00 - Tutorial 2a - Python for Data Analysis.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",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
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   "source": [
    "## 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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   "source": [
    "### ndarrays\n",
    "There are several ways to create numpy arrays."
   ]
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       "array([ 1,  2, 53])"
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     "execution_count": 95,
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   "source": [
    "# Convert normal Python array to 1-dimensional numpy array\n",
    "np.array((1, 2, 53))"
   ]
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   "cell_type": "code",
   "execution_count": 96,
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     "data": {
      "text/plain": [
       "array([[1.5, 2. , 3. ],\n",
       "       [4. , 5. , 6. ]])"
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     "execution_count": 96,
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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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   "cell_type": "code",
   "execution_count": 97,
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     "data": {
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       "array([[1.+0.j, 2.+0.j],\n",
       "       [3.+0.j, 4.+0.j]])"
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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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       "(2, 3)"
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       "6"
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       "dtype('int64')"
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       "8"
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   "source": [
    "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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   "source": [
    "Quick array creation.  \n",
    "It is cheaper to create an array with placeholders than extending it later."
   ]
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     "execution_count": 99,
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      "text/plain": [
       "array([[0.221, 0.861, 0.549],\n",
       "       [0.778, 0.125, 0.538]])"
      ]
     },
     "execution_count": 99,
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     "data": {
      "text/plain": [
       "array([[3, 1, 1, 1],\n",
       "       [2, 0, 3, 3]])"
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     "execution_count": 99,
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   "source": [
    "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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      "text/plain": [
       "array([0.   , 0.333, 0.667, 1.   ])"
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     "execution_count": 100,
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     "data": {
      "text/plain": [
       "array([0. , 0.3, 0.6, 0.9])"
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     "execution_count": 100,
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "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": 100,
     "metadata": {},
     "output_type": "execute_result"
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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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([20, 30, 40, 50]), array([0, 1, 2, 3]))"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([20, 29, 38, 47])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
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     "data": {
      "text/plain": [
       "array([0, 1, 4, 9])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "array([False, False,  True,  True])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([21, 31, 41, 51])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The product operator * operates elementwise.  \n",
    "The matrix product can be performed using dot() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [0, 1]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[2, 0],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[2, 0],\n",
       "       [0, 4]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[5, 4],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dtype('int64'), dtype('float64'), dtype('float64'))"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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"
    }
   },
   "source": [
    "ndarrays have most unary operations (max,min,sum,...) built in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.057, 0.725, 0.083],\n",
       "       [0.395, 0.017, 0.825]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(2.1028801738915552, 0.016881707307918936, 0.8254116092022105)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([12, 15, 18, 21])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ 6, 22, 38])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(12).reshape(3,4)\n",
    "b\n",
    "b.sum()\n",
    "b.sum(axis=0) \n",
    "b.sum(axis=1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.   ,  0.841,  0.909,  0.141, -0.757, -0.959, -0.279,  0.657,\n",
       "        0.989,  0.412])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sin(np.arange(0, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Shape Manipulation\n",
    "Transpose, flatten, reshape,..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6., 6., 8., 4.],\n",
       "       [3., 5., 9., 4.],\n",
       "       [2., 9., 2., 3.]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[6., 3., 2.],\n",
       "       [6., 5., 9.],\n",
       "       [8., 9., 2.],\n",
       "       [4., 4., 3.]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([6., 6., 8., 4., 3., 5., 9., 4., 2., 9., 2., 3.])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[6., 6., 8., 4.],\n",
       "       [3., 5., 9., 4.],\n",
       "       [2., 9., 2., 3.]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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 3 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": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7., 4., 2., 6., 8., 5.],\n",
       "       [6., 1., 4., 3., 7., 1.]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[7., 4., 2.],\n",
       "       [6., 1., 4.]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[6., 8., 5.],\n",
       "       [3., 7., 1.]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[7., 4., 2., 6., 8., 5.],\n",
       "       [6., 1., 4., 3., 7., 1.]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  4,  9, 16, 25, 36, 49, 64, 81])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)**2\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9, 25, 49, 81])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3:10:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([81, 64, 49, 36, 25, 16,  9,  4,  1,  0])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[::-1] # Defaults are used if indices not stated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  4, 16, 36, 64])"
      ]
     },
     "execution_count": 113,
     "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": 114,
   "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": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 114,
     "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": 115,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 5, 9])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ 1,  5,  9, 13])"
      ]
     },
     "execution_count": 115,
     "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": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[1:3, : ] # Rows 1:3, all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 13, 14, 15])"
      ]
     },
     "execution_count": 117,
     "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": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100, 121])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  9, 64, 25])"
      ]
     },
     "execution_count": 118,
     "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": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9, 16],\n",
       "       [81, 49]])"
      ]
     },
     "execution_count": 119,
     "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": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False],\n",
       "       [False,  True,  True,  True],\n",
       "       [ True,  True,  True,  True]])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ 5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 120,
     "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": 121,
   "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": 122,
   "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": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(12)\n",
    "b = a\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-100,    1,    2,    3,    4,    5,    6,    7,    8,    9,   10,\n",
       "         11])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[0] = -100\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-100,    1,    2,    3,    4,    5,    6,    7,    8,    9,   10,\n",
       "         11])"
      ]
     },
     "execution_count": 125,
     "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": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10, 11]])"
      ]
     },
     "execution_count": 126,
     "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": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[123,   1,   2,   3,   4,   5],\n",
       "       [  6,   7,   8,   9,  10,  11]])"
      ]
     },
     "execution_count": 127,
     "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": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[123,   1,   2,   3,   4,   5],\n",
       "       [  6,   7,   8,   9,  10,  11]])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[10, 10],\n",
       "       [10, 10]])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[123,  10,  10,   3,   4,   5],\n",
       "       [  6,  10,  10,   9,  10,  11]])"
      ]
     },
     "execution_count": 128,
     "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": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-42,  10,  10,   3,   4,   5,   6,  10,  10,   9,  10,  11])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = a.copy()      \n",
    "d[0] = -42\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([123,  10,  10,   3,   4,   5,   6,  10,  10,   9,  10,  11])"
      ]
     },
     "execution_count": 130,
     "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": 131,
   "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": 131,
     "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": 131,
     "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": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 0)\t1.0\n",
      "  (1, 2)\t1.0\n",
      "  (2, 4)\t1.0\n",
      "  (3, 6)\t1.0\n"
     ]
    }
   ],
   "source": [
    "data = np.ones(4)                         # [1,1,1,1]\n",
