{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "589a1326",
   "metadata": {},
   "source": [
    "# Numpy examples with plots\n",
    "NumPy is a foundation package for scientific computing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb93ebe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a2ef351",
   "metadata": {},
   "source": [
    " ## Creating NumPy Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a1d06454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a numpy array from a Python list\n",
    "\n",
    "my_list = [1, 2, 3, 4, 5]\n",
    "arr = np.array(my_list)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7dc63ba4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a numpy array with evenly spaced values\n",
    "\n",
    "arr = np.arange(start=0, stop=10, step=2)  \n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b1272d35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.31334374, 0.84585485, 0.30977214, 0.70435732, 0.68546196,\n",
       "       0.81527989])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a 1D array with 6 random values between 0 and 1\n",
    "\n",
    "arr = np.random.rand(6)  \n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d583552d",
   "metadata": {},
   "source": [
    "## NumPy Array Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c2fb1348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3133437374403655"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[0]  # Accesses the element at index 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "622e5686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.84585485, 0.30977214, 0.70435732])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[1:4]  # Accesses a slice of elements from index 1 to 3 (exclusive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d0079b12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape  # Returns the shape of the array (dimensions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d417fb9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.size  # Returns the total number of elements in the array\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a80e4414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6123449836881373"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(arr)  # Computes the mean of the array\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6c8ae366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8458548489635508"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(arr)  # Finds the maximum value in the array\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8c7d1b27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.6740699021288235"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(arr)  # Computes the sum of all elements in the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8b04476a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.31334374, 0.84585485, 0.30977214],\n",
       "       [0.70435732, 0.68546196, 0.81527989]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reshaping\n",
    "arr.reshape((2, 3))  # Reshapes the array into a 2x3 matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "120deab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.31334374, 5.84585485, 5.30977214, 5.70435732, 5.68546196,\n",
       "       5.81527989])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Broadcasting\n",
    "arr = arr + 5  # Adds 5 to each element of the array\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cb5fbaf",
   "metadata": {},
   "source": [
    "## Array manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "40d9155e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.31334374, 5.84585485, 5.30977214, 5.70435732, 5.68546196,\n",
       "       5.81527989])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.T  # Returns the transpose of the array\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3cac35ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.31334374, 5.84585485, 5.30977214, 5.70435732, 5.68546196,\n",
       "       5.81527989, 0.        , 2.        , 4.        , 6.        ,\n",
       "       8.        ])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenation\n",
    "arr1 = np.arange(start=0, stop=10, step=2)  \n",
    "\n",
    "arr_new = np.concatenate((arr, arr1))  # Concatenates two arrays along a specified axis\n",
    "\n",
    "arr_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "30d822c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([5.31334374, 5.84585485, 5.30977214]),\n",
       " array([5.70435732, 5.68546196, 5.81527989])]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting\n",
    "np.split(arr, 2)  # Splits the array into two equal parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9489d1c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.84585485, 5.30977214])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract a slice form the array\n",
    "arr[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5750037f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11 22 33]\n"
     ]
    }
   ],
   "source": [
    "# Sum of 2 arrays\n",
    "\n",
    "arr1 = np.array([1, 2, 3])\n",
    "arr2 = np.array([10, 20, 30])\n",
    "result = arr1 + arr2  # Adds the arrays element-wise\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ebeb337",
   "metadata": {},
   "source": [
    "## NumPy and Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6328cfc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.array([10, 15, 20, 25, 30])\n",
    "\n",
    "mean = np.mean(data)  # Computes the mean of the data\n",
    "mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8f4c7c2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.0710678118654755"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std = np.std(data)  # Computes the standard deviation of the data\n",
    "std"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145a6cbc",
   "metadata": {},
   "source": [
    "## Creating Multidimensional Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eb34488a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "print(arr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6b7da1e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeros_arr = np.zeros((3, 4))  # Creates a 3x4 array filled with zeros\n",
