAAAEC2JMnXzLADBCgAh/EBAAAAAAClYwcIAAAAAJAtaTryyTMOYIRpgAAAAAAAmY7cAVL7cVbuAAGKQAMEAAAAAMiWJBE5LkF3BwhQBBogAAAAAECmJEkiydHMyDMGYKhpgAAAAAAA2Zqa8u0AyTMGYIj5NxEAAAAAAFA6doAAAAAAAJmSJOcl6DUcgfXQQw/F5s2bY/v27dHW1hadnZ3x2c9+NqZMmXLMMS+99FKsWLHiXc9/8IMfxLhx4/q/b9q0KR544IHo6emJyZMnx5IlS2LevHk1rQVoXBogAAAAAEC2JIlI6nsJ+pYtW+KCCy6IU045JSqVStx3331x8803x2233RajRo16z7H/8A//EKNHj+7//qEPfaj/f7/88stx++23x+WXXx7z5s2LjRs3xre//e245ZZbYtq0abWvCWg4jsACAAAAALI1Jfk/g3TjjTfG/PnzY+rUqTFjxoxYtmxZ7Nq1K15//fX3HTt27NgYN25c/6fpHXePPProozFnzpy46KKLoqOjIxYvXhyzZs2KdevW5fpHATQeO0AAAAAAgExJ0hRJjh0gecYctX///oiIOPHEE9/33RtuuCEOHToUU6dOjUsvvTQ++tGP9v/ZK6+8Ep/+9KcHvH/mmWfGL3/5y9xzAxqLBggAAAAAUBcHDhyINE37v7e2tkZra+sx369Wq3H33XfHqaee+p7HVI0fPz7+7u/+Lk455ZQ4dOhQPP7447FixYr4xje+EbNmzYqIiL1798bYsWMHjBs7dmzs3bv3gy0KaBgaIAAAAABAtiRqOs5qwLiI6Orqiq1bt/Y/XrhwYSxatOiYw1atWhVvvPFGfP3rX3/P8lOmTBlwSfqpp54af/zjH+ORRx6Jq6++uvb5AqWkAQIAAAAAZGtqiqQpx3FW/zKmq6vrXTtAjmXVqlXx7LPPxooVK2LChAk1R37kIx+J3/3ud/3fx40bF/v27Rvwzr59+2LcuHE11wYaU5K+899AQMOpVNLY3XuwLrUnjG+L5uakoTPKsAYZxakvo1gZR+unaRqVSmXI60dENDc3R5LIKEJGGdYw7BmHK9G3o6cuGaMmnxRJS3PdMupdX0axMsqwBhnFqS+jWBllWMM7M45XB356Z6S7/1fN45IJJ0f7Z744qHfTNI0f/vCHsXnz5ujq6oqTTz655ryIiL//+7+P9vb2uO666yIi4nvf+168/fbb8dWvfrX/nZtuuimmTZsWX/jCF3JlAI1FAwQazLp162L9+vXR0dERy5cvH+npAAAAACV2YO1dke7ZUfO45N9MjvaL/s9Bvftf/+t/jY0bN8YNN9ww4Fir0aNHR1tbW0RErF69Ovbs2RNXXXVVREQ88sgjMWnSpJg6dWocPHgwnnjiifjv//2/x0033RSnn356RES8/PLL0dXVFZdffnnMmzcvnnrqqXjooYfilltuec/7RYDy0ACBBtfIv94ejowyrEFGcerLKFaGHSDHV0YZ1jDsGQ38a9uy/WJYxsjWl1GsjDKsQUZx6g93xvHqwMM/yN8A+feD22VxrDtBrrzyypg/f35ERKxcuTJ6enqiq6srIiJ++tOfxs9+9rPYs2dPnHDCCTF9+vS45JJL4mMf+9iAGps2bYr7778/enp64uSTT44lS5bEvHnzal4P0Jg0QKDB7ek9GP/+85vqUvuhH50dkyaeEDt3vR0XX/F0Q2aUYQ0yilNfRrEyjtY/fPhwbPt995DXj4iYNn1GtLS0yChARhnWMNwZB/6wIx6fcW5dMs7vfjLaOybXLaPe9WUUK6MMa5BRnPoyipVRhjVERHxi+1Mx6sMT61K7EQxHAwSgXlyCDgAAAABkSpqa+i80r3kcwAjTAAEAAAAAsiVJRJKjmZEkQz8XgBppgAAAAAAA2ZIkoilHM0MDBCgADRAAAAAAIFOSNOXaAZLk2TUCMMQ0QAAAAACAbE05d4DkGQMwxLRiAQAAAACA0rEDBAAAAAA4hpyXoIcdIMDI0wABAAAAALIlSb4LzV2CDhSABggAAAAAkK2p6cgnzziAEaYBAgAAAABkS3IegWUHCFAAGiAAAAAAQLYkiWhyBBbQmOxFAwAAAAAASscOEAAAAAAgW9KU8wgsv7sGRp4GCAAAAACQLYl8x1k5AQsoAA0QAAAAACBb0hTRZAcI0Jg0QAAAAACAbEmScweILSDAyNOKBQAAAAAASscOEAAAAAAgW5LkvATdDhBg5GmAAAAAAADZmnLeAZJnDMAQ0wABAAAAAI4h5x0gYQcIMPKSNE3TkZ4EkF+lksbu3oN1qT1hfFs0NycNnVGGNcgoTn0Zxco4Wj9N06hUKkNePyKiubk5kkRGETLKsIZhzzhcib4dPXXJGDX5pEhamuuWUe/6MoqVUYY1yChOfRnFyijDGt6Zcbx6e9PaSP95d83jkj+bECecc1EdZgQweBog0GDWrVsX69evj46Ojli+fPlITwcAAAAosbefXhvpP++peVzyZ/8mTjhbAwQYWRog0OAa+dfbw5FRhjXIKE59GcXKsAPk+MoowxqGPaOBf21btl8MyxjZ+jKKlVGGNcgoTv3hzjheaYAAjcwdINDg9v3pUFx8xdN1qf3Qj86OSRNPiN29Bxs2owxrkFGc+jKKlXG0fqVSiW2/7x7y+hER06bPiJaWFhkFyCjDGoY7o29HTzw+49y6ZJzf/WS0d0yuW0a968soVkYZ1iCjOPVlFCujDGuIiPjE9qdi1Icn1qV2Q3AJOtDANEAAAAAAgExpJJG6BB1oUBogAAAAAEC2JIlIcuzmyNU0ARhaGiAAAAAAQLakKWcDxBFYwMjTAAEAAAAAMqVJziOw7AABCkADBAAAAAAYMQ899FBs3rw5tm/fHm1tbdHZ2Rmf/exnY8qUKccc88wzz8SGDRuiu7s7Dh8+HB0dHXHppZfGnDlz+t/5xS9+EXfccceAca2trfGTn/ykXksBCkYDBAAAAADINgx3gGzZsiUuuOCCOOWUU6JSqcR9990XN998c9x2220xatSozDG//e1v44wzzojLLrssxowZEz//+c/jlltuiW9+85sxc+bM/vfa29vj9ttvr33+QClogAAAAAAAx5DkPM5q8GNuvPHGAd+XLVsWf/u3fxuvv/56/MVf/EXmmKVLlw74fvnll8evfvWr+PWvfz2gAZIkSYwbN27QcwHKRQMEAAAAAMjW1HTkk2dcRBw4cCDSNO1/3NraGq2tre85dP/+/RERceKJJw46rlqtxoEDB941pq+vL6688spI0zRmzpwZl112WUydOnXQdYHGpgECAAAAAGTLeQl68i9jurq6YuvWrf3PFy5cGIsWLTrmuGq1GnfffXeceuqpMW3atEHnPfzww9HX1xfnnHNO/7MpU6bEl770pZg+fXrs378/1q5dGzfddFPcdtttMWHChJrXBDQeDRAAAAAAINsHvAOkq6vrXTtA3suqVavijTfeiK9//euDjtq4cWM8+OCDcf3118fYsWP7n3d2dkZnZ+eA79dee2089thjsXjx4kHXBxqXBggAAAAAUBft7e2DfnfVqlXx7LPPxooVKwa9Q+Opp56KO++8M77yla/EGWec8Z7vtrS0xMyZM2PHjh2DnhPQ2HK0bwEAAACA40EaTZEmOT41/LVjmqaxatWq2Lx5c/yn//SfYtKkSYMat3HjxrjjjjvimmuuiXnz5r3v+9VqNbZt2xbjx48f9NyAxmYHCAAAAACQLUn6j7OqedwgrVq1KjZu3Bg33HBDtLe3x969eyMiYvTo0dHW1hYREatXr449e/bEVVddFRFHmh8rV66MpUuXxuzZs/vHtLW1xejRoyMi4sEHH4zZs2fH5MmT46233oq1a9dGT09PnH/++bWvB2hIGiAAAAAAQKY0SSLNcQdILRenb9iwISKO3BfyTldeeWXMnz8/IiJ6e3tj165d/X/2s5/9LCqVSqxatSpWrVrV//zcc8+NZcuWRUTEm2++GXfddVfs3bs3xowZE7NmzYqbb745Ojo6al4P0Jg0QAAAAACAbMOwA2TNmjXv+87RpsZR/7pZkmXp0qWxdOnSQc8DKB93gAAAAAAAAKVjBwgAAAAAkC1pOvLJMw5ghGmAAAAAAACZ0qjtPo93jgMYaRogAAAAAEA2O0CABqYBAgAAAABkSiOJNPLsAMlxcTrAENMAAQAAAACyJUmkuXaAaIAAIy9J09SRfNDAKpU0dvcerEvtCePbork5aeiMMqxBRnHqyyhWxtH6aZpGpVIZ8voREc3NzZEkMoqQUYY1DHvG4Ur07eipS8aoySdF0tJct4x615dRrIwyrEFGcerLKFZGGdbwzozj1T//bnNUDrxZ87jm9hPjzz56Vh1mBDB4GiDQYNatWxfr16+Pjo6OWL58+UhPBwAAACixf375V/kbIKf+ZR1mBDB4GiDQ4Br519vDkVGGNcgoTn0ZxcqwA+T4yijDGoY9o4F/bVu2XwzLGNn6MoqVUYY1yChO/eHOOF796ZVf526AfKjzf6vDjAAGzx0g0OD2/elQXHzF03Wp/dCPzo5JE0+I3b0HGzajDGuQUZz6MoqVcbR+pVKJbb/vHvL6ERHTps+IlpYWGQXIKMMahjujb0dPPD7j3LpknN/9ZLR3TK5bRr3ryyhWRhnWIKM49WUUK6MMa4iI+MT2p2LUhyfWpXYjSHPeAZK6AwQoAA0QAAAAAOAYkpwXmmuAACNPAwQAAAAAyJQmTTl3gNQ+BmCo+TcRAAAAAABQOnaAAAAAAACZ0kgizXGcVZ4xAENNAwQAAAAAyJbzEvR894YADC0NEAAAAAAgW5LzEnQNEKAANEAAAAAAgExpNEWa4xrhPGMAhpoGCAAAAACQKU0i0hy7OVIbQIAC0IoFAAAAAABKxw4QAAAAACBTGk25LkF3BBZQBBogAAAAAECmNJJII8cRWDnGAAw1DRAAAAAAIFuSbwdI5BkDMMQ0QAAAAACATC5BBxqZViwAAAAAAFA6doAAAAAAAJncAQI0Mg0QAAAAACBTmvMOkFz3hgAMMQ0QAAAAACCTHSBAI9MAAQAAAACyJUm+3Rw1XJz+0EMPxebNm2P79u3R1tYWnZ2d8dnPfjamTJnynuNeeumluOeee+KNN96ICRMmxCWXXBLz588f8M6mTZvigQceiJ6enpg8eXIsWbIk5s2bV/t6gIZkLxoAAAAAkOnoDpA8n8HasmVLXHDBBfGNb3wjbrrppqhUKnHzzTdHX1/fMcfs3LkzvvWtb8Vpp50Wt956a1x44YVx5513xnPPPdf/zssvvxy33357nHfeeXHLLbfEX/3VX8W3v/3t2LZt2wf5RwI0EA0QAAAAAGDE3HjjjTF//vyYOnVqzJgxI5YtWxa7du2K119//ZhjNmzYEJMmTYrPf/7z0dHREQsWLIizzz47Hnnkkf53Hn300ZgzZ05cdNFF0dHREYsXL45Zs2bFunXrhmNZQAFogAAAAAAAmdJo6r8IvabPB/hrx/3790dExIknnnjMd1599dU4/fTTBzw788wz45VXXun//sorr2S+8+qrr+aeG9BYkjRN05GeBJBfpZLG7t6Ddak9YXxbNDcnDZ1RhjXIKE59GcXKOFo/TdOoVCpDXj8iorm5OZJERhEyyrCGYc84XIm+HT11yRg1+aRIWprrllHv+jKKlVGGNcgoTn0ZxcoowxremXG8+sMbb8TBg7X///m2trbomDo1Dhw4EO/868fW1tZobW095rhqtRq33nprvPXWW/H3f//3x3zvmmuuifnz58fFF1/c/+zZZ5+Nb33rW3HvvfdGW1tbXHbZZbFs2bL4d//u3/W/s379+njwwQfjv/yX/1LzmoDG4xJ0aDDr1q2L9evXR0dHRyxfvjyam5OYNPGEumaWIaMMa5BRnPoyipWRJEm0tNT3/9LIKE5GGdYwbBktzdHeMbmhM8qwBhnFqS+jWBllWIOM4tQfrozjVZokkdZwofk7x0VEdHV1xdatW/ufL1y4MBYtWnTMcatWrYo33ngjvv71r9c+WYB/RQMEGsyCBQtiwYIF/d8b+dfbw5FRhjXIKE59GcXKsAPk+MoowxqGPaOBf21btl8MyxjZ+jKKlVGGNcgoTv3hzjhupUmkae0NkEj//wbIv94BciyrVq2KZ599NlasWBETJkx4z/Ljxo2Lffv2DXi2b9++aG9vj7a2tvd8Z9y4cbWsBGhgGiDQ4Pb96VBcfMXTdan90I/OjkkTT4jdvQcbNqMMa5BRnPoyipVxtH6lUoltv+8e8voREdOmz4iWlhYZBcgowxqGO6NvR088PuPcumSc3/1ktHdMrltGvevLKFZGGdYgozj1ZRQrowxriIj4xPanYtSHJ9aldiNII999HkfHtLe3v/+7aRo//OEPY/PmzdHV1RWTJk163zGzZ8+Of/qnfxrw7IUXXojOzs7+752dnfHiiy/GhRdeOOCd2bNnD3YZQINzCToAAAAAMGJWrVoV//iP/xjXXHNNtLe3x969e2Pv3r0D7h5ZvXp1fP/73+///slPfjJ27twZ9957b2zfvj3Wr18fmzZtGtDs+NSnPhXPP/98PPzww7F9+/ZYs2ZNvPbaawNO1gDKzQ4QAAAAACBTGhFp5LgDpIZ3N2zYEBFHjst6pyuvvDLmz58fERG9vb2xa9eu/j+bNGlSfPWrX40f//jH8eijj8aECRPii1/8YsyZM6f/nVNPPTW+/OUvx/333x/33XdfnHzyyXH99dfHtGnTal4P0Jg0QAAAAACATGkkORsggx+zZs2a931n2bJl73p22mmnxa233vqe484555w455xzBj0XoFw0QAAAAACATMPRAAGoFw0QAAAAAOAY8jVAQgMEKAANEAAAAAAgUxpJpKkdIEBjahrpCQAAAAAAAAw1O0AAAAAAgExp5NvNkQ79VABqpgECAAAAAGRyCTrQyDRAAAAAAIBMGiBAI9MAAQAAAACypfkuQY88YwCGmEvQAQAAAACA0rEDBAAAAADIVI0kqjmOs8ozBmCoaYAAAAAAAJnSyHefRzr0UwGomQYIAAAAAJApzXkHSK57QwCGmAYIAAAAAJApjSTnDhANEGDkaYAAAAAAANly7gAJO0CAAmga6QkAAAAAAAAMNTtAAAAAAIBMjsACGpkGCAAAAACQKU3zXWiepnWYDECNNEAAAAAAgEzVf/nkGQcw0pI01Y+FRlappLG792Bdak8Y3xbNzUlDZ5RhDTKKU19GsTKO1k/TNCqVypDXj4hobm6OJJFRhIwyrGHYMw5Xom9HT10yRk0+KZKW5rpl1Lu+jGJllGENMopTX0axMsqwhndmHK9e2PpmvNVXeztjzKimOGPmiXWYEcDgaYBAg1m3bl2sX78+Ojo6Yvny5SM9HQAAAKDEnt/6Vu4GyJkzx9RhRgCDpwECDa6Rf709HBllWIOM4tSXUawMO0COr4wyrGFARkl+CesXwzIaob6MYmWUYQ0yilN/uDOOVxogQCNzBwg0uH1/OhQXX/F0XWo/9KOzY9LEE2J378GGzSjDGmQUp76MYmUcrV+pVGLb77uHvH5ExLTpM6KlpUVGATLKsIZ3ZvTt6InHZ5xbl4zzu5+M9o7JDZ1RhjXIKE59GcXKKMMaZBSn/nBlfGL7UzHqwxPrUrsRpGmS8xL02scADDUNEAAAAAAgUxpJpJGjAZJjDMBQ0wABAAAAADKlaUQ1xwH6Dt0HikADBAAAAADIlEa+3Rz6H0ARaIAAAAAAAJmO3AGSbxzASGsa6QkAAAAAAAAMNTtAAAAAAIBMaZrvPg93gABFoAECAAAAAGRKI4lqznG12LJlS6xduza2bt0avb29cd1118VZZ511zPdXrlwZTz755Lued3R0xG233RYREb/4xS/ijjvuGPDnra2t8ZOf/KSmuQGNSwMEAAAAAMiURs47QGpsgLz99tsxY8aMOO+88+I73/nO+75/xRVXxJIlS/q/VyqVuP766+Pss88e8F57e3vcfvvtNc0FKA8NEAAAAAAg03AdgTV37tyYO3fuoN8fPXp0jB49uv/75s2b46233oq//uu/HvBekiQxbty42iYDlIYGCAAAAADQ0J544ok4/fTT46STThrwvK+vL6688spI0zRmzpwZl112WUydOnWEZgkMt6aRngAAAAAAUExpJLk/EREHDhyI/fv3938OHTo05HPcs2dPPPfcc3HeeecNeD5lypT40pe+FDfccENcffXVUa1W46abbordu3cP+RyAYrIDBAAAAADIlKYR1Q9wBFZXV1ds3bq1//nChQtj0aJFQzS7I5588skYM2bMuy5N7+zsjM7OzgHfr7322njsscdi8eLFQzoHoJg0QAAAAACATEfuAKntQvOj4yKONEDSd1wI0traOlRT+5ecNH7+85/Hxz/+8Whpee+/6mxpaYmZM2fGjh07hnQOQHFpgAAAAAAAmT7oJejt7e1DO6F/ZcuWLbFjx453HX+VpVqtxrZt22q6bB1obBogAAAAAECmaiRRzTWuNn19fQN2ZuzcuTO6u7vjxBNPjIkTJ8bq1atjz549cdVVVw0Y98QTT8Ts2bNj2rRp76r54IMPxuzZs2Py5Mnx1ltvxdq1a6OnpyfOP//8HCsCGpEGCAAAAAAwol577bVYsWJF//d77rknIiLOPffcWLZsWfT29sauXbsGjNm/f38888wzsXTp0syab775Ztx1112xd+/eGDNmTMyaNStuvvnm6OjoqNs6gGLRAAEAAAAAsuU8AitqHHPaaafFmjVrjvnny5Yte9ez0aNHx7333nvMMUuXLj1mcwQ4PmiAAAAAAACZ0kjy3QEy9FMBqJkGCAAAAACQqZoe+eQZBzDSNEAAAAAAgExpziOwch2bBTDENEAAAAAAgGNIHGcFNKwkTfVjoZFVKmns7j1Yl9oTxrdFc3PS0BllWIOM4tSXUayMo/XTNI1KpTLk9SMimpubI0lkFCGjDGsYkHG4En07euqSMWrySZG0NDd0RhnWIKM49WUUK6MMa5BRnPrDnXG8euI31di7v/Zx40ZHnPexpqGfEEANNECgwaxbty7Wr18fHR0dsXz58pGeDgAAAFBiP3sxfwPkE6drgAAjSwMEGlwj/3p7ODLKsAYZxakvo1gZdoAcXxllWMOAjJL8EtYvhmU0Qn0ZxcoowxpkFKf+cGccrx57IX8D5G/O0AABRpY7QKDB7fvTobj4iqfrUvuhH50dkyaeELt7DzZsRhnWIKM49WUUK+No/UqlEtt+3z3k9SMipk2fES0tLTIKkFGGNbwzo29HTzw+49y6ZJzf/WS0d0xu6IwyrEFGcerLKFZGGdYgozj1hyvjE9ufilEfnliX2o0gjZyXoA/5TABqpwECAAAAAGRK0ySqeRogOiBAAWiAAAAAAACZ0jTnDhANEKAAHMQHAAAAAACUjh0gAAAAAEAmO0CARqYBAgAAAABkStNwBwjQsDRAAAAAAIBMaST5doAM/VQAaqYBAgAAAABkcgQW0Mhcgg4AAAAAAJSOHSAAAAAAQKZqzjtA8owBGGoaIAAAAABAtpxHYLkEBCgCDRAAAAAAIJM7QIBGpgECAAAAAGSqRs4jsIZ8JgC10wABAAAAADLZAQI0sqaRngAAAAAAAMBQswMEAAAAAMiUViOqOc6zSp2BBRSABggAAAAAkMkRWEAj0wABAAAAADKlkbMBMuQzAaidBggAAAAAkKmaHvnkGQcw0jRAAAAAAIBsaZrvOKs0IiIZ4skA1KZppCcAAAAAAAAw1JI0dSURNLJKJY3dvQfrUnvC+LZobk4aOqMMa5BRnPoyipVxtH6aplGpVIa8fkREc3NzJImMImSUYQ0DMg5Xom9HT10yRk0+KZKW5obOKMMaZBSnvoxiZZRhDTKKU3+4M45X9/9jGj1/qn3cSR+KWPzxwe8A2bJlS6xduza2bt0avb29cd1118VZZ511zPdfeumlWLFixbue/+AHP4hx48b1f9+0aVM88MAD0dPTE5MnT44lS5bEvHnzaloL0Lg0QKDBrFu3LtavXx8dHR2xfPnykZ4OAAAAUGKr/980evbVPu6ksRGX/x+Db4D80z/9U7z88ssxa9as+M53vjPoBsg//MM/xOjRo/uff+hDH4qmpiOH3rz88svxta99LS6//PKYN29ebNy4MX7605/GLbfcEtOmTat9UUDDcQcINJgFCxbEggUL+r838q+3hyOjDGuQUZz6MoqVYQfI8ZVRhjUMyCjJL2H9YlhGI9SXUayMMqxBRnHqD3fG8SpNI9cdILWOmTt3bsydO7fmnLFjx8aYMWMy/+zRRx+NOXPmxEUXXRQREYsXL44XX3wx1q1bF1/4whdqzgIajwYINLh9fzoUF1/xdF1qP/Sjs2PSxBNid+/Bhs0owxpkFKe+jGJlHK1fqVRi2++7h7x+RMS06TOipaVFRgEyyrCGd2b07eiJx2ecW5eM87ufjPaOyQ2dUYY1yChOfRnFyijDGmQUp/5wZXxi+1Mx6sMT61K7EaRpRHUYGiB53XDDDXHo0KGYOnVqXHrppfHRj360/89eeeWV+PSnPz3g/TPPPDN++ctfDs/kgBGnAQIAAAAA1MWBAwfinSfwt7a2Rmtr6weuO378+Pi7v/u7OOWUU+LQoUPx+OOPx4oVK+Ib3/hGzJo1KyIi9u7dG2PHjh0wbuzYsbF3794PnA80Bg0QAAAAACDTBz0Cq6urK7Zu3dr/fOHChbFo0aIPPK8pU6bElClT+r+feuqp8cc//jEeeeSRuPrqqz9wfaAcNEAAAAAAgExpmkZazTMuIiKJrq6ud+0AqZePfOQj8bvf/a7/+7hx42LfvoE3uO/bty/GjRtXtzkAxdI00hMAAAAAAIrp6B0gtX6O9jza29tj9OjR/Z96NkC6u7tj/Pjx/d87OzvjxRdfHPDOCy+8ELNnz67bHIBi0QABAAAAADIdPQIrz6cWfX190d3dHd3d3RERsXPnzuju7o5du3ZFRMTq1avj+9//fv/7jzzySPzyl7+MHTt2xLZt2+Luu++O3/zmN3HBBRf0v/OpT30qnn/++Xj44Ydj+/btsWbNmnjttddiwYIFH/ifC9AYHIEFAAAAAGSqVtOo5jgCq9Yxr732WqxYsaL/+z333BMREeeee24sW7Ysent7+5shERGHDx+Oe+65J/bs2RMnnHBCTJ8+Pf7jf/yP8bGPfaz/nVNPPTW+/OUvx/333x/33XdfnHzyyXH99dfHtGnTal8Q0JA0QAAAAACAEXXaaafFmjVrjvnny5YtG/D9M5/5THzmM59537rnnHNOnHPOOR94fkBj0gABAAAAADKlUftxVkfHAYw0DRAAAAAAIFuO+zyOjgMYaRogAAAAAECmappGNUczI88YgKGmAQIAAAAAZEqrRz55xgGMNA0QAAAAACBTGqk7QICG1TTSEwAAAAAAABhqdoAAAAAAAJnSakTVEVhAg9IAAQAAAAAypWlEmuMMrDRN6jAbgNpogAAAAAAAmarpkU+ecQAjTQMEAAAAAMiWppHm6WZogAAFoAECAAAAAGQ6cgRWvnEAI61ppCcAAAAAAAAw1OwAAQAAAAAyVatpVHMcgVWt1mEyADXSAAEAAAAAMqVpGmmO86wcgQUUQZLm+TcYUBiVShq7ew/WpfaE8W3R3Jw0dEYZ1iCjOPVlFCvjaP00TaNSqQx5/YiI5ubmSBIZRcgowxoGZByuRN+OnrpkjJp8UiQtzQ2dUYY1yChOfRnFyijDGmQUp/5wZxyvbn+wL7bvqv2vD/98YhLXLBxVhxkBDJ4GCDSYdevWxfr166OjoyOWL18+0tMBAAAASux7/8+B3A2Qay9tr8OMAAbPEVjQYBYsWBALFizo/97Iv94ejowyrEFGcerLyJdhR4CMRqg/7Bkl+SWsXwzLaIT6MoqVUYY1yChO/eHOAKDxaIBAg9v3p0Nx8RVP16X2Qz86OyZNPCF29x5s2IwyrEFGcerLyJdRqVRi2++7h7z+tOkzoqWlpW71ZRQrowxreGdG346eeHzGuXXJOL/7yWjvmNzQGWVYg4zi1JdRrIwyrEFGceoPV8Yntj8Voz48sS61G0GaRs47QJI6zAagNhogAAAAAECmNE2jWs3TAHHqPjDyNEAAAAAAgExHdoDkGwcw0jRAAAAAAIBMaTWNNM8OkBxjAIaaBggAAAAAkKmaplHNsZ0jzxiAodY00hMAAAAAAAAYanaAAAAAAACZ0jTnEVh2gAAFoAECAAAAAGSr5rzPozr0UwGolQYIAAAAAJCpmh755BkHMNI0QAAAAACATI7AAhqZBggAAAAAkClN01zNDA0QoAg0QAAAAACAEbVly5ZYu3ZtbN26NXp7e+O6666Ls84665jvP/PMM7Fhw4bo7u6Ow4cPR0dHR1x66aUxZ86c/nd+8YtfxB133DFgXGtra/zkJz+p1zKAgtEAAQAAAAAypdU0qnmOwKpxzNtvvx0zZsyI8847L77zne+87/u//e1v44wzzojLLrssxowZEz//+c/jlltuiW9+85sxc+bM/vfa29vj9ttvr3n+QDlogAAAAAAAmdLIeQRW1DZm7ty5MXfu3EG/v3Tp0gHfL7/88vjVr34Vv/71rwc0QJIkiXHjxtU0F6A8NEAAAAAAgExptfbdHEfHRUQcOHBgQAOltbU1Wltbh2p6/arVahw4cCBOPPHEAc/7+vriyiuvjDRNY+bMmXHZZZfF1KlThzwfKCYNEAAAAAAgU1pNczZAjozp6uqKrVu39j9fuHBhLFq0aMjmd9TDDz8cfX19cc455/Q/mzJlSnzpS1+K6dOnx/79+2Pt2rVx0003xW233RYTJkwY8jkAxaMBAgAAAABkSiON6gc4Aqurq+tdO0CG2saNG+PBBx+M66+/PsaOHdv/vLOzMzo7Owd8v/baa+Oxxx6LxYsXD/k8gOLRAAEAAAAA6qK9vb2u9Z966qm488474ytf+UqcccYZ7/luS0tLzJw5M3bs2FHXOQHF0TTSEwAAAAAAiunoEVh5PvW2cePGuOOOO+Kaa66JefPmve/71Wo1tm3bFuPHj6/73IBisAMEAAAAAMiUpumAI6xqGVeLvr6+ATszdu7cGd3d3XHiiSfGxIkTY/Xq1bFnz5646qqrIuJI82PlypWxdOnSmD17duzduzciItra2mL06NEREfHggw/G7NmzY/LkyfHWW2/F2rVro6enJ84///ya1wM0Jg0QAAAAACBTmkZU81yCXuOQ1157LVasWNH//Z577omIiHPPPTeWLVsWvb29sWvXrv4//9nPfhaVSiVWrVoVq1at6n9+9P2IiDfffDPuuuuu2Lt3b4wZMyZmzZoVN998c3R0dNS8HqAxaYAAAAAAAJnyHmdV65jTTjst1qxZc8w/P9rUOKqrq+t9ay5dujSWLl1a0zyAcknSPHvYgMKoVNLY96dDdak99kOt0dycNHRGGdYgozj1ZeTLSNM0KpXKkNdvbm6OJKlffRnFyijDGgZkHK7E27t765JxwoTxkbQ0N3RGGdYgozj1ZRQrowxrkFGc+sOdcby68f/eGd3/s/b/ZpgxpTW+cfWkOswIYPA0QKDBrFu3LtavXx/z5s2Lz33ucyM9HQAAAKDE/q///MfcDZBvfvnDdZgRwOA1jfQEgNosWLAgvve978XnPve5+G//7b/VPe+73/1uw2eUYQ0yilNfRrEyyrAGGcWpL6NYGWVYg4zi1JdRrIwyrEFGceoPV8Zw/Pd3UaVpNdJqjk9aHempA2iAQCN79tln657xhz/8oeEzyrAGGcWpL6NYGWVYg4zi1JdRrIwyrEFGcerLKFZGGdYgozj1hytjOP77u6jS6pFL0Gv96H8AReASdGhgF1xwgYwC1JdRrIwyrEFGcerLKFZGGdYgozj1ZRQrowxrkFGc+jKKlVGGNQxXRlGlaRp5TtB36j5QBO4AAQAAAAAy3fDd7bH1DwdrHjezoy1uXf7ndZgRwOA5AgsAAAAAACgdR2ABAAAAAJnSahppNccRWDnGAAw1DRAAAAAAIFOaplHNcaO5U/eBItAAAQAAAAAy2QECNDINEAAAAAAgU5rmbIDYAQIUgAYIAAAAAJApTdNczQwNEKAImkZ6AgAAAAAAAEPNDhAAAAAAIFNaTaNazXEJujtAgALQAAEAAAAAMrkDBGhkGiAAAAAAQKY0rUaa5tgBkmMMwFDTAAEAAAAAMqXVnDtAHIEFFIAGCAAAAACQKU3zNTOcgAUUQdNITwAAAAAAAGCo2QECAAAAAGRK02pU3QECNCgNEAAAAAAgkztAgEamAQIAAAAAZErTaqRVO0CAxqQBAgAAAABkSqs5L0HX/wAKwCXoAAAAAABA6dgBAgAAAABkS6v5jrOyBQQoAA0QAAAAACBTNU2jmuMIrGrqEnRg5GmAAAAAAACZ0mqa7xL0HE0TgKGmAQIAAAAAZErTNN8l6HaAAAWgAQIAAAAAZBumO0C2bNkSa9euja1bt0Zvb29cd911cdZZZ73nmJdeeinuueeeeOONN2LChAlxySWXxPz58we8s2nTpnjggQeip6cnJk+eHEuWLIl58+bVuhqgQTWN9AQAAAAAgOPb22+/HTNmzIj/8B/+w6De37lzZ3zrW9+K0047LW699da48MIL484774znnnuu/52XX345br/99jjvvPPilltuib/6q7+Kb3/727Ft27Y6rQIoGjtAAAAAAIBMR+4AyXEEVo1j5s6dG3Pnzh30+xs2bIhJkybF5z//+YiI6OjoiN/97nfxyCOPxJw5cyIi4tFHH405c+bERRddFBERixcvjhdffDHWrVsXX/jCF2qaH9CYNEAAAAAAgEzT/rw9130e0/68PSIiDhw4MGB8a2trtLa2fuB5vfrqq3H66acPeHbmmWfG3Xff3f/9lVdeiU9/+tPveueXv/zlB84HGoMGCAAAAACQ6WvX/dvcYw8fPhxf+cpXYvfu3f3PFi5cGIsWLfrA89q7d2+MHTt2wLOxY8fGgQMH4uDBg9HW1nbMd/bu3fuB84HGoAECAAAAAAy5NE3ju9/97oBnQ7H7A2CwNEAAAAAAgCE3VMddZRk3blzs27dvwLN9+/ZFe3t7tLW1vec748aNq8ucgOJpGukJAAAAAADUYvbs2fHiiy8OePbCCy9EZ2dn//fOzs7Md2bPnj0scwRGngYIAAAAADCi+vr6oru7O7q7uyMiYufOndHd3R27du2KiIjVq1fH97///f73P/nJT8bOnTvj3nvvje3bt8f69etj06ZNceGFF/a/86lPfSqef/75ePjhh2P79u2xZs2aeO2112LBggXDujZg5CRpmqYjPQkAAAAA4Pj10ksvxYoVK971/Nxzz41ly5bFypUro6enJ7q6ugaM+fGPfxx/+MMfYsKECXHJJZfE/PnzB4zftGlT3H///dHT0xMnn3xyLFmyJObNm1fn1QBFoQECAAAAAACUjiOwAAAAAACA0tEAAQAAAAAASkcDBAAAAAAAKB0NEAAAAAAAoHQ0QAAAAAAAgNLRAAEAAAAAAEpHAwQAAAAAACgdDRAAAAAAAKB0NEAAAAAAAIDS0QABAAAAAABKRwMEAAAAAAAoHQ0QAAAAAACgdDRAAAAAAACA0tEAAQAAAAAASkcDBAAAAAAAKB0NEAAAAAAAoHQ0QAAAAAAAgNLRAAEAAAAAAEpHAwQAAAAAACgdDRAAAAAAAKB0NEAAAAAAAIDS0QABAAAAAABKRwMEAAAAAAAoHQ0QAAAAAACgdDRAAAAAAACA0tEAAQAAAAAASkcDBAAAAAAAKB0NEAAAAAAAoHQ0QAAAAAAAgNL5/wDBaHYIOAunjAAAAABJRU5ErkJggg=="
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1600x400 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "**Step 7.** Create a legend and add it to chart.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# instantiate a new figure object\nfig = plt.figure()\n\n# use matshow to display the waffle chart\ncolormap = plt.cm.coolwarm\nplt.matshow(waffle_chart, cmap=colormap)\nplt.colorbar()\n\n# get the axis\nax = plt.gca()\n\n# set minor ticks\nax.set_xticks(np.arange(-.5, (width), 1), minor=True)\nax.set_yticks(np.arange(-.5, (height), 1), minor=True)\n    \n# add gridlines based on minor ticks\nax.grid(which='minor', color='w', linestyle='-', linewidth=2)\n\nplt.xticks([])\nplt.yticks([])\n\n# compute cumulative sum of individual categories to match color schemes between chart and legend\nvalues_cumsum = np.cumsum(df_dsn['Total'])\ntotal_values = values_cumsum[len(values_cumsum) - 1]\n\n# create legend\nlegend_handles = []\nfor i, category in enumerate(df_dsn.index.values):\n    label_str = category + ' (' + str(df_dsn['Total'][i]) + ')'\n    color_val = colormap(float(values_cumsum[i])/total_values)\n    legend_handles.append(mpatches.Patch(color=color_val, label=label_str))\n\n# add legend to chart\nplt.legend(handles=legend_handles,\n           loc='lower center', \n           ncol=len(df_dsn.index.values),\n           bbox_to_anchor=(0., -0.2, 0.95, .1)\n          )\nplt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 16,