    "row_indices = col_indices = np.arange(4)  # [0,1,2,3]\n",
    "col_indices = np.arange(4) * 2\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": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    3\n",
       "c    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "b    2.0\n",
       "c    3.0\n",
       "d    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 133,
     "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": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 134,
     "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": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "a    2\n",
       "b    4\n",
       "c    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "a    1.00\n",
       "b    1.41\n",
       "c    1.73\n",
       "dtype: float64"
      ]
     },
     "execution_count": 135,
     "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": 136,
   "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": 136,
     "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": 137,
   "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": 137,
     "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": 137,
     "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": 138,
   "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>1.53</td>\n",
       "      <td>0.74</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.62</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.98</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-2.14</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01  1.53  0.74 -0.03 -1.84\n",
       "2013-01-02 -0.62 -1.15  0.34  0.60\n",
       "2013-01-03 -0.98 -0.66 -1.93  0.87\n",
       "2013-01-04 -2.14 -0.31 -1.48  1.40"
      ]
     },
     "execution_count": 138,
     "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": 139,
   "metadata": {
    "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": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1.528,  0.744, -0.035, -1.843],\n",
       "       [-0.623, -1.15 ,  0.345,  0.599],\n",
       "       [-0.984, -0.661, -1.932,  0.87 ],\n",
       "       [-2.142, -0.306, -1.477,  1.399]])"
      ]
     },
     "execution_count": 139,
     "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": 140,
   "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>D</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.53</td>\n",
       "      <td>0.74</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.62</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.98</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.14</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A     B     C     D\n",
       "0  1.53  0.74 -0.03 -1.84\n",
       "1 -0.62 -1.15  0.34  0.60\n",
       "2 -0.98 -0.66 -1.93  0.87\n",
       "3 -2.14 -0.31 -1.48  1.40"
      ]
     },
     "execution_count": 140,
     "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.00</td>\n",
       "      <td>0.74</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.62</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.98</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.14</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       A     B     C     D\n",
       "0  10.00  0.74 -0.03 -1.84\n",
       "1  -0.62 -1.15  0.34  0.60\n",
       "2  -0.98 -0.66 -1.93  0.87\n",
       "3  -2.14 -0.31 -1.48  1.40"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_csv('data.csv', index=False) # Don't export the row index\n",
    "dfs = pd.read_csv('data.csv')\n",
    "dfs\n",
    "dfs.set_value(0, 'A', 10) # Set value in row 0, column 'A' to '10'\n",
    "dfs.to_csv('data.csv', index=False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Simple operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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>1.53</td>\n",
       "      <td>0.74</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.62</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.98</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-2.14</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01  1.53  0.74 -0.03 -1.84\n",
       "2013-01-02 -0.62 -1.15  0.34  0.60\n",
       "2013-01-03 -0.98 -0.66 -1.93  0.87\n",
       "2013-01-04 -2.14 -0.31 -1.48  1.40"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "  <tbody>\n",
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       "      <th>2013-01-01</th>\n",
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       "      <td>-1.84</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.62</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.98</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-2.14</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01  1.53  0.74 -0.03 -1.84\n",
       "2013-01-02 -0.62 -1.15  0.34  0.60\n",
       "2013-01-03 -0.98 -0.66 -1.93  0.87\n",
       "2013-01-04 -2.14 -0.31 -1.48  1.40"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # First 5 rows\n",
    "df.tail() # Last 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "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>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.53</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.14</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.27</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-1.59</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.53</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A     B     C     D\n",
       "count  4.00  4.00  4.00  4.00\n",
       "mean  -0.56 -0.34 -0.77  0.26\n",
       "std    1.53  0.80  1.10  1.44\n",
       "min   -2.14 -1.15 -1.93 -1.84\n",
       "25%   -1.27 -0.78 -1.59 -0.01\n",
       "50%   -0.80 -0.48 -0.76  0.73\n",
       "75%   -0.08 -0.04  0.06  1.00\n",
       "max    1.53  0.74  0.34  1.40"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quick stats\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "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>1.53</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>-2.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.74</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-1.84</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1.40</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        1.53       -0.62       -0.98       -2.14\n",
       "B        0.74       -1.15       -0.66       -0.31\n",
       "C       -0.03        0.34       -1.93       -1.48\n",
       "D       -1.84        0.60        0.87        1.40"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "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>-1.84</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.87</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.40</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-2.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               D     C     B     A\n",
       "2013-01-01 -1.84 -0.03  0.74  1.53\n",
       "2013-01-02  0.60  0.34 -1.15 -0.62\n",
       "2013-01-03  0.87 -1.93 -0.66 -0.98\n",
       "2013-01-04  1.40 -1.48 -0.31 -2.14"
      ]
     },
     "execution_count": 144,
     "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.62</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.98</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-2.14</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.53</td>\n",
       "      <td>0.74</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-1.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-02 -0.62 -1.15  0.34  0.60\n",
       "2013-01-03 -0.98 -0.66 -1.93  0.87\n",
       "2013-01-04 -2.14 -0.31 -1.48  1.40\n",
       "2013-01-01  1.53  0.74 -0.03 -1.84"
      ]
     },
     "execution_count": 144,
     "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": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    1.53\n",
       "2013-01-02   -0.62\n",
       "2013-01-03   -0.98\n",
       "2013-01-04   -2.14\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2013-01-01    1.53\n",
       "2013-01-02   -0.62\n",
       "2013-01-03   -0.98\n",
       "2013-01-04   -2.14\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A'] # Get single column by label\n",
    "df.A    # Shorthand "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "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>2013-01-01</th>\n",
       "      <td>1.53</td>\n",
       "      <td>0.74</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.62</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01  1.53  0.74 -0.03 -1.84\n",
       "2013-01-02 -0.62 -1.15  0.34  0.60"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.53</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.62</td>\n",
       "      <td>-1.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B\n",
       "2013-01-01  1.53  0.74\n",
       "2013-01-02 -0.62 -1.15"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>2013-01-02</th>\n",
       "      <td>-0.62</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.98</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-02 -0.62 -1.15  0.34  0.60\n",
       "2013-01-03 -0.98 -0.66 -1.93  0.87"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.62</td>\n",
       "      <td>-1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.98</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "               A     B\n",
       "2013-01-02 -0.62 -1.15\n",
       "2013-01-03 -0.98 -0.66"
      ]
     },
     "execution_count": 146,
     "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.53</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.62</td>\n",
       "      <td>-1.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B\n",
       "2013-01-01  1.53  0.74\n",
       "2013-01-02 -0.62 -1.15"
      ]
     },
     "execution_count": 146,
     "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": 147,
   "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>1.53</td>\n",
       "      <td>0.74</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.62</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.98</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-2.14</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01  1.53  0.74 -0.03 -1.84\n",
       "2013-01-02 -0.62 -1.15  0.34  0.60\n",
       "2013-01-03 -0.98 -0.66 -1.93  0.87\n",
       "2013-01-04 -2.14 -0.31 -1.48  1.40"
      ]
     },
     "execution_count": 147,
     "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.53</td>\n",
       "      <td>0.74</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-1.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01  1.53  0.74 -0.03 -1.84"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "<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>1.53</td>\n",
       "      <td>0.74</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.62</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01  1.53  0.74 -0.03 -1.84\n",
       "2013-01-02 -0.62 -1.15  0.34  0.60"
      ]
     },
     "execution_count": 147,
     "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": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.56\n",
       "B   -0.34\n",
       "C   -0.77\n",
       "D    0.26\n",
       "dtype: float64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2013-01-01    0.10\n",
