Numpy_InClass_3 (Numpy_Arrays & Concatenation & Indexing) 12.03.24.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='green'> <b>Importing Libraries </b><font color='black'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Numpy Source 01](https://numpy.org/),\n",
    "[Numpy Source 02](https://www.w3schools.com/python/numpy/numpy_intro.asp),\n",
    "[Numpy Source 03](https://www.geeksforgeeks.org/python-numpy/),\n",
    "[Numpy Source 04](https://scipy-lectures.org/intro/numpy/array_object.html),\n",
    "[Numpy Source 05](https://www.javatpoint.com/numpy-tutorial),\n",
    "[Numpy Source 06](https://medium.com/analytics-vidhya/introduction-to-numpy-16a6efaffdd7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "- **NumPy** is a module that is not included in the Python Standard Library and does not come pre-installed with the Python installation.\n",
    "\n",
    "- Before importing the NumPy module, we need to install it using Python's pip tool -pip install numpy-.\n",
    "\n",
    "- The **pip** tool allows us to install modules or packages that are not included in the Standard Library.\n",
    "\n",
    "- However, when you install Anaconda, many libraries including NumPy come pre-installed in addition to the core Python language."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-30T09:25:26.054190Z",
     "start_time": "2023-10-30T09:25:25.811353Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='green'> <b>Various Built-in Methods</b><font color='black'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>rand & randn & randint </b><font color='black'>\n",
    "**random.rand():** Generates random float numbers with a uniform distribution in the range 0-1.\n",
    "\n",
    "**random.randn():** Generates random numbers with a normal distribution, having a mean of 0 and a standard deviation of 1.\n",
    "\n",
    "**random.randint():** Generates random integers within the specified range. The endpoint is not included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35124786410741193"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.43141178, 0.07410544, 0.71516636],\n",
       "       [0.66245931, 0.0106047 , 0.23954876]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.15999976, -1.6819012 , -0.41397661,  0.66905777,  1.93749776,\n",
       "        1.06826877,  0.11717789, -0.15621749, -0.98343311, -0.04124263])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.08792231,  0.23423827, -0.45194254,  2.96161921],\n",
       "       [ 0.55550891,  0.39959524, -0.19406194, -1.22192067],\n",
       "       [-0.23597022, -1.02031774,  0.30197844,  0.08388839]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3136407827478132"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(3,4).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8065183671671072"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(3,4).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0031008769650416592"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(300,400).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(4, 10)  # a number between 4 and 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50, 92, 58, 21, 11, 11, 70, 95])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(1, 100, 8)  #one dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14, 42],\n",
       "       [38, 44],\n",
       "       [54, 22]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(1, 100, size = (3,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[21, 36],\n",
       "       [46, 60],\n",
       "       [ 4, 97]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(44)\n",
    "np.random.randint(1, 100, size = (3, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>max & argmax & min & argmin</b><font color='black'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "min() returns the smallest value in an array.\n",
    "\n",
    "max() returns the largest value in an array.\n",
    "\n",
    "argmin() returns the index of the smallest value in an array.\n",
    "\n",
    "argmax() returns the index of the largest value in an array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20,  3, 47, 42, 23, 22, 25,  3,  7,  3, 35, 38])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array1 = np.random.randint(0,50,12)\n",
    "my_array1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array1.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array1.argmin() #index value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array1.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array1.argmax() #index value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>size</b><font color='black'>\n",
    "The total number of elements in an array\n",
    "\n",
    "For a 2x3 matrix, size=6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20,  3, 47, 42],\n",
       "       [23, 22, 25,  3],\n",
       "       [ 7,  3, 35, 38]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa = my_array1.reshape(-1,4)\n",
    "aa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa.size  #total number of elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(aa) #returns the number of rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>itemsize</b><font color='black'>\n",
    "    \n",
    "itemsize() indicates how many bytes each element in a NumPy array occupies in memory.\n",
    "\n",
    "This property depends on the data type (dtype) of the NumPy array and varies according to the size of this data type.\n",
    "\n",
    "itemsize() can also be used to calculate the total memory occupied by a NumPy array. This calculation is done by multiplying the number of elements in the array by itemsize().\n",
    "\n",
    "A float32 type uses 32 bits (4 bytes) of memory and can represent decimal numbers with approximately 7 digits of precision.\n",
    "\n",
    "A 32-bit number can represent 2^32 different values.\n",
    "\n",
    "A float64 type uses 64 bits (8 bytes) of memory and can represent decimal numbers with approximately 15-16 digits of precision.\n",
    "\n",
    "A 64-bit number can represent 2^64 different values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20,  3, 47, 42],\n",
       "       [23, 22, 25,  3],\n",
       "       [ 7,  3, 35, 38]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa.itemsize  #hrepresents the size of each element in the array in bytes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>copy() </b><font color='black'>\n",
    "**copy()** is used to create a copy of a NumPy array.\n",
    "\n",
    "This function creates an exact duplicate of the original array, but this copy is independent. That is, any changes made to the copied array do not affect the original array, and any changes made to the original array do not affect the copied array. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12],\n",
       "       [13, 14, 15, 16]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array2 = np.arange(1,17).reshape(-1, 4)\n",
    "my_array2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12],\n",
       "       [13, 14, 15, 16]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "copyarr = my_array2.copy()  #A copy of my_array2 is created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12],\n",
       "       [13, 14, 15, 16]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copyarr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>transpose() </b><font color='black'>\n",
    "The **transpose()** function is a method that changes the dimensions of NumPy arrays. It is specifically used to obtain the transpose of a matrix.\n",
    "\n",
    "Transposing a matrix converts its rows into columns and its columns into rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12],\n",
       "       [13, 14, 15, 16]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copyarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  5,  9, 13],\n",
       "       [ 2,  6, 10, 14],\n",
       "       [ 3,  7, 11, 15],\n",
       "       [ 4,  8, 12, 16]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copyarr.transpose()   #method swaps the rows and columns of the array copyarr, effectively computing the transpose of the matrix represented by copyarr."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12],\n",
       "       [13, 14, 15, 16]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copyarr   #does not permanently modify the original array copyarr"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='green'> <b>Concatenation of the Arrays</b><font color='black'>\n",
    "concatenate() function is used to concatenate (join together) two or more arrays along a specified axis or dimension.\n",
    "\n",
    "- The first parameter of the function is a list of arrays that will be concatenated.\n",
    "\n",
    "- The second parameter specifies the axis or dimension along which the concatenation operation will be performed.\n",
    "\n",
    "For example, setting axis=0 performs vertical concatenation (stacking arrays row-wise), while axis=1 performs horizontal concatenation (joining arrays column-wise).\n",
    "![image.png](attachment:image.png)\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(1,7)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7,  8,  9, 10, 11, 12])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.arange(7,13)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xy = np.concatenate((x,y))    #In one-dimensional arrays, the axis value is 0, and it concatenates them side by side.\n",
    "xy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xy = np.concatenate((x,y), axis=0)\n",
    "xy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "AxisError",
     "evalue": "axis 1 is out of bounds for array of dimension 1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAxisError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[36], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m xy \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m xy\n",
      "\u001b[1;31mAxisError\u001b[0m: axis 1 is out of bounds for array of dimension 1"
     ]
    }
   ],
   "source": [
    "xy = np.concatenate((x,y), axis=1)  #In one-dimensional arrays, axis value of 1 cannot be used because they only have one dimension.\n",
    "xy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[1,2],[3,4]])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 6],\n",
       "       [7, 8]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.array([[5,6],[7,8]])\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [5, 6],\n",
       "       [7, 8]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab = np.concatenate((a,b))\n",
