Alexon-Abreu/DV0101EN-Exercise-Pie-Charts-Box-Plots-Scatter-Plots-and-Bubble-Plots-py.ipynb

## DV0101EN-Exercise-Pie-Charts-Box-Plots-Scatter-Plots-and-Bubble-Plots-py.ipynb
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   "source": [
    "<center>\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n",
    "\n",
    "\n",
    "# Pie Charts, Box Plots, Scatter Plots, and Bubble Plots\n",
    "\n",
    "\n",
    "Estimated time needed: **30** minutes\n",
    "    \n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "* Explore Matplotlib library further\n",
    "* Create pie charts, box plots, scatter plots and bubble charts"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Table of Contents\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "\n",
    "1. [Exploring Datasets with *p*andas](#0)<br>\n",
    "2. [Downloading and Prepping Data](#2)<br>\n",
    "3. [Visualizing Data using Matplotlib](#4) <br>\n",
    "4. [Pie Charts](#6) <br>\n",
    "5. [Box Plots](#8) <br>\n",
    "6. [Scatter Plots](#10) <br>\n",
    "7. [Bubble Plots](#12) <br> \n",
    "</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Exploring Datasets with *pandas* and Matplotlib<a id=\"0\"></a>\n",
    "\n",
    "Toolkits: The course heavily relies on [*pandas*](http://pandas.pydata.org/) and [**Numpy**](http://www.numpy.org/) for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/).\n",
    "\n",
    "Dataset: Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml) from United Nation's website.\n",
    "\n",
    "The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. In this lab, we will focus on the Canadian Immigration data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "# Downloading and Prepping Data <a id=\"2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
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    }
   },
   "source": [
    "Import primary modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [],
   "source": [
    "import numpy as np  # useful for many scientific computing in Python\n",
    "import pandas as pd # primary data structure library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Let's download and import our primary Canadian Immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
    "```\n",
    "!conda install -c anaconda xlrd --yes\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Download the dataset and read it into a *pandas* dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
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    "new_sheet": false,
    "run_control": {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data downloaded and read into a dataframe!\n"
     ]
    }
   ],
   "source": [
    "df_can = pd.read_excel('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/Canada.xlsx',\n",
    "                       sheet_name='Canada by Citizenship',\n",
    "                       skiprows=range(20),\n",
    "                       skipfooter=2\n",
    "                      )\n",
    "\n",
    "print('Data downloaded and read into a dataframe!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Let's take a look at the first five items in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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       "<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",
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       "      <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",
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       "      <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",
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       "      <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",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "<p>5 rows × 43 columns</p>\n",
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       "         Type    Coverage          OdName  AREA AreaName   REG  \\\n",
       "0  Immigrants  Foreigners     Afghanistan   935     Asia  5501   \n",
       "1  Immigrants  Foreigners         Albania   908   Europe   925   \n",
       "2  Immigrants  Foreigners         Algeria   903   Africa   912   \n",
       "3  Immigrants  Foreigners  American Samoa   909  Oceania   957   \n",
       "4  Immigrants  Foreigners         Andorra   908   Europe   925   \n",
       "\n",
       "           RegName  DEV             DevName  1980  ...  2004  2005  2006  \\\n",
       "0    Southern Asia  902  Developing regions    16  ...  2978  3436  3009   \n",
       "1  Southern Europe  901   Developed regions     1  ...  1450  1223   856   \n",
       "2  Northern Africa  902  Developing regions    80  ...  3616  3626  4807   \n",
       "3        Polynesia  902  Developing regions     0  ...     0     0     1   \n",
       "4  Southern Europe  901   Developed regions     0  ...     0     0     1   \n",
       "\n",
       "   2007  2008  2009  2010  2011  2012  2013  \n",
       "0  2652  2111  1746  1758  2203  2635  2004  \n",
       "1   702   560   716   561   539   620   603  \n",
       "2  3623  4005  5393  4752  4325  3774  4331  \n",
       "3     0     0     0     0     0     0     0  \n",
       "4     1     0     0     0     0     1     1  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's find out how many entries there are in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195, 43)\n"
     ]
    }
   ],
   "source": [
    "# print the dimensions of the dataframe\n",
    "print(df_can.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to *Introduction to Matplotlib and Line Plots* and *Area Plots, Histograms, and Bar Plots* for a detailed description of this preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data dimensions: (195, 38)\n"
     ]
    }
   ],
   "source": [
    "# clean up the dataset to remove unnecessary columns (eg. REG) \n",
    "df_can.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)\n",
    "\n",
    "# let's rename the columns so that they make sense\n",
    "df_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\n",
    "df_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\n",
    "df_can.set_index('Country', inplace=True)\n",
    "\n",
    "# add total column\n",
    "df_can['Total'] = df_can.sum(axis=1)\n",
    "\n",
    "# years that we will be using in this lesson - useful for plotting later on\n",
    "years = list(map(str, range(1980, 2014)))\n",
    "print('data dimensions:', df_can.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Visualizing Data using Matplotlib<a id=\"4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Import `Matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "collapsed": false,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  3.3.4\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.style.use('ggplot') # optional: for ggplot-like style\n",
    "\n",
    "# check for latest version of Matplotlib\n",
    "print('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Pie Charts <a id=\"6\"></a>\n",
    "\n",
    "A `pie chart` is a circualr graphic that displays numeric proportions by dividing a circle (or pie) into proportional slices. You are most likely already familiar with pie charts as it is widely used in business and media. We can create pie charts in Matplotlib by passing in the `kind=pie` keyword.\n",
    "\n",
    "Let's use a pie chart to explore the proportion (percentage) of new immigrants grouped by continents for the entire time period from 1980 to 2013. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Gather data. \n",
    "\n",
    "We will use *pandas* `groupby` method to summarize the immigration data by `Continent`. The general process of `groupby` involves the following steps:\n",
    "\n",
    "1. **Split:** Splitting the data into groups based on some criteria.\n",
    "2. **Apply:** Applying a function to each group independently:\n",
    "       .sum()\n",
    "       .count()\n",
    "       .mean() \n",
    "       .std() \n",
    "       .aggregate()\n",
    "       .apply()\n",
    "       .etc..\n",
    "3. **Combine:** Combining the results into a data structure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/Mod3Fig4SplitApplyCombine.png\" height=400 align=\"center\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas.core.groupby.generic.DataFrameGroupBy\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
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       "      <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>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>3951</td>\n",
       "      <td>4363</td>\n",
       "      <td>3819</td>\n",
       "      <td>2671</td>\n",
       "      <td>2639</td>\n",
       "      <td>2650</td>\n",
       "      <td>3782</td>\n",
       "      <td>7494</td>\n",
       "      <td>7552</td>\n",
       "      <td>9894</td>\n",
       "      <td>...</td>\n",
       "      <td>27523</td>\n",
       "      <td>29188</td>\n",
       "      <td>28284</td>\n",
       "      <td>29890</td>\n",
       "      <td>34534</td>\n",
       "      <td>40892</td>\n",
       "      <td>35441</td>\n",
       "      <td>38083</td>\n",
       "      <td>38543</td>\n",
       "      <td>618948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>31025</td>\n",
       "      <td>34314</td>\n",
       "      <td>30214</td>\n",
       "      <td>24696</td>\n",
       "      <td>27274</td>\n",
       "      <td>23850</td>\n",
       "      <td>28739</td>\n",
       "      <td>43203</td>\n",
       "      <td>47454</td>\n",
       "      <td>60256</td>\n",
       "      <td>...</td>\n",
       "      <td>159253</td>\n",
       "      <td>149054</td>\n",
       "      <td>133459</td>\n",
       "      <td>139894</td>\n",
       "      <td>141434</td>\n",
       "      <td>163845</td>\n",
       "      <td>146894</td>\n",
       "      <td>152218</td>\n",
       "      <td>155075</td>\n",
       "      <td>3317794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>39760</td>\n",
       "      <td>44802</td>\n",
       "      <td>42720</td>\n",
       "      <td>24638</td>\n",
       "      <td>22287</td>\n",
       "      <td>20844</td>\n",
       "      <td>24370</td>\n",
       "      <td>46698</td>\n",
       "      <td>54726</td>\n",
       "      <td>60893</td>\n",
       "      <td>...</td>\n",
       "      <td>35955</td>\n",
       "      <td>33053</td>\n",
       "      <td>33495</td>\n",
       "      <td>34692</td>\n",
       "      <td>35078</td>\n",
       "      <td>33425</td>\n",
       "      <td>26778</td>\n",
       "      <td>29177</td>\n",
       "      <td>28691</td>\n",
       "      <td>1410947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latin America and the Caribbean</th>\n",
       "      <td>13081</td>\n",
       "      <td>15215</td>\n",
       "      <td>16769</td>\n",
       "      <td>15427</td>\n",
       "      <td>13678</td>\n",
       "      <td>15171</td>\n",
       "      <td>21179</td>\n",
       "      <td>28471</td>\n",
       "      <td>21924</td>\n",
       "      <td>25060</td>\n",
       "      <td>...</td>\n",
       "      <td>24747</td>\n",
       "      <td>24676</td>\n",
       "      <td>26011</td>\n",
       "      <td>26547</td>\n",
       "      <td>26867</td>\n",
       "      <td>28818</td>\n",
       "      <td>27856</td>\n",
       "      <td>27173</td>\n",
       "      <td>24950</td>\n",
       "      <td>765148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern America</th>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>7705</td>\n",
       "      <td>6469</td>\n",
       "      <td>6790</td>\n",
       "      <td>...</td>\n",
       "      <td>8394</td>\n",
       "      <td>9613</td>\n",
       "      <td>9463</td>\n",
       "      <td>10190</td>\n",
       "      <td>8995</td>\n",
       "      <td>8142</td>\n",
       "      <td>7677</td>\n",
       "      <td>7892</td>\n",
       "      <td>8503</td>\n",
       "      <td>241142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  1980   1981   1982   1983   1984   1985  \\\n",
       "Continent                                                                   \n",
       "Africa                            3951   4363   3819   2671   2639   2650   \n",
       "Asia                             31025  34314  30214  24696  27274  23850   \n",
       "Europe                           39760  44802  42720  24638  22287  20844   \n",
       "Latin America and the Caribbean  13081  15215  16769  15427  13678  15171   \n",
       "Northern America                  9378  10030   9074   7100   6661   6543   \n",
       "\n",
       "                                  1986   1987   1988   1989  ...    2005  \\\n",
       "Continent                                                    ...           \n",
       "Africa                            3782   7494   7552   9894  ...   27523   \n",
       "Asia                             28739  43203  47454  60256  ...  159253   \n",
       "Europe                           24370  46698  54726  60893  ...   35955   \n",
       "Latin America and the Caribbean  21179  28471  21924  25060  ...   24747   \n",
       "Northern America                  7074   7705   6469   6790  ...    8394   \n",
       "\n",
       "                                   2006    2007    2008    2009    2010  \\\n",
       "Continent                                                                 \n",
       "Africa                            29188   28284   29890   34534   40892   \n",
       "Asia                             149054  133459  139894  141434  163845   \n",
       "Europe                            33053   33495   34692   35078   33425   \n",
       "Latin America and the Caribbean   24676   26011   26547   26867   28818   \n",
       "Northern America                   9613    9463   10190    8995    8142   \n",
       "\n",
       "                                   2011    2012    2013    Total  \n",
       "Continent                                                         \n",
       "Africa                            35441   38083   38543   618948  \n",
       "Asia                             146894  152218  155075  3317794  \n",
       "Europe                            26778   29177   28691  1410947  \n",
       "Latin America and the Caribbean   27856   27173   24950   765148  \n",
       "Northern America                   7677    7892    8503   241142  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group countries by continents and apply sum() function \n",
    "df_continents = df_can.groupby('Continent', axis=0).sum()\n",
    "\n",
    "# note: the output of the groupby method is a `groupby' object. \n",
    "# we can not use it further until we apply a function (eg .sum())\n",
    "print(type(df_can.groupby('Continent', axis=0)))\n",
    "\n",
    "df_continents.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot the data. We will pass in `kind = 'pie'` keyword, along with the following additional parameters:\n",
    "- `autopct` -  is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be `fmt%pct`.\n",
    "- `startangle` - rotates the start of the pie chart by angle degrees counterclockwise from the x-axis.\n",
    "- `shadow` - Draws a shadow beneath the pie (to give a 3D feel)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# autopct create %, start angle represent starting point\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(5, 6),\n",
    "                            autopct='%1.1f%%', # add in percentages\n",
    "                            startangle=90,     # start angle 90° (Africa)\n",
    "                            shadow=True,       # add shadow      \n",
    "                            )\n",
    "\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]')\n",
    "plt.axis('equal') # Sets the pie chart to look like a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The above visual is not very clear, the numbers and text overlap in some instances. Let's make a few modifications to improve the visuals:\n",
    "\n",
    "* Remove the text labels on the pie chart by passing in `legend` and add it as a seperate legend using `plt.legend()`.\n",
    "* Push out the percentages to sit just outside the pie chart by passing in `pctdistance` parameter.\n",
    "* Pass in a custom set of colors for continents by passing in `colors` parameter.\n",
    "* **Explode** the pie chart to emphasize the lowest three continents (Africa, North America, and Latin America and Carribbean) by pasing in `explode` parameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors_list = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'lightgreen', 'pink']\n",
    "explode_list = [0.1, 0, 0, 0, 0.1, 0.1] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "df_continents['Total'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,         # turn off labels on pie chart\n",
    "                            pctdistance=1.12,    # the ratio between the center of each pie slice and the start of the text generated by autopct \n",
    "                            colors=colors_list,  # add custom colors\n",
    "                            explode=explode_list # 'explode' lowest 3 continents\n",
    "                            )\n",
    "\n",
    "# scale the title up by 12% to match pctdistance\n",
    "plt.title('Immigration to Canada by Continent [1980 - 2013]', y=1.12) \n",
    "\n",
    "plt.axis('equal') \n",
    "\n",
    "# add legend\n",
    "plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question:** Using a pie chart, explore the proportion (percentage) of new immigrants grouped by continents in the year 2013.\n",
    "\n",
    "**Note**: You might need to play with the explore values in order to fix any overlapping slice values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='2013'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "explode_list = [0.0, 0, 0, 0.1, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
    "df_continents['2013'].plot(kind='pie',\n",
    "                            figsize=(15, 6),\n",
    "                            autopct='%1.1f%%', \n",
    "                            startangle=90,    \n",
    "                            shadow=True,       \n",
    "                            labels=None,                 # turn off labels on pie chart\n",
    "                            pctdistance=1.12,            # the ratio between the pie center and start of text label\n",
    "                            explode=explode_list         # 'explode' lowest 3 continents\n",
    "                            )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    explode_list = [0.0, 0, 0, 0.1, 0.1, 0.2] # ratio for each continent with which to offset each wedge.\n",
    "\n",
    "    df_continents['2013'].plot(kind='pie',\n",
    "                                figsize=(15, 6),\n",
    "                                autopct='%1.1f%%', \n",
    "                                startangle=90,    \n",
    "                                shadow=True,       \n",
    "                                labels=None,                 # turn off labels on pie chart\n",
    "                                pctdistance=1.12,            # the ratio between the pie center and start of text label\n",
    "                                explode=explode_list         # 'explode' lowest 3 continents\n",
    "                                )\n",
    "\n",
    "    # scale the title up by 12% to match pctdistance\n",
    "    plt.title('Immigration to Canada by Continent in 2013', y=1.12) \n",
    "    plt.axis('equal') \n",
    "\n",
    "    # add legend\n",
    "    plt.legend(labels=df_continents.index, loc='upper left') \n",
    "\n",
    "    # show plot\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Box Plots <a id=\"8\"></a>\n",
    "\n",
    "A `box plot` is a way of statistically representing the *distribution* of the data through five main dimensions: \n",
    "\n",
    "- **Minimun:** Smallest number in the dataset excluding the outliers.\n",
    "- **First quartile:** Middle number between the `minimum` and the `median`.\n",
    "- **Second quartile (Median):** Middle number of the (sorted) dataset.\n",
    "- **Third quartile:** Middle number between `median` and `maximum`.\n",
    "- **Maximum:** Highest number in the dataset excluding the outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/boxplot_complete.png\" width=440, align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "To make a `box plot`, we can use `kind=box` in `plot` method invoked on a *pandas* series or dataframe.\n",
    "\n",
    "Let's plot the box plot for the Japanese immigrants between 1980 - 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Even though we are extracting the data for just one country, we will obtain it as a dataframe. This will help us with calling the `dataframe.describe()` method to view the percentiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Japan\n",
       "1980       701\n",
       "1981       756\n",
       "1982       598\n",
       "1983       309\n",
       "1984       246"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get a dataframe, place extra square brackets around 'Japan'.\n",
    "df_japan = df_can.loc[['Japan'], years].transpose()\n",
    "df_japan.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot by passing in `kind='box'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_japan.plot(kind='box', figsize=(8, 6))\n",
    "\n",
    "plt.title('Box plot of Japanese Immigrants from 1980 - 2013')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can immediately make a few key observations from the plot above:\n",
    "1. The minimum number of immigrants is around 200 (min), maximum number is around 1300 (max), and  median number of immigrants is around 900 (median).\n",
    "2. 25% of the years for period 1980 - 2013 had an annual immigrant count of ~500 or fewer (First quartile).\n",
    "2. 75% of the years for period 1980 - 2013 had an annual immigrant count of ~1100 or fewer (Third quartile).\n",
    "\n",
    "We can view the actual numbers by calling the `describe()` method on the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>814.911765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>337.219771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>529.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>902.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1079.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1284.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country        Japan\n",
       "count      34.000000\n",
       "mean      814.911765\n",
       "std       337.219771\n",
       "min       198.000000\n",
       "25%       529.000000\n",
       "50%       902.000000\n",
       "75%      1079.000000\n",
       "max      1284.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_japan.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "One of the key benefits of box plots is comparing the distribution of multiple datasets. In one of the previous labs, we observed that China and India had very similar immigration trends. Let's analyize these two countries further using box plots.\n",
    "\n",
    "**Question:** Compare the distribution of the number of new immigrants from India and China for the period 1980 - 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset for China and India and call the dataframe **df_CI**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>5123</td>\n",
       "      <td>8880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>6682</td>\n",
       "      <td>8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>3308</td>\n",
       "      <td>8147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>1863</td>\n",
       "      <td>7338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1527</td>\n",
       "      <td>5704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  China  India\n",
       "1980      5123   8880\n",
       "1981      6682   8670\n",
       "1982      3308   8147\n",
       "1983      1863   7338\n",
       "1984      1527   5704"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_CI= df_can.loc[['China', 'India'], years].transpose()\n",
    "df_CI.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI= df_can.loc[['China', 'India'], years].transpose()\n",
    "    df_CI.head()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's view the percentages associated with both countries using the `describe()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19410.647059</td>\n",
       "      <td>20350.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13568.230790</td>\n",
       "      <td>10007.342579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1527.000000</td>\n",
       "      <td>4211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5512.750000</td>\n",
       "      <td>10637.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19945.000000</td>\n",
       "      <td>20235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31568.500000</td>\n",
       "      <td>28699.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42584.000000</td>\n",
       "      <td>36210.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country         China         India\n",
       "count       34.000000     34.000000\n",
       "mean     19410.647059  20350.117647\n",
       "std      13568.230790  10007.342579\n",
       "min       1527.000000   4211.000000\n",
       "25%       5512.750000  10637.750000\n",
       "50%      19945.000000  20235.000000\n",
       "75%      31568.500000  28699.500000\n",
       "max      42584.000000  36210.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_CI.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI.describe()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Plot data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "\n",
    "df_CI.plot(kind='box', figsize=(10, 7))\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_CI.plot(kind='box', figsize=(10, 7))\n",
    "\n",
    "    plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "    plt.ylabel('Number of Immigrants')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can observe that, while both countries have around the same median immigrant population (~20,000),  China's immigrant population range is more spread out than India's. The maximum population from India for any year (36,210) is around 15% lower than the maximum population from China (42,584).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "If you prefer to create horizontal box plots, you can pass the `vert` parameter in the **plot** function and assign it to *False*. You can also specify a different color in case you are not a big fan of the default red color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# horizontal box plots\n",
    "df_CI.plot(kind='box', figsize=(10, 7), color='blue', vert=False)\n",
    "\n",
    "plt.title('Box plots of Immigrants from China and India (1980 - 2013)')\n",
    "plt.xlabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Subplots**\n",
    "\n",
    "Often times we might want to plot multiple plots within the same figure. For example, we might want to perform a side by side comparison of the box plot with the line plot of China and India's immigration.\n",
    "\n",
    "To visualize multiple plots together, we can create a **`figure`** (overall canvas) and divide it into **`subplots`**, each containing a plot. With **subplots**, we usually work with the **artist layer** instead of the **scripting layer**. \n",
    "\n",
    "Typical syntax is : <br>\n",
    "```python\n",
    "    fig = plt.figure() # create figure\n",
    "    ax = fig.add_subplot(nrows, ncols, plot_number) # create subplots\n",
    "```\n",
    "Where\n",
    "- `nrows` and `ncols` are used to notionally split the figure into (`nrows` \\* `ncols`) sub-axes,  \n",
    "- `plot_number` is used to identify the particular subplot that this function is to create within the notional grid. `plot_number` starts at 1, increments across rows first and has a maximum of `nrows` * `ncols` as shown below.\n",
    "\n",
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/Mod3Fig5Subplots_V2.png\" width=500 align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can then specify which subplot to place each plot by passing in the `ax` paramemter in `plot()` method as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure() # create figure\n",
    "\n",
    "ax0 = fig.add_subplot(1, 2, 1) # add subplot 1 (1 row, 2 columns, first plot)\n",
    "ax1 = fig.add_subplot(1, 2, 2) # add subplot 2 (1 row, 2 columns, second plot). See tip below**\n",
    "\n",
    "# Subplot 1: Box plot\n",
    "df_CI.plot(kind='box', color='blue', vert=False, figsize=(20, 6), ax=ax0) # add to subplot 1\n",
    "ax0.set_title('Box Plots of Immigrants from China and India (1980 - 2013)')\n",
    "ax0.set_xlabel('Number of Immigrants')\n",
    "ax0.set_ylabel('Countries')\n",
    "\n",
    "# Subplot 2: Line plot\n",
    "df_CI.plot(kind='line', figsize=(20, 6), ax=ax1) # add to subplot 2\n",
    "ax1.set_title ('Line Plots of Immigrants from China and India (1980 - 2013)')\n",
    "ax1.set_ylabel('Number of Immigrants')\n",
    "ax1.set_xlabel('Years')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "** * Tip regarding subplot convention **\n",
    "\n",
    "In the case when `nrows`, `ncols`, and `plot_number` are all less than 10, a convenience exists such that the a 3 digit number can be given instead, where the hundreds represent `nrows`, the tens represent `ncols` and the units represent `plot_number`. For instance,\n",
    "```python\n",
    "   subplot(211) == subplot(2, 1, 1) \n",
    "```\n",
    "produces a subaxes in a figure which represents the top plot (i.e. the first) in a 2 rows by 1 column notional grid (no grid actually exists, but conceptually this is how the returned subplot has been positioned)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's try something a little more advanced. \n",
    "\n",
    "Previously we identified the top 15 countries based on total immigration from 1980 - 2013.\n",
    "\n",
    "**Question:** Create a box plot to visualize the distribution of the top 15 countries (based on total immigration) grouped by the *decades* `1980s`, `1990s`, and `2000s`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the dataset. Get the top 15 countries based on Total immigrant population. Name the dataframe **df_top15**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <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>India</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>8880</td>\n",
       "      <td>8670</td>\n",
       "      <td>8147</td>\n",
       "      <td>7338</td>\n",
       "      <td>5704</td>\n",
       "      <td>4211</td>\n",
       "      <td>7150</td>\n",
       "      <td>...</td>\n",
       "      <td>36210</td>\n",
       "      <td>33848</td>\n",
       "      <td>28742</td>\n",
       "      <td>28261</td>\n",
       "      <td>29456</td>\n",
       "      <td>34235</td>\n",
       "      <td>27509</td>\n",
       "      <td>30933</td>\n",
       "      <td>33087</td>\n",
       "      <td>691904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>5123</td>\n",
       "      <td>6682</td>\n",
       "      <td>3308</td>\n",
       "      <td>1863</td>\n",
       "      <td>1527</td>\n",
       "      <td>1816</td>\n",
       "      <td>1960</td>\n",
       "      <td>...</td>\n",
       "      <td>42584</td>\n",
       "      <td>33518</td>\n",
       "      <td>27642</td>\n",
       "      <td>30037</td>\n",
       "      <td>29622</td>\n",
       "      <td>30391</td>\n",
       "      <td>28502</td>\n",
       "      <td>33024</td>\n",
       "      <td>34129</td>\n",
       "      <td>659962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom of Great Britain and Northern Ireland</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>22045</td>\n",
       "      <td>24796</td>\n",
       "      <td>20620</td>\n",
       "      <td>10015</td>\n",
       "      <td>10170</td>\n",
       "      <td>9564</td>\n",
       "      <td>9470</td>\n",
       "      <td>...</td>\n",
       "      <td>7258</td>\n",
       "      <td>7140</td>\n",
       "      <td>8216</td>\n",
       "      <td>8979</td>\n",
       "      <td>8876</td>\n",
       "      <td>8724</td>\n",
       "      <td>6204</td>\n",
       "      <td>6195</td>\n",
       "      <td>5827</td>\n",
       "      <td>551500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>6051</td>\n",
       "      <td>5921</td>\n",
       "      <td>5249</td>\n",
       "      <td>4562</td>\n",
       "      <td>3801</td>\n",
       "      <td>3150</td>\n",
       "      <td>4166</td>\n",
       "      <td>...</td>\n",
       "      <td>18139</td>\n",
       "      <td>18400</td>\n",
       "      <td>19837</td>\n",
       "      <td>24887</td>\n",
       "      <td>28573</td>\n",
       "      <td>38617</td>\n",
       "      <td>36765</td>\n",
       "      <td>34315</td>\n",
       "      <td>29544</td>\n",
       "      <td>511391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>978</td>\n",
       "      <td>972</td>\n",
       "      <td>1201</td>\n",
       "      <td>900</td>\n",
       "      <td>668</td>\n",
       "      <td>514</td>\n",
       "      <td>691</td>\n",
       "      <td>...</td>\n",
       "      <td>14314</td>\n",
       "      <td>13127</td>\n",
       "      <td>10124</td>\n",
       "      <td>8994</td>\n",
       "      <td>7217</td>\n",
       "      <td>6811</td>\n",
       "      <td>7468</td>\n",
       "      <td>11227</td>\n",
       "      <td>12603</td>\n",
       "      <td>241600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States of America</th>\n",
       "      <td>Northern America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>9378</td>\n",
       "      <td>10030</td>\n",
       "      <td>9074</td>\n",
       "      <td>7100</td>\n",
       "      <td>6661</td>\n",
       "      <td>6543</td>\n",
       "      <td>7074</td>\n",
       "      <td>...</td>\n",
       "      <td>8394</td>\n",
       "      <td>9613</td>\n",
       "      <td>9463</td>\n",
       "      <td>10190</td>\n",
       "      <td>8995</td>\n",
       "      <td>8142</td>\n",
       "      <td>7676</td>\n",
       "      <td>7891</td>\n",
       "      <td>8501</td>\n",
       "      <td>241122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran (Islamic Republic of)</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1172</td>\n",
       "      <td>1429</td>\n",
       "      <td>1822</td>\n",
       "      <td>1592</td>\n",
       "      <td>1977</td>\n",
       "      <td>1648</td>\n",
       "      <td>1794</td>\n",
       "      <td>...</td>\n",
       "      <td>5837</td>\n",
       "      <td>7480</td>\n",
       "      <td>6974</td>\n",
       "      <td>6475</td>\n",
       "      <td>6580</td>\n",
       "      <td>7477</td>\n",
       "      <td>7479</td>\n",
       "      <td>7534</td>\n",
       "      <td>11291</td>\n",
       "      <td>175923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>185</td>\n",
       "      <td>371</td>\n",
       "      <td>290</td>\n",
       "      <td>197</td>\n",
       "      <td>1086</td>\n",
       "      <td>845</td>\n",
       "      <td>1838</td>\n",
       "      <td>...</td>\n",
       "      <td>4930</td>\n",
       "      <td>4714</td>\n",
       "      <td>4123</td>\n",
       "      <td>4756</td>\n",
       "      <td>4547</td>\n",
       "      <td>4422</td>\n",
       "      <td>3309</td>\n",
       "      <td>3338</td>\n",
       "      <td>2394</td>\n",
       "      <td>148358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republic of Korea</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1011</td>\n",
       "      <td>1456</td>\n",
       "      <td>1572</td>\n",
       "      <td>1081</td>\n",
       "      <td>847</td>\n",
       "      <td>962</td>\n",
       "      <td>1208</td>\n",
       "      <td>...</td>\n",
       "      <td>5832</td>\n",
       "      <td>6215</td>\n",
       "      <td>5920</td>\n",
       "      <td>7294</td>\n",
       "      <td>5874</td>\n",
       "      <td>5537</td>\n",
       "      <td>4588</td>\n",
       "      <td>5316</td>\n",
       "      <td>4509</td>\n",
       "      <td>142581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>863</td>\n",
       "      <td>2930</td>\n",
       "      <td>5881</td>\n",
       "      <td>4546</td>\n",
       "      <td>3588</td>\n",
       "      <td>2819</td>\n",
       "      <td>4808</td>\n",
       "      <td>...</td>\n",
       "      <td>1405</td>\n",
       "      <td>1263</td>\n",
       "      <td>1235</td>\n",
       "      <td>1267</td>\n",
       "      <td>1013</td>\n",
       "      <td>795</td>\n",
       "      <td>720</td>\n",
       "      <td>779</td>\n",
       "      <td>852</td>\n",
       "      <td>139241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1409</td>\n",
       "      <td>1119</td>\n",
       "      <td>1159</td>\n",
       "      <td>789</td>\n",
       "      <td>1253</td>\n",
       "      <td>1683</td>\n",
       "      <td>2576</td>\n",
       "      <td>...</td>\n",
       "      <td>3709</td>\n",
       "      <td>3802</td>\n",
       "      <td>3467</td>\n",
       "      <td>3566</td>\n",
       "      <td>3077</td>\n",
       "      <td>3432</td>\n",
       "      <td>3072</td>\n",
       "      <td>1614</td>\n",
       "      <td>2172</td>\n",
       "      <td>115359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1729</td>\n",
       "      <td>2027</td>\n",
       "      <td>2219</td>\n",
       "      <td>1490</td>\n",
       "      <td>1169</td>\n",
       "      <td>1177</td>\n",
       "      <td>1298</td>\n",
       "      <td>...</td>\n",
       "      <td>4429</td>\n",
       "      <td>4002</td>\n",
       "      <td>4290</td>\n",
       "      <td>4532</td>\n",
       "      <td>5051</td>\n",
       "      <td>4646</td>\n",
       "      <td>4080</td>\n",
       "      <td>6280</td>\n",
       "      <td>5623</td>\n",
       "      <td>109091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamaica</th>\n",
       "      <td>Latin America and the Caribbean</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>3198</td>\n",
       "      <td>2634</td>\n",
       "      <td>2661</td>\n",
       "      <td>2455</td>\n",
       "      <td>2508</td>\n",
       "      <td>2938</td>\n",
       "      <td>4649</td>\n",
       "      <td>...</td>\n",
       "      <td>1945</td>\n",
       "      <td>1722</td>\n",
       "      <td>2141</td>\n",
       "      <td>2334</td>\n",
       "      <td>2456</td>\n",
       "      <td>2321</td>\n",
       "      <td>2059</td>\n",
       "      <td>2182</td>\n",
       "      <td>2479</td>\n",
       "      <td>106431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viet Nam</th>\n",
       "      <td>Asia</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1191</td>\n",
       "      <td>1829</td>\n",
       "      <td>2162</td>\n",
       "      <td>3404</td>\n",
       "      <td>7583</td>\n",
       "      <td>5907</td>\n",
       "      <td>2741</td>\n",
       "      <td>...</td>\n",
       "      <td>1852</td>\n",
       "      <td>3153</td>\n",
       "      <td>2574</td>\n",
       "      <td>1784</td>\n",
       "      <td>2171</td>\n",
       "      <td>1942</td>\n",
       "      <td>1723</td>\n",
       "      <td>1731</td>\n",
       "      <td>2112</td>\n",
       "      <td>97146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>375</td>\n",
       "      <td>438</td>\n",
       "      <td>583</td>\n",
       "      <td>543</td>\n",
       "      <td>524</td>\n",
       "      <td>604</td>\n",
       "      <td>656</td>\n",
       "      <td>...</td>\n",
       "      <td>5048</td>\n",
       "      <td>4468</td>\n",
       "      <td>3834</td>\n",
       "      <td>2837</td>\n",
       "      <td>2076</td>\n",
       "      <td>1922</td>\n",
       "      <td>1776</td>\n",
       "      <td>1588</td>\n",
       "      <td>1512</td>\n",
       "      <td>93585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                          Continent  \\\n",
       "Country                                                                               \n",
       "India                                                                          Asia   \n",
       "China                                                                          Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...                           Europe   \n",
       "Philippines                                                                    Asia   \n",
       "Pakistan                                                                       Asia   \n",
       "United States of America                                           Northern America   \n",
       "Iran (Islamic Republic of)                                                     Asia   \n",
       "Sri Lanka                                                                      Asia   \n",
       "Republic of Korea                                                              Asia   \n",
       "Poland                                                                       Europe   \n",
       "Lebanon                                                                        Asia   \n",
       "France                                                                       Europe   \n",
       "Jamaica                                             Latin America and the Caribbean   \n",
       "Viet Nam                                                                       Asia   \n",
       "Romania                                                                      Europe   \n",
       "\n",
       "                                                                Region  \\\n",
       "Country                                                                  \n",
       "India                                                    Southern Asia   \n",
       "China                                                     Eastern Asia   \n",
       "United Kingdom of Great Britain and Northern Ir...     Northern Europe   \n",
       "Philippines                                         South-Eastern Asia   \n",
       "Pakistan                                                 Southern Asia   \n",
       "United States of America                              Northern America   \n",
       "Iran (Islamic Republic of)                               Southern Asia   \n",
       "Sri Lanka                                                Southern Asia   \n",
       "Republic of Korea                                         Eastern Asia   \n",
       "Poland                                                  Eastern Europe   \n",
       "Lebanon                                                   Western Asia   \n",
       "France                                                  Western Europe   \n",
       "Jamaica                                                      Caribbean   \n",
       "Viet Nam                                            South-Eastern Asia   \n",
       "Romania                                                 Eastern Europe   \n",
       "\n",
       "                                                               DevName   1980  \\\n",
       "Country                                                                         \n",
       "India                                               Developing regions   8880   \n",
       "China                                               Developing regions   5123   \n",
       "United Kingdom of Great Britain and Northern Ir...   Developed regions  22045   \n",
       "Philippines                                         Developing regions   6051   \n",
       "Pakistan                                            Developing regions    978   \n",
       "United States of America                             Developed regions   9378   \n",
       "Iran (Islamic Republic of)                          Developing regions   1172   \n",
       "Sri Lanka                                           Developing regions    185   \n",
       "Republic of Korea                                   Developing regions   1011   \n",
       "Poland                                               Developed regions    863   \n",
       "Lebanon                                             Developing regions   1409   \n",
       "France                                               Developed regions   1729   \n",
       "Jamaica                                             Developing regions   3198   \n",
       "Viet Nam                                            Developing regions   1191   \n",
       "Romania                                              Developed regions    375   \n",
       "\n",
       "                                                     1981   1982   1983  \\\n",
       "Country                                                                   \n",
       "India                                                8670   8147   7338   \n",
       "China                                                6682   3308   1863   \n",
       "United Kingdom of Great Britain and Northern Ir...  24796  20620  10015   \n",
       "Philippines                                          5921   5249   4562   \n",
       "Pakistan                                              972   1201    900   \n",
       "United States of America                            10030   9074   7100   \n",
       "Iran (Islamic Republic of)                           1429   1822   1592   \n",
       "Sri Lanka                                             371    290    197   \n",
       "Republic of Korea                                    1456   1572   1081   \n",
       "Poland                                               2930   5881   4546   \n",
       "Lebanon                                              1119   1159    789   \n",
       "France                                               2027   2219   1490   \n",
       "Jamaica                                              2634   2661   2455   \n",
       "Viet Nam                                             1829   2162   3404   \n",
       "Romania                                               438    583    543   \n",
       "\n",
       "                                                     1984  1985  1986  ...  \\\n",
       "Country                                                                ...   \n",
       "India                                                5704  4211  7150  ...   \n",
       "China                                                1527  1816  1960  ...   \n",
       "United Kingdom of Great Britain and Northern Ir...  10170  9564  9470  ...   \n",
       "Philippines                                          3801  3150  4166  ...   \n",
       "Pakistan                                              668   514   691  ...   \n",
       "United States of America                             6661  6543  7074  ...   \n",
       "Iran (Islamic Republic of)                           1977  1648  1794  ...   \n",
       "Sri Lanka                                            1086   845  1838  ...   \n",
       "Republic of Korea                                     847   962  1208  ...   \n",
       "Poland                                               3588  2819  4808  ...   \n",
       "Lebanon                                              1253  1683  2576  ...   \n",
       "France                                               1169  1177  1298  ...   \n",
       "Jamaica                                              2508  2938  4649  ...   \n",
       "Viet Nam                                             7583  5907  2741  ...   \n",
       "Romania                                               524   604   656  ...   \n",
       "\n",
       "                                                     2005   2006   2007  \\\n",
       "Country                                                                   \n",
       "India                                               36210  33848  28742   \n",
       "China                                               42584  33518  27642   \n",
       "United Kingdom of Great Britain and Northern Ir...   7258   7140   8216   \n",
       "Philippines                                         18139  18400  19837   \n",
       "Pakistan                                            14314  13127  10124   \n",
       "United States of America                             8394   9613   9463   \n",
       "Iran (Islamic Republic of)                           5837   7480   6974   \n",
       "Sri Lanka                                            4930   4714   4123   \n",
       "Republic of Korea                                    5832   6215   5920   \n",
       "Poland                                               1405   1263   1235   \n",
       "Lebanon                                              3709   3802   3467   \n",
       "France                                               4429   4002   4290   \n",
       "Jamaica                                              1945   1722   2141   \n",
       "Viet Nam                                             1852   3153   2574   \n",
       "Romania                                              5048   4468   3834   \n",
       "\n",
       "                                                     2008   2009   2010  \\\n",
       "Country                                                                   \n",
       "India                                               28261  29456  34235   \n",
       "China                                               30037  29622  30391   \n",
       "United Kingdom of Great Britain and Northern Ir...   8979   8876   8724   \n",
       "Philippines                                         24887  28573  38617   \n",
       "Pakistan                                             8994   7217   6811   \n",
       "United States of America                            10190   8995   8142   \n",
       "Iran (Islamic Republic of)                           6475   6580   7477   \n",
       "Sri Lanka                                            4756   4547   4422   \n",
       "Republic of Korea                                    7294   5874   5537   \n",
       "Poland                                               1267   1013    795   \n",
       "Lebanon                                              3566   3077   3432   \n",
       "France                                               4532   5051   4646   \n",
       "Jamaica                                              2334   2456   2321   \n",
       "Viet Nam                                             1784   2171   1942   \n",
       "Romania                                              2837   2076   1922   \n",
       "\n",
       "                                                     2011   2012   2013  \\\n",
       "Country                                                                   \n",
       "India                                               27509  30933  33087   \n",
       "China                                               28502  33024  34129   \n",
       "United Kingdom of Great Britain and Northern Ir...   6204   6195   5827   \n",
       "Philippines                                         36765  34315  29544   \n",
       "Pakistan                                             7468  11227  12603   \n",
       "United States of America                             7676   7891   8501   \n",
       "Iran (Islamic Republic of)                           7479   7534  11291   \n",
       "Sri Lanka                                            3309   3338   2394   \n",
       "Republic of Korea                                    4588   5316   4509   \n",
       "Poland                                                720    779    852   \n",
       "Lebanon                                              3072   1614   2172   \n",
       "France                                               4080   6280   5623   \n",
       "Jamaica                                              2059   2182   2479   \n",
       "Viet Nam                                             1723   1731   2112   \n",
       "Romania                                              1776   1588   1512   \n",
       "\n",
       "                                                     Total  \n",
       "Country                                                     \n",
       "India                                               691904  \n",
       "China                                               659962  \n",
       "United Kingdom of Great Britain and Northern Ir...  551500  \n",
       "Philippines                                         511391  \n",
       "Pakistan                                            241600  \n",
       "United States of America                            241122  \n",
       "Iran (Islamic Republic of)                          175923  \n",
       "Sri Lanka                                           148358  \n",
       "Republic of Korea                                   142581  \n",
       "Poland                                              139241  \n",
       "Lebanon                                             115359  \n",
       "France                                              109091  \n",
       "Jamaica                                             106431  \n",
       "Viet Nam                                             97146  \n",
       "Romania                                              93585  \n",
       "\n",
       "[15 rows x 38 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "df_top15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    df_top15 = df_can.sort_values(['Total'], ascending=False, axis=0).head(15)\n",
    "    df_top15\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Step 2: Create a new dataframe which contains the aggregate for each decade. One way to do that:\n",
    "   1. Create a list of all years in decades 80's, 90's, and 00's.\n",
    "   2. Slice the original dataframe df_can to create a series for each decade and sum across all years for each country.\n",
    "   3. Merge the three series into a new data frame. Call your dataframe **new_df**."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "### type your answer here\n",
    "\n",
    "years_80s = list(map(str, range(1980, 1990)))\n",
    "years_90s = list(map(str, range(1990, 2000)))\n",
    "years_00s = list(map(str, range(2000, 2010)))\n",
    "\n",
    "df_80s = df_top15.loc[:, years_80s].sum(axis=1) \n",
    "df_90s = df_top15.loc[:, years_90s].sum(axis=1)\n",
    "df_00s = df_top15.loc[:, years_00s].sum(axis=1)\n",
    "\n",
    "new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:\n",
    "    \n",
    "    # create a list of all years in decades 80's, 90's, and 00's\n",
    "    years_80s = list(map(str, range(1980, 1990))) \n",
    "    years_90s = list(map(str, range(1990, 2000))) \n",
    "    years_00s = list(map(str, range(2000, 2010))) \n",
    "\n",
    "    # slice the original dataframe df_can to create a series for each decade\n",
    "    df_80s = df_top15.loc[:, years_80s].sum(axis=1) \n",
    "    df_90s = df_top15.loc[:, years_90s].sum(axis=1) \n",
    "    df_00s = df_top15.loc[:, years_00s].sum(axis=1)\n",
    "\n",
    "    # merge the three series into a new data frame\n",
    "    new_df = pd.DataFrame({'1980s': df_80s, '1990s': df_90s, '2000s':df_00s}) \n",
    "\n",
    "    # display dataframe\n",
    "    new_df.head()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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   },
   "source": [
    "Let's learn more about the statistics associated with the dataframe using the `describe()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
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    "scrolled": true
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   "outputs": [],
   "source": [
    "### type your answer here\n",
    "new_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df.describe()\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
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   },
   "source": [
    "Step 3: Plot the box plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "### type your answer here\n",
    "\n",
    "new_df.plot(kind='box', figsize=(10, 6))\n",
    "plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df.plot(kind='box', figsize=(10, 6))\n",
    "\n",
    "    plt.title('Immigration from top 15 countries for decades 80s, 90s and 2000s')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Note how the box plot differs from the summary table created. The box plot scans the data and identifies the outliers. In order to be an outlier, the data value must be:<br>\n",
    "* larger than Q3 by at least 1.5 times the interquartile range (IQR), or,\n",
    "* smaller than Q1 by at least 1.5 times the IQR.\n",
    "\n",
    "Let's look at decade 2000s as an example: <br>\n",
    "* Q1 (25%) = 36,101.5 <br>\n",
    "* Q3 (75%) = 105,505.5 <br>\n",
    "* IQR = Q3 - Q1 = 69,404 <br>\n",
    "\n",
    "Using the definition of outlier, any value that is greater than Q3 by 1.5 times IQR will be flagged as outlier.\n",
    "\n",
    "Outlier > 105,505.5 + (1.5 * 69,404) <br>\n",
    "Outlier > 209,611.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
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    "scrolled": true
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   "outputs": [],
   "source": [
    "# let's check how many entries fall above the outlier threshold \n",
    "new_df=new_df.reset_index()\n",
    "new_df[new_df['2000s']> 209611.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:    \n",
    "    new_df=new_df.reset_index()\n",
    "    new_df[new_df['2000s']> 209611.5]\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- The correct answer is:\n",
    "new_df[new_df['2000s']> 209611.5]\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
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   },
   "source": [
    "China and India are both considered as outliers since their population for the decade exceeds 209,611.5. \n",
    "\n",
    "The box plot is an advanced visualizaiton tool, and there are many options and customizations that exceed the scope of this lab. Please refer to [Matplotlib documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.boxplot) on box plots for more information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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   },
   "source": [
    "# Scatter Plots <a id=\"10\"></a>\n",
    "\n",
    "A `scatter plot` (2D) is a useful method of comparing variables against each other. `Scatter` plots look similar to `line plots` in that they both map independent and dependent variables on a 2D graph. While the datapoints are connected together by a line in a line plot, they are not connected in a scatter plot. The data in a scatter plot is considered to express a trend. With further analysis using tools like regression, we can mathematically calculate this relationship and use it to predict trends outside the dataset.\n",
    "\n",
    "Let's start by exploring the following:\n",
    "\n",
    "Using a `scatter plot`, let's visualize the trend of total immigrantion to Canada (all countries combined) for the years 1980 - 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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   },
   "source": [
    "Step 1: Get the dataset. Since we are expecting to use the relationship betewen `years` and `total population`, we will convert `years` to `int` type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>99137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>110563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>104271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>75550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>73417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   total\n",
       "0  1980   99137\n",
       "1  1981  110563\n",
       "2  1982  104271\n",
       "3  1983   75550\n",
       "4  1984   73417"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tot = pd.DataFrame(df_can[years].sum(axis=0))\n",
    "\n",
    "df_tot.index = map(int, df_tot.index)\n",
    "\n",
    "df_tot.reset_index(inplace = True)\n",
    "\n",
    "df_tot.columns = ['year', 'total']\n",
    "\n",
    "df_tot.head()"
   ]
  },
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   "source": [
    "Step 2: Plot the data. In `Matplotlib`, we can create a `scatter` plot set by passing in `kind='scatter'` as plot argument. We will also need to pass in `x` and `y` keywords to specify the columns that go on the x- and the y-axis."