       "2013-01-02   -0.21\n",
       "2013-01-03   -0.68\n",
       "2013-01-04   -0.63\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 148,
     "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": 149,
   "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>1.53</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.62</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.93</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>2.14</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01  1.53  0.74  0.03  1.84\n",
       "2013-01-02  0.62  1.15  0.34  0.60\n",
       "2013-01-03  0.98  0.66  1.93  0.87\n",
       "2013-01-04  2.14  0.31  1.48  1.40"
      ]
     },
     "execution_count": 149,
     "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": 150,
   "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>1.53</td>\n",
       "      <td>0.74</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-1.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.62</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.98</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-2.14</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A     B     C     D\n",
       "2013-01-01  1.53  0.74 -0.03 -1.84\n",
       "2013-01-02 -0.62 -1.15  0.34  0.60\n",
       "2013-01-03 -0.98 -0.66 -1.93  0.87\n",
       "2013-01-04 -2.14 -0.31 -1.48  1.40"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "A    1.53\n",
       "B    0.74\n",
       "C    0.34\n",
       "D    1.40\n",
       "dtype: float64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "A    3.67\n",
       "B    1.89\n",
       "C    2.28\n",
       "D    3.24\n",
       "dtype: float64"
      ]
     },
     "execution_count": 150,
     "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": 151,
   "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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
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       "      <td>0.74</td>\n",
       "      <td>-0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>0.73</td>\n",
       "      <td>-0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>-1.66</td>\n",
       "      <td>-0.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B     C     D\n",
       "0  foo    one  0.74 -0.29\n",
       "1  bar    one  0.73 -0.17\n",
       "2  foo    two -1.12  1.04\n",
       "3  bar  three -1.66 -0.19"
      ]
     },
     "execution_count": 151,
     "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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>bar</th>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.74</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        C     D\n",
       "A              \n",
       "bar -0.93 -0.36\n",
       "foo -0.38  0.74"
      ]
     },
     "execution_count": 151,
     "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>0.73</td>\n",
       "      <td>-0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>-1.66</td>\n",
       "      <td>-0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>0.74</td>\n",
       "      <td>-0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-1.12</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              C     D\n",
       "A   B                \n",
       "bar one    0.73 -0.17\n",
       "    three -1.66 -0.19\n",
       "foo one    0.74 -0.29\n",
       "    two   -1.12  1.04"
      ]
     },
     "execution_count": 151,
     "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": 152,
   "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",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
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       "      <th>1</th>\n",
       "      <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": 152,
     "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>data2</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data2 key\n",
       "0      0   a\n",
       "1      1   b"
      ]
     },
     "execution_count": 152,
     "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": 152,
     "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": 153,
   "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>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>-0.01</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.38</td>\n",
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       "      <td>-0.45</td>\n",
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     "execution_count": 153,
     "metadata": {},
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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": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "    k1  k2\n",
       "0  one   1\n",
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       "2  one   2"
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     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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       "    k1  k2\n",
       "0  one   1\n",
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     "execution_count": 154,
     "metadata": {},
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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": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <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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       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   k1  k2\n",
       "0   1  -1\n",
       "1  -1   2"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": 155,
     "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": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 2, 1, 3, 2, 2, 1], dtype=int8)"
      ]
     },
     "execution_count": 156,
     "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": 156,
     "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": 157,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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+8pEzJUl1daPj9uEmBgBOy7RP1uHDPfr+99+QJH3+83Nj2/OhT1YqE2If/eh0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ejqatETT+yQRPCOyiE8KZIx5rAx5vuMMZ8yxvyEMeaHjDF/xxiTv1Bw5cfTaow5YYz5b40xP/Jg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\nSb8lqc119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jKZmZrHCDQK5VeAxrt7d1P372/JmML3NaCVcd0A9kZxwxahO1pbW5vRyAjX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SuppKtzD5u264WUgaD7APce4BKXUdY8eU9mnrfSemr+VH0Ye1OeiXel3LeXAN+ehXuhWo9n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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cdcdeb8>"
      ]
     },
     "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": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11dcd7b00>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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IyeLu8AmaUJTAyZrSgfYTV3s42WL639FAbh33ZSZsEAeSaxT52cJzQlUbvJGf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\n",
      "image/png": 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aiqVJkiQNS9fv2MHVL7/M9/fsyfRSJOmI7WEPD/AAj/JoppciZUwuh++bPMVTGVyJdJht\nnTRihWFIQ0N7ppcx4owenUsQBJlexhF797vfzfXXX594/cQTT7Bs2bIMrkiSJCl7bGqIPlH5dG1t\nhlciSUeuhuio0FK6V9lLR4u97E38/G/8jfM4L4OrSa122juFLxo+DCc0YjU0tFNc/JtML2PEqau7\nhDFjhv8fHfPnz+/0evXq1RlaiSRJUvbZ2dwMwJq6ugyvRJKOXC3RoNVwQkerkJB97ANgGtN4A2/I\n8IpSo4EGvs7X2cxmfsAPKKSw/4OUVYb/HUZJ6uDpp5/mvvvuY82aNWzcuJGqqipqa2tp66df8oED\nB9K0QkmSpOwWhiE7m5oA2NbURFVrK+Py8zO8KkkavGqqgcPhRH3sYwpTMrksKW0aaGAsY2mlle/w\nHQooyPSSUmIUo9jFLuqoYwMbOJETM70kDZDhhEas0aNzqau7JNPLGHFGj87OMrnHHnuMj370o7zw\nwguDOr6qqirFK5IkSRqeKtvaaIhEEq/X1tfz2nHjMrgiSToyHSsn1rGOa7mWmczke3wvwyuT0mMM\nY/hv/ps22sgbQbeDAwJO4AQe5mHWsMZwYhgaOV+NUhdBEIyI9kPq32233cZHPvIRwjAc9DmaY60L\nJEmSjnbxqom4NXV1hhOShrWOMycmMhGACioICQkY/jMVpWQNx2Cilloe5mEmMIGzObvb+/Fw4gUG\n97CqMmv4fUVKUgePPPJIt2AiLy+Ps846i9e85jXMnTuXKVOmUFRURGFh596D559/frqXK0mSlPV2\ndnloY6jmTjxdU8N5a9bwjYUL+fCMGUNyDUmCzuHEJCYRENBCC9VUMw7DVymblVPO//K/TGRij+HE\ncpYDsI1tVFHl/9PDjOGEpGHt05/+dKdg4uKLL+bWW29l1qxZfR5npYQkSVLP4pUTY3JyqI9Ehiyc\n+P3Bg9RHInxn927DCUlDqmM4kU8+E5jAQQ5STrk3MqUsV0W0DfdYxvb4/jjGMY95bGc7L/JijwGG\nsldOphcgSYP18ssvs3r16sTr448/nl//+tf9BhMAlZWVQ7k0SZKkYSteOfGPkydz/6texf2vetWQ\nXGdXLATZ0NDAji6tpCQplc7hHM7jvMQA7PiPFVRkclmSktB1oH1PTuAEANawJi1rUuoYTkgatp56\n6qlOrz/4wQ9SUFCQ1LHr168fiiVJkiQNe/HKiROLi7lo4kRmdGmNmSq7OlSy/vHgwSG5hiQBXMql\nfIyPMZnJAIkfyynP5LIkJSFe+dRb5QSQGIS9lrWEDH4eqdLPcELSsFVWVtbp9ZIlS5I+9uGHH071\nciRJkkaEeOXEnKKiIb1Op3DCqlZJaWQ4IR3WSCOttGZ6Gb2KV070FU4sZSl55NFAA4c4lK6lKQUM\nJyQNWx1nTQC0tLQkdVxzczN33HHHUCxJkiRp2Iu3WJqTRMVEdVsbVa0Dv6ERhmGncOKhQ4dojkQG\nfB5JGoz4Tc5aajO8Eimz7uRO3sf7eI7nMr2UXiUTThRSyE3cxF3cxQQmpGtpSgHDCUnD1rRp0zq9\nfvzxx5M67tprr+1WdSFJkiRojkTYF3vgo7/KiY+8/DLjHn+cW/buHfB1Dra20hQLIybl51MfifB4\ndfXAFyxJg1BCCQANNGR4JdLQq6eeZpp7fC+HHJpp5mEe5jEe4/N8nq/wFSJkzwMDHQfa92Uuc8kj\nLx1LUgoZTkgats4444xOr2+99VY2b97c5zG33XYbN91001AuS5IkadjaE6tmKMrJYXJ+fo/7/OeO\nHfxg717ygwCAbYMYZh2vmpiSn8/FE6JPODp3QlK6nMEZ3M3dXMu1mV6KNOR+xa94F+/iJ/yk23uv\n5bUAPMMzfJNvspGNPM/zHCR7/k5OpnJCw5fhhKRha+HChZx++umJ17W1tZxzzjnce++9tLW1ddp3\n7dq1XHrppVx99dWEYcjSpUvTvVxJknSUOtjayoUvvMA9w6Byc2eHlk5BLHzoqDkS4T927OCql1+m\nKCf6z8ltjY0Dvk48nJhdWMiFEycCzp2QlD4FFFBEEQHd/5yTRpp97AN6vrk/j3ksYEHi/UKiLR2r\nqErfAvuxkIUsY1liVoxGFmtdJA1rN910EytXrqQ11ut43759vPOd76S4uJhFixaRk5PD7t27O7Vx\nGjNmDD/96U856aSTMrVsSZJ0FPmP7dv5U2Ulf6qs5F1Tp2Z6OX3qbxj2UzU1NEYiTM3P500TJ/L1\nXbvYegSVE7OLinjD+PHkAC81NLCzqWnIB3FLknQ0iYcTM5jR4/v/xr+xjW0sZznf5JtUU51Vwd2H\n+XCml6AhZDghaVg744wzuP3227nqqqsSAQVAXV0dq1ev7rb/+PHj+fWvf82KFSvSuUxJknQU2zyI\nyoJM2dnPMOxflJcDcN748SyIhQg7m5poi0TIy0m+MH9X7DqzCwsZn5/P6aWlrKqp4V82b+bHS5cy\nJjf3SD4NSZIERIj0G05MjH0AfI7PpW1tEtjWSdIIcOWVV/K3v/2Nc845p9d9ioqKeP/738/69etZ\nuXJl+hYnSZKOei1hmPh5Y3t7BlfSv74qJ9bW1SWGX79u/HhmFBZSEAS0A7tjx+1rbuaGHTv47JYt\nfOKVV7hx504iHT7/uI5tnQA+P3cu+UHAbw4c4Mznn+fhQ4e4ZssWZj7xBKc8+yx1XVp2SlKyNrKR\ntazNqjY1UrpUUkkLLeSSyxSmZHo5UjdWTkgaEU477TT++te/sn37dlatWsW+fftobm5m3LhxLFmy\nhDPOOIPRo0d3Oibs4R/KkiRJqXagQ3XnruZmFnf5niSb9FU58XRNTeLnrxs3jpwgYG5REa80NrKt\nqYl5o0bx2a1b+XGX2RqnlJRw3vjxnbZ1DScunjiRh084gbevX8/a+npet3ZtYt+9LS18assW/mfJ\nktR8kpKOKvdwD2tZy8f5OOdybqaXI6VVOdGKx0lMIherEpV9DCckjSjz5s1j3rx5mV6GJEkSEH0Y\nomNbp51NTdkdTvRROXHVjBmMyc2lPQyZN2oUAPM7hBMrw5C/HDoEwBVTp/JMbS0bGhp4qb6+93Ci\nw3XOGjeOZ08+mX9Yv541dXW8eeJEzigt5V+3buX2fft408SJvGXSpCH5vAfjX7ds4bnaWn5x3HFM\nzM/P9HIk9aKWWgBKKc3wSqT0O0T07+V42yYp2xhOSJIkSdIQKWtpoa5DK6cdsZvy2SgMw35nTlze\nZaD3/Fi4sK2piY0NDexvaaEoJ4fbFi/mi9u3s6GhgVe6zNyIhCF7ulROxM0pKuLvJ51EUyTC6Njc\nif0tLXxj924+uGkTL5aWMrWg4Mg/2SPUHInwrd27aQtD3rtxI787/niCIHuGh0o6rJpqwHBCR6eD\nHARgAhMyvJL0uJd7+SN/5EIu5B28I9PLURKcOSFJkiRJQ6QxEuGiCYdvCMRv/mejQ21t1EciAMzq\nJZzoan6sgmJbYyOPVEX7uZ9RWkpRbi6LY++93CWcKGtpoTUMyQFm9BA05ARBIpgAuGHBAl41ZgwV\nra18/JVXOu1b3dbGHw4c6HGuxVBaV19PW+yafzh4kG/u3p3W60tKTkiYqJwYy9gMr0ZKv3jlxFCE\nE2tYw8/4GREiKT/3YEWIcIhDHOBAppeiJBlOSJIkSdIQmT9qFPcvX84N8+cDh9smZZO6tjYqW1sT\nwcmU/HxG5SbXl7pj5UQ8nDh33DiARPuqlxsaOh1TkpvLz5ct43uLFpGX0/8/SQtzcvjRsccC8KuK\nCipaWhLvfWDjRt68bh23xQZ1p8vztbE2MbFfp89t3cpT1dVpXYOk/jXTTAvRPzNKKOn03q/4FR/l\no/ye32diaVJavIf3cCd38nbentLzhoR8mS9zL/fyJE+m9NxHIh7CxCtGlP0MJyRJkiRpiMXbJGVb\n5URFSwszn3ySiatW8do1a4Ce5030Jh5ObGls5NFYOBGfL7EoVjmxvamJ5sjhpyqL8/J455QpfGTm\nzKSvs6KkhFNKSmgH7q2oAGB/czP3HYg+Gfmj/fuTPlcqrK6rA+DDM2Zw6eTJtIUhb163jt/E1iYp\nO9RQA0ABBRTR+c+2eurZwx4q8P9bjVw55DCOcSmvHNrHvsTP463ThsLLvDygKoj4bA3DieHDcEKS\nJEmShtjc2E38HVkWTjxTW0tNbCZG/MflY8YkfXw8nChrbeVAaytjcnI4tST6dPK0ggKKc3OJAFu7\ntHYajP83ZQpnlpYyMxb0/LisjPg0j7/X1qbkGsmKV06cVFzM/yxZworiYg60tvL29eu5YsMGqlpb\n07YWSb2LhxMllBDQeS5MvJIi3vZJUlQLLZRTTiu9/122jnWJn89j3pCt5Tt8h6u4itWsTmp/w4nh\nx3BCkiRJkoZYvBphV3Nz2ucj9GVL7Ib+RRMm8NRJJ3HvsmV8/Zhjkj5+Yn4+xR1aQJ01diz5sVZN\nQRAk5k50HYo9GJ+YNYvHTzqJt06aBETbQ80uLGRMTg5njR3Loba2I75GMtrDkBfq64FoRUdpXh5P\nnnQSn58zhxyiocn/27AhLWuR1Lf4E909PTVuODFyNNLIf/FfPMADtMdi681s5jEeSwRUSt5H+Agf\n5sPsZGev+8TDiUu5lGUsG5J11FDDXqJtG48hue9N4uFELbU0k32tNNWd4YQkSZIkDbEZBQXkAC1h\nSHmHmQmZtjkWGhw3ZgyvKS3lH6dMYUJ+ftLHB0GQqJ6Awy2d4t45ZQofmzkz6QHb/V2ro6tnzmTb\naadRduaZPLZiBSeXlPRyZGptamigMRJhTE5OonVVYU4OX12wgL+eeCI5wAOVlayLtX6SlFnTmc4U\npnTbbjgxcrzESzzFU9zHfeTEbnXezM18k2+yla0ZXt3wEw/zqqjq8f2QMBFOHM/xg7pGGPvoyyY2\nATCTmZRSmtR5xzCGQqLfc1RSOai1Kb0MJyRJkiRpiOXn5CTaEe3IoqHY8XBiYewm+2B0DCfiw7Dj\nPjtnDjcvWsRJQxQc5AYBY5Ic3p0qrWHIRRMmcP6ECeR0CUzOGjeOt0+eDMB39uxJ67okdXcyJ3ML\nt/BZPtvtPcOJkeMFXgDgBE5ItO+aylQA9pPemUQjwTiif5f3Fk7sZS+HOEQ++Sxm8aCu8TIvczVX\n8wt+0es+8XDiWI5N+rwBgUOxhxnDCUmSJElKg2wcih1v63TMEYQTC2LHlubmsqK4OCXrymYnFBdz\n//Ll/Ob4np8W/URs0PdPyso4kEVVMpI6i4cTtv0Z/uLhxHKWJ7bFw4lyyjOypuGsv8qJ7WwnIGAx\niymgYFDXWMUqyilnD70H+RvZCAwsnADnTgw3eZlegCRJkiQdDeYUFbGqpoadg6icWFVdzcdfeYXv\nLVrE6WO7904frAeWL+eVxkZOPoJQYdno0QC8bvx48nJ8/u3MsWM5ubiY5+rq+J99+7hm9myu37GD\nR6qquHvZskQFjaTMiocT9dTTTju59F6F1UwzVVQxiUl97qf0q6KK7WwHeg4nrJwYuHjlRHxmS1dn\ncibLWT7oYC9ChFWsSpwrvi2IfQC00cYrvALAEpYM6PxXcAUhITOZOaj1Kb0MJyRJkiQpDeKVEzsG\nUTnx4U2bWN/QwBmrVxOuXJmyNS0YNSpR+TBYV06bRksY8rbYoOqjXRAE/MusWbxn40a+t2cP9x04\nwDO10bYxd5eV8Zk5czK8QklwOJwAqKOux6HZEL1pejmXEyHCD/lhomWMssOLvAjAfOZ3+j20cmLw\n+mvrBNH/fzr+PzQQG9hAJZWMZjQrWMF1XMfLvMzX+TqzmAVEqzNaaKGY4gGHDItYNKh1KTN8rEWS\nJEmShsBvKir4RXk5+2OVEnNjsxkG09apsq0teo7CQiJh3wMk060gJ4d/njkzqYqAmtjnMdK9c8oU\nphUUsK+lJRFMAKyqsX2MlC1yyeXTfJpruZZRREPaCBGaif6Z3UorADnkJG7C9vYkuTKnp5ZOcDic\nKKMs7WvKFj/hJ3yaT/MIjwzouP7aOh2px3kcgNM4jXzyaaCBJprYwY7EPvGWTktYkhhyrpHJyglJ\nkiRJGgLX79jB83V1/Pb443lLYSFz4uHEANs6hWFIfXs7APcvX95tCPNwEYYhb1i7loKcHG5bvJil\nY8ZkekllZlhJAAAgAElEQVRDpiAnh8/PmcMnNm/mtWPH8k8zZ3LpSy+xqrqaMAwJhunvoTTSnMVZ\nnV5fz/WsZS3LWEYTTeSRx3t5L2MZS3XsQ9kjJGQta4HoMOyOpjAFiFbF1FPPGEbu3zm92c1utrKV\nJgb2UEQylRNH4jmeA+AMzgBgLnPZzGZ2sCPR5qmAAmYxa8DzJjT8GE5IkiRJUoqFYcjm2LDphbG2\nSYNt67S/pYWa9nZyOpxrOHro0CH+XltLUU4OE/LzM72cIffxWbO4ZNIkZhYW0hqGFAYBFa2tvNLY\nyOLYnA5J2aOJJtawhpCQdaxLbC+ggFJKgeSGZ4exD5/2Hnr72U8FFeSRx1KWdnpvFKMopZQaaiij\njAUsyNAqMyc+EHqgrcji4UQttf3sGf092MUupjCFucztd/8qqqiggoCAZSwDSBy3k52J/d4Q+wjJ\nrmpRpZ7hhCRJkiSl2IHWVmra2wmABbGKiXhbp8q2Nura2ijOS+6fYxsaGoDofIjCYTxw+oad0ZsO\nH5o+nakFBRleTXrMjv2eFwYBry4t5bHqalZVVxtOSFloBzsICSmllEu4hGd4hvmxj/4GBMe10MKn\n+BTjGc+X+JLDs1NoD3u6zR6YyESu4zr2s58iirods4Ql1FJLO+3pWmZWOcQhYODhxCxmcSd3JjVT\n4g/8gfu5n0u4hCu4ot/940OuZzIz0U4tHk7EB5t3FB+QrZHLcEKSJEmSUizeumlaQQFFudGbU6V5\neYzNzaW6vZ1dzc0sTTKc2BgLJ5YO4xvaq6qrebSqivwg4JrZs4fkGg3t7Xxt5042Nzbyk6VLyc2y\n1klnjh3LY9XVPF5dzfumT8/0cqQRrz32UUByYWj8xugxHMPbYh9x8R78/YUTezp8/IW/8EbeOLjF\nq5PVrOYrfIWLuIgruCLxe1pAASdyYq/H/Rv/lq4lZp122hPhxEQmDujYPPISgVx/4ueOX6s/u9kN\nwEIWJrbFw4kyymikMRFa6OgwfB+7kSRJkqQstTcWTszoUiEQnzsxkNZO8XDi2GEaTtS2tXHW6tUA\nXDltGrOKuj/dmgqFOTl8fedO7ikvH9TQ8aF21tjozc1V1fasl9JhL3u5lEv5IB9Mav9tbANgHvO6\nvZdsOBFvowPwM35GHXVJrlZ92chGQkLu537+l//N9HKGhWqqiRAhh5zE1+9QGM94oPPXfl8u4RLu\n4A4u47LEtrGMTYQhu9iV+kUqqxlOSJIkSVKK7W1pAWBGbM5EXHzuxECGYm8Y5uFEbhCQH6ti+Nch\nqpqIX2dR7NcoHuik0ue2bOG6bdvYNcjg4/TSaM/6TY2NVMS+PiQNnfh8iGQrJy7kQq7mak7n9G7v\nJRtOLGc5N3MzEO3X/wt+MZAlqxeXcRmf4TMAPMRDSc3+ONpVUglE50ekor1YfPh4K62dtscrJyqp\nJCTkG3yDn/EzGmlM7NM1uBjP+MTA8rh49cQOdhzxWjW8GE5IkiRJUopdMmkSD51wAl+Y23k45Nw0\nV06EYUh9e/de22GYvgGTo3NzWbViBU+ddFIiPBgqS2IDwzelOJxoam/ne3v28B87dlA2yGBhQn4+\nx8U+/ydqvLEmDbX4Dexknxqfy1wu4AIWsajbe/GB2P0NCC6ggNnM5jquA+ABHuBJnhzIstWLMziD\nYziGFlr4C3/J9HKyXjycGOi8id5sZztf4ktcxVWdZnjEz19JJRVU8DiP8xt+Qx551FPPJ/gEV3M1\n9dT3ef5UhxMP8RA3ciNP8ERKzqehYzghSZIkSSk2paCA88aP5zWxp+XjZscqJ/YkWTlR29bG7ti+\ngwknbt+3j+LHHuNXFRWdtv+4rIypq1bxiVdeGfA5B+PU0tJuvxZD4dghqpz4v0OHqI9EmFVYyMkl\n/Q8I7c2ZsdZOj9vaSRpy8XAimaG+/VnBCn7Ej7iBG5La/0RO5GROpp12buTGbk+ba+ACAt7Em4Bo\n6NNGW4ZXlN1SHU48wzMALGZxp0qM+PkbaWQjG4HoQO188hnDGEJC2mjj7/y9z/NfyIV8i29xJVem\nZL3b2MaTPMlmNqfkfBo6hhOSJEmSlCYzY+HE7iTDiZcbo20RpuTnMyE/f8DXu2PfPgB+UlbWafvm\nxkbKW1tpikQGfM5stiQWTmxqbOxnz4G578ABAN42aRLBEQzaHolzJ/Y0N3N3WRmra/t+olxKt2Qq\nJyqo4C7u4m7u7vNchRRSSik5A7iN9gE+wDzmMZvZ/T41ruScyZmMZzyVVFqR0o9TOIXP8TnezJtT\ncr54OHEqp3baPopRjCb6d+9qovOlOs5tOYMzAPr9/ZrGNOYxj3wG/r1OT+KzMJId1K3MMZyQJEmS\npDSZNcBwYkN99IZWx6qJDfX1vPnFF/nHdev6PPZQayvPxG4YP1ZVRaRDK6fNsZv3C2NtkEaKJSmo\nnLivooKq1sNPObeHIb87GO2XfcmkSUe0vnjlxLO1tTT20G5rOFpVXc3lGzbwsTRV4UjJSqZyoo46\nfsNvhqRN0HSm8y2+xc3cnBj2m4wGGriFW1hH33/GH43yyeeNvBGA3/E7QtLXonC4mcQkXsNrOJ7j\nj/hclVQmKhBO4ZRu78erJ57neeBwiyY4HE6sYU1aQzrDieHDcEKSJEmS0qRj5UQycx96mjcRAH84\neJC/HOr7H9yPVFURr4s42NaWGKwNsCUWThwzQsOJ/S0t1LS1caClhQtfeIFL1q3jlj17eKWf0GJr\nYyOXrF/PrCef5FAsoHiiupqK2M/PHptc7/rezC8qYmJeHq1hmKiKGe62xj6P+SPsa0nDXzyciM+L\n6Ek8uKihJmtudP+Mn/EX/sK1XJvppWTUn/gTz/M8zXQO89/AG8gnnx3soIyyXo5WKrTTzqM8yr/z\n7wAsYlHipn9Hs5nNPOYl/p/rWDkxhznMYhZttCWqL9IhHpgYTmS/vEwvQJIkSZKOFvFwoj4Soaa9\nnbF5ff+TLB5OLO0QTsyODdWuaW+nuq2t13N0DS/+VlXFcWPGEIYhr4zQyomxeXlMKyhgf0sLmxoa\nuLu8nD9VRvtux1szfXHuXL48f36Px9+5fz8AZ4wdy/hYG61DbW2MysnhrZMmkZ9zZM/3BUHA5VOn\n0hKJMOYIz5UttsaGuy+IfV1K2WIg4UQ77TTSmGhPk4wneZKpTGUOc8hL4e21V7AKqZlmbud2IkT4\nIT+kkMLEe+MYxzVcw2IWJzXsvJ12DhKtfpvClCFb80izhS18hs8kXpdSyhVc0eO+/8q/0kwzl3M5\nESKdKicATud07uVenuAJVrJyKJedYOXE8DEyvhuSJEmSpGFgTG4u42NhQjJDsXuqnBiTm8vE2Dl2\nxm4M9+QvsZvyp8YGOP+1qgqIDmM+1NbGmJwcFo2wcAIO/1o9WlXFf+/dC8BHZszg3HHRtir/sWNH\ntwHhAK2RCP8bCyc+MH16Yvs5Y8fypXnz+PbChSlZ382LFnHrkiUsHMSA82y0LfY1+GhVFf+1c2eG\nVyMdlkw4UUghBRQAUEvyc1OaaebrfJ1P8+mUt6ppovc/148W29hGhAjjGd/jQOdTOTWpYALgPu7j\nw3y437ki6mwc48gjjzGM4d28m1u5tc8WUbvYRYQIpZR2a2N2JmcC8BzP0cDg2y4ORDycqKXWgfRZ\nznBCkiRJktIo2aHYbZFIovXPsV1uZM+JPaW+s5dzbGtsZEtTE7nAtXOjTzD+rbqaMAwZm5fHpZMn\nc8W0aYzKzT2STyUrLYkFLl/evp2mSITTS0v5/qJFPHziiXxm9mwA3rtxIy/Vd76heMvevexubmZy\nfj5v6zBbYlx+Pv86Zw5TCwrS90kMI/G2Tn+rruZzW7dS19aW4RVJUddxHd/hOyxjWZ/7xasnBhJO\n7GY3ISGllCZ9k7yjJ3mS7/E9XuCFbu91bGMUSTTnO3qEhDzKowAcwzFHfL6pTAVgP/uP+FxHk4lM\n5HZu5wf8gH/gHxhF3w8zbGc7EG3pFBB0em8Oc4Do1/Od3Dkk6+2qhJJERZPVE9nNcEKSJEmS0ig+\nFLu/yoltTU20hiGjcnISYUTcnNg5dsWeWg/DkO/u3s1vY62L4i2dTist5fXjx1MQBOxraWFLYyPL\ni4u557jj+P6iRSn9vLJFPMipj0Rv6n153jyCIHqj5Gvz53PuuHHUtbdzybp1ibkSFS0tXLdtGwDX\nz59P4QhpuTTU2iKRbgHZvpaWDK1G6qyUUuYwp99WTfGWPw/xUNLn3sUuINprP24ta7mWa/k5P+/3\n+DWs4SEeYg1rur3XSDTwexfvypo5GOlSTTWf4lP8mT8DqQ0nyik/4nMdbcYxjiKSa9l3LMfyXt7L\n63l9t/cCAj7JJ5nKVC7iolQvs0cBga2dhglnTkiSJElSGs2MPYHfX+VEvKXTktGjyQk6P4U4r6iI\nGQUFiWdqV9fV8fHNmwG4bfHiRDhx/oQJjMrN5e2TJ1MQBJ2ewQ26nHOkWNKhyuTMWDgTl5eTw8+X\nLePk557j5cZGzlmzhj8tX86Xt2+nur2dFcXFnVo6qX8PnXACWxob+erOnWxubGRvSwuLRkjLKh1d\nBjLroadwYg97WMe6RCVGXxaykAd5sMdrXsEVlFHGxVxMLiOvuq0vpZR2mt+RynCikkqaae40v0Kp\nMyv20ZtzYh/p9FbeSjvtTGRiWq+rgfFxEEnD2qOPPkoQBIn/vvSlL2V6SZIk6Sj3tR07OPW55/hB\nbN5BV/9v6lRuW7yYt3ZoHdSTzbF2OT3NhfjWwoXsOeMM/mnmTAD+XlOTeO/DL7/M72MVFOfHbszf\nvWwZP1q6lMVHwU3jji2wvtShaiJuckEB97/qVUwvKGBdfX3092rfPgBuXriQ3BEa2gyFvJwczhk3\njvdNn54I3fYlMUtFyiYf4AOUUsrH+Fif+z3O43yST3IHd/QYTsQrMHqakdDVIqKVa1vY0q110+t4\nHZdzOcUUD+jzGK5+yk95gAeopZaAgHfwjsR7qQgnSihJtCSqoPu8IY1cF3Mxb+EtTKLv77eUWVZO\nSJIkSVIKvdTQwLO1tbxz8uQe3z93/HjO7fA0f2+2xlo2HdNDONH1hvuztdFe6bMKC9nd3ExzGFKa\nm8urS/p/gnekmVdUxGdmzyY/CHhdL7/Oryou5smTTuKCtWvZFAuBLpsyhbPGjetxf/VvRqzV2F7b\nOmmYeRNv4mIu7tYnv6tmmtnOdiYwgb1Ew+eO4UQllcDhQbx9mc1sCimkkUb2sKfTeY4m9dRzH/fR\nRhuv4lWUUMKpnMpbeSuFFCYV9PQnIGAqU9nOdsoo6/Pp/pHit/yW/eznXM5lMYszvRypT1ZOSJIk\nSVIKxWdJxAdfD1Z80PCCov77PcfDie8uXMjHY9UUb544kbyjcHZCEAR8/Zhj+OqCBX22rppbVMTj\nK1Zw/vjxLBo1ihsXLEjjKkee6fHKCcMJDUP9BRNAYvB1BRWUUQZEhwZvYhM72JHoa5/MDfVccllA\n9M+cgbSTGmme5VnaaGMWsxIBTUDAe3kvl3FZyq4Tb+0U/30b6Z7iKf7En5yzoWHByglJkiRJSqG9\nsXBiRuxm7WDFKycW9FA50VFDezvr6+sBOLW0lLdOmsR7pk7t1N5IPZtUUMCDJ5xAGIYjdgZHuiQq\nJ2zrpBEqHk7EWzqVUMKTPMlP+Amv5bWJtk7J9rdfxCI2sIHNbOY8zkt6HXvYQwstzGIW+eQP8LPI\nHu2083t+D8DpnD6k1zrawol9RFsVTscZSsp+R99jNJIkSZI0hOJtbWYcQeVEJAzZlmTlxJq6OtqB\naQUFzCgoIAgCTiktpTjPZ9GSZTBx5Kyc0EgXDycAjo19xG/+7mPfgNo6weG5EwOtnPgdv+NTfIp7\nuGdAx2WbP/AHtrCF0YzmjbxxSK81lankkksTTUN6nWzQQAPVVAMwjWkZXo3UP79blSRJkqQUqW1r\no7a9HTiyyom9sbkReUHA7H5CjnhLp1NKSrzJPkz8taqKp2tqOGvsWE4fO7b/A4aB+Ne7lRMaqUop\nTfz8i3yRUYxiG9sA2M52WogGc8nOSVjIwsSxrbQmXQWxgx0AzGNeskvPOmWUcTd3A3AlV6ZktkRf\nXs/ruYALyCV3SK+TDeJVE6WUMoYxGV6N1D/DCUmSJElKkXjVRGlu7hFVLsRbOs0tLOx3bkQ8nDj1\nKBx+PVz9tKyM2/ft40vz5o2YcGJ6LESzckLZ4Jf8kvWs5wIu4DROS8k5i2IfTTRRRRWjGJV4Mj0e\nTBRRxGiSa6k3lam8hbewjGVJzbwAiBAZ9uFESMh1XEczzRzHcbye1w/5NQs4sjaLw8l+9gMwgxkZ\nXomUHNs6SZIkSVKK7E31MOx+5k0APNOhckLDw6zY18fuEVRlEK+cqGlvpz5WPSRlymY2s4Y1iSHV\nqRJv7RRvmzOKUYk2ThdyIVdwRdJBQ0DA+3gfr+E15HV5driccn7CT/g5P++0vYyyRGuiNazhF/zi\niD6fTNjFLsooI5dcPsJHyPHWZErtZS9gSycNH/4JIEmSJEkpsifVw7D7mTdR09bGpoYGwHBiOBmJ\n4URJXh5jYlU++0bQ56XhqZZoaNuxFVMqdA0n4PDQ4SUs4UIuHPS5D3KQu7mbx3iMWmr5Fb/iz/y5\n0z7b2Q5Eh27fwR3cwz200jroaw6lTWyinHJCwk7bd7ObiUzkQ3yImczM0OpGLisnOnuap/ktv6WC\nikwvRb2wrZOkEa2trY2nnnqKdevWUVlZSWlpKbNnz2blypWMHSEl9JKkkW9NbS27mps5fswY5ifx\nJL1Sq7K1lb8cOsSjVVU8W1vLe6ZO5eOzZvW4byqGYQNsSaJyojkS4e7yckJgTmEhU44wEFH6jIRw\n4ra9e7lt717ePXUqn5o9G4h+3b/S2MjelhYWjk6utY00FOLhQarDicu4jBZaWMzixLYZzOAlXkrc\nFB6s7WznF/yCeczjC3wBgBpqCAkT1Rjxlk7LWc7jPE4rrRzkYFY+Jf8NvkEFFdzADSxjWWL7GbEP\nDY145UQ8NDva3cu9bGYzM5jBZCZnejnqgeGEpBGptbWVb37zm3zjG9+goqJ7Ql5YWMgll1zCTTfd\nxMyZPq0hScpud+zfz3f37OFzc+bwtQULMr2co0pDezvHPv00Fa2Hn0xdXVvLWWPHclIPlQrJtnV6\norqa7+7Zw8JRo/jK/Pnd3o9XThzTRzjxyKFDXP3yy4BVE8PNSAgnXqyrY3VdHRdMODzIdnpBAa80\nNjp3QhnVTDNllAGk/GbkiZzYbVs8GIjfFB6s+JPdk5mcCFXaaaeeeoopBg5XTsxnPhvZyD72cYAD\nWRdO1FCT+HyGfDbGnj2waxfMmwfTsuvXIRMu4iKO5ViO4ZhMLyUrxNuuVVKZ4ZWoN7Z1kjTiVFVV\nce655/K5z32ux2ACoLm5mXvuuYdly5bxxz/+Mc0rlCRpYBI3vH0yPu02NzZS0dpKUU4On5g5kzeM\nH0878MFNm2iLRLrtvydeOdHP71V5Swv3lJfzYGXP/1hOzJzoo63TnA7vGU4ML/Fwoqqtjbq2tgyv\nZnDW1tcDsLRDhUS8YmjvMA5dNPxtYhNttDGRiUxl6pBfbwYzGM94iui7DV9/yikHooOyCyhgFNFw\numMLqXg4MZe5TGFKp+OyyTa2AdGn95MdED5oX/wiXHYZPPzw0F5nmDibs7mSK62ciImHE6meP6PU\nMZyQNKJEIhHe8Y53sGrVqsS2iRMnsmLFCpYuXUpRl3/g19TU8Pa3v51HHnkk3UuVJClpe1LUKkgD\nF58hsWTUKL69aBF3LV3K+Lw8VtfV8a3du7vtH78p29/vVfzm9J4ebuLWtbVRHqvU6Kut0+wO11hi\nC51hpTQvj0dOOIGXX/1qRuXmZno5A9YehjwfG8R+aodgbHoslLNyQpm0nvUAHMdxSQ+nPhKncRp3\ncAcf4SNHdJ54tUc8dIhXT9RQA0SrKOIhyDzmJapCsrGX/la2AtEKjyE3PXYTvqxs6K+lYWcC0eo+\nw4nsZTghaUS56667+L//+z8ATjrpJB555BEqKip4/vnneemll6ioqOC2225j3LhxiWOampq4/PLL\nqaqqytSyJUnqU/yG979v28Ypzz7rsNk02tOlTdPUggK+eUy0VcIXt29nc2wYNUAYhuxMssolHk7s\na2npVoGxLdbSaUJeHmPzeu/EW5KXx4nFxUzKz+f148cP5NNSFlg5fjyLRo8mNxj6m6eptrGhgYZI\nhOLcXBZ3rJyIfd1bOaFMWsc6IBpOpEOqApB4BUQ8nIgP346HE7nk8jW+xk/5KaWUDotwYgFpaEU5\nNVYdsz+5mR/ZOkBcQ8PKiexnOCFpRNmxIzog7KKLLuKpp55i5cqVBB3+wVdcXMyHPvQhnn76aaZM\nmZLYvn//fr7whS+kfb2SJPUnEoaJIctbGxt5rq6OlzrcENfQiletzOpQpXDltGm8btw4miIRrtm6\nNbH9kaoqdjc3U5ST028lw5SCAvKCgAiwv8tT5snMm4h7csUKtr7mNZT2EWJIqfZsrGripOLiTuHK\n9A6hm5QJLbTwCq8A6QsnUqVrOBGvnOjY1gkgJ3YrL5vDiXhbp7SEE/E5E/1UTtRQwwf4AJdzOe20\nD/26lBWcOZH9DCckjTjTp0/nnnvuIT8/v9d9Fi1axF133dVp25133kl1dXUvR0iSlBkHWltpC0MC\n4HWxp+PXx3q9a+jt7mHAdRAEfHfRInKA+w4c4Jma6FOtX409JHHV9OmM7+P7EICcIEg8Zb6ny43c\nLUnMm4grys2lxGBCaRb/mu866yRROdHha/pASwt/PHiQ7+7ebUWFhlweedzIjXyYDzODGZleTtLW\nsS5RIdFbW6eusjWcaKQxMRw8LW2dkqycKKaYWmppo40DHBj6dSkr2NYp+/ldrEa+oXyysKAAkvnH\nYHMztA9RMp+bC8n0