    "ab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 5, 6],\n",
       "       [3, 4, 7, 8]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate((a,b), axis=1)"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
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bVWeGL8PXIhVsRPsG6Fr3LVzw8yrXSkw9oyh1p+d37+Fq8CmLXE/7pmX4Wha+F/290bluDfT94X1cC7FeWpl2OdKyNeFLIbKBjT8RYbKM5Fvl6iJ1sXaYNkRkPDjOm1Buuy55K7uO4cvwtUgFO7zLSXgL6+eNLdcnPsDHQ1T2iV1/xL0s63hQDF/LwZcekN2+fRddv/o3An324/GDfIvUEWMhZU345mUnYXznBqI+HtyxvpxeFNMe1/E7sf3scetmdTB8Gb7lKqCxN4n6fvk5SSJHstWHf8JWhzninNSbKCzoFPr88KEY2ezEge0qr5heay+d9cHw1l/Dc5f5sTV7hC+1ph/dtwkDG32McxboLWWLVLP4G5cxvFVdAR+/I/uMAi9lP8we1g30cLXE2B3WhK8Ip3jtRfP3fo8udf8DchpoTAuqy9mpcVg0theav/c7jOnaWKOXX/7dFEzu0wrLxvcGZX+o1/3KzjN8Gb4WqUhUqa9fPovhbb5Bq5p/FjcvVS6q5B0//ye2rV6gcR3ypnasniNGO2tb668a2ypTme0RvpQpsHhMTzSu8ZIYKawyeqkfY2340hjFa6cPUZUd2dTQb/eGpaLcYq5dQMsP/4Sm776MveueP3jV5TZ13prwlWQ57bUPXb/6F1rV/JNKRxp5r8Nn/8T8kT3F+NPSvjQlJ4KGoGzx/h8Q6nfI7PpK5yTbmtLD7enDAoT4eqD/z7WweuVyuLq6IjAwEPHx8cjPz8fjx481aSazJe5kYWbKFI0KdWTfJlXu5O6183H9cmC5ykqwvhVxARvmjsbZY/vLbVev+MbMVwV870SHCj3Jo9cl45MH+Qj198ba6YNxJcj8WLe14UvZDacObFGV3YoJvQzOB5/2EnpTTH/P+sXYvGi83u7luuyjb50t4EvXzkqJhfdeJ5WOG+eNQkSof7mwGe1L3vG2lbPg5rQIlJKmT3ZT1psK3wu+HhjZrh5mTRoNJycn7N+/H+fPn0dCQgIKCgoYvnJ52Fh6MHVTKp019q0K+FpDD0PntDZ8DV3blttsBV9b6qTrWqbAN+SkOwY2/hTTx4+Ag4MDNm/ejMOHD+PChQtISkpCUVERnjx5Ihf86JSTPV8zPV9dlcwW6xi+lmtws0V5GboGw1czX/uyvyd6f/cuGr/9G7Su9Rpaf/x3bHJyxLFjx3DlyhWkpqbi/v37DF+dSLfDlez5alZwQzCwl23s+VavD2hS2tuNS36YPaAFUuPCkXQzDOM61BOx9S2bnODr64vIyEhkZmaipKQET58+tUPSGC8Se77s+VoknmcNYDN8qxd846+HwGnuSFwJ9Matq4EY3+k7LBnVBaPb1sV2F2f4+fkhKioK2dnZKC0tZfgaz/mq3ZM9X/Z8rfGAsMQ5OexQjOTYq9g4dySoke1K4FEMb1Ebi0Z0QkZilPh0vLrnm5GRwZ5v1eLUtKszfBm+lgClNc5R3eGbmXgDa6cNRPCJ/SLsQOBdMKwDivPSxFvZ4pGd4bx+Hby9vREWFoaUlBQUFxdzzNc0BFbd3tUBvgV3k8RrWlxEkN2GEkyBl66wA30SJzHmkhh1zJRz2fO+uuD75GE+TrhthMuicYooS7J/uWyHx0XITY+Dq+M8BBzZqYrxLhzeEZSDLZUZwddxzSocPHgQISEhSExMRGFhIaeaVR1OTbuy0uH74H4OTnm4YGSrL6FU+FKudNKtMOxznKto+Ip86TOHsWxsd0XDt+heCnz2rseZw9sRfdlfFeOlQYAk8NKU4Ltm5TK4ubnh3LlzIs+XO1mYxr8q3VvJ8CVvkCoxfaxzxfieioXv3bRYuG2Yj6vnvBULX3rAXA7wEtAN8NypWPhSnT2ycw2O79uAC6cOYHiLL7BoZGcR41UHrwRfhxVLuYdblRLUjIsrGb5Hd67BpgVjEXvtvGgZVqLnS1DaunQigo674dgeR8XC9/rFU3CeNxr52XewalIfxcLXdf187FkzGxHBJwR4Fwx/EeNl+JoBOns8VInwnd67CU4f3IrNC8eK2NmUHj+JQVKUBt+Lpw+KBhl6yPgd2ore9d9RJHwToi7Aae4I3Ao/J8BL4zAoLebrMKkvvHY4wGXxOFWMl7IaytRivAxfeySoGTIpEb7dv/4/7Fo1HeFBPpjU9QfM6t8CBGSlwXd678bw3r1OxAYppr1v3RxFwpcGnTnv44p10wdhwE81sdthhuLg27/hhyJmf/3i6ecx3tFdkaUV42X4mgE6ezyU4Nur/tuqQUUqGkTF3rc3efslLBreCUHHXTGxSwNM79MEKXFXsWnBGAFhe5ffGPmm9WqETp//U8SzqTFxSNNP4bp+HkoKMkTLuTHnkMM+3er+GwMa1gR1q3WcMRhj23+L6xd8cfNqIAY1qqWYOksj/lFKWaD3HhFqoIY0SjPThq32Mu3HMV97pKqRMpWVleLm9XDMG94Vcwa2Ep9VoVZVuf6ykqMRHeaPCZ2/F7Atyk0RlZhakEmn1v97VQx3KFf9JLmpke3orrVoW+sv2LNmFiirg25O2n726G5xE1PcUNpfrtN7mQlYPaWf0IfivhTjpk8AkT7k+QtQybi+kh7ZyTGi9xo1rlE6mZTHqw1b7WWGr5GQs9fdHjwog9duZ8wf2h4FOYkVPm21K4C9LdN3uSjGO7NfM+RmvPjCLlXoKd0bYu/a2bLX8fGDPBHjpTeWnSunaeiTnXJTpNVdCzqusd7eyskYeciTXzmxN/r98D7CAo6o9Hlcdg/7nRYIz/dxWZ5qvTHntMd9EmMui7EaRIy3ONtofRi+9kpVI+U6sX8betV/B9uWTYav+2bZ/yZ0+g7Dm38u8l6lG428J6rY1BdeCTpSWhmBd/vyKRo3KnmG7T75m+hQogQ9KZ+XBja/dOawhp4HNy9Fvx8/wL51cxVRnqPafIUp3X8CvbVJddaYKcPXSMjZ627H3baACl8JNyvpoGtISYIvhVR2r56pCD23LB4v9Iy8oPlBS4Jvoxq/VoSOVJZDmnws9NQGkfvGReLjmkqqs6Z8yUKyB8PXXqlqpFzZ6SkibiYVqNynuuBLOlGDm9KyHbThS3o2rsHj+cqtDpfrXmzkiIIMXyMhZ6+7KTHVbPaAluVe3xi+8htASNfYDgRWynZQWp4ve74vCFktxvN99uwZstKS2fM10tOwF49K18A6kmzs+crvIcOe7wvw0ly1gC8pmsnwLeclSyCz1ynDlz1fqpscdtCEtuyWGL7y85QYvgxfhq/sUFteYI75Mnzt1cPnmK/husmeb3meyWoNw9dwBbdHMLHny54ve76ywqxuYRm+DF97fMCQTOz5Gq6b7PnqZpps1jJ8DVdwewQTe77s+bLnKxvE6hfUVvAtupcmuk9mp8SAPgdjLajZqpPF/fx0ZCXHGBy45lFprlX0rAr40oA2D+7fRcHdZKFzfk4i6KsL1ipHOq81Pd+yomzQOBjGDDiUk3pLDOZjLV051UyTT9Um1cwW8C28l4blE/ujyTsvo22tvyI90bQ+7KZUelvBd/eaOWj67m9F91e6pq4ffX/LFNmN3dfW8C0tzsbFM15wXjgBYzo1FLouGNEF+TnJVtFPsoM14evntQdtP/mHznLTLsse39ZATESI1XSl63EnixcAZvhaqOPBs0eFCPRxQ8cv/p+o6EqAL3nuG+eNEvo4zR+ndyyF/GzrjBJnS/g+LL0Hl+Uz0KXOvzGgyedYNXWI0DcixBcPS6zj2dsCvhmJ0fD33Km37GjcCCrbZv/9Hfo3rIl7WXcYvi/4aNU5hq+F4FtSmIWxXRupPAwlwLekMBMrJvYVOt28Zj2PSIKQ9tSW8HVaPB3N3/8D5g5pj5T4SJQWGT/kobbcpi5b0/OtSBZ6wK6bPVqMqrZ/y2oxjnBFx1R2O3u+mixn+FoAvhQT3Ou0THgP80Z0Q+cv31RE2KHgbgoWjuwi4JuedNNqHpG+m9lW8L187gQav/0Sxnb8DtmpcTbXsyrhG3/jMgY3/Rytav4Zqbcr/qqEvrIyZj3Dl+Fr8Zsr6so5tPv8DUzr2woBPgfQ7ev/KAK+OekJmNG/lYAvfVXBmBvMkvvYAr7UuLZgZHeh46XAEzbXkexVVfB9/CAfB7etRsuaf4bz0hlW153hy/C1aCUrzE3F9L4t0fLDVxBwzB03rgYpBr4ZyTcxsXsjdPm6hkVtZiygbQHfOzfDMajxJ2j32evIzbyDAB93rJ89Qnw9wmXxBNy5FY4nDwusqn9VwZfiu+QwtPrfX5F466pVdaQyZ/gyfC1aybx2O6H1R3/B0klDRJpOzLVgxcD3dswVDGpWG/0a14a/jweGtqyLnvVqiN/h3RtBcDYWpJXZzxbwDfE7gq51/4MJvVtj5pBO4o2lydu/EaBo+u7L6PzlGzh9aIdVY6FVBd9Q/yOgj1qunPq87lamjEw5huHL8LUYMJLjIjC24/fihqX4KFVEJcH35rVg9PjufQGidp+8hp7fviXA2+XLN0U6Xb+GNRF5KcBqebC2gK/3Pme0+ugvQse+P34E30O7VY1t+zevQOuP/ixCSklxERarN9rAqgr4Uj7z4vH9BHzPH3cDHhdZTT9JX4Yvw9cilexRWR62LpuGVh+9imNuLqpzKgm+sZEhmNyrGcZ2aoCQM0dBMUK6kZLjr8Nh+jA0/+AVjOnYAGl3rNNQYwv40heRCQota76KS4GaH+OkePD2VTNFFsSicf2tFn6oCvgmxUUKvaf0aoqslFhV/ZVAaY0pw5fha5GKFn31PDrX+Tdm9G+D3MzbqnMqCb5l93OQdida/LRvRkpDmzO0E5q993uc8dqj0l97P3OWrQ1fys3etWq6gNCi8brhGhHqj17fvyfgnJP24ivR5uilfWxVwHfhmD4iw2G/83KQI6EtkzWWGb4MX7Mr2oOSXIzv3gxdv34LwacOa8QDlQRfQzcgvbZ673US4Nq0VPOz7oaOM2WbteFLKYI7VkwVOuxwXKyzXqQnxWBYi9pin+th53TuY4pOuva1NXzjoi6jTa2/ome9dxB7PdQqOunSk+HL8DWrspG35Ln7OXQmd2uIpFtXNfrNB/keQpc6/0Kb/72KiIt+YtvDkrtmXVNfRa7qb7gFn3QXUFo1fbjF9SOdrQ1fugZ9lp0+SbRm7nidOtzNuI2Rrb8SekaEntW5j67yMWWdLeFLD5wtK2eDGhMdZo6yij76dGf4MnzNqnDUsNb3h+eNUFSZjPmFB1k+f5Sua0340kMmPPgkAr33oTgvrZzNyPM9e3SP0H/N7NHltuu7AU1Zbwv4njywDW0/eQ2DmtfRGdNNvR2FIc0+F3rGRl2yip62hG9SbASGtfxS6HMr8qJV9NFXxgxfhq9ZFY4GXznvs09vX/mdDrPQ/tO/o+UHf4S78xKxX25mglnX1FWZrQ1fym11mDFcJOBHXDhVTn7yoPasXyhu4n0bl5bbrktmU9fZAr4EoP4/fST0iLtxuZweV4NPo2f9d0UecFlxTrntpuqka39bwffpwwIc2eUoYr0zBrbT+bDRJZ+l1jF8Gb5WuYGkCqqUmC95tifdXQSUVk4bVs5mOenx6PH9B2hd6zWEB/uW2y7Zw5ypLeBLGRwrpgxBoxq/xuKxfVCqBlgar8Nx9kgx7oHHtnVW0ZHsYyv45mUnYfbgDqJMI0MDrKaPvjJn+DJ8rVrp9MGXPMUzR/ahwxdvwHnxZLNlsLbnSzcQAXbe8M5o+s7LWDljJKKvBIoYdoifJwY2qyvGsnCYNhiF91KFPtQQucdxkfCWD243H1a2gC/pST3bun71b7Sq+SrWzBqNhBuhQk/HOWNEPvPI9g1AIJagci8rEUNa1xMe8fVL5kPMVvCNuHAarT/+O6b2ayPGLJb00TXNSo1Dn58+wbjODZB2O0qlu659jV1XGfjSiHLzBreBw4qlcHV1RWBgIOLj45Gfn4/Hjx9r0kxmSzywjgUG1lGvfPrgS6NHubusRIsPXsGgJp+ZXZLivLUAACAASURBVJltAV/Si2KeDlMGYkTrr4XHRNelH92UzgvGCUBL+hflpWPNjOFi/N8xHb83W0dbwZfkp04Ucwe3RY96b6v07P7tO1g6vi9uRV7Q0IW6HHeq839o/dGrOLrbUWObZAtTpraAL9W/5VMGi/rntWt9hR1josODxTjANEgUDatpij769jUVvg/u5+Dw1hUY0vpbrF29iuErs4eLSlxbDKZOlS7/bgoCvffC//B2DW+JXuOzUm4h4Mge3LLAgNW2gi/pdD8/Q4xxQGO/Sj8aKF47Bkre/Z2YKzhzeLuAmb6b0Nj1toQvyVScn4FrwSdVOl4N8lV59eoy0yBDF88cwTkfV+TnJJkNJlvBN+jkQQQc3WPUmL30FhPsexCXAo5o1GN1O5g6byp8D7osE+BdMHs6nJyc4O7ujqCgINy+fRsFBQXs+aroZucztoKvqRWysvvbEr6VldHc42wNX3PlrezxtoBvZWWz5HGmwNdz20p0rvsWFs6bg9WrV8PFxQWenp64ePEikpKSUFRUhCdPntg5dQyLx2EHC4cdLFlZDZ2L4ftrsz1OQ/a15TaG74uPuz4uyxNvHoMbf4zuX/8HLd7/vQgDbdrgCB8fH1y9ehVpaWm4f/8+nj59aphudr6V4cvwtVuIsedbvb5e/LjsHs4d2weHyf1AH/O84OuBUW2+Eh2Wtm7aiFOnTuH69evIyspCSUkJw9fOHy4q8Tjs8MK7sKVXZ861GL7VC75hAUfENwPjr4fg7JFdGNSoFrYunYQpPX7Cdhdn+Pn5ISoqSsC3tLSU4auim53PMHwZvuY8CKx5LIcdihEd5o8V43shNvwc/D13oHPtN7Bp4ViUFWVh8cjO2OK8ASdPnkRERATS09M57GDnvNUQrzrA99njQjjPG4W4iCC7DSWYAjF9ni+lTdF4DKacy5731QffG5fOwGXROMXoqavBjTJkyNNdNakvrl/wRYivh/iK8qYFL7qsE3w3Oq7FkSNHcOnSJaSkpKC4uJgb3DQIZ8cLiofv4yJkJEZhdv8Wiobvo7J7CD/vo3j4lhZlYf+GBYqGL40fknQrDK6O83At6LgA76g2dbF16USoD0ZF8F23eiUOHDiA4OBg3Llzh1PN7Ji15URTOnzv56eD8iJHt/1asfAlL+nGJT/sXTdH0fB9VJqLY3vXw2FSX0XDNzcjHh6bliDs7BERapBivIV3kzW8fYLv6pXL4ObmJnq4JSQkcA+3coSz4xVKhu/TRwXYtmwyjrs6YcOsYYqFb1pChGiAuRMdqlj4UujI12Mzti6ZILxBpYYdpDobeHQ3/A5tRefar2PzLzFe7RARwZe7F9sxXCsSTanwLSvOxt61s8WrW+QFXwz4qaYi4VtwNwlOc0bgkr8ndq+eoUj4Uiw7+MR+bFs6CXlZtzG9TxNFer6lhZkCtNSRIuSkO/o3/BCbFozR8HbVAczwrYhudr5difAd37E+Dm1ZDq/tqxARchJj23+D9p++pjj4eu9eB4fJfUX60b51czCk6SeKhC81NtFnja6e88a0Xo3Fx0qV5vnOH9pODGDvs9dRxHhHt6mLbcsmacR41cFL8wxfO4drReIpEb7dvvq3AG+A107h8TrOHIIlo7ooDr6Tu/2IyJCTwsMf2bqOiA8qMdth49wRuHDqAGb2a45JXX/ACbeNivN8+zZ4Dyf3O+PMoW0Y3LiWAK92jJfhWxHNZLad4Nuq5ivCm+hZr4bsp03e+Q02zh0lBnbv8/1/Ra+gksIM8frWtc6bstePyqjzl6+jxzf/QUTwCWxfMQW9v3sX/p7bQa/nlLakhHIkHWjgfWoovX0jFDP6NMXUnj/jXmYCbl4NRNtaf1GOnh/+EfTmcspjs1qMN1tvuEGCMHu+MoOttrgPHz5ERloqlozri/Wzyg8OLhW0XKY0Shp5g8Oaf4Y10wZoVGC6cbvV/Tf8Dm7VWC8X3dTlpGEFdznMEBAij0l9W9BxNwGrzCTrfLpe/VrWni/KTVF5vKnx11R6lhRkYN7gtiL9ytoyWPv89CUNY2K82nIwfLVpJrPl+8VF2LZ8hgj0U1qWdgHLbTki5ISI8a6fNRSFuSkqfRKiLmBG36bi1VxuOmnLS+UkxXhPH9yi0pH2O3t0N+YMbInbUbb9Dpm2jJZYTo69imm9GoHCK+odZAjImxeMwZ41s4S3b4lrVeU5CLwvYry5GuVpSC6Gr8xgqy3u+jmj0PHzf4hPtqyY0Aty//X94T1QgxvlSkoVNzn2CsZ1qIfe9d+RvX5UPvQFgw6f/QPU4CbpSFOfvevR8sM/guK/ci9Hkn9027po/PZLuBOt+YHOlRP7iNfzOQNbKULPXvXfBjW4FeZq5vGql62ueYavNs1ktnwr4rLo/ZWZFC0+ESP3qa4hJalX0NppA3Hx1AFF6EiDspOelEKnflMW5CSJbAe5l6Ek/8QuDYSe6jrSfOjpQ1gzdYAiypJ01dW9WFtnXcsMX5nBVlvc7PQUkbKiq3DluE4XfEkPypdUf3WVo26SzPrGdqDtSsx2kPSWptTgprRUs/Uzh2o8SCVdDU0Zvto0k9myElPNZg9oWa4iM3zlN3qbvoF1GL7Py5LhKzPYaoublZbMnq/MBo5nz5c9X/KIGb7aNJPZcibDt5yXbOhVzx62MXwZvgxfmYFWl7gMX/m9jjN8Gb4MX100k9k6jvkyfO3Bm9clA8d8DddNDjvIDLba4jJ8DVdwXVCo6nXs+bLnS3WQ4atNM5ktM3wZvlX9MNF3ffZ8DddNhq/MYKstLsPXcAXXB4aqXM+eL3u+VP8Yvto0k9myteCbfzcZERfPwNd9s/hd9PNE2p1oq2cW2KqTBX26JzstDmePuap0jIu6iAf371pdR2vCl+SPv3EJ8VGhIB0NPWTuZtzGlaCTKv3DAn2QlRJr8BhD59PeZi3P935BJm6EBSIzKcZoWUsKs5AQHYb4G5eNPkZbH33L3MNNE5q/0lxU7pKl4UujitHNu2xiP/T8/n3RdZIqV4cvXse4Tg3ge2g3Hj/It3gFliq2LeD7qPQeznjtwdTezdCy5qsqHYc0+xxLx3TDvexEq+lHeloDvrmZd+DvvR+LRnTG0Oa1sWXpZNBHOSW7qk+fPCxA2LnjmD2oHbp9845K/y51/4OJ3X5C0ClP0D7qx1Rm3tLwTUuMxnGP7Zg7pB36NvwIfod3gOqrIdkI1MGnD2Pe0PYY2rIOtq6eb3B/Q+fSt43hq8lXhm8lOx7cz8/A8Lb10fTdl7F04vP+9ylx13Bg6xq0+/TvAsIBPu4Wr8BSxbY2fOlmDfU/ii5130KH2v+Gz/6tYowBghHpTd175w9tj7LiHKvpaEn4ZiTdxM6188VQm21q/VUFUuf5Y/TCNyXhOoa1/lYMerN99TyhP31Hzn3LajR5+yUBtivBp8zW31LwTYi+gtXTh6JLnTfR+qMXD8tTHlv0wjf19g24rJyDXvXfQbtP/qayC62T6pqlpgxfhq9FKpW32xZRUZeM7Y2ivDSNcx7a5SS2TejyA8jTslTlVT+PteFL3uCcoZ3R4oM/IuTUIQ0d6DW87cd/Q6cv/4VAH3e9N7a6vJWZtyR8m7//R3Sq/TrGdfkJR9224PBuZ1FGhuC7a+0CAd6da+aV0/HAjg3i+FWTB4Be1Sujn3SMpeDb4sNX0PnLNzFjYHv4+3hg87IZQkZ98C28l4oVkwei4+f/RM9va8DvqBuc5o0RxzB8NUFpjSX2fCvp+c4Z0QPN3vsDvPdsKHdj3s1IEBV4YONPcDPiglk3pnSDak+tDd/kuAihw5yhHZGXnVROh6OuLmL7mulDzYaPtm7SsiXhu2vdQsTduIyHJc/HkfX13CvkNwTfsV0bodu374i4qSSTNC3Oz0DH2m9iVPvvkJpwvZx9pP2MmVoKvge2rUFyfKQq3LXLcaHQUR98S4uzERZ0CtfDAkEDnZOsh7esEMcwfK2BW81zMnwrCd/Breqh3af/xNlj+3XeeATHPj98iMjQAJ3bjbkpDe1jbfj6HXUVN6HTvNE6Qwt30+PF9sm9miEn/cWYwoZkNnWbJeGrfe2K4Etwbf7BK5jcqymyUnU3rk0f2AGt//dXRF4wL/RgKfhq61gRfLX3p2WGryYgrbnE8K0kfJdNGYrGb/8Gh7euwjOt1vKc9Oee7+CmnyMuSnOAbF0VvjLrrA3fY+7bBFy3L5+Kx2V55R4g1BjXs0FN9G/8GZLjI8ptr4xO2sdUJXzjb4QJ/WcNaovczNs69VsxdajYhz56qS27KcsMX8NpkJxqZs1HgA3Obelsh8hQf7T88BVM79cKGWqpPGXF2di6ag4a13gJi0d3s1pKlrXh6+OxQ4Bl08JxeFBSPq2MYsJ9fvoUXb95F7ejw8yCjz5QVSV8KVxENl4wogvyc3R/eWHltGFin4Aju83Sn+HL8LUBAqvuEpaG78PSe9