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-16-6db0426703ff>:7: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n  plt.colorbar()\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x49f1510>",
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1600x400 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "And there you go! What a good looking *delicious* `waffle` chart, don't you think?\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "Now it would very inefficient to repeat these seven steps every time we wish to create a `waffle` chart. So let's combine all seven steps into one function called *create_waffle_chart*. This function would take the following parameters as input:\n\n> 1.  **categories**: Unique categories or classes in dataframe.\n> 2.  **values**: Values corresponding to categories or classes.\n> 3.  **height**: Defined height of waffle chart.\n> 4.  **width**: Defined width of waffle chart.\n> 5.  **colormap**: Colormap class\n> 6.  **value_sign**: In order to make our function more generalizable, we will add this parameter to address signs that could be associated with a value such as %, $, and so on. **value_sign** has a default value of empty string.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "def create_waffle_chart(categories, values, height, width, colormap, value_sign=''):\n\n    # compute the proportion of each category with respect to the total\n    total_values = sum(values)\n    category_proportions = [(float(value) / total_values) for value in values]\n\n    # compute the total number of tiles\n    total_num_tiles = width * height # total number of tiles\n    print ('Total number of tiles is', total_num_tiles)\n    \n    # compute the number of tiles for each catagory\n    tiles_per_category = [round(proportion * total_num_tiles) for proportion in category_proportions]\n\n    # print out number of tiles per category\n    for i, tiles in enumerate(tiles_per_category):\n        print (df_dsn.index.values[i] + ': ' + str(tiles))\n    \n    # initialize the waffle chart as an empty matrix\n    waffle_chart = np.zeros((height, width))\n\n    # define indices to loop through waffle chart\n    category_index = 0\n    tile_index = 0\n\n    # populate the waffle chart\n    for col in range(width):\n        for row in range(height):\n            tile_index += 1\n\n            # if the number of tiles populated for the current category \n            # is equal to its corresponding allocated tiles...\n            if tile_index > sum(tiles_per_category[0:category_index]):\n                # ...proceed to the next category\n                category_index += 1       \n            \n            # set the class value to an integer, which increases with class\n            waffle_chart[row, col] = category_index\n    \n    # instantiate a new figure object\n    fig = plt.figure()\n\n    # use matshow to display the waffle chart\n    colormap = plt.cm.coolwarm\n    plt.matshow(waffle_chart, cmap=colormap)\n    plt.colorbar()\n\n    # get the axis\n    ax = plt.gca()\n\n    # set minor ticks\n    ax.set_xticks(np.arange(-.5, (width), 1), minor=True)\n    ax.set_yticks(np.arange(-.5, (height), 1), minor=True)\n    \n    # add dridlines based on minor ticks\n    ax.grid(which='minor', color='w', linestyle='-', linewidth=2)\n\n    plt.xticks([])\n    plt.yticks([])\n\n    # compute cumulative sum of individual categories to match color schemes between chart and legend\n    values_cumsum = np.cumsum(values)\n    total_values = values_cumsum[len(values_cumsum) - 1]\n\n    # create legend\n    legend_handles = []\n    for i, category in enumerate(categories):\n        if value_sign == '%':\n            label_str = category + ' (' + str(values[i]) + value_sign + ')'\n        else:\n            label_str = category + ' (' + value_sign + str(values[i]) + ')'\n            \n        color_val = colormap(float(values_cumsum[i])/total_values)\n        legend_handles.append(mpatches.Patch(color=color_val, label=label_str))\n\n    # add legend to chart\n    plt.legend(\n        handles=legend_handles,\n        loc='lower center', \n        ncol=len(categories),\n        bbox_to_anchor=(0., -0.2, 0.95, .1)\n    )\n    plt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Now to create a `waffle` chart, all we have to do is call the function `create_waffle_chart`. Let's define the input parameters:\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "width = 40 # width of chart\nheight = 10 # height of chart\n\ncategories = df_dsn.index.values # categories\nvalues = df_dsn['Total'] # correponding values of categories\n\ncolormap = plt.cm.coolwarm # color map class",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "And now let's call our function to create a `waffle` chart.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "create_waffle_chart(categories, values, height, width, colormap)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 19,
      "outputs": [
        {
          "name": "stdout",
          "text": "Total number of tiles is 400\nDenmark: 129\nNorway: 77\nSweden: 194\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "<ipython-input-17-adf5f5431502>:45: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n  plt.colorbar()\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
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mTkxM5IvlE3jooYc0Z84cbdmypcxn3333XZmDmDt27KgffvjB/4WzpICClVSy1VTdunW1atUqrV69Wm3atNFFF10UUAA555xz/Pf/8MMPuvnmm9WhQwelpKTorLPOkiTt3LlTUsl5JVdddZX/LIJvvvlG3377rfr371/uvIqKilSzZs1yP//tt980aNAgpaWladSoUWU+HzZsmJYuXaq5c+cqIiJCt912m+W2E5s2bdLf/vY33X333Zbn0Uglh8sfOHBAV199teXnNWvWZAssC5GRkXr22Wc1Z84cpaam6pJLLtGjjz6qDRs2+O/JyMjQgQMHtG7dOpmmqdWrV+vWW2/1/1lbuXKlGjZsqJSUFEklq/CysrJ06aWXqkmTJrr00kt18803+wtcb731lnw+n5555hmddtpp6tGjh4YNGxYwruzsbJ1xxhl64IEHlJqaqjZt2mjixIlasWKFtm7d6r+vVatWGjVqlFJSUnT11VerXbt2ZX7XH+unn35SYmJimes33HCDXnzxRb311lu69tprNXHiRGVlZfk/r127tubPn6958+YpOTlZzZo107JlyzRr1iz/yqYff/xRP/30kxYuXKhJkybp2Wef1ddffx3whdiPP/6otWvX6ttvv9W0adM0duxY/fvf/9Y999wTMJ7SMf7000/H+V8PAHA8tra/+t8rGO3bt1f//v3L/PdceZYsWaKEhARde+21SkpKUq9evZSRkRHwwNeiRYvUrl07XX755UpKSlL//v2VkpIS8KAKAHezXjsPAABcyTRN/xPoGzZs0MGDB9WyZcuAe4qKigIOjG3cuHHAE76l5wMcq1WrVv5/joiIUL169XT66af7ryUkJEiS/3wNqeRLuVmzZmnnzp0qLCzUkSNHdMYZZwT0e/rppweswCj1/PPP69ChQ/4CxvEUFRWpRo0alk/en3POOVq2bJn27dunGTNm6MYbb9R7772nU089VfXr19fLL7+s0aNH66WXXpLH41Hv3r3Vtm3b4z6l/0fwxfKJderUSRdccIHGjh173ILC8ZSu5ChlGIYyMjK0cuVKRUdHq3PnzmrdurWKi4u1adMmffrppxo6dKj//muuuUZJSUmaMGGCEhMT5fP5dP755wecUzNo0CB169ZNP//8s2bNmuXfEqk89erVK/eA99JiRK1atTRt2jTLra3i4+MVHx+v5s2bKy0tTe3atdNnn33m37ZLKnkCsk+fPho8eLDuuuuucsfy+uuv66KLLvL///b3CgoKyhT+UOKyyy7TRRddpNWrV+vzzz/X0qVLNWnSJD399NPq37+/6tSpo9atW2vlypWKiopSVFSUBg8erPHjx+vAgQNatWqVOnXqJKlkq7Xc3FyNGDEi4H8vr9erP/3pT5JKin6tWrUKKJ516NAhYEwbNmzQypUrLX9X5ubm+s/MOPb3uFTyu/7Y39m/V1hYqBo1apS5fuutt/r/uXXr1oqOjtbdd9+tMWPGqEaNGiosLNSIESPUsWNHTZ06VV6vV1OmTNGgQYO0ePFixcTEyDRNHT58WJMmTfKP75lnntGFF16o77//Xi1atJDP55NhGHr++ed1yimnSJKysrL097//XePGjVNMTIwk+X82/G4FAPuS/xxj6zyP5D+X/C4uLCwMaF/678A/6rvvvlObNm0Crp155pkBZz9t2bJFl156aZl7Pv300z+cDyA8UAABAOAksmXLFv8WIwcPHlSDBg00f/78MvfVqVPH/8+/P2vAMIwyfwH6/V9gDMMIuFZafCg9I2H+/PnKyspSZmamzj77bNWqVUuTJ08usx/v77+oLpWRkaH3339fCxYs0B133HHcOderV0+HDh1ScXFxmWJKrVq1lJKSopSUFHXo0EF/+ctfNHPmTA0fPlySdMEFF+jTTz/V3r17FRkZ6f/y8oorrpBUUtj5/ReER48eVX5+frlfHh9PQUGBfwUKyjdmzBh169ZNLVq0CLiemppaZq/otWvXqnnz5oqIiDhun507d9aMGTMUHR2t+++/Xx6PRxkZGZo8ebKKi4v9TyLu27dP33//vSZMmOA/H2P16tVl+mvVqpXatWun1157TW+99Zb++c9/Hje/TZs22r17twoKCgLO5vjtt9/Ur18/1ahRQ6+99tpxV4mUKv3/2bEFmW+//VZXXnmlrr76at1///3ltv3xxx+1YsUKvfbaa5afb9u2TUVFRWWKlfg/NWvWVNeuXdW1a1eNHDlSI0aM0Pjx4/0Fu9IzZ2rUqKHOnTurbt26/j+7n3zyib+AcPDgQUnSU089VWaV24n+PB/r4MGD6tGjhx588MEynzVo0MD/z1ZfRJX+WbISHx+vTZs2nTA/PT1dR48e1Y4dO9SiRQu99dZb2r59uxYtWuQvJr/wwgtKS0tTTk6OevfurYSEBEVGRgYcaJ6amiqpZCVHixYt1KBBAyUmJvqLH5KUlpYm0zT13//+17+KpnTbLH63AoB9D999+olvKsfRo0d11113BZw91bdvX/Xr1+8Pj6ugoCDg7y1Syd9jCgsL/f/tX9495T14AsB9KIAAAHCS+Pjjj7Vp0ybdcsstkqS2bdtq9+7dioyM9BdFqsratWt19tln6/rrr/dfO3bVyYm0b99e119/vfr376+IiIgyW74cq/SL2s2bN5d5Quz3fD6f5d79pV+cffzxx9qzZ4//QOMOHTpo//79+vrrr/1njHz88cfy+Xz+bZEqqnTlzYnGiJLiQp8+ffTyyy8HXB86dKh69Oihp556SldccYU+/fRTvfLKKxo3btwJ++zcubMefPBBRUdH+4sd55xzjjIzM9WuXTv/Ic5xcXGqV6+epk+froSEBO3cuVNjx4617HPQoEG67777FBsbq4svvvi4+W3atFF8fLzWrl2rHj16SPq/4sehQ4c0ZcoU/fbbb/rtt98kSfXr11dERIQ+//xzffXVV+rYsaPi4uKUm5urxx9/XE2bNvWvBNi0aZP69Omjrl276pZbbvFv4RYREVFmJcesWbPUoEEDde/e3XKca9asUZMmTdSsWbMT/kxRIi0tTe+9957/fefOnTVr1ixFRESoW7dukkr+rL311lvaunWrf7u1hIQEJSYm6scff1Tfvn0t+05NTdUbb7wRsIXa788fatu2rf79738rOTm5TEH7j2jTpo2mTZsWsLLQyvr16+XxePx/1goLC+XxeALalBZCSgsuHTt21NGjR7Vt2zb/n7XS7bpKV1J17NhRCxcu1IEDB/yrFLdu3SqPx6OGDRv6+/72228VFRVVZrUjAKBqmKapp556KuBaZaz+AICK4gwQAABcqLi4WL/88ov++9//6ptvvtEzzzyja6+9Vj169PA/bdWlSxd16NBBf/vb3/Thhx9q+/btWrt2rR577LGAgwNDISUlRV999ZWWLVumrVu36vHHHw86s2PHjpo5c6aefPJJTZ06tdz76tevr7Zt2wasDDh48KAeffRRffbZZ9qxY4e+/vprDR8+XLt27dLll1/uv2/WrFn67LPPtG3bNr3xxhu64YYbdPPNN/tXHqSlpalbt26666679MUXX2jNmjW677771Lt374C98X/44QetW7dOu3fvVlFRkdatW6d169YFPKH/+eefKzo6usz2NbB2zz33lHk6vW3btnrppZf09ttv6/zzz9f48eM1evToCm2V1apVK9WpU0dnnHGG/8vUzp07y+v1Bpz/4fF4NHXqVH3zzTfq0qWLHnzwQT388MOWffbu3VuRkZHq3bv3CVduREREqH///nrzzTf917755ht9/vnn2rRpk/7yl7+oTZs2/lfpeSMxMTF699131bdvX3Xu3FkjRoxQq1at9M477/i3J1q4cKH27NmjefPmBfTRs2fPgDH4fD7Nnj3bX1i08tZbb5V7ePrJbt++fbryyiv1xhtvaMOGDfrxxx+1YMECTZo0yV80lUq2cTtw4IDef/99/5+tzp07680331SDBg0CVj2MHj1azz77rF566SVt3bpVGzdu1KxZs/T8889Lkq688koZhqG77rpLmzdv1gcffKApU6YEjOv6669XQUGBbr75Zn355Zfatm2bli1bpjvuuCPgbJxgnXPOOTp48KC+/fZb/7VPP/1UU6dO1fr165Wbm6t58+bpoYceUt++ff0rm7p06aL9+/frnnvu0ZYtW/Ttt9/qjjvuUGRkpM4991z/PW3bttWdd96pdevW6euvv9aoUaPUpUsX/8/nyiuvVN26dTV8+HBt3rxZq1atUlZWlgYOHOjf/koqWaGVkZERcA0AUHWioqIUGxsb8KqsAkhcXJz2798fcG3//v2KiYnxr/wu755jV9wCcDdWgAAAEKSaUT5JR6s4LzjLli1TmzZtFBkZqbi4OLVq1UqPPfaYrr76av+TtoZhaNasWXrsscc0fPhw7d27VwkJCcrIyLA8MLwyXXvttVq3bp1uuukmGYah3r1767rrrtPSpUuD6icjI0MzZ87UgAEDFBERoRtuuMHyvkGDBmnu3Ln+A3QjIiL03Xffac6cOdq3b5/q1q2r9u3ba8GCBQGHrX///fcaO3asCgoK1LhxY915553+FTSlnn/+ed13333q06ePPB6PLr30Uj366KMB99x1113+Q44l+Z+u/+yzz/yrb9566y316dOn3G2/Qs0wPFX2aIxhBBf03HPPlbmWnJxseajxZZddpssuu6zcvn7/dHwpj8dT5mD10m2pfq9Lly5asWJFwDWr+/bt26eioiINGjSo3PEc6+abb9b555+vHTt2qHHjxjrnnHMs+z1Wq1at9NZbbx33ntGjR2v06NEnzPd4PMctRH777bdav359mZU3ValGUoMT31RNebVq1VJ6erqmTp2q3NxcHT16VI0aNdLgwYP92+pJJV/EnH766crLy/Nv69SpUyf5fD517tw5oM9rrrlGMTExmjx5srKyshQbG6vTTz9dN910k6SSA8Vfe+01jRo1St27d1daWpoefPDBgNV1iYmJ+ve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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1600x400 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "There seems to be a new Python package for generating `waffle charts` called [PyWaffle](https://github.com/ligyxy/PyWaffle), but it looks like the repository is still being built. But feel free to check it out and play with it.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "# Word Clouds <a id=\"8\"></a>\n\n#### <p style=\"color: red\">Please skip this section</p>\n\n#### We are currently working on `wordcloud` compadibility.\n\n#### For now, you may download this notebook and run this section locally via `jupyterlab`.\n\n`Word` clouds (also known as text clouds or tag clouds) work in a simple way: the more a specific word appears in a source of textual data (such as a speech, blog post, or database), the bigger and bolder it appears in the word cloud.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "Luckily, a Python package already exists in Python for generating `word` clouds. The package, called `word_cloud` was developed by **Andreas Mueller**. You can learn more about the package by following this [link](https://github.com/amueller/word_cloud/).\n\nLet's use this package to learn how to generate a word cloud for a given text document.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "First, let's install the package.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# # install wordcloud\n# await piplite.install(['wordcloud==1.8.1'])\n\n# # import package and its set of stopwords\n# from wordcloud import WordCloud, STOPWORDS\n\n# print ('Wordcloud is installed and imported!')",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "`Word` clouds are commonly used to perform high-level analysis and visualization of text data. Accordinly, let's digress from the immigration dataset and work with an example that involves analyzing text data. Let's try to analyze a short novel written by **Lewis Carroll** titled *Alice's Adventures in Wonderland*. Let's go ahead and download a *.txt* file of the novel.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# import urllib\n\n# # open the file and read it into a variable alice_novel\n# alice_novel = urllib.request.urlopen('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/alice_novel.txt').read().decode(\"utf-8\")",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Next, let's use the stopwords that we imported from `word_cloud`. We use the function *set* to remove any redundant stopwords.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# stopwords = set(STOPWORDS)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Create a word cloud object and generate a word cloud. For simplicity, let's generate a word cloud using only the first 2000 words in the novel.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# # instantiate a word cloud object\n# alice_wc = WordCloud(\n#     background_color='white',\n#     max_words=2000,\n#     stopwords=stopwords\n# )\n\n# # generate the word cloud\n# alice_wc.generate(alice_novel)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Awesome! Now that the `word` cloud is created, let's visualize it.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# # display the word cloud\n# plt.imshow(alice_wc, interpolation='bilinear')\n# plt.axis('off')\n# plt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Interesting! So in the first 2000 words in the novel, the most common words are **Alice**, **said**, **little**, **Queen**, and so on. Let's resize the cloud so that we can see the less frequent words a little better.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# fig = plt.figure(figsize=(14, 18))\n\n# # display the cloud\n# plt.imshow(alice_wc, interpolation='bilinear')\n# plt.axis('off')\n# plt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Much better! However, **said** isn't really an informative word. So let's add it to our stopwords and re-generate the cloud.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# stopwords.add('said') # add the words said to stopwords\n\n# # re-generate the word cloud\n# alice_wc.generate(alice_novel)\n\n# # display the cloud\n# fig = plt.figure(figsize=(14, 18))\n\n# plt.imshow(alice_wc, interpolation='bilinear')\n# plt.axis('off')\n# plt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Excellent! This looks really interesting! Another cool thing you can implement with the `word_cloud` package is superimposing the words onto a mask of any shape. Let's use a mask of Alice and her rabbit. We already created the mask for you, so let's go ahead and download it and call it *alice_mask.png*.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# save mask to alice_mask\n# alice_mask = np.array(Image.open(urllib.request.urlopen('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%204/images/alice_mask.png')))",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Let's take a look at how the mask looks like.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# fig = plt.figure(figsize=(14, 18))\n\n# plt.imshow(alice_mask, cmap=plt.cm.gray, interpolation='bilinear')\n# plt.axis('off')\n# plt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Shaping the `word` cloud according to the mask is straightforward using `word_cloud` package. For simplicity, we will continue using the first 2000 words in the novel.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# # instantiate a word cloud object\n# alice_wc = WordCloud(background_color='white', max_words=2000, mask=alice_mask, stopwords=stopwords)\n\n# # generate the word cloud\n# alice_wc.generate(alice_novel)\n\n# # display the word cloud\n# fig = plt.figure(figsize=(14, 18))\n\n# plt.imshow(alice_wc, interpolation='bilinear')\n# plt.axis('off')\n# plt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Really impressive!\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "Unfortunately, our immigration data does not have any text data, but where there is a will there is a way. Let's generate sample text data from our immigration dataset, say text data of 90 words.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "Let's recall how our data looks like.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# df_can.head()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "And what was the total immigration from 1980 to 2013?