n25rgyN9gmmY9DL+93//d0qSGAp5wQUXcN555/FwbHBWY2Mjd999N1dfffVQ\nL1GSpKQV5+byu+OP50BrK680NvJAZSUvGU6kzT/PmMHZY8dyQnFxp+1Lx4zhPVOn8qOyMr6wbRvX\nz5/PQ1VV5AUBn5k9O6lzzyosJABG5XR+ZiwxDDuJygkpE57tYd4EHJ45saOpictfeomna2rYEqsE\nAnisuppfHDe8nmbX8JJDDvNiH8NJfPg1wBjGAPAqXkUeeb3e4J/OdE7hlKwLYbaznZCQiUxkHOP6\nP+BI9VQ5EYbwq1/BihUQa8WYQw6Tmcxe9lJGWVqGpSvz4pUTVVTRTju5DL85TyOd4YRGvqH85vd7\n34OLL+5/v09+Ev74x6FZw4UXwi239L/fn/8MH/3okV1r27YjOz4N8vPzede73pX0/ldeeWUinAB4\n9NFHDSckSVlldG4ub540CYCfxv7hvd62TmlzYkkJJ/by0MN18+bxs/Jy/nLoEDtjN2DfPXUqc5Ko\neAD4/qJFLBszhoKu4UTsXMlUTkjp1hqJsKauDuihciL20FRjJMLd5eWJ7XMLC9nR3MyfKytpjUTI\nz7GJg9TRMRzDl/lyoloCYGXsozcTmcgXyL7WxM00M5vZ6QtN4uFEVRU0NUFRETz5JFxzDcyaBY88\nkniodCpTE+HEcFZPPXdzNwtZyNmc7Q33PoxlLEtZyjjG0UJLpyBQ2cFwQtKIsnz5ciZMmJD0/itX\nruz0+umnn07xiiRJSp1lsSrG9fX1hGHYaa6S0m/+qFFcNX06t+zdy6bGRgLgs0lWTQC9hh5WTiib\nra+vpzkMGZeX120uSmleHtfPn88zNTWcUlLCq0tLObWkhLF5eUx94gkOtLbyVE0NZ49Lw9PU0jCz\nnOWZXkJKnMiJ3MzNRIgM7YXq6uBnP4O5c2HUKGhsjLZ2mjcPNmyI7rN7Nzz4IFx0EQDTiAYZ+0lu\neHa2WsUq7ud+AH7Nr1nCEi7lUiYxKcMryz655PJVvprpZagPhhMa+davH7pzx8qW+/Wtb8FNNw3N\nGnKTTMgvuGBofy2yxPHHHz+g/efMmUNpaSk1sb65O3bs8GaPJClrHTt6NAFwqK2N8tZWpib7vYiG\nzBfmzuWO/ftpikT4h8mTOXbMmCM6X9hhAHoyA7GldHsm1tLplJKSHr9n/sLcuT0ed/748dxdXs6f\nKysNJ6SjQM5Qj7ndvh2+9jWYNCk6d2L79sPhRMeuDz/8YSKcmM50APaxb2jXNsR2szvx812xj5d4\niZu52SoKDTuGExr5smFOQjIzIYZaXl5y8zGGuYkTJw74mAkTJiTCiUgkQk1NDWPHjk310iRJOmKj\ncnNZUFTElqYm1tfX9xhOPFFdzc6mJl5dWuqT92kwo7CQGxcs4Na9e/nKvHlHfL4gCDh01lnsaW7u\nNIRbyhbPdggnBuKNEyZwd3k5f6qs5PoFC4ZiaRoh/s7f+SW/5EZuJMCHxtSLnTujP86ZE31wdPv2\nw3MnOoYTzz8Pq1fDihWJyonh3tYpPnD8PbyHOup4nud5D+8xmNCwZKNHSSPK6EGEUWO6POFYF+uh\nK0lSNjou9vdWb0Ox79i3j8s2bOCu/cO7ZcFw8rFZs1j/6lcfcdVEXG4QMKeoiFwrOY8KkTCkrq0t\n08tI2mDDiTeMjw4lfa6ujvJYdZDUkwd4gAoq2MnOTtsjRKillhaOrq+fNtp4iqeopjrTS8kuHcOJ\nqbHh1vHvfbZujf64ZEn0xzvuAA63ddoX7iWMtKdrpSkXDycWs5gruIJv821O5uQMr0oaHMMJSSNK\nwyAGhNb/f/buOzyO8trj+Hd2Ja16b5aL3GUbY+MGGGxTE3ovoSVcCCROo4SQxiUkBEJI6DckoQVC\nTKghkNAhBIzB4N4LbnJTtbpk1d25f7wz27Rdu6t2Pn54djU7u/PKshZpfnPO8Tq5k56eHq3lCCGE\nEFE3zTgB7m8o9jZj+5SBUD0qhAjoyYoK8j/9lFvNE2kDXIfdzkbjZ+dww4lim42jjJ+z329oiPra\nxHTEPfoAACAASURBVNCxl7000dQrhPgZP+MbfIP1rO+nlfWP3ezmXu7l+3wfHb2/lzNwuIcT5lDs\n6mpoa3OFFL/8pbp9+2347DOKttaj6dCuddD07+fivuRosGOnHTWbKm5Dx4WIoaHf40UIMawcOnQo\n7OfU19c771ssFjIzM6O5JCGEECKqphvhxDo/lX7e4YTMUhJi4MpLTKShp4dPmwbHFdEb2tro1nUK\nEhMZE0HbsdNzc1nX2so79fVcaV7pLISbRhppogkNjTGM8XgsHRVutdDS63lb2MIUpsR+zkE/2Ioa\n7jyVqdLmyp17OGG0aaaqCvbuVfdzc+HYY+G44+Czz+DKK0kC7p2RQsHBbrKmpsF53+iXpfeFFStP\n8zTNNJNBeCGxEAPR0HvXFkIMa5s2bQpr/7179zrnTQCUlpbKCRwhhBAD2nFGiL6qpYXDds+WBIe6\nuqgz2sM8WlHB6OXLeS2C4F4IER/HGXPONrW10djd3c+rCW610dJpjp9h2MGcnpsLwHv19Th0uQJc\n9LYXdWK5mGJseAZgmaj//zXT7LF9N7u5jdu4iZvoZuB/H4VrM5sBmMa0fl5JlDz9NDz+OFT2cSi1\nv8oJsxLNnG3zox/BhAlqn4ICJjUXkX3zL9GefqZvx+9nmWRKWCWGBKmcEEIMKRs3bqS+vp5c4xef\nYD7++GOPj48++uhYLEsIIYSImrHJyYyy2TjQ2cnnzc2cbPRxB9jersr8x9hsdDkcHOjsZFNbGxcU\nFPTXcoUQARQlJTHBGHL/RUsLp4X4M2x/WW9UbM2KsA3q/MxM0q1Warq7WdfayuwwW0OJoa+ccgDG\nMrbXY+ZV4t7hxOu8DkAppSSSGNP1xdte9rKSlYD/cKKKKnawg6lMJZ/8eC4vMk88oYKJo4+GwkI1\nyLqzE6aFEb50d0OFmrvAmDGQYJzerKpyhRPjxqnbWbPggw+itnwhRHRJ5YQQYkjp7u7mhRdeCHn/\nv/71rx4fn3DCCdFekhBCCBGxtS0tnLB2LTfv3Oncpmkai4yrrZc2Nnrs797SyWz/tMnP4GwR3HXb\ntnHBpk3OE7JCxIJZPTEYWjttMN5PZkQ4/D3JYuHk7GwA/uv1/iUGvnWsYzWraSV274nmEOxSSns9\nZoYT7m2daqllGcsAOJ/zY7au/nI3dzvvj2e8z30e5VEe4AHWsCZey/JrP/tpI8DPHboOdXXqfn4+\nvPoqnHoq3H23/+f4UlEBdjvYbFBQ4BqIXVPTO5wY6FpbVUAjYqqBBrawhQZ6zzyqoIJ7uIc97OmH\nlQkJJ4QQQ85dd91FS0vvPqTe3n33XT788EPnxykpKVx++eWxXJoQQggRll3t7SxtamJls+dVoguN\nk5mfeJ3MdA8njjAHZ0s4EbH3Ghp47dAhOhyO/l6KGMKON76fPxvg4YRD153DsGdGWDkBMMN47g7j\n/UoMHi/xEndxl/NK/lgwKyd8hRO+2jotYxkOHExnOhOYELN19Zdaap33E/w0P5nEJAB2stPn4/Gi\no/NjfsxVXEUFFb53am6GLmPQeUEBTJmi7m/bpoKLULm3dLJY1GtpGvT0wKpV6rHBEE5s2QJHHgkX\nX9zfKxnyHuZhbuM2nyHe7dzOClbwKq/2w8qEhBNCiCGnsrKSyy67jO4AfXt37tzJN77hOfzq6quv\nJtu4kksIIYQYCCqMX+BLvAbPLjL+f7W8uZkutxPnviontre3e+wjQuPQdSqNv/+RSUn9vBoxlJlz\nZD5vbqYnxO/Vjxsb2Rbn4HFPRwetdjs2TWNSSkrErzPReO5Oow2dGBzs2J1XFT/CI9zFXTE5xgEO\nAKGHE+bJ+zLKor6egeAKrgDgIi7yu89ACScaaKCDDixYKMBPO0lzDlZGhqp6mDRJhQv19a7HQrF/\nv7odPVrdJiaqSgyAuXPh/PNdwccA9CEfso51MGqU2lBXBxLYxlQRqrqmmmqP7Z10chj1d38O58R9\nXULCCSHEEFNaqn6Ifeutt5g/fz4ff/wxutsVGG1tbTzxxBMcffTR1NTUOLcXFRXxm9/8Ju7rFUII\nIQI52NkJwEivcGJqair5iYm0OxzOAbXgGU6MttnIsFrp0XV2yEnAsNV0ddGj61iAYgknRAwdkZZG\nptVKm8PhrEwIZFNrKyeuW8fUlbG7et2XDUZ7syPS0kiwRH4q4czcXFbNmcM/pk+P1tJEHFRQQQcd\nzo/dr+iPlg46OJZjmchE54lEd77aOtVTD0AeeVFfz0BwIRdyL/dyOf4r/CcyEVDzKTrpjNfSeqlE\nDbguoMD/7A8zgMgzvl7JyTB2rLq/dWvoB9urBqczZoxrmzkU++yz4cEHXa87wFRRxf/xf/yKX9Ge\nmaiCGoADB/p3YUNcMerfh3c4sYY1dNBBAQXOoE/El4QTQogh5Rvf+AZf+cpXAFi9ejUnnngiBQUF\nzJkzhyOOOIKCggK+9a1v0dDg6jNos9lYsmQJOW4DRYUQQoiBwFk54XVyXNM0FphzJ4xWMJ0OB3uM\nEKIsNRVN0/hKTg5n5uZiD6dVggDggBEMFScl9elErBDBWDSN+Ub1RChzJ951+zm2O45VURui0NIJ\noCApiTkZGWQl+G5RIwamXewCIJlkAJqIfhuyNNK4mZv5Pb/H4uN0la/KCbN/fC4De5h8pKxYmcxk\nrFj97pNPPllk4cAR+575hw7Bs8/6fMgMJ0YwIvDzwVXlAFBmVL1s3x76OvYYn6d7OGHOnaiu7r3/\nAOL+73cd61zVH2Y1iIgJf5UTn/IpAMdxHBpa3NclJJwQQgwxFouFl19+mQULFji31dXVsWbNGrZs\n2UK715WjGRkZvPLKK5x66qnxXqoQQggRlL/KCcA5FPsTY6jsrvZ27ECG1coII8z4x/TpvDljhrPH\nuwjdQbOlk4+/eyGizZw7sSyEcMIMIW8aNYrEOAZn5mD4SIdhi8FtN2rI8CxmAap6wUF8WwaOYASP\nGX9MdajhyjkM3wvNNDRn9cQOdsTuQG1tcN55cMcd8MorvR6OOJxwnzsRqo0b1e20aa5tZuVEVVXo\nr/PQQ3DrrVAb/Uogf8wWQmCEfmY4EULlRBdd6MgFJ5HwFU500MEq1IySBSzw+TwRexJOCCGGnKys\nLD788EPuuece8t1/6HGTlJTEpZdeypYtWzj77LPjvEIhhBAiNBVGOOFdOQGuuRPLmpqw67pHSydN\nkyu/+qrD4SA/MZHREk6IODjR+H5+r6Eh6NyJVUYrt3lmK5A4Mds6Sdg5PJmVE2Y44cBBK61xXUMi\niRRSSApqbomOzhjGMJKR5OP7977hIi5zJ9LS4NJL1f1HHun1cNzCiZoaFUBYLODeHi6Myom3eItb\nuIU39DdU0BLHlkptqCq0Agq4iqtccydCqJx4mZe5nMt5mZdjucQhyQwnGml0tqhbzWo66aSIIiYw\noT+XN6xJHaUQYlA78cQTPWZKmBITE/npT3/Kj370I5YvX87GjRtpaGggMzOTUaNGcdJJJ8nwayGE\nEANau93OXiOcGOXjBPnMtDQyrFaa7HY2trZ6hBOi775WWMjXCgtxSEssEQfHZWWRn5jIoe5uPmlq\n4iQ/7Ua7HQ7WGSFBPMOJ1p4ednWokzlSOTH8OHA4KycmM5k00mijjSaanK2W+oOGxi/4Rb8dfyAx\nKydiPhT7619Hf+ghah1VtHVsZVzyVOdDfQ4ndu6Enh4I1vJt/Xp1O2mSCkxMxcWgaRDCnK0WWtjN\nbsZPN95H41U5UVFBW0EjJMI4xqltZjgRQkBykIN00okNuXAiXOmkO9+7aqhhDGOkpdMAIeGEEGJI\nS0hIYOHChSxcuLC/lyKEEEKE5Z36ejocDsbYbExISen1eILFwnGZmbzb0MCZGzc6i/wlnIicQ9ex\neFWdeH8sRCxYNY1z8vJ4uqqK1w4d8htO1HV3syAri10dHUz08b4QK+ag7pKkJPJlQPywYw7DTiKJ\nUYwiiyzaaPPonS/61yQmMY5xTGISDhw+Z3Z4+wt/wYqVq7k64H46OmtYw2xmo+Xk8NklJdz3u0Im\nH/4T9/KIc58qVDulgOFEnWrD5RyIDerkfFqaahu1Z48KHQIxw4kZMzy3n3OOajuV6GcYtxvzKvqq\nMca+8ZpT8ZOf0DZzN/yokDSMYCWMmRMVVABQQkmsVjikFVHEbnZTTTUFFLCa1QAcz/H9vLLhTdo6\nCSGEEEIIMQC9ZFzFd0lBgd82TVcVFWEBKru6qDJmJPR1WO1wVdnZSfayZXxl/XoZIC76xfnGlcSv\nHTrkszIYoNhm44OjjmL3McfEtX2bOQxbWjoNTzo6x3Ec85iHFavPwdSif2WSyQM8wHf4TkjBhAMH\noxjFa7wWtNqillru4i5+za/RNRjfWgjAHlsF3XQD0EoriSRiwUIhhf5fzFflhMUCkyer+6EMxd6w\nQd3OnOm53WYLKZgAV4BSWWy819bUhPS8PunshJUrSW7qpKQr39WKLMSZEw4czuqUkYyM5UqHrBGM\noIACuugiiSR+yk85j/MYz/j+XtqwJpUTQgghhBBCDDDtdjv/Nn6Bv7TQ/y/5VxUXc0ZeHlvb2th6\n+DAacEZubpxWObSsaGmhxW6nsrMTq1RLiH7wlZwcUi0W9nV2sq61lVkB2jbFe67MBhmGPayNZjS3\ncqvz4yzUAPcmgg9wFwOTBQtb2ALAS7zEz/m57x2ffJJd9mXwbfX11tAoThlHeuMOWrNhH/uYwAQy\nyOBZnqWNNhIJEBD4CidAtXZauxa2boVAMyF13RVOeFdOhKEYNTy7LquHTpuGLR7hxMqV0NnJGe9n\nc8adj4PZRshs69TcrP7L9N0qrZZauugigYTAAZDw6xZu8WjfNMv4AyqEPcxheuhxvseJ+JDKCSGE\nEEIIIQaYt+vraXM4KLXZgvaVz0tMZEF2NteXlHBdSYm0IYrQF83qCuBj/JwUECLWUqxWTjPCxdfM\nE3gDxHojnJDKrOHhPd7jBV7gIAd9Pn4Jl3AXd3Esx8Z5ZSKaLuESLFhYyUrnwPNeli5lV8+XAM6r\ny7Vx45m0Xs252sEOj92drYr88RdOlJWp22BDsffuhaYmSEpyPScCmWSSimqDWTMqKT6VE8uWqdsF\nC9RsDFNqqqvNVYDWTmtZC6ivgxVrrFY5pAWaK/EGb3AVV/EUT8VxRQIknBBCCCGEEGLAecn4Jfni\nAC2dRHRJOCEGArO10+sDKJzQdd3V1inKlRN2XafH4Yjqa4q+e4d3eJEXe514Nk1gAkdwBNlkR+V4\nnXTyMi+zhz3oSFu9eBnJSBawAFDVE73oOmzaxK4j1Xwbc+g248Yx0U84EVBbm2tYta/KCQje1sms\nmpg2TQUUEdLQyEHN9mksSIzPzAkznDjex3wDs3oiQDixghUAHM3R0V6ZAOf7WT31/byS4UfCCSGE\nEEIIIQaQw3Y7bxgDIwO1dBLRY9d1Vra0ABJOiP51Vl4eVmB9Wxt7zJN4/WxnezstdjtJmkZZamrU\nXvf8jRtJWbqU9xoaovaaou/qqGMPe9DQnO1OYm0DG/g7f+ce7onL8YSLWT2xghXsZrfngwcPojc0\nsPNI9X0/gQlq+7hxfisnAjKHYdtsagC2OzOcOHgQGhv9v4a/YdgRMFv3NOclxL5yYsMG2LwZEhJU\n5YS3IHMn2mhjIxsBpGIpRnJRlYsSTsSfhBNCCCGEEELEyWu1tfxw504WrFnDORs30u3jiuFwWjqF\noqari5dqang5Hi0LBpEP6uup7OwEYNvhw7TY7aRaLBwRxZOvQoQrLzGRRdnq6s1g1RMOXaemqyvm\nlQe/3rsXgAVZWSRaoncKwappdOs6OwdICCOU1awGYBKT4tZ33f2K8EBtVwDWs57buI3HeCweSxvy\nRjHKWT3xJm96Prh5M7Ujk2jNSSCBBEopVdvHjWPSBhVOHNAPcJjDoR3MvaWTd1VoVpareuDMM+H5\n56G7u/dr+BuGHQHn7JS8BBWc+DpetDz+uLo9+2woKPB4qJtuds3NoSE/wW/lxAEOkEwyI40/Ivrc\nwwmp4IovCSeEEEIIIYSIk7fr63nwwAE+bW7mjbo6thzu/Qv9P2prAbiksDAqLZ02trXxtS1b+EV5\neZ9fa6hY1tjIVzZs4KR16+h2OJwtneZmZJAQxZOvQkTiTGPuxH8DXT0MFHz6KUWffcaujo6YreXz\npib+ZrQ7+e348VF97YkpqlXMDgknBhQznJjL3Lgcz46dlawEQmtX00EHW9jCbnZTQQXtyL+fvjKv\nxO81Y2TjRmdLpzGMcQ26TkkhO6mQggNd6Jruf16FN3/zJkz33APFxVBZCT//OVx2Gdjtnvt89atw\n6qkwq+9VPWdzNj9x/Ji5H7V5ri/a9u2Dt99W96+/vtfDv+N3/OjqjSw/I9tv5UQZZTzN09zO7bFZ\no3CGE510hh64iaiQn7yFEEIIIYSIk0NeV+WtNloJuVtrDJ79Sk5OVI5ZarMBsLejA12XK8EAljY1\nAbC9vZ2nKitZIfMmxABiVk4sa2rCEeB7tiBRnSisMCqAos2h69ywcycA1xQXMy/K3x+TjHBCKicG\njm662YC6Mn0Oc+JyzB3soIkmUkllGtOC7p+J+ndYTz3f43tcwRU00RTrZQ563XSzjW0+wxzzpGwd\ndZ4PbNzIrumqmtAchu00bhxla9sY3ZRFJyG+BwULJxYsgI8+gttvV22f1qyBDz/03Of66+GJJ2Dc\nuNCOGcA0pnGsZT6F3cbslFjNnXjqKXA4YOFCNSvDyzjU57JzRmrAmRMJJFBEUWzWKLBhcw50l9ZO\n8SXhhBBCCCGEEHFihhNjjMBglVc40eNwsMs4UTfZOHHXV6OTk9GAdoejVzgyXLmHQr8sL+djI6yQ\ncEIMBLPS00m1WKjv6WGLMYjal5HG+0hFV1dM1vFsVRUrW1rIsFr5TRROBHqbKOHEgLOZzXTQQS65\nzhOmsWa2dJrDHBJICLq/GU4cQp3oTiCBDPreAnGoaKGFDWxgC1s8tu9jHz/jZyxmca+WNSMYwRVc\nwZVc6dpYWwvLl9OUn4Cma65h2KZx47j5xr088vgRoVfZBAsnQM2juPZauNJYy9/+Ftpr94U53ysW\n7S8bGuDll9X9b33L5y6TmATAzhkpqnJCLiTpN36DOhFTEk4IIYQQQggRJ3VGOHC60bbFO5zY19lJ\nt65j0zRGJydH5Zg2i4URSUkAlMew/ctgssaoTkm2WKju7mab0V7rmCjM+BCirxItFuYbQdknRnD2\nr0OH+HV5OVvdwooSI5w4GIPKCV3X+d89ewC4vbSUYuNY0WSGE3va233O3xHxt4pVAMxmdtDZD9HQ\nQgv/5J8AzGNeSM/xDiJyyMEip7acVrCCO7iDl3jJY3s55QCUUtrra5tJJpdwCSdyomvjSy9Bdzff\nezGf57TnWMQizwONH49FB3Z7DdEOxAwn8vKC73vVVWouxSefhHeMSPgLJ3RdhQsbNqjKh0gsWQLt\n7api4vjjceCgC89A2Qx+DkxMpt3S6RocLuJOhmL3D3kHF0IIIYQQIk7MyoXTjHBifWsrXW6/8Jq9\n1yekpGCNwrwJU6kRdOwNEE4Ml5ZP9d3dzpDm4YmuK0FLkpIYFaVASIi+Wmi0djLDiacqK/lFeTmv\nuvVEH2mEjmZbp02trVy8aRO7o1CJ0Gy3c9CoyPhOSUmfX8+XEpuNr+Tk8D/FxRyWcKLf6ehxnzdx\nJ3c6789mdkjPSSPNI4wwTyYKpQT1/VpBhcd2M5wYy9jgL2K3w9//ru5fdRUpxh8PZjWVEWKGJJTK\nCdPo0XDSSer+c8+FfoxIFBmtkrzDCbsd5s2D884LXlXR3Q1//jNs8axY4f331e0114CmUUklX+Nr\nXM3Vzl1yyCGPPHSLxu4jUgK2dhKxlYcKzhoJPPNJRJeEE0IIIYQQQsSBruvOcGJeRgbZCQkUJiV5\nXPX8pXEF/+TU1Kgee6wZTgS4wvq56mpGfPYZN+7YEdVjDzRrjGqVCcnJXDdiBHONaglp6SQGkoVZ\nWQAsbWykw27ng4YGAM7KdZ2IdVZOGCHCHeXl/OPQIR72M1A1HGaVV4rFQnpC8FY7kbBoGu/NnMmT\nU6aQFaNjiPD8lJ9yFVcxgxlB9/0H/+BBHmQPYZyc9vJdvkseeZzGac5e78FYsZJOuvNjCSc8meHE\nIQ55zIJwhhPLq+CPfwz8Ih9+CBUVkJMDZ53lex8znCgvD72qwAwnCgpC2//rX4ekpNi3OTLX4z1z\nIiEBRo5U9/ftC/wazz0H996r5mWYenpg+3Z1f64K/MxBy0kkeTzdrJ7YOSMVHn88Ni2mRFBXczXP\n8RwXcmF/L2VYkXBCCCGEEEKIOGjq6cFu3M9PTGTPMcewf/58xrnNljArJyZFad6EyaycCNTWaWd7\nO1VdXUP+CmazpdPsjAwsmsaTZWWckp3ND0eN6ueVCeFybGYmCZrGwa4unqmq4rDDwcikJGamu07K\nOmdOdHZi13U+bFRXem4OMKciVO0OB6NsNkbHoJ2TGJg0NEop5SIu6n2VvA+rWMVSlva6Qt+XbWzj\ncR5nGcs8to9jHE/wBItZHNZa3Vs7STjhKZNM0khDR6eKKkBVxexlLwCldy+B3/8e9u71/yJLlqjb\nSy9VMyB8GTkSEhOhs1MFGaGYNAmOPNJ1wj+YRYtg+XL4xS9C2z9SZuVEba26feUVV5AyZoy6DVTN\noOvw4ovq/qZNqooCVDuqri413Nt4nTbU+7N3GOeaO5EK77wDr77at89JRCSTTFKJ7gVCIji5PEEI\nIYQQQog4qOvpASDNYiHFaiXFau21j1k5EfVwwji5EKit0y7jsQlDvLWROQx7jlExMTM9nQ+OOqo/\nlyREL6lWK3MzMvi8uZlfGScRz8rLQ3Nr91ZitnXq6mJ1SwuNxnvMZuN9pC+OSEtj//z5fX4dMXRl\noap7mmkOuu82tvE2b9NCCwtY4PFYJLMtMsnkIAcBCSe8aWiUUMIOdlBBBaWUUk89LbRg0S2M3mH8\nHOCvtVJ5OSxdquY9XHml731AVRWUlsLOnaq1UygB/913h/fJWCyQG4evrzlzYs8euOMOePZZSE2F\nVatc4USgyomNG2HbNnW/qwt27FAzJrZuVdvKytTngv9wwqyc2DEzDebMcVWmCDEMSOWEEEIIIYQQ\ncWC2dMpLTPS7j1k5Eau2ToEqJ3a5zbsYypyVE25XoAsxEJmtnaqMtk1neQ2RLXGrnHjfaPtk7m+2\nZQJY3tTEu/Uy3FNEVzjhRCWVAIxgRFSOfQEXOO9LONHbSFRlglnVYrZ0GtmeS1KXDmPHqqv5fXn5\nZXV70klq7kMgkcydGIjMyonychVMAFx2GaSkuP4OAlWavOQ5fJyNG9WtGU5Mnep8KFA4kUIK+aNn\n0f3K83DaaQCsZS011KAzPOaCieFJwgkhhBBCCCHiwAwn8v2EE10OhzM8iFVbp4CVE17hRI/DwUcN\nDbzi1vf4mcpKsj75hKWNg3NQYFNPDzuNz3N2RkaQvYXoX2Y4AWDTNE7JyfF4fIRROdGl67zg1Z/c\nbO3U6XBw8vr1nL5hg8d8GzE8ddPNDnZE5URnJmpOTyiDY82T5NEKJ+Yxj3GoE+MSTvTmPRTbbOk0\nts74/15xsf8nb96sbk85JfiBzHBi9+6I1jkQvMu7PDnhI/YeYfzdHH88/O1v8L//qz4O1tapvR3+\n9S91f4Yxq2XTJnUbRjiRRhpLWMJd2l0kon5O7KSTu7mbb/NtaqmN/JMUYoCTcEIIIYQQQog4CBZO\n7G5vx4Fq+2SedIyWMUY40Wy30+h2RbWppaeHGmO7GU6839DASevXc+POnTh0nU6Hg5/t2UOz3c4/\nzV7Mg8xao6VTqc0WsIJFiIHgeLdw4qScHNK8WsElWSwUGP+ONxlhRJnx/WuGE6tbWugw5siURPl9\nRQw+r/M6P+bHPMVTfX6t/qycALif+1nCEqYyNfjOw4wZTpitr3R0ssii9KDR2X1EgK9Debm6HT8+\n+IGmTFG3n38e4Ur73yd8wpu299n32M/h7bfVvI0FC1RbKwje1umtt6ClRVVYfPObalsElRMAFq9T\ntLvZjR072WRTQIhDxIUYhCScEEIIIYQQIg7qgoQTzmHYqakefeWjIc1qdZ7E9NXaqdVu59y8PI7P\nzCQrQZ28OCk7m3SrlYquLla2tPBCTY2zvcyOKPS07w9mS6c5UjUhBoHcxESONFqvnOWn7/pIt2G1\nY2w2zjP6yJvhxGdNTQCcn58f9fcVMfiYIcGbvMkh+hYyhxpOdNJJHXWA66R5NGhopJHmvMpcuIxk\nJMUUU4iapXARF/EMz3D+O8b7hY9woosu3rG/yd8v7kYH2sYX00VX4AOdfLIair19O+zaFeXPIj7M\nCqDmkRmusMWdGU4cOgS+fvYxWzpdcoka9g0qlKiuVgO2NU3NnDCY4UQoQ5e/5EsAyiiLaDaL6JtG\nGvmCL1jL2v5eypAn4YQQQgghhBBxEGzmhDOciNHMB2drJx+tXUbYbLx+5JEsmz3buS3ZauVM44To\nP2tredCtpYHZGmmwMYdhS0snMVg8OmkSt4waxXV+rnR2r4Y4NSeHI4www6yk+KxZnTg+LjMzxisV\ng8H3+T7TmAbAEpb06bXMk7pNNAXcr4oqQF0pnoG898bDWMbyJ/7ED/mhx3brQfW18BVOaGg8Zn2S\nl79fSEtJOq8UfMzlXM6LvOj/QFlZamj2zTeDr/eYp56CM86AZ57pw2cTW2bI5vffcWam+jyhd2un\n2lpYsUIFEBdfrAaEZ2Soodhmq6fSUo/5HodRAYevyglv29kOwGQmh/EZiWi5lVv5Lb/lTu7kPd7r\n7+UMaRJOCCGEEEIIEQfB2jp9aVyRNzlG4cTYEOZOeLvAuAr70YoK1re1YTaV2d3RgV2P/XDGl2pq\nmPLFF2wxTrSGotNoYWPe73L7WIZhi8FmYXY2902cSLJXSyeTe+XEV3JymG6cBNt8+DC6rjsrdOuD\nogAAIABJREFUJ45zaxElhi8NjWu4BoCP+Zi/8Bc+4IOIZlAEPalrcG/pJFd/97NK9bXwFU4kkkh2\np7qav27WGPZr+3HgcH6d/brjDrjhBijw0XZo2zb1n3FhwEAU0r9js3rCeyh2lRH2FBWpv1OLBebN\ngzlzYK1xtb1XNcZ1XMcf+AMncmLQtZmVExJO9I+pTHVWHz3O42xgQz+vaOiScEIIIYQQQog4CKet\nUyyUGicxfbV18ufMvDySNI1Wux2A60aMwKZpdOs6+8J4nUh9bcsWtre380ZdXUj7O3Sd0cuXM3Pl\nSo5auZK0pUv5T0MDoOZqbDcCIKmcEEOFe+XEyTk5TElNRUOFoV80N1Pd3U2SpjFngAZyuq7zUk0N\nfzhwgHbjfUbE1kQmcgInAPBv/s2jPMpKVob9OnnkcQ7ncD7nB9wvFvMmRISq/FdOAOQ1q59PDk0r\n4gAHABjFqMiPt2ePujUHZw9AvsKJbro9W/mMHq1uvSsnzPlbeXmubU89Ba+8AmZwPG2ax1MyyGAk\nI4OGPoc4RB11WHSNiQeSQ/+ERNTcyI38mT+ziEXYsXMv9zq/L0R0STghhBBCCCFEHPhr63Soq4t/\n1NbyYWMjEPu2TnvCCBUyExI4JSfH+fHNo0cz3lhfsNZOXQ4HfzhwgAMRhhgddjtJRo/8C40KjmC2\nHz5MbXc3O9rbGZeSgh3YYgQSy5ub0VEVJEUyGFgMEaON7+uj0tMpTEoi1WplvLGtoquLp8rK+MXY\nsX4rL/qbpmlcs20bP9i5k8quIP3tRdRcyZUkod4HZzGLucwN+zUyyOBaruUCLgi4XxllnM/5zGNe\nRGsVUdLZCWbQX+J79kfuIVVpWDklixpqgKEfTjhnThizU7roYjGLuZM7Kadc7VRaqm69h2Kbf5/u\n4YTp+uvhrrvglFMiWpdZNVG6pY3k790S0WuIvrFiRUPje3yPKUzhMId5gAf6e1lDkoQTQgghhBBC\nxIG/tk7P19Rw8ebNzo9j1dZpmtHuZaPR2ihUVxSqkvbz8/MpS01lYojhxJOVlfxg505+FOGQzDWt\nrXTpOgWJiUwI8e/kU6OFzdEZGcw0Pl+zJdRSI/xZJO1txBByaUEBVxcV8eCECc5t5tyJg52dXDti\nBLeZJ9ZCpOs6W9vaqOnqQo9D+7ZCIyyslnAibgooYDGLWcACbuRGLDE8NTSNaVzN1SxkYcyOIUJg\ntnRKTnbNUPCSd0D9f33D1C50dNJJD97WyZ+mJqivV/fHjo3sNeLAu3IiiSTKUAOsP+ADtVOwyglf\nF1BMm6bmcXhVToTK2dJp7WEV8kTyXrxnD6xcCW7tLUX4kkjiZm4GYA97sCNVftEm4YQQQgghhBBx\n4C+cWOh2kiDDavU7MLuvZhltXXZ1dNBorCUUVxYV8Z+ZM3nW6JtshhM7goQTa4we019E2Gt6udsg\nX00LrU/5MiOcOD4ri6nGCdqtRuXEUuOxRdnZEa1HiIEoIyGBZ6ZO5US3CicznNgcxqwWd212O9NW\nrqTos884HIeTWoXGe15NGO9Lou9O4iRu4ZbITz6LwcW9pdOvfw2XXgobN3rskt+igsL1RdWAqpqI\neE6IWTVRWOgxEHqgMf/9m5UTAKdyKgAf8RGddLpmToRTOdFH6aRj022M39yuZnaYQUio7Hb42tfU\n1/m00+CFF1T1jIhIHq6vcQsDd4bKYCXhhBBCCCGEEDGm67rfmRNHuvWCb3c4Qj4RH67cxETnUOx1\nYVRPaJrGyTk5ZCQkAK62U8EqJzYboUB5RwcNEZx0DGWQr13XuW/fPt4yThB8agQax2dlMc2Y3bGl\nrY0Ou50vjMekckIMdefm5XH/hAlc46evfDBmkGrTNFItsT9lYLZZq5HKiSGhkUb+xJ/Yzvb+Xsrw\ns2cPHH+8mnvgraJC3ZaUqFBi5cpeJ9vzLrwegB6LujK8Ty2dysvV7QBu6QSQSy4nciKncIpzMPxM\nZlJAAW208Tmfu8KJ/fs9qxB8zZyIkhnM4HztfE5eblSO7t4d3guUl0Ntrbq/cyf87Gdw7bVRXeNw\nYsXKeMYzkYn00NPfyxlyJJwQQgghhBAixpp6epxF4HnGSX6T1S2MsMe4hYpZPbEmzNZO7kJp66Tr\nurOdEoQXhpjP/8wIE+ZnZvrdb2ljI7fu3s1ZGzdy/saNzjXNz8xkcmoqFqDJbuf1ujq6dJ3ipCTn\n+oUYqo7NyuKHo0dzjI/vnc+bmrhhxw7+cMD/UM+6HnXiJT8xMWZhqTupnBhanuRJ3uM9fs2vOcjB\n/l7O8PL++yqEuO++3lfam22dRoyAoiJ1v6bGYxf3q8Nh6M+bAEgjjRu5kf/hf5xVIlasnIKaFfE+\n76u/M6sVurqgutr15LIyFQa5tdUztRP4Ao5gJjOZy7iMhNLxakO44cS2bep2yhQVTAB8/jlEWFEn\n4H7u5/f8nnxCm4MmQifhhBBCCCGEEDFmXomcZrH4HEz7otGT+IUIexOHarYZTkTYaglc4cSu9nZn\nmLK3o4NHDhzgzwfViagDnZ002109edeGGU7s7eigqquLBE1jbkaG3/12uw3bft2onpielkZOYiI2\ni4XLCwv5TkkJH7nNm4jHyVYhBqrt7e3838GD/DNAixDz/SpWLea8ycyJ+Kinnr3sjWlLkm1s41M+\nBaCNNp7kyZgdS/hgVkJ0dMDTT3s+ZoYTxcWucML9RDtQRBFzmOP8eDiEE/6cwilYsLCZzRxMqIaR\nI9UD7tUmixfDkiVw6qkez13KUq7kSj7kw74vZHyE4cR2o3Jpxgz41rfU19zhgC1b+r4mIaJMwgkh\nhBBCCCFizP1KZF8uLSyke9EiLjWGT8fKbONEv3tY8F59Pc9VV1MepE2TaUxyMomaRpeuc8DoX7z9\n8GFu3LmTh4yrsb173YcbTphVE7PT00nxEeaY9hvhxEnZ2c4B2F9x672/ZNo0/jh5MruNz22htHQS\nw9ykEGbG+JuPEyvS1ik+/sN/uImbeJZnY/L6Dhz8hb8AcBzHsZCF3MRNMTmW8GPvXtf9v/0Nml1z\nFDwqJ8yfNbzCiUIK+V/+l1d5lSd4gulMD38NXV3Q0OBq6zSAh2EHkk8+s5gFqNkTfodi+/AgD6Kj\n83/8X98XEmk44V45ATDd+Fpu2tT3NQkRZRJOCCGEEEIIEWOhXImcEIfe7mblxLbDh2kzKhv+cPAg\nV23dylv19SG9hlXTGG/MrjDbKE03goEd7e202+3OeRPZRgurtWFWaoQybwJwhiMnZmezcs4c/jtz\nJnf5uErzznHj+M24cZyemxvWOoQYasxwYn9nJ+1u1U3u/M3HiRVp6xQfTaj31Uz8t8rri2UsYwc7\nSCaZ67iOH/JDGbYdb+ZV/SkpaojyX//qeswMJ0pK/LZ1Mmlo5JOPDVt4x//732HWLPj9712VE+bJ\n9UHoOI4DYCMbXZUT5uyOCOxgB7/iVzzDM6E/KdJw4r774KWX4PTT1cdHHqluvYagCzEQSDghhBBC\nCCFEjMX7SmR/im02RiQl4QA2tLbSbrezzAgCzAHSoTBbO923fz+/2buXuu5uchMScKCCD7Ny4tKC\nAjC2+TsR6otZOXFcgHkT4AonRttsJFosnJiTQ6qPSotjMjP5WWkpE8P4HIUYivITE8kyvkd2+ame\nmJuRwc/GjOHc/Pj01Za2TtHzD/7BYzyGnd7vt82o99VYBAZf8iUP8iAAF3EROeQEeYaIuu5uMFor\ncpNRsfL0064ZAyG0deqz4mI4fBjeegtaW8FicVUcDHA6Og/zMC/yIm2ov7PpTGchCzmVU13hxMHQ\n56hMYYrHx7XUso51fMmXoS/MDCf271dVKaHKzIR581SlDEjlhBjQEoLvIoQQQgghhOiLgRJOgBqK\nXVlfz5rWVna0t9PQ00OpzcbC7OyQX2NSairU1/OO8V+ipnFkWhofNzWxsa3NGU6cmpPDa4cOUdPd\nzca2No4OEjYAtPb0sMFoA2UOw36iooJVLS38ZMwYxrsNtN5vhBOjbGFe3SnEMKVpGhNTUljd2srO\n9namG9VU7o7LygpatRRNUjkRHQ4cLGEJAOMYx1f5qsfjZjgRi8qJBBLQ0CiggHM4J+qvL0JQUQF2\nO9hscM01ajj2KaeogKC9HYzZS5SUgPmziJ/KiYgdeywkJYFx0QMjR6r1DAINNPARH2HBwgVcAKg2\nVz/kh2qHkcbnFEI4kU46rbSymMUe283QI4200BdWXKz+Djs7VcBUWhr6c92ZlRO7dqkASS7WEAOI\nVE4IIYQQQggRY/FukxKIc+5ESwt/NtoTfKukBGsYg6InugUEoEKEI42TnBtaW9litHU6Ii2NWcb2\nUOdOrGxpwY6qhhhltI96qrKSxysrew3ydq+cEEKEZpJxUirQ3Il4MmdO1HV30+Nw9PNqBi8LFi7j\nMgDe5V10dI/HzbZOsaicGM947uVe7uGe8FsBiegw502MGaPCh5deUgObU1JcVRNpaZCR4aqcaGlx\nVVZEQ2qqulrfNIiGYZdTDkAJJSSR1HuHECsnOumkFfXzTj6e1WeHUT8bhRVOaBrk5an7Ibbf9Kmw\nUIZiiwFLwgkhhBBCCCFiLJSZE/Fizp14va6O5c3NJGga1xYXh/Ua7uFEgqYxJyPDOXfi3YYGWu12\nEjWNSSkpzHILQ0KxxggxjnGrsphmvLYZegA09/TQbLSKGinhhBAhC2UodjzlJSaiATqu90oRmTM4\nAxs2drOb9az3eCyWlRMAk5hELjLXp9+Y8ybGjFG37hccuA/D1jRIT1dBBUS/emLhQtf9QRhOjGWs\n7x3McKKlBXTd9z5APSpAsGEjFc/qBLNywnt7UOa8rIaG8J7nzWztJHMnxAAj4YQQQgghhBAxNpDa\nOpmVE+aaLsjPpzjMk/uT3MKJWenppFitXJifz+Z587jbOBkxOSWFRIsl7MqJSqMaotRtTeY8jC1u\nV3iaVRNZVisZCdKtVohQeYcTuq6zr6MDPcAJt1iyaprzvVFaO/VNJpmqPz7wGq85t+voMZ05IQYA\ns3LCV9sf93kTpsJCdVtdrYZXf/45HDrU93WccILr/iAJJ9po4w3eAIKEE+vXw6pVnsGPlzzy+C2/\n5VZuRcNzv4jaOgHkGDNc+hpOyFDsPuum2/leKqJHwgkhhBBCCCFibCC1dRpjs5HjdjJ/cUlJRK+R\nYPxybs6FKEhKYlpaGl+6tXQCnOHEhra2kFq2VBkDH4uTXG0VfFVOOFs6Ga2fhBChcYYTxvfT7Xv2\nUPr55/w92ldQh8Fs7VQjQ7H77FzOxYKF9axnF7sA1U6mhx4gdpUTop95V064mzABvvlNOO001zYz\nqKiuhl/8Ai6/HP74x76vo6zM9dqTJvX99eKgnHIaUCf+/YYTFosaMh1EEkmUUcYc5vR6LOJwItqV\nEzIUOyK72c2lXOqaQyKiRi4xEkIIIYQQIsYGUlsnTdOYnZ7OfxobmZySwklhDMI2JVgsTEhOZnt7\nuzOcMF1YUEBhUhIlxsnGCSkpZFittNjtbG9vd4YW/pjhRJF7OGFUTmw/fJgeh4MEi8UZTsgwbCHC\nY86cONjVxfbDh7lv/34A/lFby5VmL/o4M4diV0vlRJ8VUsgCFrCUpfyTf/IjfkQbbaSSio7uu5++\nGPwCVU7MmqX+c2dWTrz7LixbBgkJcO21fV+HpsEjj8DatTB/ft9fLw7cw4JSQhw4/cUXKiyYPh1G\njQrpKQOmckKGYkckHXWxTQuhtSkVoZNwQggxJO3bt49Vq1ZRW1tLXV0dSUlJ5ObmUlZWxlFHHUVa\nkBMjQgghRDTVmuHEAGk/dHZeHv9pbOTHY8aghTEI2939Eyfyfn09FxYUeGwfn5LCeLe2TxZNY2Z6\nOsuamljT0hI0nDBPTrpXToxJTibVYsGu6xzs6qI0OZn9Ek4IEZHchASyExJo7Onh2m3b6DTaOS1r\nakLX9YjfE/qiUConouoczmEpS1nJSuzYKaSQ53jOWT0hhhhdd1VO+AonfDGDyLffVrcXXBDySfag\n5s3zHIw9wI1gBDnkkEEGeeSF9qRnnoF33oE774Svfz2kp/Q5nOjLQGxQgVRhoZozsmULzJ3bt9cb\nZsxwoosuOunEhvz8GS0D47cjIYSIgtbWVh555BGeeeYZduzY4Xc/m83GokWL+OY3v8mFF15I4gC4\nilUIIcTQ1drT4wwnSgdIC6IbRo3iksLCPg2SPisvj7PyQvslflpqKsuamtgZwgBeX22dLJrGmrlz\nGZ+cTKJFdaZ1tnWScEKIsGjGsPqVLS181qx6Z1tRIer2w4eZ0g8X8RTKzImoGsc4kknGgoU66ihE\nXSWfIKeAhqbaWmhvV62HzMHNwbhXSVkssHhxbNY2CNiw8Uf+iBVrrzkRfpnzOUL8OQjgbM5mHvP8\nt47yJ5zKieZmNezcavX9+PTp8OGHau6Ev3DiwQehrQ1uuy3gfI3hJoUUrFixY6eFFgknokhmTggh\nhoTXX3+dcePGcdtttwUMJgA6Ozt5//33ueyyy/j000/jtEIhhBDD1ZfGCfmCxERyBkggbtG0PgUT\n4TKPVRHkquhuh8PZAsu9rRNAWWqqM5gApK2TEH3gPtT+rNxcFmSpIcmfNDX1y3pk5kR0WbHyKI+y\nhCXOYEIMYWZLp5ISSAqxbVeh27+Ls8+G8eOjv65BJJlkEvH/M9rHfMz93M8mjHkNdXXq1k840U47\n5cYf03EcxwVcQAlhzvoKp3Lil79UAcTzz/t+fMYMdbthg+/HDxxQbbmeegqCnFcZbjQ0MsgApLVT\ntEk4IYQY9B544AEuvPBCDplXLxg0TWP06NHMmTOHo446ipIIBn4KIYQQfWUOiJ7sdjJwuHGGE0ag\n4I9ZYWIl+HyO/R0dgFROCBEJ93Di7vHjWWjMnnm8spKf797Ns1VVcV2Pc+aEhBNRk0tu6FeBi8Et\n3JZO4Fk58d3vRnc9Q9A61rGMZTzCI2p4thlO5Of73H81q7mZm3mCJ/p+8HAGYu/cCR0d/is6Zs9W\nt8uXq3Zg3pYudd1fsSK8dQ4DMnciNiScEEIMaq+99hq33HILDofDua2oqIhHHnmEiooK5+yJtWvX\ncvDgQaqrq/n73//Oueeei8Uib4FCCCFiz6ycmDyMBw+aw7EPBgknzJZOBUlJWIO0EpDKCSEid7Jx\nJe43i4uZmZ7OIqNyYlVLC/fs28dfKivjuh7nzAlp6xSxB3iAb/EtPkUqw4cds3JizJjQnzNzJnz1\nq3DzzVBWFpt1DSHXcA0jGEEttfzGcRcd3a3qAT8hQDYq8G2kse8HD6etU0WFuvU3P+Too+E3v4FX\nX/Xdssk9nFi5Mrx1DgNm5UQrrf28kqFFGg4KIQat6upqrrnmGo9tCxcu5F//+hfZxtVf3goLC7n8\n8su5/PLL+fLLL0kdxieKhBBCxIdUToTe1qnax7wJX1p6emiy2wEJJ4SIxKLsbA7Mn88I43ttfmYm\nVsBuPJ4f5xZ0zpkTUjkRsTrqqKW2v5ch+sP+/eo2nHAiKQkeeyw26xmCMsnkdm7nJ/yEnZbdPPhQ\nKT/5/n4sRrDrLQcVKDQQQqAQjFk50dgIDoeaEeJLR4erosPf7BGbDS6/3Pdj3d3g3vZ6xQpVXSFz\nJ5ykrVNsyGXDQohB6+GHH6ax0XUlwqRJk3j77bf9BhPeJk+ezCh/VxQIIYQQUWJWTpQN40DcrJyo\n7e6m063a0ZuvYdi+mFUTWVYrGQlyvZUQkRhps2ExTjqlJyQwKyPD+Vi8wwlz5kR1dze6r1YjIijz\nZJnZdkQMI/fdB598Ahdf3N8rGdJGMIKf239Cot3Kiq9m8cnlIz1O3O9jH7/jd7zES85wop12Oujo\n24HN8xt2O7QEOCluVk2kpUFmZvjHWbsWWlvV8RIToarKFXwJQMKJWJFwQgx5HSH8ceD/l2RTF11B\nX6eb4GXIduxBX6eTwC0PBurnFk9dXV386U9/8tj25z//mbS0tH5akRBCCNGbruuuyolhHE7kJSaS\nZPwCXxXgymjzsaIgJ0alpZMQ0bfQ7QrgYDNfoq04KYkkTaPD4eB1rzlyIjRmmxEJJ4Yhq1W18fEz\n/0BEz5Tzf8qlD6gT9s99L5suXD/THOQgy1nOGtaQQgpJqNC1z62dbDZIN76vAw3FNsOJkpLIqh3M\nlk6LFsGRR6r70trJw1VcxVM8xXmcF3C/TjrZzvboVM4MA3KZkRjyLsdPyZqbh3mYMQQugXyIh1jO\n8oD7fI2vcRmXBdzncz7nPu4LuM9oRvMIjwTcBwbe5xZPK1as8KiamD59OieffHI/rkgIIYTorba7\nmya7HQ2YkJzc38vpN5qmUWKzUd7RQUVnJ6V+/i5Cbeu0X8IJIaJuYVYWDx44AMS/ciLFauWW0aO5\nZ98+vrtjBydmZ5Md5zUMdm20Aa4re4UQMVBUxDl/2cw7V+ZTW5LEm7zJBVwAqNZqAHnkoaGRQw7V\nVNNAA8UU9+24b7yhqiH8tJECPMOJSHzyibpdtEgN1l6zRrV2uuiiyF5vCDJniQRzL/eylrV8m29z\nOqfHeFWDn1ROBKEp4zRNO1fTtO9qmvZzTdNu0TTtWk3TFmqaFvXfMjVNS9Y07WTjeLdpmnarpmmX\na5o2PtrHEmKw+sT8H6fhjDPO6KeVCCGEEP5lJSTw8VFHsWTqVJKt1v5eTr8KZSh2uG2dRg/jwEeI\naFvgdtIr3uEEwO2lpUxKSaGyq4uf7N4d9+MPZp10Oq/gTsOzklxH52quZjGL5SpeIfpq5EhsHTpX\n3l8JwBrWoKNa0bmHE+CaOxGVodilpWowtr95E9C3cKKuDjZuVPcXLoR589T9FSt677trlxqqLVVu\nfo1Hnb7dxa5+XsngIJUTPmialgOcD5wOnAwEqo3r1jTtTeAhXdc/7uNxC4A7gP8BfPam0TRtNfBr\nXddf78uxhpPneT7oPma5XSA3cRM3cEPAfawEP+lwLMcGXZNGaCV4A+1zi6dduzzf5OfOndtPKxFC\nCCH8s1ksLApxFtJQVxLCUGxnW6cA4US73c4rtWroq1ROCBE9BUlJTE9LY1Nbm/P7NZ5SrFaeLCvj\nhHXreLyykiuKijhB3j9DYlZNWLCQimcLwQ46aDb+eD8mhAiTceL/hH82kDL/ZI6++FfO8zfe4YR5\nlf0udrGFLYxjHCcTw24PfQknli1Tw6+nToXCQpg7V7WGKi+Hmhq1zfTQQ6qSIzcXFi+OytKHmglM\nACScCJWEE140TXsUuA5COKOrJKKCjPM1TXsW+IGu680RHPdE4GUCByEAc4DXjGNdr+u6/9/uBADJ\nROeKulBO8ofCavyJhoH2ucVTvVevxUL3/1kKIYQQYsAZGULlRHW3mnEVqHLivE2b2NimTsSNlnBC\niKh6uqyMj5uaOLGfQoFF2dl8e8QIHqus5NZdu1gxZ06/rGOwcR+G7X6hmx07V3CF82Mb8p4pRJ+M\nHAmARYdjD03EvSGNGU7kkguocCKNNHayk/Wsp5TSgRlOvPUWPP64ur9okbrNzIQpU2DrVlU9cfbZ\nrv3Xr1e3NTV9W+8QZoYT+9hHF12D8pxbPElbp96OwXcwYQcOAKuBDUCTj32+AbyvaVpYE6g0TVsA\nvEXvYKIRWAuUG8f3PtbzmhbJlBshBr+WlhaPj9PTZfCbEEIIMZCFUzkRKJyY7fb/fKmcECK65mZm\ncsvo0Vj78dfMX4wdC8CqlhYajcBSBOZvGPZAq34XYtAzwgmg1wBy78qJ67meJSxhJjMBNVs0pg4e\nVLfhhhPLl8OWLer+V7/q2n700aqN1P79rm0NDa6P6+oiX+sQV0ABmWRix85e9vb3cgY8CScCawT+\nCJwF5Oi6PlrX9bm6rs8E8oCTgE+8nnM08EyoBzBaSL0IpLht3ouqxsjVdX22ruvjgLHAY15PvxC4\nOeTPRoghJCPDc9Bba2trP61ECCGEEKH4n+Jidhx9NI9Nnuzz8U6Hg8aeHiBwW6d5mZnO+xJOCDH0\nlNhsTEpJQQc+bQ67KcGwNJax3MVdfIfv9Hosk0wfzxBCRMQ9nHCb06OjU4/q7pBvXHdsMU657ked\nzI9pOKHrrsoJ9zWG4vTT4aab4OWXYfZs1/bvfQ/WrYPvuL2vmHMpQMKJADQ0ae0UBgknfCtHtXYq\n0XX9e7quv6Xrusdl2rqu23Vd/wgVUDzu9fyLNE07KcRj3Qq4x5p7gON0XX9d13Xd7XgHdF1fDNzm\n9fxfGAGHEMNKbm6ux8c1UlIohBBCDGiFSUlMTE0l1c9g8GqjaiJR08hJ8N991r1yQto6CTE0LTJO\n+i1tjMIg2WEgjTSO4AimM73XY/OZD0SvJbAQw5p7tYTbzyottNCNqvQyB2Gb4hJO1NVBV5eaE1FU\nFN5zjz8ebrxRzZlwV1AAXheFsmGD674MxA5IhmKHTsKJ3u4AynRdf0rX9fZgO+u6bge+C6zyeui6\nYM81BmD/wGvz9bquVwR42j3AUrePs4AfBTuWEEPNpEmTPD5etcr7W1AIIYQQg4l7S6dAnUvHpaRw\n59ix/Hb8eDIChBhCiMFrkTHzYmmTr27KIhxXczUXcAF3c3d/L2V4a2tTV7a7rkGNjmi/ngjMYoGr\nroKZM+G445ybrVi5hmu4mItJJNG53YGDAxwAYhxOmFUTBQUQoPq0z6RyIqCd7GQ3u+mmWyonwiDh\nhBdd198Md8i0EVD8zmvzaSE89TLwaAq5VNf1/wQ5lg78ymvztTJ7Qgw3Cxcu9Pj47bff7qeVCCGE\nECIazMqJQC2dTLePHctPxoyJ9ZKEEP3ErJxY1dJCm917/KIIRwopfINvOK/iFXFUX69OZM+eDdOn\nqyvUn346use47jqYMQPeeCO6ryv8+/Wv4bXXwK16M400zuVcruRKj10PcYgOOkgggWKKY7emSIdh\nh8s9nKivB4cjtscbgB7mYX7MjymnvNdjS1jCLdzCf/kvE5lIGmnkkIOOhIiBSDgRPd55KpnXAAAg\nAElEQVSzJ/I0TUsN8pzzvD5+KsRj/RfV/slUDBwb4nOFGBLmzZvn0dpp06ZNfPjhh/24IiGEEEL0\nRSjDsIUQw0NpcjKjbTZ6dJ3PZe6EGKw++gg+/VQNETY9+ii0B23SEbr6emhpie3V8iJiZkunEkpI\nIArVnv4qZeIRTtTWQmWlah0FKpgYhq33yilnBzucM0ZMOjq72Q2olk755PM3/sbt3I6GXE8eiIQT\n0dPgY1uWj20AaJqWDizy2vxeKAcyqic+8Np8dijPFWKoSExM5Lvf/a7HtsWLF9PW1tZPKxJCCCFE\nX0g4IYQwaZrmrJ74ZBie/BJDhDkX8fTTYe1aGDNGhQkvvBC9Y5jfH14zGcXAELV5Ew0NcOyxMHUq\nmNVk1dXQreZcxCWcMOdNTJwIS5bAm2/2nkkxDGSgPucWPEYTc4hDtNCCFStjGINm/BHBSTgRPSN9\nbAvUgO0IcGtEB3t0Xa8K43ifen18VBjPFWJIuOGGGzyqJ3bs2MGZZ55JY4i/wGzfvp0DBw7EanlC\nCCGGuYs3bWLuqlV81ODrGhbhzdnWKTExyJ5CiOFA5k6IQc8MJ8aOhexs+Pa31cePP66GF0dDvXH1\ndk5O4P1Ev5jIRM7iLOYyN/jOgaSnqzCisxOamuDzz+GEE+Bf/1KPxyOcMFs6HXmkalE2bRoMw5/Z\nzHCilVaP7WbVxGhGk4RcaBMOCSeiZ6HXx3uDzK6Y6vXxljCP572/9+sJMeQVFBTwzDPPeAzNXLp0\nKVOnTuUPf/gD1dXVvZ5TU1PD888/z7nnnsu0adPYuXNnPJcshBBiGNnQ1sbq1tbgOwpAKieEEJ7M\nyonlzc10DcO+5mII+PnPYfly+OY31ccXXQRFRVBVBf/8Z99fv6cHzLZnRpgnBpbpTOc6ruNETuzb\nCyUmuqoUGhrUv5+0NDh4UG0zw4mRvq6bjhL3cGIY81c54d7SSYRHwonoudbr47eC7F/m9fH+MI/n\nvX+ppmnJYb6GEIPeOeecwwMPPOARUFRVVfGDH/yAESNGMHbsWObNm8fs2bMZOXIkRUVFXHHFFfz7\n3//GIb/kCCGEiBGHrrO3owOAcSkp/byawaHaaE0g4YQQAqAsNZWCxEQ6HA5WtbQEf4IQA01CAhQX\nQ36++thmg+uvV/f//GdXe55IuVcVZfntKi6GCrNrREMDrFihqmamTVPbYl05oeuutk4zZ8bmGIOE\nv3BijzEaeBzj4r6mwU7CiSjQNO1Mes+PeCbI0wq9Pg63t0w10OP2sQXIC/M1hBgSbrrpJl555RWP\nFk8Auq6zd+9eVq1axdq1a6kw/4cthBBCxFhlVxdduo4VGCkn23tx+BjoaFZOFMnflxACz7kTS2Xu\nREB/5I88yZM0I8PDB7zLLlMtmMrLYdmyvr2W+X2RmamCEDG0ma27Nm5U/36sVjjmGNXqqbZWPRar\ncOKLL+DQIXV/6vBu3JJOOtC7rdM+9gEwlrE+n9dJJ1vYwhrWxHR9g5G8e/WRpmm5wGNem1/TdX1F\nkKeme30c1hRfXdd1TdPaAffpM96vGTZN0wqBgjCfNqGvxxWiry688EJOOeUU7r//fp599ln27t3r\nd9+0tDROOeUUrr32WhYu9O7IJoQQQvTdnvZ2AMYkJ5NgkeuBTO/X13PV1q1MTk3lk1mzPB5bMXs2\nVV1dlCZLMbAQQrlh1ChOz83l2MzM/l7KgGXHzgd8gI7OxVzc38sRwaSlwfz58NZbsHu3mhsQKXPe\nhAzDHh7Mr/Obb6rbo45SrZ7Mcx/JybGbPXLTTa77w/znNF+VE510UoOaMeNv+HkdddzGbSSTzPM8\nH/uFDiISTvSBpmkWYAkwym1zE3BDCE/3DhI6IlhC1MMJ4LvAHVF4HSHiLisrizvvvJM777yTrVu3\nsmHDBmpra2lsbCQ1NZWCggKmTJnCjBkzsNls/b1cIYQQQ9ges6XTMP8Fzlua1UpNdzepnZ29HstJ\nTCRnGA5WFEL4tyg72zkYW/h2mMPoqGq0NNL6eTUiJMXF6raqqm+v09CgbuV7ZHgwg4fVq9Xt8cer\nW/eWTm7trqPqoYfgyis9Q4phKh/Vpq0K1/dvCy2UUUYTTWThu8Waub2DDrrokqHZbiSc6JvfA2d4\nbfu2ruuhzI/w/k010PBsf7x/q5OGxkIYpk6dytRhXm4ohBCi/6RYLMzNyGBmejSuHRk6SoyWTRWd\nnei67jEzSgghRPjM1iLJJJOIBLyDghlOVFf37XXMtk6xulpeDCzeIZQZTsyeDe+8A4cPx+7Yxx4L\nmzYN+6oJUAOvr+Zqj8HX+eRzD/cEfF4qqVixYsdOM83OkENIOBExTdNuAH7otfl3uq6/GOJLeFdK\nRBKZeV/6HUn1hRBCCCGEiLKLCwu5uNB7xJgYYVQuduk6dd3d5Mt8CSGE6BMznEiPSiMFERcjRqjb\nysq+vY7Z1kkqJ4YH9/ZdqamqrROoQetlZbE/fopcDw3qvfZ8zg/7eRoamWTSQIOEE14knIiApmlX\nAA95bX4G+GkYL9Pq9XEk8aP3O4P3a0bij8DLYT5nAvB6FI4thBBCCCGGMJvb/I1Ltmzhv+Yv1kII\nISIi4cQgVFSkbvva1smsnJCZE8ODe4XMMceAXOAx6GSQ4QwnhIuEE2HSNO1s4K+Aew36q8B1uq7r\nYbyUd5AQVnNITdXARz2c0HW9BowpLqGvpa+HFUIIIYQQw8RXc3J4r6GBq82TM0IIISIm4cQgZFZO\nVFWBrveeE7B5Mzz7LNz5/+zdeXxcdb3/8deZyWRPmmZp0qR7m5alZZOlRQoFEdl6QeTKDwH1ooCC\nqFcB/alXRe/ld1GQTRCueAG9uFxUFgVllaXQ0tJCS/clXZI0S7M0e2aSmfP745wzmaRZZpLJLMn7\n2QePM8s5J5+WJDNzPt/P5/Mja1X8UDRzYnIJTUI5LZ3ipasLrr4aGhrgpZeG/z6VoFxyAZScGEDJ\niQgYhnE2VlVB6L/by8CVpmn6IzzdwATAjEH3GlrxgDgCQEOE5xARERGRUTBNky/v3ElpWhrfnzMn\n3uEklaeOPZZan4+FmZnxDkVEJOkpOZGgenshZYhLbk7bR5/PSjCEXnT2++H6660hx4sXwzXXDP01\nNHNicgn9/xzv5ER6Onz4IfT0WAmKsrL4xpMknKHYLbTEOZLE4hp5FwEwDOM04Dn6t196B/ikaZqj\nGWa9Y8D9WREeP3D//aZpauaEiIiISAxsaG/nkZoafrBvH93+SNeoTG65KSlKTIiIRImSEwnqttus\n5MJvf3vkc6mpUFBg3XZaO110EZx9NuzYAV/8ovXYH/84/NfQzInJZeZMq8qmtDQ2MyaGYxh938ON\njfGNJYnkkANAG21xjiSxKDkRBsMwjgP+Bv1e7d8HLjRNs2OUp90+4P4xER5/9AjnExEREZFxUtHV\nFby9p1vrQ0REJD4WspBLuIQT0AyfhFJfDx0d1grzwYQOxTZNqKiAffsgKwvOP996bvNmaB2m/Ysq\nJyaX6dPhD3+AJ588shVYPCg5EbFC+48HT7xDSShq6zQCwzAWYbVuCv1tvw34hGmaY6nD2QL0QPA7\nco5hGNNN06wJ8/iBNVwfjCEWEREREYnAts7O4O1dnZ0cmxXR+DAREZGoON7+Iwnm0CFr67RwGqik\nxEo+1NVZrZ2chQ4lJVb//jlzrGTFunXwsY8Nfg5n5oSSE5PHKafEO4I+Sk5E7FP2H+lPlRPDMAxj\nNvAKEPpqshf4uGmah8ZybtM024A3Bzz88TDjMoBzBzz8l7HEIyIiIiLh29rRVzy7K6SKQkREostv\nmly5dSunrF9Pc09PvMMRCU+9PWa0qGjw58vL4eijrUTEwYN9+zqDhZcutbarVw9+vGmqckLiy0lO\nNAwYf+v3w513wquvxj6mOKigggYaMDHjHUrSUnJiCIZhTAdepf+g6mrgY6ZpVkfpyzw34P4Xwjzu\nbGBuyP064N2oRCQiIiIiI9oaWjmh5ISIyLhxGwZvHD7Me21t7NHvW0kGXm9f4mCoyonbboMXXoBP\nfaovOVFa2vf8smXWds2awY9va7MuAoOSExIfQ1VOPPccPPxw3+yUCaqGGh7iIb7JN7mO69jM5niH\nlLSUnBiEYRj5WK2c5oc8fAirYmJvFL/U74HQmRVnGoZxzgixGcAPBjz8mGmagSjGJSIiIiJD6A0E\n2DFMcuKvDQ38o7mZtt7eWIcmIjIhzc/IAGC3khOSDJyWTqmp4Q2rrrbXv4YmJ5zKia1b+xIdoZyW\nThkZfdUWIrE0VHJiy5a+2xO42i1AgJd5OXh/JjPjGE1yU3JiAMMwcoC/A8eGPHwYOM80zW3R/Fqm\nadYDPx/w8KOGYZQOtr/t/wJnhtxvAX4azbhEREREZGhuw2DPaadx+5w5gDVzItQNO3dyzsaN/eZS\niIjI6C1QckKSiZOcKCwMb3DxYJUT06bBggVW+6a1a488RvMmJN4KC63twOREaJun2trYxRNj05ne\n7/4UpsQpkuSngdhHeg4YOGHmZ0ChYRgD5zyMZL1pms0j7PMT4HNAiX1/LvCOYRhfBf5imqYJYBjG\nDOB7wA0Djv8P0zSbIoxLREREREbJMAxmpKdzU1kZP9i3j2qfj06/n0y3m26/n4M+HwBz09PjHKmI\nyMSg5IQklZGGYQ/kJCfKyvo/vnQp7N5tzZ0477z+z2nehMTbUJUT27f33a6shJkTs6LANWC9v0EY\niUgZlJITR1oxyGM/GuW5zgZeH24H0zSbDMO4AngRcD7BzgaeBQ4bhrEXyANmAe4Bhz8L3DXK2ERE\nRERkDAo8HqampNDc28vuri6Oy87mgNcLQJbLRaHHE+cIRUQmBic5sae7O86RiITBGYYdaXKidEAT\njWXL4H/+Z/Ch2E32GtVw2kaJjIfBKidME446CnbssO5XVfU99/778MQT8K1vwfT+VQfJaiEL2clO\n5vebCiCRUlunBGCa5pvARcDACog84ESsaoqBiYnfAlc4lRUiIiIiEnvl9gUzZ+7EXvvC2dyMDIxw\nWjmIiMiI5qenk+N2k+XSJQxJAtFKTjhzJ3bsOHJ1+umnwyOPwE03jT5OkbEIrZxwLk0aBtx7L1x1\nlXW/srJv/1/8Ap59Fp5+OrZxjqNv821WspJv8+24xWBi4scft68fDXplTxCmab4GHAP8AhiuQfH7\nwKdM07zKNE1vTIITERERkUEFkxP2fIm9dpJijlo6iYhEzUdycmg54wz+fvzx8Q4lYTTRxOM8zloG\nmUcg8eUkJ4qKRt7X6+3bf2ByIj8fFi2ybq9Z0/+5khKr1dOyZWOLVWS0nORETw+0tfV/bsYMaxua\nnNi1y9ru3z/+scXIVKZyLddSSGFEx/nx08PYh4U/z/PcyI28xVtjPlc8KTkxgGmaRhT/ez3Cr11n\nmuaNQCFwLnAz8G/At4CrgXLTNE8yTfPP0f57i4iIiEjkyjMzgb7Kiffb2wFrla+IiESHYRiqRhtg\nG9t4lmf5Pb+PdygyUCQzJ+rqrG1ampWMGOijH7W2r78eldBEoiYtDXJyrNvO97zDSU5UV1vb7m44\ncMC67WwnqZ/xM/6Zf+Yf/GPM52qhhVpqWcOakXdOYEpOJCDTNLtM03zVNM2fm6b576Zp/sQ0zSdN\n09wd79hEREREpE9oW6eeQIA/NzQAcJGzmkxERGQc7MDq6b6IRXGORI4QSVun0JZOgyXgPvEJa/vi\ni1aVhUgicd7vOkkIhzME26mcqKiAQMC6PcmTEx48mJi00hrxsa200kJL8P4yrMqp93mfLrqiFmOs\nKTkhIiIiIjJKTnJid1cXrx0+TENPD0UeD2drQKWIiIwjJzlxFEfFORI5QrjJieZmeOYZ6/bAlk6O\nk0+2Wji1tcGbb0YvRpFoOPlka/vcc/0fdyon6uqspNrukLXWNTXg88UmvgQ0hSmAlWjopZdv8k3u\n5346h+3wD3vZy/f5PtdxHc9g/d6YwxxKKMGHjw1sGPfYx4uSEyIiIiIio+QkJ2p8Ph6tqQHg8qIi\nUjS0VURExokXLxVUAEpOJBy/H+wqyhGTEy++CH/4g3W7rGzwfVwuuPBC6/bzz0cnRpFoufJKa/vX\nv0JL34p+8vPBbn1KdXXfvAmwhmcPrLSYRHKwWmG10kollVRQwbu8SzqDt4QNEODX/JpbuIX97CeF\nlGDFnIHBUpYCJHVrJ31qEhEREREJU4ffj2mawft5Hg+FHg8Af7L77f6fcNo4iIiIjNIe9tBLL1OZ\nyjT0mpNQGhut9jUuV1/Lm6GUlPTdHqpyAuDii63tK69AV/K2bpEJ6MQTraHtXi/8OWQ8rmH0VU9U\nVfWvnIBJ3doptHJiJzsBKKcc1xCX6Neylqd5mgABlrGMB3iAozk6+LzT2uk93ovKkO14UHJCRERE\nRCRM527cSNHbb/Nac3PwMad6wgRKU1M5Y8qUOEUnIiKJrooqruEavsJX+C2/ZT/7Iz6H09JpIQsx\n0KDwhLJli7WdNQvc7uH3DTc5ccIJ1oXejg74x9iH6IpEjWHAZz5j3f7d76yqCEdocsKpnHAGaE/i\n5ERo5cQurH+XBSwYcv+97AVgBSu4jdsooH/ScwELyCGHbro5QHL+uyo5ISIiIiISpp2dnTT29gar\nJaAvOQFwxbRpuAYbaCkiIoK1anYlK6mmmqd4iq/zdV7n9YjOoXkTCWz9emv7kY+MvO/06X238/OH\n3s8w4KKLrNtPPz362ETGw6WXQnq6lYBwvv+hLzlRUQH79lm3ly+3tpM4ORFaOeEkJxaycMj9a6kF\nYCYzB33ehYvZzAYYVbI7ESg5ISIiIiIShm6/n6beXgBmpKUFHw9NTqilk4iIDCeHHC7mYr7O1zmO\n4wD4E3/CxBzhSIuJyXa2A0pOJKRIkhO5uX23nQu5Qwlt7fTGG6OLTWQ85ObCypXW7SuugNZW6/ZM\n+2L6qlXWLJacHDjlFOuxSZycyMX6uT/EISqpBIavnHCSEyWUDLmPk5yooSZaYcaUkhMiIiIiIoA3\nEOBfd+/m5aamQZ+v9fkASDMMpqakBB8/yh74Nzc9nVOccnUREYk6byDAm4cP81hNcl6AcWSSyVmc\nxW3cRiqpVFEVXEE7kgABruRKzuZs5jFvnCOViPT0wAcfWLfDSU4YBjz+ONx5p9W3fzjHHgtz5li3\nH3xwLFGKRJ8zGDsQgLY267aTnDh4ED71KbjgAphtXURXcsJiYlJIIfkMXTkVTnLiMi7jMR7jKq6K\nXqAxlDLyLiIiIiIiE99D1dXcW1XFvVVVmCtWHPH8QTs5UZqWhhHSuunSwkK+P3s25+fn93tcRESi\n65DPx1kffIAbq41e5kg9/RNcFlksYxlv8Aav8uqwrT0cbtx8wv4jCWbbNujutlaSLxh6JXQ/Z50V\n3n6GAXffDa+/DtddN+oQRcbFCSfATTdZt8vKrK1TDZSaCnfdZd3es8faVlZa8ykm4fvmDDK4i7t4\njdd4gRcop5weemimmRxyyKCvIjtAgPM4j1pqh01ODJfcSAaqnBARERERAQIht03zyPYaB71eAKan\npvZ7PMXl4va5c1mmQdgiIuNqRno6Zamp+IH1zurcJPcxPgbAKlbhxRvnaGRMQls6ucbhcttJJ8E3\nvtE3VFgkURgG3HKL9Z/DqZxobITOTuv2jBnWvj090NIS+zgTgIHBfObThFWpXU453+W73MANbGJT\nv31duLiKq/gm3ySTzHiEGxNKToiIiIiIADeVlgZvN/b0HPF8UWoqlxcVcc7UqbEMS0REQpxm9+lf\n4/Q1T3LHcizTmEYnnaxmdbzDkbHYZF9YDKelk8hEl5vbl0irrra2aWmwZo1VZZSXF7/YEsAX+SK3\ncRtLWRqsfGikMc5RxYfaOomIiIiIAOluN6WpqRz0+djb3U3hgAqJs/LyOGuSf5ASEYm3pbm5/Lmh\nYcIkJ1y4uJIrMTFZytJ4hyNjcdddcMMNk/6iq0jQ009DUVH/4e/TpsUvngRSQAHLWAZAIYWAkhMi\nIiIiIpPe3PT0YHLilNAPUiIikhCW2r+bV7e2YprmhJj1s4IV8Q5BosHthqOOincUIolj/vx4R5AU\nCigACLZ6mmzU1klEJqV9+/ZhGEbwv89//vPxDklERBLA3AxrCF1FV1ecIxERkcF8JCcHN1Dj81Hp\nTY4ZDSYmvfTGOwwREUlATnKigYY4RxIfSk6IiIiIiNjmpqcDsLe7O86RiIjIYDLdbo7JygLgw46O\nOEcTnnrq+Ryf4z7ui3coIiKSYMZr5kQPR87QS0RKToiIiIiI2JScEBFJfIsyMwHY0dkZ50jC00IL\nnXSyhS3xDkVERBKMUznRSCMm5pjOFSCAHz/v8R7XcA1v8VY0QhxXSk6IiIiIyKRz3Y4dlL/7Lo09\n/VcUKTkhIpL4Ftkt+HYmSXKinXYAsske9TmqqeZu7uZlXo5WWCIikgCc5IQPHx2MviLwPu7jKq7i\nFV7hAR7AwGAuc6MV5rhRckJEREREJp1Ha2rY3dXFPZWV/R53Zk7s7+7Gb45t5ZKIiIyPYOVEkswH\nGktywrlQ9RRPscr+IyIiE0cqqeSSixs3zTQDVkL6NV5jD3siOlc33fyKX9FKKyWUUEzxeIQcVSnx\nDkBEREREJJY6/f7g7ZWFhf2em5GWRoph0GOaHPR6mWlXUoiISOJItrZOo0lO1FDD/dxPM818mk/z\nBm/gwsUVXDFeYUqkXn0V8vJg8WJIS4t3NCKSxB7gAbLIwo0bgI1s5Jf8klM4he/wnbDOMYtZgDVr\nIoUUvs7X8eAZt5ijRZUTIiIiIjKp1Pp8AKS7XJyak4NpmjxYXc1LTU0ETJNZ9gUGtXYSEUlMC+0q\ntxqfj7be3jhHM7I22gDIISfsY/LJp5JK6qjjQR4E4NN8mmM4ZlxilAj5/XDDDXD55dDcHO9oRJKL\naVo/N/rZCXIqJxy11AJQQknY55jN7ODtq7m63/1EpuSEiIiIiEwqTnJiemoqhmFw0OfjK7t2ceGm\nTZjAPPuiV0VIu5C1ra38qqaG99va4