   ]
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    {
     "data": {
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sWZUe69Chw0Uv/LhUDdUJCQlRVFSU4uPj9eabbyooKEhLlixx+3XQdBD00Oz07dtXGzdurLRu48aNslgszhGlFi1aVBo5k6StW7fq1KlTWrhwoa677jr16tXL7ZGkxqhPnz5VplhwdfqVtLQ03X333Ro/fryuueYadevWTXv37q20zcV6eaG+ffsqLy+v0ojq6dOn9eWXX7o8IlKdi71/xWumpaVVWr9p06Ya3++ee+7RunXrqp2SomJ6FVf64oq0tDRNnTpVY8aMUb9+/dShQwfl5ORU+Rzp6enOdaWlpZWuKs/Pz1dWVpZmzpypm266SX369FFAQECtR80qREVFqWXLllX+TqWlpdX6u6stHx8fderUSQEBAfrf//1ftW7dWjfeeKMk6aeffpLFYpGPz79/DVosFvn6+joDdGxsrFq2bKnVq1c7tykvL9fatWudo/kXc8UVVygqKkpRUVG64ooraqzxo48+0ujRo/XMM8/ot7/9bZXHr7vuOuXm5mrfvn3Odd98842+/fbbGmtwVXl5uU6fPl3r10HjRdBDs/PEE09o+/btmjFjhnbv3q1Vq1YpOTlZd999tyIjIyVJXbt21e7du7Vr1y7l5eXp9OnT6tGjhywWi1JSUpSbm6sPPvhAzz33nIc/Te395je/0V//+lctXrxY2dnZWrp0qZYuXSrp0iN9vXr10ocffqgvv/xSWVlZevDBB3XkyJFK23Tt2lUZGRnav3+/8vLyLjotyw033KBBgwbprrvu0hdffKGdO3fqvvvuU0lJiR555JFafb6uXbvq2LFj2rx5s/Ly8nTy5El1795dv/zlLzV16lStXr1au3fv1mOPPaadO3fqiSeeqPa1HnvsMY0cOVI33XST/vjHP2rbtm06ePCgVq1apdtvv93ZN1f64opevXrpnXfe0b/+9S9lZmZqwoQJlUJrVFSUxowZo1//+tdav369srKy9MADD6ioqMi5TWhoqCIiIvTaa69p79692rx5syZMmKDAwEC367mYoKAgPfroo/qv//ovvf/++9q3b5/mzZunDz/88KLBpS5UjJY5HA4VFxc7lysUFBQoNTVVWVlZ+te//qU5c+bohRde0Pz58xUcHCxJuummm1RaWqopU6Zo165d2rNnj2bMmKH9+/drzJgxkqTg4GA9/PDD+u1vf6t//OMf2rVrlyZPnqxTp07poYceqvXneP/99/WLX/xCjz/+uO6++24dO3ZMx44d0w8//ODcJjExUQMGDNA999yjL7/8Uv/85z917733avDgwUpISHBul52drczMTOdh2IqeFBcXS5J27typl156SRkZGTp06JC2bt2qyZMnKzc3V3fddVetPwsaMc+dHgg0jEtNrxIeHm4efvjhStNa5Ofnm5/97GcmODi40vQqdrvddO7c2QQEBJjrrrvOeSVfxTQHtZle5XxvvfWWufCv57vvvmskmbKyMmPMxadJudiFB88//7zp1KmTc/liz5s/f77p2LGjCQgIMKNGjTKvvvqqkeS8+rG6E7wPHTpkRo0aZYKCgkz79u3N7NmzzeTJkytdyLJ//34zbNgw06pVK7emVxk+fPhFp1epuECkgq+vr/P7uZjS0lIzYcIEExoaWml6lcLCQuf0Ki1atHBpehVjjCkrKzMLFy40sbGxJigoyLRu3drExMSYuXPnOq/+daUvFdOrnO/C7+/rr782Q4YMMQEBAeaKK64wqampVS5uycvLM7/85S9NUFCQCQ8PNzNnzqwyvcqGDRtMdHS0admypenZs6dZtmyZ6d69+yWnCLnwO6ruO3B1epXzX8uYi393LVu2NK+99tol67rYnwoOh8PEx8eb1q1bm4CAADNo0CCzYsWKKq+zfv16k5CQYNq0aWOCg4PNtddeaz788MMqn+2JJ54w7dq1My1btjTx8fGV9svaSEhIuOjnuPDv8JEjR8zYsWON1Wo1rVu3NuPGjXNeGXyp16r4+7Zv3z4zatQo07ZtW+Pv7286duxofv7zn5vNmzfXyWdB42UxxsWTPAA0G88995xefvll5efne7oUAEAteO4+TAAahbKyMqWkpGj06NFq1aqV1q9fr5deekm//vWvPV0aAKCWGNEDmrkzZ87o1ltvVUZGhoqKitS1a1fdd999euKJJzx6T14AQO0R9AAAALwUV90CAAB4KYIeAACAlyLoAQAAeCnOtK7G5Uxu6q7w8HDl5eXV+/t4E3rmPnrmHvrlPnrmPnrmPnpWvY4dO1b7GCN6AAAAXoqgBwAA4KUIegAAAF6KoAcAAOClCHoAAABeiqAHAADgpQh6AAAAXoqgBwAA4KUIegAAAF6KoAcAAOCluAUaAABotA4eLFJy8mYVFpYpJMRfdnu8IiOtni6ryWBEDwAANFrJyZuVkZGn7OxCZWTkKSkp3dMlNSkEPQAA0Gg5HCU1LqNmBD0AANBo2WwBNS6jZgQ9AADQaNnt8YqNDVdUVIhiY8Nlt8d7uqQmhYsxAABAoxUZadXKlaMUHh6uvLw8T5fT5DCiBwAA4KUIegAAAF6KoAcAAOClCHoAAABeiqAHAADgpQh6AAAAXoqgBwAA4KUIegAAAF6KoAcAAOClCHoAAABeiqAHAADgpQh6AAAAXoqgBwAA4KUIegAAAF6KoAcAAOClCHoAAABeiqAHAADgpQh6AAAAXoqgBwAA4KUIegAAAF6KoAcAAOClCHoAAABeys/TBQAA0FwcPFik5OTNKiwsU0iIv+z2eEVGWj1dFupBxXftcJTIZgvw2HfNiB4AAA0kOXmzMjLylJ1dqIyMPCUlpXu6JNSTiu86N7fYo981I3oAADQQh6OkxmU0LrUZlWss3zUjegAANBCbLaDGZTQutRmVayzfNUEPAIAGYrfHKzY2XFFRIYqNDZfdHu/pklCD2ozKVXzXXbtaPfpdc+gWAIAGEhlp1cqVoxQeHq68vDxPl4NLsNkClJtbXGnZVRXftacxogcAAHARjWVUrjYY0QMAALiIxjIqVxuM6AEAAHgpgh4AAICXapBDt3l5eUpNTdWJEydksViUmJio0aNH629/+5s+++wzBQcHS5ImTJigAQMGSJJWrFihdevWycfHR5MmTVJMTIwkKScnR6mpqSotLVX//v01adIkWSwWlZWVyW63KycnR61bt9a0adPUtm1bSdKGDRu0fPlySdIdd9yhESNGNMTHBgAA8KgGCXq+vr6699571a1bN506dUozZ85UdHS0JOmWW27RmDFjKm1/+PBhpaena/78+SooKNDvfvc7vfzyy/Lx8dFrr72mhx56SD169NDzzz+vzMxM9e/fX+vWrVOrVq20ePFiffHFF3rnnXc0ffp0FRcXa9myZXrhhRckSTNnzlRcXJysVm45AwAAvFuDHLoNDQ1Vt27dJEmBgYHq1KmTHA5Htdtv3bpV8fHx8vf3V9u2bdW+fXtlZ2eroKBAp06dUs+ePWWxWDR8+HBt3bpVkrRt2zbnSN3gwYO1c+dOGWOUmZmp6OhoWa1WWa1WRUdHKzMzs74/MgAAgMc1+FW3x48fV25urqKiorR7926tXr1aaWlp6tatm+677z5ZrVY5HA716NHD+RybzSaHwyFfX1+FhYU514eFhTkDo8PhcD7m6+uroKAgFRUVVVp//mtdaO3atVq7dq0k6YUXXlB4eHi9fP7z+fn5Ncj7eBN65j565h765T565r7m1rPc3BOaOHGN8vJOKTw8UG++eZO6dg1x6zWaW8/qSoMGvZKSEqWkpGjixIkKCgrSqFGjNHbsWEnSe++9p6VLl2rq1Kkyxlz0+dWtr+4xi8Vy0W0vtj4xMVGJiYnO5YaYyJIJM91Hz9xHz9xDv9xHz9zX3Hp2991rlJFx7vNmZxfq7rs/cnvakubWM3d07Nix2sca7KrbM2fOKCUlRcOGDdO1114rSWrTpo18fHzk4+OjkSNHav/+/ZLOjdTl5+c7n+twOGSz2aqsz8/Pl81mq/Kcs2fP6uTJk7JarbLZbFVeKzQ0tN4/LwAAdengwSKNGbNGQ4eu1Jgxa3ToUPGln9RI1OZWYqidBgl6xhi98sor6tSpk2699Vbn+oKCAufPX375pbp06SJJiouLU3p6usrKynT8+HEdPXpUUVFRCg0NVWBgoPbu3StjjNLS0hQXFydJio2N1YYNGyRJW7ZsUd++fWWxWBQTE6MdO3aouLhYxcXF2rFjh/MKXgAAmork5M3KyMhTbm6xMjLylJSU7umSXHbhrcPcuZUYaqdBDt3u2bNHaWlpioyM1BNPPCHp3FQqX3zxhQ4cOCCLxaKIiAg9+OCDkqQuXbpoyJAhmjFjhnx8fDRlyhT5+JzLpA888ICWLFmi0tJSxcTEqH///pKkG264QXa7XcnJybJarZo2bZokyWq16s4779R//ud/SpLGjh3LFbcAgCanKY+K2e3xSkpKl8NRIpstoMFuJXbwYJGSkzdXet/IyOaVASymphPfmrEjR47U+3twvoH76Jn76Jl76Jf76Jn7LqdnY8b8+zw3SYqNDW/yt+dyBz2rXqM4Rw8AAFw+uz1esbHh6trVqtjY8AYbFWvKmvIoaF1p8OlVAACA+yIjrV45GlWfbLYA5eYWV1pubhjRAwAANWqqV/wyCsqIHgDAQzhRvumouOJXknJzi5WUlN4kRhcZBWVEDwDgIU15upDmhnPdmi6CHgDAIwgPTQfz4DVdBD0AgEcQHpoOznVrujhHDwDgEZ6aRBfu41y3pougBwDwCMIDUP84dAsAAOClCHoAAABeiqAHAADgpQh6AAAAXoqgBwAA4KUIegAAAF6KoAcAAOClCHoAAABeigmTAQBoBg4eLFJy8uZKdyKJjLR6uizUM0b0AABww8GDRRozZo2GDl2pMWPW6NChYk+X5JLk5M3KyMhTbm6xMjLylJSU7umS0AAIegAAuKGpBiaHo6TGZXgngh4AAG5oqoHJZguocRneiaAHAIAbmmpgstvjFRsbrq5drYqNDZfdHu/pktAAuBgDAAA32O3xSkpKr3RRQ1MQGWnVypWjPF0GGhhBDwAANxCY0JRw6BYAAMBLEfQAAAC8FEEPAADASxH0AAAAvBRBDwAAwEsR9AAAALwUQQ8AAMBLMY8eAKDJOXiwSMnJmytNWhwZafV0WUCjw4gegEbl4MEijRmzRkOHrtSYMWt06FCxp0tCI5ScvFkZGXnKzS1WRkaekpLSPV0S0CgR9ADUqdoGNX6BwxUOR0mNywDOIegBqFO1DWr8AocrbLaAGpcBnEPQA1CnahvU+AUOV9jt8YqNDVfXrlbFxobLbo/3dElAo8TFGADqlM0WoNzc4krL7rDb45WUlF7pJHvgQpGRVq1cOcrTZQCN3mUFvdLSUvn4+MjPj5wIoLLaBjV+gQNA3XEpqS1dulTx8fGKiorS9u3blZKSIovFomnTpikuLq6+awTQhBDUAKDxcOkcvc8//1xdunSRJC1btkzJycl68skn9e6779ZrcQAA1DWm8EFz4tKI3unTp9WyZUsVFRXp+++/1+DBgyVJeXl59VocAKDxqpi0uLCwTCEh/k1m0uKKK8MlKTe3WElJ6YxCw2u5FPQ6duyoTZs26dixY4qOjpYk/fjjj2rRokW9FgcAaLzOD0ySmkxgYgofNCcuHbqdMmWKVq9erV27dmn8+PGSpB07djhDHwCg+WmqgYkpfNCcuDSiFx4ert///veV1g0bNkz9+vWrl6IAAI1fbafS8RSm8EFz4tKI3mOPPXbR9dOnT6/TYgAATUfFpMVRUSFNatLiiivDP/98jFauHNUkzisELpdLI3rGmCrrTp48KR8fbqwBAM1VRWAKDw/n4jygkaox6D3yyCOSzk2QXPFzheLiYl133XX1VxkAAABqpcagl5ycLGOMnn/+eSUnJ1d6rE2bNurYsWO9FgcAAIDLV2PQ69OnjyTp9ddfV8uWLRukIAAAANQNl87R8/X11dq1a3XgwAGVlFS+fD4pKaleCgMAAEDtuBT07Ha7Dh48qNjYWIWEhNR3TQAAAKgDLgW9HTt2yG63q1WrVvVdDwAAAOqIS/OjhIeHq6ysrL5rAQAAQB1yaURv+PDheumll/Szn/1Mbdq0qfTY1VdfXR91AfCgipvVn3/nACaVBYCmx6Wgt2rVKknSu+++W2m9xWKR3W6v+6oAeNT5N6vPzS1uMjerBwBU5lLQS01Nre86ADQiTfVm9QCAyriHGYAqLrw5fVO5WT0AoDKXRvROnjyp999/X1lZWSoqKqp079s//elP9VYcAM+w2+OVlJRe6Rw9AEDT41LQ+/Of/yyHw6GxY8dq8eLFSk5O1sqVK3XttdfWd30APKDiZvUAgKbNpUO3X3/9tX7zm99o4MCB8vHx0cCBAzV9+nRt2rSpvusDAADAZXIp6BljFBQUJEkKCAjQTz/9pDZt2ujYsWP1WhwAAAAun0uHbq+44gplZWWpX79+6t27t15//XUFBASoQ4cO9V0fAAAALpNLQe+hhx5yXoAxefJk/e///q9++uknJSUlufQmeXl5Sk1N1YkTJ2SxWJSYmKjRo0eruLhYCxYs0A8//KCIiAhNnz5dVuu5SVlXrFihdevWycfHR5MmTVJMTIwkKScnR6mpqSotLVX//v01adIkWSwWlZWVyW63KycnR61bt9a0adPUtm1bSdKGDRu0fPlySdIdd9yhESNGuNMjAACAJumSh27Ly8u1YcMGhYWFSZKCg4P18MMPa/r06ercubNLb+Lr66t7771XCxYs0Ny5c7V69WodPnxYH3zwgfr166dFixapX79++uCDDyRJhw8fVnp6uubPn69Zs2bp9ddfV3l5uSTptdde00MPPaRFixbp2LFjyszMlCStW7dOrVq10uLFi3XLLbfonXfekSQVFxdr2bJlmjdvnubNm6dly5apuLjY3T4BAAA0OZcMej4+Plq9erV8fX0v+01CQ0PVrVs3SVJgYKA6deokh8OhrVu3KiEhQZKUkJCgrVu3SpK2bt2q+Ph4+fv7q23btmrfvr2ys7NVUFCgU6dOqWfPnrJYLBo+fLjzOdu2bXOO1A0ePFg7d+6UMUaZmZmKjo6W1WqV1WpVdHS0MxwCQF05eLBIY8as0dChKzVmzBodOsR/KAF4nksXYyQkJOjTTz+tkzc8fvy4cnNzFRUVpcLCQoWGhko6FwZ//PFHSZLD4XCOIEqSzWaTw+Gosj4sLEwOh6PKc3x9fRUUFKSioqJqXwsA6lLFbeNyc4uVkZGnpKR0T5cEAK6do5edna1Vq1Zp5cqVCgsLk8VicT727LPPuvxmJSUlSklJ0cSJE51X8V7M+RMyu7K+usfOr/NS69euXau1a9dKkl544QWFh4dX+151xc/Pr0Hex5vQM/fRM/dcbr8KC8uqLDeXvrOPuY+euY+eXR6Xgt7IkSM1cuTIWr3RmTNnlJKSomHDhjknWg4JCVFBQYFCQ0NVUFCg4OBgSedG6vLz853PdTgcstlsVdbn5+fLZrNVek5YWJjOnj2rkydPymq1ymazKSsrq9Jr9enTp0p9iYmJSkxMdC7n5eXV6vO6Ijw8vEHex5vQM/fRM/dcbr9CQvyrLDeXvrOPuY+euY+eVa9jx47VPuZS0KvtVarGGL3yyivq1KmTbr31Vuf6uLg4bdy4Ubfffrs2btyogQMHOtcvWrRIt956qwoKCnT06FFFRUXJx8dHgYGB2rt3r3r06KG0tDTdfPPNkqTY2Fht2LBBPXv21JYtW9S3b19ZLBbFxMTo3XffdV6AsWPHDt111121+jwAcCFuGwegMbKYmo6H/n/r1q276Hp/f3+FhYWpR48e8vf3v+g2krR7927Nnj1bkZGRzsOmEyZMUI8ePbRgwQLl5eUpPDxcM2bMcE6vsnz5cq1fv14+Pj6aOHGi+vfvL0nav3+/lixZotLSUsXExGjy5MmyWCwqLS2V3W5Xbm6urFarpk2bpnbt2jnrX7FihaRz06tcf/31l2zMkSNHLrlNbfG/E/fRM/fRM/fQL/fRM/fRM/fRs+rVNKLnUtB75plntHfvXoWEhDgPkRYWFqp79+46fvy4JOnJJ59U9+7d665qDyPoNU70zH30zD30y330zH30zH30rHq1PnTbuXNnDRo0SKNHj3auW7Vqlb777js999xzWr58uf7yl79o7ty5ta8WAAAAdcKl6VW++OIL57lwFUaNGqXPP/9cFotFY8aM0eHDh+ulQAAAAFwel4JeSEiIMjIyKq3bvn278yrZsrIy+fm5NDgIAPWGSYsBoDKX0tmkSZM0f/58RUZGOs/RO3TokGbMmCFJ2rdvX5URPwBoaBWTFktSbm6xkpLStXLlKA9XdWkHDxYpOXlzpSt2IyOtni4LgBdwKehdc801Wrx4sTIzM+VwONS/f38NGDBArVu3dj5+zTXX1GuhAHApDkdJjcuNVVMNqAAaP5ePtwYHB2v48OH1WQsA1IrNFqDc3OJKy01BUw2oABq/aoPe3LlzNWvWLEnS7Nmzq72dmDu3QAOA+tRUJy1uqgEVQONXbdBLSEhw/nzDDTc0SDEAUBuRkdYmecizqQZUAI1ftUFv6NChzp9rews0AED1mmpABdD4uXyO3jfffKPc3FyVlFQ+d+SOO+6o86IAAABQey4Fvb/85S/avHmzevfurRYtWjjXV3feHgDPq5iyo7CwTCEh/kzZcQn0C4A3cinobdq0SSkpKbLZbPVdD4A6cv6UHZKYsuMS6BcAb+TSnTHCw8Pl7+9f37UAqENM2eEe+gXAG7k0ovfwww/r1Vdf1XXXXaeQkJBKj/Xp06deCgNQO0zZ4R76BcAbuRT0cnJy9NVXX+mbb76pdI6eJP3pT3+ql8IA1E7FlB3nn3OG6tEvAN7IpaD37rvv6qmnnlJ0dHR91wOgjlRM2REeHq68vLxLP6GZo18AvJFL5+i1bNmSQ7QAAABNjEtBb/z48XrzzTd14sQJlZeXV/oDAACAxsmlQ7cV5+F9+umnVR5777336rYiAAAA1AmXgp7dbq/vOgAAAFDHXAp6ERER9V0HAAAA6phLQe/kyZP6+OOPdeDAgSr3un366afrpTAAAADUjktBb/78+SovL9egQYOqzKMHAGi6Ku7x63CUyGYL4B6/gJdxKejt27dPr7/+uvz8XNocANBEnH+P39zcYu7xC3gZl6ZX6d27t7777rv6rgUA0MC4xy/g3Vwaops6daqef/55RUVFqU2bNpUeGzt2bH3UBQBoANzjF/BuLt8CLT8/XxERETp16pRzvcViqbfCAAD1r+Iev+efowfAe7gU9NLT0/Xyyy8rNDS0vusBADSginv8AvBOLp2j165dO/n6+tZ3LQAAAKhDLo3oDRs2TC+++KJuvvnmKufoXX311fVRFwAAAGrJpaC3evVqSefO1TufxWLh9mgAAACNlEtBLzU1tb7rAAAAQB1z6Rw9AAAAND01jujNnj37klOoPPvss3VaEAAAAOpGjUHvhhtuaKg6AAAAUMdqDHojRoxooDIAAABQ1zhHDwAAwEsR9AAAALwUQQ8AAMBLVRv0Zs2a5fz5/fffb5BiAAAAUHeqDXpHjhxRaWmpJOkf//hHgxUEAACAulHtVbcDBw7UY489prZt26q0tFRz5sy56HbMowcAANA4VRv0pk6dqt27d+v48ePKzs7W9ddf35B1AQAAoJZqnEevd+/e6t27t86cOcOcegAAAE1MjUGvwg033KCdO3cqLS1NBQUFCg0N1fDhw3X11VfXd31As3XwYJGSkzfL4SiRzRYguz1ekZFWT5cFAGhCXJpe5bPPPtPChQvVpk0bDRo0SKGhoXr55Ze1du3a+q4PaLaSkzcrIyNPubnFysjIU1JSuqdLAgA0MS6N6K1cuVJPP/20rrzySue6+Ph4paSkKDExsb5qA5o1h6OkxmUAAC7FpRG9oqIide7cudK6jh07qri4uF6KAiDZbAE1LgMAcCkuBb3evXtr6dKlOn36tCSppKREb731lnr27FmvxQHNmd0er9jYcHXtalVsbLjs9nhPlwQAaGJcOnT7q1/9SgsXLtTEiRNltVpVXFysnj176rHHHqvv+oBmKzLSqpUrR3m6DDQBXLgDoDouBb3Q0FA9++yzys/Pd151GxYWVt+1AQBcUHHhjiTl5hYrKSmd/yQAkORi0KsQFhZGwAOARoYLdwBUx6Vz9AAAjRcX7gCoDkEPAJo4LtwBUJ1LHrotLy9XVlaWevfuLT8/t470AgAaABfuAKjOJUf0fHx89OKLLxLyAAAAmhiXDt1eddVV2rt3b33XAgAAgDrk0jBdRESEnn/+ecXFxSksLEwWi8X52Pjx4+utOAAAAFw+l4JeaWmpBg4cKElyOBz1WhAAAADqhktBb+rUqfVdBwAAAOqYy1dYHD58WFu2bFFhYaGmTJmiI0eOqKysTFdccUV91gcAAIDL5NLFGJs3b9acOXPkcDiUlpYmSTp16pSWLl1ar8UBAADg8rk0ove3v/1N//Vf/6Urr7xSmzdvliRdccUVOnDgQH3WBgAAgFpwKegVFhZWOURrsVgqXX1bkyVLlmj79u0KCQlRSkqKpHPh8bPPPlNwcLAkacKECRowYIAkacWKFVq3bp18fHw0adIkxcTESJJycnKUmpqq0tJS9e/fX5MmTZLFYlFZWZnsdrtycnLUunVrTZs2TW3btpUkbdiwQcuXL5ck3XHHHRoxYoRLNQMAADR1Lh267datm/OQbYUvvvhCUVFRLr3JiBEj9Nvf/rbK+ltuuUUvvfSSXnrpJWfIO3z4sNLT0zV//nzNmjVLr7/+usrLyyVJr732mh566CEtWrRIx44dU2ZmpiRp3bp1atWqlRYvXqxbbrlF77zzjiSpuLhYy5Yt07x58zRv3jwtW7ZMxcXFLtUMAADQ1LkU9CZNmqS//vWvmjNnjk6fPq25c+fqvffe0/333+/Sm/Tp00dWq9Wlbbdu3ar4+Hj5+/urbdu2at++vbKzs1VQUKBTp06pZ8+eslgsGj58uLZu3SpJ2rZtm3OkbvDgwdq5c6eMMcrMzFR0dLSsVqusVquio6Od4RAAAMDbuXTotlOnTlq4cKEyMjIUGxursLAwxcbGKiAgoFZvvnr1aqWlpalbt2667777ZLVa5XA41KNHD+c2NptNDodDvr6+CgsLc64PCwtzzunncDicj/n6+iooKEhFRUWV1p//Whezdu1arV27VpL0wgsvKDw8vFafzRV+fn4N8j7ehJ65j565h365j565j565j55dHpenV2nZsqV69+4th8Mhm81W65A3atQojR07VpL03nvvaenSpZo6daqMMRfdvrr11T1W3fmD1a1PTExUYmKiczkvL6/a96sr4eHhDfI+3oSeuY+euYd+uY+euY+euY+eVa9jx47VPuZS0MvLy9OiRYu0b98+tWrVSj/99JOioqL06KOPKiIi4rKKatOmjfPnkSNH6g9/+IOkcyN1+fn5zscqguWF6/Pz82Wz2So9JywsTGfPntXJkydltVpls9mUlZVV6bX69OlzWfUCAAA0NS6do5eamqpu3brpjTfe0J///Ge98cYb6t69u1JTUy/7jQsKCpw/f/nll+rSpYskKS4uTunp6SorK9Px48d19OhRRUVFKTQ0VIGBgdq7d6+MMUpLS1NcXJwkKTY2Vhs2bJAkbdmyRX379pXFYlFMTIx27Nih4uJiFRcXa8eOHc4reAEAALydSyN6OTk5mjVrlvz8zm0eEBCge+65R5MnT3bpTRYuXKisrCwVFRXp4Ycf1rhx47Rr1y4dOHBAFotFERERevDBByVJXbp00ZAhQzRjxgz5+PhoypQp8vE5l0cfeOABLVmyRKWlpYqJiVH//v0lSTfccIPsdruSk5NltVo1bdo0SZLVatWdd96p//zP/5QkjR071uWLQgAAAJo6i6np5Lf/7/e//73Gjh2r3r17O9ft2bNH77//vp5++ul6LdBTjhw5Uu/vwfkG7qNn7qNn7qFf7qNn7qNn7qNn1busc/Tee+8958/t2rXT888/rwEDBjjPh/vqq680dOjQuq0UAAAAdabaoHf+hQ+SdO2110qSfvzxR/n7+2vQoEEqLS2t3+oAAABw2aoNelOnTm3IOgAAAFDHXJ5H7/Tp0zp27JhKSkoqre/Vq1edFwUAAIDacynobdy4UX/5y1/k5+enFi1aVHrsT3/6U70UBgAAgNpxKei9/fbb+s1vfqPo6Oj6rgcAAAB1xKUJk/38/LijBAAAQBPjUtAbP368li5dqh9//LG+6wEAAEAdcenQbceOHfW3v/1Nq1evrvLY+fPtAajs4MEiJSdvlsNRIpstQHZ7vCIjuTsLAKBhuBT0Fi9erOHDhys+Pr7KxRgAqpecvFkZGedmcs/NLVZSUrpWrhzl4aoAAM2FS0GvuLhY48ePl8Viqe96AK/icJTUuAwAQH1y6Ry9ESNGKC0trb5rAbyOzRZQ4zIAAPXJpRG97OxsrVq1SsuXL1ebNm0qPfbss8/WR12AV7Db45WUlF7pHD0AABqKS0Fv5MiRGjlyZH3XAnidyEgr5+QBADzGpaA3YsSIei4DaJy4ahYA0JS5FPTWrVtX7WM33HBDnRUDNDZcNQsAaMpcCnqbNm2qtHzixAkdO3ZMvXv3JujBq3HVLACgKXMp6M2ZM6fKunXr1um7776r84KAxsRmC1BubnGlZQAAmgqXple5mBEjRtR4SBfwBnZ7vGJjw9W1q1WxseFcNQsAaFJcGtErLy+vtFxaWqq0tDS1atWqXooCGguumgUANGUuBb0JEyZUWWez2fTQQw/VeUEAAACoGy4FPbvdXmm5ZcuWCg4OrpeCAAAAUDdcCnoRERH1XQcAAADqWI1B71K3N7NYLJo9e3adFgQAAIC6UWPQGzZs2EXXOxwOffLJJzp9+nS9FAUAAIDaqzHoXTgZclFRkVasWKHPPvtM8fHxGjt2bL0WBwAAgMvn0jl6J0+e1MqVK7V69WoNGDBAf/jDH9S+ffv6rg0AAAC1UGPQKy0t1UcffaR//OMf6tOnj5577jl16dKloWoDAABALdQY9H7961+rvLxcY8aMUffu3VVYWKjCwsJK21x99dX1WiAAAAAuT41Br0WLFpKkNWvWXPRxi8VSZY49AAAANA41Br3U1NSGqgMAAAB1zMfTBQAAAKB+EPQAAAC8FEEPAADASxH0AAAAvBRBDwAAwEsR9AAAALwUQQ8AAMBLEfQAAAC8FEEPAADASxH0AAAAvBRBDwAAwEsR9AAAALwUQQ8AAMBLEfQAAAC8FEEPAADASxH0AAAAvBRBDwAAwEsR9AAAALyUn6cLgHsOHixScvJmORwlstkCZLfHKzLS6umyAABAI8SIXhOTnLxZGRl5ys0tVkZGnpKS0j1dEgAAaKQIek2Mw1FS4zIAAEAFgl4TY7MF1LgMAABQgaDXxNjt8YqNDVfXrlbFxobLbo/3dEkAAKCR4mKMJiYy0qqVK0d5ugwAANAEMKIHAADgpQh6AAAAXoqgBwAA4KU4R6+ZYcJlAACaD0b0mhkmXAYAoPkg6DUzTLgMAEDzQdBrZphwGQCA5oOg18ww4TIAAM1Hg1yMsWTJEm3fvl0hISFKSUmRJBUXF2vBggX64YcfFBERoenTp8tqPXdRwIoVK7Ru3Tr5+Pho0qRJiomJkSTl5OQoNTVVpaWl6t+/vyZNmiSLxaKysjLZ7Xbl5OSodevWmjZtmtq2bStJ2rBhg5YvXy5JuuOOOzRixIiG+MiNFhMuAwDQfDTIiN6IESP029/+ttK6Dz74QP369dOiRYvUr18/ffDBB5Kkw4cPKz09XfPnz9esWbP0+uuvq7y8XJL02muv6aGHHtKiRYt07NgxZWZmSpLWrVunVq1aafHixbrlllv0zjvvSDoXJpctW6Z58+Zp3rx5WrZsmYqLixviIwMAAHhcgwS9Pn36OEfrKmzdulUJCQmSpISEBG3dutW5Pj4+Xv7+/mrbtq3at2+v7OxsFRQU6NSpU+rZs6csFouGDx/ufM62bducI3WDBw/Wzp07ZYxRZmamoqOjZbVaZbVaFR0d7QyHnnTwYJHGjFmjvn3/R2PGrNGhQ4RPAABQ9zw2j15hYaFCQ0MlSaGhofrxxx8lSQ6HQz169HBuZ7PZ5HA45Ovrq7CwMOf6sLAwORwO53MqHvP19VVQUJCKiooqrT//tS5m7dq1Wrt2rSTphRdeUHh4eB1+2sruvHOdMjLynMvTp3+pjRvH1dv7eRM/P796/W68ET1zD/1yHz1zHz1zHz27PI1uwmRjjFvrq3vMYrFcdNvq1icmJioxMdG5nJeXd9Ht6sL33xdXWa7P9/Mm4eHh9MpN9Mw99Mt99Mx99Mx99Kx6HTt2rPYxj111GxISooKCAklSQUGBgoODJZ0bqcvPz3du53A4ZLPZqqzPz8+XzWar8pyzZ8/q5MmTslqtstlsVV6rYhTRk5jiBAAANASPBb24uDht3LhRkrRx40YNHDjQuT49PV1lZWU6fvy4jh49qqioKIWGhiowMFB79+6VMUZpaWmKi4uTJMXGxmrDhg2SpC1btqhv376yWCyKiYnRjh07VFxcrOLiYu3YscN5Ba8nVUxxEhUVwhQnAACg3lhMTcdE68jChQuVlZWloqIihYSEaNy4cRo4cKAWLFigvLw8hYeHa8aMGc4LNpYvX67169fLx8dHEydOVP/+/SVJ+/fv15IlS1RaWqqYmBhNnjxZFotFpaWlstvtys3NldVq1bRp09SuXTtJ567IXbFihaRz06tcf/31LtV85MiReuhEZQxDu4+euY+euYd+uY+euY+euY+eVa+mQ7cNEvSaIoJe40TP3EfP3EO/3EfP3EfP3EfPqtcoz9EDAABA/SLoAQAAeCmCHgAAgJci6AEAAHgpgh4AAICXIugBAAB4KYIeAACAlyLoAQAAeCmCHgAAgJci6AEAAHgpgh4AAICXIugBAAB4KT9PFwDUt4MHi5ScvFkOR4lstgDZ7fGKjLR6uiwAAOodI3rwesnJm5WRkafc3GJlZOQpKSnd0yUBANAgCHrweg5HSY3LAAB4K4IevJ7NFlDjMgAA3oqgB69nt8crNjZcXbtaFRsbLrs93tMlAQDQILgYA14vMtKqlStHeboMAAAaHCN6AAAAXoqgBwAA4KUIegAAAF6KoAcAAOClCHoAAABeiqAHAADgpQh6AAAAXoqgBwAA4KUIegAAAF6KO2PAZQcPFik5ebMcjhLZbAGy2+MVGWn1dFkAAKAajOjBZcnJm5WRkafc3GJlZOQpKSnd0yUBAIAaEPTgMoejpMZlAADQuBD04DKbLaDG5ZocPFikMWPWaOjQlRozZo0OHSqu6/IAAMAFCHpwmd0er9jYcHXtalVsbLjs9niXn8thXwAAGh4XY8BlkZFWrVw56rKey2FfAAAaHiN6aBC1OewLAAAuD0EPDaI2h30BAMDl4dAtGkRtDvtK/57Dr7CwTCEh/szhBwCACxjRQ5NQcTFHdnYhF3MAAOAigh6aBC7mAADAfQQ9NAlczAEAgPsIemgSKi7miIoK4WIOAABcxMUYaBIqLuYIDw9XXl6ep8sBAKBJYEQPAADASxH0AAAAvBRBDwAAwEsR9AAAALwUQQ8AAMBLEfQAAAC8FEEPAADASxH0AAAAvBRBDwAAwEsR9AAAALwUQQ8AAMBLWYwxxtNFAAAAoO4xoudBM2fO9HQJTQ49cx89cw/9ch89cx89cx89uzwEPQAAAC9F0AMAAPBSBD0PSkxM9HQJTQ49cx89cw/9ch89cx89cx89uzxcjAEAAOClGNEDAADwUn6eLsCbLFmyRNu3b1dISIhSUlIkSQcOHNBrr72mkpISRURE6NFHH1VQUJDOnDmjV155Rbm5uSovL9fw4cP1i1/8QpKUk5Oj1NRUlZaWqn///po0aZIsFosnP1q9qauePfPMMyooKFCLFi0kSU8//bRCQkI89rnqk7s9++///m/t379fPj4+mjhxovr27SuJ/exyetZc9rO8vDylpqbqxIkTslgsSkxM1OjRo1VcXKwFCxbohx9+UEREhKZPny6r1SpJWrFihdatWycfHx9NmjRJMTExkprPflaXPWM/u3jPioqKNH/+fGVnZ2vEiBGaMmWK87Way352WQzqzK5du8z+/fvNjBkznOtmzpxpdu3aZYwx5rPPPjPvvvuuMcaYTZs2mQULFhhjjCkpKTFTp04133//vfM5e/bsMeXl5Wbu3Llm+/btDftBGlBd9WzOnDkmOzu7YYv3EHd69sknn5jU1FRjjDEnTpwwTz75pDl79qzzOexn7vWsuexnDofD7N+/3xhjzMmTJ82jjz5qvv32W/PWW2+ZFStWGGOMWbFihXnrrbeMMcZ8++235vHHHzelpaXm+++/N0lJSc1uP6vLnrGfXbxnp06dMt98841ZvXq1+fOf/1zptZrLfnY5OHRbh/r06eP8n1qFI0eO6KqrrpIkRUdH65///KfzsZKSEp09e1alpaXy8/NTUFCQCgoKdOrUKfXs2VMWi0XDhw/X1q1bG/RzNKS66Flz407PDh8+rKuvvlqSFBISolatWiknJ4f9TO73rDkJDQ1Vt27dJEmBgYHq1KmTHA6Htm7dqoSEBElSQkKCc5/ZunWr4uPj5e/vr7Zt26p9+/bKzs5uVvtZXfWsOXG3ZwEBAerdu7dzpLNCc9rPLgdBr5516dJF27ZtkyRt2bJF+fn5kqTBgwcrICBADz74oKZOnarbbrtNVqtVDodDYWFhzueHhYXJ4XB4pHZPcbdnFZYsWaInnnhCy5Ytk2lm1xhV17Mrr7xS27Zt09mzZ3X8+HHl5OQoLy+P/Uzu96xCc9vPjh8/rtzcXEVFRamwsFChoaGSzv2S/vHHHyWpyv5ks9nkcDia7X5Wm55VYD+r2rPqNNf9zFWco1fPHnnkEb3xxhtatmyZ4uLi5Od3ruXZ2dny8fHRq6++qp9++kmzZ89Wv379msVf6Etxt2ft2rXTo48+KpvNplOnTiklJUVpaWnO/xE2B9X17Prrr9fhw4c1c+ZMRUREqFevXvL19WU/k/s9k9Ts9rOSkhKlpKRo4sSJNY6eV7c/Ncf9rLY9k9jP3NUc9zN3EPTqWadOnfT0009LOneoaPv27ZKkzz//XDExMfLz81NISIh69eql/fv366qrrnKOLEhSfn6+bDabR2r3FHd71q5dO2ePAgMDNXToUGVnZ3v1P4wXqq5nvr6+mjhxonO7p59+Wh06dFCrVq3Yz9zsmaRmtZ+dOXNGKSkpGjZsmK699lpJ5w5lFxQUKDQ0VAUFBQoODpZ0bgTl/P3J4XDIZrNVWe/t+1ld9ExiP6uuZ9VpbvuZuzh0W88KCwslSeXl5Vq+fLluvPFGSVJ4eLh27twpY4xKSkq0b98+derUSaGhoQoMDNTevXtljFFaWpri4uI8+REanLs9O3v2rHNo/8yZM8rIyFCXLl08Vr8nVNez06dPq6SkRJL09ddfy9fXV507d2Y/k/s9a077mTFGr7zyijp16qRbb73VuT4uLk4bN26UJG3cuFEDBw50rk9PT1dZWZmOHz+uo0ePKioqqlntZ3XVM/az6ntWnea0n10OJkyuQwsXLlRWVpaKiooUEhKicePGqaSkRKtXr5YkDRo0SHfddZcsFotKSkq0ZMkSHT58WMYYXX/99RozZowkaf/+/VqyZIlKS0sVExOjyZMne+1l4nXRs5KSEs2ZM0dnz55VeXm5+vXrp/vvv18+Pt75/xh3enb8+HHNnTtXPj4+stlsevjhhxURESGJ/czdnjWn/Wz37t2aPXu2IiMjnfvEhAkT1KNHDy1YsEB5eXkKDw/XjBkznOfJLl++XOvXr3dOSdO/f39JzWc/q6uesZ/V3LNf//rXOnnypM6cOaNWrVrp6aefVufOnZvNfnY5CHoAAABeyvv+iwAAAABJBD0AAACvRdADAADwUgQ9AAAAL0XQAwAA8FIEPQAAAC9F0AOAy7Ro0SItWbKk0rqsrCxNnjxZBQUFHqoKAP6NoAcAl2nSpEn66quv9PXXX0uSSktL9eqrr+q+++5z3pS9Ns6ePVvr1wDQvDFhMgDUwubNm/X2228rJSVFy5cv14EDBzR27FgtXbpUhw8fVkREhCZOnKi+fftKktavX6+VK1cqPz9fwcHB+vnPf+68/dquXbu0ePFi3Xzzzfroo48UHR2t5ORkT348AE2cn6cLAICmbMiQIUpPT9fLL7+sPXv26A9/+IOeeuopJSUlKSYmRjt37lRKSooWLlyo4OBghYSE6KmnnlK7du30zTffaN68eerevbu6desmSTpx4oSKi4u1ZMkS8f9wALXFoVsAqKUpU6Zo586dGjt2rL744gv1799fAwYMkI+Pj6Kjo9W9e3dt375dkjRgwAC1b99eFotFffr0UXR0tHbv3u18LYvFonHjxsnf318tWrTw1EcC4CUY0QOAWmrTpo2Cg4PVuXNnffnll9qyZYsyMjKcj589e9Z56Parr77SsmXLdOTIERljdPr0aUVGRjq3DQ4OJuABqDMEPQCoQ2FhYRo2bJgefvjhKo+VlZUpJSVFSUlJiouLk5+fn1588cVK21gsloYqFUAzwKFbAKhDw4YNU0ZGhjIzM1VeXq7S0lLt2rVL+fn5OnPmjMrKyhQcHCxfX99KV+wCQH1gRA8A6lB4eLiefPJJvf3223r55Zfl4+OjqKgo/epXv1JgYKAmTZqkBQsWqKysTLGxsYqLi/N0yQC8GNOrAAAAeCkO3QIAAHgpgh4AAICXIugBAAB4KYIeAACAlyLoAQAAeCmCHgAAgJci6AEAAHgpgh4AAICXIugBAAB4qf8H4o8GdT1enMYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Total Immigration to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Notice how the scatter plot does not connect the datapoints together. We can clearly observe an upward trend in the data: as the years go by, the total number of immigrants increases. We can mathematically analyze this upward trend using a regression line (line of best fit). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "So let's try to plot a linear line of best fit, and use it to  predict the number of immigrants in 2015.\n",