\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# total_immigration = df_can['Total'].sum()\n# total_immigration",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Using countries with single-word names, let's duplicate each country's name based on how much they contribute to the total immigration.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# max_words = 90\n# word_string = ''\n# for country in df_can.index.values:\n#     # check if country's name is a single-word name\n#     if country.count(\" \") == 0:\n#         repeat_num_times = int(df_can.loc[country, 'Total'] / total_immigration * max_words)\n#         word_string = word_string + ((country + ' ') * repeat_num_times)\n\n# # display the generated text\n# word_string",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "We are not dealing with any stopwords here, so there is no need to pass them when creating the word cloud.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# # create the word cloud\n# wordcloud = WordCloud(background_color='white').generate(word_string)\n\n# print('Word cloud created!')",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "# # display the cloud\n# plt.figure(figsize=(14, 18))\n\n# plt.imshow(wordcloud, interpolation='bilinear')\n# plt.axis('off')\n# plt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "According to the above word cloud, it looks like the majority of the people who immigrated came from one of 15 countries that are displayed by the word cloud. One cool visual that you could build, is perhaps using the map of Canada and a mask and superimposing the word cloud on top of the map of Canada. That would be an interesting visual to build!\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "# Regression Plots <a id=\"10\"></a>\n\n> Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics. You can learn more about *seaborn* by following this [link](https://seaborn.pydata.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDV0101ENSkillsNetwork20297740-2021-01-01) and more about *seaborn* regression plots by following this [link](http://seaborn.pydata.org/generated/seaborn.regplot.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": "In lab *Pie Charts, Box Plots, Scatter Plots, and Bubble Plots*, we learned how to create a scatter plot and then fit a regression line. It took \\~20 lines of code to create the scatter plot along with the regression fit. In this final section, we will explore *seaborn* and see how efficient it is to create regression lines and fits using this library!\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "Let's first install *seaborn*\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# install seaborn\n# !pip3 install seaborn\n\n# import library\nimport seaborn as sns\n\nprint('Seaborn installed and imported!')",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 35,
      "outputs": [
        {
          "name": "stdout",
          "text": "Seaborn installed and imported!\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Create a new dataframe that stores that total number of landed immigrants to Canada per year from 1980 to 2013.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "# we can use the sum() method to get the total population per year\ndf_tot = pd.DataFrame(df_can[years].sum(axis=0))\n\n# change the years to type float (useful for regression later on)\ndf_tot.index = map(float, df_tot.index)\n\n# reset the index to put in back in as a column in the df_tot dataframe\ndf_tot.reset_index(inplace=True)\n\n# rename columns\ndf_tot.columns = ['year', 'total']\n\n# view the final dataframe\ndf_tot.head()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 36,
      "outputs": [
        {
          "execution_count": 36,
          "output_type": "execute_result",
          "data": {
            "text/plain": "     year   total\n0  1980.0   99137\n1  1981.0  110563\n2  1982.0  104271\n3  1983.0   75550\n4  1984.0   73417",
            "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>year</th>\n      <th>total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1980.0</td>\n      <td>99137</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1981.0</td>\n      <td>110563</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1982.0</td>\n      <td>104271</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1983.0</td>\n      <td>75550</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1984.0</td>\n      <td>73417</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "With *seaborn*, generating a regression plot is as simple as calling the **regplot** function.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "sns.regplot(x='year', y='total', data=df_tot)",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "trusted": true
      },
      "execution_count": 37,
      "outputs": [
        {
          "execution_count": 37,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='year', ylabel='total'>"
          },
          "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": "This is not magic; it is *seaborn*! You can also customize the color of the scatter plot and regression line. Let's change the color to green.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "sns.regplot(x='year', y='total', data=df_tot, color='green')\nplt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x4bf8900>",
            "image/png": 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WTFqAVENqn/SFeo4JIBER0QDlDrhh89gQlIJ9cr8aew1W7liJiqYKRfuIhBFYVLQIRelFfdIP6j0mgERERAOMJEto8jSh1d/aJ/dzBpzYULkBbx94GxKkcLtercfdF92NW/NuhUbVscA09V9MAImIiAYQT9ADm9t2ah/fKJNlGX+t/SueLX8Wzd5mxbHpQ6fjgcIHkGHMiPh9NSoNkvXJSNAmRPzadAoTQCIiogFAlmU0e5th99n75H5HWo9g1Y5V2GnbqWgfahqKh4sexmWZl0X8nmqVGsm6ZCRqExW7lVDkMQEkIiLq57xBL2weG/whf9Tv5Q66sXHvRrxe/TpC8ukVxVqVFneOvxO3j70dOrUuovdUCSok6ZJg0Vm4L3AfYQJIRETUT8myjBZfC1q8LX1yr8+OfYZndj0Dm8emOHb5kMuxsHAhssxZEb2nIAhI0CQgWZ/MkjF9jNEmIiLqh3whH2xuG3whX9TvVddWh6d2PoXtJ7cr2jONmXiw8EFMy5oW8UeyJo0JVr0VWrU2otelrmECSERE1M+0eFvQ4mvp9S4dF+IL+vBS1Ut4Zf8rikUloiDih2N/iDvH3wm9qI/oPQ2iAVa9NeLXpe5hAkhERNRPBKQAGtwN8Aa9Ub/X3w//HY99+hhOuE4o2i/OuBiLJi/CsIRhEb2fXtTDqrfCIBoiel3qGSaARERE/YDD50CTtynqo34nXCfwzK5nUHq8VNGeqk/FA4UP4D+y/yOij3s1Kg2seivMWnPErkm9xwSQiIgohoJSEDaPDe6AO6r38Yf82FK9BS9VvaSYV6gW1Lh1zK24+6K7YdQYI3Y/tUoNi86CJG0SS7r0Q0wAiYiIYsQVcMHmsSEkhS58ci98dfIrPLnjSdQ56xTtBWkFWDR5EUYnjY7YvQRBQJI2Ccn6ZJZ06ceYABIREfUxSZbQ6GlEm78tqvdpcDdgze41+PvRvyvak3XJWHLlElyefHlE75egTYBVb2VJlwGA3yEiIqI+5A64YfPYEJSCUbtHUArijQNv4MXKF+EJecLtKqgwM3cm7s2/FznZOWhoaIjInEOjxgir3hrxAtEUPUwAiYiI+kBfjfrtsu3Cqh2rcLj1sKJ9gnUCFhUtQl5yXsTm5HFl78DFBJCIiAY1SZZQ2ViJclc54AYmpEzo87lpfTHq1+Rtwtrda/FR7UeK9iRtEu6bdB+uG3ldxN53tFf2SrKEisYKNHubYdVbkZ+az/mEEcYEkIiIBq3SY6VYs3sNDtoPIiSEoJbVyLHkoLigGNOGTov6/SVZQpOnCa3+1qjdIygF8e7Bd/FCxQtwBV3hdgECrh99PX6c/2Mk6ZIicq++WNnb/j2rsdcgIAWgUWmQa8nts+9ZvGACSEREg1LpsVIsKV0CZ8AJi84Cs94Mp9eJquYqLCldguXTlkc1ofAEPbC5bYodNiKtsqkSK3esxAH7AUV7niUPi4sW46KUiyJyH5WgQpIuCRadpdsjcd0ZzTv7e6ZT6+AL+frsexZPmAASEdGgI8kS1uxeA2fAiUxjJgRBgEpQwSAaoFfrUe+ux5rdazA1a2rEHy3KsowmbxMcPkdEr3smu8+OdXvW4c+H/6xoN2vMmJ8/Hz/I+QHUgrrX9xEEAYnaRCTrkqFWdf963RnN6+x7BqBPvmfxiAkgERENOhWNFaix18Cis3R4VCkIAiw6C2rsNahorMCktEkRu6836IXNY4M/5I/YNc8kyRK2Ht6K5/c83+Gx8owRM3D/pPuRrE+OyL0StAlI1idDo9L06PXdHc2L1fcsXjEBJCKiQafZ24yAFDhnWRKdWgeHz4Fmb3NE7ifLMlp8LWjxtkTkep3Z37IfK3esRFVzlaJ9VOIoLC5ajIK0gojcJxIlXXoymtfX37N4xwSQiIgGHaveCo1KA1/I12mJEl/IB1Elwqq39vpevpAPNrdNsb1aJLX527C+Yj3ePfguZJyu2WcQDbh3wr24KfemiBRejmRJl56M5vXl94yYABIR0SCUn5qPXEsuqpqroFfrFUmILMuw++wYbx2P/NT8Xt3H7rOj2dsckWLKZ5NlGR9+/SHWlq+F3WdXHPvWsG+huKAYqYbUXt9Hq9YiWZcc0ZIuPRnN66vvGZ3CBJCIiAYdlaBCcUExlpQuQb27HhadBWpRDU/QA7vPDrPGjOKC4h4vJghJITR4GuAOuCPc81MOOg5i1Y5VKG8sV7QPTxiORZMXYUrGlF7fQ1SJSDOmwSyaI17SpSejeZ19z9rnDUbie0ZKTACJiGhQmjZ0GpZPWx6uA9gWbINaVmO8dXyvasq5A240eBoQkkIR7vGpa2/YuwFvHXgLIfn09fVqPX500Y8wK29WjxdltBMEARa9BSMtI3HSezIqo5c9Hc0783tWY6+Bw+eAqBJ7/T2jjpgAEhHRoDVt6DRMzZqKyqZKwIhe7QQSzfIusizjk6OfYM2uNWj0NiqOXTn0SjxU+BAyjBm9vo9JY0KKIQVatTaqI2m9Gc1r/55xJ5DoilgC+M4772D79u04duwYtFot8vLycMcddyArKyt8zpo1a/DZZ58pXldQUIBf/OIX4a/9fj82bdqEbdu2IRAIoKCgAPPmzYPFYgmf43Q6sWHDBpSVlUEQBFx22WW45557oNfrw+c0NjZi/fr1qKyshF6vx/Tp0zF79myo1afrGH399dcoKSnBwYMHkZiYiBkzZuCGG26IVEiIiKgfUAkqTEqbhCFDhuDEiRM9GvHyhXxocDdEpbzL161f48mdT6KsoUzRPtQ0FAsnL8Q3hnyj1/fQqrVINaT26Z69vRnNa/+eUfRELAHcu3cvrr32WuTk5CAUCuHVV1/F0qVLsXLlSkViVlhYiAULFpzugKjswsaNG7Fjxw4sXrwYRqMRJSUlWLFiBX7zm9+Ez3n66afR0tKCRx99FKFQCM8++yzWrVuHhQsXAgAkScKyZctgsViwdOlStLS0YPXq1VCr1Zg9ezYAwO12Y+nSpZg4cSLmz5+P2tparF27FiaTCddcc02kwkJERAOcw+dAk7cp4o9KPUEPNlVtwmv7X0NQPr1HsFalxZxxczB73OxelWIBTiVSyfrkqG7ddj4czeu/IpYAnjmKBwDFxcWYN28eDh06hIsuOr0VjSiKitG8M7ndbnzyySdYuHAh8vNPzQtYsGABFi1ahOrqauTl5eHo0aPYtWsXli1bhpycHADA3LlzsWzZMtx5552wWq3YvXs3jh49il/+8pewWCwYOXIkZs2ahc2bN+O2226DKIooLS1FMBjEggULIIoihg0bhiNHjmDr1q1MAImICAEpAJvbBk/QE9HryrKM0uOleHrX0zjpPqk4dlnmZXh48sMYah7atWtBRnVLNRw+B5J0SchLzoOAU4legjYBVr01IiVieoOjef1T1D4VbveplVFms3JZ+d69ezFv3jyYTCbk5+fj9ttvR0JCAgDg0KFDCIVCmDhxYvj8oUOHIjU1NZwAVldXw2QyhZM/AJg4cSIEQUBNTQ0uvfRSVFdXY/jw4YpEs7CwEC+88ALq6uowatQoVFdXY/z48YoRyIKCArz77rtwOp0d+k1ERPEjWuVdjjmP4cmdT+Jf9f9StGcYM/BQ4UOYljWtyyN1ZSfLsHnfZtS21Ya3WRueMBz3TLgHM0bOgF7UX/giFLeikgBKkoQXX3wRY8eOxfDhw8PthYWFuOyyy5Ceno76+nq8+uqr+O1vf4vHH38cKpUKdrsdoijCZDIprpeUlAS73Q4AsNvtSExMVBxXq9Uwm82Kc84eZUxKSgofa///9PR0xTntr7Hb7Z0mgIFAAIHA6U29BUGAwWAI/3cktV8vFkP2/RHjocR4dMSYKDEeSl2Nhy/kg81jgy/ogyz/3+ia34Ek7f+NrvUwnr6QD6/sewUvV70Mv3R6HqGoEjErbxbuuuiubs3PKztZhhVlK+AKupCoTUSSOgmBUABHWo/gD2V/QJox7bxz7Pj5oKgkgCUlJairq8Ovf/1rRfsVV1wR/u/hw4djxIgRePDBB1FZWakY9euv3nnnHbz55pvhr0eNGoXf/e53SEtLi9o9MzMzo3btgYjxUGI8OmJMlBgPpXPFQ5ZlNHua4fQ4kWRIwpdHv8Tz/34eh1sOIyAHoBE0GJU8Cj+++Me4PPvybt3zs68/w9J/LEWto1bR/o3sb+BX3/wVcqw553hl5yRZwuvbXodH8mBIwhCoBBVElQi1Sg2rbMWxtmNYX7UeN0+5+YJz7fj5iF8RTwBLSkqwY8cOPPbYY0hJSTnvuRkZGUhISEB9fT0mTpwIi8WCYDAIl8ulGAV0OBzh0TmLxYLWVuUG2KFQCE6nU3FOTU2N4hyHwxE+1v7/7aOB7dq/PtccxZkzZ+L6668Pf93+l5PNZkMwGOz0NT0lCAIyMzNRX18flRpNAw3jocR4dMSYKDEeSueLhzfohc1jC6/wLTtZhj/8+w/h0TWz2gx/yI8qWxUe/eujeOTiR7pUiPmk6ySe3vU0/nHsH4r2FH0KHih8AN8a9i0IQQENDQ3dei/7m/ejpqkGZtEMSKfemyRLkEISACBRk4i9J/fir3v+es65dz35fEiyNGgWc4iiGNXBm4EgYgmgLMvYsGEDtm/fjv/5n//p8Hi1M01NTXA6nUhOTgYAjB49Gmq1Gnv27ME3vnFq2fvx48fR2NiIvLw8AEBeXh5cLhcOHTqE0aNHAwAqKiogyzJyc3PD57z99ttwOBzhR7/l5eUwGAzIzs4On/Pqq68iGAyG5wGWl5cjKyvrnPP/NBoNNJrOC3BG6x9YWZb5j/cZGA8lxqMjxkSJ8VA6Mx6yLKPZ26zYZk2GjJerXoYr6EKaIS38h75e1EOn1sHmseHlqpcxOX1yeLHF2QJSAK9Vv4ZNezfBG/KG29WCGjfn3ox7JtwDk8YU7kN3OXwOhOQQzBoz1Cp1h+Pt26w1eS68crmrn4/SY6Xhci7t8w1zLbkszjyARSx1Lykpweeff46FCxfCYDDAbrfDbrfD7z/1F5XX68VLL72E6upqNDQ0YM+ePfj973+PzMxMFBQUAACMRiOuvvpqbNq0CRUVFTh06BCeffZZ5OXlhRPA7OxsFBYWYt26daipqcG+ffuwYcMGTJ06FVbrqS1lCgoKkJ2djdWrV+PIkSPYtWsXtmzZgmuvvTacwE2bNg2iKOK5555DXV0dtm3bhg8++EAxwkdERIOTJ+hBXVtdhz12q1uqUdtWi0RtYof5cYIgIFGbiNq2WlS3VHd63bKGMtzz0T14fs/ziuRvUuokvPDtF/BA4QPh5K8nTBoT8pLzoFPrFHMJz9TZNmu9UXqsFEtKl6CquQomjQkZxgyYNCZUNVdhSekSlB4rjch9qG9FbATwo48+AgD8z//8j6J9wYIFuOqqq6BSqVBbW4vPPvsMLpcLVqsVkyZNwqxZsxSjanfddRcEQcCKFSsQDAbDhaDP9NBDD6GkpAS//vWvw4Wg586dGz6uUqmwZMkSvPDCC3j00Ueh0+kwffp0zJo1K3yO0WjEo48+ipKSEixZsgQJCQm4+eabWQKGiGgQC0khNHoa0eZv6/S4w+dAQAogSZ3U6XGtWos2f1uH3UAaPY1Ys3sN/lb3N0V7si4Z90+6H9eOuLZXCy50ah1SDCkwiAakG9N7tM1aT0iyhDW718AZcCLTmBm+l0E0QK/Wo95djzW712Bq1tQB+zg4Xgkynw30ms1mU6wOjgRBEHpVtX6wYTyUGI+OGBMlxkNJEASYrWZUHKlAMHTuOdv7W/bjF1/84lSC00kZFW/QC0/Qg8eveBxjk8ciKAXxds3b2FC5Ae6gO3yeCir8IOcHmJ8/HwnahAv271z1/NQqNax6KxK1yuoX7aNyzoCz023Wlk9bfsFVwF35fJTbynHvX++FSWPqdJWyJ+iBK+BCybdLBlStP41GwzmAse4AERHF3mCa4H82f8iPJm8TErQJCEmh856bl5yH4QnDcdBxEDq1rsPoWqu/FTlJOchLzkN5YzlW7liJQ45DimuMt47H4qLFGJs8tkv966ye34jEEZifPx/fHfXdc+6X29Nt1rqj2duMgBQ4544k7fMNm73NEbkf9R0mgEREcW6wTvCXZRktvpbwPL8EXHgkToCAOePmYEXZCtg8NiRqE6FVa+EP+dHqb4VJNOEHo3+AZduX4cOvP1S8NlGbiPsm3ofvjfpel5PnTuv5SQEcdhzGsq+WIUmXdM7vQV9ss2bVW6FRaeAL+TodAYz0fEPqO0wAiYji2LkeJbZP8L/Qo8T+yhP0wOa2ISCdmp7Tnfl3UzKm4KdTfhoelWvzt0FUiRidOBojk0bi92W/hzPgVLzme6O+h/sm3geLztLl+8iQsXnf5vCKY5WgglqlhkE0IEGT0KX5ddHeZi0/Nb/P5htS32ICSEQUpwbjBP+QFEKjtxFOv/PCJ5/HlIwpKMooCs/La/Y2462at/BWzVuK88ZYxmBx0WJMSJnQ7Xu0rzhO0iZBo9IoSroIggCLzoIaew0qGitiNr9OJahQXFCMJaVLUO+u73S+YXFB8YD5fNBpTACJiOJURWMFauw1sOgsnZY86Q8JSHe0+lvR7G2+4Dy/rhIgINOYifcOvYeth7ZCxunFEibRhHkT5+HGnBuhFjrW4uuKNn8bQnIIJo3pvPX8Yj2/rq/mG1LfYgJIRBSnBssEf1/Ih0ZPI7xB74VP7iJJlvD+kfexrnwdHH5lyZfvDP8O7i+4Hyn68+92dS6CICBJm4SxyWPD9fwMqv49v64v5htS32ICSEQUpwb6BP+QFEKLr6VDTb7eqm6pxqodq1DZXKloH5U4CouKFqEwrbDH1zaIBqQaUqFVa5GsTx5Q8+uiPd+Q+hYTQCKiODWQJ/hH+nEvADgDTpRUlOCdmncgQQq3G9QG3D3hbtw65laIqp792tSoNEgxpCh2AeH8OoolJoBERHFqICYg0XjcK8sy/lr7Vzy7+1k0+5SPu/8j+z9QXFCMdOOF97fvjFqlhkVnQZI2qdOVyJxfR7HCBJCIKI4NlATkzJp+kdzZ5LDjMFbtXIVdtl2K9mHmYXi46GFcknFJj67bPs/PorN0usDjTJxfR7HABJCIKM719wTEG/TC5rHBH/JH7JruoBsvVr6INw68gZB8+jGyTq3Dj8b/CLPyZkGr1vbo2kaNEamGVGhUmguf/H84v476GhNAIiLqlwmIJEto8Z7eySMSZFnG3+v+jtW7V8PmsSmOTcuahgcLH8QQ05AeXVtUiUg1pCrm+RH1V0wAiYio33EH3Gj0NIZ38oiEwy2H8ct//BJfnfxK0T7ENAQPT34Ylw+5vEfXba+ZmKxL7taOI0SxxASQiIj6jUjt5HEmb9CLl/e9jFf3v6pIKDUqDeaMm4M54+acsxbihRg1RqToU3r8uJgoVpgAEhFRv+AKuGDz2CJa2qX0eCme3vk06t31ivZLMy7FwskLMSxhWI+u21lZF6KBhAkgERHFVFdH/WTI4b15k3RJyEvOg4DOH7kedx3H0zufxrYT2xTt6YZ0PFD4AKYPnd6jx7UqQYUkXdI5H/dKstRvF9MQnYkJIBERxYw74IbNY0NQCp73vLKTZdi8bzNq22oRkALQqDQYnjAcc8bNwZSMKeHzfCEftuzfgpeqXoJfOr1qWC2ocXfh3bht5G2d7nrSFQnaBFj11nMWgy49Vhoup9Pex1xLbr8qp0PUjn+WEBFRnwtJITS4G3DCdaJLyd+KshU46DgY3krNIBpw0HEQK8pWoOxkGQBge/123P3R3SipLFEkf5PTJuOP3/kjfnbFz2DUGLvdV4NoQHZCNtKN6edN/paULkFVcxVMGhMyjBkwaUyoaq7CktIlKD1W2u37EkUTRwCJiKhPdWcbNxkyNu/bDFfQhTRDWvixq17UQ6fWweax4Y+Vf8T/HvxffHbsM8VrrToriguLcc2wa6BSdX+8Q6vWwqq3XnCenyRLWLN7DZwBJzKNmeE+GkQD9Go96t31WLN7DaZmTeXjYOo3mAASEVGf8If8sHls3drGrbqlGrVttUjUJnY6506WZexp2gMZp3cHUUGFm8bchLkT5sKsMXe7n2qVGsm65HPe82wVjRWosdfAorN0OL+9REyNvQYVjRX9rtYixS8mgEREFFW92cbN4XMgIAWQpE5StLsDbpx0n1Q86gWA/JR8LC5ajFxLbrf72Z3t287U7G1GQAqcs5SMTq2Dw+dAs7e50+NEscAEkIiIoqa3BZ2TdEnQqDTwh/zQi3oEpSAaPA1o87cpzjNrzHiw8EFcO+LaHj1mTdAmIFmf3K3t29pZ9VZoVBr4Qr5OF5j4Qj6IKhFWvbXb1yaKFiaAREQUcUEpiEZPI1wBV6+uk5ech+EJw1Fjr4E76EaTpwkSJMU5aYY0/PE7f0SiNrHb1zdpTLDqrb0q5Jyfmo9cSy6qmqugV+sVj4FlWYbdZ8d463jkp+b3+B5EkcbZqEREFFFt/jbUtdX1OvkDAAECpg6ZClfwVJHoM5M/taBGuj4d/3XJf3U7+dOLemSZs5Bpyuz1Lh4qQYXigmKYNWbUu+vhCXogyRI8QQ/q3fUwa8woLijmAhDqVzgCSEREERHpbdzsPjueK38O7x95X9EuQIBZY0aeJQ93jL9DUQfwQkSViGRdMhK0CRHpY7tpQ6dh+bTl4TqADp8DokrEeOt41gGkfokJIBER9Zon6EGDu+GCNf26IiSHsPXQVjy/53m0BZRz/a4YcgW+PfzbGJow9Lw7gZxNEARYDVaYEkxdfk13TRs6DVOzpnInEBoQmAASEVGPhaQQmr3NaPW3RuR6+5r3YeWOldjXsk/RnpOUg8VFizExdWK3r2kQDUgzpiHVmIoTjhPdXoncHSpBxVIvNCAwASQioh5p87ehydvUpYLOF9Lqb8X6Pevx3qH3FDX9jKIR9+bfi5k5M8+5C8e5iCoRKfoUmLXmHu37SzSYMQEkIqJu6UlB53ORZAkfHvkQa8vXwuF3KI5dM/waLJi0AKmG1G5f16KzIFmfzMevROfABJCIiLrM4XOgydsUfowqQ0Z1SzUcPgeSdEndmpdXY6/Byh0rUdFUoWgfkTACi4oWoSi9qNv904t6pBpSz1mUmYhOYQJIRBEnyRInwg8yISmEBk8D3AF3uK3sZBk279uM2rZaBKQANCoNhicMx5xxc867MtcVcKGksgTv1LyDkHz68bFercddF92F2/Ju63ZBZrVKDave2qNagETxiAkgEZ1Xd5O50mOl4VIY7UlBriWXpTAGMHfAjQZPg2KuX9nJMqwoWwFX0IVEbSKS1Enwh/w46DiIFWUr8NMpP+2QBMqyjI/rPsaa3Ws6bIs2feh0PFD4ADKMGd3uX4I2ASn6lG5t30YU75gAEtE5dTeZKz1WiiWlS+AMOGHRWaBT6+AL+VDVXIUlpUuwfNpyJoEDyLlW+MqQsXnfZriCLqQZ0sILLPSiHjq1DjaPDZv3bUZRRlH4cfCR1iNYtWMVdtp2Kq411DQUDxc9jMsyL+t2/7RqLVINqZ1uv0ZE58cEkIg61d1kTpIlrNm9Bs6AE5nGzHBSYBAN0Kv1qHfXY83uNZiaNZWPgwcAp9+JRm9jpyt8q1uqUdtWi0RtYofVtYIgIFGbiNq2WlS3VGNYwjBs3LsRr1e/rnjcq1Vpcef4O3H72Nu7PV9PEAQk65Jh0Vm4upeoh5gAElEHPUnmKhorUGOv6fSXsiAIsOgsqLHXoKKxgnXS+rGu7OHr8DkQkAJIUid1elyr1qLV14rPj32OD458AJvHpjg+dchUPFT4ELLMWd3uX3tNv+7OESQiJSaARNRBT5K5Zm8zAlLgnKM5OrUODp+jw9wv6h8kWYLdZ4fdZ79goeQkXRI0Kg38IT/0or7DcVfABVfQhU1VmxTtmcZMPFj4IK4cemW3+6dWqZGiT4n4Fm5E8YoJIBF10JNkzqq3QqPSwBfydTonyxfyQVSJsOqtUes3dZ8sy3D4HbD77F0u6JyXnIfhCcNx0HEQOrUu/EeCJEto8jSh2adM8jUqDW4fezvuHHdnpwnjhSRqE2HVW7nIgyiCmAASUQc9SebyU/ORa8lFVXMV9Gq9YuRQlmXYfXaMt45Hfmp+n7wHurA2fxtavC0ISIFuvU6AgDnj5mBF2QrYPDYkahPhl/ywuW0Iysq9gC/OuBiLJi/CsIRhivau1A/kIg+i6GECSEQd9CSZUwkqFBcUY0npEtS76xULR+w+O8waM4oLirkApA9IsoTKxkqUu8oBNzAhZYIi7p6gB02eJvhCvh7fY0rGFPx0yk+xoXID9rfsh1/yK46nGdLwQMEDuCr7qg7TCC5UP5CLPIiijwkgEXXQ02Ru2tBpWD5tebh0jMPngKgSMd46nnUA+0h76Z6D9oMICSGoZTVyLDkoLijGJZmXoMnTBE/Q0+v7+EN+VDZXotperUj+1IIat465FXdfdDeMGmOH112ofuAvLvsFvjvqu1zkQRRlTACJqFM9TeamDZ2GqVlTuRNIDJxdusesN8PpdaKquQr/+Y//xOIpi3u0vdrZvjr5FZ7c8STqnHWK9oLUAiwuWoxRSaM6fd356gfq1Xo0eZvw6v5Xcf3o63vdRyI6PyaARHROPU3mVIKKpV76WGele1SCCnq1HladFQ2eBrxc9TImp0/u8l69Z2twN2DN7jX4+9G/K9qtOisWFCzAt4d/+7yPbM9VP1AtqKFWqZGsT2apIKI+wgSQiM6LydzA0FnpnpAUOvV4VoCiOPPY5LHdunZQCuKNA2/gxcoX4Qmdfnysggozc2di7oS5XSrPcnb9QAECRJUY/oOCpYKI+g4TQCKiQeDM0j2SLCEoBaEWTpdN0aq1aPO3weFzdOu6u2y7sGrHKhxuPaxon2CdgEVFi5CXnNfla7XXDwyEAjBpTB3KurBUEFHfYQJIRDQIWPVWiIIIV8AFrVrb4bg/5IeoEpGk63z3jrM1eZuwdvdafFT7kaI9SZuE+ybdh+tGXtfteZ1jk8ciJykHB+wHOowYslQQUd/irGwiogFOkiVkmbOQnZDd6U4esiyj1d+K4QnDLzhiF5SCeOvAW7jjgzsUyZ8AAd8f/X28PONlXD/q+m4nf0aNEcMTh+PhooeRoE1AvbsenqAHkizBE/Sg3l3PUkFEfYgjgEREA1irvxXN3maEpBBmj52tKM5sVBvhDXrR6m+FSTRhzrg5510AUtlUiZU7VuKA/YCiPc+Sh8VFi3FRykXd7p9GpUGqITVcEoalgoj6ByaAREQDUGfFnNuLM7cXWXaFXFDJKuQk5YSLLHfG7rNj3Z51+PPhPyvazRoz5ufPxw9yfqCYT9gV5yvmzFJBRLHHBJCIaAAJSAE0e5vh9Ds7PT4lYwqKMopwoOUAYADgAcYkj+l05E+SJWw9vBXP73kerf5WxbEZI2bg/kn3I1mf3O0+mjQmpBpSIarO/SuGq8uJYosJIBHRACDJEuw+e6dz/M4mQMBY61ikp6ejoaGh0/P3t+zHyh0rUdVcpWgflTgKi4sWoyCtoNt9PPtxLxH1X0wAiYj6uTPn+fVWm78N6yvW492D70LG6cTQIBowd8Jc3Jx783lH7