i1bgE6ffkGZvRvI3JAT7o5Y+3M4aKleWb/ViL+pg8s5q63NnyvBp9+Dp+RXUG5zNryFuelo9NX/1EsfC+fO8nwreRboXZdkZapzvJg6i8YyGGHylawX/J8h7f+RtykVLHUf4EnD5cDllQJLTG1NnwJrv0bfYZOtd/EtRDNmCaFIXauWyT0VarnG3T6CMO3sveGnuMYvi/AS3MMXz0VpSJApidGY2T7BuhQ+01sWTlL9Z0t/6Ou6FT7DXSt+x+cPLjLagC2Nnwpjn1o22oBoP6NPkX01WChC/XsWj1zNFp88IrYNrh5HbNb+/XZuirDDgxfzvPVRKXllxi+lYAvdUddOK4fmv33t3BzWlSuQYoaa1rV/DOoJ5g1umkSrKwNX7pGwd0UOEwdhPaf/UNcj65Jeb+UxB9yxls0uE3p3QLUwKgPoOasr0r4xl4PFTrPH94ZeRXEfM8e3WOW/hzz5Ziv5dFuR2e0ZMyXeksRiIY0ry36wesCzOrZY9Hsvd/Dc8eaCscO0HV8RetsAV+SgToQUC8u6RPt7puWI+3ODaTERwobrJjYDxSiqEjeymyvSvgm/ZLnPKN/a1Deti75l04aiKb//R0u+R/RuV3XMbrWMXwZvnaESsuLYkn4RoQGCPCMaV8P6WqZDuo31oHtjmKfHatmgNKy1LdZYt5W8NUn6+HdG9Hiwz/h4FYHVYK+vn0ru74q4UsD77Su9RrGdvoRFGLSpcOojg3R9Zu3ERN+Xud2XcfoWsfwZfhannh2dEZLwpdGLSP4DW3xJW7HXNF5462bP1Hs4+68WNaery5YSN2LBzX5VK/+uo4zdV1VwpdknTm4I9p//jrCg33LlXFG8i3h9U7o1gj0JmSqbur7M3wZvnaESsuLYkn40o0zvnsz0QNqj+NCjddu8nKjr5wT4O3d4ANEXPRX3Zg0CE126i0U55v/ml4Vnm9pURbirl/EmE4/Cq93v4uDSjeyCQ3GQwMOZafEoLQoW2ObOmyMna9q+B5z3SxCR0sn9EPRvefjd5COpNvKGSPR9J2XsVWMivaiEwplguSmxyE3I8Hoh67c4EtdtLOTY5Cfk1Sug5GhsqU6y6lmL9jGDW6VaHCjCnbzWgiGNvtceEYrJg1QjfW602EW2n/6d7T95O/Y5TBL1TWX+s57u25Cm1qvYe6wTmaDyVbwLcxNxXkfV5zcvwkrpwxCz+/eE7+daxegVGtEM4oPb14yFTSIzZqZI8zWsarhSz3bFo/tg+bv/wGzh3QUZXzCdSNWTR0sOtFM7tkUSbHXNPQMv+CHXt+9hx7ffWh0nrfc4Hvac49oeB3frTGyU+M09Lc0fPOybmNKj5/gsGIpXF1dERgYiPj4eOTn5+Px48cvSCbDOYZvJeFLGQ83w4PgsnQKenz3YjzfLl/XwOJxfRB08oDGgDPq8J3ev7XRFVZfZbYVfG9dD0Xv798To3tN6dMS21fOwNUgXzzRMVaxOnyXTexvto5VDV/ycqlxcfuqWRjcoq54myG7D2j8OdbPHY0EHQOOS/Bt99nriofv8NZfWxW+9/PTsXHuSIzs2hSOa9fAzc2N4SvDhwwsHXYgKNLNSa9g9IqZmRQtfjTgTElBpk7wSGEH6RVWH1iNWW8r+D4qy8PdtFhkJUWj6F6qaDwkvXXJSOulsENJoW4b6DpO3zprwpdCB1RmRfdSyo1Kpy3Pg5Jc5GUnqso4L+uO6o1Ge18p7JCTFlvlYQcKb5GOFC7SllPfcklBxnO7aA2Tqr6/FHa4l3XbqmEHx5lDMKDFN1i2ZDE2btwIDw8PBAcH4/bt2ygoKGDPVy4gtgZ81SukredtBV9b66V+PWvCV/06VT1vrbBDVeulfX2qs8bEfGl8lM0Lx6L1J//AvJlTsXjeLLi4uMDLywuhoaFITk5GcXExnjx5Ihf86JSTww6VDDtoVyxbLzN8f220N2frsjH1egzfF9kO9Hbl6jgXy8Z2x6IRndC59usi3LPZyRHHjx9HeHg40tPTcf/+fTx9+lQn1OSykuHL8LVbiLHnW72+XvygOAde2x2wffkUFOUm46DLMgxv8QU61/5/2LZ5I06fPo2oqChkZWWhtLSU4SuXpwyHHV54F6Z6ZlW1P8O3esHX7+A2OM8fjZy0W3DbsABdv/oXvHevw7zBbbDdxRl+fn4MX7kAV11Ohi/Dt6oeIhVdt7qHHShzhgakd5jcD0k3w3Bw81LxXbnD21aKtzIaTN1l4/pyYQeO+aoTzo7nqwN8Hz/Iw/pZQxEXEWS3oYSKQKS+XZ/nW1qYKb6erL6vnOf1wZf0d1k0ThFlSeWjq8HtUWkurpw9is0LxyD22nkc3rpC5M97716r0pvg6+S4RqPBraioiBvc7Ji3GqIpHb6U5pUQdQGTuv6gaPhSS3iA107Fw7fwbjI2LRijaPhS7nv05TNwWz8PiTcvq2K8PnvXgxwJ6YFK8F3nsEKkmgUFBXGqmQbZZLCgdPgW5CRhx4qpGNehnmLh++RhAS6ePoi9a2crGr7U8LRj5TSsmzFY0fDNuBOFnaumIy4yGK7r56PrV/8WMV7tgagIvqu5h5sMKKtHRCXDlzoKULiBPEJKTFdq2CHmSgC2L5+MxJjLioUvefaHtizH/o0LcSXQW7HwLbibCKe5I0Ws9wDFeD/7Jzx/ifFKHq80Jfhy92I9YJPDaqXCNys5GtuWTYa/5w74um9Cj2/+o0j4Rl/2x5YlE3DpzGE4TOqrSPhSLPv4vg3w3L4KNy6fwYhWXyoSvqnx1+A0dwQCj+75Jcb7mfB4JdhqTxm+ciCsARmVCN9hzT/HjpVTEeLrjuOuThja7DNFwpe83RXje+HqOW/RIk6hlcY1lNfJwmuHg4h7nju2F4Mbf6xI+E7p3lA8UKgxUeTxtvwCPvs2aMR4Gb4GQCbHTUqEL33OJ8Bzh3hda//pa9jvtECRYYcJnb5DfvYd8TWNka3r4ObVs4qELzWwXfb3wpAmn2DxqC6KDDv0/u4doaOr4zx0q/tvHNvjWOHHBtjzlSNx1WTOyUxH92/eQuuP/oxWNf+EnvVqyPrX5/t3RSL6CbeNwtulRijyGPY5zhV6NX77JXT49O+y1pHKaHyn73DzaiCWjumGgT9/hGvBx4WetK3Ll2+g+Xu/Fzex3MuTAEMNT6Pb1hVda6m1n7JXSK9WH74i6q3cdRz400civHBg0xJ0/PwfemO87PmqgUsJsw8fPkTsjUgsGNFFNGhoF7DclilNh2K8I1rWhvvGRaq0HNLjcoCXWB9+3kdjvdx0JHlzUm9h1aS+GPBTTZEPqq7D/g0LsHb6INCYr+rr5ThP3vy4jvWxckIvMYqcpEPyrSuYO7gN6CErrZPr9MH9HHHvDWtuOMarrR97vjIncGH+PSwf30f0Hafvc2kXsNyWqWGGYrzkRZSpfTUi5KQ75g5qLbxFuemkLe+9zARVjDcmLECjzDbOG4V10weJrqjax8lt+eq5YxjcuBZWjO8pvgIiyU9fBFk+rgdOH9hi9PCU0rH2OKWea8NbfiEaFXWNB61PZoavzOE7ruN3aP7e78Srqtxf30h+Cp9M790ED0tePEiiw/yfv6YqIKxCOlJMkMqMcnvVb0zq/9/knd+A4txKKEsKDzWq8WvQ4OHqelJ8u8X7vxdjHChBz1Y1XxGZKo/KTPugLMNX5vDNTk8BFaJ65ZbzPA8pqbxsB+36SLFupXcv1tZZ1zLDl+FrV+Bm+DJ8dYHKntfpGtvBGHkZvjKG77Nnz6zyGSFjKo619mH4MnytVbesdV6GryZEq81g6plpyRx2kNnA8fpGNSM4KLGThTb0OOzwfBhU9nw1oS27JYYvj+erDTd7WdY3pCTDl+ErO9DqEjiLPV+7ilkbAz72fLnBjeoJe766iCajdUrsXjx7QMtyQKUuqkod1Uwd2Bx2kN+bDMd8NYFZbWK+DF/53azs+bLny56vJrBlucTwZfiqe872NM8xX8N1k8MOskTuC6EZvoYruD3BSJKFPV/2fKkuMHxfcEyWc5aA7+MH+UiKi8CuNfNwvyCzXLxVggZNC3NTEXBsP9ZOHyKGQnSY3B/+3vuRm2mZQWCslef7sCQXsddD4bJ0KsqKcwzqmJMeD+99m+Awpb/Q0WXJRFy/HAhLjZ1hTfgW52cg6NRhBPseBH2eSL3sDM3Tvkd2b8DKCb3FJ3CK8jS7BBs6Vt82a3m+97IScfLAdtwMPw/6xp++659w34KVE/uIMlwxoZfOKX1hQ9/xxq7nmK8mOjnma2Tua8gZb8wf2QOdv3wDzd77PfKyk/RWxvgbYej+9f+hba2/onGNl8RXW6mBqE2tv2J8t0ZIvR2l91hTKrIlG9yePirE6SOumNyzKTrVfh2tav4ZhffS9Mp57aK/+OYW7Ue60Y3V9N3fik9+r509Ru9xxupH+1kDvumJMdjjtFwMw9nu479h08LxMGWsgZhrIej/88dC3yFNP8Pd9ASzdbU0fOOiLmHTsllibIzW/3sVpw5uMwjfeUPaCX2oDPX9iu+lmq0nw5fha3Qlys9OhLfrZnT84v+h5Yd/QofP/onm7/9BVFB98I29fhE9vqmBVh+9Cucl05CbdUdcLzs1Dksm9Eez//4Ow9t9j4eluUbLoQtYVJEtAd/0OzfgvmW10Kn1R6+iw2f/QNN3XkaL9/+gF75Xgk6J/XvU/y+89jijpDALeFyE2MiLmNSzKZq8/Rvsclxs9khcloRvePApLJ80UDwg2n36d7T/9B9CB+f5Y4yGL+lJbzISoOwNviGnPDGldzPhHHT4/J/iYU+ynvLYohe+pUVZmNarsdApIlRz5Dhd9c6cdQxfhq/R0As+6YGBjT/FvCHtcXjXBlwMOIbxXRuKiqoLvvSavnbWCDR552WsmT0WFKZQr6wZSTGY2O1ncXzgyUMa29T3M2beUvDdvWYOhreui8WjuuK4x3acPXEQPeu/qxe+BKAJ3X4Wo4q5blpZ7pX9avBpdP+mhoCcuR6+JeHbofabmNT9ZzgvnIDQsz444rpFlIMp8L1w5gg61/k/9G7wgfD27Q2+Xb95G9P6NMMOhzkgkG5ZOUvoaAi+uZl3ML5zA7FfSoL5b2SG6i7Dl+FrNPRyMxJwKyIEJYXP47uZKbcwqcdzL0EXfLNSYjGlZxNRkaPDg8tdh8YwpcG/qRI6L5tVbruhiqu9zVLwTYoNx52bV1Vx2luRF9H7+/f0wjc5PhL9G9YUHrwuHfNzkkHfliPvmUI12nKbsmxJ+F7w80J2Wpzqgejrufd5ORjp+VKMeGznhhjY+BPs3bhcPHzsDb5Xzp8ExXkphER23uW4UOhoCL70gBzZ5muxX5GBMJMp5aZvX4Yvw7fSQKgIvlTxTxzcCfeta/Q2yDnNGS4q+urZ4yotB1VuS8FX+0apCL7k3Wen3AR9NflRWV45Hcgzpq/u0ni75309y23Xvp6hZUvCV/s6psJ3p+MStK71N7huXIYQf2+7hK+2jsbANzH2Goa1rCPqk6FGOe1zV2aZ4cvwrTQQKoJvRRWSMiSWjOkuKvrJQ3sqLQddp6rgW5GOFPft0+B90WB3M+KiWTraC3xvRVwQsB3e6iuk3rmBy+d9FQPfqLBz6NfwfxjSuj7u52eIB2tmUjToR/XVlEyQiuoGw5fhW2kgmANf8iqirwSK19Zu37xtMFuiokpsj/Al/VLiI7F6xgg0f/+PcFo0GY91eMbG6CbtYw/wLcpLw9xhXdDorV/jrI+HqDtKgu9Ff290/boGutd/H/NHdEXLmq+KBzuBctGYXgg4uhelFkgzk+rs+plDTb7/OM9XE9qyW7JEnq858H1QcheLx/YWFdtzp6PJFVACkjS1B8+XdDrh5vw8L3R8L4zt+B36NKyFTYsmijxnSdbKTqsavuT1HdvrBEpJIxA9Kn3++Rslwff04Z1o8/Frol4um9APx12d4Ou+GRsXjEebj/8u0gmPuW6yiAfMnq8mNjnP18g8XwKIOfD13L1RpADNGdIBeVmJioAvvZaumNhX5SnRzdX0v7/Fng2LzdaP7F3V8E1PjMboDt+jZ4OPkKOWz6sk+MZFhcJj21rRTkHxeinuSx1lzh13R+uP/47u9d/DvV9SJiv7IKXjGL4M30qDoTLwpXSzkwd3ig8kDmn+JWKulc+CqEyFtgfPV11uummpd9vUPi3Q9N2XsWrGSDz8xVNU38+U+aqEL+mzd/0ikdXh7rJSBSWSX0nwNVQexfnpWD6xn4Bm4PEDlb5vpGswfBm+la5EpsKXwBt4zFV00qA4b5Cvebm9UiWmqb3BV5ItJeE6Bvz8P5FqdsHfflLNJPmkaUXZDnE3LqNz3RqY2rsZslNjNepMdYEv1d/da2aLurZr/RING0h2NGXK8GX4VroSmQJf8pwiQ89gQKOPRezs7LH9eGrCGAIVVWp7hS/lmM4d1hmN334J2xzmVdrWpH9Veb6lRdkY3b6+gM7Anz/C8nE9NMY7mNqriehkrXo1UwAAIABJREFUQZ+uXzi8o9hGY3lUVGb6tlu6e7F0HWNSzaR9dU2pLN2dFws7uKycU2n9pHMzfBm+la5EpsCX4rrD2tQTYzuc9NiqSnyXKqK506qC7/WwQIxu9y36/fA+qOOBLj1WTB0m4OQ417xc5qqCb9qdaJFKRjY29kcdbHTZwph1VQVfiuMuGNUd47s1hq6BcyiPe8eqGcIGezeuqLR+kg0YvgzfSlciY+F7vyADo7s0QvP3fo896+aBRgqTKqClplUF34SYK+j7wwdo9t/fgnJEtfUhr3FkqzoCXj4e28tt197f0HJVwdeQTLRNKWEHGpGNMhyoLt3U0RaRl52IuUM7ibE6Lp31MassyW4MX4ZvpSuRMfClXm7LJg0QYxs4zh4J6m5b0c1M26nbrr/nTpErK7U4GzququBLD5LlE/oKj37+yO5IvBUO6jZNshbnpePo3o3oVPsN9PjuPdEhQdKBdLoVEYxA731GD6spR/gS0EJOeuCCn6fRDY5V5flSGOykuwvaffIaJvRogoToMJWjcC87EW6blou83xFt62mkDpLHfObQNlwLOW1SLjfDl+FrFAwlaKhPK4Ivdb3d6TAbLT98RcCXBjnRNz6qw+R+CDt3UiXLgjF9RCraxoUTn48SVkEKXFXBl+yRkXwTC0f3FJ0paFyApWO6CT3nDGqDTl++iUHNaoOS99VtRzf6pF4t0LLmn+G1e4PGNvX91OflCN+Q017oUvc/aP/5G4i5dsEoPasKvmTrwnup2L1uvsjqGNqyDhaN7CzKcnqf5mj7yT9Ep6DQgKMaYbNDOxzFW93Qll8Jp0G9zAzNVwa+qXHhGN22LhxWLIWrqysCAwMRHx+P/Px8PH78WJNmMlviPN8KIKdemSqCL3k9C0dSb6iKY4U0JvChHS8g5LbZAc0/eAVuG5fqHDNBXQ6ar0r40vUp3ht86jAm9Wik0rdHvXfhsXUN0u5EaaRm0f7UeLNu7hgxPkLwKeOyPuQI31uRF9D/5/+hU923cTfDuIHzqxK+VDaU0xsa4C16J6rXXa+9zs/fbLQaiml0PxpilQYaKjChodFU+ObnJGLOwFYY178z1q93hJubG86dO8fwldlDBpbo4UYV1V5+1oKvvehHclgTvvakp7Xga086kiymwLc4Lw1zBrXG4PY/YeXKlXB2dsaBAwcQHByMO3fuoLCwEE+ePJEbhjTkZc/XjoBqys3C8P213TwITSk3XfsyfDWdmqzkGPHdtoFtf8Ty5cuxdu1abNu2Dd7e3rh8+TJSUlJQXFzM8NVAuR0vsOerWcF1QcDe1rHnW/0+oJmTeguOM4dgdK+2WLZsGVavXo1NmzZh//798Pf3R3R0NLKzs1FaWoqnT5/aMXEqFo09X/Z87daDZPhWL/iWFGRg27LJmDigM5YuWQwHBwds3LgR+/btg6+vL65evYrk5GQUFBTg0aNHePbsWcWEs+M9GL4MX4ZvFdcBDjsU42HJXRx0WY5x/Tpg0aKFArxOTk4CvMePHxfhBor13rt3D2VlZbL3eumZwPCt4huvsq/6HPPlmG9l605VHaevwY08Xp+96zG5fwcsmDNTBd69e/eCwHvp0iXcvn0bubm5ItxADW1y93oZvjIFL908uuBL3sOaaQMQFxFkt96sKTe+vrBDQU6i6L5syrnseV99nm/o6YNwWWReF2170lsXfMuKsnDm8HZMHtgZC+fOUoF39+7d8PHxEeBNSEhQHHgZvgqCL/Ugu37xFEa2rqNo+FLXbc9tKxUP37tpcWJAHyXDl3pGXjrjiZmj+mDB3Nka4D127BhCQ0NFTu/du3cV5fFKYWgOO8gUwNqeb0pcOBaM7omu9d5TLHypM4CvhwsmD+ioaPjmZd3GmumDMbhtA0V7vjfDz2HuyO6YO2MyVq1ahQ0bNmDXrl0ipezixYuIi4sDgbekpESklSkh1CCBlz1fmYKXXiXV4ZsQdUEM4zht7HAMbNNAsfANOLIL04Z0E15S4xrKjPneTYvFpsUTMH5wT8yYMEqx8L159SxmD+uCmZPGqMC7c+dOHD16FCEhIYiNjRUpZffv3xfdiJUGXoavAuAbe+0c5o7ohpkTRomeQIPb/ahI+J7ycMHEfu2xYM4McbMqEb4FOUnYtGQypo7oLzoXzJ48VpHwjQw5ian922LW5HEa4D1y5IjowXbr1i3Fg5fhK3P40pi6s4Z0EpV4xYoVWLduHYZ1/Flx8KXBzPs1/waLFswXccH169crMuywbtZwjBvQDcuWLsGaNWuwcOZkxcF3RMvamNy3NebNnCacBSrLHTt2wMvLS4D35s2byMrKgpI9Xin0UK1ivrP7t0BmUrQifhR2aP3JPzFp5CBI4N26dSvGdG+Oi6cOKEJHagWnL2J0bVALS5cuFV4S5X7SzUqer1LKcmKXBmjyzssY3Lm58HgJvJs3b4bD4rlYM3WAYvRsVOPXaPvFvzB9wmhRZx0dHbF9+3Z4enri/PnziImJQWZmpug6TCOWKTHUIIG3Wnm+ebk5WDiqB2YN6oAJPZphePsfMLDlt+jbrC76Nv1Kfr9mdTFxaB/hPVAlJvDSwCNrF07HpN4tML57U4zs2BCDW9dH/+Zfy0+/X8pkULsfRaiBejsReCkFibwkGp6SfmO7Nsaw9g1EWfaTa1k2/QrDu7UQQKJxDAi81J3Wddc2oeOEns0wuvPPGNLmOwxo8Y1sy7Jfi28wdfRg8RCVwHv48GExUhmBNyMjA0VFRYrovaYOWX3z1cbzpe6I1DuGWlBpTFAPDw9s2bIF9NpDr+ty/JHsNNoTNVRQJQ4ICBA9gagbZlBQkGg1JljRPnLUj2Smm5RawV1cXERvJ8r9JN2uXLmCsLAw0d//4MGDYuAV2k/OelJXWhpAhurm6dOnQS3+VJY0PXnypBjPluxANpGrnlRnaawGymqghygNEXnjxg0VeB8+fKiI3mv6gKu+vlrBl/qEJyYmCkCdOHEC7u7u2LNnj6gIVBnk9COoUg8g0oFGe6LXtuvXr4MS0qk3UFRUlHjIUCMGjYMqJ93UZaXyob795NUTgCj3k+KC1NWUHqQEYQIVbSd7qB8rl3kqS9KTyomARAPIhIeHC/2oLMkrpKEU6cFD5U37y0U3dTnV6yzl8dJDlAbKSU9PV3m8ch8sRx2uFc1XG/hSl0Qaho5iSnTzUpfFs2fPihv31KlTkOPvzJkzwnOgYfaohTgtLU3kRebk5CApKQmRkZEibYduZjnqRzITWEl+gg8BiR4uVIbU1ZSm9FUDAjB5UH5+frLVk2Sn+kheLnmCNIAMlSPluaampgpI0YNHCXWWHAV6a6F0suoWalAHcrWBLz1R6ZWGBmGmm5Y8YCp88iro6Su3H8lNwCX40PimdKNSvIwS0qmlOC8vT8CYPCfaT276SfKSnlROpAc9XCh0RPrRkIKkL+lNoCIvmB6q0nFym5LspAM9NKm1n97SqCzpR/MEKaXUWXqAUp2lBws5RNUp1FAt4Ustp+T9PnjwQBQ4VWi6kakCyPVH3h9BliBEMKK4NrUS05RGfqL1tJ32k7OOVE5UXnSjUvmRjvSjm5bgRA9UpZQl6UI6kW6SnlKdpe+WybksqQ6S/KQH1U2qo1RXq1OooVrCl5QmAFNBS4CigqdKLuefBFzSi/STfvSgkfSUs34ku6Qj6aSuJ83TOtqupLIknagcddVZuZclyS89VKSyVAdSdZqvNmGH6lSorCtbgC1g/xZg+Np/GbGEbAG2gAItwPBVYKGySmwBtoD9W4Dha/9lxBIasMCDslJcvxyEUP/juB0TaWBP3sQWsC8LMHztqzxYGhMtkJ2WjKHNvxBDbK6aNMDEo3l3tkDVWYDhW3W25ytbwAIMXwsYkU9RJRZg+FaJ2fmilrIAw9dSluTz2NoCDF9bW5yvZ1ELMHwtak4+mQ0twPC1obH5Upa3AMPX8jblM9rGAgxf29iZr2IlCzB8rWRYPq3VLcDwtbqJ+QLWtADD15rW5XNb0wIMX2tal89tdQswfK1uYr6AlSzA8LWSYfm0trEAw9c2duarWN4CDF/L25TPaEMLMHxtaGy+lEUtwPC1qDn5ZLa2AMPX1hbn61nKAgxfS1mSz1MlFmD4VonZ+aIWsADD1wJG5FNUnQUYvlVne76yeRZg+JpnPz66ii3A8K3iAuDLV9oCDN9Km44PtAcLMHztoRRYhspYgOFbGavxMXZjAYav3RQFC2KiBRi+JhqMd7cvCzB87as8WBrjLcDwNd5WvKcdWoDha4eFwiIZZQGGr1Fm4p3s1QIMX3stGZarIgswfCuyEG+3awswfO26eFg4AxZg+BowDm+yfwswfO2/jFhC3RZg+Oq2C6+ViQUYvjIpKBaznAUYvuVMwivkZAGGr5xKi2VVtwDDV90aPC87CzB8ZVdkLPAvFmD4clWQtQUYvrIuvmotPMO3Whe//JVn+Mq/DKurBgzf6lryCtGb4auQgqyGajB8q2Ghy1XlB2WlKLh3F8+ePVOpYAi+D0pLUJCbo9qXZ9gC9mQBhq89lQbLYtACBN8T+7chLuqqaj998CVAX/TzRoivp2pfnmEL2JMFGL72VBosS4UW8N7rjPlDOyI3K13sqw++YYG+mND5B8Rev1LhOXkHtkBVWIDhWxVW52tW2gIRF86iV/13MWdQOxTm5UIXfCNDz6FnvbcxvlMDlBQXVvpafCBbwJoWYPha07p8botb4F5OJka0rIPGb7+EdTOHIyXhFoY2/wKN3voVVk0aIJZHt/tWLK+fPdLi1+cTsgUsZQGGr6UsyeexmQUWj+ou4Nq21l+wedFEDGxUSyzPGtAGy8f3QZO3XxLLAUf320wmvhBbwFQLMHxNtRjvX+UWOLLLScCVvN12H/8NLd7/g1hu98lrqvmm77yMrNSkKpeVBWAL6LMAw1efZXi93VogIfqaCr4EYF2/wU0+wbOnT+1WBxaMLcDw5TogOws8efIYnWu/rhO6Eoi3r5gpO71Y4OplAYZv9SpvxWi7clJ/vfBtXOPXiAkPVYyurIgyLcDwVWa5Kl6rYF8vvfDt/OUbePL4seJtwArK2wIMX3mXX7WVvrgwH60/+rNOAM8d1K7a2oUVl48FGL7yKSuWVM0C1H14fKfvdcLXY/MqtT15li1gnxZg+NpnubBURlhg67Jp5eDb4oM/gHrB8R9bwN4twPC19xJi+fRaIPikJ5q9+1sNAFOKWXJctN5jeANbwF4swPC1l5JgOUy2QMKNa+hV/x0N+C4Y3gX5PIykybbkA2xvAYav7W3OV7SQBXIyUjG9dzMVfKlb8U6HOXjy5ImFrsCnYQtYzwIMX+vZls9sZQs8LCvFxnnjQHm91Lmiw6d/xxmvfVa+Kp+eLWAZCzB8LWNHPksVWeDY3k2qlLN+P36IOB6/t4pKgi9rqgUYvqZajPe3KwtEXDyLnvWex33Hd/6Bx++1q9JhYQxZgOFryDq8ze4tII3vS2GHDTx+r92XFwv4wgIM3xe24DmZWkAa35fH75VpAVZTsRm+1bTglaQ2je8rxu9N4/F7lVSuSteF4av0Eq4G+tH4vjx+bzUoaIWpyPBVWIFWR3VofF+v7auro+qss4wtwPCVceGx6C8s8JiHkHxhDJ6ThQUYvrIoJhaSLcAWUJoFGL5KK1HWhy3AFpCFBRi+sigmFpItwBZQmgUYvkorUdaHLcAWkIUFGL6yKCYWki3AFlCaBRi+SitR1octwBaQhQUYvrIoJhbSmha4evUqzpw5Y81LmH3uhIQEeHl5mX0ePoH9WIDhaz9lIStJaMDyLS4uOHbsGEpLS20ue+DZs9ixfTsyMzNV1y4tKUFJSQmePn2qWmdohvYLDw9H165dceTIEUO7WnQbXbesrAyFhYXiV1xcjEePHhm8RlRUFFq1agVPT08eLN6gpeSzkeErn7KyK0ntEb5HjxzBoUOHUFRUJGz18OFD3LlzB6kpKTptl5ycjLFjx2Ljxo2gryHb4u/BgweIiIiA5+HDWO3gIH4umzfj9KlTGg8SXbL4+PhgwIABuHLlis3k1SUHr7OMBRi+lrFjtTuLPcI3KSkJiYmJKi8yPz8f+93ccMzbW2f5bN68GSNGjECKHjjrPMiMldQLj0IcG52ccOjgQURGRODGjRvwPXkSa1avxgEPD+EJ67sE6TNt2jSMHz9epaO+fXm9/VuA4Wv/ZWSXEtojfLUNZQi+eXl5+PHHH7Ft2zajwxTa5zd1ubCgQDwM3PfvF+ERydsmD/2Mnx82OTsjPi5O72lpf4pNN2/eHBSG4D95W4DhK+/yKyf95cuX4bhunXjdvnjxIrZs2SJebXfu2CG8QikeSl4YxWwDAgIQfvUqtm3dKvbbvn07rl27horGSpDgS7HS69ev48CBA+J48uouXrigcTxB4+7du/D29hb70Ov2vn37cOvWLdV+tE9QUBBcaf3Nmzjl66val+bv37+voauumC95uPQ6TzFU0ld6rZemHu7uqnOQvG3bthWep2qlnpnHjx4hLjYWXp6eqnPS8RS2IDsY+0c2oDh12OXL5cIGN2NisGH9ekRGRho8HXn2PXr0wLx58wzuxxvt3wIMX/svI5MklOBLr9turq44eeIEfI4dg7OzswBSRkaGOJ8EX+eNG7Fv717x6nvi+HHs3bMHa9esQXBwsEGPUIIvHb9z504cP34cJ06cEFCl40kO6Y9e6wksu3ftEvvRazZBlkBN4KYHggRf2o8ARVCn/Qh40rqiwkLplKgIvkHnz8P76FHhTW7dskWc69KlS+J4kr1bt24CYrm5uapz6pqhhjB6mFBYgDxWkonsRDJu3rQJlIVAshvzR/He5KQknaGF8+fPg2K/FDox9EdeMoUdfvjhhypp6DQkG28zzQIMX9PsZfd7S/A94uWFvHv3BBgItNSqv27tWhFzJFhI8CXYkgdHfwRByh7Ys3u3AF6WWiaBtuISfAlCcXFxwgOk86anp2P7tm0CJJJXSGAmeMXHx6uATtAjGFMjGXm1EnxJxrCwMBCo6I8yKYKDgoTHeS4wUCVGRfClHfWFHSjkULt2bYwePVolj+rEWjPUeEdg9Dt9WpyPNpOd0tPSsN7RUYQLKspU0DplucWc7Gy4/JI5Iuldbie1FdRAWKdOHfGGoraaZ2VmAYavzAqsInEl+EZHR2vsSmlN9PpNHi1BUYIvecXaqWIEavJeaarvT4IvhRIovUv6IxCRV0vHS+c9ffq0gO+VsDCNhiI6jsCr7vmSJ62ePkbnpfOQ7OTNS+c0B77kbRN8V6+ueAxgko100gVYCtuQt28MMCX7aE/pQUDhEHoLoHlj/ijroW7dujh16pQxu/M+dmoBhq+dFkxlxZLgS/FU9T+Ch4BvUJAmfH18VECT9qe4InmqZ8+elVaVm0rw1ZXnS5BUh29WVpbwhCkWTT/yIqnVXwIvnVzyfAm+tL/2H8Vwd+/erQKUOfClhwrBlxrbjPmjV/0bUVEaMV8pjmwOfOnhQyEMpw0bhCdtjCy0D4VPvv76axw+fNjYQ3g/O7QAw9cOC8UckSwJX38Dvb5MgS/pQ6/vFE4gT5lip9Syv2PHDlUowhj47tq1C1KM1hz4EryMhS95tWcDAlSeN8GW4r70o7BDZeFL5w3w9xeNnpRuRvob+8fwNdZS9r0fw9e+y8dk6SwB35s3bwrPN/TiRb3XNxW+0onIA6dY7O3bt0XMV8pOMARfuhZ5h9QwSJkM9GcOfCmbg+BLsdOK/qixcOvWrSI2TWEBdUhSQ15l4EueNMGb4ryUMkYhIFP+/P39RdiBwg/8J18LMHzlW3Y6JTcVvtTgpR6zJdBRYx2FDQzlnBoLXwLtwYMHRRxYXWCCGIUfCKj5v0CNUs3Im0z5pQFQ2j/xzh3heZJcEvzMgS81ChJ8qcNCRX+3ExJEVsOFCxc0diWAUpzWVPjSw4fS+8i+1yMjTUpVkwSgMR6++uorBKo1QErbeCofCzB85VNWRklqKnwptksQpAY5eqWnOC8BkDIeJI+MGp0oDkt5qjRPf8bCl/YjD026Dp2DrkWNXuTNUoMfwV/yfGk/ygGmuDPtRxkS5GFSrDgpMVFlA2PgSzFlAjaFNwoKClS5wgTOevXqoW/fvmKMBdVJdczk5OQIWxBoKauDZKKcXIpBE0DVGxwptEL6SXbTPh3Z4vKlS0KXkOBgcS46n/pPPQ6ufTwtk51WrFghPN/s7Gxdu/A6mViA4SuTgjJWTFPhSxDZv3+/SC2jRiQCooeHBwg60h9BuWnTpmJcAYIY/RkLX9qXoEQdICgFTWqooutQDm9aWpo4nwRfyuk9eOAAdu3cqdqX5kNDQ8V+0j9j4EsQpAwLuhZdV72TxfLly9GxY0cR/pDOqWtKD5uIa9ewd+9elTykB4UN6AFFXYIlm1AYY+DAgXq7KxNkKd4t2UDXlDJF7hnIPaZzDB48WDw4dMnL6+RjAYavfMrKKEnJ84qJjlYBQTqIIEINO+QtEegITJQqRV4pebUUYqDt5GnSDa7+R+ldlC5GaWpSyhWdIzY2FqmpqeVenWkwG0p1U/cA6TgCLV1Duo56pwkJvpTtQHFWSjeT9tVOPSPZaB3JLKWe0ToaQIc6MUgy0joaPYx0onNR+EL6i4mJER0VjImbku3oYSTJQ3rQNehaFLt++EtO8tq1azF06FCR6yxdR31Kx5BtpPPomtL5DKWuUaYG9cyjAYT4T94WYPjKu/wqLb06fNUBVukTmnmgOnx1pZqZefpyhxNQp06dilmzZpV7UJXb2cgVQ4YMwezZs1Wjqhl5mNG7UZlR41+7du3KPSCNPgnvaDcWYPjaTVHYVpDqDl+yNqVsde/eXYwpYa71ydNv1qwZ/Pz8zD2V3uPJa6awBnm99PDgP3lbgOEr7/KrtPQM3+chCcodHjNmjKqLdWUNSg2EpmY+mHItiruTV71kyRJVRxNTjud97c8CDF/7KxObSESek76YrU0E0HERCjfQiGa2DINQfFvk8R49qkMi+1lF+cBz585VNVDaj2QsSWUtwPCtrOX4OMVYgBq41HOd7VExaqyTOpjYo3wsk+kWYPiabjM+gi3AFmALmG0Bhq/ZJuQTsAXYAmwB0y3A8DXdZnwEW4AtwBYw2wIMX7NNyCdgC7AF2AKmW4Dha7rN+Ag7sgB1znhYVoqy0vt49PD51y/sSDwWhS2g1wIMX72m4Q1ysEBR/j24OS2F46yR8PPcKweRWUa2gLAAw5crgqwtkJ2WjKHNv0Cjt36FVZMGyFoXFr56WYDhW73KW3HaMnwVV6TVRiGGb7UpamUqyvBVZrlWB60YvtWhlBWsI8NXwYWrcNUYvgovYKWrx/BVegkrVz+Gr3LLtlpoxvCtFsWsSCUZvoos1uqjFMO3+pS10jRl+CqtRKuZPgzfalbgClKX4augwqyOqjB8q2OpK0Nnhq8yyrHaasHwrbZFL3vFGb6yL8LqrQDDt3qXv5y1Z/jKufRYdjB8uRLI1QIMX7mWHMstLMDw5YogVwswfOVaciy3sADDlyuCXC3A8JVrybHcwgIMX64IcrUAw1euJcdyCwswfLkiyNUCDF+5lhzLLSzA8OWKIFcLMHzlWnIst7AAw5crglwtwPCVa8mx3MICDF+uCHK1AMNXriXHcgsLMHy5IsjVAgxfuZYcyy0swPDliiBXCzB85VpyLLewAMOXK4JcLcDwlWvJsdzCAgxfrghytQDDV64lx3ILCzB8uSLI1QIMX7mWHMstLMDw5YogVwswfOVaciy3sADDlyuCXC3A8JVryVVDuZ89e4anT55oaG4Ivrr21ziYF9gCVWgBhm8VGp8vbZoFykpLEHf9Cu4XFagONARf2nY7JlK1L8+wBezJAgxfeyoNlsWgBZ48eQyvHetx3G2Laj998H388CFclkzGlXO+qn15hi1gTxZg+NpTabAsFVog4Oh+9PvhA1y/dF7sqwu+FG5YO2MYhrf8EulJCRWek3dgC1SFBRi+VWF1vmalLRB/4xr6N6yJ1h+9ioToa8hKTcLQ5l+g0Vu/wqpJA/D40UMc3LpGLM/o17JcjLjSF+YD2QIWtgDD18IG5dNZ1wKl94sxsWtDAdfxnb7HpbMnMLTZ52J52bjeCPQ5iG51/08s71230LrC8NnZAmZYgOFrhvH40KqxgPP88QKuzd/7nQBx16/+JZbJAyYQN67xa7F87UJA1QjIV2ULGGEBhq8RRuJd7MsC544fFHClUEOTd36DxjVeEsvq8y0++CMoO4L/2AL2agGGr72WDMul1wK5WekCugRffb+ZfZvrPZ43sAXswQIMX3soBZbBZAsMa1FbL3gJyD6uL9LRTD45H8AWsIEFGL42MDJfwvIW2L12vl74Nn33t7ibmWr5i/IZ2QIWtADD14LG5FPZzgIJN67phe/gxh+Dcn35jy1gzxZg+Npz6bBsei1AcO1a53mWg3bcd+30YXqP4w1sAXuxAMPXXkqC5TDRAs+wYHjnct5v4//P3nmARXFu//+Xcu/93Zryuy33f2NMvIlJ1GtJ0yRGjYoVe+8N7F2sscQaWwRUrIgVUayIxF6wgAh2pRcR6R1pon7/z3l1NwvsAgszuzPj4Xn22d2Zd94557xzPns4875n3nsVJ/ZtM7Mvbs4WsLwFGL6WtzmfUSIL7Nu8qhR8u9X/O4Kv+0t0Bu6GLSCfBRi+8tmWe5bZAjf9zqFdzT8WA/CEro2RcD9K5jNz92yBqluA4Vt1G3IPVrLA/fBgDH9R10GX91051Q65OVlWkohPyxaouAUYvhW3FbdUmAUy01KwYFRPfeTb5j//C8+NK3img8LGicUxbgGGr3G78FYVWIDq+277eS5s3n9dALjn5/+C/2lvFUjOIrIFAIYvXwWqtsDZw7vRpe5fBXztbP7L9XtVPZovl/AM35drvDWnLdX0pfq+lPOdOagd1++0ngM7AAAgAElEQVTV3AhrVyGGr3bH9qXQzLC+704nrt/7Ugy6RpRk+GpkIF9mNXT1fW9cPvsym4F1V5kFGL4qGzAWt7QFqL4v1+8tbRfeomwLMHyVPT4sXQUsQPV9fxjcrgItuQlbQDkWYPgqZyxYkipY4Mbl01U4mg9lC1jeAgxfy9uczyiDBZ4+fSpDr9wlW0A+CzB85bMt98wWYAuwBUxagOFr0jS8gy3AFmALyGcBhq98tuWe2QJsAbaASQswfE2ahnewBdgCbAH5LMDwlc+2qumZHslj+KKbV/R68uSJql86Pcp615KOhmOomovvJRaU4fsSDz6prnPYxAcxoihNXHQ4YiNDERMRgphwegUjOoxe99TzCg8WcsfHxiA/Px8FBQUoLCzEw5hIPIgKR2xUGO6X0lFF+oXdE/rR2KQkxgsdSb+ioiLxY6kb05f80la8+gxfxQ+RfAKSk1JUGOh7EqNtv8DSiX018RrV/jNRaGdsp6+RlpaG9PR0HN+/A5O6N9aEfjROQ77/WOjoOGu00C8rKwt5eXniR4aieQawfH4jVc8MX6ksqbJ+dOD1P30U80d2Q3DQWeBJjupfEbcvY1rf5pjc4zuMbP8VoqKisHv9Cswa1AYxwVdVrx+N0TVfb9i1rI2R7RpgqYMd7t+/j4SEBGRkZODRo0fFAKyyy/KlEpfh+1IN93NldeANvhGARWN7IfzWJU1AKSH6Dqb2/h7T+7dEVup9DGvVAFuclmDh6B7ISrmvCR1v+5+AnU1tLBzVDdd8j+DHsf1x8+ZNhIeHIz4+XgCYImBKQdA4859yLcDwVe7YyCKZDryJcfexeFwf3PI7rgkoEWyn92sp4EsQpgix/advYP6Y3oiPvq0JHelHckyHL7F4bE88zk9D6HVfTB/aFWfOnEFAQIAAMEXA2dnZIvqllBL/KdcCDF/ljo3kkunAGx16Gz9PtxP/vmoh1UDphMk9mmBqn+agtAPpFHbjAqb2+V7/Xe16Bgedw/guX2PphL5IjgsROhJ8J/Rth/379wsA37p1C7GxsSIHTDcZGb6Su5CkHTJ8JTWncjsj8NIrJuwe3JbPwI1Lv2giGiTYEmQJvrqcbsi181gzZ5QAsNqhS/JTjndYi0+xZHxvJN6/px83gu/I7i2xbds2eHt7i+g3MjISqampYgYEw1e5/kiSMXyVPT6SSKcDb2piPOhpD/4n9+HZ4yy9E6sVUJROcOjdTJ/jJT0ehF/H+vnjxL/kz4qyVa8j5XiHtayFhaO74VHGw2L6EHztOjXF+vXrsW/fPly6dAlhYWFISUkRMx8YvpK4j2ydMHxlM60yOtaBlx63Q49V379xKfKzE4s5sRrhSzfQpvdr8TzHG/M8x5uWEAG3ZdNw7vA21etHYxJ+8yLGdPhC5HiL8tNL6UTwHdLhO6xevRoeHh7w9fVFSEgIEhMTkZuby2kHZbigSSkYviZNo/4dOvA+LizErtWLse7HMUiLDy/lxGqDr8jx9mwippRF3vYT+jwpyMCWpVOxx2UhcjPjVa8jTf3T5XhT4kKN6kPwHdz+Wzg6OsLd3V3ANzg4mOGrEtdl+KpkoMwVUwdemnC/eelMzLPviKTYYKNOrCb4RtyiHG9zTOn5a46X5KcZAJTnzUiOVr2ONIVsWIta+Gl8n2I53pLjxPA11yuU1Z7hq6zxkEQaQ/DucFqAOUPbIyf9geqhFB9VOsdLQFo4qquAb25Wgup1pKl/tIBi4ejupXK8DF9J3EMxnTB8FTMU0giiB29REY57bsWsga2R8jBM9VDKTIl5nuPt8z0SXuR4C3NTsXnxZCyf1F/1+hFYw17keJeM64WigtI5XoavND6ilF4YvkoZCYnkIPjSXW4fD1cR8cYEB6geTNHBAZgscrwtEHnneY6XQLR79Y9YNX0o0hMjVa+jLse7bGJfmMrxMnwlchKFdMPwVchASCEGgZdyvHvWL8MPQ9ohNjRI9VB6nuP9HlN6NUVMyK+1GdbOHQ3H6UP1Cw5KgklN34POU473Uyyd0AdJsb/O4y1PB875SuE11uuD4Ws920t+ZoLv4e1r0bHWG7D95M/o06ia6l9d6/8NLau/Jmo16GDkMm+M2Napzluq14/GqHOdt9G82ivl5nh1+uveGb6Su5BFO2T4WtTc8p6M0g1U13V46/qqj3h1gNm6fLoonaj7rntvUe0Vzeg4o39LozrqdDX1zvCV15/k7p3hK7eFLdg/pRyoopV963qaARPD13SZT4avBZ1LhlMxfGUwqjW6pJQDlRHMycmBXSuGr6loUYnbOfK1hsdY/5wMX+uPgSQSEHwfP34sygnatarLka+KCsMzfCVxAdV1wvBV3ZCZFpjgS4+TsbNh+CoxwjUlE8PX9DWt5T0MXw2NLt1sy8zMZPiqKOolIDN8NeSEZqjC8DXDWEpvyvA1fXPKVNSphO0MX6V7ljzyMXzlsatVeqW0A0e+6gMww9cq7mL1kzJ8rT4E0gnA8FUfeDntIN31r7aeGL5qG7Ey5GX4Mny5nm8ZDqKwXQxfhQ1IVcRh+DJ8Gb5V8SDLHsvwtay9ZT2bnPB9+jgLh3ZuwPjuTUU9hX7fvI8da5ciPkbeAu3WWOEWctMPW1cvxtmjey0yX5pzvrK6hWI7Z/gqdmjMF0wu+MZF3cWQVl+g1Qe/QY/P3hHw7f3Vu2j38Z8wuGVd3LxyRjZIWQK+BbmpSE+KRqCvD6b1byPqLDR/939w1HOrbHoZzrJg+Jp/rWvhCIavFkbxhQ5ywDcn4yHmjeyJFu+9ip8mDUT8/eeRbsGjFOzZuAJdG/wTIzs2wqNMeZ4iITd8s9PisMN5Poa2+RztP/kzJnRv8rzKGMNXQ56hTFUYvsocl0pJJQd8r10+hV5fVUPXz/+NxNjiD3IkcC0a21tEiuePHZAlSpQbvhF3r2LFjBFwnjMWvt67RBqFHlzJkW+lLkE+yAwLMHzNMJbSm8oB3z0blqHNh3/Az3Mm4FlRdjHA0nevXeth++mbmDduYLF9hv9WV+Wz3PClR7I/yoxH/qNkvfxTejZl+Cr9YteAfAxfDQyiTgWp4UvPSHOcaS9AdPzgLj2cDGF6+bQXun/+/9C6xu9KwdmwXWU/yw1fY3IxfHVXFL/LaQGGr5zWtXDfUsOXIsKfJvYX8D3tvccofAMvnkDPr6qhdY3fIi87yWgbY4Cr6DaGr+npc1zP18IOJvHpGL4SG9Sa3UkN3+z0X3O6F08eNgpWQ/hmpcUZbVNR0Bprx/Bl+FrTp+Q8N8NXTutauG+p4ZueFIO59l1E5MvwNQ1BYz8a5mzjqWYWdhSFnI7hq5CBkEIMOeA7x74zw1fmEpUMXymufvX1wfBV35iZlFhq+GamxGL+qB4MX4avyWuOd1TeAgzfyttOcUdKDV++4cYr3BR3kWtIIIavhgZTavgaTjU7uHOD0Ztpuqlmvb75kKeaVTJC5rSDhpzQDFUYvmYYS+lNpYYv3TQqb5GF5+af0bbmn1S7yMLYjTGe56v0K10b8jF8tTGOQgs54HvT/wx6N3xPLC+mAjuGsMpIjsG8EV1FTvjSKa9i+wzbVeUzTzUzPcvCcJ7v7t274evrCy4pqR6HZviqZ6zKlVQO+OZmJeKnyUNEYZ0FY3rjQeRtAVkqrOOxYTk61/0bJvRoBvpuCNkbV87AcYa9qBT2pDCz2D7DduV9Vip8bweex6rpw+B/2gtPCjIqrR/pX9m0w/aVMzHYtjGcnJzg4eEh4BsSEoKkpCTk5eXh6dOn5V4z3MB6FmD4Ws/2kp9ZDvgSHNISozCupw1s3v8Nun/2z1IlJUNvXi4Fnwm92whgLxrfDzRrojzImtqvVPhOGdABLaq9inkjuiMtMbrS+lUWvttX/YDuX1XHnFkzsHr1anh6euLixYsICwtDcnIyw1dy75K+Q4av9Da1Wo9ywZcAYW4x9TPeezCwaU0c2rYaRVWIDK0B38VjeogfmLKKqVMVtwFNPsLejcvxOD/dYvAtzE2B++of0e7Tt+DgMAULFizAunXrsH//fvj5+SE8PBwpKSnIz8/nyNdqnlixEzN8K2YnVbSSE76mIlO5t1sDvnLrVLL/iqYdCh4l45DbSnSr/3f0s22God3bYsakMdi4cSMOHTqEK1euICoqCmlpaSgoKGD4KtxrGb4KHyBzxGP4mr45VRJ4SvpeUfge37MBq6YNgc+utZjap7m40WnfuRlcXV1x5MgRBAYGIjo6Gunp6QxfcxzHSm0ZvlYyvBynZfhqF74Xju7GnKG2eBh5Cyc8N6JTnbcwo78Nhnf5Hm5ubvDx8UFQUBBiYmKQkZGBwsJCjnzlcDIJ+2T4SmhMa3fF8NUefJ8UZuDqmUOY3r8V7ocG4pzXdgxoXAMbF00ETTVj+Frb6yp/foZv5W2nuCMZvtqCL01hu3n5mABt5G0/nD+yA6Pafwa3ZdPE9DaCL6UdNm/eLNIOV69e5Zyv4rzStEAMX9O2Ud0ehq+24Btx+zJ2Os7B3YBTOLV/M4a1qIWtK6YjJ+2BmF1B8LXr1BQbNmzQ33CLjIxEamoqz3ZQgfcyfFUwSBUV8WWBb0ZyNFpUe6VK07uUfsMtJS4MW5Y64JrvERzzWCdyvJt/moL8nF+fFkLwHdaxCVxcXLBv3z5cvnyZp5pV1FkU0I7hq4BBkEqElwG+2WkP4L7mR03DNz0xEmvnjsK5w9tf5Hg/EKmHkj8YBN+hHb7TL7K4cOECQkNDeZGFVA4lcz8MX5kNbMnutQ7fRxkP4bXNEUd3rdEsfFPiQrHTaQ7OHt6G814vcrzLpxtdwkzwHdz++fJiXW0HWl6cmJiI3Nxcnu1gSeerxLkYvpUwmlIP0TJ86TH1B1yX48h2RwElLaYdMpNjsMt5Ls4f2YmTnpswrOWLHG/68xyvsch3cPtv4ejoCHd3dy6so1THNCEXw9eEYdS4Wcvw3bJ0KratnAm/43sxuFlNTUa+GxaMxy/uLvBxX4vOdd6C608OxXK8DF81eqVpmRm+pm2juj0E36ysLNjZ1EVCzF1NvNbMHonWH/wWPzsMwr2rZzCqXQMsm9RPwFcrOk7q3hht/vO/2PHzLP083k2LJ5V7Q/F52oEjX9U56guBGb5qHTkjcuvgu3b+ZEwd0A62H/8J0/o0x9KJfVX92rZihphuNb5zQ/w8dTDohhTpNL1fS3St9zfMteugav1Il91r5gvwjm7/GaieRUXKcDJ8jTiBijYxfFU0WOWJSvDNzs5GXFwc5o7oCaeZdkiNDy83gir576zSvgecOYjBTT/CSoeBoBtSQr6ibCyf1A/uq+chNzNe9Tqe2u8Ku5a1QD80OSZyvCXHheFbnkcoez/DV9njY5Z0OvhSjdmVDoM0ASWa50o53iXjexfTZ7Tt59ix6gdQpa+SUFLb9xN7n9dqcF3qgAKDebzl6cHwNcs9FNeY4au4Iam8QEVFRdjv6oT2H/9J1KPt06ia6t871npDVO8yBJHLvDFimxb0Ix3afvj7Ujoa6mvqM8O38r6ihCMZvkoYBYlkIPjm5OTArlU91UeDOuBwPV/TS6YZvhI5jpW6YfhayfBSn/bZs2fQpR3sWtVl+FbyMe466FvyvaL1fEvKxPCV2oss2x/D17L2lu1sOvjqppqVdFS1fufIlyNf2ZzGyh0zfK08AFKdnuFrGlJK/+HhyFcqL1BXPwxfdY1XmdLS0wsyMzPFIgulA6ei8nHka/pHhdMOZbqD4ncyfBU/RBUXkOFrGlQVhb012nHkW/FrXEstGb4aGk2GL8M3ODiYq5qpxKcZvioZqIqISbMdOO2gPgBz5FuRq1t7bRi+GhpThq/6wEtpDoavhpzQDFUYvmYYS+lNGb4MX047KN1Lf5WP4furLVT/ieHL8GX4qseNGb7qGatyJbUkfOnJEuePeqDdJ89rL4Te8pdlVZ0lp5p1qvdPUWOh+bv/Y/R9XKeGsujIaYdyL21NNmD4amhYLQnfzNRYjO3SWA8ptcO34FGK0KXP19WxcFQ3o/WBdznNYfhqyF+srQrD19ojIOH5LQnfzSvm6sFLkaLa4RsTdkPo89OE/shJfygLZE3NIebIV0InUFFXDF8VDVZ5oloKvtcun0KbD/+A3o3ex0jbrwS01A7f636nhR6OM+2Rn2PZGsEM3/KubG3uZ/hqaFwtAd+M5PuY1McG7Wr+CUf3bsXMwbaagO+xAzuEHvTstKePszjy1ZBfKFUVhq9SR6YScskN36L8dBzY6oQOtd7EylljkRAbohn4Hty5ATbv/xbuqxdYFLw8z7cSF7pGDmH4amQgSQ254RsTeh0j2n2Boa0aICkuDCnxkZqBr+O8KWj/yRvYtHwOdq1djGUvHjq6a81CRAYHyQpkTjtoyAnNUIXha4axlN5UTvjSv+JbVs5By+qv4/TB7QJGWoLvDPueaFHtVbT/5C9o85/nj/WhG4n0SPdu9f+BM0f3ygZghq/SPUse+Ri+8tjVKr3KCd+w21fQ4r1X8ePoXqC8L/27rCX4Dmv7JTrX/Stm23fBg8g7Qr/s9IfYtGwWOtb5P5EPPuPtAZrfbGrWQmW3M3yt4i5WPynD1+pDIJ0AcsE3M/UBRnf+Dj2/fBeXThzUA0hL8L199TxuXT2HxAcvHk3/4jFEhXlp2Lh0JlpUewWTejUHAbmykDV1HMNXOh9QU08MXzWNVjmyygHfJ4WZ2Onyk4h6V80ajUeZCXr4aAm+psBI26NCrqFl9dcw4LsPQf8BlNW2MvsYvuVc2BrdzfDV0MDKAd/gG5cxsOnHaF3jt0iMLR4VvizwpXm/NtVfR++G7+Ha5ZMMXw35jDVVYfha0/oSn1tq+BbmpoIWHZiqdWBs+6YlU0BT0ioTARo7xhK1HXKzEpAYcxdZqbFG5Wb4SnyhcnfCAgxfDV0IUsOX6h0c3bXWaJ2DpRP7Yv6Iruj11bsCzrMGtxPtzh/ZCUpVGANpZbZZAr7HD+wQMx3mjuiBooKMUrJz2kFDTqIgVRi+ChqMqooiNXzLg6VW0g6xEbfR8r1XRU435KZfMfgq/4ZbYzg6OmL37t3w9fUFl5SsqhdZ7niGr+VsLfuZlATfZ4+zsHPNIozu9A3uXD1XDGjlQd1wvyUiX5rD7Oa4QMxhdujfBrcDfYW8uVmJ2L1hBbo2eMfoVLM9G5djVIeGuH7pRKX1I10re8Nt+eQBGGzbGM7OzvDw8BDwDQkJQVJSEvLy8vD06VPZrzk+QeUtwPCtvO0Ud6SS4PukIAM0d5bmBm93rvySXUvAVwd7t5/noWu9v8Pm/d/o89xtP/oDun/2jtFFFva2jYR+rit+qFI9CHPhm5sZDwJv10YfYv78+VizZg08PT1x6dIlhIWFISUlBfn5+QxfxXlocYEYvsXtoepvloYvlV7ct2m5yPVSnQcdxOidFiOc8doFx5nDEXXvarF9hu3K+2xJ+BbmpYrSmJsWT9bnufdvXoH4mGCj8p/32YNVM+wRevOy0f3l6abbbw580xMi4DTTDt2+/RQODg5YuHAh1q1bhwMHDsDPzw/h4eFITU1l+KrAkxm+Khikiopoafjq4CHnuyXhK6ceZfVdUfjmpMdh06KJ6PDpX9Dl65ro8V1tTBzWFxs3bsThw4cREBCAqKgopKWloaCggCPfijqOldoxfK1keDlOy/DV9jPcdjrNwbofx+BB+HUsn9xfpEbsO38PV1dXeHt7IzAwEDExMUhPT2f4yuFgEvfJ8JXYoNbsjuGrTfg+zkvFIbdVWDKuF1IehsF99Tx0rP2myPsO79Icbm5u8PHxQVBQkIBvRkYGCgsLOfK1pjNW4NwM3woYSS1NGL7ag29ediKO79kAx+nDRMTr4bIA9jZ1cHTXGoRe98XwLt9jy5YtOHr0aDH4ctpB+V7L8FX+GFVYQoavtuBLKwwvHN2FHat+QHJsMHavmY+RbevjmMd6PH2cKeBr37kZNm/eDC8vL1y9epVzvhX2Fus3ZPhafwwkk4Dhqy343rp8DNtWzBAR7/afZ6H3V//GL7td9Mu3KfId1rEp1q9fj4MHD8Lf3x8RERE820Eyj5K3I4avvPa1aO8vA3xpCltc5E1R4rGsGQRq2ldytgPpGHn3CmjK2x3/k3B3noeOtd6E1zbHYlPansO3CVxcXPTzfGmqGc3z5UUWFnW9Sp2M4VspsynzoJcBvg+jbol/w6m+rpoAW5ashvAl8Ebcvox1P47F1bOHoMvx+rivLaUvwXeI7XdYvXo19u7diwu+vggNDUVycjLDV5kuWkwqhm8xc6j7i9bhm5YQgV3Oc+F/cp9mI9/oe1fg+pMDgoPOYveaH0WO9/geyvGWfqIywXdw+2/h5OTEtR1U6LoMXxUOmimRtQxfigg3LJyAc4e3wWu7oybhm/wgBC7zRuPOlVPYtnImejd8t1iOt2T0rIMvFdZxd3fnwjqmHEOh2xm+Ch2YyoilZfj+PHUw9m1YgpP7NqHf19U1Cd9V04bg7KGtIrrvVPtN8SNTEriG3xm+lfES5RzD8FXOWFRZEoJvVlYW7GzqiulINCVJ7a/ZQ9qhc523cWDTMpza74pR7Rpgp+NsNK/2iup1042NXcta6N7gH/DZtRYeaxfAvlUdGMvxGoKXPjN8q+wyVu2A4WtV80t7coJvdnY2ju7Zig1LZ6Nrvb9hw4LxqofU5eN7xUIDO5s6Iiqkql4Ers2LJ6N/4xpi/qsOZGp9Dzh9EO6rf8TIdg2ErsZyvAxfaf3F2r0xfK09AhKeXwffuLg4DGr2iXDmwtyUUnfJSzqx0r/Tai4qJkNzXQseJQt9KAc8rlNDnD7gCipfqXQdypOPcrx9Gr6LYx7r9PN4yzuGI18JnccKXTF8rWB0uU5J8E2Mj8PYTl+Ldf8JMXeh9hf9+92t/t/htmyaHrCZyTHPty2fpnr9aHzoR8Xm/ddxZLuTXsfywEv7Gb5yeZJl+mX4WsbOFjlLUVERzvvsR9f6f9fXo6Vnran5RXUM6EGdhjDy3uEstqlZL0PZe37xr1I6Gupr6jPD1yJuJdtJGL6ymdbyHRN8c3JyYNeqXjFYmXJeNWzner6ml0wzfC3vY1KekeErpTWt2NezZ8+gy/natarL8H1iGlpK+9ExXOFmjmwMXys6nASnZvhKYEQldKGDr26qmTlOrOS2HPma/hFh+CrB8yovA8O38rZT1JEMX9OQUvKPC8nGka+iXMliwjB8LWZq+U9ETy/IzMwUiyyUDpyKyseRr+kfFY585fcpOc/A8JXTuhbum+FrGlQVhb012nHka2FHUcjpGL4KGQgpxGD4MnyDg4ORmJiI3NxcfoabFE4lYx8MXxmNa+muabYDpx3UB2COfC3tKco4H8NXGeMgiRQMX/WBl9IcDF9JLn/VdcLwVd2QmRaY4cvw5bSDaf9Q2h6Gr9JGpAryWAK+j/PTkZlyX19TITU+HPR4c7luVFl6tsPj/DSkJUQi8UVdjIykqAoXuqmsDTjyrcJFr+JDGb4qHrySossN35T4COzduAJjujQRtQio5kLvRh9gmcNQ3Ao4i6eFmZJD2JLwjY+5hx3OCzCwWS29fvZt6mOn84/ISLkvuW46WDN8S17JL8d3hq+GxllO+FLZxsUTB6H9J29gQo9mop4ulXpcNXMEOtR6EwOafoqrF45JDihLwTcnIx6zhnZG+1pv48dRPUVpx0NuP2NSr+aw+eC3mDmwDfKykyTXj3O+GnJAM1Vh+JppMCU3lxO+l095web932Jy75aICbshIEQ1dfNzkrHVaYGIFB36NEdGsrQRoqXgu3eLM1pWfw0/TeiHtMRooR8VNH8YdRcj2zYQ+nlucWb4KtkBVCYbw1dlA1aWuHLCd9nMMWjx3qvY5Tyv1JN046LvCjgNbPox7l67KCmgLAHf3MwEtHzvNfT5+gPcvHK2lPxnvPeg5Xuvwr51XT2YdSkDKd457VDWVa3dfQxfDY2tnPC1t/0WtrXewjHPLaXgRAB6nv99H1d9fzG6v7KQsgR87wRdeA7XVnWRFBdeSn6KhG2qv4a+33yAW1fPldpfWd10xzF8NeSEZqjC8DXDWEpvKid8540dgFYf/A57NywFpRt04KD3rNQHAr79v/tIcjhZAr4Bvr+IqH5U+89BuV9D3XSf+zX5FJ3r/R2nvXYb3a9rV5l3hq/SPUse+Ri+8tjVKr3KCV+KaCm6nTuiW6m8rtfuTWLfhK7fIDv9oaRwsiR8R7b7TPyQGAPoyC7NxM3GwztcJNWPzsXwtYq7WP2kDF+rD4F0AsgJ3yeFmVg61Q5tPvw9lk21E/N8H0bdwuGd69Cl/j/Q4/N/4WaA9P+SWwK+965fEmmHIc1rITbiVim40o03u/aN0O6Tv+DQ9rWl9huDtTnbGL7S+YCaemL4qmm0ypFVTvgSTAhM47s3FVEuRcGGr3M++ySHEp3TEvClKWQDmnyMjrXfxpFd60A/NDp4Enhv+J0SN+QYvuVcgLzbLAswfM0yl7IbywnfzNQHWDF9OHo2fB9zhncT83yPeayH67IZ6Fz3b5jQvQn8zxzRQ0sHr6q+WwK+JOO5o57ix8S+TQNcOecNFGWj4FEKznp7YGSHhmJfp//+FScObJdcR458le1XcknH8JXLslboV074bloxV8zz/Xn6MKQnPZ8HS9AqzEsT0KUoeEzHRrgfflNSOFkKvkUFGVi/yEHceOtU5230aVQNvb96VywgcVkwGcPaNkTPr6rJspCE4WsFZ1HAKRm+ChgEqUSQC7401YrgOqBJTdwJ8jUK15+mjkTr//wenptWlJoNUZXo11LwFT8kuam4cs4HE7o1FvAd2/lr+J31RnpSDGyqv46BTWsi4l6gUf2roiPDVyoPUFc/DF91jVeZ0soF37tBFwR8h7epj9jI20bhs3/bWtHG5cdxKMhNNdqmMoCyJHxNyXft8kmR83Xo01KWJcYM3zIva63ThnwAACAASURBVM3uZPhqaGjlgm906HUB1qEtaiPi7lWjYHV1fL7E2PWnqaB/4U2BzNzt1oYv3YxbOnkQWlR7BT57jS8wMVenku0ZvhpyQjNUYfiaYSylN5ULvnT3f0irL9Du479gn+uqUnBNiY+E7Sd/QdcG7+Dc0T2SgZcgZU34Ut2KnWsWwfbTNzC+VysU5kkX0RsCuErwtW0MJycneHh4wNfXF1zPV+le+qt8DN9fbaH6T3LBl0Bx3e80OtZ+C90+fxfbnBbo6/lePnUQfRpVh837v8GcoR2KrRBLehCKqf1aoW+TTxEfE1wpKFsSvjStLCMpWuh2xH0jpg/uiNY1foex3ZohLTGqlPypCZGYOaQD+jSuidgI4+kYQ8ia+lwZ+GanxmLmoDaw69oSzs7OAr4XLlxASEgIkpOTkZeXx89wU7hHM3wVPkDmiCcnfGlJ8aUT+zG9fyt0qf9PkYagm3C2td7ESNsvsX7RFKQZzIIg0ETdC8QQm7po/eEfEHjxRCl4mYKR4XZLwpdurM0Z3lXo1rfxR5jc63tsWT4LSQ/CjMr+IOIW7Nt+LvTzP+tttI2hLqY+mwvf2NAgTO/XAv3bfoP58+dj7dq12LdvHy5duoSwsDCkpKQgPz+f4WuO81ihLcPXCkaX65RywpfAQZEhAdb/1CH9PN8LPh6IDrlmFDw0TzbI9yjOHNou5syagk9Z2y0J38LcVFy/+IvQ7ab/GQHdsvLXov2lY0K//JzK1/o1B74E3h8Gt0X/Ds0xdaoDFi5ciPXr1+PAgQPw9/dHREQEUlNTGb5yOZmE/TJ8JTSmtbuSG75lQVKufZaEr1w6lNdvReGbnhiJRWN6iFRI65p/RpuP38DInm2wadMmeHl5ISAgANHR0UhLS0NBQQFHvtZ2yHLOz/Atx0Bq2s3w1e4DNOk5eS7zxsDdeS6yUmKwfPIAtPvoD7Dv8j22bNkCb29vBAYGIiYmBunp6QxfFTguw1cFg1RRERm+2oQvPcSTpvBtXTEDsWHX4DhjGAY3q4ntK2dieJfmcHNzg4+PD4KCggR8MzIyUFhYyJFvRR3HSu0YvlYyvBynZfhqD76ZKTHwWLsAu9fMx8PIm3CaYYexnb7CzUu/IPS6L4a/iHyPHj0qIl9KO3DkK4d3Sd8nw1d6m1qtR4avtuBLN/G8d66G54YlyMtKxEqHQRjeui5uXj4mlnATfO07NxM538OHD4ucb2RkJOd8reaB5p2Y4WuevRTd+mWBLz2inlaclXcjSy37jd1we1KYgcvH9ogc74OIG1g+uT/6N/4AAWcO6vUm+A7r2ETMdjh48CD8/Px4toOiPbS4cAzf4vZQ9beXAb4E3tv+JzUN3ycFGfA/tR87neaIKFfkeJvWhP/J4jWTCb5DOzYR83w9PT2LzfPlRRbKd2WGr/LHqMISah++2SLP6bZsmmbhS4tZKLrdsGA8ggPP/prjvXxMH/HqInqC7xDbxmKF2549e8TyYlrhlpSUxCvcKuw11mvI8LWe7SU/s9bhGx91G+vnj0P4zUuahe8tv+NwmTsGNMNBl+OlbSUfWkoAJvgObv8tHB0d4e7uzrUdJPcoeTtk+MprX4v2rmX40l1/51n2uHx8L7atnKFJ+Ebcvow1c0Yi5Np5keMd8F2NYjleXcSre2f4WtS9JD8Zw1dyk1qvQy3Dd+mEvji5bxN2Os6GnU1tTcLXeaY9fL13wXH6UDGPt2SOVwdd3TvD13q+JsWZGb5SWFEhfRB8s7KyYGdTF0sn9tXEy96mDvo0rIZrvkewY9UPGNX+M5w+sEUUv9GKjj2/+BcGN62JgNMH4DRjGMZ1aohbRnK8Oujq3hm+CnG8SorB8K2k4ZR4GME3Ozsbd25ew0nvg+IZZCf3bdaXf0yIuavKz6nx4XBd6oB+37yP0wdcQbMBSJezh7dhRJt6uHHRR5V6GY5HWkIEVjoMFPqYyvHqoKt7Z/gq0QsrLhPDt+K2UnxLHXzv3b7xHLyem0rdIdc5rlreCx4li2W1HT79C37Z7aLXJyk2GKPaNcD1C0f129SiU0k5czPjsWxSPwz87j+4euZQhfVh+CreJcsUkOFbpnnUtZPge/eaP4Y0r4VpfZvryz7SI97V+lo7dxQ61noTJzw36qFEkWHH2m9i0ejuqtXLcDyWjOuFltVfw5VT+/U6lgS0se8MX3X5Z0lpGb4lLaLi70VFRbh77Qr6fl1dE1AiQM0e0k7kdw3hQ/BtXu0Vzeho17JWKR0N9TX1meGrYmcFwPBV9/gVk57gm5OTA7tW9cyKoEw5txK2cz1f0/UqGL7FLn/VfWH4qm7IjAv87Nkz6HK+dq3qMnyfmIaWEn5UDGUwVtvBcL+pzwxf476glq0MX7WMVDly6uCrm2pmymHVtp0jX9M/IgzfcpxC4bsZvgofoIqKp4NvZmammOerNsiakpfhy/CtqA+orR3DV20jVoa89PQChq9pWJkCvLW3c9qhjItaw7sYvhoaXIav+sBL4Gf4asgJzVCF4WuGsZTelOHL8A0ODkZiYiJyc3P5GW4Kd1iGr8IHyBzxaLYDpx3UB2COfM25yrXTluGrnbEUU80YvgxfjnzV4dQMX3WMU4Wk5MhXfeDlnG+FLm1NNmL4amhYpYZvdvpD3PQ7idT4iHIXbSTGhuLaxWP6Jb8n9m5A4oMwPM5LK/fYsmYbyDHVjGSKi76LoAs+KCrIKFO+R5kJCL8TgON7Ngjdzh7egYi7V8s9riydSu7jtIOGnNAMVRi+ZhhL6U2lgu/98Fs4vHM9Zg+1Rd9va+DKmcMmAfWkMBO+x/ZjUs9m6NWouqhR0Pzd/xHFzif3+h4HtqxCXnaSyeNLgqjkdynhS3JcPu2FVTPsMb5rY0zs0RS5WYkmZUt6EIY18ybArk0DUUuC9Grz0R9h3+YzOM0Zi9SEKJPHltSjrO8MX6V7ljzyMXzlsatVeq0KfOkZYSE3/bBwbB90b/BPtKv5Jz1I/U8dMAmZs9570KvR+6Iq155NKxEXcUPU1j20wwUd6/4D7Wr+ET4ev1YkKwtCxvZJAV+CpI/nVvT+6t/oWPstISuBdEyHL8uE7/QhndGqxu+weOJARN+7KvSi6H5st6awef83WDxhYJV+WHT6Mnyt4i5WPynD1+pDIJ0AVYEv1c1tW/OP6P7ZPzF7RE+c9fHEz7NGCwCbgi9FhlP7txYw8z1eHNAE81MHtqJ1jd/Brl1D0CPfdbAx510K+E7t0xyd6/4Vw2z+ix0uS+HlvllE5mXB99CujaLNjyN7ID05ppjs8feD0f6Tv6DHF/+G/1nvYvvM0U3XluErnQ+oqSeGr5pGqxxZqwJfyn0e2LYGcVF3QakEerksnFImfCkf3KX+Oxja+nOjAHoYfRfjuzcVfTyMvme0jQ5Apt6lgO8xTzfcCjinj1IDzv9SLnwn9++AtjX/jANuTkbl/mnaSBH9bls1x+iThU3pY2w7w7ecC1ujuxm+GhrYqsC3JBQqAt+QGxexfJod9m9dYxRQKfGRmDnEVsA39Ja/0TYlz1vyuxTwLdlnReB73mcvTh9ww8OoO0bl3rbmJ6HX+gXjKx3V6+Ri+GrICc1QheFrhrGU3tTS8KVnqdFNrIJHKUYBFRtxC6M7fSMgRTMndLAx591a8KW0SVlyTuzbVkTG+7c4ltmurD50+xi+SvcseeRj+MpjV6v0amn46uBh9L0oG74+e9DukzewZPLQSgPKWvA1qtOTHBTmpeG0lztavvca7NvUR1TItUrrpjsHw9cq7mL1kzJ8rT4E0gmgJPgmx4VjbJfG6P75v8qcqqYDkKl3JcA38l4gju9ZLx7guXnpdHT/7B2M6/ItLp44WGXwkt4MX+l8QE09MXzVNFrlyKoU+FIq4scxfcSc2I1LZ5Q5ncsUdHXblQDfXeuWo0W1V0X6hKao0ctlyUxkpcUxfMu5Jnm3aQswfE3bRnV7lADfovx0rF0wBS2rv45R7b9AUlxYlQClBPg+yohHYsxdMc/3ytkjGN25Mdp9/GdM72eDbAkAzJGv6lxNEoEZvpKYURmdWBu+NF1t/1Zn2NZ6U8wXjg69XiXwUvSrBPjqonDde05GPIa2+ULccPPc/DNPNVPG5a86KRi+qhsy0wJbE740O8D3l73o+00NDG5eC7cDfasMXqXCl+Q6unerSD8snTQAVP9BB+bKvHPka/qa1vIehq+GRtea8L3udxoDm36M/t/VhN9p07UgzIWTNSJfylkvn9wf6+aPR0zYDaNgvXDikIDvvOFdS62AM1dHhq+GnNAMVRi+ZhhL6U2tBd/I4CCxfLdrg3dw4sB2PCksu1KYOXCyBnyfPs5Cj0Y10Knu33Dy0E6j8D24c4OA78qpQ/Qr58zRy7Atw1fpniWPfAxfeexqlV4tDV9KNdAS4qHNP0WbD/+AnS5LK1RqkeBGZSpTHoaBbtAZgqjkZ2vAl2TYu2W1qFkxe1hHoaNOLlr5lxwXJuDcofbb8NmzWS8/2SM3MwFJscHIyzZdLU3Xl+69svC95nsEg20bw8nJCR4eHvD19QU9RigpKYkfI2QVDzTvpAxf8+yl6NaWhm9G8n0smThI1Djo1+QTHHL7WV/P95jH+mKfqb6vLjdKtXR7f/sRenzx/+BXRsU0gpO14Es31Wbbd0Xbmn/C5N4tcGz3OqHPnvVLMLrTt6LEJJWmJBvoIJqfk4wtK2eLKXbLHIbot+v2m3qvDHyDg85hXJdvMG5wT6xevRp79uzBhQsXEBoaiuTkZOTl5fEz3BTtrQDDV+EDZI54loYvLT4YYlNP/Putm/9q6t3m/dfxIPK2AJIOvt0+ewcXftlbJqSsBV8CJU2T27txORz6tRWFeEi3DrX/D3OGd4PXNidkpT4oJrshfOeP6VNsnynw0nZz4XvN1xvDWtTC8H5dsXDhQri4uODAgQO4fPkywsPDkZKSgvz8fIavOc5jhbYMXysYXa5TSglfgkJ22gMxt5XKTRqDB6UMUh6GizYJL+bBmnpPvH9Xn5LQpx3iyn/ShRzwLchNEfN20+LDy50mRjpmpT3Qz/NNfhCCnIyHRo8zTDvoonxjdiu5zRz43gs8g+Gt62Lk0AGYOXMmfvrpJ7i6uuLIkSO4evUqoqOjkZaWhoKCAoavXI4mUb8MX4kMqYRupIZvSUhY47sc8LWGHmWds6LwjQ0NwrR+NhjWuzMcHKZg/vz5IuWwe/dunDp1Crdu3cKDBw/EE6wLCwsZvkpwyjJkYPiWYRy17WL4avcBmg8jb2HR2N4Y1qM9Jk+ahLlz52LlypVwc3ODt7c3/P39RcqB8r2PHj1CUVERnj17prZL+KWSl+GroeFm+GoTvvRopmWT+sO+RxtMnjQBs2fPxrJly7Bp0yaR66VZDnfu3EFcXBwyMjJEyuHJkycMX4X7NsNX4QNkjngMX+3BNzkuBC7zxmB47w4CvD/88AOWLl2KDRs2wNPTE2fOnMGNGzcQExMjcr25ubmg64CiXo58zfEey7dl+Fre5rKdkeGrLfhmpdyH+5r5GNnbFpMnTcSsWbP04N27dy9Onz6Na9euISoqSkwvo3SDLtfL4JXNzSTrmOErmSmt39HLAl96ckaLaq8YnYFR1o0tpe4zdsONFml4bXfG+IFdMWHcGFDESzMb1q9fDwIv3WAj8EZGRopFFTk5OQK8unQDw9f6/lieBAzf8iykov0vA3yLCtLh671L0/B9nJeKIzucMa5ve4wbPVIP3nXr1onFFCdPnkRQUBCDV0W+aUxUhq8xq6h0m9bhS/NoA88dxrheNpqF77OiLBzbsx6D2zbC+LFjxFzeJUuWiIUUNKXsxIkTCAwMREREhIh4s7Ozi91g44hXPc7L8FXPWJUrqdbhey/wLCb0aQ37wf01CV96IOn5Izth37kpJowfJ8C7ePFirF27Fjrw0kIKWsWWmJgIBm+5LqHoBgxfRQ+PecJpGb53A05hUr+2sB/YGxMmTNAkfM9778LY3m0wdqSdAO+iRYsEeN3d3XH8+HEEBAQgLCwMCQkJyMrK4ojXPPdQXGuGr+KGpPICaRm+Izp+gxGD+8HBwQHTp0/XJHyHd2mKMcOH6sG7Zs0a7Nq1C7/88guDt/JuodgjGb6KHRrzBSP4UkRk16ou+jSqpolXx1pvoF3tv2LS+Of5z3nz5oH+FaciN1rRse2Hv4dtvXcwZeJ4cXONcryUatCBl1avUbUyingzMzNF0Rye1WC+fyjtCIav0kakCvLQklKachQfH4979+7h3LlzYloSTU+iFVFUC4CmLFFBFjW8aF4rvWhFF8lOiwscHR1Bd/03b94sCsrQYgOqZ0v6LViwAHPmzBHHqEE/klGnI8lN8q9YsULoR6kGinivXLmCkJAQMaY68OqWDvPNtSo4iwIOZfgqYBCkEoGckibaUzFtuilDEdPRo0dFBKWDFDn38uXLVfMieVetWgX6F5yAu3PnTrGy69ChQzh8+LD4TNtoqS1Fi9RWTfqRrFSjgX5USP4tW7YInWhWgw68Dx8+FBEv1eilMX769CmvXpPKaazYD8PXisaX+tT0ryg5KJUUjI2NFVWuaN0/AXjfvn0Cwtu2bcPWrVtV8SJZt2/fLuSmYuEEW7rxRBH9xYsXxevs2bMiQjx48KCYA0sgVquOtFyYiuSQTjSdjG6uEXipXgONK6WVGLxSe431+mP4Ws/2kp+ZHJOWl9IUJKpuRbVdb9++LW7WEISpDgCtjKJJ+mp5kbwEI5KfIsHr16+LlAr9K04vKihDCw78/Pxw/vx5seRWLbrp5NTpSE+ioKlkd+/eFUuGdTleBq/krqKIDhm+ihgGaYQg+NK/peSsdOONAEwRME3Ip2d7Eaio5quaXvTjQTCiG060lPb+/fsi/0lgohdV8qIfGUqzqFVHGhfK0ZOOVKeBot3U1FTxI0pPpOCIVxr/UFovDF+ljUgV5KEbMJR6IGclANPNt/T0dAFhAhXdiCNYqelFICLZKY9NQKJ/wemHhXSjCJ++U5qFfmioHbVXk34kq6GOpAvpR7l7ehoF53ir4BAKP5Thq/ABMkc8gi+9dBEwpSAocqIyg+TMOmARtNT0IrlJB/pBISCRXroXfaftataR9KPx0elIutEPKP2Qco7XHA9QV1uGr7rGq1xpdQDWQZgcmF4UQan1pdPBEEY6PQlOuv1q1Y/k1ulgTMdyB50bqNICDF9VDhsLzRZgC6jdAgxftY8gy88WYAuo0gIMX1UOGwvNFmALqN0CDF+1jyDLzxZgC6jSAgxfVQ6btELrbl5p6SadsZtYdHNO99Ld4FLzTTpDHXWzInRjKe0Vwr3JYQGGrxxWVVGfOuCePuQOHw9X+Ox2hbf7JhzZtVH/8tq5AWp6Hdn5XPYLxw/rp2/RlLQT+7bj6O7N4qV6HV+Mz80rF/RT8Gh6GgFZB2IVXYYvpagM35dy2J8rTeClCHDf5lX4YXBbHPNYr4nXwtHdRcnJMR0bicUXtDjDec5YLJ3QVxP60ThN7dNc6Lhq5ij94hNemKEuZ2b4qmu8JJNWF/Ee2bkBi8b0RHZqrCaeBnztgjeGNKuJxWN7YkT7L8Wy4xXT7LF0Yl/kZsZrQseTnpvQ+b9vY0Z/G/w0eahYXk2r5Gh1HC3YoCifflRpjPlPuRZg+Cp3bGSTTBfx+p0+iqWT+iMlLlQTUKJnvI1q1wDLJvYT+gxqXgfO8x3gONNOE/o9KczAOa/tGPBdDWxcNBGh130xb3RfUViIigzRUmUCMK2Uo/QDw1c2F5KkY4avJGZUTye6iDfo4mk4/zAS0fcCNAGmu1dPY1znRljpMAjpiZFCp46134bLgolIS4jQhI7nj+zA6Pafw235NNDDNgm+Uwd3Fk801j3tgup3UG0IWqJMuV/+U64FGL7KHRvJJdNFvIG+x+A0czhiQ4M0AaWrZw5hUJOPsGLKACQ/CBE6EaiWT/71O57kqFrX0wdcMaxFLWxdMQM5aQ+ELgTfcb3biuLrVJbyxo0bouobFVOi1APDV3IXkrRDhq+k5lRuZ7qI906gH9YvmIjE+3dVDSMdTK/5emNw04+weGwvfU73yqn9cJplj7R4bUS8Jzw3oVOdt7D5pynIz0nSjxvBd0TXFnBzc4OXl5eod0zlQ+kGIxVUYvgq1x9JMoavssdHEul04I2NDIXb8pm4c+WU3oF1EFPj+73AMxhJOd5Jz3O8zx5nITjoLNb/OBbx0bdVr6M+x9v4A5HjLTlGBN9hnZrCxcVFRL/0dA+qCUzlNanSG8NXEveRrROGr2ymVUbHBF56pSUnwnXpDJzatxmFuSmqB9PdAMrxNiyW470fEoiNCyci6LyX6vUj0J732oFR7T+D2/LpIsdrDL5DbBvD2dkZu3fvFk/7oBtviYmJ4qYbw1cZPmhKCoavKctoYLsOvPn5eZg/qif2uCzEo4yHqgcT5Xgp1bBiykAkxz3P8RbmpYrUA03D0sKPy6n9rhjWknK805GT/jzHawy+g9t/Kx6+SU87pkct0dM8GL7qcF6GrzrGyWwpdeCl+Z4zB7bDmtkjUZCTrHrwXvM9gsHNamLJuF9zvASl3g3fhcfaBXicl6p6HU94bkTnOm/B9SeHYjlehq/ZbqDoAxi+ih6eygmnA2/uoxw4/zAaP08drHogEXjuXT3zfB7vixwvbctJj8OUHk2wZelU1etI08fEPN7GNbBp8aRy9aGcL0e+lfMRJRzF8FXCKEgogw68hQUFcFsxG86z7JGVcr9cRy4ZVSnt+92AUxjfuaH4IdHN46X3n6cOwU7HOarXj+xN4B3d/jNspRxvYWa5OjF8JXQcK3TF8LWC0eU8JcGXUg0/T7eH86zhSI0PL9eJlQbakvIEnDkocrwrHQb+uhqvKBvLJ/WD++p5+ilmJY9T03fK8dq1rIVtNI/XRI63pD4MXzk9Sf6+Gb7y29hiZ9CBd8mE/lg5dZAmoKTP8Y7vXUyf0bafY8eqH1DwSP157BN7N4p5vK5LHVBgMI+3JGxLfmf4Wsy1ZDkRw1cWs1qnU4Lv4W1rYfvJn9GnUTVNvDrWekNU7zIEj8u8MWKbVnRs++HvS+loqK+pzwxf6/iZVGdl+EplSQX0Q/M6aVnp8Nb1VZ9q0AGH8p/N3/2fUvq0qPZKqW26Y9T2PqN/S6M6lqcHw1cBTlcFERi+VTCekg7VpRyoopV963qaARPD13RNCoavkjzQfFkYvubbTJFHEHypjCDVc7VrxfAtL2pU0n6OfBXpUrILxfCV3cSWOQHBlx4jk52dDbtWdTnyVVEVM4avZXxEaWdh+CptRKogD8GXarna2TB8lRTZlicLw7cKF72KD2X4qnjwSopOBbQzMzMZviqKegnMDN+SV/LL8Z3hq6FxZviavjlVXvRpzf0MXw05oRmqMHzNMJbSm1LagSNf9QGY4at0z5JHPoavPHa1Sq8MX/WBl9MOVnEVRZyU4auIYZBGCIYvw5fr+UrjS5boheFrCStb6BwMX4Yvw9dCzibBaRi+EhhRKV3IDd+nj7OQEHMPR9w3oOsX74klsbT0d0LPltjr6ojs9Dg8K8qWdI6xpVa4FeamItDXB3NH9kKrGv8rdOvxxf/D7g0r8CDylqQ6lby5xzlfpXiQZeVg+FrW3rKeTU74ElTvBp7D6A5foUPttzGtfyssndgXi8f0xIj2X4BqLcwZ0RNxkdI+uNIS8M1Ke4CNS6aidY3fwb5NAywa3UPoNnNQW3Sp/w8Mb/8looKDZAMww1dWt1Bs5wxfxQ6N+YLJCd+8rERM7NkcHf/7N+zduAJpidECRkUFGQJMk/u2Ruv//B5bVlCZR+ke0GkJ+O5YvVD8eJAOoTf98Dg/TeiWmRIL2teh1lsYafsV8rJ/fWx7yei1Kt8ZvuZf61o4guGrhVF8oYOc8L126SRaVn8Ns4Z2RGpCVKko8E6gr/hXfeqAtkh5GFFqf2XhJDd8k+LCMdr2SyH7uV88S6VNYiNui+XalF4JunRCMr0M7cHw1ZATmqEKw9cMYym9qZzw3bd1LTrX/Su2rpiFwrznkaEhQOjBlQQo+7Zf4IGEqQe54RsXfRfT+tmI2sdRwddKwZWi3QldvxG6nfHeW2q/oQ0q+5nhq3TPkkc+hq88drVKr3LCNzr0Bm5dPYeE2JBS0SFBJyP5vgDU2K7fIeF+sGSQkhu+9Ky0Z4+zTMqbnhSDMR2eR8aXTnmZbFdZ8NJxDF+ruIvVT8rwtfoQSCeAnPAtDy4XTxwU8F3uMASPMuMlg5Tc8C1LL7rJeOHYfvT44t/o/e1HoBRFWe0ru4/hK50PqKknhq+aRqscWa0F3+z0hyIl0bXBOzh7xMNoZFxZMFkTvud/2YcRtg3R4r1X4X/miKR6GdqD4VvOha3R3QxfDQ2sNeBL+d+Vs+iZaq9g9lBb5OdI+0BLS8KX8rurZtiLCJ7y1/Tq/XUNnDu6VzbwEoQZvhpyQjNUYfiaYSylN7U0fGlK2eEddCPubxjWqh6iQkrfsDKM8Crz2ZLwLcxLxfG9m8QcX5rDPL1/KzHPd/rAtrgbdEGWlAPDV+leJZ98DF/5bGvxni0JX8qHnvPejV5fvYt+TT5G8I1LssDJkvAt+eNAc5kPbHVGt8/ewaDmtREbIc9KN458Le4qijghw1cRwyCNEJaE7/3wm+hY+y10+/xdXDlzRBbwEgytCV86v+FUs0M718uiJ8NXmutfbb0wfNU2YmXIawn4UsT7IOK2qO1g++mbOLjVGbTKrWTUKNV3ueFLNR1S4kKREHMXVLvCmNyrZo8TK+CWTRthdL+xY8zZxvAt46LW8C6Gr4YG1xLwvR92AxN7tUTL917FxsVTkCfxDbaS0JIbvneCLqD/dx+Jm2uJsaGl4EpAXjSun7ih6LJ4eqn9JeWtzHeGr4ac0AxVGL5mGEvpTeWGb2pCJBZNGIBWNX6HtfMn4lFmTzyhxQAAIABJREFUgiwwMgSY3PClpdKj2n8u4Htwx7pS+hCQR7RtIPb7Hj9Qar+hrJX9XDX4NoajoyN2794NX19fcElJpXvpr/IxfH+1heo/yQnfp4WZWL94mqj85Tx3fIWL59AMiEk9muCH4T2Rk2H+4gu54UtplF883dCi2qvo1ag6Du/aoC+g8zDmHuaN7IFWH/wG43u1As2GMATsno3LMapDQ1yvYs2HysJ3+eQBGGzbGM7OzvDw8BDwDQkJQVJSEvLy8vD06VPVX9NaVoDhq6HRlRO+t6+eF9Gfbv5rWe9TB3XUQ+qoxyZRFax1jf/Fdf/T+u2GECvrs9zw1Z3bZ9829PjsHbT96A96PW2qvy4WjywYNwB52YmlZLe3bSQWYLiu+MFkvljXf1nv5sI3NzMeBN6ujT7E/PnzsWbNGnh6euLSpUsICwtDSkoK8vPzGb4K922Gr8IHyBzx5IRvXNQd/fxXmgNb1mvPxhV6UFEtiI2LJmHdwin6iLIsEJXcZyn40nnj74fgoNsqvW7r5o/HzStnTS4cOe+zRyzKCL15Wa9vSfkr8t0c+KYnRMBpph26ffspHBwcsHDhQqxbtw4HDhyAn58fwsPDkZqayvA1x3Gs1JbhayXDy3FaOeFbEYjI0caS8JVD/or0WVH45qTHYdOiiejw6V/Q5eua6PFdbUwc1hcbN27E4cOHERAQgKioKKSlpaGgoIAjXzmcTMI+Gb4SGtPaXTF8tf0Mt51Oc7DuxzF4EH4dyyf3F+kR+87fw9XVFd7e3ggMDERMTAzS09MZvtZ2xgqcn+FbASOppQnDV5vwpVrJh9xWYcm4Xkh5GAb31fPQsfabIu87vEtzuLm5wcfHB0FBQQK+GRkZKCws5MhX4Y7L8FX4AJkjHsNXe/ClG33H92yA4/RhIuL1cFkAe5s6OLprDUKv+2J4l++xZcsWHD16tBh8Oe1gjudYpy3D1zp2l+WsDF9twZdW3104ugs7Vv2A5Nhg7F4zHyPb1scxj/V4+jhTwNe+czNs3rwZXl5euHr1Kud8ZfEseTpl+MpjV6v0yvDVFnxvXT6GbStmiIh3+8+z0Purf+OX3S4oyk8Xsyso8h3WsSnWr1+PgwcPwt/fHxERETzbwSreZ/5JGb7m20yxR7wM8KVFEXGRN0WthYrMJFBDm5KzHUjHyLtXsGnxZNzxPwl353noWOtNeG1zLDal7Tl8m8DFxUU/z5emmtE8X15koVg31QvG8NWbQv0fXgb4Poy6Jf4Nb1HtlWIgUgNkTcloCF8Cb8Tty1j341hcPXsIuhyvj/vaUvoSfIfYfofVq1dj7969uODri9DQUCQnJzN8VeDODF8VDFJFRdQ6fNMSIrDLeS78T+7TbOQbfe8KXH9yQHDQWexe86PI8R7fQzne0hXXCL6D238LJycnru1QUSdRUDuGr4IGo6qiaBm+FBFuWDgB5w5vg9d2R03CN/lBCFzmjcadK6ewbeVM9G74brEcb8nIWQdfKqzj7u7OhXWq6kAWPp7ha2GDy3k6LcP356mDsW/DEpzctwn9vq6uSfiumjYEZw9tFdF9p9pvih+ZksA1/M7wldOb5O+b4Su/jS12BoJvVlYW7GzqiulINCVJ7a/ZQ9qhc523cWDTMpza74pR7Rpgp+NsNK/2iup1042NXcta6N7gH/DZtRYeaxfAvlUdGMvxGoKXPjN8LeZaspyI4SuLWa3TKcE3OzsbR/dsxYals9G13t+wYcF41UPq8vG9YqGBnU0dERVSVS8C1+bFk9G/cQ0x/1UHMrW+B5w+CPfVP2JkuwZCV2M5XoavdfxKrrMyfOWyrBX61cE3Li4Og5p9Ipy5MDel1F3ykk6s9O+0mouKydBc14JHzx9NTzngcZ0a4vQBVzyR8TFGlrIN5Xj7NHwXxzzW6efxlndujnyt4GQSnpLhK6Exrd0VwTcxPg5jO30t1v3Tc8nU/qJ/v7vV/zvclk3T/4hkJsc837Z8mur1o/GhHxWb91/Hke1Oeh3LAy/tZ/ha2+Oqdn6Gb9Xsp6iji4qKcN5nP7rW/7u+Jm1ZdXfVsI/qGFDhdkMYee9wFtvUIH9FZOz5xb9K6Wior6nPDF9FuZ/ZwjB8zTaZcg8g+Obk5MCuVb1isDLlvGrYzvV8TS+ZZvgq1xcrIhnDtyJWUkGbZ8+eQZfztWtVl+H7xDS0lPajY7jCzRzZGL4qcMwyRGT4lmEcNe3SwVc31cwcJ1ZyW458Tf+IMHzV5KGlZWX4lraJKrcwfE1DSsk/LiQbR76qdLkqC83wrbIJldMBPb0gMzNTLLJQOnAqKh9HvqZ/VDjyVY7vVUYShm9lrKbQYxi+pkFVUdhbox1Hvgp1KJnFYvjKbGBLds/wZfgGBwcjMTERubm5/Aw3SzpfJc7F8K2E0ZR6CM124LSD+gDMka9SPUpeuRi+8trXor0zfNUHXkpzMHwt6iaKORnDVzFDUXVBGL4MX047VN2PLNUDw9dSlrbAeawB38d5achKfSBqLKTGh6NI4iI3cs52eJyfjuQHoRWuD1HwSJ4iRRz5WsA5FHgKhq8CB6WyIlkSvlRV7MblE9iyfCYm920tahPYt66P2Ijbkq6ukxO+Ybf80P3L94TsVD+ivJf/2aOS6qabWcHwrewVr+7jGL7qHr9i0lsKvvTo8t0bVqBf4/+gd6MPsGh8f1Ffl+ruPspMkBRQcsKXIvazXjvLrHfstmIWun32jgBzfEywpLoxfItdvi/dF4avhobcUvD1dHVGmw//gGE2dRB8/SJys6QFrg5K9C4nfA3PY+wzFTQ/4OaEdh//Gc7zJ4OifWPtqrqNI18NOaEZqjB8zTCW0ptaAr6hN/3Q79saomxldOgNWWBkCDNrwjc1IRIzB9uizYe/R/idK7LpyvBVumfJIx/DVx67WqVXueFL6Yatq+ai9X/+FycO7ZINRkqBb8DZI+hY569YOH6g0Ue3G8pZlc8MX6u4i9VPyvC1+hBIJ4Dc8E1NiMIPQzugfa23kZMRD78zR7Bh0RRRuH355AG4d+0iCvPSJIWyNSPfZVPt0ebDP+LsEXdJdSoJaoavdD6gpp4YvmoarXJklRu+UcFBGNb6M4zo+C0WTR6GjrXfhs37v3kxS+AVdKn3N2xZNh35Oc+fs1YSMpX5bi340s01yms79GuFpAehDN9yrj3ebb4FGL7m20yxR8gN39sBZ9ClwfM7/x3rvA2PTauQkXxfgOn0EQ90rP0W2n/6Jnx/2SsZrKwBX7qxtnbRNNhUfx1bV8yUNeVAP0gc+SrWpWQVjOErq3kt27nc8KXHm9NcWPpX/MTBncUAS8A6sc8N7T95Aw4D2kn2RGFrwDchNgQtq7+GAd99iNBbfsX0rEz0Xt4xDF/L+olSzsbwVcpISCCH3PClebwEX4cB7Y3CNS7qDsZ3ayL+XafP5UGnIvutAV/XFbPRotorWD7NHvkvHlVfEVkr24bhK8HFr8IuGL4qHDRTIssN34s+uwV8l08faRSsmakPsGBML9Hm8mlvo23MBZSl4fsw+h5GtPtM6BDge0wSHcrTmeFr6orW9naGr4bGV274Xj1zCC3eexVzRvY2CqVHmfFYOmmAANeFk4eNtikPRCX3WxK+lDrx2euKjnX+DxP6tJFE/pL6GPvO8NWQE5qhCsPXDGMpvanc8L0XdB49vnoP/Zp8YjTtQDfffhzZXcD3ut8ZSeBlSfimJ8Vg7vCuQv7Aiyckkd8YbEtuY/gq3bPkkY/hK49drdKr3PBNehCGqf2fF9G5caU0XGMjbmF0p2/QvtZbktV4sCR8r186hk7//SvGdv0OVK2tJCTl+s7wtYq7WP2kDF+rD4F0AsgNX6p14LFhOdrW/BOG2zYSCy10QKJ/2Q9scUSrD36L1T9OlAxcloSv45xxIur12rFWtjoOOnsZvlcJvraN4eTkBA8PD/j6+oLr+UrnT3L3xPCV28IW7F9u+BIwstPiMKVXM7So9ipm2XXH7SunRT1c9/XLRD54UPP/gko1GsLlnI+nmD2weOJg5KQ/LLbPsJ2xz5aCb3pyDDrV+yeGt/sS98PKr1lxcMc6tK35Z2xzWoCq1vmtDHyzU2Mxc1Ab2HVtCWdnZwHfCxcuICQkBMnJycjLy+NnuFnQ9ypzKoZvZaym0GMsAV8CZML9EMwf0RVDWtQWkSJNP+tc/5/4YVgn+J85AqoBYQjSbc6LRLuxnb+FuWUZLQHfp4WZ2Lh8Dmze/y1Wzx2H3KzEYvIb6qL7vMTBHi3few1LJg4UxeR12yvzbi58Y0ODML1fC/Rv+w3mz5+PtWvXYt++fbh06RLCwsKQkpKC/Px8hq9C/VQnFsNXZwkNvFsKvgQYWkIccv2ivhau36lDSEmINAqt5IcROL5nPa5fPGb0Rl1ZwLIUfEn+U/tdERteftRL8j6MvosTnptElE/pmLJ0KG+fOfAl8P4wuC36d2iOqVMdsHDhQqxfvx4HDhyAv78/IiIikJqayvBVgT8zfFUwSBUV0ZLwLQ8oUu23BHylkrWy/VQUvumJkVg0pgda1/gdWtf8M9p8/AZG9myDTZs2wcvLCwEBAYiOjkZaWhoKCgo48q2o41ipHcPXSoaX47QMX+0+QDMvOxEu88bA3XkuslJiQFXk2n30B9h3+R5btmyBt7c3AgMDERMTg/T0dIavHA4mcZ8MX4kNas3uGL7ahG9GUhRcf5qKrStmIDbsGhxnDMPgZjWxfeVMDO/SHG5ubvDx8UFQUJCAb0ZGBgoLCznytaYzVuDcDN8KGEktTRi+2oNvZkoMPNYuwO418/Ew8iacZthhbKevcPPSLwi97ovhLyLfo0ePisiX0g4c+arDYxm+6hinCknJ8NUWfPNzkuC9czU8NyxBXlYiVjoMwvDWdXHz8jExD5nga9+5mcj5Hj58WOR8IyMjOedbIW+xfiOGr/XHQDIJXhb40tQwqjpW2RtcSjvO2A23J4UZuHxsj8jxPoi4geWT+6N/4w8QcOagXm+C77COTcRsh4MHD8LPz49nO0jmTfJ3xPCV38YWO8PLAF8C723/k5qG75OCDPif2o+dTnNElCtyvE1rwv/kPj146QeE4Du0YxMxz9fT07PYPF9eZGExt6v0iRi+lTad8g7UPnyzBXDclk3TLHxpmTZFtxsWjEdw4Nlfc7yXS5e3JPgOsW0sVrjt2bNHLC+mFW5JSUm8wk157llKIoZvKZOod4PW4RsfdRvr549D+M1LmoXvLb/jcJk7BjTDQZfjpW0E5ZLpEoLv4PbfwtHREe7u7lzbQWWuy/BV2YCVJa6W4Ut3/Z1n2YOeprFt5QxNwjfi9mWsmTMSIdfOixzvgO9qFMvxMnzLuvrVt4/hq74xMymxluG7dEJfnNy3CTsdZ8POprYm4es80x6+3rvgOH2omMdbMsfL8DV56atyB8NXlcNmXGiCb1ZWFuxs6mLpxL6aeNnb1EGfhtVwzfcIdqz6AaPaf4bTB7aIQj1a0bHnF//C4KY1EXD6AJxmDMO4Tg1xy0iOl+Fr/LpX61aGr1pHzojcBN/s7GzcuXkNJ70PovdX7+Lkvs2i5GNCzF3VvqfGh8N1qQP6ffM+Th9wFcV5SJ+zh7dhRJt6uHHRR7W66cYlLSECKx0GCn1M5XgZvkYuehVvYviqePBKiq6D773bN56D13NTqZs0JR1Y6d8LHiWLZbUdPv0LftntotcnKTYYo9o1wPULR/XblK6LKflyM+OxbFI/DPzuP6Dn5JlqV3I733Ar6QHq+s7wVdd4lSktwffuNX8MaV4L0/o215d7POaxXrWf184dhY613sQJz416KFFk2LH2m1g0urtq9TIckyXjeqFl9ddw5dR+vY4lQWvsO8O3THdQ/E6Gr+KHqOICFhUV4e61K+j7dXVNQIkANXtIO5HfNYQPwbd5tVc0o6Ndy1qldDTU19Rnhm/FfUOJLRm+ShyVSspE8M3JyYFdq3pmRVCmnFsJ27mer+l6FQzfSjqKQg5j+CpkIKoqxrNnz6DL+dq1qsvwfWIaWkr4UTGUwVhtB8P9pj4zfKvqNdY9nuFrXftLdnYdfHVTzUw5rNq2c+Rr+keE4SuZ+1ilI4avVcwu/Ul18M3MzBTzfNUGWVPyMnwZvtJ7izJ6ZPgqYxwkkYKeXsDwNQ0rU4C39nZOO0hy+auuE4av6obMtMAMX/WBl8DP8DV9TWt5D8NXQ6PL8GX4BgcHIzExEbm5ufwMN4X7NsNX4QNkjng024HTDuoDMEe+5lzl2mnL8NXOWIqpZgxfhi9HvupwaoavOsapQlJy5Ks+8HLOt0KXtiYbMXw1NKxSw/fp4yykJUbD79QhFDxKKXfhRmpCJK6e89Iv+734iwfSk2NA/VR2RoFcU81In/A7VxB+27/C8pEepM+Vs17Iz0mutE4lbcFpBw05oRmqMHzNMJbSm0oJ35Cbfti1djGm92+FLvX/KSBcEhqG32/4n8bUvi3R4/N/iToFzd/9H3Ss/RZmDGiDgHNHKg0qqeGbmhCF0167sWBkN9i3ro+Ni6fgcX5aufKF3r4Cj/XLMGNAa3T77F9IehBW7jGG9inrM8NX6Z4lj3wMX3nsapVeqwpfigYDLxzHuM5fo2u9v6PNh7/Xg5QiYFMAiQwOQue6f0XH//4VrivmICYkEPHRt7FjzWJ0afAvdKrzNuLvh5g83lS/tF0q+MbHBMNt1TxQ4XLbT9/Q6+Uyb6xJ+BbmpuLa5VOY3LMZutYne/xBfxzD1yqXuKZOyvDV0HBWFb4Bpw/C9tO/CEAtnT4SJw67Y0ofGwEcU/Clf8UXTRiIFu+9CpdF01BUkKGH7OP8dGxaPAU21V/HshmjjD4EsizwSgnf1v/5vQDo+O7NcNh9I/ZvcxF6lQXfO1dOof0nfxHR/OIp9jjptRszB3cQxzF8NeQ4VlKF4Wslw8tx2qrCNy7yFk4e2gn615zAl/wwAjMGPS/paAq+CbEhGNqiFlq9/xsE37isB68OqveuX0S/b2vA5v3Xy01d6I4xfJcq8t3qOB9hdwJA0Sz1f+zAjnLhm/QgBMf2bRV2oGMo3zt3RDeGrxwX70vYJ8NXQ4NeVfgaQo8+VwS+N6+cRe9G1dG6xm+N3oQikNu3qiuKhd+4crYUnEues+R3qeBbst+KwLfkMQxfDTmLAlRh+CpgEKQSwRrwPefjKW7I9frmQ6NgfZSZgFHtPhPwvXD8oNE2JSFn+J3ha3r6HFc1k8pzrNMPw9c6dpflrNaA75kju9G53t8xultzo2DNzUoUTxxu+d5rOH5wh9E2hrAt+Znhy/CVxVkU0CnDVwGDIJUI1oCv9+6NsK31FsO3CsXbeaqZVB6grn4YvuoarzKltQZ8D25bg3Yf/4Xhy/At89rknaUtwPAtbRPVbmH4mv4XvWQ6g2+4qfYy14zgDF/NDCUkL6xTkdkOZ733oEu9f2Bk56ZG87mGOd8Th3YZbVMSjIbfOedr+geFb7ip23kZvuoev2LSWyPyvXjiILp99g66ff5vPC3MLAXXnIx4jGzXQMx2uHTqcKn9hqA19pnhy/AtdpFr6AvDV0ODaQ343gm6gL7ffIDWH/wWGSmxpeD6MOaeWIRhU/01UFtjgC1rG8OX4ashFy2mCsO3mDnU/cUa8KWVb3Y2/4XN+7/Bdb/TpeB65dxR9PzyXbHCLTcrodT+ssBL+xi+DF91e6Vp6Rm+pm2juj3WgC8Bcsuq+Whe7RXMGNwRKfGR+hoO6UkxWDKhn1iOu3fL6mLgfVKYiZSHYchIii6zpKPa4PusKBtZqbFIjLlboTKcZL/KTjW75nsEg20bw8nJCR4eHvD19QU9RigpKYkfI6QC72X4qmCQKiqiteBLS4gn9miKNh/9EXOGd8OR7c445rEOP00ahA6138a4zo1AuV/DKDfsdoDIE49o9zmopoThPsPPaoNvevJ9zBneHS2qvYJdaxeZ1MtQx8rANzjoHMZ1+QbjBvfE6tWrsWfPHly4cAGhoaFITk5GXl4eP8Otoo5jpXYMXysZXo7TWgu+BJL4+8FwXTYDQ1rUFpEu1fPt+eW/xbb7oddKQUgHX7tW9RAbfqPUfh2c1Axft5/nmNRLpx+9mwvfa77eGNaiFuz7dsH8+fOxdu1a7Nu3D5cvX0ZYWBhSUlKQn5/P8JXDySTsk+EroTGt3ZXU8KXUQHpiFBJi7hqdyWAIEPpcmJeG9MRI0Z6OobQCbaN/xUu21aUd0pOiyuxbLvjmZScJObPTYo3KV1Je+k7lMylNQrqR/MbaGKYdKvq0C3Pgey/wDIa3rgv7QX0xdepULFq0CBs2bMDBgwfh7++PiIgIpKamMnyt7YwVOD/DtwJGUksTqeFrDC6W3iYXfC2tR1nnqyh8Y0ODMGeYLfp9XR22n/xZ/Idh37kZNm3aBC8vL1y9ehXR0dFIT09HQUEBR74Kd1yGr8IHyBzxGL6mZwaUBT9r76sIfB9G3sLPUwfjpOcmRN29gun9WqBPw2oY3uV7bNmyBUePHkVQUBBiYmIYvuY4jRXbMnytaHypT83w1SZ84yJuYNmk/jixdwPCblzArIGtMal7Y3FTc3iX5nBzc4OPj48evhkZGSgsLOTIV2oHk7g/hq/EBrVmdwxf7cE3OS4ELvPG4MxBNyTE3MGM/jZw6N0MqfHhoOXF9l2+h6urK7y9vREYGMhpB2s6oJnnZviaaTAlN2f4agu+WSn34blhCX7Z7YKM5GjMfBHxxoY9nz1C8LXr9Dzne/jwYQQEBCAyMhJpaWl8w03JjvpCNoavCgapoiK+LPClpyzTPFpr52qlOr+xnG9ediKOe6zHke1OuBtwGtP6tsCkHt8h9IavXm+C77COTbBu3TocOHAAfn5+YrYDTzWrqMdYtx3D17r2l/TsLwN8iwrS4eu9S9PwfZyXiiM7nLF/01Jcv+jzPOLt0RhRd/314CXwE3yHdvhOzPP19PTExYsX9fN8eZGFpK4lS2cMX1nMap1OtQ5fmkMbeO4wNi2epFn4PivKwrE967Fp0URQeuF5jvd7pMWHFwOvDr5DbBvD2dlZv7w4JCSElxdbx/3MPivD12yTKfcArcM35Np5rJ8/DpF3/DUJ3ycFGbjosxubF08Wi1V0Od4HL3K8JdMcFPkObv8tHB0d4e7urq/tkJiYyLUdlOumeskYvnpTqP+DluFLec91P47B5eN7sWLyAE3C99KxPaKKW9B5L5HjnVwix8vwVb+PGmrA8DW0hso/axm+i0Z3x9Uzh7BiykCM79xIk/BdO2ek+HGhiJfAS4spSgLX8DtHvup2WIavusevmPQE36ysLNjZ1EWfRtU08epY6w0Mbf6p+Dd86cS+4jH0wUFnxdJarejY9sPfY0yHL0U6hXK8U/t8j7SEiDLBSxBm+Ba7/FX3heGruiEzLTDBNzs7G3FxcTjl4yUKsNz2P1GuExtGU0r8/CjzIRaP7Ykh33+M6xeP6vW5cNRd3JCixQdKlNscmbJTY8WsBop4H4Rfr5A+DF/TvqCGPQxfNYxSBWXUwff4wd0Y1f5zUOlBcwCgxLbJD0KwfPIADG5WE4Fnf30G3NnD2zB7SFtE3vZTvY73/397ZwIdRZnt8TNvfJ43Z+aMMvrU8TEoh0VDBEWGQfQhSyBAICFICAmSOAQYwyKQjYgCIjuIoIhA2BcRIqsCEoYlbCI7yhK2QAAJISEhCWEP8H/n/2n1a0KadKe3qsqtc+okXV311b336+/Xt+93v1vH9yHxbT/Ed2qslg/b2w8CXzsHhk5PE/jqtGPKIxbhu2XNMoT942/KS+TPdKPv8WFNEFDzD0jbs8kC2dULJqljvdrWM7x+7J8+QfXR/Ln/KDPGWxLKAt/yjBL9XCPw1U9fOC1JcXExsi9kIqqZr6WmLmvPGnmfPLiniu9ag6cg54yacDOyXtays0gOi89b62jP/wJfp4eMVxsQ+HrV/K69OT3foqIi8OkQ9gxeI5wj9Xxt16sQ+Lp2/Hi6NYGvpy3upvvdu3cPWsy3R8uXBb53bENLb186pdV2sEdGga+bBpOHmhX4esjQ7r6NBl8t1cyewWuEc8Tztf0lIvB196hyb/sCX/fa16Ots4B2QUGByvM1AljtkVHgK/D16CDy4M0Evh40trtvJfC1DSp7QO+tcyTs4O6Roc/2Bb767JdySSXwFfgePXoUUlinXMPH4xcJfD1ucvfdkBNuEnYwHoDF83XfmNBzywJfPfeOg7IJfI0HXoY6BL4OftBNcrrA1yQdSTUEvgJfCTsYZ0ALfI3TV2VKKvAV+Ap8yxwmujlB4KubrnBeEFfDl09WOHvyIOZPGo6i/AsOLdzgI382fbdQ1V5Inj7eoWutsw7clWpGfbatW4btKUtw51aBTfmyz5/ArLEDHlpDgnpSX2u5Hflfwg7Of/aN2ILA14i9ZkNmV8J3Z+oaDO8bgY6vPoNW1f8LeRczHILLpcx0BNV6TNUsGPBOkEPXWoPL1fDNzEjD/MljEN6gMtr5Po6pw/rj9o08m/Klp+1FlJ+v0oP1F0rbp44agLu3C222Ya1Paf8LfG18oE1+WOBrog52Fr6FueewetEMdKpfGQE1/4j2dZ5E6xp/UMBxBL63b1zG6PgeFlDpAb77t6/DmLgo+Ff9T7Sr/RcE135CyTdl6HsPhe+BHzfg7TeqoVeHZuWGa2nAtT4m8DXRIHRAFYGvA8bS+6nOwnffllXo6ueLId2DsGTW59i5eQ0SurR0CL70AP+9fIGqOtalUQ11rR7g277uM4gLa4Ypw2KwM3U1Vi6cbhd8t6xdgg6v/hVj3+8l8NX7ADCYfAJfg3XYw8R1Fr752RlI278NV3+L7+ZkpmPgP9s4BN9z6QfRO/gNvPXqs0hOGqsb+O5YvwKM3xbfzFcQTVm+wC74rlo4FW18/oy5X4wW+D7swyfvOWwBga/DJtPvBc7C1/qnMP8qT7EyAAAPsElEQVR3FL43r15SMVT/qo9g2bzJ2L1xhW7gW1I3e+G7cNJHyotfnTxb4Kvfj74hJRP4GrLbShfa2/A9sm8r3mnqi17BjRSo+NgfTlDpIexQHvjevp6HpBExSocN3y1Cfs4ZS2H63Avp4JdNyXbL81pivqV/ns1+VOBroh72JnxvXcvFsN7h6Pj3Z7Er9TtTwPda4UVMSOyu4Duox1voGdRQ/c8vlPD/rYmk0QNwOm2vU5kOhLXA10SD0AFVBL4OGEvvp3oTvvQM2/g8jklD+uJqQZYp4FuQew7D+4Qp4BK2cycMRsriaVg5ZwISItuozIm4zv7IOLbPKQ9Y4Kv3keUe+QS+7rGrV1r1FnzPZxxBkO/j+GdTH6Tt325ZcGD0sMONohxsW7ccSeMG4cShXWAKHT1VZnRcyTuPsQnd0eL5RzB7whCnvF+Br1eGi9dvKvD1ehe4TgBvwJdAmvBhH/X03RmfDL7PAzQ6fMuK3x7enYr2rzyDbq3ro/g3MJd1TWnvC3xdNwaM1JLA10i9VYasnoYvPcCtKUvRsd6z6BnYAHnZ96+CMzt8L/5yHDFhzVVY4nL2mfu+eEqDrK1jAt8yPtgmfVvga6KO9TR8c7NOY2jPULR47vf4V0C9B+ofDOjcTIEp9B9/U+8ljYgF84BtQai0465eXqzdw95UM+380v7mZp3CwK6BSseL5447pJd1ewJfEw1CB1QR+DpgLL2f6mn4nj66D91bvqrgU1rNg5LHujSqjp93pToEKW/C94d1S9G5YRUsnTXREse2hmbW2aPoG9JE6c+l2dbvOfK/wFfvI8s98gl83WNXr7TqafiWBRijhx0O7U7FW3X/iiHRoWDOb0l9mVLXttbj6Bvqhzu3fl05V/Ice14LfL0yXLx+U4Gv17vAdQIYCb7MJNj6fTIObF8L5gjbgpQ3Pd/8S2cxsl+Eymig98sVf5Tz7q0CpB/Zo+petK75Ryyd/f+eMctwHt6zGeuSk5CTedKmXtb6lhe+KYunomtgI3z++edYvHgxtm7dimPHjiE7OxvXr1/H3bt3XffhkpZcbgGBr8tN6r0GjQTfn3ZuQqBvJYQ3fA6nDu+0CSlvwpcTigRp/1A/BLz4ZyR0bq5i16P7hqkYd8ALf8L4gdHIOX/CIn/2+ZOIf9sfflV+h4kDe1iOW8O25P/lge/G5bPRtUUdDOgbjS+++ALffPMNtm/fjuPHjyMnJ0fg671haPedBb52m0r/JxoJvqyr27nRi4gN88PFc8dsQsqb8FVe7u1CFFw6i29mTkSPNq9Z4tuD3g1DyrK5uFaYdV88mEXaR8d1+7UexKIZNvWyBrCj8F2/ZAaCa1dCzHu9MXLkSEydOhUrVqzAjz/+iPT0dOTm5uLGjRvi+ep8yAp8dd5BjojnavhaA8Jb/7sLvt7Sp7T72gtfhjt2pCQjonFN9HuvDz788EOMGzcOc+bMwZo1a7B3715kZGTg8uXLuHnzpsDXkcHjhXMFvl4wurtuKfA19zPcWG+5fyc/vPtOOBITEzFixAh8+eWXSE5OxqZNm3D48GGcP38ehYWFuHXrFu7du+euj5q06wILCHxdYES9NCHwNS98D+/aiAERrRAd2QkJCfH4+OOP8dlnn2H+/Pn4/vvvsXv3bpw6dQqXLl3CtWvXUFxcLPDVy8C0IYfA14ZhjHhY4GtO+B4/sBUD3m6O6IgQBd6PPvoIn376qQo3fPvtt/jhhx/ApxZfuHBBeb1ayEE8X32PYoGvvvvHIekEvuaCL5+IfOboHoyNjUR0ZEcF3iFDhmD8+PGYOXOmmmTbsmULDh06hHPnzqlYL1PM+DkgeAW+Dg0fj58s8PW4yd13Q4GvueCblXEI00bGo1dkCBLi42AN3uXLl4PgPXjwIM6cOaMyHBhu4GeA+b0CXveNM1e1LPB1lSV10E5Fge/V/EyVylVa5oARj5WW7VB46SzmThiE3pEhiI3pr8D7ySefKI932bJlSE1Nxc8//2wB79WrV9Ukm4BXBwPRThEEvnYayginVQT43r6ei2/nTjQ1fG8W5WD2uET06OBvAS9TyqZPn46lS5feB15OsAl4jTA6H5RR4PugTQx7xOzwLb6Rh/VLZ+Jf7ZuaFr43irLx9eRhiGjdELGxsRg0aBDGjh2LpKQkLFmyRKWUHThwQOXzErxFRUXi8Rp0xAp8DdpxpYltdvhuWjkPfcJbI7p7lCnhe70wC0tmjMO7HVshNibGAt5p06Yp8G7cuBH79+9XKWVcQlwSvBLnLW1U6PeYwFe/feOwZGaGLwvVRHfwQ68eUYiJiTElfBdPHYV3Q1uhf98+CrxjxowBwctFFBs2bLCAl4Vzrly5ojzeO3fuyOSawyNFHxcIfPXRDy6RwszwDWvsi359equVXR988IEp4dulVQPE9OurwDt69GhVs4HgXb9+Pfbt26c8XgGvS4aKLhoR+OqiG1wjBOFLjyihsz86/v1/0KHuMwh++b8R+NITaFurknq6cIDPYwh40UC7z2N4q0E1vP/++xg8eDCGDx+uYqBBtZ8Ad+oW6PuX+/UzoI4hb/hg4MCB4AIKTq5pHi/By5oNLJijgZeLKMTjdc2Y8WYrAl9vWt/F9+aSUsYBudKJK55Y35Wz45wl54oo1gLg4CbEjLAzr5U7l9Kyehd1mDx5MmbMmIG5c+di3rx5KvWK9Q343qhRo9S5vMYI+lFGyso+oY70didOnKj0Y4lIDbwnT57ExYsXLavXBLwuHjheak7g6yXDu+O2hC/TjjgZQ0+J6/3Xrl2rCm1zRRTBxXoAHOBG2Skv69XSEyRwWTSc5RNZxYs7/+ex2bNnq3N4rlF00+RkMXT2DTMaWKuBOrFQDj1egjcrK0uBl2Ui2cdSJN0do8fzbQp8PW9zt92RHhGXl7Kk4C+//IIjR46odf/r1q3DypUr1cTN119/jYULFxpmX7RokZKbK7pYQIYz/qxlsHPnTrVv27ZNeYirV68GFx8QxEbSj/1BHenpEropKSmgTj/99JOK8Qp43TZcvN6wwNfrXeA6AegRsZQgQw/MAT179qwKPzA9iYW2Oai5JHXz5s2G2Skvn9BA+TnpxDoGfFoDPULufGwOV3rt2bMHO3bsUKEWI+lHWTUd+YXCHF7qxL7jLxiWhxSP13VjRE8tCXz11BtOysI8T/4s5WDlxBufaJCZmakS8jVQpaWlwUg7Y9eELcMorGHAerWMfxJMnIBifJtFZU6fPo0TJ04ocBlJP8pKHSk7S0ISuvR28/Ly1JcoJ9ck1ODkwNDp5QJfnXZMecQifOn9MuuBAGb8t6CgQA1kDVYc2EbaNdDSk2c4hZ4gPXvqxp2v8/Pz1RcNdeT5RtKPsmo68suSulA/ho/4K0bAW56RYIxrBL7G6Ce7pdQAzPgvIUzPiSDmYGbVKw1aRvlLmSk7daAuBBL10na+NpuO1E2Drqxas/ujb7gTBb6G67KyBeaA1XZ6wtwJY6Pumg78q+ll/Vd736j6UW5Nh5I6lt3brjuDNuUvJU5ahoeHo127YAQGBqkca4ZGKKe9Gyd8+WBPLhKhRy/bgxYQ+D5oEzkiFqiQFmBoZ8SIX5+GzBAOY+3MumCWSbdu3VQ2Cb8c7NmYvUGA16tXD/3791e/Tuy5riKdI/CtSL0tuooFHmKBr776SuVIc/KSC0CYh8z8ai7U4VORu3aNUumLWhNMi9Pi8KtWrVKHOcHLrBrGrhky4sKR5s2bqwlgLYWOWSnaXATrEnOykVAn/Jm7zfO0sAsnWnk+QzH0vJntQq+aG49TLmbD8L5MQ2QMnZ42Uw8pH+PnnHzW8qY12bW/lJPXMc2P4S3elymalIGvuTHfmkXr2SYnePmlxMc3ae9rbTn6V+DrqMXkfLGASS0QFhauMi/i4uJAmBKQGrwYkmD+NBexaFvTpk2xYMECzJo1C08//bQq/MP3o6OjFZjo/T711FMK5IzNE8I+Pj6oUqWKuqZBgwZ4/fXX4evrq+BG77p+/frqNT1vLjypXr06atasiZdeeklltLCuB/O5uYWEhKjn2AUFBeGFF15AnTp1EBERgVatWoFt165dW4VQIiMj0b59e+WFc0GLthH27dq1U9c9//zzKn+cBetr1KiBqlWrokmTJirLhvpUq1ZNefJ8r1GjRup9LnV3ZhP4OmM9uVYsYCILvPLKK8qrjOoahcLCKw9oRg8wISHBcpxLuoODgxWMKlWqhMDAQLRt21aVvySs6S0SjI0bN1ZZKa+99poqjETPlItmKleurDxOgpHxYVar8/PzU7nahHWtWrUsHimvHTZsmDqHi1K4tW7dWq0KbNasmfpSoNdK+IeGhqr78TW/RAh4XkMvPCoqSn2h8HrmU/MLhH+50csmjFlBjhshzuflEd6MXdO7JtTpUXMlIr8QnNkEvs5YT64VC5jIAgQfc427dOli+WlvrR4XhLD4j7bxXAKSXiJ/tj/66KNo2bKl8lC5sIc5zJMmTUKLFi0UDAlhhjK4can4k08+CZbNJMR5PRfKEMD0aPnTnl4yV2Yy99nf318Vlyf8ueScP/8bNmxogS9DI9zonXfq1EktwGGuNK9nO6xrwhDKnDlzLLnhDFdQJi5uYbofZaYNeA3bpx0SExMt8OUxnk/4ctEP9XZmE/g6Yz25VixgIgvEx8djw/r1mDJlCrp3767inpxwY1yWXiQn47Sf/FSbMV3CkECmp0qvkZDjuYQovUSGEfgkDr5vDV8ukKE3S4C++eablnsS3vQoCTp6ugQ3PVB6qIzFsv5F3bp11b0YKuBrer4afAltwpfXccKPT//ga94jICBAycU6J/S2Ga/u2bOn5R4s2ETvmOEGykEvnqsqNc9X4GuiD7uoIhbQkwV27dqFfv36qRWDnKBitTXunJDiEuiQkI6Wn+ya3Jzw4s9xbvSEudqQG0MLXCrN0ANzyrlxMoyLYrSNr+lNM5zB41zByEpuXI3JCThex5AAPVLCWjvGiTZ+KXDijZNpbIdfBNx4Du9Nz5SFpXickOVrts1YMiHK11pqHdvjPSgD78kJQNYPYUxYmwjkcU748V6caGNuuTbxp+nj6F/xfB21mJwvFjCpBeixLljwFWJj4xQ0CSn+5GcWBOO5BI9srrOAwNd1tpSWxAKGtwA9PXqsLOg+dOhQFUbgogvNszS8gjpSQOCro84QUcQCYoGKYwGBb8Xpa9FULCAW0JEFBL466gwRRSwgFqg4FhD4Vpy+Fk3FAmIBHVlA4KujzhBRxAJigYpjAYFvxelr0VQsIBbQkQUEvjrqDBFFLCAWqDgWEPhWnL4WTcUCYgEdWUDgq6POEFHEAmKBimMBgW/F6WvRVCwgFtCRBf4P5GUzBsMytaUAAAAASUVORK5CYII="
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='green'> <b>Splitting of the Arrays</b><font color='black'>\n",
    "[numpy.split 01](https://www.w3resource.com/numpy/manipulation/split.php),\n",
    "[numpy.split 02](https://sparkbyexamples.com/numpy/numpy-split-array/),\n",
    "[numpy.split 03](https://numpy.org/doc/stable/reference/generated/numpy.array_split.html),\n",
    "[numpy.split 04](https://www.w3schools.com/python/numpy/numpy_array_split.asp)\n",
    "\n",
    "**split():** It is used in NumPy to split an array into equal parts along a specified axis.\n",
    "\n",
    "**array_split():** This function in NumPy is used to split an array into a specified number of unequal parts. Unlike other splitting functions (like hsplit, vsplit), it splits the array into the number of parts specified by the user, not along a specific axis.\n",
    "\n",
    "**vsplit():** In NumPy, it is used to split an array vertically (along rows), creating multiple sub-arrays.\n",
    "\n",
    "**hsplit():** In NumPy, it is used to split an array horizontally (along columns), creating multiple sub-arrays. Essentially, it divides a matrix into parts at specified column indices.\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "![image-2.png](attachment:image-2.png)\n",
    "![image-3.png](attachment:image-3.png)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array3 = np.arange(1,9)\n",
    "my_array3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([1, 2]), array([3, 4]), array([5, 6]), array([7, 8])]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.split(my_array3, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "array split does not result in an equal division",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[42], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmy_array3\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\numpy\\lib\\shape_base.py:864\u001b[0m, in \u001b[0;36msplit\u001b[1;34m(ary, indices_or_sections, axis)\u001b[0m\n\u001b[0;32m    862\u001b[0m     N \u001b[38;5;241m=\u001b[39m ary\u001b[38;5;241m.\u001b[39mshape[axis]\n\u001b[0;32m    863\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m N \u001b[38;5;241m%\u001b[39m sections:\n\u001b[1;32m--> 864\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    865\u001b[0m             \u001b[38;5;124m'\u001b[39m\u001b[38;5;124marray split does not result in an equal division\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    866\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m array_split(ary, indices_or_sections, axis)\n",
      "\u001b[1;31mValueError\u001b[0m: array split does not result in an equal division"
     ]
    }
   ],
   "source": [
    "np.split(my_array3, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([1, 2]), array([3, 4]), array([5, 6]), array([7]), array([8])]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array_split(my_array3, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([1, 2, 3, 4, 5]), array([6, 7, 8])]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.split(my_array3, [5])  #Up to 5 (but not including) the 5th index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa = np.arange(1,18)\n",
    "aa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([1, 2, 3, 4, 5]),\n",
       " array([ 6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17])]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.split(aa, [5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([1, 2, 3, 4, 5]),\n",
       " array([6, 7, 8]),\n",
       " array([ 9, 10, 11, 12, 13, 14, 15, 16, 17])]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.split(aa, [5, 8])  #splits the array \"aa\" into segments based on the indices [5, 8]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12],\n",
       "       [13, 14, 15, 16]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[1, 2, 3, 4]]),\n",
       " array([[ 5,  6,  7,  8],\n",
       "        [ 9, 10, 11, 12],\n",
       "        [13, 14, 15, 16]])]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.split(my_array2, [1])  #It took row-based indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[1, 2, 3, 4]]),\n",
       " array([[ 5,  6,  7,  8],\n",
       "        [ 9, 10, 11, 12],\n",
       "        [13, 14, 15, 16]])]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.split(my_array2, [1], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[ 1],\n",
       "        [ 5],\n",
       "        [ 9],\n",
       "        [13]]),\n",
       " array([[ 2,  3,  4],\n",
       "        [ 6,  7,  8],\n",
       "        [10, 11, 12],\n",
       "        [14, 15, 16]])]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.split(my_array2, [1], axis = 1)  #It took column-based indices because of axis=1."
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='green'> <b>Sorting of the Arrays</b><font color='black'>\n",
    "[numpy.sort 01](https://www.w3schools.com/python/numpy/numpy_array_sort.asp),\n",
    "[numpy.sort 02](https://sparkbyexamples.com/python/numpy-sort-arrays-examples/),\n",
    "[numpy.sort 03](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.sort.html),\n",
    "[numpy.sort 04](https://www.geeksforgeeks.org/how-to-sort-a-numpy-array-python/)\n",
    "**sort()** arranges the elements of a numerical or alphabetical array in ascending order.\n",
    "![image.png](attachment:image.png)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 1, 5, 3])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb = np.array([2,4,1,5,3])\n",
    "bb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(bb) #It sorted the array but did not make permanent changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "bb.sort() #This method made permanent changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[41,  6, 19],\n",
       "       [44, 41, 34],\n",
       "       [26, 24, 37]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array4 = np.random.randint(5, 50, (3,3))\n",
    "my_array4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6, 19, 41],\n",
       "       [34, 41, 44],\n",
       "       [24, 26, 37]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(my_array4) #It sorted the rows internally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[26,  6, 19],\n",
       "       [41, 24, 34],\n",
       "       [44, 41, 37]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(my_array4, axis = 0) #sort() method sorts along columns while keeping rows intact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6, 19, 41],\n",
       "       [34, 41, 44],\n",
       "       [24, 26, 37]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(my_array4, axis = 1)  #sort() method sorts along rows while keeping columns intact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6, 19, 24, 26, 34, 37, 41, 41, 44])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(my_array4, axis = None)  #Creates a one-dimensional array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example with axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1 = np.array([1,2,3])\n",
    "arr2 = np.array([4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 35,
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   "cell_type": "code",
   "execution_count": 53,
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    "arr3 = np.array([[1,2],[3,4]])\n",
    "arr4 = np.array([[5,6],[7,8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
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       "2"
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     "execution_count": 54,
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     "output_type": "execute_result"
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   "execution_count": 55,
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     "execution_count": 55,
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     "output_type": "execute_result"
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   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [5, 6],\n",
       "       [7, 8]])"
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     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
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    "np.concatenate((arr3, arr4), axis = 0)"
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  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 5, 6],\n",
       "       [3, 4, 7, 8]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "np.concatenate((arr3, arr4), axis = 1)"
   ]
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