    "\n",
    "Step 1: Get the equation of line of best fit. We will use **Numpy**'s `polyfit()` method by passing in the following:\n",
    "- `x`: x-coordinates of the data. \n",
    "- `y`: y-coordinates of the data. \n",
    "- `deg`: Degree of fitting polynomial. 1 = linear, 2 = quadratic, and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.56709228e+03, -1.09261952e+07])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_tot['year']\n",
    "y = df_tot['total']\n",
    "fit = np.polyfit(x, y, deg=1)\n",
    "\n",
    "fit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The output is an array with the polynomial coefficients, highest powers first. Since we are plotting a linear regression `y= a*x + b`, our output has 2 elements `[5.56709228e+03, -1.09261952e+07]` with the the slope in position 0 and intercept in position 1. \n",
    "\n",
    "Step 2: Plot the regression line on the `scatter plot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'No. Immigrants = 5567 * Year + -10926195'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tot.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Total Immigration to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "\n",
    "plt.plot(x, fit[0] * x + fit[1], color='red') # recall that x is the Years\n",
    "plt.annotate('y={0:.0f} x + {1:.0f}'.format(fit[0], fit[1]), xy=(2000, 150000))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "'No. Immigrants = {0:.0f} * Year + {1:.0f}'.format(fit[0], fit[1]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
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   "source": [
    "Using the equation of line of best fit, we can estimate the number of immigrants in 2015:\n",
    "```python\n",
    "No. Immigrants = 5567 * Year - 10926195\n",
    "No. Immigrants = 5567 * 2015 - 10926195\n",
    "No. Immigrants = 291,310\n",
    "```\n",
    "When compared to the actuals from Citizenship and Immigration Canada's (CIC) [2016 Annual Report](http://www.cic.gc.ca/english/resources/publications/annual-report-2016/index.asp), we see that Canada accepted 271,845 immigrants in 2015. Our estimated value of 291,310 is within 7% of the actual number, which is pretty good considering our original data came from United Nations (and might differ slightly from CIC data).\n",
    "\n",
    "As a side note, we can observe that immigration took a dip around 1993 - 1997. Further analysis into the topic revealed that in 1993 Canada introcuded Bill C-86 which introduced revisions to the refugee determination system, mostly restrictive. Further amendments to the Immigration Regulations cancelled the sponsorship required for \"assisted relatives\" and reduced the points awarded to them, making it more difficult for family members (other than nuclear family) to immigrate to Canada. These restrictive measures had a direct impact on the immigration numbers for the next several years."
   ]
  },
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   "source": [
    "**Question**: Create a scatter plot of the total immigration from Denmark, Norway, and Sweden to Canada from 1980 to 2013?"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Step 1: Get the data:\n",
    "   1. Create a dataframe the consists of the numbers associated with Denmark, Norway, and Sweden only. Name it **df_countries**.\n",
    "   2. Sum the immigration numbers across all three countries for each year and turn the result into a dataframe. Name this new dataframe **df_total**.\n",
    "   3. Reset the index in place.\n",
    "   4. Rename the columns to **year** and **total**.\n",
    "   5. Display the resulting dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
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    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  total\n",
       "0  1980    669\n",
       "1  1981    678\n",
       "2  1982    627\n",
       "3  1983    333\n",
       "4  1984    252"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_countries = df_can.loc[['Denmark', 'Norway', 'Sweden'], years].transpose()\n",
    "\n",
    "df_total = pd.DataFrame(df_countries.sum(axis=1))\n",
    "\n",
    "df_total.reset_index(inplace=True)\n",
    "\n",
    "df_total.columns = ['year', 'total']\n",
    "\n",
    "df_total['year'] = df_total['year'].astype(int)\n",
    "\n",
    "df_total.head()"
   ]
  },
  {
   "cell_type": "markdown",
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   "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",
    "    # show resulting dataframe\n",
    "    df_total.head()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
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   "metadata": {
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    "new_sheet": false,
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   },
   "source": [
    "Step 2: Generate the scatter plot by plotting the total versus year in **df_total**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
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   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "plt.title('Immigration from Denmark, Norway, and Sweden to Canada from 1980 - 2013')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # generate scatter plot\n",
    "    df_total.plot(kind='scatter', x='year', y='total', figsize=(10, 6), color='darkblue')\n",
    "\n",
    "    # add title and label to axes\n",
    "    plt.title('Immigration from Denmark, Norway, and Sweden to Canada from 1980 - 2013')\n",
    "    plt.xlabel('Year')\n",
    "    plt.ylabel('Number of Immigrants')\n",
    "\n",
    "    # show plot\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Bubble Plots <a id=\"12\"></a>\n",
    "\n",
    "A `bubble plot` is a variation of the `scatter plot` that displays three dimensions of data (x, y, z). The datapoints are replaced with bubbles, and the size of the bubble is determined by the third variable 'z', also known as the weight. In `maplotlib`, we can pass in an array or scalar to the keyword `s` to `plot()`, that contains the weight of each point.\n",
    "\n",
    "**Let's start by analyzing the effect of Argentina's great depression**.\n",
    "\n",
    "Argentina suffered a great depression from 1998 - 2002, which caused widespread unemployment, riots, the fall of the government, and a default on the country's foreign debt. In terms of income, over 50% of Argentines were poor, and seven out of ten Argentine children were poor at the depth of the crisis in 2002. \n",
    "\n",
    "Let's analyze the effect of this crisis, and compare Argentina's immigration to that of it's neighbour Brazil. Let's do that using a `bubble plot` of immigration from Brazil and Argentina for the years 1980 - 2013. We will set the weights for the bubble as the *normalized* value of the population for each year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Get the data for Brazil and Argentina. Like in the previous example, we will convert the `Years` to type int and bring it in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>American Samoa</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>...</th>\n",
       "      <th>United States of America</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Venezuela (Bolivarian Republic of)</th>\n",
       "      <th>Viet Nam</th>\n",
       "      <th>Western Sahara</th>\n",
       "      <th>Yemen</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>368</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9378</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>103</td>\n",
       "      <td>1191</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>426</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10030</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>1829</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>626</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9074</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>2162</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7100</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>3404</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>237</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6661</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>7583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 196 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Year  Afghanistan  Albania  Algeria  American Samoa  Andorra  Angola  \\\n",
       "0        1980           16        1       80               0        0       1   \n",
       "1        1981           39        0       67               1        0       3   \n",
       "2        1982           39        0       71               0        0       6   \n",
       "3        1983           47        0       69               0        0       6   \n",
       "4        1984           71        0       63               0        0       4   \n",
       "\n",
       "Country  Antigua and Barbuda  Argentina  Armenia  ...  \\\n",
       "0                          0        368        0  ...   \n",
       "1                          0        426        0  ...   \n",
       "2                          0        626        0  ...   \n",
       "3                          0        241        0  ...   \n",
       "4                         42        237        0  ...   \n",
       "\n",
       "Country  United States of America  Uruguay  Uzbekistan  Vanuatu  \\\n",
       "0                            9378      128           0        0   \n",
       "1                           10030      132           0        0   \n",
       "2                            9074      146           0        0   \n",
       "3                            7100      105           0        0   \n",
       "4                            6661       90           0        0   \n",
       "\n",
       "Country  Venezuela (Bolivarian Republic of)  Viet Nam  Western Sahara  Yemen  \\\n",
       "0                                       103      1191               0      1   \n",
       "1                                       117      1829               0      2   \n",
       "2                                       174      2162               0      1   \n",
       "3                                       124      3404               0      6   \n",
       "4                                       142      7583               0      0   \n",
       "\n",
       "Country  Zambia  Zimbabwe  \n",
       "0            11        72  \n",
       "1            17       114  \n",
       "2            11       102  \n",
       "3             7        44  \n",
       "4            16        32  \n",
       "\n",
       "[5 rows x 196 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can_t = df_can[years].transpose()\n",
    "\n",
    "df_can_t.index = map(int, df_can_t.index)\n",
    "\n",
    "df_can_t.index.name = 'Year'\n",
    "\n",
    "df_can_t.reset_index(inplace=True)\n",
    "\n",
    "df_can_t.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Create the normalized weights. \n",
    "\n",
    "There are several methods of normalizations in statistics, each with its own use. In this case, we will use [feature scaling](https://en.wikipedia.org/wiki/Feature_scaling) to bring all values into the range [0,1]. The general formula is:\n",
    "\n",
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/labs/Module%203/images/Mod3Fig3FeatureScaling.png\" align=\"center\">\n",
    "\n",
    "where *`X`* is an original value, *`X'`* is the normalized value. The formula sets the max value in the dataset to 1, and sets the min value to 0. The rest of the datapoints are scaled to a value between 0-1 accordingly.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# normalize Brazil data\n",
    "norm_brazil = (df_can_t['Brazil'] - df_can_t['Brazil'].min()) / (df_can_t['Brazil'].max() - df_can_t['Brazil'].min())\n",
    "\n",
    "# normalize Argentina data\n",
    "norm_argentina = (df_can_t['Argentina'] - df_can_t['Argentina'].min()) / (df_can_t['Argentina'].max() - df_can_t['Argentina'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 3: Plot the data. \n",
    "- To plot two different scatter plots in one plot, we can include the axes one plot into the other by passing it via the `ax` parameter. \n",
    "- We will also pass in the weights using the `s` parameter. Given that the normalized weights are between 0-1, they won't be visible on the plot. Therefore we will:\n",
    "    - multiply weights by 2000 to scale it up on the graph, and,\n",
    "    - add 10 to compensate for the min value (which has a 0 weight and therefore scale with x2000)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f642554dd30>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For Brazil\n",
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Brazil',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_brazil * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "\n",
    "# For Argentina\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='Argentina',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_argentina * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from Brazil and Argentina from 1980 - 2013')\n",
    "ax0.legend(['Brazil', 'Argentina'], loc='upper left', fontsize='x-large')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The size of the bubble corresponds to the magnitude of immigrating population for that year, compared to the 1980 - 2013 data. The larger the bubble, the more immigrants in that year.\n",
    "\n",
    "From the plot above, we can see a corresponding increase in immigration from Argentina during the 1998 - 2002 great depression. We can also observe a similar spike around 1985 to 1993. In fact, Argentina had suffered a great depression from 1974 - 1990, just before the onset of 1998 - 2002 great depression. \n",
    "\n",
    "On a similar note, Brazil suffered the *Samba Effect* where the Brazilian real (currency) dropped nearly 35% in 1999. There was a fear of a South American financial crisis as many South American countries were heavily dependent on industrial exports from Brazil. The Brazilian government subsequently adopted an austerity program, and the economy slowly recovered over the years, culminating in a surge in 2010. The immigration data reflect these events."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Question**: Previously in this lab, we created box plots to compare immigration from China and India to Canada. Create bubble plots of immigration from China and India to visualize any differences with time from 1980 to 2013. You can use **df_can_t** that we defined and used in the previous example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Normalize the data pertaining to China and India."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "### type your answer here\n",
    "norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # normalize China data\n",
    "    norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())\n",
    "    # normalize India data\n",