jojCAIsOguSdcncu5dogGACSETUTzn9TrT4WuAP+S988gXIsowPv/4Qa8vXwu6zK45dPexqFBcUI82Q1u3rGkQDUg2pnZaeIaL+iwkgUZyQZImT7gcId8CNZm+zYoFHbxx0HMSqHatQ3liuaB+eMBwPT34YF2dc3O1riioRKfoUmLXmiPSRiPoWE0CiOFB6rDRcdiMgBaBRaZBryWXZjX7GF/KhydMET9Bz4ZO7wOl3YvWu1XjzwJsIyacfH+vVevzooh9hVt4saFSabl/XorMgWZ/MPyCIBjAmgESDXOmxUiwpXQJnwAmLzgKdWgdfyIeq5iosKV2C5dOWMwmMMUmW0OJtgcPvuOACj66QZRmf1H2CZ//8LBpcDYpjV2ZdiQcLH0SmKbPb19WLeqQaUqFT63rdRyKKLSaARIOYJEtYs3sNnAEnMo2Z4Qn6BtEAvVqPenc91uxeg6lZUzmaEyNt/jY0e5sRlIIRud7XrV/jyZ1PoqyhTNGeZcrCwskLcfmQy7t9TbVKjRR9Soft24ho4GICSDSIVTRWoMZe02kx3vaVmzX2GlQ0VrAmWx/zBr1o8jbBG/RG5HqeoAebqjbhtf2vISifTia1Ki1mj5uNOePm9GjkLlGbCKveCrWqe4Wgiah/YwJINIg1e5sRkALn/MWvU+vg8DnQ7G3u457Fr6AURJO36ZyFnLtLlmV8fvxzPLPrGZx0n1Qc++aIb2LBhAXIMmV1+7oalQZpxjQYRENE+klE/QsTQKJBzKq3QqPSwBfydfqL3BfyQVSJsOqtMehdfOlOIeeuOuY8hid3Pol/1f9L0Z5hzMBDkx/CzYU3w2azdft+Fp0FVr2VNf2IBjEmgESDWH5qPnItuahqroJerVf8QpdlGXafHeOt45Gfmh/DXg5+rf5WtHhbIjbPzxfyYfO+zXhl3yvwS6drBIqCiNvH3o47x98Jo8bY7QROq9YizZAGvaiPSD+JqP9iAkg0iKkEFYoLirGkdAnq3fWKVcB2nx1mjRnFBcVcABIlnqAHTZ6miNXzA4AvT3yJp3Y+heOu44r2ovQiLJq8CCMSR3T7mtzJgyj+MAEkGuSmDZ2G5dOWh+sAOnwOiCoR463jWQcwSgJSAI2eRrgD7ohds95Vj2d2PYPPj3+uaE/Rp+CBggdw9bCre5S86UU90gxp3MmDKM4wASSKA9OGTsPUrKncCSTKZFlGi68lovP8AlIAr+1/DRurNipGEtWCGjfn3ox7JtwDk8bU7euqBBWS9cmw6CwR6ScRDSxMAInihEpQsdTLGSK9NZ7T70STtyli8/wAoKyhDKt2rEJtW62ifVLqJCyavAg5lpweXdeoMSLVkNqjXUCIaHCIWAL4zjvvYPv27Th27Bi0Wi3y8vJwxx13ICvrdPkBWZbx+uuv429/+xtcLhfGjRuHefPmYciQIeFz/H4/Nm3ahG3btiEQCKCgoADz5s2DxWIJn+N0OrFhwwaUlZVBEARcdtlluOeee6DXn5643NjYiPXr16OyshJ6vR7Tp0/H7NmzoVafrmX19ddfo6SkBAcPHkRiYiJmzJiBG264IVIhIaJ+KpJb47kDbrT4WiJWzw8AGj2NWLN7Df5W9zdFu0Vnwf2T7seMETN69LhXrVIjVZ/K/XuJCBF7/rN3715ce+21ePzxx/Hoo48iFAph6dKl8HpP/6P47rvv4oMPPsD8+fPx29/+FjqdDo8//jj8/tOr2DZu3IiysjIsXrwYjz32GFpaWrBixQrFvZ5++mnU1dXh0UcfxZIlS1BVVYV169aFj0uShGXLliEYDGLp0qUoLi7Gp59+itdeey18jtvtxtKlS5Gamorly5fjjjvuwBtvvIGPP/44UiEhon6ofWu8quYqmDQmZBgzYNKYwlvjlR4r7dJ1PEEPjjmP4YTrRMSSv6AUxOvVr+OOD+9QJH8CBNyYcyM2z9iM7478bo+SvwRtAoaZhzH5IyIAEUwAf/GLX+Cqq67CsGHDMHLkSBQXF6OxsRGHDh0CcGr07/3338dNN92ESy65BCNGjMADDzyAlpYWfPXVVwBOJWWffPIJ7rrrLuTn52P06NFYsGAB9u/fj+rqagDA0aNHsWvXLvzkJz/BmDFjMG7cOMydOxfbtm1Dc/OpYra7d+/G0aNH8eCDD2LkyJGYPHkyZs2ahb/85S8IBk89niktLUUwGMSCBQswbNgwXHHFFfjud7+LrVu3RiokRNTPnL01nkE0QCWoYBANyDRmwhlwYs3uNZBk6ZzXCEgB1Lvqcdx5PKKjfuWN5Zj38Tys3r0a7uDpxSPjrePx/DXPY3HR4h5txaZRaZBlzkK6MZ27eRBRWNTmALrdp/4BM5tP/bXZ0NAAu92OSZNOz0EyGo3Izc1FdXU1rrjiChw6dAihUAgTJ04MnzN06FCkpqaiuroaeXl5qK6uhslkQk7O6bkvEydOhCAIqKmpwaWXXorq6moMHz5c8di4sLAQL7zwAurq6jBq1ChUV1dj/PjxEMXTISgoKMC7774Lp9MZ7veZAoEAAoFA+GtBEGAwGML/HUnt12NJhlMYDyXGo6OuxKSysRIH7QfPuzXeQftBVDZVdpgvKckSWrwtcPgdkGU5YrFv9jZjbflafHjkQ0V7ojYR9028D9ePvr5HcxNVKhWsBisSErl/L8CfmbMxHhSVBFCSJLz44osYO3Yshg8fDgCw2+0AgKSkJMW5SUlJ4WN2ux2iKMJkMp33nMTERMVxtVoNs9msOOfM5O/M+555Tnp6uuKc9tfY7fZOE8B33nkHb775ZvjrUaNG4Xe/+x3S0tI6D0QEZGZmRu3aAxHjocR4dHS+mJS7yhESQjDrzZ0mVWpRjbZgG2CEYm6y3WtHk7sJWp0WaYjMz3tICmFL5Ras+nIV2vxtimO3XHQLfnr5T2E19GyHFr2oR4YpAzpRBxgj0dvBgz8zSoxH/IpKAlhSUoK6ujr8+te/jsblY2bmzJm4/vrrw1+3/+Vks9nCj5YjRRAEZGZmor6+PmLlJAYyxkOJ8eioSzFxA2pZDafX2enWeJ6gB2pZDbiBEydOwBfyweaxwReMXCFnAKhsqsTKHStR3VKtaB9jGYOfTvkpJqRMQLAtiIa2hm5dVxCEU9v/6TRo8bTwM3IG/swoxXs8RFGM6uDNQBDxBLCkpAQ7duzAY489hpSUlHB7++iaw+FAcnJyuN3hcGDkyJHhc4LBIFwul2IU0OFwhF9vsVjQ2tqquGcoFILT6VScU1NTozjH4XAo+mGxWMKjge3avz579LCdRqOBRtN52YRo/QDJshyXP5znwngoMR4dnS8mE1ImIMeSc8Gt8S6yXoQmT1NE6/kBgMPnwPMVz2Proa2Qcfq6JtGEeRPn4cacG6EW1D26p0E0IM2YBo1Ko3g9PyNKjIcS4xG/IrYIRJZllJSUYPv27fjVr37V4fFqeno6LBYL9uzZE25zu92oqalBXl4eAGD06NFQq9WKc44fP47GxsbwOXl5eXC5XOHFJQBQUVEBWZaRm5sbPqe2tjac9AFAeXk5DAYDsrOzw+dUVVUpRu7Ky8uRlZXV6eNfIhr42rfGM2vMqHfXwxP0QJIleIIe1LvrYdaYMS9/Ho67jqPF2xKxX4ySLGHr4a2448M78KdDf1Ikf98Z/h28/N2XcXPuzVAL3V+koRJUSDOmIcucxbp+RNRlERsBLCkpQWlpKX72s5/BYDCER9OMRiO0Wi0EQcB1112Ht99+G0OGDEF6ejq2bNmC5ORkXHLJJeFzr776amzatAlmsxlGoxEbNmxAXl5eOAHMzs5GYWEh1q1bh/nz5yMYDGLDhg2YOnUqrNZT82UKCgqQnZ2N1atXY86cObDb7diyZQuuvfba8AjetGnT8MYbb+C5557DDTfcgLq6OnzwwQe46667IhUSIuqHzrU13tjksZgzbg5GJo6EP+S/8IW6qLqlGqt2rEJlc6WifVTiKCwqWoTCtMIeX9ukMSHVkApRxZr+RNQ9ghyhP3Fvu+22TtsXLFiAq666CsDpQtAff/wx3G43xo0bh3vvvVdRLLq9EPQXX3yBYDB4zkLQJSUlikLQc+fOVRSCttlseOGFF1BZWQmdTofp06djzpw55ywEnZCQgBkzZuDGG2/s9nu32WyK1cGRIAgChgwZghMnTnB4HozH2QZrPHqzO0d3Y9J+r0ZPI0SViKHmob3tvoIz4ERJRQneqXkHEk6XlTGoDbh7wt24dcytPU7cRJWIVEPqebeAG6yfkZ5iPJTiPR4ajSbu5wBGLAGMZ0wAo4/xUBqM8ejt7hzdjYksy3D4HbD77AhJoS73U4aM6pZqOHwOJOmSkJecBwHKuYR/rf0rnt39LJp9zYrX/kf2f6C4oBjpxvSzL9tlidpEpBhSLpgYD8bPSG8wHkrxHg8mgNwLmIj6gfbdOZwBJyw6C3RqHXwhX3h3juXTlnd7i7bzafO3ocXbgoDUvT/cyk6WYfO+zahtqw0nqcMThmPOuDmYkjEFhx2HsWrnKuyy7VK8LtucjYcnP4xLMy/tcZ+1ai1SDamdrl4mIuouJoBEFFNn787RvjLXIBqgV+tR767Hmt1rMDVrao8KIp/JE/SgydMEX6j7ZV3KTpZhRdkKuIIuJGoTkaROgj/kx0HHQTzx7ycw1joW/zj2D4Tk06OJWpUWP7roR7g973Zo1doe9bm9QHWyLplFe4koYsTPPvvsnAenT5/eh10honhU0ViBGnvNeXfnqLHXoKKxosPuHF0VkAJo8jTBFXD16PUyZGzetxmuoAtphrRwP3VqHfRqPU64T+C4+7jiNVdkXYGHCh/CENOQzi7ZJTq1DmnGNOjUuh5fg4ioM+Kzzz57zoNMAIko2pq9zQhIgXMmOTq1Dg6fA83e5k6Pn8/Z27f1VHVLNWrbapGoTQwnf/6QHyfdJxX79gLAENMQLCxciKlZU3t8P0EQkKxL7jQpJiKKBHH16tWx7gMRxTGr3gqNSgNfyNfp/DZfyAdRJcKq7/q2aLIso9XfihZfS7cWeJyLw+dAQAogSZ0ESZbQ5G06VScQyqTymmHX4OeX/LxXI3Z6UY80Q1qPHxkTEXWFGO+rYIgotvJT85Fryb3g7hz5qfkXvFZ74tfsaUZQitz2jEm6JGhUGth9drT4WjpcW6/WwygaMWvsrB4nf2qVGin6FCRoEyLRZSKi8+IiEKIBRpIlVDZWotxVDrhPbW/W28URsdS+O8eS0iWod9crVgHbfXaYNWYUFxSf9z1KsoTtJ7aj7WQbgq4gxiSPUZRm6a0EbQJ8IR8cfoeiXRREpBnS4Al6MDppNPKS83p0/URtIqx6K9Sq7u8EQkTUE+If/vAHeDweSJKkOCAIAn71q1/FqFtE1Jn2WnkH7QcREkJQy2rkWHK6XCuvvzrX7hzjreMv+N7+Vvs3rN61Gkdaj0ASJKhklaI0S2/4Qj5s2b8FL1W9BL+k3B0kWZcMs8YMZ8AJs8aMOePmdDvp1Kl1SDWkQi/qL3wyEVEEiSdPnkRtbS2ys7PhdrvR3NyMjIwMpKSkxLpvRHSGs2vlmfVmOL3OqNXK62vThk7D1KypXd4JxB/y48MjH+Lxfz0eLs1i1Bnh9rlx0HEQK8pW4KdTftrjJHB7/Xas2rkKx5zHFO0JmoTwnru+kA85STndTjZZ2oWIYk385S9/ifnz5+Oee+5Bfn4+vvzyS7zwwgt46KGHYt03Ivo/ndXKUwmqqNTKiyWVoLpgqZegFAyv7C2pKFGUZlEJKuhFPXRqHWweGzbv24yijKJujcyddJ/E6l2r8dkxZYksq96K4oJifGvYt3DAfuCcO4FciF7UI9WQytIuRBRTokp16pdF+yPgyy+/HPv27cNLL72Exx57LJZ9I6L/0xe18vq7kBSC3WcPl3TprDRLO0EQkKhNRG1bLapbqjE2eewFrx+QAnij+g1s3LsRnpAn3K6CCjeNuQlzJ8yFWWMGgC5d72yCIMCqt8Kis3T7tUREkSYajUaoVCo4nc5w4/Dhw/HJJ5/EsFtEdKZo1srr7yRZgsN3as9eST49V/nM0iyd0aq1aPO3weFzdHr8TDsadmDVjlX4uu1rRXt+Sj4WFy1GriW3V+/BIBqQZkwLPzomIoo1UaVSIT09HXv27MHUqacKl+7fvx8mkynGXSOidtGoldffSbIEu8+OVn9rp7X82kuz+EP+ThdR+EN+iCoRSbrOE0QAaPQ04tndz+Ljuo+V19Ym4f5J92PGyBm9eqSuVqlh1VuRqE3s8TWIiKJBBIBvf/vbeOmll9DQ0ABZllFZWYnvf//7se4bEf2fSNbK6+9CUggOvwMOn0Mx4ne2vOQ8DE8YjoOOg9CpdR1i0upvRU5STqelWYJSEO8cfAclFSWKnTwECPjB6B9g/sT5vU7azFozUvQpEFWstkVE/Y8IAN/73vfg8/nwr3/9CyqVCjfffDNuuummWPeNiP5PZ7Xy1KIanqCny7Xy+ruuJn7tBAiYM24OVpStgM1jO7UKWG2EN+hFq78VJtHUaWmWPY17sHLHShx0HFS0j0seh0VFizDeOr5X70Oj0iDVkAqjxtir6xARRZMg92aDTAIA2Gw2BAKBiF5TEAQMGTIEJ06c6NUepoMF43HKYKwD2N3E72xlJ8uwed9m1LbVnrcOoN1nx3Plz+H9I+8rXm/WmHHfxPtw/ejroRZ6XohZEAQkaZNg1Vv7RWkX/swoMR5K8R4PjUaDeN8JrdNnExUVFXjrrbfw3//9333dHyI6j/ZaeZVNlYARA3onkN4mfu2mZExBUUYRDrQcAAwAPFDsBBKSQ9h6aCue3/M82gJtitdeN/I6/GTST3q9MpelXYhooOk0AWxtbcXevXv7ui9E1AXttfIG6l/v7eVcWv2tvUr8ziRAwFjrWKSnp4fnMgPAvuZ9WLljJfa17FOcn5OUg8VFizExdWKv7qsSVLDqreddaEJE1B91mgDW19fDYOi40pCIqKeikfidS6u/Fev3rMd7h96DjNMJslE04t78ezEzZ2avF2eYNCakGlKjushDkqUu74xCRNQd4tnFnt1uN77++mtMnjw5Rl0iosEkKAXDiV+0RyslWcKfD/8Za3evhcOvrP93zfBrsGDSAqQaUnt1D1ElItWQCpMmuqWy2ud71thrEJAC0Kg0yLXkDuj5nkTUf4itra3hLwRBgE6nw7e//W3ccsstMewWEQ10ASkAu9eOtkBbnzymrrHX4KF/PISd9TsV7SMSRmBR0SIUpRf1+h4J2gSk6FOgVvV8sUhXnL3vs06tgy/kGzT7PhNR7IkrVqyIdR+IaBBp36u3rxI/Z8CJDZUb8HbN24pHy3q1HndfdDduzbu11ztwiCoRaYa0Pint0tm+zwAG3b7PRBRbrFBKRBHRl496gVPFnv9a+1c8W/5shy3wpg+djgcKH0CGMaPX90nSnSrt0lfJFvd9JqK+IL788suw2WwAgLS0NBQVFeGiiy6KcbeIaKDo68QPAI60HsGqHauw03bW496kEXiw4EFcmnFpr+8Rq9Iu8bzvMxH1HfFPf/oTjMZTjzXcbjf+9Kc/4dJLL8XChQshihwgJKLOxSLxcwfd2Lh3I16vfh0h+fT+wFqVFneOvxMLr1wIR7OjV/2JdWmXeNz3mYj6nrhu3TpYLBYAgMPhwJ/+9Cf86U9/wptvvonbb789tr0jon6nvZyLw9+7RKs7ZFnGZ8c+wzO7noHNY1McmzpkKh4qfAhDE4ZCJ/ZutK4/7N8bT/s+E1HsiO3JHwAkJSXhjjvugMPhwOeff84EkIjCZFmGw++A3WdHSApd+AURctR5FE/ueBLbT25XtGcaM/HQ5IcwLav3q2E1Kg3SjGmdjrj1tc72fW5fBTxY9n0motjr9M/cMWPGYNu2bX3dFyLqp9r8bWj2NiMoBfvsnr6QDy9XvYxX9r+CgHR6r22NSoPbx96OO8fdCb2o79U92hdVJOuS+8X+ve2mDZ2G5dOWh+sAOnwOiCoR463jWQeQiCKi0wRw7969sFo5v4Qo3rkDbjR7m+EL+fr0vtuOb8NTu57CCdcJRfvFGRdj0eRFGJYwrNf30It6pBnSoFVre32taGjf95k7gRBRNIjHjx9HZmYmgFNbwP35z3/Gl19+idtuuy3GXSOiWPEGvWj2NsMT9PTpfU+4TuDpXU/ji+NfKNrTDGl4oOABXJV9Va9H6gRBgFVvhUVn6dV1+kL7vs9ERJEmLlq0CCrVqb8oJelUEdXp06dj5syZsewXEcWAL+RDi7cFroCrT+/rD/mxpXoLXqp6STHaqBbUuHXMrbj7orsjUoTZIBqQZkzrdWFoIqKBTvzhD3+oqAM4efJkZGZmorm5Gampvdszk4gGBm/QixZfC9wBd5/f+6uTX+HJHU+izlmnaC9ILcDiosUYlTSq1/dQCSqkGFKQqE3s9bWIiAYD8cYbb+zQuG3bNjz11FN47bXX+r5HRNRnYpn4NbgbsGb3Gvz96N8V7cm6ZCwoWIDvDP9ORBZmGDVGpBnSYlrahYiov+n0X8RgMBh+LExEg48n6EGLt6XP5/gBpwpIv3HgDbxY+SI8odP3V0GFmbkzMXfCXCRoE3p9H7VKjRR9SkSuRUQ02IiNjY2KBpfLhW3btuHM+oBENDh4gh40e5vhDXpjcv9dtl1YtWMVDrceVrRPsE7AoqJFyEvOi8h9OOpHRHR+YnFxcacHZs2a1cddIaJoiXXi1+Rtwtrda/FR7UeK9iRtEu6bdB+uG3ldRMqbqAQVUg2pnOtHRHQB4h133BH+QhAE6HQ6jB49Gjk5OTHsFhFFgjvgRouvJWaJX1AK4t2D7+KFihfgCp5eWSxAwPWjr8eP838csT13daIOI5JGoNHX2Gdb1BERDVTi97///Vj3gYgizBVwwe6zxyzxA4CKpgqs2rEKB+wHFO15ljwsnrIYF1kvish9BEFAsi4ZVoMVGjXLuxARdQUnyBANIq6ACy3elj7fueNMdp8dz5U/h/ePvK9oN2vMmJ8/Hz/I+QHUgjoi99KpdUgzpkGn1kXkekRE8YIJINEg4PQ7YffZY5r4SbKErYe34vk9z6PV36o4NmPEDNw/6X4k65Mjcq/+uocvEdFAwQSQaABr87ehxdsCf8gf037sb9mPlTtWoqq5StE+KnEUFhctRkFaQcTupVPrkGpIhV7UR+yaRETxhgkgUQxJsoSKxgo0e5th1VuRn5rfpdWwTr8TR+xH0OBuiOmChzZ/G9ZXrMe7B9+FjNP9MIgG3DvhXtyUe1PESrFw1I+IKHKYABLFSOmxUqzZvQY19hoEpAA0Kg1yLbkoLijGtKHTOn2NO+BGs7cZfsmPdHN6H/f4NFmW8eHXH2Jt+VrYfXbFsW8N+xaKC4qRaojcVpKc60dEFFlMAIlioPRYKZaULoEz4IRFZ4FOrYMv5ENVcxWWlC7B8mnLFUng2XX8YjkCdtBxEKt2rEJ5Y7mifXjCcCyavAhTMqZE7F7tK3wtOgtH/YiIIogJIFEfk2QJa3avgTPgRKYxM5zYGEQD9Go96t31WLN7DaZmTUVACqDZ2xyTvXrP5gq48MfKP+KtmrcQkkPhdr1ajx9d9CPMypsFjSpyZVj0oh5phjRo1dqIXZOIiE5hAkjUxyoaK1Bjr+l0VKt9ntuBlgP4x9F/YFjCsBj18jRZlvFJ3SdYvXs1mrxNimNXDr0SDxU+hAxjRsTupxJUsOqtESsQTUREHTEBJOpjzd5mBKRAp/PZZFmGWlDDF/LhhOtEzBPAr1u/xpM7n0RZQ5mifahpKB6a/BAuH3J5RO+nF/VIN6ZHdCSRiIg6YgJI1Meseis0Kg18IR8MogHAqcQvKAchyRK8QS9ElRjTETBP0INNVZvw2v7XEJSD4XatSos54+Zg9rjZEV2Q0T7yadVbI3bN/qSnq72JiKKFCSBRH8tPzUeuJRdVzVXQqXQIIQRJlgCcSgRb/a3IScpBXnJen/dNlmWUHi/F07uexkn3ScWxb2R+AwsnL8RQ89CI3lOr1iLNkDZo6/r1ZLU3EVG0MQEk6mMqQYX7Jt6H/1f6/3DMdQyJ2kRo1Vr4Q360+lthEk2YM24OBPTtqtdjzmN4audT+Gf9PxXtGcYMPFT4EKZlTYv4Stz2Ub/BusK3u6u9iYj6ChNAoj4UkAJo8bZgeOJwLCpahM37NqO2rRZt/jaIKhE5STmYM25OREupXIgv5MMr+17B5n2b4ZdO7ygiCiJmjZ2FH43/UfhRdaRo1VqkGlIjft3+pDurvfk4mIj6GhNAoj7gD/lh99nR5m8Lt03JmIKijCJUt1TD4XMgSZeEvOS8Ph35++eJf+KpnU/hmOuYor0ovQiLJi/CiMQREb9nsj45Lnbz6Mpq7xp7DSoaKzApbVKMeklE8YoJIFEUdZb4nUmAgLHJY/u4V0C9qx7P7HoGnx//XNGeok/BAwUP4OphV0c8QRvsc/3Odr7V3sCp3U0cPgeavc193DMiIiaARFHhD/nR4muB0++MdVcUAlIAr+1/DRurNsIX8oXb1YIaN+fejHsm3AOTxhTRe8brHr6drfY+ky/kg6gSB+3KZyLq35gAEkVQ+xy/c434xVLZyTKs2rkKtW21ivZJqZOwqGgRcpJyIn7PeN7D98zV3nq1XpH8yrIMu8+O8dbxyE/Nj2EviSheMQEkioCgFDyV+AXaIMtyrLuj0OhpxOrdq/FJ3SeK9mRdMu6fdD+uHXFtxEfm4nXU70wqQYXigmIsKV2Cene9YhWw3WeHWWNGcUExF4AQUUwwASTqBUmWYPfZYffZ+13iF5SCeKvmLWyo3ABP0BNuV0GFG3JuwLz8eUjQJkT8vvE21+98pg2dhuXTlofrADp8DogqEeOt41kHkIhiigkgUQ/IsgyH3wG7z46QFIp1dzrYbduNVTtX4ZDjkKJ9vHU8FhctjtrCk3hZ4dsd04ZOw9SsqdwJhIj6FSaARN3QvlOH3WdHUApe+AV9rNnbjLXla/GXr/+iaE/UJuK+iffhe6O+F5XEQ6PSIM2YNqjr+vWGSlCx1AsR9StMAIm6qM3fhhZvCwJSINZd6SAkh/DuwXfxQsULcAaUK4+/N+p7uG/ifbDoLFG5d4I2AamGVI5oERENIEwAic5DlmU4A07YfXb4Q/4LvyAG9jbtxcodK1Ftr1a0j7GMweKixZiQMiEq91Wr1EgzpEW8bAwREUUfE0CiTsiyjLZAG+xee78c8QOAFk8Lnvj3E/jToT9BxukFKCbRhHkT5+HGnBuhFtRRubdRY0SaIQ2iiv+EEBENRPzXm+gM/X2OH3Bq5fEHhz/Auop1sHvtimPfGf4d3F9wP1L0KVG5t0pQIcWQgkRtYlSuT0REfSOiCeDevXvx3nvv4fDhw2hpacEjjzyCSy+9NHx8zZo1+OyzzxSvKSgowC9+8Yvw136/H5s2bcK2bdsQCARQUFCAefPmwWKxhM9xOp3YsGEDysrKIAgCLrvsMtxzzz3Q60+XnWhsbMT69etRWVkJvV6P6dOnY/bs2VCrT4+IfP311ygpKcHBgweRmJiIGTNm4IYbbohkSGiAaF/V6/A5+m3iBwAH7AewcsdKVDZVKtpHJY7CoqJFKEwrjNq99aIe6cZ0aFSaqN2DiIj6RkQTQJ/Ph5EjR+Lqq6/GH/7wh07PKSwsxIIFC053QFR2YePGjdixYwcWL14Mo9GIkpISrFixAr/5zW/C5zz99NNoaWnBo48+ilAohGeffRbr1q3DwoULAQCSJGHZsmWwWCxYunQpWlpasHr1aqjVasyePRsA4Ha7sXTpUkycOBHz589HbW0t1q5dC5PJhGuuuSaSYaF+TJIltPpb+33i5ww4UVJRgndq3oEEKdxuEA24+6K7ceuYW6P2OFYlqJCsT47aIhIiIup7Ef2NMXnyZEyePPn8NxRFxWjemdxuNz755BMsXLgQ+fmntkdasGABFi1ahOrqauTl5eHo0aPYtWsXli1bhpycU1tXzZ07F8uWLcOdd94Jq9WK3bt34+jRo/jlL38Ji8WCkSNHYtasWdi8eTNuu+02iKKI0tJSBINBLFiwAKIoYtiwYThy5Ai2bt3KBDAOSLIEh88Bh9/RL+v4tZNlGX+t/Sue3f0smn3NimMzcmdg/rj5SDOkRe3+Jo0JqYZUzvUjIhpk+vxf9b1792LevHkwmUzIz8/H7bffjoSEU7sRHDp0CKFQCBMnTgyfP3ToUKSmpoYTwOrqaphMpnDyBwATJ06EIAioqanBpZdeiurqagwfPlyRaBYWFuKFF15AXV0dRo0aherqaowfP14xAllQUIB3330XTqcTZrO5Q98DgQACgdMLAgRBgMFgCP93JLVfjwV1T4lUPNp37mj1t4YTv/4a48OOw1i5YyV22XYp2rPN2Vg8ZTG+N/F7sNlsUdmBRFSJSDWkDqgVvvyZUWI8lBgPJcaD+jQBLCwsxGWXXYb09HTU19fj1VdfxW9/+1s8/vjjUKlUsNvtEEURJpPyl05SUhLsdjsAwG63IzFROQFdrVbDbDYrzjl7lDEpKSl8rP3/09PTFee0v8Zut3eaAL7zzjt48803w1+PGjUKv/vd75CWFr0RmMzMzKhdeyDqaTxCUggt3hbYvXZodBqkIDqLJCLB5Xdh9VersWn3JsVjab2ox08u/gnunXwvtGotAETls5ekS0KaKW3A1vXjz4wS46HEeCgxHvGrTxPAK664Ivzfw4cPx4gRI/Dggw+isrJSMerXX82cORPXX399+Ov2v5xsNhuCwcjOHxMEAZmZmaivr+93e8zGQk/jEZJCsPvtaPW1QpKlC78ghmRZxqdHP8Uzu56BzWNTHJuWNQ0PTX4IQ0xDYG+yQxAEpKWlRXQEUFSJSDOkQfJLONl2MiLX7Ev8mVFiPJQYD6V4j4coilEdvBkIYjqxJyMjAwkJCaivr8fEiRNhsVgQDAbhcrkUo4AOhyM8OmexWNDa2qq4TigUgtPpVJxTU1OjOMfhcISPtf9/+2hgu/avzzVHUaPRQKPpfAVktH6AZFmOyx/Oc+lqPIJSMDzHbyDEr66tDqt2rsK/T/5b0Z5pzMTDkx/G1KypADp+ziL1+TBpTEgzpEGtUg+IeJ0Pf2aUGA8lxkOJ8YhfMX3G09TUBKfTieTkZADA6NGjoVarsWfPnvA5x48fR2NjI/Ly8gAAeXl5cLlcOHTo9Cb3FRUVkGUZubm54XNqa2vDSR8AlJeXw2AwIDs7O3xOVVWVYuSuvLwcWVlZnT7+pYEhKAXR6GlEbVst7D57v/+HzRv0Yn3Fetz90d2K5E+j0uCu8XfhpRkvhZO/aFCr1Eg3piPTlAm1KjpFo4mIqP+J6Aig1+tFfX19+OuGhgYcOXIEZrMZZrMZb7zxBi677DJYLBacPHkSL7/8MjIzM1FQUAAAMBqNuPrqq7Fp0yaYzWYYjUZs2LABeXl54QQwOzsbhYWFWLduHebPn49gMIgNGzZg6tSpsFqtAE4t5sjOzsbq1asxZ84c2O12bNmyBddee214BG/atGl444038Nxzz+GGG25AXV0dPvjgA9x1112RDAn1kYAUgN1rR1ugrd8nfe1Kj5fi6Z1Po95dr2i/NONSLJy8EMMShkX1/matGSn6FK7wJSKKQ4Icwd+WlZWVeOyxxzq0T58+HfPnz8cTTzyBw4cPw+VywWq1YtKkSZg1a5bikWt7IegvvvgCwWDwnIWgS0pKFIWg586dqygEbbPZ8MILL6CyshI6nQ7Tp0/HnDlzzlkIOiEhATNmzMCNN97Y7fdts9kUq4MjQRAEDBkyBCdOnBgwCU00nSseAzHxO+46jqd3Po1tJ7Yp2tMMaXiw8EFMHzr9givzBEFAeno6Ghoauv2+B+IK367gz4wS46HEeCjFezw0Gk3czwGMaAIYr5gARt/Z8fCH/LD77HAGnAMmPr6QD1v2b8FLVS/BL/nD7WpBjdvybsNdF90Fo2js0rV6mgAmaBOQakgdsCt8z4c/M0qMhxLjoRTv8WACyL2AaYDxBD1o8bbAHXDHuivdsr1+O1btXIVjzmOK9slpk7GoaBFGJo6M6v3VKjXSDGmDbtSPiIh6hgkgDQiugAu1jlocdx4fUH+tnnSfxOpdq/HZMeUe2Fa9FcUFxbhm2DVRL8RqEA1IN6Zzrh8REYXxNwL1W7Iswxlwwu6zIyAFkG5Kv/CL+omAFMAb1W9g496N8IQ84Xa1oMbM3JmYO2EuzJrorjYXBAFWvZV7+BIRUQdMAKnfkWUZrf5W2H328E4YA2m7op0NO7Fyx0p83fa1oj0/JR+LixYj15Ib9T5o1VqkG9OhU+uifi8iIhp4mABSvyHLMhx+Bxw+h2ILtIFAhozt9dvxyr5XsNO2U3EsSZuE+yfdjxkjZ/TJ4oskXRJS9CkDKmkmIqK+xQSQYk6SpfCIX0gKxbo73ba9fjue2vkUjjqPQsbp+YkCBPxg9A8wf+J8JGoTz3OFyFCr1Eg3pMOo6dpKYiIiil9MAClmJFkKb9c2EBM/AHiz+k2s3bMWAUlZBkgtqJGiS8FV2Vf1SfJn1BiRbkjnbh5ERNQlTACpzw2GxM/us2Nt+Vp8cOQDRbtKUCHNkIZETSIavY3YvG8zijKKICA6j2NVggpWg7VPkkwiIho8mABSn5FkCXafHa3+1gGb+IXkELYe2orn9zyPtkCb4liiNhFphrRwuZVEbSJq22pR3VKNscljI94Xg2hAdkI2RIE/xkRE1D38zUFR1574OXwOSLIU6+702L7mfVi5YyX2texTtGtVWmSYMjrs4qFVa9Hmb4PD54hoPwRBQKohFcOShuGEOz6r+BMRUe8wAaSoCUmh8KregZz4tfnbsL5iPd49+K5ikYderYeoEmHVWWEQDR1e5w/5IapEJOmSItYXg2hAmjENWrU2YtckIqL4wwSQIm4gJ34yZFS3VMPhcyBBm4BDjkN4rvw5OPzKUbxrhl+DBZMW4Lfbf4uDjoPQi3pF2ZX2WoY5STnIS87rdb9UggophhTO9SMioohgAkgRM5ATPwAoO1mGzfs2o7atFp6gB96Qt8Pq3hEJI7CoaBGK0osAAHPGzcGKshWweWxI1CZCq9bCH/Kj1d8Kk2jCnHFzer0ARC/qkW5Mh0al6dV1iIiI2jEBpF4b6IkfcCr5W1G2As6AEyE51GGBh1alxdwJc3Fr3q2KRGxKxhT8dMpPw4ljm78NokpETlIO5oybgykZU3rcJ27lRkRE0cIEkHpssCzukCHj5aqX0eJrgTfkRUhWrlDWqDQYlzwOPxz3w05H86ZkTEFRRlH40XGSLgl5yXm9GvnTi3qkGTjXj4iIooMJIHXbYKjjd6ZP6z5FeVN5h8e9GpUGGcYMqAU16t315y3nIkCIWKmXZH0yknXJ3MqNiIiihgkgddlgS/zcQTc27t2I16pfU4xgCjj16NWqt0IlqCDJUlTKuZxNo9Ig3ZgOvaiP6n2IiIiYANIFDYYCzmeSZRmfHfsMq3etRoOnQXHMpDEh3ZCuePQajXIuZ0vQJiDVkAqVoIraPYiIiNoxAaRzGgyLO85W11aHp3Y+he0ntyvatSotNCoNhpqGRrWcy9nUKjXSDGkwaUwRvzYREdG5MAGkDgZj4ucL+fBy1ct4Zf8rirl+oiDih2N/iPyUfDyz65molnM5m1FjVGwdR0RE1Ff4m4fCglIwPMdvMG0vtu34Njy16ymccJ1QtF+ccTEWTV6EYQnDAJzaui0a5VzOJggCUvQpUX2kTEREdD5MAAlBKRie4zeYEr8TrhN4etfT+OL4F4r2VH0qHix8EFdlX6V43BuNci5nY3kXIiLqD5gAxrGAFIDda0dboG1QJX7+kB9bqrfgpaqX4Av5wu1qQY1bx9yKuy+6G0aNsdPXRrKci+K6goBkXTKS9ckRvzYREVF3MQGMQ4M18QOAr05+hSd3PIk6Z52ivSC1AIuKFmF00ug+7xNH/YiIqL9hAhhH/CE/7D472vxtFz55gGlwN2DN7jX4+9G/K9qTdclYULAA3xn+nT4vrMyt3IiIqL9iAhgH/CE/WnwtcPqdse5KxAWlIN448AZerHwRnpAn3K6CCjNzZ2LuhLlI0Cb0eb846kdERP0ZE8BBzBfywe6zD8rEDwB22XZh1Y5VONx6WNE+wToBi4oWRaVu34VwhS8REQ0ETAAHIW/QC7vPDlfAFeuuREWTtwlrd6/FR7UfKdqTtEm4b+J9uG7UdTHZUUMv6pFuTIdGpenzexMREXUHE8BBxBv0osXXAnfAHeuuREVQCuLNA2/ihT0vwBU8ndwKEHD96Ovx4/wfx2TkjSt8iYhooGECOAh4gh60eFvgCXoufPIAVdlUiac+eQpVjVWK9jxLHhYXLcZFKRfFpF86tQ5pxjTo1LqY3J+IiKgnmAAOYPGQ+Nl9djy/53lsPbxV0W7WmDE/fz5+kPMDqAV1TPpm0Vlg1Vv7fHUxERFRbzEBHIDiIfGTZAlbD2/F83ueR6u/VXFsxogZuH/S/TF75KpRaZBmTINBNMTk/kRERL3FBHAA8QQ9aPY2wxv0xrorUbW/ZT9W7liJquazHvda8/BQwUOYlDopRj0DErWJSDGkxGSRCRERUaQwARwA3AE3Wnwtgz7xa/O3YX3Ferx78F3IOL1DiUE0YO6Eubh/6v1oaWqJye4lokpEmiHtnFvIERERDSRMAPsxl9+Fo86j8AYGd+InyzI+/PpDrC1fC7vPrjh29bCrUVxQfKq8ijo25VVMGhPSDGlQq2Iz15CIiCjSmAD2Uw6fA21tbfAFfbHuSlQddBzEqh2rUN5YrmgfnjAcD09+GBdnXByjngEqQYVUQ2pMdhIhIiKKJiaA/dSZj0AHI3fAjQ17N+CtA28hJIfC7Tq1DneNvwu35d0W023UWNSZiIgGMyaA1KdkWcYnRz/Bml1r0OhtVBy7MutKPFj4IDJNmTHqHYs6ExFRfGACSH2mtq0WT+54Ev9u+LeiPcuUhYWTF+LyIZfHqGenaNVapBvTWdSZiIgGPSaAFHWeoAcvVb2ELfu3ICgHw+1alRazx83GnHFzYp50JemSkKJPYVFnIiKKC0wAKWpkWUbp8VI8vetpnHSfVBy7LPMyPDz5YQw1D41R705Rq9RIN6SzvAsREcUVJoAUFcecx/DUzqfwz/p/KtozjBl4sPBBXJl1ZcxH21jehYiI4hUTQIooX8iHzfs245V9r8Av+cPtoiDi9rG3487xd8Z8CzWVoEKKIQWJ2sSY9oOIiChWmABSxHx54ks8tfMpHHcdV7QXpRdh0eRFGJE4IkY9O43lXYiIiJgAUgTUu+rxzK5n8PnxzxXtKfoUPFDwAK4ednXMH/eyvAsREdFpTACpxwJSAK9Vv4aNezfCFzq9Y4laUOPm3Jtxz4R7YNKYYtjDU7RqLdIMadCL+lh3hYiIqF9gAkg9UtZQhlU7VqG2rVbRPjFlIhYXLUaOJSdGPVNieRciIqKOmAAOEjJkVLdUw+FzIEmXhLzkPAiIfNLT6GnEmt1r8Le6vynaLToL7p90P2aMmNEvki1RJSLdmB7zBSdERET9ERPAQaDsZBk279uM2rZaBKQANCoNhicMx5xxczAlY0pE7hGUgni75m1sqNwAd9Adbhcg4IacGzA/fz4StAkRuVdvmbVmpOpTWd6FiIjoHJgADnBlJ8uwomwFXEEXErWJSFInwR/y46DjIFaUrcBPp/y010lgeWM5Vu5YiUOOQ4r2ccnjsLhoMcZZx/Xq+pGiElRINaT2m0SUiIiov2ICOIDJkLF532a4gi6kGdLCj171oh46tQ42jw2b921GUUZRjx4Ht3hbsLZ8LT78+kNFe4ImAfdNug/Xj7oeKkEVkffSW3pRjwxjBkQVP9JEREQXwt+WA1h1SzVq22qRqE3sMO9OEAQkahNR21aL6pZqjE0e2+XrhuQQ3jv4HtZXrIcz4FQc+96o7+G+iffBorNE4i30miAIsOqt/aY/REREAwETwAHM4XMgIAWQpE7q9LhWrUWbvw0On6PL19zbvBerdqzC/pb9ivYxljFYVLQI+Sn5vepzJOnUOqQZ06BT62LdFSIiogGFCeAAlqRLgkalgT/k77TGnT/kh6gSkaTrPEE8k8PnwPMVz2Proa2QIYfbTaIJ8/Ln4YacG/rV41WLzgKr3tovVhwTERENNP3nNzp1W15yHoYnDMdBx0Ho1DpFMiTLMlr9rchJykFect45ryHJEt4/8j7Wla+Dw68cKfzO8O/g/oL7kaJPidp76C6NSoNUQyrLuxAREfUCE8ABTICAOePmYEXZCtg8NiRqE6FVa+EP+dHqb4VJNGHOuDnnXABywH4AK3esRGVTpaJ9VOIoLCpahMK0wj54F12XqEuEKcEUlfqGRERE8YQJ4AA3JWMKfjrlp+E6gG3+NogqETlJOeesA+gMOFFSUYJ3at6BBCncrlPrMHfCXNw65tZ+9bhXrVIj3ZiOTHMmTrSdgCzLF34RERERnVP/+S1PPTYlYwqKMoouuBOILMv4a+1f8ezuZ9Hsa1Yc06g0MGvM+Kr+K+RZ8iJWQLq3TBoT0gxpENX8qBIREUUKf6sOEgKE85Z6Oew4jFU7V2GXbZeiXSWokKpPRZIu8gWke4NFnYmIiKKHCeAg5w668WLli3jjwBsIyaFwuwABOrUO2ebs8JZpkSog3VsG0YB0Y3q/egxNREQ0mPA37CAlyzI+PfopVu9eDZvHpjhWkFqAo21HYdaaO+yX25sC0r0lCAJS9CldKltDREREPccEcBCqa6vDqp2r8O+T/1a0Zxoz8fDkhyGqRDy+/XFo1dpOX9+TAtK9pRf1SDOknbNPREREFDlMAAcRb9CLl/a9hC37tyAgBcLtGpUGs8fOxh3j74BOrcP+lv0RKyDdW4IgIFmXjGR9ctTvRURERKdENAHcu3cv3nvvPRw+fBgtLS145JFHcOmll4aPy7KM119/HX/729/gcrkwbtw4zJs3D0OGDAmf4/f7sWnTJmzbtg2BQAAFBQWYN28eLBZL+Byn04kNGzagrKwMgiDgsssuwz333AO9/nQy09jYiPXr16OyshJ6vR7Tp0/H7NmzoVaffuT59ddfo6SkBAcPHkRiYiJmzJiBG264IZIh6TOlx0vx9M6nUe+uV7RfknEJHp78MIYlDAu3RaKAdCRo1VqkG9O5lRsREVEfU0XyYj6fDyNHjsS9997b6fF3330XH3zwAebPn4/f/va30Ol0ePzxx+H3+8PnbNy4EWVlZVi8eDEee+wxtLS0YMWKFYrrPP3006irq8Ojjz6KJUuWoKqqCuvWrQsflyQJy5YtQzAYxNKlS1FcXIxPP/0Ur732Wvgct9uNpUuXIjU1FcuXL8cdd9yBN954Ax9//HEkQ9IjkiyhsrESn3/9OfY371dszXa2467jWFK6BP/1xX8pkr80Qxp+ffmv8Ycr/6BI/oDTBaRNogk2jw3eoBeSLMEb9MLmsV2wgHQkJOmSkG3OZvJHREQUAxEdAZw8eTImT57c6TFZlvH+++/jpptuwiWXXAIAeOCBBzB//nx89dVXuOKKK+B2u/HJJ59g4cKFyM/PBwAsWLAAixYtQnV1NfLy8nD06FHs2rULy5YtQ05ODgBg7ty5WLZsGe68805YrVbs3r0bR48exS9/+UtYLBaMHDkSs2bNwubNm3HbbbdBFEWUlpYiGAxiwYIFEEURw4YNw5EjR7B161Zcc801kQxLt5QeK8Wa3WtwoOUAAghAJaswPGF4h6LO/pAfr+5/FS9VvQS/dDqBVgtq3JZ3G+666C4YReM579OTAtKRIKpEpBvTuZUbERFRDPXZHMCGhgbY7XZMmjQp3GY0GpGbm4vq6mpcccUVOHToEEKhECZOnBg+Z+jQoUhNTQ0ngNXV1TCZTOHkDwAmTpwIQRBQU1ODSy+9FNXV1Rg+fLjisXFhYSFeeOEF1NXVYdSoUaiursb48eMhiqdDUFBQgHfffRdOpxNmszm6AelE6bFSLCldAmfAiQRNAixaC9w+d4fafNvrt2PVzlU45jymeP3ktMlYVLQIIxNHdul+XS0gHSlmrRmp+tQOK4+JiIiob/VZAmi32wEASUnKhQVJSUnhY3a7HaIowmQynfecxMRExXG1Wg2z2aw458zk78z7nnlOenq64pz219jt9k4TwEAggEDg9OIKQRBgMBjC/90bkixhze41cAacyDRmIiSHIAiCojbfHyv/iP89+L/47Nhnitda9VY8UPAArhl+Tbf7IUDAOOu4XvX9QiJR1Ln9ffU2zoMF49ERY6LEeCgxHkqMB3EVcDe88847ePPNN8Nfjxo1Cr/73e+QlpbW62uXHS/DkbYjSDOlQavRIigFEZSCEEURkixBUAnY07RHMR9QJagwZ+IcLLxsIRJ0/XPHDINoQKY5Exq1JiLXy8zMjMh1BgvGoyPGRInxUGI8lBiP+NVnCWD76JrD4UBy8umSHw6HAyNHjgyfEwwG4XK5FKOADocj/HqLxYLW1lbFtUOhEJxOp+KcmpoaxTkOh0PRD4vFEh4NbNf+9dmjh+1mzpyJ66+/Pvx1+19ONpsNwWDwnO+9K6qPVsMb8CJRk4hAIICgFISgFtDqacVJ90nFPD8AyE/Jx+KixRiTPAYehwceeHp1/0hrL+8i6kU0uhsjcr3MzEzU19dDls+9KCZeMB4dMSZKjIcS46EU7/EQRTEigzcDWZ8lgOnp6bBYLNizZ0844XO73aipqcF3vvMdAMDo0aOhVquxZ88efOMb3wAAHD9+HI2NjcjLO1WSJC8vDy6XC4cOHcLo0aMBABUVFZBlGbm5ueFz3n77bTgcjvCj3/LychgMBmRnZ4fPefXVVxEMBsPzAMvLy5GVlXXO+X8ajQYaTecjWb39AUrWJUNUifCFfDCIBgSkABpcDR2KMZs1ZjxQ8ABmjJwBlaDqlz+4Z5Z3iXT/ZFnul+85VhiPjhgTJcZDifFQYjziV0QTQK/Xi/r606VIGhoacOTIEZjNZqSmpuK6667D22+/jSFDhiA9PR1btmxBcnJyeFWw0WjE1VdfjU2bNsFsNsNoNGLDhg3Iy8sLJ4DZ2dkoLCzEunXrMH/+fASDQWzYsAFTp06F1WoFcGoxR3Z2NlavXo05c+bAbrdjy5YtuPbaa8MJ3LRp0/DGG2/gueeeww033IC6ujp88MEHuOuuuyIZki7LT81HriUXe5v2wqly4qTnJCRZUpyTpk/Dhms3IEnbf7dKs+gssOqtnFdCRETUjwlyBFP/yspKPPbYYx3ap0+fjuLi4nAh6I8//hhutxvjxo3Dvffei6ysrPC57YWgv/jiCwSDwXMWgi4pKVEUgp47d66iELTNZsMLL7yAyspK6HQ6TJ8+HXPmzDlnIeiEhATMmDEDN954Y7fft81mUywO6anSY6Uo/qQYjV7lI1O1oEaKLgX/79L/F7XyLL0V7fIugiBgyJAhOHHiBP9aBePRGcZEifFQYjyU4j0eGo0m7h8BRzQBjFeRSgAB4NO6TzH/4/lwB90QIMCsMSPPkoc7xt/Rb5O/vijvEu//WJ2N8eiIMVFiPJQYD6V4jwcTQK4C7neuGnYVXprxElbvWo0fXPQDJEgJGJM8Jqq7cvSUSlAhzZAGs7bvayYSERFRzzEB7Ie+MeQbGJ8yHuoENRoaGvrlX2cG0YB0YzpEFT9CREREAw1/e1O3CIIAq94Ki84S664QERFRDzEBpC7TqXVIM6ZBp9bFuitERETUC0wAqUtY3oWIiGjwYAJI56VRaZBmTItaeRciIiLqe0wA6ZwStAlINaRCJahi3RUiIiKKICaA1IFapUaqPpXlXYiIiAYpJoCkwPIuREREgx9/yxOAU+VdknXJSNYnx7orREREFGVMAAlatRbpxvRBV95FkiVUNFag2dsMq96K/NR8zmckIiICE8C4l6RLQoo+ZdCVdyk9Voo1u9egxl6DgBSARqVBriUXxQXFmDZ0Wqy7R0REFFMcDolTapUaQ0xDkGpIHZTJ35LSJahqroJJY0KGMQMmjQlVzVVYUroEpcdKY91FIiKimGICGIeMGiOGmYfBqDHGuisRJ8kS1uxeA2fAiUxjJgyiASpBBYNoQKYxE86AE2t2r4EkS7HuKhERUcwwAYwjKkGFNGMahpiGQK1Sx7o7UVHRWIEaew0sOkuHkU1BEGDRWVBjr0FFY0WMekhERBR7nAMYJ/SiHunGdGhUmlh3Jaqavc0ISIFzLmjRqXVw+Bxo9jb3cc+IiIj6DyaAg1y8lXex6q3QqDTwhXydbl/nC/kgqkRY9dYY9I6IiKh/4CPgQUyr1iLLlBU3yR8A5KfmI9eSC7vPDlmWFcdkWYbdZ0euJRf5qfkx6iEREVHsMQEcpJJ0Scg2Z0Mv6mPdlT6lElQoLiiGWWNGvbsenqAHkizBE/Sg3l0Ps8aM4oJi1gMkIqK4xt+Cg4yoEpFlzhqU5V26atrQaVg+bTnGW8fDFXChwd0AV8CF8dbxWD5tOesAEhFR3OMcwEHErDUjVZ86aFf4dse0odMwNWsqdwIhIiLqBBPAQUAlqJBqSEWCNiHWXelXVIIKk9ImxbobRERE/Q4TwAHOIBqQbkyHqOK3koiIiLqGWcMAJQgCrHorLDpLrLtCREREAwwTwAFIp9YhzZh2zmLHREREROfDBHCAsegssOqtMV/hK8kSF1gQERENUEwABwiNSoM0Y1qnu1v0tdJjpVizew1q7DUISAFoVBrkWnJRXFDMEitEREQDAIdsBoAEbQKyE7L7TfK3pHQJqpqrYNKYkGHMgEljQlVzFZaULkHpsdJYd5GIiIgugCOA/ZhaUCPTlAmjaIx1VwCceuy7ZvcaOANOZBozw4+hDaIBerUe9e56rNm9BlOzpnb6OJiPjYmIiPoHJoD9lE6twwjLCDT4GjrsaRsrFY0VqLHXwKKzdJiDKAgCLDoLauw1qGis6FB/j4+NiYiI+g8Ov/RTBtHQ73b0aPY2IyAFzrn6WKfWISgF0extVrTzsTEREVH/wgSQusyqt0Kj0sAX8nV63BfyQVSJsOqt4bazHxsbRANUggoG0YBMYyacASfW7F4DSZb66m0QERHFPSaA1GX5qfnIteTC7rN3eCwtyzLsPjtyLbnIT80Pt3fnsTERERH1DSaA1GUqQYXigmKYNWbUu+vhCXogyRI8QQ/q3fUwa8woLihWLOzo6WPjM0myhHJbOf5S8xeU28o5WkhERNRLXARC3TJt6DQsn7Y8vKDD4XNAVIkYbx3f6YKOMx8bd1bGprPHxmdqXzxy0H4QISEEtaxGjiWHi0eIiIh6gQkgddu0odMwNWtql0q6tD82rmqugl6tVzwGbn9sPN46XvHYuF374hFnwAmLzgKz3gyn1xlePLJ82nImgURERD3AR8DUIypBhUlpk3DVsKswKW3SOev59eSxMcDFI0RERNHEBJCirv2x8XjreLgCLjS4G+AKuDDeOv6co3hcPEJERBQ9fARMfaI7j42Bri0ecfgc5108QkRERJ1jAkh9pv2xcVf0dvEIERERnRsfAVO/1JOag0RERNQ1TACpX+rp4hEiIiK6MP72pH7rzMUj7oAbJ5wn4A64z7t4hIiIiC6McwCpX2tfPFLZVAkYAbiBCSkTOPJHRETUC0wAqd9rXzwyZMgQnDhxosOcQCIiIuoeDqMQERERxRkmgERERERxhgkgERERUZxhAkhEREQUZ5gAEhEREcUZJoBEREREcYYJIBEREVGcYQJIREREFGeYABIRERHFGe4EEgGiGL0wRvPaAxHjocR4dMSYKDEeSoyHUrzGI17f95kEmftqEREREcUVPgLupzweD37+85/D4/HEuiv9AuOhxHh0xJgoMR5KjIcS40FMAPspWZZx+PBhcID2FMZDifHoiDFRYjyUGA8lxoOYABIRERHFGSaARERERHGGCWA/pdFocMstt0Cj0cS6K/0C46HEeHTEmCgxHkqMhxLjQVwFTERERBRnOAJIREREFGeYABIRERHFGSaARERERHGGCSARERFRnOFmeFGyd+9evPfeezh8+DBaWlrwyCOP4NJLLw0ft9vt2Lx5M8rLy+FyuTB+/HjMnTsXQ4YMUZzz0ksvoby8HF6vF1lZWZg5cya+8Y1vhM9xOp3YsGEDysrKIAgCLrvsMtxzzz3Q6/V9+n67oq9iUlxcDJvNprj37NmzceONN0b9PXZHJOJRX1+Pl156Cfv27UMwGERBQQHmzp0Li8USPmegfEb6Kh4D5fPxzjvvYPv27Th27Bi0Wi3y8vJwxx13ICsrK3yOLMt4/fXX8be//Q0ulwvjxo3DvHnzFDHx+/3YtGkTtm3bhkAggIKCAsybN2/AfUb6Mh7x9hn5+OOPUVpaisOHD8Pj8eCPf/wjTCaT4l4D4TNC3cMRwCjx+XwYOXIk7r333g7HZFnGE088gYaGBvznf/4nfv/73yMtLQ2/+c1v4PV6w+etXr0ax48fx89//nP84Q9/wKWXXopVq1bh8OHD4XOefvpp1NXV4dFHH8WSJUtQVVWFdevW9cl77K6+igkA3HbbbXj++efD/5sxY0bU31939TYeXq8Xjz/+OARBwH//93/jN7/5DYLBIH73u99BkqTwtQbKZ6Sv4gEMjM/H3r17ce211+Lxxx/Ho48+ilAohKVLlyp+Ht5991188MEHmD9/Pn77299Cp9Ph8ccfh9/vD5+zceNGlJWVYfHixXjsscfQ0tKCFStWKO41ED4jfRkPIL4+Iz6fD4WFhZg5c+Y57zUQPiPUTTJF3a233ir/61//Cn997Ngx+dZbb5Vra2vDbaFQSL733nvljz/+ONx2xx13yJ999pniWvfcc0/4nLq6OvnWW2+Va2pqwsd37twp33bbbXJTU1O03k5ERCsmsizLCxYskLdu3RrF3kdeT+Kxa9cu+bbbbpNdLlf4HJfLJd92223y7t27ZVkeuJ+RaMVDlgfm50OWZdnhcMi33nqrXFlZKcuyLEuSJM+fP19+9913w+e4XC559uzZcmlpafjr22+/Xf7yyy/D5xw9elS+9dZb5f3798uyPHA/I9GKhyzH12fkTBUVFfKtt94qO51ORftA/YzQ+XEEMAaCwSAAKApwqlQqaDQa7Nu3L9w2duxYbNu2DU6nE5Ik4YsvvkAgEMCECRMAANXV1TCZTMjJyQm/ZuLEiRAEATU1NX30biIjUjFp97//+7+YO3cufvazn+G9995DKBTqmzcSIV2JRyAQgCAIinM0Gg0EQQifM1g+I5GKR7uB+Plwu90AALPZDABoaGiA3W7HpEmTwucYjUbk5uaiuroaAHDo0CGEQiFMnDgxfM7QoUORmpoaPmegfkaiFY928fIZ6YqB+hmh8+McwBjIyspCamoqXnnlFfz4xz+GXq/H1q1b0dTUBLvdHj5v0aJFePLJJzF37lyo1WpotVo88sgjyMzMBHBqTlRiYqLi2mq1GmazWXGdgSBSMQGA7373uxg1ahTMZjP279+PV199FS0tLbjrrrti8M56pivxyMvLg06nw+bNm/HDH/4QsizjlVdegSRJ4XMGy2ckUvEABubnQ5IkvPjiixg7diyGDx8OAOH3lJSUpDg3KSlJ8f0XRbHDfK6zzxlon5FoxgOIr89IVwzEzwhdGBPAGBBFEY888gjWrl2LuXPnQqVSYeLEiZg8eTLkMzZmee211+ByufDLX/4SCQkJ+Oqrr7Bq1Sr8+te/Dv+ADxaRjMn1118fPn/EiBEQRRHr16/H7NmzB8y2R12JR2JiIhYvXowXXngBH3zwAQRBwBVXXIFRo0ZBEIQYv4PIimQ8BuLno6SkBHV1dfj1r38d6670C9GOBz8jFA+YAMbI6NGj8cQTT8DtdiMYDCIxMRH/9V//hdGjRwM4tZrxww8/xIoVKzBs2DAAwMiRI7Fv3z58+OGH+PGPfwyLxYLW1lbFdUOhEJxOp2JF20ARiZh0ZsyYMQiFQrDZbIrVcf3dheIBAAUFBXjmmWfQ2toKtVoNk8mE+fPnIyMjAwAG1WckEvHoTH//fJSUlGDHjh147LHHkJKSEm5v//45HA4kJyeH2x0OB0aOHBk+JxgMwuVyKUa9HA5H+PUD7TMS7Xh0ZjB/RrpioH1GqGs4BzDGjEYjEhMTceLECRw8eBCXXHIJAIRXaJ09kqNSqcIjHnl5eXC5XDh06FD4eEVFBWRZRm5ubh+9g8jrTUw6c+TIEQiC0OERxkBxrnicKTExESaTCRUVFWhtbcXFF18MYHB+RnoTj87018+HLMsoKSnB9u3b8atf/Qrp6emK4+np6bBYLNizZ0+4ze12o6amBnl5eQBOJc1qtVpxzvHjx9HY2Bg+Z6B8RvoqHp0ZzJ+RrhgonxHqHo4ARonX60V9fX3464aGBhw5cgRmsxmpqan48ssvkZiYiNTUVNTW1uLFF1/EJZdcgoKCAgCn5jxlZmZi/fr1uPPOO2E2m/HVV1+hvLwcP//5zwEA2dnZKCwsxLp16zB//nwEg0Fs2LABU6dOhdVqjcn7Pp++iEl1dTUOHDiACRMmwGAwoLq6Ghs3bsSVV14ZnhjdX/Q2HgDw97//HUOHDkViYiKqq6vx4osv4nvf+154lGIgfUb6Ih4D6fNRUlKC0tJS/OxnP4PBYAjPtTIajdBqtRAEAddddx3efvttDBkyBOnp6diyZQuSk5PDSbHRaMTVV1+NTZs2wWw2w2g0YsOGDcjLywsnAAPlM9JX8Yi3zwhwao6f3W4P//zV1tbCYDAgNTUVZrN5wHxGqHsE+XxDJ9RjlZWVeOyxxzq0T58+HcXFxXj//ffxpz/9CXa7HcnJyfjmN7+JW265BaJ4Oic/ceIENm/ejP3798Pr9SIzMxPf//738c1vfjN8jtPpRElJiaI459y5c/tlcc6+iMmhQ4dQUlKCY8eOIRAIID09Hd/85jdx/fXX97u5O5GIx+bNm/Hpp5/C6XQiPT0d3/72t/G9731PMUo6UD4jfRGPgfT5uO222zptX7BgAa666ioAp4v8fvzxx3C73Rg3bhzuvfdexWPK9sLHX3zxRbg4dmeFoPv7Z6Sv4hGPn5HXX38db7755nmvMxA+I9Q9TACJiIiI4gznABIRERHFGSaARERERHGGCSARERFRnGECSERERBRnmAASERERxRkmgERERERxhgkgERERUZxhAkhEREQUZ5gAEhEREcUZJoBEREREcYYJIBFRFPn9fkiSFOtuEBEpiBc+hYhoYKmoqMCvf/1rPPLII7j00ksVx0pLS/H0009j6dKlyMvLw7Fjx7BlyxZUVFTA7/dj2LBhuOWWW3DxxReHX+N0OvH2229j9+7daGhogEqlwtixYzF79myMHDkyfF5lZSUee+wxLFy4EHV1dfj73/8Ou92ODRs2wGQy9dXbJyK6II4AEtGgM2HCBKSkpODzzz/vcOzzzz9HRkYG8vLyUFdXh1/84hc4duwYbrzxRtx5553Q6XR44oknsH379vBrTp48ia+++gpTpkzBXXfdhe9///uora3F//zP/6C5ubnDPd566y3s2LED3//+9/HDH/4Qosi/tYmof+G/SkQ06AiCgCuvvBJ//vOf4Xa7YTQaAQCtra0oLy/HzJkzAQAvvvgiUlNTsWzZMmg0GgDAtddei1/96lfYvHlzePRw+PDheOqpp6BSnf6b+Zvf/CYWLVqETz75BLfccovi/oFAAMuXL4dWq+2Lt0tE1G0cASSiQWn69OkIBAL45z//GW7btm0bQqEQvvnNb8LpdKKiogKXX345PB4PWltb0draira2NhQUFODEiRPh0T2NRhNO/iRJQltbG/R6PbKysnD48OFO783kj4j6M44AEtGgNHToUOTk5ODzzz/H1VdfDeDU498xY8YgMzMTNTU1kGUZr732Gl577bVOr+FwOGC1WiFJEt5//3189NFHaGhoUCzqMJvNHV6Xnp4enTdFRBQhTACJaNCaPn06/vjHP6KpqQmBQAAHDhzA3LlzASCcxH3/+99HQUFBp6/PzMwEALzzzjt47bXX8B//8R+YNWsWzGYzBEHAxo0bIctyh9dx9I+I+jsmgEQ0aE2dOhUbN27EF198Ab/fD7VajalTpwIAMjIyAABqtRqTJk0673X++c9/YsKECbj//vsV7S6XCwkJCdHpPBFRFHEOIBENWomJiZg8eTI+//xzfP755ygsLERiYiIAICkpCRMmTMDHH3+MlpaWDq9tbW0N//eZiz/affnll52uACYiGgg4AkhEg9o3v/lNrFy5EgAwa9YsxbF7770Xv/zlL/HII4/gW9/6FtLT0+FwOFBdXY3m5mY88cQTAIApU6bgzTffxLPPPou8vDzU1taitLQ0PIpIRDTQMAEkokHt4osvhslkgizLiuLOAJCdnY3ly5fjjTfewKeffoq2tjYkJSVh5MiRuPnmm8PnzZw5E16vF1988QW2bduGUaNGYcmSJXjllVf6+u0QEUWEIHc2g5mIaJAIhUK47777MGXKlA5z+IiI4hXnABLRoPbVV1+htbUV06dPj3VXiIj6DT4CJqJB6cCBA/j666/x1ltvYdSoUbjoooti3SUion6DCSARDUofffQRPv/8c4wcORILFiyIdXeIiPoVzgEkIiIiijOcA0hEREQUZ5gAEhEREcUZJoBEREREcYYJIBEREVGcYQJIREREFGf+P83IqXJ3Z/Y2AAAAAElFTkSuQmCC"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "You can always customize the marker shape, so instead of circular markers, let's use `+`.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "ax = sns.regplot(x='year', y='total', data=df_tot, color='green', marker='+')\nplt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x44c9168>",
            "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": "Let's blow up the plot a little so that it is more appealing to the sight.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "plt.figure(figsize=(15, 10))\nsns.regplot(x='year', y='total', data=df_tot, color='green', marker='+')\nplt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x49fca40>",
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1500x1000 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "And let's increase the size of markers so they match the new size of the figure, and add a title and x- and y-labels.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "plt.figure(figsize=(15, 10))\nax = sns.regplot(x='year', y='total', data=df_tot, color='green', marker='+', scatter_kws={'s': 200})\n\nax.set(xlabel='Year', ylabel='Total Immigration') # add x- and y-labels\nax.set_title('Total Immigration to Canada from 1980 - 2013') # add title\nplt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x5646200>",
            "image/png": 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M+xEjRmjVqlWqrq6ulxsAAAAAkP5iZiw+PsY0TavjnJaqUJWe3va0XvziRYViiR8aDG03VHcOv1PD2w+3KN3pcdld8rg8ysnIkWEYVscBAEtRuEOxWExPPPGEBg4cqB49esS3jxw5UqNGjVLHjh116NAhLVu2TL/5zW/061//WjabTT6fTw6HQ9nZ2Qn35/F45PP5JEk+n095eXkJ63a7XTk5OQn7HFu2191H3Vrd/zt2TPx0v+42Pp+vwcI9HA4rHP6/q9QbhqHMzMz4r9Nd3XNoDc8F6YfjD1bjGISVOP5gJY4/WKmlj7/KUKXKA+WKxCIKRoP6/nvflyT9+dI/W3bx0FMRjAa14osVemrbU6oKVSWs9cjtodlFs/Xtrt9O6ff1sdkMw4j/PtORqXxXvrIyslosQyq/TgAgUbhD0qJFi7R//3498MADCdsvvPDC+K979Oihnj176gc/+IE2b96ccFZ7qnrppZf0wgsvxH/fu3dvPfLII+rQoYOFqZKvc+fOVkdAG8bxB6txDMJKHH+wEscfrNTcx19tuFYlNSUynIYKcwolSYFwIP4Tzx3ad5A7w32iu0gJ0VhUr+x4RX/82x91sOpgwlqHrA76wXk/0HVDrpPDlvrVzDdf/w7ZHVSQWSC3o+W/Dnz/A5DqUv+7OprVokWL9Pe//13333+/2rVrd8J9O3XqpNzcXB06dEhFRUXKz89XJBJRTU1NwlnuFRUV8bPP8/PzVVlZmXA/0WhU1dXVCfvs3LkzYZ+Kior4Wt3/6852r1P3+2+eHV/n2muv1dVXXx3/fd2n4CUlJfHZ8OnMMAx17txZhw4dSvsfrUT64fiD1TgGYSWOP1iJ4w9Wau7jLxQNyRvwqiZcU28tGA3G/x1XUlqS0me4m6apvx36mx7b8Jh2VyReJy3LkaWbBt2k6wdcr0xHprylXotSNk3d62837HIFXZIplVeXt2gGvv8h2RwOR6s7KROpgcK9jTJNU48//rg++eQT3XffffXGtTSkrKxM1dXVKigokCT16dNHdrtdGzdu1Pnnny9JOnjwoEpLSzVgwABJ0oABA1RTU6Pdu3erT58+kqRNmzbJNE3169cvvs+KFStUUVERHyWzYcMGZWZmqlu3bvF9li1bpkgkEv9UfcOGDerSpctx57dnZGQoIyPjuM+/tTBNs1U9H6QXjj9YjWMQVuL4g5U4/mClZB9/0VhUvqBPFaGK497vsdtT+fjf6t2q4g3F+kfJPxK2OwyHJvabqFsH36p8V76k1Pp3aTAaPO6aYRhy2pxy2pwyDEORaES14doT3l+mIzPZEeOs+Pr7I35NemWSJGnFhBXN+vwApD8K9zZq0aJF+vDDD/Wzn/1MmZmZ8bPFs7Ky5HQ6FQgE9Pzzz2vUqFHKz8/X4cOH9fTTT6tz584aMWJEfN9LL71UTz75pHJycpSVlaXHH39cAwYMiBfu3bp108iRIzVv3jzNmjVLkUhEjz/+uC644AIVFh790cARI0aoW7dumjt3rm666Sb5fD49++yzuuKKK+KF+ejRo/X888+ruLhY11xzjfbv36/Vq1dr2rRpLf/iAQAAAABOm2maqghVyBf0KRqLWh3ntOyv2q+FmxZqzYE19dbG9hirmUNnqktOFwuSNc7da+5ucLvdsMtu2BPmtk9dPfWk97f62tVJzQcA6YTCvY166623JEn33Xdfwva77rpLF198sWw2m/bt26e1a9eqpqZGhYWFGj58uKZMmZJw1vi0adNkGIYeffRRRSIRjRgxQjNnzky4z3/5l3/RokWL9MADD8gwDI0aNUrTp0+Pr9tsNt1zzz1auHChfvWrX8nlcmnMmDGaMmVKfJ+srCz96le/0qJFi3TPPfcoNzdX1113ncaOHdsMrw4AAAAAoLmYpqmqcFX8gqjpzBvw6oktT+iV3a8oaiZ+aHBOx3M0e/hsDSwYaFG6U3ds0Q4AaBrDTKWfYQJaQElJicLhsNUxTpthGDrjjDP09ddfp9SPIqJt4PiD1TgGYSWOP1iJ4w9WSsbxVx2qVnmwXKFoqEm3C0aD8bOw514y1/IZ7rXhWj2741k9t/05+aP+hLX++f01Z/gcndvpXIvSNV0wGpTNsCnPmSePyyObYUtYD0QC8TPbl45fetKLpTbHyBUrv/8xUqZ1ysjIYIY7mgVnuAMAAAAAgGZVG66VN+A94azwdBCOhfXK7le0ZMsSlQcTLxraOauzZhXN0mXdL6tXWKcyu82uM9xnNFi0N8TtcFM4A8AJULgDAAAAAIBmEYgE5A145Y/4T7jfyYr4Y9cbU9on+wx40zS15sAaLdi4QF/VfJWw5nF6dOuQW3VNn2vktDuT+rjNyW6zy+P0NLpoBwA0DoU7AAAAAABIqlA0JG/Aq5pwTaP2P95FOxvykw9+ctJ9Foxd0Oj7O5m/H/m7ijcUa1v5toTtLrtL3xvwPd048EblZOQk7fGam82wyePyKN+VT9EOAM2Awh0AAAAAACRFOBZWeaBcVaEqq6Octl2+XSreWKy/Hfpbwna7YdeVva/U7UNuV/vM9halazqKdgBoGRTuAAAAAADgtERikaNFe7jqlC5oOfeSuSdcD0aD8TPbH73o0Wa9aOqhmkNatHmR3vryLZlKfC7f7vpt3THsDvXM69lsj59shmEo35Uvj9Mju81udZyUdLKRR4FIoMFfHw8z7oG2jcIdAAAAAACckkgsIl/Qp8pQ5SkV7XWaUqC77K5mKdwrghV6etvTWrFzhcKxcMLa8PbDNWf4HA1rNyzpj9sYwWgwPnZn7iVzG/X8DcOQx3n0jHaK9hOb9MqkRu87dfXUk+6z+trVpxMHQJqjcAcAAAAAAE0SjUVVEaqQL+g7raI9FQSjQb3wxQt6Ztszqg5XJ6z1yuul2UWzdcEZF8gwDIsSNo1hGMpz5inflS+HjdoHAFoa33kBAAAAACflj/jjZ4GumLCCkQltVMyMyRvwyhfwKWbGrI5zWqJmVG/sfUOPb35cJf6ShLUOmR00feh0XdHzirQprW2GTXnOPHlcnrTJnCpWTFhxwvVAJBA/s33p+KVyO9wtEQtAmuI7MAAAAAAAOKmKYIVqy2tVHihP67PaTdPUuq/Xad7GedpbuTdhLScjRzcNuknX9bsubUrVuouhNueM9kxHZqsek9KUDxDdDjcfOAI4IQp3AAAAAABwXMFoUKX+UgWjQXXM7Wh1nNOyqWyTijcUa0PphoTtGbYMTeo3STcPulkel8eidE1jt9njo2Nshs3qOACA/0XhDgAAAAAA6omZMZUHylURqpBpmmkzw7wh+6r2af7G+frgqw8Sthsy9J2e39GMoTPUObuzRemaxmbYVOgulMfloWgHgBRE4Q4AAAAAABLUhmtV6i9VOBa2OsppKfWXavGWxXp9z+uKmtGEtVGdR2l20Wz1y+9nUbqmsxt29cjtoayMLKujAACOg8IdAAAAAABIkiKxiEr9paoJ11gd5bTUhGu0dPtSPb/jeQWigYS1QQWDNGf4HJ3V8SyL0jVNhi1Duc5cOW1OGYaR1j9pAABtAYU7AAAAAABQRbBC3oBXMTNmdZR6XHaXFoxdcNL9QtGQXt79spZsWaKKUEXCWtfsrppVNEuXdLsk5UrrYDRYb1uGLUMel0e5zlwFIoF45kAkUG/fb+KingBgHQp3AAAAAADasLqLojamyE1VMTOmd/e/q4WbFurrmq8T1gpcBZo2ZJom9JmgDFuGRQlP7O41d8d/bciQ3WaX3bA3uO/U1VNPen+rr12dtGwAgKahcAcAAAAAyB/xn3D92DKWM2xbh2gsqvJguSqCFSffOYV9evhTFW8o1he+LxK2Z9ozNWXgFN0w4Ia0mHluyJDD5uBCqACQ5ijcAQAAAACa9MqkRu/LGbbprzJUKW/Aq2gsevKdU9T28u2at3GePjv8WcJ2u2HXhD4TdNuQ21ToLrQoXeNlZWTpxatflNvhPu4+gUgg/r5bOn7pCfdF8mU6MvmeBqDRKNwBAAAAAGgjApGAygJlaT0+5mDNQS3ctFDv7Hun3tol3S7RzGEz1T23uwXJmibTkalCd2GTy3O3w81PkABACqNwBwAAAABoxYQVJ1znDNv0Fo1F5Q14VRmqtDrKKfMFfXpy65NauXOlImYkYe3MDmdqzvA5Glw42KJ0jZfpyFSBu4DSHABaKQp3AAAAAECTyj/OsE0vFcEKlQfL03Z8jD/i1/NfPK+l25aqNlKbsNbH00dziuZoVOdRMgzDooSN43a4Vegu5L0DAK0chTsAAAAAAK2QP+JXmb9MwWjQ6iinJBKL6LU9r2nxlsXyBrwJa52yOmnG0Bm6vOflsht2ixI2DkU7ALQtFO4AAAAAALQikVhE3oBXVaEqq6OcEtM09ZeDf9H8jfO1r2pfwlpuRq5uGXyLru13rVx2l0UJG8ftcKvAVaCsjCyrowAAWhCFOwAAAAAArUDMjMkX9MkX9Mk0TavjnJINpRv02IbHtLlsc8J2p82pyf0n66ZBNynXmWtRusZx2V0qcBcoOyPb6igAAAtQuAMAAAAAkOaqQlXyBryKxCIn3zkF7a3cq+INxVr39bqE7TbZNK7XOE0fOl0dszpalK5xKNoBABKFOwAAAAAAaSvd57QfqT2ixzc/rjf2vqGYYglrF5xxgWYXzVZvT2+L0jWO0+5UgatAOc4cq6MAAFIAhTsAAAAAAGkmHAurzF+mmnCN1VFOSVWoSs9se0YvfPGCQrFQwtrQwqGaM3yORnQYYVG6xnHancp35bfIiJtMR6ZWX7u62R8HAHD6KNwBAAAAAEgTMTOm8kC5KkIVaTmnPRgNauWulXpq61OqDFUmrHXP6a47iu7QRV0vkmEYFiU8uQxbhgrcBSk/Sx4AYA0KdwAAAADASXGGrfUqQ5XyBryKxqJWR2myqBnVO/ve0cJNC3W49nDCWqG7ULcPuV1X9b5KDlvq1hQOm+No0Z6Rm9IfCAAArJW6f5IBAAAAAIC0ntNumqY+OfyJijcUa1fFroS1LEeWbhx4o7434HvKdGRalPDkbIZN+a585bvyKdoBACdF4Q4AAAAAQAqKxCIqC5SpOlRtdZRTstW7VcUbivWPkn8kbHcYDl3T9xpNGzJN+a58a8I1gmEYynPmqcBVILvNbnUcAECaoHAHAAAAACCFmKap8mC5fEFfWs5pP1B9QAs2LtCaA2vqrY3tPlYzh81Ul5wuFiRrvOyMbBW6C+W0O62OAgBIMxTuAAAAAACkiJpwjcr8ZQrHwlZHabLyQLme2PKEXt79sqJm4pz5czqeo9nDZ2tgwUCL0jWO2+FWobswpUfcAABSG4U7AAAAAAAWC0VDKvWXyh/xWx2lyWojtXpu+3N6dsez9fL3z++v2UWzdV7n8yxK1zgZtgwVuguV48yxOgoAIM1RuAMAAAAAYJGYGVN5oFwVoYq0Gx8TiUX0yu5X9MSWJ1QeLE9Y65zVWTOHzdTYHmNlM2wWJTw5u82ufFe+PE4PF0QFACQFhTsAAAAAABaoCFaoPFiuaCx68p1TiGmaWrN/jeZvmq+vqr9KWPM4Pbp1yK26ps81KT3/3GbY5HF5lO/KT+kPBAAA6YfCHQAAAACAFuSP+FXmL1MwGrQ6SpP97cDf9NAHD2mrd2vCdpfdpe8N+J5uHHijcjJSdyyLYRjyOI8W7Xab3eo4AIBWiMIdAAAAAIAWEI6FVeYvU024xuooTbbLt0vzNs3Tx19/nLDdbth1Ze8rdfuQ29U+s71F6Ron15mrAneBMmwZVkcBALRiFO4AAAAAADSjmBmTL+iTL+hLuznth2sPa9GmRXrzyzdlKjH7t7t8W3cU3aGeeT0tStc42RnZKnQXpvSIGwBA60HhDgAAAABAM6kKVckb8CoSi1gdpUkqQ5V6euvTWrFzhUKxUMJaUfsizSmao6L2RRala5xMR6YK3YVyO9ynfV/+iF+TXpkkSVoxYYUyHZmnfZ8AgNaJwh0AAAAAgCQLRAIqC5QpEAkk5f6C0aDuXnO3JGnuJXPlsruScr8NPc6LX7yop7c9repwdcJaz7ye+tnon6koO7WLdrfDrUJ3IaU4AMASFO4AAAAAACRJOBaWN+BVdaj65DunkKgZ1Zt739SizYtU4i9JWGvvbq/pw6ZrfK/x6tK5i44cOZKSo3EyHZkqcBdQtAMALEXhDgAAAADAaYrGovIFfaoIVaRkGX08pmnqo68/0ryN87Snck/CWk5GjqYOmqrJ/SbL7XDLMAyLUp5YVkaWClwFSRkdAwDA6aJwBwAAAADgFJmmqYpQhXxBn6KxqNVxmmRz2WYVbyjWP0v/mbA9w5aha/tdq1sG3SKPy2NRupPLzshWgbug2cbrAABwKijcAQAAAAA4BdWhankDXoVjYaujNMm+qn2av3G+Pvjqg4Tthgx9p+d3NGPoDHXO7mxRupPLceaowFUgp91pdRQAAOqhcAcAAAAAoAn8Eb+8AW/SLojaUkr9pXpiyxN6bc9ripqJZ+OP6jxKs4tmq19+P4vSnVyuM1f5rnyKdgBASqNwBwAAAACgEdL1gqg14Rot275My3csVyCa+CHBoIJBmj18ts7ueLZF6U4u15mrAneBMmwZVkcBAOCkKNwBAAAAADiBdL0gajgW1qpdq7RkyxJVhCoS1rpmd9Wsolm6uNvFshk2ixKeWEsW7f6I/4Trx/40Q2N+siHTkXnamQAA6YnCHQAAAACABtRdELU8UK6YGWvWxwpGg41eP9m+MTOmvx78qxZuWqiDNQcT1vJd+bptyG2a0GdCSp4xbhiGcjJyWvyM9kmvTGr0vlNXTz3pPquvXX06cQAAaYzCHQAAAACAb2jpC6LevebuRu/7kw9+cty1mnCNSvwl9Ur5THumpgycohsG3KCsjKxTztlcrCraAQBINgp3AAAAAAD+V7peEDUQCajEX6LaSG3Cdrth14Q+EzRtyDS1c7ezKN2JpcKM9hUTVpxwPRAJxM9sXzp+qdwOd0vEAgCkIQp3AAAAAECbF4qG5A14VROuseTx514y94TrwWgwfmb7oxc9KpfdJUn6uuZrLdmyRO/uf7febS7udrFmDZul7rndkx84CbIzslXoLpTT7rQ6SpNmrrsdbma0AwCOi8IdAAAAANBmRWNRlQfLVRmqtPSCqHUFemP39Uf8emrrU1q5a2W9sTcjO4zUnOFzNKRwSLJjJkWmI1OF7kLOEgcAtEoU7gAAAACANqclL4iaTDEzpqXblmr5juWqiSSejd/H00ezi2br/M7nyzAMixIen9vhVqG7kLPDAQCtGoU7AAAAAKBNqQnXyBvwKhQNWR2l0aKxqHxBn0r9pfrC90XCWsfMjpo5bKYu73m57IbdooTH57Q7VeguVHZGttVRAABodhTuAAAAAIA2IRgNqsxfJn/Eb3WURjNNUx8e/FDFG4t1uPZwwlpuRq5uHnyzJvWb1KSRNC3FYXOowF2gPGee1VEAAGgxFO4AAAAAgFYtGovKG/CqMlRpdZQm2Vi6UY9teEybyjYlbM+wZWhy/8m6edDNynXmWpTu+Bw2hzwujzxOT0qOtgEAoDlRuAMAAAAAWqV0ndO+t3Kv5m2cp78e/Gu9tTxnnh679DF1z+1uQbITo2gHAIDCHQAAAADQCtWEa1TmL1M4FrY6SqOV+Ev0+ObHtXrPasWU+AHB+Z3P16HaQ3LZXeqY1dGihA1z2BzKd+Urz5lH0Q4AaPMo3AEAAAAArYY/4pc34FUgErA6SqNVhaq0dPtSPb/jeYViiRdyHVI4RHOGz9HgwsG6e83dFiVsWFsq2jMdmVp97WqrYwAA0gCFOwAAAAAg7YWiIXkDXtWEa6yO0mihaEgv7XpJT219qt58+e453XVH0R26qOtF8TJ7wdgFVsSsx2FzyOP0tImiHQCApqJwBwAAAACkrWgsqvJguSpDlTJN0+o4jRIzY3p739tatGmRDtUeSlgrdBfq9iG366reV8lhS61/sjtsDnXM7qicYI7VUQAASFmp9ac3AAAAAACNEDNj8gV9qghWpM0FUU3T1CeHP9G8DfO0s2JnwlqWI0s3DrxR3xvwPWU6Mi1K2LC60TEel0f57nz5DX/afLgBAEBLo3AHAAAAAKSVylClygPlisQiVkdptG3ebSreWKy/H/l7wnaH4dA1fa/RrYNvVYG7wKJ0DfvmjHbGxwAAcHIU7gAAAACAtFAbrlVZoEyhaOjkO6eIA9UHtGDjAq05sKbe2tjuYzVj2Ax1zelqQbLja0sXQwUAINko3AEAAAAAKS0UDaksUKbacK3VURqtPFCuJVuXaNWuVYqa0YS1szuerTnD52hgwUCL0jXMYXPI4/LI4/RQtAMAcIoo3AEAAAAAKSkdL4haG6nV8h3LtWz7Mvkj/oS1/vn9Nbtots7tdG5KFdp2m/3ojHaKdgAAThuFOwAAAAAgpZimqYpQhXxBn6Kx6MlvkAIisYhe3fOqntj8hLxBb8Ja56zOmjlspsb2GCubYbMoYX02wyaPy6MCVwFFOwAASULhDgAAAABIGceb0x6MBnX3mrslSXMvmSuX3WVFvHpM09Tar9Zq/sb5OlB9IGEtz5mnWwffqol9J8ppd1qUsD7DMJSbkatCd6HsNrvVcQAAaFUo3AEAAAAAlkvHOe3/OPIPFW8s1lbv1oTtLrtL1/e/XjcOvFG5zlyL0jUsOyNbhe7ClPoAAACA1oTCHQAAAABgmXSc076rYpfmbZinjw99nLDdJpuu7H2lbh96uzpkdrAoXcPcDrcK3YXKdGRaHQUAgFaNwh0AAAAA0OLScU774drDWrRpkd788k2ZSvxw4Ntdvq1ZRbPUK6+XNeGOI8OWoUJ3oXKcOVZHAQCgTaBwBwAAQKP5I35NemWSJGnFhBWcKQnglBxvTnuqqgxV6umtT2vFzhUKxRIzD2s3THcOv1NF7YssStcwu82ufFe+PE4PF0QFAKAFUbgDAACkEQpvAOksGA2qzF8mf8RvdZRGCUaDevGLF/X0tqdVHa5OWOuR20Ozi2ZrdJfRKVVoG4Yhj9OjfFc+F0QFAMACFO4AAAAAgGYVjUXlDXhVGaq0OkqjRM2o3tz7phZtXqQSf0nCWnt3e00fOl3jeo2Tw5Za/6TOdeaq0F2YcrkAAGhL+FMYAAAAANAs6ua0lwfKFTNjVsc5KdM09fGhj1W8oVh7KvckrGU7snXT4Js0ud9kuR1uixI2LCsjS+3c7eS0O62OAgBAm0fhDgAAAABIuppwjcr8ZQrHwo3aPxgNNnr9ZPtKksvuatTj1tlStkWPbXhM/yz9Z8L2DFuGru17rW4ZfIs8Lk+T7rO5uR1uFboLGS8GAEAKoXAHAAAAACRNMBpUqb9UgUigSbe7e83djd73Jx/85KT7LBi7oFH3tb9qv+ZvnK+1X61N2G7I0OU9LteMYTN0RvYZjc7WEpx2pwrdhcrOyLY6CgAA+AYKdwAAAADAaYvEIvIGvKoKVVkdpVHKAmV6YvMTenXPq4qa0YS18zqdp9nDZ6t/fn+L0jXMYXOowF2gPGee1VEAAMBxULgDAAAAAE6ZaZoqD5arIlhxWnPa514y94TrwWgwfmb7oxc92uSRMXVqwjV6dvuzem7HcwpEE8/CH1gwUHOK5ujsTmef0n03F4fNoXxXvvKceTIMw+o4AADgBCjcAQAAEOeP+E+4fuyIiMaMi2CuMNC6VYWq5A14FYlFTvu+mlKgu+yuJhfu4VhYL+96WUu2LpEv6EtY65LdRbOGzdIl3S+RzbA16X6bk91mV74rXx6nh6IdAIA0QeEOAACQQqwuvCe9MqnR+05dPfWk+6y+dnWTHh9AevBH/PIGvE2e026FmBnTmv1rtGDTAh2sOZiw5nF6dNuQ2/Tdvt9Vhi3DooT12Qzb0aLd5UmpDwAAAMDJUbgDAACkEApvAKksHAvLG/CqOlRtdZRGWX94vYo3Fmt7+faE7Zn2TE0ZOEVTBkxJqQuP2gybPC6P8l35FO0AAKQpCncAAADErZiw4oTrgUggXvQvHb9Uboe7JWIBsFjMjMkX9MkX9Mk0TavjnNQXvi9UvKFYnx7+NGG73bBrQp8JmjZkmtq521mUrj7DMOKjY+w2u9VxAADAaaBwBwAASCFWF95NGUHjdriZ0Q60Acmc097cDtYc1KJNi/T2vrfrrV3c7WLNGjZL3XO7W5CsYYZhyOM8ekY7RTsAAK0DhTsAAEAKofAGkCrSaU67L+jTU1uf0spdKxWOhRPWRrQfoTuH36kh7YZYlK4+wzCUm5GrAneBHDb+WQ4AQGvCn+wAAAAAgLhILKKyQFlazGkPRAJ6/ovntXTbUtVEahLWeuf11pzhc3R+5/NlGMZpP1YwGtTda+6WJM29ZK5cdtcp3U+u82jRnkoXaQUAAMlD4Q4AAAAASKs57dFYVK98+Yoe3/y4ygJlCWsdMjto5rCZ+k7P78hupM6YllxnrvJd+XLanVZHAQAAzYjCHQAAAADSgD/i16RXJikjI0PLxy+X2568azikw5x2l92l+ZfN14cHP9Qd796hfVX7EtZzMnJ0y+BbNKnfpFM++7w5ZGdkq9BdSNEOAEAbQeEOAAAAAG1UOs1p31i6UY9teEybyjYlbHfanLqu/3W6adBNynPmWZSuvqyMLBW6C1Oq/AcAAM2Pwh0AAAAA2phQNCRvwKuacM3Jd7bY3sq9mr/x6JntxzJkaFyvcZo+dLo6ZXWyKF19TrtT7dztlJWRZXUUAABgAQp3AAAAAGgjorGoyoPlqgxVpvyc9hJ/iRZvXqzX97yumGIJa+d3Pl+zh89WX09fi9LVZzNsynflK9+Vn5SLtAIAgPRE4Q4AAIBGy3RkavW1q62OAaCJTNNURahC5YFyxczYyW9goepwtZZuW6rnv3hewWgwYW1w4WDNKZqjMzueaVG6huU4c9TO3U4OG//EBgCgreNvAwAAAGmEwhtAU1WHquUNeBWOha2OckKhaEgrd63Uk1ufVGWoMmGtW0433VF0h8Z0HZNSZ4+77C61y2ynTEem1VEAAECKoHAHAAAAgFYoEAmoLFCW8hdEjZkxvbPvHS3ctFCHag8lrBW6CnXb0Nt0de+rm/3s8W+eTX+i9XAsrPaZ7ZXrzJV09OKz30QJDwBA20ThDgAAAACtSDgWljfgVXWo2uooJ2Sapj49/KnmbZynL3xfJKxlOjJ148Ab9b0B31OWo2UuPnr3mrsbtZ/dsOvnf/n5Sc+056eRAABomyjcAQAAACAFNHSW9LGOPVM9EAnUu+hpNBaVL+RTVbBKpky57K5myZkM28u3q3hDsdYfWZ+w3W7YdU3fazRt8DQVuAssStcwQ4YcNodshs3qKAAAIIVRuAMAAABACpj0yqRG73vj6zfGf22apqJmVFEzmrDPgrELkpYtWb6q/koLNy3Uu/vfrbd2afdLNXPYTHXL6WZBMmnuJXMb3J5hy1Chu1A2w6apq6dKkpaOXyq3w92S8QAAQJqgcAcAAACANBWNHS3aTZkn39lC5YFyPbn1Sa3atUoRM5KwdlbHszSnaI4GFQ6yKN1R3/yJALvNrgJXgfKceTIMI+EnENwONzPaAQBAgyjcAQAAACAFrJiw4oTrgUggfob1E1c8oepQ9Ukv9Gm12kitnt/xvJZuX1pvZE4/Tz/NGT5H53Y696Tz0FuSYRjyOD0qcBcwPgYAADQZhTsAAAAApIDGnDFtmqbC0aMXRXXanCk7pz0Si+i1Pa9p8ebF8ga9CWudszpr5rCZGttjbMoV2tkZ2WqX2U4ZtgyrowAAgDRF4Q4AAAAAKS4ai6osUKZQLCSHLXX/GWeaptZ+tVYLNi7Q/ur9CWt5zjzdOvhWXdP3mpT7oMDtcKvQXciYGAAAcNpS929qAAAAANDGmaapilCFfEGfasO1Vsc5oc9LPlfxhmJt8W5J2O6yu3R9/+t148AblevMtShdwxw2hwrdhSmXCwAApC8KdwAAAABIQdWhankDXoVjYaujnNDuit2av3G+1n29LmG7TTaN7z1e04dOV4fMDhala5hhGMp35SvflZ9yY20AAEB6o3AHAAAAgBTij/jlDXgViASsjnJCh2sPa/HmxXpj7xuKKZawNrrLaN1RdId65fWyJtwJMKcdAAA0Jwp3AAAAAEgB4VhYZf4y1YRrrI5yQlWhKj297Wm9+MWLCsVCCWvD2g3TnOFzNLz9cIvSHZ/L7lK7zHbMaQcAAM2Kwh0AAAAALBSNRVUeLFdlqFKmaVod57iC0aBW7Fyhp7c+rapwVcJaj9weml00W6O7jJZhGBYlbJjdZlehu1B5zrzTup9MR6ZWX7s6SakAAEBrReEOAAAAABYwTVO+oE++oE8xM3byG1gkakb11pdvadGmRTriP5Kw1s7dTtOHTtf4XuPlsKXWPy8Nw5DH6VGBu4A57QAAoMWk1t+IAAAAAKANqAxVqjxQrkgs0ujbuOwuLbx8oTp27KgjR440+9nwpmnq40Mfa97GedpdsTthLduRramDpmpy/8kpOaIlx5mjQnchc9oBAECLo3AHAAAAgBZSG66VN+BVMBq0OsoJbfFuUfGGYn1e8nnCdofh0MR+E3Xr4FuV78q3JNuJZDoy1S6znVx2l9VRAABAG0XhDgAAAADNLBAJyBvwyh/xWx3lhPZX7deCTQv0/oH3661d3uNyzRg2Q12yu7R8sJPggqgAACBVULgDAAAAQDMJx8LyBryqDlVbHeWEvAGvntjyhF7Z/YqiZjRh7dxO52p20WwNKBhgUbrjy7BlqNBdqBxnjtVRAAAAJFG4AwAAAEDSRWIRVQQrVBGqaPZZ66ejNlyrZ3c8q+e2Pyd/NPHs+wH5AzRn+Byd0+kci9Idn91mV4GrQHnOPBmGYXUcAACAOAp3AAAAAEiSdCnaw7GwXt79spZsWSJf0Jewdkb2GZo1bJYu7X6pbIbNmoDHYTNs8rg8ynflp1w2AAAAicIdAAAAAE5bJBaRL+hTZagypYv2mBnTmgNrtHDjQn1V81XCmsfp0bQh03RN32uUYcuwKGHDDMNQnjNPBa4C2W12q+MAAAAcF4U7AAAAAJyidCnaJWn9kfWat2GetpVvS9jutrt1/YDrNXXgVGVnZFuU7vhynbkqcBek3IcAAAAADaFwBwAAAIAmSqeifadvp+ZtnKe/Hfpbwna7YddVva/SbUNuU/vM9halO76sjCwVugvlsrusjgIAANBoFO4AAABN4I/4NemVSZKkFRNWKNORaXEiAC0pEouoPFCuqnBVyhftX9d8rUWbFuntfW/LVGLWMV3HaFbRLPXI7WFRuuNzO9wqdBfy/RUAAKQlCncAAAAAOIlwLCxfwJcWRXtFsEJPbXtKL+18SeFYOGFtePvhunP4nRrabqhF6Y7PaXeq0F2YkmNtAAAAGovCHQAAAACOI52K9kAkoBe+eEHPbHtGNZGahLXeeb01u2i2vnXGt2QYhkUJG+awOVTgLlCeM8/qKAAAAKeNwh0AAAAAviGdivZILKI39r6hxzc/rtJAacJah8wOmjF0hq7odYXsht2ihA0zDEP5rnzlu/JlM2xWxwEAAEgKCncAAAAA+F/hWFjlgXJVh6tTvmg3TVMfHvxQ8zfO15dVXyas5WTk6OZBN+u6/tel5EVHszOy1S6znTJsGVZHAQAASCoKdwAAAABtXigaki/oU1WoyuoojbKxdKMe++dj2li2MWG70+bUpH6TdPPgm1NyRIvL7lK7zHZcEBUAALRaFO4AAAAA2qy6M9rTpWj/svJL3f/Z/Xpn9zsJ2w0ZuqLnFZoxbIY6ZXWyKN3x2W12FbgK5HF5rI4CAADQrCjcAQAAALQ5kVhEvqBPlaHKlB8dI0ml/lIt3rxYr+19TTEzlrB2fufzNbtotvrm97Uo3fEZhqE8Z54KXAWy21JrhjwAAEBzoHAHAAA4hj/iP+F6IBJo8NfHw9gEILVEY1FVhCrkC/rSomivDldr2bZlWv7FcgWjwYS1QQWDdOfwO3VmxzMtSndimY5Mtc9sL6fdaXUUAACAFkPhDgAAcIxJr0xq9L5TV0896T6rr119OnEAJEnMjMkX9KkiWFHvDPFUFIqGtHLXSj219SlVhCoS1np6emrGkBka03WMDMOwKOHxOe1OtXO3U1ZGltVRAAAAWhyFOwAAAIBWyzTN+Bnt0VjU6jgnFTNjemffO1q4aaEO1R5KWCtwFej2obdr+qjpKi8rT7kz9JnTDgAAQOEOAACQYMWEFSdcD0QC8TPbl45fKrfD3RKxADSRaZqqClepPFCuSCxidZxG+eTQJ5q3cZ6+8H2RsD3TnqkbBt6gKQOnKDsjWxn2DIsSNswwDOW78pXvypfNsFkdBwAAwFIU7gAAAMdoysx1t8PNjHYgBVWHquUNeBWOha2O0ijby7dr3oZ5+uzIZwnb7YZd3+3zXU0bMk2F7kKL0p1YjjNHhe5CZdhS60MAAAAAq1C4AwAAAGgVasO18ga89S4umqoOVh/Uwk0L9c7+d+qtXdLtEs0cNlPdc7tbkOzk3A632rnb8VM+AAAA30DhDgAAACCt+SN+eQNeBSIBq6M0ii/o05ItS7Rq1ypFzMRxN2d2OFNzhs/R4MLBFqU7sQxbhgrdhcpx5lgdBQAAICVRuAMAAABIS8FoUN6AV7XhWqujNIo/4tfyHcu1bPsy1UYSM/fx9NGcojka1XmUDMOwKOHxOWwOFbgLlJuRm5L5AAAAUgWFOwAAAIC0Eo6F5Q14VR2qtjpKo0RiEb225zUt3rJY3oA3Ya1TVifNGDpDl/e8XHbDblHC43PYHMp35SvPmUfRDgAA0AgU7gAAAADSQiQWUXmgXFXhKpmmaXWckzJNU2u/WqsFGxdof/X+hLXcjFzdMvgWXdvvWrnsLosSHp/dZle+K18ep4eiHQAAoAko3NPA559/rvfee09HjhxRTU1NvX9cGIah//7v/7YoHQAAANC8wrGwfAFf2hTtkvTPkn+qeEOxNns3J2x32pya3H+ybhp0k3KduRalOz6bYTtatLs8shk2q+MAAACkHQr3FPfyyy/rmWeeUX5+vvr27asePXpYHQkAAABoEelYtO+p2KN5G+dp3dfrErbbZNO4XuM0feh0dczqaFG647MZNnlcHuW78inaAQAATgOFe4p7/fXXNWzYMP3iF7+Qw8GXCwAAq2U6MrX62tVWxwBatXAsrPJAuarD1WlTtB+pPaLHNz+uN/a+oZhiCWsXnHGBZhfNVm9Pb4vSHZ9hGMpz5qnAVSC7LfVmyAMAAKQbGtwUV1NTo/PPP5+yHQAAAK1eKBqSL+hTVajK6iiNVhWq0tPbntaLX7yoUCyUsDa03VDNKZqjER1GWJTuxHKcOSp0FyrDlmF1FAAAgFaDFjfF9evXTwcPHrQ6BgAAANBs6s5oT6eiPRgNasXOFXp669OqCifm7pHbQ3cU3aFvd/l2Sl5w1O1wq9BdqExHptVRAAAAWh0K9xQ3Y8YMPfTQQ+rbt69Gjx5tdRwAAABYyB/xa9IrkyRJKyasSPvCNBKLyBf0qTJUmTajY6JmVG9/+bYWbV6kw7WHE9YK3YWaPnS6rux1pRy21PunVoYtQ+0y2yk7I9vqKAAAAK1W6v0tEAn+8Ic/KBqN6r//+7+1YMECtWvXTjZb4kWMDMPQ7373O4sSAgAAAE0TM2PyBX3yBX1pU7Sbpqm/Hfqb5m2cp10VuxLWshxZmjpoqq7vf31Kfghit9lV4Cr4/9m78/A2ywPf+79Hu+RNluRsJCH77gSGsgzQBihLoGVJCqSEJSRkMZSenmnnnTNztedMOaXD2zPTnnlnmOKskLCEBBq20gBlGShQ9oKdBLKHJGQnlmzZ1v68f2RwUUN227ckfz/X1avWcz+yf1YebOmn2/etSk9lQc64BwAAKCUU7gWuvLxcFRUV6tu3r+koAAAAwEmxbVuxVEzRZFTZXNZ0nGO29sBazWuYpz/t+1PecZfl0jXDrtEto29R0Bs0E+4ILMtS0BtU0BuUw3Ic/Q4AAAA4aRTuBe6nP/2p6QgAAADASWtONasp0aRMLmM6yjHb3rJdC1cv1Cs7Xjlk7OKBF2vW2FnqV97PQLKjq/BUKOQLFeTSNgAAAKWMZ18AAAAAukxrulUHEgeUyqZMRzlmBxIH9MDaB/TM5meUtfNn4n+t99c0t3auRlaPNJTuyPwuv8L+sLxOr+koAAAAPRKFexHI5XJ67bXX9MEHH2j//v2SpEgkojPOOENf//rXD1nTHQAAADCtNd2qaDKqRCZhOsoxa0u36dH1j2r5uuVqz7bnjQ0PDlfd+Dqd2ftMQ+mOzOP0KOwLK+AOmI4CAADQo1G4F7i2tjb9/Oc/18aNG+X3+9W7d29JUmNjo95++2298MIL+vGPf6xA4PieWD/xxBN655139Nlnn8nj8WjEiBG66aab1K/fn/8k1rZtrVixQi+99JJaW1s1atQozZo1K289+VQqpaVLl+rNN99UOp3WhAkTNGvWLAWDwY5z4vG4Fi9erPfff1+WZenss8/WjBkz5PP5Os7Zv3+/FixYoDVr1sjn82nixImaNm2anE5nxzmffvqpFi1apE2bNqmyslKTJk3S1VdffbwPKQAAALqIbduKp+OKJqNFNaM9nUvrmc3PaMnaJWpKNuWN9Qn00eza2frmgG8W5DroLodL1b6DG6ICAADAPAr3Ards2TJt3rxZM2fO1De/+U25XAf/yTKZjF5++WXdf//9evTRRzVz5szj+rxr167VZZddpqFDhyqbzWrZsmW6++679atf/aqjCH/qqae0atUqfe9731OvXr20fPly/fznP9evfvUreTweSdKSJUv0wQcf6Ic//KECgYAWLVqkX/7yl/rZz37W8bX+7d/+TU1NTfrJT36ibDarX//615o3b55+8IMfSDo4g/+ee+5RMBjU3XffraamJt17771yOp2aNm2apINvPNx9992qra3V7NmztW3bNt13330qKyvTxRdffNKPMwAAAE6cbdtqTjUrlowpnUubjnPMbNvWKzte0YLVC/RZ/LO8sSpPlaaPma6rhlwlj9NjKOHhOSyHKr2VbIgKAABQYHhmVuDeeecdXXrppbrssss6ynZJcrlcuvTSS3XJJZfo7bffPu7P++Mf/1gXXHCBBgwYoEGDBul73/ue9u/fr82bN0s6+OLjd7/7naZMmaIzzzxTp556qu688041NTXp3XfflXSwBH/55Zc1ffp0jRs3TkOGDNEdd9yhdevWaf369ZKkHTt26MMPP1RdXZ2GDx+uUaNGaebMmXrzzTd14MABSdJHH32kHTt26Pvf/74GDRqk008/XVOnTtXzzz+vTObgplqvv/66MpmM7rjjDg0YMEDnnXeeLr/8cv32t789qccXAACgkLRn2o/4vy8vz5LIJI56flfL2Tk1JZr0acun2t++v6jK9g/2fqC5L83VT9/6aV7Z7nP6dMvoW/ToFY/q2uHXFmTZXuWt0oCKAQr5QpTtAAAABYYZ7gUuHo/nLfPyl0455RTF4/GT/jptbW2SpPLycknS3r17FY1GNX78+I5zAoGAhg0bpvXr1+u8887T5s2blc1mVVtbm5cnEolo/fr1GjFihNavX6+ysjINHTq045za2lpZlqWNGzfqrLPO0vr16zVw4MC8ZWhOO+00LVy4UNu3b9fgwYO1fv16jR49Ou9NhwkTJuipp55SPB7vyP1l6XRa6fSfX/RZliW/39/xcbH74nsohe8FxYfrD6ZxDcKkrrz+pjwz5ZjPnbZq2lHPeW7KcycT57Cyuayiqaiak83K2TlJxfPf48boRtU31Ovt3fmTVpyWU98a/C3NGDtDEX/EULoj87v8igQi6l3eW7vju2XbtulI6GH4/QuTuP4AFAsK9wLXp08fvffee7rsssu+cvy9997rWNf9ROVyOT3wwAMaOXKkBg4cKEmKRqOSpKqqqrxzq6qqOsai0ahcLpfKysqOeE5lZf56kk6nU+Xl5XnnfLls//LX/fI5vXr1yjvni/tEo9GvLNyfeOIJPf744x23Bw8erF/84heqqan56geiSPXp08d0BPRgXH8wjWsQJnXF9ed2uzv18315753OkMll1NTepHgiLo/Po4gKs5j+Kp81f6b/7+3/T0+ve1q28ovqS4Zcor/567/R0Oqhh7m3WW6HWzVlNSr3/Pk5Lz//YBLXH0zi+gNQ6CjcC9yll16qxYsX65577tEVV1zR8aJp586dWrVqlRoaGnTbbbed1NdYtGiRtm/frv/9v/93Z0QuGJMnT9a3v/3tjttfvAu+b9++jqVqipllWerTp49272Z2E7of1x9M4xqESV15/a24fMURxxOZhG743Q2SpGVXLJPP5Tvi+bt27eqUXNlcVtFkVLFUrOj+m4slY3ro44f0m42/OWTJm/GR8bp9/O0aFxknpQ/+lWchsSxLQW9Q5d5ytSRa1KIWfv7BKK4/mMT1h87mcrlKblImCgOFe4G77LLLFIvF9NRTT+nDDz/MG3O5XLr22mt16aWXnvDnX7RokT744APdddddCofDHce/mD0ei8VUXV3dcTwWi2nQoEEd52QyGbW2tubNco/FYh33DwaDam5uzvua2WxW8Xg875yNGzfmnROLxfJyBIPBjtnuX/ji9l/Ojv+C2+0+7CyxUvrlbNt2SX0/KC5cfzCNaxAmdcX153MeuUD/8tfzOr3Hdf6JyOayiqViiiVjHUvHFItkNqnHNzyuhz95WPF0/hKMgyoHaW7tXJ3b91xZllWQP0fKPeUK+8JyOQ6+ZPvLjPz8g0lcfzCJ6w9AoaNwLwLXX3+9Jk2apMbGRu3bt0+SVFNTo9ra2kOWazlWtm1r8eLFeuedd/TTn/70kOVaevXqpWAwqMbGxo6Cva2tTRs3buwo+IcMGSKn06nGxkadc845kg7OvN+/f79GjBghSRoxYoRaW1u1efNmDRkyRJK0evVq2batYcOGdZyzcuVKxWKxjqVkGhoa5Pf71b9//45zli1bpkwm07GOe0NDg/r16/eVy8kAAADgxOXs3MEZ7UVYtGftrJ7b+pwWr1msfe378sZq/DWaOXamJg2aJKflNJTwyLxOr8L+sPwuv+koAAAAOAEU7kWisrJS5513Xqd9vkWLFun111/X3/3d38nv93fMFg8EAvJ4PLIsS1dccYVWrlypvn37qlevXnr00UdVXV2tM888s+Pciy66SEuXLlV5ebkCgYAWL16sESNGdBTu/fv312mnnaZ58+Zp9uzZymQyWrx4sc4991yFQiFJBzc/7d+/v+69917deOONikajevTRR3XZZZd1zFA///zz9dhjj6m+vl5XX321tm/frlWrVmn69Omd9pgAAAD0dDk7p1gyplgqpmwuazrOcbFtW2/uelPzGudpa/PWvLFyd7luHHWjrh1+rbxOr5mAR+F0OBXyhVTpObEJNQAAACgMls3f4RSU/fv3S5IikUje7aP54vxjdf3113/l8TvuuEMXXHCBpIMvWlasWKEXX3xRbW1tGjVqlG677Tb169ev4/xUKqWlS5fqjTfeUCaT0YQJEzRr1qy8ZV7i8bgWLVqk999/X5Zl6eyzz9bMmTPl8/35T6D37dunhQsXas2aNfJ6vZo4caJuvPFGOZ1/nnn06aefatGiRdq0aZMqKio0adIkXXPNNcf1fX/xtdLp9NFPLHCWZalv377atWsXf06Hbsf1B9O4BmGSyeuvPdOuKc9MkSStvHJlp82Ctm1bsVRM0WS06Ip2SVr9+WrVN9SrYX9D3nG3w60pw6bo5tE3F2yRbVmWqjxVqvZVy2E5jul8fv7BFK4/mMT1h87mdrtZwx1dgsK9wEydOlWS9PDDD8vlcnXcPprly5d3ZaySQuEOnDyuP5jGNQiTSqlwL/aifVvLNs1vnK/XPnst77glS5eeeqluG3ub+pT1MZTu6ALugMK+sDxOzzHfh59/MInrDyZx/aGzUbijq7CkTIG5/fbbJaljZvcXtwEAAIDOYtu2WtItako0KZPLmI5z3Pa379cDax/Qs1ueVdbOf6Pg7D5na27tXA0LDjOU7ug8To/CvrAC7oDpKAAAAOhkFO4F5ovlXA53GwAAADgZzalmRRNRpXPF9xd/relWLVu3TCvWr1Aim8gbG1U9SnXj6/RXvf7KULqjc1gOVfuqVeWpkmVZpuMAAACgC1C4F7hf//rXuuSSSzR8+PCvHN+4caNeeOEF3XHHHd2cDAAAAMWkJdWiaDKqVDZlOspxS2VTenrz01qydoliqVje2Cllp2h27Wxd2P/Cgi6xKz2VCvlCcjqcRz8ZAAAARYvCvcC9+uqrGj9+/GEL97179+rVV1+lcAcAAD1CV20aWiz8Lr9WTV51XPeJp+JqSjYVZdGes3N6aftLWrh6oXa17sobC3qDunXMrbpyyJVyO9yGEh6d3+VX2B+W1+k1HQUAAADdgMK9yB04cEAez7FvsgQAAICeoTXdqqZEk5LZpOkoJ+TdPe+qvqFeG6Ib8o77nX5NHTlV3x3x3YJeA93j9CjkC6nMXWY6CgAAALoRhXsBevfdd/Xuu+923H7xxRfV0NBwyHltbW1qbGzUsGGFuyEUAAAAuldbuk1NySYlMomjn3ycktmk7nzlTknSvRfe2yWzttc1rdO8xnl6b897ecedllNXDrlS08dMV9gX7vSv21lcDpeqfdWqcFcU9BI3AAAA6BoU7gVox44deuuttzpub9iwQZs3b847x7Iseb1ejR49Wrfcckt3RwQAAECB6cqivTvsbN2phasX6sVtLx4ydmH/CzVr3CwNqBhgINmxcVgOBb1BBb1BinYAAIAejMK9AE2ePFmTJ0+WJE2dOlW33367zj//fMOpAAAAUIjaM+06kDhQtEV7NBnV0o+X6smNTypjZ/LGTqs5TXXj6zQmNMZQuqOzLEtVnioFvUE2RAUAAACFe6Fbvny56QgAAAAoQMVetLdn2rVi/QotW7dMbZm2vLHBlYNVN75O5/Q5p6Bni1d4KlTtqy7oTVsBAADQvSjcAQAAgCLSnmlXU6JJ7Zl201FOSCaX0bNbntX9a+/XgcSBvLFe/l66bdxtuvTUS+W0Cne2eMAdUMgX6pI17AEAAFDcKNyLwJ/+9Cf99re/1ZYtW9TW1ibbtg85h5nwAAAApa3YZ7Tbtq0/7PyD5jfO17aWbXljFe4K3TT6Jk0ZNqWgS2yfy6eQLyS/y286CgAAAAoUhXuBe+utt/R//+//1YABA3Tuuefq97//vc477zxJ0rvvvqu+ffvqzDPPNJwSAACgcxxt1vaXy+ZjKZ5LoRgt9s1QJalhf4Pua7hPaz5fk3fc4/DoO8O/o5tG3aQKT4WhdEfncXpU7a1WuafcdBQAAAAUOAr3Avfkk09q2LBh+tnPfqZ4PK7f//73uuiiizRu3Djt3btXP/7xj9WrVy/TMQEAADrFlGemHPO501ZNO+o5qyavOpk4RsVTcUWTUSWzSdNRTtjW5q2qb6jXm7vezDvukEOTBk3SjLEz1DvQ21C6o3M5XKr2VavCXVHQa8kDAACgcFC4F7gdO3Zo2rRpcjgccjoPrmOZyWQkSb169dJll12mp556ShMnTjQZEwAAAJ2kJdWiaDKqVDbV7V/7aOX+l8ePdO6+tn1a+vFSvfDpC8oplzd2bt9zNad2joZUDTm5sF3IYTkU9AYV9AYp2gEAAHBcKNwLnNfrlct18J+prKxMLpdL0Wi0Y7yqqkp79+41lA4AAKBzrbxy5RHHE5lEx8z2Ry5/RD6XrztidTnbttWSblE0EVU6lzaW485X7jzmc3/02o8OOZbNZXUgcUBNySbZyt93aGxorOaOn6vTak472ZhdxrIsVXmqFPQG5XQU7qatAAAAKFwU7gWuX79+2rFjR8ftQYMG6bXXXtPXv/51ZbNZvf7664pEIgYTAgAAdJ7jWXPd5/IV/Rrttm2rOdWsaDKqTC5jOs4Jy9k5RZNRfZ74XDk7f0b7gPIBmlM7R9845RsFPVu8wlOhal+13A636SgAAAAoYhTuBe7MM8/UqlWrdPPNN8vtdmvKlCn6P//n/+jWW2+VZVlKJpO6/fbbTccEAADAccjZOcWSMcVSMWVzWdNxOtx74b1HHE9mkx0z23/5jV/K5XDp5W0v64G1D2hf+768c0PekGaMnaFvDf6WXI7Cfdnhc/kU9oVL5q8lAAAAYFbhPvOFJOmqq67SVVdd1XH7jDPO0E9/+lO9/fbbcjgc+qu/+iuNGzfOYEIAAAAcq2wuq1gqpuZUc0EV7V/wOr3HdJ5t2/po30davGaxNsU25Y0FXAHdMPIGXT/i+oL+CwSXw6WwL6xyT7npKAAAACghFO4FLJ1O66OPPlJNTY1OPfXUjuOjR4/W6NGjDSYDAADA8cjmsoomo2pONR+y5Eqxac+0a3/7fv34zR/nHXdZLl099GpNHzNdQW/QTLhjYFmWgt6gqr3VBb3EDQAAAIoThXsBc7lc+tWvfqVbb701r3AHAABAccjkMh1Fu23bR79DAdsR36F5DfO0rWXbIWMXD7hYs8bNUr/yfgaSHbtyT7nCvnBBL3EDAACA4sYzzQJmWZb69u2rlpYW01EAAABwHFLZlKLJqOLpeNEX7QcSB/TA2gf0zOZnlLXzl8H5Wq+vae74uRpZPdJQumPjc/kU8oUKeokbAAAAlAYK9wI3efJkLVmyRH/913+tfv0Ke8YQAADdoT3TrinPTJHb7daKy1fI52SjQxSORCahaDKq1nSr6SgnrS3dpuXrl+vR9Y+qPdOeN+Z1enXXOXfp3H7nGkp3bFwOl0K+kCo8FaajAAAAoIegcC9w69evV0VFhX70ox9pzJgxqqmpkcfjyTvHsizNmDHDUEIAAICe43Bv+LSl29SUbFIikzCc8ORlchk9s/kZPbD2ATUlm/LG+gT6yLIsVbgrdEbvMwwlPDrLslTlqVK1r1oOy2E6DgAAAHoQCvcC9/zzz3d8vHr16sOeR+EOAAB6Ar/Lr1WTV5mO0SGeiiuajCqZTZqOctJs29YrO17RgtUL9Fn8s7yxKk+VbhlziyadOkk/fO2HhhIem4A7oLAvLI/Tc/STAQAAgE5G4V7gli9fbjoCAAAAvsS2bWVyGW1v2S6HSmP29J/2/kn3NdynT5o+yTvudXp1/YjrdcPIG1TuLi/oNxY8To/CvrAC7oDpKAAAAOjBKNwBAACAY5DNZdWUaFIql5Ir51Iml5HHUdyzqDdFN6m+sV5v734777jTcuqKwVdoxpgZivgjHce9Tq8WXLygu2MekcNyKOgNKugNyrIs03EAAADQw1G4AwAAAEeQyqYUS8bUkm4piTXaJWlP2x4tXL1QL3z6gmzZeWNf7/d1zamdo1MrTzWU7thVeCoU8oXkcvCyBgAAAIWBZ6YFburUqUc9x+PxKBQKaezYsbrqqqvUp0+fbkgGAABQ2toz7Yomo2pLt5mO0mmaU8168OMH9cTGJ5TKpfLGasO1qhtfp9pIraF0x87n8insC8vn8pmOAgAAAOShcC9w3/nOd/Tee+9p+/btOv300zvK9F27dunDDz/UwIEDNW7cOO3evVv/+Z//qTfeeEN33XWXBg0aZDY4AABAEbJtW/F0XLFkrKDXKz9eyWxSj294XA9/8rDi6Xje2KkVp2ru+Lk6r+95Bb8ki9PhVMgXUqWn0nQUAAAA4CtRuBe4UCiklpYW/eu//qt69+6dN7Z792799Kc/Vf/+/XXzzTdr165d+slPfqJly5bpH/7hHwwlBgDg5LRn2o84/uUlPRKZhGzbPsLZkt/l75RcKG05O6dYMqbmVLMyuYzpOJ0ma2f1/NbntWjNIu1r35c3FvFFNHPcTE06dVLBL8liWZaqPFWq9lXLYZXGRrUAAAAoTYX9zBp6+umnddlllx1StktSnz59dNlll+nJJ5/UhRdeqL59++qSSy7R888/byApAACdY8ozU4753Bt+d8NRz1k1edXJxEGJy9k5RZNRxZIx5ezcUWe1f3k8mU0e9Q0fr9PbKTmPl23b+uOuP2pe4zxtad6SN1bmKtONo2/UtcOuLYolWfwuvyL+iDzO4t6gFgAAAD0DhXuB+/zzz+VwHH4Wj9Pp1P79+ztu19TUKJ1Od0c0AACAovXFjPZYKqZsLttx/M5X7jzmz/HDV3941HMWXLzghPKdjDWfr1F9Q70+2v9R3nG3w63Jwybr5lE3q8pb1e25jpfb4VbYH1aZu8x0FAAAAOCYUbgXuAEDBuj3v/+9vvGNbygYDOaNRaNRvfDCCxowYEDHsT179hxyHgAAxWTllSuPOJ7IJDRt1TRJ0rIrlhmbQYziZNu2YqmYosloXtFeCra1bNP8xvl67bPX8o5bsnTpqZdq5tiZ6lvW11C6Y+ewHAp6gwp6gwW/pjwAAADwlyjcC9zNN9+sf/qnf9J/+2//TWeeeWbHpqm7d+/Wu+++q2w2q9tvv12SlEql9Oqrr+q0004zmBgAgJNzPGuu+1w++ZyFvyQGzLNtW82pZkWT0SOu0X7vhfce8fMks0n96LUfSZJ+NfFX8jjML3Oyv32/Hlj7gJ7d8qyydv6bCGf3OVtzaudoeHC4oXTHp9xTrrAvXPBrygMAAACHwzPZAjd27FjdfffdWrFihd555x2lUilJktvtVm1tra677joNGTJEkuTxeDRv3jyTcQEAAAqKbdtqSbeoKdF0TJuhHs9fTHidXqOFe2u6VcvWLdOK9SuUyCbyxkZWj1Td+Dqd0esMQ+mOj9fpVdgfZpNjAAAAFD0K9yIwePBg/Y//8T+Uy+XU3NwsSaqsrDzi2u4AAAA92Rcz2mPJmNK50trfJp1L66lNT2nJ2iWKpWJ5Y/3K+ml27Wxd2P9COazCf67odDgV8oVU6ak0HQUAAADoFBTuRcThcLA+OwAAwBEcbjPUUpCzc3p5+8tauHqhdrbuzBsLeoOaPma6rhpyldwOt6GEx86yLFV5qlTtqy6KNwYAAACAY0XhXmAef/xxSdKUKVPkcDg6bh/Ntdde25WxAAAACloml1EsGVNzqlk5O2c6Tqd7b897qm+o1/ro+rzjfqdfU0dO1XdHfFcBd8BQuuMTcAcU9oXlcZpf/x4AAADobBTuBeaxxx6TJF1zzTVyOBwdt4+Gwh0AAPREqWxKsWRMLekW2bZtOk6nW9+0XvWN9Xpvz3t5x52WU1cOuVLTx0xX2Bc2lO74eJwehX3honljAAAAADgRFO4FZvny5Ue8DQAAACmRSSiajKo13Wo6SpfY2bpTC1cv1IvbXjxk7IL+F2j2uNkaUDHAQLLj57AcqvZVq8pTJcuyTMcBAAAAuhSFOwAAAIpGe6ZdTYkmtWfaTUfpEtFkVEs/XqonNz6pjJ3JGzut5jTV1dZpTHiMoXTHr9JTqZAvJKfDaToKAAAA0C0o3AEAQFHxu/x6bspz6tu3r3bt2lWSy4jgUPFUXLFUTIlMwnSULtGeaddjGx7Tsk+WqTWTP2t/cOVg1Y2v0zl9zimaGeI+l08Rf0Rep9d0FAAAAKBbUbgXgddee02vvPKK9u7dq3g8/pXnLFmypJtTAQAAdC3bttWSblEsGVMqmzIdR5LkdXq18JKF6tWrl/bu3XvSb/hkchmt2rpKi9cs1ueJz/PGevl76bZxt+nSUy+V0yqOGeIuh0thX1jlnnLTUQAAAAAjKNwL3EMPPaRnnnlGoVBIQ4cOVSDAJlMAAKC05eycmlPNiiVjyuQyR79DEbJtW6/vfF3zGudpW8u2vLEKd4VuGn2TpgybUjQzxC3LUtAbVLW3umhm4QMAAABdgcK9wL300ks644wz9Ld/+7dyOBym4wAAAHSZbC6rWCqmWDKmnJ0zHafLNO5v1H0N92n156vzjnscHn1n+Hd006ibVOGpMJTu+JW5yxT2h+V2uE1HAQAAAIyjcC8Cp59+OmU7AAAoWelcWrFkTM2p5pJek39r81bNa5ynN3a+kXfckqXLB12uGWNnqHegt6F0x8/r9CrsD8vv8puOAgAAABQMCvcCd8YZZ+iTTz7RJZdcYjoKAABAp0plU4omo4qn4yVdtO9r36fFaxZr1ZZVyil/5v65fc/VnNo5GlI1xFC64+d0OFXtrVaVt8p0lG7XnmnXlGemSJJWXrmSNxsAAABwCAr3Ajdz5kz94he/0KJFi3ThhRcqEol85Wz38nI2pgIAAMUhkUkomoyqNd1qOkqXakm1aNm6ZXpsw2NKZpN5Y2NCY1Q3vk6n1ZxmJtwJsCxLlZ5KVXur5XQUxyauAAAAQHejcC9wXq9XI0aM0NNPP60XXnjhsOctX768G1MBAAAcv/ZMu5oSTWrPtJuO0qVS2ZSe3PSkln68VM2p5ryxAeUDNLt2tiaeMrGoNhcNuAMK+8LyOD2mowAAAAAFjcK9wC1atEgvvfSSRowYoWHDhikQCJiOBAAAcFziqbhiqZgSmYTpKF0qZ+f04rYXtXD1Qu1u2503FvKGNGPsDH1r8LfkchTPU3CP06OwL6yAm+egAAAAwLEonmf7PdQf//hHfeMb39D3vvc901EAAACOmW3biqfjiiajSmVTpuN0Kdu29e6ed1XfUK+NsY15Y36XX9NGTtN1I65TwFU8pXVPXqcdAAAAOBkU7gXO6XRq+PDhpmMAAAAcE9u21ZxqViwZUzqXNh2ny31y4BPVN9brg70f5B13WS5dPfRq3TL6FlX7qg2lO36WZanKU6VqX7Uc1qH7BgEAAAA4Mgr3Anfuuefq/fff16WXXmo6CgAAwGHl7JxiyZiaU83K5DKm43S5bbFtuueP9+jl7S8fMvbNAd/UrHGzdEr5KQaSnbgyd5lCvhDrtAMAAAAngcK9wJ177rm6//77dc899+jCCy9UJBKRw3HobKMhQ4YYSAcAAHq6bC6rWOpg0Z7NZU3H6XJNiSYt+XiJnt789CFvLPxVr79SXW2dRoVGGUp3YrxOr8L+sPwuv+koAAAAQNGjcC9w//iP/9jx8YcffnjY85YvX94NaQAAAA7K5DIdM9pzds50nC7XlmnTivUrtGzdMrVn2vPGhlUNU934Op3Z+0xZlmUo4fFzOVwK+UKq8FSYjlIw/vLf9i99eePfY9kEmDcxAAAAeh4K9wJ3++23m44AAADQIZ1LdxTttm2bjtPlMrmMfrvlt3pgzQM6kDyQN9Yn0Eezxs3SxQMvLqr1zh2WQ0FvUEFvsKjeIOgOU56ZcsznTls17ajnrJq86mTiAAAAoAhRuBe4Cy64wHQEAAAAJbNJRZNRxVNx01G6hW3bevWzVzW/cb52xHfkjVV6KvW9s76ni3tfLLfDbSjhian0VCrkC8npcJqOAgAAAJQkCncAAAAcVnumXU2JpqMutVFKPtz3oe5ruE8fH/g477jX6dV1w6/TjaNu1JD+Q7R3796imeUfcAcU9oXZEPUoVl658ojjiUyiY2b7I5c/Ip/L1x2xAAAAUEQo3IvAvn379Oqrr2rPnj1qbW095IWdZVn6u7/7O0PpAABAKYqn4oqlYse0TnWp2BTbpPmN8/XHXX/MO+6QQ1cMvkIzxs5Qjb+mqJZhYUPU43M8j5PP5eNxBQAAwCEo3Avc66+/rv/4j/9QLpdTIBBQIBA45JxietEHAAAKl23bakm3KJaMKZVNmY7Tbfa07dGi1Yv0/KfPy1b+xIbz+52vObVzNKhykJlwJ8jpcCrkC6nSU2k6CgAAANCjULgXuGXLlumUU07RD3/4Q/Xr1890HAAAUIJydq5jI9RMLmM6TrdpTjXroY8f0sqNK5XK5b/BMC48TrePv121kVpD6U6MZVkdG6IW00auAAAAQKmgcC9wzc3NuuqqqyjbAQBAp8vmsoqlYoolY8rZOdNxuk0ym9RvNvxGD33ykOLp/E1gB1YM1NzauTq/3/lF91eEZe4yhf3hotvIFQAAACglFO4Fbvjw4dq/f7/pGAAAoISkc+mOGe3FsulnZ8jaWT2/9XktWrNI+9r35Y1FfBHNGDtDlw+6XC5HcT1FZp12AAAAoHAU16uJHujWW2/VP/3TP2no0KE655xzTMcBAABFLJVNKZqMKp6O96ii3bZtvbX7LdU31GtL85a8sTJXmW4cdaOuHX6tfC6foYQnhnXaAQAAgMJD4V7gBg4cqO9+97v613/9V3m9XoXDYTkc+etxWpalf/7nfzaUEAAAFLpEJqFoMqrWdKvpKN1u7edrdV/Dffpo/0d5x90OtyYPnaybRt+koDdoJtwJsixLVZ4qVfuqWacdAAAAKDAU7gXu+eef1+LFi+XxeNSnTx8FAgHTkQAAQJFoz7SrKdGk9ky76SjdbnvLds1vnK9XP3s177glS5cMvES3jbtNfcv6Gkp34gLugMK+sDxOj+koAAAAAL4ChXuBe+KJJzRy5Ej9/d//PWU7AAA4Jq3pVkWTUSUyCdNRut3+9v1asnaJfrvlt8ra2byxs3qfpbnj52p4cLihdCfO4/Qo7Asr4Ob5oEl+l1+rJq8yHQMAAAAFjMK9wLW1ten888+nbAcAAEdk27bi6biiyahS2ZTpON2uNd2qR9c9quXrlyuRzX+jYWT1SNXV1umM3mcYSnfiHJZD1b5qVXmqZFmW6TgAAAAAjoLCvcCNGTNG27ZtMx0DAAAUKNu2FUvFFEvGlMllTMfpdulcWk9velpLPl6iaDKaN9avrJ9mj5utCwdcWJRrnVd4KhTyheRy8JQdAAAAKBY8ey9ws2bN0j333KOnnnpKF110kSoqKkxHAgAABSCbyyqWiqk51axsLnv0O5SYnJ3TK9tf0YLVC7SzdWfeWJWnSreOuVVXDb1KbofbUMIT53P5FPaF5XP5TEcBAAAAcJwo3AvcD3/4Q9m2rUceeUSPPPKIPB6PHI5DZ2gtWbLEQDoAANDdMrmMYsmDRXvOzpmOY8T7e95XfWO91jWtyzvuc/o0dcRUfXfkd1XmLjOU7sS5HC6FfCFVeJhgAQAAABQrCvcCd/bZZ7NeJwAAUDqXVjQRVUu6RbZtm45jxIboBs1rmKd39ryTd9xpOfXtwd/WrWNvVdgXNpTuxFmWpSpPlap91UW59A0AAACAP6NwL3Df+973TEcAAAAGpbIpRZNRtaRaTEcxZlfrLi1avUi/3/Z72cp/s2HiKRM1p3aOBlQMMJTu5ATcAYV9YXmcHtNRAAAAAHQCCncAAIAClMgkFE1G1ZpuNR3FmGgyqgc/flBPbnpS6Vw6b2xCZILqxtdpbHisoXQnx+P0KOwLK+AOmI4CAAAAoBNRuBegzZs3H/d9hgwZ0gVJAABAd2vPtKsp0aT2TLvpKMYkMgk9vuFxPfzJw2rN5L/hMLhysOrG1+mcPucU5bJ7Dsuhal+1qjxVRZkfAAAAwJFRuBegf/iHfzju+yxfvrwLkgAAgO7Smm5VNBlVIpMwHcWYTC6jVVtX6f4192t/Yn/eWI2/RrPGzdKlp14qp+U0lPDkVHgqFPKF5HLwFBwAAAAoVTzbL0C333676QgAAKAb2LateDquaDKqVDZlOo4xtm3r9Z2va17jPG1r2ZY3Vu4u182jb9aUYVPkdXoNJTw5PpdPYV9YPpfPdBQAAAAAXYzCvQBdcMEFpiMAAIAulLNzak41K5aMKZPLmI5jVOP+Rt3XcJ9Wf74677jH4dF3hn9HN466UZWeSkPpTo7T4VTYF1aFp8J0FAAAAADdhMIdAACgm2RzWcVSMTWnmpXNZU3HMWpr81bNb5yv13e+nnfckqVJgyZp5tiZ6h3obSjdyavyVinkC8lhOUxHAQAAANCNKNwBAAC6WCaXUSx5sGjP2TnTcYza175P96+5X7/b8jvllP9YnNPnHM0dP1dDq4YaSnfyfC6fIv5I0S5/AwAAAODkULgDAAB0kVQ2pWgyqng6Ltu2TccxKp6O65FPHtFjGx5TMpvMGxsdGq262jqd3ut0Q+lOntPhVMgXKtrlbwAAAAB0Dgp3AACATpbIJBRNRtWabjUdxbhUNqUnNz2ppR8vVXOqOW+sf3l/zamdo4mnTJRlWYYSnrxKT6VCvpCcDqfpKAAAAAAMo3AHAADoJO2ZdjUlmtSeaTcdxbicndOL217UwtULtbttd95YyBvSrWNv1bcHf1suR/E+HfW6vAp7w/K5fKajAAAAACgQxfsKBwAAoEC0plsVTUaVyCRMRzHOtm29u+dd1TfUa2NsY96Y3+XXDSNv0PUjrlfAFTCU8OQ5HU71LuutilRFj18qCAAAAEA+CvcCs3bt2hO635gxYzo5CQAAOBLbthVPxxVNRpXKpkzHKQjrmtapvqFe7+99P++403LqqiFXafqY6Qr5QobSdY5KT6XC/rCqfFVqU5vpOAAAAAAKDIV7gbnrrrtO6H7Lly/v5CQAAOCr2Lat5lSzosmoMrmM6TgFYWd8pxasXqCXtr90yNiF/S/U7NrZ6l/e30CyzuN1ehX2h+V3+Yt6vXkAAAAAXYvCvcD84z/+o+kIAADgK9i2rVgqplgyRtH+X6LJqJasXaKnNj2ljJ3/mJxec7rqxtdpdGi0oXSdw2E5FPKFVOWtMh0FAAAAQBGgcC8wLA0DAEBhoWg/VHumXSvWr9CydcvUlslfVmVo1VDVja/TWb3PKvqZ4BWeCoV8oaLe2BUAAABA9+LVAwAAwFfI2bmOpWOyuazpOAUhk8vo2S3P6v619+tA4kDeWO9Ab80aN0uXDLxEDsthKGHn8Dg9ivgj8rv8pqMAAAAAKDIU7kUglUrp7bff1pYtW9TW1qZcLpc3blmWbr/9dkPpAAAoLTk7p1gyplgqRtH+X2zb1qufvaoFjQu0Pb49b6zSU6lbRt+iq4deLa/Tayhh53BYDlX7qlXlqSr62fkAAAAAzKBwL3D79u3TXXfdpX379ikQCKitrU3l5eUdxXtFRYV8Pp/pmAAAFD2K9q/20b6PVN9QrzUH1uQd9zq9um74dbph5A2q8FQYStd5yj3lCvvCLB8DAAAA4KTwiqLAPfjgg2pra9PPf/5z9erVS7Nnz9bf/M3faOTIkVq1apWee+45/fjHPzYdEwCAopXJZRRLxtScalbOzh39Dj3E5thmzW+crzd3vZl33CGHrhh8hWaMnaEaf42hdJ3H6/Qq7A+zfAwAAACATkHhXuDWrFmjSy+9VMOGDVM8Hpd08M+63W63rrrqKu3YsUMPPPCA/uEf/sFwUgAAiksik1AsFVNrulW2bZuOUzD2tO3R/Wvu13Nbn1NO+W9AnN/vfM2pnaNBlYPMhOtETodT1d5qVXmrTEcBAAAAUEIo3AtcMplUr169JEl+/8GZV21tbR3jI0aM0IMPPmgkGwAAxagt3aamZJMSmYTpKAWlJdWihz55SL/Z8Bulcqm8sXHhcaobX6fxkfGG0nWuKm+Vqr3VcjqcpqMAAAAAKDEU7gUuEono888/lyQ5nU6FQiFt2LBBZ599tiRpx44d8ng8JiMCAHqY9ky7pjwzRZK08sqVRbEUh23bakm3KJaMKZVNHf0OPUgym9TKjSv10McPqSXdkjc2sGKg5tbO1fn9zi+JTUR9Lp8i/kjRb+4KAAAAoHBRuBe4cePG6b333tN1110nSbrgggv05JNPKh6Py7Ztvfbaa5o4caLhlAAAFKZsLqtY6uD67GyEmi9rZ/XCpy9o0epF2tu+N28s7Atr5tiZunzQ5SWxiajL4VLYF1a5p9x0FAAAAAAlrvhfQZW4a665Rhs3blQ6nZbb7dbkyZPV1NSkt99+Ww6HQ+eff75uueUW0zEBACgobIR6eLZt663db2le4zxtjm3OGytzlWnaqGm6dvi1RfGXC0djWZaC3qCqvdUlMUMfAAAAQOGjcC9wkUhEkUik47bH41FdXZ3q6uoMpgIAoDClsilFk1HF03E2Qv0Kaz9fq/rGen2478O84y7LpcnDJuvm0Tcr6A0aydbZyj3lCvlCcjvcpqMAAAAA6EEo3Avcr3/9a11yySUaPnz4V45v3LhRL7zwgu64445uTgYAQOFIZBKKJqNqTbeajlKQtrds14LVC/SfO/7zkLFLBl6i28bdpn5l/bo/WBfwuXwK+UIlMUMfAAAAQPGhcC9wr776qsaPH3/Ywn3v3r169dVXKdwBAD1SW6ZNTe1Nas+0m45SkD5PfK4la5fomc3PKGvnr2F/Zu8zNbd2rkZUjzCUrnO5HC6FfCFVeCpMRwEAAADQg1G4F7kDBw7I4/GYjgEAQLdqTbdqW2ybdsV3sXTMV2hLt2nZumVavn65EtlE3tiI4AjVja/T13p/zVC6zsU67QAAAAAKCYV7AXr33Xf17rvvdtx+8cUX1dDQcMh5bW1tamxs1LBhw7ozHgAARti2rXg6rmgyqnQurV5lvUxHKjjpXFpPb35aS9YuUTQZzRvrW9ZXs8fN1kUDLpLDcpgJ2MnKPeUK+8JyOXhKCwAAAKAw8OqkAO3YsUNvvfVWx+0NGzZo8+bNeedYliWv16vRo0frlltu6e6IAIASdrTlWRKZxFd+fDgnu5a2bdtqTjUrmowqk8tIEjOZ/0LOzumVHa9oYeNCfdb6Wd5YladK08dM19VDry6ZDUR9Lp/CvrB8Lp/pKAAAAACQh8K9AE2ePFmTJ0+WJE2dOlW33367zj//fMOpAAA9xZRnphzzudNWTTvqOasmrzqhHNlcVrFUTM2pZmVz2aPfoYd6f+/7qm+o17qmdXnHfU6fpo6Yqu+O/K7K3GWG0nUup8OpkC+kSk+l6SgAAAAA8JUo3Avc8uXLTUcAAKBbZXIZxZIHi/acnTMdp2BtjG5UfUO93tnzTt5xp+XUtwd/W9PHTFfEHzGUrvNVeCoU9oXldDhNRwEAAACAw6JwLxJ79+7Vn/70J+3bt0+SVFNTo9NPP129erF+LQCgc628cuURxxOZRMfM9kcuf6TTlvVI59KKJqJqSbewEeoR7G7drYWrF+r3234vW/mP08RTJmp27WwNrBhoKF3n8zq9CvvDJ700EQAAAAB0Bwr3IrB06VL97ne/O6R8sCxLV1xxBWu4AwA61fEUmz6X76SL0GQ2qWgyqngqflKfp9TFkjE9+PGDemLTE0rn0nlj4yPjdfv42zU2PNZQus7nsByq9lWrylPFmv3o0J5p71j2auWVK3kjBgAAAAWHwr3APfPMM3r22Wd19tln68orr9Qpp5wiSfrss8/07LPP6tlnn1UoFNK3v/1tw0kBADg+7Zl2RZNRtaXbTEcpaIlMQo9veFwPf/KwWjOteWODKwdrbu1c/XXfvy6pUrrcU66wLyyXg6eqAAAAAIoLr2IK3EsvvaQzzjhDP/zhD/OODx8+XP/9v/93pVIpvfjiixTuAICi0ZZuU1OySYlMwnSUgpbJZfTc1ue0eM1i7U/szxur8dfotrG36bJBl8lplc6a5h6nRxF/hFnLAAAAAIoWhXuB27dvn6644orDjp922mn66KOPujERAAAnJp6KK5qMKplNmo5S0Gzb1us7X9f8xvn6tOXTvLFyd7luGnWTvjP8O/I6vYYSdj7LshT0BlXtrS6pmfoAAAAAeh4K9wJXWVmprVu3HnZ869atqqys7L5AAAAcB9u21ZJuUTQRPWTdcRyqcX+j6hvq1fh5Y95xj8OjKcOm6KbRN6nSU1q/98vcZQr7w3I73KajAAAAAMBJo3AvQGvXrlX//v1VWVmpv/7rv9bvfvc79erVS5MmTZLP55MkJRIJPffcc3r55ZePOAMeAAATcnZOsWRMzalmZXIZ03EK3tbmrVrQuEB/2PmHvOOWLF126mW6bdxt6h3obShd13A73Ir4Iwq4A6ajAAAAAECnoXAvQHfddZe+//3v6/zzz9fUqVO1detWLVu2TMuXL1coFJIkHThwQLlcTmPHjtXUqVMNJwYA4KBsLqtYKqZYMqacnTMdp+Dtb9+v+9fcr2e3PKuc8h+vc/qco7m1czU0ONRQuq7B8jEAAAAAShmFe4Hzer36X//rf+ndd9/Vn/70J+3ff3DTtAkTJuiv/uqvdMYZZ/BiFQBgXCaXUTQZVXOqWbZtm45T8OLpuJZ9skwrNqw4ZE37UdWjdPv423V6r9MNpes6AXdAEX+E5WMAAAAAlCwK9yJx5pln6swzzzQdAwAA+V1+rZq8SpKUyqa0t22v4uk4RfsxSGVTenLTk3rw4wcVS8Xyxk4pP0Vzxs3RBf0vKLk301k+BseqPdN+xPFEJvGVHx+O3+U/6UwAAADA8aBwBwAAxy2RSSiajKo13Wo6SlHI2Tm9uO1FLVy9ULvbdueNhbwh3Tr2Vn178LflcpTWUzOWj8HxmvLMlGM+d9qqaUc954s3BwEAAIDuUlqv6krIv//7v+vf//3fj+lcy7L06KOPdnEiAAAOzj5tSjQddRYq/uzdPe+qvqFeG6Ib8o77XX7dMPIGXT/iegVcpTfzu9xTrpAvxPIxAAAAAHoUCvcCNX78ePXt29d0DAAAJEmt6VZFk9FjWsIBB61rWqd5jfP03p738o47LaeuGnKVpo+ZrpAvZChd1/E6vQr7wyzlgROy8sqVRxxPZBIdM9sfufwR+Vy+7ogFAAAAHDMK9wI1ceJEnX/++aZjAAB6MNu2FU/HFU1GlcqmTMcpGjvjO7Vw9UK9uP3FQ8Yu7H+hZtfOVv/y/gaSdS2nw6lqb7WqvFWmo6CIHc8bNT6Xjzd2AAAAUHAo3AEAQB7bttWcalYsGVM6lzYdp2hEk1EtWbtET216Shk7kzd2es3pqhtfp9Gh0YbSda1KT6VCvpCcDqfpKAAAAABgFIU7AACQdHBjz1gyplgqpmwuazpO0WjPtGvF+hVatm6Z2jJteWNDq4aqbnydzup9VkluGupz+RTxR+R1ek1HAQAAAICCQOEOAEAPl8llOma05+yc6ThFI5PL6Nktz+r+tffrQOJA3ljvQG/NGjdLFw+8WE6r9GZ9uxwuhX1hlXvKTUcBAAAAgIJC4V6Ali9fbjoCAKAHSOfSiiaiakm3yLZt03GKhm3beu2z1zS/cb62x7fnjVV6KnXz6Jt1zdBrSnLWt2VZCnqDqvZWl+SMfQAAAAA4WRTuAAD0MMlsUtFkVPFU3HSUovPRvo9U31CvNQfW5B33ODy6bsR1mjZymio8FYbSda0yd5nC/rDcDrfpKAAAAABQsCjcAQDoIdoz7Yomo2pLtx39ZOTZEtuieY3z9OauN/OOO+TQ5YMv14wxM9Qr0MtQuq7lcXoU8Ufkd/lNRwEAAACAgkfhDgBAiWtLt6kp2aREJmE6StHZ27ZXi9cs1nNbn1NO+evbn9v3XM2tnavBVYMNpetaDsuhal+1qjxVLB8DAAAAAMeIwh0AgBJk27Za0i2KJWNKZVOm4xSdllSLHvrkIf1mw2+UyuU/fmPDY1VXW6cJNRMMpet6lZ5KhXwhOR2lt+Eripvf5deqyatMxwAAAAAOi8IdAIASks1lFUvF1JxqVjaXNR2n6CSzSa3cuFIPffyQWtIteWMDKwZqTu0cfb3f10t2xrfP5VPYF5bP5TMdBQAAAACKEoU7AAAlIJVNKZaMqSXdItu2TccpOlk7q99/+nstWrNIe9r25I2FfCHNHDtTVwy6Qi5HaT51cjlcCvlCJbvhKwAAAAB0l9J81QgAQA/RnmlXLBlTa7rVdJSiZNu23t79tuob67U5tjlvLOAKaNqoabpu+HUlu2GoZVmq8lSp2lcth+UwHQcAAAAAih6FOwAARSieiiuWirER6kn4+MDHqm+o15/2/SnvuMty6Zph1+iW0bco6A2aCdcNAu6Awr6wPE6P6SgAAAAAUDIo3AEAKCItqRZFk1E2Qj0J21u2a+HqhXplxyuHjF084GLNGjdL/cr7GUjWPdwOtyL+iALugOkoAAAAAFByKNwBAChwtm2rJd2iaCKqdC5tOk7ROpA4oAfWPqBnNj+jrJ2/oezXen9NdbV1GlE9wlC6ruewHAp6gwp6gyW76SsAAAAAmEbhDgBAgbJtW82pZsWSMYr2k9CWbtOj6x/V8nXL1Z5tzxsbHhyuuvF1OrP3mYbSdY8KT4VCvlDJbvoKAAAAAIWCV10AABQY27YVS8UUS8aUyWVMxylamVxGT29+WkvWLlFTsilvrE+gj2bXztY3B3yzpDcL9Tq9CvvDJbvpKwAAAAAUGgp3AAAKRM7OKZaMKZaKKZvLHv0O+Eq2beuVHa9oweoF+iz+Wd5YladKt4y5RVcPubqkNwt1Opyq9laryltlOgoAAAAA9CgU7gAAGJbNZRVLxdScaqZoP0kf7P1A9Q31+qTpk7zjXqdX14+4XjeMvEHl7nJD6bpHpadSIV9ITofTdBQAAAAA6HEo3AEAMOSLoj2WjCln50zHKWqboptU31ivt3e/nXfcaTl1xaArNGPsDEX8EUPpuofP5VPYF5bP5TMdBQAAAAB6LAp3AAC6WSaXUTQZVXOqWbZtm45T1Pa07tGC1Qv0wqcvyFb+Y/n1U76uOePm6NTKUw2l6x5Oh1NhX1gVngrTUQAAAACgx6NwBwCgm6RzaUUTUbWkWyjaT1Jzsln3v36/Hmp4SKlcKm+sNlyruvF1qo3UGkrXfaq8VQr5QiW98SsAAAAAFBMKdwAAulgqm1I0GVVLqsV0lKKXzCb1+IbH9fAnDyuejueNnVpxquaOn6vz+p4ny7IMJewePpdPEX9EXqfXdBQAAAAAwJdQuAMA0EXaM+2KJWNqTbeajlL0snZWz219TovXLNa+9n15YzX+Gs0cO1OXnXqZXI7SfmrD8jEAAAAAUNhK+1UpAAAGtKZbFU1GlcgkTEcperZt681db2p+43xtad6SN1bhqdCNI2/UlGFTesRGoZWeSoV8ITkdTtNRAAAAAACHQeEOAEAnsG1bLekWxZIxpbKpo98BR7X689Wqb6hXw/6GvONuh1tThk3R33z9b5RuSZf8evhep1dhf1h+l990FAAAAADAUVC491Br167V008/rS1btqipqUl/+7d/q7POOqtj/D/+4z/06quv5t1nwoQJ+vGPf9xxO5VKaenSpXrzzTeVTqc1YcIEzZo1S8FgsOOceDyuxYsX6/3335dlWTr77LM1Y8YM+Xx/nom4f/9+LViwQGvWrJHP59PEiRM1bdo0OZ1/nsH36aefatGiRdq0aZMqKys1adIkXX311V3wyADA8cnZOcWSMTWnmpXJZUzHKQnbWrZpfuN8vfbZa3nHLVm69NRLddvY29S3vK+q/dXa27LXUMquZ1mWqr3VCnqDJb8mPQAAAACUCgr3HiqZTGrQoEG66KKL9C//8i9fec5pp52mO+64o+O2y5V/uSxZskQffPCBfvjDHyoQCGjRokX65S9/qZ/97Gcd5/zbv/2bmpqa9JOf/ETZbFa//vWvNW/ePP3gBz+QJOVyOd1zzz0KBoO6++671dTUpHvvvVdOp1PTpk2TJLW1tenuu+9WbW2tZs+erW3btum+++5TWVmZLr744s5+aADgmGRymY6iPWfnTMcpCfvb9+uBtQ/o2S3PKmtn88bO7nO25tbO1bDgMEPpulfAHVDEH5Hb4TYdBQAAAABwHCjce6jTTz9dp59++hHPcblcebPVv6ytrU0vv/yyfvCDH2jcuHGSpDvuuEN/8zd/o/Xr12vEiBHasWOHPvzwQ91zzz0aOnSoJGnmzJm65557dPPNNysUCumjjz7Sjh079D//5/9UMBjUoEGDNHXqVD388MO6/vrr5XK59PrrryuTyeiOO+6Qy+XSgAEDtHXrVv32t7+lcAfQ7dK5tKKJqFrSLSW/lEl3aU23atm6ZVqxfoUS2fx170dVj9Lc8XN1Rq8zDKXrXi6HSxF/RGXuMtNRAAAAAAAngMIdh7V27VrNmjVLZWVlGjdunL773e+qoqJCkrR582Zls1nV1tZ2nH/KKacoEol0FO7r169XWVlZR9kuSbW1tbIsSxs3btRZZ52l9evXa+DAgXnF/mmnnaaFCxdq+/btGjx4sNavX6/Ro0fnzbCfMGGCnnrqKcXjcZWXl3f9gwGgx0tlU2pKNimeipuOUjJS2ZSe2vyUlq5dqlgqljd2Stkpml07Wxf0v0AOy2EoYfcKeoOq9lX3mO8XAAAAAEoRhTu+0mmnnaazzz5bvXr10u7du7Vs2TL90z/9k37+85/L4XAoGo3K5XKprCx/Bl5VVZWi0agkKRqNqrKyMm/c6XSqvLw875y/nEVfVVXVMfbF//fq1SvvnC/uE41GD1u4p9NppdPpjtuWZcnv93d8XOy++B5K4XtB8elJ118ik1A0GVVrulVSz/ieu1rOzumlbS9pweoF2tW6K28s6A3q1jG36qohV8ntPPxyKqV0DXqdXkX8EflcvqOfjIJQStcfig/XH0zi+oNJXH8AigWFO77Seeed1/HxwIEDdeqpp+r73/++1qxZkzervZA98cQTevzxxztuDx48WL/4xS9UU1NjMFXn69Onj+kI6MFK+fprT7frQPsBpdNplQXKVCaW+OgMb2x7Q//yx3/R2n1r844H3AHNPG2mZpw+Q+WeY//LpWL+mW7JUjgQVrWvmheORaqUfwai8HH9wSSuP5jE9Qeg0FG445j07t1bFRUV2r17t2praxUMBpXJZNTa2po3yz0Wi3XMPg8Gg2pubs77PNlsVvF4PO+cjRs35p0Ti8U6xr74/y9mu3/hi9uHW2NekiZPnqxvf/vbHbe/KDP27dunTCZzLN92QbMsS3369NHu3btZRxrdrpSvv7Z0m5qSTUpkEkc/GcdsXdM6zWuYp3f3vJt33Gk5ddWQqzR9zHSF/WG1RdvUprajfj7LslRTU6N9+/YV5TXod/lVE6hRMpXUbu02HQfHqZR/BqLwcf3BJK4/mMT1h87mcrmKegIPCheFO47J559/rng8rurqaknSkCFD5HQ61djYqHPOOUeStHPnTu3fv18jRoyQJI0YMUKtra3avHmzhgwZIklavXq1bNvWsGHDOs5ZuXKlYrFYx1IyDQ0N8vv96t+/f8c5y5YtUyaT6VjHvaGhQf369Tvi+u1ut1tu91cvR1BKv5xt2y6p7wfFpZSuv3gqrmgyqmQ2aTpKSdkZ36mFqxfqxe0vHjJ2Yf8LNWvcLA2oGCDpxH42F9s16HQ4FfaFVeE5uCdKMWXHoYrt+kNp4fqDSVx/MInrD0Cho3DvoRKJhHbv/vOMur1792rr1q0qLy9XeXm5HnvsMZ199tkKBoPas2ePHnroIfXp00cTJkyQJAUCAV100UVaunSpysvLFQgEtHjxYo0YMaKjcO/fv79OO+00zZs3T7Nnz1Ymk9HixYt17rnnKhQKSTq4+Wn//v1177336sYbb1Q0GtWjjz6qyy67rKMsP//88/XYY4+pvr5eV199tbZv365Vq1Zp+vTp3fyoASg1OTunllSLYsmY0rn00e+AYxZNRrVk7RI9tekpZez8vyo6veZ0zR0/V2NCYwylM6PCU6GwLyynw2k6CgAAAACgi1g2bwv2SGvWrNFdd911yPGJEydq9uzZ+ud//mdt2bJFra2tCoVCGj9+vKZOnZq3hEsqldLSpUv1xhtvKJPJaMKECZo1a1beOfF4XIsWLdL7778vy7J09tlna+bMmfL5/rwx3L59+7Rw4UKtWbNGXq9XEydO1I033iin88+FxKeffqpFixZp06ZNqqio0KRJk3TNNdec0Pe+b9++vM1Ui5VlWerbt6927drFu/vodsV+/WVzWcVSMTWnmpXNZU3HKSntmXatWL9Cy9YtU1smf2mYIVVDVFdbp7P7nH3Sa5ZblqVevXpp7969BX8Nuh1u1QRq5Hf5TUdBJyn2n4Eoblx/MInrDyZx/aGzud1ulpRBl6BwR49D4Q6cvGK9/lLZlGLJmFrSLUWVuxhkchk9u+VZ3b/2fh1IHMgb6+XvpVnjZumSUy+R0+qc2d3FULhblqWgN6hqL5uilppi/RmI0sD1B5O4/mAS1x86G4U7ugpLygAASl4ik1A0GVVrutV0lJJj27b+sPMPmt84X9tatuWNVbgrdPPomzV52GR5nV5DCc0IuAOK+CNyO756LxEAAAAAQGmicAcAlKzWdKuiyagSmYTpKCWpYX+D7mu4T2s+X5N33OPw6Nrh1+rGUTd2bA7aU7gdbkX8EQXcAdNRAAAAAAAGULgDAEqKbdtqSR/cCDWVTZmOU5K2Nm9VfUO93tz1Zt5xhxyaNGiSZoydod6B3obSmcHyMQAAAAAAicIdAFAisrmsmlPNiqVibITaRfa27dXiNYv13NbnlFMub+zcvudqbu1cDa4abCidOSwfAwAAAAD4AoU7AKCopXNpNSeb1ZxqVs7OHf0OOG4tqRY9/MnDenzD40rl8v9qYGxorOrG12lCzQRD6cxxO9wK+8Mqc5eZjgIAAAAAKBAU7gCAopTMJjs2QrVt23SckpTMJvXkpif14McPqjnVnDc2oHyA5tTO0TdO+UaPW0KF5WMAAAAAAIdD4Q4AKCpt6TZFk1G1Z9pNRylZWTurF7e9qIWrF2pP2568sZAvpBljZuhbg78ll6PnPY3wu/yK+CPyOD2mowAAAAAAClDPe6UMACg6tm0rno4rlowpmU2ajlOybNvWO3veUX1DvTbFNuWNBVwB3TDyBl0/4nr5XX5DCc1xOpwK+8Kq8FSYjgIAAAAAKGAU7gCAgpWzc4olY2pONSuTy5iOU9I+OfCJ6hvr9cHeD/KOuyyXrh56taaPma6gN2gmnGGVnkqFfCE5HU7TUQAAAAAABY7CHQBQcNK5tGLJmFpSLWyE2sV2xHdoQeMCvbLjlUPGLh5wsWaNm6V+5f0MJDPP6/Qq7A/3yBn9AAAAAIATQ+EOACgYiUxCsVRM8VTcdJSS15Ro0pKPl+ipTU8pa2fzxr7W62uaO36uRlaPNJTOLMuyFPKFVOWpYlNUAAAAAMBxoXAHABj1xfrszalmJTIJ03FKXlumTSvWr9CydcsO2Xh2eHC45tbO1Vl9zjKUzryAO6CIPyK3w206CgAAAACgCFG4AwCMyOQyak41qznVrGwue/Q74KRkchn9dstv9cCaB3QgeSBvrE+gj2aNm6WLB14sh+UwlNAsl8OliD+iMneZ6SgAAAAAgCJG4Q4A6FbtmXY1p5rVmm6Vbdum45Q827b16mevan7jfO2I78gbq/JU6ZYxt+jqIVfL4/QYSmhe0BtUta+6x77ZAAAAAADoPBTuAIAu98WyMbFkTMls0nScHuPDfR/qvob79PGBj/OOe51eXT/iet0w8gaVu8sNpTPP5/Ip4o/I6/SajgIAAAAAKBEU7gCALsOyMWZsim3SvIZ5emv3W3nHnZZTVwy+QjPGzFDEHzGUzjyH5Ti4Kaq3ynQUAAAAAECJoXAHAHS69ky7YsmYWtOtpqP0KHva9mjR6kV6/tPnZSt/uZ6v9/u65tTO0amVpxpKVxjKPeUK+8JyOXgKBAAAAADofLzaBAB0Ctu21ZJuUXOymWVjulgym9Sdr9wpSbr3wnuVzCb10McPaeXGlUrlUnnn1oZrVTe+TrWRWhNRC4bb4VbEH1HAHTAdBQAAAABQwijcAQAnJZ1LqyXVwrIxBuTsnJavX65H1z2qeDqeN3ZqxamaO36uzut7nizLMpTQPMuyDm6K6q3u0Y8DAAAAAKB7ULgDAE5IW7pNu1t3K56KH/1kdKqsnVUsGdP+9v3aEN2QNxbxRTRz3ExNOnVSj182xe/yK+KPyOP0mI4CAAAAAOghevYrcQDAcfli2ZiWVItaPC2s0d7NbNvWH3f9UfWN9drdtjtvrNxdrmmjpunaYdfK5/IZSlgYXA6Xwr6wyj3lpqMAAAAAAHoYCncAwFGlc2k1J5vVkm5RNpdlaQ4D1ny+RvUN9fpo/0d5x90OtyYPm6ybR92sKm+VoXSFwbIsVXmqVO2rlsNymI4DAAAAAOiBKNwBAIfVnmlXNBlVW7rNdJQea1vLNi1oXKBXP3v1kLFKT6V+fdGvNbBioIFkhYXlYwAAAAAAhYDCHQCQJ2fnOjZBTWVTpuP0WPvb9+uBtQ/o2S3PKmvnb0Z7Zu8ztbdtr3wun3oHehtKWBhcDpdC3hDLxwAAAAAACgKFOwBA0p+XjWlONStn50zH6bFa061atm6ZVqxfoUQ2kTc2snqk6sbXaVx4nO585U5DCQuDZVmq9lWrrKJMlljiCAAAAABQGCjcAaCHa8+0K5aMsQGqYelcWk9tekpL1i5RLBXLG+tb1lczx87UN075hhyWQ8lssmPsyx8fjtfp7fS8JvlcPtUEalRTVqNdzbtk27bpSAAAAAAASKJwB4AeybZtxdNxxZKxYyps0XVydk4vb39ZC1cv1M7WnXljTsupsC+sCneFHt/wuB7f8Pgh9//Raz866tdYcPGCTstrktPxX4+Hp4KNewEAAAAABYnCHQB6kGwuq1gqppZUizK5jOk4Pd57e95TfUO91kfX5x23ZCnkC6naVy2n5TSUrrBUeasU8oXksBymowAAAAAAcFgU7gDQAySzSTUnm9WSbmH5jQKwvmm95jXO07t73s077rScunLIlbph5A0K+UKHvX8ym+yY2f7Lb/yy5JaM+TKfy6eIP1LS3yMAAAAAoHRQuANACWvPtCuajKot3WY6CiTtbN2pRasX6ffbfn/I2AX9L9DscbM1oGLAcX1Or9NbkmX0l5ePAQAAAACgWFC4A0AJaku3qSnZpEQmYToKJEWTUT348YN6ctOTSufSeWMTIhN0+/jbNSY8xlC6wsPyMQAAAACAYkXhDgAlJJ6KK5qMshFqF0tmk7rzlTslSfdeeO9hZ5gnMgk9tuExPfLJI2rNtOaNDa4crLrxdTqnzzlsAPpfWD4GAAAAAFDsKNwBoMjZtq2WdIuiieghs6dhRiaX0aqtq7R4zWJ9nvg8b6zGX6NZ42bp0lMvZUPU/8LyMQAAAACAUkHhDgBFKmfn1JxqViwZUyaXMR0HOvjmx+s7X9e8xnna1rItb6zcXa6bR9+sKcOmMIP7S1g+BgAAAABQSijcAaDIZHNZxVIxNaealc1lTcfBf2nc36j7Gu7T6s9X5x33ODz6zvDv6KZRNzGD+0tYPgYAAAAAUIoo3AGgSGRyGcWSB4v2nJ0zHQf/5dPmT3X/2vv1xs438o5bsnT5oMs1Y+wM9Q70NpSu8LB8DAAAAACglFG4A0CBS+fSiiaiakm3yLZt03HwX9K5tD5v/1xzXpyjnPLfADm377maUztHQ6qGGEpXmFg+BgAAAABQ6ijcAaBAJbNJRZNRxVNx01HwJS2pFj348YPaEtsiW/lvgIwJjVHd+DqdVnNal2bwOr1acPGCLv0anYnlYwAAAAAAPQWFOwAUmPZMu6LJqNrSbaaj4EtS2ZSe2PSEHvz4QTWnmvPGBpQP0Oza2Zp4ykRZlmUoYeFxOVwK+UIsHwMAAAAA6DEo3AGgQLSl29SUbFIikzAdpcdLZpMdH+fsnF7a/pKWrF2iPW178s5zWk7NrZ2rK4dcKZfDpVQu9ZWfr6fN7LYsS0FvUEFvkOVjAAAAAAA9CoU7ABgWT8UVTUbzSl6Ydecrd8q2bbVl2rSvfd8h/zaWLIV8IYV8Ib20/SW9tP2lI36+Ylr+5WSVucsU9ofldrhNRwEAAAAAoNtRuAOAAbZtqyXdolgyplT2q2dFw5xEJqF97fvUljl0WZ+gN6iwLyyXg1+hX+ZxehTxR+R3+U1HAQAAAADAGNoCAOhGOTun5lSzYsmYMrmM6Tj4CzviO7Rw9UJ92vLpIWMX9L9AM8bMUL/yfkpmk/rRaz+SJP3yG7/scUvGfJnDcqjaV60qTxXr1wMAAAAAejwKdwDoBtlcVrFUTM2pZmVzWdNx8BeaEk1a8vESPbXpKWXt/H+fM3qdobrxdRpZPfIr7+t1ents4V7pqVTIF5LT4TQdBQAAAACAgkDhDgBdKJPLKJY8WLTn7JzpOPgLbZk2rVi/QsvWLVN7pj1vbHhwuObWztWZvc9k5vZf8Ll8ivgjPfaNBgAAAAAADofCHQC6QDqXVjQRVUu6RbZtm46Dv5DJZfTbLb/VA2se0IHkgbyxPoE+mjVuli4eeLEclsNQwsLkcrgU9oVV7ik3HQUAAAAAgIJE4Q4AnSiZTSqajCqeipuOgq9g27Ze/exVzW+crx3xHXljVZ4q3Tz6Zl0z9Bp5nB5DCQuTZVkKeoOq9lYz2x8AAAAAgCOgcAeATtCeaVc0GVVbus10FBzGh/s+1H0N9+njAx/nHfc6vbp++PW6YdQNKnczc/svlXvKFfKF5Ha4TUcBAAAAAKDgUbgDwEloS7epKdmkRCZhOgoOY1Nsk+Y3ztcfd/0x77hDDl0x+ArNGDtDNf4aQ+kKl9fpVdgflt/lNx0FAAAAAICiQeEOACcgnoormowqmU2ajoLD2NO2R4vXLNZzW5+Trfx19M/vd77m1M7RoMpBZsIVMKfDqWpvtaq8VaajAAAAAABQdCjcAeAY2batlnSLYsmYUtmU6Tg4jJZUix765CH9ZsNvlMrl/zuNC4/T7eNvV22k1lC6wlblrVK1t1pOh9N0FAAAAAAAihKFOwAchW3biqViiiVjyuQypuPgMJLZpFZuXKkHP35Q8XT+prUDKwZqbu1cnd/v/E7Z9NPr9GrBxQtO+vMUCr/Lr7A/LK/TazoKAAAAAABFjcIdAA4jm8sqloqpOdWsbC5rOg4OI2tn9cKnL2jR6kXa2743byzii2jm2JmaNGiSXA5+5f0lt8OtsD+sMneZ6SgAAAAAAJQE2gcA+AuZXEax5MGiPWfnTMfBYdi2rbd2v6X6hnptad6SN1bmKtONo27UtcOvlc/lM5SwcDksh4LeoILeYKfM+AcAAAAAAAdRuAPAf0nn0oomompJt8i27aPfAcas/Xyt6hvr9eG+D/OOux1uTR46WTeNvklBb9BItkJX4alQyBdixj8AAAAAAF2AV9sAerxkNqloMqp4Kn70k2HU9pbtWrB6gf5zx3/mHbdk6ZKBl+i2cbepb1lfM+EKnM/lU8gXkt/lNx0FAAAAAICSReEOoMdKZBJqSjapLd1mOgqO4vPE51qydome2fyMsnb+evpn9T5Lc8fP1fDgcEPpCpvT4VTIF1Klp9J0FAAAAAAASh6FO4Aepz3TrgOJA0pkEqaj4Cja0m1atm6Zlq9frkQ2/99rRHCE6sbX6Wu9v2YoXWGzLEtVnipV+6rlsBym4wAAAAAA0CNQuAPoMeKpuGKpGEV7EUjn0np689NasnaJoslo3li/sn6aPW62LhxwIUXyYQTcAYV9YXmcHtNRAAAAAADoUSjcAZQ027YVT8cVTUaVyqZMx8FR5OycXtnxihY2LtRnrZ/ljVV5qnTrmFt11dCr5Ha4DSUsbG6HW2F/WGXuMtNRAAAAAADokSjcAZQk27bVnGpWNBlVJpcxHQfH4P2972tewzx90vRJ3nG/06+pI6dq6oipFMmH4bAcCnqDCnqDsizLdBwAAAAAAHosCncAJSVn5xRLxhRLxZTNZY9+Bxi3MbpR9Q31emfPO3nHnZZTVw65UtPHTFfYFzaUrvCVe8oV9oXlcvArHQAAAAAA03h1DqAkZHIZNaeaFUvGlLNzpuPgGOxq3aVFqxfp99t+L1t23tgF/S/Q7HGzNaBigKF0hc/r9CrsD8vv8puOAgAAAAAA/guFO4Cils6lFUvG1Jxqlm3bR78DjIslY3rw4wf1xKYnlM6l88YmRCbo9vG3a0x4jKF0hc+yLFV7q1XtqzYdBQAAAAAA/AUKdwBFKZVNKZqMKp6OU7QXiUQmocc3PK6HP3lYrZnWvLHBlYNVN75O5/Q5hzXIj8Dv8qsmUMOmsQAAAAAAFCgKdwBFJZFJqCnZpLZ0m+koOEaZXEbPbX1Oi9cs1v7E/ryxGn+NZo2bpUtPvVROy2koYeFzWA6F/WFVeipNRwEAAAAAAEdA4Q6gKLRn2tWUaFJ7pt10FBwj27b1+s7XNb9xvj5t+TRvrNxdrptH36wpw6bI6/QaSlgcytxlivgjbIoKAAAAAEAR4NU7gIIWT8UVS8WUyCRMR8FxaNzfqPqGejV+3ph33OPw6DvDv6MbR93IbO2jcDqcivgiKveUm44CAAAAAACOEYU7gIJj27Za0i2KJWNKZVOm4+A4bG3eqvmN8/X6ztfzjluydNmpl+m2cbepd6C3oXTFo8JTobAvLKeDZXYAAAAAACgmFO4ACkbOzqk51axYMqZMLmM6Do7D/vb9un/N/Xp2y7PKKZc3dk6fczR3/FwNrRpqKF3xcDvcqgnUyO/ym44CAAAAAABOAIU7AOOyuaxiqZiaU83K5rKm4+A4xFNxPfLJI1qxYYWS2WTe2OjQaNXV1un0XqcbSldcgt6gqn3VclgO01EAAAAAAMAJonAHYEwqm1IsGVNLukW2bZuOg+OQyqb0wIcP6Nfv/FqxVCxvrH95f82pnaOJp0yUZVmGEhYPr9OriD8in8tnOgoAAAAAADhJFO4Aul17pl2xZEyt6VbTUXCccnZOL257UYvWLNKu1l15YyFvSLeOvVXfHvxtuRz8ejkay7JU7a1W0BvkjQkAAAAAAEoEjQiAbtOablU0GVUikzAdBSfgnd3vaF7jPG2Ibsg77nf5dcPIG3T9iOsVcAUMpSsufpdfNYEauR1u01EAAAAAAEAnonAH0KVs21ZLukWxZEypbMp0HJyAdU3rNK9hnt7b+17ecZfDpauGXKVbRt+ikC9kKF1xcTqcCvlCqvRUmo4CAAAAAAC6AIU7gC6RzWXVnGpWc6pZmVzGdBycgJ3xnVq4eqFe3P7iIWMXDbhIfz/x7+VP+Vl//xiVe8oV8UXkdDhNRwEAAAAAAF2Ewh1Ap8rkMoolY2pONStn50zHwQmIJqNasnaJntr0lDJ2/pslp9ecrrrxdRoTHqNewV7au3evoZTFw+1wK+KPKOBmuR0AAAAAAEodhTuATpHMJhVLxhRPx5nxXKTaM+1asX6Flq1bprZMW97Y0Kqhqhtfp7N6n8UGn8fIsiwFvUFVe6t5zAAAAAAA6CEo3AGclPZMu5oSTWrPtJuOghOUyWX07JZndf/a+3UgcSBvrHegt2aNm6VLBl4ih+UwlLD4+Fw+1fhr5HF6TEcBAAAAAADdiMIdwAmJp+KKJqNKZpOmo+AE2batVz97VQsaF2h7fHveWKWnUjePvlnXDL1GXqfXUMLi43Q4FfaFVeGpMB0FAAAAAAAYQOEO4Jjl7JxaUi2KJWNK59Km4+AkfLTvI9U31GvNgTV5xz0Oj64bcZ2mjZxGaXycKj2VCvlCbIoKAAAAAEAPRuEO4KiyuaxiqYMboWZzWdNxcBK2xLZoXuM8vbnrzbzjDjl0+eDLNXPsTNX4awylK05ep1cRf0Q+l890FAAAAAAAYBiFO4DDSmVTiiVjakm3sBFqkdvbtleL1yzWc1ufU065vLHz+52vObVzNKhykJlwRcphOVTtq1bQGzQdBQAAAAAAFAgKdwCHSGQSiiajak23mo6Ck9SSatFDnzyk32z4jVK5VN7YuPA41Y2v0/jIeEPpilfAHVCNv0YuB79GAQAAAADAn9EUAOjQlm5TU7JJiUzCdBScpGQ2qZUbV+qhjx9SS7olb2xgxUDNrZ2r8/udL8uyDCUsTmyKCgAAAAAAjoTCHejhbNtWS/rgRqipbOrod0BBy9pZvfDpC1q0epH2tu/NGwv7wpo5dqYuH3Q5M7NPQLmnXBFfhE1RAQAAAADAYdG4AD1Uzs4pljy4EWomlzEdByfJtm29tfstzWucp82xzXljZa4yTRs1TdcNv46NPU+Ay+FSjb9GAXfAdBQAAAAAAFDgKNyBHiaTy3QU7Tk7d/Q7oOCtPbBW9Q31+nDfh3nHXZZLk4dN1s2jb2ZjzxNU6alU2B+Ww3KYjgIAAAAAAIoAhTvQQySzScWSMcXTcdm2bToOOsH2lu1asHqB/nPHfx4ydsnAS3TbuNvUr6xf9wcrAW6HWzWBGvldftNRAAAAAABAEaFwB0pce6Zd0WRUbek201HQST5PfK4la5fomc3PKGtn88bO7H2m5tbO1YjqEYbSFb+gN6iQL8SGsgAAAAAA4LhRuAMlyLZttaZbFU1GlcwmTcdBJ2lLt+nR9Y9q+brlas+2542NCI5Q3fg6fa331wylK34+l08Rf0Rep9d0FAAAAAAAUKQo3IESkrNzak41qznZrHQubToOOkk6l9bTm5/WkrVLFE1G88b6lvXV7HGzddGAi1hn/AQ5HU6FfWFVeCpMRwEAAAAAAEWOwh0oAZlc5mDRnmpWNpc9+h1QFHJ2Tq/seEULGxfqs9bP8saqPFWaPma6rh56tdwOt6GExc2yLFV5qlTtq+bNCgAAAAAA0Cko3IEilsqmtK9tn5pTzWyEWmLe3/u+5jXM0ydNn+Qd9zl9un7E9bph5A0qc5cZSlf8WD4GAAAAAAB0BQp3oIh93vY5ZXuJ2RjdqHmN8/T27rfzjjstp741+Fu6dcytivgjhtIVP5fDpZAvxPIxAAAAAACgS1C4A0AB2N26W4vWLNILn74gW/lvoHzjlG9oTu0cDawYaChd8WP5GAAAAAAA0B0o3AHAoFgypgc/eVBPbHzikI1ux0fGq258ncaFxxlKVxr8Lr8i/og8To/pKAAAAAAAoMRRuAOAAclsUo9teEyPfPKI4ul43tigykGaWztX5/Y9V5ZlGUpY/DxOj8K+sALugOkoAAAAAACgh6BwB4BulMll9Nynz+n+NfdrX/u+vLEaf41mjp2pSYMmyWk5DSUsfk6HU9XealV6KnnDAgAAAAAAdCsKdwDoBrZt641db2h+43xtbd6aN1buLtdNo27Sd4Z/R16n10zAEsA67QAAAAAAwDQKdwDoYqs/X636hno17G/IO+5xeDRl2BTdNPomVXoqDaUrDeWecoV8IbkdbtNRAAAAAABAD0bhDgBd5NPmTzW/cb7+sPMPecctWbr01Et129jb1Kesj6F0pcHn8inkC8nv8puOAgAAAAAAQOEOAJ1tf/t+3b/2fv1uy++UtbN5Y+f0OUdza+dqaHCooXSlweVwKeQLqcJTYToKAAAAAABABwp3AOgk8XRcy9Yt04r1K5TMJvPGRlWP0u3jb9fpvU43lK40WJaloDeooDfIOu0AAAAAAKDgULgDwElKZVN6ctOTevDjBxVLxfLGTik/RXPGzdEF/S+QZVmGEpaGgDugsC8sj9NjOgoAAAAAAMBXonAHgBOUs3N6cduLWrh6oXa37c4bq/ZW69Yxt+rKIVfK5eBH7clwO9yK+CMKuAOmowAAAAAAABwRLRAAnIB3dr+jeY3ztCG6Ie+43+nXd0d+V1NHTlXARUF8MhyWo2P5GP46AAAAAAAAFAMKdwDHLZlN6s5X7pQk3XvhvfI6vYYTdZ91Tes0r2Ge3tv7Xt5xp+XUVUOu0vQx0xXyhQylKx3lnnKFfWH+OgAAAAAAABQVmgwAOAY74zu1cPVCvbj9xUPGLux/oWaNm6UBFQMMJCstHqdHEX9EfpffdBQAAAAAAIDjRuEOAEcQTUa1ZO0SPbXpKWXsTN7Y6TWna+74uRoTGmMoXemwLEtBb1DV3mqWjwEAAAAAAEWLwh0AvkJ7pl0r1q/QsnXL1JZpyxsbUjVEdbV1OrvP2ZTDncDn8inij/SopYkAAAAAAEBponAHgC/J5DJ6dsuzun/t/TqQOJA31jvQW7PGzdLFAy+W03IaSlg6LMtStbda1b5q01EAAAAAAAA6BYU7AEiybVt/2PkHzW+cr20t2/LGKj2Vunn0zbpm6DXMwu4kPpdPNf4aeZwe01EAAAAAAAA6DYU7gB7vo30fqb6hXmsOrMk77nF4dN2I6zRt5DRVeCoMpSstDsuhkC+kKm+V6SgAAAAAAACdjsIdQI+1JbZF8xrn6c1db+Ydd8ihywdfrhljZqhXoJehdKXH7/KrJlAjt8NtOgoAAAAAAECXoHAHcIhkNnnM40c7V1LBLcOyt22vFq9ZrOe2Pqeccnlj5/U7T3PGzdHgqsGG0pUeh+VQ2B9WpafSdBQAAAAAAIAuReEO4BB3vnLnMZ/7o9d+dNR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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1500x1000 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "And finally increase the font size of the tickmark labels, the title, and the x- and y-labels so they don't feel left out!\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "plt.figure(figsize=(15, 10))\n\nsns.set(font_scale=1.5)\n\nax = sns.regplot(x='year', y='total', data=df_tot, color='green', marker='+', scatter_kws={'s': 200})\nax.set(xlabel='Year', ylabel='Total Immigration')\nax.set_title('Total Immigration to Canada from 1980 - 2013')\nplt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x2387b80>",
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1500x1000 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Amazing! A complete scatter plot with a regression fit with 5 lines of code only. Isn't this really amazing?\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "If you are not a big fan of the purple background, you can easily change the style to a white plain background.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "plt.figure(figsize=(15, 10))\n\nsns.set(font_scale=1.5)\nsns.set_style('ticks')  # change background to white background\n\nax = sns.regplot(x='year', y='total', data=df_tot, color='green', marker='+', scatter_kws={'s': 200})\nax.set(xlabel='Year', ylabel='Total Immigration')\nax.set_title('Total Immigration to Canada from 1980 - 2013')\nplt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x4b1aef0>",
            "image/png": 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"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1500x1000 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Or to a white background with gridlines.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "plt.figure(figsize=(15, 10))\n\nsns.set(font_scale=1.5)\nsns.set_style('whitegrid')\n\nax = sns.regplot(x='year', y='total', data=df_tot, color='green', marker='+', scatter_kws={'s': 200})\nax.set(xlabel='Year', ylabel='Total Immigration')\nax.set_title('Total Immigration to Canada from 1980 - 2013')\nplt.show()",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x566cdf0>",
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1500x1000 with 0 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "**Question**: Use seaborn to create a scatter plot with a regression line to visualize the total immigration from Denmark, Sweden, and Norway to Canada from 1980 to 2013.\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    },
    {
      "cell_type": "code",
      "source": "### type your answer here\n# create df_countries dataframe\ndf_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n\n# create df_total by summing across three countries for each year\ndf_total = pd.DataFrame(df_countries.sum(axis=1))\n\n# reset index in place\ndf_total.reset_index(inplace=True)\n\n# rename columns\ndf_total.columns = ['year', 'total']\n\n# change column year from string to int to create scatter plot\ndf_total['year'] = df_total['year'].astype(int)\n\n# define figure size\nplt.figure(figsize=(15, 10))\n\n# define background style and font size\nsns.set(font_scale=1.5)\nsns.set_style('whitegrid')\n\n# generate plot and add title and axes labels\nax = sns.regplot(x='year', y='total', data=df_total, color='green', marker='+', scatter_kws={'s': 200})\nax.set(xlabel='Year', ylabel='Total Immigration')\nax.set_title('Total Immigrationn from Denmark, Sweden, and Norway to Canada from 1980 - 2013')\n\n\n\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "trusted": true
      },
      "execution_count": 46,
      "outputs": [
        {
          "execution_count": 46,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Text(0.5, 1.0, 'Total Immigrationn from Denmark, Sweden, and Norway to Canada from 1980 - 2013')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1500x1000 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for a sample python solution</summary>\n\n```python\n    #The correct answer is:\n    \n    # create df_countries dataframe\n    df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n\n    # create df_total by summing across three countries for each year\n    df_total = pd.DataFrame(df_countries.sum(axis=1))\n\n    # reset index in place\n    df_total.reset_index(inplace=True)\n\n    # rename columns\n    df_total.columns = ['year', 'total']\n\n    # change column year from string to int to create scatter plot\n    df_total['year'] = df_total['year'].astype(int)\n\n    # define figure size\n    plt.figure(figsize=(15, 10))\n\n    # define background style and font size\n    sns.set(font_scale=1.5)\n    sns.set_style('whitegrid')\n\n    # generate plot and add title and axes labels\n    ax = sns.regplot(x='year', y='total', data=df_total, color='green', marker='+', scatter_kws={'s': 200})\n    ax.set(xlabel='Year', ylabel='Total Immigration')\n    ax.set_title('Total Immigrationn from Denmark, Sweden, and Norway to Canada from 1980 - 2013')\n\n```\n\n</details>\n",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        }
      }
    }
  ]
}