    "zeros_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "33c9430d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ones_arr = np.ones((2, 3))  # Creates a 2x3 array filled with ones\n",
    "ones_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1718cc43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "identity_mat = np.eye(3)  # Creates a 3x3 identity matrix\n",
    "identity_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cf13054b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.17128251, 0.01012276],\n",
       "       [0.50822073, 0.90303273]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_arr = np.random.rand(2, 2)  # Creates a 2x2 array with random values between 0 and 1\n",
    "random_arr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a03501f5",
   "metadata": {},
   "source": [
    "## Plots and visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09bae6c0",
   "metadata": {},
   "source": [
    "### Line Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "088774f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qfpcjIiIi4oeK3bc+C24nmVnEgTNjmlkCgcXG9wOfH3DMp8BFwMnAS5XON5bALJoznXNF9ZZaJAQ555i5ZidPzMpk1pqdNIuO5PzhXTkvvSsDOiV6f8bNzEhLbsFPTurLj0/sw+y1O3l1/kb++cUGnpmbRb+OiVwxOoWzjupMVAMUTxEREZHDaXLFzsyigTSgxDmXUfG6cy7DzKYRmPnyBuCBAw77HRAPPOacyz/g9deAu4DzzeyBirXszCwOuDO4zyP19mFEQkxRaRlvLdrCE7MzWb19H+0SYvnJSX24aEQ3WjY/stss60tkhDGudzLjeiezp6CYt7/ewktfbuSnry3hkekZ3HJCL04b3ImICE24IiIiIv7YEazp3WiY2RnAGcFvOwAnEbj9cVbwtZ3Ouf8L7psCrAPWO+dSKp0nDZgLtAPeIrBEwQhgAoFbMEc553Yd5NqvAYXAy0AO8AMCSyG8BpxXnYXT09PT3fz58w+3m0hIKi93/GvBRu6ZtprsvUX07ZDANWNSOW1IJ2KiQm/EyznHR8u3c+9Hq1m5bS992idw64m9OWlAe++jjSIiIhLezGyBcy79O6+HSbH7LfCbQ+zy3xJ3qGIXfL8r8HsCt1e2AbYCbwK/c87lVHH90cAvgZFAHLAWeAq43zlXVp3PoGIn4WrB+hx++/Zyvtmcy7Durfjxib0ZldYmLApQebnj3W+2ct/Hq8nMzmdQ5yR+PKk343snh8XnExERkcYnrItdOFCxk3CzLbeQv7y/gjcXb6F9Yiy/OLUfPxjSKSwLT2lZOW8u3sI/PlnNxpz9HJvamj+fNZgebeN9RxMREZEwo2LXyKnYSbgoLCnjydnreOiztZSWO64Z04Prx/ckPjb8H+ktLi3nlfkbufuDlRSXlnPbpN5cObqHJlgRERGROqNi18ip2Ek4+DxzFz99bQkbcgqY1L89d3yvP93aNPcdq8FtzyvkjjeX8tHy7QzuksRdZw+mX8fEwx8oIiIichhVFTv9GllEjlhJWTl3f7CSCx7/nAiD568aztRL05tkqQNonxjH1EuG8dCFR7Nlz35Oe2A2905bRVFptR65FREREamx8L83SkTqVdbOfH708iK+3pTLD9O78uvT+jeJ2y4Px8z43uCOjEprwx/eWc79n67l/aXbuPucwRzVrZXveCIiIhJmNGInIrXinOPV+Rs59f5ZZO0q4JGLjuaucwar1FXSKj6Ge384lKevOIb8olLOeXQeT8zKRLfBi4iISF1SsRORGsstKOHGfy7ip68tYXCXJN7/0RhOGdTRd6xGbUKfdnxw61hO6NeOO99dwY0vLSK/qNR3LBEREQkT+tW6iNTIgvW7uemfC9mxt4ifndyXyWNTiYwIvyUM6kNiXDSPXjyMx2ZmcvcHK1m1bS+PXjyMnu1a+I4mIiIiIU4jdiJSbW8u2swFUz8nOiqC168bxXXj01TqasjMuHZcGi9cNYLd+cWc/uBs3v9mq+9YIiIiEuJU7ETksMrLHfdOW8UtryzmqG4tefP60Qzp2tJ3rJA2qmdb/nPTcfRqn8B1Ly7kT++toLSs3HcsERERCVEqdiJySIUlZdz08iLu/3Qt56V34fmrRtAqPsZ3rLDQqWUzXplyLJcc252pMzO5+Mkv2FNQ7DuWiIiIhCAVOxGp0o69hfxw6ue8981Wfn5KX+46ezAxUfq/jboUGxXJH84YyN/OHcLC9Xs477F5bM3d7zuWiIiIhBj9hCYiB7Viax5nPDiH1cEJPqaMS8NMz9PVl7OHdeGZK45hy55CznlkHmt37PMdSUREREKIip2IfMdnq3ZwziNzKXOOf107kpMGdPAdqUkY1bMtL08+lqLScs59dC6LNuz2HUlERERChIqdiPyP97/ZyjXPzielbTxv3XAcAzsn+Y7UpAzsnMTr140kIS6aCx//gumrdviOJCIiIiFAxU5E/uutxZu58aVFDO6SxEuTj6VDUpzvSE1S9zbxvH7dKFKT47n62fn8e9Em35FERESkkVOxExEAXluwiVtfWUx691Y8d9UIEuOifUdq0pITYnl58rEck9KaW1/5midmZfqOJCIiIo2Yip2I8NKXG/jJa18zKq0tz1wxnBaxUb4jCZAQF83TVxzDqYM6cOe7K3jw0zW+I4mIiEgjpZ/eRJq4Z+dm8Zu3lzGhTzKPXDyMuOhI35HkAHHRkTxwwdHERn3NPdNWExsVyTVjU33HEhERkUZGxU6kCXt8ZiZ/fG8Fk/q354ELjyI2SqWuMYqMMP56zmCKS8v543sriI2O4NKRKb5jiYiISCOiYifSRD08fS13f7CK7w3uyN9/OJToSN2Z3ZhFRUbw9/OHUlxWzq/fWkZMZATnD+/mO5aIiIg0EvpJTqQJev7z9dz9wSpOH9qJf6jUhYzoyAgevPAoxvVO5uf//oY3Fmq2TBEREQnQT3MiTcy7S7by67eWcnzfdvzt3CFEqdSFlNioSB67ZBgjU9vwf//6mneWbPEdSURERBoB/UQn0oTMXrOTW15ZRHr3Vjx44dEqdSEqLjqSJy5LZ1j3Vtzy8mKmLdvmO5KIiIh4pp/qRJqIrzfuYfLz80lLbsETlx1DsxhNlBLKmsdE8dTlxzCwcxI3/HMhM1dn+44kIiIiHqnYiTQBGdn7uOKZr2gdH8NzVw4nqZkWHw8HCXHRPHvlcHq2S+D6FxeyYmue70giIiLiiYqdSJjbmrufS5/8kgiD568aQbvEON+RpA4lNYvmqcvTaREbxZXPfMW23ELfkURERMQDFTuRMLanoJhLn/yS3P0lPHPFcHq0jfcdSepBx6RmPHX5MewtLOXKZ75iX1Gp70giIiLSwFTsRMJUYUkZVz7zFet3FTD10mEM7JzkO5LUo/6dEnnooqNZtX0vN/5zIaVl5b4jiYiISANSsRMJQ845bn99CQs37OEf5w9lVFpb35GkAYzrncydZwxk+qpsfv32MpxzviOJiIhIA4nyHUBE6t4jMzJ4c/EW/m9Sb04Z1NF3HGlAFwzvxsacAh6enkG31s25dlya70giIiLSAFTsRMLMR8u389cPV3HakE7cMKGn7zjiwf9N6sPG3fv5y/sr6dKqGd8f3Ml3JBEREalnKnYiYWTltjxueXkRgzon8ddzBmNmviOJBxERxl/PGcy23P38+NWv6ZAYR3pKa9+xREREpB7pGTuRMLFrXxFXPzuf+Ngopl6STly0FiBvyuKiI5l6STqdWzbj2hcWsj1PyyCIiIiEMxU7kTBQXFrOdS8uZMfeIqZemk6HJK1VJ9AqPoaplwyjoLiU615YQHGpZsoUEREJVyp2IiHOOcdv3l7Gl+ty+Os5gxnataXvSNKI9GqfwD3nDmHhhj384Z3lvuOIiIhIPVGxEwlxz81bz0tfbuC68WmcPrSz7zjSCJ06qCNTxqXy/Ofr+df8jb7jiIiISD1QsRMJYZ9n7uL37yznhH7t+MmkPr7jSCP2k0l9GJXWhl++uZSlm3N9xxEREZE6pmInEqKy9xZx00uL6N66Off9cCgREZoBU6oWFRnBAxccRdv4GKY8v4Cc/GLfkURERKQOqdiJhKCycsctrywib38JD110NAlx0b4jSQho0yKWRy8ZRva+Im5+aRFl5c53JBEREakjKnYiIeiBT9cwZ+0ufn/6APp1TPQdR0LI4C4tufP0gcxeu5N7pq3yHUdERETqiIqdSIiZs3Yn//hkDWcd1Znz0rv6jiMh6LxjunLhiG48Mj2D97/Z6juOiIiI1AEVO5EQsiOvkB+9vIieyS2488yBmOm5Oqmd35zWn6FdW/LT15awMafAdxwRERE5Qip2IiGitKycm15aRH5RGQ9fdDTNY6J8R5IQFhsVyQMXHAUGN7+8iJIyLV4uIiISylTsRELE3z9ewxfrcvjjmQPp1T7BdxwJA11bN+dPZw5i0YY9/P3j1b7jiIiIyBFQsRMJAdNX7eDBz9byw/SunHV0F99xJIycNqQTP0zvysPTM5ibsdN3HBEREaklFTuRRm5r7n5ufWUxfTsk8LvTB/iOI2HoNz/oT4+28dz6ymKtbyciIhKiVOxEGrHycsdtr35NUWk5D110NHHRkb4jSRhqHhPFAxccxe78En762tc4p/XtREREQk3YFDsz62JmT5nZFjMrMrMsM/u7mbWq5vGXm5k7zFdZpWNSDrP/y/XzaaWpeHpuFnMzdvGr7/cnLbmF7zgSxgZ0SuL2U/ry8YodPP/5et9xREREpIbCYlo9M0sD5gLtgLeAlcBw4EfAyWY22jm36zCnWQz8ror3xgATgfereP9r4M2DvL70MNcUqdKqbXu564OVnNCvPecfo/XqpP5dMTqF2Wt3cue7KzgmpTX9Oib6jiQiIiLVFBbFDniYQKm72Tn3QMWLZnYvcCvwR+DaQ53AObeYQLn7DjObF/zj1CoOX+yc+22NEoscQlFpGbe8spjEuCj+cvYgrVcnDcLM+Os5gzn5H7O46aVF/OfG42gWo9t/RUREQkHI34ppZqnAJCALeKjS278B8oFLzCy+lucfCBwLbAberX1Skeq796PVrNiax11nD6Zti1jfcaQJadMilvvOG0pG9j5+/85y33FERESkmkK+2BG4RRJgmnPuf1bYdc7tBeYAzQmUs9qYEtw+6Zwrq2KfTmY2xcx+EdwOruW1RPg8cxdTZ2ZywfBuHN+vve840gQd16stk8em8tKXG/hs5Q7fcURERKQawqHY9Qluq1pdd01w27umJzazZsDFQDnwxCF2PRF4lMAtn48CX5vZZ2bWrabXlKYtr7CE2179mu6tm3PH9/r5jiNN2I9P7E2f9gnc/sYScgtKfMcRERGRwwiHYpcU3OZW8X7F6y1rce7zgse975zbeJD3C4A/AMOAVsGvccBnwHjgk0PdAmpmk81svpnNz87OrkU8CTe/fXsZ2/IKufeHQ4mPDZdHYCUUxUZF8rfzhrBrXzG/+88y33FERETkMMKh2B1OxawTtVmYaXJw+9jB3nTO7XDO/do5t9A5tyf4NZPAM39fAD2Bq6s6uXNuqnMu3TmXnpycXIt4Ek7e+2YrbyzczA0TenJ0t2qt0iFSrwZ2TuKGCT15Y9Fmpi3b5juOiIiIHEI4FLuKEbmkKt5PrLRftZhZf2AUsAl4rybHOudK+fbWzbE1OVaapu15hfzi398wpEsSN03s6TuOyH/dOLEn/Tsm8ot/