0_Introduction_to_python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <h1>Introduction to Python </h1> </center> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepared by: Ransalu Senanayake \n",
    "\n",
    "**Instructions:**\n",
    "\n",
    "* We will be using python 3 for our tutorials. You can complete these exercies on an online Juyter notebook such as http://try.jupyter.org/. If you have installed Jupyter on your computer, you may run it locally on your computer.\n",
    "* Go to File->Open. Drag and drop \"intro.ipynb\" file to the middele of home interface and click upload. \n",
    "* To run a cell you can press Ctrl+Enter or hit the Play button at the top. \n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "## 1. Numbers and Variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example 1: integers\n",
    "\n",
    "\n",
    "```python\n",
    "x = 5\n",
    "``` \n",
    "indicates `5` is assigned to a variable named `x`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = 12500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12500\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example 2: floating-point numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = 12500.50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12500.5\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example 3: string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "z = \"MI like Python\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MI like Python\n"
     ]
    }
   ],
   "source": [
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Arithmetic Operators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n"
     ]
    }
   ],
   "source": [
    "x = 11\n",
    "y = 2\n",
    "z = x + y\n",
    "\n",
    "print(z) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 2.1**\n",
    "Identify the use of following arithmetic operators.  $+, -, *, /, **, \\% $"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Lists\n",
    "\n",
    "A list is a mutable, i.e. values can be changed (added/deleted/updated), sequence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example 1: a list  of integers/float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 10, 20, 30, 50, 80, 130, 210, 340]\n"
     ]
    }
   ],
   "source": [
    "x = [2, 10, 20, 30, 50, 80, 130, 210, 340]\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example 2: list of strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pad thai', 'phở', 'butter chicken', 'souvlaki', 'pizza']\n"
     ]
    }
   ],
   "source": [
    "my_lunch = ['pad thai', 'phở', 'butter chicken','souvlaki', 'pizza']\n",
    "\n",
    "print(my_lunch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be using different python modules/libraries thoughout this course. \n",
    "\n",
    "* NumPy - linear albegra (to deal with arrays and matrices)\n",
    "* Matplotlib - plotting\n",
    "* SciPy - scientific computing (i.e. optimization/management science, linear algebra)\n",
    "* StatsModels - statistics\n",
    "* Scikit-learn - data mining and machine learning\n",
    "* Pandas - data pre-processing\n",
    "* Keras - deep learning\n",
    "* Tensorflow - as the backend of Keras\n",
    "\n",
    "In order to use any module, firstly you need to import the module. \n",
    "\n",
    "```python\n",
    "import actual_module_name as abbreviation\n",
    "```\n",
    "\n",
    "E.g. \n",
    "```python\n",
    "import numpy as np\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Numpy library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rather than learning generic python to manipulate python \"lists\", we will represent our data as arrays using the numpy module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array( [2, 10, 20, 30, 50, 80, 130, 210, 340] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  2  10  20  30  50  80 130 210 340]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The printed output looks similar to that of section 4. Nevertheless, representing the number list as a numpy array facilitate,\n",
    "\n",
    "**1) performing algebra**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[        3      -949     -7899    -26849   -124749   -511599  -2196349\n",
      "  -9259949 -39302299]\n"
     ]
    }
   ],
   "source": [
    "x = np.array( [2, 10, 20, 30, 50, 80, 130, 210, 340] )\n",
    "\n",
    "y = 5*x + 1 - x**3\n",
    "\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2) using inbuilt numpy functions** such as getting the sum, average, etc. \n",
    "\n",
    "There are many such functions which you will learn over time. http://docs.scipy.org/doc/numpy/reference/routines.math.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "872\n"
     ]
    }
   ],
   "source": [
    "y = np.sum(x)\n",
    "\n",
    "print(y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96.8888888889\n"
     ]
    }
   ],
   "source": [
    "y = np.average(x)\n",
    "\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 4.1** Calculate z.\n",
    "\\begin{equation}\n",
    "    z = \\bar{x} + \\sum_{i=1}^{N} x_i^2 \n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#ANSWER\n",
    "z = np.average(x) + np.sum(x**2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 4.2** Let $x$ be a numpy array.\n",
    "```python\n",
    "x = np.array([1, 2, 3, 6, 9, 13])\n",
    "```\n",
    "Calculate $y_1, y_2, y_3$ and $y_1$.\n",
    "\n",
    "$$ y_1 = 2 x $$\n",
    "$$ y_2 = x^2 + 2 x + 1 $$\n",
    "$$ y_3 = \\log_{10} x$$\n",
    "$$ y_4 = \\sum_i e^{x_i} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  6  9 13]\n",
      "y1 =  [ 2  4  6 12 18 26]\n",
      "y2 =  [  4   9  16  49 100 196]\n",
      "y3 =  [ 0.          0.30103     0.47712125  0.77815125  0.95424251  1.11394335] \n",
      " and rounded y3 =  [ 0.      0.301   0.4771  0.7782  0.9542  1.1139]\n",
      "y4 =  450950.097605\n"
     ]
    }
   ],
   "source": [
    "#ANSWER\n",
    "x = np.array([1, 2, 3, 6, 9, 13])\n",
    "print(x)\n",
    "\n",
    "y1 = 2*x\n",
    "print( 'y1 = ', y1 )\n",
    "\n",
    "y2 = x**2 + 2*x + 1\n",
    "print( 'y2 = ',  y2 )\n",
    "\n",
    "y3 = np.log10(x)\n",
    "print('y3 = ',  y3, '\\n and rounded y3 = ',  np.round(y3, 4) )\n",
    "\n",
    "y4 = np.sum( np.exp(x) )\n",
    "print( 'y4 = ',  y4 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **Exercise 4.3** Eucledian distance (a.k.a $\\mathsf{L}^2$ distance) measures similarity between two vectors/arrays,\n",
    " \n",
    " $\\mathrm{distance}(\\mathbf{x,y})  = \\| \\mathbf{x} - \\mathbf{y}\\|_2 = \\sqrt{\\sum_{i=1}^{N} ( x_i - y_i)^2}$\n",
    " \n",
    " Calculate the Eucledian distance between the following two arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.68114574787\n"
     ]
    }
   ],
   "source": [
    "#ANSWER\n",
    "x = np.array([3, 20, 5, -3])\n",
    "y = np.array([2, 20, -2, -6])\n",
    "\n",
    "distance = np.sqrt(np.sum((x-y)**2))\n",
    "print(distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3) slicing** numpy arrays\n",
    "\n",
    "Reading from left to right (note that we start counting from zero!),\n",
    "\n",
    "```python\n",
    "x = np.array([0th_element, 1st_element, 2nd_element, so on])\n",
    "```\n",
    "\n",
    "or reading from right to left,\n",
    "\n",
    "```python\n",
    "x = np.array([so on, -3rd_element, -2nd_element, -1st_elemnt])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3 20  5 -3]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "slice_1 = x[0] #0th element\n",
    "print(slice_1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3\n"
     ]
    }
   ],
   "source": [
    "slice_2 = x[3] #3rd element\n",
    "print(slice_2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3\n"
     ]
    }
   ],
   "source": [
    "slice_3 = x[-1] #-1st element\n",
    "print(slice_3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3 20  5]\n"
     ]
    }
   ],
   "source": [
    "slice_4 = x[0:3] #elements 0, 1 and 2\n",
    "print(slice_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5 -3]\n"
     ]
    }
   ],
   "source": [
    "slice_5 = x[2:5] #elements 2, 3 and 4\n",
    "print(slice_5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4) Automatically generating a sequence**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(0, 10, 1) #arguments are (start, stop, step); by default start is zero and step is 1\n",
    "print(x) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "x2 = np.linspace(0, 10, 11) #arguments are (start, stop, number_of_elements)\n",
    "print(x) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Plotting with Matplotlib Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us import the plotting module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as pl\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Basic plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f06752bff60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#step 1: define your x and y arrays\n",
    "x = np.array([1, 2, 3, 4, 5, 6, 7])\n",
    "y = np.array([0, 0.1, 0.3, 0.2, 0.4, 0.5, 0.7]) #make sure x and y have the same number of elements\n",
    "\n",
    "#step 2: plot \n",
    "pl.scatter(x, y)\n",
    "\n",
    "#step 3 (optinal): decorate\n",
    "pl.title('My first plot!')\n",
    "pl.xlabel('x values')\n",
    "pl.ylabel('y values')\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Exercise 5.1.1** Make the following changes.\n",
    "\n",
    "* Change the marker color to red.\n",
    "Hint: matplotlib.org/api/markers_api.html\n",
    "\n",
    "* Remove the marker edgecolor.\n",
    "\n",
    "* Increase the marker size.\n",
    "\n",
    "* Chnage the marker shape to square."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JiSTHk9w56poWI8l3JvmnJP/cO4/fHnVNw0iyKsmnk/zFqGtZrCRPJPlskseSTI66nsVK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\nS8vcPGAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f06752bf828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ANSWER\n",
    "\n",
    "#step 2: plot \n",
    "pl.scatter(x, y, c='r', s=400, marker='s', edgecolor='b');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 6.1.2** Change the scatter plot to,\n",
    "\n",
    "1. line plot\n",
    "2. stem plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rX87PtzazZ5I/f9vMmqS70M1yD4Ojv/oVXHIJNG8ejrsbPVrBLiK1VoXhbmZ5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\nRHKQwl1EJAcp3EVEcpDCXUQkB/1/rAqb8Lq7aBAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0672f82438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Answer\n",
    "pl.plot(x, y, c='r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UIMtdkgpkuUtSgSx3SSqQ5S5JBbLcJalAlrskFaitco+IQxGxEBFXI+LUGu9/T0Q813z/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\n+v+VfI7TrNIS2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0672f1e0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Answer\n",
    "pl.stem(x, y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2. Multiple plots on the same figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zvwMtkx9eLbd1K4weHYZxuPLKMHTHjBnw3HPQrVuqo3OZolUrOP308ANk8+ZU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\nnHPOJeT/AQC3yBRLYTIQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0672ebb6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#step 1: define your x and y arrays\n",
    "x = np.array([1, 2, 3, 4, 5, 6, 7])\n",
    "gbp_eur = np.array([1.28, 1.30, 1.30, 1.32, 1.22, 1.23, 1.22]) \n",
    "gbp_eur_predicted = np.array([1.28, 1.29, 1.30, 1.31, 1.32, 1.36, 1.37]) \n",
    "\n",
    "#step 2: plot \n",
    "pl.plot(x, gbp_eur, c='r', label='actual')\n",
    "pl.scatter(x, gbp_eur_predicted, c='b', label='predicted')\n",
    "\n",
    "#step 4: activate the legend and set its location\n",
    "pl.legend(loc='lower left')\n",
    "\n",
    "#step 3 (optinal): decorations \n",
    "pl.title('Effect of Brexit on GBP/EUR exchange rate')\n",
    "pl.xlabel('days of the week')\n",
    "pl.ylabel('GBP/EUR')\n",
    "pl.grid()\n",
    "pl.show()\n",
    "\n",
    "#Note: GBP/EUR = EUR per 1 GBP as in www.xe.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3. Subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "day = np.array([1, 2, 3, 4, 5, 6, 7])\n",
    "eur_aud = np.array([1.25, 1.27, 1.20, 1.36, 1.25, 1.26, 1.31])\n",
    "eur_hkd = np.array([8.6, 8.7, 9.0, 9.0, 9.1, 9.0, 9.2])\n",
    "eur_lkr = np.array([150, 153, 160, 155, 156, 158, 161])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6RAmPnMlrfswE/s1ljAlvq3fkMmP5dm4d14tubeO8jmOakNlp2QzqlECHxGak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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0672e5e3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.figure(figsize=(15, 4)) #width, height in inches\n",
    "\n",
    "pl.subplot(1,3,1) #1 row, 3 columns, 1st plot\n",
    "pl.plot(day, eur_aud)\n",
    "pl.title('EUR/AUD Exchange Rate')\n",
    "pl.xlabel('Days of the week')\n",
    "pl.ylabel('EUR/AUD')\n",
    "pl.grid()\n",
    "\n",
    "pl.subplot(1,3,2) #1 row, 3 columns, 2nd plot\n",
    "pl.plot(day, eur_hkd)\n",
    "pl.title('EUR/HKD Exchange Rate')\n",
    "pl.xlabel('Days of the week')\n",
    "pl.ylabel('EUR/HKD')\n",
    "pl.grid()\n",
    "\n",
    "pl.subplot(1,3,3) #1 row, 3 columns, 3rd plot\n",
    "pl.plot(day, eur_lkr)\n",
    "pl.title('EUR/LKR Exchange Rate')\n",
    "pl.xlabel('Days of the week')\n",
    "pl.ylabel('EUR/LKR')\n",
    "pl.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Exercise 5.3.1** Create a figure of size $10^{\"} \\times 10^{\"}$. Plot the folowing four exchange rates on four different plots ($2 \\times 2$). Color actual exchange rates in red and predicyed exchange rates in blue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "day = np.array([1, 2, 3, 4, 5, 6, 7])\n",
    "\n",
    "#actual exchange rates\n",
    "eur_aud = np.array([1.25, 1.27, 1.20, 1.36, 1.25, 1.26, 1.31])\n",
    "eur_hkd = np.array([8.6, 8.7, 9.0, 9.0, 9.1, 9.0, 9.2])\n",
    "eur_lkr = np.array([150, 153, 160, 155, 156, 158, 161])\n",
    "eur_usd = np.array([1.12, 1.13, 1.12, 1.14, 1.16, 1.17, 1.2])\n",
    "\n",
    "#predicted exchange rates\n",
    "eur_aud_pred = np.array([1.25, 1.26, 1.25, 1.35, 1.25, 1.26, 1.30])\n",
    "eur_hkd_pred = np.array([8.7, 8.6, 9.1, 9.1, 9.0, 9.0, 9.1])\n",
    "eur_lkr_pred = np.array([151, 162, 161, 163, 155, 158, 160])\n",
    "eur_usd_pred = np.array([1.11, 1.13, 1.13, 1.12, 1.17, 1.18, 1.21])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+eNyHLVpk/uisWbYy1XaEuVUI7qw5KUskAm3qLaFVgz9g1123+zxHHWU3UVum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D/Ae+7RFGnv3qohk1dENE3foYOmHKdOhfLlw2R4mIiL06Hxq1cfeV/DMAyjcPL77+qk3XVXCFmu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\nxsessWaMMcYY42PWWDPGGGOM8bH/B745WAF77g3xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0672d28358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ANSWER\n",
    "pl.figure(figsize=(10, 10)) #width, height in inches\n",
    "\n",
    "pl.subplot(221) #1 row, 3 columns, 1st plot\n",
    "pl.plot(day, eur_aud, c='r', label='actual')\n",
    "pl.plot(day, eur_aud_pred, c='b', label='predicted')\n",
    "pl.legend(loc='lower right')\n",
    "pl.title('EUR/AUD Exchange Rate')\n",
    "pl.xlabel('Days of the week')\n",
    "pl.ylabel('EUR/AUD')\n",
    "pl.grid()\n",
    "\n",
    "pl.subplot(222) #1 row, 3 columns, 2nd plot\n",
    "pl.plot(day, eur_hkd, c='r', label='actual')\n",
    "pl.plot(day, eur_hkd_pred, c='b', label='predicted')\n",
    "pl.legend(loc='lower right')\n",
    "pl.title('EUR/HKD Exchange Rate')\n",
    "pl.xlabel('Days of the week')\n",
    "pl.ylabel('EUR/HKD')\n",
    "pl.grid()\n",
    "\n",
    "pl.subplot(223) #1 row, 3 columns, 3rd plot\n",
    "pl.plot(day, eur_lkr, c='r', label='actual')\n",
    "pl.plot(day, eur_lkr_pred, c='b', label='predicted')\n",
    "pl.legend(loc='lower right')\n",
    "pl.title('EUR/LKR Exchange Rate')\n",
    "pl.xlabel('Days of the week')\n",
    "pl.ylabel('EUR/LKR')\n",
    "pl.grid()\n",
    "\n",
    "pl.subplot(224) #1 row, 3 columns, 3rd plot\n",
    "pl.plot(day, eur_usd, c='r', label='actual')\n",
    "pl.plot(day, eur_usd_pred, c='b', label='predicted')\n",
    "pl.legend(loc='lower right')\n",
    "pl.title('EUR/USD Exchange Rate')\n",
    "pl.xlabel('Days of the week')\n",
    "pl.ylabel('EUR/USD')\n",
    "pl.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise** Use numpy to generate 500 equi-distance points between 0 and 10 and assign it to a variable $x$. Use `matplotlib.pyplot` to plot $y$ vs $x$ where,\n",
    "$$\n",
    "    y = \\frac{1}{\\sqrt{2 \\pi \\sigma^2}} \\exp{\\Big[ - \\frac{(x-\\mu)^2}{2 \\sigma^2} \\Big]}\n",
    "$$\n",
    "\n",
    "Initially, set $\\mu=0$ and $\\sigma^2=1$ (i.e. standard normal distribution)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pm+KTF2wIRdMtS/omYrxxqJJh6alMyhnodihRZ4HzRfmmNfGYbljSNxGhvV15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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0672d677b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as pl\n",