    "    norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Generate the bubble plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "button": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f64253722e8>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1K0uWLOm3rz2dd3/n3NNrjuzP//pf/4vHH3+cn/70p8yePZtgMMjXvva1Pot/rFmzho9//OPceuut/PjHPyY/P5/Vq1fzmc98pstn3tt71NHno9/TI/cZqOE41v79+7n44ov51Kc+xXe+8x2Kioqoqqri/PPPH9R3e7DfNej5e9XTtsH8Bo5UUFDAAw88QDwep66ujvLycn79618DMGXKFAD+4z/+gwULFvDv//7vna/761//ysSJE3nxxRc5//zzKSsr44UXXujWfm1tba8FQMrLy9m4cWPn43SKBP3kJz/hu9/9Lk888US3IhplZWVdbggAnY9760NfSktLO2+KLVq0iPLycp5//nk++MEPDrgtIUTmSAZMCHFMmj59Oh6Ph9dff73L9tWrV/f72oaGBrZt28Y3v/lNLrzwQubOnYvP5+t2J97j8fR7t3jevHm8/vrrXS5uN23aREtLC/PmzRvAGQ3evHnz2Lp1a5c5OLW1tVRWVma8D4sWLWLBggX813/9V+dcpSOFQqEet2fSyy+/zCc/+UmuuuoqFi1axNSpU6msrOzzNa+++ipFRUX88Ic/5JRTTmHmzJmDKsk9b9481qxZ0+V7MRzzgwZ7rHXr1hGJRLjjjjs4/fTTmTVrVr8Zx4H26fDhw2zbtq1zWywWY+3atSP2fe+Nx+PpXMfsb3/7G8uXL+/MOre3t3erXNoR/HYE96effjp79uxhx44dnfu8/fbbHDhwoNfMo2VZTJ8+vfNfSUlJn338zne+w/e//32efvrpHisYnn766Tz//PNdgtJnnnmGQCCQ1g2PvnS0GYvFhtSOEGLoJAATQhyTgsEgN954I//n//wfnnrqKSorK/n2t7/N22+/3W+WID8/n+LiYn77299SWVnJ66+/zjXXXNOtzPuUKVN48cUXqa6u7lZgoMO//uu/0traynXXXceWLVt49dVX+dSnPsUZZ5zBmWeembHz7csnPvEJiouLueqqq1i/fj1vvvkmV199NRUVFRlfO6xjQeiqqipOOeUUHnvsMXbs2MH27du5++67WbhwIaFQKKPHPNqsWbN4/PHHWbt2Ldu2beMLX/gC1dXV/b6mvr6e3/3ud+zevZs//elPPRZs6c9NN91EfX09X/jCF3j77bf5n//5ny4lwjNpMMeaMWMGSil++tOfsmfPHh577DH+7//9vxnr07nnnsvJJ5/MJz7xCV577TW2bNnCpz/9aaLRKDfddFPGjtMhFAqxceNGNm7c2DlcdOPGjV3W71q3bh0PPvggu3bt4vXXX+djH/sYGzdu5Oc//3nnPpdffjnPPPMMt99+O7t27eKNN97guuuuo7y8nFNOOQWA888/nxNOOIFrr72WtWvXsmbNGj71qU9x6qmnctZZZw35XG6++WZ+/OMfc9999zFr1iwOHTrEoUOHumRub7rpJlpaWvj85z/P1q1beeKJJ/j3f/93vvzlL3fJRna8J42NjV3eow4PP/wwf/rTn9iyZQv79u3jhRde4Morr2T8+PGce+65Qz4XIcTQSAAmhDhm/dd//Rcf/vCH+cQnPsHJJ59MU1MT1113Xb9rIhmG0XnBtnDhQq677jpuvvlmysrKuuz305/+lDfffJMpU6Z0mb91pHHjxvHcc89RVVXFSSedxCWXXML8+fM7q8qNBL/fz3PPPYfX62X58uWcddZZBINBnnnmmW7D+TLhhBNOYMOGDSxdupSbb76ZefPmce655/Loo49yxx13DHuVtdtvv51JkyZxzjnncN5551FRUdHvQsSXXHIJ3/72t/nWt77FggUL+Pvf/86Pf/zjAR+7oqKCJ598krVr17J48WL+7d/+bdgWBx/MsRYuXMgvfvEL7r77bubOnctPfvIT7rjjjoz1SSnFY489xuzZs/nQhz7ESSedxKFDh3j++ee7zEHMlDfeeIMlS5awZMkSampq+OUvf8mSJUv43Oc+17lPLBbj+9//PvPnz+eDH/wgsViMVatWdZmD9qlPfYq77rqLP/zhDyxcuJCLL74Yn8/Hs88+2zls0DAMnnrqKSZOnMh5553HBz7wAaZNm8bjjz8+6GGmR/rZz35GNBrlIx/5CGVlZZ3/jqxsOWHCBJ577jnefvttTjzxRL7whS/whS98gf/3//5fl7Y63pMnn3ySNWvWdD7u4PV6ueuuuzjjjDOYNWsWN954IwsXLmTVqlVkZ2cP+VyEEEOjdG8D/YUQ4hh07rnnkp+fP6IBkBBCCCFEuqQIhxDimLV582bWr1/PsmXLiMfj3Hfffbz44os8/fTTo901IYQQQogeSQAmhDhmKaX41a9+xVe+8hVc12X27Nk8+uijXHTRRaPdNSGEEEKIHskQRCGEEEIIIYQYIVKEQwghhBBCCCFGiARgQgghhBBCCDFCJAATQgghhBBCiBFy3Bfh6G/xTjH8ioqKel3kVowM+QzGBvkcxgb5HEaffAaZ47gOL+x/gbW1a/Gb/gGtaRYIBAiHw2nt62qXmBPjtLLTOHvC2RhK7vFnivweRl95eXlG2zvuAzAhhBBCiPejaDLKvdvupSHaQMAKDOuxDGXgt/ysqlnF7pbdfHrup/GYmV8IXoj3A7k9IYQQQgjxPhNJRvjN5t/QHGvGZ/pG7Lh+y099pJ57ttxDNBkdseMKcSyRDNgw0FrTlmjjQOsBdjbvJJwM42oXQxkErADT86YzMWciWXbWgIYCCCGEEEL0J+bEuGfzPUSSEWzDHvHje0wPrfFW7t12LzfMv2FU+iDEWCYBWAZFk1Fer3mdtw6/RUusBTT4bX+XcdCudtlUvwmlFDneHBYWLWRZ2TJ81sjdnRJCCCHE+5PWmr9u/yvtyfZRDXxsw6Y52sz979zPJ2d/Um44C3EECcAywHEdnt33LJvqN+FqF5/lI2gHe9zXUAZBT+q5pJtkVfUqVtesZnHxYi6YdAGmYY5k14UQQgjxPrK6ZjUHQwfxW/7R7gq2abOnZQ8b6zayZNyS0e6OEGOGBGB9cByHaDQ1frm3OzfV7dU8uvtRwskwXtOLiUkikUj7GCYmaFhbvZYtdVv4yNSPUB7MbKWVsUxrjeM4AJimBJ9CCCHEYLXEWlhRtWJMBF8dAnaAZ/c/y8yCmb3enBbieCMBWC8cxyESiRAMBnsNvjbVb+KJXU/gt/xkWVlDOp5t22it+duuv3HZtMtYWLxwSO0dS0zTpKWlBb/fL0GYEEIIMQhaax6ofGBMzrdSKB7a8RCfmfuZ0e6KEGOCVEHsRTQa7Tf4enLXkwTsQMbGNSulCNgBntj1BG/Vv5WRNo8FSimCwWBntlEIIYQQA7OreRfV7dWYauzdyLQMi30t+zjYdnC0uyLEmCABWB96C6wOhg6mMl/28KT4/bafx3c9zsHQ8fOHSibnCiGEEIP3SvUrBK2xO8TPb/t56eBLo90NIcYECcB60VtA4LgOD1U+NOzjq/2Wn4d3PIzjOsN6nLFEgjAhhBBi4NribVSFqsb0/48aymBf6z4iychod0WIUScB2AA9t+852pPtw/5HTilFKBHiuf3PDetxhBBCCHFse+nAS2Ny7ldPXqt+bbS7IMSokwBsAKLJKBvrN+I1vSNyPK/pZWPdxmFbSf5jH/sYX//61/vc56c//Smnn376sBxfCCGEEEO3t3XvMRGAeU0vlU2Vo90NIUadBGAD8HrN67jaHdFjutplzaE1g3ptY2MjP/zhDznzzDOZOnUqCxcu5KMf/SgPPvggyWQyrTa++MUv8uSTTw7q+EIIIYQYXnEnTmu8dbS7kbaWWMuIX0sJMdZIGfo0aa156/Bb+CzfiB7XZ/nYWL+R5RXLBzTssbq6mssvvxzLsvj617/O/PnzsSyLN954g7vvvps5c+ak1U4wGCQYHLuTeoUQQojjWW24loSbwMfIXp8MVtyJ0xBpoDhQPNpdEWLUSAYsTW2JNlpiLaNy7NZYK6FEaECvufXWW4nH4zzzzDN89KMfZebMmUydOpUrr7ySf/7zn0ydOrVz39tvv53Fixczb948br75ZsLhcOdzRw9B7Hj87LPPsnz5cqZPn87HPvYx9u7d27lPc3MzX/7ylznppJOYNm0aZ555Jr/+9a/RWg/+TRBCCCFEN5VNlfjMYyP4glRJ+p3NO0e7G0KMKgnA0nSg9QCMUvygteZA24G0929qamLFihVcd9115OTkdHvetm0CgQAA//jHP2hubuahhx7izjvv5Nlnn+Wuu+7qs/3a2lr+9Kc/ceedd/L444/T2trK1772tc7n4/E4s2fP5ve//z0vvvgiN998Mz/96U954IEH0j4HIYQQQvSvLlyHbY79+V8dvKb3uFpmR4ieyBDENO1s3jls6371x2/72dm8k7mFc9Paf+/evbiuy8yZM/vdt6Kigu9///sATJ8+nUsvvZSVK1f2WZwjHo/z85//nMLCQgC+9KUv8aUvfYloNIrP56OkpIQvfelLnftPnDiRTZs28eijj3LVVVeldQ5CCCGE6F/STW9O91ihlCKhE6PdDSFGlQRgaQonwxhqdBKGhjJoT7SnvX/HUL905ozNnds1qCstLeXll1/u8zXjxo3rDL46XqO1pqGhgYqKClzX5a677uLxxx+npqaGWCxGMpmkoqIi7XMQQgghRP9cjr2CFq577PVZiEySIYhpGu2KPQM5/pQpUzAMg3feeafffT0eT5fHSql+/zD29Bp47w/q3XffzZ133slnP/tZ/v73v/Pcc89xzTXXkEjIHS8hhBAikxRjd/Hl3ozWDW0hxgr5BaRptP9YDOT4+fn5nHPOOdx77720tnYvTZtIJLoU2si01atXc/bZZ3PNNdcwf/58pkyZwp49e4bteEIIIcTxarSvTwbDVOZod0GIUXXs/WpHScAKjFoWzNUuQXtgpeB/9KMfYVkWF110EY8++iiVlZXs2bOHhx9+mIsuuojdu3cPU29h2rRpvP7667z22mvs2rWL//qv/2LDhg3DdjwhhBDieJXjyRn1UToDkXST5PnyRrsbQowqmQOWpul509lUv4mgZ+TXxIokIkzPmz6g11RUVPDss89y55138tOf/pTq6mqysrKYMWMGN910E7Nnzx6m3sLNN9/MwYMH+exnP4tlWVx22WV89rOf5eGHHx62YwohhBDHo+l509lQt4EsT9ZodyUt4WSYGXkzRrsbQowqpY/zxZmqq6t73B4OhztLtQO0xlu5Y/0dA85EZUI4EebmE24m25M94sceCZZlkUwmu73nYuQUFRVx+PDh0e7GcU8+h7FBPofRJ59B+tribdyx/g4Cdub//zMQCGR8ykIkGeHrJ34dn3XsrF022uT3MPrKy8sz2p5kwNKUbWeT681Nq9yr1hAP+2ipy6fxQAmJqBetFUppbF+Mggl15I5rxOOPkUahQnK8OWTZx8adLSGEEEKMnCw7i4B17Ny8zLKzJPgSxz0JwNKklGJh0UJWVa/q9Q9HMm5xYPM06naNJxLyo7TC8sZRxntJRu0qandMBEPjC0YomVbFhAW7sDw9B3bRZJQzKs5Iq6S8EEIIIY4vSimKAkXUh+vH/LWCq13Kg5nNJAhxLJIiHAOwrGxZj9WGXEexY9V8Xv/7BziweRpO0sTji2P7Y12CLwBlaGx/DNsbx0maHNg8jdf//gF2vD4P1+n+h9NQBqeUnjJs5ySEEEKIY9uZFWfSnkx/vdDREk6GOWv8WaPdDSFGnQRgA+CzfCwqXkTMiXVua6vPZe1D51KzYyKm5fSayeqN5UliWg41lZNY+9C5tNXndj4Xc2IsLlksqXohhBBC9GpKzhTyvfmj3Y1+lfhLKM+SDJgQEoAN0IWTLiRoBdFac2hHBRueOgMnaWLZAwu8jmbZSZykyYanzqB2ZwVaa7LsLC6YeEGGei6EEEKI9yOlFIuLFxNLxvrfeZREkhGWjls62t0QYkyQAGyATMPkYzM/xv7txVS+thjLm0irkEY6lALLm+CdVxez/50irphxBaYhixUKIYQQom+nlJ0yZq8ZtNb4LT9LSpaMdleEGBMkABuMlgmw7SqwIsPTvhWBrVenjiOEEEII0Q+v6eXiyRcTTmS2bHwmRJIRPjz1w1iG1H4TAiQAGzDHgYceCjCxoJiZ+TNJuImMtp9wE8zKn8XEgmIeftiP42S0eSGEEEK8Ty0oXsDU3KkZvzYZirgTZ3bBbGbmzxztrggxZkgANkDPPeelvT01XLA0WMqSkiUYysDRQ4uUHO1gKpMlJUsYFxyHUhAKKZ57zpuhngshhBDi/e6jMz6KQqG17n/nYaa1xjZsLp126Wh3RYgxRQKwAYhGYeNGD17ve5O+cjw5nFx6MqWBUhztpLVQ85GSbhJHO5QGSzmp9CRyPDmdz3m9io0bPUSjGTuFAVm1ahUVFRVUV1f3+FgIIYQQY4vf8vPxmR8n6ozSxcO7tNbEnBhXz7oaryk3k4U4kgRgA/D6615ct/t2QxnMyJ/BsrJlTMyeiKEMEm6ChJNA0/UOlEaTcBIk3ASGMpiYM5FlZcuYkTej5zXGXFizZuB/uG6++WauuuqqAb+uL0uXLmXDhg2UlpZmtF0hhBBCZM7U3KlcMeMKwsnRmQ+mtSbqRLlm9jWMzx4/Kn0QYiyT2ZBp0hreesvG18eSXJZhMTl3MpNyJhF347TEWmiKNhF342itUUrhMTwU+ArI8ebgMTz9rlrv88HGjTbLl8cyVm1xsDweDyUlJaPbCSGEEEL0a07BHK6ceSUPVj6Iz/T1e72RKR3B1ydnf5IpuVNG5JhCHGskA5amtjZFS0t6b5dSCq/ppSRQwqyCWSwoWsDC4oUsKFrArIJZFAeK8ZretP8YtrYahEKD/8PZkQ3785//zMknn8ysWbO4/vrraWho6LLf73//e0488USmTZvGJz7xCQ4ePNjl+aOHIGqt+V//639x2mmnMW3aNJYtW8aPfvQjYrGxuw6JEEIIcbyYlT+L6+ddj2VYI7JGWMyJ4TE9fH7+5yX4EqIPEoCl6cABExidCa1a63ePP3ibNm1i1apV/OlPf+LPf/4zW7du5f/+3//b+fyzzz7L9773Pb7whS/w3HPP8eEPf5gf/vCH/farqKiIO++8k5deeonvf//7PPDAA/ziF78YUl+FEEIIkRkVWRX86+J/ZWHxQsKJ8LAU59BaE06GObHkRL606EuMC47L+DGEeD+RIYhp2rnTwu8fnWP7/anjz507sAIfR7Jtm9tvvx2vNzWf7NOf/jS/+93vOp//1a9+xaWXXsqNN94IwLRp09ixYwd33313r20ahsE3vvGNzscTJkxg7969/PGPf+TrX//6oPsqhBBCiMyxDItLpl7CouJFPL7rcRqiDQSsQI9zzwfC1S6RZIRifzGfmP0JyrPKM9RjId7fJABLUzisMEYpX2gY0N4+tLHbM2bM6Ay+AEpLS6mvr+98vGPHDi6//PIurzn55JP7DMAA/vKXv/C3v/2NAwcOEA6HcRwHt6dKJUIIIYQYVROyJ/ClRV+iKlTFS1UvcaD1ACgGXKUwmoxiKINJOZM4q+IsKrIrhqnHQrw/jWgA5rou3/zmNykoKOCb3/wmoVCI22+/nfr6eoqLi/nqV79KVlYWAI8++igrVqzAMAyuv/56Fi9eDMDu3bv55S9/STweZ8mSJVx//fUopUgkEtx5553s3r2b7Oxsbr755owWjBjtmGKox7dtu8tjpYa+RsiTTz7Jt7/9bW699VaWLVtGVlYWTz31FP/1X/81pHaFEEIIMTyUUkzInsCn5nyKcCLMutp1HAgdoCnSRGu8lYSbQKEwlIFOaNrj7Wg0HtNDjieHPG8ek3ImsXTcUvzWKA0NEuIYN6IB2NNPP01FRQWRSASAxx57jAULFnD55Zfz2GOP8dhjj3HttddSVVXFqlWruO2222hqauIHP/gBP/vZzzAMg9/+9rfceOONzJgxgx/96Eds3LiRJUuWsGLFCoLBIL/4xS947bXX+Mtf/sJXv/rVjPV9tLJfI3X8GTNm8MYbb3Ddddd1blu3bl2fr1mzZg3z58/vHLYIUFVVNVxdFEIIIUQGBewAZ40/q/Nx3IlTF66jLdFGLBkjLy+PSChCjieHkkAJtmH30ZoQIl0jFlY0NDSwfv16zjvvvM5t69at46yzUj/8s846q/OCf926dZx22mnYtk1JSQmlpaXs3LmTpqYmIpEIM2fORCnF8uXLO1/zxhtvcPbZZwNw6qmnsmXLloxONA0E9KhlwVwXgsHhLQBy44038sQTT3DPPfewe/du7r//fh5++OE+XzNt2jTefvttnn32Wfbu3cs999zD008/Paz9FEIIIcTw8JgexmePZ07BHBaXLGZp+VLmFMyhIqtCgi8hMmjEArB7772Xa6+9tkvp9ZaWFvLz8wHIz8+ntbUVgMbGRgoLCzv3KygooLGxsdv2wsJCGhsbu73GNE0CgQBtbW0Z6//06UneTdyNuEgkdfzhdNFFF/Gd73yHX/3qV3zgAx/gkUce4Vvf+lafr7n22mu54ooruOWWW7jwwgvZsGEDX/va14a1n0IIIYQQQhzLRmQI4ptvvklubi5Tp05l69at/e7fW+aqr4xWT8/1tM7WCy+8wAsvvADAf/7nf1JUVNRje7W1tVjWe2/PlCkKwzBGZSiiYaSOf2R/+nPnnXf2+N8drrrqKq666qou2774xS/yxS9+scu2T3ziE53/vXz5cmprazsfW5bFbbfdxm233dblNZ///OfT7ueRbXm93l4/DzG8LMuS934MkM9hbJDPYfTJZzA2yOcwNsjn8P4zIgHYO++8wxtvvMGGDRuIx+NEIhF+/vOfk5ubS1NTE/n5+TQ1NZGTkwOkMltHLhLc2NhIQUFBt+0NDQ0UFBR0eU1hYSGO4xAOhzsLehzp/PPP5/zzz+98fPjw4R77HIvFMM331t7y+yE72yGZHJmV5I+Una3x+RIkhzcJNmosyyKZTBKLxXr9PMTwKioqkvd+DJDPYWyQz2H0yWcwNsjnMDbI5zD6ysszu8TCiORzPvGJT/DrX/+aX/7yl9x8883Mnz+fr3zlKyxdupSVK1cCsHLlSk466SQAli5dyqpVq0gkEtTV1VFTU8P06dPJz8/H7/dTWVmJ1pqXX36ZpUuXAnDiiSfy0ksvAbB69WrmzZvXYwZssJSChQsTRKMZazIt0SgsXpwgg6cihBBCCCGEGCWjug7Y5Zdfzu23386KFSsoKirilltuAVIL+i5btoxbbrkFwzC44YYbMN4d+/e5z32Ou+66i3g8zuLFi1myZAkA5557LnfeeSdf/vKXycrK4uabb854f5cti7F6tSfj7fbFMOCUU2IjekwhhBBCCCHE8FA6k6UCj0HV1dU9bg+HwwQCgW7bn37ay8aNNl7v8KekYjHNkiUJLrro/R2AdQxB7O09F8NPhjeMDfI5jA3yOYw++QzGBvkcxgb5HEbfMTkE8f3kwgtjBIMw3GGr1pCVpbnggvd38CWEEEIIIcTxRAKwXvSWGDRN+NjHwsNekj4SgSuuiHBEHZD3veM8GSuEEEIIIY4DEoD1obeAoKLC5bLLIsMWhEUicNllESoqRmnl51EgwZcQQgghhDgeSADWC5/PR3t7e6+BwcKFST784SjhcOaGI2oN4TBcemmEhQvfpzXne6C1pr29HZ/PN9pdEUIIIYQQYliNahXEscw0Tfx+P+FwGOh5UecZM+Caa0I8+mg27e0GXu/gjxeLQVaWyzXXtFFe7vDuYd/3tNb4fD78fn+XddeEEEIIIYR4P5IArA+maRIMBvvcZ/p0uOWWOM8952XjRg+uCwNJ5ESjqVLzJ58c54IL4pjmEKK4Y5RU9xFCCCGEEMcLCcAywDThootinHNOjNWrvWzaZNPaaqC1xu9PBVgdXDc1x0spRU6OyxlnJDjllNiAgjYhhBBCHL8SboK6cB07mnZwMHSQ1kQrjuvgahdDGViGRa4nlwnZE5ieN51ifzGmIaNMhBgrJADLIJ8Pzj47xllnxQiFFPv3m+zaZdHernDdVCAWDGqmT08yYYJDVpamh5GNQgghhMgQV7soVI9TCUaL1hpHOyTdJEopLGVhKKPPPmqtOdB2gJcOvkRVWxVxJ47X9OIxPd32jTkxQvEQe1r38ML+F/BZPibnTObs8WdTGiwdzlMTQqRBArBhoBRkZ2vmzUsyb97xU0xDCCGEGC2RZIS9rXvZ2bST+kg9TbEm4k4cV7ugwMAgaAfJ9+VTGihlZv5MxmeNH5HMUHuinR1NO9jVsovGaCNN0SbibrzzeYXCZ/nI9+ZT5C9iRv4MpuZOxWt60VrzRu0brDm0hsZoIwErgNf04u1nyoJSqst+B9oO8JvNv6HYX8zyiuXMK5rXtY/tioMHDXbssAiHFa6ryM42aG/3kZWlmTEjSUWFi98vVYuFGCoJwIQQQghxTNJaUxWqYmXVSva37ifhJgjYAQyVGvt/dHaoY+jewdBBXj34KlmeLGbnz+asCWeR48nJeN/2tu7l5YMvc6DtAK52CVgBlFKYhonf8Hd7TVOsiYZoAxvqNuC1vJQGSmmKNtGebMdv+Qnafc9L74uhUgFoOBnmkZ2P8GbtBhY417BpfTaNjUbnaB2/n841SAMBRThs4ziwdq0Hw0jdYC4ocDnllBgzZzoykkeIQZAATAghhBDHnHca3+G5/c/RFG3Cb/nxWl68pFfIyjZsbK8NwNbGrWw8vJGJ2RO5bOpl5Pnyhty3txvf5vl9z9McbSZgB/Bb3YOt3nQESgdDB1lVvQqtNVmeLGblzyLbkz3kviWiHqo3zWX9rjIejFQye9wkSrPGEQj0/hrThKys1H87jqKuzuT++wNkZ2sWLkxw2mlxyYwJMQASgAkhhBDimBFzYjy+83G2N20naAeHlBWCd4Mxw6YuXMddm+5i+fjlnF5++qDmjEWTUR7f9TiVTZUE7ABBz8D7prWmsqmS2nAttpEKEuNOnPV166nIqmBq7tTODN+A2nUVezfM5MDm6SjlYtoOHn+Sd5q3E3UiTM6dnHZbSkEwCK6rWLPGw5o1Hs48M8oZZyQkIyZEGiQAE0IIIcQxYWfTTh7d9SiO6ww58DqaoQy8lpeXql5iW8M2rpl1Ddne9DNOR/YtYPeRTuqD1prtTds5HD6MZXS9RLMNm5r2GhoiDcwvmj+g8w83B9n6PycRaQtgeRLd2t3fth8Hh2m50wbc5441UFeu9LFtm4crrwyTny/ZMCH6MvBbKEIIIYQQI2xj3Ub+Vvk3FArbtIftOH7LT3O8mV9v/jUNkYa0XrOpbhN/q/wbhjKG1LcdzTuoD9f3WhjEVCaOdlhft57WWGtabe5/axpvPHo28YgHy9NzYTDLsDjYdpD9rfsH3Xe/H1pbDX71qyzWrRu+z0eI9wMJwIQQQggxpm2s28hTe57qLGIx3EyVCoB+u+W3NEWb+tx3U/0mntj9BAFrcFmvDvWRemraa7plvnrr38b6jbTGew/CtIadq+ey9805WN4E/Y1atAyLPa17aIu3DbTrnQwjlRF75hkfK1d2L48vhEiRAEwIIYQQY1ZlUyVP7XlqQIUsMsFQBqYy+d2W3xFJRnrcZ1fzLp7c/eSghxx2SLpJKhsrO+d8pcMyLDbVb+q9b2vmUv32FCxvvMfne2IbNlsbtqZK9w9BIACvvOLjpZckCBOiJxKACSGEEGLMcLVL0k2ite4sajHSwVcHQxk42uHByge7PRdzYjy681F8pm/Ix9neuH1QQY+hDLY2bEXrrnOu9m+aTvXbU7G8iV5e2buEm2BXy64Bv+5ofr/mlVe8MhxRiB5IEQ4hhBBCjDitNTXtNVQ2VVIVqqIl1kJrvBVXu/j8PqLRKJVNlUQTUYKeIAW+Agp8Bf0uQJxplmGxr3Ufm+o3sah4Uef2x3c9TsJNdFtrbKBaY60cjh7GYwy8HYUinAizv20/k3ImARBqyGbv+lkDynwdyVQm1aFqJmZPJMDQMnuBADz/vI/p05NSmEOII0gAJoQQQogRE01Geb3mdd6qf4umWBNe09sZxHT8b8AOUN9aT3O0GcuwaIm1cDhyGIBsTzYTsiZQ5C8akflgHf15Zu8zzMibQcAOUNlUyfbG7RmpxLi3dS+2GnyWqCNALAmU4DMCbF2xFNMz8MzXkUxlsrdlL/nZ+UNqB8C24f77A9x4Y7uUqBfiXRKACSGEEGLYxZ04/9jzD95ufButNT7L1+vCwlprdjfv7lKQomN+VDQZZVvjNjymh0nZkygLlo1YIPbcvue4fPrlPLfvuSEX3YDUcL+WWEuvVQ/TZRomO5p3ENx9FbF2f6/VDtNlKIPD0cNDngsGqcIchw8bvPqqhzPPHFxWToj3G5kDJoQQQohhtaNpB7/Y8Au2N27Ha3rxWX3Pm2qMNBJ1or0+bxs2Wmt2NO9gU/0m4s7wX9hbhkVlUyV7W/bSGGnMSNB3oO0AmqEPzVMoGpocDmydNOTgq0PSTVITqslIW34/vPyyl3BYUmBCgARgQgghhBgmjuvw8I6H+ds7f0Oj054vtadlD5bqf5CObdiEEiHWHFrDofZDQ+1uv+JunL9s/wt+OzNFQRqjjWmVnU9Hy7ZTaHP6Lpk/ELZhcyiUuffUMODVV6UqohAgAZgQQgghhkHCTfCnbX+isqmSoB1MO2MUd+I0R5vT3r+jXPw7Te+wv23wCwmnwzIsNtRvwOhvUa00dFR5zATtmMSqZxBxB7+GV08iiZ5L3A+GxwNbtti4Qx/VKMQxTwIwIYQQQmRU0k3yx61/pDZcO+AqgY2Rxm5l1dNhGzZ7WvZwoO3AgF+brpZoC+2JdpLu0If5RZIRHO1koFcQ2jcbN+nBcZ2M9K1D1IlmtL1QSPHOO0Ob7ybE+4EEYEIIIYTIGK01D1Q+QF24DtsceHW/xljjgBYkPpJt2Oxu2U1tuHZQr+9PQ6wBE5NQPDTktppimRsu2LZnHoYnikYTc2IZa9fVbkbOtUMgAKtXj+wyAkKMRRKACSGEECJj3qx7k90tu/Fag7vQjiajQypwYRs2O5p2kHCGVoq9J+FEGNu0aYg2DLmtUCKU1jy3/mgNyVAekCofH0lmbtigqUxCicwFYEpBY6PBIBKcQryvSAAmhBBCiIxoT7Tz/P7n8VuDK1Khtc5YALGtcVtG2jlS1IliKCMjQYmr3YxUUky25+Im3hvmmckhg0qpjA2T7BAOK0IhqYYojm8SgAkhhBAiIx6sfBBjCJcWGp2RC35DGTTHmjNeGbEjuMlkkDNU0boJoN6rbJGJtbuONJj5eH1xXaiqkstPcXyThZiFEEIIMWTVoWr2te0jy84adBuZDB5sw2Zv617GBcZlJNN0ZCCSqbW7tNZD7lu0bjyG/d68r0z0rbMtrYe8SPTRAgHYudNmzpzMZtaOdfE41Naa7NhhUVNj0NJikEyC6yqCQYN4PEgwqCkpcZgxI8n48S5+v4zlPFZJACaEEEKIIVtZtZKAFRjtbnQRc2I0x5rJ9+WPdle68Vt+HO0MeR6Ym/CQgfiy57a1O+jhpL0xDGRB5ndpDbt3m7z8spfqapN4XOH3a+yjatAkk4po1CAahbo6k3XrPBgGFBW5nHRSnMWLE1hyRX9MkY9LCCGEEEMSTUbZ07oHrzm0CneZWF/rSLZhs691X0YCMKUUilTgMJRhlh3yvfnsYc+Q29Fu1wxVRx8zQaHI8eRkrL0OznGe/HIceO01Dxs2eGhpUQQC4POBz9d/Rss0IevdJHM4bPDMMz5WrPAya1aSCy6IHXNZMa01USdKwk3gahdLWfgsX8YWKB+r3t9nJ4QQQohht/bQ2ozMFVKojF94tcZbSTiJQZXEP5pt2jiuk5G2gp5gZgLOo952w8hcEOsxPQNex60nR341lOK4roJ46JDBAw/4aWsz8PkgGBxae34/gOLtt222b7f44AdjLFqU+QqgmZJwE2xv3M6O5h00RhppjDYSc2Odfz8MZWBgkOPNocBXQHmwnIVFCynwF4xyzzNLAjAhhBBCDMne1r34LN+Q21FK4bf8JMlckQtXuzTHmyn2Fw+5Lb/pp8VpyUhWyFQmPtM39IIexnvz5jQajzH0gKnDQIcfap0aXtjYqGhpMYhEFPG4wnVTzymVGoJYXGximjBxYpLp0x2Ki91hG0Y5VjgOvPCCl7VrPfj9qYxXJqWGLSqeeMLPW2/ZfOxjkTGVDWuMNLLy4Ep2NO0g5sTwWT4MZWAaJgGj+9DlmBOjpr2Gfa37eOXgK5QGSzmt/DTmFMzJeKZ8NEgAJoQQQoghaYw2Zqwtv+WnNdmasfZs06Yx0piRACzLk8XhyOGMzSnLtrNpiDYM6YLStOLE3w1uXO1mbB6eox3y/emdZzIJBw6Y1NYaRKMGhqExzVSflEoNm+ugdaqwRFWVxa5dFs8/r8jLc1m4MMGyZbGMByZjQSwGf/xjkPp6g8AwT5MMBDTV1Sa/+EUWn/1sO0VFma2KOVDhRJiHdzzM3ta9eEwPlmH1GHD1piML2xJr4ZEdjxCwA1w0+SLmFs4dxl4Pv2M/hBRCCCHEqIkmoxldrLfQV0jCzdwQKoUinAxnpK1CXyFaa4L2EMeNvWtSzqQhl933FlWjk6msl0JlZMhgh0m5k/p8Ph6HLVssXn/dw4EDJq6r8Hg0lkWvGa1kMhVwKZXKAmVlaZJJxapVHm67LZv77/fT3v7+SYdFo3DPPUEaG40RCy4tK5Vp/O1vg1RXj86lvtaaDbUb+PnGn1PdXk3ADgxpeLFSioCdCtwe2fkIf9v+N6LJaKa6O+IkABNCCCHEoNWF6zIaMOX78jNe+jxTF2pZdhZ5vjxMlZn+BewAWXbWkObP+cYdQDupOWm2aWdkeJZGk+fN63Wum9ZQU2Owdq2H5mYD02QAVfg0eXndszI+H3i9sHevxc9/nsWbb9rH/FyxeBz+8IcgbW1Gt8qGw02p1LDEP/0pQEPDyAa0STfJX7f/laf2PIVt2Bmf1+m3/Oxr28fPN/ycfa37Mtr2SJEATAghhBCD1p5sz0hVwA6WYVHoL8zommCZaivqRFlesTyjAef47PEk9eDngdnZzRhWDFe7GatYmHATTMruOfuVSMCmTTY7dlgYRirT0h+NxtUumlR2zNtHsUzLAo8Hnn7axx//GDims2EPP+ynuXnkg68OSoFlKf74xyCJEarLEXfi/H7r79nftr8zYzUcbCN1s+HPb/+Zd5reGbbjDBcJwIQQQggxaAk3kZGFjo80LW8ajpu5WuUumQnAsj3ZXDP7mowGYCX+EgJWYNBZMGW4WMFWDGVkZGikq13yvHnkeLsHc5EIrF3rIRRSvWa8Em6CllgLdeE6qkPV7G/bz/7W/RxoO8D+1v1Ux3fxRu06Nh/ezL7WfYQTPQ8PDQRSCxPfdVfWiGdwMmHzZoudO208mRsROihKpeag/eMfwz/+MekmuXfbvTRGGjM6FLY3Sil8lo+HKh9id8vuYT9eJkkAJoQQQohBMzHRR9dCH6IsbxZBTzAjpe0hM2tjxZIxFhQtINeby8TsiRnLqimlmFc4D4fBB5z+Cdvx6byMrQE2r2Bet23hMLz5Zuqi+uisl0YTSoSobq/mYOggLfEWYk4MRzsoVKq0uDLAtckpbCXhJmiLt7G/bT9ra9fyRu0b1LTXdHtPO4K83/wmeEwFYZGI4umnfQQCY2MMpcejeOstmz17Mju092gP73iYhkhDRpZpGAiv6eVv7/yNlljLiB53KCQAE0IIIcSg+S0/2s38heas/FlDLlDRYajzorTWBOwAZ48/G4DLpl5GwslcFixgB5iQNWHQJelzp2+hIDD04YdJN8m03GndLqCjUdiwwdNZ1fBIbfE2qtqqaIg04LgOpjJ7DQQN0yGr8L0Kl6Yy8Rge4k6cyqZKXq95naq2qi6Bt2GAbSt+97ssWlqOjSDs0Ud9aD22+hoIwKOP+klmboWHLrYc3sKOph0Dynw5CZOW2nwOvj2J/W9NY9/GGVRtmUL93lJi7b605wAqpbANmwcqH8jYTZvhJmXohRBCCDFoxYHijBfNgNRwv7JgGTXhGiw1tMsVr9nHpKM0RJIRPjPvM53FBPJ8eZw1/ixerHpxwGtl9WZyzmQao42EE+EBvZ8JN8HCcXOorjhMU00RhjG4C1BHOxT4CijLKuuyXWvYvDkVkB0ZfDnaoS5cR9yJv5fh6oPWikBeCKV67p9tpI6xq2UXdZE65hXMw2t5O4+rFPz5zwFuuqk9rXlno6WlRbFrlzXs5eYHSqnUGm0bN9osXZrZCWGRZIR/7PkHfrvv34LrKGp3jqd+TzmRtiCxdh+uY6IMN/WP1PdEOyYoje2N48sOk13UwsRFO/AGey+mYyqT2nAtq2tWs6x8WUbPbziM4a+wEEIIIca6LDtr2CbbT8ubhtfwDvmu9lAWiY4lYywuWczE7Ildtp9Wfhol/pKMZemUUiwuXozX8qY9vNHRDiWBEvJ9+Uw+cTvJ+OCGfjnaIcvO6nFtpb17TSIR1SX4ak+0c7DtIEk3mXZ2UTsGeaWH+93PNmwiyQhra9dSG67t3G4Y0NRk8NJLQwumh9tLL3lHfd5Xb/z+1By+oYo5MVpiLTRGG2mONfNg5YN9/kZj7T4qX13I6r9/gMrXFhJqzMVJWFieJB5/DNubwLIdTNvB8iSx/TFsXxwUREMBandWsPqB89jw1OkcPlDca2bMb/l5sepF2hPtQz7H4SYZMCGEEEIMmlKKPG8ezbHmjLdtKIP5RfNZX7ceQxuDKvaRcBMU+AoGdfyEm6DAX8BFky/q9pxSimtmXcOvN/8aV7sZKf9uGiYnlJzAhroNRJPRPjNhrnbxWT5m5s8EIJjXTum0Kur3lWFa6QeFjnbItrNZWLyw2zm0t6cWWD6y4EZbvI3GaOOAztd1DbKLmrE86Y1/UyhMZfJO4zsk3ATjs8YDqQBi1SoPCxcmRn2B4Z4kk/DOO9YASvKPvPp6g5oag7Ky9N+/unAdG+s3UtteS0O0gfZEO4520GiSTpI3697EZ/nwW378lp8iXxFF/iJcx2THqgXU7RyPYTkYlovtG3j2zbBcDMsl3BJk6/On4MsOM/ecN8gu6r5gu4HBy1Uvc9GU7r/ZsUQyYEIIIYQYknGBcYOev9SfoB1kYfHC1AXfIDJhHWtaDVTCTZBtZ3P9vOt7Xcco25vNZ+d9Fkc7GSvKYRkWJ4w7gTxfXq/VFl3tYhs2S4qXdFmTbMbpb2HZybTnziTcBMX+YhYVL+oxoNq8GcwjYsBQIjTg4AvAtBzyx9cP6DWQei92N++mJlTTuc3rTZV3H4s2b7aJxcbW3K+jBQLw8sv9ZxEd12FD3Qbufutufv3Wr1lft566SB2OdvBZPoJ2kCw7i8PRw3gMDwpFNBmlMdrItsZtrHhrH0/ft4hDu0uxfAkMa+i/D6XA9sVJxi02PHkmO1fPw3W6vt+2abOtYVtGl7EYDhKACSGEEGJITi8/nbgTH7b2cz25LClZgos74CAsaAUHPE8r7sTJ8+bxuQWf63f+WKG/kBsX3IhCZSwINZXJgqIFzMmfg4vb5WLS0Q5ey8uJ407sFhialsvss9bjxPoeitgxbHJh0UJmF8zuMbPY2qoIhd4behhzYjREGgYcfLmOScmU6l7nfvXHMiwqmys7K9wZBtTWGhw8OPYuYbdvt/CPzdiwU8f715fN9Zu5ff3tPLX7KUKJEEE72DlH70haa+rCdV0ztVrR+ta51K38CKFYhJroXg5HDme0UqpSYHkTVL8ziXUPn0M01HWIcXuyna0NWzN2vOEw9r69QgghhDim5PvyKQuWDWsFsmxPNktLluK1vGkHOgk3QUVWRdrH0FoTToaZUzCHG+bfkHbxjnxfPl9a/CXKgmW9rms1GCXBEk4pPYU8bx5JN0k0GaXYX8wJJSf0mpXLL2+gbPY+kvHuzyfdJI52KPYXc0rZKeT78ns99r597w091GjqI/WDCL4Mckoa8QR6L56QDtuw2db4XlbD74eVK8feXLDmZqNblcixqK3NIBbrvj2SjPDnt//MY7seQynV79zO1nhrlxsvWsPhNR8ktGsBhjeKoVJLEISTYaraqogmh/Y9OJplJ0nGbd587GzCze+tgRewAqyvW5/RY2WaBGBCCCGEGLLTy08nnMxc8NETv+3nxJITmZwzGcftf0iipSxKg6VptR134iil+NTsT3H59Mt7vOPfZ98sP5+e+2kunXYpSTeZsWyYbdjMyJvBsrJlXD3rauYUzCGcCPfZ/vRlWyicWIsTt3C121mpcGLORJaVLWN2wewuQxePlkh0DSYao4247sCGdLmOQVZBG/nl/RfeSEfSSbKjaQeQyuLs3WsRiWSk6YxwnNR7diyIx1OLXB9pf+t+frb+Z1SFqtIuqtMQbejMfmkNh9deSLh6Koana3TXsSxBbbiWxmhjBs7giLYNDcpl/ZNnEmlLpR+VUsMyJzWTxvA0QSGEEEIcK2YVzCLXk0vCTQyqWEa6lFJMzJlIsb+YnS07aYo1gaZbRiipk1QEK/rM2nRkvIJWkMUlizl/4vkDDryOtqh4ETPyZvDcvueobKok7sYHVaq+o29ZdhZLS5dyzoRzsA0bV7vsbd3LO03vUBeuoynaRCgRSg3x0qn3R6FYdO5m9ryaQ6h6IhW5hWR7stP+XKqrjc55ZHEnTigeGljRDccgmN9G4cRDAz7v3piGyaHwIcqzysn2ZKM1rFvnYfny4Rv6OhCHDxvE4wqPZ+yvQ+X3w44dFhMnpoai7mzayf2V9+M1vQP67Ybioc5Avumt0wkfnIZh95Bae5ehDNribbjapchfNLSTOIJSgHLZ8OQZnPyxF7E8SVpjrUSSkYwtE5FpEoAJIYQQYsgMZfCxGR/jD9v+MCIXPX7bz4KiBSTcBFVtVdSGa4k5qYs/S1l4DS9Tcqd0e13CTRBJRrANmxJ/CRdOvpC5BXMzupZZwA5w+fTLSbgJNtRt4M3aN2mINpBwEwSsQK/DB+NOnJgTw2t6KfYXc/GUi5ldMLtL8GMog6m5U5maO7XLOSXdZGohZMPENmwsw0KfAE8/7WP9+oEFlS0tRufww6ZY0wCDL5Oc4ibyKwZedKM/lmGxp2UPC4sX4vPB/v2ZX39usJqbVdrFT0abbaf6C7CvdR/3V94/qKUaIslUCjLWUELbzsUYnv6HGBrKoD3RjlKKQl/hgI/ZG2WAk7TY/vJi5p//Bo52qA5VMy1vWsaOkUkSgAkhhBAiIyqyKzrLqA9l7a2BsA2bKblTmJwzmbgbpyXWwqHwIZYUL8Fn+joLTpiGSdAKMi44jhl5MygLlg17H23D5uTSkzm59GSiySg17TXsaN5BXbiOSDKCo53Okut+y09FVgUz8mdQEigZUCbONuwe91cKPvShKHPmJHj0Uf+7GZr+24tGUxfnrnaJObHOIWR90a6BYTqMm34AX9bwjA1UKFriLSTcBLZh09Q0dob8xWLGmF4g+miOowgnwvx1+18HvVB53I1juB7q11yEstOf32Uog1A8hMfwkO3JHtSxe2JaDg0HSqnfO46cCTGq2yUAE0IIIcRx4MJJF7KzeSdRJ9rnPKNMU0rhNb3keHI4b8J5XDD5ghE7djp8lo8puVN6zMoNt6lTHb785RBPPeVj82abQIBegwXHgVhMYZrQHG3ut22tFdoxyCpspWBC7aCrHQ7EgbYDTM2dSlubQTzOmFj4WGuOiQIcR3pox0MYanDr62mt0WgaN52BG/Wj7IGt72Uog6ZYEwE7kNG/E7Y3TuVriznhikPDWpl1qI6hWF0IIYQQY51pmFw39zoUasTX4ok6UabnTecDkz6QsTaTydScqLVrbV55xcOKFV5eecVmwwabhgaDAdamGDUeD3z0o1G++MV2JkxIEo9DtIekRTiscN5dWymcCPea/XIdE+0q/NlhyufspXDioREJvkxl0hBpAFKBYn392LiU9XrdY+a7AFDVvo99rft6HQ6bDifupX3f3AEHXx0Uivpw5oequkmD/Rtnj+m1wCQDJoQQQoiMyvHmcMP8G/jdlt/haGdEMmGRZIRpedP4+MyPD6kIiNawe7fJ2rUeGhsNahviNLS3EXKa0SqJAjRgaC+5dh5F2dmUFJoUFzssXx6nqGjsXvQBjBvncvXVEaJRWLvWw+bNHlpaFPG4wuvVJBLvZTeOrLToOgZaGxiGi+VJkFPSRE5xM8oY+fONOlFc7aKUSXv72Eg7ZWfrY2YOWDzpsDf0BjPTrHbYE6UUbdtPQqPTGKDau5gToz3RTtAO9r9zmkzboX53BR4jlLE2M00CMCGEEEJkXIGvgC8u+CL3bruX1kQrPnN45ltprYkkIywqXsSHp3540MFXNApr1njZuNFiz6EW6p13iDjvlXu3DZsj15LVaBoi+9kTVXgaPAR35bBmw2Smjg9y6qlx5s9PYI6dGhHd+HywfHmc5cvjJJNQV2ewc6fFmjU2gYAFZhwjEcUEDMPFmxXBn92O7Y+PSKarL47rEE6GMYxsYrGxEYCVlLjHRAVEgH2H68laVjWkNrSriFbNwbAGl/3qYCiD1nhrRgMwgEjYovXAJJiQ0WYzRgIwIYQQQgyLbG82/7LoX3hh/wusrV2L3/RntER93IljGzZXzbqKmfkzB9WG1vDGGzbPvWCzt7mKJqe6s12F6rUYhkLhMVOTjzSaNqeZxtA69u4IsnnPJKaNK+KKKyJMnDi2M2IAlgXl5S7l5XGKix3CYZMmXYUbrkI7Yy+oMJRBU7SJfJU9ZoJc24acHJdYbGwMiexLfayGyaVDWxS54UAJRHPB0zbk/sScWGdhlUyxvQn2vTUFTnMy1mYmSQAmhBBCiGFjGiYXTr6QRcWLeGjHQzRGGwlYgSEFYkk3SdyJM7tgNpdOu3TQVdxCIcWDD/rZtivEnsgGkm6ys4z7QCkUHsNDUic4ENtOQ3WQht/P5+QTDC66KIqduWvLYeX1gutCe7Idy7BIOEPLcAwHy7AIxUPkecDrHTsBYn6+y6FDYzsAa421krSbsL1D+1xrd07AH3BpT+q0qmT2xVQmzbFmiv3FQ2rnSB7Tpq3Jj+OExkyQfiQJwIQQQggx7EqDpXxp0ZfY3bKblw++TFVbVap8upleZKK1JuJE8BpeZufP5uwJZ5PrzR10fyorTR540Meeth3Uxw5hG3bG1gKzDIuYG2VL61qaVk9l9+5yrruunby8sRMs9CYvz8Uw9JguYACprKNSmvz8sfOeTp6cZM8eC//YXPsXgOpQDdn5Q8t+AURagwRsl7ZEa0bmeHas4ZcpPstHLGZw+LDBuHFj77ssAZgQQgghRoRSiml505iWN422eBura1ZT3V5NU7SJUCJEwk2gtSZhJAjHwpjKxGN6yPPlke/JZ1bBLBYULRhS5TaAzZstHnnMS2XbRtoT7Rkd+tShY32vA5GdxOtC/PrXM7nhhjDFxWPvYvBI+fk6FUBk9np4WPh8qazTWHHiiQlWrhyZ9e8GqzmUYOYpu4fUhnYV0ZAfn+kMaJHuvjiug6vdjLSXdJMU+YrweDQ7dpgSgAkhhBBCAGR7sruUi48kIzRFm0i4CXJyc2hvbcdreinwFaSdmYrFoKbGpLLSoq7OJBZTOE5qfSbDgEDARSmXjRtt9ibXE3XDGct69cY2bOoitWg0v/vdLL74xbGdCVMqFdQYLWN7KJ2hDPLy3DG19pbPB1OnJjlwwBq7izL7m8ivODykJiKtQZJxG4/fxW/5iSQjQx6GqNHEnXhGFkc3lEFFVgWGggMHLGAMDqMd7Q4IIYQQQvgtP/6s1NitosIiDuv0LhITCVi/3ubNNz00NRkkEqkL4Z7mXB08aLJ6tU1DvB6HCXgDMfJKG/BlhzN5Kt1YhkV9pA6P4eH3v5/Cl78cGtNzwvLzHQJVQZoSTaPdlR4l3SQBK0hBwdgrsHD22THuvtsiK2u0e9Jda3uCvCk7hhy0RtoCnSX387x5qfXihtiooQzCyfCQAzCNJt+X33ljJRIZm5GwBGBCCCGEOOaEw4pnn/WyY4dFPK7w+1OBl6+X6zetYetWi5hqwVFhDGWQiHqo3Tkey5Mkq7CZ3HFNMEwl1i3Doqr9AHmeYv7xDx+XXz70eTjDZeHCBG9sKsLR+zAYexewrnYJ6EIWLBh7mY3SUpeyMoeWFnPsZcGMBKVzK4GhBTnJuNW5FIFt2HhMT5c14wZDoXD00APqpJtkSs6UzsfO2IvRAQnAhBBCCHGM2bDB5tlnfSiVKqGeTtGDPXtM2sMOLfHmLvNMDMvBdRXNhwoJNeZSPKUaj394JkDZhs2O1q143zqFhQtNpk4dm1eHM2Y4jCsIsvuQjeOOvT6ahklRnpdZs4Y3czlYH/94hLvuyur1ZsBoCIfhzPMbeZUEAw3AtKsItwRpPFhMS20h9XvGUbtjIqblgtK4eiIh9zCmP4Kd1Yzpb0cZg/jeDPHeh+M6lAZLCQxhgemRIgGYEEIIIY4J4XCqbPyBA+aAKs2Fw3DggEljoqbXSf6G6eI6BjXvTCKnpIn8ssPDkg1LuAmqYzt57LHpfOUrIawxeCWmFCxcmGRddRCH1tHuTjeWG2TRQmdMzf86Un6+ZvnyGCtXesdERcRkEiZNcliwKMqrm9J/XTTkZ8+bs2iqKiEe8WJYDqadJBn34DoGytCAAgwsJ59oU5BYQxlKuZj+dryF1djZzekfcIifp23azMib0WXbmMtCvmuMdksIIYQQ4j2NjYpf/jKLQ4cGFnwB7N5t4ahYWqWuDdOhtS6PQzvHo3Xmr/BNZVIXqaUtlCoGMladdlqcwkDRkIeWZZqjHQq8hZx+eny0u9KnM86IU1zsjvoQuNRcLc3HPhZJLS6eRtTaUpvH+ifOYM0D59GwvxQUeAIxLE8SpcC0u38nfKYP01AoK44yHZyYj3DVTFp3LCZ6uCytvg6lkEfCTTCnYE63Gywez9gseCMBmBBCCCHGtIYGxW9+k6pqMNCMUTIJzc0GLfHmtNcrMkyXeNjHoR0ThiUIc7VLo3uQdes8GW87U/x+zeXnVOAkx1aKzklYfPC0Ivz+sXlh3UEpuPbacGpNtVGsgh6NpoZEBgKaXE9un0suuI5B5WsL2PT0GUTaAti+OIbZvfMefxxldn//szxZnUGUUqDMJGiDWP142nYtwI33vmC6q91BL6iecBNMzZ3abV3ARALGjRt7Q2hBAjAhhBBCjGGtrYrf/z4L0xzccKKDB02SrkMsObB5XcpwSUS81O6sgAwHYZZhcSh0iLo6g0OHxu6l2BWXW4zLDeLqsRHsuFpTlBPg0g+NvXWdehIMaj73uXa0ZlSCsEgErrgi0jnX0DRM8rx5Pe7bWpfL2ofOpXbnBCxvvM/hnYahMa3uWTCFIstO3SjRR0zoUqaDm7Rp27WQaH1Fj21q9KAqICbcBJNzJjMhe0K356JRmDlzbGVwO4zdX70QQgghjmtaw9/+lip5Pdi5HLW1BhEnNKj5JcpwibX7aT5UOLiD9yHshMEO8/LLg7vrPxIsC/7107nEomPjcjEeNfnXT+eOyXlzvcnL09x4YwjD0CRHKBbQOhV8XXllmDlzuh4035uPPiqgrt9bysZ/nIGTNHocXtgT29vzEFBDGWTZqUxYlyBMpQKx2OFywgend7upYShjwAuiJ9wE03KnMSlnUs99tKG0VDJgQgghhBBpe/VVm/p6A3OQayUnkxCNKmJObNDzSwzTpaW2gEQ0s8MFDQya4g0cPjy2L8WWzCzktJNT7+NoikUVpyzVnDgr88HwcMvN1fzLv7RTUZEkEhne9zEaBa/X5fOfb2fmzO7Bx9S8qUST7y2BUL+3lG0vnojlTQyoqIkvO4zr9FLQRhnkeHLwGJ5uwZ4yHRKt+bRXTe+yfSDBl6MdTGVyYsmJjM8e3+t+ubl6TFWiPNIxdA9BCCGEEMeLpibFK6/4hlRFrr1d4bqpO+VDYRgO9XsqKJ+9N2OVES3DojXWSnPzBJLJgc9tG0nf/NQcbqx9ncNVhViekR/SlYxbjJvYwK2fOXXEj50pfr/m2msjbNqU5JlnvIDq8pm72qUl1kJDtIFQIkQ0GcXRDlprPLYHJ+ngtbwErAAFvgLyffldghatU8HXySfHOe+8WK83LZYUL+HF/S8C0FRTwNsvnojtHfjvI7uomZZ+MsN+y49t2oQTYbTWnQVAlOmQDOURrp5KoHw3rnY7hy72xdUujnaoyKpgau7UXiuaAkSjmpNOGruFWsbwz10IIYQQx6snnvBjD7FI4OHDBoapcVwnrepvvVKQiFm01ueRU9I0tE4dIeJEiMcVdXUG5eVjd16TZZp876YpfOPn22mvLR/RICwZtwiOq+Z7X5yNbR77l62LFiWYOTPJU0952bnTJpKIURPbQ0OkgbgbxzbszsBCoVAq9U+jiSajRJIRattrMZRBrjeXisAk/CqXigqHiy+OUlra9/fIY3qYmjeVnQ0HePvFpZiewd2cMEyNP6edSFugz8yZpSxyPDkk3ARRJ4rruvDucMRESxGJrCbsnCayPL0HYAk3gW3YlARKmJQzKa1iHYahOOWUsRuAje28txBCCCGOO62tsH+/OeQ1fMJhBcrpMhdlsAzTpe1w3pDbOVLCSeDxaPbsGeQYyxE0IaeCb3yhmLyJezM+HLM3iaiH3Il7+d+fL2JCbs/FG45Ffr/mQ5c3UnrR3Rwq+QuNiUMkIn5MJ4jq59JcoTC0l2Q0SGs8xMHgPwmccwcfvHJnv8FXh3PHn8u2lfNxEuaQ1lLLLT2MdtL77tqGTbadTbYnuzPIVGaS9urJmHhJukkSboK4EyfuxHG0g2VYZHuymZU/i1PLTmVm/sy0gi/HgalTk3jH7vRKyYAJIYQQIn0JN0Fdex2VzZUcDB0klozh4KBQmIZJrie3c2J8njdvUJmn555TQ85+QWoOWCaCrw6JmE2s3Y83GMlIey4upgmh0LFxP3xRyUJu/ozDH198jf2rT8Z1FaaV+cydkzQwDM3U5a/x6bMXs3jcooy2r7WmKdbE/tb9tMRaOjNPWXYWk3ImUegv7HN421BtrNvIM3ufQSnFrJMs9NL9hJuzaawqpqW2kEhrgFgogOsq0AqlLRzHxeOP4c8Ok1XYTOGEOrKLmzFMF601v9vyO5aWLuWCiRdgGn0HRU0Hx+HUzkVZjQwlF+PxJ/AGI8QjHtJ9uwxlELACnY+TjktZ6AJmTAjhahfLsPCYHoJ2MO1lI44Wj8P550f733EUSQAmhBBCiD5prdnbupeXD75MVaiKhJPAa3rxmN0zIY2RRrY1bEOjybKzmFMwh+XjlxO0g2kdy3Fg27bMBGCDKn3YB9N0aK4pZNz0qoy1aRgQj49ugYuBWFKyBN8HfDxc8TSVL59Ic1U5ljeW9gV4X7QLyZiX3PHVzFz+BlfMuZi5hXOH3jAQTUZZXbOayuZKmqPNRJKRzsp7Sim01iR1Etd18Zge8rx5TMqdxPKK9L+7/XFchwd3PMiOph0E7PeCEKUgmN9GML+NCQt2d27XrsJ1FVlZfiLR9l7bVUoRsANsqNvAzqadXD/v+l6H9GkNzzzjZ2HZDNYeWjPkcyqeUs3BrVNgEDc6XO1SFCgg0ZZNwA2SlTX0myWRyLuLiBeOjaUTeiMBmBBCCCF6tal+EyurVtIcbSZgB/CZPnxm76XFOi4GIZV92lS/iTfr3mRSziQum3oZOd6cPo9XXW3S1papohR60NUPe6QgHs5cWTUDA63Bssb2xeLR5hTM4ZZTJvFgwYNs2bmVus2LaD5UiFIa0x542W8nYaK1Iq+0gZIFm5g3PcjHZ9yUkcCnPlzPC/tfYE/rHrROrTVlGmaPAYrNe1F/e7KdTXWbeLP2TSZkT+CcCecwMXvioPuRdJPc9/Z9HGo/1CX46osyNKahUUZ63w+v6SXiRPj1W7/m8ws+321hYkgN7W1sNMjKspmeN53KpkosY/A/NtN2yKs4TNPB4h4Xbe6vv9mebLSGvXtN5s8f2txC14X8fJezzx7Ymn+jQQIwIYQQQnQTToR5aMdD7GvdR8AOEPQM7mLYNm1sbGraa7hz052cO+FcTik9pdehiZWVFj6fysiaSYaRWnx2SAU4juI6Bsm4jTXI4gVHsg0b12VMz1XpTcAO8Ok5n2Zj4UZWVKygORSnYdtCGqvGEW0L4CRsDCuJaSe7zDPSGpyEheuYmFYSX1aEkmmHKJ63mbwsD2eNP4sTSk4Y8mfmuA7P73+edbXr8Jm+tOYOHa3ju1sfruferfcyr3AeH5764R4zv31xtctft/+VQ+2HBvzagTKViatd7tlyD19c+MVuQezKlR6CwVRAVxospSHaQEO0AUsNPiTILmom3JxNLOzFSDNYBCgOFAOpDGBTkzHkaqCxGFx/fWTIc0dHggRgQgghhOhie+N2Ht/1OEDad+v7YyoT0zR5Yd8LbGvYxidmfwKf1T2bVFNj4PGQkQDM69WEQgaWsnB05hZkjYb8ZBUMPQDz236iUZg4ceRLu2eCUool45awqGQRbze+zaqCVdS0vwmuiRsqoungONoO5+E6BtpVKENjGC7Zxc3kl9diZB8Gw6E0WMppZZcwt3BuRuZeHWo/xAOVD9CWaOsy32go5xm0g1Q2VfKLjb/g8mmXMy1vWufzWmtCiRC1ocOEYwk0Gr/HpjCQR4Evn+f3Pc+BtgM9ft+Hg6EMHNfhL9v/wufnf74zmA2HFQcOWF3WxppbMJe3Dr9Fa7x10HOulIKSqVVUb5+EmzT7HY6q0ZQFy7ocT2s4eNBk0qTB/U4jEfjoRyMUFY3daqJHkgBMCCGEEJ021m3kqd1P4beHsABXH/y2n7pIHb/e/Gu+MP8L3QK8piYjY2tiFRRoDh1KrbnlOJkJwAzLIdIaJKugdUjtJN0kud5cTAMqKo6Ni8beGMpgXuE85hXOoznWzI6mHexp3UNj6SFaY5UkdCI1RUiBrWxyPDkU+AuYlLOYWfmzyPPmZawvO5t2cn/l/XhNb59DZQfDY6YWFv7r9r9yVtl57NxtsGW7S12doq3FJh7xpKYdaoVWUSy7CU/WZhqNfZRMNJkzK05OcGQqSJqGSV24jlXVqzi94nQAduywcBzFkfO1lFIsKFrA1oatNEWbBj0c0TA1ZbP2U/NORxDWeyasLFjWbeFly4LGRmNQAVgkAh/6UJS5c4+dGxkSgAkhhBACgC2Ht/Dk7iczlvXqjW3YxJNxfrvlt9y44MYumYFIxCA7OzPHyc11UQq8lrez6EImuMmhl413tUuBt4Bsn8bvP7bmgPUlz5vHSaUncVLpSZ3btNY42sFUmR0OerSdTTv5e+Xf8Zm+YTtObVOMTavKeWR/A7mqnPzs1LA7S0HXZJuBxqb6cATHncCufTPYuSpObmkd8049wOQKz7C+F5BaCHll1UrmFc4jz5fHrl0mgUD375qhDOYXzmdP6x6q2qoGHYSZlkv57H0c2jGBRNTTZU6Yq1PFTUoCJb1m2qLRgb0frpsadnjFFRHmzDl2gi+QdcCEEEIIARyOHOaxXY8Ne/DVwTRMIskIf93+V7R+76IwE0MPO3g8YNuQbWdn9GJX66G3FbAD+K0AeXnHdvYrHUopLMMa1oCjrr2O+yvvH7bgq71d8fSD43nhzyfTtH88tmkSUjXE3HCvr2mLt5F0k6niJL4Ilu0Qqi9i1cMn89h906ivG/7132zT5qk9TwHQ2Nj7ul9KKabmTuXEcSdiGRZJd3A/RMN0KZu1j+ziJpykidapIYf5vvxuww6PFo+n/qUjEoGsLJebbgodc8EXSAAmhBBCHPdc7XL/O/dnfMhWf2zDpjpUzZoMlMPuiVKkAhxtjPi59SXpJikPlhMOw5IlQ59LdrxztcsDOx7Aa3ozHnxpDVs3ZPHEvfNoqc/B9iZQZmqYnKEMDkcO4+qeg+i2eFu3rKsyNJYvTizs5bm/z+P1lwpwMzc9sRtDGexr3UckGaGxsf/3JmgHOWncSUzOmYypTBLOwL+fSkFO2SFKZ+4m6PVQ4q0gx9N39VNIvdetrX33MZmEaBTOPDPGF7/YPubLzfdGhiAKIYQQx7mXDrxEU6wJvzU887764rf9rDiwgrkFc8nx5mS8gtnkyQ719Qa53lxq2msGXWjgSEO9xjeVSVlWGabSzJ8vAdhQ/c/+/6El1pLxIhfJhGLlk1M5VJWF5e05NaPRNEQbKPYXd9kec2Ik3ESv3zelwPIm2L25gvr9JZz/0V0Esobnu2Aog5UHXiGRuAIzja+/UoqJOROZkD2BlngL+1r3EUqEOrNiR8/fAnC0g+M6mIaJz/QxIXsC5eXlGNNNqqs1Bw9qIhGFZfX++zFNiES6zlHrEImAx6OZOTPJOefEyMs7NgOvDhKACSGEEMex9kQ7r9e8PirBVwfLsHhi9xNcO+da/P7MDskLBDRZWZpo1IvX9A56aNWRDHPwKYukm6QiqwI3aTJvQTytC2LRu8ZII6trVmd86GwybvHCQ7NpaKTX4AtAoQgnwkTsSJffUEusJa1g3/IkaW9XPPPXeXzwyncI5EUy0v8jeUwPW+veQQ8wZlFKkefNI684D601MSdGc6yZplgTSTeJ1hqlFAYGWZ4sCnwFBO1gt6xfRYVLRYVLW5ti716TUMggHk9lvGz7vYBMKXAchdapuV2JhMLn0+TnuyxfHmfx4kSGFmgffRKACSGEEMexVw6+krHiFINlKpO9rXtpT7STlxcg3Pu0mkGZMCHJ22/blPhLONh+cEiLM7uOiTdr8BfJXsvL1NypxGKwfHmaE15Er/7nwP/gtTK7kJqTNHjxoXk0NiWw7P6jFkMZNMeauwRgCTf9bJZhQNSN8vyDc7nwE1vwBTO/kHBbshXtJvAzuAhGKYXP8lFqlVIaLB1UG9nZmgULUjdA4nFoaVE0NhrEYqmgK5GAwkKHiROTjB+fZPp0h8JC9315k0ICMCGEEOI45WqXrQ1bh31x2HSYyuTlgy9TUnI5O3dmtu3iYk11tSYUMsnz5tEUbRp00Km1wp/dPqjXJtwEC4sWEo0qTjstTnb2sT2MaiQ0NSn27bPYtcuksdEkHFY4Tip7gpFkXcMEgrlF5JU1kF9xGF92+5CGiGoN659dTF1THMtK//OJO3GSbhLLsHC1S9JNDug7ZipFe6Kd1x5dwjnXrO1SQTAjlENbvI2cYEFm2x0kjyf1uywufi+bHI3CuedGWbbs/T8sVwIwIYQQ4jj1TuM7tCfaCdrB0e4KtvKyfs8+zi9wqKvT2LbCtnWXIUqDpRTMnZtgzRoPOZ4cIskIsWRsUAUbDMPB9g78AjHpJpmcM5mAlUUgx+XsszOf5Xi/SCRg/XqbN9/0cPiwgVIKv193+x7sb6kh0u4lHg5Sv68MXIUvt52ymfupmLMX0x74UNHqdyawe4/CGuBrlVK0xFoo9BeScFOLMQ+UaSrqmtt5+7U5zFu+dcCv70vA4+OQqgPGRgDWk0QCysre/1VBQQIwIYQQ4ri1rXEbAWtkys4fzUka1O6qoPHAOKJtQSJtAWIxl5YCg3e2KlzXg1KpAMzn0wSDmtJSh5yc7hfi6fB4YNq0JDt3WpQESjjUfoiEmxjwcETbHwc1sIvrpJukLFjGpJxJRCJw/fWRjBcbeT9wHHj+eS9vvWUTjyv8fggGoaeiDAANkYbONas6gmInbrN3/Wz2bZzBuKnVTDt1C6aV3kV9POJhwyvjca1GjAEWClcook4UIBXcD3KYq7ITbN+cS8XMfPJKmwbVRk9MZWIEmjPW3nCwLCgtHcaSkGOIBGBCCCHEcaox2jjsi8EeLdLmZ+/62TQcGIeTMLE8CZQC03KwjQT4mykvz+fQId0ZpESjikhEceiQid+vKStLUl4+8LkhZWUura0udXUGpcFSattribmxtC+23aRJ7sTaAR0z4SYYnzWeqblTiUTg4oujFBUdH3f5B+LgQYOHHgrQ3g5ebyr46k8k2fNcPMuTCsZqd42n4UAJc85eT15ZY7/tvf3SCbQn2jDMwUXHHYUpXNxB/64UipjRzJYXF3H6VStRxtCHqWqtUxUZc+oJtUSwLQPbsEd97ufRsrNdfGNntYhhNSIBWDwe57vf/S7JZBLHcTj11FO58sorCYVC3H777dTX11NcXMxXv/pVsrKyAHj00UdZsWIFhmFw/fXXs3jxYgB2797NL3/5S+LxOEuWLOH6669HKUUikeDOO+9k9+7dZGdnc/PNN1NSUjISpyeEEEIcc7TWNMWaMlKWPR3JhMk7ryzm8N4yTDuBYboY3q6BiGVYNEYbmTV1CgcP0iVLpBTYtiaZhD17LPbtg6lTk5SVuWlnxJSCWbOSOI5FQ4PBuOA4mqJNPa7X1BPTThLIDaV1LFe7aDQz82ZSllVGJALnnBPlhBPe//NbBkLrVNZr7VoPPh9406ynEXfiJNxEjyXRO5h2Etc12PTP0yibuZ8Zp23uEtC4jsJ1TFzHIB7xcHBfANdoGnD2q/Nc0MTd+KCGHx7JMBSHmxIc3j+O4smHBt4PrWmMNlIbriXiRIgmoiR1EtfTyI59xXgCMQyVWhvPZ/lSiyQHyjCN0at2oTUUFBw/cyJHJACzbZvvfve7+Hw+kskk3/nOd1i8eDFr165lwYIFXH755Tz22GM89thjXHvttVRVVbFq1Spuu+02mpqa+MEPfsDPfvYzDMPgt7/9LTfeeCMzZszgRz/6ERs3bmTJkiWsWLGCYDDIL37xC1577TX+8pe/8NWvfnUkTk8IIYQ45oQSIWLJWMbLd/ek4UAJ77yyGCdhYvv6LukdTUbJyoJgUBOLqR6DK+vdq5edOy1qazXz5iXwpFlHJDUfLMn27Rb19QYFvgKy7CzqInU4rtNrIOY6BjklzWkdI+EmyPXmMrdgLrbhIRyGiy6KsnRpZoIv14XGRoNdu0z27rVoaTFIJFIXsUqlhlvm5TlMm+YwebJDfn76QepI0hoeecTH22/baWW8jtQab0WnUVddKbB9cQ7tHE/r4VwKJxyirSGfaGuQWLsP7RqAovFAMfUNGuUpxrBjmP527JxGTF/6JTkNZRBJRjAwOku0D4ZCETUa2bdp2oACsISboKqtitpwLVEniq1slFIopbCVTaCsgVAAjHeD1oSbIBFP0BRtYm/LXvJ9+UzJmTIifxOOFonAokXHz82JEQnAlFL43s0pOo6D4zgopVi3bh3f+973ADjrrLP43ve+x7XXXsu6des47bTTsG2bkpISSktL2blzJ8XFxUQiEWbOnAnA8uXLWbduHUuWLOGNN97g4x//OACnnnoqv//974f05RdCCCHezxJuApfhHQqnNex8fQE170zE9CTSKorQcVE9Y0aSTZvszmCrJ5YF4bBizRoP8+cnyM9P7w66UjB7dpKcHIPduy0s5aEiq4LWWGvngrNHB2Km5ZJXerjPfid1kiw7ixl5MyjyF5FIgGFpPvWpMJMnD/29bm1VvPSSl3fesQiHFaYJPl/3IiXRKLS0GGzf7sFxNLm5qSD1jDPiBAJjI8swlOALUhmwdLKWWkO4KYvW+kKiIR++7AhlMw6A0u9+Hx20q4iGfWizBVwLJ2aRjGQRPVyO6Y3gya/Fm38YVN+foULhaIeAFRhyFszFoak+SKTVT6CfeEhrzcHQQfa07kFrjWVYeIyudyQ0Gq/XwDuhlob9JRjme/3rmEfXHGtmXe06iv3FzMqfNaIZsUAg9R09XozYHDDXdfnGN77BoUOHuPDCC5kxYwYtLS3k5+cDkJ+fT2trKwCNjY3MmDGj87UFBQU0NjZimiaFhYWd2wsLC2lsbOx8TcdzpmkSCARoa2sjJydnpE5RCCGEEO/SGravXEL93jKsAVQN7Lhwzc1NFd2orTX7nOulFJgmbN5sM3duMu35VUqlFogtKoqzbZtNW5si15tLrjeXaDJKS7yFhJPA0Q5O0qB0enXX4WvaTc2rUSZe00u2N5spOVPw2360Tt3RX7gwwcUXR4e8eGxDg+KJJ/xUVZnYdmrx2ndnbPR5fh3Blusq3nzTw7p1HiZPTnLZZVGyskY3EPuf//EOOvgCcHT/wXx7UxZNB0tIJiwM08HyJImHvTQcKKHwiLl8kdZAKoOI7iyeoQyNMpJoxyZaO5lY/QS8xVV4C/qeA6i1xmt6h7TWHKSyaTFaqdoyjcLS3b3uF01G2da4jVA8lAqkejls0k1S4CsgZ8l26nZXYJjdM9EKhW3YNEQbWHNoDbPyZ1HoL+yhtcxKJGDBgsT7cr2v3oxYAGYYBj/+8Y9pb2/nJz/5Cfv37+91395Syn2lmnt6rqfs1wsvvMALL7wAwH/+539SVFTUX9fFMLMsSz6HUSafwdggn8PYcLx8DlbUIuAPEPBkfriR1rBt5TyaD5bjDyZhAIu/+mwfhmEQCARYuBBeew0cp+ehiEeybaistMnK0hQMoNJ2IACnnQY1NbBvH4RCiiyfTbY/G4B4wiGYH6JgYixVZOHdi3Sv5aXIX0S2N7szg5BIQCKhmTwZLrrIZdIkL9BPpNQH14UXXlC89JLC61UM5WvZkUVpaoJ77snh4otdli3ru6LkcP0Wqqth0yaDwsLBBymBZAArbPU4B8x1DOr2lBBuCWJaLh4vQOrq3jQh3JxPXkkEX3aqiEe8PRdtxjFco8fASVmpYC9WN4lkWzFZE3Zi2N1vKmg0lmXh9XjxWJ6hZ8HMOIn2IgxjL4Ee0mCtsVY21m1EofB7+45ktaMpyyvDZymKyttpbw72WuDDfvf3ur1lO9ON6UzJnzKk8+hPJKL56Ee9ZGdnD+txxpIRr4IYDAaZO3cuGzduJDc3l6amJvLz82lqaurMVhUWFtLQ0ND5msbGRgoKCrptb2hooODdv7IdzxUWFuI4DuFwuLOgx5HOP/98zj///M7Hhw/3PpxAjIyioiL5HEaZfAZjg3wOY8Px8jkk3ASJWIJwMv05Lumq3j6R/VvHYXsjJAY4qshVLq7rEg6n+jVzpmLDBhvTTG89sHXr4NRT4wPOOuXlpf6FQoq9e02amw3icfB4NPNnWFjWxO59daDxcBzTTA3tmzUrwVlnxTuzS0P5GrW2Kv7ylwANDQZ+f2pYYSbdfz+89prD1VeHe81CDcdvwXHg7ruDaG0QHsJXTyc08Xi8I67qFG33UrdrAlqDYSRwekqUKYeanUVUzN2DMjTRdpOkmwBN30GTkSQZ9dBcuYBAxU7s7K5l4jUaTEgkEiitSLrJwZ8gEHNjtDTYXX4PHdribWyo24CpTJRSOPSdEVQonJhDOB5m6mmrWffwOVievvunULxT/w6RaITJuZOHdC69iUTgtNPixGIxYmN4abzy8vKMtjci9SdbW1tpb0+tGh+Px9m8eTMVFRUsXbqUlStXArBy5UpOOukkAJYuXcqqVatIJBLU1dVRU1PD9OnTyc/Px+/3U1lZidaal19+maVLlwJw4okn8tJLLwGwevVq5s2bJ/O/hBBCiF7Yhk2OJ/PD9OMRD7vWzMP29l5sozeOdrr1KTtbM39+6kI6jZoLKAVbtw5+zF9Wlmb+/CSnnhrntNPi/O//HWL69AT5+Q45OS5ZWS65uS6FhQ6zZiW48sowN98c4pZbQnzoQ7GMDO1raFDcfXcWoZAx6CF6/QkEoK7O5O67s2hvH7nrpZde8tLaagy5KEi2J7vbdV6kzU/tjomAxuinfLvrmDRWpaplJ2N22tkqpUAZDuGqGcSbu2YHXe3it1IfWI4nB1cPbd6fRhOPeIhHun6fI4kIG+s3dgZf/XG1S74vv3Nff3aESYt3kIz1/zuxDIt9bfuoDlUP7iT66pcL+fnH56LkI5IBa2pq4pe//CWu66K1ZtmyZZx44onMnDmT22+/nRUrVlBUVMQtt9wCwIQJE1i2bBm33HILhmFwww03YLxbi/Zzn/scd911F/F4nMWLF7NkyRIAzj33XO68806+/OUvk5WVxc033zwSpyaEEEIcs/J9+RxqH3iZ695oDVtXLEUZg7vwdFyHAl/38YMFBakgbMuW/jNhhpHKHh06ZFBaOrh+JBIQCLh87nPhES9a0dysuOeeLAyDYV+s2bIgHlf89rdBbryxHb9/eM/VcWDDBjsjaz15DE+X4YfRdh91u8ajjPQqPirDpb0pm/zywzjOwCcfKTNJpGYqynCwc1KZMIXqLH4RsAOYUXNowxA1aMcgGvJjBVtSm7Rma8NWDIy0Ew2OdpicPbnLtomLdlC/p5xou6/fYNU2bHa27KTAV4DPytxCXbHY8bsoudLp1PB8H6uuznxELwbmeBnuM5bJZzA2yOcwNhxPn8PLVS/zavWreM00F1/qx+F949j6wknY/oFnvyB1oXh6+elkBbO6DbkCaGtTbN1qEY+rPqsjQioYXLYsPuCLu3BYMXVqgiuuiIz4orDxONx5ZxaOo0b0ojSZhJwclxtvbO9y3Ez/FjZtsnn8cR/B4MBe52qXSDJCY7SRtnhb57z//W2pegJeFaR2+ywU6Q1T7WzXMckpaaC1roCw2zqojJV2LLKnbcLwpNbWqsiq6HyuMdpIKB4afDl6pShWMzj9qnV4clLXq/ta97GvdV/nvMM++6Yh2Z6D2ziZ8uRpOIlUoGkYGn9uiGBBM9tXLkUpl/4KSmo0PtPHCSUnZGSEWTgMH/rQsbMuXqaHII74HDAhhBBCjA1Lxy3llYOvZKy9/ZtmYPWxzld/cjw5fZYWz87WnHxygl27TGpqzD6zYY4Dhw4ZlJend1GdSIBSmo98JML8+UObuzNYTz7pIxZTaa9plimWBQ0NBi++6OW884ZvONjq1Z60gy+tNXWROqraqogkIyR1EgOjW+DRFGuiff843Fgrpqnwmt5uJdh7Y5gOocZcgMFXLTSStB+YSdbUt/BaXW9k5HnzaE+0D67dd/uktaYjLow5sX6DL+0atB+YQdvORSRCuSTjNiWBYg77VWeQpTU4e8twkxauo2jYV4Y/r4280iYMs+e5ZApFe6Kdg6GDjM8eP+hzgtS8r/PPP3aCr+EgAZgQQghxnArYASbkTKCuvW7Id7UjbX7aDuf1udByXxJugsk5k/vdzzBgxgyH0lKXykqLUCi1HtbRGSPLgoMHzX4DsGg09drp0xNcckl02OZc9WfXLpNt2+x+13waLn4/vP66h4ULExQXZ359uPZ2RV2d0e/5udplb+teDrUfIuEmsA0bQxl4VPegKseTQ329idueh2E5aK2JJCJEVRTbsDvnY/UlGbdBK0zTJOkkBxyIKQVu3Ef0cDnlk7s+ZyiDQn8h9eH6tNYsO5plWCjAsFKfx56WPb0vFJ7w0LR5GZGD00jGAhieCCiH3GwfWb6u56QUWLYD767LVzL9ANXbJ9NSW0AgJ0x+RT0ef/dA3DIsDoYOUpFVMai/Fx3LM2RyUfJj1XE46lIIIYQQHc6uODsjlRD3bZiJ2UNp7nT5TN+AioJkZ2tOPDHBySfHKSx00BpiMdWl6l04rAiFul4opi4CFaEQ2LbmzDNj3HJLGx//+OgFX44Djz3mH7Xjd/D54MEH/WkVOxmoAwcM3H7iurZ4G2sPreVg6CBAjyXmu9AKp246ynovY9kRGMTdOK3x1n7XC1NK4yRMbMPuc7mjPtswHZKNEzDc7kN5A1YAv+UfcNua1HpihuHiDcRxtUtDtKHHAKy9aipVT3+G0L7ZaBSmN4JSqSWgeppTeTTbm2TC/N0E80JEWwPUvDOJxqoStO4eZEWSEZpjzQM6F4BYTGNZmuuvbz/ugy+QDJgQQghxXJuQPYGyYBlNsSZMNfiVUFvrCjDMwV3AJtwE0wumD+quut8Pc+Y4uK5DKKRoajJoaVHEYgqtFTU1BtOnJzHN1MLEBQUOU6Y4TJ3qkJ+fXsGGodIaDh40eOcdm5oag6Ymg3DYwHFS2Yi6OsW+fRY5OZqcHJeiIk0wOPJT9JWCw4dNDhwwmDgxs1mwHTt6X3RZa82e1j0caDuAbdhpfw/bm3LwkEVCN3dbgLgjkxWKh/CYnl6zYablkIh4UhUFBzkMUevU/KiW2gLyK+q7PV/kL6KmvQZHO2kfo6Oiom3E8QajVNUfIukmuwSlbsLm8NoLiByajOGJdil+42qXskBZ2sczTJeSqdWEm4M07C+lpS6PSGsWxVMOdsmGeUwP+9r2ke/LT+88XIhGFUuWxPngB2P9zt08XgzqbYjH4xiGgSXvohBCCHFMU0px5cwr+eWmX2KagwvAXMcgFvZjWgOfO+Vql3xvPiWBkkEdu4Nh8G4A0zXjUVLi8KlPZX6ts3REIvD66142bbJpaTHw+XTn+mSWRefF6KFDFkop2toUzc0Ge/akgsXycoeyMpdBfiyDEghoXnrJy6c/Hclou/X1Ro/nobWmsqmS2nBt/xmvo7TW5WPZGm/SS8yJ9RjAK6WIO6kMUtDueQKaJhUkm4Y54EIcGo1lWHgsg/amHPLLD4PqGjwbyqAsWEZ1qBoXN62gyFQmHtODLzucCowjh7u8P07MR82Kq3BiXgxP10XiXO0yLjAOjznwyYSBvHZ82XtoPlRAe2MOB7dOYdyMAwRy35vL1h5vR2vd5w2TjjW9pkxJcs45sUFXJH2/SiuC+tOf/sRpp53G9OnTWb9+PT/96U9RSnHzzTd3rsMlhBBCiGNTrjeXc8afw/8c+J+05s0crb0pCyduDioAA5hTOGdQr0tHY+PIrwmaTMIzz/jYvNlGa/B6U0Mme9LermhvV52BmWmm/iWTit27Lfbtg/HjHSZOdEYkW6cUHDhgEQ5n9mCRSM/t7WjeQW24Nq2qfkeKR2wSUS+G5eCzfCTcRK8l35VKLYocToYJWN0noZmWg+uYeE0v4UR4QJlYhSJgp9p0Eibh1iCB3FC3/QxlUJ5VnlYmTKNTwaKr8Oek2oo47wXETtxLzfPX4DpWt6IZGk1psHRIlU0N06Wg4jD55YcJNwdpPlgMgO1JpIJVO0TUiXb+rdA6VcEzkVDYtiYvz+XEE5Occkps1IfVjlVpfdtfffVVrrrqKgAeeughvvzlLxMIBPjjH/8oAZgQQgjxPnBq2ansbdvLnpY9A754a6ouxrAHHnwl3STzCucNOPMxEKGQQSyWCoJGwv79Bo88EiAcTu+YVVU9Z4bgvQzZvn0m9fUG8+YlRuyCduNGi4kTM9ee08NUrJpQDTXtNYP6/EONuXDEkLuAHeiz5LtSioSTIKZi3b7ftjcVWNiGPaDgS2tNwA50BlOG5dDemNNjAAbvBWEd5el7K6ihtSbPm0ci6qFizh6SbpKYE8NSFtoxOfTix3GTFsrsOuTQa3opDhQPaSjxkZSCYH47gbx2knGbeeetw4nbHNqfiyevmvKsSQCYpqaszGX69CTFxa4MM0xDWm9RLBbD6/XS1tZGbW0tp556KsBxs06KEEII8X6nlOLKGVfy57f/zMH2gwMKwmLtPgxzYEOMkm6SWfmzKPQXDrSrA+K6qcWGvd7hnVOlNaxY4WXVKg9+f/oBXzjc/5pflpUqMLJunYeZM5PDPpzL64V9+4b3KjrhJNjVsmvQwXci7O2ygLCpTIJ2kPZEe59BWDQZ7ays2Lnd1Hj9EWLtfjymh1iy5+GMR9JavztHq2v/E7G+z0ehKPQVkmVnUR+px3GdLn3RpNo1lYkvr52ckhbCiSSu64IJjRvPxGnPQr1b8MbVLkopCnwFZHuy+zz2YCkFlifBrjULOOmjKyiZVsXUvAgfnT60YcPHs7SqIJaXl/PKK6/wzDPPsHDhQgBaW1vxjPRCFUIIIYQYNqZhcu2ca5mSM4VIIv05QG7SQqn0AhyNJukmmVMwh3HBcYPtatpcNzUkcDhpDU895eP11z0EAukvBpyqyJheQWqlUoFYZaVFVdXwFrFWCpqaMnuMI7N8Wmu2Nm4dUnuJePdrUMuwCNrBPisOKqW6Vf1UaPJKD+M6Jj7T12/J+I7gq6c5Vh1l7fvjNb2MzxpPkb8Iy7Bwtds5hLLIX0QyZlM2a2+qTTeJRhNtKCG0dx7KTuDoVOCW581jQtaEYQu+OigFsbCX3evmpoZ0OqOzVt77RVq/rhtuuIFnn32WrVu3dg5F3LRpU2cwJoQQQoj3B9MwuXrW1Zw36TxiTqzfMt5AaihUGhedSTeJx/CwdNxSigPFmehuvwyDYS9i8cwzqUIbAx0emEik/g2EZcGuXRY1NcMbhLW0GD0OGxysQOC9oKgt3kZztHlQa2MBuI7CSfScobMMiyxPFkCvc8KSbpKE+94bb1oO3mAcjy+G1qSCuB5e27EtaAd7LXChHZNEPP2sXtAOUhYsoyKrAr/pZ3zW+FRJfDNGzrRthBIhQvEQiaTD4TUX4fE6BKwApcFSKrIqyPXmDnkNv3RZdpLqtyfT1pA94GIloqu08stFRUX88Ic/7LLtzDPPZMGCBcPSKSGEEEKMHqUUy8qWMbdgLg/ueJCDoYMErECvF8y2N47rGphGz1fsjk4tkjshewKTcyaP2AVjZ/+Gb4oZb71l8cYbnkEtoByPK1xXQS+BQm9sO5UJy85OkJU1PEMrHSe1SHWmFBc71NebmCbsbd2LbQ7+Q9Gu0WfAbyqTHE8OkWSEuBPv9n0zlEHMiXUOH7S8qcXDiybVUFM5GcNMrUsXTUY7X6u1xjZTizv3WUBDg3YGEVgqmJE/g7mFcwmH4cKLW5k1fzKOdgibYX5Q+RQ1xizswOAWOs8U006w5425zJ1cPar9ONal9Q35t3/7tx63f/WrX81oZ4QQQggxduR6c7lh3g18dt5nKQuWEUvGiDmxbvvllR3GiXe9p6vRxN3UxW95sJxlZcuYkjtlxIOvYFDj9w9PkBKJKP75T9+ggi/g3YWJB9c324atW61hWTQZUoHEQLNzfZk5M0kkklrzrSXWMug1t1J9S++1fstPlicLU5lodJehiY7r4GoXN2niy0kNSfQE4mQXN+I6Bl7Ti23auNrFUAZBT5CAFei331rzblCdPkc75HpymVMwh2QSJk50OPlERa43lwJfAeNzx3P4nfmdgeJoUgY01uTjddNbB0z0LK0MWE9jacPhMEZ/s0aFEEIIcUxTSjEhewLXzrmWcCLMm3VvUtVW9f/bu/Mwuetq3/fv31Bzz2OSzjxPhIwMgZAwiKigiAwOqIAzils95+ztc7ZXr+de7x7OgygC6lbErSgbQVDkKAgECKMEAsQMkHkeOj1311y/3+/+UaShSXenurqqp3xez8NDUl3169X9TXXXqrW+60tLooX2VDuO62CUJ3CMBBnXwTItQnaIkB2iNlRLdbB6yJOud6qoKF6r1O9/H+SEE4AHYLDflmTSYNcuixkzCtgr+A6FfJk3caKLacKhrkN9tgbmzPBy6XgF3h7O4eGRdJKk3XT3fqu4Eyfg+QhEurLJmOcSqT9EtD2C6QSoDdXi4fU7sfCE0AwwzNy/voyboTJQyYKaBYCB63pcdVXP/ZduVzXJ1hoCoQJmxIOQcdN0vbkc5g53JKNXvwnYl770JSB78PLxPx/X1dXFOeecU7zIREREZEQJ+8KsaljV/ffjZytl3Aw/W1+L69gDHuVdTK4LtbXFScD27rXYtcsm0vvZvjkxTd76XuWXkNg2HDxoMXmyU5Q2S9vOnu9UCOGwR329y45dHQM+8+vdTNPDGOD3zMAgaAUJWkGA7j1MVeEgE6tqsAwTv+WnIlCBURvi1Q3Z+xlGth2xOdHcfZ2TfKIe0xn74uHhuA6TSyczpWxKdkJjAq69NtZjvxzAM8+YlIQs0oyMBMzv92jaVw8Mf0VutOr3GXDTTTfheR7/8i//wk033dTjYxUVFUyYMKGowYmIiMjIZZs2Zf4yACZP8HPggD0khwXnKhaDefOK86L16afz2/f1ToGAl9OL9f4YRjYZnDmzsFWwQMAjFMp+Dwtl5cokf/m7gznIIdqm5WLZTs6tiL1ewzBxHZsF08uYWFHS84M+WLo0xYYN/u6hHCE7xLH4MeKZeL/nbBmme9JWwbSbJmyHWVC7gLAvjOdl99tddVWMKVNOfMOgsdGgJBCiJRkfVOtmofgtP7GuAJlMSmd+5anfb9v8+fMBuPPOOwkM1QmGIiIiMuqce26Su+4aXEWo0MrKvKK058ViBvv32wSDg7uObWcTHcfJ/0W1ZcGxY9k2xEImv1VVXsGT6dlzE7i+DszcdsD0ywqkySQGl8l5Rpra+iRwYvkwFIJly1K8+qqPTMbAskzqw/UkMgnaU+3dAzrenRD5/Olej2TwPI+Ml6HEV8KMihnUheqy49wz4Loe117be/IF0NwMk0oncTR2tM/pi0PF8Rxqw7WkUgbHjpmMH69piPnI6RlgWRaPP/44e/bsIfGukThf+cpXihKYiIiIjB4TJ7pUVbkkkyNjf3gyCWeemS5KRe7ZZ/0F2x8VDHpEo4MLMpWCpiaD2trCTORwXaiqKnzimvFS1M/ewdFNC7EDgztHyh9IkY75yXOSPZ5rEq5sxjXK6S0BAwgG4Ywz0uzYYXHkiIVtQ9AOErSDOJ5De7KdlJPqsa/MsrOTFz08DAx8lo+gFSTiizC5dDJh39tl01gsO3DjqqviJ7QdHheNGnR1QYm/hBJ/CSlneNv+PM9jSukUvIzHjh2WErA85ZSA3Xbbbezdu5dly5ZRXl5e7JhERERklDGMbMXgiSeCAz4Pq1hWrjxxYmMh7N9v4S9QIaKszKO9nUG1cvl80NRkUVtbmMNx43GYNavwB+1mvAwTF71Jx74ZpOK+vJMngEhlB53N5X0efXAypu1QPuEorjep3/tZFsyZ41Bf77J1q006bWBZ2eEeVcGq7vs5nkMimaFhUhe1NWXYhp1N1qzgCXsi43Hw+TwuuyzJ4sX9t8h2dho4TvbfR0NJA9tat3WPzx9qHh7lgXL8lh/PhOZmC0bIvrTRJqen++uvv85tt91GZCT1FYiIiMiIcsYZaV56KUA6bQzrXrBEAlasSBUlEfQ8aGsrXJVv4kSH/fsHf1J0PF64b3go5DFvXuETMMuwME1YcOFLvPzgGuxA/i/eAyVxbH86eybYALmOxbgZ+zBML+fphhUVHmedlebYMYMDByw6Okxs++1JkZZhEQ5YzJliYFmlJ35ON5t4VVW5rFyZYvnydE6DUxzH6D5qYFx4HPs795NxC782uXBch1kVs4DsGy6FPKj7VJPzQczpQh4GISIiImOOZWUHCfziF5Fhq4J5HpSVuVx0UXGqX11dBrGY2WfL2ED5fNlR+Z2d5qCS1ng8+0J9sIlvJgPz5mWKMlzBb/kxTZNARZTJi7ex7/XZ2P78Xl8aBpRUt9N+pBrTyr0NznVMSmtaCYQTpF2j34EavX3OujqPurrsmWYHDlh0dRkkEibJZPbfXVdX9s0Hz/Ow7WwyW1XlUVXlcMYZKSZMcAe4Rm//OzMMgwVVC3i58eUhr4Jl3AyTSif1aKGU/OX09DrvvPP43//7f/O+972PioqKHh9buHBhMeISERGRUWjCBJelS1O89pqf4ZjflR3lHS/oGVbv1Nho4jiFPf14yhSHV1+18Pvzv24mkz04ebCtkakUrF5dnOTVZ/oo9ZWSclNMWbydZDTE0R0Tsf35VXTKatvoOFqd8/1dxyRcHqVq4rHuePIdahEKwaxZb5eA2toMPvaxKJFI9k0An8+jpMSjrGxww0x8vp7nsUX8ESaWTORg18FBj/PPlYdHyA4xtWzq27d5FOXog1NFTiv3yCOPAHDPPff0uN0wDG677bbCRyUiIiKj1nvfm+TQIZumJnNIX6TF43D++QnGjSveYIBEwsA08z+7qzdlZR5lZS6xmJF34uh5g28JS6dh9uwM5eWFTTDfqTJYydHYUQwDZp+zEYCjOyblVQkzLZeyuhbaj1adtAp2PPmqnXq4+7aQXZgybSoFixenmD+/8D15FRUutk13GyLA9PLpRNNR2lPtA6rg5eP4odmLahb12MsWjxtMnqwexHzllIDdfvvtxY5DRERExgjLgk99Ksqdd0ZoazMLNrCiP/G4wdlnJznnnOJumRjMyPi+GAbMn59m/frBfqMGlxjatsfll8cHGUP/JkQmcKDzAD7Lh2HAnHM3EojE2ffabCxfesCDOcrHNRNtLcHJ9H0GnetYlNW2UtlwrPs2D4+wPfh2Os/LHkx82WWJk985D34/VFRAa+vbtxmGwcKahWxs2khnqrNoSdjx5Gtp3VICds9ytud5TJkyPHvRxoKRMStWRERExhS/Hz7zmSi1tS6J4rw2BbIvgKNRWL06wYUXFqd17p38fhe3CAW2YBCmTcuQyfM1rWHQ6/lTuYrF4NJL40VvG10+bjkZr+cXOXXJdpZ/+Cn8oTSZ5MDa6gwDaqcfwnNPTEJc18QwPcbP3tcj+YLsnqaJpRMH/gW8SywGH/5wvKhvMlRVnXibaZicXnM61cFq0m7h33TIeBls02Z53fJeK4XBoEdFRfEqpWNdTv/KY7EY9913H1u2bKGzsxPvHXXQH//4x0ULTkREREYvvx+uvz7KE08EeOklP8Hg4IdEvFMqla0+fPzjcWbOHJp2qLKy4r3obGhwaWnxaG/PjjofCMPIf09OPA6nn55m7tzifw/L/GU0lDTQHG/u0dIWroiy/MNPsfe1WRx6YwrpRAA7kMrp34s/mKZq4lFaDtRjWg5uxsK0Hcpq2qiY0NRrYhrxRYj4BjfdOx6Hc89NMX16cb9v06d7bN7MCQd/G4bB/Or5HIsfY1vrNjwv96mO/Um7aRpKGphePr3X63ledprjcE46He1yWqWf//zn7N69myuvvJKuri5uuOEGampq+MAHPlDs+ERERGQUsyy4+OIkn/1slEDAJRYb/DVdN1v1mj07zU03dQ1Z8gVQV+cOalhGfwwDFi5ME4l4A97PFQh4A07aIJtEzJyZKVoLXW/OmXAOscyJ/xAM02Pq0m2c/dHHmbt6A8GSOJmkj1QsiOv2/Wrfcw0CkQSlNa0Yhkv15KNMXLiLyoZjvSZfaTfNxJLBVb/icVi6NMUFFxS/6rpyZf+DPGpDtZw57kyqglU4npPXmHrP80i7aQJWgGV1y5hZMbPPZC4ezx45IfnLqQK2ceNGbrnlFkpLSzFNkxUrVjBjxgz+7d/+jUsvvbTYMYqIiMgoN26cy403Rtm40ceLL/ppbDQJhxnQ0InkW691p07NsGZNkgkTijdsoy+2DeXlLolEcXZxmCYsXpxm40YfHR1GzuPgQ6GBJ4XxOMybl+aKKxJDWs2YVTGL2lAtXemuXl/kG6ZH/YxD1M84RCrup6OxkpYDdUTbSknH/XieCR4YposvmCZc0UXVxKOU17XStK+e7S8sor+9cAErQF24Lq/YPS/bdrhyZapoRx282/H21AMH7D6fL7ZpM796Phk3w4HOAxyJHSHlpHA9N7vfjhMXOONmcHHxGT4qg5VMK5tGyHfywSShkMeCBUrABiOnp7XneYTD2Y2KwWCQaDRKRUUFR44cKWpwIiIiMnZYFixZkmbJkjRHj5qsW+fn2DGLtjaTVMrAtrNVHJ8vm2yl09mhEsfPUpo+PcPZZyeH7Yyx4yorXQ4dGty5Xf0xzWxL4M6dFocOWVhW/62brgulpbknYK6bbd9csybBOeekh7yVzDAMrpl9DT/e+GOCdrDf+/pDKWqmHKVmytGcrj1h7j5Ka9rZvHY5qXgA29ezGpR20yytW5pXq1625RU+9rFYjxH0Q2HNmiQ//alN6YlnPPdgmzZTy6cytXwqKSdFe6qdlngLcSfevYXIMAx8po+KQAWVgUpCdqhHO2h/0mlYtCidV7VV3pZTAjZlyhS2bNnCaaedxty5c7nzzjsJBoOMHz++2PGJiIjIGFRf73LVVdm2t0wGjh0zOXDAIh43CIeDxOMJyss9Jk92KC8f3FlKhbZ8eYr/+i+byOC2EPXLMGDmTIf6epctW2ySyb6rYY4DEybklhDEYtnv/dVXx4Z1iEJVqIpVDatYd2BdTlWXgSitaeeMj6xl5/r5HNoyDcvOYNouGS9DQ0kDpf6TZDHv4jjZ8+Xmz0/zwQ8mhmSq57uNH+8yc2aGgwftnKuifstPbaiW2lBtweIwTY/zzx+ayt9YltMSfuELX+jOmm+44QZ++9vfEo1G+cpXvlLU4ERERGTss+3sC8zx47MthTU1Hk1NI7fFadYsh7Iyrygj6d+ttNRjxYo0Bw+aHD5sEY+b2HbPhLSszOt3eqHrvp14nX9+iiVLhr7q1ZtVDas40HmAPZ17CFiFHb9oWh6zztrMpIU72bNhDo17awl4VUwvn57zNRKJbNV2+vQ0F1yQoqZm6Fte3+kjH0lw660R6KWdcCjE43DFFYlhr0CPBSdNwFzX5amnnuKKK64AoKysjC9+8YtFD0xERERkJDKMbBvWiy/6iz62HbItiZMmuUyc6NLRYbB3r0UsZr7VppkdouF52biOH8gcj2cT29LSbGK7enWS+vrhTSDezTAMrplzDb954zfs79pP0Oq/HTEfwZIE089Zz/IL6lic/izb33Bpa4P29mzbazaOtw869vs9Skvdt1pe0yxfnh6WildvQiGP970vwR//GCYcHtrqZToNM2ZkmD9fZ38VwkkTMNM0efTRR7nqqquGIh4RERGREe/ss5OsX+9jKKsRhgHl5R6LFmVfBKdS2SrNWWelSCQMXDdbsQmHPWbOzDB+vDPiqxWWaXHtvGt5cMeDbGneQtg3+MOR3ymWjjGjYgZXz74a2/Q4c3n2oOl0GpqaTGIxg3Q6u+8wGPSoqXGHJKnO16JFGXbvTvH3v/uGbG0dB0Ihlw9/uLiHdJ9KcmpBXL16NY899hjvfe97ix2PiIiIyIgXCsEllyR56KHQkFcjjnMcuPHG6IirbA2UaZh8ZNZHmFc1j4d3PZydzGfmeajZWzJuBs/zuHTapSyuW3zCkAmfj+6W19Hmgx9MkEwabNtmFz0Jy2SyVcHPfS52wjlkkr+cErAdO3bwyCOP8NBDD1FdXd3jH/F3v/vdogUnIiIiMlKdfnp2XPyhQ1bOgxEKJZGAM85Ijfrk653mV89nWvk0/rTrT+xo2wEw4L1hx0evTyufxuUzLi94RW0kMAy46qo4Dz0UZONGf9HeAEgmPcrLPW64IZbXMQfSt5x+XFx44YVceOGFxY5FREREZFS58so4t91WgusO7EyzwUinoarKHbJzqIZSyA5x9eyriWfivHDoBTY2baQz1YnneYR8oRPGx7ueSzwTxzAMSnwlLK1byrkN5xLxFXFE5QhgGPChDyWYOtXhkUcCQO5nxp2M50E8brBkSYpLLkkO+ZsLp4KcvqVr1qwpchgiIiIio08o5HHDDV38x39E8PmMok8XzGSy+3Guvz46ZAnfcAjZIS6YfAHnTzqfznQn+zv2s71tO+2pdhwvO3LfMixK/aXMrJjJ5NLJlPvLcz7Paqw4/fQ0s2dn+P3vg+zcaQ/4cPN3On7IdHm5x0c/GmXixLFTXR1pckrA1q5d2+vtPp+P6upqZs2ahc83uF5dERGR0cp14cgRk507bfbvt2hvN3Gc7Asa08wORaitdZk1K83kya7aecaY6mqPz38+yp13lgAU7ZDaVArKylxuuCF6yuzHMQyDMn8ZC2oWsKBmwXCHMyKFQh7XXhvn0CGTp58OsHu3jeeR87+R4+ecjRvn8t73pliwID2mk/uRIKcEbN26dWzbto3y8nKqq6tpbm6mvb2dGTNm0NjYCMA//uM/MmPGjKIGKyIiMpJEowbr1vnZutVHR4eJ3+/h93NCFSSRgGPHLF55xYdtw6RJGVatSjF1qjMizmOSwauu9vjSl7q4994QR45YBR2OcLwyMXdumssvH56DgGXkmzDB5WMfi5NIwPr1fvbutWhtNensNEmnDVw3+8aPYRhYlkc47FFZ6VJX53L22Smqq1XxGio5JWATJ07kjDPO4P3vf3/3bY888ggHDx7kf/2v/8UDDzzAL37xC773ve8VLVAREZGRIpWCP/0pyNat2YTK58semNsfy4KSbIGExkabX//aR0WFwxVXxNXqM0aUlnp85jMx/vY3H2vXBrEsBr1/JpXK/vv66EdjzJ7tFCZQGdOCQVi1KsWqVdm/p1LQ1maSyYDnGdi2RyTiUVKiSvxwyanA+Nxzz3HJJZf0uO3iiy/m2WefxTAMPvjBD3LgwIGiBCgiIjKSbN9u8aMflbJtW/Ycnnw68A0DIhGPVMrkrrtKePjhABmdbzomGAacdVaam27qYs6cNI7jER/g8UmeB9EomKbHsmUpbrqpU8mX5M3vh7o6lwkTXBoaHOrrXSVfwyyn92XKy8t55ZVXWLFiRfdtGzZsoKysDIB0Oo2tESkiIjKGeR489liAv/3NTyhEQdrADCO7P2zjRh+7d9t85jOxYTtTSgqrtNTj8ssTpNPw6qs+Nm7009JiEI8bmKZBIOB177NxHEgms61hoZBLdbXL+9+fZO5cR3txRMagnLKm66+/nu9///tMnjy5ew/Yvn37+MY3vgHA9u3bT6iQiYiIjCV//nOAV1/1Ey7CsUKBQPaF+U9+EuELX4gSiSgJGyt8PjjjjDRnnJHG86CtzWDvXpvDh7P7cgwje9DtxIkOkyc7lJR42hcoMsYZnufl9FO+o6OD1157jZaWFiorK1m6dCmlpaXFjq/oDh06NNwhnPJqampoamoa7jBOaVqDkUHrMDL0tg5r1wZ4/nl/QQcr9MZxIBBw+eIXowQGdv7smKLnwsigdRgZtA7Db8KECQW9Xs59g2VlZZx33nkF/eQiIiIj3cGDJs89V5zK17tZFsRiJg89FOSqqxLF/4QiIjLk+kzAvve97/HP//zPAHz729/u82C77373u8WJTEREZJg5Dtx3X7jola938vth61Yf27enmTVLgxdERMaaPhOw1atXd//5ggsuGJJgRERERpLHHgsQjeZ+oGmhhMPw0EMh/uEfugY9xlxEREaWPn+sn3vuud1/XrNmzVDEIiIiMmJkMrBxo49gcOgnIhgGxGIGr7/uY9my9JB/fhERKZ6c31fbunUru3fvJpHo2ZN+xRVXFDwoERGR4fbaaz5SKWNI2w/fKRyGl17yKwETERljckrAfvGLX/DCCy8wd+5c/O84+KSvfWEiIiKj3fr1xZ96eDKNjSZHj5rU17vDG4iIiBRMTgnYM888w80330xVVVWx4xERERl2ySS0tJhDvvfr3QKBbBvke96THN5ARESkYHI6X72mpgafz1fsWEREREaEo0ct0iOg88/ny8YiIiJjR04VsC9+8Yv89Kc/5ZxzzqG8vLzHx+bPn1+UwERERIbL9u32sFe/jmttVbu/iMhYklMCtmvXLl599VW2bt3aYw8YwI9//OOiBCYiIjJcjh0zGc7GD8eBjg6DlhaT9nYbnw88Lzsd0eeDigqHGTMcpk7NUFHhDV+gIiIyYDklYPfccw//9E//xKJFi4odj4iIyLBzhun842jUYM8ei5YWE8cB2wbXhc5OE/MdmwZaW022bPHjuh61tS5nnJFi8eK0zgwTERkFcvpRHQgE1GooIiJSJIkEbNnio7PTwLLo/g+yCdi7GQaEw9nKVzxu8sgjQZ54IsCFFyZZtiyNhhSLiIxcOQ3huOaaa/jlL39JW1sbruv2+E9ERGSsGapKkufBgQMm69f7icUMfD56VLogm2ydLKEKhcC2DR55JMgvfxmmq0sZmIjISJXTr5jj+7wee+yxEz527733FjYiERGRYVZT47J7N0XdB+Z5sGWLTVNT//vN/H4v54pWKATHjlncdluET30qxoQJeqNURGSkySkBu+2224odh4iIyIgxe3aGZ57xFy0B8zzYtMmmre3kwz5CoYEN2bAsME2Du+6KcP31USVhIiIjTE4JWG1tbbHjEBERGTHq652iVr/efNOitdU8aauj60JJycCnHBpG9hDn//zPMF/5SpTSUk1KFBEZKXJKwGKxGH/+85/Zs2cPiUSix8e+9a1vFSUwEREZm46PUx/J/H6ornaJRnPaKj0gLS0GR49aOSV4mQzU1eVXwTIMsCyD3/0uxA03xEb891xE5FSRUwL2/e9/H9d1OeOMM044B0xERKQ3ngeHDpls3OijsdGirc0gFjNx3eMVGo+KCo/qaoe5c9PMnu2cMIBiOC1fnuIvfwkSDhfumo4Db7xh5zzkIxLx8qqAHWdZcPiwxUsv+TjzzHTe1xERkcLJ6VfA9u3bufPOO7F1wIiIiJxEOg3r1/vYsCFAc7NJMOh1Jxw938MzaGvLHjb8+us+Sko85s9Pc955KSKR4W+ZW7w4zdq1AaBwpaNduywcx+geMd+fdBqmTRv8gWShEDz1VIAlS9LoPVQRkeGX03uNc+fO5eDBg8WORURERrlt2yxuvbWUtWuDJJMGJSXeSas9pgklJQAGr7/u54c/LOGZZ3x4w5yD2XY2CUsmC3M9181OKMwl+YLsBMb6+sIM0EinDV55pYib2kREJGc5lbRuvPFG/uVf/oWZM2dSUVHR42NXXnllMeISEZFRJJ2GP/whyBtv+AiFslWXfBzfF/X000E2b/bzsY/FKC8fvkzswguTbN1qk06bg95D1dhokk7nNto+nYbTTksXrCUzFIJXXvFz1lk6pFlEZLjl9KP9nnvuobm5mfb2dg4fPtz935EjR4odn4iIjHCJBPz85xF27PARDhdmwEYoBJ2dJj/5SQnHjg3fxjDLgiuvjPOu+VN5OXTo5FMPIbtPrLbWpaqqsIlnc7NJc/MI2mQnInKKyqkC9vzzz/PDH/6QysrKYscjIiKjSCoFd90Vob3dLPj+ItPMJnN33hnm85+PFjwhyVVDg8u55yZ59tlA3pU9z4NE4uRVNM/LHrw8Z04mv0/UB9dzibsxfv/8Zspmv07KTeF5HqZhUuYvY2bFTCaXTabMX1bQzysiIifKKQGrr6/HyrVpXUREThn33x+ira3wyddxx0ep/+pXEb7yla6cpwcW2po1KeJxg1de8eeVhKVS2f/6+z65bnbf2dKl6Zz3iZ1MPB1nd8duWhOtpJw0Bze3s7jhUI/7HI0dZWPTRgwMakI1rKhfweK6xdimBm+JiBRDTj9dV61axb//+79zySWXnLAHbOHChcWIS0RERrhNm2x27bLzrgrlyjQhFoO//CXIZZcVoBcwT+97XxKfD55/3j/gVsvOzv7v7DjZsfxLlqQLcgB02k2ztXkrrclWbMPGMAwCtp9MtPSE+5qGScQXASCWifHI3kdYu38tF0+5mMV1iwcfjIiI9JBTAvboo48C2b1g72QYBrfddlvhoxIRkREtHoc//zlY9OTruEDA4LXXfCxenGLSpMJMBszHRRclmT49w4MPhkiljJwrf8mk0edAjXQaJkxwmDGjMOegNcYa2da6DQCf2TObczInL62F7Oyi/mnXn3i96XWunn11920iIjJ4OSVgt99+e7HjEBGRUWTdukDO51kVSigEf/1rkM98JjZ0n7QX06c73HRTFw8/HGTrVh+m2X9rIWTbC98tnTYIhVwWLsxQVlaY/W172vewt3PvCYnX23Iv24V9YY5Ej3DH63fw2YWfpTxQXpAYRUROdRqHJCIiA+K6sHmzb8gP9TUMOHTIor19+Oeo+/1wxRUJvv71LpYtS2GaHp2dBokEvZ5fZlnZ71sqlW03LClxWbw4xYoV6YIlX3s79p4k+QLDGFj10DZtXM/lP/7+H0TT0cGGKCIinKQC9u1vfxvjJE3u3/3udwsakIiIjGxvvmnR1WUQiQz95/b74amnAnzoQ8O3F+ydwmGPiy9OctFFSY4dM9m502LfPpv2dpNMJpuM2TZMnOjS1GQycaJDaal30n1eGTfDsfgxWhItxDNxUk4Kj2yiZhkWITtEia+EunAdpf5SWhIt7OnY02/yBeALpgf8NZqGieM5/GrLr/jioi+e9HWBiIj0r98E7IILLhiqOEREZJTYuDE7hGI42DYcPDjypvKaJtTXu9TXu6xceWKSk0rB//7fJsFg/9eJpqPsbt9Na7IVz/N6TCI03mofdD2XaDpKZ6qT/V37CVkh2pJtlPhK+r225xqEyrsG/sWRTfqaE82sO7CO1ZNW53UNERHJ6jcBW7NmzRCFISIio0VLy8nPsyqm1tZsdWm4RtLnw++HsjKXVKr3zn/Xc9neup0jsSPYpo1lWCfdrmUaJn7Dz9HYUbpSXbSn2qkN1RKwAr3eP530Uz3paN5fQ8gO8eyhZzm99nQqghV5X0dE5FSnPWAiIpIz14X29uH91ZFOGxw7Nvp+fdXXu70O4+hKdfG3I3+jMdaIz/R1V7py4XousUwMy7TwPI8j0SO0Jlp7va9lO1SMb843fCC7J+zJA08O6hoiIqe60fcbTEREhk00apBOD+8eIMuCxsaR14Z4MuedlyT2rgGOHckOXj32Kp7nYZkD/5o6Uh1475j6YRomHakOjsWP9bif50FZXQu+wMD3gL2Tbdpsb9tO2h3cdURETmVKwEREJGfZwRKFmdqXL8PwSCaHNYS8jBuX3SN2XDQd5fWm17PthnmKpqOYRs9f5aZhEkvHaI6/Xe1KJ/1MWbIt78/zTiknxaamTQW5lojIqajPBOyf//mfu/983333DUkwIiIyshXioODBG9rzxwrp3HOzVTDXc9nUvGlQyZfruWTcTK8fMw2TrnQXsUwMz4VIeRfl9S15f653ClpBtrdtL8i1RERORX3+Kj106BCpVAqAhx9+eMgCEhGRkSsQ8IY9CctkPEpLB3ae1UixcGGGKVMcdjTvIekMroyXdtPdo+l7YxomzfFm0mmLBReuL9jgFMMw+txnJiIiJ9fnDKkVK1bwD//wD9TV1ZFKpfjOd77T6/10DpiIyKkjGISSEg/HGb59YJYFEyaMzgQM4L0fbOR36w8R8A9ujGM8E8c8yU4CJ+3HnL2OcEVhD1FuT7YX9HoiIqeSPn/633jjjbzxxhs0NjayY8cOzj///KGMS0RERqiKCpfm5uHrAQyHPUpKhncf2mC83Po0889v5M21Z2EHem8hzIXruf2OqvcyNsHqI5gzH8PxzhxUu+O7pb00nufpUGYRkTz0+/bb3LlzmTt3LplMRmeCiYgIABMmOBw5YuHzDf3n9jyoqhrZ1a+0m6Yp3kRnqhPHcwhaQWpDtZT4S3Bchy3NW6ifYsJ5r/HmusV5J2EeXp8j672MD195E/XnPoRjZDjUdYhJpZMG82WJiEiB5NT/cMEFF7Bp0ybWrVtHa2srlZWVnHfeeSxcuLDY8YmIyAhz9tkp/vY3/7AkYLEYvPe9I28Eeku8hacOPMXh6GHaU+2knOweagMD13OxTZuwLwzA3o69TC2bSv2MQ1i+DFufWoZhuJjWwKp6lmH1WoVyUwFC4/ZSe/ZfMEwXG5tj8WMFTcBsw1b1S0QkTzklYE888QT33HMPF1xwAbNmzaKpqYkf/vCHXHPNNVx00UXFjlFEREaQ0lKPiRMdmputgg12yFUk4rFgwchJwA51HeLh3Q9zNHqUgBXAMi0CVoCAFej1/jvbdrK3cy9HokeYUDKByZM8zrr6CbasXUbb0Wp8gVTOnztoBWmjDYtsa6HnWGBA9fLHKZnSc+R8IpPI/4vsRVmgrKDXExE5leSUgD300EN861vfYurUqd23rVy5kptvvlkJmIjIKWj16iR33x0mEhm6z5lMeixfnh72KYwAjuvw131/5eUjLxOyQ93VrZOJpqMErSAeHns79tIYa2RB9QJOf/8LHNnewL6Ns4m1RfAFUhgn+Tr9lj9bYUv7MUyH0Lg9VC19Gst/4nTFtJsm5aTwW/58vtwePM+jKlA16OuIiJyqckrAOjs7mThxYo/bJkyYQFdXV1GCEhGRkW36dIcZMzLs328PSSui50E4DBdcMPwnMCcyCX655Ze0JFpyTryOS7lvV7hs0yblpnj56MvMr57PuFkwbtZBOhrL2fvaHLpaykjHA7iuiWk5GKYLnoHrmOCZWP40gXCS0MTXKZ25EdPuey+Z67kknERBErCEk2Bq+VSiUYP9+03277dJJLLTKcvKXKZPd6irc7EHN+RRRGTMyunH49y5c/nVr37FJz7xCQKBAIlEgt/+9rfMnj272PGJiMgIdcUVcW69tYR+R/EVSDwO110XG/YX9UknyZ2b7qQz3dlnm2F/PK/nPi8DA9u02dy8mQXVC6gJ1VBW185pF78EQDrpo/NYBV0tZThpG9Ny8YUSVNS3EiqLsrNjO4e7DmOaJ59w+O7PnY9EV5Bdryxk3XNr+GvUxnEgFMoe0O15kMnA448b+P0eVVUuK1akOP309LCvm4jISJLTj8TPfe5z/OAHP+C6666jpKSErq4uZs+ezT/8wz/k9Emampq4/fbbaWtrwzAMLrroIt7//vfT1dXFLbfcwrFjx6itreXrX/86JSUlADz44IOsXbsW0zS5/vrrWbx4MQC7du3i9ttvJ5VKsWTJEq6//noMwyCdTnPbbbexa9cuSktL+drXvkZdXV1+3xURETmpUAg+9KEE998fIhQq3ueJx+Gss1JMmjS80w89z+OeN++hM9WJz8qv7NfX4Aqf6WNL8xaW1y/vUVXzBdJUTTxG1cRjvT5uSukUDkUPDepz5yIVC7D1qaW0HqmiMhLGrLcJ91L8s6zsYd0A0ajJn/8cZO3aAGedleLcc1NDvmdQRGQkyikBq6ys5Lvf/S7Nzc3dUxCrq6tz/iSWZfHJT36S6dOnE4/H+eY3v8miRYt46qmnOO2007j88sv5wx/+wB/+8AeuvfZaDhw4wPPPP8/3v/99Wltb+X/+n/+HH/7wh5imyc9+9jO+8IUvMGvWLP7lX/6F1157jSVLlrB27VoikQg/+tGPeO655/jNb37D17/+9by/MSIicnJz52Z4//sT/PnPwaIkYYkEnH56mosuGv7Ww1caX2F/537C9sDaDt/JNmxS9D5owzIttjRvYVn9spyTJZ/loypQRVuyDbOfTWOmYeI3B95+6HlwaOtUdq2fh2G6GP4YM6vm5/z4bJJm8PTTATZt8nH11TGqq0fvGW4iIoUwoK3M1dXVzJw5c0DJF2QTuOnTpwMQCoVoaGigpaWF9evXs3r1agBWr17N+vXrAVi/fj0rV67E5/NRV1fHuHHj2LFjB62trcTjcWbPno1hGJx33nndj3n55Ze7zyo766yz2LRpU0HaLUREpH9Ll6b50IfixOPgFqhI5XkQjcKyZSkuvTTRXTlpbDRZuzbA3XeHuO22CLfcUsItt5Rw660R7rorzMMPB9ixwypYHMfF0jEe2/vYoJIvgIgvkj1AuRcGBrFMjH2d+wZ0zTlVc06asNmGPeCWSc+DrU8vYeeLC7B8Dp6ZYULJBCK+gU9eCYWgq8vkJz8pYdeu4TvEW0RkJBjyruzGxkZ2797NzJkzaW9vp7KyEsgmaR0dHQC0tLQwa9as7sdUVVXR0tKCZVk9kr/q6mpaWlq6H3P8Y5ZlEQ6H6ezspKxMo3JFRIrttNMyTJzYxb33hmlqMgdVDUulwO/3+MQn4syY4eA48OqrPtav99PYaOL3g/9dxRzXNWhpgWPHLF55xU9pqcf8+WnOOy9FONz3m3GOA01NJjt3Whw+nE3cysoMIMDs2RnGj3cIBmHdwXV9Hno8EDWhGg52HexzGIZt2hzqOsTk0sm5V8FMHzPLZ/JGyxt9tkaG7NCAWhA9D7Y8uYzmffXYwTQeHj7Tx4zyGTlf491MEwIB+O1vw3ziE1GmTRvZB2qLiBTLkCZgiUSCm2++meuuu45wb83jb+mrctVfRau3j/X2y+bxxx/n8ccfB+Bf//VfqampOVnYUmS2bWsdhpnWYGQY7etQUwP//M/w1FMGzz5r0NUFkYiR876feNzDtg2WLnX50Ic8/P4g+/fD3XebtLdnr5XL1t7S0uz/33wT3ngD3v9+l7PP9rrj8DzYtw/++leTffsMkkkPn88gEADDgJYWk1Sqgtde87BtqK722BbJ0DCvEtMaXNIQCAbY1rGt33bBtJOmy+uiPlKf83WnhaeRNJIc6DyAz+yZhGXcDOPKx/X7e/fddr40k/ZDkwhF0oAPx3M4q+GsAU997E04DH/4Q5hvftPpXqt3G+3PhbFC6zAyaB3GnpMmYK7rsmXLFubOnYs9iDFGmUyGm2++mVWrVnHmmWcCUF5e3r2nrLW1tbtaVV1dTXNzc/djW1paqKqqOuH25uZmqqqqejymuroax3GIxWLdAz3e6aKLLupxdllTU1PeX5MURk1NjdZhmGkNRoaxsg6nnQYLFsAbb1g8/3yA5mbrrTHl2el4xyfmpdOQSmVvq6hwOeusFMuXp/H7ob0dHnsswEsv+QkGs4lRLJZfPPfeC8895/Cxj8Vobze5//4QTU0W4XA2FsvKtk7G49n7h8NhUqm3Jy5uP9jCqwcm88bz05iy+E0aFuwe1DCJUquUlkRLv0nYjqYdlJp9ZCd9mBSaRDwZ52j0KLb59u9rx3Oo9dcSy/EbGGuLsH39JOxAjFTaw/VcltQugXS2FbMQHAd+/GOH66+P9fq9HCvPhdFO6zAyaB2G34QJEwp6vZNmVKZp8u///u/86le/yvuTeJ7HT37yExoaGrj00ku7b1++fDlPP/00l19+OU8//TQrVqzovv3WW2/l0ksvpbW1lcOHDzNz5kxM0yQUCrFt2zZmzZrFunXruOSSSwBYtmwZTz31FLNnz+bFF19kwYIFg5r4JCIi+TNNmD/fYf78GK4Lra0mu3dbNDaapFIGtu1RXu4xY0bmhDOjPA8eeCDI1q2+ggz2CIfhyBGLf/zHciorPUpLPUpKct8j3BRvIhQGA5edL82ncddE5l/4EsFIfoNBppZNpTHeiN/oeyhGIpMY8HUNw2B2xWxCVoi9HXuzCZ4BFYGKE6piffE82PzECixfmoybIWSHWFizkJBd2AkrlgWHDlm88oqP5cvTBb22iMhIl1NJa968eWzbti3vc7/efPNN1q1bx+TJk/kf/+N/APCxj32Myy+/nFtuuYW1a9dSU1PDN77xDQAmTZrE2WefzTe+8Q1M0+Qzn/kMppl9p/Czn/0sd9xxB6lUisWLF7NkyRIALrjgAm677TZuuukmSkpK+NrXvpZXrCIiUlimCdXVLtXVubXvPfxwkDfeKEzyBdnq1tatPtraDI4e9Vi2LI01gDkQ0XS0e/+XL5gm1hHm5QfOZ8mlzxCpjA44nrAvTG2olpZ4C1Yf53el3BQpJzXgg5MNw2By2WRqQ7VsbtlMR6qDWeWzTv7At7QdrqazNYwdSjCldMqA9qINVCgEzz8fYNmytMbTi8gpxfByGBX485//nOeee47ly5dTXV3d44fxNddcU9QAi+3QodzOT5HiUWl9+GkNRgatA2zdanH//eFez5jKh+fBxo0+OjoMLCvb+lZT4zJvXqbPx4TD4R7ten87/Dcczznhup5rsuxD6wiVDbwtz3Ed/nbkb3ie12uCk3SSLK1bSnmgfMDXPi6WirGgdgGdqU72d+7HdV1CvlCvrY8pJ0XSSbLr8UuIJKczpXxyzlWzwejqMvjkJ6NMn97z+6vnwsigdRgZtA7Db8hbEAFSqVR3e+DxqYMiIiKFlEjAn/5UuOQLYOdOi/Z2o7vF0bKyo+zHjTOorMytDbG3sfGGARgurz9yNmd8ZC2mNbBjTyzTYm7VXDY1bcI2TvxVbGCQdvNvzUs5KaaUT+HDMz6MYRjE0jF2t+9mZ/tOmuJNpNwUeNmKWYmvhEmlk2gIzuSeJ2YTqhy6clQk4vHcc36mT48P2ecUERluOSVgN954Y7HjEBGRU9xf/xrEdRlQe2B/OjsNDh608L2rkOPzwbZtNmeckVvrm2EYvU/aNSEVD7Bz/XxmnbV5wPFVBauYUzmHN1vf7DE047j+hnT0J+WkqA3X8vG5H++uroV9YRbULGBBzYI+H7d9u4XjDG0voGFAc3N+X6eIyGiV80+9AwcOcP/993PnnXcC2da9vXv3Fi0wERE5dWQy8MYb9gnJUr48D7ZutelreG88btLenluyYRl9Z4S2L8PhrdOItp44dTcX9ZF65lfPx3GdEyptfnNg+78gO6VwStkUrpt/Xa9JXX+2bbMLtu9uIDo7ze4JlCIip4KcErAXXniB73znO7S0tLBu3ToA4vH4oCYjioiIHPfaaz5SqcJVX9raDGIxs88Kl9/vsXdvbqW2oB3s9+OmL82eDXMHGmK3mlANZ44/k4gvQtpN43kelmkN6MyttJsm7aS5bPplfHzuxwecfAE0N1sFqz4ORCYDjY3D8IlFRIZJTj+hf/e73/F//V//F1OnTuWFF14AYMqUKezZs6eYsYmIyCli8+bCTT0E2LfPwufrf19WV5eJ53HSNsQyfxltybY+K2Gm6dFysJZM2sL2Ob3e52T8lp/FtYtpjDWyv2s/SSeZbXvsJzbP84g7cQJmgHmV83jPlPcM6qDkTGZ4RhEahqEKmIicUnJKwNrb25kyZUqP2wzD0DlbIiJSEG1thft94rrQ0WGetJqTTmcHf5ws8asP17OnY0+/rYieY9G4s4EJc/flEXGWYRjUR+qpDlUzPjyeimAFx+LHaE20knAS3QmZZVhUBCqoCFQwp3IOp9eenlfFa6QwjGzLqIjIqSKnn9jTp09n3bp1rF69uvu25557jpkzZxYtMBEROTUkEtl9QIWqgEWjBo5jYJ1kMqFhGLS1mYRC/Z9PFrSDlPpKSTh9H45s+VO0HqwdVAJ2XMpJcdmMy6gMVgLZSlfSSeJ4DqZh4jf9fZ4fNhgn+34Vi+t6w7L3TERkuOSUgF1//fX8v//v/8vatWtJJpN873vf49ChQ3zrW98qdnwiIjLGRaMmbm5nNOekpcXMKZmwLI9oNLfK28TSiWxt2drn2ViGAYnOyIDi7I3neYwvGd+dfGWvbZx0H9rJRKMGGzfa7Ntn09JikkoZeF72e1BS4lFb65DJZPdj9TW4pFhsG+rqCvgPQERkhMvpx2xDQwM/+MEPeOWVV1i2bBnV1dUsW7aMYHBwvxBEREQcB1zXAApTgenqMnIaJmEY5Jz41YZq2e/bT8JJYPSxMSsZDwwgyt4lnASXTbts0Nc5bu9ek6eeCrB/f/bXfTDYc8+b4xi0tMCxYxYHDhjs2WMzcaLLlCnOkCViJSUe4bB6EEXk1JHzj9dAIMDcuXNpaWmhqqpKyZeIiBSEZWUHWRRKrvuJshWg3O5rGAYLqhew/sj6Ptv/PGdw51klMgnOGHcG9ZH6QV0Hsm2df/xjiDfesIlETr7PzbJgwgSPvXsNDh2yOHLEYvbsDLW1xa1MeR5UVan6JSKnlpwSsKamJm699Va2b99OJBIhGo0yc+ZMvvrVr1JbW1vsGEVEZAwrLXULOv481/lQjmNQUpJ74he0g0yvmM6Oth29tiIaZv6JRNpNUxuu5aLJF+V9jeMOHDC5554IjgMlAziezOfLVqPicQPDgC1bbGpqXObPz+T8PR2oWAzOOitZnIuLiIxQOb1dd/vttzN9+nTuuusufv7zn3PXXXcxY8YMbr/99mLHJyIiY5zfn03CCsXn83JsLfQoLx/Y520oaWBq2VTSbvqEj1l5jqBPOSnK/GV8ev6nMY3BVdH27jX5z/+MYBjkdaj15MkZMpnsn32+7H66jRt9RZtSWFrqMXt2ft83EZHRKqef9Lt27eLaa6/tbjsMBoNce+217Nq1q6jBiYjIqaGqyi3Yi/yqKrc7ieiPbUMgj21bU8qmMKtiFo7r4L1j31qoNDbga8XSMRpKGvjsws8SsAa3h6y11eDuuyMEArlXAd+tutojGPS618KyoKPDYOvWwm8Ii8fhzDOTRauuiYiMVDklYLNmzWLHjh09btu5cyezZ88uSlAiInJqWbw4TWzg+Uuvyso8zJP8dvM8KC93837xP6FkAivGrSBoBUm7aZyMSUl1W86PTzkp0k6aS6ddyifnfRK/5c8vkLd4Htx7bxi/P//kC7KPnT8/g/OOopRlwbFjJseODa46906uC7W1LmeffWIlUURkrOvzLa177723+8/19fX8y7/8C0uXLqW6uprm5mZeffVVzj333CEJUkRExrb58zP89a8enjf4cojfD4GAh+P0fa10GqZOHVzrW9AOsrRuKU3xJnY0HqF0ynYSmQQBK4DRSxaUcTPEM3HKA+Usq1/GqoZVhOzCHID17LN+mpoKc5ZaaanHhAkOhw5Z3ZMQfT7Yts2msjJVkOmIqRRcfXVM1S8ROSX1+WO0ubm5x9/PPPNMADo6OvD5fJxxxhmkUqniRiciIqcE04SFC9O8/LKPQGDwr8rHj3fYs8fuM1mIRLwBDeDoi2EY1IZrmbawmmsuqGBL8xb2deyjNdVKykmBB6ZhUhYooy5Ux5yqOUwtmzrovV7v5Djw4ov+gh5mPGOGQyxm0tZmdH8PXRf277eYNm1wiWssBlddFaOyUqPnReTU1GcCduONNw5lHCIicoq74IIkW7b4yGQG10YH0NDgsm9f7x/LZGDRohw2ieUoGoWLLkpREahg5YSVrJywsmDXzsWmTT7icYPI4M+B7mYY2YR4y5bswc22nW1FPHLEYupUJ6/18bzsePwrr4wzd64Gb4jIqSvnRoJkMsmRI0dIJBI9bp8zZ07BgxIRkVOPbcNHPhLjl7+MEA4P7lqWBZMmOezda/WogmUy0NDgFKT6BW/vZTrttOHby7R+va+gyddxpgkLFmTYu9di3z4Ly8q2Dra1GQOuXiUS2UmX114bZ9w4nfslIqe2nBKwp59+ml/84hfYto3f33Oj8I9//OOiBCYiIqeeSZNcVq1K8uyzgUG31E2e7HDsmEkymT3XynWz+5sG20L3Tsf3Mp1s6EexeF52VHwhz1F7J8PI7pWrr3fYssVHKmXQ2GhSWZnb9zCR8LAsg+XLU1x0UbJocYqIjCY5JWB33303/+2//TcWLVpU7HhEROQUt2ZNikzGGPS+JsOABQvSvPyyH8/LHjK8aFG6YMlSPA4XXpigunr49jK1thokEoVtP+xNKATLlqXp6jJobjZwnOyBzaZpEAp53S2JmUz2+2JZUFnpccYZKc44I5XXuH8RkbEqpwTMtm3mz59f7FhEREQAuOiiJGVlLo89FsTnI+/KSTAIs2enOXrUYsGCTMEqMPE4nH12atjHqDc2mriuAQxNElhS4lFd7fDlL0dpbTXYs8dm3z6re2x9JOIxa1aGCRNcQiEN2RAR6U1OCdg111zDr371K6688krKysqKHZOIiAhnnJFmzpwMv/tdmEOHLCIRb0DDH+JxCIU8Pve5GOXlHnffHSaZBL8//wkfrgvJJJx/foJzzhn+M6ySyeK1H/bFdbPVxaoqj6qqNEuXDv/3QURkNMkpAZswYQK/+93vePTRR0/42DvPCxMRESmk8nKPz342yp49Fs8842f/fptMBsJhTmglPD5lz3Ggrs5l9eoUixenu4dw3HRTF3/+c5DXX/cRCp34+JOJxaC62uX66+PU1IyMQRKm6eGp0CQiMqrklID96Ec/4rzzzmPlypUnDOEQEREpJsOAadMcpk2LE4sZ7N1rsn27j+Zmk3TawPOyLYplZQ7TpjnMmJGhqurErMTngw99KMGKFSmefDLA3r02rpttU+yrspbJZJO6ujqXNWtSLFlSuD1khVBV5Xa3/w0V7ecSERmcnBKwrq4urrnmGgwdWS8iIsMoHPaYN89h3rz8s44JE1w+8Yk48bjBa6/Z7N1r09pq0tlp4HkGjgO27VJZ6VFf73D66ekROzq9ttbF7x+6EpjnkfMERBER6V1OCdiaNWtYt24dq1evLnY8IiIiQyIU8jj77PQJgzRqagI0NUWHKaqB8fuhrMwllRqaslw8bjB9uhIwEZHByCkB27FjB4888ggPPPAAFRUVPT723e9+txhxiYiISA6mTnXYvNnE5yv+57IsjwULMsX/RCIiY1hOCdiFF17IhRdeWOxYREREZIBWr07y2mu+oidgrguTJ2c0Xl5EZJBybkEUERGRkaeszGPy5AxHj9pFHUkfj8P55yeL9wlERE4ROSVga9eu7fNjF1xwQcGCERERkYH78IcT3HZbSdESsFQKFixI09AwMoeRiIiMJjklYM8880yPv7e1tXHkyBHmzp2rBExERGSYlZV5XHBBgsceCxIOF/bangc+n8dllyUKe2ERkVNUTgnYd77znRNuW7t2LQcPHix4QCIiIjJwZ56Z5tAhiy1bsgdNF4LnQTIJN9wQQ8eAiogURt5za9esWdNva6KIiIgMHcPItiLOn58mFhv89Rwn23p43XVRxo9X66GISKHkVAFz3Z4/eFOpFOvWrSMSiRQlKBERERm440lYQ4PDE08EsW3y2hcWj0Ndncs118QoL9fUQxGRQsopAfvYxz52wm1VVVV84QtfKHhAIiIikj/DyLYjzpuX4cEHg+zbZ+PzcdIx9Z4HsRiUlHhccEGSs89OYxhDE7OIyKkkpwTstttu6/H3QCBAWVlZUQISERGRwSsr8/j0p+N0dhqsWxdgzx6Ljg6TZNLAMLKJmuuCaXqEwx5VVS6XXJJi/vwMZt4bFERE5GRySsBqa2uLHYeIiMiQi6ajbG3eyu6O3XSkOsi4GUpLSvFSHhMiE5hfNZ/6SP1whzkopaUeH/hAdoJhOg2NjSadnQaZjEEw6FFb61JW5qnaJSIyRPpNwL773e/2+2DDMPj2t79d0IBERCQ/nufRnmqnMdZIwkngN/3UheuoDFRi6NV1Dwc7D/LE/ifY37Ufz/MIWsHu75GbdInFYuzv2M+6A+uoDlWzYtwKVtSvwDRGd2nI50NneYmIDLN+E7BVq1b1entLSwt/+ctfSCaTRQlKRERy43keuzt288zBZzgaPUrCSeB6LqZh4nouhmEQskJUh6pZOX4lc6rmjPokYjDSbpo/7/4zrx97nbAdJmgF+7yvz/Lhs3wknSSP7XmMV468wjVzrqE6VD2EEYuIyFjTbwL27kOWOzs7efDBB3niiSdYuXIlV155ZVGDExGRvh3qOsQDOx6gOdFMxI5gmRYRs/fptK2JVu7bfh+l/lI+NONDTC+fPsTRDr9oOsqdm+4kmo4S8Q1sim/IFyKaiXLH63dw5awrmVc9r0hRiojIWJfTHrBYLMZDDz3Eo48+ytKlS/m3f/s3xo0bV+zYRESkF57n8fj+x3nx0IuE7BAlvpKTPsYwDCK+CI7rcPfWu1lUs4gPzvjgKVMNi2fi/OzvPyPlpPBb+Z0obBomYV+Y+3fcz1XGVcytmlvgKEVE5FTQbwKWSqX4P//n//Dwww8zf/58/tf/+l9MmjRpqGITEZF38TyPP+78I5uaNxH2hQf8+OOJ2OaWzXSlu/j43I+P+STM8zzu3XYvSSeJbeb0vmO/wnaYB3Y8wFcXf5US/8mTXxERkXfq9zfRl7/8ZVzX5YMf/CAzZsygvb2d9vb2HvdZuHBhUQMUEZG3Pbn/STY1byJkh/q8j+uYuI6JabmYVu8DF4JWkL2de3lo50NcPvPyIkU7MrzS+AoHOg/0+z0bKNuwuW/7fVw3/zoNOBERkQHpNwHz+7NtGn/96197/bhhGCecESYiIsVxNHqU5w8/f0Ii4WRMDr85haa940h0hUnFgoAHgD+YIlgao7KhkYYFu7F9TvfjglaQvzf9ndNqTmNGxYwe1/Q8aG832LfPornZAqC01GXaNIfKSnfUnBPlei5PH3i6oMkXgGVa7OvYx+6O3afkfjoREclfvwnY7bffPlRxiIjISTyw44EeU/tcx2TX+rkc3T4Zx7Gw/WmA7v8DuK5JrL2EzqYK9m+cRfWUw8xe+XestxKxkB3ijzv/yNeWfg3TMGlvN3jqqQDbt9vEYgZg4Pdnk7l02sB1PUIhj8mTHdasSTJu3Mgeab6leUteQzdyEfFFeObgM0rARERkQAbfDC8iIkV3sPMgx+LHuhOJzqZyNj+xnFQ8iO1PY/fRanic5csA0LRnPK0H6pi7+lWqJh7DMAy60l1sanyDfeuXsmGDj0AAbBsiETheSQPw+Y7/2WD/fpuf/tRm5swMV1wRJ1TYAlPBrD+6nrA98L1yuTAMg4NdB0k6SQJWoCifQ0RExp5R0kQiInJqW3dwXXcbXcuBWl7907k4GatHtSsXls/BAzY9diZHtk8EwIhX8b9/YLJxo00kkk2+TsY0oaQE9u+3ufXWEvbsGZm/TloTrUXdo5V0khyNHi3a9UVEZOwZmb8xRUSkh6Oxo5iGSWdzKZsePwPLnybfvMIwwA6k2PbsYg6+MZENfzyfzngSfx7T2X0+sCyDu++OsGuXlV9ARRJNR4mmo0X9HCE7xPa27UX9HCIiMrYoARMRGeHimTidqU5cx2DL2jOwfPknX+9k2mme/dWleB44ZIhn4nldxzAgGIT/+q8wXV0jZyJgLB3D8ZyT33EQfKaP9lT7ye8oIiLyFu0BExEZ4dqSbWS8DLtfmUsyFsB+az/XYLUerMNJ27QcqKdi8j660l15nS0Gb1XVbLjvvhDXXRcrSILYm7Sb5vXG19netp22ZBvxTBwPD7/ppyJQwYSSCZw57kxK/CV4eBgUPyH0PO/kdxIREXmLEjARkRHOcR2cDBzdPqlgyVcmZdPZXI5lO8TbSyh3rEFXiywruyds+3aL2bMLW3lKOSke3fMoW1u2knJTBK1gj71dKTdFY7yRg10HeeHwC0wqncR5DecV/ZBpx3WKNuRDRETGJrUgioiMcJZp0bJnCumkr2DXbDtcjWlmJyd6HnQ0VmEZg9/DFQ57PPtsYScC7mzbyY9e+xGbmjdhmRYhO9TnYA2f5SNkh2iMNfKbrb/hSPRIUStUcSfOrMpZRbu+iIiMPUrARERGuMpAJe37puALDmziYV88DxIdEQwzm5iYlkuiK0KJr2TQ1zYMOHTIeusMscF7+ejL3PPmPXieh9/KfUqIaZiEfCGOxY+xpWVL0ZIwE5OGkoaiXFtERMYmJWAiIiNc0A7ixqoKdj0nbZPJ9OxAd1MBgnZhDvPKZGD//sH/etnaspVHdj/Sb8XrZCZEJnA0dpQ3W98cdDzv5nke9eH67uMBREREcqEETERkFPCna/AoTBUnGQ3x7ktZXhAnU5iqVTgM27cPrl0ynonz0M6HCPkGl9xMKJ2A3/RzNHaU5njzoK71bvFMnLMnnF3Qa4qIyNinBExEZBSYGJ5C2ilMC2I64cN4a/8XgOu5lPpLKVSXnmUx6BbEh3Y+VJCE0zIsxkfGY2DwZuubuJ578gflwPM8ygPlLKheUJDriYjIqUMJmIjIKFAWimSTpAJVwd7JMi1CVqhoo+MHKpqOsr1tOz6zMENHppdPJ2AHSDtpjkSPFOSacSfO1bOvLvqURRERGXv0m0NEZBQoLXWZVzUPxx38eHfLn8Hzsj/+Xc+lNlSLz5c9x6sQXBf8/vwTxWcPPluQiYzHGYbBwuqFmKbJwa6Dg75ePBNnzcQ1jIuMK0B0IiJyqlECJiIyClRWuoTsMFPLppJxB3cWWLAkDmSTrxJ/CQErQCjkFawCFosZzJyZf4z7O/cPaOJhLiK+CItrFxNNR0m5qbyvE01HWTl+JasaVhUwOhEROZUoARMRGQUWLMgQj8PkssmMj4wfVBJm+9MYZpqwL0x1sJpMJpvgFYphwOTJ+VXqPM+jJdlSsFjeqdRfysLqhdiGTTwTH9Bj026ajJvhyllXcsHkC4oSn4iInBoK1HAiIiIDFY0a7N5tsWOHTSxmUFpqkskEmTEjw/TpDiUlb7fxLViQ5rHHAoDBrMpZ+C0/ezv2YpkWBgMrXWW8NPXVfvypWiCbME2cOPjWxuPq6hzKyvJrQUw4CVJOqmij3SP+CGsa1uDg8Lcjf6M92U7YDvc55j6eieMzfcyqmMX7p71fI+dFRGTQlICJiAyx/ftN1q4Nsn+/heMYhMMepgnt7QZdXT7+/nc/punR0OCwZk2SadMcLAuWLEnzwgt+gkGYUjaF2lAtW1q20JXuwmf6TpqIHU9sFtYtxKosZ/367MTCmhq3YPu/olG44IL8W/xczy3aockABgYZL8PZE87mzHFnsrtjN682vkpLooWOZAcZL4PruQStIJXBSqaXT2dp3dKCt0SKiMipSwmYiMgQSafhz38OsnGjj1AIQiF494FcpgmRSPa25maLu+8OM39+mssuS7BmTZLNm22SSRPDgLAvzPL65XSmOtnTsYdoOkrSSfYYtW4aJj7TR8QXYX71fMr95dlqj9+jqsqlvd1k9uzB7Sk7zvOgutrl9NPzH5fvM31FnSzoeA5hXxjIDueYXj6d6eXTe9ynpqaGpqamosUgIiKnNiVgIiJDIJGAu+6K0NZmEA7n9hjDyB5qvG2bj5/+1OKzn41xzTVxfvazCIEA3UMzSv2lnFZzGpDdqxTPxHFcB8uwCNrBPqs306ZlSCQMzALlO/E4XHddfFDX81t+Svwlgx400hcPj0mlk4pybRERkVxoCIeISJG5Lvznf0Zobzfx+wc+atDvh1jM5Be/CFNV5fLxj0dJJun14GSf6aPMX0ZlsJKyQFmvyZfnZZOlj3wkzo03dvV5rYGIx+EDH0hQUzP4YR4VgYpBX6MvIStEZaCyaNcXERE5GSVgIjLmuS40N5vs3Wuxf79FS4sx6IRjINauDdDUZOIbxLnCtp3dI/boowGmTXP57Gej+P0uyeTArpNKeRiGx7XXRjnttAyTJ7tce22UdBoyeRSdPA9iMXj/+xMsXZp/6+E7La7JjosvNNdzaSht6HPghoiIyFBQC6KIjEmuC2++afH88wGamiwSCXAcA8PIDrwIhTxqa11WrUoyY4ZTsDOw3q293eCFF/w5tx32JxAw2LDBz1lnpamvd/nyl6OsXRvg1Vd9JJP9tzbG4+DzwcKFGS65JNEjGZwyxeWrX+3ivvtC7NtnEw7ndiZYLAZVVS7XXRcvSOXruIU1C3ls/2MFu95x8XScNQ1rCn5dERGRgVACJiJjzpEjJvfdF6K11SQczlaPSkqg58ALg+Zmi9/8JkJdncPVV8eori58WeyppwL4CzhALxCAJ57wc/XVCSwL3vOeJOefn+T1131s3WrT2mrS2Wniutk9YpGIS2Wlx6xZaZYvT/cZSyTi8elPx9i50+KZZwIcPGjhONlBIZaVvY/rZhM5w4C6OpeLLkpx2mnpgu0hO84yLc4edzZPHniyYGPfHddhUtkkGkobCnI9ERGRfCkBE5ExZf16H488EiQUgkik//saBpSUeHR1mfz4xyV88INxFi0q3PAHz4Nt2+yCjXiHbDK0e7cP1010Jz62DcuWpVm2LN39eR0nO1FxIMmRYcDMmQ4zZ8aIxw0OHDDZvt2mqyt7kWDQY8aMDJMnO5SWFreHc+WElWxq3kR7qh3LsAZ9PcdzuGrWVQWITEREZHCUgInImPHyyz4efTQ44Ha/bEsiPPRQCNuOM39+YZKw9naDeNw4aSI4UImEQXOzSW1t721/hsGgk75QyGPWLIdZswp3QPNAGIbBJ+Z+gh9v/DGu5w5qNH0sE+OqWVdR4i8pYIQiIiL50RAOERkTWloM/vrX4Ftna+UnFII//jFENFqYDWH791twksOR82FZHrt2Db4qNNKV+Ev4/GmfxzZsUs7AD3d2PZdEJsGVM69kbtXcIkQoIiIycErARGRMuP/+0KCmDB5nmvD73wcHfyGgo8PAtgvfqufzQWfnqfHjuzxQzleWfIW5VXPpSnf1OGS6L57nEU1HKfGXcOPpNzKvet4QRCoiIpIbtSCKyKh3+LDJ4cPWW4M2Bse2Ye9em/Z2g/LywSVPpjn487V643kD29uVj/ZkO5ubN7OvYx8xJwYeBOwAEyITWFi9kNpwbXEDeAef6ePDMz/MWePO4qkDT7G7YzdJJ0nIDuEzs1m34znE03FM02RceBzvnfpeFlYv1Mh5EREZcZSAicio98wzgYKMeT/O74d16wJcdlliUNcZP94lnYZgYQpq3ZJJmDChcMNC3ulg50Ee2fsIh7oOYRomASvwdhKThH0d+1h3YB114TrWTFwzpNWl8SXj+djcj5FyUhyJHmFb2zbaEm14eITsEDMrZjKpdBIRX4E33YmIiBSQEjARGfUaG82CVoRsGw4fHvweq/HjnYJOQDzONGHixMKduwXZ/VJ/2fMXXm18laAVJOzrPaP1W378lp9YJsbvt/+eGcdmcMWsKwhYgYLG0x+/5Wdy2WQml00ess8pIiJSKKfGJgIRGbMyGejoKPyPsra2wbeuBQIU9IDi4yorPUpKCtfb6Houd2+9m9eOvUbIDuXcthfyhdjbuZf/2PgfJDKDqxaKiIicKpSAiciolkoZOEWYlJ5OG7gFyJ1WrEgRiw3+OsfF47BkSbJwFwT+sPMPHOg6QNAaeK+kz/QRzUT51dZf4RVjw5uIiMgYowRMREY1w/AoxpwFw6Df6x4/7PhkOcfixWlKS72CDOPwvOz5XGeckR78xd6yrXUbm5s2D6qF0Gf6aIw28uyhZwsWl4iIyFilPWAiMqoFg+D3exT6vK1w2D0hATt61GTdOj9NTRZtbSaum92PVV7uUlfncN55KerqepbNLAs+8pEYv/xlCeHw4LKwWAw+8Yl4QcbtQ3Zc+6N7HiVkD+LwtLeEfCGePfQsZ447E7/lL0B0IiIiY5MSMBEZcTIZOHbMZN8+i0TCIBLxmDLFobraPWHYhmFAVZVHe3thE7DKyreTpY4Og/vvD7F/v0U4nE26/O/IMeJxk127TLZs8TFpksNVV8V77NGaNMnl/PPjPPlkMO9pjfE4rFqVYsaMwvVb7u3cS2uilYi/MFMDXdflpSMvcW7DuQW5noiIyFikBExERozmZoMnngiya5dNMpk9xNg0s61+jmMQDrvMnp3hwguTPRKcmTMzPPecv2Dj3mMxWLAg2+a3bZvFffeF8fno95wxy4JIBI4ds/jhD0u4+uoYs2a9nSyde24ay4InnggSDPbf3vhOnpdNvtasSbJqVWowX9YJ1h9Z3+e0w3wE7SBvtLyhBExERKQf2gMmIsPO82Dt2gA//nEJe/bYbyU73lvthRAKZf9umgZvvOHjRz8qYf36t/vwzjorWdB9YMGgx+LFaXbtsvjd78IEg9kEKxeWlW2LvPfeMLt39/wRe/bZab7whSjhsEtXl9HvvjDPg2gUAgGXz3wmWvDkC6Al0VLwg4pbEi0axiEiItIPJWAiMqw8D+69N8Tzz/sJhU6e6Nh2Nil75JEgjzySHRwRDMLy5SkSBZiEHo/DypVJHAceeCCcd1UtGITf/z5C6l15U22ty5e+FOVTn4pSX58BPGIx6Ow06OjwiMUMwKO21uHjH4/xla9EmTCh8KPsAbpSXQW/ZspNkXB6XwjXc2lJtHA4ephjsWNk3OIcJi0iIjKSqQVRRIbVX/4SYOdOm9AA50CEw/Dyy34qKlzOOivNRRcl2b7dJho1c65WvVsmA+PGuaxcmeahh4Kk0z33eg2EYUAqBY8+GuSyyxInfGzaNIdp0+IAdHUZRKMGlZV+kslOSkqKM9nx3VxczCK8D+e4To8/b2raxPqj62lONJNwEnieh2EY+AwfFYEK5lfP56zxZxG0C9RDKiIiMoKpAiYiw+bgQZMNG/LfuxUKwdq1QdrbDUwTPv3pGJbl5XUuWDoNoZDLJz4RJZOBN9/05Z18Hef3w9atNumTTI0vKfGor3eZMAFKS4cm+QKKMq3QwOi+7r7Ofdz62q38cdcfaU+1Y5s2Jb4SSv2llPhKCNgB4k6c5w49xw82/ID1R9arfVFERMY8JWAiMmwefTQ46MEZtg2PPZZtRSwp8fjiF6NUVrrE47lfIxaD8eMdPv/5KKEQbNtmD+jx/YnHDXbsyLMkV2QV/oqCX7PEV4Lf8vPswWf55eZf4rgOEV//UxaDdhCf5eORvY9w3/b7cL3itFyKiIiMBErARGRYdHQYHDxoDbraY1mwc6evu8oUiXh89rNRLrwwgWl6RKO9H5bsutnWP9v2uPTSBJ/6VKw7Gdy+3c57XPy7hcOwY0eBDu4qsAklE0g7hTvU2fM8qkPVrD+ynif3P0nEFxnQkI+wHWZ763Ye3PFgwWISEREZabQHTESGxfbt9lsvzgffchaPGzQ2mjQ0ZCsnhpGdOHjWWWm2bbPYtMlHa6vVPaQjFILqaodFi9JMm+ackAR2dponnDeWL9Ok4GeUFcrKCSv525G/4aMwCWI0HWVRzSIe3vVw3uPtg3aQrc1b2VK1hfnV8wsSl4iIyEgyJAnYHXfcwYYNGygvL+fmm28GoKuri1tuuYVjx45RW1vL17/+dUreOmTnwQcfZO3atZimyfXXX8/ixYsB2LVrF7fffjupVIolS5Zw/fXXYxgG6XSa2267jV27dlFaWsrXvvY16urqhuJLE5E87dljEQwWZr9PIOCxc6dNQ0PPkYOGAXPmOMyZM7BNYYXehuR5IzMBi/gizCifwb7Ofdjm4H4deJ5HTaiGDY0b8FmDS+hCvhB/2f0X5lTOwTJHZvumiIhIvoakBXHNmjX8z//5P3vc9oc//IHTTjuNW2+9ldNOO40//OEPABw4cIDnn3+e73//+/zzP/8zd955J66bfVf7Zz/7GV/4whe49dZbOXLkCK+99hoAa9euJRKJ8KMf/YgPfOAD/OY3vxmKL0tEBiGVMgo2bMKysu2EhRIMegVLwjwPgsGRu6fp8pmXF+Q6CSfBxVMuZl/XPkxj8L9aYpkYm5o3FSAyERGRkWVIErD58+d3V7eOW79+PatXrwZg9erVrF+/vvv2lStX4vP5qKurY9y4cezYsYPW1lbi8TizZ8/GMAzOO++87se8/PLLrFmzBoCzzjqLTZs2aZKWyAhnWYWrNLkuBAKFuRbAlCmZgpwpBpBIGEyblsdYxiESskN8aMaHiKfznzoSz8RZ3bCafZ378BuFmawYskO8duy1glxLRERkJBm2PWDt7e1UVlYCUFlZSUdHBwAtLS3MmjWr+35VVVW0tLRgWRbV1dXdt1dXV9PS0tL9mOMfsyyLcDhMZ2cnZWVlJ3zexx9/nMcffxyAf/3Xf6WmpqY4X6DkzLZtrcMwG441mDvX4NAhsyCJU0eHx5IlAWpqSgd/MeC88+DZZ03C4UJU1TzOPdfPu96D6tVwPRfOrTmXSFmE3235HUE7mHMFy/M8ouko75v5Pt474738fMPPKS8tL1hcSTM5LN8P/UwaflqDkUHrMDJoHcaeETeEo6/KVX8Vrd4+1tfkrYsuuoiLLrqo++9NTU0DjFAKraamRuswzIZjDcaNM2lvj+SUmJxMJgOhUCeF/BJqasI0NlqDGsbhulBfnyGRiOdUURvO58IkexKfnvVpfvfm72hMNBKx+59gGM/ECdthrpx5JdPKp9HU1MThtsPE0rGCxZTIJGg81liQlsaB0M+k4ac1GBm0DiOD1mH4TZgwoaDXG7YErLy8nNbWViorK2ltbe2uVlVXV9Pc