georgehc/recitation_1 - Basic Python Review.ipynb Secret

## recitation_1 - Basic Python Review.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 95-865 Recitation 1 - Basic Python Review\n",
    "\n",
    "*Contact TA: Xinyu Yao (xinyuyao at andrew.cmu.edu)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Setup the environment\n",
    "\n",
    "Download and install [Anaconda](https://www.anaconda.com/download/), [spaCy](https://spacy.io/) and run a Jupyter notebook.\n",
    "   * The tutorial slides included in HW1.zip (95-865 Anaconda, Jupyter, and spaCy setup tutorial.pdf) will guide you through this process (tested on Linux and Mac OSX).\n",
    "   * The following [video](https://www.youtube.com/watch?v=xKg2anRkg-M&index=1&list=PLcHK-U_3XaQvdjiU1d9r3dn_1xLyablE4) guides you through installing Anaconda and running Jupyer on Windows 10.\n",
    "      * Note: **DO** check the \"Add Anaconda to PATH\" option during the installation!\n",
    "      * Installation on Windows 10 requires Visual Studio 2017.\n",
    "   * The following [video](https://www.youtube.com/watch?v=hY_0YUKVNMU&index=3&list=PLcHK-U_3XaQvdjiU1d9r3dn_1xLyablE4) guides you through installing spaCy on Windows 10.\n",
    "      * Note: Run `conda config --add channels conda-forge` prior to installing the package.\n",
    "   \n",
    "If you already have a reliable way to install Python packages and run Jupyter notebooks, feel free to continue using that instead."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Python basics\n",
    "\n",
    "The subproblems below use the following two lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [13, 1, 55, 34, 2, 3, 89, 5, 8, 21]\n",
    "b = [17,55, 34, 2, 3, 8, 9, 1, 4, 5, 6, 7, 20, 21, 36]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List intersection\n",
    "\n",
    "Find the elements that are common between the lists and store it in a new list `c`. Print list `c`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 34, 3, 2, 5, 8, 21, 55]\n"
     ]
    }
   ],
   "source": [
    "### fill in here: one line\n",
    "\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List filtering\n",
    "\n",
    "Using a [list comprehension](https://www.pythonforbeginners.com/basics/list-comprehensions-in-python), construct a new list `d` containing only the **even** elements of `c`. Print list `d`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[34, 2, 8]\n"
     ]
    }
   ],
   "source": [
    "### fill in here: one line\n",
    "\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[34, 2, 8]\n"
     ]
    }
   ],
   "source": [
    "d = []\n",
    "for item in c:\n",
    "    if item % 2 == 0:\n",
    "        d.append(item)\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fancy list operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reverse the list `d` and print it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[34, 2, 8]\n"
     ]
    }
   ],
   "source": [
    "f = list(d)\n",
    "### fill in here: one line\n",
    "\n",
    "print(d)\n",
    "# print(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort the list `d` in increasing order and print it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 8, 34]\n"
     ]
    }
   ],
   "source": [
    "### fill in here: one line\n",
    "\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort the list `d` in decreasing order and print it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[34, 8, 2]\n"
     ]
    }
   ],
   "source": [
    "### fill in here: one line\n",
    "\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User-defined Functions\n",
    "\n",
    "Write a function that takes as input a list and returns the maximum element of the list, **without** using a call to the `max()` function built into Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n"
     ]
    }
   ],
   "source": [
    "def myMax(ls):\n",
    "    ls.sort(reverse = True)\n",
    "    return ls[0]\n",
    "\n",
    "print(myMax([1,2,19]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Loops to Evaluate Expressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use a for loop to compute $\\sum_{n=1}^{100} 2^{-n}$.  Print your result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 0\n",
    "\n",
    "for n in range(1,101):\n",
    "    s = s + 2**(-n)\n",
    "\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Scientific computing with numpy\n",
    "\n",
    "Install the `numpy` Python package before starting this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Numpy arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct a 4x4 \"identity matrix\" `I`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "### fill in here: one line\n",
    "\n",
    "print(I)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct a 4x4 \"zero matrix\" `O` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "### fill in here: one line\n",
    "\n",
    "print(O)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct a 4x4 \"all-ones\" matrix `X` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "### fill in here: one line\n",
    "\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct a 4x4 \"all-3s\" matrix `Y` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3. 3. 3. 3.]\n",
      " [3. 3. 3. 3.]\n",
      " [3. 3. 3. 3.]\n",
      " [3. 3. 3. 3.]]\n"
     ]
    }
   ],
   "source": [
    "### fill in here: one line\n",
    "\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the matrix `Z` defined below and use numpy functions to answer the following questions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  4]\n",
      " [ 2  3  5]\n",
      " [ 2  7 10]]\n"
     ]
    }
   ],
   "source": [
    "Z = np.array([[1,2,4],[2,3,5],[2,7,10]])\n",
    "\n",
    "print(Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the maximum element of `Z`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the maximum of each row of `Z`. Note that for this question and other questions below, your code should work even if Z is a different 2D array of numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  5, 10])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### fill in here: one line\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the maximum of each column of `Z`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  7, 10])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### fill in here: one line\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  4]\n",
      " [ 2  3  5]\n",
      " [ 2  7 10]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 2,  7, 10])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z = np.array([[1,2,4],[2,3,5],[2,7,10]])\n",
    "print(Z)\n",
    "### fill in here: one line: lambda\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print a \"row-normalized\" version of `Z` so that each row sums to 1 (the row-normalized version of `Z` has the same number of rows and columns as `Z`). \n",
    "\n",
    "For example, if a row is `[1,2,4]`, then the version of this row that sums to 1 is `[1/7,2/7,4/7]` (although when printing this out, you would get a decimal representation like `[0.14285714,0.28571429,0.57142857]`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  4]\n",
      " [ 2  3  5]\n",
      " [ 2  7 10]]\n",
      "Before reshaping: [ 7 10 19]\n",
      "After reshaping:\n",
      " [[ 7]\n",
      " [10]\n",
      " [19]]\n",
      "(3, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.14285714, 0.28571429, 0.57142857],\n",
       "       [0.2       , 0.3       , 0.5       ],\n",
       "       [0.10526316, 0.36842105, 0.52631579]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(Z)\n",
    "\n",
    "row_sum = Z.sum(axis=1)\n",
    "print(\"Before reshaping:\", row_sum)\n",
    "row_sum = row_sum.reshape(3,1)\n",
    "print(\"After reshaping:\\n\", row_sum)\n",
    "print(row_sum.shape)\n",
    "Z / row_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert `Z` into a matrix with a single row (a row vector) and print it. There are multiple two ways to do this, any of them are fine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  4,  2,  3,  5,  2,  7, 10])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### fill in here: one line: flatten\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  4,  2,  3,  5,  2,  7, 10])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### fill in here: one line: -1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  4,  2,  3,  5,  2,  7, 10])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### fill in here: one line\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return the first 3 elements from `arr`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(0,11)\n",