    "%matplotlib inline\n",
    "\n",
    "x = np.linspace(-5, 5, 500)\n",
    "\n",
    "mu = 0\n",
    "sigma_sq = 1\n",
    "\n",
    "constant = 1/np.sqrt(2*np.pi*sigma_sq)\n",
    "y = constant * np.exp(- ((x-mu)**2) / (2*sigma_sq))\n",
    "\n",
    "pl.plot(x, y)\n",
    "pl.title('PDF of Normal Distribution')\n",
    "pl.grid()\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Relational and Logical Operators\n",
    "\n",
    "Relational operators are used to identify the relationship between two entities (e.g. variables) and the output is either `True` or `False`.\n",
    "\n",
    "For instance, `20>10` in python (or in many other programming languages) means \"Is 20 greater than 10?\". The answer is `True`. Recall from tutoril 1 - section 2, we used three data types — integer, float and string. The data type whose values are always `True` or `False` is called Boolean data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(20>10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**\n",
    "Identify the use of following relational operators.  $==, !=, >, <, >=, <=$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logical operators perform Boolean operations.\n",
    "\n",
    "* a `and` b - output is True only if BOTH a AND b are `True`\n",
    "* a `or` b - output is True if EITHER a OR b is `True`\n",
    "* `not` a - inverts the logic of a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "x = 10\n",
    "\n",
    "print(x>20 and x<10) #Is x greater than 20 and x less than 10?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Conditional Statements\n",
    "\n",
    "In condional statements, conditions/relations are checked from top to bottom and the first `True` condition is executed. \n",
    "\n",
    "```python\n",
    "if relation:\n",
    "    some code\n",
    "elif relation:\n",
    "    some code\n",
    "else:\n",
    "    some code\n",
    "```\n",
    "\n",
    "E.g. \n",
    "```python\n",
    "if y>0:      \n",
    "    print('y is positive')\n",
    "else:\n",
    "    print('y is not positive')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise** Let $y = 1.1 x^2 +15x - 20$. Write an `if-else` code as in the above example to check if y is positive or not for different $x$ values. E.g. \"Is $y>0$ when $x=2$?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y is not positive\n"
     ]
    }
   ],
   "source": [
    "x = 2\n",
    "y = 1.1*(x**2) - 15*x - 20\n",
    "if y>0:      \n",
    "    print('y is positive')\n",
    "else:\n",
    "    print('y is not positive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise** Amend the answer for exercise 2.2 to print one of the following accordingly: \"y is zero\", \"y is positive\" or \"y is negative\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y is negative\n"
     ]
    }
   ],
   "source": [
    "x = 2 \n",
    "y = 1.1*(x**2) - 15*x - 20\n",
    "if y==0:\n",
    "    print('y is zero')\n",
    "elif y>0:\n",
    "    print('y is positive')\n",
    "else:\n",
    "    print('y is negative')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise** Write a program to check if an arbitrary number $x$ (say, $x=10$) is between $-15$ inclusive and $15$ exclusive and print the output \"In range!\" or \"Out of range!\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In range!\n"
     ]
    }
   ],
   "source": [
    "x = 10\n",
    "\n",
    "if x>=-15 and x<15: #alternative: a<x<b\n",
    "    print('In range!')\n",
    "else:\n",
    "    print('Out of range!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Iterations\n",
    "\n",
    "Often you need to iterate through arrays. It can be easily accomplished using `for`-loops.\n",
    "\n",
    "```python\n",
    "for i in list_or_array:\n",
    "    code\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\n",
      "20.0\n",
      "30.0\n",
      "-35.0\n",
      "5.5\n"
     ]
    }
   ],
   "source": [
    "x = np.array([10, 20, 30, -35, 5.5])\n",
    "\n",
    "for i in x:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.5\n"
     ]
    }
   ],
   "source": [
    "#Let us add numbers in the following array. \n",
    "\n",
    "x = np.array([10, 20, 30, -35, 5.5])\n",
    "\n",
    "sum_var = 0\n",
    "for i in x:\n",
    "    sum_var = sum_var + i\n",
    "    \n",
    "print(sum_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In many cases, you have to run a for loop a specific number of times. In such cases, you can use one of the following. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for i in np.arange(10): #alternative: np.arange(0,10,1)\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for i in range(10): #alternative: range(0,10,1)\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 10.0\n",
      "1 20.0\n",
      "2 30.0\n",
      "3 -35.0\n",
      "4 5.5\n"
     ]
    }
   ],
   "source": [
    "for i in np.arange(len(x)):\n",
    "    print(i, x[i]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.5\n"
     ]
    }
   ],
   "source": [
    "sum_var = 0\n",
    "for i in range(len(x)): \n",
    "    sum_var = sum_var + x[i] \n",
    "print(sum_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise** Write a for loop and access $i^{th}$ pair of values $(x_i, y_i)$. In each iteration, print a message \"Both values are positive.\", if both values are positive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least one value is not positive!\n",
      "Both values are positive.\n",
      "At least one value is not positive!\n",
      "At least one value is not positive!\n"
     ]
    }
   ],
   "source": [
    "x = np.array([3, 20, 5, -3])\n",
    "y = np.array([-2, 20, -2, -6])\n",
    "\n",
    "for i in range(len(x)):\n",
    "    if x[i]>0 and y[i]>0:\n",
    "        print('Both values are positive.')\n",
    "    else:\n",
    "        print('At least one value is not positive!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Functions\n",
    "\n",
    "What happens when you run `y = np.sum(x)` is a \"function\" named `sum` takes the input `x` and output the summation of all elements. This summation is assigned to y. If functions were not there, we might have to use a for loop everytime we want to obtain a summation, as in section 4. You can define your own functions, if you intend to use a particular piece of code frequently. \n",
    "\n",
    "We can define a function,\n",
    "```python\n",
    "def function_name(input1, input2, input3):\n",
    "    your code\n",
    "    return output1, output2\n",
    "```\n",
    "\n",
    "We can \"call\" a function,\n",
    "```python\n",
    "output1, output2 = function_name(input1, input2, input3)\n",
    "```\n",
    "\n",
    "You have \"called\" many such functions in Excel (formula)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us put the piece of code used in exercise 1.3.1 inside a function and name it \"normal_pdf\". The intention of doing so is to generate outputs for different mean and variance values.\n",
    "E.g. `normal_pdf(x, 0, 1)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def normal_pdf(x, mu, sigma_sq):\n",
    "    \n",
    "    constant = 1/np.sqrt(2*np.pi*sigma_sq)\n",
    "    y_pdf = constant * np.exp(- ((x-mu)**2) / (2*sigma_sq))\n",
    "    \n",
    "    return y_pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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byyOPPMLYsWOJiYlpthzAhAkTeOKJJ7hw4QJbtmxh3Lhx1NTUUFRU1GKsffr0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8U/LoH09atZ8vQjbHj7r5TnZtObzg8Ltwa9GV4cBuQJIQoAJElaAdwJnO2R5k7gD13bXwH/km63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\n0xURERHRQJquiIiIiAb+BrT6a0mz2aqzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0672a36240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu_array = np.array([-2, -2, -2, 0, 0, 0])\n",
    "sigma_sq_array = np.array([1, 3, 10, 1, 3, 10])\n",
    "\n",
    "pl.figure(figsize=(10, 5))\n",
    "\n",
    "for i in range(len(mu_array)):\n",
    "    mu = mu_array[i]\n",
    "    sigma_sq = sigma_sq_array[i]\n",
    "       \n",
    "    x = np.linspace(-7 + mu, 7 + mu, 500)\n",
    "    \n",
    "    y = normal_pdf(x, mu, sigma_sq)\n",
    "\n",
    "    pl.plot(x, y, label='mean='+str(mu)+', var='+str(sigma_sq))\n",
    "\n",
    "pl.title('Normal Distribution')\n",
    "pl.legend(loc='upper right')\n",
    "pl.grid()\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise** The cumulative distribution function (CDF) of the normal distribution is given by,\n",
    "\n",
    "$$\n",
    " y_{cdf} = \\frac{1}{2} \\Bigg[ 1 + \\mathrm{erf}\\Bigg( \\frac{x - \\mu}{\\sqrt{2 \\sigma^2}} \\Bigg) \\Bigg]\n",
    "$$\n",
    "\n",
    "where, in mathematics, $\\mathrm{erf}$ is called the \"error function\" which can be imported from scipy as,\n",
    "\n",
    "```python\n",
    "import scipy.special as sp\n",
    "sp.erf(z) #gives the error function value of z\n",
    "```\n",
    "\n",
    "Write a function called \"normal_pdf_cdf\" which takes input `(x, mu, sigma_sq)` and outputs `(y_pdf, y_cdf)`. Similar to the above example, call it in a for loop to plot both pdf and cdf for different vales of mu and sigma_sq. You make use `pl.subplot()`. A similar graph can be found in https://en.wikipedia.org/wiki/Normal_distribution (top-right)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.special as sp\n",
    "\n",
    "def normal_pdf_cdf(x, mu, sigma_sq):\n",
    "    \n",
    "    constant = 1/np.sqrt(2*np.pi*sigma_sq)\n",
    "    y_pdf = constant * np.exp(- ((x-mu)**2) / (2*sigma_sq))\n",
    "    \n",
    "    y_cdf = 0.5*( 1+sp.erf( (x-mu)/(np.sqrt(2*sigma_sq)) ) )\n",
    "    \n",
    "    return y_pdf, y_cdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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REWj796c+L4+6s2dtFoeiKN1jTtJ1CAgUQgQIIZyBRCC1+QVCiMBmh7OAbMP5\ngYaJ+AghhgOBwDlLBN6TcnNzCQoKYsGCBYwaNYqkpCR27dpFfHw8gYGBpKenA1BZWcnChQuJi4sj\nMjKSzZs3m8pPmjSJqKgooqKi2Gd49DstLY2EhATmzZtHUFAQSUlJXV51e+bMmQghEEIQFxdHQUHB\nTa9PTEzks88+Mx0vWLCATz755KaxTpo0idmzZzNmTMvnKNoWHBzcah9KgM8++4zExERcXFwICAhg\n5MiRpvdQsT9fnNYnOVOCBtk0jsmjBuKkFWRcuMa1yp5Zs6ru7FnqCwvRenujM2x4bwtCq8XDkPSV\n7/7CZnEoitI9Hc7pklI2CCGWANsBLZAipTwphHgRfZdaKrBECDENqAeuA78wFJ8MvCiEqAeagEel\nlNda38V8oWus84vv2198e9PXc3Jy2LBhAykpKcTGxrJ27Vr27NlDamoqy5cvZ9OmTSxbtowpU6aQ\nkpJCSUkJcXFxTJs2jUGDBrFz5050Oh3Z2dk8+OCDps2sMzMzOXnyJH5+fsTHx7N3714mTpzY4YbX\n7amvr+eDDz7ocMud+fPns379embNmkVdXR27d+/mrbfeQkrZKtaMjAwAjhw5wokTJwgwrMbdmQ2v\nm7t48eINvV/+/v4UFt6s81SxFSklX3x3BbB90tVX58T44T58k13Ml1lXmBtt3b0fAVMvl8fkyVbf\na7EjHlN+ROmnn1LxxRcM+H+/tmksiqJ0jVnbAEkpPwc+b3HuD82+f6KdchuBXrEXTEBAAKGGv3RD\nQkKYOnUqQghCQ0PJzc0FYMeOHaSmpvLKK68A+ongeXl5+Pn5sWTJEo4ePYpWq+XMmTOmeuPi4vD3\n1//jERERQW5uLhMnTuxww+v2LFq0iMmTJzNp0qSbXnfPPffwxBNPUFtby7Zt25g8eTKurq6Ulpbe\nNNaAZtufdLThteL4cq5UkH+tGh93Z8L9bbcFj9G0YF++yS5m1+nLPZR0fQWAR8JdVr9XRzzuvBPh\n7Ez18eM0FBXRZ+BAW4ekKEonOdzeix31SFmLi8sPj8lrNBrTsUajMc1xklKycePGVkNqS5cuxdfX\nl2PHjtHU1IROp2uzXq1Wa6qro56u6dOnc/nyZWJiYkzzt1544QWKiora3P+xJZ1OR0JCAtu3b2fd\nunUkJiYC8Prrr7cba8vNr7va0+Xn50d+/g8PxBYUFDB48OAOY1Z63hdZ+l6uu0YPRKMRNo4Gpo3x\n5Y+pJ/mxhUlPAAAgAElEQVTqTBG1DY249NFa7V6NpaVUZWZCnz64x8db7T7m0ri74zZhPJVffU3F\nV1/Rb948W4ekKEonOVzSZc+mT5/OihUrWLFiBUIIMjMziYyMpLS0FH9/fzQaDWvWrDFr0nhHPV3b\nt2+/4fi9995j+/bt7N69G02zYZD09HRWrlzJ+++/36qO+fPn895775GRkWHaxLszsXa1p2vmzJk8\n8sgj/O53v+PixYtkZ2cTFxfXpboU6zImXVODfG0cid7gfq6M8vXgzOUKDl+4zp0jBljtXhV79kBj\nI27jxqH19LTafTrDY/JkfdK1Z69KuhTFAam9Fy0oOTmZ+vp6wsLCCAkJITk5GdAP+a1Zs4bw8HCy\nsrJa9RhZwqOPPsrly5eZMGECERERvPjiiwDk5eXh6uraZpm7776br776imnTpuHs7GzxWD/99FP8\n/f3Zv38/s2bNYrphZe3g4GAeeOABxowZw4wZM1i1ahVarfV6LJSuKa2qJ+PCdfpoBJNGWS+56azJ\ngfphta/PFFv1PqahxbtsP7Ro5DFxIqDflkiqJ34VxfGYu7ZET32pdbos66mnnpLHjh3rkXuZy9y2\n98afuyOtWfOvY4Vy6DNbZOI7+y1WpyXa/9V3V+TQZ7bImX//uvsBtaOpoUF+N268PDU6SNacPWeR\nOi31s8+e9mN5anSQrDp61CL19RRH+uxbmmp774aF1+lSHNjLL79MWFiYrcNQHNA3hp6ku0bb14Tt\nuABvXPpoOHmxjKLyWqvco+bUaRpLSnAaPBjngGFWuUdXuU/Uzy+r2LPHxpEoitJZKulSFKUVKSXf\nZBcBMHGk/QwtAuictIwb7gPAnpwiq9yj0rA+nfuddyKE7R8gaM7DMKm/cs9eG0eiKEpnqaRLUZRW\nzhVXcrG0Bh93Z8bc3tfW4bQyOVCfCFprXpcp6Yq/0yr1d4fb+PHQpw/Vx4/TWFZm63AURekElXQp\nitLKnmx9MnPnyAF2sVRES3eN0g95fpNdRFNT13ZxaE9TdTXVR46AELiNG2fRui1B6+GBa0Q4NDZS\neeCArcNRFKUTVNKlKEor3xiSrkl2NrRoNHKQB7d76SiuqOPUJcv29lRlZCDr69GFhNCnf3+L1m0p\npqcY1RCjojgUlXQpinKD+sYmDpy7CsDEQPtMuoQQPywdkW3ZeV2Ve3+Yz2Wv3OONSdeeLu/XqihK\nz1NJl4OSUvL4448zcuRIwsLCOHLkiK1DIisriwkTJuDi4mLaCklxPMfyS6iobWD4QHf8+rW9xps9\nMK4dtjfHsvO6mk+it1e6kDFo+/Wj/uJF6vPybB2OoihmUkmXg9q6dSvZ2dlkZ2fz7rvv8thjj1n1\nfuasou/t7c0bb7zBU089ZdVYFOuy96FFowmGJxgzcq9T22CZhUIbioqoPXMG4eqKa1SkReq0BqHR\n4GbYxaHy4EEbR6MoirlU0mWG3NxcgoKCWLBgAaNGjSIpKYldu3YRHx9PYGAg6enpAFRWVrJw4ULi\n4uKIjIxk8+bNpvKTJk0iKiqKqKgo9hn+kk5LSyMhIYF58+YRFBREUlKS2UMFmzdv5uGHH0YIwfjx\n4ykpKeHSpUvtXv/222/z9NNPm45Xr17NkiVLALj//vuJjo4mJCSEd99913SNh4cHTz75JOHh4ezf\nv7/DmAYNGkRsbCxOTk5mtUGxT3sMPUfxdp50+Xi4MNrXk9qGJo7mlVikzkrD59wtJgaNYZcGe+U2\nXj/Jv+qASroUxVE43N6Lp4OCrVJvcNbpm76ek5PDhg0bSElJITY2lrVr17Jnzx5SU1NZvnw5mzZt\nYtmyZUyZMoWUlBRKSkqIi4tj2rRpDBo0iJ07d6LT6cjOzubBBx80bWadmZnJyZMn8fPzIz4+nr17\n9zJx4sQON7wuLCxkyJAhpvP+/v4UFhZy++23txn/3LlzmTBhAi+//DIA69at47nnngMgJSUFb29v\nqquriY2NZe7cufj4+FBZWcm4ceN49dVXgY434VYcX3lNPUfzS9BqBONH+Ng6nA5NGOHDd5fL2Xf2\nqmntru6o3KdPuux5aNHI3fBkZWV6OlJKu1tPTFGU1hwu6bKVgIAAQkNDAQgJCWHq1KkIIQgNDSU3\nNxeAHTt2kJqaaprPVFNTQ15eHn5+fixZsoSjR4+i1Wo5c+aMqd64uDj8/f0BiIiIIDc3l4kTJ3a4\n4XVnDRw4kOHDh3PgwAECAwPJysoi3rDI4htvvMGnn34KQH5+PtnZ2fj4+KDVapk7d66pDkvHpNif\njAvXaWySRN7Rj746+++xnDDCh9X7ctl/7iq/tUB9VYZea/cJ4y1Qm3U5Dx+OduAAGouKqTt3DpcR\nI2wdkqIoHXC4pKujHilrcXFxMX2v0WhMxxqNhoaGBkA/uX3jxo2MHj36hrJLly7F19eXY8eO0dTU\nhE6na7NerVZrqqujXqXBgweTn59vOl9QUMDgwYNv2obExETWr19PUFAQc+bMQQhBWloau3btYv/+\n/bi5uZGQkEBNTQ0AOp3uho2oVU9X73fgrP6pxfEW6DXqCeMDfBACjuaVUFPfiM6p6xun1xUUUH/x\nIhovL1xGjbJglNYhhMA9No6yzz+n8sABlXQpigNwuKTLnk2fPp0VK1awYsUKhBBkZmYSGRlJaWkp\n/v7+aDQa1qxZY9ak9I56lWbPns3KlStJTEzk4MGDeHl5mYYWg4KCyMrKalVmzpw5LFu2jMzMTF56\n6SUASktL6d+/P25ubmRlZXHgJostqp6u3s+4VISjJF1ebk6Mub0vJy+WcfjC9W7NQ6s6qO/lcouN\nQWgcY7qr2/hxlH3+OVUH0/FOSrJ1OIqidMAxfrM4iOTkZOrr6wkLCyMkJITk5GQAFi1axJo1awgP\nDycrKwt3d/du32vmzJkMHz6ckSNH8sgjj/Dmm28CUFxc3O5k/P79+xMcHMyFCxeIMzz5NGPGDBoa\nGggODubZZ59l/PiuD6t8//33+Pv789prr/GnP/0Jf39/ytQ2JQ6jvKaebwtL6aMRxAy1z0VB23Kn\nYe7ZvrPdWzrCNLRo+H/DERjndVWlpyObmmwcjaIoHVE9XWYYNmwYJ06cMB2vXr26zddcXV155513\nWpUPDAzk+PHjpuOXXnqJ8vJyEhISSEhIMJ1fuXKl2TEJIVi1alWr8wcOHGDx4sXtltuyZcsNxy4u\nLmzdurXNaysqKsyOB+C2226joKCgU2UU+5GRe50mCRFDvHB3cZxfDRNG+PA/35xnv2FotCuklFQe\nMvR02eHWP+1xuuMO+tx2Gw3ff0/tmTPogoJsHZKiKDdhVk+XEGKGEOI7IUSOEKLV5B0hxKNCiG+F\nEEeFEHuEEGOavfZ7Q7nvhBDTLRm80tq9997L448/buswFAfkaEOLRrHDvNFqBMcLSqmsbehSHfWF\nhTRcvITWywuXwEALR2g9QogfervUel2KYvc6TLqEEFpgFXAPMAZ4sHlSZbBWShkqpYwA/gq8Zig7\nBkgEQoAZwJuG+hRFsTOOmnR56pwYO9iLhibJodxrXarDNJ8rLtZh5nMZGXvmKg1tUBTFfpnz2yUO\nyJFSnpNS1gEfA/c1v0BK2XzijjtgnFR0H/CxlLJWSnkeyDHUpyiKHSlrNp8r2oHmcxkZ53V1dYix\nKl3fS+QW63i/ntzH6WOuOnQIacZDOoqi2I45EzcGA/nNjguAVpMehBCLgd8BzsCUZmWbPw5XYDjX\nsuyvgV8D+Pr6kpaWdsPrXl5elJeXmxGq42hsbOx1bTKXuW2vqalp9VlwdBUVFXbZpqNXGmiSMNxL\ncGj/Hqvdx1rtd6/QDyvuOJrLBLfLnSssJQO+/gYtcEojaLDSz8eaP3ufAQPoU1zM3g8/pGHoUKvc\no7vs9bPfE1Tb02wdht2w2GxZKeUqYJUQ4iHgeeAXnSj7LvAuQExMjGw+uRzg9OnTeHp6WipUu1Be\nXt7r2mQuc9uu0+mIjLTf/e+6wrj1k73Z9/lp4Bx3RwSQkGC9ydjWan9cXQN/z9zBhfImIsfF4+Vq\n/sKudfn5nL1+HW2/fsQ/9JDVhhet+bO/mHAXpZ9sJLihAR87/HyB/X72e4Jqe4Ktw7Ab5vx2KQSG\nNDv2N5xrz8fA/V0sqyiKDRjnc01wgK1/2uLm3Idw/340Scjo5Lwu41IRbrGON5/LqPmWQIqi2C9z\nfsMcAgKFEAFCCGf0E+NTm18ghGj+uM8sINvwfSqQKIRwEUIEAIGA+q1gAVJKHn/8cUaOHElYWBhH\njhyxdUhs3ryZsLAwIiIiiImJYc8e6w1TKZZTVlPPCQeez2UUF+ANQHpXky4HWp+rJbeYGACqj2Sq\neV2KYsc6HF6UUjYIIZYA2wEtkCKlPCmEeBHIkFKmAkuEENOAeuA6hqFFw3XrgVNAA7BYSql+I1jA\n1q1byc7OJjs7m4MHD/LYY49x0IqPjDc2Nt6wJVBbpk6dyuzZsxFCcPz4cR544IE2V8ZX7EtG7jWa\nJETe0Q83Z8dZn6ul2ABvSDvLofPmJ11SSirTDwGOnXQ53X47Tn5+1F+8SG12tlqvS1HslFl96VLK\nz6WUo6SUI6SUywzn/mBIuJBSPiGlDJFSRkgpfySlPNms7DJDudFSyrZX4bRzubm5BAUFsWDBAkaN\nGkVSUhK7du0iPj6ewMBA0g1/KVdWVrJw4ULi4uKIjIxk8+bNpvKTJk0iKiqKqKgo9u3bB/ww1j1v\n3jyCgoJISkpqdzX5ljZv3szDDz+MEILx48dTUlLCpUuX2r3+7bff5umnnzYdr169miVLlgBw//33\nEx0dTUhICO+++67pGg8PD5588knCw8PZv39/hzF5eHgghDC9F8bvFft24Jw+SRk/3NvGkXRP9ND+\nCAHfFpZSXWfe33b1BQU0XLqEtl8/XAJHWjlC63KNiQagKuOwjSNRFKU9Dvdn7apHv7BKvYvfnnLT\n13NyctiwYQMpKSnExsaydu1a9uzZQ2pqKsuXL2fTpk0sW7aMKVOmkJKSQklJCXFxcUybNo1Bgwax\nc+dOdDod2dnZPPjgg6aNozMzMzl58iR+fn7Ex8ezd+9eJk6c2OHm0oWFhQwZ8sN0OX9/fwoLC037\nL7Y0d+5cJkyYwMsvvwzAunXreO655wBISUnB29ub6upqYmNjmTt3Lj4+PlRWVjJu3DheffVVwLwN\nrz/99FN+//vfc+XKFT777LOO3nbFDqQbeobiAhxzPpdRX50Twbf15dSlMjLzr3PniI73YTQmKK4x\n0Q47n8vILTqGstR/UZWRgffP1D6MimKPHC7pspWAgABCQ0MBCAkJYerUqQghCA0NJTc3F4AdO3aQ\nmprKK6+8AuiXPMjLy8PPz48lS5Zw9OhRtFotZ86cMdUbFxeHv78/ABEREeTm5jJx4kSLby49cOBA\nhg8fzoEDBwgMDCQrK4v4+HgA3njjDT799FMA8vPzyc7OxsfHB61Wy9y5c011mBPTnDlzmDNnDl9/\n/TXJycns2rXLou1QLKuqroEThaVoBETd0c/W4XRbXIA3py6Vcei8mUnX4QxAn7A4OrdYfRuqDmcg\npVQ9zYpihxwu