3Nx9v5aWFqqqqk64vbm5maqqqh6Pqa6uxnEcYrHYCS2PMnIkk7B+vY89e2zSaYOKChPXDXLuuSnq60fuXhkprNpal+pql2RycC+uPQ8mTnQIhQoU2FsuvTTOj39cMqjrplLwgQ8UqJdxCNSEavjS6V/ijZY3eP7w8xyLHyPpJLsHVXp4BKwAlcFKVjesZnHd4p7DO4rQ+e167pAnYCIiIsU0bAnY8uXLefrpp7n88st5+umnWbFiRfftt956K5deeimtra0cPnyYmTNnYpomoVCIbdu2MWvWLNatW8cll1wCwLJly3jqqaeYPXs2L774IgsWLBjQ2TMyNNraDP761wC7dvlwHLrPXEokDLq6fGza5KO+3uXss1MsWpQu2IAGGZkMA845J8XDDwcHdeZWPA4XXFD4JKe62mP16iRPPx3IKwmLx2H16iTV1aNrP6phGMyrnse86nk4rkNzopmWRAseHmX+MurCdfjM3qcc+i0/sUzhKmC2aWMZmoIoIiJjy5AkYD/4wQ/YsmULnZ2dfPGLX+Tqq6/m8ssv55ZbbmHt2rXU1NTwjW98A4BJkyZx9tln841vfAPTNPnMZz6D+VYP0Gc/+1nuuOMOUqkUixcvZsmSJQBccMEF3Hbbbdx0002UlJTwta99bSi+LBmAvXtNfvvbCJYFPl/2v3cyTSgpgWjU5KGHguzaZfGhDyVyav9Kp2HLFh9Hj5qkUlBa6jFvXoa6OlXTRrrFi9O89pqPxkYLO4+fRsmkx2mnZZg0qThrfe65Kbq6DF5+OUA4nHsiFYsZrFiRZNWq1MnvPIJZpkVduI66cG7HelQFq2hJtBSsYlUZrNSbaSIiMuYY3ik+LvDQoUPDHcKYd/iwyZ13RggG6bWqFQ6HicV6vmsej8PChWkuv7zvykZrq8FTTwXYts1HKgV+fzaRy2SySVl9vcsZZ2SraZbeRO/XcPaXx+PwH/8RIZEwB5SEpVIedXUen/50NK/kbSBef93HI48EcByj36EhySRYlscllyQ5/fT0gD/PaO/z39G6g9+++Vsivsigr5V205xeczrvm/a+AkQ2MKN9HcYCrcHIoHUYGbQOw2/M7AGTU4PnwT33hPtMvvoSCsHGjX5mzcqwYEHmhI+//rqPhx4K4veDbdPjBbjfn/2vq8vkT38Ksn69n099Ktrd8igjSygEn/98lHvuCXPwoJVTO2IsBrNnO3zkI/GiJ18Ap5+eZvbsNM89F2DzZh8dHQaOk23Xc10P24ayMo/Fi9OsXJks+H600WJGxQzK/GU43uDH7mfcDKsaVhUgKhERkZFFCZgU1RtvWHR1GUTyeEM8HPZ4/vnACQnY66/7+OMfgzldMxKBlpZsBe7zn4+e0PooI0MoBNdfH+OVV3w8/3yA1laTQMDrsV6pVPa/2lqX978/wbx5Q3u2VigEF12U5KKLksRiBo2NJvF49vb6epdQ6JRuJgCyCenqhtU8vPthwr78N/alnBTzq+dT4tcwJRERGXuUgElRPf98IO8BC4aRbV9sbjaprs7u8WltNfjTn3JLvo7z+aCjw+TBB0NcfXX+h81KcRkGLF+eZtmyNAcOmGzd6uPYMRPXNbBtj/HjXebPT4+IvX3hsMfUqSP3cOXhtLhuMX9v/juHug7hswb+jofnefgtP5dOu7QI0YmIiAw/JWBSNPG4weHD1qDasYJBeO45Px/8YHYv2FNPBfKqYvn9sH27TSxmDGiYggw9w4BJk1wmTUoOdyhDIuNmONx1mPZUOxWZCtyYy/iS8X1OGhzpDMPgo3M+yp2b7qQt2Ybf8uf8WNdzcVyHz532uQE9TkREZDRRAiZFE40auIMsVlhW9jqQHayxfbud954f04Rnn/Vz8cV9v7A/etTkqacCHD5skUplHxMKeSxalOaMM1L9DmAQGYi2ZBtP7X+Kba3biGaiWIZFKBSiK9ZF2A4zs2Im5088n6pQ1XCHOmB+y89nFn6GB3Y8wPa27YTtk5fB45k41cFqPjrno1QGK4cgShERkeGhBEyKxnHA8wwGezqr42QTsC1bfCSTRt4tjX4/vPGGr9cErK3N4L/+K8zRoyahUDbxOz45MZEwWLcuwLPPBli6NMXFFyd1RpnkzfM8Ht71MK8dew2/5cc2bcr82YPow/4wZiY7wn1n2062NG9hfvV8Pjzzw6PuMGK/5eejcz7KGy1v8PSBpzkSO4JlWAStYPdo+ZSTIuWkqAhWcP7E81k5YaXGzouIyJinBEyKJhDwMIzBt/v5/dlrHD1q4h9kV1IiceKLu8ZGkzvvDGPbBiV97Pk/PkFx/Xo/ra0m11wTVxImA+Z5Hv/15n+xs33nSYdUWKZF2AzzZuub/Hrrr/nkvE+OuiQMYG7VXOZWzaUl0cKbLW+yv2s/GTeDiUl9pJ45lXMYHxmvxEtERE4ZSsCkaMrKPEpKvLeqYPmJRmHWrOwUxOMtgYPhvGtuQjwO//mfEXy+3Mbkh0KwY4fNX/4S4P3vPzX2KEnhPLzrYXa17yJk574xMmAFONh1kAd3PMhHZn2kiNEVV1WwirMnnM3ZnD3coYiIiAyr0fd2qowapgnz56dJpfK/xvH9V5BN6DInHgk2ID5fz4rcM88EyGQGfkbZa6/5iWugogxAZ6qT15teJ2gP/EC6gBVgc/NmWuItRYhMREREhpISMCmqVatSJ1SdcpVOw9y5me69WHPnZkin84/F86Cm5u2pIK4Lmzf78m5rfOEFTeSQ3D194GlsM/+mg4AV4MkDTxYwIhERERkOSsCkqCIRj2XLUgOuFnledgjGhRe+3eZXV+dSX5//WMVYzGDVqrevt3OnRUdHfu2RgQD8/e+jc0y4DD3HddjasnVQo+Vt02ZH2w7S7iDehRAREZFhpwRMiu6SS5LMnp3JOQlzXUinPa67LnrCmV1nnZUiGs0vjvJyl+nT3y7HHTxoD2qsfDRq4OlIMcnBsfgxutJdg75OLBPjSNeRAkQkIiIiw0UJmBSdYcBVV8VZvjxFKgXJPmZXuC50dRmEwy5f+lKU2toTq12nnZZm/Hh3wK2I8Ti8972JHnu9UqmB7f16N88jp3POOjoMtm2z2LjRZs8ea9D72GT0iWfigz2NAQDTMOlIdQz+QiIiIjJsNAVRhoRhZCthq1cnef75ABs3+ujqylaQPM8jlYLJkzOsXp1k4kS3z8TINOGTn4zyi19EaG/PbSx9PA6XXJJg7tyemU9JiUsmQ94HO9v222eF9Wb7dot16wIcPGjhugamCY7jEYl4zJ6d4fzzk5SXq4R2qjAozJh1jWsXEREZ3ZSAyZAKhbL7ui64IEk8bpBIQH19gFisE1+O22OCQfjc56I8+GCQHTt8GAYnJGKel93zVV7ucuWVJyZfAPPnZ3jiify+Ds+Durrep4t4HvzpT0FefdVPJOIRiUDP8ofBm2/62LzZxzXXxJg5M88pJSOM52XPVGttNbAsqK52qapSgglkz/wqQN7kuA6lvtLBX0hERESGjRIwGRaGAeGwRzgM5eUMuKXQ54Orr04Qjyd59lk/W7f6SCQMHCc7ar6mxmXVqiTTpzt9VtMqKjzGj3doa7MG3IoYi8EHP9h7L+Vf/pKt8JWU9J18HK+e3XtvmOuui9LQkP9wkeGWTsOLL/p59VU/LS1vdzUbhse4cS5nnZVi0aL0KX1wdW2oljJ/Gd4g+xBLfCWMLxlfoKhERERkOCgBk1EtFPJ4z3uSvOc9+R2KfM45SX73u0i/ydK7eR6UlnrMmnVi5aqlxeDllwNEIie/nmFkpyk+9FCIL30pz8kiwyweN/jFL8K0tZkEg9nvyzt1dZn88Y9BNm3y8dGPxvpt2RzLTMNkfvV8Xm18Fb+V37kHGTfD3Mq5gxplLyIiIsNPQzjklDZ3rsP8+WkSidzu73mQSMBVV8V6reg8+WSAQCD3ZM4wsm17R4+OvqdiOg2/+EWYrq5s8tWXSAT27jW5777Q0AU3Aq1qWIXj5d9umnJSrJm0pnABiYiIyLDQW6kyqqXdNBsaN/Ba42vE0jEcz8Fn+RgfGc+aiWuoC9f1+3jDgI98JM7994d44w37rf1avXOcbNLx0Y/GmDjxxJZBz4OdO+0BD/UIh+GZZ/xceWWOWeAI8dJLPlpbTUI55FWBgMGbb9ocPGiO6nbLwYj4IqyoX8HLR18maPeTsfYikUmwuG4x5YHyIkUnIiIiQ0UJmIxKnufx5P4nebnxZVJOipCdzQIMwyDjZtjTsYefbPwJ9eF6rpx1JdWh6j6vZZrZMfkbN/p48UU/R45kKzrH2+USieyesxkz0lx4YZLKyt4rXIkEpFJGTgnJuz9/Z+foqoB5HmzYEBjQ1xoOw1NPBfjEJwZ4KvcYcvGUi+lKd7G1ZWv3v9mTiWfizKiYwaXTLi1ydCIiIjIUlIDJqON5Hr/f/nveaH2DkB3q9YWsZVhEfBE6Uh389O8/5dPzP01DSUOf1zQMOP30NKefnubYMZNXX/URjWan+Y0f73D66ekcRt7nfzDzaDvQuanJpKXFoKQk98eYJuzbZ+M4/Y/vH8sMw+CKmVfw171/5eWjL2MaZp97wtJumoybYXHtYj4w7QMaPy8iIjJGKAGTUefRvY92J18nYxomftPPr7f8mhtPv5GyQNlJH1Nb63LxxQMf6hEIeDmP0n8nz8sOExlN2tsNPM9goKcLZzKQTBqEw6Pr6y0kwzB479T3sqphFc8cfIbNzZvpSHXgeR5pM00ilaDEV8JpNaexumE1Jf4BZLkiIiIy4ikBk1Elnomz4eiGnNu3IPuC1zAMHt//OFfMvKJosZlm9jDpgwdtzAF0FMZiBmedlSpaXMVgWflV7QwjO55esmeDvXfqe3nPlPfQFG8inolTWVlJvDNOTbAGyzxFy4QiIiJj3OjaeCKnvOcOPZfXgba2abOjdQdpd4AHjg3Q6tVJ4gPc4lRZ6TJ16ug6jLmqys2rjdDv9wgECh/PaGYaJnXhOqaUTWF65XTqw/VKvkRERMYwJWAyqmxu3kzAyu8VfMpN8Vrja4UN6F0aGlxmzsyQSuVW5YnH4aKLEqPukOLyco9x4zIDqoJlMjB7dmZA1UERERGRsUYvhWTUcFyHrlRX3o8PWkH2d+0vYES9u/rqOOPHu/1Wwjwvm3y9970J5s3LFD2mYjjnnCSxWO73TyRgzZr8DswWERERGSuUgMmokXbTeAMc+vBOhmGQcYqf7Ng2fOpTMVavTmLbHtGoQToNrgvJJMRiUF3tcO21UVasKG5LZDHNn+8wZ04mp0OsYzE4//wkFRXa/yUiIiKnNg3hkFHDb/kHNYrb87w+R34XmmnCqlUpzj03xe7dFrt22SQS2da9009PU1Y2+hMRw8ien/bAA0E2bfIRiXBCe2E6DalUNvlatWp0DRoRERERKQYlYDJqmIZJZaCSeCa/g3xjmRhzKucUOKr+GQZMn+4wffroGrKRK9OEK69McO65KZ56KsCePTbpdPbrDgQ8FizIsHp1ckwknCIiIiKFoARMRpUV9St4dO+jAxpDf1ypv5Q5VUObgJ0qxo1z+ehH491tltkEjFE3XERERESk2JSAyaiypG4Ja/evHfDjkk6SFfUrMI3Rve3RcWDTJh9791qYpseSJWkaGtzhDqubaUJo4LmxiIiIyClDCZiMKrZp894p7+Xh3Q/nXAVzXIdSXymrJ64ucnTF9eqrPh5/PEA8bhAOZ4d6bNjgp7ra5aMfjVFdrTY/ERERkZFudJcD5JS0uG4xF066kHgmjneSg6jSTpqgHeSGhTcM2QCOYnj1VR8PPxzENA0ikWxrn2VBJAKxmMnPflZCe7v6/URERERGOiVgMiqdPeFsrp59NWWBMqLpKI7Xc8hFPBPHcR3mVs3li4u+SMQXGaZIB891Ye3aAOFw7x83zex/jz4aHNrARERERGTA1IIoQ87zPA50HeClIy8Rz8SpKKug0qhkRf2KAVWpZlfOZnblbJpiTaw7uI7OVCcODn7Tz5zKOSypW4Jtjv5/4lu3WkSj2cpXXywLdu2ySaXAP3oLfSIiIiJj3uh/dSqjyputb/LY3sdoSbQQskOYhkknnbze8TrPHHiGeVXz+MD0DwwocaoJ13DFrCuKGPXw2rvXJphDcSuZhK4uk6qqkTOUQ0RERER6UguiDJlXG1/ld2/+jqSTJOKL9JhIGLSD+Cwfm5s3c9fmu8i4mWGMdGTx+bJtiCdjGGCaGsQhIiIiMpIpAZMhcSR6hId3PUzY18dGprcE7ABN8SYe3PHgEEU28i1ZkiaVOvn9yss9ysuVgImIiIiMZErAZEis3b+WoJ3bkAi/5Wdb6zai6WiRoxodampcxo1z+q2CxeOwZElKBx+LiIiIjHBKwKTo4pk4ezv2DugQZNMweebgM0WManT56EfjAGR66cyMx2HqVIdzz82hTCYiIiIiw0oJmBTdoa5DJJzEgB7jt/wc7DpYpIhGn7Iyjy9/uYvZs9M4jkcsBrEY+Hweq1cn+fjHY5h6NouIiIiMeJqCKEUXz8QHVP06ToM4egqHPT784QSZDESjxlsHMXtqOxQREREZRZSASdGV+ktxvYGPRveZviJEM/rZNhq2Mcw8z6Mz3UkykyRoByn1lw53SCIiIjJKKAGTomsoaSBi93OKcC/imThnjT+rSBGJ5MdxHV468hKvNL5Ca6IVFxcTk8pgJcvrl7OifgWWaQ13mCIiIjKCadeIFJ1t2syunD2glkKf6WNF/YoiRiUyMGk3zV2b7+KJ/U+QdJKEfWFKfCWEfWGSTpLH9z3OL7f8krSbHu5QRUREZARTAiZD4oJJF+B6Lp538ta5eCbOsvpl+C3/EEQmkpt737yXY/FjhOxQrx8P2SGOxY5x35v3DXFkIiIiMpooAZMhURYo45PzPknKTfW7HyyWibGgegEXTrpwCKMT6V9TrIld7btO+qaAz/Kxo30HzfHmIYpMRERERhslYDJkJpZO5AunfYGGkgZSTop4Jk7GzZByUkTTUQJWgEumXMLlMy7H0Gg/GUGePPBkzgeJB60gTx14qrgBiYiIyKilIRwypKpD1Xx87seJZ+JsbtpMa7KV2spaao1aJpRMUOI1xDwPXnjBR1OTyfz5FjNnOsMd0ojUkmjBMnIbrmGZFi2JliJHJCIiIqOVEjAZFiE7xPJxywGoqamhqalpmCM6NW3davPYYyFqaw02bgzzjW90EQ5rxP27eQzse+J4SmRFRESkd2pBFDmFNTWZ2Hb2MOdMBuJxVSB7E/FFchogA9kzwkp8JUWOSEREREYrJWAip7AzzkhRVeWSTMJpp6Wpqhr4gdmngnMmnEM0E83pvrF0jHMbzi1yRCIiIjJaqQVR5BQWDMKXvhSlujpEc3NiuMMZsaaVTaM6WE08E8c0+n7fyvVcqkPVTCmdMoTRiYiIyGiiCphILzpSHexu383BroMDOkB6tNLsk/4ZhsEn530SAMftfX/X8ds/Oe+TGiYjIiIifVIFTOQdGqONPLz74e7Ey8Oj1F/KaTWncfGUi/utfsjYVh4o58bTb+RPu/7E7vbdpN00tmnjuA62aTO9fDqXTr+UiC8y3KGKiIjICKYETOQtR6NH+fnmnxMwA4R94R4f29C4gaZYEx+f93ElYaewiC/CR+d8lEQmwba2bXQmOykNlDK7YnbO54SJiIjIqU0JmMhbHtjxAAEz0Gv7WMAKsLN9J5uaN7GoZtEwRCcjSdAO6t+BiIiI5EVv5YsAjbFGGmON/e7difgi/O3w34YwKhEREREZa5SAiQD7OvZhmVa/9zEMg7Zk29AEJCIiIiJjkloQRQDLtHI6aNdA0+1Gq7SbzlY5MagP15804RYREREpBiVgIsCsilknHR3ueR71kfohikgKxfM8/rr3r7ze9DrRVBQMKPGVsKJ+BasnrtbIeBERERlSSsBEgBJ/CVPLpnKw6yC22fvTIpqOsrph9RBHdmpKu2me3P8kW5q3kHASROwIi+sWc86EcwY8hfKhnQ/x9+a/E7JDlAXKum9/9uCzpN0075nynkKHLyIiItIn7QETectHZn2EkB0i6SR73O55HtF0lPMmnsfkssnDFN2pI+NmuGvzXbx85GUcz8Fn+ki5KZ4+8DT3brs3p1bR47pSXWxs2kjIDp3wsZAvxPqj60lkEoUMX0RERKRfSsBE3hKyQ3xh0RdYXr8c0zCJZ+IkMgkqghV8dM5HOX/S+cMd4ilh/ZH1NEYbCdiBHreH7BDbWrexu2N3ztfa0Lih371eGTfDGy1v5B2riIiIyECpBVHkHQJWgIunXMzFUy4m42YwDVMHLw+xvzf/nZDvxIoVQMSO8OLhF5lePj2nayWcBJbRdwJmGRYJRxUwERERGTp6ZSnSB9u0lXwNg7ST7vNjhmH0+/F3m1M5p98WQ8dzmFk+c0DxiYiIiAyGXl2KyIhSEazA9dxeP5Z20tSF63K+1uTSydSEanq9XsbN0FDSQE24Ju9YRURERAZKCZiIjCjnTzyfeCbe68cyXoZVDatyvpZhGHxy3ifxmT6i6Sie53UPVSnxlfDxuR8vVNgiIiIiOdEeMBEpGM/zaE+1Yxs2Jf6SvK4xoWQC75v6Pv66968YhkHACpDIJLBMi6tmXzXg65YFyvjqkq+ypXkLm5s3Y2Bwet3pzK6YrTPAREREZMgpARMZIp7njekX/C2JFn77xm9pSbRgYDCpdBIfm/sxAlbg5A9+lxXjVrCwZiEvHn6RlkQL4yPjWV6/HL/lzys20zBZWLOQhTUL83q8iIiISKEoARMpsg2NG3j6wNPE0jHqwnVcNesqKoIVwx0WkE0KNzdvJtmepNasZXJp/uec/deb/0U8EyfiiwBwJHaEh3Y+xFWzr8rreiE7pNH/IiIiMuZoD5hIER2LHeNPO/+E67kE7SBtyTZ+8+ZvhjssIJt83bvtXh7Y8QDP7n+WX27+Jc8fej6va3WlumiON/eYGukzfezr3FeocEVERETGBCVgIkX00pGXCNlvn2llGiYt8Ra6Ul3DGFXWoeghtrZsJeKL4Lf8RHwRnj7wdJ8TCPtjmVavI/ttU0V2ERERkXdSAiZSRFXBKtJuz3OrLNPCZ/mGKaK3daW7MOi5J83xHDJuZsDXCtkhZpTPIOWkum+LZ+KcXnP6oOMUERERGUuUgIkU0dL6pYTsECkn1T3+fFHNorwGUxy3u303P934U3699dd9jmvPxcSSifhNP57nAeC4DtXB6rwHXVw5+0oWVi/EMiz8lp/VE1ezeuLqvOMTERERGYvUHyRSRAErwJdO/xLPHHyGlngLC2oWsLB6cJP4HtjxAJ7n0ZZs4+FdD+c95CLii3DtvGv5464/4nkeNeEarpl9Td5x2abNZTMuy/vxIiIiIqcCJWAiRRayQ1w85eKCXc/1XEzDxMDA8ZxBXWty2WRuWnwTNTU1NDU1FShCEREREemLWhBFRpnLpl9GwApQFajiA1M/MNzhiIiIiMgAqAImMsrMrZrL3Kq5wx2GiIiIiORBFTAREREREZEhogRMRERERERkiCgBExERERERGSJKwERERERERIaIEjAREREREZEhogRMRERERERkiCgBExERERERGSJKwERERERERIbImDqI+bXXXuOuu+7CdV0uvPBCLr/88uEOSUREREREpNuYqYC5rsudd97J//yf/5NbbrmF5557jgMHDgx3WCIiIiIiIt3GTAK2Y8cOxo0bR319PbZts3LlStavXz/cYYmIiIiIiHQbMwlYS0sL1dXV3X+vrq6mpaVlGCMSERERERHpaczsAfM874TbDMM44bbHH3+cxx9/HIB//dd/paampuixSf9s29Y6DDOtwcigdRgZtA7DT2swMmgdRgatw9gzZhKw6upqmpubu//e3NxMZWXlCfe76KKLuOiii7r/3tTUNCTxSd9qamq0DsNMazAyaB1GBq3D8NMajAxah5FB6zD8JkyYUNDrjZkWxBkzZnD48GEaGxvJZDI8//zzLF++fLjDEhERERER6TZmKmCWZXHDDTfwve99D9d1Of/885k0adJwhyUiIiIiItJtzCRgAEuXLmXp0qXDHYaIiIiIiEivDK+36RUiIiIiIiJScGNmD1g+vvnNbw53CILWYSTQGowMWoeRQesw/LQGI4PWYWTQOgy/Qq/BKZ2AiYiIiIiIDCUlYCIiIiIiIkPklE7A3nkemAwfrcPw0xqMDFqHkUHrMPy0BiOD1mFk0DoMv0KvgYZwiIiIiIiIDJFTugImIiIiIiIylMbUOWB33HEHGzZsoLy8nJtvvhmAPXv28LOf/YxEIkFtbS1f/epXCYfDPPPMMzz00EPdj923bx//9m//xtSpU/m//+//m9bWVvx+PwDf+ta3KC8vH5avaTQayDpkMhl+8pOfsHv3blzX5bzzzuPDH/4wALt27eL2228nlUqxZMkSrr/+egzDGM4vbVQp1Dro+ZC/ga7Bf/zHf7Bz505M0+S6665jwYIFgJ4Lg1WoddBzIX9NTU3cfvvttLW1YRgGF110Ee9///vp6urilltu4dixY9TW1vL1r3+dkpISAB588EHWrl2LaZpcf/31LF68GNDzYTAKuQ56PuRvoOvQ2dnJ97//fXbs2MGaNWv4zGc+030tPR/yU8g1yOu54I0hmzdv9nbu3Ol94xvf6L7tm9/8prd582bP8zzviSee8O65554THrd3717vy1/+cvffv/Od73g7duwofsBj1EDW4ZlnnvFuueUWz/M8L5FIeDfeeKN39OjR7se8+eabnuu63ve+9z1vw4YNQ/uFjHKFWgc9H/I3kDX4y1/+4t1+++2e53leW1ub94//+I+e4zjdj9FzIX+FWgc9F/LX0tLi7dy50/M8z4vFYt5Xv/pVb//+/d6vf/1r78EHH/Q8z/MefPBB79e//rXneZ63f/9+77//9//upVIp7+jRo95XvvIVPR8KoJDroOdD/ga6DvF43Nu6dav36KOPej//+c97XEvPh/wUcg3yeS6MqRbE+fPnd79jc9yhQ4eYN28eAIsWLeJvf/vbCY979tlnOeecc4YkxlPBQNchkUjgOA6pVArbtgmHw7S2thKPx5k9ezaGYXDeeeexfv36If06RrtCrIMMzkDW4MCBAyxcuBCA8vJyIpEIu3bt0nOhAAqxDjI4lZWVTJ8+HYBQKERDQwMtLS2sX7+e1atXA7B69eruf9vr169n5cqV+Hw+6urqGDduHDt27NDzYZAKtQ4yOANdh2AwyNy5c7srLMfp+ZC/Qq1BvsZUAtabSZMm8fLLLwPw4osv0tzcfMJ9XnjhhRMSsDvuuIP/8T/+B/fffz+e5pQMWl/rcNZZZxEMBvn85z/PjTfeyGWXXUZJSQktLS1UV1d3P766upqWlpZhiX0sGeg6HKfnQ+H0tQZTp07l5ZdfxnEcGhsb2bVrF01NTXouFMlA1+E4PRcGr7Gxkd27dzNz5kza29uprKwEsi+IOjo6AE74d19VVUVLS4ueDwU0mHU4Ts+HwctlHfqi50NhDGYNjhvoc2FM7QHrzZe+9CXuuusu7r//fpYvX45t9/ySt2/fjt/vZ/Lkyd23ffWrX6Wqqop4PM7NN9/MunXrurNhyU9f67Bjxw5M0+SnP/0p0WiUb3/725x22mn6QV4kA12H+vp6PR8KrK81OP/88zlw4ADf/OY3qa2tZc6cOViWpedCkQx0HUC/GwohkUhw8803c9111/VbZe/r372eD4Ux2HUAPR8KIdd16IueD4M32DWA/J4LYz4Ba2ho4Fvf+haQbTnZsGFDj48/99xzJ1S/qqqqgGxJ8txzz2XHjh36oTJIfa3Ds88+y+LFi7Ftm/LycubMmcPOnTuZN29ej2plc3Nz97pI/ga6DvX19Xo+FFhfa2BZFtddd133/b71rW8xfvx4IpGIngtFMNB1AP1uGKxMJsPNN9/MqlWrOPPMM4Fsm2drayuVlZW0trZSVlYGZN/Jf+e/+5aWFqqqqk64Xc+HgSvEOoCeD4M1kHXoi54Pg1OINYD8ngtjvgWxvb0dANd1eeCBB3jPe97T/THXdXnxxRd7JGCO43SXGzOZDK+88gqTJk0a2qDHoL7Woaamhk2bNuF5HolEgu3bt9PQ0EBlZSWhUIht27bheR7r1q1j+fLlw/kljAkDXQc9HwqvrzVIJpMkEgkANm7ciGVZTJw4Uc+FIhnoOui5MDie5/GTn/yEhoYGLr300u7bly9fztNPPw3A008/zYoVK7pvf/7550mn0zQ2NnL48GFmzpyp58MgFWod9HwYnIGuQ1/0fMhfodYg3+fCmDqI+Qc/+AFbtmyhs7OT8vJyrr76ahKJBI8++igAZ5xxBh//+Me7x3Nu3ryZ3/72t3zve9/rvkYikeA73/kOjuPgui6nnXYan/70pzHNMZ+rFsxA1iGRSHDHHXdw4MABPM/j/PPP54Mf/CAAO3fu5I477iCVSrF48WJuuOEGjVYdgEKsg54PgzOQNWhsbOR73/sepmlSVVXFF7/4RWprawE9FwarEOug58LgvPHGG3z7299m8uTJ3f92P/axjzFr1ixuueUWmpqaqKmp4Rvf+Eb3/tMHHniAJ598svs4gCVLlgB6PgxGodZBz4fByWcdvvzlLxOLxchkMkQiEb71rW8xceJEPR/yVKg1qKmpyeu5MKYSMBERERERkZFMb1WIiIiIiIgMESVgIiIiIiIiQ0QJmIiIiIiIyBBRAiYiIiIiIjJElICJiIiIiIgMESVgIiIiIiIiQ0QJmIiIjFm33nord9xxR4/btmzZwg033EBra+swRSUiIqcyJWAiIjJmXX/99bz66qts3LgRgFQqxU9/+lM+9alPUVlZOejrO44z6GuIiMipRQcxi4jImPbCCy9w9913c/PNN/PAAw+wZ88errzySn71q19x4MABamtrue6661iwYAEATz75JA899BDNzc2UlZXxoQ99iPe85z0AbN68mR/96Edccskl/J//839YtGgRN91003B+eSIiMsrYwx2AiIhIMZ199tk8//zz/PCHP+TNN9/k3/7t3/inf/onvvKVr7B48WI2bdrEzTffzA9+8APKysooLy/nn/7pn6ivr2fr1q38f//f/8eMGTOYPn06AG1tbXR1dXHHHXeg9zBFRGSg1IIoIiJj3mc+8xk2bdrEwmwDcgAAAZlJREFUlVdeyXPPPceSJUtYunQppmmyaNEiZsyYwYYNGwBYunQp48aNwzAM5s+fz6JFi3jjjTe6r2UYBldffTU+nw+/3z9cX5KIiIxSqoCJiMiYV1FRQVlZGRMnTuSll17ixRdf5JVXXun+uOM43S2Ir776Kvfffz+HDh3C8zySySSTJ0/uvm9ZWZkSLxERyZsSMBEROaVUV1ezatUqvvjFL57wsXQ6zc0338xXvvIVli9fjm3b/Pu//3uP+xiGMVShiojIGKQWRBEROaWsWrWKV155hddeew3XdUmlUmzevJnm5mYymQzpdJqysjIsy+oxQVFERKQQVAETEZFTSk1NDf/4j//I3XffzQ9/+ENM02TmzJl87nOfIxQKcf3113PLLbeQTqdZtmwZy5cvH+6QRURkDNEYehERERERkSGiFkQREREREZEhogRMRERERERkiCgBExERERERGSJKwERERERERIaIEjAREREREZEhogRMRERERERkiCgBExERERERGSJKwERERERERIaIEjAREREREZEh8v8D7Meqr1iA6iwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### type your answer here\n",
    "\n",
    "\n",
    "# For China\n",
    "    ax0 = df_can_t.plot(kind='scatter',\n",
    "                        x='Year',\n",
    "                        y='China',\n",
    "                        figsize=(14, 8),\n",
    "                        alpha=0.5,                  # transparency\n",
    "                        color='green',\n",
    "                        s=norm_china * 2000 + 10,  # pass in weights \n",
    "                        xlim=(1975, 2015)\n",
    "                       )\n",
    "\n",
    "    # For India\n",
    "    ax1 = df_can_t.plot(kind='scatter',\n",
    "                        x='Year',\n",
    "                        y='India',\n",
    "                        alpha=0.5,\n",
    "                        color=\"blue\",\n",
    "                        s=norm_india * 2000 + 10,\n",
    "                        ax = ax0\n",
    "                       )\n",
    "\n",
    "    ax0.set_ylabel('Number of Immigrants')\n",
    "    ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
    "    ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<details><summary>Click here for a sample python solution</summary>\n",
    "\n",
    "```python\n",
    "    #The correct answer is:  \n",
    "    \n",
    "    # China\n",
    "    ax0 = df_can_t.plot(kind='scatter',\n",
    "                        x='Year',\n",
    "                        y='China',\n",
    "                        figsize=(14, 8),\n",
    "                        alpha=0.5,                  # transparency\n",
    "                        color='green',\n",
    "                        s=norm_china * 2000 + 10,  # pass in weights \n",
    "                        xlim=(1975, 2015)\n",
    "                       )\n",
    "\n",
    "    # India\n",
    "    ax1 = df_can_t.plot(kind='scatter',\n",
    "                        x='Year',\n",
    "                        y='India',\n",
    "                        alpha=0.5,\n",
    "                        color=\"blue\",\n",
    "                        s=norm_india * 2000 + 10,\n",
    "                        ax = ax0\n",
    "                       )\n",
    "\n",
    "    ax0.set_ylabel('Number of Immigrants')\n",
    "    ax0.set_title('Immigration from China and India from 1980 - 2013')\n",
    "    ax0.legend(['China', 'India'], loc='upper left', fontsize='x-large')\n",
    "\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "\n",
    "## Author\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/aklson/\" target=\"_blank\">Alex Aklson</a>\n",
    "\n",
    "\n",
    "### Other Contributors\n",
    "[Jay Rajasekharan](https://www.linkedin.com/in/jayrajasekharan)\n",
    "[Ehsan M. Kermani](https://www.linkedin.com/in/ehsanmkermani)\n",
    "[Slobodan Markovic](https://www.linkedin.com/in/slobodan-markovic).\n",
    "\n",
    "\n",
    "## Change Log\n",
    "\n",
    "\n",
    "|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n",
    "|---|---|---|---|\n",
    "| 2021-01-20  | 2.5 | LakshmiHolla  | Changed TOC markdown section|\n",
    "| 2021-01-05  | 2.4  | LakshmiHolla  |  Changed markdown for outliers |\n",
    "| 2020-11-12  | 2.3  | LakshmiHolla  |  Added example code for outliers |\n",
    "| 2020-11-03  | 2.2  | LakshmiHolla  |  Changed URL of excel file |\n",
    "| 2020-09-29  | 2.1  | LakshmiHolla  |  Made fix to a boxplot label |\n",
    "| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab |\n",
    "\n",
    "\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>"
   ]
  }
 ],
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