    "arr[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return the last 3 elements from `arr`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8,  9, 10])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[-3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return the elements that are larger than 5 but smaller than 10 in `arr`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False False False False False  True  True  True  True  True]\n",
      "[ True  True  True  True  True  True  True  True  True  True False]\n",
      "[False False False False False False  True  True  True  True False]\n"
     ]
    }
   ],
   "source": [
    "print(arr > 5)\n",
    "print(arr < 10)\n",
    "print((arr > 5) & (arr < 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the first 2 rows and first 2 columns of `mat` and name it `mat_slice`. Change `mat_slice[0][0]` to 1000. See what happened to the original matrix `mat`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original matrix\n",
      "[[11 12 13]\n",
      " [21 22 23]\n",
      " [31 32 33]]\n",
      "\n",
      "Sliced matrix\n",
      "[[11 12]\n",
      " [21 22]]\n",
      "\n",
      "Change the sliced matrix\n",
      "[[1000   12]\n",
      " [  21   22]]\n",
      "\n",
      "The original matrix now\n",
      "[[1000   12   13]\n",
      " [  21   22   23]\n",
      " [  31   32   33]]\n"
     ]
    }
   ],
   "source": [
    "mat = np.array([[11,12,13],[21,22,23],[31,32,33]])\n",
    "print(\"Original matrix\")\n",
    "print(mat)\n",
    "#### Shallow copy\n",
    "mat_slice = mat[:2,:2]\n",
    "print (\"\\nSliced matrix\")\n",
    "print(mat_slice)\n",
    "print (\"\\nChange the sliced matrix\")\n",
    "mat_slice[0][0] = 1000\n",
    "print (mat_slice)\n",
    "print(\"\\nThe original matrix now\")\n",
    "print(mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original matrix\n",
      "[[11 12 13]\n",
      " [21 22 23]\n",
      " [31 32 33]]\n",
      "\n",
      "Sliced matrix\n",
      "[[11 12]\n",
      " [21 22]]\n",
      "\n",
      "Change the sliced matrix\n",
      "[[1000   12]\n",
      " [  21   22]]\n",
      "\n",
      "The original matrix now\n",
      "[[11 12 13]\n",
      " [21 22 23]\n",
      " [31 32 33]]\n"
     ]
    }
   ],
   "source": [
    "mat = np.array([[11,12,13],[21,22,23],[31,32,33]])\n",
    "print(\"Original matrix\")\n",
    "print(mat)\n",
    "######## \n",
    "mat_slice = np.array(mat[:2,:2])\n",
    "print (\"\\nSliced matrix\")\n",
    "print(mat_slice)\n",
    "print (\"\\nChange the sliced matrix\")\n",
    "mat_slice[0,0] = 1000\n",
    "print (mat_slice)\n",
    "print(\"\\nThe original matrix now\")\n",
    "print(mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Plotting with matplotlib\n",
    "\n",
    "Install the `matplotlib` Python package before starting this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'  # if you use a Mac with Retina display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line lot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(10)\n",
    "y = x\n",
    "z = 2*x\n",
    "m = x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 368
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### fill in here: five lines\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.normal(size=1000)\n",
    "y = np.random.normal(size=1000)\n",
    "# y = np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OcJnYKJ6IyD0jj4xg4OqBimkYhWotI18dcbhl9ijsE2CGm7so0zmJnQmMPjqqdF+OtJKbeN4JLgYEROR5Zqu1qKZreE1sW8z4WdqlXZRpvkLgmj6UVro/R1rJbTzvBBMDAiLyPJVqLX5Q2Ceg5pma+wR4guHAtQKOtJIX8LwTTAwIiMjTrFRr8YOuLV1Yv2M9on3lp/GjfVGs37Gem1p5gKnAtRRHWslDeN4JHiYrEpGnsVoL9wmoByqB6xyOtJIH8bwTLAwIiMjTWK3lHO4T4F2qgWt4VRi9f89ggLyL551gYMoQEXka62JTPVANQDs/1MlggIhcx4CAiDyNdbGpHjBwJaJ6xoCAiDyNdbGpHjBwJaJ6xoCAiDyPdbHJ6xi4ElE9Y0BARJ7HuthUD1QD19T+FIa3DyN+dxzD24d9UzKXiOoHkxeJqC50belCOBbGodsPYXLP5IK/R/uiiG2NMRgg1xQC15qbk+UDVwDo7+svW640elUUsW38PBORMzhDQER1IbEzgfid8bLBQOsVrQwGyBOMbugECQxcPVBx74LkU0kMXD2Aka+O2NlcIiIAnCEgojow8shI1VHXiT0TGLh6AL0P9XLnTHJdrQ2dEjsTtWcRACALDN44iPDKMINdIrIVAwIi8jR2nqheVdrQKX5nvPbnuSALxO+K8zNNRLZiyhAReZpK54nIq1L7UxXThCpJPpnkQmMishUDAiLyLNXO08gjzLsmb0rsTDh6PyIiI5gyRESepdoJGrxxEBDgegLynNnxWUfv56RKayaIyPsYEBCRZyl3giTXE5A3LWpTu+yq3s8JhQpgLJ9KVL+YMkREnmWpE8T1BORBqh1jr3aoRx4ZYflUIh9gQEBEnmW1E8TFmOQ1kbURRK8qv0dBJdG+qCdTb8xWAOM6CCLvYkBARJ6l0nkqxU4IeU1sW8z41bcBiG2N2dgadawARuQf3k1KJCJCrvM0cPWA8Y5HCTcWYwZxcaWV1xy049W+uR29D/bWHl1vAHof6vVkupCV8ql+fm+J6hUDAiLytLnO042DgDR/fycXYwZxcaWV1xzE41XQtaUL4VgY8bviSD5Z5vX3RRHb6t3Xb6V8KgMCIu9hQEBEnte1JVc+dPCGQdP3dapDNfLISNUR38Liyt6Hen1TDtXKaw7i8SrVvrkd7Zvb63KGxM/lU4mCKHABgRCiA8D/BPA2AJcCuBDANIDnAPwDgH+QUiomJxCRXbq2dGF4+zBSzxpfJBy5LOJI2orZxZVeL4dq5FhYec1+O15WRdYa/5x6hR/LpxIFWRC/me8B8BUAIwCeAPAigAsAvBPAwwDeIoR4j5RSITmBiGxlw7dSR9qKyuJKJzq4ZoMcM8fCymv26vEi4/xWPpUo6IIYEBwA8A4APyyeCRBC/AWA/wbwLuSCg++50zwiKie1P4XUc+ZKiKaeTVVdxKgjbSWbznpucaVKkGPmWLRd2ab8mguPpXLfehtF97NCBTAz76VXy6cSUQDLjkopfyql/LfStCAp5SiAv8v/c5PjDSOiqqwsYqz0ex011DPjGa3tskployizx+LoV44qtS2xM6H9fST3+KV8KhEFMCCoYSb/k6ueiDxG9yJGXTXUZVYtj8mOxZWqQY7ZYzH2wzGl9s2Oz3Ixqo8UKoDV7El4uHwqEeUIpsrnCCEWAegHsA7A70opf1Tj9nsr/Onlq1evXvLggw/qbmJdmJiYAAC0tra63JJgCcJxnzkxg/Rw2vT9wt1hNC5vnPe7bDqL1K/M72AceWUEDeFzvZ+JiQnIWQnxgtDSLqvOHDiDzKTxGYtQSwjhi8NKx0JFuDsMAJbfxyB83r2m2jHPTGQwNTJV9rMXagmhuasZodaQ7W30I37W3VFPx/2mm27CwYMHn5FSXm7lcYK4hqCSe5ALBh6vFQwQkfNCbWodinL3U03zyYxn5gUEACBC5oOBSu2yIpvOmgoGACAzmcHM2EztG2qSnckqbzCn+3iRPqHWEJa0Lsl9BsczkFkJ0SAQagst+L4QkTcxIAAghLgZwG0AfgPg/UbuUykSE0LsbW1tfdWmTZv0NbCO7Nq1CwAQ1NfvlqAc9/7P95texLjh4xsW/D5+dxzxrXHTzx+7K4bYdbG5fxeOe/Qx84sry7XLiuHtwxi6bcj0/Za+dSlOPX7K9P2aY82Yik+Zvh8ANKPZ1O1Lj1dQPu9e4pdjXm97PvjluNebejruumYxAh8QCCH+FMB9AH4FYLOU0vyVkYgcEdsWw8DVA8ZGmassYtRdQ11Xu6xwOse+460dOPZ3x5RH/A3jYlTSIMi7YhMZEei5PCHEnwH4MoB9AN6QrzRERB6laxGj7hrqXlhcqRrkRC5TGx298KMXGnvNVgguRnVDan8Kw9uHEb87ntsMcL8za0zsolJ5iyhoAjtDIIT4c+TWDfw/AG+WUp50uUlEZEDXli6EY2HE74oj+WSZ0b6+KGJbq4/22VFDXUe7rFB93M7rOzH+83GlYxFZG6n6mq2KXBqpuP8D6VdtFP3M359Bc5e5VC8v4K7YRMYEMiAQQmwFcCeAvQCuZpoQUX1p39yO9s3tlvKBTaX5iHNpK8XPObNmZt5iVx3tUmUlyLGS8lTuNWdTWbx4z4vmX0SJWhvLkT61NqbLTGZw5uAZjHx1pK6CNO6KTWRM4AICIcQHkQsGMgCeBnCzEAuqhMSllI863DQiMqkwSq2ikOZjaPRQAkO3DgEi10ktSN+bK5/Z//n+eTnIVtplRWxbDANvHgCMVJMu6tgbPhZVUp6KX/Pw9mHzja8gsTPBgMBmhkfRUX4U3asLdVP7U57aFdurx4kICGBAAOBl+Z8hAH9W4TZPAnjUkdYQkWtqpfkUSz1XOY+6kIPc+1Cvq6On6bjB+v5lcvN1pjzpXODMDcnspzqK7vWFulZ2xdbZUff6cSICAhgQSCnvAHCHy80gIo8oXIgHnjaYMlNJFhjcMoiZsRlc/MmL9TTOhLlRXoN7TYZXhhf8TlfKk+oCZ7sfixZSHUWP3xlH/HPxit8ZLwTJXtgVu1YqlheOExEQwICAiKiUqRHSGp7/1PNo7Gh0/OJu6jXI6rnSVlOedI52cuTUXqqj6PE74rWDTwcW6lYLXnWXFzaLC5qpnjAgIKJAUxkhrWXwBmcv7l7LlVZZ4FxOrcpOZJ3yaLjBmSi7FuramYajq61c0Ez1JND7EBARqY6QVpUfgTdCR813K7nSdolti1m7wnh0QzK/1eh3IiWrEHzqYnRfgfH/GkfL5S2mHltXEGolSCdyA2cIiAJIR7ULv1TMsGvRaq0ReJ0jnG7lSlf7DJiq4lTKxg3cVPl1YahTbda1UNdUGs6WQXMPrjEI9cqCZiKjGBAQBYiOTo3fOkZ2jpBWurjrXmjodK600c+AmSpOc/e3eQM3FW4sDHUq4NaV3lWLrsBb53qfeTQHoV5Y0ExkBgMCooDQ0anxY8UMOzue5S7udiw0dDJX2uxnoFrlIgCemGWq1vl2emGoGwG3qY3pFOkIvO1Y7wPYE4S6vaCZyCx+8ogCQEenxq8VM+wcIR19dBTR10bnHQezCw0P3HIAV+67surNrOxSbIaVz0ClykVupkcY6Xw7uTDUrYDbUnqXieewyo41L61XtGLDrg3aH9ftBc1EZnFRMVEAqHRq7HgMr7K8ALaC9KE0Bq4ewMhXRwCojXCe3X8WT7c+jRf/5sWqtzP1GhRzpXV8BryyKNfQwtQ3V/57JaoLQ80GW7o7x11burB+x3pE+6JaHxfQt1DXjnSaiT0TtnwGC0G6GayqRW7iDAGRz+koSem1spa62TpCWjRartrxyExm8PynnseZX53By//h5WVvY/g1KOZKW/0MeGntieHOt9HSmmUe3+zn3gslKsuld02PTuPY/cfUH1ToW6hrVzqNXQt5TaViebSqFgUHZwiIfE5HSUovlrXUrdYIafhlC3f2NSzfgbM6wjn66GjVmYJaryHaF8X6HeuV0k2sfAaMlokszKTYzbaFqXlm32evlaiMrI2g++ZuxD4TQ+o568+RPpzW0Cr70mnsWshbCNJr9rQ8WFWLgoczBEQ+p6PaRVAqZlRbABtZG8nleN84qDRynHwyiehrradjHP6rw7j4kxdX/Hut16BK9b2c7J/E6NdHPbP2xK6FqcXMjmR7tUSllmMl9b2vdq33sXMhb61KW16sqkXBxICAyOd0VLsIWsWMSgtgCxf3wY8MIj1kftRTquagFMkkMzj52Eks7llctcNf6TWoUn0vTz992vVUmGJOzFqZbb9XA25tx0rj+2pHRSS7O+N2BelEOtXn1ZqIDNNR7YIVM85p39yOzg92Ir41bvq+oUhIywjnb274DWZPLuwMmsnFN9s5UX0v04fMBU52rz2xuxOtsjDUqwG3zmOl633Vvd7HyYW8uoN0Ip24hoDI53RUu2DFjPmsdOB0VDQqFwwAxnLxEzsT6O/rx+51uzF0yxDiW+MYumUIu9ftRn9ff8VRYZXPQLhHbd2FnaP4tnaiFReGhiIhpaezO+DWfax0va/aKiJxIS/RHAYERAGgoySlE2Ut64WVGRPDCw1VZYHBGwbx6z/69YKynlYX93a8vcNUUxZfstjU7QvsHMVX7kTbtDB0bu8Bk8I9Ybz0Ly/ZWrpVd8Ch831t39yODbs2YOO+jei5rwexu2Loua8HG/dtRO/DXMhLZBZThojqiJE0j3K30VGS0u6ylvWkMFo+gQnD9wmvOjdaXliLcOCWAzi7/6z+Bkrg+KPHcfzR4wByqUQdb+/A87c/b2lx79i/j5lqxtlDaq/NzlF81U3cYltj2heGGi5/WkZ6KD0vbc2O0q26F/Hqel9rnQcjayNcyAvzaYEUbAwIiOpArRrumU9kAAD9ff1V67yv37He0kWSFTPOiW2LYXhg2PDt04fS2L1u97yO25X7rsTTrU8jM5mxsaW5kX9Tnboyi0BVKs6YXT9QMDUyZes6ApX68DoWhpbe9/g3jmtbHGvXLsZzx0oDq+cFM3tZBHkhr5f2/KD6wYCAyOPmUgoqdBySTyVx5pozAIDQU+VzkYs7Cxt2bbB0kQzyhbZY++Z2hE+HTddYL+24rdy2Es9/6nmbWqmudBGoav53eFXYdGBw5J4jOHLPEds6L1Zmu1QWhlbroGlVYXbH6ve998Fe7EnusdQ0q2uKjJwHywVEKu9XPZ/bVI8TEQMCIg+zklKwQElnweoFjhUzgMaORjQ0NSDaFy07Y1JR0Xtx8ScvRmp/Cse/dty+hioqrnOvmv8d/e0o0i+klT7DdnZenJrtqtVB065odkfXSHHXli4s+bclmBqZUmuTxTVFhs+DFveyqPeRdaeOE/kTAwIiD9O+o2oWGPr4EDYObNT4oMEWag3Nzbo8d81zxkfDizpur3j0FZgemUZih7d2di4OAlTzv1s3tOK815+n3im2sfNi92yX1oDehOSTScTvjCP+ubi2keJQawhLWpdg476Nc8fqzP4zOPHdE7avKTJ1HlTc88API+tOHCfyLwYERB5l146qqWdTiN8V93UVILdYqbnf8bYOzwUExUGAlcpKtRZ51mRz58Wu2S7tAb2Z574jXntHbYVgq/RYdd3QVXOWpamzCcPbh5XXXZg9D5rd88API+tOHCfyNwYERB5lZy32+GfjiL4u6rmLWj1Tfb8KaTlefC9Kc+dVqvMUOhvFo/HHv3kcL97zoqm2JJ9M4tCnDyEUCWFmzQxCbWq1+51iV0BvmNFNsS0GW9VmWaZHpy2n4Fj9XhmhMrLe1NnkqXUGThwn8jcGBEQeZeuOqpLTxbqpvl+F++ku8WhVuUWgKtV5SkXWRtDU1aTUpiP3HAEApO/NzcTs+cwetL2mDU2dTZ7olBUkdiYw+Mfm9xZwi46R4tKZA10pOFa/V7WojqzvXrd7we9V1xnoSFmz+ziR/zEgIPIoW3dUBaeLdbOye3GBqQ63nUT5zryuvSh0dUIm905icu/kvN+5vfjT8UXEmugcKdaZgqPje1WNzplYs+sMjCxihsGJMLuPE/kfdyom8ignOjR2piVZkdqfwvD2YcTvjtu6E6tOVlIuiv/f1l2MDVrxpysqvp6uLV1Yv2M9on3Rsn+P9kWxfsdBei42AAAgAElEQVT6qh0iOzshtXZbtpNbi4h10DlSrJKCU4mO71U12kfI80FOrXOr0V3DZ8ZmDD2t3ceJ/I+hIZFHOZFC4rXp4nou+2c1x77AaDnMQg7zZP8kRr82ajxn3IAlq5dU/bvV6jy2v4cuLf60YxFxtC+KjrcZ3GUaAASUPgs6dxHWubhV1/eqEluC0xrrMszMoKQPp9HQVHuEwO7jRP7HgIDIw+xOIfHSdLEfyv7pyLEHjHe4C/8ffX1U68i00UBRtTqPI+slHC6rqHsR8bJ3LsPL7nzZ3PFtXNpoKFUrdkcM8W1x08+n6zjZsbhV1/eqHLs+H9WCHLOBo9H9H+w8TuR/TBki8jC7U0i8MtpuNufYq6lOht8vg7XZI2sj6L65G7HPxNB9c3fFDlOtNB6zigNFu9K3Yttitl+BCp0yJ+j+TBYHA4DxVK3Y1hiiV5n7HOgcKbZjcavu71WxQnBqh3KfCZXAMTOZMfQ5tvM4kf95Z3iQiMoykkKSWZ3B1BFzu4h6abrYTxvqOLUDbqnSWYXTT5zGycdOKqWP6NzlttpzGFqgbNHRrxzFmi+vse8J8nSm31X6bpa+x2cOnsH0sWk0X9iMxT2L0dSZq97k5kixXYtb7fxe2TUTW+4zYXd5ULfOP1T/GBAQ1YFaKSS7du1C80XNuZGhOpsu9uOGOnbvgFtNIY2n++ZuJHYmsO9d+5BJZgzfP9oXxfgvxx1J36rVedFh7IdjwJdteeh5tKXfGfhuTo9O46XvvVT2exNeFUbnBzqx/LrlOPGdE9UDQhtGiu1c3GrX98qu4LTcZ8KJ8qBunn+ofjEgIKoj1XK2Q60h5ZKQui8cZh7Pzxvq2LUDrlHtm9ux7nvrMPDmAWMzBQ1Ax9s6HN21tVznJZvKmt64rJKp+JQjwaOWTrWBDnqttTbpQ2nEPxuv+VR2jRQ7sbjVju+VHcFpuWPrZHlQo8eJgQMBDAiIfMXsdLGVtBBdO5NyQx17tW9uR+9DxgPF0a+NWk7fUulglHZekj9Paluk60TwGFkbQfPKZkwdNpe6V2Ckg265rKkAlr9vOVb+5Upbj0e9Lm6tNrJ+4KMHtAQ5XioPWs9V3Ug/BgREPmN0uli1qk+1i0g1lR6PG+pUpmvkzkwp08Et5nbYLU7fqvbZaF7ZjI63d+DCP7nQ0GvQmdd95uAZ6w9iQMfbO3Ds/mOm73fx7Rfjks9fUvN2lsuaSuDEd06ga0uXrQGBrg3s3FJuZF1XkKMygxJqCWl/v/xQ1Y308v8VlSigqk0Xq+4kankX1jJpJl4aMfMKO0bujASKw9uHldtba93B1OEpHLv/GI7dfwzhVWFEfzuK1g2tFYMcnXndx758DKlnU7aPeF74JxcqBQSLexbXvI22sqYOLcr32+JWnUGO2WC3uavZXGNr0LmTNPkHAwKiAFLdSVTLoruSDgk31JnP7pG7aoGiahrWZP8kRr9uPNUofSiN9KE0jj96HEDlIEdnXrcTI56q+yuMfmMUXVuqt0lnWdPSRfl25ZD7bXGrriDHTHARXhlGqDVkseXz+amqG+nDgIAoYFSr+hyaPKStAkdph0THdLwfOh1uj9yppmGdfvq0pc9Gtc56++Z2jP3HmJ6Fng6MeHZ+oNOWqlm618wkdiaU1vyocHtxvU66ghyjwcVAaEBn831Z1Y30YEBAFDCqI42Teye1t6NwgbEyHe+nhXFuj9ypPlb6UNr6k1forCd2JjB8r1oqU6Xn0XncSjuGRneVLVVr4bPuNTOJJxIYunWIOeSKdAQ5hoKLXdbbWszPVd3IGgYERAHjleo8pe1QmY6vp4VxtUYUvTByp5LyEu4JIz2kISAAynbWD/35IT2PXUTHcVNdXF9Jre+l7qB27LGx2qVoi4I06M1aoSJOzqCwqhtVwoCAKGC8Up2nXDvMTMe7nV5jlNEZDLdH7grHPHxJGMmnk4b3LYi+PqovIMD8znpqf0r7zFSBleNmeXF9GbW+l6rrEyoyuoN1YQ3RHXqeltzFqm5UCd9hooDxSvpMtXYYGTFzO73GCDMzGG6N3CmPdOfTtxI/1rfYtbhNhTKmdjFy3CrttaE7GABqfy8TOxOYTbgzSpt8MolsOouGcIPyY/hhjY8fsKobVcKAgChgtI80KrBaJcgL6TW1mJ3BWPGRFUrPM/6LcQxvH1bqYKmOdBfStwDgxHdPmLuzAYXOup1pCtVGPKsFSaFoSHswEO2LAsiVfS3XYdY6IyFgfHagSGY8oxQQ+GmNjx+wqhtVwoCAKIB0bvpkmoadSd1OrzHC7AzGxH9PKD3PqcdP4dTjpwAA4VVhrPz0ypolLAGTu94KoPODnWjZ0DKvo9rf12/LZ6jQWbczTeHMwTNlA6lane9MMqO3IQKYTcxi97rdC/4UvSqKjrd34Pnbn9dznBuAZe9YhpM/OGn6rjJrPoqopzU+QVKvO0mTvRgQkLJyU8BUH3Ru+mSKpp1J3VwYV/y5n1kzg1DbwtWWKjMYE3sm0HJ5i6Wc+fShNAZvGMSL/+tFrHlgTdXjbCpgkcDZF87i5f/w8rlfadsoqwyrm9YZcezL5zYQK4xUA5r22jBK5H6knk2V/XPyqaTWYxxZG0HThU1K9xUNwtTtTc2QbRnEzNgMLv7kxUptI3PqfSdpsgcDAjKt2hTwmb8/o31XRbJH15YuTDwzgWMPmN9ZVYXOnUndWBhX7nOfvje3mLb/8/3zUh9UZzDaXtOGyf5Jyx3SswfOYuDNA+h9uPzIq46Uq9FvjlprZAXNsea553Aqva0wUr24Z7GzAbJC6o4VqedSSO0rH3zUUi7wrcZUwAng+U89j8aORs4UOMRvO0mTdQwIyJSa0+mTGZw5eAYjXx3hib0ONHWpjRY2x5oxFV9Yb71wEWnqbLJ1AaHTC+PMpj6ozkQ0dTbpm7mRlasrWUm5qraZlQ4db+uY92/H0tuyuUDK9xSCkGhfFMmw8fdbdfZo8Ab3qoEFkd92kiZrGBCQYWZyjt0s80jGqY6YX3TrRXOlMitdRIxeUFQuRk4ujFMpb2plBqPWyJ0pFaorqQYstTaz0uHCP7lw3r/NpLcte/cyzLw0o2dXY8rJ55APwPiOucrVoaQ71cCCzk87SZM6BgRkWD2UeSRzrIy0W72IWK0+4tTCOJXP/Zr71yg9V3hleK7SzPnvPB8X3XoR0ofTOPWjcwuHzSpXXUk1YDG0mZUFpUFbcbC44k9WYPy/xjG5Z+Eai5YrWrDqnlVzn5fC/c4OncXRvz1qX4PrXQOM55DvMv6wVtbqOF0NjIhyGBCQIfVQ5pHMc6sEnY7qI04sjFP93AMwfVxD0RD2Xbtvwe+jV0Wx+JLFptpQqrS6UnhlWO2B7Mx5LwraqgWLrVe0Yur8KYhGgZ77esrOKBWC1eHtwzY2uP51vL0Ds8lZ7TnkVqtDOVkNjIhyGBCQIfVQ5pHUOF2CbuSREQzeOFi7c2lgh2G7F8ZZ+dybzX2vVM4y+VQyt3OwBYURW+VNyOwmzgVttYLFiT0TmH5pGuGVYXTf3F31Ye3cx8APxv5tDL0P92LN/Wu05pC7VUWMiNQFLiAQQrwbQB+A/wFgPYBWAN+SUl7vasM8zs0yj2Qvp0rQKXVGDaSe2bkwzsrnXmtpV4sj84vaFund3EqzFR9dga4Pd5lap5Q+nEZiZ6LqZ8POfQx8Ib/wfP2O9TWDKzOsVocafXQU0ddGfZVyyoW75HVBPFt+BrlAYBLAMICXV785Ae6UeSTn2D3SbnhWoAyjqWd2LIyz+rmvdVxD0ZD+ja7KCEVCng0GAGD66DQA86UqjQSLVINN672sVIdKH0r7ZsMy7tRM9SKIvbVbkQsEhpCbKXjC3ebUB6fLPJLzSkfazw6dxdTRKTStaMKS1UvQ1KlWojSxM6EcDBQ/hhujaTo+98XHdfSbo3ObUDWvaMbIwyNa2llNtC+K0a+PejYYAICTj53EyCMj2tcpqYxUL+pYhNmxYM1s2rHea26G7IZBtQcwkDLoddypmepJ4AICKeVcACCEuZ0Xg8ytxafkvOnRabz0vZe0jWgN3TpkOeXFrdQzXZ9713L3G4DO93eqd8qcIoEDHzugdNdawaLZkWqvBwOtV7RiJjGD9KG01se1I+ju2tKFmVMzeP5Tz6s9QBYY/ONBXPrYpXV3LVEpV1yvgQ/5Q+ACAlLn9OJTcp7REa3YHTEsii6qmQ+b2p9C6jm1nVGLuZl6ZvVz72bufu9DvchM2p+SpINMq0WNtYJFrWs5PGBizwR6H9b/ekqPY2nOe3ZNFg3hBtOPe/EnL0bj0kblWcL0oTR2r9utLb3GqVx+lummeiOkdHjvdA8RQmxCLmXI9KJiIcTeCn96+erVq5c8+OCDVpvnSTNjM0gfrjwyle3OnQGXhJagsaPRqWYF3sTEBACgtbVV+TEyE7ldplWFWkJo7mpGqDU097upo1OYPj6t/JgFkVdGlDojulT63Bc+7w3DubaFV4bnfe6tHlMrhBBoXNaI7FTWd4v7i497uDuMxuW1zzWZiQymRqbqJkCqJtwdBkKoei5WeczG5Y0Vj1O2Ows0AC3NLfO+40ZlJjJIH04jO20tiin9js1rYzqLzHgGMishGgRCbaG580a197/cucuKbDqL1K/MD4SUO8/pOLd7VbX3y231dNxvuukmHDx48Bkp5eVWHoczBGRKY0cjGpoaKl9YG4CGxgY0RhkM1JupkSlL989M5jq/xRfs7FnrQ5ihFvcvErU+95U6FFaPqRVSSky/ZD0YU9HQ1GC542dUqM1YJy7UGsKS1iVznZDpE9OOtVE3mZVoWt5U/VxsUqgtVHPAB1ks+I4bfvzWEBqXNWLqmLXvRPpwGg1NDfO+a7U6+6ElIUyfqPxdKHfusiIzrvZ+ZMYzrp/rnOBkcEbGMSBQVCkSE0LsbW1tfdWmTZscbpHzyk297n5pNwAgCK/fS3bt2gVA/bin9qew+w279TSmAVi/Yz3aN7fj2b95VnmH3XmPtcm9qfTSz3l4VRjpw2nMjs/iIA4i1BbCG697Y9n7aTumdaJpRRMaOxq1pIlVM3FvbvSu+1+7seHjG+b9zUhKSGp/CrvfW7/vzaKORVj116vQtaVrwWL1lstacOrHpzC5d+GOzpVE+6KIrY9h4LoBNGbLd4gLx7z1ttZ533EzKTjD24cxdNuQyVdbvr0bduXe90JK3pLskqr3aUZz7Qcuel1WxO+OI741bvp+sbtiiF0Xm/c7q+d2rzH0fuVLXLu50LqejruuWQwGBKSsbJnHXa40hSxS3YCrrKJ82KYutcpEACzve2CVkXKBjaHKo4laj2mdmD42jeljzs1KFK/XMFPesd7fm9mxWQzeMIiD/9/BBbNwpx4/hchlEUDAWM5+ft2L2Zz3oY8PYdF5i0wVH9D1XS5URZoenda7lkJTLj/LdJfHhdbe5v+5KSKqSXeOefLJJPZu3IvRR0aV7t96RSvW71jv2gjRyCMjGLh6oGJVoMLi6pmxmYqP4be8fa8p7iwYfb9Gvpor8+qX96ZSSl5htgC1Cunlg+6mzibTFbBSz6YMH++CQtUuHQoBoO6F4oVgwwqW6S5PZaE1OYcBARHZMjI1sWdC6X6L1yzG5bsvd3VmwOgoVvpwGpmJ8vnCfh/ts12Fw9d6RSuWrD5XtMDsqGNiZyIY701+diByWfn0nWhfdC7otmXGJAsM3jCIAx87gPjdcQxvH0ZqfwqxbTEtPY+zQ2dtK+P73DXPWTomKoGP38t0p/ZXDiAr0RGckXEBOCsSUS1eGplqe10bhrcP21YOsBazo46VFg6rHtOLbr8IoUgImVQGR+45ovQYvlBhEH9izwSmRqbQON2I4e3DOPKlI6ZHHS+69SJtzfQ0CSxqX4SN+zZWzfO3bcZEAsfuPzbvV9Groui+tRvDXxq2NLo/ddS+Bfs6dkpmme75VAMstzalDKLABQRCiGsBXJv/Z2f+52uFEI/m//+klPITjjeMyEUqG3DZ5fijx3H80eMA1DZCs0JlFCszmSm7y6vKMW29ohXNXc2YHZ9FYoe9ee5L37oUba9tw7GvHHM071+HzGQGmcmM0gLV5JNJtL22zYZWeVPyydznr/vm7oq3cXLGJPlUEsmfJdF9azcm9kzMtc+sphUW1icZYTGP3fD+Fy6vlXKKatDpl/S+ehDElKH/AeCD+f9+J/+7S4p+926X2kXkqti2WO2cY4dVykW2i5VRrHLMpkdM7JnA0C1DiG+NY/IZ41ViVCz9naU4/3+eX3fBgA5zOfYWLV6zWMvj2K3a5zqxM4Hj3zjuYGsAZIHhLw0jtjWGjfs2IrwqbOru0b4olqyuXlVIC4t57F1burB+x3pE+8qnDxWnbfkdF1p7X+COtJTyDgB3uNwMIjLKoYoTiZ0JHPmiWorO2aGzZX/v5V1y2ze31321Hbed/87z8eI9L7rdjJoqjbK6uYt2obO9YdcG9P59r+n0mqZOm2cI8gp57KppK+2b202XZ/UjLrT2viDOEBBRGfE748bKFLrB5ooThSo1U4fV8pKP/u1R9Pf1l+1g1xoldENhAWNQp+NbLmvR8jgXXH+Btqo5dio3ymp4MbaNCp3tQuBspEfSfWv3XGfaqWOvI3COrI2g++ZuxD4TQ/fN3YEKBgAutK4HDAiIfCy1P4Xh7cPzqnxUup0X1g9UY1fFCV0do2rpTe2b27Fh1wZs3LcRPff1IHZXDD339aDlcj0dU1OKFjAGdTr+gusvgAhby48rdFbMpoWFos7vwFpulNWOkp0qChV9urZ0ofvWyuscCoa/NDz3HdNVsaiWoAbOupl6vwKw0Nprgnk1IPI5M5s0FW5vVcsVLZjcY2/eux0VJ7R2jGqkNxVv5nfysZOmdpPVomQBY1Cn48d/OQ6ZtjYdVuismE0LyyTLl6m1S7lRVi8NABQq+sxVHqql5DtmdOFu963dOPnYSaSH0qbbGNTAWTcutPY2zhAQ+YzZTZoA9RGw8zafh577erBx30ZcsfsK26fwdY/U2dIxqpHelNiZQH9fP/Zdu0/v89ZQbgHj+C/HHW2DVxy+57DlxyjOYS+khVWq+e8m0SwWzBJ6bu1IFhi+10QZ0qLvmNGFuz1f6MGlP7hUqXnsmOrDhdbexbCXyEdUt4ZXHQFb9o5l88oZmqq9rcDqSF3pwr7pEXsq7FRaiOjWIs6e7T2ABJK/OJezPT06nWtLAKmMEpcqna1q39yORed575J6esdpnN5xet7vmlc2u9QafYq/Y0YX7qqUAmYeu346F1oHfbG2Tt47exGRMpWt4QsnZxWl9zMzJazSKVZtZ7UUKruUdhjdXMQ5dPPCev2haMgTOeT1qnS2yktpOLWoLp73mtLvWHFKXiXcMMw7jLxflZhNi6XamDJE5BPZdFZ5a3idFSCMTgk7VXGiVgqVXUo7jF5ZxFngdC673xRmqwoL9w996pDLLQoelRRCwxWNmMfuWSppsVQbZwiIfCIznkGDQow/+s1RrPr8Kq0jZ0anhO