6eqoR8paXFxcTN9rNBrTsUajoaFBv0aQlJKNGzcyevToG8ouXboUX19fjh07RlNT\nEzqdrs16tVqtqa6OepUGDx5Mfv4Py6cVFBQweHCrJdBalV2/fj1BQUHMmTMHIQRpaWns2rWL/fv3\n4+bmRkJCAjU1NYB+yYbm87jM6ekymjx5MufOnaO4uJgBAzr+x0+xjaP5JTQ0SUL8+uKpM3+ZBXsV\nF+DN6n25Zq9MX31Y/wCKW3SUNcPqEc4BAWi9vWksKqY+Lw9nO12vS1FuZQ6XdNmz6dOns2LFClas\nWIEQgszMTCIjIyktLcXf3x+NRsOaNWtoNOPpoo56lWbPns3KlStJTEzk4MGDeHl5mYYWg4KC2pzA\nPmfOHJYtW0ZmZiYvvfQSAKWlpfTv3x83NzeysrI4cOBAq3LmxpSTk8OIESMQQnDkyBFqa2vx8XHs\nIaveLiP3OqDfv7A3MLbj8IXr1Dc24aRtf8iw4do16s6fR7i6ogsO7qkQrUYIgVt0FOU7d1GVcVgl\nXYpihxx7EoOdSU5Opr6+nrCwMEJCQkhOTgZg0aJFrFmzhvDwcLKysnB3d+/2vWbOnMnw4cMZOXIk\njzzyCG+++SYAxcXF7U7G79+/P8HBwVy4cIE4w5NaM2bMoKGhgeDgYJ599lnGjx/f5Zg2btzI2LFj\niYiIYPHixaxbt04Ncdg5Y49Qb0m6Bnq6MHyAO9X1jZy8WHbTa6sNy6y4hocjnBy/lw9+WDqiKiPD\nxpEoitIW1dNlhmHDhnHixAnT8erVq9t8zdXVlXfeeadV+cDAQI4fP246fumllygvLychIeGGlXpX\nrlxpdkxCCFatWtXq/IEDB1i8eHG75bZs2XLDsYuLC1u3tv1QaUVFhdnxADzzzDM888wznSqj2E5D\nYxNHLuh7umKGOe76XC3FDvPmXHElh85fI2JI+/PUjJPo3aIcf2jRyDXaOK9LPcGoKPZI9XT1Mvfe\ney+PP/64rcNQHEDW9+VU1jVyh7cbvn11HRdwELGGRVIPdrBeV5Wxp6sXzOcy0gWNRuPmRn1eHvVX\nrtg6HEVRWlBJl6LcooxDi72plwsgzjBUmnHhGk1NbQ+1N1VVUXPqFGg0uIZH9GR4ViX69MHVsF9p\ntertUhS7o5IuRblFGSfRx/WS+VxGQ7xdua2vjpKqenKK2h4irz7+LTQ0oAsORuvR/TmW9sS0dMQh\nNa9LUeyNSroU5RYkpTTtURjTy5IuIUSHQ4zG9bl609CikVu0YWV61dOlKHZHJV2KcgvKu1ZFUXkt\n/d2cGDGwd/X0AMQZhkzb24fRtD5XVHSPxdRTdGFhCCcnas+cobG01NbhKIrSjEq6FOUWdCjX+NSi\nd69c1sO4pVH6+WutllCRDQ1UHz0K9I5FUVvSuLigCwsDKU0PCyiKYh9U0uWgpJQ8/vjjjBw5krCw\nMI7YwS/XrKwsJkyYgIuLi2krJKNt27YxevRoRo4cyWuvvWajCBWjDMPQYm+bz2UUOMgDL1cnvi+r\noeB69Q2v1Xz3HU1VVTgNvYM+AwfaKELrMg4xqsn0imJfVNLloLZu3Up2djbZ2dm8++67PPbYY1a9\nnzmr6Ht7e/PGG2/w1FNPtSq7ePFitm7dyqlTp/jkk084deqUtUJVzJDeS59cNNJoBLGGtqW3GGI0\nJiK9cWjRyC3GMK8rQyVdimJPVNJlhtzcXIKCgliwYAGjRo0iKSmJXbt2ER8fT2BgIOnp6QBUVlay\ncOFC4uLiiIyMZPPmzabykyZNIioqiqioKPbt2wdAWloaCQkJzJs3j6CgIJKSktpdTb6lzZs38/DD\nDyOEYPz48ZSUlHDp0qV2r3/77bd5+umnTcerV69myZIlANx///1ER0cTEhLCu+++a7rGw8ODJ598\nkvDwcPbv399hTIMGDSI2NhanFqt7p6enM3LkSIYPH46zszNz5841vTdKz7taUcu5okp0ThpC/Lxs\nHY7VGFfZb5l0VfWi/Rbb4xoZCUJQffIkTdXVHRdQFKVHONyK9K/Ov9cq9T65bstNX8/JyWHDhg2k\npKQQGxvL2rVr2bNnD6mpqSxfvpxNmzaxbNkypkyZQkpKCiUlJcTFxTFt2jQGDRrEzp070el0ZGdn\n8+CDD5o2js7MzOTkyZP4+fkRHx/P3r17mThxYoebSxcWFjJkyBDTeX9/fwoLC037L7Y0d+5cJkyY\nwMsvvwzAunXreO655wBISUnB29ub6upqYmNjmTt3Lj4+PlRWVjJu3DheffVVoHMbXjfXMlY/P78b\nVuhXelaGYRX6iCH9cO7Te//uMj7BeOjCD0mXlJKqI/reH9de3NOl9fTEJSiI2tOnqT7+Le7j4mwd\nkqIoOGDSZSsBAQGEhoYCEBISwtSpUxFCEBoaSm5uLgA7duwgNTXVNJ+ppqaGvLw8/Pz8WLJkCUeP\nHkWr1XLmzBlTvXFxcfj7+wMQERFBbm4uEydO7HBz6c4aOHAgw4cP58CBAwQGBpKVlUV8fDwAb7zx\nBp9++ikA+fn5ZGdn4+Pjg1arZe7cuaY6LB2TYhu9fT6X0Vg/L3ROGs4VVXK1ohYfDxfq8/NpLCpG\n6+2Nc8AwW4doVW7R0fqk68hhlXQpip1wuKSrox4pa3FxcTF9r9FoTMcajYaGhgZA/1f0xo0bGT16\n9A1lly5diq+vL8eOHaOpqQmdTtdmvVqt1lRXR71KgwcPJj8/33S+oKCAwYMH37QNiYmJrF+/nqCg\nIObMmYMQgrS0NHbt2sX+/ftxc3MjISGBmpoaAHQ6HVqt1lS+qz1dLWO9ePFih7Eq1pPe7MnF3sy5\nj4aIIf04cO4ah3KvM2PsbT/stxgd1Suf2mzOLTqK6x9+qOZ1KYodcbiky55Nnz6dFStWsGLFCoQQ\nZGZmEhkZSWlpKf7+/mg0GtasWWPWpPSOepVmz57NypUrSUxM5ODBg3h5eZmGFoOCgsjKympVZs6c\nOSxbtozMzExeeuklAEpLS+nfvz9ubm5kZWVx4MCBLsfUntjYWLKzszl//jyDBw9m48aNfPzxx12q\nS+meqroGThaWohEQeUf7m0H3FnHDvDlw7hoZudf0SdctMLRoZGxjdWYmsqEB0Uf9ulcUWzNrQocQ\nYoYQ4jshRI4QolWXhhDid0KIU0KI40KI3UKIoc1eaxRCHDV8pVoyeHuTnJxMfX09YWFhhISEkJyc\nDMCiRYtYs2YN4eHhZGVl4e7e/cUoZ86cyfDhwxk5ciSPPPIIb775JgDFxcXtTsbv378/wcHBXLhw\ngbg4/XDDjBkzaGhoIDg4mGeffZbx48d3Oabvv/8ef39/XnvtNf70pz/h7+9PWVkZffr0YeXKlUyf\nPp3g4GDmzJlDSEhIl++jdN3R/BIamiTBt/fFU+fUcQEHZ+zNM+4zWX0LTKI3cvIdhNOQIfp9Jr/7\nztbhKIqCGT1dQggtsAr4MVAAHBJCpEopmz/znwnESCmrhBCPAX8F5hteq5ZSOvSOssOGDePEiROm\n49WrV7f5mqurK++8806r8oGBgTdMHH/ppZcoLy8nISGBhIQE0/mVK1eaHZMQglWrVrU6f+DAARYv\nXtxuuS1bbhyedXFxYevWrW1eW1HR9r517bntttsoKCho87WZM2cyc+ZMAMrLyztVr2I5xv0WY3v5\n0KJR1ND+aAScuFhG+fdXqDt/HuHqii442Nah9Qi36GhK8/OpPnwEV/WHjqLYnDk9XXFAjpTynJSy\nDvgYuK/5BVLKL6WUVYbDA4C/ZcNUzHXvvffy+OOP2zoMxU4Ze3xulaTLw6UPY/z60tgkOb1jDwCu\n4eEIp97fywc/7C2p9mFUFPtgziD/YCC/2XEBMO4m1/870LzrRCeEyAAagL9IKTe1LCCE+DXwawBf\nX1/S0tJueN3Ly6vX9Y40Njb2ujaZy9y219TUtPosOLqKigqbtamxSXLonP5vo7qLp0m71vNDTrZo\n/+19ajkBnNu+k3Dgio83523wM7BF27WNjQwASvbvJ+fLL8GGDw/Y8rNva6rtabYOw25YdGalEOJn\nQAxwV7PTQ6WUhUKI4cAXQohvpZRnm5eTUr4LvAsQExMjmw+5AZw+fRoPD49e9bRReXk5np6etg7D\nJsxpu5QSnU5HZGRkD0XVM4wL4trCicJSanbs4Q5vN+bM+JFNYrBF+6t8LrHzwhEGFOYBEPzTn+Jh\nWC6lJ9mi7VJKst9YAdeuceeIETjfcUeP3r85W372bU21PcHWYdgNc4YXC4EhzY79DeduIISYBjwH\nzJZS1hrPSykLDf89B6QBnf5XVKfTcfXqVbNXa1ccm5SSq1ev3rC0htJ9t9rQolHMsP64NNTiezkX\ntFpcwx16immnCCFMDw2opSMUxfbM6ek6BAQKIQLQJ1uJwEPNLxBCRALvADOklFeane8PVEkpa4UQ\nA4B49JPsO8Xf35+CggKKioo6W9Ru1dTU3LJJhTlt1+l0pkVjFcv4IenqnfsttmeQp46Exiv0kU00\njQhC69H9p4cdiWtUNOU7d1F15DD9fjrH1uEoyi2tw6RLStkghFgCbAe0QIqU8qQQ4kUgQ0qZCrwM\neAAbDEOAeVLK2UAw8I4Qogl9r9pfWjz1aBYnJycCAgI6W8yupaWl9bqhM3Pdym23FSklh26RRVHb\nMrn2IgDfDxnNrfYMn3Hz62rV06UoNmfWnC4p5efA5y3O/aHZ99PaKbcPCO1OgIqidF/etSqKymvx\ndndmxMBbq6cHIPCKfhppptdQpto4lp6mCwpCuLpSl5tLw9Wr9PHxsXVIinLL6r273SqKYmLq5Rra\nv1c9kGIO2dCA51n9Dg2fM+iWmxsqnJxwDQ8H1NIRimJrKulSlFvAofO35iR6gJqs76C6iu89B3Ku\nScf54kpbh9Tj3KINQ4yGFfkVRbENlXQpyi3g0AV90hVzi02iB6g27Ld4LSAI+OGBgluJm1okVVHs\ngkq6FKWXu1pRy7miSnROGkL8vGwdTo+rMvTuOEfqEw/jUOutxDU8HLRaak6fpqny1uvpUxR7obad\nV5ReLuOCPsmIGNIP5z499HdWTRl8/y1cOQWVRVB1FeoqGXXlGtRsB/eBMCAQBowCn5Gg0VolDCkl\nVYaerqEJE2BzgdV7upqaJCXfV1FcUE75tRqqK+qprazn+8tNfP39GZx1WrwGudLP1x0fP3ecXa3/\na1jj7o4uOJiaEyeoPnYM9zvvtPo9FUVpTSVditLLGedzxVlzPldTExQcgu8+hzPboeh0m5f5AVxq\ncdK1PwybBCOmwJj7wM1ycdbn5dFYVIzW25uRsWNx23qJC1eruFJWw6C+llsnr+J6LbnfFnP+WDEX\ns6/TUNfU5nUl527cEF4IGDSsL/5B/RkROYgBQ6y384ZbdDQ1J05QdfiISroUxUZU0qUovZyxZ8cq\n66oTuH8AACAASURBVHNVXoWjH0JGClzP/eG81hkGjYHbxkJff30i5ezBmVPHGRXgD2WX4Go2XD4F\nZQVwOlX/tfU/YfRMiF4AwxO6vVegcRV2t+gonPpoibqjP3tyism4cJ2Zobd3q+6mJkneyauc+LqQ\nCyeuQrOHIj29dQwY4kE/Xzd0Hk7o3J34Lus7hgeMpLaynpIr1ZRcruJqQQWXz5dx+XwZh7dewGew\nB8F33k7wnbdbvAfMNToK1qwx9fwpitLzVNKlKL1YVV0DJy6WoREQNdSCk+gri+Gb1yDjf6GhRn+u\nrz8E/wRG3wN3TIA+zq2KXSwdzKg7E248ee08nPsSTv8Lzn4JpzbpvwZHw+T/hFHTu5x8GRMMV8PT\ne7HDvNmTU0z6+WtdTrpkkyTnyBXS/3Weksv6DcQ1fQR3jPEhIHwAQ8f64O7l0qrclfozhCcMueFc\nXU0DF7NLuHDiKtkZl7laWMGeDdkc+uw8YVOGEPYjf3TuTl2KsyXTE4xHjyHr6xFOlqlXURTzqaRL\nUXqxzLwSGpskYwf3xcPFAv+711fDnr/BvhVQb5iQPfLHEPcIjJzWtblZ3gH6r5iFUFoAR9fCwbeh\n8DD8Yz4MGQ8z/wq3h3e66mpTT5cx6dInnhkXujavKz/rGns/yeFqQQWg79Eae9dggu+8HVfP1klm\nR5x1fRgWOoBhoQOYOC+Q3G+LOfZFPpdySjm05TzHv8gn7ifDGTvZD422e/Px+vj44DxsGHW5udRk\nZeEaqtatVpSeppIuRenF0i25PtfZL2HLb+H6ef1x4HSY8jzcHtb9uo28/OH/s3ff4VFd1+L3v2eK\neu+9S6ghBIjee8fYYGOw4xo7uYlznTf9l9jpjnNvinOT2LEdGzuJDZhmei+igwoSAlWEekW9jdrM\nnPePI4liAQJ1sT/PowdpTpm9QaDF3uusNetHMOXbkPAJnP0LFF6AD2fD+Bdh/i/BzKZHt9JXVNCW\nn49kYYFZWBgA0T52aFQSaSX1NLS0Y23Ws9We5oY2zm7LJvNiGQBW9qbELPUjdKo76l4GQ53UWhWB\n41wIHOdCcVYN8ftyKc6s5fQXWaSdKWH2M6NwC+jd06fmMeNpy8tDl5Aogi5BGASiZIQgjGCdKzq9\nSqJva4Jdr8F/VikBl3MYvLAfntnStwHXrUwsYepr8J1EmPwtQFK2Mv8xDfLO9ugWnaUiLKLHIGmU\n/19amGiI8LTFKCurgD2Rk1TB57+8QObFMtRaFZNWBvDMrycTMcOzzwKuO3mG2PPYd8ey5BujsXYw\no6q4kR1/SOTCzusY9N0n6feExbiOLUaR1yUIg0IEXYIwQrUbjFzKVwKLh06iL70MH8yCpP+A2hTm\n/Ry+cQr8pvXhSO/BzBYWvw3fPAPu0VBXAJ8ugyO/AIP+npfemc/VaWLHFuP9Skfo2wzEbszkwAdX\naG3S4xVqz9NvTiRmqR8abf+UuLiVJEkEjHVm/S8nMW6RDzKQeDCfbf+T0JVL9qA6m1/rEi89cu2Q\nBGEoEEGXIIxQqSX1NLcbCHCyxNn6q4nd95X4L/hovvKUoXMYvBoLM77fbYJ8v3MNh68fhVk/Bkml\nbDv+ZxU0Vtz1kjvzuTp1BqD3CrrqKprZ9j8JpJ4qRqWRmP5UMCtfj8bOxaIPJvNgNCZqpjwexOPf\nH4eNkxmVhY1sfTue3JTKB76X1tsbtbMThupq2nLz+n6wgiDckwi6BGGE6qzP9cCtfwx62P8j2PPf\nYGhTEtxfPaEEPoNJrYU5P4UX94OVK+Sdhg9nQfFX+wkaGhtpycgAjaar2XOnmI6nOJMKamnrZquu\nKLOGrb+Pp6q4CTtXC9b8OIYxc70HvVG4R5Ada382kcCxzrS1GNj/XgoX9+Q80IqVJElYjI8BQJeY\n0F9DFQThLkTQJQgjVOdKzgMl0bfUw+erIe4DUGlh5d9h+TugNe+nUT4En8nw6knwngT1xcp2Y+bB\n205pTkoGoxGziHBU5reP3dHKlEBnS1r1Rq6W1N12LO1MCXv+L5nWJj2+kY6s+UkMzt7W/T6lnjIx\n17Do1UgmrwpAkiBhXx5HP0l7oDwvi3FKOyTR/FoQBp4IugRhBJJluav9z0T/HgZdjTeUACYnVmnT\n88JeGPe1/htkb9i4w/N7Ycx6aNfB5nVKgdYOnas4nYnjd+oMRDtXA2VZJmF/Lic+y8BolBm7wIel\n34rCdABa9DwoSZIYv9iPZd8eg8ZUTVZcOXv+dpnW5nvnuHUyF82vBWHQiKBLEEag6xWNVDe14WJt\nio9DD/KQavLg44VQlgIOAfDyEWVFaSjTmMCq95QCqrJRKWdx+k/AzVWczsTxO3UFXXk1yEaZ01uu\ncXF3Lkgwa/0opq4OQqUa3O3E+/GNdOTx743F3MaE4swadv75Ei2N7fe9zmzUKFSWlrQXFtJefmMA\nRioIQicRdAnCCBSXq6xyTfBzuH8uUnUOfLJUKQfhFgUvHVKKlQ4HkgRzfwbL/wJIcOzXGI/8juaU\nFADMO7bS7tQZdCXkVXP8swyunChCpZFY/EokkTM9B2r0vebia8OaH43H1tmcysJGdr5zieaGtnte\nI2k0mEdHA6J0hCAMNBF0CcIIlNCVz3WfJPrqXPh0hZIb5TMFXtgHVi4DMMI+FvMirPoHSCpadv0Z\nubUVk8BANPbdz9/bwRxXa1MmVkHGuVI0WhXLXxtD4LjhN3cbJ3Me//447FwtqCpuYuc7Sejq7x14\n3Vo6QhCEgdOjoEuSpMWSJGVKkpQtSdJPujn+PUmS0iRJSpEk6ZgkSb63HHtekqRrHR/P9+XgBUHo\nXlxn0HWvfK7qXPh0udJw2nsyPLO1x9Xeh6TodfD4h+gqzACwcLvHuTKsaDdjTJsG1BLLvh2Fd2g/\nNAQfIJZ2pqz63ljs3S2pLmli558v3TPwMu/IdRPNrwVhYN036JIkSQ28CywBwoF1kiTd+ex4EhAj\ny3IUsA34345rHYBfAJOAicAvJEnqw667giDcqbSumaKaZqxNNYS63SWIqiuCf63oCLgmwbPbwHTo\nPKX30KKepFkzAQALQ4LSJ/IOsixz6oss7MvaaEemONIKr2EccHWytDXl8e+NxdHTkpoyHbv/mkyr\nrvscL/Oo0aDV0pqRiaGxcYBHKgiPrp6sdE0EsmVZzpFluQ3YDDx26wmyLJ+QZbmzRPIFwKvj80XA\nEVmWq2VZrgGOAIv7ZuiCIHQnPk/J5xrvZ4+6u2RwXTX85wmoKwSvifDMCAm4ANloRJddCoCFczsc\n/QVc+vdt58Tvy+PqyWIktcSXlm2cqm8YjKH2C3NrE1a+PhZbF3OqihrZ914K7W2Gr5ynMjfHPDwc\njEalvIYgCAOiJ89DewKFt3xdhLJydTcvAwfuce1XslQlSXoVeBXA1dWV2NjYHgxreGtsbHwk5tkd\nMffYfn2PnWmtADgaa7/yXipDK2Muv4ltfSaNlr4k+76O/sLA5fX09/w1xcU41tVhsLcnb8xygrM/\nRN79OqnXi6l0nkJ1tkxpggwSeE6RuZFhpLmmmR0Hj+Ng1r8prgP5fe8ySabpKJRm17Hx9yfxmS4h\n3RGAW7m4YAmk79hO031aKvUF8fc+drCHMSge5bl3p0+L0EiS9CwQA8x6kOtkWf4Q+BAgJiZGnj17\ndl8Oa0iKjY3lUZhnd8TcZ/fre/w++RTQwNq5MbfX6DK0w+ZnoD4TbL2xevkg0208+nUsd+rv+Vdv\n3Eg5YD91Kp7P/gFiHZFi3yYy489ct95GWqJSRHTOM6GET/dgx4Y4TmZVoHEPZfaY/v29GOjv++qx\nTez4UyKNJXoMBa7Mez7stidZG4xGio4cwaWyCt8BGJf4ez97sIcxKB7luXenJ/+1Kwa8b/naq+O1\n20iSNB/4GbBSluXWB7lWEIS+UadrJ7O8ARO1iigv25sHZFmpY3XtEJjbw7M7YIADroHwlfpcs34M\nE79BSXMgR3a2IMswcYU/4dOVuXcGpZ1FUkcSBw9LVrwWjcZUTeaFMhL259123HzsWACaU1Iwtt37\naUdBEPpGT4KueCBYkiR/SZJMgKeB3beeIEnSWOADlIDr1mp7h4CFkiTZdyTQL+x4TRCEfpBYUI0s\nwxhvW8y06psHzv0Vkv4DGnNYvxWcQwZvkP1EluWuKuvmnU2uJYm6mJ9zoOFNDGiJtDtLzKyb+Wud\nfRjv1fx6OHP1t2HhyxFIEsTtySUrvqzrmMbeHpOgQOTWVlpSUwdxlILw6Lhv0CXLsh54DSVYSge2\nyLKcKknSryVJWtlx2h8AK2CrJEnJkiTt7ri2GvgNSuAWD/y64zVBEPpBZ1HUmFv7LWbshyO/UD5/\n/H3wnjAII+t/7cUl6MvKUNnaYhoUBEBrs559/0ilpd0cH+tsZpj+GWnLs6BXFuPHeNtholaRWd5A\nXfP9q7kPR/5RTkxbEwzAsX+lU5pd23Wss/l1s2gJJAgDokeZo7Is75dlOUSW5UBZlt/qeO3nsix3\nBlfzZVl2lWU5uuNj5S3XbpBlOajj45P+mYYgCHBzxWZiZ9BVmgLbvw7IMPcNiFg1eIPrZ82d/RbH\njkVSqTAajBz+6Co1pU3Yu1uy8AdLUdm4QcF52P0dkGXMtGpGe9kiy3Cpo1flSBQ114vIWZ4Y9TL7\n379CXUUzABZdfRhFkVRBGAiiIr0gjBDNbQauFNUhSTDO115pYL1pHbQ3QdRamPGDwR5iv9IlKKs1\nnflcZ7dnU5BajZmVlmXfisLU1QvWfwFaS0j5As6/C9xsCRQ3QrcYQWmSPeOpYHwiHGhpbOfA+1do\nbzVgMb6zSOolZKNxkEcpCCOfCLoEYYRIKqihzWAkwsMGWxNgy/NK8VOvibDir0qfwhFMFxcHgMXE\niWReKCXleBEqtcSSb4zG1tlcOck9StliBTjyc8g52dUqKWEEB10AKrWKhV+P7GgX1MiJzzJQu7uj\ncXPDWFdH2/Xrgz1EQRjxRNAlCCPEhZwqACb7OyoBRcE5sHKDtZ+B1myQR9e/2svLacvPR2VhQaON\nDyc+zwRgxtoQPILtbj85fCVM/x7IBtj2IjF2SkX2y4V1tLR/tZDoSGJqrmHJN0ajNVVzLb6clONF\nWIzr3GIUeV2C0N9E0CUII8SFHGWl5jHNebjwHqg08NS/wdp1kEfW/3Rx8QCoYyZz8KM0DO1GQqe6\nEzHjLmUx5r4BgfNAV4Xt7hcZ7WJCm8FISlHdAI56cDh4WDLvhTAAzm3Ppi5wCnBze1YQhP4jgi5B\nGAGa2wwkF9YySlVIZOIbyouL3gafezWPGDl0cXHISKTYL6G+sgVnH2tmPR1yWzHQ26jUsPojsPeD\n0sv8Wv0RIHOxY7VwpAsc68K4xb7IMpzPdqLZ1AFdfDyyLA/20ARhRBNBlyCMAEkFNZgaGtlg9n9I\n7TolcX7iK4M9rAGji4sj128JpXUWmFpqWPxqJBoT9b0vsnCAtZ+D1oKxNQd5Vn2U849I0AUwaWUA\nPuEOtDQbuTrmm7TdqKI9P3+whyUII5oIugRhBLhwvZI/at/H01gCrqNh+V9GfOJ8p/byckobLMnz\nWwYSLHw5Ahsn855d7BYJK/8GwJuaz9DlX6JVP7LzujqpVBILXo7A2tGMBgtPsgNX0XQxbrCHJQgj\nmgi6BGEEsL/6CYvUCbRrrWHtf8DEYrCHNGAqTyWQFvocAJNWBOAT7vhgNxi9Bsa/iKnUzjuqv5CS\nXdgPoxyazCy1LPp6JCpJpshrDtlnxUqXIPQnEXQJwjDXkp/AM/X/BKBt2d/AwX+QRzRwjAYjp061\n0m5ijZuNjvGLfR/uRovfpsw8CH9VOdZHf6j0qnxEuPrbMHG2UqvsUnM4dRW6QR6RIIxcIugShOGs\npR5564uYSAb2mi7DMvrxwR7RgIrfn0dlux0mrXXMecILSfWQW6pac67N/BuNshmhlYch8dM+HedQ\nN/bJMTjXp6NXm3Po3UsY9KJQqiD0BxF0CcJwJcuw53XMGwtINfpyJeKHgz2iAVWUUU3C/jyQjUTm\nbMI+JrJX9xs9Joaf6V8GQD7wYyi70gejHB5UKhUTXAsxa6mioqyN81+KQqmC0B9E0CUIw9Wlf0Hq\nDlokM15r/28mBN2lJtUIpKtv48iGNJDBL/8gnqPskDSaXt3TzsKEay5L2KSfg2Roha0vQGtD3wx4\nGLCbPI6I1A1IGLl8rJCc5IrBHpIgjDgi6BKE4ag8DQ78GICftX+dPNyZ4O8wyIMaGLJR5tinaejq\n23DS1uKftx/LiRP75N5TAh35lf45KiwCoSob9n2/T+47HFhOmohtQx5BJYcAOP7vdBqqWwZ5VIIw\nsoigSxCGmzadsgqjb6E8cA3b9VOVfovm2sEe2YBIOlJAQZrSyDry+kYkZCz6KOiaHOBIC6b8xvzH\noLVQGmOnbO2Tew91Wl9fNC4ueGXtxTvAjFadnqOfpGE0PjoPFQhCfxNBlyAMN4ffgMpMcBrFZqfv\nADDJ/wHLJAxTN/LrubgrB4DZj7mjyklFZWGBWXh4n9x/or8DKgkOlFnTOv8t5cV934OakV9KQZIk\nLCZNQgJinPIwtzGh5FotSYdH/twFYaCIoEsQhpPMg5DwMai0sPojYnObAJgWNPKDrvY2A0c2KCsv\nUXO8cKy8CoDFhAm9zufqZGuuJcLDlnaDzEW75RC6HFrrYcerYND3yXsMZZaTlBVDY9J55j+v9GeM\n251LeV79YA5LEEYMEXQJwnDReAN2fVv5fN7PqbcP43JhLRqV9EisdJ3dlk1tuQ4HD0umPB5I0/lz\nAFhOm9qn7zMlUPm9PJ9brVSrt3aHwgtw5p0+fZ+hyGKS0qtTFxePd5g9Y+Z6YzTKHNmQSlvLyA86\nBaG/iaBLEIYDWVYCLl0l+M+EKa9x4XoVRhnG+dhjado3Kz1DVW5KJamnilFpJBa8FIFaq6Lp/HkA\nLKdM6dP3mhLQEXRdr1L6M676h3Ig9m0ojO/T9xpqtF5eaDzcMdTV0ZqZyeTHA3D0tKTuRjNntl4b\n7OEJwrAngi5BGA7iP4Jrh8HMFla9DyoVZ7IrAZgW5DTIg+tfuvo2TvwnHYApqwJx8rKiLTsbQ0Ul\namcnTIKC+vT9Jvg7oFFJpBTVUtfcDoFzYOp3QDbAjq+P6DISkiRhOWkyAE3nzqPRqpUgV6Mi/Wwp\n1y/dGOQRCsLwJoIuQRjqKjKV5HlQGlnbegJ0BV3Tg0fu1qIsyxz/dzrNDe14hSrbXQBN5zq2FqdM\nQerjxt5WphrG+dhjlDtWuwDmvgluo6Emr6tUx0hlOW0aAE1nzwLg6GnF1NWBAJz4LIPGGlFGQhAe\nVo+CLkmSFkuSlClJUrYkST/p5vhMSZIuSZKklyRpzR3HDJIkJXd87O6rgQvCI0HfBjteAX0LjFkH\nkU8AUFLbTE5FE1amGsZ42Q3yIPvP1ZPF5F+twtRCw7znw7ra/DSd69hanNq3+Vydpgcrq4enr3UU\nCNWYwuqPQWMOyZ/D1R398r5DgeUUZaVLl5iIsUUJsEbP9sI30lEpI/FpOrIoIyEID+W+QZckSWrg\nXWAJEA6skyTpzuezC4AXgI3d3KJZluXojo+VvRyvIDxaTrwFpZfBzheW/G/Xy52rXJMDHNGoR+aC\ndXVpE2e3ZwMw+5lQrOzNAJDb22mKV3Kr+jqfq9OMrqCr8uaLzqNgUUcZib3fhdrCfnnvwaZxdMQ0\nPAy5tRVdYiKgbDvOfS4Mc2stxZk1JB0tGORRCsLw1JN/rScC2bIs58iy3AZsBh679QRZlvNkWU4B\nRJdUQegruafh7P+BpIInPgQzm65DZzu3FkdoqQiD3siRDakY2o2ETnEjaLxL17HmlBRknQ6TwEC0\nrq798v5RXnbYmGkoqNaRX9V080DMSxCyBFrq4MtvgtHQL+8/2Kw6txg7tnEBLGxMmPucUkbi4q4c\nKgpGbm6bIPSXnjzy5Anc+l+6ImDSA7yHmSRJCYAe+L0syzvvPEGSpFeBVwFcXV2JjY19gNsPT42N\njY/EPLsj5h573/M07Y3EJLyOGTJ5Pk+Sl9MCOcp1sixzIk0HgElNLrGxw6d4ZU/nX5ZspKoQtJaA\nRzmxsTcTuC337MUKqPXx6dfvoxBbmYQW+HjfOeb63Kz2r3VaR0zeBUzzz3D9s+9S6LO6R/cbTt/3\nJpaW2APlhw6TPmHCbcccgqH6mszOv8cTuFBCpelZTt1wmn9fE3OPHexhDBkD8Zy5ryzLxZIkBQDH\nJUm6IsvybS3sZVn+EPgQICYmRp49e/YADGtwxcbG8ijMszti7rPvfZIsw/aXobUSPMfj99x7+Klv\n/tDPKKun/tBp3GzMWLdsTp8nkvennsy/OLOG1C+SkCRY+e3xuAXY3nY874MPaQZCnnwS6378Piox\nLyDhyyvckByYPXv87Qf9LeHz1QTmbSJw/svgEX3f+w2n73vjlClkvf8B2qIipkdGonG6+YSsfqqB\nLb+Lp6ZMh6bCg5nrRvXonsNp/n1NzH32YA9jyOjJ9mIx4H3L114dr/WILMvFHb/mALHA2AcYnyA8\neq5shavblWWeJ/4J6tt7Kp65drNUxHAKuHqipamdo5+mgQzjl/p9JeAyNDbSnJICajUWEyfc5S59\nozOv6+z1SvSGOzIngufDxG+AsV150KFN169jGWgqU1MsYmIAuuqhddKYKGUkVGqJKyeLybtS2d0t\nBEHoRk+CrnggWJIkf0mSTICngR49hShJkr0kSaYdnzsB04C0hx2sIIx4Nfmw7/vK54vfBsfAr5wy\nUktFyLLMqU2ZNNa04upvw4Slfl85RxcXDwYD5lFRqK2s+nU83g4W+Dla0NCiJ6W47qsnLPgVOIdC\nZRYcebNfxzIYukpHnDn7lWPOPtZMWhkAwPF/p6OrbxvQsQnCcHXfoEuWZT3wGnAISAe2yLKcKknS\nryVJWgkgSdIESZKKgCeBDyRJSu24PAxIkCTpMnACJadLBF2C0B2jQUnObq1Xev6Ne+4rp7TqDVzM\nqQZGXlHUrLhyriXcQGOqZv6L4ai6eSqzqz5XP5WKuFNX6YisblZztObKSqRKqxSvzTo8IGMaKJ2/\nx03nziHLXy0REb3AB88QO5ob2jnxWUa35wiCcLsePWsuy/J+WZZDZFkOlGX5rY7Xfi7L8u6Oz+Nl\nWfaSZdlSlmVHWZYjOl4/J8vyaFmWx3T8+nH/TUUQhrmzf4GCc2DlCiv+Ct1sHSbk1dDcbiDUzRoX\na7NBGGT/qK9s5tSmTABmPBWMnYtFt+c1nT4N9H2/xbuZEewMwJnsiu5PcI+CeR2rXLu+BY13OW8Y\nMg0JRu3shL6igtZrX20BpFJJzHshHBNzDXkplaSeLhmEUQrC8DIyC/wIwnBTkgQnfqd8vuo9sOx+\n6zA2U3mKb/Yol26PD0dGo8zRT9NoazEQEO1M2FT3bs9ry8+nLT8fla0t5lFRAzK2KYGOqFUSlwpq\naWhpv8tJ3wG/GdBUAbtfUx6EGAEkScJqamd1+nPdnmPtYMbs9Uoi/dmt16gpa+r2PEEQFCLoEoTB\n1qaD7a+AUQ+TvglB8+966olMZSVl9ijngRpdv0s6nE9pdh0WtibMfnbUXR8OaDylrHJZTZuKpBmY\nBt82Zlqive0wGOWbLYHupFLB4+8rfTGzDkLiJwMytoHQuaLY2RKoO8ETXAmZ5Iq+3ciRDWkY7nzo\nQBCELiLoEoTBduRNqLqmJGXP/+VdTyus1pF9oxFrUw3jfe0HbHj9qaKggbjduQDMey4McyuTu57b\nePoUAJYzZg7I2DrN7NhijM26x9ahrRcsf0f5/OBPofKr23HDUWdely4+HmNz813Pm/n0KKwdzKgo\naCB+T+5ADU8Qhh0RdAnCYMo6rCRhq7RKUrbW/K6ndv7Qnx7shHYEtP5pbzNwZEMqRqNM1BwvfCLu\n/jSmsaUF3cU4AKxmTB+oIQIwJ1QJuk5k3Lh3snjkaohaC/pm2P51MNxlO3IY0Tg5YRYZidzaStOF\nC3c9z9Rcw/wXw0GCxEP5lFyrHcBRCsLwMfz/5RaE4aqxQkm+BiUZ2/3eeUonu/K5RsbW4vnt2dSU\n6bB3t2TK418tjXErXVwccmsrZhERtxXqHAiRHrY4W5tSWtdCRtl9Wt8s/QPY+kBpMsS+PTAD7GdW\nHYUtG0+evOd5HsF2jFvkCzIc/SSN1mb9AIxOEIYXEXQJwmCQZdj9HSX52m8GTHntnqe3tBs4m63k\nFM0KGf5J9HlXKrlyshiVWmLBS+FoTNT3PL/xZMfW4swZAzG826hUEnM6At3jGTfufbKZLTzxgdIv\n8/SfIb/7BPThxGrWLED5M7hfWYiJy/1x9rGmobqFU5szB2J4gjCsDEw2qiAIt0v8FLIOgKktrPoH\nqO4ddMTnVdPcbiDM3QY328EpFdGib6G0qZTSplKqmquoba2lpqWG2tZaaltrqW+tp1nfTLOhmeb2\nZloMLbToWzDISlNoWZYxGA1YbLBmVdL3MMeaK/7H2J/wZ0yTTDHXmGNratv1YWdqh62JLc4WzrjF\nHkMCLGcMfNAFMGeUE/suZBJ3MYkF5mU0N9TTXF+v/NpQT3NjA23NzehbW2lva6W9bg76liYMP/kt\naM2RAYPBwOWPlFIgGq0WjYkJGhPTjl9NMLW0wszSCjNrG8ytrDCzssbCxhYrRyesHZywcnBEY3L3\nnLf+YhYRjtrJCX1pKa1ZWZiNunvbH7VGxYKXwtnyVjxZF8vxi3QieEL/NCUXhOFIBF2CMNAqs+HQ\nT5XPl/8Z7LzvfT4QO0BPLVa3VJNTm0NufS65dbmUNJZQ2lRKWVMZ1S3VvX8DGSZfewLzdmuKba5x\nxmk3cv29V0/cqmX+WmygwQxmpLyEc44r7lbu+Nn44Wvji6+NL342fnhZe2GifvigRN/WRk1ZCdXF\nhVQXF1FbVkJ9VQUNlRU0VFXxkkEPhbAnqad37Gjf1Np68z263qsVHqK6gpm1DdaOTti7eWDvSf/Y\naAAAIABJREFU7om9u0fHhyfm1jYPfsMekFQqrGbOpG7HDhpjT94z6AKwd7Nk2pPBnNyYyclNmbgF\n2mLtMHJqyglCb4igSxAGkGTUK7362nUw+ikYvea+18iyzLH0cgDm9FF9rhZ9C1k1WaRVpZFRnUFO\nXQ65dbnUtt49AVqj0uBm4Ya7lTtOZk7Ym9ljZ2aHvanyq62JLeYa864PM40ZZhoz1JIaCaUMxL5P\nz