01b - Introduction - Scientific Python.ipynb

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    "# Python for scientific computing\n",
    "Python has extensive packages to help with data analysis:\n",
    "\n",
    "* numpy: matrices, linear algebra, Fourier transform, pseudorandom number generators\n",
    "* scipy: advanced linear algebra and maths, signal processing, statistics\n",
    "* pandas: DataFrames, data wrangling and analysis\n",
    "* matplotlib: visualizations such as line charts, histograms, scatter plots. "
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    "# Global imports and settings\n",
    "from preamble import *\n",
    "%matplotlib inline\n",
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    "## NumPy\n",
    "NumPy is the fundamental package required for high performance scientific computing in Python. It provides:\n",
    "\n",
    "* `ndarray`: fast and space-efficient n-dimensional numeric array with vectorized arithmetic operations\n",
    "* Functions for fast operations on arrays without having to write loops\n",
    "* Linear algebra, random number generation, Fourier transform\n",
    "* Integrating code written in C, C++, and Fortran (for faster operations)\n",
    "\n",
    "`pandas` provides a richer, simpler interface to many operations. We'll focus on using `ndarrays` here because they are heavily used in scikit-learn."
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    "### ndarrays\n",
    "There are several ways to create numpy arrays."
   ]
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    "# Convert normal Python array to 1-dimensional numpy array\n",
    "np.array((1, 2, 53))"
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   "source": [
    "# Convert sequences of sequences of sequences ... to n-dim array\n",
    "np.array([(1.5, 2, 3), (4, 5, 6)])"
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   "source": [
    "# Define element type at creation time\n",
    "np.array([[1, 2], [3, 4]], dtype=complex)"
   ]
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   "source": [
    "Useful properties of ndarrays:"
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    "my_array = np.array([[1, 0, 3], [0, 1, 2]])\n",
    "my_array.ndim     # number of dimensions (axes), also called the rank\n",
    "my_array.shape    # a matrix with n rows and m columns has shape (n,m)\n",
    "my_array.size     # the total number of elements of the array\n",
    "my_array.dtype    # type of the elements in the array\n",
    "my_array.itemsize # the size in bytes of each element of the array"
   ]
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    "Quick array creation.  \n",
    "It is cheaper to create an array with placeholders than extending it later."
   ]
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    "np.ones(3) # Default type is float64\n",
    "np.zeros([2, 2]) \n",
    "np.empty([2, 2]) # Fills the array with whatever sits in memory\n",
    "np.random.random((2,3))\n",
    "np.random.randint(5, size=(2, 4))"
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    "Create sequences of numbers"
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       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
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     "execution_count": 79,
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   "source": [
    "np.linspace(0, 1, num=4)   # Linearly distributed numbers between 0 and 1\n",
    "np.arange(0, 1, step=0.3)  # Fixed step size\n",
    "np.arange(12).reshape(3,4) # Create and reshape\n",
    "np.eye(4)                  # Identity matrix"
   ]
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   "source": [
    "### Basic Operations\n",
    "Arithmetic operators on arrays apply elementwise. A new array is created and filled with the result. Some operations, such as += and *=, act in place to modify an existing array rather than create a new one."
   ]
  },
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     "data": {
      "text/plain": [
       "(array([20, 30, 40, 50]), array([0, 1, 2, 3]))"
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     "execution_count": 80,
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     "data": {
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       "array([False, False,  True,  True], dtype=bool)"
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     "data": {
      "text/plain": [
       "array([21, 31, 41, 51])"
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     "execution_count": 80,
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   ],
   "source": [
    "a = np.array([20, 30, 40, 50])\n",
    "b = np.arange(4)\n",
    "a, b    # Just printing\n",
    "a-b\n",
    "b**2\n",
    "a > 32\n",
    "a += 1\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "The product operator * operates elementwise.  \n",
    "The matrix product can be performed using dot() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [0, 1]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "array([[2, 0],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "array([[2, 0],\n",
       "       [0, 4]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "array([[5, 4],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
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    }
   ],
   "source": [
    "A, B = np.array([[1,1], [0,1]]), np.array([[2,0], [3,4]]) # assign multiple variables in one line\n",
    "A\n",
    "B\n",
    "A * B\n",
    "np.dot(A, B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Upcasting: Operations with arrays of different types choose the more general/precise one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
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     "data": {
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       "(dtype('int64'), dtype('float64'), dtype('float64'))"
      ]
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     "execution_count": 82,
     "metadata": {},
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   ],
   "source": [
    "a = np.ones(3, dtype=np.int) # initialize to integers\n",
    "b = np.linspace(0, np.pi, 3) # default type is float\n",
    "a.dtype, b.dtype, (a + b).dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
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   "source": [
    "ndarrays have most unary operations (max,min,sum,...) built in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.988,  0.559,  0.106],\n",
       "       [ 0.376,  0.511,  0.252]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(2.7933370658694319, 0.10627410057221587, 0.98806590974860331)"
      ]
     },
     "execution_count": 83,
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   ],
   "source": [
    "a = np.random.random((2,3))\n",
    "a\n",
    "a.sum(), a.min(), a.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "By specifying the axis parameter you can apply an operation along a specified axis of an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
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    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
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     "data": {
      "text/plain": [
       "array([12, 15, 18, 21])"
      ]
     },
     "execution_count": 84,
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     "data": {
      "text/plain": [
       "array([ 6, 22, 38])"
      ]
     },
     "execution_count": 84,
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   ],
   "source": [
    "b = np.arange(12).reshape(3,4)\n",
    "b\n",
    "b.sum(axis=0) \n",
    "b.sum(axis=1) "
   ]
  },
  {
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   },
   "source": [
    "### Universal Functions\n",
    "\n",
    "NumPy provides familiar mathematical functions such as sin, cos, exp, sqrt, floor,... In NumPy, these are called \"universal functions\" (ufunc), and operate elementwise on an array, producing an array as output. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.   ,  1.   ,  1.414,  1.732,  2.   ,  2.236,  2.449,  2.646,\n",
       "        2.828,  3.   ])"
      ]
     },
     "execution_count": 85,
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   ],
   "source": [
    "np.sqrt(np.arange(0, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Shape Manipulation\n",
    "Transpose, flatten, reshape,..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.,  4.,  4.,  7.],\n",
       "       [ 1.,  8.,  0.,  4.],\n",
       "       [ 9.,  6.,  2.,  1.]])"
      ]
     },
     "execution_count": 86,
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    {
     "data": {
      "text/plain": [
       "array([[ 5.,  1.,  9.],\n",
       "       [ 4.,  8.,  6.],\n",
       "       [ 4.,  0.,  2.],\n",
       "       [ 7.,  4.,  1.]])"
      ]
     },
     "execution_count": 86,
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    },
    {
     "data": {
      "text/plain": [
       "array([ 5.,  4.,  4.,  7.,  1.,  8.,  0.,  4.,  9.,  6.,  2.,  1.])"
      ]
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     "execution_count": 86,
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    {
     "data": {
      "text/plain": [
       "array([[ 5.,  4.,  4.,  7.],\n",
       "       [ 1.,  8.,  0.,  4.],\n",
       "       [ 9.,  6.,  2.,  1.]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
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    }
   ],
   "source": [
    "a = np.floor(10*np.random.random((3,4)))\n",
    "a\n",
    "a.transpose()\n",
    "b = a.ravel() # flatten array\n",
    "b\n",
    "b.reshape(3, -1) # reshape in 2 rows (and as many columns as needed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Arrays can be split and stacked together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.,  9.,  3.,  5.,  6.,  2.],\n",
       "       [ 4.,  6.,  4.,  9.,  4.,  4.]])"
      ]
     },
     "execution_count": 87,
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     "data": {
      "text/plain": [
       "array([[ 5.,  9.,  3.],\n",
       "       [ 4.,  6.,  4.]])"
      ]
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     "execution_count": 87,
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     "data": {
      "text/plain": [
       "array([[ 5.,  6.,  2.],\n",
       "       [ 9.,  4.,  4.]])"
      ]
     },
     "execution_count": 87,
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       "array([[ 5.,  9.,  3.,  5.,  6.,  2.],\n",
       "       [ 4.,  6.,  4.,  9.,  4.,  4.]])"
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     "execution_count": 87,
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   ],
   "source": [
    "a = np.floor(10*np.random.random((2,6)))\n",
    "a\n",
    "b, c = np.hsplit(a, 2) # Idem: vsplit for vertical splits\n",
    "b\n",
    "c\n",
    "np.hstack((b, c)) # Idenm: vstack for vertical stacks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Indexing and Slicing\n",
    "\n",
    "Arrays can be indexed and sliced using [start:stop:stepsize]. Defaults are [0:ndim:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  4,  9, 16, 25, 36, 49, 64, 81])"
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   ],
   "source": [
    "a = np.arange(10)**2\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9, 25, 49, 81])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3:10:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([81, 64, 49, 36, 25, 16,  9,  4,  1,  0])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[::-1] # Defaults are used if indices not stated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  4, 16, 36, 64])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[::2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For multi-dimensional arrays, axes are comma-separated: [x,y,z]. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(16).reshape(4,4)\n",
    "b\n",
    "b[2,3] # row 2, column 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 5, 9])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ 1,  5,  9, 13])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[0:3,1] # Values 0 to 3 in column 1\n",
    "b[ : ,1] # The whole column 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[1:3, : ] # Rows 1:3, all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 13, 14, 15])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Return the last row\n",
    "b[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Note: dots (...) represent as many colons (:) as needed\n",
    "* x[1,2,...] = x[1,2,:,:,:]\n",
    "* x[...,3] = x[:,:,:,:,3]\n",
    "* x[4,...,5,:] = x[4,:,:,5,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Arrays can also be indexed by arrays of integers and booleans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100, 121])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  9, 64, 25])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(12)**2         \n",
    "i = np.array([ 1,1,3,8,5 ])\n",
    "a\n",
    "a[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A matrix of indices returns a matrix with the corresponding values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9, 16],\n",
       "       [81, 49]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j = np.array([[ 3, 4], [9, 7]])\n",
    "a[j]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "With boolean indices we explicitly choose which items in the array we want and which ones we don't."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False],\n",
       "       [False,  True,  True,  True],\n",
       "       [ True,  True,  True,  True]], dtype=bool)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ 5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(12).reshape(3,4)\n",
    "a\n",
    "a[np.array([False,True,True]), :]\n",
    "b = a > 4\n",
    "b\n",
    "a[b]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Iterating \n",
    "Iterating is done with respect to the first axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False False False]\n",
      "[False  True  True  True]\n",
      "[ True  True  True  True]\n"
     ]
    }
   ],
   "source": [
    "for row in b:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Operations on each element can be done by flattening the array (or nested loops)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "for element in b.flat: # flat returns an iterator\n",
    "    print(element)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Copies and Views (or: how to shoot yourself in a foot)\n",
    "Assigning an array to another variable does NOT create a copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(12)\n",
    "b = a\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-100,    1,    2,    3,    4,    5,    6,    7,    8,    9,   10,\n",
       "         11])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[0] = -100\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-100,    1,    2,    3,    4,    5,    6,    7,    8,    9,   10,\n",
       "         11])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The view() method creates a NEW array object that looks at the same data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10, 11]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(12)\n",
    "a\n",
    "c = a.view()\n",
    "c.resize((2, 6))\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[123,   1,   2,   3,   4,   5],\n",
       "       [  6,   7,   8,   9,  10,  11]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0] = 123\n",
    "c # c is also changed now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Slicing an array returns a view of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[123,   1,   2,   3,   4,   5],\n",
       "       [  6,   7,   8,   9,  10,  11]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[10, 10],\n",
       "       [10, 10]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[123,  10,  10,   3,   4,   5],\n",
       "       [  6,  10,  10,   9,  10,  11]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c\n",
    "s = c[ : , 1:3]  \n",
    "s[:] = 10\n",
    "s\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The copy() method makes a deep copy of the array and its data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-42,  10,  10,   3,   4,   5,   6,  10,  10,   9,  10,  11])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = a.copy()     \n",
    "d[0] = -42\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([123,  10,  10,   3,   4,   5,   6,  10,  10,   9,  10,  11])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Numpy: further reading\n",
    "\n",
    "* Numpy Tutorial: http://wiki.scipy.org/Tentative_NumPy_Tutorial\n",
    "* \"Python for Data Analysis\" by Wes McKinney (O'Reilly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## SciPy\n",
    "SciPy is a collection of packages for scientific computing, among others:\n",
    "\n",
    "* scipy.integrate: numerical integration and differential equation solvers\n",
    "* scipy.linalg: linear algebra routines and matrix decompositions\n",
    "* scipy.optimize: function optimizers (minimizers) and root finding algorithms\n",
    "* scipy.signal: signal processing tools\n",
    "* scipy.sparse: sparse matrices and sparse linear system solvers\n",
    "* scipy.stats: probability distributions, statistical tests, descriptive statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Sparse matrices\n",
    "Sparse matrices are used in scikit-learn for (large) arrays that contain mostly zeros. You can convert a dense (numpy) matrix to a sparse matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.,  0.],\n",
       "       [ 0.,  0.,  1.,  0.],\n",
       "       [ 0.,  0.,  0.,  1.]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<4x4 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 4 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 0)\t1.0\n",
      "  (1, 1)\t1.0\n",
      "  (2, 2)\t1.0\n",
      "  (3, 3)\t1.0\n"
     ]
    }
   ],
   "source": [
    "from scipy import sparse\n",
    "eye = np.eye(4)\n",
    "eye\n",
    "sparse_matrix = sparse.csr_matrix(eye) # Compressed Sparse Row matrix\n",
    "sparse_matrix\n",
    "print(\"{}\".format(sparse_matrix))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "When the data is too large, you can create a sparse matrix by passing the values and coordinates (COO format)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 0)\t1.0\n",
      "  (1, 1)\t1.0\n",
      "  (2, 2)\t1.0\n",
      "  (3, 3)\t1.0\n"
     ]
    }
   ],
   "source": [
    "data = np.ones(4)                         # [1,1,1,1]\n",
    "row_indices = col_indices = np.arange(4)  # [1,2,3,4]\n",
    "eye_coo = sparse.coo_matrix((data, (row_indices, col_indices)))\n",
    "print(\"{}\".format(eye_coo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Further reading\n",
    "Check the [SciPy reference guide](https://docs.scipy.org/doc/scipy/reference/) for tutorials and examples of all SciPy capabilities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## pandas\n",
    "pandas is a Python library for data wrangling and analysis. It provides:\n",
    "\n",
    "* ``DataFrame``: a table, similar to an R DataFrame that holds any structured data\n",
    "    * Every column can have its own data type (strings, dates, floats,...)\n",
    "* A great range of methods to apply to this table (sorting, querying, joining,...)\n",
    "* Imports data from a wide range of data formats (CVS, Excel) and databases (e.g. SQL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Series\n",
    "A one-dimensional array of data (of any numpy type), with indexed values. It can be created by passing a Python list or dict, a numpy array, a csv file,..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    3\n",
       "c    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "b    2.0\n",
       "c    3.0\n",
       "d    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.Series([1,3,np.nan]) # Default integers are integers\n",
    "pd.Series([1,3,5], index=['a','b','c'])\n",
    "pd.Series({'a' : 1, 'b': 2, 'c': 3 }) # when given a dict, the keys will be used for the index\n",
    "pd.Series({'a' : 1, 'b': 2, 'c': 3 }, index = ['b', 'c', 'd']) # this will try to match labels with keys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Functions like a numpy array, however with index labels as indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.Series({'a' : 1, 'b': 2, 'c': 3 })\n",
    "a\n",
    "a['b']       # Retrieves a value\n",
    "a[['a','b']] # and can also be sliced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "numpy array operations on Series preserve the index value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "a    2\n",
       "b    4\n",
       "c    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "a    1.00\n",
       "b    1.41\n",
       "c    1.73\n",
       "dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a\n",
    "a[a > 1]\n",
    "a * 2\n",
    "np.sqrt(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Operations over multiple Series will align the indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Andre      3200.0\n",
       "Cecilia       NaN\n",
       "John       1100.0\n",
       "Mary          NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.Series({'John' : 1000, 'Mary': 2000, 'Andre': 3000 })\n",
    "b = pd.Series({'John' : 100, 'Andre': 200, 'Cecilia': 300 })\n",
    "a + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### DataFrame\n",
    "A DataFrame is a tabular data structure with both a row and a column index. It can be created by passing a dict of arrays, a csv file,..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop</th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.5</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.7</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.4</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.9</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pop   state  year\n",
       "0  1.5    Ohio  2000\n",
       "1  1.7    Ohio  2001\n",
       "2  2.4  Nevada  2001\n",
       "3  2.9  Nevada  2002"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   state  pop color\n",
       "0  2000    Ohio  1.5   NaN\n",
       "1  2001    Ohio  1.7   NaN\n",
       "2  2001  Nevada  2.4   NaN\n",
       "3  2002  Nevada  2.9   NaN"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'state': ['Ohio', 'Ohio', 'Nevada', 'Nevada'], 'year': [2000, 2001, 2001, 2002],\n",
    "'pop': [1.5, 1.7, 2.4, 2.9]}\n",
    "pd.DataFrame(data)\n",
    "pd.DataFrame(data, columns=['year', 'state', 'pop', 'color']) # Will match indices "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "It can be composed with a numpy array and row and column indices, and decomposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.61</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.65</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01 -0.61 -1.84  0.53  1.73\n",
       "2013-01-02 -0.20 -0.49 -1.39 -1.28\n",
       "2013-01-03 -0.65 -1.38 -1.79 -0.32\n",
       "2013-01-04  0.16 -0.60  0.04 -0.11"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('20130101',periods=4)\n",
    "df = pd.DataFrame(np.random.randn(4,4),index=dates,columns=list('ABCD'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04'], dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.609, -1.84 ,  0.526,  1.726],\n",
       "       [-0.203, -0.489, -1.394, -1.277],\n",
       "       [-0.653, -1.378, -1.789, -0.321],\n",
       "       [ 0.16 , -0.597,  0.037, -0.114]])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index\n",
    "df.columns\n",
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "DataFrames can easily read/write data from/to files\n",
    "\n",
    "* `read_csv(source)`: load CSV data from file or url\n",
    "* `read_table(source, sep=',')`: load delimited data with separator\n",
    "* `df.to_csv(target)`: writes the DataFrame to a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>hello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b   c   d message \n",
       "0  10   2   3   4    hello\n",
       "1   5   6   7   8   world \n",
       "2   9  10  11  12      foo"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>hello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b   c   d message \n",
       "0  10   2   3   4    hello\n",