0eqXNzVD6bymJ4+zBmx2eRSWXqZvQ4KMKrwkgfUk8dCkVC6O/r5/vqItUUwq4tXQjHwojfFS+7U3K0L4rYVo4we5FqWizVxoCAyCdkVq1qypF7jmD85+OIbYuZqgDR1Nk01+GtlLtZbUrYiYoTbo7K18OGVVZcdPtFaO5qxol/PoHxp60vTl7+vuVI/jKJqbgz6SwrP71SOVhcvGYxBm8c9O6+HS5riDSgqatJy1qNaqx09LhhWH1STYul2hgQ+ARPaiQa1OuqF6ZYu2/tRmRdBKlny9f7j1wWwQXXX4DRr41icMvCBalmczftHKmrp5zuetR5fefcgs7d63Zbfry217bhlf/0ygXnsvDKMNKH03P/DkVC6M/0IzOpnvLUckULurbkqpgM3mB+YfXZg2eDEwwImH6t2VQWPV/osbWSlq7Fvlby2MlZKud0qztNBwkDgjrHhTX+ZzTYC7VZ3PCoUPqvWlueS+H5P3++YgehEFj0PtRruGyc2ZE6o7fzXGlFHynujKlUbimnkA9upIM2uGsQ2XQWPff1YHZ8FtMj0zj2d8cMjxxO7plEf18/Oj/QqdZYnwUDzbHmyjMziq81fTit5XNRFhf7BpLqOd2O/Wv8iAFBHatVwlClc0beYTbYawg32HcBLjDSOVDM3azVEax2PFoub8Gqv1417/m4u6hNynTGdJSbrZQPXi4ABHKf9+KSt62vajWVApR8Konk0/Uzg3TR7Rfh1OOnKs7eWRF9TRQnXjyhNb1udnwWHW/vMHU+WvbuZTj5/ZPctIrKUj2n81pgDKsM1SmzC2s4WlpfVKsomNoa3k41Nucyq9bxmNw7iYE3DWDoE+fKa3J3URtU6IwZrtxSReljFjZw271uN4ZuGUJ8axxDtwxh97rdOHPgDDIT81OGalW3KquORvo7r++0beO/E9/VGwwAue/f2L+PmbrPzEsz3LTKokLVq+KN6PxC9ZzOa4ExPEp1igtr/EuligLy2UKGF+o6QFfupplKQYWUp54v9PDzboPuW7srdsZqrQepJhQNYXp0eu6zUmv2MzOZwZmDZzDy1ZF57Smkn+3duBcTeyZMtcHLCilaS1Yv0f7YoWjIlhK04ZVhpXzvpvubsGHXBq6LMykI6cO69suh8rwwlkgmWVlYQ96nEuwVUxoptYmOmSmzlYKG7x2eyxm1a0Q1qIa/NFz7PVUYdc8kM3MzXmYCwHKzn6n9KV8FAxDnUrS0d2yEPftRRPuiSB9WqzBUeD8jayPovrkbsc/E0H1zN4OBKoJSl1/nfjm0EAOCOmRlYQ15m2qwl03P7z21b27Hhl0bcPHtF+tsnmlWczdVKwU9f/vzAEymUKkXaQqOKqlgljeAy894DX18yFJA7KvznAB6Hz6XohVZG0HkUk2dm4ZcqVft8mtMmO/tjKClD5s6p3PxuSlMGapDPNH6l+rJOjOeQUN44VnygusvcLUevtXcTdXjMbFnAqn9KdN7HYx+bZSlSmtIPpnEoU8fQigSQjaVhYTEzOgMRr82aj0nPwvTi2ZLU9P8cp6LXBZBzxfnp76NPDKC1D7rM72Fx07+QvNnvWiNieqM9GT/JOJ3x5kmVEa5NKqgpQ87sX9NUDEgqENcWONfqp2ZSpuS6SoJqcrqydhK566QNmRmr4PwyrDlajlBcOSeI243YZ7isoLK5zmFevu6RS6NYOnbls7t8VBsbiRYQxsXtS9CU2cTJvsnrT9YXumeIarf/ZPfP5mrNFR4XIX8d7+tP8hMZLTtiu2Huvzcadoe7CHWIS6s8S/Vzky1Tcl0lIRUoSN300oQWxxMGN3roH1zO2KfjSF+R7x6x6sBWNyzGBPwUa56HSt9r1Wc/97zkR5Ku7r+ILU/hZ4v9ZT93ujcdTv5ZNLSZnLrfrBu3mZxlXYp1zEYYaZ8tt0La3UGGkYfa2ZsBunDaYSesrjPTBE/1OXnTtP6MSCoQyonWi6sqQ+qF6tqm5K5UnlIU+6mlYt3uWCi2l4H1ToTxQqjTwBwYuCEcvtIn8J7XegcNK1owvSxaVOP8dK3XwKQ29Oi7TVtaOpswuijo0gfUlscq6RCSoeXdt2O9kWx7Jplhm6rbTDCwN4mhvblefMAYp+LmT436Qw0zDxWYmdCeXF2NX5JqwO407ROXFRcp7iwxp9UqyiUWz9QrFbloWhfFN23ddf+TAnUXnxrMHfTSL3syNoIWi5vqfGE5ZkJJgwtiBVA7M4YNuzaMDc6FV4ZNt8wASy71liHiowJRULz9iwwGwwUm9w7iWNfOQZIOBsM5JWrCOelhaBmriU69qeYU2VBu+GFtRKIb4tj9/rdho+pzgo+Zh8rfmfcUBvNYvowlcNPRZ3iwhr/MjWqlg/2BjBQ86ZGplg73tJRMy8TgKXcTbOjbav+ehUG3lT79RULrwrjpX95CYmdCYRXhqumN5jqTNwRR/R10bn2NXY0oqGpAdG+qKHa+4Xjk9qfwskfnKx5e6pt8ZrF+me/skD8s3GND2hOcUpHan8Kp350yrW2FDO7+3hqfwqZVAadH+jE6adPWw6wKuW/m02nSj2bMpSGpLInTKXjY/axIHMBAq6pcXsF7A9QOQwI6hgX1viTUrC3y/jjV5tiNZNrr5K7aWhav+RC3b65Hd23dc9tOmZE+lAa8a3xin8vDjysVukItYbKbqRULRBp6mwy/FqoCgGcPXjWnsXALi4wnh2fRWJnAof+/BAm9+pb+Ksq2hdFZnUGoVZjeezVgv7wqjCivx1FZjwzb/GwUS9sewEvu/Nl8wImpXQqA514nRV8zD7W4XsOG7yxOUwfpkoYENQ5LqzxJ7eDPSN5mWZzN62MtvV8oQcATAUF1RQCj9gdMeVN/sotojR6PNyu/uQXi1cvxtkDZ91uhnaJnyaqBrROWfbOZXOd7127dhm6T62gP30ojfQLaXS8o0OpTYUqRIWg3tKGm1U68VY2AC09D6g8VnrIhnQ1pg9TFQwIfIILa/zHb8GeldG2xM4EJnZrrv5iIS3k6ANHseb+NZae3q3qT37R3NXsy2AAAJJPeCNQbNnQYlvQP/bYmKW2FYL6Ze+wth6nUifeygaguh5LK6YPUw0MCIg8zg/BnpXRtvFfjttXIUkxLWTscWudGcCl6k8+EWoJLdidm/Qzu/jUVNCvIyUrCy1rccp14nVuAOp2VR+mD5MRDAiIfMpLMwuqI2RHHziKY393zHMd5qn4lLVUhbxaqWFUhgAWRRdh6ugUwlCo8qTwfG5vWOaWah3Icmtm6jUFrtBhL35Nqpu2lQuinKrqE+2LYs39ayyf97107SDnMCAg8hm7N+dRoTpCNvb4mOeCgYLEzgRwmfXHKaSGjTwygkO3H8LsSf/UCLdD78O96E/2O/Z8sTsMbFTnQ5UWnxrdr6OeTI9Oa9sJuNy51ZHzbRd90C4AACAASURBVH59gJUZZS9eO8g5DAiIfESlio8OtUaUVEfIpuJTupqonc40gFrvG+Use88ytF3ZhsyPMo48X3hVGM0XNtv+PKFoCJmkM6/JEFF+8enM2AwGrrNh3YvLszDH7j+m5XEqBVG2FxHQsD7ArWsHeQcDAiKfyExktNXMNsroiJIfR5WMBDlGpt4NL8QknPzeSTSd3wSscub5sunsXE14q1quaMGqe1ahqbNpQarNvmv3WX8CneS5TbEK393MRAbpw2k0ZhttecrIZRGknrWehueaGhV87CoiUPhcWTnH6txvgeoXdyom8ompkSnTVXysMLPrpsoOzM0x+0dmrai1+VrxzrnxrXEM3TKE3et2o7+vf96aCrObKgVaFjj+7eOOPd300WltI9ed7++cFwxkUhlMj0zj2IN6Rqd1Sz6VxMCbz+2aOzVi42ydBBa1L8JFt19k33PYycAIvdZdm4t0vr/TcudcpQIc+Q9nCIh8IJvOIjNpLuWgUrm9WlL7Uzj6laM49sCx2p2lohElszswd7ytQ9tUvm5zqQG7Fv7NzNR725VtvsrFLgi1h5BJ2JMCkznlodQaE4588QiGbhlyuxnmSGBwyyBSv0ohs8Le4558MonIpfW3cNVMBR87ighYTV3Uud8C1TcGBEQ+kBlXu1iXK7dX7bZKiwnzI0obdm0wtQNz25Vt3gwIqqQGmJ16X/EnK7Q3zwuWrFmS2zeCMx9zpg57dz1MLcP3DgP32v88yZ87ExxbWbOx7J3L0LKhRbn6TqX9ZaZHpvHiPS+abo/VCkY691ug+saAgMgHZFYtr8Ho6JLVRa+FESWzOzB7bjffBmD5e5cj+Yvc68muyaIhfC4HwOzU+9gPre9n4EXtb2jHihtXaMu/p2CwZXfeEsv/YDlO/OMJ5fu3bGhB7DMxy+0orQaU2p9SCgispgvp3G+B6hsDAiIfEA1C6X7Fo0uVFsDqWvRaGFEyswOzl3bzLYwqnvinc52J1L0phFpCSGQSaOpsMh28eLmKkhUSEsmfJRkMkCmqM51mWN3Qzq49BVQqEVWqamSG6utxam8Fcg7fUSIfCLWFlO7Xvrm9ZqWg2cSslg757Pis6Q1v5nbzdWmkeelbl2JR2yKc+M6JiikGmckMBq4ewPL3Lne4dd515J4jbjeBqKymFU2W7q+7uk7xOTGyLpILpA2us6pW1cgo1dfDKkP+w4CAyAcawg0ItZgLCqJ9UYz/crzmAlhdRh8dRXxrfGE7amx4E445sBttBY3LG3H868cNLZ4+8W21NITGCxoxc3xG6b5EZFy0L4olq5co37/lihZtefOWNnjTsO9AgVszE+Q9DAjIF7jVOtDc1ZwraWeiio+T9e/Th8rnB9fa8CZ+Z9y11JPk0wZH6wDlNjIYIHJGx9s6LHWi265s09IOK2uyzFQ1MspsBTgdMxPkPQwIyJOMdvC51fo5odaQqSo+o18b9URuPoCKG96olMQry2igVKJSEENE9Wfsh2O4+JMXoznWrLR+p6nTWroRYHIjQgE0nd8E0SjQc1+PbQNdc6mZBq8dQbmmBo3tG5MJIRqFEBuEEL12PxfVPzMbOpnZGCsourZ0Yf2O9Yj2ld8ELNoXxfod671Z/77MhjeqJfHmEbmLmNmN0cKr3EtVIqIaFOooFKqddby1Q+kpdSykNVWJTAKZsxk0dTah++ZuW2e9jV47ys3ikj9omyEQQlwH4N0APiKlPJX/3SoA/4H8RvNCiMcAXCelZL0qWsDMhk7hlWHfb7WumgZlpIrP8PZhu5uvpHTDG12l7VQ2Rjvvt8/D6KFRLc9PRPoUctiPPWB+n5IXtr2Azg90Kt13amQK8bvjymmpKjOemcmM5cpIRpmpAEf+ozNl6MMAVhSCgbx7AfQA+CmADgDXAPgjAA9pfF7yAbMbOkXWRkxvtV4vAYGuNKjSOtfFnKghHV4VVkq5Kd7wRktpO6m2MVpmMgM8av3piUiPpW9dilX/axUiayOI3x1XeoyT3z+Jk98/qbQ5WWn1LLNpqaoznk6UYy1W7dpB/qUzZeiVAHYX/iGEaAPwVgDflVK+CcCrAfwGuYCAaB6zGzqlnkuZevzCyLPXOZUGZVsNaQGs+OgKbNy3EZ0f6lR6iOJgRVcQV7wxmtFp8XoJIImCouWyFm2DBao7FRczez5WHYhR3XiSyAydvYLzARR/K16bf/xvA4CUckYI8WMAv6/xOckHtC0crcHrW62bnSWxkgZlS2dXALHPxeYqUBz9ylGlhym+0KuUxKvE6MZoqf0pDG8fxuz4rPIsBxHpJ4tKeXkmYDdxPlYNYlQ3niQyQ2dAMAGgeNitD7lCfD8r+l0aQKvG5yQf0LJw1ACvb7VudpbEShqUzo72HAnEt8WR+EkCrRtblXJ0gYUXel27FZe+/6XT4omdCRz46AHvLbYm0iGfCnf24Fm8eM+LbrdGSShybq8VnecwlfSheQyej1XP16obT1rBdQTBozMgOAjgLUKIZuQCgfcAeFZKebLoNisBqO3eQ77lVEfdy1utq8ySlC7ANSu2LYaBNw9or/GffCqpfJEut+HNXEm8GwYttava+2+lLjiR10Uui6Dniz1zM2P1GhCUfod1DRZkkhms+8E6pA+nMTs+i2wqa/oYJZ9M4uRjJ7HsmmUVb6MSxIRaQmgI214Qcg5LeQeXzk/ZgwAuQS4w+HX+/79acpsrAezX+JzkA0511L18ElOdJbEyu5KOeywVRlTe8KbtNdY3BJpNlg88TdUFJ9t0XNuB1ldzAtkOqX0ppA/nvu+FTmk9Kj2HFwYLdPRk0ofT6L65G7HPxNDUpbbfwL5r9y0oj10qti1mvL0N+Q0nHcJS3sGmLSCQUn4NwD0AliCXOvTl/H8AACHEGwHEADyh6znJH5zoqNu51Xoh5zx+dxzD24eVFi+rzpKc/NeTGN4+bLos3Vwn2ENr1RavXlzxs6AjrSx+R7zs45hK1SLbjD02htYr1AKChlbnRlDrUj7PvfD5N9Up9YhK5/BahQKMKj4HW5m1rtVpNhzE5FO8Qq3W04WMXKPMrmEzek7WcX0kZ2gdmpVS/gWAv6jw558BaAfATwPNozKNGrksgtS+lKtbreucWlWdJTm98zRO7zyN1L0phFpCSGQShp7Ti53gswfOzqVAleavnjl4xvoTlMnzdWpBOxkggcRP1AK/7ITHPsxeVPT5n0vDu9FbgwIV1TiHFxcKGPzIIMZ/Nm76KYrPwZZnrWssNO7a0oVwLIz4XXEknyxz/eiLIrY1d/0Y3KWeKmnmGqV7DRtTj+qPY0nVUsppANNOPV81QohuAHcC+F3k9kcYAfADAJ+TUjqzwpXmMbtpVM8Xe5COp13bat3MJmpGdnbU0b7MZMbQc3q5E3z0gaNI7bOvfaXrLpxa0E7GnD1wFs0rmzF1eMrtpvhS8ee/a0sXTv/sNI4/etztZlUnjJ/DI2sjyJ5VCw6LH1/L9aJGp9nuTcDMXKNUdq6vtoZN9/WRnFFnk4bW5XdP3ovcfgj/DeBLAJ4HcAuAXwgh1PY0J0vMTqO2b253bat1O6ZWteX1GnhOL3eCj33lmO3BygvbXpibtj79xGlbn4vMmzo8BbDKom2KywG3bvD2mo2m7ias/7Hxc3hqfwqTeydNP0/rFa3zOra6zsdG9r+JrI3MrV3ovrlbSzBg9hp19AG1EtHlriV2pR55lZ9SopRnCIQQWeTe8ldKKQ/k/21k8lFKKd0s9/IAgOUAbpZS/m3hl0KILwK4FcBfAfiIS20LNDPTqAVubLVuV3lQXRUzaj2np8uvOpC+UNiptPGCRswcn7H/Ccm05oubMXVkynNpbX4w9sOxudV9Xk/ZWLyq8rqiclQ7l+UWs+s6Hx994CjW3L/G2oOYZPYaNfb4mNLzlLuWOFk+201+TImy0jF/CrnL95mSf3uWEOISAFcDiAO4v+TPnwVwE4D3CyFuk1LWb5hXx1Q7+E5ttW5nedC5vF4NFW+qPaeXy686icGAd00dnsKijkUQTQIzI3yfdJqKT82dG2zZj0Qjs6WVVQc7ylUV0nU+Vu1sq1K5Rk3F1VL0Sq8lbpTPdoNfU6KUewZSyk3V/u1Rb8z/3CGlnPdWSiknhBD/F7mA4TUAdjrdODrHqQ6+WVbKgxp5PYVZkudvfx4TeyaUnqvWc9bbqAUF0+yYh2eyHLBo6SJ0vKMDMydmcOrxU1ofe/Sbo1j1+VUANM5MVtD7cC9GvzFadtbXCDM7zKsOdlS6X61ZayOKAzAnOJmCU3otsfv66AVmU6KM7GDtFUJKTw/qayWE+BsAnwDwCSnlvWX+/mUAfwrgo1LKr9R4rL0V/vTy1atXL3nwwQctt7ceTUzkOrGtrd7OTVU1PTqNqWPmR1OaVzSjqbN2bevMRAZTI1PITJrbNTPbnTs7NQyfW4RR7TnPHDhj+jloIblSQkqJhhcDtxzLVeU+737V2N6ImYT+WZJQSwjNXc0ItYYwMzYzt09BJarHPNwdRuPyRkwdmcL0S+brijSEG9C4tBGiQSDUVn2Trmw6i9SvzE/uR14ZKfu42XQWmfEMZFYiO5XFzJja+1A4BirMXlNVr1EiJCAzxvuDoZYQlqxZouW5jV4fnVTpuJu9dpY7TrrddNNNOHjw4DNSysutPI623AEhxBop5QEDt7tGSvmYruc1qbBKqFKoX/j9eQ60heqQaFBb7WjkfkYuyrqes7mrWU8pzyqaljdhNjmL7JR/E8FFo4CQXAFL9rEjGAByVcnOHDyD8MowGjsa0dDUgKmjU8ic0TtQUPj+NzSrBW/ZdHZeJ7M4kCnVEG5AqCVkusNWGgyoDsxUIrPODbyqXqPMBANA+Q3T7Lw+ekE2nTX9mchMZpBNZx3dbVqVzmTiPUKIj0opv1nuj0KIRgD3IjcCb32nDXsUPpU1vxmVIjEhxN7W1tZXbdq0SWe76sauXbsAAH59/an9Kex+727T99u4b2PVKdHEzgQGrhtAY1ZxFOne/GjGbedGM2o9Z608SFWli79HHhnB4c8fRvrQwmAn2hdFZF0Ex+4/prcRDoj2RZG8IzeGEJuMIf7ZuLsNCpByn3c/C0VDyCRtmtFrANbvWI/232vH8PZhDN02VPZmVo559KooOj/QicHb1Gvqz5OvNlcuPzuRSZgqYb1+x3q0bzqX0lE4Ly7J6hvV7bmvB93v61a6r9lrquo1yozFvYtx5W+u1Pbcta5Vbih33Kt9P6rpua8H3Tervf9G6MrI0BkQnAXwNSHEZuRSbs4W/pAv9fkdAK9CrtSnWwozAJXqibWV3I5oHpVFeEZ2Sda9UZiR59SRHwsAizoWofvPuisu/u7a0oWuLV0VF4qn9qfqLyDIb5Q0gAEAQPS3NJSMJaogk8zkhqvsGGguqvRiVwWy5FNJJJ9OovH8Rsy8pGHGo0p+tuHFwGX2qDGcH26SkznkTiwUPzt4tuy6iMjaiOngNRQNeS4YqET1++Hpyn5FdM5hrAewC8AHkZstWAcAQojfB/AMgA0AvgDg9Rqf06zC8ESlGmCr8z9rpj5RcMW2xYx/cwzskqx9ozATOzO3b27Hhl0bsHHfRvTc14PYXTGs+OgKU3XgZ0/NonlFc80a2pXqbWvbg8EpJR2JzEQG+961z+VG2avxArWZK9Jn+XuX27ZzUKHSi60VyCT0BAMF+UCmHNU9auzYwd3I4Ixupq5Ritk65RYQp/anTM9kZZKZuqndr3vRutdoa6WUclQI8SYA2wB8BsB/CSGeAPAWACcBXCel/JGu51P0RP7n1UKIhuJKQ0KIVgC/hdxMxy/daBzVBysjUOXorgoRu8N8/ePSqk6tr2rF4I2DBncWsV5Nwe5KJ7qUpkMV1n2Ekl7NgtSDJVrdt2TtEqzfsV5LBbJyEjsTdVMNpaBayUqzJaxt2cHdxOCMTmauUR3v6MDYD8yXRi036u33KkOq3496+V5pHW+QOZ8DcDOAxTgXDLzKA8EApJSHAOwAEENuLUOxzwGIAPg69yCgWnTukqx7OnFR1Hqc37WlC5F1Jk7QVUbrjDC8U7UbBLD895dj476N2LBrw7y1EToXgRNVU+jMXvD+C2x5/Nnx2fqbrUPtTqjRnYC1l+s0OCBkF6PXqPY3qLWv3Ki331NqVL4fbswQqdI+jyGEuBG51KAMgBcBvAzAN4QQfyilHNH9fAo+CuDnALbn1zv8GsCVAN6AXKrQX7rYNqojunZJ1j2dqOPkmtqfQuo5c3Gx1Q1mdK1p0Knc7tjzdqhcULyYyB7hlWEA9nWeFrUtyn1/10WQfDrp8W1Gz9F1PHQe13LnDTc0dTbh/Heej8ilEUwfm0bzhc1Y3HNu9+fEzoRytblyr83vKTWAydlsl2aIVOksO9oK4CEA7wFwDMAfILeAeDuAGwH8PyHEh6WUP9T1nCqklIeEEFcAuBPA7wJ4K4AR5Nr5OSml3h1gyPesbqKm+6Kh4+Tq5NRvaUC15v41GP/lOAZv0FSRRMHy31+OlX+5csFrsasyE1Et+67dh8hlEaVa/kaMfn0UQ7eYr6DiNjPnu2qDN6rnzYtuvwjNXc3KA0J2mDdoUSJyaQQjj4wg9ax6IkSlUW+/p9QA+lOGvURnWNYP4BIAPwTwISllISntj4UQP0EuWPhXIcR9UsqPa3xe06SURwD8kZttICrQXRVCxwlIdbTs1I9OVbwwll6MQ5EQRr8+WvZ1h6Lu5uSf+M6JXNpUUfvtqkBC9S/cE0bHWzpw4rsnbF1vYaUTV8vk3knbHttORs531TrI0auiiG1TH83vvL7T9QCgWK1BC7MzvwtUGfW2qwqf19SazfbKDJFZOgOCi5DbAfiLpX+QUv6f/M6+3wZwCwBXAwIir9G1qFbXyVV1tOzU46dw6vFzk2zRq6LoeHsHxv59zNRFwraa60YVlWIssKMCCflDeiiNo3971O1mBI6R812tDnLyqSQGrh5A70O9dd+ZtX3QwsCot59TaorpShn2Ep0BwW9LKSvuMSClfF4I8ToA92h8TqK6VnwyWX7dcpz47glLJ3NdJ1ddIxvJp5K21sO2U/GaCK0VSOyqJ08UJAY6k4Y7yPl9DS655xIkf5as286snYMWRke9/ZxSU47VlGEv0VbTo1owUHSbWSnlJ3Q9J1G9SuxMoL+vH7vX7cbQLUOIb43jxLdzwYBqukzT8iZtJ9d6rDZih8JaCm0VSATQ+3Avjy2RFQY7k6Y6yFlg7IdjxqqdebAza0vZ1LyWy1tMpcDorMJHzqmfpd1EPlFrCruwK2njMnO7embO6E2zqZe9AexUWEtxduhsjVvWFoqGsO5769C+uR3hleHAH1uialquaMHknoXrGoyOVKt0kJNPJrHm/jVYv2N9xfzwcE8Yne/vRGYyg/jdcdvTRLLpLDLjtZ9Le9nUIpN7J+fSqox24v2YUuN3dpQd3QjgdwBcCKC5zE2klHKL7uclqgeGp7AVdvXMTGYMlf00eoI2PPXrY9Oj0+jv67c+8taAuWAA4LElnwohV3Bcg7ZXt+EVj75CuTNppVJa983daN/cjpFHRvDC517A9JFzlZ3SQ2nEPxtfcD+ri5PLtSN+Zxypa3KLgONbzz1nueeyvZZ/Vm0DSj+l1PidzrKjAsCjAK7HuSzZ4k2xZdHvGRBQINm9MLVa2U+VShte3BvAScceOGY9379CekHQjy35UAZY94N1OPHtEzjxnROWvjvHvnIMrZe3ovvmbqX7W90ka+gTQxi+d9jw/YoXJ1tNhZk3i3yNsedypJZ/mWIL5B869wX9GID3A/gGgCuQ6/z/bwCvA/AXACaQqzJ0icbnJKobduZ4FlS6CI48MoKBqwcqPn/hAjPy1YV7B7ZvbseGXRuwcd9G9NzXg9hdMSx961Kt7fYsi8FArVzZwrHtfbgX4VVha09G5AHpw2m88p9eifU/rpxDbojMjUirjvRb2STLbDAwJ2utzYD5hdCF53Kqk14otkD+ozMg+CCAQSnlh6SUz+R/d1pK+Usp5T3I7QT8LgBv1PicRHXDzhzPgnIXQdULTKnI2gi6b+5G7DMxLP2dgAQEFhnJdS6MBqYPpR1qFZF9CoMShWB33Q/WqT9YfkRahWoHORQJqQUDBRXanNqfwvD2YcTvjmN4+3DFTrXZhdCF53KyEIQT1zJyns6AoBfAT0t+N9c7kVL2A/h3AB/V+JxEdcP2HE+UvwiqXmDMPo9dGiI6T1NGnlDfQ9U6lm5vdrZ4zWK9VwEKvNJBiWXXLLPUUVUdkVbpIEf7ojj2d8dMP1ep4jaXqyg3dMsQdq/bjf6+/nmda9WF0IXnim2LOfJ9duJaRs7T+dERAIo/ySkApcOIBwG8XONzEtUNu3M8Qy2hBesHrF5gKomsjWDx6sWm26gim3Kutxzti2LFR1Zoe7xax9LVzc4agDUP5CqqtFzR4lIjyG/KDRZY7ag+d81zSqPSpp5XAIvO+//bu/fwuK7yXODv0sjSSCN5rNhJpFiJh1i2ArajGuKm5SZTN2lJzyFAC7Q9EFKchJKCc2goTXoak9s5SQukxJRbEock3A6cBhIo0BoESqAU6gShxA6RLYdxoljydSzJY10saZ0/9ow8Hs1l77XXvr+/58nj6DIzS3vv2bO+tb71rVqMPzVu+XVKyfRkLKdq2lkIDZwuVuB0UODKegUfmJucMzWrExY6z+rLMCoL5b0A4DVFv7MKRqBA5EtOlkhzelS9vm1hUS87HzCV/u5MT0ZLKU6/qD2rFiv/cSXaNrchfWda63P/Zutv0LS+acH15MaakrKKFjq3vqcVg08NetMWCo1yO/fOV9W6dkBpXc7kvkmlBbuWqnlJ4OjjR603rowTfScw8siI6VTN+Iq47YXQQPViBYmLc/efZ9S7YmFfVJzpyeDkvpOYPTGLwRvPvC/qriblJzoDgv/CmQHA9wH8jRDiFgDfBLARxnr5f9X4mkRaqFTgsSo/hW2lE9iwusHoeFfZ8TG+Io5Y88INzXR8wJSSvj0dqt12Z47NGJ0GoX/068g3j+DIN4/Mf52/nrwabapP1eOiBy6av54zPRm8dM9LnrTFNgEsfctSHP320crXY77enV+u2TDuVl1l5962zW0Y7xvHgc8opuTMAQPXGAFF22bzQYFX1byO/+S45VTNs99+ttJrFd+zzOwBkP/ZS/e8hKn9U6Zfq1zQFxb5NV2zHy9dP1dnNSm/0Tmx9CiAmBDiFbmv/xHAfgC3AXgGwKcBHAdwk8bXJLLNTgUeqyxNYRekdFTb8XHR0kUlf26n0kY5no5sOyk3UhdLqO0UbVb+ejr0jUOOvk4553/4/PlgIH/tW+kQ+EWsMYauH3Rh3WPrKla0ab6kGV0/6ELn/c6nUpgWwmDAzM69yz+wvOLPq5JGUFCce19NcaW0c68+98yi6A6wWiRg9IlRxFeoVRord9wLC0G0b2k/oyOf/9lF2y+y9JlUKegLOl0FOIJK2+1RSvmYlPKVUsrf5L4+BmA9gI8CuA/AzQDWSSmf1/WaRHa5fQMwneNZ8AFbquxnx70d2LBrA9b3rq/4Iaw6q1HpcarH4IKbLkDHvR3+Llk6B4x8acT5ah1zwNh/jDn7GmUUBgMD1wZzY7RYYwyNFzWesdFb6pYUml6zcC3E+FPjSN+eRjwVrxhck5pqpXUL6aqEY3eQ5viPjzsalMU71Dr2k/snlRZC2xmxV/lMCisnCnAEiaMrQ6SUowA+4eRrENmhcgOwe0OMp+JIrE2UzeFMXJxAxz0dC15HZcdHlTSlah8wqmlINYkatG9px8zYDI5975jSc7hh9IlRdD7QidGfjgays1xJQ2cDpkem8fzVz2tbPOm6GqB++ZnrZc7YyKmE0SdH0X9ZP87503PQ8vstaFzViOEH7M/4Rd3ax9Zi2ZXLLD0mtTWF/sv77b+3LKQQVUoJdcLsuNp2zTNjM9aOj6YR+2ppVcnupKkSykFmpwBHWFKoHAsIhBCLASyRUr7o1GsQ2eHFDaBaxwUAsruymNyvrya97g8Yu2lIQahQMZudNb8YMUAmBibQ//v9XjfDlvYPt2Oo+XSdeNOzfBI49DVv0rTCqqHDeqUxSwt9q8mlEI08MlJ2nZeZe65upw6eUnpcPs/f1PHRPGJvZt1BmDlVgCNInMyo/DCA3zj4/ES22C3xpvI4L/ITdU8Jq+bYF6Z3+N3M2AzaNrcxxcSHimc2PC3dGnGq96j8e0vX7tzlUoi83ufDqvy9sdq9x0qallWV1h2EmVMFOILE/0N1RA5x+wbgRXoSYMyEzGZn0XpVK47/5HjJxW5WpoRHHhmx3IbCNCSVNCa35WcxikfNTu49iQP/bH/jIlI3+sQo5ibnUBOvCe8C94Cw0xlq2dSCdY+vw861O/U0pqB8Z/4+FqRgsThVM3/vyfxbBrNjs0jdkYrUiL3bnCjAETTh+UuILHLzBuBFelKlvNn4yjiSb0iieX2zpQ8Y1Q5Y63taz/haWx6xQ8qt3xjaNlTmEeSm2bFZ1MRrQlflI2jsdoa0Dw4UDKToDhaXf2g5ahI1eOluB0r01hj3yKFtQwtSdWriNaiJ1yD1zpT+16V5ThTgCBoGBBRZbt4A3M5PrJY3O7lvEpO/mcSSNyyx9Pyqf8ds9sxFdnY3KnKSqBeYHpkueVzCND0cZHNTxoXN82GCg3se5DveI18emS+S0HRxE85997mm7yu6BwfyAyk6g8WGzgas2rbKmQEBYazFGLhmYMGPkm9MYvYjsyX3mCG9nCjAETROriEQcLzSL5E6lRJ4qjcAN9OTnFyroPPvaNvchsQ6/91M5ZQsW9IwTNPDQTZ9eBon95zE9PC0103xtXhHHOe+91zUnVen/bkTFyfw3J89h51rd+Klu1/Cse8dw7HvHcOLd7+InWt3YmfXTlP3lpZNLWj/cLvWtuUXxeoysWcCmZ4MTvSdDXOh3AAAIABJREFUUHp8fWrhLvKAsfFk/vlLGX1yFCf3nsSpo6cXKWd3ZzG0bQjpO9MY2jbk2QaHYWR1n6Cw7cmg7dNNCHGOlLKwhMM/AfiirucncoJbJd7cTE9ycq2Czr8juztbtvSqVfGVcZz15rMwfWAa9cvr0dDRgBPPnMDIduvrHQCUzEcGwjU9HHSzJ2Zx4HNcz1HJ5OAkJgf1VSwrVO29m30mi/7L+tH5gLGja7nqNcPbhzH0T3pH3vOvoY007pNTQ2ob+OU3Ayz8+2OJmDFwY2L2ZnL/JF78+Is4+q9HS45g53c/5/3JnjMqPFUS0j0ZdA53vSSEeAzAF6SUP8rtQcDVXuRrbpV4c+txRx4/4uhaBZ1/h84p/cl9k1j+l8vP+BteuPkFe09aIlgKwoLoSPFZulmQJbuTaL6kGUP3DOk7rhIY2DyAoXuHSgYQiXUJZHdltZ/HfMChU6n6/Gblg5/C+1Nfd5+lNKkXPlr+fpavstR5f6cjlYeiJL8nw3/u+0/Mnli4n0SY92TQGRDsAfAOAH8ihNgH4AsAHpJSHtX4GkTaubEpi9P5ibPjs5gansKu9+9Sap/ZtQo6/w7d+d/Ff0NNwn5GZKlgye8LoomqEsB5HzgPdW11Z4zWZ3oyRkCgWbnZhOyzzqS75P8ePwTv8Y74gvufI9WxysxqknUtm1rQGGvE3OQcOu7tiMyeDNoCAinlOiHEawFcByMw+DiAO4UQ34Qxa/Ckrtci0s2NTVmcSk8a3j6Mk6MnbbWtuHNe6Tjo+jt05+MX/w26nr840DA9rUyBkrg4gcn9k5gdtbjLrIOLdh0jgcwPM7h04NIzvp2+PR28v6VI4QCEH4L35OsXrlNzrDrWHPD8Nc/PpyiFufPqhpp4Ddq36F3f4mdaFxVLKX8mpbwawHkAbgAwCODPAPxYCPFrIcQNQgiGruRbTm7KonuDMKBgAbFN+c5zpieDvu4+7Fy7E4M3DCJ9SxqDNwxi59qd6OvuQ6Yno+3v0D2KVRwA6Hr+cguiu3Z0oabO3i002Z3Ehl0b0HFvB1J3pGwv/owlS1cj0TFbEmoCaN/Sbj0YAJC6NaW/PS6Y2DOB4e2nF86HYk+HogEI0/cqBzWvb17wPSerY02lpxbcs4nMcORtIqUclVJ+Wkq5DsDrATwC4AIA9wB4WQjxkBDiEidem8jPdO9AOfjXg1pGv1o2tWB4+zD6L+8v2yko3A1Ux9+RWJNAw6oG+43PcWqavNxMQ8umFtSdY6MDn+u85IPQs992NqYPWK+as/xDy9Fxbwc27NqANxx/Azbs2oClb1kKUX+6yNtclvlNlaRuSy0ojWtW7ZJay9XK/GL/3fvn/z/wHccyAxBe7zhe6r7kVrWycjs4E5XixlV5FEAGwCSABgB1AK4C8B4hxHcAvE9KecyFdhD5gq70pPTtaS1VepLdSUyPTJsvVXrN6TxVO39HpieDicHS5fZU/obi19TVwZkZLT+aF1usWB+8ROdFtb0NHQ1nTGu/+PEXcfTbXLplVuLiBFK3pJC+M630+CPfPoLE2gRGfzoauHUlk4OT82tkgrynQ7V1XoX3qpEvjyhtLtZ0SRNijTEt66dczfHn2gIyyZGAQAixCMAfA3g/gDfCyLLcA+AOAA8B+C0AHwXwFgCfgZFWRBQKZjvIxVUnrMj0ZJC+NW2zpZgfpbZUqlQCe/5qDy593sg/Vv07tOUrl1mnoKuDk741jeRrkyU/TGviNYg1WQsKYskY1j66dsHzqba3sDb6ix9/EQcfPqj0PJFUA3Tc0wFAfdT2eM9xHO85rrNVrsqvkQnSHhtnXXEWFv/uYssDKYk1CdS3ld4ToJr68+rRvqVdy/op1xc8WywvTdGk9Q4ghOiAsaj4agBLAcwCeAzAZ6WUPQW/2gugVwjxLwD+UGcbiLyS6ckgfXvalTrRWjrTuVHqutY6yx9MEwNG/nHbZrUSd9rylSusU9DWwanyYVrfVm8kX5rpJAiUDAYAGx3Sn5zujO7/3/sr/CYVK7x2/NpZqmuvM1LJHJp9yAeifv37SznrD84ytdiz1OCMcuD9zAmtZardXvBspbw0RZO2NQRCiB8CGADwEQDTMGYDUlLKPy4KBgo9DWCxrjYQecVK/r1dOjrThTn+qqkq++9S73zqSOeptk5BZwcn/2FaSqw5Zm7hogA6HyjfSYiviCu1bXKfkfZx5PEjSotio2zxpcbHT6Yngz3X7/G4NaVND00j9bGUYwtj84Goys7tXqn23q5UHGHkIbXNCqfSU8juzmpbB+bFgufArxMhR+mcIfg9AD8G8FkAj0kpzXwyfQcAt5qkQJuv9GMm//5aY2fK2eys8toBLTf1gtkF1RGzfEdUZcRJ9TWXbFqCZW9ZZuqY6Z6Wr7RXQ7W9LAAAEhh5eKRsLu/kfvUdZTM9GYw8rLgrs5t8VqIz05PB2M/HzL1/PVS7pBZdO7oq7pXS/JpmpT0ECq9FP5TprKba/izD24crns/JffbeZ4k1CW3rwMzsgTO9fBpTL6vtkFwsyOtEyHk6A4JXSikt1T+UUu4CoLaTEpFPWMq/zy3KLWYlpUjHTb1wZ0s7qTXFnWSzH5Cqr7nsLcss1YVuvapVW0BQ7bi3bGrBZHoSoz8pv7i00o6ix3+snod+5LtHcOKXJ6r/oocSFyeQfH0SBz7rnzGg8b5xHHzkoK87wIBx7ZnphB7916OY2GN+oX5x59p0SowJi1oX4dTBU3oDwCr7s5genFFUfA+wsw4sr9p57e3thagV5tMSKwjSOhFyn86NybhLD0WOrlx4K1vP68yNH7h2wJi2VpT/gLS6fkI1ncfK47K7szj6PX3Vdqodd6szRYUzBZmeDI48fkS5bcd3+H9Ra21LLZZfv9xXAUGl4M1P5rJzCzqMZ7/t7AWd0dWfXY3+y/rNdcLLdK5NzXZVIowyrvliBemPpa0/Rykm8vItDc4ocLJDXSm4WLR0UcUZIrOCtE6E3MdwkcgGrTmZJsvDab2pzwEjXxpBfGVcaSq9dnFt1Sn6UsGOSjpPtVSBvErBiR3Vjvvghy3sCVG0UDkMO8RWk+/IqF5rTvBLO6p58e4X8eLdLy74fnGw3bKpBZ3321/0Wtdah7PffjaaLm7C1MtTqDuvDvvb9yO2OIauH1ZOXSos/1m7RE8Xo1pZUcCdjdW87FAXzyS8dM9LmNpvPpXI7P2ToosBAZEN2nMyTZSH050bP/rEKFK3pZRG8mKJmPKouKV85SqpAnnVghNVTZc0IdOTweFvHS6ZCvXrq3+N7LPW9oQoXKgc+B1iTcr0ZLDi5hUl0+bcJuICcjLYUVipYDtf+Wv/3fsxObgw4KnUua4UTJ9adwo1DTVo+cPqqUv5nx35tvqsF2AEj+seX2d6IMBJiYvtpwdpaUduJiGxJqH9/knRxoCAyAYnppDNlIfTvfivdkktGlY1YGKvtfzjkUdGlEfFTecrC+Ccd52DutbKuwI7mT984qkTGHxq8IzvJd+YxOxHZjEzOqNc+z9qVT9mxmbQvqUdL/7ji5Zy3Z0Q9GBgXkGwDaBshz6+Mo4VN68oWyq4WjA9e2IWJ/eeNHYqf19byRQX3bNzVmZw/L5g1u4C5GI6S6ASAQwIiGxx6iZbqaJN/nV1Lf4DjA/T1Z+zln/c+p5WyyO9o0+M4oWbX0BNomb+Q7FqbqwEDn3tEA597VDFxddO5w8XG31yFCevPAkAqIfaZkdHvn0ENfUu1h30WD6AtpTrTtXNAXuu32Ps/F2hus7AdQOAwIJ1SlaC6XJpjU7NzlW7F+Y5vWA2+0xWqaqak/vTmKlSVC3VKih0B1S0EAMCIhuc2nHSzGhX4YfBOMZtvV7+Bmsl/3j2hFrN++Jc6PyHYt1n6rD/zv049PVDZTuK5RZfu5E/7IQg73CrIj+KbfpaUyGA2qW1mDni7xFj3UzNuOSqnBV36K1WSitOa3Ryds7syL8bnV6zwUmeyvoqq3SVQPUrNzf8jLroDE0ROSS1NaX9nWR2tKtlUwvW965H4lUJxNvjOO+D5ym9Xv6GamXTHV1T9PObtn1xGIe+UT4YmJdLkShMt4la6o1ONU3ufQwU7rVQ7VpTJhG5YMASCez5q9ObsKkE08Ub9Tk5O2f2XujGxmpW7nlWq47ZvYcl1iTQvqUdqb9PoX1LeyiCATc3/CQGBES2ObHjpNkRj+zuLIa2DWHmuPFBtfwvl1v+UCxVi3x973ps2LUBHfd2IHVHCh33dmDDrg1Y37v+dAURnVP0c8DQJ4csj1Lm6cwfbrqkSdtzBcHcCffyrIrPU+G1dsFNF7jWjqibGJjA8HajE6XaEc0/zunZOSujv6mtKWPzO4dYueepzLrQaW4HVMSAgEgLnaOdZsrDZXoy6Ovuw861OzF4wyCmDkxhcmgSO9fuNIIDsx+KFapPVBtx8nqatnCUUltwIoDFly7W81y0QLnzlFiTwIV3Xej4CC+dtv+u/QDUg+nCPUicYrVU5mTa2TKyVgZq7M66RB0DKvdxDQGRJuVyOU2X5gRMlYerlpeafSZrBAQCldNvbFafcGr9hBX5nF5twYkEjn5X32ZmdKZq50l39Swqb3LfJLK7s8rBdP5xjlX3sVgqc35E2aGF6laCEzuzLmFI9bHLTkDF46eOAQGRZqXK8U29PIX0rWnbHXTT06gSgDBqZ2efWTjqpKv6hNcduHxnRGdwMpU2v9kPmWemQ6W7elYQJLuTgPRmL4pMT8b2ruGOVPdRGKxwtMqYxeBENUg69u/HQrcoWAUDKm8wICBykNm63GY76JY+9CRQ21KLDbs2OFZ9wusOXGFnxOvgRLdYSwyzGbVKTr5joUNVrZRiqAijfO/4L8cx+pNR18uwzozN2N41XHfqoMpghaPrGBSCE9Ug6dj3juHY947Nf53f6yTWHFN6vqCym8ZGahgQEDnEVF1uAaRuS5nqLKlOowJA+5Z2S4+zwssOXOGHtM7gpD5V7/lMwbl/fi4OfO5A8AMchQ5VYfrdy599GcPbhyGnwrdpQcOqBvu7NtcAi5YuwqnDpyw/NN9xtbNruLbZOWFcJ+U2TqvEqXUMqjOpuoKk/F4n+XK9UWE3jY3UcFExkQOspPakb02b+kCzWw3ESaUqE51/0/mOvmapFBRdi7uXXrHU87vj8g8s1169ym0Nqxvmy9SqGPv5GA58/kAogwEIk3sHVNB8STO6dnThwrsuVHp88a7hZq61UsGdltLLEhj50ojSQ3WNDNcuq8Wyty4rWVXNCt0lUCf3T0aqio7dNDZSE+CPGiL/cqJCQhCmUQsrE628ayUaVjc480IVUlB0lLJcfr23nfF8sNO2uQ0X3q3W2fODiUH1Dq+Tm135goYYZ/ypcaRvTyOeiqNhlbX3WnzlmaPO1YLpWFMMjasaSwZ3ukovq1baUR5RXnrm42aOzODIY0dw+NHDmB6ZVnrOPN3700Spio5KQGW1IhUtxICASDOnSs4FbRo105PBxF4LHUIBtN/YXv2uZDIFRbWUZWFn3JGNs6opCnaO/muAqx7ZKAfo6CLREMlvzrT0LUst1eCf3GeUKe7r7psffa60B0nj6saKuez590u8w156i8pIuOrI8MzR0oMlOja80r0/TdTKkloKqCwu+qbSIhUQCCEWCSFuEEJ8UQjxKyHEtBBCCiGu8bptFB5OpfYEbRo1fXva0ihow6oGdHyiw/ROyWZZ+mARQPwVcQxtG0J2d7ZkB2nZ25eZfm3LCoKd7O4sXrj5BU/Luuqg0pFxerOr0JkDhv5pCO1/bSKgLlKq86u6623Lpha0vrfVWgOKqMxoOrJLsYYNr3QPKgQ5bSi/iWb6zvT8/bUS0wGVzfLZdFrUVmAkAHwq9/8HAYwAcDbRmSLHqdQeu9VA3KTSoZvYMzHfCS+1n4NqdSRLi40lcPChgzj40EEARpWP1NbUGa+dvjONI988YrkdeU2XNOHEUycWfD+/gBEA+rr7QtUhtloOMMgdH8/MGSlEXTu6rC/wz3V+4yvitjtWdmckVR/vSJWx3AyXnWNS6n429p9jZ1QTMiuIVXQqVdorvL+WUq1gha7y2WSIWkBwEsAVAH4lpRwWQtwK4GPeNonCxsnUHjvVQNyko450qf0cVKlWQsqPnnbe3zk/K2Gnw5PsTmJ97/qSwQ4ADP7NIDLfD19n2GpHJogdHz8YfWIUdZ+pm7/Gnr3yWUzuM7l7r4bOL2B/RtLOTKgTJZB1bXhVeD8b2jakFBAErYpOtUp7pe6vxXQPEFF5wbq6bJJSTgP4vtftoHBzMrXH9Ieex9Ooqh26k3tPYmjbkGN7JhR+sJzoO4GRh0eqpzUVjZ4qH9OCAK2wc5DpyeC5P3sO2WfDmx9stSMTtI6PnxQG1aaDgRwdnV87ZUjtzmhWC/zjHXFMDlo7JoD+Da+Clv6pwnRRAJOzUzoHiKg03nWJNHM6tScI06iqHboD/3xgwfeqTStblf9g6evuM7/GoWD0VLXDUypAG94+jIFrB1zfkMptVs9dLBGtjZh0ygfjXu72qpS+o2lGs9KI8uFvHUb6lrTl59Q9YxWk9E9VKpX2ghTwhJGQMuSfRBUUpAxdK6V8wOJjny7zo4tWrVrVeN9999ltXiCNj48DAJqbmz1uibdmx2dxcu9J07/fuKpyBY9y5ibnMDs2i4lFRjWfpoYm1MS9rxUwNzmH7HN6R7zjK+JYtHSRludSbV/iVQnUxGvmz+9cu/GJVzNU+ZjXL69H3bl1Z3zP6jUSVDV1NUistdaRObnnJGZPlN+l2exxj6J4exyLzlmE6ZFpTB2wvrle/Xn1qGutW/B9q/f2U0dPYXK/+dF4ne/vck4dOoXJIeszBPljqpPZ93/+Wm9qaArMjsV2769+EKS+zHXXXYe9e/f+Ukr5GjvP448jTxQyseaY6d0l4yviyjf6mngNFp2zCKJWQNQK39xMa+I1iDXp/fCa3D+J2fHynUQrZsfUnmd2bBZzk3OYm5hDbdLcLEh8RXxBMAAAU8Pe7oTsFrPHKW9ucq5iMECVxRYb7ztRY6EGaYHix81NzuHUoVOQMxJyRmJu0tyw76Kli4yBjir3gfz+Bk4HA8DpY+PW4yo+p4XPCFEnAhMMAPbur+SdwKUMCSHSAFZYeMhXpJTv1t2OcpGYEOLp5ubmV2/cuFH3SwZCb28vAED33x/UBUWZnowrqT06jrvqMS73uMxsRnvVj/yiXLvSd6aVUgfqU/WYSk+hBjVYhEWY+aSRStB848JRpErnN7s7i51v2mn59YNow64Nlt6rQ9uGMHjjYMXfGf9kbvSuxHGPuq4fdqFlo5Eys/Nd1q+x/PkqrA5TgxpMftIYWa+5scZyGl/+HjExOIGpl6dQd14dGlc1enIf77vLegWvzu3lF73aVe0z4tiNxxBrjmn/THWS6v01dUcKqXemtLdHhVN9GSfomsUIXEAAYB8AK3N+C5OSKTDslCzzgyBUSFA9xmYep7vqh66KH6prHKbSFUb1BbD0yqVoeVNL1fMblbKaKnnPga8wJICz3nyWUhUZHQrXujSsarC0OWD+fOmoDlNIx4JQXfdQlfUNukqyllLtMyLfMQ2SoG2iSYbAHX0p5Sav20Du0P2h5CW/VkhQPcZWHqdUF70CHYseHQkiJXD020fR/sF2AKhYLSnz4wgEBIqLRIPQKYglY5gdLZPeIGErGFj+oeVYduUy7LtpX8n9KqrJB83TI9OYGLS2U3jqlpT26jB2qQxYVAoe5iu1XTNgvhEuLHr162eEiihUUQoj/995KZL89qEURqrH2OrjunZ0lay9PzE4gZc//bLldusYQbZTGrGiOWDXH+8q2VnMd14A4OjjR/W+rkXxlXHLJSktsVH21nfv4xrgnHedg8ZXNZ7RuUzfnkb61rT2ClENHQ1o2dSCS3Zegn0378NLd79k+TkyPRkcfvSwtZ3CVxuv29fd55vqMFYHLMwGD4t/Z7HltuianYyCKFRRCiMGBORLLFnmPNVjrPq44hGwoW1DFlts0DWC7MjOpkDZkeN856Who8HzMqOtV7fi7LedbXvn1FLsro1xLFhTNQcc+vohdO3omv+bMj0ZpG9LO3IeC4+bavnVQ/9yCGM/GbP0mImBCRx5/Ijl4+5UR9nSwMM1AxjePoyx/xwre04KgwfVReu69yMIs6BsokmnRS4gEELcBOCi3Je/lfv3L4QQr8/9/0+tliAlvbK7s775UAor1WOss8Pg9bSyUzubVjQHTOyxkMbhkNrFtVp2Tr3gpgtQ11anfW2MU8GaslxgW9dah0xPBi/d85IjbSseJVUNfq0GA3kjj4woPa5cR9lO3r+lgQcJjP3MxN+cm7U896pzTT7xmQK/vsVFQdlEk06LXEAA4A8BdBd977W5//IYEHjIyw11okL1GOvsMPhhWrnaJm/5qkJhU/zhq/phfO67z3XsPdfQ0eCL4Clv9IlR7FzrYGUosXCU1O1O0syoWoe3uKNstxiEyoCFaXPA6E/VnttMgObnAhJuC8ImmnRa5AICKeVGr9tAlamOwnD0xjzlY6ypw5Dnh2nlSlU+Mj0ZDN5QuQRm0BQHVfnOm93n0aVa3riqmsU1mBvzy5RDEQF0PrBwlNTt9Cmre0bMP66go6yjGITTVbgmB9XWz1TquAa9Ip5TglBpjwyRCwjI/1iyzHnKx1hDh6GQn6aVw1Tlo6yioEq58+1QcGY6b9yiJW9aghX/awX6L+v3fP1GscTFCXTc01H22nYzfar1qlYc+eYRy48rXFuhoxiEG4M7VhfWVwqAw1QRzymRuL8GnD+2NSUq4HVueRSoHqvWq1q1v17b5jZ07ehCsjtZ8ufJ7iS6dnR58kGaH6ENhGob0xYFVcqdbwEsfctSZHdnkd2dNf2w7O4shrYNIX1nGkPbhko+1lLeuAWrPr3KCD7v76x+nBxywU0X4PybzsdZV5yFs644CxfcdAE27NqADf0bKr4/8kGz05/Wye4kll25zPL1XthRVik4UIobgztL3rDE/DGtEABbDYKisgcJBQ+HVMl3/JBbHnYqxzhxcWK+w6D73Ph5Wtl3C1zLOOdd52BqeMpUrm52dxYD71cciZfA0ceO4uhjRunUaukQZlMpnMobb+hsmL+G2jYbQeVvbvsNpl+a1v5aldhZb1EtF9u2gg6vahqfzmIQbgzuNK1vQufr7c9OsiIehQUDAvIlP+SWh53Vjm722SyGHxx29Nz4cVq5ZVML2j/cjqFPqpVJdUt9qh6v+tqrKgZVlTrnqiqlQ1hJpVAtBVnNxJ6J+VFZ3X+7WToGLEoFzVrKxRZ1eFXT+HQWg3Bj7UT+fWFn0Ssr4lGYMCAgX/JTbnlYzR/jawfM5VXL0xuNBfncqMxCjO8cd6l16vI168sFVU4t1gVQMifcaiqFainIqiSw5/o9xq69XszyaB6w0FEuNq9ch1elOozuYhBOzswVBmh2ZidZEY/ChAEB+RZLljmvbXObkc/9jMlc8NyU9/re9YE7N6pVQLSnstQAibUJ88fcpEp5104t1j1DUTqE1VQK1VKQZnhZvjR1q3PvA9XnveCmC6qmMBV3lJ875zmIGoENuzaUfJzuYhCO7RNSJkBTmZ1kRTwKEwYE5Gt+zi0Pg+zurOWOaX7KO0jnxk4VEK2LAHOzJvEVce2jn5U6h04t1i2WvzYAWA6iVEtB+p1qZS4zVNdbXXjXhZZeI7EmgcHewfmvS3GiGIT2tROaZy1ZEY/ChFclBYIfc8vDQMeUt9/Pjd1SiLpG84pnTXSOfsY74mV/5ugmTyXYCaCsloIMgvE+Z9PN/LLeyqliEIUDD89e+azy9eHErCUr4lGYsOwoUQlmSiSGQRSmvO2WQrQzmlefqkfHvR3YsGsD1veuP6MjUK3cqhWTg5PYuXYnnrrkqQUdcrfLHM6MzShfH5ZKQQbEwYcPYvjBYcee33RZUhfW9KS2prSU8iwlsSaBzi9YK7+6+HWLy77/dFApS8yKeORXIbv1EtmT6cmgr7sPO9fuxOANg0jfksbgDYPYuXYn+rr7QldDOuxT3naqgOTFV5Qffa9mKj1VMYWqZVML1veux6LzFim/RqETT59A/+/3Y/Ajp3dXdjt4q11cq3x9NK1vcqXmvquk8/Xn/bKXh+ngRKitrbCyJ0P7je149U9fjfYt7Y52wJ0MgojcFKbbLpEtw9uH0X95f9kOZD7P3MnRPreFfcrbTkpUPjjc9dZdjrYhuzuLUwdO2XqNYkOfHJoPCtwO3vIpHqqPrda5ja9UD9A8U2ETLq3KVAubTE9i+IFh7Lt5n+OznqZmviSQ3ppWGmSp9vxNlzSh64dd6PhEh6XnVeWnGRoiO4IxzEfkMLt55kEV9k3gVEfHD3z+AE4+f9JcOVabbXBq5Hjok0NY+ualmDmuPkMQS8YwO2p+f4DCa8POdVVpwfrhbx3GYRy29of4gJP156stmp/aP4VD+w8t+H61TeVU5c9f+vY00remy76PKi3mN/P8filowIp4FAYMCIgQ7d0m/bIo0Qmqo+Mnf33StTY4mdIzX3/fonhHHOseW4fpkWnla0PHdVVqwXqmJwMEZwnLGV7+3MtY/c+rtT6nnZKyqh1ys+1K35auHlTbGGTxU0EDvwUpRFYxZYgiT0eeeZCFecrbD22t1gYnU3om9ihsxlUDdH6+E4k1CVvXhlPXlR/Oqaqj3z2q/Tltl5Sdc2aNg93F/EGVWJNA+5Z2pP4+5fj6BSKdGBBQ5NnJM/cb1epIflmUqJtKFRCd4ivjpsoq+knxYk+r10bhNTibnUXnfZ1ar6vEmgRiTTELfxHQsLpB6dMuliz9OrVL1YK4qfSU1oEEbSVlNXfIoz7IQhRETBmiyAtD6c3Z8VlMDU9h55t2LviZ2TzhsE55W0pd0WzFzSuq/o7KOg4nldpIy8y1UW0n6M4HOjGbndX77W65AAAgAElEQVRyXcUaY5g9YXJtQw2w+rNGms7gXw+a34ivBlj76FrUtdYh05PBeN84Rn8yisl9k5g5qv7eL9zDwy6dgxI61zjo2N+EiNzFgIAiL+ilN4e3D+PkaPmcd6t5wn7Ky9Uhn7qiaxMwsxo6G9C22dzIt5dBS7FKgW65a8PMTtCjPx1F5/2daN/Sbqt9mZ4Mpg9Nm/799g+3zwfDG/o3VF3oCmBBWcyxn4/h4CMHtZwfnQMJugcldHXIwzDI4jdhG6gh//FHj4bIQ0EsvZn/cBjvG8fBhw8Cn6jygJBVR7KqWhUQ7QSw+jPmF496FbSUYjXQdbtCV/r2NHCl+d8ff+rMnYJTW1NIvi5Z+VrIlcXM/DCDpf9tKV646QVt50XnQILuQQldHfKgD7L4iY7ZXyIzuIaAIi9Iu00Wb5x28KGD5ktjhmjhnor8JmAbdm1Ax70dWLJpiTMvVAN0PmB98XW1XP3Exe5cb1bb7ebiUV256flrIXVbChAVHvvkKF74qL5gIP/aZlVbE6S7I6irQx7EQRY/Gt4+jJN7T5ZNjwvj3jjkHYbjRAhG6c1qaRlmOFkLPSgK016O9xzX+tx2640X5+pPDE5g6uUp1J1Xh8ZVjRj/ZW5GyIwa42/NPmt+oabVQNdOB13lGtSZm266LKZGZo9vtfUY+VHhxJoE4ivjmNw3qaV9ujrkYd/fxA3zM28fr/KLEZ/9JX0YEBDBQsqGR6U37dQaL/Vc/ODV1/lZtHQROu7t0JrTOz0yjcOPHlZfaCyM6zS+Iu5ooOvk4tFSOdM6c9Ntl+u0yuTxNbMeo3BN0IqbV2DgmgHbzdPdIfdqkCUsufZR3huHvMGAgCjHz7tN6uy8cOGewXZ1nxogviKORUsXoX2jvYWyhXTMBAGYHzF0MtB1YvFopdHx+Mq40usVp8JoK9dpVu741rXWYWjbUNnOqsp6jLbNbXjxH17ExF7rG9AVtk/3rKdT1165Dr/ZWZUgcHvmjQhgQEB0Bj+W3tTdeeHCvdNUq/vUp+qx9IqlGEoMaW2PtpkgeXrE0MlAV/fi0WrBkGpqTPHf5uYeIsnuJJb+0VKMPDyCgc0LR/ILO6uqo8KrP7ca/Zf1q6U/OTjrqfPaq9Thb1jdYAREZf5+J3dkdgLLtpIX2DMgX/FLR9xPpTd1d16CMkqmQ7XryfQopgBqz6qdrz8/lZ7Cgc8eQHZlFrGmGDKzGS3HVedMUOGIoVOBrs7FozrT4gqVSoVxepbsvA+eh8ZVjWjZ1IKxn4+ZSgFK3ZpSHhVu2dSCzvutV6lyY9ZTx7VXLVCc2GNidiRAufYs20peYEBAvhCm6V7ddN7ko7Jwz8r1VG0UMz/6WG4zqtkTs1pGH51IYykeMdQd6OpcPOpITn+ZVJi5rHOLB5LdSaz+tFFy1koKUPpjaaXXO/jlg7jwrgurXsd159ch+Trj2Hsx2KJ67WV6Mhi4dkDP4u+A5NqzbCt5gVcPec7qIjo3+GWmAtB4k/eoOpLbVK6ncqOYsUTMeK5qnZE5YOAae6OPTqSxuDFiaCntSpS+Bh3J6a+QCiOdKi1U9B6zFOQoNunFu1/E6M9G54Ncv6U82rXnA3u0VoIKQq49y7aSFxgQkKfc3tTITHv8NlOh5fUKOkdh6iwUG94+bG40scz1VDyK2dfdZ6lDt+uPd2Hto2uVzpkTnXc3Rgzn065MjuJO7l+4DkA1GIp3xDE5uPD5qqXCxBIxpderqCgAcXPhcnGQa2Y0Pgj3geHtw/YWS5fh91x7lm0lLzAgIE/5qbSaH2cqAPvVcPKdI8Do4Pop2NGlUiBXVpXrSaVDNzuqnj7kROfdrfMZT5ms/iMXBmLZ3Vkc+/djSq/b+t5WnP22sy13bFWPtZUAxM2FywBMD5r4cdCjnP137XfkeYOQaz8/82ZGRGZ/yVkMCMgzfiqt5reZimJW0zJa39uKpvVN850jvwY7Otgp01npelLu0CleI7qvJzdHDNO3p5V2zLYcxBWpXVyrlJuueqzXPbYOAEwFIJ50OqsEuUG6D2R3Z7VtuFYsCLn2+Zm3p0afqvyLHu2NQ+Hj/3cFhZafSqv5aaaiFKs1vQs/zP0Y7OhKV9BRmabc9WSrQ2fiGil1DGzti1DIxRFD1cC+/0nFMpkFVK9TuykZpa6X4vPp5MLlSsoFuX68D1Ti5AxLUDrPbZvb0PidRkwNT5X8uZd741D4MCAgz/iltJqfZioqqVZFJNYUQ9eOrgUfDn4KdnSnK+ioTFPuerI7ilipY1buGCQuTgAC9jrKLo8YKnfcbAYDsaaYrfefrp10ldLVHFYqyPXTfcAMp2ZYgpZrH2uOobG5ERt2bfD9mg8KNgYE5Bm/lFbz00xFNeWqiOxZvQc18Rq0bDzzA9xPwY7udAVdizbLXU86OkPF10i1Y5B9JmsEBIpBgRcjhl7lY9e31QNQn23SsZOurl2ldSs+J366D5jlSFpPgHPt/bQ3DoUTAwLyjF9Kq/llpsKK4g+Hwd7Bkr/nl2DHiXQFXSkF5V7H7mJu4MxrxPQxkACEMVuQfSa74Mf5Tn9da13ZjrCbFWS8yseem56zvUjezk66Tm2kpkPxOfHLfcAK7UEtc+2JKmJAQJ7xS2k1v8xUOMEvwY4T6Qo62ljterKUVlLC9Mj0/P9brUlf21JbNU3ASjqSUxVkvOpgTe6fLHvvsDLbpFq735GN1DQpPid+uQ9YofL5UNNQg7mJhSeFufZE1fm/R0OhpiuP1w6/zFQ4wQ/BjlPpCrbbWGaTrEJWa+wXG/vFGAD1YwAA7VvaTf2+VxVkdMykOMLi4lgrKRlu7jFgVakg1w/3ARVWq6ut+866ijNnRFRejdcNoGjLd7iqXokOTvfmOzRWBGVhmh+CHTvpCpW4FZC1bW5D1w+6UNNk/XZ54qkT8yPPKsw+zmpKlu4KLqmtKX9+mhSUONXJ9T0GhMnfKzNo4of7gOrrm/58eKBzvvPfvqUdqb9PoX1LeyDu0+S+7O4shrYNIX1nGkPbhpDdvTA9M2r8eAuniGnb3IauHV1IdpfulCe7k+ja0eVoXWxLHZoALUzzQ7DjVLqCyt92Bmm+s9iyqQWtV7UqvUx+tFKF2ceppGTpZKXj5rb8bJNObqfSnPOuc2wNmvjhPqDKD58PFB6Zngz6uvuwc+1ODN4wiPQtaQzeMIida3eir7vP/WDfR5gyRL6gmser8/XtVhzxK6/TspxMV7Cb42+lkkpdW53Sa+SvZRVmHueXCjJmF+h6UaJT9+JYt1NpVvz9CrRd01bx2La+pxWzJ2aRvjNd8t5pNf3GT4MeXn8+UDhYSavEhe62zQ8YEJCveFlazU7FET/zOthxMl3B9N9WgdnOop1OvZPHQHcFGTudLrMdNztBnArdI/pu3gPyI/WJNYmSxzaWiGHkkREMXDOw8LEFC8mtroeZ3O/MLsGFrF5rLL1JqqymVc4+NotYc8yVtvkFAwKiAmEdifIy2HG6mlT+b3v+mucxlS69o2clZjuLdjr1Oo5BuWtSVzqSzgpFlTputoI4xf0ZdI/ou7aQWgANr2jA0Lah+fNdeGytLiSPp+LmXlc6u1uxzmstbPdqcobVtMqp4Sk0Njc62STfYUBAVEIYR6K8DHacTltq2dSC8z98PgZvKL0fQyVmO4t2O/Wqx6Ba5ymxVu3cFf7dblcoqhaglpJPiyk1El6NE53a1NYU+n+/X/vznkECIw+NAA8ZXxZ2llX29kjfnjYfUDm0W7Gua82LErsUTCpplbMnZjE36dO6wg5hQECRFOVRJS+CHTfSltyopGInsFE5BmY6T6M/VRuljq8wRoud2DTOjEoBar5dpd6fI4+MYBzjpl/HqcWxda1qa0rsKOwsjzw8YmnE84WbXsD4U+aPG6B/rYmua82rErsUTKpplbNjs5pb4m8MCChSOKrkHafTltzY6M5uYGPlGFjpPKnY9dZdSL4xiZnjM9o3jbOiXIBa7ryktqYw1D9k7skdrAimuxpJ8283Y3znePUR/DkYsyQWU6esBgN5Ohdk69ig0KsAloJLNa1SzinkJwYYAwKKDI4qec/ptCU3KioVdupLjVRXC2zMHgM3dsJVyYHXNWqseg20bGpB/Hi8+qJXhyuCaS89OgfznXwX+ym6/k5d1bCc2PWcwk11DZGoMbsBSDgwIKBI4KiSvziVtuRWRaV8pz7zbxnMjs0idUfKcmBT6Rgo74SruOjWKjujxjpm6RYtXYSauhoku5OuLpIvDGJO9J3Q9rxNlzQpj+A7TdeCbB3VsPxSYpeCRfU+EFvMKkNEocNRpehws6JSTbwGNfEapN6Zsv1chVQ7T+ddfx6yu7KmF+qqUh011jlLF2uOYX3velfWA1UKYmyrARZfuhgnntIXYOik6z6ooxqW7hK7FA0q6aSxphhq4tHau5cBAYUeR5WiJ+jlY1U7T3WtdVj9z6tx5PEj2PXWXZpbdZrKqLFTs3ROL5KvFsTYkpupmjpgvVyuiqbXNOHE0+YDD50LsnVszuf0jt8UXlbTSevb6h1vk99EK/yhSLIzqkTBlliTQPuWdqT+PoX2Le2BCAYA+50npzeVUhk1Vpml85rpIEZBsjuJrh1daHtfmys7Hye7k1j5DyvNf+prXpCtowqYkzt+U7jl00mrXv+5ID1qm5IBnCGgCOCoUrQFcZbAbufJyWtXZdTYq1k6u+feiYXd8ZVxrLh5Bdo2n06JUk7LqYGlBfRe7lquowqYG6WFKbyspJMO9Frf7yToIhUQCCFWAXg7gD8AsArAuQAyAH4O4FNSyh972DxyiOro0Im+E0jfmQ5MJ5LOFOQSs3Y7T46NiCqOGrud+63j3Csv7K5ict+k0SEXmF8noXq+W9/Tarlzn+8UDf71ILLPZEs+JLE2Mb9PhU52q4C5UVqYwi3o6aROilrK0B0A7oYRCHwPwCcB/AeAPwLwIyHEFg/bRg5R7fQd+eYRpG9JY/CGQexcuxN93X1MIwqI4e3D6L+8v2zHIb94dfjBYZdbZl5qa0o5vcORQMfGqLGbs3S6zr2j7/XcOonC11A5322b29C1owvJ7mTJXytMSyo0mZ5EdlfpYAAAss9kHXl/WE3bKHWt2XlfEOUFNZ3USVELCP4NwKullGuklO+XUt4spXw7gE0ATgH4uBCCBehDJj+qZFcQOpFkffGqX4M8O50nlWs+cXHCcsfSLLdyv3Wee8dTBovWSaie75ZNLVjfux4bdm1Ax70dSN2RQse9HdiwawPW9663vbGX7veHahCTpyOoIKKFIpUyJKV8qMz3nxBC9AK4DMBrATzqYrPIBZamqivhPgW+F6YSs3ZKqFpNz+i4p8OxqXS3cr91nns3FqIWr5Owc77NVlvyw/vDbtqGm6WFiaIiUgFBFady/3IlaQiZXkxnhs87kVEWxhKzqp0n1QWkTpTxdCP32+65Lz6+TuTQl1K8TsLJHGc7x8gJdq415oIT6SWkdHEPdJ8SQqwAMABgFkC7lLLqHKkQ4ukyP7po1apVjffdd5/OJgbG+Lix22Zzc7PHLSltdnwWU8NTmD0xa/u5Eq9K+GbjEr8fd7ecOnQKk0PWS27G2+NYdM4iy48LwnGvdM3HmmKob6tfUGJvbnIOs2OzkHMSokYgttj+Jj2z47M4ufek6d9vXNVYtvRfqeOueu7rzq7D7MRsyeMjYgJy1tnPyPrz6lHXWufoa+TZeX9MNhiP8/O1HkZBuMeEUZCO+3XXXYe9e/f+Ukr5GjvPE/kZAiFEPYCvAKgH8FEzwQAFV6w5hsbmxjM6PLMnZzFz3PrE0OzYrG8CAjLIObXOm+rjgqDUNV+uk68SPFhpR3xF3NQeCfEVccuvo3oOpw9Pl39Oh4MBABA1wvHXyHPr/eFEQElEzgpcQCCESANYYeEhX5FSvrvMc8UAfAnA6wB8HcAnzD5puUhMCPF0c3Pzqzdu3GihieHR29sLAAjS35++M430LWnLj0vdkULqnSnt7VERxOPuhKFtQxi8cdDy4zru7UD7n7ZbflyYjnt+R97Gucbyv5RLL1JdXAzkSoLazP0uddxVz73XNuza4FqKi533x+DFxuMqXeuF5V5rilb9+r3Ur1+F6R4TJEE67rpmMQIXEADYB8DKnOeBUt/MBQNfBvAOAN8A8G7J/KlI4u6X4cGNi9RYrTxjZ1G9U7nfTp/DWDKG2VH7qYaF3K6Rb+v9cbjy7+QDynLXUL5Km92AkoicEbgejZRyk93nEELUAvgqjGDgqwCuklLqvdNTYLATGR7cuEiNF5VndC9eVjn3VsyOzmLtY2sxuX9yPoiZGp7CS3e/pPaEFmrk6wqebL0/esv/jpsBJRE5I3ABgV1CiDoYMwJXAngEwF9IKTVvTk9Bwk5kuNjdDbWcUp2yMAhTZSZt5YXLmNw/ifYtp1PL0nem1Z5ImKuR78Ru2068P/xQypSI7InUKp/cAuJvwQgGtoPBAOVw90v/y+7OYmjbENJ3pjG0bahsKUTdGxdlejLo6+7DzrU7MXjD4Bm7V5/ccxKz48GeXFTdeMqPG7qZPveK63iLNytTTRs87wPnVU2bcWq3bd3vD7+VMiUiNVGbIfg8gCsAHAHwMoCtQiz4ZOiVUva63C7ymGrNdnKeyiipro2LquVFz54wSmkOPzgc2Lxo1R15Hd/JV5GZc59Ym8CBz5RcXlZRcQCgeh9Yfv3yij93OgVH58ZedgJKv80wEUVZ1AKCV+T+XQZga4Xf63W+KeQ33P3Sf+wsVLS7eNV0pwzBzosO46L6auc+uzurFBAUn1+n0g3dSMExc4yGtg3Nf39u9VzJ0qFhCyiJosq/d3QHSCk3et0G8jfufukfukZJVRevRiUvOsyL6sude50ded05+W6v6Sg+RpmeDPZcv2dBG7KfzCLWFENmNnPGuQ9jQEkURXxHEpWguwIKWedlhzxMC22rieqiel0ded3phl6m4JhJkeu/rB/LrlyGJW9aMj+AoiIIASVRlERqUTERBYPXCxXDtNDWjCguqte5uLZtcxu6dnQh2Z0s+fNkdxJdO7pMrTPxKgXH9IycBI48dmR+Yf2e6/cgsc5aIBKGgJIobDhDQES+4/VCxajlRUd1Ub3OdUO60g29SsGxNCNXYPTJUaNqkwBgZmvPkASURGHDgICIfMfrDnkU86Kjuqhe97ohu+mGXqTgqMzInUHCXFAQsoCSKEyC++lFRKHldYc8qnnRUV5U75d1Q16s6dCS6iaBxMUJ1LbURiqgJAoLBgRE5Dted8ijutA2zy+d46hyarftcnTNrGWfyWLDrg0AELmAkijoGBAQke/4oUPudqeMCDDSd7K7s1j2lmU48vgRV1JwdKa6ZXoyaN/SzgCAKGAYEBCRr+TTVeIXxjH6k1HPFiqaXmgL5kWTfZV25C5FZwqOzms3qAvriaKOAQER+YLVDtE8BxcqVltoG2uKob6tHm3/vXo5SaJyqtX/BwAIYOmVS9HyphbtKTgqM3LlBHlhPVGU8Z1LRJ4z1SEqwY2FipUW2u48vNOx16VosFL//+i3j6L9g86k41hKkauAM2VEwcSAgIg8ZbpDBAACaH1vK5rWN7m+ULHkQtte116eQsrLHbkLWUmRKydMC+uJooY7FRORpyx1iCQw8ZsJLlqkUPB6R+5i1XZcrogL64kCjTMEROQZOx0iBgQUdF7vyF1KcYpc5scZHH38aOUHccMxosDjDAERecZOh4go6LzekbuSxJoE2re0Y9231qHrB12INcVK/l6yO4muHV1oex8X1hMFGWcIiMgzfu4QETnN6x25zWrZ1ILGWCPmJufQcW8HNxwjCiEGBETkmaB0iIic4PWO3FbVxGvQvqXdk9cmImcxZYiIPBO0DhGRTvn6/1awkg8ROYEBARF5hh0iirrU1pT5T2JW8iEih3DenYg8ZWlDJHaIAqPURm4M5BYyXf/foUo+PE9EBDAgICKPed0hIr0yPRmkb0+XLCebfGMSqa3O7iwdRG2b2xBPxZG+I43RJ0ocNwd25OZ5IqJCDAiIyHNedIhIv+HtwxUDu9EnR9F/eT867+9kmcoixfX/nRyx53kiomIMCIjIF9zsEJF+mZ5M9VkeAJgDBq4dQHxFnAFeCYk1CUev97CfJ94/iNQwICAiX3G6Q0TOSN+eNrcOBADmgPQd6UB1NMMirOeJKVBE9rDKEBER2ZLdnS3ZEatk9IlRZHdnHWoRlRLW8zS8fRj9l/eX/dvyKVDDDw673DKi4GBAQEREtmR6Mq4+jtSE8TxZTYHy899C5CUGBEREZMvM2IyrjyM1YTxPKilQRLQQAwIiIrKldrHacjTVx5GasJ2nsKZAEXmBAQEREdmiuliTizzdFbbzFMYUKCKvMCAgIiJbEmsSSL4xaekxye4kq0m5LGznKYwpUEReYUBARIGT3Z3F0LYhpO9MY2jbEFMAfCC1NWX+E6UGSN2ScrA1VE6YzlPYUqCIvMR3BREFBmuN+1fLphZ03tdZveJLDdB5fyfPk0fCdJ7ClgJF5CXOEBBRILDWuP+1bW5D144uJLtLp6Uku5Po2tGFtve1udwyKhSW8xS2FCgiL3GGgIh8z2qt8fiKOEcBPdKyqQUtm1qQ3Z1FpieDmbEZ1C6uRcumFnbEfCQs5ym1NYX+y/vNlR71eQoUkZcYEBCR76nUGmdA4K3EmkSgOpZRFfTzFKYUKCIvMWWIiHyNtcaJqJKwpEAReYkzBETka3ZqjQd55JOIzAtLChSRVxgQEJGvsdY4EZkV9BQoIq8wZYiIfI21xomIiJzFgICIfI21xomIiJzFgICIfI21xomIiJzFgICIfC+1NWX+bsVa40RERJYwICAi38vXGq96x2KtcSIiIssYEBBRILDWOBERkTNYhoOIAoO1xomIiPRjQEBEgcNa40RERPowZYiIiIiIKMI4Q0BEREQUQkyvJLMYEBARERGFSKYng/TtaYw+ObrgZ8k3JpHammI1NjoDAwIiIiITONoaXUE698PbhzFw3QAwV/rno0+Oov/yfnTe38mqbDQvUgGBEOJ8ADcDeA2AFQBaABwFsA/AgwC+LKU85V0LiYjIbzjaGl1BO/eZnkzFYGDeHDBw7QDiK+K+aj95J2qLilcC+B8ARgE8BuCTAL4DIzh4EMAOIUSkgiQiIipvePsw+i/vL9khBE6Ptg4/OOxyy8hpQTz36dvT1YOBvDkgfUfawdZQkESt8/szAC1SyjPeLkKIRQB2ANgI4O0AvuF+04iIyE842hpdQTz32d3ZssFLOaNPjCK7O+vb9CdyT6RmCKSU08XBQO77p2DMGADAKndbRUREfsTR1ugK4rnP9GRcfRyFS6QCgnKEEDEAV+S+fMbLthARkffsjLZSsAX13M+Mzbj6OAoXIaX0ug2uE0IsA/BBAALA2QAuA9AB4KsA3i1NHBQhxNNlfnTRqlWrGu+77z5dzQ2U8fFxAEBzc7PHLYkWHndv8Lh7w43jfurQKUwOTVp+XLw9jkXnLHKgRd6K0rXup3Nv5bj7qd1BF6Tr/brrrsPevXt/KaV8jZ3nidoagrxlAD5W8LUE8AkAf2cmGCAionCTc2ofBaqPI/8I6rmPLY65+jgKl8AFBEKINIyqQGZ9RUr57sJvSCmfN55KxAAsB/A2ALcDeL0Q4o+klMeqPWm5SEwI8XRzc/OrN27caKGJ4dHb2wsAiOrf7xUed2/wuHvDjeM+tG0IgzcOWn5cx70daP/Tdgda5K0oXet+OvdWj3vfXX2W0p2S3Ums/+v1Ci0LtyBd77pmMQIXEMDYM8DKnNiBcj+QUs4CeBHAvUKIgwC+BiMw+KCtFhIRUaCpVozxutIM2Rfkc5/amkL/5f3mFkTXAKlbUk43iQIicAGBlHKTQ0/9/dy/Gx16fiIiCojEmgSSb0xaHm1l+cbgC/K5b9nUgs77OquXTK0BOu/v9EUQQ/7AKkOnLc/9y+X2RESE1NaU+U9JjraGSpDPfdvmNnTt6EKyO1ny58nuJLp2dKHtfW0ut4z8LHAzBHYIIS4F8KyU8mTR95sA3Jv78ruuN4yIiHyHo63RFfRz37KpBS2bWpDdnUWmJ4OZsRnULq5Fy6YWX8xkkP9EKiAAcDOAjUKIJ2CsHTgJ4HwAbwawBMZOxnd51zwiIvKTts1tiKfiSN+RxugTC1NIkt1JpG5J+a5DSPaF4dwn1iQYAJApUQsI7geQBbABxlqBRgAZAE8D+AaAB6WUTBkiIqJ5HG2NLp57iopIBQRSyu+CKUFERKSAo63RxXNPYRepgICIiBbi6CcRUbQxICAiiqhMTwbp29Mlyysm35hEaqu/86OJiEgPlh0lIoqg4e3D6L+8v2yt9dEnR9F/eT+GHxx2uWVEROQ2BgRERBGT6clUL6cIAHPAwLUDyPRkXGkXERF5gwEBEVHEpG9PVw8G8uaA9B1pB1tDREReY0BARBQh2d3ZsmlC5Yw+MYrs7qxDLSIiIq8xICAiihDV9B+mDRERhRcDAiKiCJkZU9t7UfVxRETkfwwIiIgipHaxWrVp1ccREZH/MSAgIooQ1X0FuB8BEVF4MSAgIoqQxJoEkm9MWnpMsjvJnYuJiEKMAQERUcSktqbM3/1rgNQtKQdbQ0REXmNAQEQUMS2bWtB5X2f1T4AaoPP+TqYLERGFHAMCIqIIatvchq4dXUh2l04fSnYn0bWjC23va3O5ZURE5DaWjSAiiqiWTS1o2dSC7O4sMj0ZzIzNoHZxLVo2tXDNABFRhDAgICKKuMSaBAMAIqIIY8oQEREREVGEMSAgIiIiIoowBgRERERERBHGgICIiIiIKMIYEBARERERRRgDAiIiIiKiCGNAQEREREQUYQwIiIiIiIgijAEBEREREVGEMSAgIiIiIoowBgRERERERBEmpJRetyFUhBBHGxoaznrlK1/pdVM8MT4+DgBobm72uOruq80AAAzvSURBVCXRwuPuDR53b/C4u4/H3Bs87t4I0nH/9a9/jYmJiWNSyqV2nocBgWZCiN8AWAwg7XFTvHJR7t/nPW1F9PC4e4PH3Rs87u7jMfcGj7s3gnTcUwDGpJSvsPMkDAhIKyHE0wAgpXyN122JEh53b/C4e4PH3X085t7gcfdGFI871xAQEREREUUYAwIiIiIioghjQEBEREREFGEMCIiIiIiIIowBARERERFRhLHKEBERERFRhHGGgIiIiIgowhgQEBERERFFGAMCIiIiIqIIY0BARERERBRhDAiIiIiIiCKMAQERERERUYQxICAiIiIiijAGBOQoIcT5QojPCiF+IYQYEUJMCSEOCCF+IoT4CyHEIq/bGEZCiFVCiL8VQvxICPGSEGJaCHFQCPG4EOJNXrcvjIQQi4QQNwghviiE+FXumEshxDVety0MhBDtQogHc/ePKSFEWgjxKSFEi9dtCyshxJ8IIT6du1+P5a7nL3vdrjATQiwVQlwjhPiWEGJQCDEhhBgVQvxUCLFZCMF+m0OEEP8ghOjJfWZOCCGOCSH6hBAfE0Is9bp9TuPGZOQoIcRGAI8D+AWAFwAcA7AUwJsBnA+gF8BlUsoZj5oYSkKI/wvgXQCeA/BTGMe9E8BbAMQA3CCl3OZdC8NHCLEEQCb35UEA0zCu8WullA941rAQEEKsBPAzAOfAuJ88D+C3AbwJwACA10kpj3rXwnASQvwKQBeAEwCGAFwE4CtSynd72rAQE0L8JYDPARgG8GMALwI4F8DbASQBPArgHZKdN+2EENMAfgnjc/MQgASA3wFwCYADAH5HSvmSdy10FgMCcpQQog7AjJRyruj7iwDsALARwLuklN/woHmhJYS4GkC/lLKv6PvdAH4AQAJISSmHPWheKOWu9U0AfiWlHBZC3ArgY2BAYJsQ4t8BXA5gi5Ty0wXfvwfAhwF8QUr5l161L6xys4lDAAYBdMPooDIgcJAQ4vdgdES/W/i5KYRoBfBfMAYZ/kRK+ahHTQwtIURcSjlZ4vv/G8DfAficlPJ691vmDk49kaOklNPFwUDu+6cAPJb7cpW7rQo/KeVDxcFA7vtPwJiVqQPwWrfbFWa5a/37DLL0EkJcCCMYSAP4TNGPPwYgC+A9QoiEy00LPSnlj6WUezka7R4p5Y+klN8p/tyUUo4A+Hzuy42uNywCSgUDOfkBy1D3VRgQkCeEEDEAV+S+fMbLtkTQqdy/TNOiIPi93L87SnSSxgH8B4BGGFP7RGHGe7c3/nvu31D3VWq9bgBFgxBiGYAPAhAAzgZwGYAOAF8F8K8eNi1ShBArYKS1nATwpMfNITKjM/fvnjI/3wtjBmE1gB5XWkTkMiFELYCrcl/+m5dtCTshxEcANMFYs3EJgNfDCAbu9rJdTmNAQG5ZBmN6P08C+ASAv+N0tDuEEPUAvgKgHsBHpZSZKg8h8oNk7t/RMj/Pf3+JC20h8srdANYC+J6U8t+9bkzIfQTGQu68fwNwtZTysEftcQVThqiqXHk/aeG/BWXppJTPSykFjCB0BYyFgNcBeFIIcZbLf1Ig6DjuBc8VA/AlAK8D8HUYwRgV0XnMyTUi9y8HFiiUhBBbANwIo7rWezxuTuhJKVtz/ZVWGNWdLgTQJ4R4tbctcxZnCMiMfQDKLbYp5UC5H0gpZ2GUUbtXCHEQwNcA3A4jnYjOpOW454KBLwN4B4zFUe/mrExZ2q510iY/A5As8/PFRb9HFBpCiL8CcC+MUpibpJTHPG5SZEgpDwL4lhDilzBSFh+BMUsTSgwIqCop5SaHnvr7uX83OvT8gabjuOfyTr8KIxj4KoCrckEZleDgtU7qBnL/ri7z83zlj3JrDIgCSQjxPwH8E4BdMIKBQx43KZKklPuFEM8B+C0hxDIp5RGv2+QEpgyRl5bn/mXFBAfk6uL/C4xg4BEA72EwQAH049y/lxfv0iqEaIaRBjcB4OduN4zIKUKIv4URDPwKwJsYDHjuvNy/of0MZUBAjhJCXCqEaCzx/SYY06AA8F13WxV+uQXE3wJwJYDtAP6i1H4QRH4npdwHYxPDFIC/KvrxbTA2cXpESpl1uWlEjhBC3AJjEfHTMGYGQjki7SdCiItym78Vf78mtzHZOQB+FuZiHNypmBwlhHgMRkrQEzDWDpyEsdPim2FUBfkZgD+QUp7wqo1hJIT4IoCrARwB8FmUXnDZK6XsdbFZoSeEuAnARbkvfwtAF4xrfG/uez/lrsXWCSFWwjiO5wB4HMCvAVwK4E0wUoVeK6U86l0Lw0kI8VYAb8192QrgDwC8AOAnue8dkVJ+xIu2hZUQ4r0AHoIxEv1plF4bk5ZSPuRis0Ivl571cRjluPcBOAqj0lA3jEXFIzCCs+c8a6TDGBCQo4QQfwTgzwFsgPHmagSQgVHT9xsAHpRSMmVIMyFEL4wbWSW3SSlvdb410WHiuD8spbzandaEixDifBgFCP4QwFIAwzB2O7+NCy2dIYS4FWeWiy62X0qZcqc10WDimAPAE1LKjc63JjqEEGsBfABGCmI7jAHLLIwBh+8C2Bb2+wwDAiIiIiKiCOMaAiIiIiKiCGNAQEREREQUYQwIiIiIiIgijAEBEREREVGEMSAgIiIiIoowBgRERERERBHGgICIiIiIKMIYEBARERERRRgDAiIiIiKiCGNAQEREREQUYQwIiIiIiIgijAEBEREFnhCiVwghvW4HEVEQMSAgIiIiIoowISUHVIiIKNiEEBcAaJRSPu91W4iIgoYBARERERFRhDFliIiIShJCPCaEkEKID5X42R25nz1g8rneKoT4shBijxAiK4Q4IYR4WgixRQhRU/S7lwohpoUQLwghkkU/axNCHMw9/qKC7y9YQyAM7xVC/EwIcVgIMSmEeEkI8e9CiHdZOxpEROHFGQIiIipJCHEWgD4A5wL4XSllX+77mwDsAPA8gA1SypMmnut5AHMAngbwMoAkgN8DsBrAl6WU7yn6/Y8A+DiA/yelfGfuezUAfpB73NVSyocLfr8XQLeUUhR87/8AuBnAbwB8H8AogDYAGwA8L6X8E4uHhIgolBgQEBFRWUKI1wJ4Akan+tUAGgH0w+jQb5BS7jb5PCullPuKvlcD4IsArgLwO1LKXxT8TAD4DoA/AvCXUsovCCE+BuBWAF+SUl5V9Fy9WBgQHAUwAWB1cdAihFgmpTxipu1ERGHHlCEiIipLSvkzALcAWAXgCwC+DKAVwBazwUDuefaV+N4cgHtzX/5B0c8kgKthzCZ8Sghxfa4dAwA+YOFPOAVgtsRrMxggIsqp9boBRETke/8AYCOAP899/TUp5fzaASHEEgD/s8TjPiWlPJ77naUA/gbAFQAuBJAo+t3lxQ+WUh4RQvw5gB8B+AyASQDvklJmTbb7KwA+BGC3EOL/wZjp+E8p5ajJxxMRRQJThoiIqCohxPsBfD735aVSyv8q+FkKRkpRsVdIKdO5gOGXAF4B4L9grCM4BmAGwBIANwB4WEp5dYnXjQN4LvfY70spryjTvl4sTBmKAfgggPcBuDj37RkA3wNwo5Ry0MSfTkQUegwIiIioIiHEKhgd+lMw1g7sBvDbUspJk4/PLxC+TUp5a9HPfhfAz1A+IPgCgOsAHAGwDMC7pZRfKfF7vSgKCIp+fg6A1wP4UwDvALAPwBop5ZSZv4GIKMy4hoCIiMoSQtQD+DqMFJ8/BXAXgHUAPmXhaTpy/z5a4mfdFV77HTCCgSdhLGg+DODzuQDFEinlISnlN3MVi34EYCWAtVafh4gojBgQEBFRJZ8AsB7AP0opdwD4GID/APB+IcQ7TT5HOvfvxsJvCiHWwygLuoAQ4kIA9wM4CuB/SClfglGNKAHg67lApSwhRL0QYlOuWlHh9xcBOCv3ZdVyqUREUcCUISIiKkkI8VYA3wLwCwCvl1LO5L5/PoBfwShMsV5K+UKV5zkPwLMw1gs8DmAvjKpF/w3ANwG8CwUpQ7lO+3/A2C/gSinltwue6+MAPgLgn6WUHyr4fi8KUoZy6xYyMIKRXwDYDyAO4DIArwTwbSnllWpHhogoXDhDQERECwghLgDwIIzNvP4sHwwAQG60/n0AFgP4v0KIukrPJaU8AOANAL4LI4//gwBWALgewE0lHnI3jGBgW2EwkPN3MBYmf1AI8bYKL5sF8LcwNk97LYyFy38OYAxG2dJ3VGozEVGUcIaAiIiIiCjCOENARERERBRhDAiIiIiIiCKMAQERERERUYQxICAiIiIiijAGBEREREREEcaAgIiIiIgowhgQEBERERFFGAMCIiIiIqIIY0BARERERBRhDAiIiIiIiCKMAQERERERUYQxICAiIiIiijAGBEREREREEcaAgIiIiIgowhgQEBERERFFGAMCIiIiIqIIY0BARERERBRh/x+T00/tn6Xe5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 386
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### fill in here: one line\n",
    "\n",
    "plt.xlabel('x-axis')\n",
    "plt.ylabel('y-axis')\n",
    "plt.title('My plot')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 377
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### fill in here: one line\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [3, 10, 7, 5, 3, 4.5, 6, 8.1]\n",
    "N = len(y)\n",
    "x = range(1, N+1)\n",
    "### fill in here: one line\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chnage ticks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 373
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = {\"a\":1,\"b\":2,\"c\":3,\"d\":4}\n",
    "plt.bar(range(len(z)), z.values())\n",
    "### fill in here: one line\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Just for fun: [XKCD](https://xkcd.com/)-style sketch plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 267,
       "width": 383
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.xkcd():\n",
    "    # Based on \"Stove Ownership\" from XKCD by Randall Monroe\n",
    "    # http://xkcd.com/418/\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_axes((0.1, 0.2, 0.8, 0.7)) #[left, bottom, width, height] \n",
    "    ax.spines['right'].set_color('none')\n",
    "    ax.spines['top'].set_color('none')\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    ax.set_ylim([-30, 10])\n",
    "\n",
    "    data = np.ones(100)\n",
    "    data[70:] -= np.arange(30)\n",
    "\n",
    "    plt.annotate(\n",
    "        'THE DAY I REALIZED\\nI COULD COOK BACON\\nWHENEVER I WANTED',\n",
    "        xy=(70, 1), arrowprops=dict(arrowstyle='->'), xytext=(15, -10))\n",
    "    \n",
    "    # xy(float, float)\n",
    "    # The point (x, y) to annotate. The coordinate system is determined by xycoords.\n",
    "    # xytext(float, float), default: xy\n",
    "    # The position (x, y) to place the text at. The coordinate system is determined by textcoords.\n",
    "    \n",
    "    plt.plot(data)\n",
    "\n",
    "    plt.xlabel('time')\n",
    "    plt.ylabel('my overall health')\n",
    "    fig.text(\n",
    "        0.5, 0.05,\n",
    "        '\"Stove Ownership\" from xkcd by Randall Monroe',\n",
    "        ha='center')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}