3KjRo3WXM03vruCb9ssp9XQSqu+FZ1eR11r3c2PtjpqWmrw3JsIZJMeZEq7ZKCkqYSSphISyxNv\nG59KUuFp5UmoQygh9iGEOoQS6hCKq4XrbWUoZFmm7kY55TnZ3MjNprIwn+riIupulCPLdy930K61\noFqyxN/Xg0AfN8ytbW77MDE3R2tqhsbUFK2pKZrGYtT/Xo7U3gyr3uV0tSMzZ84EGfTtbejb2tC3\ntaJva6O9tZU2XRPNjQ20NDbQ3KD82lRXS2N1FY3VlTRWV9HSUE9LQz0VeTlfGZ+ZlTVOPr64+Abg\n7BeAi18Ajl7eqDXabmbzYKxmzVKCrpMncfrGq/c9P2KGB/lXKsm7UsWxT9NY+V3RclcQQARdgjCg\nfPO/gJJLYOutJF33QPaNRvKqdNhbaBnnY/fA76k36smqyeJyxWXSqtJIq0rjeu31rm2/W1lqLQmw\nDcDf1h9/W3+8rL3wsPTA3dIdR3NHVNLDp4HWluuoSFa2UWevH4Wvh1uPrst/72vogFUvvsXaRfO4\nobtBUWMR+fX55Nfnk1efR0F9AcWNxRQ2FFLYUMiR/CNd17sa7Rjd6ouPzg6rapnWkkpam766zCRJ\nKuzdPXDw9MbB0xt7Nw9snFywdnLG2smJf8eV8P7eNFZGePBf63oQRDi5wNLfwN7/Dw7/GIuxf0Jr\nqqz4aM0efOVHNhrR1ddRX3mD2tISqktLqCkt7vgooaWxgaK0qxSlXe26RqXW4OjphWtgCB6jQvEI\nCcPB3RNJ9WB/jpbTpoJWS3NyMvqaGjT29y5ZIkkSc74WxubfXKQ4q5bkowUw8DujgjDkiKBLEAZK\nwUV887cBklJM07xnAdThNGWVa26oK5oelIpo1jdzpeIKiTcSSSpP4nLFZXR63W3nqCQVQXZBhDuG\nE+YQRpB9EP42/rhYuNy1OGlvGAxGjnyShmzoKKY5oWcBl76mBt2lS6DVYjVzJmqNGT42PvjY+DDV\n4/ZWQO2GdnLqcriaeZGctGTqcwrQljRhqVMDtRippb7j3DZTkNxscPb3Jyx0AsGB0di7e6LR3n1V\naG6oC7/Zm0Zs5g30BmOP/iwY/6JSFiTrAKEZf4GFT9w3f+9uJJUKSzt7LO3scQ+6fYtPlmUaa6qo\nzM/jRl4ON/JzqcjLoaashIqCPCoK8rh6QukNaWZphXuIEoB5hobjHhx6z3kDqK2ssIgZj+78BZrO\nnMF2xYr7jtfCxoS5z4Wx790ULu7Kwe/uNX8F4ZEhgi5BGAgtdbDjFSSMMO274NfzWlNHOoKuBeHd\nJyS3G9u5WnmV8yXnOVdyjtTKVPTy7Y/r+1j7EO0STbhjOBGOEYTYh2Ch7b6/YX+I35vLjbx6tBYw\na11Ij69rjD0JRiOWkyejtrbu9pz6ihvkpSSRfyWZwquXaW6oRwUoIa0ajZkZ5j6uNDhJ5JlVkay6\nTq22GWXH8woU7Ma9yp2JbhOZ5D6JSe6TcLH46jauv5MlAU6W5FQ2EZ9Xw5TAu9cV6yJJsPJv8I8p\n2NdehfN/h2mv93j+PSVJEtYOSsK9/9iYrtfbWpqpyM+jLDuTksx0SrLSaaypJjcpgdykBAA0JqZ4\nhobjEzkGn4goXAICUXUTGFrPno3u/AUajh/vUdAF4DfaichZnlw9WUzxedAvM9z3SVVBGMlE0CUI\n/U2WYc93oTafBqtArOf8rMeX3qhvIbmwFlONipkhN+tTFdQXcLbkLOdLzhNXFkdT+83tMpWkIswh\njHGu4xjnMo6xLmNxthi82l5FGdUkHsxHksBzsoSpRc9zjBqPHwPAat7crtfamnUUpF4hP+US+SnJ\n1JQW33aNpb0DnqEReI4KxzM0HGdfv9uCiHZDOxnVGSRXJJN0I4n4snhKm0rZdX0Xu67vAsDf1p9p\nHtOY6TWTGNcYtGplzAsiXPngZA6HUst6FnQBWDnDY+/Bxifh2G8gYDa4j+nx70FvmJiZ4zkqDM9R\nYYxftgpZlmmoqugIwDIoTLtCZUEe+SlJ5KcoTwiYWlriHR5FwLgJ+I+NwcreQZnGvPmUv/17mk6e\nwtjaisrUtEdjmLo6iOLMGmrKdJzbcZ2ZT/c86BaEkUYEXYLQ35I+g9QdYGJFWvgPmKTpeXLL0XSl\nLtS0IAcya69wovAEsYWx5Nbd3mrFz8aPqR5TmeIxhRjXGKxMrPp0Cg+rubGNI5+kgQzjl/nRbF3Q\n42uNLS00nuno+TcumuTD+7meeJHCq5cx6G+u5JmYW+ATGYXv6LH4RkVj5+Zxzy1SrVrLaOfRjHYe\nzdfCv4ZRNpJVk8XF0otcKL1AYnkiuXXK05ufpX+GhcaCqR5Tmek1k8nBUXxwEg6nlvGLFeE934oN\nWUixxxI8Sw4ofTZfjQWTgVtp7CRJEjZOLtg4uRA6Tam/paurpeDq5a6PuhvlZMefJzv+PACuAUEE\njJtAwLiJmISF0paeQdP581h3FE29H62JmgUvRbDl7XiuxBbhG+mIb2QPA1ZBGGFE0CUI/akiEw78\nSPl82Z9orulZLhOArl3HlrT9mLlfIFVzjecO1HcdszaxZqrHVCXQcp+Cu5V7X4+812RZ5vi/0tHV\nteEeZMuEpX6cOt2zoEuWZfL37CTLxoyKYE9qf/njmwclCfeQUPyixuEbNRb3oBBU6offslJJqq4n\nHZ+PeJ52QzsplSmcKT7DyaKTXKu5xtGCoxwtOAqATaAPlbWjOJTlxOJRPV+xuh74Ip5t16EyE478\nHJb98aHH3JcsbO0InTarKwiru1FG3uVL5FyKp+DKZcpzsinPyeb8tk2YW5ni6OVM087tjJ0+rcdP\nRjr7WOMyWuJGisyxf6ez7s2JmFuLzHrh0SOCLkHoL+0tsO0lpTxE1NMw5mmIjb3nJbp2HaeKTnEw\n7yBnis/QqmpFawc6A3hZeTHHZw5zvOcQ7RKNVtX7UgD9KeV4EXlXqjC10LDgpQhU90k8l2WZ8pxs\nMs+fJuvCGeorboCbAyCjMTXFL2ocgTGTCBgbg4Xtgz/F2VNatZbxruMZ7zqe18e9TkljCaeLTnOy\n6CQXSy/SZlKAqUsBP7xwhA8ygljou5AFvgsIsg+6532NalOlv+ZH8yH+n8o2Y9jyfpvHw7J1cWPM\ngqWMWbCU9rZWClNTyEmMJ+dSPA1VFRQ52lBUmsuFV54hMGYyIZOn4Tt67H0LtzqFgqbZjpJrtRz9\nNJ3l345CUvX9QxuCMJSJoEsQ+suRN6H8KjgE3HNVo0Xfwuni0xzMPcipolO0GFoAkJAw6Hxw0Yzj\n4ydfIMA2oF+eLOwPFQUNnPsyG4C5Xwu7a3FMWZapyM9VAq3zZ6gtL+06Zmow4lLTQNTr3yNo8bJB\nqcYO4GHlwdrQtawNXUuzvplPEo/w1ws7MLFJI7s2m+zabN67/B4BtgEs8F3AQr+FhNjfJW/JIxrm\n/wIOv6H03XSPAjufgZ3QA9CamBIwdgIBYycof1YFecR/51sUo6cBSDt1nLRTxzExNydw/CSCJ0/D\nf8z4bv+sJJXE/BfD+eKtOApSq0g6UqD0ahSER4gIugShP2Tsh7gPQaWFNRvA9PYn79oN7ZwpPsOB\nvAPEFsbSrG/uOjbGeQyL/RZzKtmdw/ktrFsSSqDdvRtCDyVtLXoOf5yKUS8TOdOTgLFfTeKvKiok\n42wsmefP3JYIb2lnT/CkafjaOdH20zcx8fYmcMWqIRNsmmvMeSVmOR8cNKOutJk/PGfB1dozHCs4\nRk5dDh+kfMAHKR8QbB/M8oDlLPVfipvlHVvKU16DvDOQdVBZCX3xAKiH9qolKPlgLr7+xMyYS8DH\nG1CvXUNN9GiyLpzlRt510s/Ekn4mFlMLS0ImTyNsxhy8QiNuqwlm7WDG/OfD2fdeChd25eAeZId7\noO0gzkoQBpYIugShr9UVK6sYAPN/CR5KIU1ZliloLeB3F3/HgdwDt1V/j3SMZJHfIhb5LcLdyp3m\nNgO/26wU+Fw6eujla93L6S+yqC3X4ehpybQ1N7fcmhvquXHlEp8d2kV5zrWu181tbAmZNJVRU2bg\nGRaBSqWm7Ne/pgawWbx4yARcnbRqFfNCXdiRVEzFDX9+NXsBb0x+g/iyeA7nHeZI/hGu1VzjncR3\n+EviX5jgNoHlAcsxM3as9kmS0m/z/elQFA/HfgULfzu4k3oA1vPnU/3xBqTTZ5n4y18z6fGnqC0r\nJeviWTLPneZG3nWuHD/MleOHsXZ0Jmz6LMJmzOm63i/Kiej53iQfLeTwR1dZ+8ZEzCyHftApCH2h\nR0GXJEmLgf8D1MBHsiz//o7jM4G/AFHA07Isb7vl2PPAGx1f/laW5X/1xcAFYUgy6GHHq9BcA0EL\nYPK3KG8qZ1/uPnZn7+Z63XUoU04NsgtiWcAyFvktwtv69v6LsZk30LUZGONth7fDwD/l9rCy4srI\nOF+GRqti4cuRSCoj2fHxpJ48Rs6leIwG5alDE3MLQiZPJ3TqTLwjRt+WCC8bDNQfUgp52ixZPCjz\nuJ+FEW7sSCrm4NUyvjU7CK1K2/Vgw88m/YzTxafZm7OXk4UniSuLI64sDg0ajsYeZUXgCqZ7Tkez\nZgN8shTO/Q38ZkDIosGeVo+YjxmDxtkZfUkpLVdTMR8diZ2bOxMfW8PEx9ZQVVTQtepVX3GDuF3b\niNu1DXNHZywbqgmfMYfJqwIpya7jRl49x/6VztL/Gj3kgmtB6A/3DbokSVID7wILgCIgXpKk3bIs\np91yWgHwAvCDO651AH4BxAAykNhxbU3fDF8QhpjY30H+GZqtXDk+bg27j/0XF0ovYOzo6WelsmLV\nqFWsCFxBmEPYXX/Q7L2i5DYtH0arXNWlTZz4PBNZlhk924TkQ5+RcfYkzQ3KU5eSpMLG248Zjz9J\n4ITJaE26r/Oki0/AUFWF1tcH07CwgZxCj80e5YyliZqUojryKpvwc7LsOqZVa5nrM5e5PnOpb6vn\naP5R9ubsJaEsgcP5hzmcfxgncydWBq7k8enfwe/0X+DLb8A3z4Kt5yDOqmcklQrrhQup+fxz6vfv\nx3x05G3HHb18mP70c0x76lmKM9NIPx1L5oXTNFdVcOqzDZze+CkB4yYQOnEWteUq8lIquXyskOj5\nQze3TRD6Sk9WuiYC2bIs5wBIkrQZeAzoCrpkWc7rOHZnt9hFwBFZlqs7jh8BFgObej1yQRhqMg+S\nefGvbHV0YJ+9LY3xbwFKo+i53nNZGbgS43Uj8ybOu+dtdG16jnfU51oyuuclJgZTW4ue/e/F01J3\nCY06nfNbbibEO3r5EDFrHmHTZ5OQcqWrNMHd1B88AIDN4iVDdvXDTKtmYYQbXyYVszelhNfmBnd7\nno2JDU8EP8ETwU/w5dEvqXatZmf2TvLq89hwdQMbgHH+o3iioogF21/E4vn9oB76WR82y5YpQdeB\nA7j88Afd9nKUVCq8wiLxCotkzovfYM9/PkWqKic3KZ7rCRe5nnARMys72vXBnN1Wi3ugHa7+NoMw\nG0EYOJIsy/c+QZLWAItlWf56x9dfAybJsvxaN+d+Cuzt3F6UJOkHgJksy7/t+PpNoFmW5T/ecd2r\nwKsArq6u4zdv3tzbeQ15jY2NWFkNjQKWA22kzb3V2EpK7QkuVe7kqunN3BQfEx8mWU1ivMV4LNXK\nSkhP5h5Xpue95FYCbVW8OcW8X8feW7Is01hWQsHJFFpqMgFl+1BtZo5jcBiOoyIwd7rZz/G+8zcY\ncP7JT1A1NFL1xs/Qe3kNwCwezuUKPe8ktuJpJfHW9PtvAXfOXZZlcltzOd94nku6S7TJbQBYGo3M\nwJUIj+fwNfEdsgEnAEYjTm+8ibq6murvf4/24O6Dzlt1zr9d10RVZiqV6Vdorbu56aE28cRz8mgc\nQ0ahuk8vyOFmpP2b9yAehbnPmTMnUZblmPufOUQS6WVZ/hD4ECAmJkae3cNKx8NZbGwsj8I8uzNS\n5p5elc62rG3sy9lHk74JTLVYo2LFqLWsHrWm27IBPZn7ls8TgTLWTR/F7BkB/TP4XmppbCTt9Amu\nHDtIZWF+1+vuIZGMX7qMwJjJ3TZRvt/8m86fp6ChERM/P6Y988yQDjym6o18kn6U4sZ23EPHM8qt\n+96QnW6d+xzm8BIv0dTexMHcg+y4+i9SGnI5SAUHy/5EiH0IT4U8xfLA5VhqLe9538Fy44lLVH30\nMYGlpbi98sp9z7/tz37pMmRZpjgzjStHD5N+9hSGtmIKThVTFn+K8JlzGLNgKU7eI6OkxEj5N+9h\nPMpz705Pgq5i4NYsX6+O13qiGJh9x7WxPbxWEIacpvYmDuQeYFvWNlKrUrteH9vSwhqDGQu+dhBz\n64fPw2ps1XM8Q9laHGpPLcqyTElmOinHDpJ1/gz6dmWFBskCtUkEs559nLELo3r1HvX7la1F6yVD\n76nFO5loVCyJdGdTXAG7LxfzQ7fQB76HpdaS1SGrWR2ymuyjP+PLtM/YY2VFVk0Wv734W/6c+GeW\nByznqVFPMcphVD/M4uHZLF1K1UcfU3/wEK4//SmS5sH+Dy9JEl6hEXiFRhCz8mtsfWsTLQ2XaWsu\nI/nQPpIP7cMzNIIxC5cSPHFqt0G8IAw3PflbEg8ES5LkjxJEPQ2s7+H9DwG/kyTJvuPrhcD/e+BR\nCsIgu1Zzjc0Zm9mbsxedXgco+TorbUJZnbyLIKMKXt4KvQi4AA5eLaOl3cgEP3s87IbG1mJbs47U\nU8e5fHg/VUU32/h4h4+htiqAtlYfoub4MnZh7xoZG1tbqT94EACbJUt6da+BsmKMEnTtuVzKDxaO\n6lWgGDT3N/ywIpfXM/dx1D2EL7xDuVSRzJasLWzJ2kK0czRPjXqKhX4LMVX3rNl0fzINC8PE35+2\n3Fyazl/Aasb0h76Xs48TS19bx773IjDqK/AKLqIw9QLFGakUZ6RibmPL6DkLiJq/GFuX4ZHnKAjd\nuW/QJcuyXpKk11ACKDWwQZblVEmSfg0kyLK8W5KkCcCXgD2wQpKkX8myHCHLcrUkSb9BCdwAft2Z\nVC8IQ53eqCe2MJZNGZuIK4vren2cyzjWhKxhgbk3Zp8sgfZ2WP4Xpdp4L21PLALgiXGDn8tUXVJM\n8qG9pJ48SluzUrzVwtaOyDkLiJi9kHPbb1BRWoWrvw3TVt+7BU5PNJ44gbGhAbPwcMxCehfADZRJ\n/o64WJtSUK3jclEd0d69aE+kUsHj/8Dkn3NZWprFUscorq3YztZr29hzfQ/JFckkVyTzv/H/y+NB\nj/NkyJN423jf/779RJIkbJYupfLdd6nfv79XQReA32gnJi73J24PVJW5s/6tFylKu8DlI/upLMhT\nSk/s3o5/9HjGLFiK/9jxqFQP33NTEAZDj9aDZVneD+y/47Wf3/J5PMrWYXfXbgA29GKMgjCgalpq\n2H5tO1syt1DapDyFZ64xZ2XgStaFrlOqw+uq4Z9zbvZVHP9Cr9+3qEbH+ZwqTDQqlkUNztaibDSS\nk5RA8qG95F2+1PW6V1gk0YuWEzRhMmqNhgs7r5N/tQpTSw2LXolArb13X8WeqNu5CwDbVat6fa+B\nolZJLI/yYMPZXL68VNS7oAvAzBbWfg7/nAtXtxHsOZ6fTvkp3x33Xfbn7mdL5hbSq9P5JPUTPkn9\nhGke01g7ai0zvWaiHoQAxGbZMirffZeGw4cxvvkGKove1ZSLWeLHjbx68q5Ucexf2Tzxw0WMWbCE\nksx0Lh/ZT9aFM+QmJZCblIC1kzNj5i9h9LxFWNiIqvbC8DAkEukFYShIq0pjY/pGDuQeoM2o5Cv5\n2viyLnQdKwNXYm3SkSht0CvtW2rywH0MLH9HqTLeS7uSSwBYGO6KjdnA5q+0NDZyNfYIyYf3UVeu\nVG/VmJgSNn0W0YuW4+J3M6H/WkI5iQfzkVQSi16JxMax99ug+spKGk+fBo0Gm2VLe32/gbR6vCcb\nzuayM7mE/7c0DDNtL4Mfl1BY9R5sfV7p0egehYXfdNaErGF18GquVF7hi8wvOJR3iLMlZzlbchZP\nK0/Wha5jVdAqbE0HLgAxDfDHbEwULZdTaDhyBNvHHuvV/Tr7M259O4HKwkZOfp7JvBfC8AwNxzM0\nnNnPv8LVE0dIOXaQuvIyzmz+N+e3byJ06kzGLl6Ba0DvV1wFoT+JoEt4pLUb2jmSf4RNGZtIrkgG\nlEbTM71msi50HVM9pqKS7ljFOfoLyDkBFk7KqoRJ7yvGy7LctbW4egC3FisK8kg+uJe0MyfQt7YC\nYOPsSvSiZUTOWYC51e1P5FUUNnD83+kATFsdhHeoQ5+Mo37fPjAYsJozB42jY5/cc6BEeNgS4WFD\nakk9R9PLWR7l0Qc3XQUlr8PZ/4OtL8CrJ8HWE0mSiHKOIso5ih9N+BE7s3eyKWMTxY3F/DHhj7yb\n/C7LApaxPnQ9wfb3L+PQF+yeWE3Z5RRqt+/oddAFYGqhZck3R7PtfxLIvFiGi581UXOUbVQLG1sm\nPraGCSueIC8lieRDe8lJSiD15DFSTx7DIySMsYuXEzxpGuoHTOwXhIEgviuFR1KFroKtWVvZmrWV\nyuZKAKy11qwKXsXTo57Gx+Yu1bGTN8H5v4NKA2v/A3Z9k1OTXFhLTmUTTlamzAh26pN73o3RYCA7\n4QJJB/dQlHa163XfqLFEL1pOwLiYbnNlmhvaOPCPK+jbjIROdiNqbt8Fh7W7OrYW++CH9mB4crwX\nqSVpbE0o6pugC2Duz6EkGXJPwub1SmPsWwJ8W1Nbno94nmfDnuVU0Sk2ZmzkQukFtmVtY1vWNia6\nTWR96Hpme8/u161Hm6VLKH/7bXRxcbQVFmLi3fu/E46eVsx9LozDH6VyZms29m6WeIfdDPAllQr/\n6PH4R4+npqyEy4f3cfXEUUqy0inJSsfS/mPGLFhC1LzFWNrZ3+OdBGFgiaBLeGTIsszlistszNjI\nkbwj6GWlkGeQXRDrQtexPGA5Ftp7rFoVJ8Ke15XPl/4BfKf22di2X1JWuVZFe6BR9z4/qju6+jqu\nHDtE8pH9NFYpgabWzJyIWXOJXrQcR8+7/7A0GIwc+udVGqpbcPGzYdYzvXtS71YtmVm0pqWjsrHB\nau6c+18wBD0W7cnv9mdw6loFpXXNuNv2wZOnag2s+QQ+mgulyUqroCf/pSTc33qaSs0cnznM8ZlD\nTm0OGzM2svv67q6ejx6WHqwNXcsTQU9gZ9bLnLPuhmltjfXCBdTv3kPdl1/i/N//3Sf3DY5xpbKw\nkUuH8jn0z6us/tF47N2+WrPM3s2D2c+9wtSnniX99AmSDu6lqqiAc1s+5+KOLwiZMoOxi5fjHjS0\nSm4IjyYRdAkjXquhlQO5B9iYvpH0amVrTCWpmO8zn/Vh64lxjbl/AFFXBJvWg6EVxr8IMS/12fh0\nbXp2JSn5XKvH9/3WYtn1ayQd3EPmuVMY9Eqgae/uSfSi5UTMmofpfZKfZVnm1OYsirNqsbAxYek3\nR6Ppbd7SLWq3bgXAZtlSVCYmfXbfgWRvacKCcFf2XSllx6Vivj2nj3KLLB1h/Rb4aAGk74YTb8G8\nN+96eoBdAG9MfoPXx73OzuydbM7YTEFDAe8kvsN7ye91bT32dc0vuydWU797D7Vf7sTp299GUvfN\n98fkxwKoKWsi93Il+95NYc2PYzCz6j7f0cTMnDELlhI1fwkFVy+TdHAv1xMvkn76BOmnT+AWFMK4\nxSsImTIdtUbU/BIGhwi6hBGrtLGULzK/YPu17dS21gJgZ2rHmpA1PBXyFO5WPXxCsKUePn8KGsvA\ndzos+d8+Hefu5BIaWvWM87EjzL1ves8Z9O1knT9D0sG9lGZnKi9KEgHjJjB28Qp8R0d32y+vO0mH\nC0g7XYJaq2LJN0djadd3NaKMOh11O3cCYL92bZ/ddzA8GePFviulbEko5FuzA/uuuKvzKHjyE/j8\nSTj9R3AKhjFP3/MSaxNrvhb+NZ4Je4YzxWfYmLGRs8Vn2XFtBzuu7WC863jWh65nrs9cNKre/xiw\nmDgBracn7cXFNF24gNW0ab2+JyiJ9QteimDHHxOpLGzk4IdXWPHf0ag1d//elSQJ39HR+I6Opu5G\nGcmH93P1+GHKsrPY//c/Efufj4mav4QxC5ZgZd83OYmC0FMi6BJGFFmWiS+LZ2PGRk4UnsAoKz3Y\nwxzCWB+2niX+Sx6ssKShXUlkvpEKjsFKHpemb1djPr+oFBx9ZlLvW540Vldx+ehBUo4eQFenBJqm\nlpZEzllI9IKl2Lk9WCmK7MQbnP/yOgDzXwjHLaBvn4yr378fY2Mj5tHRmIU+eEX3oWRGsDNuNmbk\nV+k4d72KaUF9mJsXNA+W/A/s/wHs/g7Y+4HP5PteppJUzPSayUyvmeTV5bE5czM7s3eSWJ5IYnki\nrhaurB21ltUhq3Ewe/gARFKpsH3icSr/9ndqv9jSZ0EXgNZUzbJvRbH19wkUZ9VyalMms58N7VFQ\na+vixqxnX2Lqk+tJPxNL0sG9VBbkcWH7JuJ2biF40jTGLl6BR0jP7icIvSWCLmFE0LXr2Juzl00Z\nm8iuzQZAI2lY5L+I9aHrGeM85sH/UZVl5Yfc9WNg4QjPbAGLvv2f8eXCWq4U12FnoX3o2lyd7XmS\nDu7hWtw5jAYDAE7evoxdvIKw6bPRmpk98H3Lcuo4+kkaAFMeDyRovMtDje9eajYpze3tnh7eq1yg\n1OxaP8mHPx/J4l/n8vo26AKY+ApUZEL8P5XE+pePgGNgjy/3s/XjJxN/wnfGfodd2bttKbbLAAAg\nAElEQVTYlLGJvPo8/pr0V96//D6L/RezPnQ9EU4RDzU8uzVPUvmP92k4doz20lK07n1Xa87K3oyl\n/xXFl3+6RNrZUmyczRm/2K/H12tNzYiat5jRcxdRlH6VpIN7yI67QOa5U2SeO4WLfyBjF68gdOpM\nNMN0i1sYHkTQJQxr+fX5bM7YzK7sXTS0NwDgZO7EUyFPsSZkDc4Wzg9/83N/hcRPQW0K6zaDQ983\nn/78otIses04rweu79Te1krGmZMkHdpLRV4OoKw4BE+aytjFK/AKi/z/2TvvOCuq8/+/5/a7vffe\n2YWlrbt0FkFAioLBAmqsMdFvokaTGBOjfs0v+ZrEJJpYYou9gSAgXZCl97LLsr333th6y8zvj1mW\npbrAlrsw7xfzmjMzZ2bOYefOfM45z3meK269N9d2sP7NNKwWkdipfoydfZHZnFdBx4l0Ok+eRO3s\njNPcuf1+/aHgrsRA/v19LlszqylrbCfA9erdiZzF3JehsRDytsIni2Xh5eh9WZew19qzbMQy7oq5\ni/0V+/k863N2lu1kbf5a1uavJd4znqUxS5kTPAetuu+2T1pvL5xm30TLho00fvUVXk8+ebm1uyTe\nIU7Muj+Wze+ls391AfbOemImXp6wEwSBwNhRBMaOoqWuhtQtG0j7fgs1hflsfutVdnz6X+JvnM3o\nm+bh5Nn/jQwFBUV0KQw7RElkd/luvsj6gt3lu3v2j/YczbKYZdwUfNNlfSwuyImv4bvuoAu3vQ2B\niVd3vQvQ3GFmbapsQL8sqe+iprmmmuNb1pO+/Ts6W2WhaXR0YtTMOfLHwuMqhCbQ3mJi3eupdLaa\nCYp1Y/pdUQMy9NL4ldzL5bx4Maor6ImzRbwcDcwb5cua4xV8ur+E397cz0Omao08g/GjBVBxDD5b\nAvevB8Pl2wKqBBWT/CcxyX8SpS2lfJX9FavyVpFWm0ZabRqvHHqFJVFLuD3qdrzt+ybsXO++m5YN\nG2lavgKPRx9Fpe/fGJER471ob4lk11e5fP9JFkZHHcEjr8yvm5OHF1OX3c/EJcvI2ruTY5u+paYw\nn4NrvubQ2lWEJyQydu5CAuPilaFHhX5DEV0Kw4YWUwurc1fzZfaXlJ4qBUCn0jEvbB5LY5YS6x7b\nPzfK2SJPzwe46SWIW9w/1z2HFYdL6TSLTI5wJ8zT4ZJ5JUmi+MRxjm9eR/6Rg/LQJ+AdFsnYuQuI\nnji1X4ZFTB0W1r2eSlN1O+4BDsz5yUhUA+DCwtLYSMu69QC43HlHv19/KLlvUghrjlfw1aESnpwV\nefUe6s9F7wDLVsB/Z0NVGnx1D9z99VXZGgY6BfKrG37FY2MeY0PhBj7P+pzcxlzeTnub90+8z8zg\nmSyNWco4r3GXFCDGcePQjxhBV2YmpzZtGhC/a/EzAmlrMnF0czGb3jnBol+Owzv0yiegaHQ6RibP\nIm76TCpzszi2aR05+/eQd2g/eYf24x4QxJg5C4idNgOdwTaC0CsMXxTRpWDz5DTm8GXWl6wrWEeH\nRQ687Gvvy53Rd3Jb5G24GvrR+WHxPlj+YxAtMOlxmPxE/127F2aryH93FwJw/6TQi+YzdbRzcsc2\njm1eT2OF7MtLpdYQPWkqY+cswDey/6b+W0xW1r+ZRm3JKZw8jSz8xWh0xoF5RTR+8QVSZyf2U6ei\nD714/YcjYwNdGOXvzInyZr5NreD2hAEISu3gCfesgvdny85TVz8Kt717ng+vy8VOa9cTbuhI9RE+\nz/qc70u+Z3PRZjYXbSbaNZqlMUuZFzYPo+Z8ASIIAm53L6PyuT/Q8NnnA+bsdsKiMNqbu8jaX8W6\nN1L50a/H4+J9dUO5giDgFzUCv6gRTL/3IU5s20zq1o3Ul5Ww7f032fX5h4xMnsWYOfNx9fXvp5oo\nXG8ookvBJrGIFraXbueLrC84VHWoZ3+SbxLLYpYxPWB6/3vZrjoBn98Jlg4Ye6/cyzVArE+rpKK5\nk3BPe2bGnG87Ul9eyvHN68nYuQ1Thyw0HdzcewL89reXbdEqsvm9k1TkNmHvrOPWJ8Zg79y/Q0M9\n9+rspPHTzwBwf6j//J3ZCoIg8OOJwfz66zT+u6eIJeMDBmZ4yi0U7vkaPpgP6V+D0QXmvdIvcUAF\nQSDBJ4EEnwSq2qpYnr2clbkryW7M5sV9L/KPI//gtsjbuDP6TgIcz/Yt5zR/PtV/e4XOtDTajx7D\nbtzYqy7PhcqXfG8M7afMlJysZ81rx1j89Lh+iQMK4ODqxsQlS0lctITcg/s4tmkdFdkZHN24lqMb\n1xIyZjxj5ywgdMz4PrteUVAARXQp2Bj1HfWszF3J8uzlVLdXA2CnseOW8FtYGrOUMJf+N2aXb5wP\nn9wGXc0wYiEseLVfPl4XQpIk3t4pG77/ZGoYKpV8H1G0UnD0MMc3r6M47VhP/oARIxk7dwHhCRMG\nJJ6cJEp8/0kWRWl16O01LHxiDE4eAzeM0rx6DdaGBgyxsdglJQ3YfYaShaP9+OvmbDIrW0jJqWVG\n9AAZZfuOhrs+lf3IHXpPnvQx50/9+uz62Pvw+LjH+dnon7G5aDOfZ35Oen06H578kI9OfsT0gOks\nHbGUib4TEQQBldGI6113Uf/229S/+y52b73Zb2XpjVqtYu4jI1nz6jGqC1tY8+pxFj81DgfX/mss\nqDVaYiZNI2bSNKoL8zm+eR1Zu3dQdPwIRceP4Oztw5jZ8xmZfBMGh0ubCCgogCK6FGwASZI4Xnuc\n5dnL2Vy0GbNoBiDEKYS7Yu7i1vBbcdAN4AutoRA+vhXaaiB0OvzofdlgeYDYnVdHZmULHg56Fo31\np62pkfSUraRt3URLrSw0NTo9sVNnMGbOfDyDB274TRIlUr7IJnt/FRq9mgU/H4273wD+X4siDR98\nAIDbQw9eswbKBq2ah6eE8n8bs3hze97AiS6AsGTZf9yXd8P+N0Cjh5nP93ujQafWsTB8IQvDF3Ki\n9gSfZ33O5qLNpJSlkFKW0vN7vSX8Ftx+fC8NH35I6/btdGbnYIiO6teynEarV7PwF6NZ8+pxaktO\nseZVucfLzqn/3T54h4Yz52dPMO3uBzjx/RZSv9tAc3UVOz55nz3LPyV2ygxGz56HV8gANQwVrgkU\n0aUwZJwyneLb/G9ZkbOix7eWgEByQDJLRyxlgu8EVMIAd903FMKHC6ClDAIS4a7P5I/WAPLOzgKQ\nJH4cYua7N14h7+A+RKscnmcwW86SKJHyeTYZu2Vv8/MeHYVPaP86Pz0XfWoapuJitP7+OM2ZM6D3\nGmrunhDMG9vzOFTUyMHChoG9WdQc2Wv98vtg9z9AY4DkZwbsdqM8R/F/nv/H0wlPszJnJctzllPU\nUsTLB1/mtaOvMTdkLncumIFq5Sbq330X/1f+NmBl0dtpueXxMaz+5zHqy1tZ8+oxFj01FqPDwPjb\nMjo6kXjrEhIWLqbgyCGObfqWkvRU0rZtIm3bJnwjo4mfdTPRE6eg1V8bs3IV+g9FdCkMKpIkUdxV\nzPN7nmdT0aYew3g3gxu3Rd7GjyJ/dJ6NyIDRUAAfLpQFV2AS3LMS9I4Desu86lZOHd7LvacyMBc1\nkQMIgorwhCTiZ80ldPTg2IhIokTKZ1lk7KlErVUx/7F4AmMGNiSKJEnYb9oEgNt99yEMwFCpLeGg\n13D/pBD+9X0eb6bkcf9AzxcYsRB+9B6sfAhS/gxqLUx9akBv6WH04Kejf8qDox7k+5LvWZ69nINV\nB/km7xt2eku8roLmDeuxf+zhAS2HwUHLLU/Iwquhoo21rx3n1ifHYrAfuBiLKpWaiBsmEHHDBOrL\nSjm+ZR0ZO7dTmZtNZW42KR+/S+y0Gxk96+YBK4PC8OPafusp2Azt5nbWF65nRfaKnqDTAEk+SSyJ\nXsLMwJlX71vrcmgo6O7hKofACbJB8gAJrtMe41O3bqRh906mSrLHeAdXN0bNnMPIGbOv2rfWZZXn\nXMH1PwMvuABat21DW1yM2tMDl9uXDPj9bIH7J4fy7q5CUrJrmeE+CL0eI2+TQ1d981PY9r9g6YTk\nZwfMPvE0WpWWOSFzmBMyh6LmIr7O+Zo1+WvYGVfPjBMSnz93O2k/moR3vTcj3EcMSBnsnHTc+uQY\nvvn7UepKW/nm70e5ZQAnhPTGPSCQmQ8+yrRlD5C1bydpWzdRlZfDsY3fcmzjtzj4+OOtlohMmqx4\nvL/OEaRufz+2QkJCgnT48OGhLsaAk5KSQnJy8lAXY8DJbshmRc4K1hWso83cBoCdyo4lMUtYErWE\nUOchcBdQmwOfLJIFV9BEuHvFgAiurvY2MnZtJ+27jdSVyp7nJaDcPoj7HlxG/MSJqNT9PAPzBxCt\nIts/zSJrXxUarYp5gyS4JFGk8NZFdOXm4v373+N27z0Dfk9b4Y/rMnh/dyGjPdWseXqQPO+nfiW7\nkZCsMOExmPPnARde59Jl7SJl92cE/uxvWAX45SNqql0FRrqP5Pbo25kbMhc7bT977AdaG7tY+9ox\nGqvacfY0csuTY/ptVuPlUFNUQNrWjWTsSsHcKffoGxydiJt2I/GzbsbN7/pwO3E9fOsEQTgiSVJC\nn/L2RXQJgjAXeA1QA+9JkvTyOcf1wMfAeKAeuFOSpCJBEEKATCC7O+t+SZJ+dql7KaJr+NNiamFj\nwUa+yfuGk/Une/aP9RrL7VG3YygxcNOMm4amcOVHZS/e7fUDIrgkUaQsM5307d+Rc2AvFlMXAHbO\nLuS5xPKdNZQH5o7nlzcNjGHxpbCYrWx57ySFqXWDKrhADmxd/tTTWF1did2Rguo6au3XtXYx/a/b\naTNZWf7TiSSGDs7/OZnfwtcPgtUku0BZ+Br0t5uVPlDx22dpXr2anHhv/rLYxCmTHEXBQevA/LD5\nLI5YTKx7bL9Oqug4ZWLtv45TV9qKg6ueW58ce9V+vK4UU0c7az54l86SAmoK83v2B8bFM3LGTUQm\nTrymbb+u5W/dafpVdAmCoAZygJuAMuAQsFSSpIxeeR4D4iVJ+pkgCHcBiyVJurNbdK2TJGlkXwuv\niK7hiSiJHKo6xKrcVWwr2UaXVRYbDloHFoYv5Pao24l0jQSGsO4FKfIML1MrRNwEd3wMuv55EbfU\n1ZKxYxvpO7bSXF3Vsz8wLp7RN91MtUskP/7oCPZa2Pf72TgZBnEoFdnT/Ia30ijPaUJvp2H+Y/H4\nRrgMyr0li4WChbdgKiyk5e67SfrDc4NyX1vi1a05vLo1l3FBLqx8dNLgzdrM2yY/85YOObLC4neu\nynP9lWAuLyd/7s1IZjN+K74gxVjMipwVpNam9uSJcIlgccRiFoQvwM3QP6K0q8PC+tdTqcxvxugo\n23x5BAyszebFOP3Oq8rPJfW7jWTt3YGlS35H6ox2xEyaRlzyLHwjo6+5Gb3X2rfuQlyO6OqLTVci\nkCdJUkH3xb8EbgUyeuW5FXixO/018LpwrT05ChekorWCNflrWJO3hvLW8p79Sb5JLIpYxMygmRf0\nXD3onFwNq34it/pH3QGL3pQNja8Ci9lM/uH9pG//jqK0Yz2heRzcPRg5fSZx02fh4uOLVZT41Rt7\nALg5VDvogut0LMXaklPYOeu45fExuPsPnk+hplWrMBUWog0MpGPSxEG7ry3x8NQw3t+Zy9GSJrZk\nVDMnzmdwbhwxE+79Bj6/A05+I/fw3vGJ7Eh1kND6++N69900fPghza++zq3vv8etEbeS05jD6rzV\nrC9YT15THn87/Df+eeSfTAuYxuLIxUzxn4JGdeVmx3qjhoWPj2Hj2ycozWhg1StHufmRUQTGDlJP\n4wXwCY/EJzyS5B8/RPbeXaSnfEdlbnbPzEc3/0BGJs8idtqN/e4AWcE26EtP1xJgriRJD3dv3wsk\nSZL081550rvzlHVv5wNJgANwErmnrAV4TpKkXRe4xyPAIwDe3t7jv/zyy36omm3T2tqKwzB1pmcS\nTaR1pHGg9QDZndlIyM+Qq9qVCQ4TSLRPxEPrcdHzB7XukkRg6SrCCz4GoMx/PnkRD8MVuqKQJImO\nuhrqstNpyMnE2tUJgKBS4xIWgUfMKBz9g86agbij1MwHJ0246gWeGyfi7jx4f/fOJomSnRLmdtA5\nQHCygM5h8NpDQlsbHi+8iKq1laaHH6I+JmbYPvdXy/qcVlYUCPjaC/y/yUbUqsH7OzicymfUiT+i\nNzXSZhdIWvzzdBkG0HfYOQitrbg/9wfUnZ00PvE4phFnjOktkoWTHSfZ37qfjI4MREQAHFWOJDok\nkmSfhK/O94rvLVolyvdLtJQCAvjdIOAaNrh9Apd653U01FGffZL67JNYOtrlnYKAc1AY7jEjcQ4O\nG3Tbz/5kOH/r+sqMGTP6dXjxakTXKcBBkqR6QRDGA6uBOEmSWi52P2V40TaxilYOVh1kXcE6thZv\npd0ivxx0Kh0zg2eyOGIxSb5JffKrNWh1t5hg/VNw7BN5e9aLMPnJKzIobq6pJnN3Cpm7ttPQHQMR\nwCsknJEzZhEzJRmjw/lDF80dZma8kkJDm4l/LR2LU2POoP3dS07Ws+nddMydVrxDnZj3aPyAOI28\nFFV/+jONn3yCXUICQZ98zI4dO4bVc9+fbP1+O388AsX17Tw3fwQPTx1kJ5pNJfDZ7VCbBQ7esOwr\n8Ov/ED0X4+Azz+C4Zi268HDCvlmFcAG7vrqOOr7N/5bVeaspaC7o2R/nHsf8sPncHHozHsaLN+gu\nhiRK7Fudz7EtJQAkzAshcWHooA3l9eWdZ7VYKEo9Qvr27yg4egjRKs9yNjo6ETVxKiOmJOMXFTPs\nhh+H27fuSujv4cVyoHfE1oDufRfKUyYIggZwBuolWdF1AUiSdKRbjEUB176qugaQJImshizWFaxj\nY+FGajtqe47Fe8SzIHwB80Ln4awfWIeaV0R7A6y4Dwp3gsYIt70NsZcXfLej9RQ5+3aTuXs75Vln\nRtONTs49NhjeoeGXvMZrW3NpaDORGOrGwnhfduzIuaLqXA6SJJG+o5xdy3ORRImI8V7MvG8EGt3g\ntpY7c3Jo/PxzUKnwfu73w+5j0d9oVAIvLozlgQ8P8c/vcpgf74uv8yAOvbsEwYObYfm98u/ivzfD\nra/DqMFx39E+axZux49jys+n/sOP8HjkJ+fl8TB68MDIB7g/7n7S6tJYnbeaTYWbOFl/kpP1J3nl\n8CtM8J3A/LD5zAyaib3Wvk/3FlQCk26LwMnDyM4vsjm8oYjGyjZuvG8EOoNteE5SazSEj08ifHwS\n7c1NZOzazsmUrdSVFpO6ZT2pW9bj7OXNiCnJxExJxt1/AAKpKww4fXnaDgGRgiCEIouru4Bl5+RZ\nC9wH7AOWAN9LkiQJguAJNEiSZBUEIQyIBApQsGkqWivYULiBdfnryG/uNdvGMZAFYQuYHzafYKfg\nISzhD1BxXP6wNJXILfqlX4D/+D6dajGZKDh6kMzdKRQcPdzjKV6j0xNxwwRGTE0meNTYPsVAzKk+\nxUf7ilAJ8OLCuEERHRaTlR2fZ5O1XzbmH39zMEkLwxAGcSgLZOFX/ac/g9WK67JlGGJiBvX+tsqM\nGC/mxHmz+WQ1L32bwVv39O257DeMLnD3Slj/Szj2qexIteIYzPrfAQ19BYBWi8/zz1P60MPUvfkm\nTvPmoQu4sNsEQRAY7Tma0Z6jeeaGZ9hRtoP1BevZVb6LvRV72Vuxlz+q/8iMwBnMD5vPJP9JaFU/\nbCs5cpo/jm4GtryXTv6xWhqr27n5Z6Nw8RqamY0Xw87ZhYQFixk/fxG1xYVk7k4ha88Ommuq2b/q\nK/av+gqv0HBZgE2ejoPr0NmpKVweP/grkyTJIgjCz4HNyC4j/itJ0klBEF4CDkuStBZ4H/hEEIQ8\noAFZmAFMA14SBMEMiMDPJEka4HgYCldCRWsF3xV/x5aiLaTVpfXsd9W7Mjd0LvPD5hPvEW/7vRXH\nPoV1T4G1C/zGyTHpnC/t4d5iMlGYeoScfbvJP3Kwx6eOIKgIjh9L7NQZRNwwAZ2x7y9mi1Xk1ytS\nsYoS904IJtbP6aqq1Rda6jrY+PYJ6kpb0WhVJN8TQ3TSIBlsn0PTihW0HziA2sUFz8d/MSRlsFVe\nWBjHrtw6NqZXsT27ZmDjMl4IjQ5ueR18x8Cm38K+16HqBCz5AOzdB/TWDpMn4zTvZlo2bKT6T38i\nsA/BsA0aQ4/j1eauZjYXbWZ9wXqO1hxlY9FGNhZtxEXvwsygmcwOmc0NPjdcUoAFj3Tn9mdvYMNb\naTRUtPH1y4e56cE4gkcObN2vBEEQ8AoJwyskjKnL7qMs4ySZu7eTs38PNYX51BTms/PTDwiMG0nU\nhClEJk7CznnwJkkoXD59atpIkrQB2HDOvud7pTuB2y9w3kpg5VWWUWGAqGytZEvxlvOEllFjZHrA\ndBaGL2Si38Q+tSCHHFO7/AE5+pG8Pe4+uPmvoL2w/5veQqvg6EFMHR09x7zDIhgxJZnoSdOuuAX5\n9s4CUsua8XM28Ju50Vd0jcuhMK2ObR9m0NVuwcnTyM0/HYVHwNAYr5rKyql5+S8AeP/hOdQuykeg\nN34uRp6cFcmfN2Tx+1Un2PjkNJyNg/wbEwRI/Al4x8HyH0PhDvjPZLjtXQidOqC39nrmt7Tu2Enr\n9u00f7sO54UL+nyus96ZO6Lv4I7oO3p65E/PflyZu5KVuStx0btwY9CNzA6eTaJv4gXfXy7edix5\nJoGtH2ZQmFrHutdTGTs7iKRbw1CrBz4M15WgUqkJGhlP0Mh4Zj74KAXHDpG5azuFxw5Tkp5GSXoa\n297/DwEj4mQBljRJmQFpg9jGYLbCoHGxHi2jxshU/6nMCZnDFP8pA+IpesCoTJOHSepyQK2H+a/A\nuB+fl81s6qI49RjZ+3adJ7S8QsOJnjiVqKTJuPhc+UwpgMzKFl7dKttu/XXJaBwH0EWExWRlz8o8\n0nfIZpbBo9yZdX/sgMacuxSSKFL53HOI7e04zpmD07x5Q1IOW+eByaGsT6sktayZ59ek89pdg2fQ\nfhbBk+CRHbIT1dL98NFCmP4bmPabARtu1Hp74fXMb6h6/gWqXnoJu3Fj0fpfvnd2Pwc/Hh71MA+N\nfIjcply2FG1hS/EWCpsLWZW7ilW5q3DSOfUIsAm+E84KNaYzarj5p6M4uqWYA2sLObalhIrcJmY/\nFIeThw24ubkEGp2OqKTJRCVNprO1lfwjB8jZv5ui1GOUZpygNOME2z74DwExcURNmExk4iQc3Gyv\nJ+96RBFd1ziSJJHRkEFKaQrbS7aT3Zjdc2xYCy0AUYQD/4GtL8j+tzyi5YC/vvE9Wdqbmyg4eoj8\nIwcoSjvW45AQ+ldonabLYuXp5amYrRL3TAhiSuTlz7TqK/XlrWx5/yQNFW2o1AITFoUzZmbgoNtv\n9abx8y9o378ftZsbPi88b/vD0UOEVq3in3eOYf6/drPmeAU3xnhx65ghCgvj7A/3r4cdL8POV2DH\nX2RD+0VvgtvAzLB0uf12ubdr2zYqnvktQR99iHCFbhEEQSDKNYoo1yh+Pvbn5DXm9fTg5zfnszpv\nNavzVuOoc2Sq/1SSA5OZ7D8ZJ50Tgkpg/NwQ/CJc2PL+SaoLW/jq/x1k2l1RRCX5DIvn1+DgQNz0\nmcRNn0lnWysFRw6SvX83xalHKctMpywzne8/fAe/yBjCE5IIH5+Im3/gsKjbtYgiuq5BTFYTh6oO\nsb10O9tLt1PTXtNzzE5jxxT/KcNXaJ2mPh/WPg7Fu+XthAdh9p9AZ0dDRRl5h/aTf+QgFTmZPU5L\nAbzDIomaMLlfhVZv/vfbDDIqWwh0M/LszQMT2NdqFTm2uZhDG4oQLRIu3nbMfigOz6Ch8bZ9mo7U\nVKr/Ig8r+rzwAho3xbj3UoR5OvD8wlieXXWC575JZ1yQK4FuQ/R7VGvgxucgZKrsRLhkH7w1GW56\nCRIeAlX/DrkJgoDvH1+iIC2V9sOHqX/vfTx++ki/XDvCNYII1wgeG/MY+U35PQIsrymPDYUb2FC4\nAY2gYbzPeGYEzmB6wHQCIgK487lEtn+SRcHxWrZ+mEne0VqS744elIDZ/YXB3oHYaTcSO+1Gutrb\nKThygOz9eyhKPUJFTiYVOZns+vxDXLx9CU9IJHx8En7RsX2aGKTQPyj/09cIdR117K3Yy47SHeyp\n2NMTXBrAy86LGYEzSA5MJtEnEZ16GMe9E61y79a2P8qhTew8sN78d8qFMAqXf0n+4QM0Vp7xaKLW\naAgaOZrwhCTCxifi6DZwPU/LD5fy+YESdBoVby4bj72+/39etSWn+P6TTOpKWwGInerHlCWRaPVD\n6zzR0tBA2RNPgtmM6z334DRn9pCWZ7hw1w2BbM+qYUtGNT/95AhfPzoRO90QvpbDpsOj+2DjryF9\nJWz4FWSsgVv+1e+9Xho3N/z+/GdKf/IIta+9hiEuDocpk/v1HuEu4Tzq8iiPjn6U4pZiUkpTSClN\n4VjNMQ5UHuBA5QFePvgyES4RzAicwbTbphE0Kpq9K/IoSqvji7wmpt4ZRVSi97DrGdLb2TFi6gxG\nTJ2BqbOD4tRj5B85QP7RQzRVV3Jk/RqOrF+D3t6e0DEJhCckERI/DsM17sh0qFFE1zDFbDVzrOYY\neyr2sLdiL1kNWWcdj3aNJjkwmRlBM4h1699gskNG+VH5I1B+hCaTgUKneRRZwyl95bOeGYcABgdH\nwsYmEH7DBELix17WrMMrJb28mT+sTgfg/906klEB/eu7zNRp4dD6IlK3lSKJEo7uBmbcGzNoAasv\nhWS1Uv7001iqqjCOGYP3b3491EUaNgiCwF+XxJPzxh4yKlv41YpU3lg2bmh/r/busOS/sl+7dU9B\n0S54YwJMfRomP3HRySlXgsPUqbg/+jPq3/oP5U89Rejyr9CFhPTb9XsT7BTMfXH3cV/cfTR3NbOr\nfBcppSnsLt9NXlMeeU15vHviXZx0TkyenUxU2jS6imDrBxlk7q1g2l3RuPn2zZdcOxwAACAASURB\nVC+YraEzGIlMmkRk0iRE0UplTrYswA4foKGijKw9O8jaswNBUOETEUnI6HGEjB6HT3jUsPaGb4so\nomsYUdpSyp6KPewp38PBqoM9XuEBDGoDCT4JTPGfQnJgMv4OQ2QfMhC01WPa/CKle9ZT2OZCcXsS\nTV065IAHxwHwCAwmePQ4wscn4h8dO6gvivKmDh7+6DBdFpGliYHccUP/OS2UJImcg9XsXZVHe7MJ\nBIifEUDSrWE24dRRkiSq/vcl2vftR+3ujv9rr17Q07jCxXGx0/HefQksfmMvG05U8a9teTwxK3Ko\niyWLruDJsPl3kPYVpPwZUr+Aea9A5Kx+u43nL35BV04urdu2UfrY/xDy1ZeoHQd2qNxZ78yCsAUs\nCFuA2WrmUPUhUkpT2Fm2k/LWcjbWrmWjz1qi1UlMLllMeTZ8+ccDjJrpx4QFQ9+zfDWoVGr8Y2Lx\nj4ll2t0P0FBRTsGRA+QfPUhFdiaVudlU5maz7+sv0NvbEzRytCzC4sfh5DnI7k2uQYb+ra1wUSpb\nKzlUfYhDVfLSO6A0QIRLBJP9JjPZfzLjvMehVw8f24MfwtzVSUVmGqXbPqP0ZBpVbUZEYnuOG+wd\nCIofS8josYTEj8PRfeCGDS9FQ5uJe98/QFVLJ4khbrywMK7frl1V0MzeVXlU5jUD4B3qxLS7ovAK\nHnifX32l7t//pmn5cgS9noB/vYbW23uoizQsifBy5F9Lx/LgR4f459YcvJ303JUYNNTFAnsPuO0d\neTbw+qflEEKf/QjCZ8r2Xj4jr/oWgkqF31/+QvHSpXTl5lL26GMEvvsOKuPgzCDUqrVM8pvEJL9J\n/C7pd5S0lPSMIBzUHuQz1xMklSwktmYSad9VcGRnProJLSROjyTeO354m2sAbn7+uPndRsLC2zB1\ntFNy8gTFaUcpSj1KU1UluQf2kntgLwCufgEExcUTEDuSwNhRikuKK0ARXTZEVVsVB6sOXlRkOemc\nmOA7gSn+U5joNxEf+6FxfDkQmLs6qcjJovTkCUoz0qjKzUYUxe6j9giAb0gwIQmT5W7viEhUqqFt\nbbZ1WXjgw0MU1LYR4+PIu/clYNBefZkaKtrYvyafwtQ6AIyOWiYuDidmgu+Qzkw8l4ZPP6PuzbdA\nrcb/n//Abvwge1e/xpgR48ULC2J58dsMnv3mBAatmkVjbaTHOmQK/HQXHHhLnuGYvw3yv4cxyyD5\nWXC5ut5dtYM9AW+9RfHdd9N++DBl//NzAt56E5V+8BuSQU5BBDkFsTRmKWarmdTaVPZW7OXYyW/w\nPz4Or7ZgpB1GNh7M5k8hr+EWrSPBJ4EEnwTiPeOHdeNXZ7QjIiGJiIQkAJqqq3oEWEl6Ko0VZTRW\nlJH6ney209UvgMDYkQTEjiJwxEjFLUUfUETXECFKItkN2aTWppJam8rR6qOUtZadlcdR68h47/Ek\n+CSQ6JNIlGsU6iEWGv1FW1Nj92yaLCpysqjKy+kJuQMgIOFtaCXAQ0Pg9CX433gPhgsElB4qmtpN\n3P/BIVJLmwhwNfLxg4lX7eCyvqKVo5uLyT1YjSSBRqdi9MxAxs4ORm+0rZ9q/QcfUtM9U9H3pf/F\n8cYbh7hE1wb3Tw6l3Wzlr5uyeXpFKlq1ivnx/T/L9orQ6GSbrjH3wM6/waH34PhnkLYcxiyFKU+B\nW+gVX14X4E/QBx9QfO+9tO3dS/kTT+L/2qtDIrxOo1VrewQV46D+R/Vs33GEim0W3Fp9mZX5ALUl\npWzw38Kbbm+hU2uJ94xnvPd4RnuOJt4z3jZj0/YRF28fXG6ax+ib5mG1WKjKy6EsM53SjBOUZ2f0\niLC0rZsAcPX1I2DESPyiY/GNjMbN10YaDTaEIPWaTm8LJCQkSIcPX3vxsJu7mkmtTeV4zXHSatM4\nXn2cLqnrrDy9RdYNPjcQ7Rp9TYgsq8VCXUlRj8gqSDuG6VTLWXkEAbzsugjQ1xJo14y/jyOGm56F\n+DvAxv4Pqls6uff9A+RUt+LvYuSzh5MI8eibgW1KSgrJycln7asqaObIpmKK0uSeLUElEDfFj4T5\nITY3XV2SJGpfe436/7wNgPfvfofbj+/t8/kXqv/1wuXU/R9bsvnX93kI3XE775sUMqBluyIaCmD7\nn+VZjpIIghpG3S4b3HtGnZe9r/XvzM6h5Mc/xtrcjF1CAgGv/9vmohpYzSLpO8s5vLmQzha5sdjq\nUM8+328pcE9FEsSevCFOIXhZvZgzag6jPUcT4RJxzbzXqwtyKc1IpyzjBOXZmWdNaALZDETn5kFc\n0iT8omLwiYjCYH/tzY4UBOGIJEkJfcqriK7+p9XUSlZDFhn1GWQ2ZJJel05RS9F5+fwd/HuCuo7x\nGnNNiCxRtNJYUU51YT7VBXk9i8V0tsDUGoz4hgbjZ2zFt2U/fqoSDGoruIXDlCch/k7Q2JbgAMio\naOGRTw5T1thBhJcDnz6UhI9z32dznf7wWK0ihcfrOJFSRkVuEwBqrYoRk3wZe1OQTXrEFru6qHrp\nJZpXrgK1Gt8//T9cFi26rGsooiu5T3klSeL17/P4+3dyZINHpoXx27kxqGxoeLmHujzY/U9I+xLE\n7t7q8JmQ+AhE3tTTaLqc+ndmZ1P6k0ew1NSgCw8n8O23LxoceyixmKxk7Knk2JZiWhvld5zGSaIj\nqoI0j52knjqKSTSddY5RY2SUxyhGeoxkhPsIYt1iCXAMQCXYZvihvmK1WKgpzKcs6ySVOVlU5GbR\n1nhOqGVBwN0/EN/IaLxDI/AKDcczOAStvv9mxA4FiugaRJq7mslsyCSzXl4yGjIobik+L59erSfO\nPa5HZLXltXHLzFuGoMT9h9VioaG8tEdg1RTmU1NccJbX99O4+vrhGxmDX0Q0p8qOMskuE1XOJpCs\ncgafeJj6FIy4xeZ6tk6z6mgZz646QZdFZHSAMx88kIib/eUZ0X63fjtO1mAydlfQ3iK/jHUGNSOn\nBzB6ZiB2TrZplGsuL6fsiSfpTE9H0Ovx/8ffcZw587Kvo4iu5Ms6Z+WRMp5ZmYZFlJge5cnf7xiN\nh4PtNUYAaCyWxVfqF2DplPe5BMMND8OYZaQcSr+s+psrKyl95Kd05eaidnXF7+X/w2H69IEp+1Vi\ntYhk7avk2JYSmmvl3h61RkX4eA/sx5jZUrKGdtd20mrTzrPVBXDQOhDtFs0ItxHEuscywm0EIc4h\naFS2ZVZwOUiSxKn6WrauXoWrVk1lbhY1hflYLZaz8gmCCjf/ALxCw/EODccrJAzPkLBh1SOmiK4B\noMPSQUFzAXmNsj+X3KZc8hrzqG6vPi+vVqUlyjWKEe4jGOE2gjj3OKJco86K+zWcPj6SKNJSV0Nd\naTF1JcXUlRZTX1pMQ0XZeT8gACdPL7xCwvEOi8A7NBzv8EjsOish/Ws4sQIai+SMKg3ELJC9yYdO\nk8cYbZCWTjN/Xp/Jl4dKAbh9fAB/XDSyz0bzpg4LBcdryTlUTWlmA3T/5Fx97Rk5zZ/oCT42Z7PV\nm5ZNm6l68UWsTU1o/f0J+Pe/MMTG/vCJF2A4Pff9zZXWfXduHb/44iiN7Wa8HPW8eucYJkUMzWzd\nPtHeAMc+lW2+mroboCoNda5j8ZjxGETfDNq+9eRaW1oof+pp2nbLkSfcHngAr18+abNuSSRRoiSz\ngRMpZRSn1/f81nWOMHZGKFGJPpjs20itTZVHQuozyWrIoraj9rxr6dV6IlwiCHcJJ9IlUl67RuJt\nN7wctfZ+7i0mEzVFBVTlZVNTVEB1YT71ZSVIonjeec7ePngEBuMeEIRHQBDugcG4+QWgscG/vSK6\nroLmrmaKW4opbimmsLmQ/KZ88pryKD1VisT5/1cGtYEot6gecTXCfQThzuFnCawLYYsfH6vFTFN1\nFY2VFTRVllNfXtotsEowd3Ve8BwXb1+8usXV6ZaK0bHbpUF9PmR+Cye+huoTPed06j0wTPoZjLsX\nHG17Bub3WdX8blU6VS2d6NQqXrgllmWJQT/40jN1Wig52UDekWqK0uqxWuSXiqCCiHFejJzuj2+E\ni02/PC21tVS99EdOffcdAPZTp+L/t79elX2NLT73g8XV1L2yuYMnvjjOwSJ5uOauGwJ5Zm4MrpfZ\n0zqoiFbI/Q4O/xfytp7p1dY7wYiFEDMfwmaA7tLOiyVRpP7996l99TWwWtGFh+PzwvPYJyYOQiWu\nnObadtJ3lJN9sJqOljNDjL4RzoSP9SJ0tEePGUFtey2ZDbIAy6zPJLMh84I9YiD3ioW5hPUIsVDn\nUIKdgvGz97NJ85Qfeu7Npi7qS4qpLsyXR0uK8qktKcJqNp+XVxBUuPj6dYuwINwDgnD18cPV129Q\nnGBfDEV0/QDt5nZKT5VS1FLUI7BOL01dTRc8RyNoCHEOIcIlQl5cI4h0icTfwf+KHvSh+viIopVT\ndXU0VpbTWFUhryvldUtNDZJ0fosDwN7VDY/AYDwC5RbH6RaIztCrxWq1QOl+yN4IOZugPu/MMYOz\n7Gxx1O2kFJlJnnH5Q1ODSU71Kf6yMYttWXLcytGBLvz1R/FE+1x8BmVLfQdFafUUnaijPKcR0dL9\n2xLAP9KFyBu8qWzPYdacGYNRhStG7Oig4aOPqX/vPcTWVlR2dnj9+le43HknwlXG4FNEV/IVn2+x\niry+PY83tudhtkq42et4enYUdyQEolXbuD1Qay25q18msv0IVBw7s19jhPAbIWaevHbyu+gl2o8e\no/LZZzEVy71nzrfegufjj6P1tz1br96IVpH1y3egb/ei8HgtFvOZd6y7vwOhoz0IGeWBZ7DjWTZ7\nzV3Nsqf87tGV/OZ88hrzaOxqvOB9NCoNgY6BBDsGE+wUTJBTECFOIQQ5BeFl5zVkNmNX8txbLRYa\nK8qoKyuhvqyEupJi6stKaKqqvPg3ysUVV19/XLpFmLz44+LtO+C9Y5cjumx3TGOAOFx1mAc2P3DR\n43YaO4Kdzjy0p0VWiFPID/ZeDQc+/vUvqC8rueAxQVDh7O2Dq6+/3Hrw88czMAT3wKAzvVe9Ea3y\nC7RwlxwqpHgfmE6dOW5wkQ1p4xZDxKwzhvHFKf1fsX4ip/oU7+wsYNXRMkQJ7HRqfjkrigenhKI+\nx4i5rbmL8pxGyrObKM9u7LHlAHmk1DfcmdDRnkQkeOHoJhuK1qbkDmp9LgexrY2mVd9Q/+67WGpk\nsWk/fRq+L7yA1u/iH0OFwUGjVvHkrCgWxPvy3Op09hc08Ptv0vnPjnx+MSOSRWP90WlsVHw5eFIe\nsJDI5L9DbQ5kroWs9VBxFLLXywuARxSETpdjQAZPBrszYa7sxo0ldO0a6t99j/p33qF5zVqaN2zE\n5Ue34fHIIzb7jKrUKhx9BZKT4zB1WihKq6MwtY7i9Hrqy1upL2/l8IYidEYN/lEuBMS4ERDtiquP\nE+O9xzPe+2z/d/Ud9T1hi/Kb8ns6D2raayhsLqSwufC8Mkz0ncg7s98ZrCpfNWqNBo+gEDyCQs7a\nbzGZaKgoo76028ylvIymqgqaqitpa2qkramRssz0sy8mCDz4z//gaiPuK6470eXv4I9WpZVbBE7B\nPS2B02kPo4dND/lcLU6eXnS1teLS3QroWXz8cPb2QaO9hLBsqYTyI/JScRTKj0FX89l53CMhao5s\ntxE4AdS2/4iZLCI7cmr5ZH8xO3Nk2wqNSuDupCAenxmJp6Mei8lKZWkrNUUtVBe1UFPUcpbIAtkg\nPjDWjZB4D4JHumN0sOGhn150FRTQtHIlTctXIJ6SRbMhNhavX/8K+4kTh7h0CucS4eXIFz+ZwPoT\nlfzzuxzya9v4zco0/ro5m6WJgSxNDMLPxfZmv/bgGQWev4Jpv4KWCsjeADmboWgP1OXIy6F35bzu\nEeCfAAEJEHADKu84PH/+PzgvXEDt62/Qsm4dTV9+RdPyFTjcOAO3ZcuwmzDhqntkBwqdQUNUog9R\niT5YzSJlOY0UptZRmlFPS10nhal1PU6R9XYavEOc8Ap1wifUGe8QJwwOWtyN7rgb3UnyTTrr2qdH\ncIpbiik5VUJRcxElp0oobinGz8E2BenlotHp8AoJwyvk7ODromiltb6ehspymrpHbhqrKmiqqqCl\nrtamwhfZ/hexn/Gx9+HQ3Ydscux7MFj06z/8cFzCrlaoy4aaLKjNlNfV6XCq8vy8LsEQOhVCpsnr\nSwwP2BKdZisHCxvYmlnNt6kVNLbL9gMGjYq7Yn25OdADTZuF4yvyqK9oo6GiDUk8eyheo1fjF+6M\nf7Qr/lGueAY5oLL1YR7kWUWmggJad+6iZf16OtPPtAyN48bhdv99OM6aZbMfLgU5UPaCeD9uHunL\nt6kVvJmSR051K//+Po/Xt+dxQ7AbC0b7MmuEt20LMCc/eXbjDQ+D1Sw36Ap2QOEOOV2fJy9pX8r5\n1XrwjEbnFYv/olg85v6WujX7afl+F61bt9G6dRsab2+c5s7Bcc4cjPHxCBrb/MyptSqC49wJjpO9\nuLfUdVCW3UhZViMVOY20NZsoyWigJOOM2wUHNz3u/g64+9nj5ueAu789rt72qLUq7LR2RLtFE+0W\nfd69LOL5E56uJVQqNU6eXrK4ih971jHRarWpoN22+TQOIIIgoBZs5w8w2KjUatn2qq0Gmkrkad6N\nRfIso9PplrILn6x3Bv+x4DcO/MeD/7hhI7LaTRZOVrRwrLiRgzl1ZBY0YjBJuFgFxooq/LVGfDRa\n1M1WrDsbOMzZ/mUEQba/8ApxlFufwU64+dujHg4iSxQxFRfTeeIE7UeO0rZrF+aKip7jKnt7HOfO\nwfWOOzCOHj2EJVW4XNQqgUVj/bl1jB8HCxv4ZH8xWzKqOVjUwMGiBp5fc5IwD3smR3gwKdyd+EAX\n/JwNttmbr9ZC0AR5SX5GFmHV6VB2WF7KD8sCrCpNXgA94O8G3rfa0VjmTVOWiKW6moaPPqbho49R\n2dthlzAe+ylTMY4dhz4qEpUNzn4DcPIwEuthJHay/E5tbeykqkDuWa8ubKa2+BStDV20NnRRfKL+\nzIkCOLjocfY04uRpxMnDKKc9jNg767Fz0qIZBiMOA4UtCS7oo+gSBGEu8BqgBt6TJOnlc47rgY+B\n8UA9cKckSUXdx54FHgKswOOSJG3ut9IryFi6oKMJOpugo/HsdGsNtFbDqarudBW01cEFZmL2oNKC\nRyR4xoDXiO51LLiFgY31fkiiRFeHha52Cx2tJuobO6msbae6rp26+g5ONXXR2WJC6rRiJ4K9KDAW\ngbGc8+I1AZixAvYuetz87HHzte9Zu/s7oNXb1o/3XMSODsxVVZiKizEVFclLYRGdmZmILWdHAFC7\numI/ZQqOM5JxuPFGVIbh7ZzwekcQBJLC3EkKc+dUp5ltmTWsP1HJvvx6CuraKKhr45P9sgG6i52W\nkX7ORHo7EOxmR7C7PcHudgS42tmWTZhaC35j5SXxJ/K+zhaoyYSajO5FTmva6/EMLcQjBDrrtbSU\nGGmtNGA61U7rjl207tgln68CvZcdhiAPdH6eaP190QUFoQsJQ+Xpj2DnBkZX0DkMuQsbB1cDEeMN\nRIyXh8ZEq0hzbQf15W00VLRSX9FGfXkrLXWdtDZ20drYRXnOBSaCCWDnqMPeRY+9sw47Zz12zjoM\n9loM9lr0dpozaXsNeqNmWPTYD1d+UHQJgqAG3gBuAsqAQ4IgrJUkKaNXtoeARkmSIgRBuAv4C3Cn\nIAixwF1AHOAHbBUEIUqSTs8dthEkSQ5j0bPutdB7n9SHPL3yiWawmuSepZ60GaxmPGqPQXqDvN07\nn9UkOxY0t4O5A0xtctrU3r2vV9rUBp3NYOn4oRqeXV0EsPcE50BE52BwCUZ0CUZ0DkFyDkJy9EMU\ntEiSJFdNkhAlCVpMSJLUXUWpu5qn85xJi6KEJEqIVgmrVUS0SogWEVGUsFok6ktETh6oxGqRsFis\nmMwiZrOIxSxiNlsxdVkxm6xYuqxYTCJmkxVzlxWLyYrVJCKaRUSLiGCWECwSF3s1agDXnq0zLxFB\nI+DgqsfV04iTuwEnDyOObnqcPQw4uhtkn1m9ZvXKKSuiyXrmeen97Jz7LF3imNDZidjWJm+KIpLZ\nfGYx9U6betJiRztiaxtiaytiWxtiWyvW1lasTU1Ya+uw1NZiqavrue6FUHt6YIwfjXHUKOwnT8IQ\nF6cMH16jOBq0LBrrz6Kx/pitImllzezNq+NgUQPp5c00tpvZnVfH7ry68851sdPibq/Dw0GPh4Me\ndwcdjgYN9noN9jp57aBXY6fTYNCq0aoFtGoVOo0KjepMWqtWoVELqAUBlSAgCGAWJUwWEZUgi8TT\n68vC4ARBSfLSm85maChEaCzE2FCIsbEQ74ZCzBXltOU20FYBnY1aTC0auqra6aoqAc6eUCSoRTR6\nEbVBRGOQUNurUdtpURm0qAx6VHYG1EYDgp0dKjs7BIMdgsEeQWdA0OnlxWDsThsQdAZ58pBGh2t1\nNlIucvxKtQ7UalCpEVQa2Y+MSi2ve9Lqc9ICKpUaV1c1rm4OMNpZPo6AVZQ4Vd9FS30nzbWdtNR1\n0FzbwamGTtqauuhoNdPeYqK9xcT53sAujNagRqvvtejkteactEanQq1WodaoUGkE1BoVao2A6vQ+\ntcCpSomyrAZUGjmvXE1BnqkpgEolIAiCvF8QEHptX/BYdxr5HwhC9/oKnqch4AddRgiCMBF4UZKk\nOd3bzwJIkvR/vfJs7s6zTxAEDVAFeAK/7Z23d76L3W+gXUYUnNhH4NfzEZAQkFALtuUy40qwSCqa\ncKBZsqcFe5ole5q717WSM7W4UCPJyy1lzliMvkinH85hHnoCQG3pQGtpR2NuR2Npl9OWdrTmNvRd\nzehMLehN8lpnakFjPd9j/rWAoNWi8fREFxKMLiSkZ9FHRqLx8bGJF5LiMiJ5SMsgSRKVzZ2cKG+m\noLaNkoY2ShraKa5vp6KpA3EIXofd388ecSZ0f0RPb/cHdnTgRSN+5hqim4sIaqrCva0Jh9Z2jK2d\naNvMCENl9nT6G9QjIqBnJKIf6i8KKjq1rpjtvTBpnejSOdGldcSiMWJR22HWnF6MWDR2mNXG4f9d\n6HYrISB3BNz7XDyOQd4Ddrv+dhnhD5T22i4Dki6WR5IkiyAIzYB79/7955x73rxNQRAeAR4B8Pb2\nJiUlpS9lvyJaqvIIE87vaBMlARF5kVB1r3vv672t6kmftU8SkKD7OFjQYEaDGTVmNFhQY5bOpE3d\nawsaTJK8tqCmEx0dko529HRg6JXW0yHpz0q3YEcbBi716+x95BYakM6dRCCJPQ8niAiSRHd/WK9j\n3fvOXXcfOzePgIggWlFJVgTJeiYtWs7fJ1lQiaK8T7KgtppQW7tQi2ZU1i55W+zeZzWhEk3deTrR\nWDpQXcRvS0/1znlzn7V9qbf6leT7geNS721BAI0GSa2W1xo1kkYLajWSRiOv1WokvR7JYJAXowHR\nYEQy6JHs7LA6OyM6OSE6OSHZ2Z1fFlGE7Gx5sQFaW1sH9Pdty9hS3fXACGCEG+AGRKgQJTtazdDS\nJdFikpdTXRLtFokuK3RaJDp71hJmK1gksIhglSSs4uk0WEQJiwgiyL3fAJJ0+o2BJPXIip602NMB\n0P/Krx0ddXiTgTdbnUbBuR5wJAmD1YRLVysuXafw6GrGq6sRN9MpHKwdOFjasbN0YWfpxGgxobOa\n0YoW1KIVtSiiFkVUkiS/x0QJlSghdCtY4azeb7r39b630HPszO7+ayAJgNHciLH9wv69zkVCwKIx\nIKp0WNR6RLUeq1qHVa0/s6hOb+uQBDWiSo0kaBBVGkRBjaSS0/KxXvsEjfz+FeRvqNTdu3c6LXX3\n3kmC0CutOiuNIH+jEXq+UueLxO5tqfs/4OCBg6gLLu5jcTCxCes6SZLeAd4BuadrIFuDVutUWjqX\nIjcr5D947x6Ac3sDBGRDNvkY5xzrfd6l73vW9xuBnTt3MHva9AscO/c84RLHLpzvUpiaTgEiAt2t\nyO7u2wuV4LxLXkqE9PGYAOzYuZPp06Zd1XXOPWYLvTh9wRZ6O4aS67n+13Pd4cL1P22mIEpSj/Dq\nsc4YBMfdA32H0yNJu3fvZsqUKecf66lsd8NRlM1TJEnsU+HOy9Ld8D13rVGpsDvHbnPg/3u7675r\nF1OmTh2gW0jnbEpnxKuEHEVGAo2Tg80Y1PdFdJUDgb22A7r3XShPWffwojOyQX1fzh1U1Go1Tvb2\nQ1kEQPYDNRRGqzoXG1D7Go3Nxk5TUFAYPITuIURVP/bs2CJGjYCjYfg7174SJKMRtcPwCV490PTl\nq38IiBQEIVQQBB2yYfzac/KsBe7rTi8Bvpdkib8WuEsQBL0gCKFAJHCwf4quoKCgoKCgoDB8+MGe\nrm4brZ8Dm5FH2v4rSdJJQRBeAg5LkrQWeB/4RBCEPKABWZjRnW85kAFYgP+xuZmLCgoKCgoKCgqD\nQJ9suiRJ2gBsOGff873SncDtFzn3T8CfrqKMCgoKCgoKCgrDnmE+L1RBQUFBQUFBYXigiC4FBQUF\nBQUFhUFAEV0KCgoKCgoKCoOAIroUFBQUFBQUFAYBRXQpKCgoKCgoKAwCPxh7cbARBKEWKB7qcgwC\nHsD5EWevD5S6X79cz/W/nusO13f9lbpf2wRLkuTZl4w2J7quFwRBONzXAJnXGkrdr8+6w/Vd/+u5\n7nB911+p+/VZ9wuhDC8qKCgoKCgoKAwCiuhSUFBQUFBQUBgEFNE1dLwz1AUYQpS6X79cz/W/nusO\n13f9lborAIpNl4KCgoKCgoLCoKD0dCkoKCgoKCgoDAKK6FJQUFBQUFBQGAQU0TVECIIwRhCE/YIg\nHBcE4bAgCIlDXabBRhCEXwiCkCUIwklBEP461OUZbARBeFoQBEkQBI+hLstgIQjC37r/5mmCIHwj\nCILLUJdpMBAEYa4gCNmCIOQJ/5+9O4+PurgfP/6a3U2yue9EQoAEuSEQK+dGTQAAIABJREFUIAly\nIyB3RcWvgJgWbb0QvFqtrUVjW9RftbUKFmkREYEqiAKiJSgSFVG5wpEYIBxLLkhC7jt7zO+PXUIC\nCYEcu0mY5+Oxj93PZz87n/dsjn3vzHxmhHjO0fHYixCiixBilxDiZ9vf+ROOjsnehBBaIUSiEGKb\no2OxNyGEjxDiY9vffIoQYrijY3I0lXQ5zt+Al6SUkcALtu0bhhDiVmAmMEhK2R943cEh2ZUQogsw\nCUhzdCx29iUwQEo5EDgB/MHB8bQ6IYQWeBuYCvQD5goh+jk2KrsxAb+VUvYDbgEeu4HqftETQIqj\ng3CQN4HtUso+wCBu3Pehhkq6HEcCXrbH3kCWA2NxhEeBV6WUVQBSyhwHx2NvbwDPYv09uGFIKXdI\nKU22zR+BUEfGYycxwEkp5WkpZTXwIdYvHB2elPKclPKg7XEJ1g/dzo6Nyn6EEKHAdGClo2OxNyGE\nNzAGeBdASlktpSx0bFSOp5Iux3kSeE0IkY61lafDf+O/TC9gtBDiJyHEN