f0NOfrHvOCIiIlKFcCh2q4Lbqp6h6xXcVvUMXlWqM2nKoVTcW1mr2Til6XDOcfvrSygsKeO+Hw4lOjIc/mcp4SI6MoJ7fziE3P0l/PotLc0pIiLSWIXDT5CfBbeTzOx/Po+ZJQCjgf3A59U9oZnFAZcQmDTlyVrmqpiFM7OWx0sT8ebizXy2KpufnNSX1OQWvuOIfEffDoncckJv3lmylXeXbPUdR0RERA4i5Iudcy4DmAakADdUevt3BEbMnnPO5QOYWbSZ9TWztEOc9lwCE6G8V8WkKQTPNcLMYg7y+kQCC6MDvFDdzyJNT/beIn73n+Uc3a0ll49K8R1HpEpTxqYypEsSd7z5Ddl7i3zHERERkUpCvtgFXQ/sAO43szfN7M9m9imBcrUa+OUB+3YGVgCfHOJ8FZOmTD3Mde8CNpvZv8zsvuDXJ8FzxwK/cs7NrcXnkSbit28vo6CojLvPGUxkhB3+ABFPoiIjuOfcIeQXl3HHm9/gXG3moxIREZH6EhbFLjhqlw48A4wAbgPSgPuBkc65XdU9l5n1A46jepOmPE9g9stjgGsIFMxewKvAWOfcnTX6INKkfLB0G+9+s5Wbj+9Jz3YJvuOIHFav9gncdmJvPly2nbcWb/EdR0RERA5g+q1r45Cenu7mz5/vO4Y0kNyCEk64bwbJLWJ568bRmjBFQkZZueO8x+axdsc+PvrxWNolxPmOJCIi0qSY2QLnXHrl1/XTpIgHf3h3OTn5xdx9zmCVOgkpkRHGX88ZzP6SMn7/n+W+44iIiEiQfqIUaWAzV2fz2oJNTBmbysDOVS2/KNJ4pSa34KYJPXlnyVY+XbnddxwRERFBxU6kQeUXlfLzN74hLTmem4/vdfgDRBqpKePS6NWuBb96cxn5RaW+44iIiDR5KnYiDejuD1ayJXc/d58zmLjoSN9xRGotJiqCv5w9iM179nPvR6t9xxEREWnyVOxEGshXWTk8O289l41MYVj31r7jiByxYd1bc/Gx3Xh6zjq+2ZTrO46IiEiTpmIn0gCKS8u5/fUldGnVjJ+c1Md3HJE689OT+9K2RSy3v7GE0rJy33FERESaLBU7kQbw+KxMMrLz+cPpA4mPjfIdR6TOJMZF87sfDGDZljyenpPlO46IiEiTpWInUs825hRw/ydrOGVgByb0bec7jkidO3lgB07o1557P1rNxpwC33FERESaJBU7kXrknOPXby0lKsL49Wn9fccRqRdmxu9PH0CEwR1vLsU55zuSiIhIk6NiJ1KPPly2jc9WZXPrib3pmNTMdxyRetOpZTP+76Q+zFidzX+WbPUdR0REpMlRsROpJ/lFpfzuP8vp2yGBy0el+I4jUu8uHZnCkC5J/P4/y9hTUOw7joiISJOiYidST/7+8Wq25hbyxzMHEhWp/6lJ+IuMMP581mB2F5Rwz7RVvuOIiIg0KfppU6QerNiax1Nzsjj/mK5as06alP6dErnk2O68+MUGlm7W2nYiIiINRcVOpI6VlzvueHMpSc2i+dnJfX3HEWlwt57YmzbxMfzqraWUl2siFRERkYagYidSx/61YCML1u/m56f0pVV8jO84Ig0uqVk0t5/Sj0Ub9vDawk2+44iIiDQJKnYidSgnv5g/v7+S4SmtOWdYF99xRLw566jODOveirveX0luQYnvOCIiImFPxU6kDv3l/RXsKyzlzjMHYma+44h4ExERWNtud0Ex936kiVRERETqm4qdSB1ZtGE3r87fxFXH9aB3+wTfcUS8G9ApiYuP7c7zn69n2RZNpCIiIlKfVOxE6kB5ueO3/1lOckIsNx3fy3cckUbjthP70Kp5DL95axnOaSIVERGR+qJiJ1IH3li0ma837uFnJ/elRWyU7zgijUZS88DssPPX7+aNhZt9xxEREQlbKnYiR2hfUSl3fbCSIV1bctZRnX3HEWl0zhnWhaFdW/Ln91eSV6iJVEREROqDip3IEXrg0zVk7y3it6f1JyJCE6aIVFYxkcqu/CLu+2i17zgiIiJhScVO5Ais25nPU7PXcfbRXTiqWyvfcUQarcFdWnLB8G48N289K7fl+Y4jIiISdlTsRI7AH99dTkxkBD87uY/vKCKN3k8m9SEhLoo/vLNcE6mIiIjUMRU7kVqasTqbj1fs4Kbje9EuMc53HJFGr1V8DLee0Js5a3fx8YodvuOIiIiEFRU7kVooKSvn9/9ZRkqb5lwxOsV3HJGQceGIbqQlx/On91ZQXFruO46IiEjYULETqYVn52aRkZ3Pr77fn9ioSN9xREJGdGQEd3y/P+t25vPcvCzfcURERMKGip1IDe3cV8Q/Pl7DuN7JTOzbzncckZAzoU87xvZO5v5P1pCTX+w7joiISFhQsROpoXs+XMX+kjJ+9f3+mGl5A5HauON7/cgvLuPvH2v5AxERkbqgYidSA8u25PLK/I1cNiqFnu1a+I4jErJ6t0/gwuHdePGLDazZvtd3HBERkZCnYidSTc45/vTeCpKaRXPz8b18xxEJebee2JvmMZHc+e4K31FERERCnoqdSDVNX53NnLW7+NHxvUhqFu07jkjIax0fw4+O78WM1dl8tkrLH4iIiBwJFTuRaigtK+dP764gpU1zLhrR3XcckbBx6cgUUto054/vrqCkTMsfiIiI1JaKnUg1vDp/E2t27OP2U/oSE6X/2YjUlZioCH5xaj/W7tjHS19u8B1HREQkZOknVJHD2FdUyr0freaYlFacNKCD7zgiYefE/u0ZmdqGez9aTW5Bie84IiIiIUnFTuQwps7IYOe+In5xaj8tbyBSD8yMX32/P7n7S7j/0zW+44iIiIQkFTuRQ9iWW8jUWZmcNqQTR3Vr5TuOSNjq3ymR84Z15bl5WWzMKfAdR0REJOSo2Ikcwt+mraK8HH56Uh/fUUTC3q0n9iYywrhn2irfUUREREKOip1IFZZvyeO1hZu4fHQKXVs39x1HJOx1SIrjytE9eGvxFpZuzvUdR0REJKSo2IkcxIGLkd8woafvOCJNxrXj02jVPJq/vL/SdxQREZGQomInchDTV2cze+1OLUYu0sAS46K5aWIvZq/dyczV2b7jiIiIhAwVO5FKtBi5iF8XHduNrq2b8ef3V1Je7nzHERERCQkqdiKVvL4wsBj5z07WYuQiPsRGRfJ/k/qwYmseby7e7DuOiIhISNBPrSIHKCwp4+8fr+Gobi05eaAWIxfx5bTBnRjUOYm/TVtNYUmZ7zgiIiKNnoqdyAGem5fF1txCfnZyXy1GLuJRRIRx+yl92bxnP8/PW+87joiISKOnYicSlLu/hIc+y2Bc72SOTW3jO45Ikze6Z1vG9U7mwc/WkltQ4juOiIhIo6ZiJxI0dWYGuftL+OnJWoxcpLG4/ZS+5BWW8PD0tb6jiIiINGphU+zMrIuZPWVmW8ysyMyyzOzvZtaqBufIMjNXxde2Qxw3yszeM7McMyswsyVmdouZRdbNp5P6tiOvkKdmZ/GDIZ0Y0CnJdxwRCerXMZEzj+rM03Oz2Lxnv+84IiIijVaU7wB1wczSgLlAO+AtYCUwHPgRcLKZjXbO7arm6XKBvx/k9X1VXPt04HWgEHgFyAFOA+4DRgPnVvuDiDf3f7qGkrJyfnxib99RRKSS2yb14Z0lW7l32mr+dt4Q33FEREQapbAodsDDBErdzc65BypeNLN7gVuBPwLXVvNce5xzv63OjmaWCDwOlAHjnXPzg6//CvgUOMfMznfOvVzdDyINL2tnPi9/uZELhncjpW287zgiUknnls24fFQKj8/KZMq4VHq3T/AdSUREpNEJ+VsxzSwVmARkAQ9Vevs3QD5wiZnVx0/s5wDJwMsVpQ7AOVcI3BH89rp6uK7Uob99tJroyAhuOr6n7ygiUoXrxqURHxPFvdNW+44iIiLSKIV8sQMmBrfTnHPlB77hnNsLzAGaA8dW83yxZnaxmf3CzH5kZhMO8axcxbU/OMh7M4ECYJSZxVbz2tLAlm7O5T9fb+Gq43rQLiHOdxwRqUKr+BiuGZPKB8u28fXGPb7jiIiINDrhUOwqpjCs6te4a4Lb6j481QF4nsDtm38ncEvlGjMbV5NrO+dKgXUEbndNPdiFzGyymc03s/nZ2dnVjCd16e4PV9GyeTSTxx30PyIRaUSuPC6FVs2juWfaKt9RREREGp1wKHYVUxjmVvF+xestq3Gup4HjCZS7eGAQ8BiQArxvZpWf2j+iazvnpjrn0p1z6cnJydWIJ3VpbsZOZq7O5obxPUmMi/YdR0QOIyEumuvH92TWmp3My6jufFgiIiJNQzgUu8Ox4NYdbkfn3O+cc58657Y75wqcc0udc9cC9wLNgN/W17WlYTnnuOuDVXRMiuOSkd19xxGRarpkZHfaJ8Zyz7RVOKf/axUREakQDsWuYlSsqsXHEivtVxuPBrdjPVxb6sGHy7bz9cY93HpCb+KitdygSKiIi47k5uN7sWD9bj5btcN3HBERkUYjHIpdxcMWVT1D1yu4PZKp1Cp+eqg8s2aV1zazKKAHUApkHsG1pY6VlTvu/WgVqcnxnHV0Z99xRKSGzkvvSrfWzfnrh6spL9eonYiICIRHsfssuJ1kZv/zecwsgcAi4fuBz4/gGiOD28oF7dPg9uSDHDOWwGycc51zRUdwbalj7yzZwurt+7j1hN5ERYbD/wREmpboyAh+fGJvVmzN491vtvqOIyIi0iiE/E+1zrkMYBqBCU5uqPT27wiMsj3nnMsHMLNoM+trZmkH7mhmA8ysdeXzm1l34MHgty9Uevs1YCdwvpmlH3BMHHBn8NtHavO5pH6UlpXzj4/X0LdDAt8b1NF3HBGppdOGdKJP+wTu/Wg1pWXlhz9AREQkzIV8sQu6nsDtkveb2Ztm9mcz+xS4lcAtmL88YN/OwArgk0rnOBfYYmbvm9nDZnaXmb0GrAR6Au8B9xx4gHMuD7gGiASmm9kTZnY3sJjAKN9rwCt1+1HlSPx70WYyd+Zz64m9iYiwwx8gIo1SZIRx26TerNuZz+sLN/mOIyIi4l1YFLvgqF068AwwArgNSAPuB0Y656ozL/ZnwL8JPBd3IfBjYBwwG7gM+L5zrvgg134zuN9M4GzgJqAkePz5TtO2NRrFpeX845M1DOqcxKT+7X3HEZEjdGL/9gzp2pJ/fLyGwpIy33FERES8ivIdoK445zYCV1Rjvyy+XYbgwNdnADNqee05wKm1OVYazqvzN7Jp937uPGMgZhqtEwl1ZsZPT+rDRU98wT+/2MCVx/XwHUlERMSbsBixEzmcwpIyHvx0LcO6t2Jcby0GLxIuRvdsy6i0Njz02VoKikt9xxEREfFGxU6ahH9+sYFteYXcNqm3RutEwsxtk/qwK7+Y5+at9x1FRETEGxU7CXsFxaU8PH0to9LaMCqtre84IlLHKkbiH5uRwb4ijdqJiEjTpGInYe/ZuevZua+Y2yZVtYa9iIS6W0/sze6CEp6dm+U7ioiIiBcqdhLW8gpLeHRGBuP7JDOs+3eWKRSRMDG0a0sm9m3H1JmZ7C0s8R1HRESkwanYSVh7avY6cveXcNuJfXxHEZF6dusJvcndX8LTc7J8RxEREWlwKnYStvYUFPPkrHWcNKA9g7ok+Y4jIvVsUJckTuzfnsdnZZK7X6N2IiLStKjYSdiaOjOTfcWl3Hqinq0TaSpuOaEXewtLeWr2Ot9RREREGpSKnYSlnPxinp2bxamDOtK3Q6LvOCLSQAZ0SuLkAR14avY69hQU+44jIiLSYFTsJCw9MSuTgpIyfnR8L99RRKSB3XJiL/YWlfLELI3aiYhI06FiJ2Fnd3C07nuDOtK7fYLvOCLSwPp2SOR7gzry9Jx15ORr1E5ERJoGFTsJO0/MDozW3azROpEm60cn9KKgpIzHZ2X6jiIiItIgVOwkrOzOL+aZOYFn6zRaJ9J09W6fwGmDO/Hs3Cx27ivyHUdERKTeqdhJWPnvaN1EjdaJNHU3H9+LwpIyps7UqJ2IiIQ/FTsJGweO1vXpoNE6kaauZ7sWnD60M8/NyyJ7r0btREQkvKnYSdh4cvY6jdaJyP+4aWJPikvL9aydiIiEPRU7CQu784t5Zq5G60Tkf6Umt+AHQzrx/Lz17NKzdiIiEsZU7CQsPDl7HfnFpRqtE5HvuHFiTwpLy3hitta1ExGR8KViJyFPo3Uicig92yXw/cGdeG5uFru1rp2IiIQpFTsJeRqtE5HDuWliT/KLy3hqjkbtREQkPKnYSUjbU6DROhE5vN7tEzh1UAeemZNFbkGJ7zgiIiJ1TsVOQppG60Skum6c0Iu9RaUatRMRkbCkYichK3d/Cc/MyeKUgR00Wicih9W/UyKT+rfnqTnryCvUqJ2IiIQXFTsJWc/MyWJvUSk3abRORKrp5uN7sbewlGfnZPmOIiIiUqdU7CQk7QveTnVCv/b065joO46IhIiBnZM4oV87npi9jn1Fpb7jiIiI1BkVOwlJz89bT+7+Em6a2NN3FBEJMTdN7EXu/hKem5flO4qIiEidUbGTkFNQXMoTszIZ2zuZIV1b+o4jIiFmSNeWjO+TzBOz1pGvUTsREQkTKnYScl76ciO78ou5WaN1IlJLN03sRU5+MS9+sd53FBERkTqhYichpbCkjMdmZDAytQ3pKa19xxGREDWseyvG9GrL1JmZ7C8u8x1HRETkiKnYSUj51/yN7NhbpGfrROSI3Xx8L3buK+afX27wHUVEROSIqdhJyCguLefRGZkM696KkWltfMcRkRB3TEprRvRozdSZGRSVatRORERCm4qdhIx/L9rE5j37uXFiT8zMdxwRCQM3TezF9rwiXl+w2XcUERGRI6JiJyGhtKych6dnMKhzEuN7J/uOIyJhYnTPNgzp2pJHZqyltKzcdxwREZFaU7GTkPCfJVtYv6tAo3UiUqfMjBsn9GRjzn7e/nqL7zgiIiK1pmInjV5ZuePBT9fSt0MCJ/Zr7zuOiISZ4/u2o2+HBB6enkF5ufMdR0REpFZU7KTR+2DpNjKy87lxYk8iIjRaJyJ1KyLCuGFCT9bu2MeHy7b5jiMiIlIrKnbSqDnneODTNaQmx3PKwI6+44hImDp1UEdS28bz4GdrcU6jdiIiEnpU7KRR+3TlDlZu28v143sSqdE6EaknkRHGtePTWLYlj+mrsn3HERERqTEVO2m0nHM8+NlaOrdsxulDO/mOIyJh7syjOtO5ZTON2omISEhSsZNG6/PMHBZt2MO141KJjtR/VUWkfkVHRnDtuFQWrN/N55k5vuOIiIjUiH5alkbr4elradsilnPTu/qOIiJNxLnpXUlOiOXBz9b4jiIiIlIjKnbSKH29cQ+z1uzk6jE9iIuO9B1HRJqIuOhIJo9JZc7aXSzasNt3HBERkWpTsZNG6eHpa0mMi+LiY7v7jiIiTcyFI7rRsnk0D3221ncUERGRalOxk0Znzfa9fLhsO5eP7kGL2CjfcUSkiYmPjeLK0T34eMUOlm/J8x1HRESkWlTspNF5ZHoGzWMiuWJUiu8oItJEXTYqhRaxUTw8XaN2IiISGlTspFHZmFPAW19v4cLh3WgVH+M7jog0UUnNorn42O68981Wsnbm+44jIiJyWCp20qg8NjODSDOuHpPqO4qINHFXHpdCVGQEj83M8B1FRETksMKm2JlZFzN7ysy2mFmRmWWZ2d/NrFU1j29jZleb2b/NbK2Z7TezXDObbWZXmdl3/q7MLMXM3CG+Xq77Txq+duQV8ur8TZw9rAsdkuJ8xxGRJq5dQhznpXfh9QWb2Z5X6DuOiIjIIYXFzBRmlgbMBdoBbwErgeHAj4CTzWy0c27XYU5zLvAIsBX4DNgAtAfOAp4ATjGzc51z7iDHfg28eZDXl9b80zRdT8xeR2lZOdeO02idiDQOU8am8dKXG3liVia//F5/33FERESqFBbFDniYQKm72Tn3QMWLZnYvcCvwR+Daw5xjNfAD4F3nXPkB5/gF8CVwNoGS9/pBjl3snPvtkXyApm5PQTEvfL6e04Z0onubeN9xREQA6Nq6OacN7siLX2zghgk9adlcz/6KiEjjFPK3YppZKjAJyAIeqvT2b4B84BIzO2RbcM596pz7z4GlLvj6NuDR4Lfj6yKzfNczc7MoKC7juvFpvqOIiPyPa8enUVBcxrNz1/uOIiIiUqWQL3bAxOB22kFK2V5gDtAcOPYIrlES3JZW8X4nM5tiZr8IbgcfwbWanPyiUp6Zm8UJ/drTt0Oi7zgiIv+jb4dEju/bjmfmrqOguKp/BkRERPwKh2LXJ7hdXcX7a4Lb3rU5uZlFAZcGv/2git1OJDCq98fg9msz+8zMuh3m3JPNbL6Zzc/Ozq5NvLDw0pcb2FNQwvUTNFonIo3T9RPS2F1QwktfbvQdRURE5KDCodglBbe5Vbxf8XrLWp7/L8BA4D3n3IeV3isA/gAMA1oFv8YRmHxlPPDJoW4Bdc5Ndc6lO+fSk5OTaxkvtBWXlvPErHUcm9qao7tVawJTEZEGN6x7a4b3aM0TszIpLi0//AEiIiINLByK3eFYcHuw2SwPfaDZzcBtBGbZvKTy+865Hc65XzvnFjrn9gS/ZhJ45u8LoCdwde2jh783F29mW14h143v6TuKiMghXT8+ja25hby5aLPvKCIiIt8RDsWuYkQuqYr3EyvtVy1mdgPwD2A5MME5l1PdY51zpQSWSAAYW5PrNiXl5Y5HZ2TQv2MiY3u19R1HROSQxvVOZkCnRB6dkUFZeY1/VygiIlKvwqHYrQpuq3qGrldwW9UzeN9hZrcADxJYh25CcGbMmqp4aE5z91dh2vLtZGbnc934NMzs8AeIiHhkZlw3Po3Mnfl8uKw2/yyIiIjUn3Aodp8Ft5PM7H8+j5klAKOB/cDn1TmZmf0MuA9YTKDU7ahlropZODNreXxYc87xyIwMurdpzikDO/iOIyJSLacM7EiPtvE8PH0tzmnUTkREGo+QL3bOuQxgGpAC3FDp7d8RGDF7zjmXD2Bm0WbW18y+MwWjmf2KwGQpC4DjnXM7D3VtMxthZt9ZrdbMJhJYGB3ghZp9oqZhXuYuvt64h8ljU4mKDPn/GopIExEZYUwZm8rSzXnMWnPIfyJEREQaVJTvAHXkemAucL+ZHQ+sAEYAEwjcgvnLA/btHHx/PYEyCICZXQb8HigDZgE3H+T2wCzn3DMHfH8XMMDMpgObgq8N5tu19X7lnJt7ZB8tPD0yPYO2LWI5++guvqOIiNTImUd35r6PV/PI9AzG9m6aMxqLiEjjExbFzjmXYWbpBIrZycCpwFbgfuB31Zz4pEdwGwncUsU+M4BnDvj+eeBM4BjgFCAa2A68CjzonJtVow/SRCzdnMusNTv52cl9iYuO9B1HRKRGYqMiufq4VP743goWb9zD0K4tfUcSERHB9IxA45Cenu7mz5/vO0aDuOHFhcxcnc2cn08kMS7adxwRkRrbV1TKqD9/wqi0tjx6yTDfcUREpAkxswXOufTKr+vhJmlQ63bm897SrVw8srtKnYiErBaxUVwysjsfLt9GRvY+33FERERU7KRhTZ2ZQXRkBFeMTvEdRUTkiFw+qgcxkRE8PlOTH4uIiH8qdtJgtucV8vqCzZyX3oV2CXG+44iIHJHkhFjOTe/CGws3sz2v0HccERFp4lTspME8NXsdpeXlTB7znZUmRERC0uQxaZSWl/PU7HW+o4iISBOnYicNInd/CS9+sYHvD+5EtzbNfccREakT3do053uDO/HiFxvI3V/iO46IiDRhKnbSIF74fD37ikqZMi7VdxQRkTo1ZWwq+4pKefGL9b6jiIhIE6ZiJ/WusKSMp+dkMbZ3MgM6JfmOIyJSpwZ2TmJMr7Y8NTuLwpIy33FERKSJUrGTevf6wk3s3FfEtRqtE5Ewdd34NHbuK+L1hZt8RxERkSZKxU7qVVm54/GZmQzuksTI1Da+44iI1IuRqW0Y0iWJx2dmUlbufMcREZEmSMVO6tWHy7aRtauAa8elYWa+44iI1Asz49pxaWTtKuCDpdt8xxERkSZIxU7qjXOOx2ZkkNKmOScN6OA7johIvZo0oAOpbeN5ZMZanNOonYiINCwVO6k38zJ38fWmXK4Zm0pkhEbrRCS8RUYYk8emsnRzHnPW7vIdR0REmhgVO6k3j87IpG2LGM4+uovvKCIiDeLMozvTLiGWR2dk+I4iIiJNjIqd1IvlW/KYuTqbK0b3IC460nccEZEGERsVyZXH9WD22p0s3ZzrO46IiDQhKnZSLx6bmUF8TCQXj+juO4qISIO6cEQ3EmKjeGxmpu8oIiLShKjYSZ3bmFPAO0u2csHwbiQ1j/YdR0SkQSXGRXPhsd14d8kWNuYU+I4jIiJNhIqd1LknZ68jwuCqMT18RxER8eLK0T2IjDCemKVROxERaRgqdlKncvKLefmrDZw+tDMdk5r5jiMi4kX7xDjOPKozr8zfSE5+se84IiLSBKjYSZ16bl4WhSXlTBmb6juKiIhXk8emUlhSzrNzs3xHERGRJkDFTupMQXEpz87N4oR+7ejVPsF3HBERr3q2S+CEfu15bl4WBcWlvuOIiEiYU7GTOvOv+ZvYXVDClHFpvqOIiDQK145LZXdBCf+av8l3FBERCXMqdlInSsvKeXxWJkd3a8kxKa19xxERaRTSU1ozrHsrHp+VSWlZue84IiISxlTspE68v3Qbm3bv12idiEglU8amsmn3ft5bus13FBERCWMqdnLEnHM8NjOD1LbxnNivve84IiKNygn92pOaHM9jMzJwzvmOIyIiYUrFTo7Y3IxdLN2cx+SxqUREmO84IiKNSkSEMWVsKsu25DFn7S7fcUREJEyp2MkRe2xmJm1bxHLGUZ19RxERaZTOOKozyQmxPDYzw3cUEREJUyp2ckSWb8lj5upsrhidQlx0pO84IiKNUmxUJFeO7sGsNTtZujnXdxwREQlDKnZyRKbOzCA+JpKLR3T3HUVEpFG7cEQ3WsRGMXVmpu8oIiIShlTspNY27S7gP0u2cv7wbiQ1j/YdR0SkUUtqFs2FI7rxzpItbMwp8B1HRETCjIqd1NpTs7Mw4MrjeviOIiISEq4YnUKEGU/OXuc7ioiIhBkVO6mV3IISXv5qAz8Y0onOLZv5jiMiEhI6JjXj9KGdeeWrjezOL/YdR0REwoiKndTKC1+sp6C4jGvGpvqOIiISUiaPTWV/SRkvfL7edxQREQkjKnZSY4UlZTw9Zx3jeifTr2Oi7zgiIiGlT4cExvdJ5pm5WRSWlPmOIyIiYULFTmrsjYWb2bmvmCnjNFonIlIbU8amsSu/mNcXbvIdRUREwoSKndRIWbnjiVmZDOqcxMjUNr7jiIiEpGNTWzO4SxJPzFpHWbnzHUdERMKAip3UyEfLt5O5M58p41IxM99xRERCkpkxeWwq63bm89Hybb7jiIhIGFCxk2pzzvHYzAy6tm7GyQM6+I4jIhLSTh7QgW6tm/PYzEyc06idiIgcGRU7qbb563ezaMMerhmTSlSk/qsjInIkoiIjuHpMDxZt2MP89bt9xxERkRCnn86l2h6bkUmr5tGcO6yr7ygiImHh3GFdadU8msdmZPiOIiIiIU7FTqpl7Y59fLxiO5eMTKFZTKTvOCIiYaFZTCSXjkzh4xU7WLtjr+84IiISwlTspFqemJVJbFQEl43s7juKiEhYuXRkd2KjInh85jrfUUREJISp2Mlh7dhbyBsLN3POsC60aRHrO46ISFhp0yKWc9O78O9Fm9mRV+g7joiIhCgVOzmsZ+ZkUVJezjVjtCC5iEh9uPq4VErLy3l6bpbvKCIiEqJU7OSQ9hWV8sLn6zl5QAdS2sb7jiMiEpZS2sZz8sAOvPD5evYVlfqOIyIiIUjFTg7pla82kldYyuSxGq0TEalPk8emsbewlJe/3OA7ioiIhCAVO6lSSVk5T81ex/CU1hzVrZXvOCIiYW1o15YM79Gap2avo6Ss3HccEREJMWFT7Mysi5k9ZWZbzKzIzLLM7O9mVqNGUpvzmNkoM3vPzHLMrMDMlpjZLWYW0usCvPfNVjbv2a/ROhGRBjJlbCpbcgt5Z8kW31FERCTEhEWxM7M0YAFwBfAlcB+QCfwImGdmberrPGZ2OjATGAv8G3gIiAke+/IRfTCPnHM8NiOTnu1aMLFvO99xRESahAl92tGrXQsem5GJc853HBERCSE1KnZm1lh/wn8YaAfc7Jw7wzl3u3NuIoFy1Qf4Y32cx8wSgceBMmC8c+4q59xPgKHAPOAcMzv/yD9ew5uzdhfLt+YxeUwqERHmO46ISJMQEWFcMzaVldv2MmvNTt9xREQkhNR0xG6jmb1iZhPrJU0tmFkqMAnIIjBadqDfAPnAJWZ2yCkda3mec4Bk4GXn3PyKF51zhcAdwW+vq8HHaTQem5lBu4RYTj+qk+8oIiJNyulDO9EuIZapMzN9RxERkRBS02K3GjgX+MjMVpvZbdW9zbEeVZTMac65/3na3Dm3F5gDNAeOrYfzVBzzwUHONxMoAEaZWUit6r1sSy6z1uzk8tEpxEaF9GOCIiIhJzYqkitG92D22p0s3ZzrO46IiISIGhU759wg4DjgeaAz8Fdgk5m9aGZj6yFfdfQJbldX8f6a4LZ3PZynymOcc6XAOiAKCKnZRx6fmUl8TCQXjejuO4qISJN04YhuxMdE8vgsjdqJiPj0xKxMbnhxIUWlZb6jHFaNJ09xzs11zl0OdCIwqcha4ALgMzNbYWY/qulMlEcoKbit6teaFa+3rIfzHNG1zWyymc03s/nZ2dmHidcwnHM0i4ni0lEpJDWL9h1HRKRJSmoWzQXDu/HOkq1s2l3gO46ISJNUXFrO47My2V1QHBJ3sdV6VkznXK5z7oEDRvGeA7oB9wKbzewZM0uvo5xHomLmjyOdXqw25znkMc65qc65dOdcenJy8hGFqytmxp/PGsTPTu7rO4qISJN25XE9MODJ2et8RxERaZLeWryZ7XlFIbP0V10td7AL2A0UEigzMcClwBdm9qaZta6j6xxMxahYUhXvJ1bary7PU1fXFhER+R+dWjbjtCGdeOWrjeQWlPiOIyLSpDjneHxWJn07JDCud+MYgDmcWhc7M4s2s/PN7DNgOXALkA38GGhLYGKRD4Ef8N1ZJuvSquC2qmfoegW3VT07dyTnqfIYM4sCegClBNbCExERqZFrxqRSUFzGC1+s9x1FRKRJmb4qm9Xb9zF5bCpmobH0V42LnZn1NLO7gc3Ai8Bo4E3gROdcX+fc351ze5xz051zpwJvACfXZehKPgtuJ5nZ/3weM0sI5tsPfF4P5/k0uD3Y5xtLYBbNuc65osN9CBERkcr6d0pkTK+2PD0ni8KSxv/gvohIuHhsZgYdk+I4bUjoLP1V0wXKPyYwSvV/QDHwByDFOXe2c+6TKg5bwLe3JNY551wGMA1IAW6o9PbvgHjgOedcPvx3pLGvmaUdyXmCXgN2Aucf+DyhmcUBdwa/faTWH05ERJq8KWPT2LmviDcXbfYdRUSkSfh64x4+z8zhytE9iI6sqyfX6l9UDfefSGBk62HgTedcdX59+B9gS02D1dD1wFzgfjM7HlgBjAAmELh18pcH7Ns5+P56AiWutufBOZdnZtcQKHjTzexlIIfA7ad9gq+/UmefUkREmpzRPdswoFMiU2dlcl56VyIiQuOWIBGRUDV1ZiYJsVGcP7yr7yg1UtMK2s85d7xz7vVqljqcc0udc8/WIlu1BUfb0oFnCBSx24A04H5gpHNuV32dxzn3JjCOwILkZwM3ASUEnjU83zl3pLNxiohIE2ZmTB6bSmZ2Ph+v2O47johIWNuwq4D3l27lomO7kxAXWkt/1WjEzjm36vB7+eGc2whcUY39svh2GYJan6fSMXOAU2tyjIiISHV9b1BH7v5gFVNnZjJpQAffcUREwtYTszOJjDCuGJ3iO0qNhc5NoyIiIk1UVGQEVx3Xg/nrd7NgfY7vOCIiYSknv5hX52/kjKGdaZ8Y5ztOjanYiYiIhIAfHtOVpGbRPDZDK+iIiNSH5+ZlUVhSHjILklemYiciIhIC4mOjuOTY7ny0YjuZ2ft8xxERCSv7i8t4bt56JvZtR6/2Cb7j1IqKnYiISIi4bFQK0ZERPD5rne8oIiJh5bWFm8jJL2ZKiI7WgYqdiIhIyEhOiOXso7vw+sJNZO8t8h1HRCQslJU7npiVyZCuLRneo7XvOLWmYiciIhJCrhnTg5Kycp6dm+U7iohIWJi2bBvrdxUwZWwqZqG7VqiKnYiISAhJTW7BpP7tef7z9eQXlfqOIyIS0pxzPDYzk+5tmnNSiC8no2InIiISYiaPTSN3fwmvfLXRdxQRkZD25bocFm/cw9VjUomMCN3ROlCxExERCTnDurcivXsrnpy9jtKyct9xRERC1tSZmbSJj+HcYV18RzliKnYiIiIhaMq4NDbv2c+732z1HUVEJCSt3r6XT1bu4NKRKcRFR/qOc8RU7ERERELQ8X3bkZYcz2MzMnHO+Y4jIhJyps7MpFl0JJeO7O47Sp1QsRMREQlBERHGNWNSWb41jzlrd/mOIyISUrblFvLW4s2cl96FVvExvuPUCRU7ERGREHXGUZ1JTojlsZkZvqOIiISUp+eso6zccfWY0F2QvDIVOxERkRAVFx3J5aNSmLVmJ8u25PqOIyISEvIKS3jxiw2cOqgjXVs39x2nzqjYiYiIhLCLR3QnPiaSqTMzfUcREQkJL32xgX1FpUwZm+Y7Sp1SsRMREQlhSc2juWB4N95ZspVNuwt8xxERadSKS8t5as46Rvdsw6AuSb7j1CkVOxERkRB35XE9MODJ2et8RxERadTeWryZ7XlFTA6z0TpQsRMREQl5nVo24wdDO/HylxvZnV/sO46ISKNUXu6YOjOTvh0SGNurre84dU7FTkREJAxMHpvK/pIyXvh8ve8oIiKN0merdrBmxz6mjEvFzHzHqXMqdiIiImGgb4dEJvRJ5pm5WRSWlPmOIyLS6Dw2M5NOSXF8f3An31HqhYqdiIhImJgyLo1d+cW8tmCT7ygiIo3Kwg27+XJdDleNSSU6MjwrUHh+KhERkSZoRI/WDOnaksdnZVJW7nzHERFpNKbOyCQxLorzj+nqO0q9UbETEREJE2bGtWNTWb+rgA+XbfMdR0SkUcjM3seHy7dx8bHdiY+N8h2n3qjYiYiIhJFJAzqQ0qY5j83IwDmN2omIPD5rHdGREVwxuofvKPVKxU5ERCSMREYY14xN5etNuXyemeM7joiIVzv2FvL6wk2cM6wLyQmxvuPUKxU7ERGRMHP20V1o2yKGx2Zm+I4iIuLVM3OyKCkr55oxqb6j1DsVOxERkTATFx3JFaN7MH1VNiu25vmOIyLixd7CEp7/fD2nDOxAj7bxvuPUOxU7ERGRMHTxiO40j4nk8ZmZvqOIiHjx0pcb2FtYypSxab6jNAgVOxERkTCU1DyaC4Z34+2vt7B5z37fcUREGlRxaTlPzl7HyNQ2DOna0necBqFiJyIiEqauPC4wA9yTs9Z5TiIi0rDeXLyZ7XlFXDu+aYzWgYqdiIhI2Orcshk/GNKJl7/awJ6CYt9xREQaRHm5Y+rMTPp1TGRsr7a+4zQYFTsREZEwNmVcGgXFZTw3b73vKCIiDeKTlTtYu2Mf145Lxcx8x2kwKnYiIiJhrE+HBCb2bcczc7PYX1zmO46ISL17bEYGnVs243uDOvqO0qBU7ERERMLctePSyMkv5l8LNvqOIiJSr+Zn5TB//W6uGdODqMimVXWa1qcVERFpgo5JacWw7q2YOjOT0rJy33FEROrNozMyaNU8mvOO6eo7SoNTsRMREQlzZsa149LYtHs/736z1XccEZF6sWb7Xj5esYNLR6bQPCbKd5wGp2InIiLSBBzftx292rXg0RmZOOd8xxERqXOPzcwkLjqCy0al+I7ihYqdiIhIExARYUwem8qKrXnMWJ3tO46ISJ3amruftxZv5vxjutE6PsZ3HC9U7ERERJqI04d2pmNSHI9Mz/AdRUSkTj05ax3lDq46rofvKN6o2ImIiDQRMVERXHVcD75Yl8OiDbt9xxERqRN7Cor555cb+MGQTnRt3dx3HG9U7ERERJqQC4Z3I6lZNI/O0KidiISH5+atp6C4jCnjUn1H8UrFTkREpAmJj43i0pHdmbZ8O2t37PMdR0TkiBQUl/L0nHUc37cdfTsk+o7jlYqdiIhIE3PZqBRiIiOYOlOjdiIS2l75aiO7C0q4bnya7yjeqdiJiIg0MW1bxHJeelf+vWgz23ILfccREamVkrJyHp+ZyTEprUhPae07jncqdiIiIk3Q5LGplDt4as4631FERGrl7cVb2JJbyPXje/qO0iio2ImIiDRBXVs353uDOvLi5+vJLSjxHUdEpEbKyx2Pzsigb4cExvdJ9h2nUVCxExERaaKuHZdGfnEZz83L8h1FRKRGPlm5gzU79nHd+DTMzHecRiEsip2ZjTKz98wsx8wKzGyJmd1iZpE1OEcvM/uZmX1qZhvNrNjMtpvZW2Y2oYpjLjczd4iva+vuU4qIiNSt/p0SmdAnmafnZrG/uMx3HBGRanHO8fD0tXRp1YzvDeroO06jEeU7wJEys9OB14FC4BUgBzgNuA8YDZxbzVP9AfghsBx4L3iePsAPgB+Y2Y+cc/dXcexbwOKDvD6/mtcWERHx4voJPTn30Xm8/NUGrhjdw3ccEZHD+nJdDos27OEPpw8gKjIsxqnqREgXOzNLBB4HyoDxzrn5wdd/BXwKnGNm5zvnXq7G6T4A7nLOLap0jXHAR8BfzexfzrmtBzn2TefcM0fwUURERLw4JqU1x6S04vGZmVw0ojsxUfohSUQat0dmZNAmPoZz07v6jtKohPr/e58DJAMvV5Q6AOdcIXBH8NvrqnMi59wzlUtd8PUZwHQgBhh1pIFFREQam+vH92RLbiFvLd7sO4qIyCEt35LH9FXZXHlcD+Kiq/3UVZMQ6sVuYnD7wUHemwkUAKPMLPYIr1MxXVhpFe8PDT7Td7uZXWJmXY7weiIiIg1mfJ9k+nVM5NEZGZSXO99xRESq9OiMDFrERnHxsd19R2l0Qr3Y9QluV1d+wzlXCqwjcLtpam0vYGbdgeMJlMSZVez2IwLP9P0ZeA7IMrNHzSyuttcVERFpKGbGdePTyMjOZ9rybb7jiIgc1IZdBbyzZAsXjehGUrNo33EanVAvdknBbW4V71e83rI2Jw+O9L0IxAK/dc7trrTLOuAmAgUzHugEnAdkAVOApw5z/slmNt/M5mdnZ9cmooiISJ04dWAHurdpzsPTM3BOo3Yi0vg8OjODqIgIrjxOEz0djPdiZ2ZZh1kyoPLXCzU5fXBb43+hgkslPE9gZs1XgHsq7+Ocm+Gce9A5t9o5V+Cc2+qc+xcwAdgNXGBmQ6q6hnNuqnMu3TmXnpyshRVFRMSfqMgIpoxNY8mmXOas3eU7jojI/9ieV8hr8zdxTnoX2ifqpriD8V7sgAxgVQ2+thxwbMWIXBIHl1hpv2oJlroXCCyV8CpwsavBry+dcxsJLJkAMLYm1xYREfHl7GGdaZcQy8PT1/qOIiLyP56YlUlpeTnXjk3zHaXR8r7cgXPu+CM4fBWQDvQGFhz4hplFAT0ITHiSWd0TBo/7J4FS90/gUudcbVZtrbi3Mr4Wx4qIiDS42KhIrh7Tgz+9t5LFG/cwtGtL35FERNidX8yLX2zgB0M60a1Nc99xGq3GMGJ3JD4Nbk8+yHtjgebAXOdcUXVOZmYxwGsESt1zwCW1LHUAI4LbapdKERER3y4c0Z2kZtE8/JlG7USkcXhmbhYFxWVcP6Gn7yiNWqgXu9eAncD5ZpZe8WJwNso7g98+cuABZpZkZn3NrGOl12OBfwOnA08CVzjnyg91cTMbc5DXzMx+DowMZjvYUgwiIiKNUovYKC4b2Z1py7ezZvte33FEpInbV1TKM3OzmNS/Pb3bJ/iO06h5vxXzSDjn8szsGgIFb7qZvQzkAD8gMFPlawQmPjnQmcDTwLPA5Qe8/ihwKoEythn4tZlVOpTpzrnpB3w/08xWA18Fj0kiMNnKQALLI1zknMs7sk8pIiLSsC4f3YPHZ63jkRkZ3HveUN9xRKQJ++cX68ndX6LRumoI6WIH4Jx708zGAb8EzgbigLXAj4H7azDpScW8qW2BXx9iv+kH/PkeYDiBhdJbA+XABuAh4F7nnG7DFBGRkNM6Pobzh3fluXnrufWE3nRtrWdaRKThFZaU8fisdRzXs62e+a2GkC92AM65OQRG26qz7zPAMwd5fXwtrvuTmh4jIiISCiaPTeWFz9fz2MwM7jxjkO84ItIEvbZgE9l7i/jH+UN9RwkJof6MnYiIiNSDjknNOGdYF16dv4kdeYW+44hIE1NaVs6jMzI4qltLRqa28R0nJKjYiYiIyEFdOy6N0rJyHp+lJwtEpGG9/fUWNu3ezw3je3KQeS/kIFTsRERE5KC6t4nn9KGdeeHzDeTkF/uOIyJNRHm54+HpGfTtkMDEvu18xwkZKnYiIiJSpevHp7G/pIyn56zzHUVEmohpy7ezdsc+rhufRkSERuuqS8VOREREqtSrfQInD+jAM3OzyCss8R1HRMKcc46Hp6+le5vmfG9Qx8MfIP+lYiciIiKHdOPEnuwtLOX5eet9RxGRMDd77U6WbMrl2nFpREWqqtSE/rZERETkkAZ2TmJ8n2SenL2OguJS33FEJIw98Ola2ifGctbRnX1HCTkqdiIiInJYN07oSU5+MS99udF3FBEJU19k7uLLdTlcOy6N2KhI33FCjoqdiIiIHFZ6SmuOTW3N1JkZFJWW+Y4jImHowc/W0rZFDOcf0813lJCkYiciIiLVcuOEXmzPK+K1BZt8RxGRMLNow25mrdnJNWNSaRaj0braULETERGRahndsw1Durbk0RkZlJaV+44jImHkwU/X0rJ5NBcf2913lJClYiciIiLVYmbcNKEnG3P28/bXW3zHEZEwsXRzLp+s3MFVo3sQHxvlO07IUrETERGRaju+Xzv6dkjgoc/WUl7ufMcRkTDw4KdrSYiL4rLRKb6jhDQVOxEREak2M+OGCT3JyM7n/aXbfMcRkRC3evtePli2jctHpZAYF+07TkhTsRMREZEaOXVQR9KS43ng0zUatRORI/Lgp2uJj4nkytE9fEcJeSp2IiIiUiOREcZNE3uxcttepi3f7juOiISozOx9vLNkCxeP7E6r+BjfcUKeip2IiIjU2PcHd6RH23ju/2QNzmnUTkRq7uHpGcRERXD1cam+o4QFFTsRERGpsajICG6Y0JPlW/P4ZMUO33FEJMRszCng34s2c8HwbiQnxPqOExZU7ERERKRWTh/aiW6tm3P/pxq1E5GaeXh6BpFmTBmb5jtK2FCxExERkVqJjozghglpLNmUy/TV2b7jiEiI2Jq7n9cWbOTc9C50SIrzHSdsqNiJiIhIrZ15VBc6t2zGPz7WqJ2IVM9jMzJxDq4br9G6uqRiJyIiIrUWExXB9RPSWLxxD7PX7vQdR0Qaue15hfzzyw2cdXRnurRq7jtOWFGxExERkSNyzrAudEqK06idiBzWI9MzKC933Dihl+8oYUfFTkRERI5IbFQk141PY/763czL2OU7jog0UhWjdWcf3YVubTRaV9dU7EREROSInZvelfaJsfzjkzW+o4hII/XI9AzKyh03TOjpO0pYUrETERGRIxYXHcm149L4Yl0On2dq1E5E/te3o3WdNVpXT1TsREREpE5cMLwbbVvE8sCnGrUTkf9VMVqnZ+vqj4qdiIiI1InAqF0qc9buYn5Wju84ItJI7Mgr5CWN1tU7FTsRERGpMxeO6EbbFjH8/WON2olIwCMzMijVaF29U7ETERGROtM8Joprx6Uxe+1OvlynUTuRpm5HXiH//EKjdQ1BxU5ERETq1EUjupOcEMt9H632HUVEPNNoXcNRsRMREZE61SwmkuvGpTEvcxdzM3b6jiMinlSM1p11lEbrGoKKnYiIiNS5C0d0o31iLH//aA3OOd9xRMSDR2dkBkbrJmrduoagYiciIiJ1Li46khsm9OTLrBzmrNW6diJNzY68Ql78Yj1nHdWZ7m3ifcdpElTsREREpF788JiudEyK476PV2vUTqSJ0Whdw1OxExERkXoRGxUYtVuwfjcz1+hZO5GmYluuRut8ULETERGRenNeelc6t2zGvR9p1E6kqXjos7WUlTtuPl4zYTYkFTsRERGpNzFREdw4sSdfb9zDZ6t2+I4jIvVsY04BL3+1gR8e05WurTUTZkNSsRMREZF6dc6wLnRt3Yz7NEOmSNh74NM1mJmerfNAxU5ERETqVXRkBDdN7MU3m3P5eIVG7UTC1bqd+by+cDMXj+hOx6RmvuM0OSp2IiIiUu8Ckyg05z49aycStv7x8WpiIiO4bnya7yhNkoqdiIiI1LuoyAhuntiL5Vvz+HDZNt9xRKSOrd6+l7e+3sJlo1JIToj1HadJUrETERGRBnH60E6kto3n3o9WU1auUTuRcHLfR6uJj4liythU31GaLBU7ERERaRBRkRH8eFJvVm/fx9tfb/YdR0TqyNLNuby/dBtXHdeDVvExvuM0WSp2IiIi0mBOHdiR/h0Tue+jNRSXlvuOIyJ14L6PVpPULJqrxvTwHaVJU7ETERGRBhMRYfzkpD5syCng1fkbfccRkSO0cMNuPlm5g8ljU0mMi/Ydp0lTsRMREZEGNb5PMundW3H/J2soLCnzHUdEjsC901bTJj6Gy0el+I7S5IVFsTOzUWb2npnlmFmBmS0xs1vMLLIG50gxM3eIr5cPcexlZvalme0zs1wzm25m36+bTyciIhJezAKjdjv2FvHcvCzfcUSklj7P3MXstTu5bnwa8bFRvuM0eSH/n4CZnQ68DhQCrwA5wGnAfcBo4NwanvJr4M2DvL60iuvfA9wGbAIeB2KA84H/mNlNzrkHa3h9ERGRsDcitQ1jeyfz8PQMLhjejQTdwiUSUpxz3DttNe0SYrn42O6+4wghXuzMLJFAmSoDxjvn5gdf/xXwKXCOmZ3vnKtytO0gFjvnflvN648iUOoygGOcc7uDr/8VWADcY2bvOOeyanB9ERGRJuEnk/pw2oOzeWLWOm49sbfvOCJSA9NXZ/NlVg5/OH0AcdHVvklO6lGo34p5DpAMvFxR6gCcc4XAHcFvr6vH618b3P6xotQFr58FPATEAlfU4/VFRERC1qAuSZwysANPzMokJ7/YdxwRqabycsfdH6yiW+vm/PCYbr7jSFCoF7uJwe0HB3lvJlAAjDKz2Bqcs5OZTTGzXwS3g2t5/fcr7SMiIiKV/PjE3uwvKeOR6Wt9RxGRavrPki2s2JrHbZN6ExMV6nUifIT6fxJ9gtvVld9wzpUC6wjcbppag3OeCDwK/DG4/drMPjOz//l1hJnFA52Bfc65rQc5z5rgVveWiIiIVKFX+wTOPKoLz85bz7bcQt9xROQwikvL+du01fTvmMhpgzv5jiMHCPVilxTc5lbxfsXrLatxrgLgD8AwoFXwaxzwGTAe+CRY5urs2mY22czmm9n87OzsakQUEREJP7ec0AvnHA98uubwO4uIVy9/tYENOQX89OQ+RESY7zhyAO/FzsyyDrPMQOWvF2py+uDWHW5H59wO59yvnXMLnXN7gl8zgUnAF0BP4Oqaf8Kqr+2cm+qcS3fOpScnJ9fi1CIiIqGva+vmXDC8G698tZH1u/J9xxGRKuQXlXL/J2sY0aM143rrZ9fGxnuxIzCj5KoafG054NiKUbEkDi6x0n41Fryl84ngt2NrcO3DjeiJiIhI0I0TehIVadz70XeerhCRRuKp2evYua+Yn53SFzON1jU23pc7cM4dfwSHrwLSCTzHtuDAN8wsCugBlAKZR3ANgIr7JP97K6ZzLt/MNgOdzazjQZ6z6xXc6l8oERGRw2iXGMcVo3vwyPQMrhmTysDOVf3eVER8yMkv5rGZmUzq356ju7XyHUcOojGM2B2JT4Pbkw/y3ligOTDXOVd0hNc5NritXBAPdf1TKu0jIiIih3DtuDRaNo/mrg9W+o4iIpU8/NlaCopL+clJfQ6/s3gR6sXuNWAncL6ZpVe8aGZxwJ3Bbx858AAzSzKzvmbWsdLrI8wspvIFzGwicGvw28rP9z0a3P7SzFodcEwKcANQBDxd0w8lIiLSFCU1i+bGCT2ZtWYns9ZoUjGRxmLznv089/l6zj66C73aJ/iOI1UI6WLnnMsDrgEigelm9oSZ3Q0sBkYSKH6vVDrsTGAF8OdKr98FbDazf5nZfcGvT4BPCCw0/ivn3NxK158L3AukAUuCxzwEzAdaA/8XXKxcREREquGSkd3p0qoZf35vJeXlh537TEQawN+Dz77ecqJW8WrMQrrYATjn3iSwLMFM4GzgJqAE+DFwvnOuuv8qPE9g9stjCJTF6wk8J/cqMNY5d+fBDnLO3QZcDmwDJgOXAsuA05xzD9bqQ4mIiDRRsVGR/OSkPizfmsdbX2/2HUekyVuzfS+vL9zEpcd2p3PLZr7jyCFY9XuP1Kf09HQ3f/583zFERES8Ky93/OCh2ezOL+GT28YRFx3pO5JIkzX5ufnMy9jFzJ9OoFX8d55aEg/MbIFzLr3y6yE/YiciIiLhJSLCuP3kfmzes58XPl/vO45Ik7VgfQ7Tlm9n8thUlboQoGInIiIijc5xvdoypldbHvxsLbn7S3zHEWlynHPc+e4K2iXEctWYHr7jSDWo2ImIiEijdPspfcndX8Ij0zN8RxFpct77ZhuLNuzh/yb1oXmM96WvpRpU7ERERKRRGtApiTOHduapOevYsme/7zgiTUZRaRl3fbCSvh0SOHtYF99xpJpU7ERERKTR+vGk3uDg3uB06yJS/56ft54NOQX88nv9iIww33GkmlTsREREpNHq0qo5l43qzusLN7FyW57vOCJhb09BMfd/soZxvZMZ0yvZdxypARU7ERERadRumNCThNgo/vL+St9RRMLeA5+uZV9RKb84tZ/vKFJDKnYiIiLSqLVsHsONE3syfVU2s9Zk+44jErbW78rnuXlZnJfelT4dEnzHkRpSsRMREZFG77JRKXRr3Zw731lBaVm57zgiYenuD1YRFRHBj0/s7TuK1IKKnYiIiDR6sVGR/OLUvqzavpeXv9roO45I2FmwPod3v9nKlHGptEuM8x1HakHFTkRERELCSQM6MKJHa+79aDV5hVq0XKSuHLgY+eSxqb7jSC2p2ImIiEhIMDN+9f3+7C4o5sFP1/qOIxI2tBh5eFCxExERkZAxsHMS5xzdhafnrGP9rnzfcURCnhYjDx8qdiIiIhJSfnJSH6IjI/jze1r+QORIPTs3iw05Bfz8VC1GHupU7ERERCSktEuM4/rxaXywbBvzMnb5jiMSsnbsLeT+T9YysW87xvXWYuShTsVOREREQs7VY1Lp3LIZd767nLJy5zuOSEj66werKCot41ff7+87itQBFTsREREJOXHRkfz05D4s25LH6ws2+Y4jEnK+3riHfy3YxJWje9CjbbzvOFIHVOxEREQkJP1gSCeO6taSv05bxb6iUt9xREJGebnjt/9ZRtsWsdw4safvOFJHVOxEREQkJJkZv/5+f7L3FvHo9AzfcURCxpuLN7Nowx5+dnIfEuKifceROqJiJyIiIiHrqG6tOGNoJ6bOymRjToHvOCKN3r6iUv7y/kqGdG3J2UdreYNwomInIiIiIe1np/Ql0ozfv7PcdxSRRu+hz9ayY28Rvz2tPxFa3iCsqNiJiIhISOuY1Iybju/JR8u389mqHb7jiDRaWTvzeXLWOs46ujNHdWvlO47UMRU7ERERCXlXH5dKatt4fvf2MopKy3zHEWmU7nx3BdGRxu0n9/UdReqBip2IiIiEvJioCH77gwFk7SrgiVnrfMcRaXRmrs7m4xXbuXFiL9olxvmOI/VAxU5ERETCwtjeyZw8oAMPfLqGzXv2+44j0miUlJXz+3eWk9KmOVcel+I7jtQTFTsREREJG786rT8Ad2oiFZH/enZuFmt37OOO7/UnNirSdxypJyp2IiIiEjY6t2zGjRN68v7Sbcxak+07joh323ILue+j1Yzvk8zx/dr5jiP1SMVOREREwso1Y1NJadOc37y9jOLSct9xRLz6wzvLKS13/P4HAzHT8gbhTMVOREREwkpsVCS/+cEAMrPzeXK2JlKRpmv6qh28+81WbprYk25tmvuOI/VMxU5ERETCzoQ+7Tixf3se+HQNW3M1kYo0PYUlZfz6rWWkJsdzzdhU33GkAajYiYiISFj69ff7U1buuPPdFb6jiDS4hz9by4acAu48faAmTGkiVOxEREQkLHVt3Zzrx/fk3SVbmb1mp+84Ig0mM3sfj87I5IyhnRjVs63vONJAVOxEREQkbE0Zl0qPtvH88s1vKCwp8x1HpN455/jVW0uJjY7gl9/r7zuONCAVOxEREQlbcdGR/PHMgazfVcD9n6zxHUek3r399RbmrN3FT0/qQ3JCrO840oBU7ERERCSsjUpryznDujB1ZiYrtub5jiNSb/IKS7jz3RUM6ZLEhSO6+44jDUzFTkRERMLeL0/tR2KzaH7+xjeUlTvfcUTqxd8+XMWufUX88cxBREZozbqmRsVOREREwl6r+Bh+/f3+LN64hxe/WO87jkidW7JpD899vp5LR6YwsHOS7zjigYqdiIiINAmnD+3EmF5tufuDVVrbTsJKaVk5v/j3N7RtEcuPJ/X2HUc8UbETERGRJsHM+OMZgygtL+c3by3zHUekzkydlcnSzXn84fQBJMZF+44jnqjYiYiISJPRrU1zbjmhN9OWb+eDpdt8xxE5YhnZ+/j7x2s4ZWAHTh7Y0Xcc8UjFTkRERJqUq47rQb+Oifzm7aXkFZb4jiNSa+Xljp+9toRm0ZH87vQBvuOIZyp2IiIi0qRER0bwl7MGsWNvEX/9YJXvOCK19ty8LOav382vv9+fdglxvuOIZyp2IiIi0uQM6dqSy0el8MIX61mwPsd3HJEa25hTwN0frmJc72TOOrqz7zjSCKjYiYiISJN026Q+dEpqxk9eW0JhSZnvOCLV5pzj5298gwF/OmsQZlqzTlTsREREpIlqERvFXWcPJjM7n79+qFsyJXT8a/4mZq/dye2n9qNzy2a+40gjoWInIiIiTdZxvdpy8bHdeGrOOr5cp1sypfHbnlfIH95dzvAerbloeDffcaQRUbETERGRJu3np/SjS6tm/OS1rykoLvUdR6RKzjl++e+lFJeWc9fZg4mI0C2Y8q2wKHZmNsrM3jOzHDMrMLMlZnaLmUXW4BzPmJk7zNcnlY65/DD7X1v3n1ZERETqUnxsFH89ZwjrdxVw1/srfccRqdJ/lmzl4xXbuW1Sb3q0jfcdRxqZKN8BjpSZnQ68DhQCrwA5wGnAfcBo4NxqnupNIKuK9y4BUoH3q3j/LWDxQV6fX81ri4iIiEfHprbhitEpPD0ni5MGdGBUz7a+I4n8jx17C/nt28sY3CWJK0f38B1HGiFzzvnOUGtmlgisBZKA0c65+cHX44BPgZHABc65l4/gGi2BLUAk0Nk5t/OA9y4HngaucM49U9trAKSnp7v589UDRUREfNlfXMap98+iuLScD28dS4vYkP/9t4QJ5xxXPPMV8zJ28e7Nx9GzXYLvSOKRmS1wzqVXfj3Ub8U8B0gGXq4odQDOuULgjuC31x3hNS4BmgFvHFjqREREJLw0i4nknnMHszV3P398d4XvOCL/9eIXG5i+Kpufn9JXpU6qFOrFbmJw+8FB3psJFACjzCz2CK5xTXA79RD7DA0+03e7mV1iZl2O4HoiIiLiybDurblmTCovfbmBGauzfccRITN7H398dwVjerXl0pEpvuNIIxbqxa5PcLu68hvOuVJgHYHnCFNrc3IzGwkMAlY75z47xK4/IvBM35+B54AsM3s0eEvooc4/2czmm9n87Gz94yEiItIY3Hpib3q2a8HPXltC7v4S33GkCSspK+fWVxYTGx3BPecO0SyYckihXuySgtvcKt6veL1lLc8/Obh9vIr31wE3ESiY8UAn4DwCk7BMAZ461Mmdc1Odc+nOufTk5ORaRhQREZG6FBcdyd/OHUL2viJ+9/Yy33GkCXvg07V8vSmXP505iPaJhxwvEPFf7MwsqxrLDBz49UJNTh/c1niGGDNLIlDSioFnDraPc26Gc+5B59xq51yBc26rc+5fwARgN3CBmQ2p6bVFRETEryFdW3LjhJ68sWgzby7a7DuONEELN+zmoc/WctbRnTl1UEffcSQENIbpnjIILFVQXVsO+HPFiFzSwXYEEivtVxMXA80JTMxSo0lTnHMbzew94CJgLPB1La4vIiIiHt00sSdz1u7kjjeXclS3lnRvo3XDpGHkF5Xy41cW0yExjt/+YIDvOBIivBc759zxR3D4KiAd6A0sOPANM4sCegClQGYtzl0xacpjtcxW8dCc/hUQEREJQVGREfz9/KGc+o9Z3PzSIv517Shiorzf7CRNwJ3vrmB9TgEvX3MsiXHRvuNIiAj1/3f6NLg9+SDvjSUw4jbXOVdUk5Oa2QhgCIFJU6bXMtuI4LY2pVJEREQagS6tmvOXswfz9aZc7v3oO3O1idS5j5dv56UvNzB5bCojUtv4jiMhJNSL3WvATuB8M/vvIn3B2SjvDH77yIEHmFmSmfU1s0PdrFwxacqhljjAzMYc5DUzs58TWBx9JwdfikFERERCxKmDOnLB8G48NjOD2Wu0pK3Unx15hdz+xhL6dUzkxyf29h1HQkxIFzvnXB6BWyYjgelm9oSZ3Q0sJlCsXgNeqXTYmcAKAksTfIeZJQI/JDBpyrOHiTDTzFaZ2QtmdpeZPQosAf5EYA29i4IZRUREJIT9+vv9SUtuwa2vLmbXvhrdCCRSLaVl5dz88iLyi8q4//yhxEZF+o4kISakix2Ac+5NYByBBcnPJrD8QAnwY+B851xNZ8S8iMBzcW9UY9KUe4BtBBZK/xFwKRANPAQMcs5Nq+G1RUREpBFqFhPJAxccRe7+En7y2hJq/uOFyKH945M1fJ6Zw51nDKRX+wTfcSQEmf6PqXFIT0938+fP9x1DREREDuHZuVn85u1l/Oa0/lwxuofvOBImZqzO5vKnv+S8YV2565zBvuNII2dmC5xz6ZVfD/kROxEREZGGcunI7pzQrx1/fm8ly7bUZjUlkf+1NXc/t76ymD7tE/jd6VraQGpPxU5ERESkmsyMu88ZQqv4aG765yLyCkt8R5IQVlJWzs0vLaKopIyHLjqauGg9Vye1p2InIiIiUgOt42N44IKj2ZBTwG2vfk15uR5rkdq5Z9oqvsrazZ/OGkRacgvfcSTEqdiJiIiI1NDwHq355ff68dHy7TwyI8N3HAlBn6zYzmMzMrloRDdOH9rZdxwJAyp2IiIiIrVw+agUzhjaiXumrWLG6mzfcSSEbNpdwI9f/ZoBnRL51ff7+44jYULFTkRERKQWzIw/nzWYPu0TuPmlRWzMKfAdSUJAcWk5N/5zEWXljocu1HN1UndU7ERERERqqVlMJI9dMgznHFOeX8D+4jLfkaQRc87x67eWsnjjHu4+ZzApbeN9R5IwomInIiIicgS6t4nnHxccxYptefzy399o8XKp0jNzs3j5q43cOKEnpw7q6DuOhBkVOxEREZEjNKFPO249oTdvLNrM85+v9x1HGqGZq7P5wzvLmdS/PT8+sbfvOBKGVOxERERE6sCNE3pyQr92/P4/y5mfleM7jjQimdn7uPGfC+ndPoH7fjiUiAjzHUnCkIqdiIiISB2IiDDu/eFQurZuznUvLmTznv2+I0kjkFtQwtXPzicqMoLHL00nPjbKdyQJUyp2IiIiInUkMS6aqZcMo7CkjCue/pK8whLfkcSj0rJybnxpIRt3F/DoxcPo2rq570gSxlTsREREROpQr/YJPHbxMDKz87nuhQUUl5b7jiSe/Om9lcxas5M7zxjI8B6tfceRMKdiJyIiIlLHRvVsy1/OHsyctbs0U2YT9cpXG3hqzjquGJ3CD4/p5juONAG6yVdERESkHpwzrAsbcwr4xydr6Na6OTcd38t3JGkg8zJ2ccebSxnTqy2/PLWf7zjSRKjYiYiIiNSTW07oxcbdBfzto9V0ad2MM4/q4juS1LNlW3KZ/Nx8ureJ58ELjiYqUjfIScNQsRMRERGpJ2bGX84azNY9hfz0tSV0SGzGyLQ2vmNJPVm/K5/LnvqKhLgonrtyOEnNo31HkiZEv0IQERERqUcxURE8eskwUtrEM+X5+azdsdd3JKkHO/IKueTJLykrL+e5q0bQqWUz35GkiVGxExEREalnSc2ieeryY4iJiuTyp79ie16h70hSh3L3l3DZ01+xc18RT18xnJ7tWviOJE2Qip2IiIhIA+jaujlPXZ7O7vxiLnz8c7L3FvmOJHWgsKSMa54LjMQ+dskwhnZt6TuSNFEqdiIiIiINZHCXljx9xXC27Cnk4ie+ICe/2HckOQKlZeXc9NIivsrK4d7zhjKmV7LvSNKEqdiJiIiINKDhPVrz5GXpZO3K55InvyB3f4nvSFILzjl+8e9v+Gj5dn572gBOG9LJdyRp4lTsRERERBrYqJ5teeySYazZvo/LnvqSvYUqd6HEOced767g1fmbuPn4Xlw2KsV3JBEVOxEREREfxvdpx4MXHsXSzblc+cxXFBSX+o4k1VBe7vj1W8t4cvY6Lh+Vwq0naOF5aRxU7EREREQ8mTSgA/84/ygWrN/N1c/Op7CkzHckOYSy8sDtl89/vp4pY1P5zWn9MTPfsUQAFTsRERERr743uCN/O28I8zJ3MeX5BSp3jVRpWTk/+dfXvPzVRm6a2JPbT+mrUieNioqdiIiIiGdnHtWFu84azIzV2Vzx9Fd65q6RKSkr55ZXFvPGos3cdmJvbpvUR6VOGh0VOxEREZFG4LxjunLfD4fwVVYO50/VOneNRVFpGTe8uJB3lmzlF6f25abj9UydNE4qdiIiIiKNxJlHdeHxy9LJzM7nnEfnsn5Xvu9ITVphSRnXPr+Aacu389vT+jN5bJrvSCJVUrETERERaUQm9GnHP68ZQd7+Es5+ZB5LN+f6jtQk5RaUcPnTXzJ9dTZ/OnMQl4/u4TuSyCGp2ImIiIg0Mkd1a8W/rh1FTKRx/tTPmZux03ekJmXdznzOfHgOC9bv5t7zhnDhiG6+I4kcloqdiIiISCPUs10LXr9+FJ1axnH5U1/x3jdbfUdqEj7P3MWZD89hd0ExL159LGce1cV3JJFqUbETERERaaQ6JjXj1SkjGdQliRv+uZDHZ2binPMdK2y9On8jlzz5BW3iY3jzhtEM79HadySRalOxExEREWnEWjaP4YWrRnBS/w788b0V3PjPRewrKvUdK6yUlzv+/P4KfvraEo5NbcMb14+me5t437FEakTFTkRERKSRaxYTySMXH83tp/Tl/aVbOeOhOazdsc93rLBQUFzKtS8s4LEZmVx8bDeevvwYkppF+44lUmMqdiIiIiIhwMy4dlwaL1w1gt35xZz+4Gze13N3R2Ttjn2c/cg8Pl4RWM7gD6cPJCpSPx5LaNJ/c0VERERCyKiebXnn5uPo1T6B615cyJ/eW0FpWbnvWCHFOceLX6zn+w/MYnteIU9efgyXj+6BmfmOJlJrUb4DiIiIiEjNdExqxitTjuXOd1YwdWYmSzbt4YELjiY5IdZ3tEYvJ7+Y219fwrTl2xnTqy1/O3cI7RLjfMcSOWIasRMREREJQbFRkfzhjIHce94QFm/cw6T7ZvDW4s2aNfMQZq/Zycl/n8n0Vdnc8b1+PHvFcJU6CRsqdiIiIiIh7Kyju/D2jcfRvU08P3p5MVc/O5+tuft9x2pUikvL+dN7K7j4yS9IiIvi3zeM4uoxqURE6NZLCR+m3+o0Dunp6W7+/Pm+Y4iIiEiIKit3PD1nHfdMW0VURAQ/P7UvFxzTrcmXl4UbdnPHv5eyfGseFx/bjV+e2p9mMZG+Y4nUmpktcM6lf+d1FbvGQcVORERE6sKGXQXc/sYS5mbs4tjU1vzlrMGktG16a7JtzyvkrvdX8saizbRLiOWPZw7ixP7tfccSOWIqdo2cip2IiIjUFeccr87fyJ3vrqC4tJybj+/FlaN7NImRqqLSMp6ancWDn66hpMxx1Zge3DChJy1iNWeghAcVu0ZOxU5ERETq2va8Qn715lKmLd9OckIsN07oyfnDuxIbFX4FzznHJyt2cOe7y8naVcAJ/dpzx/f6NcnRSglvKnaNnIqdiIiI1JevsnK458NVfLEuh05Jcdx8fC/OHtaF6DBZjHvB+t3845M1zFydTVpyPL85bQBjeyf7jiVSL1TsGjkVOxEREalPzjnmZuzinmmrWLRhD91aN+eWE3px+tDORIbgBCtl5Y4Pl23j8VmZLNqwh6Rm0dx8fC8uHdk9bAqryMGo2DVyKnYiIiLSEJxzfLZqB/d8uJrlW/NIbRvPD4/pyplHd6ZdQuNf021fUSmvfrWRp+euY2POfrq1bs5Vx/XgnGFdiNdzdNIEhGWxM7No4HpgKHAU0B+IBq5xzj1Ry3OOAu4AjgXigLXAU8ADzrmyKo65DLgheP0yYBFwj3PunepeV8VOREREGlJ5ueODZdt4cvY6FqzfTWSEMb53Muemd2Vi33bERDWeUS/nHGt37OO1BZv455cb2FtYSnr3Vlw9JpUT+7cPyRFHkdqqqtiF+q814oG/B/+8HdgGdK3tyczsdOB1oBB4BcgBTgPuA0YD5x7kmHuA24BNwONADHA+8B8zu8k592Bt84iIiIjUl4gI49RBHTl1UEcysgOl6fUFm/hk5Q5ax8dwxtDOnD2sM/07JmLW8MWprNyxaMNupi3fzrRl28jaVUCEwSmDOnL1cT04qlurBs8k0piF+ohdDHA8sNg5t9XMfgv8hlqM2JlZIoHRuSRgtHNufvD1OOBTYCRwgXPu5QOOGQXMATKAY5xzu4OvpwALCBTPvs65rMNdXyN2IiIi4ltpWTmz1uzkXws28tHy7ZSUOdq2iGF4j9YMT2nN8B5t6NMhod5GyPIKS/gyM4ePlm/n4xXb2ZVfTHSkMTKtLZP6t+fE/u1pn9j4bxcVqU9hOWLnnCsG3q+j050DJAPPVZS64DUKzewO4BPgOuDlA465Nrj9Y0WpCx6TZWYPAb8CriBQNkVEREQatajICCb0bceEvu3YnV/MtOXb+GJdDl9k5vDeN9sASIyL4piU1qSntKZH2+a0T4yjfWIcyQmx1Z60pLCkjIzsfazevpeV2/ayetteVm/fx+Y9+wFIiI1ifN92TOrfnnF9kkmMi663zywSLkK62NWxicHtBwd5byZQAIwys1jnXFE1jnmfQLGbiIqdiIiIhJhW8TH88Jhu/PCYbgBs2l3AV1k5fLkuhy/W5fDJyh3/s78ZtImPpUNSLO0T4oiIMApLyigqKaewtCzw59JyCkvK2LmvmLLywF1j0ZFGWnIL0lNacWH7bgzuksSIHm0a1TN+IqFAxe5bfYLb1ZXfcM6Vmtk6YACQCqwws3igM7DPObf1IOdbE9z2ruqCZjYZmAzQrVu3I4guIiIiUr+6tGpOl1bNOfOoLgDsKShm8579bM8rZHteEdtyC4N/LmRrbiHlzhEXHUlcdASt42OIiwr8OS46krYtYunTIYG+HRJIaRuv5QlE6oCK3beSgtvcKt6veL1lLff/DufcVGAqBJ6xq05IERERkcagZfMYWjaPYUCnpMPvLCL1zvuvR8wsy8xcDb5e8BU1uK1pAVNhExERERGRetUYRuwyCCwvUF1b6ilHxQhbVb92Sqy03+H2P9yInoiIyP+3d+9BlpT1Gce/DxJFV0WFACZRBGsVSy2EGhGQsCoRUSsKKIllQpQKKCVx44WqGC4BYizxEgohXtBVMFBliBhIRVE3IgFxo4SLUpIoIiwxySIuF+Wu4C9/vD06OcywZ4bd06fD91M11Tvdc4pnq9lz+pl+37clSdooei92VbVP3xk63wNmaHPiLp97IMnmwA7AfcB1AFV1Z5L/Bn4zyZPnmWe3vNs+YM6eJEmSJG1MvQ/FnCJf7bb7zXNsb+AxwJo5K2Ju6DUvH/kZSZIkSdokHnbFLsmWSXZK8uSRQ+cA64HXJZmZ8/NbAH/VffvRkdd8rNseneSJc17zNOAI4F7g9I0YX5IkSZIeoPehmA9VkncBO3XfPq/bHpJkr+7Pl1TVqjkvOYBWtj4NvHF2Z1X9NMlhtIL3L0n+DrgFeBXtUQjnAGfP/W9X1ZokJwHvAK5Kcg7wSOD3gScBb62qtRvnbypJkiRJ8xt8saMNg1wxsm/P7mvWKsZQVeclWQEcDbwG2AK4llbcTqmqB6xwWVXvTHIV8Ce0Z9L9ArgC+EBVfX6RfxdJkiRJWrTM01XUg5mZmbrsssv6jiFJkiRpiiW5vKpmRvc/7ObYSZIkSdL/NxY7SZIkSRo4i50kSZIkDZzFTpIkSZIGzmInSZIkSQNnsZMkSZKkgbPYSZIkSdLAWewkSZIkaeAsdpIkSZI0cBY7SZIkSRo4i50kSZIkDZzFTpIkSZIGzmInSZIkSQNnsZMkSZKkgbPYSZIkSdLApar6ziAgyY+BG/rOMcfWwPq+Q+gh8RwOn+dw+DyHw+c5HD7P4fB5Dv+v7avq10d3Wuw0rySXVdVM3zm0dJ7D4fMcDp/ncPg8h8PnORw+z+F4HIopSZIkSQNnsZMkSZKkgbPYaSEf7zuAHjLP4fB5DofPczh8nsPh8xwOn+dwDM6xkyRJkqSB846dJEmSJA2cxU6SJEmSBs5iJ0mSJEkDZ7HTLyX5rSSfSvI/Se5NsjbJyUme2Hc2bViS1yY5NcnXkvw0SSU5q+9cGk+SrZIcmuTcJNcmuTvJT5JckuSPk/h+PQBJ3pfkgiQ/7M7hLUmuTHJckq36zqelSXJw955aSQ7tO48eXHf9Ugt83dh3Po0vyW8n+VySdd216bokq5O8ou9s02jzvgNoOiR5OrAG2Ab4R+C7wG7AnwL7JXlhVd3cY0Rt2DHAzsAdwH8BO/UbR4t0EPBRYB1wIfCfwLbAgcAq4OVJDipXvJp2bweuAP4ZuAlYBuwOHA+8KcnuVfXD/uJpsZI8BTiV9t762J7jaHw/AU6eZ/8dE86hJUpyDPBuYD3wedrn49bALsCLgPN7CzelLHaa9RFaqVtZVafO7kxyEu1C5T3A4T1l03jeTit01wIraOVAw3EN8CrgC1X1i9mdSY4CLgVeQyt5n+snnsb0+Kq6Z3RnkvcARwF/Drxl4qm0JEkCnA7cDPwDcGS/ibQIt1XV8X2H0NIkOYhW6r4CHFhVt48c/7Vegk05h/aIJDsC+wJrgQ+PHD4OuBM4OMmyCUfTIlTVhVX1fe/oDFNVfbWq/mluqev23wh8rPv2RRMPpkWZr9R1/r7bLp9UFm0UK4GXAIfQPgslbWLd1IP3AXcBrx8tdQBV9fOJBxsA79gJ2ocWwOp5LipvT/J1WvHbHbhg0uEkMfsBdl+vKfRQ/G63varXFBpbkmcBJwIfqqqLk7xkQ6/RVHlUkj8Enkor5VcBF1fV/f3G0hj2BHYAzgFuTfJK4DnAPcClVfWvfYabZhY7ATyz216zwPHv04rdM7DYSROVZHPgj7pvv9RnFo0vyZG0+VhbAjPAXrQLyxP7zKXxdP/uzqTNdT2q5zhamu1o53Cu65McUlUX9RFIY3t+t/0Rbc7yc+ceTHIx8Nqq+vGkg007h2IK2oUHtInG85nd/4RNH0XSiBNpv6k8v6q+3HcYje1I2lD2t9FK3ZeAfb0QGYy/oC3Q8MaqurvvMFq004F9aOVuGa0YnAY8Dfhikp37i6YxbNNtDwceDfwO8DjaZ+GXgb2Bz/YTbbpZ7DSOdFvnbkkTlGQl8E7aKrUH9xxHi1BV21VVaBeWBwI7Alcm2bXfZNqQJLvR7tL9tUO+hqmqTujmLf+oqu6qqu9U1eHASbSicHy/CbUBj+i2od2Zu6Cq7qiqq4EDaAvFrUiyR28Jp5TFTvCrO3JbLnD88SM/J2kTS3IE8CHg34EXV9UtPUfSEnQXlufShrNvBfxtz5H0IOYMwbwGOLbnONr4Zhei2rvXFNqQW7vtdVX17bkHujvos6NXdptoqgGw2Ange932GQscn13FbaE5eJI2oiRvA/4G+A6t1PlA3YGrqhtoJf3ZSbbuO48W9FjaZ+GzgHvmPtiaNrQW4BPdvpP7Cqklu6nbusr3dJu9Lr1tgeOzxe/Rmz7KsLh4iuBXzzvbN8lmI8/QehzwQuBu4Bt9hJMeTpL8GW1e3beAl1bV+n4TaSP6jW7rqnzT617gkwsc25U27+4S2oWnwzSHZ3bo3nW9ptCGXExbBXp5kkdW1c9Gjj+n266daKoBsNiJqvpBktW0oUJHAKfOOXwC7Tdbp1WVz/CRNqEkxwJ/CVxOW2jD4ZcDkmQn2kORbxzZvxntQbvbAGuq6tb5Xq/+dcO8Dp3vWJLjacXu01W1apK5NL4kzwbWjb5/JtmeNhIC4KyJB9PYqmp9krOBP6AtZHTM7LEkLwVeRpse5ErRIyx2mvUWYA1wSpJ9gP8AXgC8mDYE8+ges2kMSfYH9u++3a7b7pHkjO7P66vqyAnH0piSvIFW6u4HvgasTDL6Y2ur6owJR9P49gM+0C3F/QPgZmBbYAVt8ZQbgcP6iyc9LBwEvCvJhcD1wO3A04FXAlsA5wMf7C+exvQO2nXo0Un2Bi4FtqctnnI/cFhV3dZfvOlksRPwy7t2M7QLy/2AVwDrgFOAE7xzMAjPA94wsm/H7gvgBtoS7JpOO3TbR9CWyJ/PRcAZkwijJfkK8HHa8PWdaY+IuZP2y7EzgVN8L5U2uQtpz+fdhTb0chltrtYltH+HZ1aVq3xPuaq6KckLaHfrDgB2p5X0LwDvrSqnB80j/r8tSZIkScPmqpiSJEmSNHAWO0mSJEkaOIudJEmSJA2cxU6SJEmSBs5iJ0mSJEkDZ7GTJEmSpIGz2EmSJEnSwFnsJEmSJGngLHaSJEmSNHAWO0mSJEkaOIudJEmSJA2cxU6SpCmR5LwkleSt8xx7d3dsVR/ZJEnTLVXVdwZJkgQkeRJwJbAtsEdVXdnt3wdYDXwXeH5V3dVfSknSNLLYSZI0RZLsCVwEXA/sCjwG+DawJa3UXd1jPEnSlHIopiRJU6Sq1gDHAsuB04CzgO2AlZY6SdJCvGMnSdKUSRLgi8DLul2fqarX9xhJkjTlvGMnSdKUqfZb13Pn7Dq5pyiSpIHwjp0kSVMmyXLgCuDntLl1VwO7VdU9vQaTJE0t79hJkjRFkjwKOBtYBrwOeC/wXLxrJ0l6EBY7SZKmyweBXYD3V9Vq4Djg68Cbk/xer8kkSVPLoZiSJE2JJPvT5tZ9E9irqu7r9j8F+BawObBLVV3XV0ZJ0nSy2EmSNAWSPJVW3jajlbfrR46/GjgP+Dda6fvZpDNKkqaXxU6SJEmSBs45dpIkSZI0cBY7SZIkSRo4i50kSZIkDZzFTpIkSZIGzmInSZIkSQNnsZMkSZKkgbPYSZIkSdLAWewkSZIkaeAsdpIkSZI0cP8LeOMmC2Vc5rYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams.update({'font.size': 20, 'figure.figsize': [14,10]})\n",
    "\n",
    "# Generate data\n",
    "x = np.linspace(0, 2*np.pi, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "# Create a line plot\n",
    "plt.plot(x, y)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Sine Wave')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c89832d2",
   "metadata": {},
   "source": [
    "### Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "33cc2622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate random data\n",
    "x = np.random.rand(100)\n",
    "y = np.random.rand(100)\n",
    "colors = np.random.rand(100)\n",
    "sizes = 100 * np.random.rand(100)\n",
    "\n",
    "# Create a scatter plot\n",
    "plt.scatter(x, y, c=colors, s=sizes, alpha=0.5)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Scatter Plot')\n",
    "plt.colorbar()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cdf6ad7",
   "metadata": {},
   "source": [
    "### Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d83471ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate random data\n",
    "data = np.random.randn(1000)\n",
    "\n",
    "# Create a histogram\n",
    "plt.hist(data, bins=30, alpha=0.7)\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99a00d60",
   "metadata": {},
   "source": [
    "### Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a99f3583",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate data\n",
    "labels = ['A', 'B', 'C', 'D']\n",
    "values = [10, 15, 7, 12]\n",
    "\n",
    "# Create a bar plot\n",
    "plt.bar(labels, values)\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Value')\n",
    "plt.title('Bar Plot')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24dff6c5",
   "metadata": {},
   "source": [
    "### Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "39f4a93e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate random data\n",
    "data = np.random.rand(10, 10)\n",
    "\n",
    "# Create a heatmap\n",
    "plt.imshow(data, cmap='hot')\n",
    "plt.colorbar()\n",
    "plt.title('Heatmap')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3c97098",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}