       "1   5   6   7   8   world \n",
       "2   9  10  11  12      foo"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs = pd.read_csv('data.csv')\n",
    "dfs\n",
    "dfs.set_value(0, 'a', 10)\n",
    "dfs.to_csv('data.csv', index=False) # Don't export the row index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Simple operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.61</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.65</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01 -0.61 -1.84  0.53  1.73\n",
       "2013-01-02 -0.20 -0.49 -1.39 -1.28\n",
       "2013-01-03 -0.65 -1.38 -1.79 -0.32\n",
       "2013-01-04  0.16 -0.60  0.04 -0.11"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.61</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.65</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01 -0.61 -1.84  0.53  1.73\n",
       "2013-01-02 -0.20 -0.49 -1.39 -1.28\n",
       "2013-01-03 -0.65 -1.38 -1.79 -0.32\n",
       "2013-01-04  0.16 -0.60  0.04 -0.11"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # First 5 rows\n",
    "df.tail() # Last 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.00e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.33</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>3.58e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.25e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.65</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-1.28e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.62</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>-5.60e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-2.17e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>0.16</td>\n",
       "      <td>3.46e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.73e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A     B     C         D\n",
       "count  4.00  4.00  4.00  4.00e+00\n",
       "mean  -0.33 -1.08 -0.66  3.58e-03\n",
       "std    0.38  0.64  1.11  1.25e+00\n",
       "min   -0.65 -1.84 -1.79 -1.28e+00\n",
       "25%   -0.62 -1.49 -1.49 -5.60e-01\n",
       "50%   -0.41 -0.99 -0.68 -2.17e-01\n",
       "75%   -0.11 -0.57  0.16  3.46e-01\n",
       "max    0.16 -0.49  0.53  1.73e+00"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quick stats\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2013-01-01 00:00:00</th>\n",
       "      <th>2013-01-02 00:00:00</th>\n",
       "      <th>2013-01-03 00:00:00</th>\n",
       "      <th>2013-01-04 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.84</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>-0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.53</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>1.73</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2013-01-01  2013-01-02  2013-01-03  2013-01-04\n",
       "A       -0.61       -0.20       -0.65        0.16\n",
       "B       -1.84       -0.49       -1.38       -0.60\n",
       "C        0.53       -1.39       -1.79        0.04\n",
       "D        1.73       -1.28       -0.32       -0.11"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.73</td>\n",
       "      <td>0.53</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>-0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.28</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.32</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>-0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               D     C     B     A\n",
       "2013-01-01  1.73  0.53 -1.84 -0.61\n",
       "2013-01-02 -1.28 -1.39 -0.49 -0.20\n",
       "2013-01-03 -0.32 -1.79 -1.38 -0.65\n",
       "2013-01-04 -0.11  0.04 -0.60  0.16"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.61</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.65</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01 -0.61 -1.84  0.53  1.73\n",
       "2013-01-03 -0.65 -1.38 -1.79 -0.32\n",
       "2013-01-04  0.16 -0.60  0.04 -0.11\n",
       "2013-01-02 -0.20 -0.49 -1.39 -1.28"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1, ascending=False) # Sort by index labels\n",
    "df.sort(columns='B') # Sort by values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Selecting and slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01   -0.61\n",
       "2013-01-02   -0.20\n",
       "2013-01-03   -0.65\n",
       "2013-01-04    0.16\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2013-01-01   -0.61\n",
       "2013-01-02   -0.20\n",
       "2013-01-03   -0.65\n",
       "2013-01-04    0.16\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A'] # Get single column by label\n",
    "df.A    # Shorthand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>C</th>\n",
       "      <th>D</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.61</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.73</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.20</td>\n",
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      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01 -0.61 -1.84  0.53  1.73\n",
       "2013-01-02 -0.20 -0.49 -1.39 -1.28"
      ]
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     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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       "      <th>2013-01-01</th>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.20</td>\n",
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      "text/plain": [
       "               A     B\n",
       "2013-01-01 -0.61 -1.84\n",
       "2013-01-02 -0.20 -0.49"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
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       "      <td>-1.28</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.65</td>\n",
       "      <td>-1.38</td>\n",
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      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-02 -0.20 -0.49 -1.39 -1.28\n",
       "2013-01-03 -0.65 -1.38 -1.79 -0.32"
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       "      <th>2013-01-03</th>\n",
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       "               A     B\n",
       "2013-01-02 -0.20 -0.49\n",
       "2013-01-03 -0.65 -1.38"
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     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.61</td>\n",
       "      <td>-1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.20</td>\n",
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       "    </tr>\n",
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      "text/plain": [
       "               A     B\n",
       "2013-01-01 -0.61 -1.84\n",
       "2013-01-02 -0.20 -0.49"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:2]          # Get rows by index number\n",
    "df.iloc[0:2,0:2] # Get rows and columns by index number\n",
    "df['20130102':'20130103']                # or row label\n",
    "df.loc['20130102':'20130103', ['A','B']] # or row and column label\n",
    "df.ix[0:2, ['A','B']]   # allows mixing integers and labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "query() retrieves data matching a boolean expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.61</td>\n",
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       "      <td>0.53</td>\n",
       "      <td>1.73</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.20</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.65</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01 -0.61 -1.84  0.53  1.73\n",
       "2013-01-02 -0.20 -0.49 -1.39 -1.28\n",
       "2013-01-03 -0.65 -1.38 -1.79 -0.32\n",
       "2013-01-04  0.16 -0.60  0.04 -0.11"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [A, B, C, D]\n",
       "Index: []"
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     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>A</th>\n",
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       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.61</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.65</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01 -0.61 -1.84  0.53  1.73\n",
       "2013-01-02 -0.20 -0.49 -1.39 -1.28\n",
       "2013-01-03 -0.65 -1.38 -1.79 -0.32\n",
       "2013-01-04  0.16 -0.60  0.04 -0.11"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n",
    "df.query('A > 0.4') # Identical to df[df.A > 0.4]\n",
    "df.query('A > B')   # Identical to df[df.A > df.B]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Note: similar to NumPy, indexing and slicing returns a _view_ on the data. Use copy() to make a deep copy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Operations\n",
    "DataFrames offer a [wide range of operations](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html): max, mean, min, sum, std,... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -3.26e-01\n",
       "B   -1.08e+00\n",
       "C   -6.55e-01\n",
       "D    3.58e-03\n",
       "dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2013-01-01   -0.05\n",
       "2013-01-02   -0.84\n",
       "2013-01-03   -1.04\n",
       "2013-01-04   -0.13\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()       # Mean of all values per column\n",
    "df.mean(axis=1) # Other axis: means per row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "All of numpy's universal functions also work with dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.61</td>\n",
       "      <td>1.84</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.65</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.79</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01  0.61  1.84  0.53  1.73\n",
       "2013-01-02  0.20  0.49  1.39  1.28\n",
       "2013-01-03  0.65  1.38  1.79  0.32\n",
       "2013-01-04  0.16  0.60  0.04  0.11"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.abs(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Other (custom) functions can be applied with apply(funct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.61</td>\n",
       "      <td>-1.84</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>-1.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.65</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01 -0.61 -1.84  0.53  1.73\n",
       "2013-01-02 -0.20 -0.49 -1.39 -1.28\n",
       "2013-01-03 -0.65 -1.38 -1.79 -0.32\n",
       "2013-01-04  0.16 -0.60  0.04 -0.11"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "A    0.16\n",
       "B   -0.49\n",
       "C    0.53\n",
       "D    1.73\n",
       "dtype: float64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "A    0.81\n",
       "B    1.35\n",
       "C    2.32\n",
       "D    3.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n",
    "df.apply(np.max)\n",
    "df.apply(lambda x: x.max() - x.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Data can be aggregated with groupby()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>foo</td>\n",
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       "      <td>0.91</td>\n",
       "      <td>-0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>-1.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B     C     D\n",
       "0  foo    one  0.91 -0.58\n",
       "1  bar    one  0.20 -0.70\n",
       "2  foo    two -0.60 -0.28\n",
       "3  bar  three -1.14 -1.07"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>-0.94</td>\n",
       "      <td>-1.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>0.30</td>\n",
       "      <td>-0.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        C     D\n",
       "A              \n",
       "bar -0.94 -1.76\n",
       "foo  0.30 -0.86"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
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       "    <tr>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>-1.14</td>\n",
       "      <td>-1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>0.91</td>\n",
       "      <td>-0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              C     D\n",
       "A   B                \n",
       "bar one    0.20 -0.70\n",
       "    three -1.14 -1.07\n",
       "foo one    0.91 -0.58\n",
       "    two   -0.60 -0.28"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar'], 'B' : ['one', 'one', 'two', 'three'],\n",
    "                   'C' : np.random.randn(4), 'D' : np.random.randn(4)})\n",
    "df\n",
    "df.groupby('A').sum()\n",
    "df.groupby(['A','B']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Data wrangling (some examples)\n",
    "Merge: combine two dataframes based on common keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1 key\n",
       "0      0   b\n",
       "1      1   b\n",
       "2      2   a"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>data2</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data2 key\n",
       "0      0   a\n",
       "1      1   b"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1 key  data2\n",
       "0      0   b      1\n",
       "1      1   b      1\n",
       "2      2   a      0"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'key': ['b', 'b', 'a'], 'data1': range(3)})\n",
    "df2 = pd.DataFrame({'key': ['a', 'b'], 'data2': range(2)})\n",
    "df1\n",
    "df2\n",
    "pd.merge(df1, df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Append: append one dataframe to another"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.55</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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     "execution_count": 132,
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   "source": [
    "df = pd.DataFrame(np.random.randn(2, 4))\n",
    "df\n",
    "s = pd.DataFrame(np.random.randn(1,4))\n",
    "s\n",
    "df.append(s, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Remove duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "    k1  k2\n",
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       "2  one   2"
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     "execution_count": 133,
     "metadata": {},
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       "    k1  k2\n",
       "0  one   1\n",
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   "source": [
    "df = pd.DataFrame({'k1': ['one'] * 3, 'k2': [1, 1, 2]})\n",
    "df\n",
    "df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Replace values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>0</th>\n",
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       "      <td>-1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "   k1  k2\n",
       "0   1  -1\n",
       "1  -1   2"
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     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "    k1   k2\n",
       "0  1.0  NaN\n",
       "1  NaN  2.0"
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     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.DataFrame({'k1': [1, -1], 'k2': [-1, 2]}) # Say that -1 is a sentinel for missing data\n",
    "df\n",
    "df.replace(-1, np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Discretization and binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 2, 1, 3, 2, 2, 1], dtype=int8)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(18, 25]     5\n",
       "(35, 60]     3\n",
       "(25, 35]     3\n",
       "(60, 100]    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32]\n",
    "bins = [18, 25, 35, 60, 100]\n",
    "cats = pd.cut(ages, bins)\n",
    "cats.labels\n",
    "pd.value_counts(cats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Further reading\n",
    "\n",
    "* Pandas docs: http://pandas.pydata.org/pandas-docs/stable/\n",
    "* https://bitbucket.org/hrojas/learn-pandas\n",
    "* Python for Data Analysis (O'Reilly) by Wes McKinney (the author of pandas)          "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## matplotlib\n",
    "[matplotlib](http://matplotlib.sourceforge.net) is the primary scientific plotting library in Python. It provides:\n",
    "\n",
    "* Publication-quality [visualizations](http://matplotlib.org/gallery.html) such as line charts, histograms, and scatter plots.\n",
    "* Integration in pandas to make plotting much easier.\n",
    "* Interactive plotting in Jupyter notebooks for quick visualizations.\n",
    "    * Requires some setup. See preamble and [%matplotlib](http://ipython.readthedocs.io/en/stable/interactive/plotting.html?highlight=matplotlib).\n",
    "* Many GUI backends, export to PDF, SVG, JPG, PNG, BMP, GIF, etc.\n",
    "* Ecosystem of libraries for more advanced plotting, e.g. [Seaborn](http://seaborn.pydata.org/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Low-level usage\n",
    "`plot()` is the [main function](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot) to generate a plot (but many more exist):\n",
    "```\n",
    "plot(x, y)        Plot x vs y, default settings\n",
    "plot(x, y, 'bo')  Plot x vs y, blue circle markers\n",
    "plot(y, 'r+')     Plot y (x = array 0..N-1), red plusses\n",
    "```\n",
    "\n",
    "Every plotting function is completely customizable through a large set of options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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KuXgGgBTS0NAU9ef1O++c1QsvtCkrS7rnnguGPe5wOJOmh1Ukc+5nP2uFHzt3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\nrz5xokWzZ7ulrzqXdEzFeSycwPH27ZsnSVq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11553a518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-10, 10, 100) # Sequence for X-axis \n",
    "y = np.sin(x) # sine values\n",
    "p = plt.plot(x, y, marker=\"x\") # Line plot with marker x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### pandas + matplotlib\n",
    "pandas DataFrames offer an easier, higher-level interface for matplotlib functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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HhpaBOFZWlvwwtFg10Hj9O4YW1zfuoouhPbRsouvZlTMKtCwb35azaQGWZdq3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\nIDEgMCBSID4+CnN0YXJ0eHJlZgoyMzgwOAolJUVPRgo=\n",
      "image/png": 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MPOh58+bFViD85S/lbNpUDSQXLrzrXWMpKsqjtHQ/v/3ttjRU3LeUUuoNil4o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SAAAg\nAElEQVRERtYCuAYX9J17er+FtWuzhu16MpmMjoWE0tLgvjH12u2sz8xkTXIyt06fzm052vfwsv37\nuaa4mFcaGui12YI6xm6bjVLZHSCE8LE3eIOXeZkaaoI9lAlZwQpO4zSsWPk1v+Yf/CPYQ3IxhSkk\noN2M1VHn9jhncMFEJ53ag+ZYurpsHDzYMWwhXi+NtHev++DC4zzOUzxFG2M3fv7/7J1pYFx12b6v\nM/tk37emaZq2abrvtIW2gCCgiFCkiIiAIKLCq8JfEHxFEFBRUEBlERCVIouAKL6AKNBS2tLSNd2S\nbmm2Zmv2fdbz/3DmTGaSSTKTzJr+rnyByZlzfkmmZ3nu57lvdTLhxImhxeqODhu9vQPXwtLSdvdx\nPS2RQCk+5+crDRgvvVTNsWMD1/m7797vDqh2OGRqa/u8fqakJD1bt36GJ55YPGYLn3Cyo7OTu0+c\nAODXYRcXRrdFslgc7Nmj/O2XLw+fFeedBQU8U1zMOT7sdGbHKX/rst5eNBqJGTOUn6P6aC+LExMx\naSdG402s0mi10myzoWHgbyWIDh6urublxsZIL0MgOC04yUk2sYmjHI30UqKKeSLUOWRE/12vQCAY\ngizLXuJCY6PF/fCVm2v2ax/x8TqSknSAYo1kNGq48MIB7+QpU+JZvToDWYaXXx7o1JtoeQsA2dmK\nuGC1OmlvV+wUBsSFLJ/vUR8mjxzpCsMKh2eyycSfZ81iw8KFAPyiqIjFCQl0Oxy81NTEVYcO8cua\nmlH2Elq+e/QoxZ9+yluuCQuBQCAIBv/lv7zMy5witoPstWi5jdv4PJ8HwIx/1/FQ4sBblM6WlfuD\nkcUFpbiflWXkLM7iaq4m+5RiNbFjR+sQcWH+/IHcBV9YsPA+7/MO7+DEOeJ6LRYHra1K5oMvcUEV\nHlJS9CQl6ejvH7B99AxzVlEzFB555DAAX/7yZPLyTFRW9vL73x8DBjKrdDrJ78aOaOPJOkWY0wI7\nlywJ67HVQOe2NhttbVaf25SWdmC1OklPN7gnHcLBWcnJ3JSXx0wfxenZrsKEaoPkmbsgiDx9TieX\nZ2Tw2dRUzBEQeqxOJ13Cy3sIB3t6uLOigqvLyvhva2uklyMQRBQZmXbaR723GQ+v8AqP8igP8mDI\njhGLqLlIe8XkQtAR4oJAEIN0dtrp6VEe/NWHrfJy5aEmkAdcTyHi/POzSUjQeX3/mmumALB+fRWy\nLON0yuzcqdwQLls2ccQFk0lLSopiXdDQ0E9z84BYc9552T7fo/rsRnpyQUWdrkjU6dixZAlbFy3i\nh5Mnc3j5cu4tLIzo2tRMiCsPHozoOiLFOy0tfK2sjD/UxXZ39WCO9vby18ZG9nZNzIJKv8NBaXd3\nxCd//KWGGl7lVfoYGjo7EbFhox7F/mYyI4ezxgJatHyDb/Bzfs5n+WxE19JDD1dxFbdyKzZsfP/7\ne9nyuh1JlkacIPC0RVrMYtaxjsXxswF4771G931Lfr5SsFWLsseP+76OHuc4Tpykur5GQrWGBN/i\ngtqQkZ9vdgcrf/BBEwCzZiUO2V4VQNSQ6ltumcYDD8wF4MEHy2httbrFkvx8s19WO+HA4gysUPHc\nzJk8Om0aTWedRbbLhzhcJCToyMpSbId8/c0Atm9XwsCXLUuLGu98tRv+kKvrUYgL0cVUs5k35s7l\n3wsWjGs/sixz5cGDrD1wAKefeSQPVFaSunkzj4V5CigW8Jxg/lpZGU1W34KiQDCRaaKJJ3iCG7mR\nr/N13uCNkB1LbRLppDNkx4hFliYq1+zd3d1+n9sF/iHEBYEgBlEfklNS9KxaleH1PX8zFwDy8ga2\nveyyvCHfX7cuH7NZy/79HWzceIqjR7vp7LRjNmuZM2dieVN6WiNt2NCELMPcuUnDijXRMrngC40k\nsTI5mYemTWOaObIdsJ4Bke/Mnx/BlUSOst5eXmxs5KP20W09Yon3Wlu5pqyMB6qqRt84Bpm+fTsL\nd+5kX4yMzd7O7bzCK7zO65FeSlioow4nTuKIG7XwHCtISMxiVqSXQT312LHTSy9v/6OJxx8/yu5v\nLWXOA/dyIRcO+z5PWyQVtRHh//5PEVczM43uvKYpU5QirVqkH8wRjgBQTDESIxeWPfMUfHn4q9/P\nyzOzZInyeVEvT4NtkTzXBjB9egKrVmVw3XWFzJuXTHu7jTvuKB0yiREoJzjBAQ6M6b3DUbhtG8/X\nD82cGA6tJPH9yZNJ00cmG2K03IVPPlHEhZUrxxfmHExmuSYXjvT1YXM63c0m0Xg/KBg7h3p7ee3U\nKf7R3ExVf//obwDS9Hp6nU42dXRwqKeHaj/fdzpQ4fG7aLTZuK68XBT2BKcVDTTwMi/zPu/ThmIz\nbSJ0U4+XcRkAZ3BGyI4Ri5TExRGn0dDtcHBkAtg2y7LMF/bt47qyMjojPDUnxAWBIAZRxYVJkwY6\n8FTGMrkgSXDJJUPFhdRUAzfcUAjAL39Z7rZEWrw4BZ1uYp0+1N9bTU0vv/udYnkw3NQCRN/kQrTS\nZFNspiRgVXJyZBcTIdJ1ykRQ+wQbk690PSgWmmLTDmQ0Zg7qTo127Cifr3LKI7yS8GDAwMVczLmc\nO2rhWRAY6kRIujWbm27aCYCt1cinWwbsi/r7HZx99gZuuGGH+7XGxgFbJBVVXFCnFmbdUsVDPMTH\nfOw1HdDTM/T8OJe5XMEVrGb1qGtWbY9AESvsdu8Ofs/7JlVcUPFli+QpGFx/fSGSJKHVSjzxxCIk\nCZ5/vpJnnqkYsq2/lFPOXdzFPdzDO7yDzPiLbGU9PTRYrTxx8uS49zUSrzQ2sr6hgXpX6PF4UO2n\nqqp8n2e3bVPuO6NJXJhsNBKv0WCXZY739YnJhQnKh21t7v8+2uffROAa133u1o4OfnD8OFO2beOp\nEP97jBUqXL/DL6anY9Jo+HdrK4+MYNsqhAfBRCODDOKJ5xIu4Sf8hFd5lUu4JGTHK6aYZ3mWO7gj\nZMeIRbSSxG35+TxUVESyTjf6G6KcDrudt1tbeaGxEUOEJzwnVnVQIDhNGO4h2WjUuO19/EGdXFi5\nMt2dOzCY228vRqNRLA3++Ecl9G8iWSKpqOLC3Xfv5+OPm0lM1HHLLdOG3V6dXDh6tBunU9wAD8cJ\n18NEvtGIQXN6XnKShbgQk8xxdacejJGulrWsBWAB47OCiBVyyeUbrq9YoYIK7uM+XuTFSC9lRNSA\n7CMf6Whutrqvj9u3t7qvdxs2NLFpUzPvdW7ib5a/0067ly2SSmFhHBkZA3Y7aYs62M52KqggOVlP\nYqJyfqypGfrvbDrT+Spf5SzOGnXN9fUDxT/PoGX3z+RjcgGUSYqMDCODUScXJAmuvXaK+/XVqzO5\n/fZi1+/glGvbwLMAiiiigAIAnuVZNrAh4H0MRp0+2N3dTU0IO6Z/UV3NteXlfBoESzz191xVNfTv\n39jYz4kTPUhSeMOcR0MjSe7phUO9vRQXK/eD9fX9dHXZIrk0QRD50GPa9Jif4sKc+HjSdDp6nU7e\ndeUKnHWaNtYMRp1cuCQ9ncemTwfgrooK/nHKOzOpur+f5bt2MWXbNq/pZ4Eg1tGh4xt8gxu4gUUs\nwsBQK0I7dp/2pp10jsn2NIMMdMR+AT3YPFhUxA8LCsg1Dr3/izXURs4krRZTBHKGPDk9Kz0CQYzj\nKS4sXJiCKlLm5JgC8qRdt24y06bF86MfDW/DUFSUwLp1ip/1Rx8pN4ATWVxQux/Xrz+DGTOG+jCr\nFBbGo9NJ9PU5vOwYBN5M9AK0P6TEmLjQZbfzWE0NrzY1+fx+ncXCozU1vOEK6J6of1vVVztWfIGv\n5Vre5E2u5MpILyXsyMh00omVwP5WH/IhD/AAG9kYmoUNopZaSimN+umSi7mYCzbcxsbbCzAYNLzz\nziri4rR0dNjc3dnuvIKflfKycT211HoEOg+cEyRJ8rpnSJFSAOigA0mSRrVG8hfPyQUYarOj3jfl\n5ZmYPj3BLWr4ylsAOPPMDJYvT+P224uZPNl7MuHBB+d6vW8skwsGDNzJnaSTTjbZfgkoo5FtMHBm\nkjKF8VZLy7j35wur00mZS3BdED9UVKm3WHixoYFXGhv92t9I4oJqiTRnThJJSZGxbRoOz9yFlBSD\nW1A7ckRMs4aCbrud68vKmLF9e8C5ImPBIcts9BAX/J1c0EgSqz3EhEKTiXk+/p2cjtS6Jp2KzGa+\nmZvLt/Ly0EsS/YP+ntkGA3u6u6m1WPy2oxIIJgq72c23+TbNNHu9/hZvcQ3X8BIvRWhlgmhFfU7N\nCnNuli+EuCAQxCCe4kJCgs49kh2IJRLAGWekcezY57n44twRt7vzzplD3jfR8Pzd3XffbC69dNKI\n2+v1GqZOdfnuiofJYRHiQuyJC9UWC7cdP863jxzx+f0T/f3cfvy4+/8n6t/2muxsOlet4i+zIu+B\nL/Cmjz53UB3Aj/gR13FdwP71hzjEbna7O/VDTRNKQT6LrLAcb6wkkMDhfybReSCFm26ayqJFqW6B\nYNs2peCrigv9dYq9YhttPicXwLshIdOgTA2owdBqYb6qqhc7dsooYy97A17zYJF/cECwKj5MmmRG\no5Hc0wu+LJFACRvetu08Hnlk6CSQyaTlhRfOcIc4e+YzBEImmTzBE/yW32IkON1zN+bm8r1Jk1iZ\nFLxcLKcsc8uRI5yzZw+f27cPmyyTrNUyxce5v6y3l6+Vl3O/n1k8I9kiqeLCihXRY4mkMksVF1xC\ni8hdCC3xWi3/aWvjWF8fmzs6Rn/DONnb3e11z+bv5ALAmpQU939flpERNUHkkWbv0qXUrlzJyqQk\nJEnid9Ons33xYq7K9ragNWo0bkFmZxCmowSCWOIwh8kll0/4ZMjrTpxkkDHMOwWnK42uyYWsCGVn\neSLEBYEgBlEfoidNUh7q1dyFQMKcA2Hx4lTOP18phqSm6pk2beJ14agj91dckc8998z26z0D1kji\n5nc4TrjEhakTtADtD6q40BEj4kKjqwMie5gOiDMSvTt9fRWYJgIJOh2JE8CLczx8xEe8yIv0ED25\nE9/lu1zN1V6CQDJKp2gDDUO276CDMsp87usEitXfx3zMvdzrDhAeL3XUeYkfKqq4kM3weT7RwqlT\nilCgiugrVqjiQivNzRb27lXEgf565T6kwdJMb6/yMw8VFwZsiGZIM7iSKzmP84ABcaG6upctbOFH\n/Ij1rA94vfX1yrVGnUgYKi4M2CIBXHmlMpF56aVD86b8YenSNNavP4NvfrOIz3xm7GKREaNPa4Sx\nckNuLo/NmMHixOEnLwNlS0cHT9bV8VFHh9sqZrmrQDiYPNd1o87PPIbCwuEnF1QhK5ryFlRmu4qf\n6hSHyF0ILZIkcUGqch75j8tuKJQUGI08XVzMF9OVz14g4sLZHuLCpenR99mNFBpJYpLRiNll26HT\naFg4zHlqmet1IS4ITjeSSaaccnay0/2aAwfHUPIgZzJzuLcKTlOaRnluDydCXBAIYhDPyQWA885T\nHmznzg2dr+c998xGp5P4whfyJmQXzjnnZNHYeAl/+9sKNBr/fr6BTjUxuTAcYnJhQFzodTqxhmGc\nf7y4xYVhOiD0Go2XWJR0mhfgJyoWLKxnPW/wBh/yYaSX40bt8K6l1v1aDjmAb3HhDu7gR/xoyFSD\nHTvVVANgw8Y+9rmL/+OhiSbu5m5+xs+GiDJrWMM1XBMTuRiquJCZqfy+1e7xbdta2LBh4PfUsTsN\nzcEpSB2K2G4yaUhI8D4neE4uLEiZzlf4itsGSO1cr67uZT7zASWbooPAupNV8UAtRFdUDFyXHQ7Z\nLT6o903f+lYRvb2X87nPjTy5ORJf+UoBf/jDEvT68DxO1fT3c+7evczbsWP0jYPIP10WeOenpvLc\nzJn8fsYMnp3pu8CR5/Iv7nA46HEMFdgGo/7929ttdHYO5BXYbE527FACdaNSXHBNLpT39uKQZUpK\nlPvB3bvbRnqbYBxckKacR94Lg7iQaTBwc14ev50xA4B+p9Nv//8F8fGsSEpieWIiq0Tewpj4Vl4e\n/50/n7sKCiK9FIEgrCxlKQAHOUgvinjdTjt69MQRx2QmR3J5EcPmdHJtWRl3HT9Ovx/3FqcTTVE0\nuSAqAgJBDDJYXLj++kLmz09m/vyUkd42LtasyaS6+mLS0iKvioYKT59ofxCTC6NzOokLT548yYuN\njVyTnc13Jg3YankW3zvsdjKjoLNgJJJ1Os5OTmbJCJ2vF6Sm8of6+jCuShBu/s2/aaGFDDK4kAsj\nvRw3+eRzjGN+iQtOnJxCyQrazW7mMtf9PRmZ7/JdqqiillpaaKGTznGv72meppNOOugYEqI31/UV\nCwwWF9TpvrrUo/wtcQd5l5vQ7yrh2K9nkboplcm/WwR8SFbW0Oyn7GwTq1dncOxYN7Nne9v1eE4u\npJJKIYVUUsk+9rGa1X6vVxUXzjorg//8p9FrcuHUKQsOh4xGMzBVIUkSZnNwg+8+6ejgndZWLs/I\nYFEQJwdUUnU6tw98s9VKRhiuJbIs809XfsPNublckTXylEaiVku8RkOP08lJi4XiuJEtoxISdKSl\nGWhttVJV1cu8eUoxdt++Dvr6HKSk6N1TAdHEVLOZ+wsLmR0fj1OWufDCHO64Yx//+U8jXV02EhMj\n/5A/0bggNZXfTp/OhWnhs2adbDTStWoVCQE0Ueg0Gj5ZvDiEq4oMsizTZLMhyzI5IQ5BHW6iQSCY\n6OS5vrRoOcUppjCFdNJ5lmcpowzNBO8Nf66uji6Hgy9mZDDNbHa/3mC1sr6xEb0k8fOiogiuMPpo\njKLMBSEuCAQxht3upLFRKdiq4/0ajcTSpaG/2c7NNY++0WmEmFwYGacsu8WF08EW6WBPD590dnJe\naqrX61pJIkmrpdPhoD0GxIXPp6fz+VFG+X81bRrdDgdXjlJoEsQmFiy8zusAXMVVQbVtGS/55APe\nkwtnciYLWUgmmV7begY8F1Lo9T09ela7vp7iKQC6GJ9Q7MTJIQ4BcCu3Bs1HPxIMFhdyc81MmRKH\n8awm7BftI6dhKuuWXczdd+/n8OEuGhuV7bOzff/MGzeeg83mxGj0Luh7Zi4ALGABMjIS/k9I9vU5\naGtTOrfOOks5d3mKC2pDRk6OCZ0udA/mvz15kleamtBASMSFBJ2OqSYTJ/r7Odjby9lhuJYc7u3l\nWF8fBknyq6grSRKFJhMHe3up7O8fVVwAJbNCERd63OKCZ96Cv9Ok4UQrSdxTWOj+/7lzkyguTuDI\nkW7efrueq64SHdfBJsNg4H/y88N6TI0kBSQsTGTuOXGCn1VXc0teHr8vLo70cgSCCcuv+BXxeFtQ\nGzGykIURWlH4+P3Jk5T29FAcF+clLpx0WS3mGgxoJqCDxngQtkgCgSAgWlutHDumFLAbGvpxOkGn\nk4b4GgvCizq5UFHRjd0e/XY34cYuyzxUVMR3J00iP8RdTtFAg+vinuPj4p4cY6HOo5Gk0/Hi7Nl8\nMUMEi01EdOi4h3u4lms5h3MivRwvzuEcHuMxbuEW92tJJJFDDlq8C9cmTLzp+lrDmmH3uZKVXM/1\nLGLRuNbWSScJJKBHTwGxW1yUZXmIuABKoTduqlK0761M5MYbpyJJ0NlpZ/9+xcZouAlAjUYaIiwA\nOKfXsXrLfzDfsA2HQ+ZaruUxHmMVq/xeb0ODImKbTBp3BlVjo4WeHuV8OzhvIVSc5/Ja/6BtwBpn\nW0eH3/kD/jDX5fV/oCc8OShvuaYWzk1J8TuDpshVEKjw06deDcT2zF2I5rwFX0iSxBVXKIXv11+v\nHWVrgSD2UO/jq4J4PhMIBEMZLCycLsiyTIWrKXHaoKbEk65n7EmnQT0hUKLJFkmICwJBDHDZZVuY\nPfvf7NvX7u7Ay801RWU31+nE5MlxGI0abDbZZxjh6Y5Bo+G2yZN5fMYMdJqJf7kZSVyItVBnwemN\nFi3FFLOWtUMK9pEmnXSmMCWo0xQLWcilXEox4+vGTCGF53iOv/CXoP3e/sSf+B7f41M+Dcr+/KGr\ny47Vqgjm3uJCGvFFSqNDjpRFZqaRwkLlIXjzZsWXPyvLSDfd9NCDk9FF947MatLPbCZpQQuNjf1j\nGvn3FA9SUw2kpCgPWJWVSgF+sJVkqDjfNbW2vauLLte5ft2hQ0z65BM2u+yMxku4xYU+p5MkrTYg\nIbnIVRRQiwSjoeYueN5HqZMLsSIuAG5x4Z13GtzClmDisr6hgRW7dvH0yZORXkpYmOL6d13l579r\ngUAgCIQWm40uV57CYDtldXJBiAtDiSZbpIlf7REIYpyODhubNzdjs8n8+c+VYXtIFoyORiMxfbqa\nuyCskU536kcQF4rNZubGx6MVo5wCwYTHTPCuzy20UE01jTQGbZ+j0dSkPMTFxWmJixvoVl+xIp2G\ntyZR82IhS9IVIUb1w9+6dUBceJEXuYZr+Bt/G/VYldoTALTvSRuzSD94MmHqVKVYrVojhWtyodBs\npshkwi7LbOrooLq/n1qLBS1DbZKcskz1GIp0c1ziwsEwiQv3FhZy6qyzuD4nx+/3TB3z5ILyMzU1\n9VNR0YMkwRlnhM9ff7wsXJhCUVE8fX0O3n13IP/F6fQvCFgQfmRZ5q3mZn5VXe1XALknH7a3s72r\ni+rTpJNfiAsCgSCUWGWZa7OzuSwjA5PWu0FHFRfyoqCAHm2okwvZYnJBIBB4sn9/BzNmvOs1Ur1j\nRyuy67nklVdqqK5WHr6FuBAdDOQuiFDn0xlZlt2TC7k+bnxenzuX/cuWce6gPAaBQCAYiVSUc0Yb\nbaNsGTx8WSIBLFqUQs1Ts9n1tTO5eM4CYEBc6OxUOrWzskx0o4jtCSSMeqwTKOJCx55U9/1NoNTX\nK8Wu3Fyl+KWKCxUVqrig5lSFPvtHnV64v7KStQcOAIqwEO/xoHyst5fsrVtZuHMnTjmwwrPn5IIc\n4HvHikGjIU7r/yRO4JML3rZI6hTMnDlJJCdH/mHZXwZbI1VW9nD22RvIzf2X27pLEF1IksQ3Dh/m\nhxUVHO4N7PyzuUOxgluVnByKpUUdqrjQ6XDQ7ipmCQQCQbDIMxr5y6xZvDl3rtfrDllmo2v6U0wu\neOOUZRYlJDA7Lk5MLggEAm/eeKOWY8e6eeSRw+7XVN9ZUB6g//rXakCIC9GCmrsgxIXTmy6Hgz6n\nYgESDYFKAoFgYpCG0rkdDeKC0ajld79bxO23F3P22Up4dnGxt4Cg2iKBf+LCdVyH/tWzad2WMWZx\nYfBkQlGR9+RCOCc+z3OJC592dbG7W/k9XDUo+L7QZMLidNJmt1PaHdjU40yzGS3QZre7p+WijcAz\nF7xtkTZtUsSFNWsyh31PtKKKC2+9VceiRf9l06ZmmposvP9++CaPBIExyxU6Xh6AuNBgsXCsrw8J\nODMpKUQriy7itVrSXRaf0ZK78MapUyzeuZP7KysjvRSBQBAi/lBXx6ddSp1FiAveaCSJDxcu5OAZ\nZ5AWBZML/iVzCQSCsKB23+3Y0Up7u5WUFAPbtrUCkJSko7PTzs6dSoFBiAvRQUmJ0rVZVibEhfHQ\nZLUSr9V6dXfGEurUQmIM/wyC2OMYx9jKVuYwhyUsifRyBD5w4OBP/IkKKvgxPyaOuIDev4QlpJLK\nZCaHaIUKrbSykY1cyIXDigsA3/xmkdf/q5MLKllZRva6xIVEvL/niwUsYHqpBktDudsWJ1AGTyZE\nyhYJ4NKMDG7IycEuy5ydksLZKSlMM3sfV6fRsCY5mbdbW/mwvX2IZdJImLRaZsTFUd7by4GeHvKi\n8EF7qqvDucPhoM1mI3WUB151cqGhoZ/+fgebNp0CYlNcWLo0lYKCOKqre+nrc2AwaLBaneza1cY1\n10yJ9PIEPiiJi2NTR0dA4kKqXs9/58+nvLeXlCgo6ISLKSYTLd3dVPX3syBhdPE41Ozr7mZPdzdL\nAjiHCgSC2GJe/EDA9STRwBfViMkFgSCKqK9XHoCdTvjoo1PIssz27crkwo9/PNtrWyEuRAfz56cA\nUFraHhaLgm3bWpgx413+/vfa0TeOIX5aWUnGli38vjY2f676ESyRBIJQsYMdvMmbbGSj+7WHeIhb\nuZV66iO3MIEbLVq2sY0yytwWQIFQQAHncA7TmDbidrIs8//+XykPPHAooP3LyPyW33ITN7Ge9Wxm\ns1tcyMoavXA9VFww8SAP8izPMoc5fq2hoEApLgdrcmGwLVI4JxeMGg1/LCnhL7NmcUNu7hBhQUW1\nyBtLdsIcV6d1uEKdAyVOqyXHYEAnSdT40eGcnm4gLk4R5Q8e7KS0VLE/WL3a/xDpaEGSJG65ZRpa\nrcQdd8zkiScWAbBrV/gmjwSBsSwxkdXJyQF1xBo1Gs5PS+PW/PwQriz6GC534cmTJ2mOwCTVEdd0\nVPEw59lAkWWZFmH5JBBEFXM9xIVM8Zwd1QhxQSCIItTJBYD332+ioqKH5mYrBoOGW2+dzuTJAzdP\nQlyIDmbPTkKjgZYWq9ffL1Tcd99Bjh3r5o9/DLxIFa3Issw/m5vpdzrddgqxRsMIYc6CiYPN6eRA\ndzcfubw/I4kTJxvYAOA1tXDS9XWKU5FammAQqjBQQUXIjnHiRA+/+c0RfvKTg/T1+R9MWk89G9iA\nEycllJBG2oiTC4PJyzO7C8OgCBImTGSQgQn/Mg7UzvVgZS4UFSkdtSdO9HD8eDctLVb3WoPBrUeO\n8NVDhwK2NPLk2uxsTixfzvMlJQG/1zN3IVrZs2QJ/WvWMN+P7mZJktyfgZdeqsbphGnT4sMyaRIK\n7ryzhJ6etfzqV/NZsSIdgD172kWwc5Tyjbw8Ni1axM15eZFeStTjS1z4bW0ttxw9yuq9e6kJc9iz\nmpMxMy6wiUBfNFutpG/ZQvaWLVhdNqcCgSDypOr1/M+kSVyRmem2sRNEJ0JcEAiiCLX7DuD99xvd\neQuLFqVgNmv5ylcK3N8X4kJ0YDZr3Z2barfdYPZ2dfG1sjI67fZxHauysof33og02U4AACAASURB\nVG8ESQn/niisb2zkpNVKglbLZ1JSIr2cMSHEhdODOquVeTt3ckFpadjCVIejlFJOcYp44lnJSvfr\nmShWIkJciB6KUKyEjnM8ZMcoLx+w5vO8lxgNCxaKKKKEEn7BL1jGsoDEBY1Gorh4YHohIyNwmx51\nckH13A+UwZMLU6bEodFAd7ed6dPfBcBk0pCaGhz7krdaWnipqYleh/8izmAyDQYKxyimq+LCWKYe\nwkWO0YhWkvzeXs1deOklJVds9erwWyJ12e1YglRUNBoVwa2kJBGzWUt3t11kcwlinimu6Q7PzIXP\npqaSbzRS3tvLlG3bwhb23OdwcMQlLgRjciFdr8cmyzjwPy9GIBCEh9/OmMFrc+agCeC+QhB+hLgg\nEAzCho0HeIA3eCOsx3U4ZBobB27Wysu7eOONkwDuzqevflURF7RaKWY7uiYiqjXSvn1DC/5OWeaq\nQ4d4sbGRuyvG17X6/PMnYIUB3smg5jodbW1jH0G2OJ3jFjuChVoo+XJmJqYYzSsQ4sLpgWp7ZY2C\n0fmpTOUarmEtazEyUNDNQLESaaY5UksTDGIJS7iaq7mAC0J2jMOHBwqXtbX+F0amMpVf82t+wS/c\nrwUiLsCANVL2HGXSMlBUcaG93UZn5+j/rg4f7mLq1Ld59NEj9Pbaabcq1zL1vshk0vLwwwuYNy8Z\nrVZ5EF22LA0pCA+lfQ6H2+pnRoQm7ZYkJvKtvDy+GUOd1nUWC2fv2cNPKyux+yjgFxYO5C4ArFkT\nfkukx2trSfj443Hfq3mi02lYuFC5RxTWSIJYx9fkwqz4eLYsWsRMsxmZwIKxx8qPKyqI+/hjepxO\ntBCUqWdJktwiRTh+BoFAIJhoCHFBIBjEPvaxm928y7thPW5zswWHQ0aSYPFi5UHkzTcVcWH58jRA\nKWI/8cQinn12CfHxIo89WliwIBnwPblwuLeXw319mDUaHpg6dczHsNudihVSkQ5MSoHkwIGxTS/8\noqoK06ZN3BXEB+jxMDsuju2LF/OHmTMjvZQxUxIXx6Xp6SJUboJj0GjIdIU31kXAX9iTFFL4kuvL\nk3M5l9u4jVWsitDKogMLw3u972MflVRiJzwC63Sms451zGb26BuPEU9xoaZmfIWRQMWFGcXxLHt1\nMyv2vc4RjgR8vMREvXuqwJ+1v/BCJZWVvfzwh/v4ztZyeDsDwwVmkpIG7otuv72YffsuoLt7Lfv2\nXcA776wOeF2+qHAV1ZK1WtLDFOT6QGUlv6yuptF1zplqNvNUcTE35uYGvK8D3d1858gRnj55MtjL\nHJHn6+vZ1NHBfZWVnL13L/2Dpj7UyQWVSIQ57+3uxi7LZAT577pkiZKvIcQFQawzXOZCgcnEJ4sX\n8+eSEnLDEDLvmeUy1WzGoAlOSUu1VzosJhcEAoEgYIS4IBAMogdlzDyXwB/axoPqGZyZaeSii3K8\nvqdOLgB85zvT+frXx16kFgSfkSYXdnUpBZ/FCQmkjeOB9d13G6ir60d3luumvdQ2ZmsktSBSPQ5v\n1L1dXbzd0jLm93ti0mo5IykpIAuFsbK7q4tfVlezpWPo7+4vDQ1cceDAmKwursvJ4R/z5nFtTs7o\nGwtiGjX08aQfQaWRYBazWMMaJjEp0kuJCNVU832+z23c5vP7Dhz8lJ9yG7fRSWeYVxc6PG2RAplc\n8EWg4sK8uSk4+jVIGnibt8d0zECskTZtUqZybPka/uKy/9J/1uxzMsFk0jJvXjIJCcFpyMjQ6/nt\n9On875QpQZmEGA1ZlvlldTV3VVTQEYRpw309PTxVV8erp8Jrm1bput+QgEUJCUOmFNXMBVCyM4qK\nvMWGcLDXlaGx0I+ciEAQ4oJgolBsNvPHmTN5afZQoTxVr+e6nBy3ABFKvjNp4P4mXRe8Zju3uCAm\nFwSCkGJxOrnp8GEeqa6O9FK82NTeztMnT0btM160I8QFgWAQLSgF03TSR9kyuNTXK8WA3FwT55+f\n7X49K8voHhcXRCfq5EJ5eRf9/d6F6Z0ucWHpMB3tj9fWMm/HDh6vrR3xGM8+WwFmCaa7bqI/sY5Z\nXChwFUdrxnHhXLRrF1/Yv39cgZaR4N3WVu6qqODpujqv161OJ9eXl/NGczMvNTZGaHWCWCDPZY0U\n6cmFYLOFLTzKo2xjW6SXMi5SSKGKKuqp507uBJSf7X7u503epJ12nDjRoCGZZPf77NhppZV2Ih/W\nPRa8bZHGXhiRZZmmpoFmB3+47LJJfNb6OQC2spU2Ai+iquLCaKHOfX0OPv20ValS/79E0CkFflux\nJiw5KNkGA/+Tn88dBQWjbxwE2u12elw2QvlB6Aiuc133J4XZwu+5khLqV66kdOlSHp42bcj3PcWF\nNWsywyLceNJpt3PcJYAEW1xYulQRF3bvFqHOgtgmQafjhtxczktNjeg6zvB4pgrmpNFMly2SEBcE\ngtDyQkMDz9XXc0dFBSeiaFLojuPH+fbRo2zvVJqPuu12Ligt5Qv79vHGqVMRD3s/1tvL1YcO8bOq\nqoiuYziEuCAQDKKVVgDSSAvrcdXJhdxcMytXpmM2K11dy5cHxydYEDomTTKTmqrH4ZApK/PuhB1N\nXGi32znQ08O+EYr0dXV9vP12PfTJbMtbyF0tOdDs9Dkp4Q+TXV1F1WMUF7o9uid9TQBEM+rveV68\nd1fkB20DxbCucYR0CiY+0T65MFb2spdNbApp6HA4SCLJff2ezGRAua7vYQ8VVLgbCNJIQ8tA9/Qb\nvMGN3MjLvBzwMT/hE+7iLv7JP4PwEwROR4fN7VUP45tc6Olx0N+vPDz5Ky4YDBoe+sYFFFOMAQMn\nOBHwcVVbnMrKkUOKt21rwWp1knxVIszRQ68T7DLWJGmIVcdEQG0CyNDriQtCJtFJlyiaFwbrksHk\nGI3MS0jA7OPn8LRFWr06/HkLaqPEZKMx6HZXItRZIAgukiSxZdEiPpOS4lOsHCtickEgCD1OWeY3\nHk2VT4TZpnEkprsExmMuwaPb4WBRQgJvt7ZyxcGD5G3dyv81Ry7Trry3l5ebmvh7mKdP/UWICwLB\nIL7O13me57mUS8N63AFxwYTJpHU/XK1cGd4JCsHoyLLMb2pq2OAqSEuSxIIFijVSaelAsd3udLLH\n9cA6nLgw04/wsL1723E6Ye7cJJbMSuGrMxXLrgMHOsbUqTnDbKZ06VIqli8P+L0AVR5F1eMh7DbY\n3dXFvSdO8HIQJwn29yiFq8HiwmseF+mJVjQWBBd1ciHWPifllLOb3cN2lVegZLAUURTOZYWEh3iI\ntazlOq4DcE8otNNOP/2kkOIOvlZJRDlHdxF48e8oRznMYRpoGOfKx4bn1AKMT1xQLZGMRk3AVkLf\n5bs8x3MsZnHAx50+XekWP3p05Gm4jz5SztXn65K5MS0H0/p+OKoI3ls6J47NlYpJo+HrOTl8KWP8\nBffq/n6edD3ET4qAuDASubkm4uIU0eHssyOTtwDBn1oAEeosEISCM5OT+WDhQmbFB89CrdglLrTY\n7TRPsOlUgSBaeLulxV33ODclhS8G4f4mWAwWF3KMRm6dNIm7CwrINRhosdt5pKYmYutTG0MLwmA/\nNxaEuCAQDEKDhlRSvewSwoGnLRLAY48t5Ac/KObWW6eHdR2C0am2WHi8tpYrDx1yF/fnz1c+L/v2\nDVhqlPf20ut0kqDVum9YB1PiR5dMa6tyg5uTo3w2Zs5MRK+X6Oy0j2oh4QuDRsP8hARSx9id5zm+\nuDuEtkibOzq4v6qKl5uagrI/WZY5LzWVM5OSmO9RQLA5nfzDowthrBMdgtMDtSgXa7ZIf+fvPMAD\nbGe71+t211c1iu/pRBAXMsnkWq4liSRgQFzooIP5zOdP/Imf8TOv96jbBprDYMHCRjYCMIMZ41z5\n2FDFhbQ0Rfgajy2SZ95CoFOTk5iEGfOYjjtzpiLuDBZKBrNpk0tcODOL5+aX8NSFszEeVabNtsbY\nJJ0/FMfF8XxJCU/PnDnufX29vByr654lL8y2SKOh0Ui8+OJynnpqMXPnhvf+G0IrLoDIXRAIYoF4\nrZbJrns8EeosEIQGtTh/5+TJfLhwIWtSUiK8ogHmxsezOCHBfR4AxfHh50VFbFy4EIBtnZ30R8jl\nQJ1mLYiyBhGV4CXgCASCceFpiwQwa1YSDz+8IJJLEgxDpl7PSYsFB1BrsTDZZPI5ubDTI8xZM0yR\nZmZcHM8UF1MSF4csyz6LOW1tShEzNVUpBuj1GkpKkti/v4P9+zu87ATCQaWH9cTe7u5h1z1eFrse\n8nd3BcdGQJIkfjdjaPFvQ3s7bS6rp3SdjvggWE8IJi55MWqLpNoBqR371VTzK36FEyeP8ig3ciNV\nVJFFViSXGRLyyedrfM3rZ9MM6q8ZTVyQkXmMxyimmPM4DxOK2KtOg2SSyWpWh+gnGJnycmXNn/lM\nFq+/XktjowWr1YnBEHgPkSouZGWF98GluFg53x871o3DIaPVDr2mWCwOPvlE+Ryr3e3XX19IQlMc\n6w4dCqpNn1OWscsyBs3E6cNamJDAh+1KA0S0TS4ArF0buRB6IS4IBAJQnstqLBYO9/ZyVnL4hU6B\nYCLzaWcnmzo60EsS383Pj/RyhrAuK4t1Wb6fg2aYzeQaDNRbrWzr7OScCGTPnJOSgl2WWROl56aJ\nc8csEMQ4nrZIgugmTqtlnusB9FNX4VudXCgtbXdPM+waxRIJwKzVclNeHqtTUoYt0Le12YABcQFg\n3jx1UiL8nZqquJCp17N50aKQHWdhQgISikd0Ywi7xFVLpG/l5dG8ahV/KikJ2bEEsY8ahFoXBeLC\na01NfNze7lfAmCoupKNY7aWSyklOUk89duxcxEXczM1ITLyMn3TSuZzLWcWqYbcpoYQ/8Sce5VGf\n36+kkk1sYj3rvX5HK1nJQzzEd/gOeoLr1a7iwEEVVexjn8/vq93+Z56ZjtGo3NrX1Y2t69JzciGc\nTJkSj8GgwWJxUlPje/Jix442+vudZGYaKSkZuK6qBaD9PT10emQCjZWbDx8m/uOPWR9ES75oYIFH\n4TzaJhciic3p5IDLMjHU4sKePSLUWSCIZkSos0AQOh52TS1cnZUVlU0OIyFJEue4piw+itCk7IVp\naTw8bRqXRJGVlCdCXBCctuxgB3/gD+xmd6SXAgy1RRJEN8tdgsGnLo/nOXOS0WigpcXqDtYcLczZ\nXwYmFwYKV6qYsX9/cC5udj+KkyqquHDPlCnMiY8PWeB4gk7nDlfbE6TphcHYnU7edIkL6zLD7/Ms\niD3Um+Emmw1bAP9ugk2vw8GVhw6xZu9e+kZZhwMH6aSTRJJbXEgkkUyUz/xYAngnGgYMpJDiFfLs\nyad8CsBCFmLE+4FoJjNZyMKQra2XXr7P97mXe7EyVGhVxYWSkkTy85XCyFhzFyIlLmi1EoULE+Bv\naXyx4gBOl0i/t6uLrR0d9DocbkukNWsyvK47uUYjU00mDBpNUApCekmi3+nk6AQrLs30sGfMjbGH\n+lByuLcXqyyTpNVSGCIfY3Uyp6vLTlfX+AUwgUAQGkSos0AQGqxOJ/902RDfNnlyhFczNlRxYWN7\n+yhbnp4IWyTBacsudvEe72HCNKbwwWAiy/IQWyRBdPPlrCyKzGYucI3Emc1aiosTKS/vorS0g4xs\no3vMPljiguqnDQOTC8EQF2xOJ4kff8wko5FPlywhfZQsBlVcCNVDuCeLExIo7+1lV3c3F6UHP9y8\nz+nkm3l5bGhvD2jE8Bvl5Zzo7+fBqVNZGaWjiX+urydOq+X81FTS/MjX2NnZyT+am1mcmMjlQmgZ\nlnS9Hr0kYZNlGqxWJkcoVEu1ZUrQakkaxcpLi5Zf8+shrxdRxClOUUEFc5kbknVOFHawA4BlLAv7\nsRNIQI8eGzbaafeyd3I4ZHcIcklJEvn5cRw/3jPm3IVIiQsAOYviOJLpoNZqRSNJtNpsLNq1C4D7\nCwvZ7Apz9hX4+/6CBeQbjUGxMVIzko5OMM/tZYmJfD0nh2yDAeMEsnsaL2p+zhSTaVgLy/FiNGox\nGDRYrU46O20kJ4dmykkgEIyPz6am8uSMGSwe57ObQCDw5nBvLzaXkD8/iEHs4UQVFz7p6KDf4cAk\nrJS9EOKC4LTlOMcBmE7kA5Pb221YLErnqZhciA3OTU3l3EFeewsWpFBe3sWePW2sOC+d63NyKOvp\nYZp5fIKRGujsyxbp8OGuMXtrq1RbLFhkmXqrlVTd6JeFF2bN4nhfHyuSknx+P5gZDEsSE3mpqSlo\nuQuDSdTp+HlR4AG22zo7OegK7B4rz9XV8X5bG3+ZNSskhZ4fHD9Oi93OniVL/BIXtnV28rPqar6U\nkSHEhRHQSBLvzJtHul5PdgStRWpd4kK+0XfwrizL7Ojq4l8tLaTrdNycl4d50E3wOtZxOZczhSlh\nWXMs8yW+xKd8yhKWhP3YEhKppNJEE620eokLVVU9WCxOjEYNBQVxMTu5AJBUYgJ6MLs08yYPO7y5\n5nh+uUXpePMlLhSN4zr7QkMD7XY7l6SnM9VsZoZrX0cmmLigkSSeF7Z/QzhlU6wnM/24To6HpCQd\nzc1WMbkgEEQxJfHxlMRo4VMgiGZU+8G5IXQ9CDWeuQvbu7o4O4rCqKMBIS4ITkts2KikEoBpTIvs\nYhjIW0hJ0WMyCQU0VjnjjDRefbWGbdtauVuv56ni4qDs11fmQn6+meRkPR0dNsrLO5k/f+wXt2Ou\nAso0s9mvrr3Z8fHMHubGW5Zl8j/5hEy9nrfnzx+3n6I71Nk1BRItqAHQ/ogxw3HTkSMAfD49nWtz\ncoKyLpU2m40W1xqn+1l0a3AV8iJZMI8Vzk9LC/sxt7Od93mfNaxhNau9xIXB2J1Orikr41WX5VeW\nXs+NublDtouG61+ssNL1FSnSSKPf9eWJaok0Y0YCWq0UkLhwN3djwsQt3OIO+o6kuKAvcBV36xyA\nMkEw02ym2Wbj8Out9PQ4SE83MHducKfFfltby67ubrL0ei9x4VhfH05ZDlk3uyA6WJ2czCuzZ5My\njuu5PyQm6mluttLZaQvpcQQCgUAgiDYKTSZuzs31+7k0GlFzF15uamJje7sQFwYhxAXBhMGJk1pq\nKaBg1G3t2Lmaq6mmmmyyfW7TQw8NNGDCxCQmBXu5XqjBi2JqIbZZsUIpOG7b1hLU7n1fmQuSJDFv\nXjKbNzezd297UMSFYFzsT9ls1Fmt1FutpAfhQV0NV6zs76fFZhvVskmlur+fDL2euBCNK45XXGjw\nCANutQW/0KDaeeQaDCT4ucaTLnEh1gK2Thd2ur7yyPMSFyb5EIOuKSvjb6dOYdTAquQkrs/OIzHE\nhTNBaPkZP0PjIyqtvFzNW1AmyfLzFUuf0WyRLFgopxzAK0MikuKCNR2Qoee4ci7SSBJbFy9mf3kn\nF/1gIwA//ekcNJrgFfu77Hb2uMRrNRi60GRC58pdqLVYKIiQ9ZkgPBSYTGH5GyclKedgIS4IBAKB\n4HRjZXJy1FoJB4IqLhwRuSxDEIabgglBF138hJ/wQ35IE02jbm/GzFrW8j2+h4Tvh9R/8k9+wA94\ni7eCvVxkWeaWW3Zz8827vPIW8vJiV8kVwOLFqej1Ek1NFk6c6AnafgfEBe8ioipmbNhwalz7D6a4\noBa1C4zGoPgQpuj1THM99AcS6nzj4cMkb97M602jnw8CRZZlnpwxg0enTQuoy/9Yby8vNDTw39ZW\nSnsGPh8N1qEBreNFAs5PTQ0oR2KkTnhB5NnPfgDmMx8Y+e/1q2nTuGD+B3z2rCd4YkE81wR5MkYQ\nfnwJCzAwuTBzpuIPPXhyoaPDxv33H+LkSe9JhmYUiyEzZhJIcL/e1KTcj0RCXGg3KzZzHWX99PUp\n0wsJspbbrttNf7+Tiy7K4TvfCe60zdbOTpzAVJPJnZ+i02gocv33RMtdEESOpCSlOaKzU9giCQQC\ngUAQi1yZmUnNihX8dfbsSC8l6hDigmBCEEccDhz008+TPImMPO59qp7G/ogVgXLqlIUnnzzOM89U\nsHNnG/X1YnJhImAyaVm8WMlh2LatNWj7VTMXPAOdAS68UCkYvvdeA7I89s+82xYpCJ17qoo/wxWI\nGQyWuELV/LVGsjudbOvsxC7L7mDOYCJJEtfn5vL9yZMDmoz4sL2d68rLeeLkSbrsA8WFE/39I7xr\nbCxLSuK/Cxbwypw5fr9HiAvRSyWVNNKIDh2zUW5mR/p7FZhMLElzotPaQ3INE0QPA5MLvsWFn/zk\nAPfee5D77jvo9b5GGgHlXsezySKSkws1DtdEV52DY8eU8/199x1kz5520tMNPP/80qD79Gbo9Xw9\nJ4ersrK8XneHOovONEGQSExUJhe6usTkgkAgEAgEsUiKXk++mGj1iRAXBBMCLVpu5VYMGCillPd5\n3+/37mMfP+fn/JW/er2u2iWpD+DBxNML+fXXa92TC7m5YnIh1lmxIh2ATz5pCcr++vsd9Pcr3ZyD\nJxdWrcogLk5LfX0/+/d3jPkYwZxcuDQjgw8WLOAnU4IXELvYJS7s8nNy4UBPD90OB0laLXOiKJRN\n9XNut9u5IiuLv7sK/6EQFzz534oKzty9m/9rbh52G1mWqXGtQ4gL0cckJnEHd3AVV2FG+Xd6chQx\nSBXIQ3ENE0QP5eWdgOfkglIUr6/vw2p18tprtQDs3Nnm9b5CCrmN21jHOvdrfX0OenqUiYFwiwtW\np5Ma1S6u3sGRI100NPTzyCOHAXjmmSUhuUdakpjI8yUl/LyoyOv1iRrqLIgcYnJBIBAIBALBREWI\nC4IJwyQmcTVXk0kmOfhvAdFFFzvYwQEO8AIv8BZv0U8/2WSjQYOEFJRJCE88xYXXXvMUF4QKGuus\nXBlccUG1RNJoBrreVEwmLeeckwnAe++NrYDokGUqgigupOn1fCY1ldVBDDhampjITLPZb0/krZ1K\nsW1FUhLaKAri9BQXQLHhACVPIpQc6evjk87OEY/T6XDQ41RELJG5EH3o0XMmZ/IlvuR+bbRJkxxy\nSCV1WOs/Qexz6pSFxkYLkgSzZyuZC1lZRnQ6CafTu3nh4MEOrFan+71ppLGGNZzFWV77A9DrJZKT\n/cu3CRZV/f3IgNYGtMkcOdLFH/94AptNZuXKdC6/PD+s61HFBWGLJAgWA+KCmFwQCAQCgUAwsRDp\nfoIJxRf4Ahdwgbuz0x9UIeIYx9zhhhdyIZlk8iqvogvSP5OfVlZy0mLhm7m51NQMjNmfONHjHpEW\n4kLk+Ki9HYMkMS8+3u8AXF+oOQilpe309tqJixvf56etTflspKQYfIZYXnRRDu+808C//93AHXfM\nDHj/tRYLVllGL0luv2mL08nR3l6yDQYyA8gUCBXnpaZSvny539tv7VCmOM6MstCobIOBBfHxlLjs\nNgpdv+9TNhvddvu4Pncjkebab6t9+G5JtVCdqtMRH6IQbEHwsDqdNLqCwIcTF67kSr7Ml8O5rKhn\nPetppZWLuZjpTI/0csaNOrFWVBRPfLzy71yjkZg0yUxVVS+/+c0R97Y2m8yhQ50sXDi88OtpiRRs\n+6HRqHCJn+lWLU3AoUOdfPSRkicU7JwFf1BtkT5qb2dTeztrgiiYD6bTbuffra102O3clJcXsuMI\nIosIdBYIBAKBQDBREZMLggmFFm1AwgIMiAt2lMJbIokYMSIhBU1YAHjz1Cmera+n0WbzmlwAaC4B\nvp/AyRxH0I4nCIzvHT3KmXv2sLG9fVz7KSiIIzfXhN0us2tX2+hvGAV1cmFw3oKKmruweXMzPT2B\nj9ofd3VlFplM7i7/yw8cYN7Onbw5go1OtCLLMhtcf8NVUSYuLEhIYO+yZe4chBS93j3NEMrphTS9\n0i3Zahu+oKGKC2JqYWT2sY+XeAkHkT1X17n+XkZJIl3vu8NcTCx4IyOzgQ1sZCPdjJ7f4sQ56jaR\nZvfROqb/4BDzfrfD63U1d0G9BiUkKOeZvXtHvr6FIm/B4nTyQVsbdx4/zq9raobdTp2gK9Apx379\n9VpqavrIyDBwxRXjm1rY3tlJZYATCGuSk1mRlESXw0Gpn3k/Y+WUzcaXDx3iu8eOjSs/SRDdJCYK\nWySBQCAQCAQTEyEuCE574onnKq7iTM4EIJ30oB9DlmWv0NzaWmVyYckSJfyXpQa41MyxBEvQjy0Y\nHacsu32VZ44zAFiSJHfuQjBCndUw59RU3wXEGTMSKCyMw2p1snHjqYD37ytvQf0dHOrpCXh/keZg\nTw91VitmjYazkpIivZxRmWoykaDVcmqEwv94SQ9gckHkLYzMvdzLa7xGGWURXYenGBTuDvNYpZde\n2lCK7Ykkjrjti7zITdxEFVXhWNqYOXjiFHMf3gsXlrKRje7X1dwFgJQUPdddp2Tg7NkzsuB98qRy\nPQhWtsFzdXWkb97M+aWlPFxTww+OHx82IFm1HypOUtauZg3dcMNUTKaxT1OV9fSwYvdupm7fHtD7\n9BoNHy5YwPMzZ3LrpEljPr4/qOfdfqeT5hBeCwSRRZ1cEIHOAoFAIBAIJhpCXBAIgC/zZS7jMtaw\nhkUsCvr+m2w2epxOJGCq2eyeXLj55iJMJg3UKF2wdTrxwBEJaiwW+pxO9JLk9sEfD8HMXVAnFwaH\nOatIksRFFynTC//+d0PA+/clLsxyiQuPnzwZ8P4izXttSvHs7JQUTDFg77Np4UI6V63i3NTUkB1D\nnVxo8WNyQYgLIxOPEhCeTGSnYlYkJVG1YgX/nDs3ouuIJeKIw4Ryfs9jZOuZWmpppZVNbArH0sbM\noQ0aal8pAI3M4zzO8zwPDEwuAKxdO4kzzlDs+kabXKiqUgTlKVPGJ7Kr/O7kSXqcTnIMBhJc5+Oq\nYaa0zk9N5abcXC4ryHS/JknKfdJ42ObK4AGoDnBCzKzV8vXc3JALeEaNhhyXBaE71Po0pN1mY2dn\nJ90jCOGxTKQCnS0WB7ffvpcPPhhbNpdAIBAIBALBaAhxQSBwMYMZ3MZtHocdKwAAIABJREFUXMu1\nQd+3XpL4xdSp/L/JkzFqNG5xoaQkkc9/PheqlQeN41YRHBgJ2mw2ZprNzIqLQ6cZ/2nRU1wYr8WB\nmrkwnLgAA9ZI770XuLjwxfR0Hpw6lS+kD0zsnJeaSppOx9qMjID3Fy7+1tTEg5WV1A4qFr3XqkyL\nXBjCYn0wSdDpQl648idzwS7LJGi1QlwYAStWelCKr6lE9vOl02goMJmYm5Aw6rYf8iH3ci/v8V4Y\nVha9SEg8x3P8mT+Pap+4mtUAbGYzMtFpU+N0yhw82MGua87k/FNr0aJlMYuBQeLCV7I4dskrnLv3\nHUoPjnxNqqpSpgqCIS7YnE7KXVMKnyxaxErXJNlJq9Xn9p9PT+eZmTNZNynbvf6LLsqhqGj0z/hI\n1Hkcb4srjycamew698bixGCwWLZ7N8t272ZnV1eklxISIhXo/NZbdTz66FH+938PhPW4AsHpSp3F\nwhUHDrAtiq85AoFAEGyEuCAQ+El7u5W//a2G/v7AvbbT9HrumjKFh6dNQ5Zlty1Sfn4c69blQ7Wy\nz6N9fdid0e/zPNFYmJhI+fLl7Fm6NCj7W7IkFZ1OoqGhn+pq3xYQ/jIwueDbFgngM5/JQqeTOHq0\nmyNHAnsoX5WSwv9OmcL5aWnu14rMZurPPJNniovHtugw8POqKu6prOSARyGmz+Fgkytv4QKPn+d0\nx5/MhQemTqVr9Wp+PGVKuJYVc7SjfLZ06NwTDLHACU6wj300IrpW44n3a+pkKcq1oIkmaqkN9bLG\nxIkTPfT0ODDodNyceg1P8zQLWQjA5MmKOJCaqufCc/KpSDlA8oJ25EmtnDgxfPFavV4VFIxfXDjW\n14fVJVoWmExu4bLWj878s8/ORJLg9tvHfw2K82gY2BzFhZ4C1+/na+XlvNbUFOHVRAZ1gvJYgPkY\nsUJiYmQCndXg98bG0GU7CQSCAe6qqOCN5mZW7tkT6aUIBAJB2BDigkDgJ/fff4gvf3kbzzxTMa79\ntLZa3V7CeXkm1q2bzL3fKcEgS1hlOaTBroKR0QzqIL+7ooKFO3bwdktg9kZms5b585UC1s6dYw91\nbrRa+b+8DrjIOGygMyjdcOefnw3AH/94YszH88Sg0ZBhGP6YkUYtVHlaSGzq6MAiy+QbjW5rJ4F/\nkwsqWuHfPyzppPM0T/MQD4UtLLndZmN/d7c7wHkstKCcvzKI3kmkaMOIkZu4icUsJo3oFCrVguHs\n2UnodBqvv+/nPpfDFVfk8/jjizDotUyRFNEweX77iNZIA5ML4xfPWu12ZpjNzIuPRyNJ7rD4k358\nlp9+egmHD1/kvq6Nh9smT+a12bOBKJ9c8LBkLAyCPWMsMtHFBXVyoasrvLZIhw4p1mAtLb6nhgQC\nQXDxtOCrnKDnM4FAIBiMEBcEMYUN726fLrp4gRd4l3dHfe9zPMcbvEEnnaNu64ujR7sB2LVr7MVi\nwG2JlJVlxGjUotVK3PeTOZQkKMXQw+ImJGqo6u+ntKeHsjHYFCxYkAIMFIDGwicdHewqtsJX4ka0\nRQL49renAYq4MJbpmljDVxfsDpe39oWpqSLk1oN0j8kF5zhtusLNcY7zDM+wh8h3f2nRkk0205g2\n5HtOnGxhCw6C+2/vtuPHmb9zJ39pCNzyTKWZZkARRwT+83k+zz3cE7VTKuq1Ze7coZMYcXE6Xntt\nJV/7miIqTGUqAEkjiAsOx8BUZTAmF85KTubI8uV8vEjJsQpkciEhQceMGSOHbge6FoD9PT10RKmf\nf4pLBAYoPk3F8ZHEhS0dHfy7pYWGGM6kUAOdwz25cPCgcm/U1WXHahXT0QJBqGn3uM7s6u6O4EoE\nAoEgfAhxQRAz9NLLV/gK/8P/YEF5uGikkTd5k9d5fcT3ttDCu7zLi7xIB2Mr9qrjxGoH0FhRxQVP\nT2SAEtfDpOpRLIg84+miUycXSksHCjkOh8zdd+/nX/+qG/X9l+3fz9qDB5X/qXOMKi5cfHEuBQVx\ntLRYee216LTxCCZLExP5XFoaRR5B1D8uLOTE8uXcLax9vEh1Fa2cQGeUFtaG42M+5l3e5QM+CNo+\n97KXO7mTd3gnaPvcyEYe4RF+wk+C6tGf65oeahjGp94fvsSXuJZr3QVmwcRAFRfmzRvd5ukszmLm\nhsup/stU9uzxLS40NPRjs8lotRJ5ecHrnFenodZmZFC6dCl/KSkJ2r79JddopMhkwol3wHM0ofMQ\nxJM9hIbTiZHuuX5ZXc3n9u/nzebmcC8raEQi0Nlicbibo0CZnhYIBKHDKcvuRsEdixfzpczMCK9I\nIBAIwoMQFwQxQw01OHDQSy9GlA44fzsyt7IVJ05KKGEyk8d0/MZGRdAoL+/yGYjYTju/5tf8l/+O\nuB/PvAVPhLgQfXg+6NqdTmZ9+imf2bvXL4sSdXJh374BMeudd+p56KFyrr9+B3b7yN1jmZ6WRHXO\nETMXALRaiZtvLgLgySePjbq+WOcbeXm8M38+1+XkeL1eaDYzzTxyWOvphkmrdfuO+2ONFE2owbo7\n2EEfwZnq2sEOjnKUSiqDsr9WWnmRFwHFq3+8lkn11FNGGTIyOa7zQP04xIXlLGcta8kia1zrEkQX\ngYgLs5jFF/Sfo+tgyrCTC1VVyoRefr4ZnS74jwdZBgPzExJI1Y98LQsV6vRCtFojfSM3lzyDge/n\n50d6KRHD855r8H12lctmJJYto9TMBavVicUSngnTo0e7cTgGfpctLbE7+SEQxAJV/f30O50YJImF\nCQmRXo5AIIhynLLM1YcOcUN5uc8aYywhxAVBzFBNNQAFFLhf89dLupRSQCmyjAVZlt2TC93ddvf0\ngSdOnGxmM3/lryPuq6bG9+TCTJe4cFiIC1GD+qB7tK+Pbx89SnlvL7u6utyd4COhFnwqKnro6lJG\n4LdsUcSw1lYrmzeP3H03zfMB2o/JBYAbb5yKXi+xbVsru3ePz77rdKeqv58fVVTwcHV1pJcSFC5K\nS+PS9PSYu+gXUUQeeVixsp3t496fjMxOdgKwjGVB2d/DPEwbbeSSyxf4Ah12+7huDt/iLX7Ej1jP\nevfkwnjEBcHEo79/oBvZH3EBBqbpurJO8l7nJndzhkoww5yjkVUucSFaQ52zDQZqV67k0enTI72U\niFFoMqEBepxOGj3OebJHHllsiwsDwlq4chdUSySV5mZxLREIQonaJFgcF4dOE2t33QKBINwc6+vj\n5aYm/tTQ4NO6s7S7m5Lt27murCwCqwsMccYTxAy+xIVsslnFKmYze8j2Dhy0o3ToXeL6WsKSMR27\ns9OOxTLQae7LGsmMUojuoIMuuobd18DkgrBFinZUcaHaYuG5+no0wPpZszBrtaO+NyPD6LaWOHBA\n+bxs3ToQDP2Pf4xsjeTVfV/nGDHQWSU728QVVyhdj089dXzU7QHeeKOWK67Yyuc+9zFnn72BX/6y\n3K/3TXR2d3Xxi+pqfl1Tg80Z+x7Fb8ydyz/mzaMwxqY6JCS+yBe5iqt8nucDpYYammjCgIH5zA/K\n+m7mZqYznS9138EV+8tJ2byZn1VVjWl/MjK72AXAHOa4JxfGY4skmHiUlXXicMikpur9tjBKStIz\nfXoCBddV8HTSo/yLf3l9fyDMeWKKC6uTkzkjMdE9wRCNnO5ZQUaNhgKXeOBpjdTvdFIcF0eGXs+U\nGBYXtFqJ+Hjl/jFcuQsHD3qLaWJyQSAILepzfMlpmp0jEAgCQ20sXpSQQIqP6d7Dvb0c7usbk013\nuBHiguD/s3eegW2Vd9v/HcmW5W3HK7bjlcSJ4wyH7EGYCXsHKKu0zNICbaEUKO3zFlqgUEqhZZXC\nU2bhKQVa9m4ZgSQkhCxnOMsj3tuybO3zfjg68pJsSdZ07p8/JJHOuGNb59znvv7/64oaLuZifsfv\nOImTXK8tYhE/42ecwRlDtj3IQS7iIm7lVgAqqOBKrvTLEqmSSh6S/sjM/7fD9dru3e7FBdWe6TCe\nPe8HMheGTjpmOBf9Wq1WOqxDHzpkWabPbqffPvGDeiOJrNhYkgcJCY+UlnJW5uhdMoOZN0+1RurC\nanWwaVOH67033qgftbp56jBxwZvOBYAf/UipenzxxRr27x89RMxmc3DllZt47bV63n+/ic8/b+MX\nv9hBX190WecEgzMyMsiJjaXZauXt9vaxdwgjTWYzLzc3sz5CK3LHy8mczHf4TkBsfXLJ5S7u4kqu\ndNnrjZdiirms9/+xZnMNbzp/V97t6BhjL/c00kgrrcQQw2xmD3QuRHGIaSTxz5YWcr78krU7d4Z7\nKONisCWSpwXpTWziYz6mhRbXaytXZpBaoRRd5FsKh2yvigsToXPhw44OnmpoGFIBNisxkY0LF/Lb\nEpE9EsmoRR0HnJ0KAPFaLZsWLqR15UrqzGYWf/MNS7/5JlxDHBehzl0Y3rnQ3i6EaoHAH35fW8s5\nO3ZQNUYRoBAXBAKBL+we45qxzykqTI+CAkEhLgiihgQSKKOMfPLZwAae4RkO4L46O4MMbNhoow0L\n45tId9HFtpQNZJ/U6HrNU6jzQhaynOXo8LwQ7CnQOSkmhoVJSRybmjqiJeqS3btJ/OIL/rexEUHo\nkCSJCqdf5q0FBfwoP9+n/VUbiu3bu9m6tQuTyUFaWizx8Vqqq/tcC0TuGGKL1GgfM3NBZeXKDFav\nzsZkcnDNNZtxODwLGJs3d9LTYyM9PZZnnllMenossgxVVaOLEkcCsRoN33fmOTwd4Z+7zQYDl+ze\nzY379nm9z18bGni5uTmIo4pMYollHvM4mZMDetx5iUkcm5bGcWlpPDRtGq+UD3RZmPFeGOigg1xy\nKaMMPXpX54LR4cAQhLwMuyxTHQWVMIFCK0m0WK1R3wniTd7Ce7zHYzzGFra4Xrvn3jmkzVfEhdce\nHPp7qdoiFRUlBnq4IeeqvXu5tqpqzEUgQeQxWqgzQLxGw2aDgS29vTii0JtYzV0IVeeC+ryidjgJ\ncUEg8I832tp4o72d98coHhlroVAgEAgGsyY9nfunTuWibPdFdOp8qFSICwJBcPiMz3iTN9nFLrfv\np5BCAgnIyDQzvgU0tVo2ocToem33bve2Rz/kh9zKrUxjmtv3ZVn2aIsEsHnRIj496ihKhl081Or5\nzigLY50IPFdWxuuzZ/O7qVPH3PYwh6mmmj6Un/FgcUG1RFq5MpM1a3KA0a2R0mJjeSmvDG7qQmuX\nSEoaO+cBFEHkyScXkpCg5dNPW3n66UMet/3Pf5SK1uOPz+b73y9m9mxlvO46c45ErsrN5Sf5+dzn\nxc8+nKgLpZN13nW37DIa+UFVFZfs3k1rlC+yRgqSJPHBvHn8d/58flpQQL4+joMc5EEe5Gqu5mu+\n9uo4c5jD4zzOHdwBKKJzkvP6H+gF8a0GAyUbNrB627aoXKTzh0nOvJxoCzYfzqeftgIwf36a2/dl\nZFfxxeD5SHoeFFmn0l8fz1O/7uH11we6LNVA54lgizQlTulKOiw6fqKOscSFXJ0OCbDJMq3Duny7\nbTY+6YzsvCm1cyEUmQtm80A2yzHHZAHCFkkg8JeTJ00C4Kb9+3mywfPz2/ykJBYlJzMnMfqFeoFg\nPPTabNgmgLVwsDkqOZlbCws9umPscwqWonNBIAgSau6CmsMwHAmJyShVx000jetcOSgLwfF5/Uyd\nqTyw7trV41dgZ3e3FaNRsTbKz/f+AjFRFkSikanx8ZyblYXGCy/k53mem7iJdawDhtoiqWHOK1Zk\ncPbZeYBijTQacy3xsNXKpEk6n7yYp05N4p575gBwyy3bXILWcFRx4YQTFAGtrCwZgD17PGeGHEmU\nJiTwcGkpc53dK5GKr+KCWnU1JzGRLC/3EYyNblBwn4TE3dzNOtbRSy+v8ioy3t8z1AwfIGihzqUJ\nCXTZbBwwmfhikKXWLfv380xjI8YJaMM3yellOtx6MJrYs6eHzZs70Wolzjorz+027bTTQw8aNBRR\n5Ho9gQQezfwdFX++Hdmq5corN7sWGydSoLMQF6KXscSFWI2GbOfnuH7Qz/dgfz/5X33FmTt20Onl\n5/uwyUTmunVUbNo0zlF7z4AtUvCvQVVVvdjtMikpMa5iF9G5IBD4xx2FhVw1eTIOYLQnsj+XlrJp\n4UJX57tAcKTy+7o6Utet414/c+gECq7OhSjohhLigiAqUcWFGjxfrG7mZp7hGRaxaFznSiaZzC+O\nZtsNC1m5KgNJgo4OC62tQx9an29qIv+rr7hp/36Px1ItkSZN0pGQ4F0lOkC680HK2wcmQXhQg7yT\nURbpZ85MJjZWoqfHxrvvKtY6K1ZkcOaZeWg0sGVLl2tBxx0dHcpDoLeWSIO58cZSli2bhMFg47bb\ndox432SyuwQPVVyYNSsFEJ0L0YYqLuTGeZch8IFTXLjSafskCA6rWAXAYhZzJ3cijfo46pnJQRIX\nErVaVwuuarl32GTiwcOHuXrv3gnVzVBrMvHLgwd5o0255nXYbH4VCEQCL76oFFWccspksrPdh9sa\nMJBAAumku7VpvPvuOcyalUx3t5W33mqkq8vi8oCfCOLC7IQEFiYlDclMEkQHK1NSeG/uXP4xyFpu\nOPnOe91gcaFEr2dafDz9DgfPNHlXVNRitdJus9EWwrl1KG2RVEuk2bNTychQvmdCXBAI/CNGo+Gp\nmTP5pKKCa/PcC/sCgWCAjT099DkcpMd4v+YVjTSZzdxXU8PtB9zbtY+HHpuNZuccRXQuCARBYipT\nWer88kQ++aSR5veCjoqEhObF4zj02Eym5qdRXKy0OQ63RjrY30+DxTJqtaenvIWxuHLyZPYtWcKf\nSkt9HL0glAwXF3Q6DeXlyoK90WhHq5VYvHgSWVlxrFihtL69+abn1trOTlVc8L26XKuVuOeeuQCs\nXz8ykHjDhnZMJgeTJ+tdHQvqn0Jc8MwzjY1UbNrEb6qrwz0UF752LpyRkcGJaWmc4mzxjhQcOLiL\nu/gbf8OIcewdIpzLuIw/8kdu53YS8H/BVu1cCEZOwFW5uQC82tpKu9Xq6mA4KimJ5An0MLCnr497\na2t5wZkzYpNlDOPozNjX18czYchicThkXnxRKaq47LJCj9sVU8wVXMH/8D9u34+N1XDBBQUAvPVW\ngyvMOTNTR2Ji9P/c7ywpYfOiRXzf+fstiB4ydTpOychg6igP0e7EBUmSuMGZi/XXUSxLBqOKCpmx\nvhdw+EsoA50rK5Xr+ezZKWRkKPcRYYskEPiPJEmckJ4e7mEIBBGPQ5bZ2KOsJyxNSQnzaIJLr93O\nLw4d4uHDh7EE2AbqgLNrISs2ltQoeC4T4oIgKskll9u5nbWsDcn5mptNAOTk6Jk1S1mAHR7qXON8\nyCnSu68kBFz2NAUFvi00Zel0TE9IiIqLypGOhOQSF2DAGgmgoiLVtXBzzjlK1cu//uXZGqmzU3nw\n9UdcACgtVVpyDx/uw24fWqX7yScDlkiq5ZLauaC20gtGUm0ysd1oDHgV+Xho9FFcuHHKFD6eP59Z\nEeYH20gjW9nKB3xAHN51YYyHL/mS67mex3hszG1rqOF93veY8+OOWGIpoQTNOKdars6FIFi8LElO\npjwhgX6Hg9KNG7m3VqmKPybNvZd/tKIKM8V6PXqnfZW/1kgH+vuZu2kT11ZVsaO3N2Bj9IYvv2yj\npqaP5OQYzj473+N2EhKrWT3EEmk4Z56pLLx/+GGzy5d9InQtCCY+LnFh2H34O9nZpGi1FOv19Hkh\nHqqZDVkhFReUOaDBEPzOhcpKtXNhsLgQOXMXgUAgEExMDvb30223E6/RMDfCnjcDzbT4eFK1Wsyy\nTKUxsMVx+5ziQjR0LYAQFwQCrxgQF+JclejDq7trTMo2RaNYk/jbuRBs2iwWfldTQ5ewXRoXj/Io\n/+SfFFDgek31uQVc3QoA5503BYDPPmsdYbGlMtC5oDz4+uLZDpCbq0erlbBaZZqaTEPeG563AEqQ\np16vwWJxcOhQ9FeOBwM1VD2SWjx97VyIVKqoApTOtBh8+/7WUsuv+BUf8AEf8qFX+8jINDi/xmIH\nO3iSJ3mXd30aVyC4MjeXd+bO5bogtOFLksT/zpxJaXw8nTYbO52T4lWpqWPsGV0M/oyMN8NoWnw8\np2dkYJNlrq2qCql91AsvKF0L558/hfj48Vn+LFiQTm6unt5eG889Vw1AUdHEfgAUTAzynfe6+mGC\na0pMDG0rV/J+RQUJXlhinZSezscVFfympCQo43RHKDsX1CKo8vIUYYskEAgEgpAxPSGBlhUr+Kii\ngljNxF5yliSJRclKYelmQ2BzK115C0JcEEwU7uEeruVaejiyrFKazGYeqK3l864umpuVB5icHD05\nx7Yjxdo9iwvDOhc2bGjn3HO/5Nlnq10LtpEmLvy5vp47Dh3ivMpKj9s819TEKdu2cemuXZgmYNBn\noNCiHVKpPFRcyHD9vaQkkaOOSsNulz0GO6viwqRJyoP0G7zBb/gNO9np1VhiYjSu4PDB2Q4Gg5Wv\nv1Z89weLCxqNxMyZaqjzkfV59xZVXJgUIeKCLMsDmQsTRFyYwQyf9pOReYzHqKSSv/AXnuZpDnN4\nzP0yUcS+VlrH3LaLLgDSCH1Ff0VSEqdlZFAcpInlstRUdi9Zwivl5SxNTmZJcjKrJ1jbf2l8PBdk\nZbE8JSUgoc5/mj6dJK2WDT09PO+lv/t4MZnsvPKK8nt92WWeOxK8RaOROOMMpXvhnXcUiyfRuSCI\nBtzZIqn4soiRpdNxYno6K0IopoYqc8Fstrs6kpTMhQFbJIdDdKYKBIKJQ73ZzBddXa61GE9U9fVR\n1dcXtZlb0UaWTsfKCVas5Im1WVlcn5fH7AB3aYjOBcGEo4kmWmmlnZG+7cHCgYNtbGMf+7ATnoXs\nL3t6uPXgQX66f7+rcyE238CXZzzCqc2vs/tAx8B4ZZk6D7ZIv/51Jf/+dwNXXLGJ559Xqg6nTImc\nB/gem41H6pXFbdWv1h3fGgx80NnJSy0tXLV3b6iGF/UMtkVavjxjyHtr1yrdC6+95l5cGAh01iEj\n8yEf8i3f0oT3i1nqYtFgcWHdujZsNpmSkkRKSobeBAdCnQOrvAeCDjq4i7vYwhafuzgChRqqnh5C\nG4XR6LXb6XP6O+ZEubhwLMdyEReNmqXjDgmJn/ATkkhCg4ZzOIfJjB1WnUUWAO20j3mf6aQTgHQm\n1qK7ilaSuCA7mw0LF7Jx4UJX3sK6ri5eb22lP8oF5XOzsnhl9mx+mJ9Pxjg7FwCm6PX8rqSEn+Tn\nc0F29tg7jIO6uj6eeOIAZ5/9Jd3dVqZMiee447ICcuwzz1S6YdTn7KKiyJmbCASeGE1ciHQGOheC\nKy6sW9eG3S6TkxNHXp7eJS44HNDdLbqUBQLBxOHNtjaO2bqVn+7fP+T1JrOZh+rq6HHO9+6tqWHm\n11/z25qacAxTMIH5YX4+j86Y4bZYQZZlvwWtaOtciIzSS0FEk046TTTRRhsljGwd7qCD3exGh47F\nLA7IOfvp507uBOAf/AMt42v/9we1ralCn8i3fcrCSsNkxW+7Z2ca9YdsdHdbSU2NpdFiwSrLaIG8\nQQt8NpuDr75SRJnU1FjXhD6SqgOfaGigy2ajLCGBczIzPW63ZtIk/lRfT6pWyw+DYM8RDA709/NF\nVxc5Oh2nZmSMvUMQmDxZz+9/Pw+HQ3aFgausXZvPr361k08+aaary0Ja2tDF4cGZC5VU0kgjevSs\nZKXX53cnLrizRFJRQ53VzgWHQ2bDhnYWLkwnLi70n8PBvM3bbHV+rWIVN3NzyMcQabZIatdCslZL\nohc2EJFMmfPLH/LI48/8GQsWcsjxap800riJm1wiw2jkkMNMZpJL8AJi681mkrVaUiLkdwvgtB07\nMNjt7F2yhBkJkXPfGg9q50L7OG0Ab5gyJRDDGZV9+wzMmfMhFstAQNx1101Do5ECcvwTT8xGr9dg\nMinHF+KCIBrwlLkQDYTKFumtt5RupNNPz0WSJOLitCQmajEa7bS3W/zO8hIIBIJIo8U5n8seVvj1\n0/37+UdrKx91dvLuvHlscAYMqxY2AkGwubu6mhebm3ll9mzmJSX5vP95mZkUxcVR4ce+4UB0LgjG\nZBKTAEZ0LuxmN3dzN/dwD3/gD/yLfwXsnEYU+6BYYtERngnwN05xYbpd6USIj9dSGbcVq01H27fl\nkC7x1VdtwIAl0pS4OGIGtWRv395Nb6+N1NRYDh8+gz/9aT4///lMVq3yvIgfSvrtdv5YVwfALwoL\n0UieFyxOmzSJf5SX8+2iRRwdJWGfH3R0cMXevfylYWxP9WDy85/P5LbbRi6alpWlUF6egtUqux4E\nBzM4c+G//BeAYziGeLxXr92JC6olkrvfw+GdC3ffvZuVK//LQw/t8/qcweI0TuMszkKDhi/4gno8\nh2EHi2CJC51WK/9qbeWVlhaf9vM1zHkik06618ICKBZmx3AMs5g1poB9IRdyH/exghXjHaZHfrB3\nL9M3buT9ds9dgpt7etjX14fV4fC4TSBRg05bJ1Aez3gzF0LJt992YbE4yMmJ45575rB582ruuMM/\nAc4dCQkxrF498JkJVeFDj81GpdEY0rwKwcRBzVzostm8Cm6OJEIR6CzLMm+9pcx71e4kYFDuQvR1\nfAgEAoEnWpzPQlnDxAW1sPC9jg5eaGpir7MKfGlKSmgHKDhi2WwwsLe/n9dax7bgdcdNBQW8WF7O\nrCgJxY6c8jhBxHIyJ3MGZzCd6UNeP8QhvuEbMlAu3N6EYnpLH8pCaCLh+SDJsuzqXMg1KB+TnBwd\n/VI/m3edQvO8EjjawA03fMvWrZke8xa++EIRH1asyCApKYYf/7g0hP+LsXmppYUWq5WiuDguHsPa\nQZIkLgyy/UOgUf3p1JaySGTt2nx27erhtdcO893vDvXRHpy5cDHXMJvZTGOaT8d3Jy7s2aP8bs+e\nPXJypXYu7N7dQ2+vjYcfVnzwt27t8um8wSCTTK7gCpppBsBG6Be/UNZFAAAgAElEQVQHgyUuHDKZ\nOK+yklydzqfP2UTJWzjS+byri3c6OoiRJKaO0vp64a5dHDKZ+Hz+fFaFQOTNio3loMnkenCbCJw0\naRKpMTEsiYLKtcZGZW5xzDFZ3HHHrKCc48wz83j7bUXcDlWg88ednaytrGRlSgrrFiwIyTkFE4fU\nmBguys4mOzYWi8PhVXhzpJCcHPzOhd27DRw8aCQuTsOaNQPiYWamjtraPhHqLBAIJhQaSSJZqyV7\n2LPQ9yZPZqfRyB/q6rjaaek8Iz6ejAixthVMfNZmZfFGezuvtbZyV8lIB5iJhuhcEIzJLGaxnOUj\nrCNU3/cFKA+G3XS7Og7GixUrqc6vcFBtMtFps6GTJJKcQmNOTjx3czfnJywBIHm2noMHjfz0p1vd\nigt27Hy+URFcIqVTYTiqgHJJTo5PIXjRgiouHDSZRlRIruvq4tYDB3irrS2oYzBj5hCHPL6v5i68\n/37TiEq2wZkLevScwAkU4VuQ53BxoaPDQkuLUrVWVjZSXJgxIxlJgq4uK/ffv8dlzdTQEDkCza3c\nyu3c7vP3YrzIsuwKgQ105oJqp9ZssWDzoSr93MxMapct429lgatmFgQHAwae5Enu474hmSFGjLzV\nrtxoLs3O9mg/ZHM4qHXea0qGCdnBoiQ+nqkhOleouDA7mwenT2fNpEnhHsqYNDYq193c3OD9DM48\nM5fERC1FRQlkZoZGpFStCeZESSWWILKQJImXy8v5U2kpaVG2SKR2LoyVufDxx838/vd7sNl871JT\nuxZOOCGbxMSBQoiBzgUhLggEgonDI6Wl9KxaxY1usiPvLC6mKC4Oi3MdYJnoWhCEkDMzMoiVJCr7\n+tjb1zf2DlHOxFtNFISMRpRKt6lMZTrTqaDC1XEwXkop5Vme5WEeDsjxfMFstnPl/VsBmJuYSEez\nMgnPyVEe7isSlEDPWSenI0nwt79V89/dio2FKi48z/NcJF/EodlfALBqVWDCFwONWtE/I0pCYnyl\nMC6OuYmJrElPp3dY6/ynXV08UFfH60EUF3ro4VIu5RZuoR/3i/Pz5qUybVoiZrODl1+uG/Le4MwF\nf1E9tFVxYfduZVGnoCCepKSR1fd6vdYV8vz73w8Ed9fXR464oAnTravf4XBNTicFuHMhS6dDCzgY\n8A71hliNhgK9nmkT9DM8kdCh433eZyMbMTAQmP4ar/Gq9SMAlozy0FNvsWAHdJJEntNzPNi8XF7O\ngWXLODcrMu9hEx21cyEvL3if79zceLZsWcPnnx+PNIo14mjIsuwKTPQGVVwQD/mCIw1vA52///1N\n3HbbDh59dP+o27njzTdHWiIBrlBnYYskEAgmIu7mMIlaLY/PmOH693Ix7wgIN+7bx/Fbt2IJkU1r\ntJIWG8uJ6cra4at+WiNFE0JcEPjNGtZwARcwi1k8wAPcyZ1eBWNGOu+808inDZ0AHJWQRHOz8nCf\nk6Ms5sxKSEADJKbFcOutMwH4dJdTXHAu+CSQgE2yEVfRiE6nYdGi9BD/L7zjgFNcmKgLkzEaDdsX\nL+bNuXNHhKQecFYATwtiVW4KKaSRhgMHBznodhtJkrj22qkA3HzzVleQsizLQzIXPDG4AtodaudC\nR4eF3l6bS1xQsxXcob5nsTjQ6ZTbRENDP/IR7o+tWiJpgaQA2zBoJYnFKSmsSEmhX0zUwo6MzAEO\nYCVwvthxxJGJ0sU22EbwIAeZX/YRb6yycHmO58yIVouF9JgYivT6UfNxBOFHlmW6rFbs47xmquJC\nMDsXQOlY8ydvwWi3c19NDQu/+YbUdev4TmXlmPvIsuzK8BDiguBIQxUXDAabxzlVS4vJVdDx619X\n0tRk8vr4ra1m1q9XnknOOCN3yHsD4oLoXBAIBEcOp2VkcH1eHgVxcZyVOdRNYkdvL4/V1/PeKHln\ngqHYHA4era/n064uPu0Kv21ypPMdZ4HW3xobx/1cEOkIcUHgN0tYwiVcEnJrkmDzwQfNMFNZiE5p\nlmhuVip81M6FZSkp9B1zDP+ZP5/f/GYOCQlaLB/2c2liFgudHs5LWQqyxOQzGlhxuRW9Prh+sN8a\nDDSYzT4t/locDped0/QJKi6MRqiEFTWrZD+eq89+9rOZHHdcFkajnQsuWE93t5Vf/GIHNpuMJCmZ\nC8N5l3e5juv4P/5v1POnpMSSmqo8zNbV9bmCmkcTF9TcBYAbb1TGbzI56OoautAqI2MnusIUx0Pn\nIEskfyt8R2P9ggV8uWDBhBX7ogU7dv7Mn7mFW7iTOwMqMOShVJKqnX+qiAEwWzuNpFE6YhalpNBx\n9NFsXbQoYOMRjM4NVVWs2LKFd3186Mz56ivSv/zSdZ/xF9WOLjc38q4JJrudvzY0cG9tLd/29gIw\nyQuLGkmS2L1kCfXLl3u0ABMIJirJyco1XpbBaHQ/f9q2rdv1954eG7fdtt3r47/7biOyDPPnp1FQ\nMPTzJWyRBILw8UJTE9fs3cvnYjE2LDw6Ywa1y5eP6Pz9uLOTG/bt4/nm5jCNLDL4W2Mjn3R2snbn\nTg6bRhe0GwblsB2VlBTsoUU9F2Znkx4Tw0GTacKLWEJcEBzR/OEPe1m06GPXA7wsy7z/QRPMUCb/\nXRv7BnUuKOJCjEZDnDOfQKfTKBYyH5j5XlcmRznFhQIK0H01B6shhlmrgx86e35lJfnr1/OZDxOW\napMJB5Cg0TD5CAyDDaW4ICHRQYfHbbRaiZdeWkpOThw7d/ZQWPg299+vWBLdeutMEhJGLjg6cNBM\nM9VUjzmGwbkLamfErFkjw0zV3BRVeNDpNNxyy0wmTdIx5ZJDbOzc6QpRfoZnuJiLeYd3xjz/RCFe\nq+Xi7GzOysgI91AEQcKBgwd4gE/5FFCyhXroCdjxj+VYLuESSlBCvdppx4iRGGIopNCrY0RyeKlD\nlvnpvn3cX1sb7qEEhC29vazv6fG5myjNKRI1jTMIO1SdC/4Qq9FwoL+fUydN4s/Tp/PtwoU8Xlrq\n9f55cXGiA0dwxJGQoEWNOPNkjbRtmzKXLy9PQZLg+edr+PJL7yw81byFM8/MHfGesEUSCMLHBx0d\nPN3YyMaewM0pBeMny1kU0eaDJe1Eo6qvj+uqqli9bRuvt7Vxye7doxastlgsxGs0lOj1ZB2Ba0i+\nkqDVclWuck9+tL4+zKMJLoE1jRYIooxHH91PTU0fjz66n3vvncu+fb3U2syQnAgWme1vdhCnUR5+\nVVuk4RQXJ1JZ2UN19dAw6+23VHDwQCn/evbEoP4fem02DjoVZl/CEQcvrgejCjuS6bfbXap7sMWF\nUzmV0zmdeEY/T25uPC+9tIw1az6jp8dGRoaOp55axLnnjgynAiimGMBrcWHHjm5qawc6F4aHORsx\ncgM3oEPH7WfdQ0VFKhddVMjkyXqmTI+h5IX1PKlZzzKeIY00tGgxY6aFlrG/CROEafHxvFReHu5h\nRCwGDNixk0ZauIfiNxo0zGQmW9jCWZzFeZxHAoGrrj6BE4b8O5NM/s7faaCBWKIrmNQdh81m/uSc\nOC9MSmJ1FIQmj8Zhs7IIN8XHjIvJOh37+vvHJS6YzXY6OpT9I1Fc0EoSjw7yMRYIBGMjSRIpKbF0\ndVnp6bG6zVNRxYWLLy6gpqaPp58+xI9+tIWNG08csxN63TpFhDj1VM/iQlub6FwQCEJNsdOGt3qM\nqnBBaFEXx1vHWQwSzSRqtZybmckrra2kaLU8OWPGqGtDi1JSMK5ahcF+5LgXjJcf5uXxYF0dH3R2\nUtXXN2E7d0XngiAoOHDQRWS3/RmNNmpqlJDbZ5+txmZz8MEHTXCccxFhp5UtGzvYv19p91c7F4ZT\nXKxcHAaLC01NJvZscGBt07NiRXCrnHcalfPm6nRk+qAeZ8bG8r2cHM48AquwVTEmVasNWDDvMzzD\nszxLJ51DXk8kcUxhQeWEE7J58cWlXH/9NLZvP8mjsAC47MhaaMGI0eN2MNC5sHevwfV7OrxzYTvb\n6aWXDjpoyzrE1q0ncfvtZQDkr+xB0oDemOpaOFbzVVqJ7HCiT/mUt3hrzGwKwfj5lE+5git4lEfD\nPZRxcS7n8mf+zGVcFlBhwRPxxDONaUE/Tygo1Ov5cb5y3bp+3z7MUZwfYpdlGp0Pm/k+Vmap3YDj\nERdUn3WdTuPWGk8gEEQnqjWSweC+s1m1RaqoSOPee+eSkaFj+/Zurrvum1GrSc1mu8vKtbR0pFXF\ngC2S6FwQCEKNKi4cEuJCRFEQF8fKlBQWH8EZUPlxcfxj9my2LlrE9sWLmeVFsaokSSPyLAWemRof\nz+nONbfHJ3D3ghAXBEHhER7hCq6giqpwD8Uje/YYXH9vbDTx/vtNSt6CRSbJqmHyTgmHgxGZC8Mp\nLlYuwNXVfa7X1PbluXNTSUsL7qJAo8VCokbDPDc3gu29vbzZ1uZWjV+cksKzs2Zxz9SpQR2fL7zQ\n1MSJW7fy2LCL7ou8yCu8EjDBKhhdG+/zPm/wBmbG99B28cWFPProArfVbINJJpmbuZkHeRA9o1e1\nquLCRx81I8tKhkNW1tBK3O0onr5llLGc5UPeS1ugfN/jGia7XlPFhUjvXPiSL3mRF12+9qB46n/E\nR9RRF8aRTTzUXJEcPIcSRwuTmTz2Rj6wv6+PP9TW8lRDw9gbRzl3FReTExtLVX8/Tzc2hns4ftNi\nsWCTZTTgs3VgrnP7xnGIC6ol0uTJ+iOuu1AgmMiooc7ubJHMZju7dyu2KRUVaWRlxfF//7cMjQae\ne66GRx/1nN+lhkDr9RpXl8JgRKCzQBA+FiQnc21uLmud4a6CyKA8MZF1Cxbw1MyZ4R5K2KlISqJI\nH3mdshOFG53FV880NdFrC75tejgQcpMgKKiV1LvYxQwis21+166hnoePP36Azz5rhT47/71jJX9L\nOsQTgxazR7NFgqGdC99+q+y3dGnwLSHOzcqiJzOTHjcXqSv27GFLby9vz53rUksjme1GI//p6qJi\nUDiQAwdv8iZWrBzDMQE5T6DzFsyYsaA8rCUzMssgWKxilVfbqeLC9u1KNdysWckjFquO53gSSeQo\njhqxf1pSPM27UkiuzgGnpXYZZdzN3UFdSH6VV9nFLk7lVBaz2K9jaNFiwcLXfO0K136bt3mWZ5GQ\nuJqrOZVTkRCLd+NlH/uAgRBzgDd4gzzyqKACHe4XaDeykWyyKaQQLZGbJzAethuN/PzgQY5KSuKa\nvLxwDyeopMXG8qfSUqpNJq7JHWnNES3UOy2RcnU6YjS+1eIEonMhkvMWBAKB/wyICyPn7bt3G7DZ\nZNLTYykoUOaoq1fn8MADFfzsZ9u46aZtzJ2bynHHZY/Y9/BhZW47ZUqCW0EyM1MEOgsE4WJBcjJP\nigVsgeCIZXV6OpNiYiiIi+Ow2UzZBOz8EJ0LgqCgBlNGclWzKi4cd5xSQfDee0309dmZPFnPwoo0\nTj5xYNFUp9OQmureD1u1RTp0aEBcUI89e3ZqUMY+HI0kkRY7cnyu6klzdLRAu/O37qILK1Y0aMgk\nMyDnUW2RAiUuGFC6YLRoQ2Kj4iuquKCiBjYPZgYzuIzLmM3sEe/NbVzFf2afgeGpgQX+JJKYzeyA\n/UzcsYMdfMu34+pYWcYyADawwfXayZxMPvnIyDzFUzzCI+Me65GOCZPLEky1+DFi5AVe4F7upY2B\nMEoHDqqpRkamn37u4z5u5mb66HN77ImAKphWGo1Yo9gqyFu+k53NbYWF6HxclI8k1PtRvo95CxAo\ncUFZKBTigkAwsUhJURYU3HUubN2qzHcqKtKGCAQ33VTKpZcWYrfL/OIXO9wed0BccD+3VTsX+vvt\n9PcLr2yBQCAQCEKFRpL45+zZTI2PJ0E7MYvpovepTxDRZKNU1DTTHLRz3Lx/Pzfv388hZxW6r6ht\nx2vXTmHZsoEOg5NOykGSJI47Lht1XSQnJ86jLYHaudDYaMJkUibrqrgw3Nc+1OQ6F0XGY80QStyJ\nC+rvUCaZxASo2crVuRCg1r8YYjiDM1jDGr8q4J/maZ7giaDZ9AwXF8rKfPu9zMtTvk8NDaEVqdSf\nfT6esyfGYhGL0KKljjoaUSxa9Oh5hEe4mquJJdZtV4QVK5vYxBd84fe5JxpmzNzJnfyRP2Jn6MKE\nHj0v8iKP8AgpKOLVN3yDHTsFFJCHUq1vx84P+AE3cRMNzi+AFFJC2vUTakr0epK0WiyyzN6+iSui\nTCTqnfdNX8OcYUBcGI+wP9C5EBgRfLw4ZJkuq3VUz3eBQDA2yclKMZC7zAU1zLmiIm3I65IkuXKw\n9u41jNgP4PBh5d7iSVxITo4hJkaZo4rcBYFAIBAIQssJ6em8PmcOhRPUfmri9WIIgoIZMzXU0Ecf\n85k/5vZzmctP+AkFFARlPLIs87fGRrrtdq6c7J83tioAlJencNVVJWzY0AHAKacox0tNjWXJkkls\n2NDhMW8BlEqgxEQtRqOd2to+iosTXSHQ5eXhDQcKhO+zv7RaLBw2mzkq2fsFQ3fiQiKJnMzJAe0I\nCLQtUhppXMVVfu0rI/MZn9FLL2tYM+L9PeyhmmpKKfU79DUvT49WK2G3K4tC7joXRt9f+T41NPgn\n5PmDAwfttAOM2h3RRReHOUwKKa6OqcEkkcT1XE8JJUN89CUkTud0lrLU7fG3sIX7uI8ssjiao4Vt\nEkquyDa2AYqAfBmXDXlfi5YpTHH9+2u+BmAJS4Zsk0cebbSxjW0uQUEVHyYqGkliXmIiX/X0sM1o\nZE7SyLBNQWQxns6F3Aloi1RnNlO8YQNJWi09Rx8tciAEAj8ZrXNhQFwY2flcVKTMgzs7rXR3W0d0\nVNfVDdgiuUOSJDIydDQ3m2lvt3jcTiAQCASekWVZzIEEAjeIzgWBVzTRxG3cxh/5o1fb55LLcRzn\n92LomOOxWOi229EApQm+T45NJjsHDgwIAN/5TgEZGTqSkmJYs2bADmn1auXvkyd7friXJGlI7sK+\nfQbsdpnk5Bjy8wNXcSjLMgabjVqTCZuXthrhEhfeaW8nb/16Vm/bxpV79vBsY6PbTIjBOGTZ5XE9\nWFwopJDruI7LuTxg41udno5E4MSF8dBEE730EkMMRRSNeH8DG3iSJ/mcz/0+R0yMZsjvor/iQmOj\nCYcjNFWrMjI/4kdcwiVMwnN2yVu8xf/wP3zABx63OZ7jKabYrUDgSbiYz3z06Gml1RVUfKQTy8BC\nxna2Y2XkwshgcsghnfQh4gLAPOYBsI1t2LGTRdYQUWKiolojbevtDfNIBABNZjNTN2zgwspKHG6q\n8dWf03g6F1qtVux+VvpHmi1Sq3MekaLViodqgWAceAp0lmWZbduUbKzhnQugdDyo1kY1NcYR76ud\nC2pWgzsyMkTugkAgEPjLwf5+lm/ZwlaD+w6yiYjV4eC31dX8sKoK8xFg7SrwH9G5IPCKDJQwYAMG\nzJiJw/eHbW+xYOEJniCRRL7H94YsaKnsdtpKTI2PJ84PT+eqKgMOB6Slxbosj77++kQsFocr8Azg\nxhuns3evgeuvnz7K0RRrpMrKHqqr++juVh4WZs1KCfgDeOaXX2KRZWqWLfOqnUoVFxpCnLmwIiWF\n9JgYWq1Wnmlq4pmmJv7d1sa/5871uE+b1YpVlpEYGLe/yMijVpo/NmMG1+fnR0RLmrpwXUyx2991\ntXp/tAV2bygsTKC2to/4eK2r+s1bcnLi0GjAbpdpaTGPKrYFCi1ajuf4MbdTLdgCne8SRxwLWMBX\nfMV61lOqJlkfwZzGaRzLsWxhC8tY5vb3dTBzmEMuucxgxpDXK6jgRV6kjTaO5ViO4zgcTPzJqhAX\nIotve3s5ZDKh12jQDLtXG+123uvo8PvYWTodGsCBsig/2Q+BQu1cUMXdcNNqVeY22eO8PwsERzqe\nAp3r6/vp6LCg1UoeO59LShJpb7dQXd3HvHlDBYjBgc6eUMUJYYskEAgEvmFzOLhs9242Ggz87MAB\nPpk/tpvHRCBGkrinpgazLHNbQQHFEVCcKYhMROeCwCsSSXT9fT3rg3quXnr5lE95j/c8euxPiYvj\nf4qKuGoMS6RKo5EmNwvru3cranN5+YAAMHVqEmVlQyfz2dl6XnllOccemzXqedRQ5+pq45BjBxJJ\nkpjkDG3usI5eMawSrsyF9NhYDixdyrtz5/LTKUpF8oednZjsngPkVAuKyTodseMIAV3Per7P93md\n10fdrjwxcdT3Q8U0pnE5l3MyJ7t9vwNlgUsV+PxFzV2YOTMZjcY30SsmRuOyBgulNZI35KB0FwUj\n32UFKyijzK3d0pFKIomsYtWYwgLAURzFalaPeL2EEv7CX3iQB10ioOYImI5UOK85240jK04FgeHt\ntjYeqK31atstTpFngRuLqkStlqNTFVuSszN9D63XSpJrEd7f+2+k2SKp4kJW7NiffYEgUnimsZFZ\nX3/NrQcOALDLaGSX0eh1B3AwSE5Wnm0MhqFzebVroawsGb3efdjj4E7p4YwV6AyDxQXRuSAQCAS+\ncE9tLet7ekjRavnfmTPDPZyQIUkSec41pfox5rRbDAZ2GY2jrvkIJi6icyFCkSRpKrAEmALogE5g\nD/CVLMshLzeRkDiP86iiigoqgnquPpSuhAQSPFafz0hI4DclJaMe51+traytrGReYiJbFw8NbB2c\ntxAIBk/21ecVT8e2YeNZnuVUTvU5qDY9JoYmi4XOMSyGVAb7PofaHzA5JoZTMzI4ZdIk/tHSQqPF\nwlc9PZyQnu52e3d5C+5Yz3q+4itO5MQR+R9f8IXLuutlXuYczon4Rcs88jiXcz2+X0ghFiyuCn1/\nUcUFf0PG8/LiaWw00dDQz4IF7n+G4SCLLPToiSfwVRQrnV/hxiHLlH39NZN1Ot6YM4f0KF/c06J1\niUJHEnOTkpBQrsctFouoAA8wX3R1cdbOncjAguRkTvRwr1HZ4mxp95QL9N7cuXTYbH53uP26qAiN\nJPmV2WCzOWhujqxAZyEuCKIRuyyzp6+PIufn8K7qal5pbeW+qVO5rTA8hQOeOhc8hTkPRi1mOnRo\nqLhgsQxcM0YXF5TvQ1ub6FwQCAQCbzHZ7TxYVwfAEzNmHHHV+3k6HYdMJpeFtSe+t2cPO41G3p83\nj5Mnjc91QRB9CHEhwpAk6RzgV8ACD5v0SpL0LHCXLMvtIRsY8F2+G5LzGFEmzIO7JXxlR28v3929\nGxnYZjRitNtJ1A5UAQVaXCgpUcWFPnp7baMe+z/8h3d4h/d4j9M5nSu50uvzpMcoH1lvxQXV99kq\ny7RbrWSGYTFLkiRWp6fzQnMzH3d2jltcqKSSdaxDQhohLgyupl7NasyYg7LoPF7s2NHiviptOD/g\nBwE55+WXF7FtWxc33uifvU9enp5vvoGGBlNAxhMo8sjjJV4KWeByFVXsZCdllFFOeUjO2WyxsK+/\nnwP9/SRrvfu9EUQeiVot0+Pj2dffz7beXtaISXdAeaKhARk4LzOTY1JHhqEO57clJZyRkcEKD9sm\nxcSQFOP/NPm6fN+KBwbT0mJGlkGjgays4NlQ+oKauSDEBUE0oXap7urrY3tvL2+0tQGwxIOoGAo8\nBTp//bXSqeouzFnFU+dCQ0M/sgw6nWbUa4baudDWJjoXBAKBwFv0Wi3tK1eyt6+POW46Xic6aqHM\nWFbbdSZlnaDAj8IaQfQT2SW9RxCSJOkkSXoReB04ClATAOVBfwdIBG4AdkmStCq0owwNKaRwNmd7\n5bfujjaLhbN37sQ4qOV5nzOjQWX3bkVc8DXY1hPqZP/AgV6qqgzOY7t/cJnBDBazGAcO3uIteujx\n+jy+2iLpNBoynIsjobZGGsxqp6DwUWenx23GEhfaLBb+3txMe9MiAL7ma/oZatGzjGX81nEfmXt/\nR1vNKcQ4IuvGtp3t/Jgfcz/3h/zcs2al8O67q1i+3D97JTUQur7eO1ukzWzmci7nUz7163zeIjm/\nQsWnfMoLvMDXfB2yc6qfjVydjphxWIYJwo/IXQgOBpuNfzsXDW8vLPTKWq88MZErcnOZmeBbBk0o\nUC2RcnL0aLWREZ7s6lwQHTeCKGKW8/NdZzZzQWUlZlnmjIwMjkvz3B0QbNTOBTWjDaC318YHHzQB\ncNJJni1fB8SFoc81gy2RRutSLihQvh/uAqEFAoFA4JlYjeaIFBZgQFwYzRbJYLPR7bRDEuLCkYlY\npYgAJGUW+ApwCYqQoM4KbcAhYCvQNeg9GcgC3pUkaWnIBxxkcsnl+3yf7/Adv/Z/rrmZQyYTU/V6\n5jgrlvb2DyyIWq0OlwAQOFskZbLe0mLGbHY4Q3Pdd14UU8wd3EE66aSQQjfdXp/H184FwOWRFwni\nwjcGg0dhZCxxodZs5rLdu3n4YB+55GLG7HaBN95cyNONTdxTU0NMCG2gvCGZZOqoYwtbaKIp4Mff\nyEae4RmqqAr4sdVQUW8zF/7BPzBg4N/8O+BjCSdTmQrAQQ6G7JzedvUIIh81d2GbyF0IKP9ua6Pf\n4WBGfDyLwliRHCgaG5XrbKTkLQC0qIHOonNBEEWkx8a6LEKr+vuZrNPxt5kzQ2oTOpzp05XFqR07\nul2f9XfeacRkcjB9etIYnQsDGW+DOXxYERtGs0QCKC1Vzr1vnxC4BQKBQOAdLnFhlM6FOud7qVot\nyePo/BVEL0JciAxuBc5iqHjwBFAoy/J0WZYXyrI8CTgPqBm0TSLwiiRJ0f8kHUBunjKFh6ZN4405\nc1jsXGTYO6hz4cCBXqxWmaSkGAoKAmOZM2mSjqSkgYtoWVnymNWGf+EvPMdzFFDg/XmcF+oOH8QF\n9aFqrDa2YJIXF0d5QgIy8B8P3QtzExNZk57ObA9By+pNrdli4TzHhfyQH7KQhSO2q3G24xXGxYX1\n4dEdJZQwn/nYsfNP/hnw469jHW/yJpVUBvzYvogLNmwc5jAAN3JjwMfiDb30Uk/9iO6W0einn13s\nGnWfEpSsl0McQh7SVBY8lqWk8Ors2fyqqCgk5xMEj4qkJKgBzUEAACAASURBVPJ1OtLEpDug/L1Z\nCXS/NCcn4q77/jAQ5hw5tn7CFkkQrZQP6k56rqws7N03M2Yks2JFBjabzLPPVgPw6qvKnOn886eM\neg1TC5e6uqx0dQ0UDdXVKfMWtTPBE6q4cOBALw5HaOYwAoFAIIhu8p33TW/EhQI/s8oE0Y8QF8KM\nJEmTgDsYKizcLsvyDbIsDyltlmX5DWAlUD3o5SnAzaEZbXQgSRI/LShgTlISZc4Hij2DxAU1b6Gs\nLDlgixCSJLmqicA7uyUdvj/cqEGunV7aIgE8PH06uxYv5sLs8QUCj5c1Y1gj/bywkA8rKjg9w71t\nT1ZsLLGShAzMtCzjJE4iiZGtifudXSpFEXpju5iLAailFive/xy9QQ0Ib6BhzG23s52HeZitbPXq\n2Hl5yvdzeObCfvbzPu+zl72u1+qpx4aNZJJdi/GhZjObuYEbuI/7vN5nL3v5Jb/kFm7xuE0hhZRR\nxkpWYiE03UC5cXGszcrijMzMoJ7HgYNDHArqOY50zsjI4PCKFTxS6l/2SSTgkCNrQarJbHbdVy4J\n830uUAyIC5FzHxO2SIJoZZXTAulnU6ZwUoRk3Vx7rdIF+dRTh+jttfHuu40AXHDBlFH3S0qKITNT\n+QzW1Aw823jbuVBYmEBsrITJ5HBZKQkEAkE0sr23l9dbW9kT4G7gz7q6OHnbNg70i2ukiuqE0TCK\nE8ZzTcrSZXkE2o0KQoMQF8LPbYDaeSADn8my/ICnjWVZbgCuZkCIkICbJElyn5J7hJNnVybge92I\nC4GyRFJRfVCDcWwVf2yRZiUmMisxkYQwB8GumTSJBI0Gx9ibukUjSaxKTeXEtDQsoyxufdmt2Ewt\nSQnOz2C8zGAG93AP93P/kADq4Rgw+LzQq4oL9dSPue1nzq9NbPLu2B4yF/7Lf3mSJ4dYVBVRxAu8\nwF3chSZMt5l2lLz7SXi/kHCAA8CA9ZE7Yonld/yO67iOOJSJVj/9Xgk6kc5bvMW93BvuYUxoormq\nfovBwGnbt3PV3r1jbxxC/tHaigNYmpzM9Ah+oHn08GFO3b6d11pbx9w2Em2RXOKC6FwQRBm/KCxk\ny8KFPDBtWriH4uKCC6aQmhrLoUNGfvazbfT12SkpSeSoo8bOgnAX6jyQuTD6NTAmRkNJibL/vn0G\nf4cvEAgEYefllhbWVlbyREPgnsFkWebH+/bxYWcnD9bVBey40c5gWyTZzTrM5p4eXm5pQULJPhMc\nmQhxIYw4sxa+z9CchTvH2k+W5f8AXwzaJxm4MPAjjG7q6/v5wWkbAajq60eWZWw2B8/+qxbO1FO+\nyLOnqT+EQlzwxxYpUjgpPZ2Oo4/mqZkz/T7GJ/Pn8/H8+UyL91yZ9blTXFiVGtifrydk55cvlFM+\n6qL7VrbyPb7HQzzk03GLKWY5y1nM4lG3s2FziQHLWe7VsVVbpNZWMxbLgESUjVIp3MrQBTM9+qB0\nLVgsDk499QvuuGPHqNt1olQy+yIuvMM7AKThW9DjZjbzc35ODTU+7RdJ2LHzNm/TRpvoXhC4xSrL\nvNfRwcvNzR6zc8LBYEukSKayr4/3Ozq8CvOONFsks8OBwRnSJ8QFQbSh02g4Kjlw3cqBICEhhu9+\nV7E6/OtflQynsSyRVNyFOg8OdB6L0lKlpm3/fpG7IBAIopcWZxV9dgA7KjcbDGx3dkK8097udiE9\nmvnWYKDFYvH5/5Xn/B73Oxx0DVuHkmWZWw4oBXqX5eRw1ATIPhP4hxAXwssKlGBmlYOyLH/u5b7/\n6/xTvTKcE7BRBZAWWtjM5rAsur322mH69lngoI3i9hiMdjvPP1/DgQQz2gsT+Ney3oDaOwy2RQpa\n54IftkiRQqxGQ5zGt0uOEd/aHOtMJqpNJrTA8hB1LtRTz4VcyA3cELBjTkOprqujzlWB7w0FFHAr\nt3Iu54663U520ksvKaQwi1leHTsjQ0dsrPLQ29Q0YI2U5byEtdDi9TjHw+bNHbz/fhN/+MNeTCa7\nx+0MKBV5Gbi32XJHDsri5CpW+TSmTWwilli2sMWn/TzxrcHAXdXVvNAU+NBvT2xmM220kUyyqwNG\nIBjMkuRk5iclYZZl/tbYGO7hAHCgv59NBgNa4DsRbomkBrIf9iL/KNJskWRZ5skZM/htcbHICxEI\nAoRqjaQyliWSirtQ57o6RWgYK3MBRKizQCCYGKgdldkBLHp4etD8ttZspjLAlkvhpN9uZ8E335Dz\n1Vc+uWAAxGu1riLX4dZI77S381l3N3GSxN0l4bFDFkQGQlwIL6cP+rsMfOTDvoO3lYDjJEmKjBK3\nQbzJm9zDPXzGZyE/9xtvNIAduKqT6subaK0zc+edlbDHRuzkGDYaDdxfWxuw86mVRLGxEtOmuQ8l\nHi/R3LngKyZMXMZlXMqlXofyfuHsWliQnExyiBZADBiwOb8CRTLJLoFhG9sCdlyVYoq5hms4n/PR\n4p1dliRJbkOdc8llOtMpptjncdhxLw600EIddZgwjXjv0CFlkme1yuzY0e3x2DdxEy/zMidwgtfj\n+SW/5GEeZgYzvN4HlO9nN91UUeXTfp7YZDBwZ3U1L7eERrAB+JAPAVjNar/yYAQTH0mSuCFfEZ7+\nUFdHn92zuBcq3m1XxNdj0tICWrkWDLwJw1OJNFskvVbLtXl5/Kq4OKKqvwWCaGbu3FSWLVO6K4uK\nEli0yDuHW9XWaGA+5HAVfXjXuSDEBYFAEP2onQuB6qjstdl4yfnspc7Z1vf0BOTYwaLGZOJ/Gxsx\nO8Y2nlaLWxI1GpfVti+U6PUUxcXRO2j+/43BwJk7dwLw0ylTKIzQzEtBaBDiQniZ7/xTfVL7ytsd\nZVluZGiwsw4oD8ywAodaCdxE6CpwATo7LXz2mWLTMnNmMr29No4++j/U1fWTHxfHQ6XTAfjVoUN8\n3tUVkHMuWJCORgPLl2cQExOcj1ZpfDyPl5byYAT5xgaLwZXw8Xinm6k/y1BZIsFAhXwygW0BnM98\ndOhc9j6BJI00TuM0zuRMn/Zzl7tQQgkP8ADXcZ3Xx+mii/u4j+u4boTA8O23nVz15hP8mB/zKq+O\n2Fd9mAbYvHn0743e+eUtSSRRRJHX26uUUQYogdC+WmS5o9Rp+7U/hEFiN3Ijl3IpJ3NyyM4piD4u\nz8mhRK+n2Wrl8fp6rF48zASTDzo6ADg1QkJaR2NpSgp3l5Two/zRO4NkWXYtFEaKLZJAIAgOt99e\nhiTB9ddP91q4G5650NhoQpaV4qasrLgx958+XRUXROaCQCCIXlrUzoUAFZf8o7WVXrud0vh4Pqio\noHbZMq7JywvIsYOBLMss27KFq/fuZX2354I7lVqnuFCo1/tVKLJp4UKqly9n6SB3iIK4OPQajcha\nEAAgepvDyyyG5i3s8nH/XTCkXHgW8M34hxU4VD/2UFmmqLz3XhN2u0x5eQrPP7+ExYs/pqFBeVj/\nf/+vnGsK81jf18O67m6SAxR0XFKSSFXVqV5N7N3RSCMHOEAxxUzBfWt0pk7HD8dYmJgoNKP4aKsC\nlTeonQvHpPnmmT8eNM6vQIsL53AOF3BBRFWRq50Lw0OdfSWJJHaxCwMGKqlkHvNc7z399CFaT2ol\nF8iQMwaujk6GigsdQPiFtmlMI488pjMdCxZX0LO/THeKC4dMJmwOBzE+2on5QxppnM/5QT+PILqJ\n1Wj4n6Iirty7l/vr6rivtpY5iYn8c/ZsssLQOfDEjBl80NHB8eneVfyGk1mJifwyceyuxqqqXqxW\nmZgYicmTRQWYQDCROfvsfHp6ziUx0ftnkeGZC4cPK3/m58ej0Yy9YKRmLhw4YMRul9FqRTeSQBAJ\nyLIsugN9INC2SKol0tW5ucz2Yr4WbiRJ4oS0NF5qaeHjzk6OG2MuXGtS1sIK4vx7TnX3u5mt07F5\n4UJStFrSRCbXEY/oXAgTkiTpgeHynq+R9MO39z8pN0hMZjKppJJCaPzvVd58swGAs87KY+HCdK65\nRvE1nT49iSuuUNr6H58xgy2LFgU0dGbatCRSUvy7sH7LtzzIgzzP8wEbz3Bu3r+fo7ds4a22tqCd\nI1CoFfveeua3Wizs6lMesI5207nQQQdf8AXb2R64QQILWchDPMRKVgb0uIkkRpSwAAPt9uMVF2KI\nYSlLAfhqWMNWc7OJ+ALl51j11chJzFidC2bGthwJNHHE8RiPcRM3jVtYAMh3VoHYZJlq00hrKIEg\nnHw3J4fp8fG0Wa2022zsNBrJCNMDRYFez9V5eUyLj5wK/3/+s44f/WjLkOB7X3j99cMAnHhiNjqd\nmKYLBBOdpKQYnxYUi4qUXIXubitdXRbq6pQ5mTd5CwCFhQnodBosFodLmBAIBOHj/5qbOWrzZn51\n6FC4hxI1tFksLnuePD8XywfzQlMTG5wWSN+bPHncxwsVq52CwideOHEM7lwIJLMTEykQdkgCROdC\nOMlEqclVPTSssiy3+niMeuef6jEiLs2wiCKe5dmQntNicfDee4ryfPbZSivbAw/MIzs7jrVrpxAb\nqzysJwaoYyFQ1Dt/nL308iIvUkwxR3N0QM/xZXc3XxsMmMJsZeENx3Isc5mLNLx03QPrnF0LsxMS\n3C50fc7nPMdzLGHJkEr58SIhUej8muiotkiHD4/frmc5y/mYj2ljqNDV2momNl6ZLD77QDs3LBta\nVTdYXKis7KGvz0ZCQgwddPAUT3GQgzzO415nSUQiGklienw8O41G9vf3Mz3BuwUDgSAUxGg0/Lqo\niO/u2QPAienpaMZYGKs1mdhkMJCi1bImCiyMxsNNN22jvr6fY47J5KKLfL8vvPqqIi6sXetduKtA\nIDiySEyMISsrjtZWM9XVfS6BwJu8BQCtVmLq1ET27DGwb18vRUWRX6ErEExkzLLM1t5e4kPQqTxR\n2Ou0ji2MiyMhAGs66rrQipQUciI8w2swJ6ankxETQ4lej0OWR52Pq50LhQEQYwQCd4grWPhIGvZv\nf0pHhsfXDz9m1CEj42B8C9+ffdZKT4+NnJw4lixRFjFSUmL57W/nMH9+6OxyfGWwuPAar7GRjQE9\nvkOWqTQqvzJzoqDVL444csllMt5VD6THxHBWRgZnZma6fV/1xd/DnoD44h+JBKpzAaCCCk7lVG7m\n5iGvt7SY+aT8DN6Mv5Ctb8Xw8ssDoes2m8NVoafXa7DbZbZtU0Ql1WqphRbWs37c4ws3qjXSvhDm\nLggE3nJxTg5lTtHrZC/Egi+6uzm/spKLdu2i22YL9vDCRkeHxXV9/OijZp/3P3iwly1butBo4Jxz\njgwLRIFA4DvFxcr1t7ra6Cr4mDLF+0IEEeosEEQOxzg77jcZDPTZ7W63+WdLC788eJA9xuHLP0cm\ne51uBTMDVIB1TmYm/6mo4KOKioAcL1QU6vW0rFzJS+XlYxb61AWpc0EgUBHiQvgYLgT4430xfNUp\n6sUFAwYu4AIu47IRQa/e8sYbyiL9mWfmeeU9Gimkk04WWSxgARD4nIoakwmjw4FOklyBsROJ49LT\neWPuXH43darb96cxjVhi6aGHRhpDPLqJwUDnwvjb6LVouZZrSWDopLClRZn4XHrBNHBo+PWvK7Fa\nFcGxrq4fu10mLk7D8ccrjVpK7gLo0HEKpwDwFm+Ne3zhJhyhzgKBt2glibfmzOHP06dzec7YuTgX\nZWdTlpBAh83GQ3W+OkBGDzt2DATqffRRM7Lsm5D9+uvK/OW447L9zm8SCAQTHzV34Re/2MFjj+0H\noKDA+7m9CHUWCCKHYr2egrg4bLLMeqc1z3D+0tDAvbW1bDaIzywEXlzQSBLHp6f73QVhl2WsYXKG\nGEtUUBGdC4JgI8SF8DFcMrT4cYzh5uJRv2LcRx8OHNiwcbDPzK8OHvR5IeLtt4daIkUyrbTyKI/y\nJ/7ET/gJf+WvrGIVEHhxIVun4+25c3l8xoyQBMRGGrHEMp/5LGQhVqzhHk5UolbF1df3+7xo5g1W\nq4OODuVS+NvfziY7O46DB428847ymVYtkYqKElxdSYNzF07lVGKIoY66EXZL0YboXBBEOtMTErhx\nyhSv7idaSeI3xcUA/PHwYdqtE/MavH37gOdtXV0/VVW+VQUPWCKJrgWBQOAZVVzYs8eA1Spz9NGZ\nnH++91Zqaqiz6FwQCMKPJEmu7oXPPXjnFzmrzUUWm4JLXIiAgsnzd+4k7rPPeD2CMy1lWQ5a5oJA\noCIyF8LH8DuDP+Zuw2XHoNxtjEYjCX6qwole2u+00cYudpGP8kCdSCI7jEbuqa1lml7PT6dM8Srs\nrL/fTk2NM9T3aPf2OJGEjMwnfEIMMdzIjWjQkEMOS1lKNtnIyF5nDoxFolbL6RnehSNPVO7gjnAP\nIarJy1MmIyaTIgJkZAS28qGtTZn0aDRKMOE55+Tz178eZMOGds45J98lLpSUJLJokRJgNVhcSCON\nO7iDmcwc0RERbZQKcUEwwViblcX8pCS29vZyf20tv582LdxDCjiDOxdA6V6YOTPZq33r6vrYuLED\nSYJzzxXigkAg8Mzllxfx4YdNLFyYzvXXT2fBgnSf9ldtkfbvF+KCQBAJHJOWxt9bWvh7czN3FBUR\nN6xwo9i5IHxIiAvAQGd3oDoXxoNOo8EO1JuH1/1GDu1WK/3OzooponMh4jH6aX/m736BQogL4WP4\nbM4fCXG4VBuUGWJJSYnf+3pT3dxPP9dxHXbs/IgfAZBAAidPmoReo+GAyUSl0cicpLFdnxoblRtN\nfLyW1NSRob6RRgYZaNBgw0YnnWSQQRJJ3M7t4R7aEYGMzOd8Tg45rkwGgWfi4rSuEMH6+v6Aiwuq\nJVJWVhwajcTSpZP4618PsnGjYn00WFxYuFB5kN69u4feXhv/n70zj4+qvNv3dWYy2fcFQhYg7GEL\nsoiIokVwra3WpVRb21pb61tba62+tZvtr1Wrr9Wq1S52sbZqq1alalEEwQVEQHZkTyBAIJB9T2Y5\nvz+enDMzySQzSWZL8r38+Ekyc86ZZ8Jk5pznfu77Tk5WH2dncEZQxxQpDHHhcFsbdpcL2zB0GwlD\nC4um8cuiIj69cye/PX6cb+XnmyvxhgqGuDBrVjrbttXx9tuV3HrrhID2NSKRzjknm1GjIr8STxCE\n6GX69DS2bbuw3/sb4kJpaTNOp47VOnhiZAVhKHJeuuqFPNTWRsr77/PitGl81qNHcE5KCldmZzM3\nJbAFC0OdLXPnUtbWFhUT5fmdYzgWxeKC4VrIjY3tJlwJ0UdyAPOe0Yi8siJHVyGgP7JrV1vAoFx+\nkkACk5kMqFikh3mY27mdJKuVpRlqAvHZU6eo6vCfHFVRodT8UaPiA3I6RBorVrJRJw7BjkES/FNP\nPctZzo/4ERvYEOnhBJ0yyvgH/+h3f4kv3L0LwV9Rb4gLI0aoCUfP6COnU/cSF0aNSiA/PwFdh61b\na30fcBCTFxdHvMWCQ9c5IquUhCHCpZmZLExNpdXl4qLt2zkVwOf6YMHl0k1x4Y47JgGwZs0pszPG\nHy+/bEQiBR5tIgiC0B8KCxOJi7PQ0eGivHzgPVqCIAyMSQkJJHZO+tp1nSldVuRflpXFy9Onc0v+\n8HM2tjmdvFdXh8tj0WqsxcLkxESS+tmREEwMgSOanQtG30JhFIgxwtBFxIXIUQV4Luu3aZqW08dj\nGJ8uxix6SGany8rKaGpq6tf/gTKLWQDsYQ9FFDEOVcp7Radi/3/l5eSsX8/ndu3q9TiGcyEvb/Cs\n+ruMy7iBG8hieEcWRYJ00lnIQly4+DW/ZjObIz2koGHHzk/4Cf/m3zzGY7R3q2jpHwUFoRQX1InP\niBHqxKe4OJWkJCtNTQ727m3wEhcAn9FIQwWLplGcmMikhATqHI4BH+8d3uGP/JHDHB744AShn2ia\nxnNTpzI6Lo59ra0s3b6dmiHSv3D4cDPNzU5iYy1ce20hWVmxNDY62LixJqD9d+9WJY6f+lRfTwUF\nQRD6hsWiMW6cOpeSUmdBiDyapnmtKJ8UBV0C0cJ95eWct20bPykri/RQfJIfq9LNg+VceOHUKTY3\nNOAIYkH0ovR0VpWU8Ktx44J2TCF09HfutSzCfyMiLkQIXdfbgPIuN4/u42G6br+3/yPqmaSkpH7/\nHyizmEUyyaSS6nX75VlZWMBcd53vR231dC4MFj7DZ7iSKxnBiEgPZVhyBVdwDufgwMHv+F3QJuEj\njQ0bJZQA8B7v4WDgE9Tgdi4cPx4650JOjvo7t1o15s5V7oWNG2t6FBfWr68O+liigY/nzGHf/PnM\nTU31v7EfVnT+t4veBVpBCDWj4+NZVVJCbmwsO5qbuWjHDiqieLVXoBiuhalTU4mNtXDBBeoz/e23\nK/3u29rqpLpauTgKCiKfHywIwtBn6lR1brFp09BboCEIg5E/TlKux1+MHTsoEhjCxS+OHAGUyBCN\nFAQxFqnD5eIre/cyb8sW9gexdy/TZuOCjAwWZ/Stn0eIDOGYfw0FIi5Elr3g1dY7tY/7F+PtfgiJ\nuBAOxjOep3na7FwwyImNZWFamvnz2X4m2QznwmASF4TIYsXKbdzGUpbyU35KXLee9MHLMpYRTzyL\nWUxStxS1/uF2LgTfRu+ORXL/GxjRSO++e5qTJ5V4aIgLF16YC8Dy5cdDMp5IE6wLi2Mc4yAHsWDh\nHM4BoI027uEenubpoAlPghAoExMTWVVSQlZMDJsbGzlj82Z2R7iEbKAY4sKMGeqcZenSkUBg4kJF\nhTp3iY+3kJER/X1RgiAMfoz3qDfeOBHhkQiCAHD1iBGUn3UWPxwzJtJDiSrSOqOPnisujvBIfGOI\nCxUdHV7RTf1hc2MjrS4X2TYbxVFQVi0IfUHEhciyrfOr8S50dqA7apqWC4z1uMkOfBKcYfnmN7/Z\nz9e/vhmnc2Bvmr6wYMGK78y8KzzKjM72EBp8ceKEmnwcTLFIQuSJIYb/4X8Yg/+TuXrqOczhQeFw\nKKSQZ3iGb/GtoB0ztM4FIxbJLQ4a4sKrr1YAkJISQ2ZmrHnfokXZ2O06jzxyIOjj6S8OHGxjG5X4\nn1QMB2tZC8BsZpOOKow7zGF2sIP3eI8YYiI4OmG4Mi0piQ9nz2ZmUhKFcXFMGOQRADt2KHFh5kxv\nceGjj2pobvYW8PQuF5/G+2l+foKsVhQEISxceukoADZsqKaqKvrPaQVhOFAYH49FzgO82DlvHqtL\nSrg0KzojpHNjY7EADl0fcJfYu3V1ACxKS5PzQWHQIeJCZHnd43sNWNKHfS/0+F4H1ui6HrKlu42N\ndu68cwd/+lMZmzcHlh8cLK7KySHBYmFaYiKj/cYiiXNBCC0f8RG3czsP8ECkhxIQNmxYgvhWb0R2\nhLbQubtzob5e5bIXFSV5nWz94AdTAPjDHw5RUxMd5bCP8zg/5+esZnWkhwLAEpawjGVcwiXmbWWo\nTMYiiiI1LEFgYmIiG2bP5j8zZnhlDfvjn/8s57HHIisoNjU5+MMfDpmuqa7OhTFjksjJicPp1Nmz\np8Hcb/36KnJy/sPTTx82bzPEBYlEEgQhXBQWJjJzZhq6Dm+9dTLSwxEEQfBJYXw8izMySIuJzsVQ\nMRYLuZ29C8eDJC6cl54+4HEJQrgRcSGyrEcVOxuM0zTtvAD3/VrnV2OWbXnQRuWDNWtO43ColXaH\nD4c3fmRMfDy75s3jnVmz/Cq44lwYPBxqbWXchg18de/ebqs4yyjjLd5iD3siNLqeqUCtoM83+9SH\nF+HoXPAUFwoLExg50v2zEYlkcPHFucycmUZzs5MnnjgY9DH1hzM4A4AP+bDbfTrBd375I5dcPs/n\nmc1s8zYRF4RoIcFqJc/PwgFPHA4XX/nKJm67bVtI3ocCwenUufbaD/nmN7ewaNFaKipa2b9flaIa\n4gLA9OkqynHXLre48NJLx6iu7uCll46ZtxlirfH+KgiCEA4uu0y5FyQaSRAEof/kB6F3ocZuZ22n\nuLBYxAVhECLiQgTR1Yzq0yiBwJhxusfffpqmXQCc67FPI/BCCIZosnKle0XL4cPhz0Uel5DAiE5F\nuDfEuTB42NDQQFlbG3tbWrqJRlvZyu/5PStZGaHR9cwx1ITQcBUXjM6Fujp7t6iPgeIrFknTNObP\nd9tgu4oLmqaZ7oXHHjtAS0vk+wPmMY8YYjjGMY5yFID7uI8v8sWoEcwWsYhruMZLcBCEwcCxY620\nt7sAd89SuPnhD3eyYoU6Lyora2bp0vdwuSArK9br/GPaNCU07N5db962bZu6cCwtbTJv84xFEgRB\nCBeGuPDmmydxOFwRHo0gCEMZXdep6uhgW2NjpIcSdIJR6vxcZSXtus6s5GSmJycHa2iCEDZEXIg8\nDwDGFaYGnKdp2l09baxpWj7wFEpYMESJ3+i6HtKsorfecmeHHzkSncWpbW1OamtVdMqoUXKBHu18\nZa/qH0+xdu/aaESddKSQEtYxBUIrahIoj7wIjyQypKbaSE5WttRgrxr25VwAdzQSdBcXAK65poCi\noiSqqjr4y18OB3VM/SGJJEooYSITaUaJsa200kwzpzgV4dEppjOd67iOqUyN9FAEoU+UlroXOJw+\nHf6c8OeeK+fBB/cB8NOfTiU+3sInnyhnwowZ3hm5XZ0Luq6zfbsSGsrKmk3XnogLgiBEgrPOyiIz\nM5baWjsbNoQ39lYQhOCxu7mZXx89ygedK9+jkYOtreSsX8/CrVu7pRYMdgxx4fgAxIU/n1SLVm7M\nzQ3KmAQh3Ii4EGF0Xa8G7sMtFGjArzRNe0LTtFHGdpriCmAdeLXOHgceDuUYDx9u4uDBJo+fw+9c\nCISTJ9Wq57g4CxkZtgiPJnLYsUd6CAFRkqQmib80cmS3+xpQEzGppIZ1TIFwL/fyPM9TTHGkhxIx\nDPdCMHsXmpsdtLQ4gb6LCzExFu68czIADz20D7s98qvv/pf/5UEeZArKVTES9TqPlpJnIfqorGwb\nchdboaCszH0OYgiS4eL48Va+9rVNgOp7+fnPp/HUd69nsAAAIABJREFUU3PN+z0jkaC7c+HYsVaz\nG6atzWWet/QkLtTZ7SzYsoWHystD8GwEQRjuWK0aF1+sJrIkGkkQBi9/rKjg+4cO8fwp/4uYqjo6\nuO6TTzgvzJP8Y+PjsQItLhcVA+wmiDYuz8rivqIiLs3M9L+xD7Y2NrKtqYlYTeM6H3MjgjAYEHEh\nOngA73JnHbgFKNc07aCmaR8D1cDLQCFKgNCAFuBaXdcbCCFr154GICFBrTCPVnHBMxLJXzfDYMGJ\nk1WsMlfyB8Iv+AXXcR0b2RjCkQ2c12fM4K2ZM/mSD3U+l1wmMYlcolO5jyceG8NXwApF74KxAjk+\n3mI6Iwzmzs0wv/clLgB85StjGTEijiNHWvjXv44GbVz9pevrwxAXosW5IEQXy5cfJzf3Ne69Nzpi\ns6IZT3Eh3M6FtWtP0dbmYubMNH75y+kAfPGLY7jrrslYrRqf+Yy3o23aNCWQHz3aSn29ne3bvVcU\nGi4Md6Gzt7jwZEUFGxoauLO0NCTPxx8PlZcz+sMP+eXhwxF5fEEQQo/0LgjC4Of8zoz+tQE4F5Kt\nVv556hTv1ddzyh6+RYk2i4VxCeo8Z39LdCZh9JelmZncPWYM5/SzK+HPJ9T775XZ2WTZhu8cgzC4\nEXEhCujsXrga+Cd4tX1agHHAGYDxTmU4HKqAS3Rd3xDq8a1ZoybDPv/5QkDFIkXj6kqjzHkoRSI9\nzMM8wRP8m38HvE8VVbTSSiKJIRzZwMmNi+PCHtT9a7iGB3iAczgnzKMSAsHtXAjeiaE7Eqm7OJiR\nEcsdd0ziqqvymTrVt5slIcHKd787EYBf/WovLld0vUctZCH3cA9f4AuRHooQhaxZo0T81atFfPJF\njd2Os/O8I5LiwpYt6qJ90aIcrFb3+9QDD8ykuflKlizxXm2WkRFLXp7qYPjkkwazb8GgtLQZl0s3\nF0d0dS5Ue1z0tzmdwXsiAXKwtZWj7e10ROE5nyAIweGii0ZiscDOnfVBj7sUBCE8LOqc1P6kpYVT\nflwB8VYrY+LVucm+ME/yT0pIwKZpnPQY43cPHODKXbvocEXeeR5MXLrOnuZm2v08rzank2c7HSc3\njhrV67aCEM2IuBAl6Lreoev69SiRYRtukUHv8n8T8AQwVdf198Mxtvc+qALgppuKAGhpcVJVFX1W\nNuPi3LiQHwosZjEAb/BGQCuedXSqqQYgiyw/WwtC/wiFc8Fd5hzn8/6HHirhpZfOxmLp2ZV0yy3j\nSUmJYffuBv773+hagZdHHrOYRTbZkR6KEIXs36/caXv2hNSIOChxuFx8ZudOYt59l5+WlfFJQ+Ri\nkT7+uBaA2bO7r0yLi+veHwQwfbqKRtq1q97sW+DxdHghkx3HGzh1qh2HQ0fTIDfX+/ylwUNQKG1r\nC8ZT6BNlnY85Nn7onFcJguBNVlacGeHWVQAVBGFwkGWzMbMzcvjdANwLkzsdBOEWF54pLqbl3HP5\nQmf0z+mODh49fpxXq6p4rbo6rGMJNTM2bWLqpk1sbOj93P7Vqio6XC4K4+K4ICOj120FIZoRcSHK\n0HX9FV3X5wATgS8C/wv8BPg2sATI1nX9O7quV4VrTM1jNLKzY1mwIMucuA92NNK6+nq+tncvf6yo\n6PcxhqJzYTazmc50HDh4nuf9bt9IIx0o4UcmMYNDK620E/7S0GimoEC5YoLZudBTmXNfSE+P5ZZb\nxgNw//17o9JhJQi+MMSFysp2amujT7yPJDuam9nWpHqffnHkCIdGun8/4XQuuFw6W7cqcWHOnMAv\n/oxopN273c6FuNE2yLGy53STKdKOHBmPzeZ9Wn6oVd0Xb7EQG4G4R0NcKBJxQRCGNIYr1CinFwRh\n8OEZjfRObS23HTiAo4dV81/Py+O3EydyXj9jfPpLps1GjMV9rrPBY+L9b52FxkOFKYnqevlDP+LC\nBRkZXJ6VxVdzc7EOkWhvYXgi4kKUout6qa7rz+u6/pCu6/fpuv6krutrdF0P/6zDuXEsXToSi0Vj\nzBiliB85ElyVe1tTE385eZK3amr6fQxDXBhKzgUNjRu4gfGM53zO97t9Ekk8yZPcy73DuhMgWDzE\nQ1zP9Wxmc6SHElWExrngjkUaCN/97kRiYy2sX19Nfv7rLFu2gVdeOR6MIQpCSOjocHlF/Yh7wZvZ\nKSlsnjOHmUlJWIDGte7zj3CKC6WlzTQ0OIiLs1Bc7DuezReGc+HDD6s5dEiJJLkx6vO5tLGtx74F\nULFEAO+UlDAhMfxRhy9Om8a/p01jVnJy2B9bEITw4RZB6yM8EkEQ+oshLrxWXc01u3fz2PHjPHzs\nmM9tr8rJ4Vv5+UyKwLmFJ54T7ytqavxGOg0mzk5T53/r63t/X212OpmalMRdo0eHY1iCEDJEXBD8\nsyCWSy5X+W9jx6oPoGA7F4zcvdzY2H4fw13oPHScCwATmcj/8X+UUOJ3WytWRjGKqUwNw8iGPskk\no6NzkIORHkpU4e5cCF8sUqCMGpXA//3fTGJjLZw40ca//nWUq65abx5fEKKN0tImPBeWycrR7kxJ\nSmLL3Ll8kDMTDrmjgsIpLhiRSDNnpnVzGPSGIS5s3FiDrqsFEEXJ6j30hL3D7K7p2rfQ5nRyrF09\nvwkJkTmvKUlO5nM5OWRIuaAgDGnEuSAIgx+jd+Foezs1DgdnpqTw7fz8kD1es9PJgQHGKhniggY4\ndJ3nTw2d7rGzU9X7anl77+eqYxMS+OnYsSRZfcdrCsJgQcQFwT8pFjKWKsfC2LHqazSKC+5YpKHj\nXDDQEItcJJjABAARF7pgTIJVVrZhtwenfCsYsUgG3/nOROrqrmDt2vMZOTIOXYf9+5sGfFxBCAUH\nDni/NvfsaYzQSKIbq6ZRe0S9T6SkxACEVTTcsqXvkUhAtxL6WbPSmZiuFmrU2ZyUlqrzqa7iQllb\nGzqQarWSLZP7giCEEE9xQSIlBWFw4tm7kB8by6vTp5MQognrDfX1JL//Phfu2DGg4xQnJjIhIYFv\n5uUBQysaaW5KCuVnncWWuXMjPRRBCAsiLggB8W6nnWvMGHVBHOxYpKUZGdyan8+C1MCjBrpy4oRR\n6Dy0nAtC5DDEhSMcQUcutgxycuKw2TR03e0YGijBikUySEiwct55OWZJYWnp8BEXOujg1/ya//Jf\nHDgiPRzBD0bfgtWqRGSJReoZIz5q7lw1wd/c7KS11dnbLkHDXebcN3EhOTnGdH0ClJSkM6FTXCBb\nRbhBd3HBiEQan5CAJhm8giCEkAkTkrHZNJqbnRw9GjxXqiAI4eUHo0dzVmoqr82Ywai4/i/YanI4\nWFNb2+P9hqPycFsbTY7+X2s8OWkSB+bP5xdFRdg0ja1NTexsGhrXbDaLhULprBKGESIuCAHxfp0q\nIQyVc+HaESN4fOJElmRm9mv/jg4XVVXK/TAUnQtCZCikkAd4gKd4StwjHlgsmrnKbd266qAc01iB\nnJMzcOeCJ+PGqfesQ4eC+54VzRzgAB/wAS/xElbEYhvtGK6ac8/NBkRc6A1DXCgpSSc2Vp3ChiMa\nSdf1fjsXAFPkBOVcKDAu+HMsbN6suqa6iguVHR1YiFwkkiAIwwebzcKkSSmA9C4IwmDmCyNH8uHs\n2ZyRktLvY7Q4neSsX8/i7ds52ubbIZodG8vITlflJwOMRgLluvh0VhYwtNwLgjCcEHFBCIiPm5po\ncTpN58Lhwy1RZZs9eVJ98NlsGllZ/Y9WEgRPrFiZxCTiCO6E91DgkktUD8t//3siKMcLZiySJ+PH\nqyLS4eRc2M1uAIopFlFsEGA4Fz77WWUJP3KkhZYWcZz4whAXioqSTCEyHNFIR460UFtrx2bTzOLT\nvjB9unufkhIPcSHbgt2uzqVWFzZx7tat1HeuALwpL4/WRYv4/aRJA38CgiAIfpDeBUEQABKtVjNe\naU3nAlNfTEtKQkO5F4LB/+Tl8YPRo/l6Z0SSIAiDCxEXBL+Mi49nUVoap+12xoxRHzRNTQ5qa+0R\nHpkbzzJniQ/wzZu8yRM8QT2yIkkYOJdckgvAm2+exOkcmNDocunm6uNgiwuGc8HINR8OfMzHAMxi\nVoRHIgSCIS6cfXY2WVmx6Drs2ye9C77wJS6Ew7lgRCLNmJFGXFzf3UCGcyEx0cqECcnkm86FzmNZ\n4O+WKj6or+e5ykpzv1iLhUzpWxAEIQyIuCAIgsHiDOXSfKeXaKS/FxfTfO65XDtiRFAec0lmJveP\nG8fkxET/GwuCEHWIuCD45cPZs1k9axZj4uNJSLAycqS6KA52NFJXWloc1NcHJmAM5TLnYPE6r7OK\nVRziUKSHIgwBFizIIi3NRnV1hxnrAUoo6Ct1dXYcDrVfqGKRhpO4cBmXcQ7nMIc5kR6K4IemJgcV\nFerza+LEZIqL1eTOUCh11nWd5cuPc/x48PK7PcUFQ4gMh7hgRCL1tW/B4IILRpCebuOqqwqwWjXy\nYzsdlvEaJGtQ4BYsvhHkFXuramr4x8mTHGqVHHVBEHrGcGWJuCAIwuL0dABW19Xxs7Iy1tbW4uyS\nWpEXFxeywmhBEAYfIi4IfunqBDDcC6ESF+rqOvjpT3eRm/saEyeuCCjywChzFnGhZ8YzHkDEBSEo\n2GwWLrxwJAD//a/Kxty4sYbs7OX8+Me7+nQs4288Lc3Wr1XBvWHEIp082UZz8/CImlnEIu7gDjLp\nX4eNED4OHFAiQk5OHBkZsebK0cHQu9DS4jBdF75YvryCK65Yz003bQ7K49XVdVBXpxYcjB3rGYsU\nDnFBxQL0p28BIC8vgerqz/K3v80DIN5qJVXvfK/LsRA/Uz2Xs1NTsQbZffm7igq+tHcvb9bU+N9Y\nEIRhi6dzIZqibwVBCD8L09KwaRrH2tv5+ZEjXLpzJx0uV6SHJQhCFCPigtBnxo5VVrUjRwZe3tOV\n5cuPM3bsf/nFL/bQ2Ojg9Ol2/vGPcr/7GSs/8/Kk+LAnxjEOiH5x4U/8ibu5my1sifRQBD9ceqm7\nd8Hp1PnmNz+mttbOyy8f69NxQtW3AJCREUt6uooVMVY9R5IDHGAVqzhG335HwtDEKHOeNEmJYMXF\nqoBvMKwc/d73tjN58pusXOm7eO/tt1W8z3vvncbhGPgFqfH3m5MTR3JyTNhikXRdN2OR+utcALBY\nNK/FGqNsne6FHCtjS618Mm8ej06YMKCx+uJou/r9mD0PgN3l4uZ9+3ji+HGZLBAEAYCJE1OwWjUa\nGhxBdZwJgjD4SLRaWZDq7otakpHRZ5fC2tpanj5xgj3Nkb/+EgQh9Ii4IPSZsWND41xwuXRuvXUr\n9fV2pk9P5StfGQvAX/9a5ncFjTgX/FNMMYtYxDzmRXooAOj4/jctpZS97KUVubCJdi6+WPUubN5c\ny7337mHrVrW6t6ysuU/xSEYhe7AjkQyiKRrpFV7hCZ7gIz6K9FCEKMBY+T9pkhIV3LFI0S8uGBPu\nf//7EZ/3r1tXBUBLi5OdOwfe9eMZiQSELRbJWOgA7pW9wWBZ7gh4uRWqnRTkJVCclMTc1OAd3+CY\nD3FhX0sLfzxxgrtLS4mRnipBEIDYWAsTJyqhezAI3IIghBajdwHg8qysPu//t8pKvrpvHy9XVQVz\nWIIgRCkiLgh9ZsyY0DgX1q+v5tixVlJTY9i0aQkPP1xCXJyFXbsazEnL8vIWrrxyHa+9VuG1rzgX\n/DOJSdzO7VzABZEeCq208iW+xE/4Ce14Tww1oC5oUgn+JIsQXHJz45k9W2Vy3nPPbvP2tjaX2YMS\nCNu3q7/vyZNTgjvAToxopEOHmkJy/L4wk5kAvMu7PQpswvChJ3HhwIEm7PboXlFuOI5ef/1Et7HW\n19u9BIUPP6we8ON1FRfCFYtkCAtJSVaSk2OCdtyfTShi1EsOOOQkPz805y66rlOSnExxYiKFHuLC\n9s5VhDOTkrCIuCAIQidG78Lu3SIuCMJwx+hdAPh0P8SFo23qWnB0XGgWjwmCEF2IuCD0mVA5F55/\nXsUfXXllPvHxVjIyYrniinwA/vrXwzidOtdf/xGvvlrBL37xide+DQeOcWnCHkaPTgzqmKKRHexg\nOcu7xapsYxsrWclRjoZ1POtZz0u8hJ3AyrdBuROaaeYkJ4nD+4SjETXZlkJoJpqF4GJEIwFMn55q\n/g2WlgY+kb9hg8oCX7Cg7yeugRBNzoVzOZdYYjnKUQ5yMNLDESJM11ikwsIEkpKsOBx6VIhhvWFM\nutfV2XnvvdNe923YUI1n2o7xNx4oR4+20N7u9LqtJ3Eh1M4Fd2xb8J2RxntTqMQFTdNYMXMmn5x5\nJiOMEmlge5N6bc1KTg7J4wqCMDjx7F0QBGF4c3ZaGl/LzeWeMWMY1Q+BoLzTOTk6XpIlBGE4IOKC\n0GcMcaGsrHlAhV8VFa04nWp/h8PFiy+qyfJly0ab23z1q2MBeO65cu6/fw8ffKBsdTt31psZzo2N\ndh5u+R1vjPwrczr29Hs8g4XlLOdpnuYTvAWW1azmd/wu4K6C9aznAAdwMLCS2xWs4FmeZR3rAt7H\nmFQ1SqY9+Qbf4Ot8nRxyBjQuITxcckmu+f1vfzvbnCQ9dCiwiXynU2fjRjXxOH9+aAqIx41TY+qL\n4BEqkkjiU3yKJSwhkf6Jod///nbmzl1FbW1HkEcnhBNd17s5FzRNY8oUIxqp57LkSNPc7KC11T35\n/+qr3m5CIxLJmDTvi3Nh5cqTjB37Bt/73nav27vHIqmL1Z7EhWAVkhqF86HohJk7V73nlZSk+9ky\nuGzrFBdKRFwQBMEDERcEQTCwahp/mjKFnxUV9XlfXdfNzqdCcS4IwrBAxAWhz4wfn0RsrIWGBke/\nC1I3bqwhP/91LrroPex2F++8c4rTp9vJzo7lggtGmNstWTKSvLx4amo6+MlPvGNXDhxQF8eH3viY\n+XFqtX5G+d4BPLPBwSjUSvGTeJdoVqEmc7LJ9nsMBw4e53Hu4i6OcxxwxxH1lTbUxEtfVmEb205k\nYrf7FrKQS7mUJJL6NR4hvCxYkMXdd0/h4YdLOO+8nD5P5O/Z00BTk4OkJCvTpqWFZIzjx6vXUqCC\nR6j5Jt/kW3yLfPL7vK/d7uKJJw7y8ce1vPLK8RCMTggXVVUd1NXZ0TR3dBdgZl5HQwF5T3Sd0H/1\n1eNek/nr1ikx4dvfVgXFBw82BewweOSRA7hcsGbNKa/bDx5U7ynG37M7Fql7BNvf/36EhISXWb58\n4H8joSycv//+GXz00QVcfXVB0I/dG9tFXBAEwQfGedju3Q09dmd1dLjYu1fEB0EQeqbKbqfN5UID\n8kVcEIRhgYgLQp+Ji7Mya5ZaZWesOO4rq1ZVArB69SnuumsHzz+vxIFrrinEZnO/LK1WjRtuGGv+\n/JnP5JnRKdu2qZx2/v1v94EPDv2YkZGMBLqLC9WoyZws/EfLHOYwbbSRRBJ55PFNvsmX+TJ11PV5\nPJdxGQCHOBTwPjdyI3dzNwtY0OfHE6ILTdO4774Z3H77JMA98RdoBNGGDep1e+aZmVitocn+NgSP\nvhZNRyO7dzfQ1qZcW//974kIj0YYCLt3q06CwsJEEhKs5u1ZWSq+JpqdKYZQkJMTR1KSlWPHWtmy\nRX1+2O0u8+/6sstGMWWKcmV89JF/98Lhw8289Zb6bDt4sMl0N9rtLvM9xehmMcSF5manl4sC4IUX\njtLe7uJHP9o1YAdDKGOREhKsnHlmJhZL+HoPTra3c8puxwJMTxIRXxAEN1OmpJCYaKW+3s6ePd0F\nBF3X+exn11Fc/Bbvv3/axxEEQRDckUgjY2OJs8iUoyAMB+QvXegXZ56prPz9FRc8ix5/85sDPPvs\nEQCWLSvstu2NN47FZtMYMSKOp56awxlnKGFj27Y6cDgo2vG2e+NDgU9wD1bGMIapTGU0o71un8hE\nxjOeEYzoYU83e1DxUVOYgq3zP+ibQOD5uLOYxRmcEfA+GWRwJmeSR16fH0+IboyJ/EDz4o0s9rPO\nCk3fAqgc+5gYjY4OFxUVrSF7nHCwaZP7PXflyko6OqK79FfomRUr1CT6Oed4u80yM5W4UFMTveKC\nMeFeUJDAxReraLRXX1Uuge3b62hpcZKebmPq1FRzQUAg0Uh//nMZhhZgt+scOaIEhbKyZhwOncRE\nK3l5KmopNTWG2Fh1GtvVFbFjhzrH2L27gVWrvB0QfSWUsUiRwChznpSYSKLV6mdrQRCGEzabxXzP\nfu+9qm73v/zycd58U312vfuuiAuCIPhGypwFYfgh4oLQLwYqLuzapS78jUkVu12noCCh2yQLwMSJ\nKWzdupStW5cyYkS86ZrYtq0O3n+ftI56OvTOC+RDhyBIOcvRykxmci/3soxlXrffyZ08xENk4j+3\n3oaNfPIpphiACXRGV/SjYDaffO7hHq7l2j7vKww9+upcMFYzh6pvASAmxsKYMarfIFqikfrL5s21\n5veNjQ4z214YfLz2muop+MxnvEVWQ1wYLM6Fz35WxXstX66ejxGJdPbZWVgsmjlR5a/U2eFw8ec/\nlwFgLHIzCq89uymMVf6apvksda6vt1Ne3mL+/Mgj+/v5LBWhjEWKBGbfgrgWBEHwwbnnqmuxrs6E\nlhaHVxfO7t0SjSQIgm/KpW9BEIYdIi4I/WLevAwAtmypw27v28pZldWpJgr+/vczzULYL35xTI/R\nANOmpZmrFQ1xYevWOlydkUh/aZqLbrFAYyOclpU0/riYi/ktv+VzfA5wiwtllEVyWMIQwHAunDrV\nTmOjvddtGxrs5sXp/Pmhcy54jisaSp0HguFcMCY6JRppcHLwYBN79jQSE6OZK/8NMjKi37ngKS5c\ndtkorFaNnTvrueWWj3n7bRV7uHChmqAyXEkbN9aYMUcGVVXtZpfCG2+c4MSJNnJy4rj0UtUtZIgK\n+/YZ4oJ3R4Cv3gVj8UJqagyaphwivuI9AiWUsUiRQPoWBEHojXPPzQHg/fervGLlHnxwH+XlLWaE\npZQ+C4LQE0aZ8+j4oXHuJAiCf0RcEPrFpEkppKbG0Nrq7PPKlX37GnE4dNLSbIwZk8jLL5/Nq6+e\nzT33TA1o/+nT07BYoKO6Dv0tFYn0d8fZUNgZqTQMeheChYa6QFjIQn7Db7iTOyM8ImGwk5ZmMzPj\n/RXSbtpUg67D2LGJ5OaG9uRz3Li+OSqikbY2pxkpd+edkwE1Iaujs5zlfMRH2Old0BGiA8O1sGhR\nDmlpNq/7BkMskiEujBgRR2ZmLD//+TQAfv/7Ut54QwlehhNx6tRUUlJiaGpymD0ToISFWbPeZuLE\nFZxxxtv8+Me7APjqV8cybVoqAAcOGM4F9dXoWzAwRDZP54IRiXTOOdmmK+Sxxw70+7kOuVikTnFh\nlogLgiD44KyzMomJ0Th2rJUjR5QL7PDhZh54YC8A9903HTCu5ySaURCE7pR3xiKJc0EQhg8iLgj9\nwmLRmDevf9FIxuTY9OmpaJpGfLyVz342n/j4wLJ/ExKsTJmSyrWJ27E6OtjZkYtrylS0CRMgJQVq\na/0fRPAinXTGMAYrkr8sDBxjIt+IIGpqcrBjR/ey8I8+Cn3fgsH48X3rgohGtm+vw+HQycmJ46ab\nirBaNfbsaWRH+XGe5mke5MFID1EIEENcuPzyUd3uc8ciRa9Q5OlcAPjRj4pZtWoRBQXKYWizuc8R\nrFbNjD1bvVr1H+i6zi23bOH4cdWBsm1bHbt2qYUKN91UxKRJSkTo7lzwFhd8xSIZ5xgzZqSZRfN/\n+9sRqqu9exkCJZpikW7Zv5+FW7awt7l/Immr08m+FjVZ2JtzocnhGHARtiAIg5PExBjmzlUO9ffe\nU27wn/1sN21tLj71qRy+//3JJCRYaW93+V1EIgjC8EScC4Iw/BBxQeg3/e1dcIsLaf1+7FklaXw7\ndT0Af22ay6wzMuDxx2H7drjssn4fVxCEgWNM5BsRRF/+8kZKSt5mzRrvYtUNG0Lft2AQ7c4FO3aO\nc5wjHOlxG6NvYd68DNLTY1m4UIkyr328G4AccsxydiF6qavr4P33VVfG5Zd3L7UfbLFIBhdcMJKd\nOy/kjjsm8eSTs0lIcIvVS5eOBOB//3cHL754lOeeK+ell44RE6OxcuUiHn/8DM4/P4cf/nAKEyem\ndBMXjK9dnQvuWCRP54ISMmfMSGPRomxmzkyjtdXJ66/3HCF2+HCzz4hHl0unqip6YpE2NjSwvqGB\nQ21t/jf2QY3DwaL0dCYnJDAqNtbnNg6Xi9QPPiDx/fc53RG9r0FBEEKHZzRSeXkLzz5bDsD998/A\nYtEoLlbvxRKNJAiCL052nj9IobMgDB9EXBD6zUDFhRkz+i8uXJFVyozYkzS6YvlL0zzVw5CQAJrv\nzgYhOPQlcsWFWKWHK57OhVOn2syi15UrK81tdF03C17D4VyIdnFhAxu4lVv5A3/ocRujb2HuXPXe\na+TSf3RURcHl0X2iWog+3nzzJA6HztSpqaYQ54lnobPLFZ2rx42oIE9xASA9PZaHHirhppvGed3+\n3e9O4vOfL8Ru11m2bAM33/wxAPfcM5WlS0dy660TWLPmfO69dwbg7lY4cqSF06fbOXGirfP23mOR\ndF03HRAzZ6ajae5OC0PQ6cqTTx6kqOi/3HXXjm731dR04Or8KMvO9j0ZH06MFYDl/RQX8uPieGfW\nLPbOn4/Ww/lSld2ODrS7XGTExPR3qIIgDGIWLTJKnat46KF9OBw6n/pUjtmPNXWqiq6TUmdBEHxx\ncP58TixYIP1OgjCMEHFB6DeGuLB7dz1NTY6A9wuGuPCpstcA+HPTmdS7EigpSe/3sYTAeYzH+Bpf\nYx3ret2ummr+h//h5/w8TCMToglP58KLLx4zS1w9hciysmZOn27HZtPMkvZQYhQ6nz7tv2g6EoxE\nreyupLLHbTydC+AWF3Z+1M5UfSoTmdhtnw0bqrnllo85fDg6RZXhyH/+03MkEkBGhnKf6LoqPY9G\nfDkXeiM21sKzz87nG98Yh8sFzc1Ozjwzkx+dI4qaAAAgAElEQVT8YIrP7XNy4khLs6HrsGKFchyM\nHBnXrZ+iayzS0aOt1NfbiYnRTJfDuecak2Snuz3Otm113H77dgBTBPXEcERkZsYSExP5U2ZjBeCR\nfooLgXDKrl5z2TYbMZbIP2dBEMLPwoXZaJqKpHvqqVIA7r672LzfEBfEuSAIgi8smkZuXByxch4h\nCMMG+WsX+k1eXgL5+Qm4XLBlS2A9Bw0NdsrLVd5vU5HGrE2b+O6BPhYt7ttH9u4NOHWNRxvOQdMG\nJlQIgVNKKTXUkEhij9u4cPEIj1BJJeMYh050rrwVQoenc+G558rN2zdvrjFXYr/7rpromzcvM+C+\nlYHgWTQdje6FXNTq6hpq6KB7FElTk4M9e9RFvOFcmDJFTZ4eea6Q71b9lC/whW773XffHn7/+1IW\nLHiHrVuljybSOJ06K1acBHxHIgHExVlJTFR/E9EajdRXcQFU98Lvfz+bX/5yOuedl8Ozz87vccJe\n0zTTvfDaa0pc6Opa8Hx8QwQwIpGmTEkhNlYd25gk27+/icpK96R8c7ODZcs20NGhrAllZc2cONHq\ndfxoK3P+XE4Oj02YwNU5OSF7jMrOKIORPcQmCYIw9MnIiDXja9vaXMyZk8GSJSPM+6dNU/eJuCAI\ngiAIAoi4IAyQvkYj7dqlXAv5+Qkcs3awvbmZA62tfvbqwl/+AsAKvYTDjkwmTUohKUms+6GmhRYq\nUCs7iyjqcbvlLGc3u4kjjsUsRkOiqoYbns6F9eur0TSIi7PQ0OAws9MNceG880I3SdaVaI5GSiGF\nBBJIIYU6updfb9lSi8sFBQUJ5OaqaBSbzWKucjcmQbtiFOaePNnGokVrWbnyZIieQe+8+eZJNm/u\nW4TeUKSiopW6Ojs2m9ZrHJgRjRSN4kJrq5PmZifQ9x4CTdP40Y+KWbv2fCZM6N0qb4gJb7110utn\nT4zHP368FV3XfTojPSfJPvjAHY307W9vZd++RvLzE5g4UY1l3bpqr+NHU5kzwHnp6Xy7oIC5qakh\newxDXBhhk/4WQRjOGNFIAHffPcUrSs1wLuzZ02C6UwVBEARBGL6IuCD4R+/5pNEQF3rKMu6K54W/\nISpMTEgIfCynT8OrrwKwZtzlAGGJVBGgllrO4ixSSCEd379zBw7WsAaAG7mRfPLDOUQhSsjLSyA2\n1mJmlZ9/fg7z5qn3ik2b1Or5995T7xnhFBe6Fk1HExoaf+EvPMMzjGBEt/vdkUje5dfG5Kqxkrwr\nJ08aWfXJNDU5+PSnP+Djj8PrYCgra+ayy97n/PPXmuW4w5Vjx9TnXn5+AlZrz8Kru3ch+mKRjNea\nzaaRmho6Yd8QExobVexi1zJngOLiFJKSrBw/3so//lFunmPMnOn9GeWORlLvO+vWVfHXvx5G0+DZ\nZ+dz4YUqlsxTfABPcSHyZc6B8E5tLRvq6wd0DCMWSZwLgjC8ueAC9b44ZUoKV17pfT5fVJREXJyF\ntjYXR45E34INQRAEQRDCi4gLgn9OnTK//fnhw3x+924+aVYnkkuXqhPP//yngjVrTvnc/fXXK3j8\n8QPY7S7zwj/77EQ2NCgr7YS+iAtvvgkdHVBSwqRl5wE951YLwSWPPGqp5SIu6nGbGGJ4gAe4mZtZ\nylKv+17kRf7MnymnvIe9haGC1aoxdqw7OusLXxht9gRs3FjD0aMtlJU1Y7VqnH126MucDTzjmqKR\neHqewDRW/c+dm+F1u7Gi2pgE9cTl0s3bV6w4l09/ehR2u86Xv7yR9nZnsIbtl48+qjZz9h99tI8x\neEOMo0dVLGBBQc/RcqBW20N0Ohc8I5F6KgUOBkYsUk8/gyqQ/vGPpwJw1107+Ogj9XfSNSqxa+/C\nww/vB+DGG4s477wcFi5U969b11VciK5YpN749+nTXLB9O+dt28be5v6/x0kskiAIAFdckcczz5zJ\nG2+cg8Xi/V5vtWpmNKOUOguCIAiCIOKC4J8jR8xvX6+u5oXTpznU6TqYMyeDm28eB8BNN22mudm7\n2PnVV4/zmc+s4zvf2call77Phg01cHcK/zi3kQ86V9dNTOx9ksWLzZvV18WL+cY3xnHixOVcd93o\nATy5wU0ppTQRnlXYGhr3cz/Xc32v2yWQwMVc7BWHpKPzNm/zOq9zCt8ilDC0MFwCNpvGVVcVmC6n\nTZtqzEik2bPTSUkJX/SGUeocjc4Ff2zfrt4vzzjDe0V218x5T2pqOnA4lPOsoCCRv/51HiNGxLF7\ndwM///knIR6xm61b3TFPjz9+kPr66FuNHy4McaGwsHdRPZpjkYwJ9770LfSHrjFIvpwLALffPpGJ\nE5M5ebLNjDzrLi4oh9S2bXXs2FHHq68eN/cFTHFh69Y6r/OYaItF6omtjY3csGcPAB26zjf370fv\nxXXaGxKLJAgCqBi7L31pjHnu1BUpdRYEQRAEwUDEBcE/5e6V5jmdF5un7e7JoQcfnElhYQKlpc38\n8Ic7zdu3bKnl+us/MlOVVq06peI4dnlPLHnFIn38MfzqV9DeQ3RGhcr8Z84cNE0jNzc+pCsno5lV\nrOIO7uBLfIm97A3LY/a3P6GCCk5zmhhimM70II9KiEYMceHii3PJzIw143y2bq3j7bcrgfBGIkF0\ndy70Rlubk337VFdFSYm3uGBMevqKRTIikTIzY4mNtZCdHcfvfz8HgAce2BtwV85A2bLFHcNUX2/n\nyScPhuVxI0FtbQff+942tm3r3psB7lgkf84FdyxSeMQFXdc5dKiJp58+zPLlx3vdtj9lzv3B6EEA\ntUq2pwmuuDgrjz46y/w5Lc3WTbzJz09g3LgkXC748pc34XIp56VRSjp6dCKFhQk4nbrpfoDBEYt0\nsr2dz+7aRYvLxblpaSRYLLxbX88zlZV+963s6OCxY8d47Ngx8zaJRRIEIRCk1FkQBEEQBAMRFwT/\neDgXRvgQF1JTbfzpT3MBtSr161/fzK9/vY/LL/+AlhYnF144ks2bl5gX+9raduI6BYEYTWN0XOcE\nha7D7bfD+vVegoYXL74I69bB3LnBfpaDDk/HQm8Fy9HAVrYCUExxr9EvwtDh1lsncNVV+fzqVzMB\nNbGfmRlLR4eLf/3rKBB+ccEQPA4fbh5UBYSffKIKE7OyYhk1yvvvx5j09FXoXFmpbjMKoAGuvDKf\n664bjcul3Gb9Xd0cKLqum86F225Tq8QfeeQALS2O3nYbtNx//14eeeQAS5e+6zOHOlDnglHUHQ7n\nwiuvHKeg4HUmTFjBV7+6iSuuWG+KWb4Il7iQkmIzX7tFRUnExvZ8ynrJJaPMiMQZM9J8LjowopEM\n4cdwLRj4ikYaDLFIX967l6Pt7UxOSOA/06fzs7FjAbjj4EGqOnp//VR2dHDbwYPc63GeJ7FIgiAE\ngjgXBEEQBEEwEHFB8I/HReens7K4e/RoFqSmem1y4YW5fP3rReg6/OlPZXz/+zuoqGhj6tRUXnhh\nAXPmZLBx4xKuuiqfn36vmCuy1UX8uPh4YiydL8ODB+HoUdi3D3Jzex5PXh7ERe+FfrhYxCJSSOEi\nLiKO6P59lFDCF/hCtx4GYegyeXIKL710tnnxqWmaGY3U3u5C0+Ccc7LDOqb8/ARsNg27XefYsZaw\nPvZA2L5dTYaWlKR3mzTtLRbJcC54igsAjz46C02DnTvrqaoK7eT1sWOtVFd3YLVq/PKX0ykqSuL0\n6XaeeqospI8bCZxOnX/8Q31eVlV1cMUV67tFBRrOhcLCwJwL4RAXfvvbg1RUtGGzaSQmWgH3a84X\n4RIXwN2z4KtvoStPPjmba64p4Kc/nerzfiMaCdT700UXeZ9nGO9HnqXO0R6LVN7WxsraWjRg+YwZ\npNts3F5QwIykJKYmJdHk7L1bxXCjVtvtuDqFRlNckFgkQRB6wTi/27OnAZdr8CzYEARBEAQh+Ii4\nIPjHQ1y4esQI7hs3jkXp6d02+93v5vCvf53FD384hauvLuCyy0bxxhvnkJamLlBzc+N56aWz+dnP\npnFrfj4xmsaSDI9y0rfeUl8XLoQU39nKgptMMnmGZ7iZmyM9FL8UUsi1XMu5nBvpoQgRxCh1BjVR\nnp4e3pWxqmh68EUj7dih+hZmzkzrdl8gsUgjR3pPjGZnxzFmjJrc3rMntCsOjUikadNSSU6O4Y47\nJgHw4otHQ/q4kWDVqkpOnGgjI8PGiBFxbNtWx1e/usnLHeIudPbnXAhfLNLx40rwePPNRSxbVgj0\n/rowXmvhiAqaMiXV62tvFBQk8sILC1i6dKTP+w3nAsB3vzuxW0Gp4Vz48MNq09kU7bFI/z6t+mvO\nTUtjcmd/lc1i4e2SEtbOmsXYhN5fZ9mdAoITqHU40HXdjEUaIc4FQRB6Yfx45ShrbnZSXj54FmwI\ngiAIghB8YiI9AGEQUFGhOhD8uAWsVo1rry3k2msL/R7ynPR0TixYQHqMx0vQEBcuumggox129LcH\nQRDCjeFcgPBHIhmMH5/MgQNNHDrUxKc+NaLH7R599ABNTQ5+9KPiMI7ON57Oha4Y4oIv50Jlpbqt\nq3MBoLg4lcOHW9izp4FFi0L3b2FEIhlF1PPnq9fAwYODr1TbH888o4T4664bzbJlo1m8eC0vvniM\nz33uKMuWjcZud3HihBJ8osm5YIgLhYUJ5iT+3r2Rj0UCJQI4HC6+9a3xAz7WxInJLF48gqqqdr70\npTHd7p8xI42UlBgaGx3s2lXPlCkpZvl4tDoXtjSpv6Orc7z/hgONNLJZLFyamUmCxYJD13HqOo9O\nmEBlR4fEIgmC0CsxMRbGj09iz55GDh5sMhdvCIIgCIIw/BDnguAfl6vnDoT+outkr11LTHW1+vnY\nMdi1CywWWLIkuI8lCEJUYJQ6AyxaFN5IJINASp0bGuzcfvs2fvzjXeZK80ih63qvzgVjgrc354Iv\ncWHKFOUO27On50nkYOAWF5Rrxei9qKxsp6lp6PQuNDTYeeUVVYR8ww1jOeecbG65RU2IGzE7J060\noetgs2l+J+bdhc72XrcbKA0NdvPfIS8vwXxd9CYuGEJWOMSF4uJU/vzneT2WOfcFTdNYvfo8tm+/\nkKSk7mtrrFaNBQuyAHj33dNmZFhMjEZ6enRGBP29uJhP5s3j+pG+3RqB8MbMmbw0fTojY2OJsVi4\nJT+fnxUVEWeRSwRBEHpnwgT13jwUFwwIgiAIghA4cuUgBMaqVcE93ptvws03w9VXQ10drFypbp83\nD7KygvtYgiBEBSNHxnP++TkUFCSweHHProFQEoi4sHdvI0aSTaSLCisq2szOAiPf2KCddnI6V1TX\n1HRgt7u87nfHIvl2LkD4YpFmz1bOhYyMWHPivLR06ExGvPTSMVpbnUyZkmLGf82Zo77u2qV+x+5I\npMRukTxdMWKRQu1cMFwLaWk2kpJiTHFh377GHjO0w+lcCDcXXqgm6V988ZhXmbOvguhooTgpiUzp\nRxAEIQKIuCAIgiAIAoi4IATKgw/CffeBj3LAvc3NNDr6uAJ1xQr1tbwcbrvN/bNEIgnCkGbVqvMo\nLb007H0LBsbK+UOHer4Q9hQUwiUu/Oc/FfzrX0e7Teju2KFW/k+enEJ8vNXrvid5kruyv0nhdYcB\nqKrydi9UVvbsXAiHuFBV1W4WGHtGOo0frwSeQ4cGT++FL379631cd90GnnjiIH/4QykAN9wwxpyI\nnjFDOU127apH13WPMufec/AhfLFIFRVqTPn5akxFRUnYbBqtrc4eXTtDWVz4/OcL0TTlNtm0SQlj\n0dq3IAiCEGlEXBAEQRAEAURcEALhG99QX596Cm68EbZtM+9aW1tL8aZNfG3fvsCPZ7fD2rXqe6sV\n3nsPNm9WP194YXDGLAhCVGK1athskfvoCcS5EG5xoba2g6uuWs+yZRs466zVbNxYY963fbuKRCop\n6R6JdIAD1Gv1xDvUxX3XaKTeYpGKi9UK9aNHW0MWT2REIk2YkExqqntltSHwDObJiOZmB3feuYPn\nnz/KrbduZePGGjQNvvhFd5b/lCmpWCxQXd3ByZNtXs4FfxjiQmurk7a27qJ+sDCcC4a4EBNjYeLE\nnqOR2tudNDaq18tQFBcKChLNPphHHz0ADM3nKQiCEAxEXBAEQRAEAURcEALh5pvhsccgNlYJAVde\naQoO/++IKrB88fTpwI+3aRM0Nqr4o4cect8+cybk5wdz5IIgCF4UFSlxoaamg7o636vCPQWFUMcG\nAWzbVofDoRwLO45W8I2t9/OVnT9RP3c6F2bO9C5zbqCBE5wAIP5oHuBd6uxwuEyxwVcsUlZWnDlp\nundvaJ5j10gkA2Myojf3SLRTVdWOrqs8/qVLR5KWZuNrXyvyKmpOSLCaz3XXroY+ORdSUmIwIu9r\na0PnXjDEhbw892vEEJ58iQvGayqaewgGyvXXjwbc7wPRWuYsCIIQaTw/z3uK0hMEQRAEYegj4oIQ\nGJdfDv/5D3zuc0pkmD4dgMoO96SH5/e9YvQ3LF4MV1yhxAuAa64J5ogFQRC6kZJiMycLy8p8uxe6\nOhd0PbQXzNu2KQHh/PNzuOaq0RTdfJC6abt45Y0jPToXDnEIgFGMIjte3ecpLlRVdaDrYLH0vPLa\nmEQOValz1zJng0CiqaKd6mr1eTdiRBwrVy6iru4Knnpqbrftpk93RyP1xblgsWhh6V3o6lwA5bgA\n38Ka8RrLzo7z2xsxWLnqqgJsNvdzE3FBEATBN6NHJxITo9HW5jJj9gRBEARBGH6IuCAEzuTJ8Otf\nw/r18OUvU+9wsLfFncm8o0lNFL1dU8NX9+7ln5WV3Y+h67B6tfr+ggvU1x/8AD76CK6/PtTPQBAE\nwYxG8pX539Li4PBh9+21tXYqK9u7bRdMtm93iwvP/PY8tLZYNAt855dr2bdPTfwXzdJYxzqcqIic\nWcziSZ7kO3zHFA88Y5GMSKScnDisVt+TwKHuXdi6VTkXzjjD27lgdC4M5hgFQ1zIyup94tmzd8EQ\nFwJxLoA7Gqm21t7fYfqlokK9TrzFBf/OhaEcFZSREcull44yf5bOBUEQBN/ExFhMR+hg/kwXBEEQ\nBGFgiLgg9J2sLEhL4/26OlweN+9sVhNy79XX8/TJk7xTV9d934MHVYlzbCycc4779hEjQBuaqyAF\nQYguxo1TK+dLS7tfCO/b14iuq8lTYxI81NFIhnNh1qx0NDTGxKp4OEd2DU6nTlZWLCtzX+AhHuIV\nXgFAQ2MUo5jCFHPy89SpNvOYhrjgKxLJwBAXfE0iD5TKyjb271e/37lzvZ0LRoxCeXkLHR3qU6S9\n3WmOeTBglGdnZfVeTG44F3burPeIRfLvXAAi6FwwxIXur/vhIC6AOxoJxLkgCILQG9K7IAiCIAiC\niAtCv1nTKR4sSE1l59y53NrZl7C7U2SYlpTUfScjEunss8HX/YIgCCHGEA18lTobkUjFxSlMnZrq\ndVso6OhwmccvKVEr/C+yXMjFp67FfkRNys+cmcYabQ0A4xjX7RjG5Kenc6GysucyZwN3LFLwn9+a\nNacAJZh0Xd2fmxtPYqIVlwuOHFH/BrfcsoX8/NfMnoZox+1c8CcuqNfQrl0NpnhSUNA350K4xYXJ\nk9XrorKyvVvfw3ARFz796TySk2MAERcEQRB6wxAXDhwQcUEQBEEQhisiLgj9Zm2nuPDt/HymJycT\n29k+aYoLiV1WZ9bWwoMPqu+XLAnbOAVBEDyZOFFNnu7cWd/tPmOif+rU1LCIC3v2NGC366Sl2Rgz\nRr1nXszF3DziCzz8/fOx2TQ+e9VIrFgBGM3obscwJno9OxeMiezexQX1/A4ebMJud/W4XX9YvVqJ\nC4sXj+h2n6ZpZjTVwYNNtLU5+ec/j+JywVtvnQzqOEJFdbW7e6A3JkxIJi7OQmurE12H2FhLwBPz\n7lik0IgLTqduvk7y8tziQkqKzRQbjFguA2P7oT7hnpBg5dFHZ3H11QUsWTIy0sMRBEGIWsS5IAiC\nIAiCiAtCv6iz29na2bFwfrp3nvat+fl8LTeXGcnJ3jvdc4/7+8WLQz1EQRAEn5x5ZiYAW7bUmrE8\nBp7iQqg7CcDdt1BSkobWJRruhhvG0th4Jdd+S8UlJZJIFlndjmFM9PoSF3qLRSooSCA5OQaHQw/6\npMA77/QsLoB7MuLQoWbeffc0ra2qS8IosI52AnUuxMRYzNcRqN9513/nnsjIsAGhcy5UVrbhdOpY\nrVq310lPvQvG68Qo5R7K3HhjES++uID4eGukhyIIghC1iLggCIIgCEJMpAcgDF4emTCBfS0tjIrz\nXsH47YIC3zvccw/U1MC4cTBqlO9tgkVVFRw4AImJUFIS2scSBGFQMWlSMhkZNmpr7WzfXse8eZnm\nfZ7iQmqqzeu2UODZt+CLuDgroxjFP/knNdSg0X1i2lehcyCxSJqmMWVKCps317JnT4PXJPhAOHy4\nmdLSZqxWjUWLsn1uY0xOHzrU5DUhYYgt0Y67c8H/Cv7p01PNf+dA+xYg9LFIFRUqEik3N75b6Xdx\ncSqrV5/q1ruwf78SGyZNGvrigiAIguAfT3FB1/WABXRBEARBEIYO4lwQ+kW6zcZtBQU8OWlS4Dtl\nZcE//gH/7/+FbmAGK1bAddfB734X+scSBGFQoWkaZ52lHAAbNlSbt7e3Ozl0SMW6TZ2aaq7erqxs\nD9kEr7FS3+hb6AkrVnLI8Xlfb4XOvYkLgIc7I3ilzoZr4cwzM0lJsfncxhAXDh5sYsWKE+bt+/c3\nmi6GaMZwLmRn9+5cAHepM0BhYWB9CxD6WCRffQsGxmvf83XhculmpvakSSkhGZMgCIIwuBg7NgmL\nBVpanOa5hyAIgiAIwwsRF4ShycjOjOTKysiOQxCEqGTBAiUufPihW1w4cKAJp1MnPd1Gbm48KSk2\nczI4FNFIuq77dS4EghGL1NDgoL1dTcy7Y5F6X1kfilJnQ1y44ALfkUjgXum4bl0V+/c3YbVqpKfb\ncLlg167oj0ZyxyL5dy7MmOEWFwoKAncuZGSE1rlgiAt5ed0FKHcskvt1cfRoC+3tLmw2zewHEQRB\nEIY3sbEWxoxx9ygJgiAIgjD8EHFBGJqM6JzUOnUqsuMQBCEqcTsXaszbjPij4uJU09YfylLn48db\nqanpwGrVzMfpD+npNmJi1HiNaKTKSvXVn3NhyhT1uF2z9fuLrut++xYAxo9XExG1tXYAFi7MYu7c\nDAB27Ih+ccEdixR650KoxQVfzgXD0XLoULPpJNm/3923EBMjp4+CIAiCQnoXBEEQBGF4I1eHwtAk\nN1d9PXUKXK7etxUEYdhx5pmZaBqUlTWb/QSefQsGxiRrKMQFw7VQXJwyoNJYTdPM3oVTp9ppb3ea\nE9L+xAVjkr+srLnfj+/Jvn2NnDjRRny8xXSH+GL06ERTEAG45JJRZjTUYOhdcMci+XcuFBYmkJoa\n0/l93zsXDAEm2PQmLowaFc+IEXE4nbr57+HuW5BIJEEQBMGNiAuCIAiCMLwRcUEYmmRng6aBwwHV\n1f63FwRhWJGWZjNFBKN3wZe4YHwfilikQPsWAsGIRjp9up1Tp9Sq+pgYzYzW6YmiIiUu1NR0UF8/\n8Ens1auVa2HhwuxeBZOYGAtjxyaZP19ySe6gERfa2500NTmAwJwLmqZx3XWjyc2NZ/78TL/bG4Q6\nFqmiQolqvsQFTdOYM0c5ST7+uBaQMmdBEATBNyIuCIIgCMLwRsQFYWgSE6MEBpDeBUEQfOIZjVRZ\n2cbKleq9wjMj33AuBCs2yJNg9C0YeDoXDCfGyJHxWCxab7uRkmIzS4l7ci/cdttWLrjgXZqbHX7H\nsWaN/0gkA8M1kZcXz8yZaZSUqN/79u316Lrud/9IYbgWLBYlUgXC7343h+PHP22WbweCZ6GzyxX8\n34e7c8F3VFN3cUHKnAVBEITuiLggCIIgCMMbEReEoYsRjSTigiAIPvAsdf7e97ZTX29nzpwMr4lx\nY2X/sWOtOJ3Bm+Btb3fy/vungeCIC8ak9alTbWaZs79IJINx49SkgC9xYc+eBh577CDvvHOKl18+\n7vdYW7cqwaS3SCSDyZPVJPXFF+eiaRpTpqRis2nU19spL28JaOyRwBAXMjNj/Yo3nvRlW4CMDCVc\n6Do0NAQ/Gqm3WCTwJS5ILJIgCILQHUNc2Lu3kZYW/wsRBEEQBEEYWoi4IAxdjFJnERcEQfDBWWep\niJoPPqjiuefKsVjgD3+Yg9XqngTOzY0nJkbD6dQ5caI1aI/9t78dobKynYKCBBYtyhnw8TxjkQxx\nYeRI/30A4BZQSku7rzj8wx9Kze+ff7681+M0NtopLVUChaf7oye+//3JfOc7E/jFL6YDEBtrMZ0i\nRmRUNFJdrWKnAulbGAhxcVYSE1W0VLCjkZqbHWYMlj9xYffuBurr7Rw+rP5tJRZJEARB8GTKlBTG\njEmkpcXJ8uUVkR6OIAiCIAhhRsQFYegycqT6KuKCIAg+KC5OJTU1xnQk3HrrBHNC1cBq1SgoUJOv\nwVpN73C4eOCBvYCaYI+N9f1R3EILpZTSin9Rw4hF2rChhgcf3AcEXh5siAtdnQutrU7+9rfD5s9v\nv11JVVV7j8fZvVv1UowaFR9g0XEijz56hlcsz2DoXTCcC4H0LQwUIxrJ6NEYCC6XzlNPlbJ9ex0V\nFeo1lZwcQ2qq72ingoIEcnJUqfMrrxzH5VLbB+qIEQRBEIYHFovGDTeMAfA6bxAEQRAEYXgg4oIw\ndBFxQRCEXrBYNObPV/E9eXnx5gr6rowerSbpgyUuvPjiMUpLm8nKiuWmm4p63O5O7uQO7uAAB/we\n03AuvPvuaQ4caKKwMIHvfW9SQOMZN863uPDCC0epq7MzdmwiJSVpOBx6r9FIO3cqt0EgroWecPcu\nRK+4YAgsWVmhdS4ATJ+ufh8fflg94GO9884pvvGNj5k/fzWPP34QUK/7nvAsdTZcK5MmJaNpfYt3\nEgTh/7N333FylWX/xz9nZvtusi29bIzSP1oAACAASURBVHpvpJGEktBBAggiIN2GWEBFH0Gwoajg\nTxFFHxUQxIKAD72F0DEE0khCEtJ722w2W7K9zZzfHzdnZra3M233+3698prZnTln7mQ3W873vq5L\npOe79loTLrz+ekEgwBYREZHeQeGC9FwKF0SkHTfcMIohQ1J4+OG5re7gdjNcsG2bu+82VQvf/vY4\n0tMTWn3uYAYDcIQj7Z534MDgReKzzx7I2rVnd7g3frAtUuNw4S9/2QXAV74ymquuygPabo3kTrjg\nVC7EclskU7ngDMIOp7POMu393nij+9/HnEGbtbX+QLjQWkskhxMuvPmmGdSteQsiItKSceP6cNJJ\nufj98NhjbbdRFBERkZ5F4YL0XAoXRKQdl102nEOHLuS88wa1+hw3w4WXX85n48bjZGQk8I1vjG3z\nuQc5CMBjPNbueU8/fQCf/vQQfvGLqSxZcmqn5gE4lQt791bi95sWUevXl7JiRTEJCRZf/OIorrhi\nOGAqI1rbkbhhgwkEpk/vfriwa1cFa9YUd/k84RRsixT+yoWzzjLfx959t5C6On+3zuUMcHZaaEHH\nwwWndZjCBRERac31148ETGsk27bZubOCP/5xJ+Xl9dFdmIiIiISVwgXpuQZ9crHw0CGw7eiuRUTi\nlpvhwpNPHgBMNUB2dts736/lWgDO5dx2z5uRkcBzz53MHXdMajSQuiOGD0/D44GaGn9gGPRDD5lB\nzp/5zFAGDkxhxIh0TjopF9s2bZ2asm3blcqF/v2TWbDAvM7JJ7/NAw/swo6xr9/Btkjhr1yYNi2T\n/v2Tqaz0sXJl91ojHTxowoVvfWsc999/AgMHJnPxxUPbPKbpDBINcxYRkdZcfvkwkpM9fPxxGV/8\n4hqmTFnKzTev49ZbN0R7aSIiIhJGChek5xo1CjweKCuDo0ejvRoRiVNuhgvO7vE5c7LbeSaczMk8\nwAN8js91+3XbkpjoCfwdnbkLr7ySDwR3IQJ87nOmeuGee7byxS+u5p57tgb+TfLzaygursPrtZg0\nqW+31vPyy6dw0UVDqKvz89WvruWOOzZ163xuC7ZFCn/lgsdjceaZTmuk7n0fcz73hg5N5eabx3Hk\nyEVceumwNo8ZPjy1UfsnVS6IiEhrsrKS+PSnhwDw6KN7AxV3Tz55gNpaXzSXJiIiImGkcEF6ruRk\nyDN9wtnR/kBUEZGWuBkuOJUBoTMS2jKAAXgi8K06dO7Cnj2V7N1bRUKCxcKF/QPPufzy4aSleTly\npIa//W0vt9++kcsu+wAItkQaNy6DlBRvt9aSnZ3Ec8+dxF13TQHgT3/a2a3zua2oKHKVCxBsjdTd\nuQuh4UJHhQ51BvPxFRERac3NN48jIcFi9Oh0nn32JIYMSaGkpJ4lS9qfHyUiIiLxSeGC9Gzjx5tb\nhQsi0kXDh5twoaSkvtt9gwsKzIXpQYM6Fi5EihMu7NlTyVtvmR3y8+blkJERHDg9cGAKH398Lv/+\n9zzuvHMyiYkWq1YVs25dSaAlUnfmLYSyLIuvf93MpCgra+j2vAE3HTvmzFyIVLhgKhdWriymrKzr\nn39OuDBsWMfDBQi2RhowIJmsrMj8nUVEJD6dcko/Dh++kK1bz+Pii4dy1VVmo9e//rUvyisTERGR\ncFG4ID3buHHmdvv26K5DROJW376JZGUlAnDgQMvDjDuittZHcbG5MB1r4cLo0WZHemi4cMYZA5o9\nb+TIdK68Mo+f/GQKn/mMaanz0EN7XJm30FRWViKeT35KcaoFYoGzlki0RQIYMSKdMWPS8fls/vvf\nwi6do7KygePHTTDRmcoFgEWLTPVKR1p5iYiI9O+fTGKi+QZ+9dUjAHjxxXxKS+uiuSwREREJE4UL\n0rOpckFEXOBGa6SjR81F6cREi+zsRFfW5ZZgW6SKNsOFUF/5ymjA7EZ0hg27GS54PFZg6LUz5yDa\nGhr8lJaai/SRqlyA0NZIXZu74FQtZGQk0Ldv5z73zjlnEEuWnMqDD87p0muLiEjvNWNGJlOm9KWu\nzs/TTx+K9nJEREQkDBQuSM8WWrlg29Fdi4jELTfChYKC4LwFy7JcWZdbRo824cKKFcUcOVJDcrKH\n+fNz2zzmtNP6M2ZMOuXlDWzfXgG4Gy5A8AJ+rIQLJSX1gW8lOTmRDxdef71rcxcOHuz8vIVQ5503\nqMvHiohI72VZFtdcY6oX1BpJRESkZ1K4ID3b6NHg9UJ5ORR0bximiPReboQLnR3mHElO5YIz2+Dk\nk/u1O5jZ47G44YbRgbczMhIYOTLd1XU5rYdipS2Ss47MzEQSEiL3I9Tpp/fH67XYvLmM1auLO318\nV4Y5i4iIuMGZu/DOO4Xd+jlKREREYpPCBenZkpNhhNkto7kLItJV7lQuxOYwZzDDetPSgmFCey2R\nHJ///EgSEkwVxtSpffF43K3IcCoXnCHK0eZUUPTrF9nBxrm5yVx9tbk484tfbOn08V0d5iwiItJd\neXlpnHyyqYbsagWeiIiIxC6FC9Lzae6CiHSTu5ULkRkE3BmWZQWqF6Dj4cLAgSlcfPFQAGbMyHJ9\nXbm54a9cePrpg/zznx1r1eCEC866Iun22ydiWfD884cDA7Q7SpULIiISTQsWmHBh3bqSKK9ERERE\n3KZwQXq+0LkLIiJd4ObMhVisXIDg3IX0dC9z5mR3+Lh7753BV786mltvneD6msI9c6G21sdVV63k\nuutWceBAyx/blSuLuPTS99mxo5xjx2obrSuSJk7sy6WXDgPg7rs7V72gcEFERKJp5kzzc8W6daVR\nXomIiIi4TeGC9HyqXBCRbnLChYMHq/D5ujYcPpZnLkBw7sLChf1JTOz4jwd5eWn8+c+zGT06w/U1\nBWcuhCdcOHCgOjBnYsWKohafc9ddW3jmmUN85SsfhrRFik71yR13TATgyScPsHNnRYePU7ggIiLR\nNGuWqW5cv760yz9HiYiISGxSuCA93/TpcP31cPXV0V6JiMSpwYNT8Hot6uvtQAVCZznhQqxWLnz+\n8yOZMSOT733P/QqErgrOXAhPW6S9eysD91eubD4o2eezee+9Y4AZRPnvf+9vtK5Imzkzm/PPH4Tf\nDz/+8SZsu2MXaA4eNFUZChdERCQaxo3rQ1qal6oqHzt2lEd7OSIiIkHPPw+33Qa14WvF29MpXJCe\nLy8P7rwTLr002isRkTiVkOAJXJjtamukWG+LNHNmNuvXn8Ppp3ds3kIkhLst0r59wY9lS+HCpk3H\nOX68PvD2+vWljdYVDT/+8WQsCx5//AA//3n77ZEaGvyBYEsDnUVEJBq8Xiswm0mtkUREJGYUFcGv\nfgXDhkV7JXFN4YKIiEgHdHfuQqy3RYpFwbZI4a9c+PDDEurr/Y0eX7bMVC3Mm5dDdnZis3VFw7x5\nudx//0wAfvzjj3nwwd1tPr+goBa/31zYGTBAn3siIhIdTmuktWs11FlERGLEo49Cfj68/jokRW8D\nWbxTuCAiItIB3QkXqqt9lJU1ALFbuRCLwl25EBouVFf72LTpeKPHly0rBOCCCwbzwx9ObrauaLnp\nprH88IeTAPja1z7k1VePtPpcZ96C09pLREQkGjTUWUREYkp5Ofz97+b+178Oln5X6iqFCyIiIh3Q\n2XChvt4f6InvtERKTvbQt29CeBbYA+XmmgqB4uK6sAyA3LvXfCydi+6hrZFs2w5ULpx6an++8Y0x\njB5thl6PG9fH9bV01s9+NoXPf34kfj/87nfbW32ehjmLiEgsmDkzWLnQ0ZlBIiIiYfPYYyZgGDMG\nzjkn2quJawoXREREOmDkSBMu7NhR0epz/H6bpUuPcOml75OW9gxXXbUSaDzM2dKOiA7LyTEVArYN\npaXuVy84lQtnnmnmTKxYURR4bNeuSvLza0hMtDjxxBySk70sW3Y6r7++kBNOyHJ9LZ1lWRb/8z/j\nAXj33UJqanwtPs8JFzRvQUREomnKlL4kJlqUlNR3ucWkiIiIK2pq4OGHzf2vfQ08ujzeHfrXExER\n6QCnnP/DD1vecefz2Zx11rucd94ynnnmEA0NNs88c4jaWl+gckHzFjonKSlY6eF2a6S6On/gwvvl\nlw8HGlcuOC2R5s7NITXVC8CQIamcddZAV9fRHZMn92Xo0FRqavyBKoumDh40F3BUuSAiItGUnOxl\nypRMQK2RREQkyp56Co4dgyFD4KKLor2auKdwQUREpAOmT88kIcGisLC2xR13775byNtvF5KS4uHm\nm8eSm5tEXZ2f9etLG1UuSOc4rZGOHXN3qPOBA1XYNqSkeLjwwsEAbN1aHqiQcC7WL1zY39XXdZNl\nWZxzjgk7Xnut5bkLaoskIiKxIrQ1koiISNS8/ba5ve46SEyM7lp6AIULIiIiHZCS4mXaNLPjbs2a\n5r8UP/bYPgCuvXYE998/k/nzcwFYsaKYggJzYXzgwOQIrbbnCNdQ5337TEA0cmQ6AwakBOYprF5t\nPrbBeQv9XH1dtznhwtKlBS0+rnBBRERixaxZGuosIiIx4OOPze3s2dFdRw+hcEFERKSD5s7NAZqH\nCzU1Pp566iAAV189AoB588xzV64sUuVCN4QrXHDmLYwcaUKF0I9Xfn41O3dWYFlw0km5rr6u2846\nayCWBRs3Hic/v7rZ4woXREQkVqhyQUREou7YMSgoAMuCSZOivZoeQeGCiIhIB82ZY3bcrV5d3Oj9\nL7+cT1lZA8OHpwZ2ujuVCytXFgdmLihc6Lx+/Uy1R1GRu22RguGCGdQ9b575eP3udzuYPv01wLTC\nyspKcvV13davXzKzZ5vPy9dea1y9YNt2yEDntIivTUREJNSMGVlYFhw+XMO+fZVhe51Dh6pZuvRI\nizOyRESkl3OqFkaOhPT0qC6lp1C4ICIi0kFOuLBmTeOhzo89th+AK6/Mw+OxAJg71zx39+5KNm48\nDmigc1c4lQvHjrlduWDaIo0YYX6gXLjQhEJFRXUcO1aHxwNf/vJoV18zXIJzFwrw+23+/OddLFr0\nNllZz1FZ6QNgyBB97omISHRlZCSwaJGZZfTnP+8K2+tcd90qzjtvGcuXF4XtNUREJE5t3mxup0yJ\n7jp6EIULIiLdpV1RvcbUqZkkJ3s4fryeXbvMjruSkjpefjkfgKuvzgs8NysriUmT+gCwY0cFoMqF\nrnAGOoe7cmHmzGyef/5kHn10LqtXn0lZ2SXcdNNYV18zXM49dxBghjovXvweX//6Wv7732OUlTWQ\nlOThy18eRVpaQpRXKSIiArfcMh6ABx7YTUVFg+vnt22bNWtMhalzKyIiEuBULihccI3CBRGR7vjw\nQ5g6Ff7+92ivRCIgMdHDCSeYfsFOa6Snnz5IXZ2fqVP7Mn16VqPnO612HKpc6LxIzVwAuOiiIVx/\n/UjmzMkhPT1+LsbPn59LRkYCx47V8eqrR0hJ8fD//t90Nmw4h4qKS3jooTnRXqKIiAgAF1wwmLFj\nMygtrefRR/e6fv6CglrKykxosXlzmevnFxGROOeEC5MnR3cdPYjCBRGR7rj2WqiqgjvvjPZKJEJC\nWyP5/TYPPrgbCA5yDuUMCXaocqHzgjMX3AsX6uv9gVkEoeFCvEpK8nDuuaY10rRpmaxZcxbf+94E\npk3LJDFRP+qJiEjs8HgsbrllHAC///0OfD53K4C3bg0GCgoXJBLy86uZOnUpt966IdpLEZH2VFTA\n3r3mvioXXKPfOEVEuqq+Hqqro70KibC5c01gsGZNMfffv4PVq0vIyEjguuuahwvOUGeAtDQvGRnx\nsxs+VgRnLrjXFungwWr8fkhJ8TBwYLJr542m//3fWTz++DxWrTqTKVMyo70cERGRVl1//UiysxPZ\nubOCl1467Oq5t20rD9zfvLlMQ50l7O6/fycff1zGX/6yC79fn28iMW3LFnM7aBDk5rb9XOkwhQsi\nIl21alXwvmVBg/t9YyX2OJULq1eXcPvtGwG4994ZDBmS2uy5U6f2JS3NC6hqoavC0RbJaYk0YkQ6\nlmW5dt5oGjgwhc99Lo+UFG+0lyIiItKm9PQEbrxxDAD33bfD1XNv3RoMF0pK6jl61N2ZTSKhamp8\nPPSQqWIuL29g9+7KKK9IRNqklkhhoXBBRGJTaSl885vw5pvRXknr3ngjeN+2IT8/emuRiJk4sS/p\n6V6qq33U1Pg5++yB3HDDqBafm5DgCYQRmrfQNaEDnd3afRgMF9JcOZ+IiIh0zpe/bH52Wr78GPX1\nftfOG1q5AGqNJOH1xBMHGm2AWbu2JIqrEZF2bd5sbtUSyVUKF0QkNv3jH/Dii/Cd75igIRbdeis8\n8EDw7QMHorcWiRiv12LWLBMY9O2bwMMPz2lz97sz1LmntN+JtH79TOVCfb1NRYU71UF791YBPWPe\ngoiISDwaNSqdtDQvDQ02e/a4t9vbqVxwKh8VLki42LbNH/5gKm/S003l6Lp1Mfp7q0hvt2sX/OQn\n8H//Z95WuOAqhQsiEpteecXclpXBH/4Q3bW0JjUVzjkHFi0ybytc6DUuu2wYCQkWf/rTLIYPb3v3\n+xe+MJK5c7P54hdbrm6QtqWlJZCSYn5cOXasjkOHqvnBDzZSWNj1NgdO5cLIkapcEBERiQaPx2L8\n+D5A82qDrqqp8QW+x1900RAAtmxRuCDhsWJFMWvXlpKc7OH22ycBqlwQiVlHjpgNrA6FC65SuCAi\nsWfXLti2zcwxAPjnP2Hv3qguqU3Dh5vb/fujuw6JmJtvHkdFxSVcfXXzIc5NTZrUl1WrzuLCC4dE\nYGU9U2hrpG98Yy2//OVW7rtve5fP5+yQVOWCiIhI9EyY4G64sGNHBbYNWVmJLFrUH1DlgoTPH/+4\nE4Crrsrj7LMHAqZyQUPERWLQ/PmN3x46NDrr6KEULohI7FmyxNwuXGiqAurr4Ve/iu6a2jJsmLk9\neDC665CISk7W4NxIcVobbNhwnJdeMrNNNm483qVz1db6WLPG7CqbOjXTnQWKiIhIp7kdLmzdaoKE\niRP7MHlyX0DhgoRHTY2P//s/U7V+001jmTYtE6/XorCwlkOHqqO8OhFpxuuF2bODb7fR1rjT6uqg\nuBj87s0PijcKF0Qk9jgtkc4/H26/HTweePVVWLUquutqTV6euVVbJJGw6NfPVC785jfb8PnMbrCu\ntjl4//0iqqt9DByYzNSpfV1bo4iIiHTOxIlthwvPP3+I669fRVVVx2YuOeeZOLFv4NwFBbUUF9e1\ndZhIp+3bV0V9vU1GRgIzZ2aRmupl0iTzOae5CyIx6v774YwzGrdHcsOGDSa4OPNMd88bRxQuiEhs\n2bMHtmwxyfLZZ8OECXD55eax//kfM4Mh1qgtkkhYOZULW7YELz7s2VNJdbWv0+d6440CAM46a2Cb\ng7hFREQkvNqqXLBtm298Yx3/+Mc+nn76UIfO5wxznjChD336JJKXZ2Yrae6CuG3//ioARoxIC/w8\nOWtWNqBwQSSibBvWrYPnnoPadmbyDRkCDz8Mp57q7hqKi81tZu+tile4IL2XbUNDx3bBSAQ5VQsn\nnQTZ5gc0br/dXMA/cAC+/33zsYslTrhQVARVVdFdi0gP5IQLADk5SWRmJuL3w/btnW+j8PrrJlxw\neuOKiIhIdDgDnY8eraWkpHF1wfr1pYH2Mps2dawVYrBywZxXrZEkXPbtM/O7nAALYObMLEBDnUUi\n7vrr4ZZbYN++8Jy/rg5K2wgNnXAhNzc8rx8HFC5I73TnnTBxIjz6aLRXIk058xbOPz/4vr594Q9/\ngMRE8/hjj0Vnba3JzIQ+5pcYzV0QcZ/TFgng+utHMGWKuVjQ2Z2IJSV1gXkLZ52lcEFERCSaMjIS\nGDo0FWheveDMWIKOhQu2bTeqXACFCxI+oZULDlUuiESBZcGIEeZ+OMKF5583G1/vvbf15zjhQk6O\n+68fJxQuSO+UkGDSx4KCaK9EQh0/boY3Z2TAOec0fmzGDLj1VnP/rrtM66RYorkLImETWrlw441j\nmDTJCRc6V7nw1ltHsW2YNKlP4GKGiIiIRE9rrZFefPFw4P6mTe2HA/n5NVRUNOD1WowZkwEQ6IGv\ncEHctm+fCRdCKxdOOMFULuzfX0VRUTvtWUTEPc61mHC0qe7Xz3SoeO651rtUKFxQuCC91MBPdqwe\nPRrddUhjmZmwdKkZiNPSF+YvfckM4KmrM2Vv7fXUiyTNXRAJG6dtwllnDWDChD6BiwWhlQv79lW2\nOhDS4cxbUEskERGR2NBSuHDkSA2rVwdby+zfX0VZWX2b53GqFkaPTicpyVzmcCoXNHNB3BasXEgP\nvK9v30TGjjXBlqoXRCJo5Ehzu3ev++desMBURlRUwIsvtvwcJ1xw2nr3QgoXpHdywgVVLsSm1oas\nWhbcc4/pZbdtW9ulaQAbN8Lpp8Oll7q/xqaGDTO3aosk4rpPfWoQL754Mk8+uQAgpHLBXCyorfWx\nYMFbzJ37RrOezaGceQtqiSQiIhIbWgoXXnnFtESaMyc7UGn48cdtBwRN5y1A8OeFAweqKS9vO5wQ\n6YyWKhcgOHdB4YJIBIWzcsHjgc98xtxftqzl52jmgsIF6aUGDDC3qlyIP/37w913m/t//SusWNH6\ncxMSTHodrsE+odQWSSRsLMvigguGkJNj2iM5Fwu2b6+gocHPO+8Ukp9fQ3l5Q6utD/bsqWTXrkq8\nXovTTusfsbWLiIhI61oKF5yWSBdcMJipU833/PbmLjgbDpzzAWRnJ5GdnQgEd5qLdJfPZ3PwYPOZ\nCxBsjdTRIeQi4gJn5kK4ukiMGmVujx1r+XG1RVK4IL2U2iLFt7PPhiuuANuGH/+49d53TllaaSn4\n/eFdk9MWSeGCSNiNGJFGaqqXujo/u3dX8sILwb7M27e33BrJaYk0f34OffokRmSdIiIi0jYnDNix\nowKfz6amxheoNLzwwiFMnZoJtH+xdv16s1N8+vSsRu8fPtxc/D1woNrVdUvvdeRIDfX1Nl6vxeDB\nKY0ec94uLIyh9r0iPZ0TLhw8CA0N7p+/Xz9zW1TU8uMKFxQuSC/lVC5UVpreaRJ/fvhDmDABysrM\nEOiWOOGCzwflnRv82mmh4YJth/e1RHo5j8cKXIzYvLmsUbiwY0fzr+m2bfPII3sAOPfcQZFZpIiI\niLQrLy+N5GQPdXV+9u6t5J13Cqms9DFkSAozZ2aFhAutt0Xy++1AG5pZs1oLF1S5IO5wqmCGDUsl\nIaHxJbXc3GQAiopab9MpIi4bOBCSkkywcM018N3vQn6+e+d32h2pcqFVChekd0pPhz6flMxq7kJ8\nysiAF16A//7XDIJuSXKy+VgDlJS0/By3ODMXKivD/1oiEhjS+Pjj+zl4MLgbsaVwYenSAlasKCY1\n1ctXvjI6YmsUERGRtnm9FuPGmSG4H3xQxO23bwRg8eLBWJbVobZIO3dWUFHRQGqqt1FbJIDhw83M\nBoUL4pZ9+yqB5vMWAHJzTQvPoiJVLohEjMcTbFO9ciU884x5n1uccKG0tPnG1upq8wcULoj0Spq7\nEP+SksyftjjVC+G+4J+cHGy3pdZIImE3aZK5ePDUU2aIutNTeceOxlVKtm1z550fA/D1r49h4MDG\n5esiIiISXRMnmgDhS19aw/r1pQwYkMytt04EzJwly4KjR2s5erSmxePXrjU/58+YkdlsJ7naIonb\nnMqFpvMWIDRcUOWCSEQ54QKA1xtsZeSG7OxgWNH0upJTtZCYaDbA9lIKF6T3csIFVS70bE644HzR\nD6c774RHHw0O/BGRsHGGOjvjVL72tTGAqVywQ1qTvfrqEVauNFUL3/vehIivU0RERNrmVBvU1fkZ\nODCZt98+jbFjzUWa9PQERo82lcgff9xya6S1a52WSNnNHlNbJHHbvn1OuJDe7DGnLVJpaT0NDWGe\n+SciQSNHBu8PHGgCBrd4PMGqhKatkUJbIlmWe68ZZxQuSO+lcKF3CB3qHG7nnQeLFkHfvuF/LZFe\nzgkXwPy8d9NNY/F6LaqqfBw+bHY2mqqFzYCqFkRERGLVzJlmTsKgQSm8885pgdaHjqZDnd94o4Ct\nW4NBg1O50FK4MGyY2iKJu5zKhZbaIjmVtAAlJa3MBRQR94VWLgwKw4y91uYuaN4CoHBBejOnhY3a\nIvVskaxcEJGIGTs2A6/X7A45+eR+DB6cysiR5pc8pzXSm28eZdUqU7Vw662qWhAREYlFF188lCee\nmM+aNWcFWiSFCh3q/KtfbeXss//LmWe+S329H9u2Q8KFrGbHOpULBw9WN6psFOmqYOVC83AhIcFD\nVpYJGI4d09wFkYgZMSJ4PxzhgtNmqaio8fsVLgAKF6Q3c8KFRx6BZcuCQ1jA9Nk4fDg66xJ3dXXm\nQkOD+2sREdckJXkCLRMuumgIAOPGmbYKzlDnl17KB+Caa/IYMEBVCyIiIrHI67W44orhDB2a2uLj\nU6aYwOGJJ/bz/e+bgc+HD9fw6qtH2LevipKSehITLaZMyWx2rFO5UFXl005ycUVblQuguQsiUREa\nLgwe7P75FS60SeGC9F5OWySA664LXnw+dAhmzoSFC2HHjuisTdzTlXDh4YfhhhvCsx4Rcc0PfjCJ\n888fxBe+MBKAceNM2OCEC2+9ZSrTzjprYDSWJyIiIi5wKhfKyszmH2cGw9/+tjdQtTBtWiZJSc0v\nb6SkeOnf3/TBV2sk6a7jx+s5ftyEVK2HC+bzrahIlQsiETN0aHDostoiRZzCBem9BoZcbEpNDX4B\nGjoU5s0Dnw9++lNQ+Wx8c77IdzRceO01+PnP4b33oEq/gIjEsmuvHcHLL58a+CVu/HincqGcwsJa\nNm40vZlPO61/1NYoIiIi3TNhQh9SUsyli1tuGcdzz50MwIsvHmbpUjM/r6V5C47hwzV3Qdyxb18l\nYKoT0tMTWnyOKhdEoiApCYaYavawhguqXGiRwgXpvULDhVGjgiknwA9/aL44LV8Or78e+bWJe7I+\n6b3akZkL27bBd75j7l95JaS18cD9yAAAIABJREFUvBtFRGJTaOXCu+8WAjB1al+1RBIREYljSUke\nnnhiPn/+8yzuvXcG06ZlMnt2Ng0NNg8/vAdoL1wwP9MfOFDd6nNEOsJpidTSvAWHwgWRCPj5z+Fn\nP4ODB4Pvu+wyc21v3jz3X09tkdqkcEF6r9C2SP2b7GrNywu2xbnrLqipidy6xF0drVw4ftx8zCsr\nYcEC+NGPwr82EXGVEy7s3FnBm2+anYynnz6grUNEREQkDnz600P56lfHYFkWAJ///EgAfD5TZd7S\nMGdHMFxQ5QKA32/zzW+u4+67t0R7KXHHGebcWkskUFskkYh46in4298ad5v45jfhrbeaX99zgyoX\n2qRwQXqvlJCdrOnpzR//+tdNOdXBg/DooxFblrisozMXXn4ZDhyAYcPgf/8XEhPDvzYRcVVeXhqJ\niRa1tX7+8x+zi0XhgoiISM9z5ZXDSUw0QYPXazF9elvhgtoihdqw4Th/+MNO7rhjE+XlGnLdnoKC\nGn73u+3s318VUrnQwvWDT6hyQSTMamrM5lAITwuklmjmQpsULoiAKZ1qKi0NvvY1c3/58siuR9zj\nfJEvLQW/v/Xn7TEl1Zx7bjCQEJG4kpDgYfRoU71QXFyHZcGiRZq3ICIi0tPk5iZz0UWmv/akSX1I\nTfW2+ly1RWpszZpgu9hNm8qiuJL48KUvreGWWz5iwoQlPP74fqC9ygWFCyJhdeSIuU1NhT59IvOa\nTlukY8caz2V1wgUnfKiqgr//HX7wg141v1XhgvRuv/kNnH463Hhjy4+PGWNuCwoityZxlzNzweeD\n8vLWn+f06hs6NPxrEpGwGT8+I3B/xowscnKSorgaERERCZfvfncCKSkerrhieJvPGzZMlQuh1qwJ\nVnR/9FFpFFcS+/bvr2LJknwAamr8gYCq7ZkLaoskElZOuDBoEHzSKi/snPCgrg4qKsz9hgaziRWC\nG1S9XvjlL+Hf/zadMXoJhQvSu116KTzySOtpp1Ni5XzxkviTnBxse9VWayQnXBje9i8nvUZ+PtTq\nB2KJP+PGBb+en3GGWiKJiIj0VAsW5FJR8Rl++MPJbT7PqVw4eLAauxftJG3Nhx8qXOioRx7Zg98P\np53Wn2efPYlx4zJIT/dy4omtt0BR5YJImIWGC5GSmhq8ruS0RnKCBcsKhgvJyTBpkrm/fn3k1hdl\nChdE2jJwoLktLw+mkxJ/OjJ3QeFC0Nq1sHChKeUTiTPOUGeA009XSyQREZGezOttf9fq0KGpWBbU\n1fkpLHR/88y77xZyzz1b8ftjP7iorfU1ChQ++uh4FFcT23w+m0ceMa1zv/KV0Vx88VC2bj2Po0cv\nCgRWLVG4IBJmhYXmNhyDm9vitEZyhjrX1MDo0TBihKlYcJxwgrlVuCAiAGRkBKsa1BopfjnhQnFx\ny4+XlwdT52HDIrOmWPavf5kSvw0bor0SkU5zwgWPB049VeGCiIhIb5eY6GHQoBQgPHMXrr9+Fbff\nvpEVK4pcP7fbNm0qo77exvPJlaANG0rjIhSJhqVLj3DgQDU5OUlccolpnevxWKSlJbR5XGhbJFXK\niISBc3HfudgfKU5rJOf1hw2DN9+Et99u/DwnXFi3Lnxr2bED9u0L3/k7SeGCSHucUqv8/OiuQ7rO\nCRdKWyn7dXrh5eQES916q8pKePVVc7+tSg+RGLVgQS4nnZTLTTeNJTMzMdrLERERkRgQHOrs7tyF\nAweq2LfPnPPo0dhvKeq0RFq0qD/JyR4qK33s3l0Z5VXFpoce2g3AddeNICWl9YHhTTmVC/X1NhUV\nDWFZm0ivFivhQmuccGHz5vC0mi4ogIsuggsvbH8tEaJwQaQ9mrsQ/9qrXHBaIqlqAZYuhepPdnSV\nloJ220icSUtLYPnyM/j972dGeykiIiISI4YPD89Q5/ffD17YKS2td/Xc4bBmjfl9aP78XKZOzQRM\n9YI0lp9fzYsvms2FN9wwulPHpqV5SU42l9rUGkkkDJyZB87F/kgZMwamTIG01tuiAaZNUna2Gf68\nZYv763j+edOSqbwc/vIX98/fBQoXRNozeLC5VbgQv9qbuaBwIeiZZ4L3GxrMNywRERERkTgWrFxw\nty3S++8fC9wvKYn9C8lr1pjfh2bPzmb6dBMuaO5Cc88+ewifz2bBglwmT+7bqWMty9LcBZFwcnbr\nRzpcuO02eOkluPjitp9nWeGdu/Dcc8H7//xnTLRwV7gg0h5VLoTXP/8JL7wAx8P4Q217lQtOW6Te\nPsw5Px/ef9/cdwYStdZKSkREREQkToSrLdLy5fFTuVBT42PTJvM715w52cyYkQXQaMCzGFu2mA1W\nCxd2re1Kv37BuQsi4rJotUXqjHCFC1u3mmqIpCSYOtW0XfrjH919jS5QuBBjLMvKsCzrXMuybrYs\n6weWZX3HsqxLLcsaHO219VoDB5pbzVxwn98P/+//wbe+Fd5/35wcc9vahfJ4qFzYtw8qKsL7Gs8/\nb9ogzZ0b/LzX3AURERERiXPDhrnfFqmysoH164O/X5SWxvYu9Y0bj1Nfb9OvXxJ5eWnMmOFULihc\naGr7dhMujB/fp0vHq3JBJExsO3ptkTpjxgxz63a44FQtnH46/OAH5v4TTwSvaUVJ22PueznLskYB\nJwLzPrmdCaSGPOUd27bPcOm1RgJ3AZcBSS08xbYs613gJ7ZtL3PjNaWD1BYpfPbuNRfMk5Nh7Njw\nvc4ll8AFF0DfVkpaY71yYelS+NrXYOFCePTR8LyGbQdbIn3mM+bz3uuFUaPC83oiIiIiIhGSl2cq\nF/bvdy9cWLWqGJ8vOJ+spCS2KxdCWyJZlsX06aZyYe/eKo4fryczMzGay4spTrgwblxGl47PzXUq\nFxQuiLiqvNzMMoD4qFzYt8900HA2vHaHz2c2hIK5xjV/PpxyCrz3Hjz4YPfP3w2qXGjCsqwLLct6\nybKso8Au4HHg28BJQApgh/xx6zUvBzYBVwOh39GbvsZpwDuWZd3t1mtLBzhtkWKgj1mP8/HH5nbS\nJEgIY9aZng5ZWeBp4UuebUeucsHn6/wxBw/CrbeadX7wAdSH6ZeWggLYscMECuefD4sWmW9UrQUy\nIiIiIiJxYtSodAAOHaqmrs7vyjmdYc5erwXEfuXChx+acGHOHHORKycnKVDRsXGj5i44amt97Ntn\nQqjuVy6oLZKIq5yWSBkZkJIS3bW0JTMTRn8yDP6jj9w558qVZtNzZiacdpp533e/a26XLHHnNbpI\n4UJzZwDnA7k0DxGsJrfdZlnWZcC/MRUR9ifntoGjwFrgAND0p5/bLMu61601SDuccKGoyPQzE/ds\n3Ghup06N3hpKS6Gy0twPV7iwZIlJrr/85c4dV19vWkaVlZm36+pg1y731wdQWGhu+/dXoCAiIiIi\nPcqAAcmkpXnx+92rXli+3LTmOOkk05oj1mcurFxpLsrNmZMdeJ/mLjS3a1cltg19+yYwYEByl87h\nhAvHjsV24CQSd+KhJZLDqV745jfN/QULghtsu+LZZ83t4sWm+4fzGmefbVqOR5HChdaFBghOyFCB\nu8HCaOCRJudcB5xu2/Yg27bn2rY9ApgEPEPjoOPblmW1M6JcXJGVFfyPq+oFdzlfWKMZLjgtkQYM\nCH6c3ZaaagZWd/bz5777YO1a6NMHxowx7+vON6O2xNM3aRERERGRTrAsi9GjTfXC7t3dn2Pm99t8\n8IG5WL94sWmjW1ISuxeSly8/xqZNZSQmWoEwBNDchRaEzluwrK5d/gm2RdLmRBFXOZUL8XDd4vTT\nzW1FhbkedOQI/OMfXTvXpk3BeQuXXNL4se98B7r4tcotChdaZgNFwFLg58CngcHAzSGPu+HnQPon\n9y1gFbDItu3/NlqMbe+wbfsy4CGClQ0W8P8sy9LHMNwsS3MXwsG2zRdIgGnToreOSLREGjDA3B49\n2vFjiovhgQfM/XvuMW2KIHzhgvNNOpb7FoqIiIiIdJHTGmn37spun2vLljJKS+tJS/OyaFF/ILYr\nF37xiy0AXH/9SAYMCLYSmTnTVDEsWXKE6uoutHDtgbo7bwE00FkkbOLpusXixfDGG/Dyy/C735n3\nvfZa51td19TALbdAQwOcdx7Mnt348YkT4Zxz3FlzF+nCdHMPAmNt2+5v2/b5tm3/xLbtl2zb7sRV\nwfZZljUZuJxgUFALXG/bdlvbKL4F7Ah5ewzwBTfXJa1wWiMpXHDP/v2m3U9SEowbF711RGKYsxMu\nFBV1/BvJ6tWmtG3cODMDYfJk834nkHGbKhdEREREpAcbPdpcLHYjXHDmLZx4Yg79+5td6tEMFw4e\nrGLKlKWBECHU2rUlLFlyBI8Hvv/9iY0eW7x4MMOHp3LwYDW/+932SC03pu3YYS7JdHXeAihcEAmb\neLpuYVmmA8XkyXDBBWbNpaVmlmZn3HMP7NxpWlj/4hctVyl89avurLmLFC40Ydv2Ftu290Tgpb5E\n8N/fBh63bbvN7+a2bdcC9xCsXgDoZBN36ZKBA81tfn5019GTOBfJJ06ExMS2nxtOkahcyMkJDqx2\nvhm2Z9Uqcztvnrl1Wkdt2RKefnrxVF4oIiIiItJJbrZFcloinXRSLllZ5neZiooG6uuj0/f6b3/b\ny+bNZfz5z83ns/3ylyZwuPLKPMaMabwbPzXVyy9/aarI7757K0eP1oR/sTEutC1SV6ktkkiYONdT\n4qFyIZTXa6oOoHPDl999F/7+d3P/178215ZakpfXvfV1k8KF6LmQYNUCmNkLHfEk4Gy1sIC5lmUN\ncnlt0pTaIrnPCReiOW8BIhMueDwmZYaOt0ZywoUTTzS3Y8aYmRAVFbBvn/trjKcdACIiIiIinRQM\nF1quXFi2rJB77tmK399+F+QPPywBTOVCZmZwo9Tx49GpXnj22UMAHDpUTWFh8IL25s1lPP20eez2\n2ye2eOxVV+UxZ0425eUN/OQnYWrBGke2bzfhk9oiicSgeN4U+alPmdvXXjMtjtpTVgbf+pa5f+21\nwVbZMUjhQhRYljUeGBvyrkrg/Y4ca9t21SfPdUIJC1js6gKlObVFct/mzeZ2ypToriMSbZEgGC50\nZKhzWVnw32fuXHObkGCqPCA8cxfiqXehiIiIiEgnOW2Rdu2qwLYbBwhVVQ1ccsn73H77Rl5/vfHP\n66tXF3PoUHXg7dpaH5s3lwFmZkFCgoc+fUyVcjRaI+3ZU8m6dcGBzKHDme+7zzRH+MxnhjJlSmaL\nx3s8FvfeOwOABx/cHfi79UZlZfUcOWKqN9wIF8rLG6iri041i0iPFM+bIufNM5UHxcWwcmX7z+/b\n11QrnHEG/OAH4V9fNyhciI4TQu7bwCrbtjvzHWd5yLFNzyfhcMEFZhDLvfdGeyU9xyGzg4aRI6O6\nDG6+2aTB48eH93WcuQuFhe0/d+1a0/poxIhgsAXBKg+FCyIiIiIinTJyZBoAZWUNlJQ0DgH+/vd9\ngV3mGzYcD7z/44+PM3/+m3z608sD79u0qYyGBpucnCSGD08FCLRGKimJ/E7155471Ojt0KDBCUpu\nvHF0m+dYuLA/l1wyFL8f/vSnne4vMk7s3GmqFgYMSCYrK6nL58nKSgq0RS8uVvWCiGvi+bpFQkJw\n8PIrr3TsmLPPhr/+1XSxiGEKF6JjUpO3N3fy+KbPb3o+cVturmlLk5YW7ZX0HM4OfmeeRbR8+tPw\n7W8HKwvCxfl7dqQtktdr2iGdemrj9ztVHuEY6hzPOwBERERERNqRlpbA4MEpQOO5Cz6fzW9/Gxx/\nGLpz//33i/D7TRskZ0f7unWmJdKsWVlYn1xBzs42F6KjUbnwzDMmXMjLM7+rrl9vwoWDB6vYt68K\nr9diwYL2f8a/8kpTyb1yZXGYVhr73Ji3AOD1WoHPiaKiWh54YBc33vhh1GZyiPQY8RwuQLA10tKl\n4PN17JiWBjjHGIUL0THhk1vnM+RAJ48Pfb4Vcj6R+FBebmYHQOOd+T2ZU7nQkbZIp54KTz4Jd93V\n+P1OuLB5M9jt94LtlGnTzPnDHbKIiIiIiERJS3MXnn/+UGDHOsCWLcFwYePGYBXDsmWmAtmpDJg5\nMzvwmFO5UFoa2V3qBQU1LF9uNgndcYdpoeqEC8uXm4twM2Zk0qdPYssnCHHiiWZQ6EcflVJT08GL\nXj2MG/MWHE5rpDvu2MRXv7qWBx/czVtvdXD+nog0V1cHxz/5mhyvmyIXLICsLNOlorjnBLkKF6Jj\nQJO3D3by+ENN3tbVQIkvzgX2Pn0gPT26a4kU56J9R9oitWbCBFNKV1wM+fnurMvx17/CSy9Fv5JE\nRERERCRMnLkLoeHCb35jqhYuvngIYCoXnJkMoeHCf/9rLuKvXWsqF2bOzAo85rTQadpuKdxeeOEw\ntg1z5mRz4YVm/Vu3llFd7QuEDqec0rEdvnl5aQwYkEx9vR0IKHobtyoXIBguvPDC4cD7QltuiUgn\neTzwz3/CffdBZsszZGJeYiK8+io8/XSP2tipcCE6msbglS0+q3VNn59oWVb7WxFEYoUzGLu3VC1A\n59oitSY5GcaNM/fD0RpJRERERKQHC1YumB3q779/jA8+KCIpycPvfz+ThASL8vIGDh2qxrbtJuFC\nIT6fHbhAHBouZGdHp3LBaYn0mc8MZfDgFPr3T8bvh02bjvPeeyZcOPnkjoULlmUFqhdWreo5O2o7\nY8cO83kxfrwblQvBHulTpvQFYMOG3hnaiLgiIQFOOQUuvtgEDfGqB27ojOOPRlzLIDiMGaCmk8dX\nt3JOkfgQK/MWIqkzbZHacv318OMfw+TJ3V+TiIiIiEgv0rQt0u9/vwOAa67JIy8vLdAOZ/PmMvLz\naygqqgu0u9648TgrVxZRVeUjPd3LuHHB3e1O5UIkZy5UVjbw5pvmd4tLLhmKZVmccIIJPJYtO8ZH\nH5kL2R0NF4BeHS7Ytu1q5cKCBblYFvzyl1O5++5pgCoXRKRnSoj2AnqplCZvd3Z7Q20L70sFSrq2\nHJEI642VC064UFRkBvd4vV07zxVXuLcmEREREZFeJLQtUmFhLc8+a3b+33yzqQ6ePLkvW7aUf9Ia\nyRzjXGjetq2cP/xhJwAzZmTh9QaHbDozF0pKIle5sHNnBfX1Nrm5SUycaHbGz5yZxeuvF/DAA7vw\n+2HkyDSGDk3t8Dl7Y7jg89ns3FnBunUlgXBozJju7928446JfOMbY8jKSmLfPhNmbdlSRl2dn6Qk\n7fMVkZ4jZsIFy7LuA74VgZe607btn0XgddrStFIhqZPHJ7fwvs5WP3RYZWUlaWlpXTo2vbf005fO\nccKF3lS5kJtrSvf8fhMwDGg6ekVERERERMLJqVzYv7+Khx/eQ329zZw52YEd/5Mm9QUOsXlzGQ0N\nJl2YNi2T7OxEtm0r56mnzLjE0JZIANnZ7lcuHDpUzd/+todrrx3BiBHNf6/es8dcsB41KviY8/dw\nBhN3pmoBYO5cEy7s2FFBcXEdOTmdvVQRX2zb5qST3moUpowenU5qahc3goWwLCtQ0ZKXl0bfvgmU\nlTWwbVs506bFab94EQmrysrOds3v3nFuiZlwIYTd/lO6xArjuTurArMeR9NKhva0tPWgouvLaduo\nUaO6fKwzCEukkd5YueD1Qr9+ZuZCQYHCBRERERGRCBs0KIWUFA81NX5+/ettANxww+jA45MnmwqA\nzZvLqK31AyZcGDUqnYce2hMIHGbOzG50Xqdywa1wYfnyY1x66fsUFNSyb18VDz00p9lz2goXHB0d\n5uzIyUli7NgMdu6sYM2aYs45p2f/vlZUVBcIFmbOzGLSpL7ccEPXr3+0xrIspk/P4r33jrFhQ6nC\nBRFpUUZGfHa8j7VarHBeiY6lq9xNg4DObu9v+vwG27YjOzlKpDucuQO9KVwAd4Y6i4iIiIhIl3g8\nVuBifHFxHenpXq68cnjg8dBwwRnmPH16JgsXNr5IP2tW44v4brZFevjhPZx++jsUFJhuyNu2lbf4\nvJbChfHj+zTadd/ZygXoXa2RDh0y4yz7909m7dqzeeyxeZx2Wng2gU2fbgIFzV0QkZ4mlioXXgIK\nI/A6/43Aa7Sn6ZXFYZ08fmiTt8P677Znzx769+8fzpeQ3qY3Vi4AOP+PIhEu2DZs2wYZGTBsGDQ0\nwPLlMHcudLHNmYiIiIhIvBs9OoMtW8wF+yuvzKNPn8TAY+PH98HjgZKSeo4fNwORp03LZMSIdPLy\n0ti/v4rERIspUxrvPHerLdLKlUV8+ctrAJg7N5vVq0sCw6ebailc8Hotpk3LZNWqYjIzE5kypW+n\n13DiiTn8+9/7e0W4cPiwCReGDOlsM4nOU7ggIu2pqOhaU5rCwsJudZ3prpgJF2zbfhN4M9rriJBt\nn9w61RR5nTw+9Pk2sLXbK2pDenq6ZieIe+rr4dgxc783zVyAYCukSIQLP/sZPPoofOUrcPvt8KMf\nwRNPwI03wve/H/7XFxERERGJQc7cBWjcEgkgNdXL6NGmLZDfD+np3sDF+4UL+/Gvf+1nypTMZgN5\ng22Rule58MwzZsD0pz89hIcemsOAAS9w6FA1NTU+UlIazwFoKVwA095n1apiTjopF4/HorPmzQtW\nLti2jWV1/hzxwqlc6MzQ666aPt1Uu2zYUNri4z3931pE2tfVa69VVVUur6RzYq0tUm/RNAyY3Mnj\nJ7VzPpHYdfSo2VWfmGiGHPcmkWyLNOeTvqyvvQavv26CBYDVq8P/2iIiIiIiMWrsWNPTesaMTObO\nzW72uNMaCWDKlMzABfpLLzUNB84/v3n1tTO4t7uVC0uWmArvyy8fTr9+SfTpY/aDOkGCw7btVsOF\nL35xFGPGpHPTTWO7tIYTTsgiIcGioKCWAwequ3SOeBHJcGHqVPN5dfhwDceO1TZ67O23j5KU9DR/\n+cuusK9DIuedd47y3nvHor0MkbBTuBAd60PuW8Bcy7I687E4OeTYpucTiW3OvIX+/cHTy74ERbJy\nYdEiSEqCvXvhO98Jvn/LFvD5wv/6IiIiIiIx6PrrR/K1r43hkUfmtrhTPDRcCB28e/HFQ9m581P8\n5CdTmh2TnW0qF2pr/VRXm5+1n376IJs3l3V4XQcPVrFx43EsC845ZyCWZQWqLJq2RiosrKWqyodl\nwYgRjVuennhiDjt3ns/55w/u8GuHSknxMmOG2WX/+OP7u3SOeBHJcKFPn8TAx9OZ5+H4z38O0NBg\nc/fdW/H7Y2lcqHRVVVUDn/rUMs49979UVTVEezkiYdXLruzFBtu2twGhkXQ6cFJHjrUsKw1YQLCl\nko2ZVyESH0aPhgcegB/8INoriTxn5sKmTbB5c3hfKyMDTjnF3K+ogAkTICUFqqtN4CAiIiIi0gtl\nZibypz/NYtas5lUL0Hq4ADBmTEazlkgAGRkJgX1TpaV1LF9+jM9+9gOuumpFh9f16qumamHevBz6\n9UsGzHwIgN27G/fh3rvXtMAYMiSV5OTG7ZLccOONpl3Uj360qUfPXohkuACtz11Yt860Stq/v4p3\n343EKFIJt8LCWmpq/FRV+Vodyi7SUyhciJ4XMJUHTkjwpQ4e9zkgI+TtNbZtH3FzYSJhlZUF55wD\n558f7ZVE3pQpZpjykSNw4YVw//3hfb2zzza3SUlw330wcaJ5O9zBhoiIiIhInJo0qU/gvnMxuD2W\nZTVqjfTBB0WAuYhcWdmxXctOS6RPfSpYcTBmTMuVC621RHLLl788is9+dhj19TZXXPEBJSXdmyUR\nqyIfLjSfu+Dz2Y3Chn/8Y1/Y17FjRznXXLOyWQWFuKe4OPh/xhkgL9JTKVyInkcIBgsW8DnLsia0\ndYBlWcnAbZ8c5wQTfw3nIkXERUOGwNKlsHgx+P3Bi/3hcvHFcPXV8Pvfw6RJJtwA+Pjjjp/jhhvg\nvPNU7SAiIiIivcLEiX1JSvLg9VodDhcg2BqppKQusBPdtpu3wGlJfb2fN94w7WM/9angTAencmHX\nrsaVC+EOFyzL4q9/ncPo0ens3VvFF7+4Gtvuee16YqFyYfv28kArLYCnnjrY4UCqqx55ZC+PPbaf\n++/fEdbX6c0ahwsdb48mEo8ULkSJbdsfA/8hGBIkAX+3LKtPG4f9HhgX8vZu4G9hW6SIuG/YMPjj\nH+Hll4OVBeGSkgI//7kJBwAmfzI7vjPhwvbtsG0bHNMgKhERERHp+TIyEnjqqQU8+eR8cnOTO3xc\naOXC+vXBnemh91vz/vtFlJU10L9/MrNnB9s1tTZzIdzhApj2Uf/5zwISEiyee+4wO3dWtH9QjNmz\np5ITT3yDf/+7+eyI2lofx46ZC8CRDhc2bTpOfb0fCH5+zJ+fw5gx6VRUNPDMM4fCuo7Dh02osnWr\ndtSHi8IF6U0ULrTAsqwzW/oDTG7y1OzWnmtZ1qgOvNQPgSqCg5lPBP5rWdaiJusZb1nW08BXaFy1\ncJtt25rMKhKPJk+GFgbIhZVTubB5s9lG1RFZpnSXkpLwrElEREREJMZceOEQLr10WKeOycoylQuH\nD1ezdWvwYmJHwoUlS/IBOPfcgXg8wd8RxoxxZi5UNqociES4ADB7djZjx5o1HDxYHdbXCodHH93L\n6tUlfPWrH1JYWNvosfz8GgCSkz3k5CRFZD1jxmTQr18SNTX+QOss5/Nj5sxsrrtuJAD/+MfesK7j\nyBHzd9csgPBRWyTpTRQutOz1Vv58j2ArI4AZbTz32vZexLbtXZhZC36CAcMM4G3Lsgosy1ptWdY+\nYAtwCY2Dhftt236me39NEelVJkwArxeKi6GgoGPH5OSY29L2fykSEREREemtsrPNBeply47h9wff\n35Fw4ZVXms9bAMjLS8PjgepqHwUFwYvjTrgwcmRad5fdrkGDUoDgBel4smKFuYBfXt7AT3/auHrb\naYk0ZEgqVoQ2fXk8Fueea9peOQO8nRZaM2dmce21IwB4882jHDhQFbZ1OB/LwsLaHjtPI9pCw4Ud\nO8oDlSoiPZHChda1tq3BWcFKAAAgAElEQVTXCvnT2WObP9G2nwSuxlQwhB7XH5gNDG/y2jbwa9u2\nb+noa4iIAKZN0pgx5n5HWyM5lQvFxeFZk4iIiIhID+BULrz11lEgWFWwYcNxfL7WLxGUltYF5jKc\nc87ARo8lJXkYPtwECM7cBZ/PZt++yFQuQDBcKCiIr3DB77dZuTL4O8wDD+xutFM/0vMWHOedFwwX\nbNsOhE8nnJDFqFHpnHpqP2ybsLZGCg2Ktm/XrvpwCA0X6uvtZnNTRHoShQsts0Nuu/Kncy9mAoap\nwL+BulbOZwPvAIts2/5+F/5OIuG1Zg189rOmv391/JXM9hqdnbuQ/UnPV7VFEhERERFplVO54LQP\n+uxnh5GW5qW62seOHa1fwD18uOaT4xPp16/5jIfQ1kjm+dXU19skJFgMGxb+yoWBA+OzcmH79nKO\nH68nNdXLeecNoqHB5vvf3xB4PFrhghMgrVtXyrp1pRQW1uL1WkydauYxLF5sqlfeeedoWF6/ocHf\nqEWUWiOFR2i4AGqNJD2bwoUW2LbtdeHPzzr5mntt274W6AcsBr4N/AjTiukKYJht22fYtv2e639h\nka6ybdO//1vfgssugw8/NMN/162L9sqkNaFzFzpi1CfjY95/PzzrERERERHpAZzKBcfs2dmBAb5t\ntUZyLto7FQJNBYc6m53PTkukvLw0vN7wt/MJVi7UtvPM2LJihalamD07m9/+dgZerxlMvXz5MSB6\n4cKAASnMmWM2cP3qV1sBmDixD6mpXgBOO60/AO++W4jf3+m9q+0qLKxtNH5P4UJ4OOGC55Orri0N\ndbZtOywfY5FIU7gQY2zbrrBt+1Xbtv9g2/Yvbdv+rW3bT9m2nR/ttYkEFBXBj34EJ58MixfDCy+Y\n4cROf/4tW6K7PmmdEy50tHJh8WJITISNG935uP7613DKKTBrFsybB/X13T+niIiIiEiUNQ0XZs7M\n4oQTTItRd8IFEypEapizY+BAU00Rb5ULK1eaeQvz5+cwaVLfwDyDxx/fD0QvXIBga6SnnjoIEPg8\nAZg1K5uMjARKSuoD7bLc1PTjqHAhPJxwYdo0EzC2FC7ce+92kpKeZvVqd1sQHzpUzdSpS7nnnq2u\nnlekNQoXRLrC5zMXWl96iUaxf2/x6KPwr39Bfj6kpsK555qA4brrzOMKF2LXpEnm9uBBKGv+A04z\nOTlw5pnm/lNPde+1a2rgL3+BQ4dMm6WjR2H58u6dU0REREQkBjhtkQDS072MHZvhSrjgtEVyerZH\nOlyI14HOTuXCvHm5AFxwgWk39O67hUBshAvO4O/QcCEx0cMpp/QD4J13Cl1/bYULkeGECyefbD6W\nLbVFeuCB3fh8Nm++6W4LrH/8Yy8ff1zGH/+409XzirRG4YJIVzQ0wAUXwM03Q6H73/BjXkODGfR7\nww2mBdJf/gJTpwYvXCtciF1ZWTBwIIwc2fHWSJddZm6few7qGveOxOcznw8dsW2b+Qk6Jwdeew2W\nLTPVLyIiIiIicS60cmHGjCw8HquHVC7E30DnysqGwK7/+fNNdf3Chabd0KZNZRw7VhvVcGHevJxG\nny+h4QIEWyOFY+6C8/k2bpwJrXbsqFBrnjAoKTEV+k64sHVrWaN/5/37q9i50wSGR4+6+39ryZIj\ngAnQ8vM1D1PCT+GCSFckJ8MIU1bJ9u3RXUs03HabCRVuu838WziccGHnTrW7iWXz58PEiTB7dsee\nv3AhDBgAxcXw9tuNH1uyBBYtgscfb/88TpgxeTKMGwfDhpmWSyIiIiIicS4rK1i54FwsnjYtE4/H\nzCtobed/++GCuQicn19DVVVD1CoXjh6tjZuL0B9+WILPZzNkSEpg6HX//slMntwXgGXLjkU1XEhI\n8HD22QMDb7cWLoRj7oLz+TZ/fi5JSR5qa/3s31/l6mtIsHJh7txsEhIsKit9HDgQ/Hd+882CwP3Q\nAdvdVVpax/vvFwXe/vDDEtfOLdIahQsiXTVhgrntjeGCw+tt/PawYdCnj9ndvmtXdNYk7fvpT+Fz\nn4OEhI49PyEBLrnE3P/Pfxo/9uijcPgwHDnS/nlCwwURERERkR4kOzu4aWbmTHOxOC0tgfHj+wCt\nVy84FQGthQs5OUmBXe6XXfYBy5aZgcTOecOtf/9kLAt8Ppuiorr2D4gBK1Y48xZyG71/4UKzi/y5\n5w5RW2t6Eg0Z0vK/e7g5rZGGDUulX7/kRo+1N3fhrbeOdnlHuhMuDB2aytixJrhSayR3VVf7qK72\nAabyx6kSCW2NFNoK6ehR98KF118vwOcLBlIKFyQSFC6IdNW4cea2N4cLTVlWMHRRa6TYlZlpqg0s\nq+PHOK2R3n3XzEoA+Ogj+PBDU31wzTXtn8P5nFC4ICIiIiI9TEuVC6H3WwsXnIu9TvuhljitkV55\nxWzo+fa3xzFrVlarz3dTYqKH3Fzzd4uXuQsrV5p5C03DhUWLTEXAs88eAqBfvySSk5tsmIuQK64Y\nztVX53H33dOaPdbW3IU33yzgzDPfZebM19m0qfMDn0MrZSZMMAGVwgV3lZSYEM7rtejTJyFQMeMM\ndbbtxnMW3KxccFoiOWHnmjVxHi7YNixYAIsXd2xDo0SFwgWRrho/3tzu2BHddcQazV3omcaMgVmz\nzIyF737XtL3629/MY4sXQ//+bR/v9ytcEBEREZEeKzc3KfBn6tTMwPudKoZly1qe1ddeWyQwMxwA\nJk/uy3vvnc59952A1ZmNQt3krC1e5i44lQvz5uU0er8zd6G83MyMGzIk8i2RHOnpCfzrX/O45poR\nLT7e2tyFJ544AJhWW6ed9g7r1nXu4nFL4cL27QoX3OS0RMrJScKyLCZNahwubNlS3iioc6tywe+3\nA+HCLbeY61Vr1pRg2/HRzqxFxcUmVNi82cyPlJikcEGkq0LDhXj+Yu02hQs9189+Bmlp8N578K1v\nwSuvmPd/4QvtH7tvH1RVQUoKjB4d3nWKiIiIiERYYqKHtWvPZu3as0lJCe6Gv/DCIYBpV+JcdHQ0\nNPgDu5bbChd++9sZvPTSKaxbd3ZgQGwkOVUV8VC5cOBAFYcP1+D1Wsyend3osSFDgq2AIDrzFjqq\npbkLPp/N888fBszai4rqOOOMd1tsndQaVS6EX2i4AATChZUri/H5bN54w8xbGD/efC4ePVrjSgDw\n0UelHDlSQ3q6l5tvHovHYz7ehw/H/v/bVu3fb24HDzbXEiQmKVwQ6aqRI00v+vJyyM+P9mpih8KF\nnmvKFLj/fvB4zCDn+nozFHr69PaPdeYtTJjQfFaHiIiIiEgPkJeXRl5eWqP3TZrUl2nTMqmvtwPt\neByFhbXYtvnxumnf/VBZWUksXjyYpKToXMJxgo94CBecf+O5c7NJT28+Y85pjQSxHS6Ezl1wBvS+\n//4xCgtrycpKZP36s5k/P4fS0np+//uOd1MIDReci9sdDRf27q3kppvWsnt3RSf/Nr1L03Bh0aL+\npKV52bDhOD/96ceBlkhXXpkHQH29TVlZQ7df12mbduaZA8nKSmLKFFNBtWZNcbfPHTV795rbES1X\n+EhsULgg0lVJSTBqlLnf0bkLmzbB449DQ5NvHLt2QVmZO+t6803T/975IhxpEyaYn46LiqCw5dJf\niWNnngl33hl8uyNVC6BhziIiIiLSa33uc8MBePLJA43e71zoHTAgBa83cm2OOiue2iI9/rjZ6exc\nuG0qXsKFxEQPl18+DIBf/WorEAxOLrhgMP36JXPzzWYO5M6dHbvYX1XVELiIHVq5cOBANZWV7V/c\n/s1vtvG//7uLP/xhZ+f+Mr1M03Bh6NBUHnxwNgB33bWFpUtNCHDBBYPJyDAB2NGj3f+/tWSJ2fT6\nqU+ZYeFz5pjKnbieu+BULuS1/P9ZYoPCBZHucFojdTRc+J//gTvugHvvDb7v//4PzjoLbrvNnTV9\n97uwfDlceaU75+us1FRT1QHBC8rSs1x7rQkYbrgBzj23Y8d0d95CQUHwBwsRERERkThyxRUmXHjr\nraONLiJ2ZN5CLIiXtki7d1ewYkUxHg9cfvnwFp+zcGGwrVQshwsAt902EY8HXnopn/XrSwPhwiWX\nDAWCg7537epYuFBQYFpwpaR46Ns3gdzc5MCw7h072j+HMyi7o2FGb9U0XAC4+uoRfP3rYwCorfWT\nlZXIzJnZDBhgKpa6O3ehrKyeDz4wFS5Nw4UPP4zjcMHZNOtcY5KYpHDh/7N35/FR1ef+wD9nZrLv\n+8KSQFgChB1kCSAiuKBIvVaxauv19tp6tdrautW2LtWrtbTaq621P63VWvcVURSlAiqgsglkYQsJ\nJCF7QvZMJjPn98eX78wkmcnsmSWf9+vV15nMnDnny2Znzuc8z0PkCVfCBaMRKC8Xj595RvSr/+Yb\n4K67gJgY4PrrPV+PqgKtZ/st1tYC+/d7fkx3sDVS6Lv+ehGU6QaXGtvkSeVCTw/wne8Aq1eLihgi\nIiIioiCSlxeLuXOTYDSqePttS2skebE30MMFS+WCdwbP+sqrr4rKkBUr0u3+nubkxCA3V7Suys2N\nGba1uWPSpDhzMPWf//kNKiq6EBmpwYUXiovHMlyoru5GT4/R4fGswyw5EFxWL5SUDN1JQa834sCB\nMwBEeySyT4YLSUlh/Z5//PGZOOccMWR8xYp0aLWKOVyQs1fcdehQK0wmYPToKOTkiL8X8+aJcwX1\nUGd5gyHbIgU0hgtEnpgoyhBxzIkeh3V1QK/VAK877wR+/GPxeNky4JxzPF9Pba3l8f/8D5CX5/kx\n3SHDhcceA06c8M8aKHA0Noq//4oi2ma56r33xN/tzk5gzx7vr4+IiIiIyMdstUYKnsoFcQHUV5UL\nqqqir8/k8TFeeUVciLzmmqFbqLzwwjl44IGpOP/8dI/OORzuvVd8tz5wQNxEeOGFmeZZEmlpEYiJ\n0UJVgZMnuxwey9bft4ULUwDAPGTYngMHWmEwiAvU5eWdwXuxehjYqlwAgIgILTZsKMRdd03GI49M\nByD+DAHPKxeKisTfj4KCBPNzM2YkQKdT0NCgR2Vlt0fH95uTJ8WW4UJAY7hA5AlZuXDsGGBy8GGo\n8uyHyFGjgEWLgK4u4MwZYOZM4PHHxZwCTx0+bFnXXXcB8fGeH9MdMnQBHP++UOiTFSy5uaJKxxWq\nCvzjH5af/VWNQ0RERETkAdmm5/PPG3D6tLjQFyzhgq8HOv/wh3uQmPgedu92f/DsoUOtKClpQ3i4\nxtw2yJ5zz03D/fdPg0YTuHMupIKChH6/HuvHiqJg/HgxlNmZIctyZob13zfZQuejj2qHDAy++cby\nZ9PZaURjY6/dfUc6e+ECIH7vH3tshrliJD1d/Fl4OnOhuFhUnhQUWK4BRUZqzWFDUA517umxdC4Y\nPdq/a6EhMVwg8kROjmgL093dv2rAFhku5OYCTz0l7uCePBl49lkg0ksfJmW4kJ/vneO5a84ccZd6\ncjIwfrx/10L+V1wstu60RNq5s3/bsX37vLMmIiIiIqJhNHZsNBYtSoGqAhs2nAZguVgvKwMClbwY\n3dio97jCYKD33z+Nf/yjAp2dRtx0014Yje7dES+rFi65JAuJiYMv6gazX/1KVC+EhSm49NKsfq/l\n5Ymbt06ccNyqyFaYtXRpKmJitKit7cG3356x+96BwQ9bI9nX0mI/XBjIW22RbFUuAJa5C9u3N3h0\nfL+orxfbyEj/3ThLTmG4QOQJnQ7IzhaPKyuH3td6yn1Kipi5sGkTkJbmvfUESriQmgps3w58/LF3\nKjIouHkyzFlWLRQWiu2hQ4DB4J11ERERERENowsuyAAA8+DVYKlcSEmJgEYjioo9vQhqra3NgJtv\nttw8tG/fGTz7rOttdVVVxWuvie/jjloiBaO5c5Pw7ruL8eGHS5GS0j+IslQuuBcuRERocf754u/l\nRx/Zv2FSVi7odKLao7yc4YI9Q1UuDOSttkiycmHatP4X4deuFdernn22HNXVQdYaqe5sq66MDHHz\nKgUsXvUj8tQYUd7qMFyQr489+2FHo/H+hfdACRcA8fvizeCEgpPRCOzYIR7PnOnae0+eBD77TDx+\n8EEgIUGURnJQOBEREREFIdnf/quvgitcEINnvT/U+de/LkJ1dTfy8mLwu9+JHvT33nsIjY2unaO4\nuA0nT3YhKkqLSy7JcvyGIPSd74zCqlUZg56XQ52t2yI9+GAxLrvsS3R19fXb197fN+vWSLa0thpw\n+HA7AJjnVDBcsM+VcEFWLngSLjQ06FFfr4eiAFOm9A8XLrkkC4sXp6C724gHHih2+xx+IcOF9MCf\njTLSMVwg8pSr4YKvesXp9UBZmXgcCOECEQDs3i36JCYkuD60/IUXxO1Ry5eL4eSzZ4vn2RqJiIiI\niILQOeckAwCOHetAU5M+aMIFwPtDnb/+ugl//vNxAMAzz8zFL34xCTNmJKClxYB77z3k0rE++URc\nhDz33DRERWm9sr5gYQkXxMX+7m4j/vd/S7FxYw3eeae6376OwoVdu5rMLX2s7d3bAgDIzY3G/Pni\n7zDbItnnWrgg/iw8qQgqLhYtkcaNizEP+5YURcH69TMAAM8/X46Skja3zzPsZFukjMGhGgUWhgtE\nnnI2XLBui+QLZWXiLvGEBCAz0zfnIHLVRx+J7apVQFiY8++rrwdefVU8vuEGsZXhAoc6ExEREVEQ\nSk4ONw9y3b69Aa2tot1nMIQLco1yKLCnHnigBKoK/OAHOVi5MgM6nQZ//rP4vP/cc+Woqupy+lib\nN4s77mXbqZEkL0+0RSor64CqqtizpxkGg5hb8frr/a9R2AsXcnJiMGVKHIxGFZ9+WjfoHLIl0jnn\nJGPcOBFmsHLBNoPBhLY2UTHiWlsk9/9dFRXJYc4JNl9fvDgV3/lONkwm4Je/dC248yvrtkgU0Bgu\nEHnKmXChuxtoODtAx1fhgmwVM2UK+9FRYDCZgM2bxeOLLnLtvc88I6pxZs8Gli4Vz82ZI7asXCAi\nIiKiILVwobjzWw51jojQICHBhZtw/ERekPZG5cKRI+34+ONaKApw//2WuWxLl6ZhwYJkqKqlGsGR\n7m4jPv9cfNe+8MKRd5NdTk40FAXo7DSioUGPHTuazK9t3lxrrkRQVXXISpnVq0U7KVutkeQw5/nz\nGS44cuaMZT6gM4PFZVukxsZemEzuDTOXlQsD5y1Ye/TR6dBqFbz//mlzWBTwZOUC220HPIYLRJ6S\nbY6qquzvI1+LixOVBb4gwwW2RKJA8e234m6D2FhgyRLn31dbC7z8snh8++2WsGzmTPG4qsoS1hER\nERERBZEFC8TchY0bRbiQmRkJJQhuDsvIsIQLjY163HLLPmzdWu/WsWQ7pDVrss0DiSU5V8DWHfS2\nfP55A3p6TBg9OgpTpsS5tZ5gFhGhxejRUQBEa6QdOxrNrxkMKt57T7RGam01QK83AbD8WVqTrZE+\n/rh20EVu68qF3FwRLpw82eX2xfBQJlsiJSaGQat1/O86NVWEC0ajarMllS0lJW146KESdHSICglH\nlQsAkJ8fjzVrRIBk/XckoLEtUtBguEDkKVmJUFcn7rS2RbZEGjPG+aqCmhrg2WftH3OgQBrmTARY\nWiKtWAFERDj/vqefBnp7gXnz+ocScXHA9Oni+ZYW766ViIiIiGgYyMqFlpbgaYkEWNZZVdWNK6/c\nhaefLsOddx50+TitrQa88EIFAOC22yYMel2GC1u21Dl18VpWOFxwQUZQhDS+IAOa48c7sHOnqFy4\n7LJsAJbWSLJqITExDJGRg+dSLFmSipgYLWpre3DwYKv5+ZqablRVdUOjAebMScKYMVHQahXo9Sav\nzd8IJa7MWwCA8HANEhNF5ZKzQ53vvfcQ7ruvGA8+WAxVVVFU5LhyAYC5JVvQVJ2wLVLQYLhA5Knk\nZCA6WgyetVe94M68hfvuAx55BHj7bef2Z7hAgWbxYuDii4E1a5x/zz33AC+9JB7//OeDw7j33gPe\nfBOYNMl76yQiIiIiGibTpyf0Gzps6y7yQCTX+fbbVdi2TVQRHzx4Bnq90aXjvPBCBTo6+jB1ajxW\nrEgf9PrChSmIidGisbEXBw6ccXg8OW9hJLZEkuRQ548+qkVzcy+iorR49NHpAIAtW+rR2KhHcbG4\nu91emBURocXy5eln32OpGvnqK1G1MGVKPGJjddDpNOZKCQ51HszVcAGwtEZyNlw4cqQdAPDXv5ah\nqKgNZ84YoNUq5vDAnqBraSUrF9IH/3eCAgvDBSJPKYpl7oK9cEE+L/dzRK8HduwQj8vLHe/f0AA0\nNYm18KIrBYrzzhNVCCtXOv+e1rN3ySgKsGjR4NdH6N1IRERERBQadDoN5s9PMv8cbJUL6tligvBw\nDQwG1dySxRlGo4qnnjoGQFQt2Ko0CA/XmC9yO2qNVFXVheLiNigKsHLlyL27WQ51fvdd0QJp/vwk\nTJ0aj9mzE2E0qvjv/96Da6/9GgAwb16S3eOsXDk4XPjwwxoA6BcEBd1F6mHkXrgg/m01NDgOF1RV\nNYc6nZ1G3HTTXgDAxImxNitSrMmWVkERCnV2Au0iRGHlQuBjuEDkDXLugr2hzq5WLuzbJ4ZAA6L/\nvCOyaiE3F4iKcu4cRIHorruAq64CPvvM3yshIiIiIvKJhQtTzI+DLVwAgPvum4pzzxVDVvfudb5d\n6dat9Sgr60RiYhiuuy7H7n4D5y709Znw3nvVqKvr34ZHvj5/frJLF3NDjaxc6O4WVSSFhakAgHXr\nxM2NGzachl5vwpo1WXjqqdl2jyMDms8/b4Reb4TJpOKDD8RskDVrss37MVywz51wIS1NVi44bjNV\nW9uDnh6T+WfZBstRSySg/5+bqgb4vAxZtRATI2Y4UkBjuEDkDbIiwV64IJ93tnLhiy8sj50JFw4d\nEtvJk507PlGgGjcOeOwxEZQREREREYWgYAwX8vPjcPXVY3DbbRNw//1TMXeuuAPelXDh8GFxJ/KK\nFemIidHZ3U+GC1980YjubiNuvXU/Lr98J265ZV+//TZvFuHChReO7DubZbggWYcLWq0CRQEefrgA\n771XiMRE+xe9p02LR2ZmJLq7jdi1qwl79rSgrk6PuDidOUwCrO+A7/LBrya4yaHMSUm+aYskA50x\nY6KQn29pgzTUMGcpJycaiiIqHhobnRse7Tdy3gJbIgUF+/81JyLnrVgBJCUBCxbYfv3xx0V7o+nT\nnTve559bHtfUON5/0yaxtR5+S0RERERERAFnwYJk8+NgCRc0GgWvvrrQ/LMMF/bsaXb6GHIAcFbW\n0L/mKVPikJ0didOne3DDDbvNQ4m3bm2AqqpQFAWqqmLbNnF380huiQRYBjpLixaJ8Co3NwaffXYu\noqK0mD8/2dZb+1EUBStXpuNf/zqFLVvqzR1pL7wwE+HhlnuTWblgnyczF5xpi3TihPg9z8uLxQ03\n5OL663cDcK5yISJCi+zsKFRXd6O8vNNcMRGQZOUCWyIFBVYuEHnD0qXAbbfZDxemTgUuuUQMf3ak\nsREoLrb8XFcHmEz29z9+XOyv0wGrV7u2bmc1NgKvvw58/bVvjk9ERERERDRCZGdHme82lxdqg40M\nFw4danV6qLNsa+QoUFEUxVy9IIMFQFy4PXq0AwBQVtaJujo9wsM1OOccJ75nh7DU1HDExop7h6dO\nje93YXvZsjSnggVJBjWfflqHjRtlS6SsfvswXLDPs7ZIzlcujB8fg+99byymTIlDRISmXzXUUHJz\no/sdJ2DJygWGC0GB4QJRoPnyS7GdPBnQaIC+PnFx354NG8R22TJRPTGUtjbgvfeAl192bU3FxcA9\n9wD33+/a+4hGgiNHgHfeAYzOfakiIiIiInrzzUV44YX5mDnTcTuTQJSbG43k5HCXhjrLyoWMDMfV\nGjJcAIDrrhuLJUtEq5+dO8V34y+/FNv585McDrINdYqiIC9PXPBfvNi5i8z2nH++aEOze3czDhxo\nhaIAq1f3DxfkBerKyi709Q1xI+QI5OuBzjIUGDcuBmFhGnz55QqUll6EMWOinTqXDIYCfqizDBfS\n0obejwICwwWiQDNpEvBf/wWsW2fpL2evNZKqAu+/Lx6vXev42C0twO23Aw8/LN7rrOpqsR01yvn3\nEI0UN98M/OIXwF/+4u+VEBEREVGQmDMnCddfnwtF9p4JMoqiuDx3QYYLzrSCWr06C2PHRmPFinQ8\n++w880VzOcBWhgsydBjpZPXGwCDAVaNHRyM/P858uWDRohSkpvZvn5OdHYXwcA36+lRUV3d7dL5Q\n40lbJGcGOluHC/I8rlQ/BU3VSU8PoNWyciFIMFwgCjRTpwK/+Q1www1AZqZ4zt5Q52+/BU6dAqKj\ngZUrHR9bHq+nBzhzxvk1MVwgsq2iAjhxQjz+05+AHTv8uhwiIiIiouHiy3AhKSkcFRWrsWXLMkRG\nahkuOPCHP8zEzp0rcPnlnn9nt55hsWZN9qDXNRoFOTlB0l5nGDU16VFaKqp4MjKcn2fgTlskd9up\nBU248PDDokPA97/v75WQExguEAUyGQbYq1w4fBgICwNWrRIBgyMREUBKytDHtIXhApFt27aJrUYj\nqoF++lNLCScRERERUQhzZaizqqpOz1yQFEUxV3bIIcUlJW04erQdR460AwAKCxkuAEB8fJj598hT\n1i2pBs5bkHJzZXudLq+cMxQ88EAJ2tr6MGNGAubNc37OhaxcaG7uHbLNlMFgQmWl+P12N1yQf24B\nHy4AonIhIoCHTpMZwwWiQJZ19v/I7VUufO97wO7dwJ13On9MR9UQtshwYfRo599DNBJs3y62P/0p\nkJ8PNDUBP/uZa23HiIiIiIiC0Lx5zg91bmkxwGAQn5HlxVRXpKdHYsKEWADAH/94FABQUBCPpCTn\n28+Qc1asSMekSbE4//x0TJ0ab3OfoLkDfpiUlrbhr38tAwA88cQsaLXOtztLSYmAooivkE1NvXb3\nO3WqCyYTEBmpcW3LdmYAACAASURBVDqgG0j+uZ082QWTid9ZyTsYLhAFMkeVCwCQkOBaRYEMLFyp\nXKiqEltWLhBZ9PQAX30lHl9wgZi5EB4unpOtkoiIiIiIQlROjvNDnWVLpKSkMEREuDeAWd6Z/+KL\nFQDYEslXYmN1OHLkYmzZcq7dmSByqDPDBeGOOw7AaFSxdm02VqxId+m9Wq2ClBQRksnqHlusWyK5\nO6tl9OgoaLUKentNqKlxPOOByBkMF4gCmTtBgCPOBBaqCpSXA+++C/T2Wtq8MFygUPbnP4uQoLHR\nuf2//loEDJmZwOTJwPjxwLx54jXOXiAiIiKiEOfKUGdXWyLZIucu6PWidQzDBf+Rd8BXVDBc2Ly5\nFps21SIsTMH69TPcOob8/ZTtvmzxdN4CAOh0Gowdy2CIvIvhAlEgc6eFkSPOBBa9vcBFFwE//7m4\ngKqqotddKj+8UQh74w3g2DHgyy+d2//zz8V22TJA3jmyaJHY7trl/fUREREREQUY2Rrp66+bhtxP\nVi5kZHgeLkgMF/yHbZEsnnzyGADgllsmYOLEOLeOMXu2+He0b98Zu/t4I1wAWHVC3sdwgSiQySCg\nrg4w2R/s49Yx5RwFWyIigIIC8XjDBrEdNcpyAZUo1PT1AadPi8fOtjSS8xaWL7c8V1gotrt2ee/f\nLBERERFRgJIX/Hftci5c8KRyYdq0BMTF6QAAY8ZEISfHs4us5D45GLi6utvmvI36+h5cdNHneOut\nquFems/o9UbceOMec1suAOjpMWLr1gYAwA035Lp97DlzEgEA+/fbrwDyVrjAYIi8jeECUSBLTxcX\n9Ht7geZm7xxz6lSx3bsXqK+3v59s77J5s9iyJRKFspoawHj2Q7Gz4cLLLwOPPWYJFABg+nQgNhZo\nbQVKSry/TiIiIiKiALJwoQgXSkvb0dxsfxitN8IFrVbBggXJAFi14G/p6RGIjtZCVYHKyu5Br3/w\nQQ02b67D739/2A+r842NG2vw3HPluOWWfejq6gMAfPFFI7q7jcjKisT06QluH9u6ckFVbQ9aPnGC\n4QIFJoYLRIEsPNzSishbrZEmTxbBQV8f8Npr9vebO1dsOzrEluEChbJTpyyPy8qce09GBnDVVUB8\nvOU5nQ445xzxeOdO762PiIiIiCgApaZGYPJk0QpmqOoFb8xcAIAf/nAcoqO1uOGGcR4dhzyjKIq5\nesHWRWoZJh050m73Ynmw2bpV3JzZ2WnEhx+KNtObN4vrNBddlOn2kGUAmD49AVqtgoYGPU6ftj1o\nmZULFKgYLhAFOtnGqKHBe8f8/vfF9uWXAYPB9j4yXJBGj/be+YkCTWWl5XFFhWctjRYvFlvOXSAi\nIiKiEUC2Rtq5s9HuPt6YuQAAV189Fp2d/4FVqzI8Og55TvbutzXUWf55t7X1oabG9sXyYLNtm+Wa\nzGuvie+PH39sCRc8ERWlxZQpIqTbt29wa6SOjj40NOgBeGPmAodxk3cxXCAKdM8/Dxw+DJx3nveO\nedFFoiKivt7S9miglBRg/HjLz6xcoFBmHS709FjmL7hDhgvffCNamhERERERhbDCQlFtv3On/coF\nb7RFosAy1B3w8s8bAA4fbh+2NflKXV0PSkrazD9v2lSDkpI2FBe3QaMBVq70POyaM0e0Rtq/f/BQ\nZxkEJCWFITEx3KPzyD+3ysouGAycE0ieY7hAFOhSUsSAZW8KDwe+9z3x+KWX7O8n5y4ADBcotFmH\nC4DzcxdsmTwZSE4GurqAgwc9WxcRERERUYCTlQtff91s92JlXZ2465rhQuhwpi0SABw+3Dbo9WCz\nfbuoWpgxIwH5+XHo6THh5pv3AQDOOScZycmeXfAHgNmz7Q919lZLJED8G4yI0MBkEgEDkacYLhCN\nVNdcA2i14u7qw3aGLFmHC2yLRKFMhgvhZz8UehIuaDTAwoXiMecuEIU2VRX/H7p5s5hlRERENAJN\nnhyHpKQwdHcbceDA4LuujUYV9fWyLZKXb5wjv5EXum2115EzNoDQqFyQ8xbOOy8d69aNAWAJHDxt\niSTJyoV9+wb/G/LWMGcA0GgUZGdHAUDItKwi/2K4QDRSZWYCF1wgHr/6qu195s8X25gYID19eNZF\n5A8yXJChgLNDne0pLBTbLVsAo9GzYxFR4OnsBNavB1asAK68EnjtNeDoUX+vioiIyC80GgWLFsm5\nC4NbIzU26mEyAYoCpKUxXAgVI6ktkpy3cN55aeZwQfJWuDBrlqhcOHWqC01N+n6v7d7dDACYOjXe\nK+eSIV99vd7BnkSOMVwgGsmuuEJst2wRd18OlJsLPPUU8Ne/iioHolDU2Qk0nf0SdO65Ylte7tkx\nV6wAIiPF7IaBLZeIKPi9/Tbw9NNiALyiAD/+MTB1qr9XRURE5DeLF9ufuyAvNKelRUCn42WoUCHb\nItXV6dHVZang7O42oq3N8nOwt0Wqre3B4cPtUBRg6dI0TJkSj5kzEwAAycnhmDcv2SvniY8PQ16e\n+D21nrugqqq5SmLZsjSvnCs9XbQns64wIXIX/6tONJIVFgJRUeICaHGx7X0uvRRYunR410U0nOTF\n/4QEYNYs8diTtkiAqAz661+BDz4QIR0RhZYjR8T2iitE+zNZ9URERDRCFRaKyoUdOxoHvSYvYHLe\nQmhJSgpDfLwOAHDypKV3v/zz1py94lhZ2Y2OjuBtH7ltm2iJNHNmonm2wve/nwMAuPTSLGi1itfO\nZWuoc0VFF6qquqHTWSqEPMXKBfImhgtEI1lkpCU42LLFv2shctdjjwHnnw+8/75774+KAq67Drjs\nMmD8ePFcTY0YyOyJ5ctFyEBEoUe2Tlu8GIiN9e9aiIiIAsD8+cnQahVUVXUPGhIrKxcyMhguhBJF\nUWy2RpJ/3mPGRCM9XVzEPnrUe62RzpzpRXNzr93XS0vb8KMf7enXmskT1i2RpJ/9bBJef30hnnhi\nllfOIcmhzvv2WYY6y6qF+fOTEROj88p5WLlA3sRwgWikW7VKbD/91L/rIHJXc7OoNKiocO/9OTnA\nQw8Bv/0tkJgIJJ8ta/W0NRIRhS5Z3ZSX5991EBERBYiYGJ35wujzz/f/HC0v8rJyIfTI1ki2woWM\njEjk58cB8N7chbY2A2bN+hT5+R/brYb4+c8P4Nlny/HwwyVeOacc5rx8uWUOpVar4KqrxpgrGbzF\nVuXC55/LlkipXjuPDH1YuUDewHCBaKRbsULUK5aUAFVV/l4Nkeuys8X29GnvHO+3vwVefFGEDkRE\nA7W1AQ3iS5652omIiIhw++2TAAAPP1yKb7+1XBytqxMXMBkuhB5ZuVBRMThcyMyMRH6+GEDsrbkL\njz56GCdPdqGhQY8DB84Mer2pSY8tW+oAABs31kC1NVvSBY2Nehw92gEAWLrUexf37ZHhwpEj7Thy\nRAQysnLh3HO9M28BsFQR1dezcoE8x3CBaKRLTgbmzhWP2RqJgpEMF2pqvHO8Sy4Bli1jqxMisk22\nRMrIAOLi/LsWIiKiAPK9743B5ZePQl+fiuuv/wZ6vRGA9Z3sEf5cHvmArcoF6xkb3qxcqKjoxBNP\nHDX/XFTUOmif9947jb4+ESicOtWFgwcH7wMAH31U41SrJllBMGFCLJKSvFulYEtaWgTWrMkCAPzh\nD0dQVdWFEyc6odEAhYXer1yQwR+RJxguEJGlNRLDBQpG3q5cICIaClsiERER2aQoCp55Zg5SU8Nx\n8GArfvtb0ZaGbZFC11AzF6zDhdJSzysXfvnLQ9DrTeafi4oGH/ONNyoBwDxk+f33B39HPHjwDFav\n/hLf/e4uh+eUsw/mzEl0a83uuOeefADAP/95Eq+9Jn49s2cnIT4+zGvnYOUCeRPDBSICVq4U26+/\nFu0eiIKJdbjgYdkrEZFDsnKBLZGIiIgGSU+PxDPPiMr43/3uMD7+uJbhQgiztEWyDPG2rlSRbZGO\nHu2A0ej+d7Vdu5rw2muVUBTg9tsnAgAOHepfldDYqMe//y3mI9xxh2jRZStc+OqrZgAi8HC0Jlm5\nMHt2kttrd9XixalYsiQVvb0m/PrXRQC82xIJsFQutLQY0NtrcrA30dAYLhARMG4ccOGFwI9/DBgM\n/l4NkWuyRNkourqAVttlr0REXiMrFxguEBER2XTFFaPxwx+Og8kErFu3y9yPn+FC6MnNjQYANDf3\noq1NXEuwDpPGjo1GZKQGvb2mfnMZXCWHM99wQy6uu07Mxisqau03U+Hdd6thNKqYNSsRt98+CYoC\n7NnTgurq7n7HkrMa+vpUnD7d/7WB/FG5AAB33z0ZAMyVGt4c5gwAycnh5uqOhga2RiLPMFwgIuGZ\nZ4A77gBSUvy9EiLXREZa/t6yNRIR+ZqsXGBbJCIiIruefnoOli5NRVtbH7q6xOwF2YqFQkdcXJj5\nLng5V8F6gLdWq2DSJM/nLhw4IG4iu/HG8ZgyJR4aDdDU1NtvZoBsibRu3RhkZERi4ULxHfGDD/p/\nR7QeNj5U4NHWZsCxY2KY83BWLgDA6tVZKCiIN/+8dKl3Kxc0GgVpaXLugp9aI23eDNx0E/D++/45\nP3kNwwUiIgp+o0aJbXW1f9dBRKHNYABOnhSPGS4QERHZFR6uwTvvLMb48aJtjk6nIDnZ9wNxafjN\nni3u6t+3rwWqqg5qg2UZ6uxeC+bubqO5+mDChFhERWkxYUIsAEtrpIYGPT77TLREuvLK0QCAyy4T\n7XOtWyOZTGq/Ic/W7ZwGkhUOo0ZFmS/EDxeNRsHdd4vZC3PnJvnk344Mherr/VC50NwM/PrXImA4\ncmT4z09exXCBiIiCH4c6E9FwqKwUAUNUlKUlGxEREdmUmhqBDz5YgqysSFxwQQY0GsXfSyIfmDtX\n3NW/d28L2tv70N3dv1JlyhRxB74zlQtlZR2YNm0znnvuhPk5WV0QF6dDSoq4yF5QkABAtEYCgHfe\nqYLJJNaSlyeCBxku/Pvf9ejo6AMAnDjRaX5sfWxb5LyF4W6JJF177Vi8+eYivPrqAp8c329DnVVV\nBAuNjcCECcCttw7v+cnrGC4QEVHwY7hARMNBzlsYNw7Q8GM0ERGRI1OmxOPkyUvwwQdL/L0U8pE5\nc0S4sG9fi7nFTmysDjExOgDWlQuOw4UNG06jpKQNzz5bbn6urEy0JsrLi4WiiIBqYLjwxhtVAICr\nrhptft+UKXHIy4uBXm/Cp5/WAbBUI0hDhQty3sJwt0SSFEXBd787GhMnxvnk+LJywbq11LDYsAH4\n6CNApwMef1y0Oaagxm9FREQU/DIyxLauzr/rIKLQxnkLRERELgsL05gvClPokZULhw614uRJ0WbI\nenh3fr6sXHDcFunYMRFAlJa2mYc1nzghAoC8vBjzftOny3ChDXV1Pdi2TbZEGmPeR1GUQa2R5LyF\n2FgRfAzVFsnflQu+5m7lgvUQbZd1dAD33Sce33orMH26+8eigMFwgYiIgp9sT1JT4991EFHo6uwE\nNm4UjxkuEBEREQEAcnKikZQUBoNBxb//LS7yW4cLkyaJNkWNjb1obBz6Lvnjx0WVQnt7H06fFhe9\nZeXC+PGx5v3ksOPi4la89ZZoiTR/fhLGjYvpdzwZLnzwwWkYjaq5cuHiizMB2K9c6OkxorhYhCH+\nqlzwNXcqF9at24Vp0zabW1+57PhxoL0dSEkBbr7ZvWNQwGG4QEREwY+VC0TkSwcPAvPmAcXF4meG\nC0REREQARIWArF7YtEnc7GUdLkRH65CTEw0AOHJk6NZIx451mB+XloqL+7YqFyZMiEV4uAadnUY8\n/vhRAMBVV43BQIWFqUhKCkNjYy+++qrJXLmwdq0IHU6d6oLROPhO/KKiVhiNKlJSwjFmTNSQaw5W\nrlYu9PWZ8NZbVSgtbUdJiXvDudHcLLZZWaItEoUEhgtERBT8rCsXPCnTJCKyZfJkICzM8vP48f5b\nCxEREVGAkXMXDh4UMxAyMiL6ve7M3IWeHiNOnbK0KZIXsG1VLuh0GkyZIo4pw4crr7TMW5DCwjRY\nvVp8V3zhhQpUVnYDAC6+OAs6nYK+PhWnT3cPet++fSKEmD07MWRberlauVBb2wOTSTyuqRn8e+aU\npiaxTU527/0UkBguEBFR8EtPF9veXuDMmaH3JSJyVUQEMH++5edx4/y3FiIiIqIAIysXJOvKBcAy\nd0FWI9hy4kRnv/vESkvbYDKpKC8fXLkAWOYuAMDChcnIyen/uiRbI734YgUAIDc3GsnJ4Rg7VlRT\n2GqNtH+/f4c5DwdXKxdkMAPA3LLKZQwXQhLDBSIiCn4REaJvI8C5C0TkG2vXWh5HhWZ5PBEREZE7\nHIcLjisX5DBnqbS0HTU1PejpMUGrVTBmTHS/1wsKLOGCrZZI0oUXZiIsTIHBIJKLWbPEgObcXBFG\n2BrqHOrDnAFL5UJ9vR4mk+Pq/6oqy++TrWoPp8i2SPK7O4UEhgtERBQaOHeBiHxpzRrg978H3n3X\n3yshIiIiCijjx8cgIcHSQtJ+uGC/ckHOW5AVCqWlbThxQjyXkxONsLD+lzCtw4XvfndwSyQpISEM\ny5enm3+eOVOGC7YrFwwGk3k2g2z3FIrS0kS40Nen4swZg8P9+1cuMFwgC4YLREQUGjIzxZaVC0Tk\nC4oCXHklMGuWv1dCREREFFAURel3l79suSPJtkjl5Z3o6THaPMbx4yJIuPRS0caovl6P3btFe6Lx\n4we3PFq6NBXTpyfgv/4rd1BVw0CyNRJgHS7IyoX+4UJxcRv0ehMSEsIwYUIsQlVkpNYcCDnTGql/\n5YKbbZFkuMC2SCGF4QIREYUGGS6wcoGIiIiIiGhYWd/lP7ByISMjAgkJYTCZLCHCQLJyYfbsRPM8\nhA8+OA0AyMsbfJE/Pj4MBw9egL//ff6g1wZasybL/HhgW6STJ/u3RdqzR1wAnzs3CRpNaA5zllwZ\n6mxducCBzmSN4QIREYUGGS7U1vp3HURERERERCOM9dwFedFaUhTF4dwFOXNh4sRYTJki9v3ii0YA\ntisXXJGTE4MnnpiJRx+djnHjxLHstUXas0dUS8ybF7otkSRXhjp7tXKBbZFCCsMFIiIKDWyLRERE\nRERE5BcLF6ZAUYCxY6MREaEd9PqUKaI1kq25C93dRvOd8RMnxpn37esTg4ZtVS646mc/m4R77sk3\n/ywrF06d6oLRaBloPJLCBXcrF+rqetDXZ3L9hG62RertNeHGG/fg9tu/RVOT47XS8GK4QEREoYFt\nkYgoEDQ0AHfcAfzwh/5eCREREdGwGTcuBp98sgwbNxbafH2oyoWyMtESKSEhDKmp4eZwQfJGuDBQ\ndnYUdDoFBoNqbvOj1xtx8KAY5jxvXui37nG2cqGvz9SvFZKqOhdI9NPdDXSdrX5wMVz4+ONaPPdc\nOf70p2OYNOlj/O1vZf0CIfIvhgtERBQaAr1yYcsW4JJLgHvu8fdKiMiXIiKAt98GPvsMaLdd9k9E\nREQUilauzMCMGYk2XxsqXJBzGCZOjIWiKOa2SJKnbZFs0WoV82yHigpx0fvQoVYYDCqSk8PNbZNC\nmbOVCzU1PTCZgLAwBVlZkWefc3Hugpy3EBYGxMUNve8AmzeL1sfh4Ro0N/fippv24cEHi107P/kM\nwwUiIgoNMlzo6RF3RQQagwEoKQGOH/f3SojIl+LjgbQ08fjECf+uhYiIiChA5Odb2iKpav+7zuUw\n54kTRYWCdeVCamo44uPDfLIm2RpJzl2wbomkKKE9zBmwhAuOKhcqK0X4MmpUFEaNigLgxtwF65ZI\nLv7eynDhlVcW4Fe/mgIAePfdatfOTz7DcIGIiEJDXBywezdw+DAQFTX0vn19wG9+Azz5JKAfpp6N\n8mJjQ8PwnI+I/Gf8eLFluEBEREQEQFQf6HQKOjuNqK7ufzOYZZizuKM9NTUCqanhZ9/n/ZZI0sCh\nznv3jpx5C4ClLZKjyoWqKvHnNXp0NLKzZbjg4g19bg5zLivrQFlZJ3Q6BatWZeCmm8Tn7NLSdnR3\nG11bA/kEwwUiIgodqamAxon/a2tuBv71LxEu6HS+XxfAcIEo0H3yCbBhg3eOlZcntmVl3jkeERER\nUZALC9NgwgQRFLz00sl+r8nKBfk6YKleyMvzfkskSVYufPutmLMgKxfmzh0Z4YKrlQtjxkQhO1sE\nEi6HC7ItkovzFj75RMxUXLw4BfHxYRg1KgqpqeEwGlUUF7e6tgbyCYYLREQ08sgL/CkpgFY7POdM\nTRXb7m6gs3N4zklEztu0CfjFL4B//9vzY7FygYiIiGiQa64ZCwC4994i/OQn+9DXZwIwuC0SAMyc\nKWY3yHZKvrBiRToA4O23q/HPf1agqEhcrB4Jw5wBS+XC6dM96O012d3PVuVCTY2bbZFcrFyQLZEu\nvFC0QVYUBbNmib8b+/efcW0N5BMMF4iIaOSR4YK84D8cYmKA6Oj+5yeiwNHcDBiNwBkvfEmRlQsM\nF4iIiIjMfv3rKXj00ekAgL/8pQxz5mzBtdd+bW6TZB0u3HtvPtavn4Gf/GSCz9ZTWJiKe+7JBwDc\ncMNu9PWpSEuLwJgxDtrshoi8vFhkZUWiu9uIDz+ssbufdeWCHOjsdlskFyoXDAYTPvusHoAlXACA\n2bNFZYmsOCH/YrhAREQjj7y4L1sVDRe2RiIKXC2iDN7VUm2bZOVCebkILIiIiIgIiqLgnnvy8c47\nixEdrcWhQ6145ZVTAESLnpSUCPO+WVlRuOOOyUhODvfpmh56aBqWL0+D6eyN+yNlmDMAaLUKrrsu\nBwDwwgsVdvezPXPBg4HOTtq1qwnt7X1ITQ3H7NmJ5uflY29XLvz97+X485+Pe/WYI8EwNZomIiIK\nIP4MF06eZLjgLUYj0NMjqkKIPCW/8CR5ocfuqFFAeDjQ2wvU1ACjR3t+TCIiIqIQcfnlo1BaehG+\n+KIBNTU9qKvrwUUXZTp+ow/odBq8+upCzJnzKWpqejB//shoiSRdf30O1q8/gk2balBf34P09MhB\n+1hXLuh04j51lysXGhvF1oVwQbZEWrUqAxqNJfCRbZEOHDgDo1GFVut5GNTc3Isbb9wDVQWmT0/A\nuecOfa2gurobJ050YOnSYb6mEIBYuUBERKGttRXYuBHYt8/ynL/CBdmGqb5+eM8b7IxG4I03xEVa\naz//OTBrFofmkne4cTeVXVqtCBgA4NQpz49HREREFGLGjo3Gtdfm4I47JmP9+pk4//wMv60lMzMS\nH3+8FLfeOsGnbZgC0bRpCZg3LwnGPiP23v8iUFXV73WDwWSeryAqF0T40NCgh8Fgf07DIN/7HnDn\nncCcOU7t3ttrwsaN4vufdUskAJg0KQ7R0Vp0dRlx/HiH82sYwoEDZ6Cq4vH99xc73P+KK3Zi2bJt\nOHiQrZkYLhARUWj761+B224DXnrJ8py8a2K4wwV5sXHABzZy4KOPgLvvBr7/fXEnOAB88w3w/vtA\nXx/MnwKJ3NXdLapgAO9ULgDAWDGw0OvhwocfApdeClRWeve4RERERCPYjBmJePLJ2UhLi3C8c4j5\nz//MxSVRh3Hxx78DfvWrfq/V1PRAVYGwMAXp6RFITY2ATqdAVYG6OhdaI61cCdx8M5Cf73DX8vJO\nLFnyGQ4dakVEhGZQuKDVKpgxIwEAsH9/i/NrGIL1/Ibt2xuwdav9GwL1eiP27BHn3beP4QLDBSIi\nCm0rVojttm2W3uf+qlzIzRXbkyeH97zBrqREbMvKgOefF2HC+vXiuWuuASaMrLuLyAdk1UJ4OBAb\nO/S+zvJVuPD//h9QXCxCBiIiIiIiD1199RgsiRafWbsOlOLuuw+isPAzbNtWj6oq0RJp1KgoaDQK\nNBoFmZlyqLOLcxecsH17A+bM+RS7d7cgKSkM77yz2Hw+a7I1kreGOh84II4TGysmCDzwQDFUOzex\nlZS0wWgUrx092u6V8wczhgtERBTa5swBEhKAM2eA/fvFczJckG2KhkuOGJbFcMFFx62Gaj35JPDy\ny8CePUBEBHDrrf5bF4UOOcw5KQnw1gA/X/x77+mxhG1st0REREREXpCSEoELRonq/sgzjfi/9UXY\nubMJV131FfbuFZ+TR4+ONu9vGers4twFJ9x110GcOWPAggXJ2L9/FVavzrK53+zZotrYW0OdZUjx\n2GPTERGhweefN2LrVtuzEg8ebDU/ZrjAcIGIiEKdTgcsXy4ef/aZ2AZC5YLJhf6UI52cqZCUJNrX\n/OY34ufrrwcy/TP4jUKMN4c5S76oXCgqEq3AvH1cIiIiIhq5VBXTVNFyU6Oo+K8LwjB1ajwaGvS4\n++5DAMQwZ0nOXZCzGLy3DBUlJW0AgH/8Yz5ycmLs7jt7tqhc2L//jN0KA2f19prM573kkiz86Efj\nAQCPPFJqc//+4YJ3Zj4EM4YLREQU+mRrpI0bgaYmoP3s3QXDHS5kZQFhYWJuQG3t8J47WBkMlouo\njz8uBuUCQFwccNNN/lsXhRZvDnOWrCsXvDUXxHowPWcuEBEREZE31NYivN0yu+Dpe9Lw+usLERGh\nQXe3aC1sXbmQleWbyoWamh50dPRBowHy8oZuVVpQkACtVkFDg97jkKO0tA0Gg4rExDCMHRuNW24R\nbXd37GhEX9/gmwKthzgfO9YOk2lkzwBkuEBERKFvxQogPV0MUr75ZvFceLi4QD2cdDpg9GjxmK2R\nnHPqlLhTOzoaOPdc4MYbxfM/+Yl37zKnkU22RfJmuDBmjNi2twOtrUPv6yzZ2g0AqqstVQxERERE\nRO4qKur/86lTKChIwB/+MNP8lK3KBW+HC7LF0LhxMQgPH/qSdVSUFvn54vu8p62RZEukmTMToSgK\nJk6MRWysDj09JpuVCdaVCz09JlRVeb89VDBhuEBERKEvNhb4y1/Exf1vvhHPpaV5r7e6K+TdzBUV\nw3/uYCRbIuXliT+vu+4S7a1kyEDkDb5oixQVJUJNwHth4rffWh4bjUBNjXeOS0REREQjV3Fx/5/P\nVo7fcksehFFP8QAAIABJREFU/uM/RgEACgst8wotMxe82xZJXsifNMm5mwDnzBGf3d97r9qj88ph\nzjNnJgAANBrF/HjgwOja2h7U1+uh0QA5OdFn1+29uQtfftmIrKyNeP11x1XKfX0mPPbYYezf3+Jw\nX19iuEBERCPDvHnAr35l+Xm4WyJJHOrsGutwARABw7hx/gmGKHT5onIB8Gzugq25LJs3Ay++aKmA\n4twFIiIiIvKUrFyQMwLPtt9UFAVvvbUITU1rzRfyAUu4UFPjm8oFZ8OFG28cBwD4+9/L8c03zW6f\nVwYIs2Ylmp+TjweGC7Il0sSJcZg5M7Hfur1h06Ya1Nb24M03HYcLn35ah3vuOYRf/vKQ187vDoYL\nREQ0clx/PXDZZeKxvOg33BguuOb4cbGV4QKRL/iicgFw/9+7qgKzZgELFgCnT1uej48Hli0DJk4U\nPzNcICIiIiJPycqF1avF1uozpqIoSE4O77d7VpZoizSoHdA99wAPPSTad7rBEi4MPW9BWro0DT/4\nQQ5UFbjppr0wGl2ffaCqKg4cEG2OZFgADBUuiH1nzEgwr9ObQ53r6kQ1SEVFl8N9jx8X5y0r8+9Q\naYYLREQ0cigK8PvfA48+Ktrr+IO8G4RtkZwzsHKByBdkuJCS4t3julu50NEhZjXU19sOPOQ8Bw51\nJiIiIiJPqCrw2GPi+/EFF4jnKivF83bk5cVCUYCmpl7U159tjdTbC7zzDvD887YrcJ0gL9JPnuz8\nbMT162cgMTEM+/efwdNPH3f5nFVV3Whu7oVOp2Dq1Hjz89bhgmr1e9E/XBDrPHLEe5ULdXV6AEBF\nRafDfSsrRbjT2urfOWwMF4iIaGSJiACuvhoYNco/57e+k3mID2wE8ftz4oR4zHCBfEm2RfJV5YKr\n4UJdndjGx4vZDQPJ0ILhAhERERF5QlFEZez//A+Qny+e6+y03HxjQ2ysDnl54q5983DjY8cAg0F8\nfpUtPF1gMJjMd+A72xYJANLTI/Hoo9MBAL/+dZEl7LChu9vYLygALPMW8vPjEBmpNT8/bVoCNBqg\noUGPmhrLMWVbpBkzEs3r9GZbJFm50NTUi/Z2w5D7VlY6rm4YDgwXiIiIhtPo0YBGA3R3Aw0N/l5N\nYGtoEHdvi2lZ/l4NhTJftUVyt3JBhgsZGbZfl5ULbItERERERN4SEQFkZorHDj5nyoHH8uK8eW5D\nQYFb8/EqKjrR16ciKkqLUaNs3FwzhBtvHI+Cgni0tfXhk0/qbO6zc2cjYmLewX339R9ebWveAgBE\nRWmRnx/fb5/eXhNKStoA9G+LVFHRCb3e6NKa7ZHhgjju0OEBwwUiIqKRKDzcUjXBuQtDky2Rxo4V\nH3SJfEFVfT/QubYW0Oudf19trdimp9t+nW2RiIiIiMgXnPycKecTyHkF5nBh2jS3TitbIk2cGAuN\nxrVwQqtVsHy5+Nw8cEaCtGVLPVQVWL/+SL8L+HL9A8MF6+fkMY8caYfBoCIuToecnGhkZkYiNlYH\nkwk4ccJxGyNHVFU1t0UCgPLyoY8p2yL5G8MFIiKi4cahzs4ZPx743e+Am27y90oolLW3A8azdxp5\nu3IhORmIjRUBhitBQH292Mo7xwaSX/paWoC2NtfXVVkJ7Nnj+vuIiIiIKLQ52X7TEi4MqFyYPt2t\n01qGOTvfEsna7NliPfv32w4X5F3+er0JTz55DABQVdWFjz8WN/XMnTv4e8DAcMHSEikBiqJAURSr\noc6et0ZqbTWgt9cyr2KouQtGo4rqaoYLREREIxOHOjsnIwNYt078j8hXZEuk2FjvV8goiuULWlWV\n8+9z1BYpNtYyfNqV4wJATQ2wZo34d+Xqe4mIiIgotDnZ1nPGDNEWqbS0Db1dvUBpqXjB7coFGS7E\nuvV+ewOYJesWQn/5Sxna2gy47bZv0dHRh8WLU7BsWdqQxwSAvXtFtbMMVsR65dyFDrfWba2+vn+l\n81DhQm1tD4zGwJjhyHCBiIhouOXkiDkCra3+XgkR+WregvTMM8CBA8Dy5c6/x1G4ALg3d8FkAu68\nU/y3x2SytB4jIiIiIgKcbouUkxONhIQwGAwqyv99ULQAjYmx3EjnInlxfvJk9yoXpk2Lh06noLm5\n12a7IPlcWJiC1lYD1q37Cu++Ww2dTsHf/jbXZismOVfi+PEOfPFFA55+Wnx2XrIk1byPN4c6W7dr\nAoZui3TqVGDMWwAYLhAREQ2/a64BSkqAhx7y90qIyNfhwpgxQHy8a+9xJlxwZ1j0888DO3ZYfq6p\ncW1dRERERBTaZswAfvQjh9XjiqKYqxcat+0TT06dKm6ic8ORI561RYqI0GLqVPGZe//+ln6vqapq\nvhh/++2TAMDcDumOOyajoCDB5jHT0yORnR0JVQUuueRL6PUmXHppFtatG2Pex5ttkaznLQBDD3SW\nlRhpaf6fTchwgYiIaLhFR/dvv/LVV8CPfwz84Q/+WxPRSOWrYc6ekOGCvYHOgOtDnfv6gHfe6X9c\nOTiaiIiIiAgA8vKAX/4SWLvW4a6yPZDx4CHxhJvzFjo6+szzA9wNFwDL3IWBQ51bWw3o6OgDAPzy\nl/nIzo4EAIwbF4Pf/GbKkMeUrZHa2/uQmxuNF188p1+Vg6y08EZbJFm5kJ8vjjlU5YIMFwoLUzw+\nr6cYLhAREflbUxPwySfAN9/4eyVEI4+sXAiUcMFkcjzQGXC9LZJOB7z9tqiYuvZa8RwrF4iIiIhC\nT1sbYDT6/DSybVBi1VHxhJvzFo4fFxfmU1LCkZwc7vZ6ZBAwcKizbImUnByOxMRw/PGPMzF+fAz+\n8Y/5iI7WOXXM8HAN3nxz0aD1TZwogoDa2h6cOdPr9toBS7hwzjnie0lrq8HuMeWvadKkOKSn+7d6\ngeECERGRvyWeHQjFGQxEw2/MGGDVKlECHgiam0WVgaIAqan298vLE2ueMMH5Y0dFAdddB2Rni59Z\nuUBEREQUWo4dA+bOBW6+GbAx2NibZs5MhAITxnefFE8UFLh1HMswZ/erFgBg9mzR5nRg5YJsiTR2\nbDQA4Oqrx6KsbDXOPXfwEOeBrr8+FwsWJOPFF+dj3rzBNyMlJIRh9OgoAEBxcZtH65dtkcaPjzG3\nO7LXGklWLowZE23+dfnL0PEMERER+V7C2R6PDBeIht/q1eJ/gUK2REpNBcLC7O83bx6wYYN755AV\nEQwXiIiIiELL9u3iRpVPPgE++sinn3OnTYvH5PBGxCp6qBGRUMaPd+s4Mlxwd5izJCspTp7sQnNz\nr7nKwHIhPsrlY06aFIevvjp/yH0KChJQVdWNoqJWFBYOcXOQA7JyISMjEuPGxaChQY/y8k5z9YQ1\n61/T2LHR2LPH7dN6jJULRERE/iYrF86cGXo/Igp9zgxz9lRWltiyLRIRERFRaCkqsjx++GGg037f\nfk9FR+tw6dhGAEBr9gTRhtMNpaWyciHWo/UkJoZj3LgYAMCBA5bv1tZ3+ftCQYEYJF1U5FnlQn29\nqFxIT49Abq5Ya0WF7T8/2RYpECoXGC4QERH5m6xc0OuBnh7/roWI/EtWEww1zNlTMrhobwc6PB8+\nR0REREQBQoYL4eHiRpK//MWnp7s4qQIAcDR+stvHKC4WFfzTpiV4vB451Nl67oL1hXhfmD5drLuo\nyLNOBNaVC7m5IiSRQ503bKjGI4+UwmRS0dtrMu8rwgXXKzK8ieECERGRv8XGAlqteMzWSEQjmzPD\nnD0VGwvEnS07l5USRERERBTcOjuBEyfE4wcfFAFDhG+H/c7uOQwA2NbrwhwwK319Jhw+LCoXpk2L\n93g9soWQ9dyFgTMXvK2gQIQLhw61QvVgzsXAtkiAqFyoqenGunVf4Ve/KsLmzbWoru6GqgKRkRqk\npoazcoGIiGjEUxRL9QJbIxGNbMPRFglgayQiIiKiUFNSIoY4Z2QAV18NbNsG/PSnvjtfdTWS2mvR\np2rwceMYtw5RVtYJvd6EqCit+YK6JyyVCy3m5zyZueCMKVPiodEATU295qHMzujrM5nDiM7OPnR2\nGgEAGRkR5sqFioou/PGPR6HXmwAAb79dbQ5LxoyJhqIofg8XONCZiIgoECQkAM3NDBfcVVUF3H23\naPHi7pBbIn86dQr49NPhCxcyM4GjRznUmYiIiChUyJZIBQViK28m8ZWvvgIA7OkdjTI3i2FlS6Sp\nU+Oh0SgeL0lWLpSWtqO724iICA2qqnzbFikqSosJE2Jx9GgHiopakZkZ2e91k0nF9u0N2L27GXv3\ntuDIkXbU1PSgvl6PxYtT8OWX55mrFqKitIiN1ZlnLhw/3oGyMksb0/feq8aSJan9fj2jRjFcICIi\nIjnUmW2R3BMbC+zcKR53dgIxnt/1QjRsWlqAtWtFuBh19o6q4QgXAFYuEBEREYWK4mKxleGCr50N\nF7Z256GmsRuqqkJRXAsIiovFEGRvtEQCgFGjojBqVBSqq7uxY0cjpk9PQG+vCYoiXvOVgoIEc7iw\ncqXlc7yqqrjuuq/x6quVNt+3c2cTjh7tQHNzLwBRtaAoCnJyxPfZ7m5RzTBnTiJOnepCY2MvXnnl\nFABLJUZYmH8bE7EtEhERUSCQbZEYLrgnMRFIThaPKyr8uhQilyUlAddcIx53izurfDrQGbDcycbK\nBSIiIqLQICsXpk0bnvPt2gUA2NYzHgaDiqamXpcP4e1wQVEUrFwpPkd/+mmduYVQVlakTy/Cy7kL\nRUVt/Z5//vkKvPpqJXQ6BVdeORq/+910fPjhEnz77SosWpQCANi5s9FcuZCeLqoeoqK0/SogfvOb\nqVi7dpT51wX4rhLDVQwXiIiIAgFnLnhu3DixLS/37zqI3PHznwOFhZaffTnQ2fr4DBeIiIiIgtP2\n7cCLLwImk7hB5dgx8fz06b4/d2UlUF0N6HQ4EjsJAFBT0+PyYWRbpGnTEry2tFWrROXAp5/WWc1b\n8O2F+IICEY4UFVluFjx6tB233bYfAPDwwwV4441FuPvufKxenYWZMxOxbJlob7RzZ5PVMGfLAG45\ng2L69ARcdlk2rrhiVL9zMlwgIiIiC7ZF8pwMF06c8O86iNyh1QL/93/A5MnA/PmimsGXONCZiIiI\nKLh99BHwwAPAW28Bhw+LkCElxfftNQFz1QJmzkR8lvguW1PT7dIhDAYTjhxpB+C9ygUA5rZE+/ef\nwf794uY9X1+Inz5dVi60wmRS0dtrwjXXfI2uLiPOOy8Nd945edB7Fi8W4cKuXU2orxeDoDMyLNUK\nK1akQ6tV8MgjBdBoFJx/fgYSEsLMr/tqQLWrGC4QEREFArZF8pwMF9gWiYJVSgqwaRPw+uuAi/1q\nXcbKBSIiIqLglp8vto89BuzYIR4XFPj+cyRgCRcWLkR2trggfvq0a5ULx493wGBQEROjxdix3rv4\nn5ERiRkzxPfrF1+sAACvHt+WCRNiER6uQWenESdPduG++4qwd28LkpPD8c9/nmNzWLVsi1Rc3GYO\nWazDhYcemoaGhstw6aXZAIDwcA3WrLEM6WblAhEREVnIygW2RXIf2yJRKNBohucLoaxcaGmxzHkg\nIiIiouBx7bXApElAczPw5JPiueEY5qyq5mHOWLQIWVniDnpXKxdkC6GpU+NtXnz3hKxeqKwUa/L1\nXf46nQZTpsQBAJ566hh+//sjAIDnnpuH0aNthwBpaRGYODEWALBpk6gmtm6LpCgKkpLC+73niitG\nmx/7OjBxFsMFIiKiQMDKBc9Zt0VSVf+uhSjQxcUB0We/kLB6gYiIiCj4hIUBDz4oHhsMYjsc4UJ7\nOxAj5gFgzhxkZblXuWAZ5uy9eQuSnLsgDcdd/rI10hNPHIOqAjfeOA6XXz5qyPfI6oWWFvHnZ125\nYMuFF2Zi9uxEXHxxJuLjw4bcd7gwXCAiIgoEDBc8l5srtm1t4m5sIrJPUdgaiYiIiCjYLVwIrF1r\n+Xk4woW4OGDdOuCpp4CoKGRnu1e5YAkXvDdvQVq2LBXh4ZbL3sNxl39BgSUkmTw5Dk88McvhexYv\nTun3c3p6hJ09hagoLfbtW4VNm5a6t0gfYLhAREQUCNgWyXORkUC26EfJ1khETuBQZyIiIqLgd++9\nYojzlCnAqKHvlPcKRQFuvBG49FIAMFcu1NS4WrkgbqyzvijvLdHROhQWWi7cD0flgpzzEBam4JVX\nFiAmRufwPXKos+SociEQMVwgIiIKBAwXvINzF4ic99//LfrzLljg75UQERERkbvS04GtW4H33x+e\n2V0DWNoiOV+50NtrwrFjHQB8U7kAWFojhYUpDisCvHW+O+6YhDffXIQ5c5Kces/UqfGIj7eEEAwX\niIiIyD3xZz9QtbcDRqN/1xLMGC4QOW/5cmDNmuG5w42IiIiIfCcqCtA5vlPeFyxtkXqgOjn77ujR\ndvT1qYiP12H0aN8MW16zJhtarYJZsxK9PjDaFp1Og/XrZ2LtWuc/W2u1ChYuFBUWYWEKkpICY46C\nKxguEBERBQI5c0FVRcBA7hk/XmxPnPDvOoiIiIiIiEaAzExxt71eb8KZMwan3nPokGiJNHVqPBQf\nVVsUFCTgm2/OxzvvLPbJ8b1FDnVOT4/02e+FL/kn0qIRzWQyoampyd/LIApJKSkp0GiYGwel8HAg\nJgbo7BStkWSbpJFm3z7gwQeBmTOB3/7W9fezcoGIiIiIiGjYREZqkZQUhpYWA06f7kZSUrjD92zf\n3gAAWLAgxcGennG2PZE/rViRjgcfLMGECbH+XopbGC7QsGtqakJ6erq/l0EUkurr65GWlubvZZC7\nEhJEuNDa6u+V+M+pU8DBgyJoccekSWJ7/LioAImL897aiIiIiIiIaJDs7Ci0tBhQU9ODadMcD2je\nurUegLiwPtItW5aG998vNA+EDja8vZWIiChQyNZII3moc12d2LobQmdnA7m5Ym7F1197bVlERERE\nRERkmytDnauqunD0aAc0GmDZslRfLy0orFmTjZwcN2+w8zOGC0RERIFCtkKSlQsHDgAffAA0NPhv\nTcOtXtzBgsxM949RWCi2O3Z4vh4iIiIiIiIaUlaWZaizI1u3iu+3c+cmITHRcQslCmwMF4iIiALF\nXXcBr70GLFkifr7vPuDWW0XIMFLU1optRob7x2C4QERERERENGyys0XlgjPhwmefiRvKzjuPLZFC\nAWcuUEAoKSlBaipLoYhc0djYiKlTp/p7GeRNs2b1/1m2BpKtgoLJ4cNASwuwaJHjfXftAjZtAs4/\nH6ipEc95Ei4sWgQoCnDsmKiE4JwfIiIiIiIKJPv3ixvL7roLSPHtUOPhICsXHLVFUlXVHC5w3kJo\nYLgwBEVRFAATAEwHkAUgHkA3gGYApQD2q6ra5+VzxgIoBDDJ6nwnAexUVbXGm+cKJKmpqRxCS0Q0\nkLzAPtzhwlNPAc8+C7zxBpCf7/r7VRX4wQ9EO6eXXrJUYtjS1gb85CdAczPwr39ZnvckXEhMBAoK\ngEOHRPXC5Ze7fywKXc8/D0RFAatXW+adkG80Noph9Tk5/l4JERERUWC4/nqgvV18Tvr73/29Go85\nW7lQXt6JU6e6oNMpWLKENxmHAoYLAyiKkgHgCgAXAFgOcYHfnm5FUd4C8ISqqt96eN5cAA8BuBKA\nrYZjqqIo2wHcr6rqF56ci4iIgoQ/Khe6uoDHHxeP//53YP1614/R0GCZE7F+vWhTpCi29/3b30Sw\nkJYmhjA3NwNhYcDYse6tXyosdD1cKC0Vdw2x0iH0qSrw2GNAby+wdCnDBXe0tAC7d4vHF1xgfz9V\nBa68UlQl7doFJCUNz/qIiIiIAll7u9h+9pl/1+ElzlYuyKqFhQtTEBPDy9KhgDMXrCiKsgFANYA/\nA7gMQNyAXdSz/5MiAXwfwB5FUX6vKEqYm+e9CkARgGsBWB9DHbDrcgDbFEV51J3zEBFRkJF378sh\nx8Pho48sj5ua3DvGsWOWx/HxQEeH7f1qaix36Tz8sLjw+NxzotrB02o2OXdh505xcdOR3buBSy8F\nfvxjz85LwaGlRQQLgOd/10aqsjLx7+WRR4be7/RpoKIC0OuB8vJhWRoRERFRUJGfS4NYVpalckEd\n4vsXWyKFHoYL/S0GMPDWShVAL4AKAHsgQoDOs88rVts7ALyhKIpLv6eKolwJ4BUAUQOOWQ9gH4BK\nAKYBb7tbUZQ/unIeIiIKQpmZYjuclQsREZbHRUXOXZgf6PhxsZ08WQQFcQOz+rP+9CdxwXHePGDV\nKiA8XMxdWLDA9XMONG+eOF5NDXDihOP9H38cMJnErAh3fs0UXOS/qeTk/n/nyXmyN7CjELK42PK4\nJmQ7fBIRERG5JirK8vjQIf+tw0tkuNDdbURbm+0O8py3EJoYLtimAqgFsB7A+QDiVVUdr6rqAlVV\nZwJIhKhsODjgfZcB+J2zJ1EUZTyA59E/0NgP4DxVVTNVVZ2v/n/27jvOqvLa//h3z8AMMzADDL0X\nhaEqTcUCigY1xoolBpMQy43JjYl61Zd61RhLNHqjsfw0JqIxGI0NY4wtCHYRxUIApSi9SRs6Q5mZ\n5/fHw8M5c5g5/Zx9yuf9es3r2efMLgsEnLPXXmsZ00NSf0kvqn4lwxWe550Z468LAJBN/GiLdOqp\n0ldf2e316+O7tksujBnT+D4LFkgvvGC3r7++8bZJ8WrWzCYYJNsaKZyPPpJmzLDbu3YFSpSRu9yf\n60Rme+Q7l1zYvt3+vWnM3LmB7WwcTg8AAJBsO3dK1fvaB1VWSnv3+htPEpSWNlHLlrYZS0OtkRYt\n2q6TT35fa9fuVklJoUaOrEh3iEgRkgsHmiPpHEldjTHXGWPeMcbUq08yxtQaY16VdLikV1S/guFX\nnucdHOW1bpfUfN+2J+kTSccaY94Lud7XxphzJT0acq27Y62UAABkEXfjs6rKPuGfLiUl9odcKb6n\naFxy4eAw/zt85hlbKXDSSdKwYbFfIxqjRklDh0ptIwwKe/DB+q/dvAjkrm+/taurDkLsyspsdZBk\nBxE2Jji54H7fAQAA8pmr/Cwqsm1pR470N54kCW6NFOyJJ5Zq0KB/a8qUtSouLtADDwxRcXGhHyEi\nBbgxXd9Fxpghxph/GGNCWxEdYF/S4QeSVga93VTShEjHep43QNJ5CiQKdkuaYIxppDG1JOlySUGN\nrHWQpAsjXQsAkKVat7bDjaX03/AePNiuqUouuFYpY8fGfv5ojR8v9e0b+en0u++WfvCDwJPYJBdS\n56uvpEcekWoaLpVOGzfHhOHd8fO8QOIuXGskkgsAAAD1uQcz2rZNfgW3jzp3tq2e1qwJVC7U1NTp\nv//7c+3aVafjj2+v2bNP1CWX9PYrRKQAyYUgxph/xXHMTkkPqH5FwUlRHHqxAr//RtLfjTELI1xr\nt2zbJXctSbok1pgBAFnC8wI3P9M51FkKJBeCbwxGY/PmwA/LBx3U8D7G2NkGktS/f3zxRaO8XPrd\n76Thw8Pv17WrHUrr4iW5kDq33y7ddZf0j3/4GweVC/F75RXp/vulWbMCCbnGKhfWrav/94nkAgAA\nQP3kQg5xlQurVwcqFxYv3qHq6lqVlBTqzTdHq2/fRubxIWuRXEiO90Ned4/imNMUSEZIdvZCNJ6V\nHSitfcce5nken4wBIFf5MdRZCiQXZs+ObcCxq1ro3Flq0aLhfVatsnMNmjYNX92Qbn4lcvLJokV2\n/eADf+Ng5kL8Xn/dDmP/4ovIlQuu8qlg30cOZi4AAAAEfnbKseRC1662cmHp0h3735s/386z69ev\nTAUFuVOlgQCSC8mxKeR1y3A7e57XV1Lw3ZQdkqZHc6F9lRLTFUhKeJK+F12YAICs4254p/uJ3/79\npcJC+4NvLNf+el/3vsaqFqTAwOiDDw70bM8EJBdSq7o68Hv74YexJa2SjeRC/NyTdu3aBSoXGksu\nuPZnrnro22/9/e8OAACQCdzPU+5nqUimTZM++yy9c/jiMHCgvR06Z86W/e/Nn79Vkk0uIDeRXEiO\nLiGvwzSelSQNCdo2kj6JZsZDkA+Djg09HwAgl7ibn+m+4d2smZ1XIMU2d8E9md6nT+P7pKMlUjza\ntbMrbZFSY2XQiKqNG6UFC/yLhbZI8XN/P4KTC421RXL/dhx/vF337JE2hT6TAwAAkGdiqVyoq5P+\n+7+lc85p/GeuDHHIIYHkgtn3QMm8ea5yody3uJBaJBeSY1TQtpEUdnaCpNC7KV/FeL3Q/TPs7gwA\nIGlccsGPdiKRhjrv3Xvge9FULsybZ9dMTS5QuZAay5fXfz09qqLNhhkT/1DovXsDH+ioXIhdcOVC\npLZIrnJh2LBAIoK5CwAAIN+5n52iqVyoqrIPaATP48tQ/fqVqbDQ0+bNe7VypR3q7CoX+venciFX\nkVxIkOd5BZJ+rPrzE16LcFilO3zfuiLGywbv7wWdDwCQa/xs1RMuubB7t00OHHmktH174L2ZM+12\nuMqFTE0uuN9rKhdSIzS58OGHDe8XybXX2j87zz8f3/Hu71LTplLr1vGdI19VV9t5KZJNLISrXNiw\nQVqzxn4QHjAgUCVCcgEAAOS7WNoiuZ+d2ra1P79msOLiQlVW2iSCq14IzFygciFXkVxI3M8k9Q56\nvVfS0xGOCU01rmxwr8atCnndLsbjAQDZwq+BzlL95EJon/RVq6TaWnujsXlze6O3Xz9781FqfFDz\n9u3SsmV2O9OSC7RFSq0V+56NOOoou378ccPVL5EUFtpEVrw3qd3T9F26BAYN40BffSXdfbf05puB\n99wH4eJiqawsfHLB/T736mWHu199tfToo9Khh6Y2bgAAgEwXS1ukNWvs2qlT6uJJouDWSGvX7tbm\nzXtVUCD16dPC58iQKnyiSoDneQdJulOBqgUj6f8ZY1ZHODT0b9SOBvdqXOj+TT3Py+z0JQAgPn62\nRerXT2rSxJbiuh9qnVX78tydOx9YotumTeNPhBcXS888I/32t1JFRWrijpf7NWzaZEuPkVwuuXDS\nSVLJDGfWAAAgAElEQVSrVtKOHdLs2bGfJ9En4F991a4nnBDf8fliyhTpj3+UJk8OvBc8b8HzwrdF\n2rlT6to1kKQ87jjpO9+JfnAhAABAroqnciFLZoUNHmyTC7Nnb9nfEqlXr+Zq1qzQz7CQQiQX4uR5\nXomkF1Q/UbBU0s1RHN5CgWHMkrQrxstXN3JOAECucTe8t22zN2PTqbjY3hyU6g/jlQLJhS5d7Bo8\nY6FXr8bP2bSpdMQR0vjxyYszWVq1sskUKeOHpWUll1zo0cO205Lia43kntqKJ7lQXS1NnWq3v/e9\n2I/PJy758v77tlJEqp9ckALJhaoqO2ww2He/a4/9/e9THysAAEC2qKmxDzNJ0VUuZGlyYc6cLbRE\nyhMkF+I3SZKr6/Zk2yFdYIyJ5s5Ps5DXsT4eubuB90piPAcAIBu0aCGVltptP+YuuORGaOVEaHKh\nc+fA94qLUx9XKhQUMNQ5VYwJzFzo3l06+mi7HU9ywVXzxJNcePvtwBP1Q4bEfnw+GTTI/l7v3CnN\nmGHfC00uuAqlurrAh+RQLmEHAAAA+zOTMbYKNJr5X+5n3ixrizR//lbNnr1FEsOcc13G/LTved4f\nJF2ehkv9xhhzayIn8DzvXklnq347pF8ZY2ZEeYrQSoWiGENo6K5NrNUPUduxY4dK3Y2tGDVv3jzJ\n0QBAnnEth5YutTf4w1UFpEJjMx9CkwvBvevdzd9s1K6dbQFFciG5NmywVQOeZ//MjBkj/eY30jHH\nxH6uRCoX/vUvu556qo0FjfM8+9/pmWekadOkY48NVPS45ELTprbiZ/Nm2xqJlkcAAADhuZ+nKiqi\newgjyyoXuncvVXl5E23dWqOXX7Zd46lciM6OODsVxHtcsmRMciGIibxLXFwSILGTeN51kq5Q/cTC\nb4wxf4rhNNv3HeuEVjJE0lCVwvYYzxG1XgncyDKhA0ABALHr2DGQXEi3aCsXJDtH4cknpf/5n/TE\nlgrupiltkZLLtUTq1EkqKrKVLhMmxHcu98Fq61bbKizaBxm2b7eVC5JNLiCyE04IJBduueXAygXJ\nlvO75AIAAADCi2XeghSYfZclyQXP8zRoUEtNn75Rq1bZru5ULkSnRYvs7HifaW2RUnknOhmJhUsl\n3aH6iYX7jTG3xXiq0ERArI/3h+5fY4xh8iQA5KoePez6xRfpv7arQgh9kr+h5ML48dLrr9d/L9u4\nZAqVC8nlWiJ165b4ucrKbLswKbbqhWnT7OyAnj2lAQMSjyMfHH20bXO2erX03nsNJxfcB2OSCwAA\nAJG5n5mimbdgTNa1RZICcxecykqSC7kskyoXXpG0Pg3XeS+egzzPGy/pIdVPLDxhjInn8czQOxZd\nYzw+9K5NSn/flixZonbBHyIBAOl14onSs89Kr70m3XSTVFiYvms31N++pibwOpsTCQ1xyYX16fiR\nJI8Ez1tIhg4dbCXC2rX1h4mH41oinXYaLZGiVVIijR0rvfKKdOGF9rVU/8OwSy7wdwYAACCydu2k\nk0+W+vWLvO/Wrba1qJQ1lQtSYO6CJLVrV6w2bbJ0Jl+abd8eX1Oa9evXJ9R1JlEZk1wwxkyTNM3v\nOBried4Zkp6QTSq4xMILki6J85QL9q2umiLWT9rB+xtJ8+OMIyrNmzdndgIA+OmYY6TycnvzbuZM\naeTI9F27oSf5166Vamttv3X3/VzBQOfUcJUuyahckOyHq0WLAmXikcyYIb3zjt2mJVJsbr/dtrJ6\n8UU73Fk6sC2SJC1Zkv7YAAAAss3RR9uvaLifdSsqbDVplgiuXKAlUvTivfe60/2M7pNMa4uUcTzP\n+46kZyQVKpBYeEPSBSb+gQKhyYBYa/P7RzgfACCXFBVJv/qVdMcdUv/Q/wWkWNeu0lFH1U9ouBvF\nHTvWH+ScC0guJG7v3gPfu/NOe4N//PjYzlVTI+1poPNjLEOdv/1WuuwymxA76yypb9/YYsh3LVtK\n99wjPfGE/fegoqJ+tYgbRPjkk7aaBAAAAMnhftZ11eRZYtCgQHKBYc65L2MqFzKR53lHS3pJUpEC\niYX3JI0zxtQkcOpZwZeRdJjneQXGmLooj3cpThfTrDD7AgBywcUX+3PdLl2kp56q/54bzts11q5+\nWaCyUvrpT6Xevf2OJHtdfLG0bJkd8H3MMfa9goL4PhR99pl0/vnSoEGBtkZSw+26GnPLLba3bb9+\nNibE59hjpXfftcmeZs0C72/aFNjO0iF0AAAAGclVLmTRvAVJat26SN26lWjFimr160flQq7LsccN\nk8fzvGGycyBKZG/iS9JMSacZY3Yncm5jzAJJi4Leai7pqCjjKpV0pAItlcy+OAEASA/X/sTHvo4p\n06OHdP310ve/73ck2ckYac4cO2OhVavEz1dVZdeiovrvx1K5cMst0gknSH/8Y2BmAOJTUFA/sSBJ\nP/iBXUeNSn88AAAAucz9rJtF8xack0/uqIICacyYHGujiwOQXGiA53kDZFsfBdfuzJb0XWNMsuq9\nX1ag8kCSon0k9XxJwY+FfWqMieKTNQAASbJ0qV1zMbmAxKxYIW3ebJMByWg/5J6Kr6io/777gBVN\ncqF9e2niRKlnz8TjwYEOO0yaNk169FG/IwEAAMgtWZxceOSR4Vq37nQNGZKEB46Q0UguhPA8r5ek\nNyW1cW9JWihprDFmU6MHxu5xBRILnqTzPc+rjBBbsaRr9x3nEhMTkxgTAACR5XLlAhIze7Zd+/U7\nsNogHhs32jWR5AJSr3fvrBoyCAAAkBXcz7pZ1hZJkgoKPLVpw8+H+YDkQhDP8zpLmirJpQQ9SUsl\nfccYsz6Z1zLGfCnpOQWSBEWS/up5XrhmZPdL6hP0erGkvyQzLgAAwjKG5AIa55ILhxySnPO5tkht\n2tR/3yUXNmxoeOAzAAAAkO2yuHIB+YOBzvt4nlciaYqknu4tSTWS7pJUGamqoAHvG2Mifdq9UdJp\nkkplEwyHS3rP87wrjDHvBsXWV9Kdks5S/aqFa40xtTHGBSBGy5Yt0/z587V8+XJt3bpVe/bsUatW\nrdS6dWsddNBBGjZsmAoLC/0OE4jfNddITZpIV10ltW0bft+1a6XqaqmwUOrWLT3xZYJFi+zvUceO\nPKEdTqqSC6GVCxUVtjJizx5p3brcHC4OAACA/LV3r7Rwod3OwsoF5A+SCwHtJQ1Q/UHJhZL+GMe5\njKRekpaH3cmYRZ7nXSzpKQWqSA6V9Lbneev3Hd9eUlcFEgpufcAY82IcsQGIYNWqVXrxxRc1bdo0\nvf/++9q0KXxHtNLSUh177LG64oorNHbs2DRFCSRJXZ304ot2vfLKyPu7qoVu3aSmTVMbWya55hrp\niy+kRx6RTjrJ72gyU12dNHeu3U52cqF16/rve55N9Cxfbp/oIrkAAAAAv02ebD8zXH211CqBWQN1\nddKIEYHXHTokHhuQIiQXDuSl82LGmGc9z5OkxySVBF2/3b4vqf5sBiPp/4wx16UzTiBfjBo1StOn\nT5cx9q+d53na93d0/3uOe7+6ulqvv/66Xn/9dR111FF69tln1aVLl/QGDsRr+3b7w6sktWwZeX+X\nXOjdO3UxZSLX+z+0PU+q1NTYFlTZlMD55htpxw6ppEQ66KDknLOxtkiS/ZDlkgsAAACAn156ySYV\nJGnmTGnSpPiTAgUFUrt20tat9nWLFsmJEUgBZi7UZ5L4Ff1FjXlW0iBJT0va08i5jKR3JB1LYgFI\nnQ8//LDR77lEQ2jCwRiz/73p06drxIgRmjNnTrpCBhKzebNdS0qia/ezeLFde/ZMWUgZKZ3JhSuu\nkAYOlN56K/XXSqZPP7XrkCG2hVQyNNYWSQqUh69Zk5xrAQAAAPH4+GPp2mvtdnGxbWc0cqT9uT7e\nB2FOPDF58QEpROXCPsaYZbJtkPy6/lJJP/I87+eSjpEd3FwmaZdse6QPjTF8egbSxPM8FRcXa8SI\nERowYIAqKyvVunVrlZWVqaamRlu2bNH8+fP1wQcf6LPPPpOk/UmGtWvX6pxzztFnn32mFjxhgEy3\nZYtdoy3bXbrUrvk0zHnXLvtEvmSTCzt22A8MnmdvpCdbkyZ2lsCCBYEWTGvW2DkX7dsn/3rJsu/f\nQg0fnpzzGRM+ueAG261dm5zrAQAAALGaN0+69FL78/t3v2uTDBMmSMuWSf/8p3TuufENZL78cltl\nfuyxyY8ZSCKSCxnGGLNd0hv7vgCkWfPmzXXeeefp/PPP1+jRo1UcxZPc8+bN05VXXqk333xz/3vf\nfPON/vd//1cPPPBAKsMFEucqF6JpiSQF2iLlU3LBVS0UFUllZdIzz0j/+7/S6NHSX/+a/Ov17WvX\nBQvsumWLdMopUrNm0nvvZW6rJFe5ENwfNhHbt9sPaVLDFSPuQxqVCwAAAEi37dulBx+UHn/ctjQd\nMkS69177M/vzz0u//KXUvLl05JHxnb+4WLr11uTGDKQAyQUACLJ69WqVlZXFdEz//v31xhtv6JJL\nLtHjjz8uz/NkjNETTzyh2267TS2jvWkL+MFVLkTz57Smxva4l/IzudCmja1W6NPHvv7mm9Rcr7LS\nri65MG1aIAm0cKFtmZRp1q2zfzY8Txo6NDnn3LTJriUl9iuUSy5MmZKc6wEAACB+u3fbNZpWq9nu\n22+ls84KtDw6/njp7rttYkGy8xKeeca/+IA0YuYCAASJNbEQ7KGHHlK3bt32v96xY4f+/ve/JyMs\nIHXcTeto2iKtXGkTDM2axVfam61C5y0cfLBdV68ODFmT7A32ZDxF36+fXZcutR/Sgm+ez56d+PlT\nwVUt9OsnlZcn55zu972hlkhSYOZCTY20aFFyrgkAAID4vPaa1L+/9Itf+B1J6k2ebBMLnTtLEydK\njz2WntlsQAaicgE5wRijnTtr/Q4DDSgtLdw//DjXFRcX69xzz9W99967/9f8wQcf6Gc/+5nPkQFh\nxNIWaedOafBg+zRSQR49nxCaXGjVylZuLFlih7eNHWvfnzRJeugh6ZJLpBtuiP967dvb/x5btkhz\n5kjvvhv43pw50g9+EP+5UyXZ8xak8PMWJPtn8eyzpYMOsl8AAADwz4oVdmZWPswddBUL48ZJJ5zg\nbyyAz0guICfs3FmrFi3+4XcYaMD27WepefP8+aemj2uXIpv0mjFjho/RAFGIpXJhwADp5ZdTG08m\n2rDBrsFPIx11lE0uTJ8eSC68/75dXeVBY2bMsEOiBw60JdOhPM+2RvrkE/sk1K5d9v3ycjvUOVPs\n3i29+ab9fUn2vAUpcnKhsFD6/e+Tdz2k3/r19s9PPiUrAQDIVa59alA1f85y1cqukhbIY/lzxw8A\n4lBVVaX3339fc+bM0bx587Rp0yZt27ZNO3bskDGmwf2DrVixIl2hAvFxbX2iSS7kq9DKBckmF556\nSvroI/u6qspWFUjSMceEP98DD9jj/vAH6cwzG97HJRf+/W/7+sILpZtusomHTPHnP9uhdcOHS19+\nad9LZnIhUlskZC9jpBNPtHNLpkwJzDEBAADZy3327d7d3zjSwVUudOjgbxxABiC5gJxQWlqo7dvP\n8jsMNKC0NIOeso3BtGnTdN9992nKlCnau3fvAd+PttVTTU2Ndu7cqdLS0mSHCCRHLG2R8lVDN7lH\njrTrggX26esZM+wN08rKyB8y1q61a7j93FBn55RTMiuxINnEyL33BloideokdemSvPMvW2ZXkgu5\nx/MCCc3Zs0kuAACQC1xyIR8qF1xygcoFgOQCcoPneXnVegeps23bNl1yySV6/vnnJdk/Wy6REFyp\n0FDVQmO2bNlCcgGZi+RCZC650LZt4L2KCjuwbt48m1h47z37/ujRBx6/c6c9h/ugtX69Xdu3b/ya\nwcmFdu2kYcPijz9VunWTDj/cVlhIya1aWLhQeuEFux2pEgTZ6dBDbTut//zHzs4AAADZa/fuwA33\nXE8u7N4d+HzQsaO/sQAZgAanALDPtm3bdOKJJ+r555/fn1QwxuxPJLj3In2FaqjyAcgYW7bYlbZI\njWuoLZJkWyNJ0ocfBuYthCYXZsywsxUuvNC+3rFD2rbNboerXOjbN7B94omZ25M++KZwMoY5r14t\nPfusbQFVUyN95zvSccclfl5kHpcwmzpV4v+TAABkt5UrbRVvaemBPzPnmnXr7FpUJLVu7W8sQAbI\n0E+qAJB+V155pT7++ON6lQqe56lv37668sor9dxzz+mTTz7R6tWrtWXLFu3evVu1tbX1vh5//HGf\nfxVAjGIZ6JyvIiUXXnnFtjpq1kw67LD6+7ies8uW2Ruo7sNI8+ZSixaNX7O8XOrZ026ffHJC4afU\nd78rlZTY7cMPT+xce/dKJ50kXXedrYYoKZFuvjnxGJGZTjjB/p1as0Z64w2/owEAAIlYudKu3bol\n1spz6VJpzBhbEXvUUdL3vheY7ZUpXIVGx46Z17YU8AF9ZABA0ty5c/WXv/ylXmKhrKxMf/rTn3T+\n+edHfZ7q6upUhQikBm2RwjOm8eTCYYdJhYW2GkGSjjhCKi6uv0/HjvYJrp07peXLo2uJ5Nx/v7Ro\nkXT00Yn9GlKprEx67DH7Iat//8TO1bSpbYHkbjT/6ldS166Jx4jMVFws/ehH0n33SRMnSqeeygd0\nAACy1fLldk20JdKiRfbna/fz95o10uTJthI4UzDMGaiHygUAkPTss8/ub3/kKhYmTZoUU2JBkqqq\nqlIRHhCdr76SHnnEtumJxq5d0p49dpvKhYZt2xZo2RKaXCgrkw45JPC6oXkLBQXSgAF2++23A5UL\n0SQXDjlEOuuszL/heuSRNs5kOPFEu/bpI110UXLOicx1wQW2pcDs2YHB4AAAIPu45IKr2o3XCSdI\nL78svfqqdNll9r1lyxI7Z7IxzBmoh+QCAEh68803670eOHCgzjjjjJjPs3jx4mSFBMTuX/+S7rrL\nrtFwVQtNmtg2PTjQhg12bdHCtj0K5VojSQ0nFyTpzDPt+vzztn2SxJNOjTnjDFux8be/2ZvOyG1t\n2wb+fjz2mL+xAACA+AW3RUpUaal9OMe13HSJi0wR3BYJAMkFAJCkFStW7B/g7HmeRo0aFdd5Pvro\nowaHOgNp4QakuieAa2sDP/w2JLglEn9uG+aqkRobTOcSCt26SQcd1PA+p51mW8AsXGiH10rRVS7k\no4IC6fTT+f3JJ65CZcoUacWK+M5RV2dbmAEAAH8kq3IhmJs/tny5/X99plizxq4kFwBJJBcAQJK0\nwT2dvE9FRUXM55g7d67mzZuXrJCA2A0fbtdvvpHmzpXGjbMtaxprk8S8hcgam7fgHH649OCD0p/+\n1HiCprw8MJT5k0/smumVCw8+KP31r35HgXxQWWlnbdTVSS+9FN85PvrIPuH4k58kNTQAABAFYwIP\nCCSjcsHp1MnO5NqzJ/wDU+l25ZW2Fe2YMX5HAmQEkgsAIKl5SEuY0GRDNO65555khQPEp6JC6tXL\nbp91lu1jLkkff9zw/u7GeevWqY8tW/3sZ3atqWl8n1NPjTzM+Jxz6r/O5OTCypXSvfdKv/mNvWkL\npNphh9l11ar4jl+71s6Qqa1NXkwAACA6W7bYOWWS1LVr8s7bpInUpYvdzqS5C336SCedFKisAPIc\nyQUAkNRp3zAm1xppqmtdEqWpU6dq0qRJtESC/1z1Qk1NYI7CokUN7+t6oybzQ0Cu2rIlseOPOkrq\n3DnwOpPb/gQnV2++OTDQGkiVtm3tGkdiX1JgUHpo0m7rVptovf32+GMDACCf7dwp3Xmn9Omnje/j\nWiK1by+VlCT3+u4G/tKlyT0vgKQhuQAAkkaNGiUT1K958eLFeuSRR6I69vPPP9f48eMlScaYeucB\n0u6ss2wlwsUX26fPpcjJBfdEEFKnoKB+9UImVy5s2hTY/vpr2iMh9dq1s+v69fEd39ig9EcekWbN\nYlg0AADx+tOfpD//Wfrd7xrfxyUXktkSyenRo/41AGQckgsAIOm8887bv+2qF371q1/pj3/8Y6PH\n1NXV6aGHHtLxxx+vjRs3yhijlvSuh9+OOsoOdL7xRqlfP/vekiUNtwtxLUioXGjcnXdKpaXSHXck\nfq5zzrFzGYqLMzu54IZYN2tm1/vuC9y8BVIh0coF9+cztCLogw8C2+FamwEAgAPt3ClNmmS3w1UO\nuAeWUplcSHXlwtKl0htvpPYaQI5q4ncAAJAJjj/+eI0ePVrvvfeePM+T53mqra3VL37xC91///06\n66yzNGDAAJWUlGj9+vWaO3eu/vnPf2r16tX7kxEdO3bUVVddpWuuucbvXw7ynWvP1aWLVFRkh6Ct\nXn3gD/yp/CCQK84/Xzr3XKmwMPFzdesmPf64/e+T7JLxZHLJhbFj7XC+WbOkKVOkH/3I37iQu1zl\nwoYNdihkrC0GG2qLtHWr9OWXgdebNgWuAwAAInv2WWnzZru9caNNNpSWHrifqyro3j35MbjkQipn\nLsyda6u/i4vtHKg2bVJ3LSAHkVwAgH2efPJJHX744Vq3bp2MMfuTDF9//bXuuuuuA/Z33zfGqFWr\nVnr11Vc12w3QBTJBYaEd8LxggW2NFJpEuOce+4P6wIH+xJctkpFYcI47LnnnShXXFqltWzvQ+qc/\nlcaN8zcm5DZ303/PHjsQsrw8tuO//dauwcmFd96R6uoCr6uqSC4AABCtvXuliRPrv7dihVRZGfj+\n4sX2/73paosUzwMI0RgwQOrfX5ozR3r4Yemmm5J/DSCH0RYJAPbp1q2b3n77bVVWVu5PGgTPUAgd\n1uze79+/v6ZPn66hQ4ce8D3Ad71727WhuQuDB0unnmpnNACOq1xo3dp+2HrrrcBwcCAVioulsjK7\nHevcBWMClQvBbZGOPFK65ZbA6+BZIgAAILxXXrGVz23bSgcfbN9bsSLw/RtvlE4+WRo6VPrwQ/te\nKpILXbvahMKOHfG3T4ykoEBy3Qf+9rdAdTeAqJBcAIAg/fr106effqrf/va36tSp0/7qBJdYCH49\nYMAAPfjgg/rPf/6jfq63fcg+oQkJIO0OOsiujQ11BkK55EJFhV2LivyLBfnDzV2INbmwaZN9elKq\nX5nQrp304x9LI0bY1xs3Jh4jAAD5wBg7yFmSLryw4eTCRx/VP6Z580BVQzIVF0udO9vtVA51PuYY\naeRIW0X58supuw6Qg2iLBAAhSktLdd111+m6667TnDlzNGvWLG3YsEHV1dUqKytTjx49NGTIEHVv\noKfkhAkTNGHCBB+iBhpBcgGxck94U9GCdGrXzg6fj/WpRDfMuU2bhhNhrm+yS5oBAIDwvv7atlVt\n1kz64Q+lBx+077sn+o2RTj9dWrhQ+vWvpe3b7UMprVqFP2+8bY169JBWrbJDl4cPj/34aHieTTDM\nmCF9801qrgHkKJILABDG4MGDNXjwYL/DAOJHcgGxCq1cANIh3sqFhloiBXN/jmmLBABAdObOteug\nQXYOknuozlUOeJ509dXRn2/JEunnP5d275befjv2eHr0kKZPT+1QZ4nPTUCcSC4AAJDL3MyFjRul\nLVukli39jQeZz92EJbmAdHItjeKtXAge5hzMVeDQFgkAgOh89ZVdBw2yq5ulENwWKRatW9tKCEna\nuVMqLY3t+NDkRqq4z01LlqRueDSQg5i5AABALmveXOrY0W4vXuxvLMh8tbXS5s12m7ZISCdXuRBv\ncqGxygXaIgEAEBtXuTBwoF1dcmHlSnvTPVatWgV+rly6NPbje/aM/9hY9Ohhhztv2xZ7JSWQx0gu\nAACQ6yjxRbQ2bw58aIzUNxdIJle5EG9bpMYqF2iLBABA9OrqApULLrnQtatdd+yIP1nvEgRLlsR+\nbI8edk11W6Ti4kAihYeygKiRXAAAINe5El+SC4jE3YBt2VJq2tTfWJBfEq1coC0SAACJW7HCPrlf\nVCQdfLB9r7g48P/ZeFsj9epl13iqD1xbpM2bbZvXVOJzExAzZi4AAJDrqFzITR9/LL32mjR4sHTO\nOck5p3sajZZISLdDDpEefljq3Dm242iLBABA8rRoId10k72RH/ygya9+JRUWSl26xHfeRCoXmje3\nFY7r19vqhUMOiS+GaPTubYdOU7kARI3kAgAAue6QQ6RTT5UOP9zvSJBMs2ZJkyZJZ5yR/OQCw5yR\nbu3aSd/9buzHxdIWieGMAACE16aNdNFFB74/fnxi502kckGyD0utX2/nQaQ6uSDxUBYQA5ILAADk\nuqFDpQcf9DsKJJt7UjvWNjLhULmAbFJbG5jRECm5sHevbfNQXp6e2AAAQIBLLsRTuSBJF1wgnXii\nNHp08mJqiKv4pnIBiBrJBQAAgGzkkgvuye1kcDMXXCsZwE9PP23/fF9+ecMVB1VVNsFQUND4n9lm\nzaTSUmnnTrs/yQUAANLPtUWqqrJzE1q2jO34U09NekgNcpULK1dKu3bZnyMAhMVAZwAAgGzUrp1d\n3ZPbyeCSC1QuwG/btkk33CDdf7/0xRcN7+PmLbRtKzUJ88xUcGskAACQfs2bBx6Mibd6IR3atrUP\nIhgTfwsnIM+QXAAAAMhGLrmwebO0e3dyznnGGdKdd0onnZSc8wHx+vzzwPb06Q3v45ILjbVEclxy\nYePGxOMCAADxSXTuQjp4HnMXgBiRXAAAAMhGLVtKTZva7WTdNB08WDr/fDunA/DTJ58Ett9/v+F9\nXHLBPQnZGJdccDNFAABA+rnWSK5y4YsvpJ/9THr8cd9CapBLLjB3AYgKyQUAAIBsVFBgS7el5LZG\nAjLBpZdKt9xitz//XNq+/cB93LyRaCsXSC4AAOAfl1xwlQtz5kj//rf00Ud+RdQwN9SZygUgKiQX\nAAAAslUq5i4AmaC8XPrxj6UePaSaGmnGjAP3WbPGrpGSC6efLv3619KoUcmPEwAARMdVBLjKBXfz\n3t3MzxQuzuAWjQAaRXIBAAAgW7nkgnuCG8g1LiHQUGuk2lqpqEjq0iX8OY49VrrwQmngwOTHBzty\nHUMAACAASURBVAAAohNcuWBM5iYXXDwrVtifH/bu9TceIMORXAAAAMhWLrmwYYO/cQCpEi658Pvf\nS/Pm2cqEfFRXJ73wgvTNN35HAgBAZD162IHJ27ZJjzwizZ5t309ncmH+fOnbb21yozHduwe2V6wI\nzDgD0CCSCwAAANmKtkjIdSNHSoWFtoXCypUHfr+gIH8/9E+bJl1zjTR2rN+RAAAQWXGx1Lmz3b77\nbptkqKiQKivTF8P//I905JH2/6GNKS4ObH//+6mPCchyJBcAAACyFckF5LrycmnoULv93nv+xpJp\nXLKlTRt/4wAAIFpHH23XykrpppukqVOl5s3Tc+2amkArpr59w+/77LPSdddJF12U+riALNfE7wAA\nAAAQJ5ILyAejRkmffmpbI40f73c0maOw0K6HHeZvHAAAROu226QrrpA6drQtktJp2TJpzx6ppETq\n2jX8vocfbr8ARETlAgAAQLZq396uJBeQy445xq4ffxy+R3K67NghVVcHXj/9tP1Kty1b7NqyZfqv\nDQBAPIqKpE6d0p9YkKSFC+3ap49tqwggKfjbBAAAkK1c5cK6dZlx0xVIhYED7U2ATZvsn3U/XX+9\nNGSI9Npr9vXixdINN0g33hi42Z8uJBcAAIieSy5EaokEICYkFwAAALJV27Z23b3bDsUDclFxsdSr\nl92eP9/fWFq1sj2bP/7Yvv7nP+1qjLR2bXpj2brVriQXAACIjOQCkBIkFwAAALJVSYlUVma3aY2E\nXNavn139Ti6MHGlX16LJJRek9CcXqFwAACB6JBeAlCC5AAAAkM1c9QLJBeSyTEkuDB9uBykvXy69\n/rodDumku2XT5s12JbkAAEB4NTXSkiV2u08ff2MBcgzJBQAAgGzm5i5s2OBvHEAqVVbadcECf+No\n0UIaNMhu3357/e+lO7lA5QIAANFZt06qrZWaNJE6dvQ7GiCnkFwAAADIZu3b25XKBeSy/v3t+s03\n0t69/sZyxBF2XbPGri42Zi4AAJCZvv3Wru3bSwXcCgWSib9RAAAA2cxVLqT7qWkgnbp0sVUDe/dK\nixf7G4ubuyBJFRXSuHF2m8oFAAAyk0sudOrkbxxADiK5AAAAkM1ccoHKBeQyz8uc1kgjRgSeevze\n96TOne12OpMLe/ZIO3fabZILAACE55ILHTr4GweQg0guAAAAZDOSC8gXmTLUuaxMOvpoqWlT6fvf\nl/r2lX74Q+mMM9IXg6tacPEAAIDGUbkApEwTvwMAAABAAkguIF9kSuWCJD30kLR5s9Stm319223p\nvb6bt1BeLhUWpvfaAABkG5dcYJgzkHQkFwAAALLZgAHSfffZnvRALhs0SDr00EAFg5/KyvytGIh2\n3sL69TYJ0qdP6mMCAGSnDz+UHnhAOuww6eqr/Y4mNdassSvJBSDpSC4AAABks3bt0tuOBfDL0KHS\nSy/5HUVmiCa5YIw0fry0fLk0dWqgygIAgGDLlkmffJJ9M3zq6qS9e6Xi4sj7rl1rV5ILQNIxcwEA\nAAAAskk0yYVZs6RvvrHDn2fOTE9cAIDss3GjXSsq/I0jFvfcY9sl3ndf5H2NIbkApBDJBQAAAADI\nJps327W8vPF9Xn01sP3ll6mNBwCQvVxyoU0bf+OIRbNmUk2NtGFD5H2rqmyiXZLat09tXEAeIrkA\nAACQr4wJPAENIHu4gc6tWjX8/bo66fXXA6/nzEl9TACA7FRVZddsSi60a2fXaJILbphzmzZSUVHq\nYgLyFMkFANjnlltuUUFBwf6vwsJCv0MCgNRavNj2sR83ziYaANjWCbW1fkcRXqS2SLNmSatXSwX7\nPu599ZVNOAAAECob2yK1bWvX9esj7+uSC506pS4eII+RXACAEJ7n+R0CAKTHxx/bpEJxscS/fYD1\ni19Ixxxj/35kqkjJBdcS6ZRTbOuIHTukJUvSExsAILtkY1skl1yIpXKhQ4fUxQPkMZILAAAA+crd\nPB050t84gHSbN0965hlp0aL67y9ZIn32mbRundSrlz+xRcMlFxqauVBXJ732mt0+/XRpwAC7PXdu\nemIDAGSXbE4ubNwYuTJvzRq7UrkApATJBQAAgHzlbjYOGeJvHEC63XefdP310nvv1X9/8mS7Hnts\nZg99dAOdG6pc2LNHuuACafhwafRoaeBA+z7JBQBAqLo6adMmu51NyQUXa02NTbhfdZX04IOBX0uw\ntWvt2rFj+uID8gjJBQAAgHxUXS0tXWq3+/f3NRQg7fr1s+v8+YH36uqkf/zDbo8bl/6YYhFuoHOz\nZtJll0kvvGBbng0ebN8nuQAACLVlS2DOUOvW/sYSi+LiQIJ97lzpxRftgwMFDdzmdJULJBeAlGji\ndwAAAADwwddf25upbdpI7dr5HQ2QXi65MG9e4L2PPrJDkMvLpbFj/YkrWoceauOMprrCVS58+aX9\nO9/QjRcAQMOMsf92Fhb6HUlqVFXZtbxcKiryN5ZYtW1rkyMvv2xfDxrUcEXfEUfYxPtBB6U3PiBP\n8JMlAABAPnJPbPfrxzBn5B9XrbNwoW2pIAVaIp12mn0iMpPdfbf0/PNSnz6R9+3Tx94w2rZNWr48\n9bEBQLosXSrdeGOgmisVjJFOOkmaPj111/CTG4hcUeFvHPFwcxfeeMOuxxzT8H6//KU0cSJtQIEU\nIbkAAACQj9wT2+4JbiCfdO8ulZZKu3fbm1ObNwduTpx9tq+hJV3TpoG/57RGApArjJH+67+kp55K\n7Y3/V16ReveW/vWv1F3DT65ywd2ozyau8nb7drsedZR/sQB5jOQCAABAPgquXADyTUGBVFlpt+fP\nl5580s4hGTAgN59sZO4CgFzjeYEqtFRWZRUVSW++Kf3nP6m7hp82brRrNlcuSLbicMQI/2IB8hjJ\nBQAAgHxjTCC5wDBn5CuXWJs1S/rrX+32pZfmZpswN3eB5AKAXNKtm11XrEjdNVzCecECaefO1F3H\nL65yIduTCyNGZH5LQyBHMdAZAGJUVVWljz/+WIsWLdLWrVvVsmVLde7cWYceeqh69+7td3gAENm3\n39o2MIWF0sEH+x0N4A+XXHjySWnPHqlLF+mUU/yNKVVc5cKXX/obBwAkU9eudl25MnXX6NhR6tBB\nWrvWJmgPPzx11/KDq1xo08bfOOIRnFw4+mj/4gDyHMkFAIjSu+++qzvuuENvvfWWamtrG9xn2LBh\n+vnPf66LL744zdEBQAxc1ULv3jzlhfzlqnb27LHrJZdITXL041GPHnbdvFnatUtq1szfeAAgGdJR\nuSDZ6oV//9tWupFcyBxu5oLEvAXAR7RFAoAI6urqdNlll2nMmDF68803VVdXJy+oZYLnefu/Pv/8\nc/3Xf/2XRo8eraVLl/oXNACEQ0skIDBzQZJat5bOO8+/WFKtRYtA4mTTJn9jAYBkccmFlStty8dU\nOfRQu+bi3IV58+zaubO/ccSjSxe7tm4tDRrkbyxAHiO5AAARjB8/Xg8//HC9hIIUSCpIkjFGxpj9\n733wwQc67rjjtGTJEj9CBoDwGOYMSOXlgZYaP/6xVFrqbzyp5Hn25otEcgFA7ujcWSookHbvltav\nT9113NyFWbNSdw0/LFkiLV5sk89HHul3NLGrrJR+9zvpj3+0rT4B+CJH634BIDnuuecePffcc/uT\nCOXl5TrjjDN02GGHqUOHDtq8ebPmz5+vyZMna8WKFfUSDMuXL9cJJ5ygWbNmqby83OdfCQAEcU+p\nkVxAvrv6aumtt6SLLvI7ktR74gmpeXOpUye/IwGA6J1zjr1xfM89gYSw07SpnYmwerVtjdS+ff3v\nb9woPfywHcR8553xxzB4sE3Srl5tkxjB7Xiy2bRpdj3iCJtwz0bf/77fEQB5j+QCAIRx00037d++\n6KKLdM899zSYKLjnnnv0hz/8QTfeeKOqq6v3JyOWLVumK6+8Uo899ljaYgaAsHbvtk+pSSQXgDPO\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W72MgueOOO7jrrrsAti7KvM8++3DhhRfWsVWSJEmDQBYu1GPkQhYizJwJIRRG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\nwCIXAAAAAACARS4AAAAAAABL6YCtHtlAAe8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116075b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(500, 4), \n",
    "                  columns=['a', 'b', 'c', 'd']) # random 4D data\n",
    "p = df.cumsum().plot() # Plot cumulative sum of all series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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vQml0c3RyZWFtVmVyYVNhbnMtUm9tYW4gPj4KZW5kb2JqCjEzIDAgb2JqCjw8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\nPj4Kc3RhcnR4cmVmCjkyMjMKJSVFT0YK\n",
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DAAAAAEBNKP0BAAAAAKAmlP4AAAAAAFATSn8A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\njJ5Bf807EbEecCDwLOBJwOJZrOYe4HzgLOC0zPzjwDZQkqQ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sH0ppfsjMu4CDI+ILlMDd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\nARjImsQl4BX8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1165974a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = df[:10].plot(kind='bar') # First 10 arrays as bar plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x116049be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = df.boxplot() # Boxplot for each of the 4 series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false,
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117d7ca90>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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6F9UO26D+hWxGPjwsiCcmcEfRUjk5zvBxwFd6jE5REYXCW25Cd4tYmVCFCMui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\nb7EJZRGxsHEo5OZYlCdjUz2/mhVymVcmT6TITG8sflhQBPt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REHwi1npZKBdltAsciFk7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\nUIrBX6QUg78MpZi2RUrxpkaRUgz+MpRi8BcpxYm/SCnmShlKs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2xRdl7tMvSf+S4zuUlMluZMuWrH7/Rxqbv/ajn8yM885K34L5jWUAgLYZLWNH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\nV5wP8W4vbF4fQh1d8Lx/6MRAqwUJ82YgdfkSuGdMMvYvQUR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NRcbUZMy6cwJmfmY8bC6r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\nI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JT9xIU1DIomApj0fxfD4gyfVmHhoO6Qka3Qe1d7MPQxQ638/SNoKak3NEbr8QaUv6dv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\nDb7WS9H4t9/Ce7RYtziW9Cwk3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WANsAx4WWtdHflIo146zWW5AHxa65JOjnH42OemMeRq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CMrivJT6pESuu+wjjphT3ek4xduRjHFcEXRBzeyyLlgd8LhH1FrVkGaIuWTn4gRpwu/RwOYxB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117d80f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot using the 4 series for x, y, color, scale\n",
    "df[:300].plot(kind='scatter', x='a', y='b', c='c', \n",
    "               s=df['d']*50, linewidth='0', cmap='plasma')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Advanced plotting libraries\n",
    "Several libraries, such as [Seaborn](http://seaborn.pydata.org/examples/index.html) offer more advanced plots and easier interfaces. \n",
    "![Seaborn](http://ksopyla.com/wp-content/uploads/2016/11/seaborn_examples.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Further reading links\n",
    "\n",
    "* [Matplotlib examples](http://matplotlib.org/gallery.html)\n",
    "* [Plotting with pandas](http://pandas.pydata.org/pandas-docs/version/0.18.1/visualization.html)\n",
    "* [Seaborn examples](http://seaborn.pydata.org/examples/index.html)"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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   "pygments_lexer": "ipython3",
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}