0KIaEcHZC9CiJlAppTy\nsKNjcbAHgP85Ogg76Ayk19rO4AZKPC4SQoQBg4GfHBuJXf0T65cri6MDcYBwIBd4z9a9ulII4e7o\noBxN5+gAOjIhxFfATfU89TwwAXhKSrlJCHEP1m8DE+0ZX2trpP46wA9rl0M0sEEI0V12kDlMGqn7\nH7F2LXZIV6u7lHKL7ZjnsXY9rbNnbIpjCCE8gE3Ak1LKYkfHYw9CiBlAjpTygBBinKPjcQAdMARY\nJKX8SQjxJvAcsNixYTmWmqfLQYQQRYCPlFIKIQRQJKX0aux1HYUQYjvw/6SUu2zbp4BbpJS5jo2s\ndQkhIoCdQLltVyjWruUYKeV5hwVmR0KI+cDDwAQpZXkjh7d7tsHDcVLKybbtPwBIKV9xaGB2IoRw\nArYB8VLKfzg6HnsRQrwCxGL9cqHHOpzkEynlfQ4NzE6EEDcBP0opw2zbo4HnpJTTHRqYg6nuRcfJ\nAsbaHo8HUh0YiyNsBm4FEEL0Apzp+CvRI6U8KqUMklKG2f4ZZQBDbqCEawrW7pbbb4SEy2Yf0FMI\nES6EcAbmAFsdHJNd2L5Qvguk3EgJF4CU8g9SylDb3/kc4OsbJeECsP1PSxdC9LbtmgD87MCQ2gTV\nveg4DwJvCiF0QCXwkIPjsbdVwCohRBJQDfyqo3QtKle1DHABvrR+HvOjlPIRx4bUuqSUJiHEQiAe\n0AKrpJTJDg7LXkZibe05KoQ4ZNv3RynlFw6MSbGfRcA625eN08D9Do7H4VT3oqIoiqIoih2o7kVF\nURRFURQ7UEmXoiiKoiiKHaikS1EURVEUxQ5U0qUoiqIoimIHKulSFEVRFEWxA5V0KYqiKIqi2IFK\nuhRFURRFUexAJV2KoiiKoih2oJIuRVEURVEUO1BJl6IoiqIoih2opEtRFEVRFMUOVNKlKIqiKIpi\nByrpUhRFURRFsQOVdCmKoiiKotiBSroURVEURVHsQCVdiqIoiqIodqCSLkVRFEVRFDtQSZeiKIqi\nKIodqKRLUZQmE0L8VQhxQQhx3tGxNIUQwiCEmNiM188TQuxowXiShRDjbI/jhBBrW7DsPwohVrZU\neYqiXD+VdClKOyOEuFcIsV8IUSqEOCeE+J8QYpTtuTghhFEIUWK7nRBCLBNCdKr1+nFCCIvt9Rdv\nnzUhjq7Ab4F+Usqb6nl+nBBCCiH+ddn+3UKI+dddcTsTQqwWQlTXei+ThBCvCCG8Lx4jpVwnpZx0\njWX9tbHjpJT9pZQJzQz94nufcVnZL0spf9PcshVFaTqVdClKOyKEeBr4J/AyEAx0Bf4FzKx12EdS\nSk/AD7gTuAk4UDvxArKklB61br9oQjhdgTwpZc5VjikDYoUQYU0ovw4hhK65ZTTB32zvZSBwP3AL\n8L0Qwr0lT+KguimKYmcq6VKUdsLWwvJn4DEp5SdSyjIppVFK+ZmU8pnLj7c9lwzMBnKxtkpd9zmF\nEGuEELlCiLNCiD8JITS2LrkvgRBbS9nqBoooBFYDLzZQvsZW5lkhRI7tXN6258JsLWW/FkKkAV/X\n2ne/ECJdCFEghHhECBEthDgihCgUQiyrVf7NQoivhRB5tm7QdUIIn+t9H6SUlVLKfcDtgD/WBAwh\nxHwhxG7bYyGEeMNWj2IhxFEhxAAhxEPAPODZ2q2Ktq7N3wshjgBlQghdPd2deiHER7aWtoNCiEG1\n6iaFED1qba+2dfe6A//j0s+mVAgRcnl3pRDidlt3ZqEQIkEI0bfWcwYhxO9s72mRLQb99b5viqLU\npZIuRWk/hgN64NPreZGU0gxsAUY34ZxLAW+gOzAW+CVwv5TyK2Aql1rM5l+ljCXALCFE73qem2+7\n3Wo7hwew7LJjxgJ9gcm19g0DemJNKP8JPA9MBPoD9wghxtqOE8ArQIitjC5A3NWr3DApZQnWZLO+\n93ISMAbohfU9uwdrS+C/gXVYW80ub1WcC0wHfKSUpnrKnAlsxNpquR7YLIRwaiTGMur+bDyklFm1\njxFC9AL+CzyJtRXvC+AzIYRzrcPuAaYA4cBArD8nRVGaQSVditJ++AMXGvhwbkwW1g/ui0JsLRwX\nb/dc/gIhhBaYA/xBSlkipTQAfwdir+fEUsrzwDtYW+kuNw/4h5TytJSyFPgDMOey7rY4W6teRa19\nf7G1Pu3A2oX5XylljpQyE/gOGGw790kp5ZdSyiopZS7wD6xJXHNc/l5eZAQ8gT6AkFKmSCnPNVLW\nW1LK9MvqVtsBKeXHUkoj1tj1WLs4m2s28LntvTECrwOuwIjLYsuSUuYDnwGRLXBeRbmhqXEEitJ+\n5AEBQghdExKvzkB+re0sKWVoI68JAJyAs7X2nbWVdb3+H3CqdveYTUg95euwjle7KL2e8rJrPa6o\nZ9sDQAgRDLyJtWXKE+sXzYImxF/b5e8lAFLKr21dm28D3YQQnwC/k1IWX6Ws+upW7/NSSottcHxI\nE2K+XJ333VZ2OnV/trWvSC1vofMqyg1NtXQpSvvxA1AF3HE9LxJCaIBfYG0Buh4XsLbedKu1ryuQ\neZ3lIKXMw9oN+JfLnsqqp3wTdZMoeb3nq+Vl2+sjpJRewH1YuxybRAjhgbUbs973Ukr5lpRyKNAP\nazfjxbF2DdWhsbp1qXVuDRCK9T0DayLkVuvY2leQNlZunfddCCFs57run62iKNdOJV2K0k5IKYuA\nF4C3hRB3CCHchBBOQoipQoi/XX68bWB2X6xjd27C2j11PeczAxuAJUIITyFEN+BpoKlzR/0Da/dV\n31r7/gs8JYQItyU0L2O9+rIpXaj18QRKgSIhRGcuJUHXRQjhIoQYCmzG2lL2Xj3HRAshhtnGXJUB\nlYDF9nQ21jFr12uoEOIuW3frk1iT7h9tzx0C7hVCaIUQU6jbbZoN+Ita01tcZgMwXQgxwRbvb21l\n72lCjIqiXCOVdClKOyKl/DvWxOdPWK9ITAcWYk0GLpothCgFioCtWLslh14+mPoaLcKaQJwGdmMd\nzL2qibEXA3+j7nioVcAHwLfAGayJyqKmlN+Al4AhWN+Lz4FPrvP1zwohSrC+h2uAA8AI22D1y3kB\n/8GalJ21veY123PvAv1s4+c21/PahmzBOv6qAOtYurtsY7AAnsDaglmIdWxcTblSymNYE9rTtnPW\n6RqUUh7H2uq3FGuL5i+AX0gpq68jNkVRrpOQsjkt94qiKIqiKMq1UC1diqIoiqIodqCSLkVRFEVR\nFDtQSZeiKIqiKIodqKRLURRFURTFDtrc5KgBAQEyLCzM0WG0urKyMtzdW3TN3HZD1f3GrDvc2PW/\nkesON3b9Vd07dt0PHDhwQUoZeC3HtrmkKywsjP379zs6jFaXkJDAuHHjHB2GQ6i6j3N0GA5zI9f/\nRq473Nj1V3Uf5+gwWpUQ4mzjR1mp7kVFURRFURQ7UEmXoiiKoiiKHaikS1EURVEUxQ7a3Jiu+hiN\nRjIyMqisrHR0KC3G29ublJQUR4fhENdSd71eT2hoKE5OTnaKSlEURVFaV7tIujIyMvD09CQsLAwh\nhKPDaRElJSV4eno6OgyHaKzuUkry8vLIyMggPDzcjpEpiqIoSutpF92LlZWV+Pv7d5iES7k6IQT+\n/v4dqmVTURRFUZqVdAkhVgkhcoQQSQ08L4QQbwkhTgohjgghhjTjXE0PVGl31M9bURRF6Wia2724\nGlgGrGng+alAT9ttGLDcdq8oiqK0I1JK2z3I2tu2fdbHsuYxl+2vNElKq0w1ZdU6rF2QUtoqf7FS\ntR5f3L5sv/U9klSUVlCYX9TA6y+/rymwnudk3XNdI51Wh6u7Y4aziIoKzCUlV+yXtl8kCZeqVbOj\n9ttU+z2+7qoD4OLriUanvf4XtoJmJV1Sym+FEGFXOWQmsEZa37UfhRA+QohOUspzzTmvcqV58+ax\nf/9+nJyciImJYcWKFQ4fhL5x40bi4uJISUlh7969REVFOTQeRekoyqtN5JVWU1hupLDi4r2RovJq\nSipNVBrNVBjNVBottntzzb5qkwWTRWKudbu4bTJbsEgwWSw1+5vyIdegr+Kb9DINFtyoxJUqPGU5\nQdWF+FUX42cswbu6FC9jGR7VFbhVV+FsNuJkNuFkNqEzm2tuWrMFjdmCxiIR8tINCcIia3IcYeFS\nJilbpsU9DGjqh54ELBonzFoXzFpn232tm8Z6b9E6YRE6LBotFo0OKZywaHQ1Nym0l7aFDqnRIoUW\niUAKDQgNUmga2NYiRa39aJBC2B4D2N4nWw/FpX0ChI6kzT9dqpCw/6imX/6hH57dbrL7eevT2gPp\nOwPptbYzbPvq/P4JIR4CHgIIDg4mISGhTiHe3t6U1JMpt2dms7lF63TnnXeyfPlyAB544AGWLVvG\nb37zmxYrvzaTyYRO1/ivTnh4OB988AFPPPEEZWVlNfW91rpXVlZe8bvQ3pWWlna4Ol2PG7n+11N3\ns0WSUy7JKrNwrtTChUpJfqUkv8JCfqWk3NS6sTbmYipycRSAuPy5WvsvPScRQqDDjD9FBIlCAkUB\ngRThTSk+ohRvSvGmDJ+qYjyLy9AXVeFUZkKWg6lci7Fci6lS02LJ0NU1dA5Z/1Pist01G7JmWwJm\nrSvVzt5UOXtT5eJNtZMHJp0bRp07Rp17rcdutm1XhyQqdiMtFx8AIGo3fSFrvY2XWvpEQ+2kDeze\nu28f2jNt48K1NnH1opTy38C/AaKiouTlSwakpKTUXO0W9tznrRKD4dXpDT9nMDBlyhRuueUW9uzZ\nQ3R0NPfffz8vvvgiOTk5rFu3jpiYGMrKyli0aBFJSUkYjUbi4uKYOXMmBoOB2NhYysrKAFi2bBkR\nEREcOHCAuLg4AgICSEpKYujQoaxdu7ZJ45nuvvvumscjR47kwoULV71CcM6cOcTGxjJ9urXe8+fP\nZ8aMGURFRV0R64gRI0hISGDx4sX4+vpy7NgxTpw40WhMF1u2tFot7u7uNfFc65Wber2ewYMHN3pc\ne3IjLIlxNTdy/Ruqu8ls4Xh2CYlphRxMK+DnrGJO55ZRbbZcWYiNs05DgLszPm7O+Lg54ePmhLer\n9bGHiw43Zy2uTlr0tpursxa9ToOrsxZnnQadRqDVXLwXNffamm1NzWNNTSPGNf5fqiiEAgMUnoWC\ns9b7wjRKzqXiKcugLJfan47GCg0Vec5UXHCmMt+JqiId5iotEqjAiQqubLHXugq0blq0rk5o3Z3R\nuuvReriicdOjcXVBo9cjXFzQuOoRelc0ej0aNzeEiytC7wpOeoSTC8LJGXROCCcn0Frvhc7Jts8F\ntDrQahEaHWi0ILS17jXWe6GpyT7NJklJYTUledUU5VVRkldFcV4lWWdz0eFKWXE1ZmPDP9eGaHUa\nnFy0OLlo0dnu69ycNeictWh0GrQ6gVanqblZNGakxoK73s26z8l6jEarQaMRCI1AaLA+FvVva7QX\nH1PrNeJS0i3EpaSzJuG27vj2228YO3YsCHEpgRL2GbdrsZipLCnB1cu7zYwTbu2kKxPoUms71Lav\n3Tl58iQbN25k1apVREdHs379enbv3s3WrVt5+eWX2bx5M0uWLGH8+PGsWrWKwsJCYmJimDhxIkFB\nQXz55Zfo9XpSU1OZO3cuu3btAiAxMZHk5GRCQkIYOXIk33//PaNGjeKpp56qOaa2OXPm8NxzzzUY\np9Fo5IMPPuDNN9+8an1mz57Nhg0bmD59OtXV1ezcuZPly5cjpbwi1otrYR48eJCkpKSaaRxGjx5d\nb4vV66+/zsSJE6/5vVWUG016fjkJJ3L55ngOP5zKo6zafMUxnX1c6RHkQY8gD7r4utLJx5UQb1c6\n+ejxd3d27IeIlFCUAbnHrLcc231eKlQW1fuSi1+zLCYNZUWBlGW7U5puwVhgvOJYjase57BQXG6+\nGefuN+MU0hVdSCecOnVCFxyMxtm5FSvXuOoKE/nny8jPKqXgXBn558ooOFdOaUFlA92xArBeja1z\n0eLu7Yy7twtu3s64ejqjd3dC767Dxc0JvbsTLu469LbHTq5atNpLLV1SSkqNpeSW55JTkUNeRR7Z\nlfnkX7xV5JNflU9+qXW73FTO9O7TeXXYq3Z5b66oucaa4LUUs8lIWWEBpfl5lBUWUF5URHlxoe2+\niIqiQsqLbY9LikFKFr2/EWe9a4vF0BytnXRtBRYKIT7EOoC+qLnjua7WItWawsPDiYiIAKB///5M\nmDABIQQREREYDAYAduzYwdatW3n99dcBa/dYWloaISEhLFy4kEOHDqHVauu0EsXExBAaGgpAZGQk\nBoOBUaNG8cYbbzQpzgULFjBmzBhGjx591eOmTp3KE088QVVVFdu3b2fMmDG4urpSVFR01Vhrz5v1\n3XffNSlGRbkRXaiw8Pauk2w5lMmJ7NI6z3X1c2NIVx+GdPMlorM3PYM98XBpEx0RYLFA/inIPAhZ\nByErEbKTobq0/uOd3MG3G/h0q7m3uHXiyLY9+KcVULZnL9JoBKoA0Li7ox8YgWtkJK6DBqHv0wdd\ncHCbaZmoLDOSc7aYHEMJOWeLyU0robSgqt5jhQAPPxe8A1zxDHDFy1+PV4ArJ8+mMHLsMNy8nXHW\nN/xzlVKSV5lHZqmB82XnycnLIbc8l+zybHIrcskpzyGnPIcKU8U1x++kccJiuf7WNUcwVlZSlJtN\n8YUcSvPzKM3Pp7Qgj7KCfEryrfflRYXXVabe04vK0tKOkXQJIf4LjAMChBAZwItgbQuWUr4DfAFM\nA04C5cD9zTmfI7m4uNQ81mg0NdsajQaT6dIVOZs2baJ37951XhsXF0dwcDCHDx/GYrGg1+vrLVer\n1daU1VhL1+TJk8nOziYqKoqVK1cC8NJLL5Gbm8uKFSsarY9er2fcuHHEx8fz0UcfMWfOHADeeOON\nBmN1d3evU4Zq6VKUq6s2Wfji6DnW/5TGXkMFcBwAT72O0T0DGNsrkDG9Aunk3TY+EAAwVVkTLMNu\nOLsbMhOhqp7WK7cACOoLgX0gsLf1cUBvcA+o6W6rPH6cgrXrKN6+GpeSEkoBhEA/cCAeo0fjMXoU\n+ogIhLZtXFkmpaT4QiWZJwrIOlHIudNFFOdemeBodALfYHf8Qtzx62S7hbjjGaCv0yp10bmKY/gE\nuyGlJL8yn6zSLDJLM8kszSSrNIuM0gyySrPIKs2iylx/Qlebq86VILcgAl0DCXANwE/vZ725+l16\nbLt5OHm0mQTWVF1tTapycyjOzaYoJ5ui3ByKc85TlJtDRXH9raS1CaHB3ccHd19/3H19cff2wc3b\nBzcvb1y9fXDz9MbN2xs3bx9cPb3QtJHfrYuae/Xi3Eael8BjzTlHezJ58mSWLl3K0qVLEUKQmJjI\n4MGDKSoqIjQ0FI1Gw/vvv4/ZfGVXwuUaa+mKj697FdDKlSuJj49n586daDSX/uj37t3LsmXLWLPm\nylk9Zs+ezcqVK9m/fz+rV68GuK5YVUuXotSvuNLImj0G1vxwlpwS64eoswYmR4RwR2QIY3oF4tSC\nXS7NYrHAuUNwcicYvoP0vXB5S4pnJwgZAp0HQ8hg6BRpTa7qIaWk7NtvyXt3FeU/Xbpqzdi1C53v\nvRevadNwCgpqzRpdl/Lias4m5ZF5vIDMEwVXtGJpnTQEdvEgqJsXQWFeBHXzxDvQ9apdZhWmCtKK\n0zhTfAZDkYG9F/ayYtsKzhafpcR49YuIvF28CXEPoZN7J4Lcggh2DybQNZAgt6CaW1tKpC4nLRZK\n8i6Qn5VBflYmafv38vH3O8nPyqDkQu5VX6vV6fAKDMIzIAhPvwA8/Pzx8PWrc+/m7dPmEqnr0Uba\nrzuGxYsX8+STTzJw4EAsFgvh4eFs27aNBQsWMGvWLNasWcOUKVOuaDFqCY888gjdunVj+PDhANx1\n11288MILpKWl4epa/7foSZMmERsby8yZM3G2jZFoyVg//fRTFi1aRG5uLtOnTycyMvKKZFFROpLS\nKhOrvz/Dv789TXGltdW6V7AH80eE41tyiqkT28iFIdVlcGoXnNgOqTugNLvu84F9IWyU9dZlGHh1\narRIKSVle/Zw4a2lVBw+DIDGzQ3vu+7Cd85sfsjIYGAbuIhCSsmFjFIMRy5gOJpHjqG4zvMu7jo6\n9/QlpJcPIT188OvsXm/rFUC5sZxThadILUwltSCVk4UnMRRbuwavYL02CQ8nDzp7dKazR2dCPEII\n9QwlxD2EEI8QOnt0xsPZo6Wr3CqkxUJRTja5aWe4kHaWC+lnyc/KoPBcFiZjdb2v0Wi1eAYE4h0Y\njFdgMN6BQXgH2R4HBePu44vQtJEvI61EyBadhKX5oqKi5MWB2xelpKTQt29fB0XUOuy19uIzzzxD\nbGwsAwcObPVzXatrrXtH/LnfyFfvQcetv5SSTxMzefmLY1wotbaUDAv3Y9H4nozsYV3CzOF1N1ZY\nE6ykT+BEfN3WLK9Q6DUJwsdCt5HgEXhdRVedOUP2y69QZmv91vr54f/rB/C55x60tr91R9ZfSkle\nZimp+3I4eSCb4guXlhjT6jSE9vGlS18/Ovf2xT/EHaERV7w+ozSDlLyUmgQrtSCV9JJ06pvmVafR\n0cWzC2FeYYR5hVF1rorJMZPp5tUNP71fm22lakhVeRk5Z06Re/YMuWlnuZBuIC89DWNV/Uu1ufv4\n4hvSGb9OoeSXVxA1eix+IZ3xCgxGew3TDbU3QogDUsprmoiy49VeqeO1115zdAiK0qEdP1/C858e\nZf/ZAgAiu/jw7OTeDL+5DawXa7GA4Vs4tB6OfV538HtoNPSaYr0F97806db1FF9VxYW3/0Xee++B\n0YjG0xP/Bx/Eb969aFqhRf96leRXcuyHc6Tuy6bgfHnNfjdvZ8IGBhAWEUBob1+cXC51V0kpOVd6\njuS8ZOvtgvW+uLr4ivJ1QkeYdxi9fHvR07cnPX16Eu4dTohHCDrNpY/XhIQEhgQ3eRU8u6qurCDn\nzCmyT5/k/KlUsk+fpOBc/ZMOePj6EdA1zHrr0g3/0K74dgrBxe3Szz4hIYGbh8bYK/w2TyVdiqIo\nTWC2SFZ+d5q/7zhBtdlCgIczz03ty12DO6PRODjZKsmGQ+vg4BooOHNpf8hgGDAL+t8J3qHNOkVF\ncjJZv/891SdPAeA96y6Cnn4anb9/s8ptLovZguFoHj9/n0VaUl7NFA56dyduHhpEz6ggOvXwqfkZ\nlRvLOXjuKIk5iRzOPczPeT+TX5l/Rbl+ej/6+fejl2+vmiQr3CscJ61jV/5oDmmxkJ+VQebxn8k6\nnsL5U6nkZ2YgZd2rHbU6HQFdwwkK705AlzACu3YjoGsYrp5eDoq8/VJJl6IoynU6X1TJ4/9NZK/B\n+uE8J7oLf5zeFy+9gz+A0/fCD8usrVoW27T1XqEw+D4YeA/439zsU0iLhbyV75L71ltgMuEcFkan\nV17GzcETGVeUVpP0TSbJ32ZSVmQdU6TRCm6ODKTPiE6E9vFFq9WQW57LV2lfkpiTSGJOIsfyj2GW\ndS8Y8nbxpr9//0u3gP4Eu7WdaSyaylRdzfnTqWQdTyHzWDJZJ45RWVp3YL9GqyWwy80E39yDm7r3\nJLh7DwK6dkOra7/JZVuiki5FUZTr8OPpPBauP8iF0moCPV34f7MiGN8n2HEBWczWJOuHZZBuu1pQ\naKHPDBjyK+gxwTqLegswl5SQ9dwfKN25EwDf++4j6LdPo2ngYh17KMwu5/DOdI79cA6TbbZ3n2A3\n+o0Koc8tN1GmK+ancz/y7o8/se/8PjJKM+q8XiM09PXry5DgIUQGRjIgYACdPTq3+wQLwGQ0ci71\nGGlJh0lLOkL2qROYTXXXkPLw9SOkdz869+5Lp559COgWhpOzSwMlKs2lki5FUZRrtPr7M/zl8xTM\nFsmIm/1ZOncw/h4O+oAym+DIh/Dt65e6EPXeEPVriHnomq44vB5Vp8+QsWAB1QYDGi8vQv72//B0\n4IUBeZml7N12htOHcmtWFeo2wJ/e4wLJ8DpO/Pn1/PnrnzhZeLLO69x0bgwKHMTgoMFEBkUyMHAg\n7k6OH3/WEqTFQo7htC3JOkzGsWRMVbWmwBCCgC7d6Nynny3R6odXYFCHSDDbC5V0KYqiNMJikbzy\nvxT+8501uXl4bHeemdQbnSPm2rKYIWkTJLxqnSkerLO/D38MIueBS8tPOVBx6BDpjzyKubAQl169\nCF22FOeuXVv8PNei4HwZe7ed4eSBHJDWiUpvinQlt+dxNpSv5sjBI3W6C111rgwJGsKwTsOI6RRD\nb9/edQa5t3cVpSUYDh3g9MF9GI4kUllSd8C/f2hXukVE0jViEJ379Efv3j6mpOioOs5v3g1u3rx5\n7N+/HycnJ2JiYlixYgVOTo7tg1+8eDFbtmxBo9EQFBTE6tWrCQkJcWhMinK9qk0Wnvn4MFsOZaHT\nCF77v4HcObh5g9CbREo4tg2+/qt1nUMAv+4w9jnr4Hht6/w7L9ma1aBXAAAgAElEQVS1i8ynnkZW\nVuIxdiyd3/gHGje3VjnX1RTnVbB36xlO7D2PlNYe1PKeWXwTuAmD6SQYrMdphZbIwEiGdRrGsE7D\nGBQ4CGetY9dqbElSSnLPnuFM4n5OJ+7n3IljdQa+ewYE0nXAIGuiNWAQ7j6+DoxWuZxKujqIefPm\nsXbtWgDuvfdeVq5cyaOPPtoq5zKZTOiuYa6VZ555hr/85S8AvPXWW/z5z3/mnXfeaZWYFKU1VJss\nLFh3gK9ScnB31rL8vqGM6XV9c1i1iPNJsP0564zxAN5dYeyzMGhuqyVbAMXxO8h8+mkwm/GedRed\nXnoJYed5loxVZg7uOEvijrOYjRIpLBwP/ol9nbdT5lIIJvDX+zM6dDRjQscwvNPwdjPB6LWymM1k\nHkvmxE97OLn/R0rzLtQ8p9Hq6NI3gvDBUXQfEo1vp44xHq2jan9JV5x3K5Xb8JpPBoOBKVOmcMst\nt7Bnzx6io6O5//77efHFF8nJyWHdunXExMRQVlbGokWLSEpKwmg0EhcXx8yZMzEYDMTGxlJWZp2S\neNmyZURERJCQkEBcXBwBAQEkJSUxdOhQ1q5d26Q/mGnTptU8jomJISMj4ypHW9dwjI2NZfp06wLi\n8+fPZ8aMGURFRV0R64gRI0hISGDx4sX4+vpy7NixOgthN8TL69LlxGVlZeofgdKuGM0WFq4/yFcp\nOfi4OfHBA8OICG2l/z8NKcuDXUvgwHsgLeDqC+P+CEPng651W29KvvqKzN/+Fsxm/B/8DYFPP23X\nv2EpJQe/P8nezQYspdYLAVL9D7C36zZK9QUMCBjA6NB7GRM6hr5+fdGIjjWTudlkJD3pCCf27uHk\nvh/rrEvo7uNrTbIGR9M1IhIXB7Q8Kk3T/pIuBzl58iQbN25k1apVREdHs379enbv3s3WrVt5+eWX\n2bx5M0uWLGH8+PGsWrWKwsJCYmJimDhxIkFBQXz55Zfo9XpSU1OZO3duzWLWiYmJJCcnExISwsiR\nI/n+++8ZNWpUowteN8RoNPLBBx/w5ptvXrU+s2fPZsOGDUyfPp3q6mp27tzJ8uXLkVJeEevFFQIO\nHjxIUlIS4eHhwLUteP3888+zZs0avL29662PorRFJrOFJz5MZMfP2Xjpdaz99TAGdLZjwiUlJH4A\nO/4ElUXWvrSYh2Hcc+Dm1+qnL9m1i4ynngaTye4J14WKC3yR+BWZn0u88oIBLbnu6fwYvoVuvQN5\nqttCbu1yKwGu9a/92J5ZzGbOHknk2J5vOXXgJ6psX34BfG7qRM9hI+kVM4Lg7j06/HI5HVX7S7qu\n0iLVmsLDw4mIiACgf//+TJgwASEEERERGAwGAHbs2MHWrVt5/fXXAaisrCQtLY2QkBAWLlzIoUOH\n0Gq1dVqJYmJiCA21jg+JjIzEYDAwatSoRhe8bsiCBQsYM2YMo0ePvupxU6dO5YknnqCqqort27cz\nZswYXF1dKSoqumqsFxMuuLYFr5csWcKSJUt45ZVXWLZsGS+99FKT6qUo9iKl5MWtyXxx9Dyeeh1r\nf2PnhCv/DHz2OJz51rrdfRxMeRWC7LMkVsWhQ2Q++RQYjfjdf79dEq5yYzk703by+ckvqDjgxpD0\nSXhJJyqcSigclErU2J481fU9vF3s3NJoB1JKzp86Qcp3CRzb822dFi3/0K7WRGvYCAK6hqnegg6g\n/SVdDuLicumycI1GU7Ot0Wgw2eY9kVKyadMmevfuXee1cXFxBAcHc/jwYSwWC3q9vt5ytVptTVmN\ntXRNnjyZ7OxsoqKiWLlyJQAvvfQSubm5rFixotH66PV6xo0bR3x8PB999BFz5swB4I033mgw1ssX\nv76Wlq6L5s2bx7Rp01TSpbR5y785xbqf0nDWaXhvfjQDQ33sc2KLGX56B3b+xbouoqsfTP0bRNzd\npCV6mqL67FnSH12ArKrC5//uJujZZ1rtg95kMfFD1g9sO72NXem7cC/0Z9ypuQSUW7+EekSYmHvf\nOPy8Z7bK+R2t4HwWKd8lkLJ7F4Xnz9Xs9wsJpe+ocfQaPgq/EAdcsKG0KpV0taDJkyezdOlSli5d\nihCCxMREBg8eTFFREaGhoWg0Gt5//33MZnOjZTXW0hUfH19ne+XKlcTHx7Nz5040tZqd9+7dy7Jl\ny1izZs0VZcyePZuVK1eyf/9+Vq9eDXBdsTbW0pWamkrPnj0B2LJlC3369Lnq8YriaFsOZfK37ccR\nAt6cHUlUWOt35QFQcBY+efDS5KYR/2dt3XK3XxeaqaCAtIcewlxQgPuY0dz04outknCdLT7LptRN\nbD25lbzKPIQUDM68jeiMqQipwd3PmQn39aNLPzu993ZkrKrk+A+7Ofr1DrKO/1yz393Hlz4jx9B3\n1K0Ehd+sWrQ6MJV0taDFixfz5JNPMnDgQCwWC+Hh4Wzbto0FCxYwa9Ys1qxZw5QpU65oMWoJjzzy\nCN26dWP48OEA3HXXXbzwwgukpaXh2sBs0ZMmTSI2NpaZM2fi7GwdlNuSsT733HMcP34cjUZDt27d\n1JWLSpt2NKOIZz4+AsDz0/oyNaJlJxdt+MQfw7anoKoYPDvBjH9C7yn2ObeNNJnIfPIpjGfT0Pfr\nR+gbb7ToVYqVpkr2le7j/e3vsz97f83+Pk4R3Jo6D3nO+j9q4PhQbpl5c50FqDuC8txsvlr5L1J2\nJ1BdYV1428lFT89hI+g7+la6DhiIpoVWDVDaNiEvrgbaRkRFRcmLA7cvSklJoW9f+4xnsJeSkhI8\nPT1b/TzPPPMMsbGxDBw4sNXPda2ute4d8eeekJDAOAfO4u1obbX+BWXVzFi6m8zCCubGdOHlOyNa\nvLXhirpXlcAXz8Lh9dbtPjPg9qV2GSh/uezXXiP/3VVoAwII37QJp+CgFin3RMEJNp3YxLbT2yiu\ntk7a6apzZXLYZMZW/wLD1kqqKky4eTszcX4/uvTtOK1bVeXlpOxO4OjX8eScOVWzv1PP3kRMmEzv\n4aNx1jtu+SR7aat/8y1JCHFAShl1Lceqlq4O7rXXXnN0CIrSppktksc/TCSzsIJBod7E3d6/9bt3\nspPho/sg/zToXGHKK9ZpIBzQrVS8PZ78d1eBVkvoG/9odsJlspjYlb6LdSnrOJB9oGZ/V+eu/GrI\nr5gcOoXEzVkkf5cFQNjAAMbH9sHVs2NMYJqflUHi9m0kf7MTY2UFAFoXPYPGTyJi/CQCuoY5NkDF\noVTSpSjKDe2tnal8l3oBP3dnlt83FBddK3fzJG2CLQvBWA7BA2DWuxDkmPGO1WfPcu6PfwQg+Nln\ncIuObnJZhZWFbErdxIfHP+R82XkA3J3cmdF9Bnf3upvzR84TFTSc7cuOkn2mGK1Ow8i7ezBgbPuf\nzFNaLJw5fIDE/32G4fDBmv1d+kUQMXEK5yqM3HrZxUXKjUklXYqi3LAOnM1n6depCAFvzRlMiE/r\ndfcIi9k679aepdYdg+bCjDfAyTFdTNJoJPOZZ7GUl+M5dQq+v/xlk8o5nn+c/x77L9tOb6PKbF1c\nOcwrjLl95jKzx8yaxaRP5Zxjwyv7qCiuxsPPhWmPDCSwa+sPsWhNVeXlJH/zFYnbP6u5AlHn7EK/\n0bcSOWUGgbZWreyEBMcFqbQpKulSFOWGVFJp5MmPDmGR1gWsR/VsxSsFy/IYeCQOCo+ARgeTX4GY\nBx3SnXjRheXLqTxyBF1IJ+vyPtcRi5SSfef38W7Su+zJ2lOzf1TnUczrO48RISNqZoiXUpL0TSaG\nXRJkNaF9fJn0m/64erTf7sSywgIOfLGFwzu+qBkY7xkQyODJMxgwfhKuHu07mVRaj0q6FEW5IcVt\n/Zn0/Ar6h3jx29t6N/6Cpso7BWtn4Vt4BtwD4Z410G1E653vGpQfPMiFd1aAEIS8+iraWkt2XY3Z\nYmZX+i7ePfouSXlJgHVg/B097uDePvcS5h1W53iLRbL7oxMc/SYTgMjbujL8ju5otO1zNvWC81ns\n3/oJyd/uxGw0AhDadwBDpt3OzUOHodGqKxCVq1NJl6IoN5wdyefZdDADvZOGN+dE4qxrpSQgfS+s\nnw0V+ZR4dMfzwW3g3bl1znWNLBUVZD33B7BY8H/wQdxjYhp9TbW5mm2nt/Fe0nsYig0A+Lr4cm/f\ne5nbZ269M8Ubq8zseDcZw5ELaHSCTlEwclaPlq6OXWSfPsneLR+T+tMepLSAEPSMGUH0zFl06tGK\nCbvS4aikq4OYN28e+/fvx8nJiZiYGFasWIGTk5NDY9q4cSNxcXGkpKSwd+9eoqIuXVH7yiuv8O67\n76LVannrrbeYPHmyAyNVbiTFlUYWb7G20vx+Sh96BLVSV9DPW+CTh8BUCT0ncSj4AUY7OOECyF26\nDGNaGi69exO4aOFVj600VfLxiY95L+k9cipyAOjs0Zlf9vsld/a8E1dd/ePRyour+fztw+ScLcHF\nTce0RwdyIvNQi9eltWUeT+GHj9dz9kgiABqtjv5jJhJ9+11qtnilSVTS1UHMmzePtWvXAnDvvfey\ncuVKHn300VY5l8lkQncNEycOGDCATz75hIcffrjO/mPHjvHhhx+SnJxMVlYWEydO5MSJE2hV07xi\nB698cYzs4iqGdPXhl8PDWuckP/wL4v8ISOtUENP+jvm73a1zrutQcTSJ/NWrQaOh01//gnCuf1xV\nlbmKTSc2sfLoSnIrcgHo6duTXw/4NZPDJqPTNPz3X3C+jG3LDlN8oRKvAD0zFg7C9yZ3TmS2Ro1a\nR9aJY+zZuK4m2XLSuzLotqkMmXY7nn4db6FtxX7aXdIV8X5Eq5R79FdHG3zOYDAwZcoUbrnlFvbs\n2UN0dDT3338/L774Ijk5Oaxbt46YmBjKyspYtGgRSUlJGI1G4uLimDlzJgaDgdjYWMpsK8YvW7aM\niIgIEhISiIuLIyAggKSkJIYOHcratWubdPn0tGnTah7HxMSQkZFx1ePnzJlDbGws06dPB2D+/PnM\nmDGDqKioK2IdMWIECQkJLF68GF9fX44dO1ZnIeyGNDSx6eeff86cOXNwcXEhPDycHj16sHfv3prZ\n9BWltfx4Oo//7k3DSSt4ddZAtJoWHsguJex6Gb79m3V7wosw6imHDpi/SBqNnPvTn8BiwW/+fFwj\nrvxfWm2u5pPUT/jP0f+QU25t2err15dHBz3KuC7jGv3fdCGjhK1vHqKixEhQN0+mPzYIN6/2M2D+\n3Mnj/LBxPWcOWecXc3Z1ZcjU2xky/Q41OF5pEe0u6XKUkydPsnHjRlatWkV0dDTr169n9+7dbN26\nlZdffpnNmzezZMkSxo8fz6pVqygsLCQmJoaJEycSFBTEl19+iV6vJzU1lblz59YsZp2YmEhycjIh\nISGMHDmS77//nlGjRjW64HVDjEYjH3zwAW+++eZV6zN79mw2bNjA9OnTqa6uZufOnSxfvhwp5RWx\nXlwh4ODBgyQlJREeHg5c34LXtWVlZTFmzJia7dDQUDIz29HXYKVdqjKZ+eMn1i9Xj93ag17BLfwh\nKiXEPw8/vg1CAzP/BZFzW/YczZD//vtUHT+OU2gogY8vqvOc0Wzk05Of8p+j/6mZY6u3b28ejXyU\n8V3GX9MXwfNniti29DBV5Sa69vNjysMR7WY5n+zTJ9mzcR2nD+4DrC1bQ6b+gqHT78DV89ouMlCU\na9Hukq6rtUi1pvDwcCJs3wz79+/PhAkTEEIQERGBwWAAYMeOHWzdupXXX38dgMrKStLS0ggJCWHh\nwoUcOnQIrVZbp5UoJiaG0FDr2IDIyEgMBgOjRo1qdMHrhixYsIAxY8YwevToqx43depUnnjiCaqq\nqti+fTtjxozB1dWVoqKiq8Z6MeGCxhe8VpS2ZNVuA6cvlNE90J0F41p4QLfFAp8/BQdWg8YJ7l4F\n/W5v2XM0gzE7m9x/LQfgphdfROPmBoBFWthh2MFbiW+RXpIOQA+fHiyIXMCErhNqpn1oTOaJAj5/\n+wjGKjPdIwOZ9Ov+aJ3a/hWKBeez2P3fNZz40dr16+SiJ3LKDKJm3Imb15UXByhKc7W7pMtRXFxc\nah5rNJqabY1Gg8lkAqzz0WzatInevetezRIXF0dwcDCHDx/GYrGg1+vrLVer1daU1VhL1+TJk8nO\nziYqKoqVK1cC8NJLL5Gbm8uKFSsarY9er2fcuHHEx8fz0UcfMWfOHADeeOONBmO9fPHrprZ0hYSE\nkJ6eXrOdkZFB586OH2CsdFzZxZUs/ToVgLhf9G/ZqxXNJtiyAI58BDo9zF4LPW9rufJbQM7rf0eW\nl+MxcQIeo0cBsPfcXv5x4B8k5yUDEO4dzmORj3Fbt9uuOdkCOJucx//eOYrZaKFXTDATftW3zU8J\nUV5UyA+b/suRr7ZjMZvROTkTOWUG0bfPUsmW0qpU0tWCJk+ezNKlS1m6dClCCBITExk8eDBFRUWE\nhoai0Wh4//33MZvNjZbVWEtXfHx8ne2VK1cSHx/Pzp070Wgu/cPbu3cvy5YtY82aNVeUMXv2bFau\nXMn+/ftZvXo1wHXF2tSWrmnTpvHggw/y9NNPk5WVRWpqKjHXcNm6ojTVK1+kUF5tZlK/YMb0Cmy5\ngi1m+ORBSP4EnNzh3o8g/OqtzPZWfuAAxZ99hnBxIfi55zief5x/HvwnuzOtrTuBroE8FvkYM3vM\nvOoA+fqcTc7ji+VHsJgk/UaHMG5ub0RLj5NrQcbKSvZ//in7tn6CsbICITT0HzeRkffch6e/GiCv\ntD6VdLWgxYsX8+STTzJw4EAsFgvh4eFs27aNBQsWMGvWLNasWcOUKVOuaDFqCY888gjdunWrGYx+\n11138cILL5CWloara/2XdU+aNInY2FhmzpyJs+0qppaM9dNPP2XRokXk5uYyffp0IiMjiY+Pp2/f\nvtxzzz3069cPnU7H22+/ra5cVFrNPkM+mw9l4azTsHhGv5Yr2GKBLY9ZEy5nT4j9BLq0rS8P0mzm\n/F+XAODyy9m8ZFjOZ6c+QyJxd3LngQEPcF/f+3BzcrvustOP5fO/d45iMUkG3hrKqHt6ttk1FC1m\nM0e/3sEPH6+nrLAAgO5Dohk991dqAWrFroSU0tEx1BEVFSUvDty+KCUlpcEr4dqrkpISPD1b/2qY\nZ555htjYWAYOHNjq57pW11r3jvhzT0hIYNy4cY4Ow2HsXX8pJXe8/T2HM4p4fHwPnp7UQhNZSgnb\nnrSO4XJytyZcXW+56ksc8bMv3LSJc8//iUp/Dxb+RlKsqUKn0TG792weGvgQfnq/JpWblVrIZ0sP\nYaq2MGBMZ8bM7dVowuWo3/20pCPsev/fXEgzAHBTj16MmXc/Xfq1zpXw9bmR/+5vhLoLIQ5IKaMa\nP1K1dHV4r732mqNDUBSH+eLoeQ5nFBHg4cLDY29umUKlhO3PWRMunR7u/bDRhMsRzOXlpL/xGjrg\nPyPKKdZomNRtEk8OfZIunl2aXO75M0Vse/swpmoLfUZ0YsycxhMuRyjKyeabte+S+pN1bUivwGDG\nzJtPr1tGtcl4lRuDSroURemQjGYLr8UfA+DJiT1xd2mhf3dfvQg/vQNaZ5izDsLHNP4aOzuef5xd\nLz/O2AtFnAmG3JF9WHXLc0TfFN2scnPTSti29DDGSjM9o4O59b4+bW4Ml7Gykr1bP2b/1k8wGavR\nubgw7I57iJpxJ7oGJoNVFHtRSZeiKB3Sf/emYcgrp3uAO7Ojm96yU8eepfD9m6DRwf+9Dz0avkrX\nEQorC1l2aBn/O7SBN7+0LsjMo7F8ePvv0WqaN26yKLecz5YeoqrcRPfBgUyc3xdNG0q4pJQc3/Mt\n36x7j9K8CwD0GTmWMfPuV4PklTZDJV2KonQ4pVUm3vzKOkXEs1N649QSUxgc2QA7/mR9fMc70Gfa\n1Y+3Iyklm09u5h8H/kFhVSG/2iNxrwLnW6KZNuePzS6/vLiaz946TEWJkdA+vkz6df82NS1EflYG\nX638F+nJRwAICr+Z8fMfpnOfFrxwQlFaQLOSLiHEFOBNQAuslFK+etnzXYH3AR/bMc9JKb9ozjkV\nRVEa8/4eA3ll1Qzu6sPk/jc1v8BTX8PmBdbHk5bAwP9rfpkt5FThKf7y4184kG1duuZW10imHzoM\nmAl55vfNLr+60sTnbx+mKLeCgC4eTH04Am1LznPWDKbqan7avJF9WzZiNpnQe3oxeu6vGHDrRDTN\nbNlTlNbQ5KRLCKEF3gZuAzKAfUKIrVLKn2sd9idgg5RyuRCiH/AFENaMeBVFUa6qtMrEyu9OA/Db\n23o3f9B01iH4KBYsRhixCEYsbIEom6/SVMm/j/yb95Lfw2Qx4af343dRvyP6w6MUVO/H87bbcO3f\nv1nnMJstxP8niZyzJXgF6PnFokicXdtGB4nhSCI73/0XhefPATDg1tsYM+9+tWyP0qY15+tKDHBS\nSnlaSlkNfAjMvOwYCVz8C/AGsppxPqUWKSWPP/44PXr0YODAgRw8eNDRIXHs2DGGDx+Oi4tLzVJI\nimJva34wUFBuJKqbLyN7+DevsPwzsO5uqC6FiHtg4p9bJMbm2p25mzu33Ml/jv4Hk8XE3b3uZusd\nW5niOYzCDRsACHhsQbPOIaUk4YNjpCXn4+rpxC8WRbaJxavLCgv4/K3X2LRkMYXnz+Ef2pXZca8y\n+ZEnVMKltHlNnqdLCHE3MEVK+RvbdiwwTEq5sNYxnYAdgC/gDkyUUh6op6yHgIcAgoODh3744Yd1\nnvf29qZHjxZeK83BzGZzsyYEjY+PZ8WKFWzatIl9+/bx+9//vt5lg1rKtcSbm5tLWloan3/+OT4+\nPjz++ONNLgusi4wXFRU1Kd62qrS0FA8PD0eH4TCtXf9Kk+R335RTaoTfRekZEND0vzGtqYwhB5/F\nvTyDfN9Ijkb8CalxanJ5LVH3EnMJH+d/zMFy65esEKcQZvvNpru+OwAeGzbi/vXXVEZGUvTIw806\nV26yJOeoRKODsFsFrv7NazFsbv2llOSlHCXjh28wV1chtDo6RQ0neFAUmjY+ufKN/Hd/I9T91ltv\nbTPzdM0FVksp/y6EGA58IIQYIKW01D5ISvlv4N9gnRz18onUUlJSaibTTOnTOpNl9j2W0uBzBoOB\nKVOmcMstt7Bnzx6io6O5//77efHFF8nJyWHdunXExMRQVlbGokWLSEpKwmg0EhcXx8yZMzEYDMTG\nxlJWVgbAsmXLiIiI4MCBA8TFxREQEEBSUhJDhw5l7dq119Qd8uWXX/LAAw/g5eXFhAkTKCkpobS0\nlE6dOtV7/DvvvMOpU6dq5u1avXo1+/fvZ9myZdxxxx2kp6dTWVnJE088wUMPPQSAh4cHDz/8MF99\n9RVvv/02o0aNumpMnp6edO/enYSEBFxcXBqcAPVaJ0fV6/UMHjy40ePakxthosCrae36L084Ranx\nGEO7+fLYrOFN71o0m2D9PVCeAUH98HvgM8bqm9eK0py6SynZbtjOaz+9RkFVAa46Vx4d9Cj39bsP\nJ1siaMzJ4dT33yOBvi++gL4ZEwufPJBD8tEkEDDloYGED2z+1X/NqX9Rznl2rFhKWtJhAMIjhzL+\ngUfxCW6B8Xp2cCP/3d/Ida9Pc5KuTKD2ddihtn21/RqYAiCl/EEIoQcCgJxmnNchTp48ycaNG1m1\nahXR0dGsX7+e3bt3s3XrVl5++WU2b97MkiVLGD9+PKtWraKwsJCYmBgmTpxIUFAQX375JXq9ntTU\nVObOnVvTKpWYmEhycjIhISGMHDmS77//nlGjRjW64HVmZiZdulx6+0NDQ8nMzGww6Zo1axbDhw+v\nSbo++ugjnn/+eQBWrVqFn58fFRUVREdHM2vWLPz9/SkrK2PYsGH8/e9/BxpfhFtRHKmsysS/vz0F\nWOflatZYrvg/wqmd4BYAcz+EZiZczZFTnsNff/wru9Ktf3vDOg0jbngcoZ6hdY7Lf3cVsqoKz9sm\nNivhyjlbzM7V1qG5I+7q0SIJV1NJi4VDOz7nu/XvY6yqRO/pxfj5D9Fn5Fg1wanSLjUn6doH9BRC\nhGNNtuYA9152TBowAVgthOgL6IHcZpzzqi1SrSk8PJyICOuyEf3792fChAkIIYiIiMBgMACwY8cO\ntm7dWjOeqbKykrS0NEJCQli4cCGHDh1Cq9Vy4sSJmnJjYmIIDbX+84yMjMRgMDBq1KhGF7y+XoGB\ngXTv3p0ff/yRnj17cuzYMUaOHAnAW2+9xaeffgpAeno6qamp+Pv7o9VqmTVrVk0ZLR2TorSkD348\nS0G5kSFdfRjVoxmJwr6VsHfFpclPfbu1XJDXQUrJllNb+Nu+v1FSXYKHkwe/i/odd/W864qEw5Sb\nS4FtWEbAgqaP5SotqOKLfx3BZLTQd2QnIie20PxmTZCflcmOFW+SecyaAPYaPpoJ9z+Mm7ePw2JS\nlOZqctIlpTQJIRYC8Ving1glpUwWQvwZ2C+l3Ar8FviPEOIprIPq58u2ttjjNXJxcal5rNFoarY1\nGg0mkwmw/pPctGkTvXvXXd8tLi6O4OBgDh8+jMViQa/X11uuVqutKauxVqXOnTuTnp5esz8jI4PO\nnTtftQ5z5sxhw4YN9OnThzvvvBMhBAkJCXz11Vf88MMPuLm5MW7cOCorKwFr917tsVeqpUtpqyqN\n5porFp+Y2IxlaU59DV88a318+1KHLe9zrvT/s3ffcVWW/QPHP/c5h71kuAAHCgIOxIWaC1eOXERD\nK3vqsSdLzZGVlpZWj5WaWaKlPqXmL82RCxVypLjBhQNxgIqCouzNgTPu3x9HSZM9BPR6v17P63Xg\n3Nd1f++eOH3PNb5XPJ8f+5wjt48A0MOpB591/YwGFoVPp6WsXo2cl4dl377lHuXS5OkI+ukc2en5\nOLrVodeoStj5WQ56vY5TO7dxdP1vaDX5mNvUod+Ycbh1fuaxxyIIla1Ca7ru1dwK+sfvPnvgdSTQ\nrSL3qE0GDBhAQEAAAQEBSJJEeHg47dq1Iz09HWdnZxQKBcdjffMAACAASURBVL/++is6na7Evkoa\nVRo2bBiLFy9m5MiRhIWFYWNjUzC16OHhwaVLlx5p4+fnx5w5cwgPD2fu3LkApKenY2tri7m5OZcu\nXSI0NLTcMQlCddl8+hZJWfm0crSmp1s5R7lSrsHGN0DWQff3oe3ISo2xNO4XOZ17Yi7Zmmysja2Z\n7jOdIc2GFJkA6bKySV23HgCHt/9T7vvu+7+LJN7MxLquWbXV4kq9c5vgJd8Rf8Xw+dWyZx98//Uf\nzCxLXgMqCLVBzSi48oT49NNPmTx5Ml5eXuj1elxcXNixYwfjxo3D39+f1atXM3DgQCwsLCp8r8GD\nBxMUFISrqyvm5uasXLkSgKSkJIoaTLS1tcXT05PIyEh8fHwAGDhwIEuXLsXT0xN3d3e6dCn/N/s7\nd+7QsWNHMjIyUCgUfP/990RGRmJtLbZxC1VHr5cLRrne7tmsfKMz+dmw7jVQp4P7YOjzaSVHWbLk\n3GQ+P/Z5wdqtvo37MrPLTBzMik8i0zZuRJ+ZiVnHDpi1bVuue5/9K5bokwkYmSh5bpwXppbl36VZ\nHrIsc27vn4T8389o8/KwtLWj/9j3aNauYmdFCkJNI5KuUmjatCkREREFP69atarQ98zMzFi2bNkj\n7d3c3Dh37lzBz3PnziUzMxNfX9+HdnUsXry41DFJksSSJUse+X1oaCjjx48vst2OHTse+tnExITg\n4OBCr83Kyip1PAANGjQgLi6uTG0EoaL2XLzLtaRsnOqY8VybwjeSFEuWYfskSLgA9q7gtxQUj3eU\nJyQ2hFlHZ5GiTsHSyJJPOn9S7OjWfbJGQ8rq1QDY/3tMue4ddzmVo5sNGxD6vdESu4YV/1JYFlmp\nKexetojr4ScBw3mJff/9LqZPeJkB4ekkkq4nzJAhQ6o7BEF4rJYfNIxyjenugqo85wGGLYXzG8HI\nAl5eA6Y2lRxh0bI12cw/MZ9NUZsA6NSgE3O6zaGhZemSx4zgYLTx8Rg3a4alb68y3z8zRc3unyOQ\n9TLtBzahWbu6Ze6jIq6EHWHP/5agzszAxMKCfmPG4dGt7M8hCLWFSLoEQai1Tt1I4dSNVGzMjHi5\nUzl22sUchl2G0imM+BHqeVRugMUITwjn40MfcyvrFsYKYya1n8RrLV9DIZUucZRlmeRfVgBg/+83\nkco4OqfV6Phz2XlyMzU0amlH52HNyvwM5ZWXk82+FUuJPGSYSm3i1Y4B707Cyq76ylMIwuMgki5B\nEGqtZQcMo1yvdWmMhUkZP84ybv+9cL7bZGg1ovIDLIRWr2XZuWUsP7ccvazHw86Dr7p/hZutW5n6\nyT5ylLzLl1HWdcB62LAyx3FofRQJNzKxsjfl2X+3QqF4PDsVb12+SFDAfDISE1AZm9Dz1Tfwfva5\nMieNglAbiaRLEIRa6VpiFnsu3sVYqeBfzzQtW2OdFv4YA9mJ4NLrsS2cj8+KZ/qh6ZxOOI2ExJjW\nYxjvPR4jZdkXrqes+AUAu9dGozAu25mIl8PuEHn4NkojBYPGtnksC+dlvZ7Qzes5unENsl5P/Wau\nDJowFXun6qsFJgiPm0i6BEGolX49GoMsg187J+pZmZbc4EEHvoGbR8GyAfj/Asqq/yg8k3OGGdtn\nkJGfQV2zunzd42s6N+xcrr7yoqLIPnoMydwc25Evl6lt6p1sQtZeBqDnyy2o27jqyzFkpSQTtX0j\nmbcNtQU7Dn2e7iNHo1Q93l2SglDdRNIlCEKtk6nW8Mcpw07ZN7s3LVvjq/vh4LcgKcD/Z7Cs2sXj\naq2a+SfmsyFxAwC9nHvxZbcvsTW1LXefKWvWAGAzfBhKm9Iv/Nfm69j1vwto83S4daqPZ7dy7PYs\no6unjvPnT9+jzszA3KYOg8ZNoal3hyq/ryDURGISvZaSZZmJEyfi6uqKl5cXp0+fru6Q2LZtG15e\nXnh7e9OxY0cOHz5c3SEJT6jNp2+Rna+js4sdHg3KUAcu8w5s/g8gQ6/p4NKjymIEiE6NZtTOUWy4\nsgEVKqb7TCegT0CFEi5dRgbp2wIBsHv11TK1PbwxiuRbWdjUNcP31aqtOK/VaNi/ajlb532BOjMD\nK+cmvD4vQCRcwlNNjHTVUsHBwURFRREVFUVYWBjvvvsuYWFhVXY/nU730JFAhenbty/Dhg1DkiTO\nnTvHSy+9VGhlfEGoCL1e5tdjMQBlW8ul18Gmt+6t4+oJPT+oivAAw5eijVc2Mu/EPPJ0eTS1bspL\nFi/xqmfZkqTCpG/Zgpybi3mXLpi4upa6XdTJu1w4dBuFSmLAf1pjbFp1H/8pt2+x44e5JMZcQ6FU\n0n3k62RZ2WFRp/zJpiA8CWpd0rXknX1V0u/4pX2KfC8mJoaBAwfSpUsXjh49SqdOnXjzzTeZNWsW\nCQkJrFmzBh8fH7Kzs3nvvfeIiIhAo9Ewe/Zshg8fTkxMDKNHjyY7OxswFEFt06YNISEhzJ49GwcH\nByIiIujQoQO//fZbqb59btu2jddffx1JkujSpQtpaWnEx8cXHAX0T0uXLuXq1avMnz8fMBR4PXny\nJIsXL2bEiBHExsaiVquZNGkSb7/9NgCWlpaMHTuWvXv3smTJErp3715sTJYPFDPMzs6ulnPbhCff\n4egkriVm09DGlGdb1i99w4PfQswhsKgLz/8PFMV/iSivHE0Os4/OJjjGUHR4hOsIPvb5mONHjle4\nb1mvJ2XtWgDsXit9ApeWkMP+3wxfgLq/4Fal67guHT3I7mUBaNS52NRvwJCJH9HAtQUhISFVdk9B\nqC1qXdJVXaKjo9m4cSMrVqygU6dOrF27lsOHDxMYGMhXX33F1q1bmTNnDn369GHFihWkpaXh4+ND\nv379qFevHnv27MHU1JSoqChGjRpVcHB0eHg4Fy5cwNHRkW7dunHkyBG6d+9e4uHSt27dolGjv3f9\nODs7c+vWrSKTLn9/f7p27VqQdK1fv54ZMwz1iVasWIGdnR25ubl06tQJf39/7O3tyc7OpnPnzixY\nsAAo3YHXW7Zs4eOPPyYhIYGdO3dW4J+4IBTu16MxALzWpUnpi6FeP2RYPI8Ezy8Hq8IPjq6o6NRo\n3j/wPtfTr2OmMmNW11k81+y5Sus/+9AhNDduonJsiGXv3qVqo9Po2f3zBTRqHc3b16V1L6dKi+dB\nWo2GA//3C2d2GU69cO/ag/5vv4eJuXmV3E8QaqNal3QVNyJVlVxcXGjTpg0ArVq1om/fvkiSRJs2\nbYiJiQFg9+7dBAYG8u233wKgVqu5efMmjo6OTJgwgTNnzqBUKrly5UpBvz4+Pjg7OwPg7e1NTEwM\n3bt3r/TDpevWrUuzZs0IDQ3Fzc2NS5cu0a2b4SzyRYsWsWXLFgBiY2OJiorC3t4epVKJv79/QR+l\nicnPzw8/Pz8OHjzIp59+yt69eyv1OYSn283kHPZdTsBYqSh9MdScFNj8Nsh66PEBNK+az5DtV7fz\nZeiX5GpzaW7TnO98v6NZncotOHp/Ab3tqFFIJUz33xe67arhIGsHU3qP9qySEeiMxAS2f/8Nd6Kv\noFCq8P3XW4baW2K0WxAeUuuSrupiYmJS8FqhUBT8rFAo0Gq1gGEdx6ZNm3B3d3+o7ezZs6lfvz5n\nz55Fr9djampaaL9KpbKgr5JGlZycnIiNjS34fVxcHE5OxX+DHTlyJBs2bMDDwwM/Pz8kSSIkJIS9\ne/dy7NgxzM3N8fX1Ra1WA2BqavrQOq7SjHTd17NnT65du0ZSUhIODqLKtFA5/i/UUCZiSNuGOFia\nlNxAlmHHFMi8Dc6dwPfjSo8pT5fH3ONz2XhlIwBDmg3h0y6fYm5UuSM8+TExZB88hGRiQp0XXihV\nm9hLKZzZG4ukkOj/71aYmFX+R/718JMELV6AOisTK4e6DJ0ynYau7iU3FISnkEi6KtGAAQMICAgg\nICAASZIIDw+nXbt2pKen4+zsjEKh4Ndff0Wn05XYV0mjSsOGDWPx4sWMHDmSsLAwbGxsCqYWPTw8\nCl3A7ufnx5w5cwgPD2fu3LkApKenY2tri7m5OZcuXSI0NLTcMUVHR9O8eXMkSeL06dPk5eVhb29f\n0qMKQqmoNTo2nDSUifhX16ala3RuPURuNZyr+PzySq/HFZsZy9SQqVxMuYiRwoiPO3/MC24vVMkI\nT+q69QBYP/ccKtuSF6SrszX8teoiAB0HN6VBs8o9U1Kv13F0w1rCthjicmnXkUHj38fMqgy7SQXh\nKSOSrkr06aefMnnyZLy8vNDr9bi4uLBjxw7GjRuHv78/q1evZuDAgVhYWFT4XoMHDyYoKAhXV1fM\nzc1ZuXIlAElJSciyXGgbW1tbPD09iYyMxMfHB4CBAweydOlSPD09cXd3p0uXLuWOadOmTaxevRoj\nIyPMzMxYv369mF4QKk1wRDzpuRq8nG1o26hOyQ1Sb8DOezsUB80Fu8qd6tt/cz8zjswgMz8TJ0sn\nFvguoJV9q0q9x336/HzSt24FDFOLJZFlmQNrL5Odlkd9F2s6DmpSqfFkp6USFDCfmxHnkCQF3V5+\nDZ/hL4ijfAShBCLpKoWmTZsSERFR8POqVasKfc/MzIxly5Y90t7NzY1z584V/Dx37lwyMzPx9fXF\n19e34PeLFy8udUySJLFkyZJHfh8aGsr48eOLbLdjx46HfjYxMSE4OLjQa7OyskodD8C0adOYNm1a\nmdoIQmn9ftwwnT6yU+OSL9brYMtYyM8Ez6HQ7rVKi0On1/Hj2R9Zfm45AL6NfPlvt/9iY1K5I0kP\nytyzB11aGiaenpi2Ljmxu3L8LtGnElCZKOn/75YoSrvhoBTioy4TuGAOWakpmNvU4bmJH9G4tVel\n9S8ITzKRdD1hhgwZUt0hCEKli07I4vj1FMyNlQzzdiy5weGFcPOY4ZifIT9AJY24ZuRnMP3gdA7d\nOoRCUjCx3UT+3frfVT6im7bBsF7M9qUXS7xXRlIuB383HPPT4yU3bOpW3tqy8/t289cvP6LTa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rjNs7jrisOBpYNGBJ3yW0sG3xGKKvuPTA7QDYjPg7qcxXazmw5jIAPkNdsKlb9MYBvU7HvpVL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\nPvD/AWkWil1TrKomAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0672c004a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu_array = np.array([-2, -2, -2, 0, 0, 0])\n",
    "sigma_sq_array = np.array([1, 3, 10, 1, 3, 10])\n",
    "\n",
    "pl.figure(figsize=(10, 10))\n",
    "\n",
    "for i in range(len(mu_array)):\n",
    "    mu = mu_array[i]\n",
    "    sigma_sq = sigma_sq_array[i]\n",
    "       \n",
    "    x = np.linspace(-7 + mu, 7 + mu, 500)\n",
    "    \n",
    "    y, y_cdf = normal_pdf_cdf(x, mu, sigma_sq)\n",
    "\n",
    "    pl.subplot(211)\n",
    "    pl.plot(x, y, linewidth=2, label='mean='+str(mu)+', var='+str(sigma_sq))\n",
    "    pl.subplot(212)\n",
    "    pl.plot(x, y_cdf, linewidth=2, label='mean='+str(mu)+', var='+str(sigma_sq))\n",
    "\n",
    "pl.subplot(211)\n",
    "pl.title('PDF of Normal Distribution')\n",
    "pl.legend(loc='upper left')\n",
    "pl.grid()\n",
    "\n",
    "pl.subplot(212)\n",
    "pl.title('CDF of Normal Distribution')\n",
    "pl.legend(loc='upper left')\n",
    "pl.grid()\n",
    "\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10)  Using Pandas\n",
    "\n",
    "This library is typically used for importing and pre-processing data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us use pandas to read a comma-seperated values (.csv) file named \"Advertising.csv\" and assign it to a varable `advertising`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "advertising = pd.read_csv('Datasets/Advertising.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to see the content, just use the name of the variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num</th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>48.9</td>\n",
       "      <td>75.0</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>57.5</td>\n",
       "      <td>32.8</td>\n",
       "      <td>23.5</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>120.2</td>\n",
       "      <td>19.6</td>\n",
       "      <td>11.6</td>\n",
       "      <td>13.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>8.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>199.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>21.2</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>66.1</td>\n",
       "      <td>5.8</td>\n",
       "      <td>24.2</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>214.7</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>23.8</td>\n",
       "      <td>35.1</td>\n",
       "      <td>65.9</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>97.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>204.1</td>\n",
       "      <td>32.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>195.4</td>\n",
       "      <td>47.7</td>\n",
       "      <td>52.9</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>67.8</td>\n",
       "      <td>36.6</td>\n",
       "      <td>114.0</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>281.4</td>\n",
       "      <td>39.6</td>\n",
       "      <td>55.8</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>69.2</td>\n",
       "      <td>20.5</td>\n",
       "      <td>18.3</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>147.3</td>\n",
       "      <td>23.9</td>\n",
       "      <td>19.1</td>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>218.4</td>\n",
       "      <td>27.7</td>\n",
       "      <td>53.4</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>237.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>23.5</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>13.2</td>\n",
       "      <td>15.9</td>\n",
       "      <td>49.6</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>228.3</td>\n",
       "      <td>16.9</td>\n",
       "      <td>26.2</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>62.3</td>\n",
       "      <td>12.6</td>\n",
       "      <td>18.3</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>262.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>19.5</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>142.9</td>\n",
       "      <td>29.3</td>\n",
       "      <td>12.6</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>240.1</td>\n",
       "      <td>16.7</td>\n",
       "      <td>22.9</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>248.8</td>\n",
       "      <td>27.1</td>\n",
       "      <td>22.9</td>\n",
       "      <td>18.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>70.6</td>\n",
       "      <td>16.0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>171</td>\n",
       "      <td>50.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>18.4</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>172</td>\n",
       "      <td>164.5</td>\n",
       "      <td>20.9</td>\n",
       "      <td>47.4</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>173</td>\n",
       "      <td>19.6</td>\n",
       "      <td>20.1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>174</td>\n",
       "      <td>168.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>12.8</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>175</td>\n",
       "      <td>222.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>13.1</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>176</td>\n",
       "      <td>276.9</td>\n",
       "      <td>48.9</td>\n",
       "      <td>41.8</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>177</td>\n",
       "      <td>248.4</td>\n",
       "      <td>30.2</td>\n",
       "      <td>20.3</td>\n",
       "      <td>20.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>178</td>\n",
       "      <td>170.2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>35.2</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>179</td>\n",
       "      <td>276.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>23.7</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>180</td>\n",
       "      <td>165.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>181</td>\n",
       "      <td>156.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>8.3</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>182</td>\n",
       "      <td>218.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>27.4</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>183</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>29.7</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>184</td>\n",
       "      <td>287.6</td>\n",
       "      <td>43.0</td>\n",
       "      <td>71.8</td>\n",
       "      <td>26.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>185</td>\n",
       "      <td>253.8</td>\n",
       "      <td>21.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>186</td>\n",
       "      <td>205.0</td>\n",
       "      <td>45.1</td>\n",
       "      <td>19.6</td>\n",
       "      <td>22.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>187</td>\n",
       "      <td>139.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>26.6</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>188</td>\n",
       "      <td>191.1</td>\n",
       "      <td>28.7</td>\n",
       "      <td>18.2</td>\n",
       "      <td>17.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>189</td>\n",
       "      <td>286.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>190</td>\n",
       "      <td>18.7</td>\n",
       "      <td>12.1</td>\n",
       "      <td>23.4</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>191</td>\n",
       "      <td>39.5</td>\n",
       "      <td>41.1</td>\n",
       "      <td>5.8</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>192</td>\n",
       "      <td>75.5</td>\n",
       "      <td>10.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>193</td>\n",
       "      <td>17.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>31.6</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>194</td>\n",
       "      <td>166.8</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>19.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>195</td>\n",
       "      <td>149.7</td>\n",
       "      <td>35.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Num     TV  Radio  Newspaper  Sales\n",
       "0      1  230.1   37.8       69.2   22.1\n",
       "1      2   44.5   39.3       45.1   10.4\n",
       "2      3   17.2   45.9       69.3    9.3\n",
       "3      4  151.5   41.3       58.5   18.5\n",
       "4      5  180.8   10.8       58.4   12.9\n",
       "5      6    8.7   48.9       75.0    7.2\n",
       "6      7   57.5   32.8       23.5   11.8\n",
       "7      8  120.2   19.6       11.6   13.2\n",
       "8      9    8.6    2.1        1.0    4.8\n",
       "9     10  199.8    2.6       21.2   10.6\n",
       "10    11   66.1    5.8       24.2    8.6\n",
       "11    12  214.7   24.0        4.0   17.4\n",
       "12    13   23.8   35.1       65.9    9.2\n",
       "13    14   97.5    7.6        7.2    9.7\n",
       "14    15  204.1   32.9       46.0   19.0\n",
       "15    16  195.4   47.7       52.9   22.4\n",
       "16    17   67.8   36.6      114.0   12.5\n",
       "17    18  281.4   39.6       55.8   24.4\n",
       "18    19   69.2   20.5       18.3   11.3\n",
       "19    20  147.3   23.9       19.1   14.6\n",
       "20    21  218.4   27.7       53.4   18.0\n",
       "21    22  237.4    5.1       23.5   12.5\n",
       "22    23   13.2   15.9       49.6    5.6\n",
       "23    24  228.3   16.9       26.2   15.5\n",
       "24    25   62.3   12.6       18.3    9.7\n",
       "25    26  262.9    3.5       19.5   12.0\n",
       "26    27  142.9   29.3       12.6   15.0\n",
       "27    28  240.1   16.7       22.9   15.9\n",
       "28    29  248.8   27.1       22.9   18.9\n",
       "29    30   70.6   16.0       40.8   10.5\n",
       "..   ...    ...    ...        ...    ...\n",
       "170  171   50.0   11.6       18.4    8.4\n",
       "171  172  164.5   20.9       47.4   14.5\n",
       "172  173   19.6   20.1       17.0    7.6\n",
       "173  174  168.4    7.1       12.8   11.7\n",
       "174  175  222.4    3.4       13.1   11.5\n",
       "175  176  276.9   48.9       41.8   27.0\n",
       "176  177  248.4   30.2       20.3   20.2\n",
       "177  178  170.2    7.8       35.2   11.7\n",
       "178  179  276.7    2.3       23.7   11.8\n",
       "179  180  165.6   10.0       17.6   12.6\n",
       "180  181  156.6    2.6        8.3   10.5\n",
       "181  182  218.5    5.4       27.4   12.2\n",
       "182  183   56.2    5.7       29.7    8.7\n",
       "183  184  287.6   43.0       71.8   26.2\n",
       "184  185  253.8   21.3       30.0   17.6\n",
       "185  186  205.0   45.1       19.6   22.6\n",
       "186  187  139.5    2.1       26.6   10.3\n",
       "187  188  191.1   28.7       18.2   17.3\n",
       "188  189  286.0   13.9        3.7   15.9\n",
       "189  190   18.7   12.1       23.4    6.7\n",
       "190  191   39.5   41.1        5.8   10.8\n",
       "191  192   75.5   10.8        6.0    9.9\n",
       "192  193   17.2    4.1       31.6    5.9\n",
       "193  194  166.8   42.0        3.6   19.6\n",
       "194  195  149.7   35.6        6.0   17.3\n",
       "195  196   38.2    3.7       13.8    7.6\n",
       "196  197   94.2    4.9        8.1    9.7\n",
       "197  198  177.0    9.3        6.4   12.8\n",
       "198  199  283.6   42.0       66.2   25.5\n",
       "199  200  232.1    8.6        8.7   13.4\n",
       "\n",
       "[200 rows x 5 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many other such methods. Type \"`advertising.`\" (with dot, without quotation marks) and then press the Tab key to see the full list of methods.\n",
    "\n",
    "* Example: `advertising.head(N)` prints the first N rows. \n",
    "    ```python\n",
    "    advertising.head(3)\n",
    "    ```\n",
    " \n",
    "* Example: `advertising.tail(N)` prints the last N rows. \n",
    "\n",
    "* Example: `advertising.columns` prints all column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num</th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Num     TV  Radio  Newspaper  Sales\n",
       "0    1  230.1   37.8       69.2   22.1\n",
       "1    2   44.5   39.3       45.1   10.4\n",
       "2    3   17.2   45.9       69.3    9.3\n",
       "3    4  151.5   41.3       58.5   18.5\n",
       "4    5  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.head() #shows all columns and the first five rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num</th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Num     TV  Radio  Newspaper  Sales\n",
       "195  196   38.2    3.7       13.8    7.6\n",
       "196  197   94.2    4.9        8.1    9.7\n",
       "197  198  177.0    9.3        6.4   12.8\n",
       "198  199  283.6   42.0       66.2   25.5\n",
       "199  200  232.1    8.6        8.7   13.4"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.tail() #shows all columns and the last five rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num</th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Num     TV  Radio  Newspaper  Sales\n",
       "0    1  230.1   37.8       69.2   22.1\n",
       "1    2   44.5   39.3       45.1   10.4\n",
       "2    3   17.2   45.9       69.3    9.3"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.head(3) #shows all columns and a specific number of rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise** Specify the index column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Num</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio  Newspaper  Sales\n",
       "Num                                \n",
       "1    230.1   37.8       69.2   22.1\n",
       "2     44.5   39.3       45.1   10.4\n",
       "3     17.2   45.9       69.3    9.3\n",
       "4    151.5   41.3       58.5   18.5\n",
       "5    180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising = pd.read_csv('Datasets/Advertising.csv', index_col='Num')\n",
    "advertising.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise** Import the columns 0 and 2, do not import any other column. \n",
    "\n",
    "Hint:  `usecols=[0, 2]` as an argument of read_csv()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Radio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Num</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Radio\n",
       "Num       \n",
       "1     37.8\n",
       "2     39.3\n",
       "3     45.9\n",
       "4     41.3\n",
       "5     10.8"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Answer\n",
    "advertising_few_cols = pd.read_csv('Datasets/Advertising.csv', usecols=[0, 2], index_col='Num')\n",
    "advertising_few_cols.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `.info()` to see information of our dataset. What is float64?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 200 entries, 1 to 200\n",
      "Data columns (total 4 columns):\n",
      "TV           200 non-null float64\n",
      "Radio        200 non-null float64\n",
      "Newspaper    200 non-null float64\n",
      "Sales        200 non-null float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 7.8 KB\n"
     ]
    }
   ],
   "source": [
    "advertising.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are multiple ways to access columns,\n",
    "1. `varaiable_name.column_name`\n",
    "\n",
    "    E.g.\n",
    "    ```python\n",
    "    advertising.TV\n",
    "    ```\n",
    "\n",
    "2. `varaiable_name[\"column_name\"]`\n",
    "\n",
    "    E.g.\n",
    "    ```python\n",
    "    advertising[\"TV\"]\n",
    "    advertising[[\"TV\", \"Newspaper\"]]\n",
    "    ```\n",
    "    \n",
    "3. `using row/column numbers`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Num\n",
       "1    230.1\n",
       "2     44.5\n",
       "3     17.2\n",
       "4    151.5\n",
       "5    180.8\n",
       "Name: TV, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising[\"TV\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Newspaper</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Num</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>230.1</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.5</td>\n",
       "      <td>45.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.2</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.5</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>180.8</td>\n",
       "      <td>58.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Newspaper\n",
       "Num                  \n",
       "1    230.1       69.2\n",
       "2     44.5       45.1\n",
       "3     17.2       69.3\n",
       "4    151.5       58.5\n",
       "5    180.8       58.4"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising[[\"TV\", \"Newspaper\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Num</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio\n",
       "Num              \n",
       "1    230.1   37.8\n",
       "2     44.5   39.3\n",
       "3     17.2   45.9\n",
       "4    151.5   41.3\n",
       "5    180.8   10.8"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising[[0, 1]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use *loc* and *iloc*. The former uses locations given by *Num* column whereas later uses actual indexing that starts from 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "230.09999999999999"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.iloc[0,0] #row number, column number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TV           17.2\n",
       "Radio        45.9\n",
       "Newspaper    69.3\n",
       "Sales         9.3\n",
       "Name: 3, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.iloc[2,:] #row number, column number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Num</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio  Newspaper  Sales\n",
       "Num                                \n",
       "3     17.2   45.9       69.3    9.3\n",
       "4    151.5   41.3       58.5   18.5\n",
       "5    180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.iloc[2:5,:] #row number, column number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TV           230.1\n",
       "Radio         37.8\n",
       "Newspaper     69.2\n",
       "Sales         22.1\n",
       "Name: 1, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.loc[1,:] #row number, column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Newspaper</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Num</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Newspaper\n",
       "Num           \n",
       "1         69.2\n",
       "2         45.1\n",
       "3         69.3"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.loc[1:3,[\"Newspaper\"]] #row number, column names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other uses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 230.1,   44.5,   17.2,  151.5,  180.8,    8.7,   57.5,  120.2,\n",
       "          8.6,  199.8,   66.1,  214.7,   23.8,   97.5,  204.1,  195.4,\n",
       "         67.8,  281.4,   69.2,  147.3,  218.4,  237.4,   13.2,  228.3,\n",
       "         62.3,  262.9,  142.9,  240.1,  248.8,   70.6,  292.9,  112.9,\n",
       "         97.2,  265.6,   95.7,  290.7,  266.9,   74.7,   43.1,  228. ,\n",
       "        202.5,  177. ,  293.6,  206.9,   25.1,  175.1,   89.7,  239.9,\n",
       "        227.2,   66.9,  100.4,  216.4,  182.6,  262.7,  198.9,    7.3,\n",
       "        136.2,  210.8,  210.7,   53.5,  261.3,  239.3,  102.7,  131.1,\n",
       "         69. ,   31.5,  139.3,  216.8,  199.1,  109.8,   26.8,  129.4,\n",
       "        213.4,   16.9,   27.5,  120.5,    5.4,  116. ,   76.4,  239.8,\n",
       "         75.3,   68.4,  213.5,  193.2,   76.3,  110.7,   88.3,  134.3,\n",
       "         28.6,  217.7,  250.9,  107.4,  163.3,  197.6,  184.9,  289.7,\n",
       "        135.2,  222.4,  296.4,  280.2,  187.9,  238.2,  137.9,   25. ,\n",
       "         90.4,   13.1,  255.4,  225.8,  241.7,  175.7,  209.6,   78.2,\n",
       "         75.1,  139.2,  125.7,   19.4,  141.3,   18.8,  224. ,  123.1,\n",
       "        229.5,   87.2,    7.8,   80.2,  220.3,   59.6,    0.7,  265.2,\n",
       "          8.4,  219.8,   36.9,   48.3,   25.6,  273.7,   43. ,   73.4,\n",
       "        193.7,  220.5,  104.6,   96.2,  140.3,  243.2,   38. ,   44.7,\n",
       "        280.7,  121. ,  171.3,  187.8,    4.1,   93.9,  149.8,   11.7,\n",
       "        131.7,  172.5,   85.7,  188.4,  163.5,  117.2,  234.5,   17.9,\n",
       "        206.8,  215.4,  284.3,   50. ,  164.5,   19.6,  168.4,  276.9,\n",
       "        248.4,  170.2,  276.7,  165.6,  156.6,  218.5,   56.2,  287.6,\n",
       "        253.8,  205. ,  139.5,  191.1,  286. ,   18.7,   39.5,   75.5,\n",
       "        166.8,  149.7,   38.2,   94.2,  283.6,  232.1])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising[\"TV\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TV           147.0425\n",
       "Radio         23.2640\n",
       "Newspaper     30.5540\n",
       "Sales         14.0225\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>147.042500</td>\n",
       "      <td>23.264000</td>\n",
       "      <td>30.554000</td>\n",
       "      <td>14.022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>85.854236</td>\n",
       "      <td>14.846809</td>\n",
       "      <td>21.778621</td>\n",
       "      <td>5.217457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.375000</td>\n",
       "      <td>9.975000</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>10.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>149.750000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>12.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>218.825000</td>\n",
       "      <td>36.525000</td>\n",
       "      <td>45.100000</td>\n",
       "      <td>17.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>296.400000</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TV       Radio   Newspaper       Sales\n",
       "count  200.000000  200.000000  200.000000  200.000000\n",
       "mean   147.042500   23.264000   30.554000   14.022500\n",
       "std     85.854236   14.846809   21.778621    5.217457\n",
       "min      0.700000    0.000000    0.300000    1.600000\n",
       "25%     74.375000    9.975000   12.750000   10.375000\n",
       "50%    149.750000   22.900000   25.750000   12.900000\n",
       "75%    218.825000   36.525000   45.100000   17.400000\n",
       "max    296.400000   49.600000  114.000000   27.000000"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TV</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054809</td>\n",
       "      <td>0.056648</td>\n",
       "      <td>0.782224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radio</th>\n",
       "      <td>0.054809</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.354104</td>\n",
       "      <td>0.576223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Newspaper</th>\n",
       "      <td>0.056648</td>\n",
       "      <td>0.354104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.228299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>0.782224</td>\n",
       "      <td>0.576223</td>\n",
       "      <td>0.228299</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 TV     Radio  Newspaper     Sales\n",
       "TV         1.000000  0.054809   0.056648  0.782224\n",
       "Radio      0.054809  1.000000   0.354104  0.576223\n",
       "Newspaper  0.056648  0.354104   1.000000  0.228299\n",
       "Sales      0.782224  0.576223   0.228299  1.000000"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.corr()"
   ]
  }
 ],
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