hGyiIiEyPN4mOaxVoPuTILWTqOpnEgjjRWsAKwBp8dwDJdz\n+XiEJ6PR1GQlKAwDBlRhisgIfvxjOOkkePTReEeSsEaTnDiao5nDHM7gDFaycrxCizolJ0RERERk\nUqmxkxMlqalA30DVuRkZpLpcgw7F/tOhQ3xxxw5+XVcX42hFRGQwydTaabQzJz7BJ3DjJpts/pV/\n7beqVuLs0CFrm58PKeqYLhKRadOs7cGD8Y0jgY0mOZFBBvdwD9/km7iS6JK/foOKyIQSCAR45513\n2LlzJ7W1taSnpzNv3jyWL19OQUFBvMMTEZEEEFo5AX2rbp02IYMlJw7ax5Tax4iISHwtzMjgrZaW\npEhOjKatE8BMZvJTfko22RSi1fkJxUlOOBdZRSR8ZWXWVsmJQZmY1GEtiIokOZGslJwQkQnB7/dz\nzz33cM8993BwkBc4t9vNZZddxk9+8hPmzJkT+wBFRCRh1Hi9wJGVE84q3MGSE84xpRp4KSKSEJzf\n2cnQ1ukKrmAFK1jAgoiPncvccYhIxqy+3toWFcU3DpFk5CQnqqvjG0eC2sIWfPhw4aKIif87RskJ\nEUl6TU1NXHDBBaxdu3bIffx+P0899RQvvvgizzzzDHPn6k2+iMhkNWLlhL3dG3LB6+CAY0REJL6S\nqa1Tuf1HJhAnOaHKCZHIlZZa29paa3aLWy3rHK/wCo/wCADHczwePFE576M8ynu8x7Vcy6mcGpVz\nRkvyNKASERlEW1sb55133hGJCcMwmDNnDieffHK/SonW1lYuueQStm3bFuNIRUQkUQw1c2LhgMqJ\ngz4f3kDAuu1UTig5ISKSEJyE8s7OTkzTjHM0MumorZPI6E2bBh6PlZjQPLd+2mmnl16WspRbuTVq\n522hhTrqOEjitdJSckJEktqtt97K+vXrg/cNw+ArX/kK+/btY+/evaxbt469e/dSUVHBDTfcAFgJ\njRtvvDFeIYuISJw5yYnpdoumpxcv5k/HHsuJ2dag0iKPh0yXCxPY391Np99Pi98PqK2TiEiimJeR\ngRvoCASC1W0iMaO2TiKj53JBiT1LQXMn+rmES7iN27iVW8kgI2rndWZXOIO2d7IzauceKyUnRCRp\nrVmzhv/6r/8K3jcMg1//+tc88MADzJo1q9++c+fO5eGHHw7uv2/fvliGKiIiCWRgW6djsrK4rKiI\nqR6rbNowjGD1REVXVzCZkeVykaOycxGRhJDqcjHPrp5IhtZOMsE4lRNKToiMjoZiD8rAYBnLcEX5\nkr2TnHAGbT/Jk1E9/1ho5oRMeB32SsdElRXmRY6h/h5uID2Mc/QGAnjHWO4cbqyxct999/Ur4b7p\nppu4+uqrhz3muuuuY+3atTz66KPjHZ6IiCQgv2lSN6Ct02DmZWSwpbOTvd3dwde/6WlpGIYRkzhF\nRGRkCzMy2NXVxY7OTs6ZOjXe4chkopkTImPjzJ3QUOyYCK2c6KKLZprjHFEfJSdkwst+6614hzAs\nc8WKsPYb6u9xeVERTx177IjHP93QwKe3bo0ktCOEG2ssHD58mKeffjp4Pz09ndtvvz2sY++44w5+\n/etf41P5t4jIpNPQ00MAMIBpnqEHzDmVE6tbW8m399O8CRGRxLIoM5Pnm5rY2dUV71BkMjFNzZwQ\nGSsnOaHKiZgophiAQxxiP/vjHE1/auskIklp9erVeO3hpAAXXXQR+fn5YR1bVFTEhRdeOF6hiYhI\nAquxXzuKPB5SXEO/Fb6ooACA39TV8dMDBwDNmxARSTSLMjMBtXWSGGtvBychprZOIqPjtHVS5URM\n5JOPBw9+/GxgQ7zD6UeVEzLhtS9fHu8QomKov0e4jZY+WVg4Yf4tANauXdvv/ooIqzpWrFjBM888\nE8WIREQkGQycNzGU8/Lz+fe5c/ne3r2sb28HVDkhIpJolJyQuHBaOmVng/09KNwbw9sAACAASURB\nVCIRUuVETLlwUUwxVVSxjnXxDqcfJSdkwku0OQmjNda/R4rLNaF+4Pfu3dvv/uLFiyM6fsmSJdEM\nR0REkkRNGPMmHN+ZNYt93d08WlMDjJzQEBGR2FpoD8Te192NLxAgdZiKuHjZzGbe4z0WsYhlLIt3\nOBINGoYtMnahlROmCZrrNu5KKKGKKvaxL96h9JN4r9wiImE4fPhwv/sFdvuNcEW6v4iIQLffz2Wb\nN3On3eYoGQUrJ8Jo0WQYBr8oL2el/ZqxbMqUcY1NREQiU5KaSqbLRQDY390d73AGtYMdPMuzrGd9\nvEORsdi0CZzPoJo3ITJ2TuVEezu0tcU3lknici7nR/yINBKrVa2SEyKSlNrtFhuOzAjLabOysqIZ\njojIpLC2rY2nGxr4dkUF3X5/vMMZlUgqJ8CqPHx28WIaPvpRPqrkhIhIQjEMg3l29cSeBB2K3YZ1\n0S2b7DhHIqP23ntwySXwz/9s3XfaOik5ITJ6GRngzA3V3ImYWMQi5jMfL96Rd44hJSdEJCkNTC50\nRthntqOjI5rhiIhMCsunTAle1F/d2hrnaEYn3JkToQzDoMDjGa+QRERkDOalpwNQkaCVE+1Yi6qU\nnEhiL71kbXfvtgZhKzkhEh0aih1z7bSziEUUUxzvUIKUnBCRpJSXl9fvfkNDQ0THNzY2RjMcEZFJ\nwTAMzrZ///5jQHu9ZBFp5YSIiCS2+XblREWCVk4oOTEB2AkwANav72vrVFgYn3hEJgoNxY65aUzj\nP/lPvst34x1KkJITIpKU5s2b1+/+5s2bIzp+06ZN0QxHRGTSOCfJkxOjqZwQEZHE5VRO7EnQygmn\nrVMOOXGOREatrq7v9urVqpwQiRZVTghKTohIkjrllFP63X/jjTciOj7S/UVExHL21KkAvNvaSkcS\nzp2o8Vo9VlU5ISIyMcxLkMoJP4O/JqpyYgJwkhEAa9ZoILZItKhyQlByQkSS1Omnn05qyIWl559/\nnqamprCOra+v54UXXhiv0EREJrR56enMTEtjemoq+xJ0lepQ2nt76QgEAFVOiIhMFMG2Tt3dmKYZ\nlxje5V0u53Le4MgFUEpOTAC1tX23zzpLyQmRaHEqJ5ScmNSUnBCRpJSXl8cnP/nJ4P3u7m5+8IMf\nhHXsd7/7XXx2Ww8REYmMYRi8f/LJ7Fu6lGOzsuIdTkSceRNZLhfZKSlxjkZERKJhTno6BtDu93Oo\npycuMdzP/QDcy71HPOckJ9TWKYk5lRN/+xt86UvQ3GzdLyqKX0wiE4FTOaG2TpOakhMikrS+9rWv\nYRhG8P6DDz7Ik08+Oewxjz76KI8++uh4hyYiMqEVeDz9fv8mi+C8ibS0OEciIiLRkuZyMcP+vR6v\n1k6zmQ3ATdzU73E/fs7lXJazXMmJZOX1glOhX1IChw/DlCmQmgr2HC4RGSUnOVFfb/2syaSk5ISI\nJK1ly5bxhS98IXjfNE2uueYavvrVr1JZWdlv33379vHlL3+Z66+/HoA5c+bEMlQREUkATuWE5k2I\niEws8R6K7Qy9nkb/Nj9u3FzHdXyDb5BBxqjOfbinB6/dklDiwGnhlJpqJSWmTYMPPoAPP4QkXKgh\nklAKCiA7G0wTqqriHY3EiZITIpLU7r77bk488cTgfdM0eeCBB5g9ezbz58/n1FNPZd68ecydO5eH\nH34Y0zTJycnhoYceimPUIiISD05yQvMmREQmlngOxTYxaaABgEIKo3ruaq+X+e++y3kbN0b1vBIB\nZ95ESUn/ZITeS4iMnWHAn/4EGzfC/PnxjkbiRMkJEUlqubm5vPTSS5x88sn9HjdNk4qKCtatW8fe\nvXv77f/cc89x9NFHxzpUEZGk9deGBh6vqaEqyQZgD1SrygkRkQkpdCh2rLXTTjfW1y2gIKrnvqey\nkqbeXt5saSEQp2Hfk15dnbXV8GuR8bFwIeTmxjsKiSMlJ0Qk6RUWFrJ69WruvPNOpk+fPug+breb\nyy+/nI0bN7JixYrYBigikuR+Xl3Nv+zYwbONjfEOZdQae3p4u6UFUOWEiMhEE2zrFIfKCadqYgpT\nSCO6M43etF+3AJp7e6N6bgmTk5woKYlvHCIiE1RKvAMQEYmGlJQUbrvtNm655RZWrVrFzp07qa+v\nJy0tjXnz5rF8+XIKC/vKrOfMmYOp1UciIiPqCQRY09oKwNIkXNUUME1+VlnJv+/fT4vfD8CJ2dlx\njkpERKIpnm2dDmHNJCiiKOrnfmHJEoreeQeAOp+PAo8n6l9DRuAkJ4qL4xuHiMgEpeSEiEwoLpeL\nM888kzPPPDPeoYiITAh/bWykxe+n2OPh+KyseIcTscdqa7m1ogKA47KyuGv+fD6enx/nqEREJJoW\nZ2Xx8nHHBZMUsdROOymkRGXehGmaGCFzDQpTU1mUkcGOri7qfT6OScLX4aSn5ISIyLhSckJERERE\nhvRoTQ0Any8pIcWVfB1B/9HcDMCXS0t5oLwcd+gwSxERmRCy3G7OjVPi+RzOYQUr8OId03lu3bOH\nJ+vq2HzKKeSHVEgUp6ayo6uLup6esYYqo6HkhIjIuEq+T5giIiIiEhOV3d38vakJgC8MMtOnoquL\npw8dYltHR6xDC9sH7e0AXJifr8SEiIiMCxcuMrCqNtpoYx3rCBAI+/jtHR3cVVnJ6bm5pA1YCFBs\nz0mq9/miF/AY/E9tLVdt3Ur7ZJmBUVtrbTUQW0RkXCg5ISIiIjKJ+QIBHqiqGrRP92O1tQSAFXl5\nlGdmHvH8j/fv57ItW/hzQ0MMIo1ct9/P9s5OAE7QnAkRERlnfvxcz/XcwR1UUsludrOVrbTSOuxx\n39+3D4Be0yTL7e733DS7iqIuQZIT3927l9/W1/Pb+vp4hzI+Ghpg717rtmmC8/fUQGwRkXGh5ISI\niIjIJHZ3ZSVf3b2bm3bt6ve43zT5ld3S6bpBqiYAFtq9vXfaCYBEs7WzEz+Qn5JCWVpavMMREZEJ\nzo2bozkagPu5n0d4hO/yXT7ggyGP2dDWxlOHDmEAP54794jng5UTCdDWqa23lwNeq33VXxob4xzN\nOPnCF+CCC2D/fmhrA+c9jto6iYiMCyUnRERERCap3kCAXxw8CMCbhw/TE+hrQfFKczMHvF6mpqRw\nWeHgQz4X2tUUOwepukgEG+2WTidkZ/cbMCoiIjJeruVacsmlggp2sxuAbIau3vuevUr/ymnTWDJI\nld80OzmRCJUT20IWI7zS3Eyn3x/HaMZBZyds2gReL7z6qnX/+OOhvBziMGxdZFLx+2GiJj1lWEpO\niIiIiExSf21spNJeAdkZCLC+rS34nFM1cU1xMekDWkw4nMqJHZ2dmKY5ztFG7ujMTG4uK+NTRUXx\nDkVERCaJGczgdm7vl5AYKjnxdksLf2tqwg3cPmfOoPsU222dEqFyYmvIjKnuQIBXm5vjGM04cNo5\nAbzxhtXK6Zln4KWX4heTyGSwZg0ccwxcfXW8I5E4UHJCREREZJJ60K6acGoK3mhpAaDT7+ev9sql\nzw/TY3mBnZxo7u2lMQEumgy0dMoU7i8v58aysniHIiIik8gc5vBDfkgmmaSQwjQGH6ZsABfm53Nd\naSkLBpntBIlVOdHQ00OqYQTfN0y41k4VFX23330XurvjF4vIZFJcDD4f7NsHIZXcMjkoOSEiIiIy\nCe3o7OSV5mYM4Kv2xfs3Dx8G4OXmZroCAeakpw87SDrD7WaWPcthvFs7BUyTV5ub6ZpoLSRERGRC\nms98HuAB7uZu8sgbdJ/Tp0zh+eOO46Hy8iHPU5xAyYlbZs2iY/ly/veYYwCrAjMRKydHbc+evtte\nr5WgEJHxN3MmpKRYCUG7entYfj/ccgv86EfjH5uMOyUnRERERCahh6qrAbi4oIDP2tURq1pa8Jsm\nTx86BMClhYUjzmoIzp0Y56HY/1NXx7kbN3Jr6IUDERGROAow/ArffPKZxawRzzPca+00u61TZyBA\nRwIk6FNcLlYWFpLlclHj87HBnu80ITiVE3ZCiDffjF8sIpNJSgrMsn9XhrZXG8q6dfCnP8Fjj0FI\nuzlJTkpOiIiIiEwyHX4/j9fWAnBTWRnHZ2czxe2m1e/nvba2YJuGS4cYhB3KmTvhVE70BgK09vZG\nPeY1ra0APN3QMLFWaYqISNR8Yft2it9+O5hkH04lldzJndzBHaP+eg/zMJ/n87zE+M0kyHa7yXBZ\nl24SoXoCIM3l4rz8fAD+0tAQ52iiyFkAcfHF1lbJCUky77a28sjBg/iT8b3yvHnWNrS92lDeftva\nnn46DNEST5KHkhMiIiIik8yGtjZa/X5KU1P5+NSpuA2DM6ZMAeCO/ftp6u2l0OPho7m5I54rtHLC\nNE0+uWUL0995h7V2MiFadtiVGQd9PraNc5WGiIgkpw6/n/qeHvaFMSsglVTWsIYNbKCL0bUmPMQh\nWmjBjXtUx4fDMIxg9UR9giQnAFYWFADDz52o9nq588ABfl5Vxe/r6vigrS1W4UUuEOhbsX3NNeB2\nw+7dUFUV37hEwnC4p4cbduxg6YYNfGnnzuQcVj93rrUNp3LijTes7WWXwQhV3pL4lJwQERERmWS8\ngQCLMjI4MTsbl/2G/sw8qx/2c/ZFhpUFBaS4Rn6rGFo58XxjI39tbKQzEOCGnTvpjeJAu9CZFi81\nNUXtvCIiMnHMTk8HCCs5UWz/8eNnM5tH9fUOYVVoFFE0quPDFZw70dMzrl8nEhcVFOACNrS388oQ\nr8s37NjBtysquHn3bq7cto0T169P3IumtbXQ1WW1lzn2WDjxROtxVU9IgtvY3s7R69bxXyGzGvYm\n4zB3p3LinXdg9WoYqhL70CH48EPr9plnxiY2GVdKToiIiIhMMufm57P9tNP4y5IlwcfOyus/rPOT\nYbR0gr7KiV1dXdwaUob9QXs7P7fnWoxVh99PldcbvP9yol7YEBGRuJpjJyf2h3lh7niOB2ATmyL+\nWiYmDVgtjWKVnEikyolpqal8qbQUgC/s2HFES8fdnZ28YCctPllYyLH2+4Wv7NqFL4qLF6LGaek0\naxZ4PPDxj8NZZ4E9l0skEfzy4EFeG/A++D8PHKDW52NhRgbL7UrompD3zUlj8WJru3MnfOYzcPLJ\n8NprR+7nVE0cdxwUje/vXokNJSdEREREJqnQAZwnZWeTZVdKZLpcnDt1aljnmJ2Whscw6A4E2N7Z\nSUFKCnfNnw/Av+3bR9UIF4gCpklVdzeBYXrj7rLbODlvXN84fDgxL2yIiEhchVs58St+xQ/5IS77\nlWUjGyP+Wm204cW6AFhAQcTHR8Jp65QoMyccd86bx7z0dA54vXxj9+5+zz108CAmcEF+Pn9evJhV\nJ55IkcfD9s5O7k/EVknOAgv7PQzXXw+PPw7nnBO3kERCbeno4PqdO/v9rJmmyVuHDwPwyMKFfMx+\n/34wwX5XhGXxYnjiCbj8cpg6FVpa4A9/OHK/11+3tmedFdPwZPwoOSEJxxikX5wGX0qyGOx7dbDv\naRGRRONxuTjdXm11fn4+Ge7w+menuFzMt1s7Afxwzhz+dcYMTs/Npd3v52sDLlZA3+/KgGlS8Pbb\nzFyzhsphVng5LZ1Oy81lmsdDRyDA6ijPtBARkeQ3J8zkxA52sJGNLGABBgaVVNJEZC0DnZZOeeSR\nSuroAg5TsHIigdo6AWSnpPDYUUdhAL+qreUFuzVke28v/223mPlKWRkAeR4Pd9ptW27fv5+Dibay\n26mccFrLiCQYp3LKG3LN4YDXS7XPR4phcGpuLqX274qaZExOgNWm6ac/hZ/9zLq/f3//53t7+1qt\nnX12bGOTcaPkhCQc1yD9rf1+fxwiEYncYN+rg31Pi4gkoq+WlTEvPZ1bZs6M6Dhn7kR5RgY3lJbi\nMgweXrgQN/DnhgY2DBiA+dnt27lo0yY2tLVRYn+I2jXMkGtnGPZRmZnBio7h5k78x/79fGbrVta0\ntET09xARkeQ2Oz2d47OyOHvq1GEr7A5jrTSewQzmYV2MjrS1k9PSqZDw2iCCNfOpchS94Kc5MycS\n8ILjmXl5fG3GDACu3LqV15qbebK+nha/n/np6Zyfnx/c93MlJSy1Fy/c6iQDEsXAygmRBNNgJyeL\n7EoqgFX2e92TsrPJdLuZbv+uSLjkX6TmzLG2+/dD6ALQDRugrc2qrDjuuLiEJtGnK2aScAzDwD1g\ntWZXyBBMkUTWOeDimtvtVuWEiCSNiwsL2bN0KcvsCopwXV1czMy0NB5euBCPnZBdkp3NFdOmAXBf\nSPuGDr+fPx86FOxDvcBObOwe5rXeqZxYmJHBx+3kxFBzJ0zT5InaWn5XX8+BZP9gJiIiEclyu/ng\nlFN4evFiUodYIGRi0oJ1QW8KU4JzJyJt7eQkOKYSXhtEgOcbG5m1Zg2f2bo1oq9VHOe2TvU+H2tb\nW2kfYkDtHXPncuaUKbT6/Zy/aRM/3LcPgJvKynCFfBZyGQY/Ly8H4Hf19TQnUiWIk5xQ5YQkKCc5\nURiSnHjbTk6cYb93L01LA5K4csJRVgZuN3R3Q11d3+NOS6czz7SelwlByQlJSFlZWf3utw1YcSmS\nqNrb2/vdz87OjlMkIiKWbr9/3FdP/fO0aRxYtoxzBsypcFZS/q6+PjiY73/q6ugMBJifns5HcnIo\nt5MTu4ZLTtiJ34WZmXzcXoH5XlsbTYNc1NjW2cmuri7SDIMLQlZrioiIAHTTjQ/rwl0eeRzDMQDs\nIbKV/KEJjnD91r7INsO+gBiuaXFu6/R8YyOnbdjApZs3D/p8htvNi8cdx6eLiugxTWp9PjJdLv5l\nkGHSH8nJ4YwpU7i4oIDWROmQYJrwyU/CJz6hyglJWIMlJ1YNSE5MD6my8idze3SPB+zPEdjJTqBv\nGLZaOk0oKfEOQGQwOTk5tIb0ku7s7MTn85GaOr69PEXGwufzHVE5oeSEiMTbl3bu5Mn6etacdBIf\nycmJ6dc+NTeX03Nzeae1lV8cPMhni4u5xW7jcGNZGYZhjFg5YZpmsK3TosxMytLSWFlQwKy0NLoH\nadnxdIPVZuPcqVPJSdFbXRER6c+peEi3/xzFUdzJnRRRFNF5zud8TuIkMsgYeWegtbeXv9ozGa60\nKwvDVRzntk4b7QVYxw5YRBgq3e3md8ccw8w9e7i7qoovlZaSF3IRNdRbJ544LnGOmmHArbfGOwqR\nYQ1s69Tc08Pmjg6A4Ny4aampuIAAVsXT9AgToQll9myrrdP+/bB0KXi9sGOH9dypp/btZ5rWz7Ak\nLVVOSEIaWDlhmiaVlZX0DlFGKhJvvb29VFZWHjEQe+D3sohIrD1RV0evafK9vXvj8vW/bq96+sXB\ng1y5bRvtfj8rQvpTj5ScONTTQ4vfjwHMtwedPrdkCT9fuDBYuh7q6UPWgNJLC8PvAS4iIpPHwIqH\nLLJYyMKI2jMB5JLLAhZQRllY+z/T0IDXNFmUkcEJES5gmmZfjGzq7aVnmFka46Hb7+e39fUALB+h\n7aPLMLhrwQIOLlvGT1SBIBJ1OW53sHJidWsrJtbMNyeB6TaM4O2kb+0UOncCYOdO8PuteRMlJVBb\nC8uWQaIlOyViWk4mCcntdpOTk9OvnZPP52PPnj3k5uaSm5uLx+PRoGGJq0AgQE9PD62trbS2thIY\n8EEhJyfniPkpIiKxVB/yoaQxTq0gPllYyKy0NA54vTT09DA1JYXfHHUUbnuF07FZWXyuuHjI1ZhO\nS6fZ6emkj/A79UB3N+vb23EB/6TkhIiIDMKpnIikHVM0/M6+wH9lcXHEM+kKPJ7gauhDPT2DJufH\ny5P19Rzq6WFWWlrYif+kXq0tkqDuKy/nvvLy4ILIgfMmHNNTU6nx+Tjo9XJSjKumo8pJTjgLrLZt\ns7ZHH21VSuTkWAkKgM5OyMyMeYgSHUpOSMKaPn06Pp8Pb0if7EAgwOHDhzl8+HAcIxMZWVpaGtOn\nT493GCIyyX1ol3oDbGpvxxsI8Nbhw+SlpHBsVhYZMUigprhc3FxWxq32oMlHFy1ihl0BAVCWlsbj\nRx895PE77IqKRRkjt81o7e3lgvx8ekwz2J9bREQklJOcyCMvZl+zuaeHl5uagMhbOoFVkVDk8VDX\n00O9zxez5IRpmvysshKAr86YQUo0FgeqBYvImDjJzYHzJhylaWlsaG9P/sqJ2bOtrVM5sXWrtXU+\nN2RmQlqa1e6pqUnJiSSm5IQkLLfbzcyZM9m3b5/aOUlS8Xg8zJw5U1UTIhJ3H9o9ogG+MXMmXX4/\nn92+nRqfj7UnncQpubkxieOG0lLeamnh1NxcLisavqe3aZr829699Jom/2/evH7DsEeyODubF447\njkAyDwAUEZFxtYQl3MRNEbdxGoutnZ34gVlpaWG9ng2mODWVup4e6mJYCbm9s5OK7m6y3W6+ONaF\nV1VVcNdd1vaPf4xOgCKTlDcQYK3daeSjg1ROwARo6xSanDDN/pUTYCU5Cwrg4EFoaOgboC1JR8kJ\nSWgej4dZs2ZRV1dHR8jqT5FElZWVRXFxMZ4hhr+JiMSSUznx/dmzuX3uXDr8/uAHlQVhVCJES05K\nCs8uWRLWvm+2tPAfBw4AMCs9nZ125cTCCOJ1aUWmiIgMocz+E0sV9mvZ/DG89hanpkJHR7+WjePt\n6KwsKpcuZUN7O1NSxnj5KDsbXngBenpg+3Y46qjoBCkyCd1dWUm33VZ64HvkUjs5cTCkC0lSmjkT\nXC6rZdOhQ0cmJwDy863khF2ZJslJyQlJeGlpacyaNYuenh5aWlpoaWmhp6fniMHDIvFgGAYej4cp\nU6YwZcoUJSVEJKE4yYkl9jwH5+LI1JQUpibo76u77fYRALfs2UOuXYU22pWmIiIi8VbR3Q2MLTnh\nDMWui/Fq6MLUVM7Lzx/7ifLy4Oyz4aWX4NlnlZwQGYPj7Pf2nx1kho0z8yXpKydSU6GsDCor4e23\noa0NPB5YsKBvn4ICa6vkRFJTckKShsfjobCwkMLCQkzTxDTNIwYQi8SSy+XCMIyIB9qJiMSC3zTZ\n4iQnsrMB2B2FlZvjaUdnJ39pbATgpOxsNrS302W/1i9SckJERMK0oa2NzR0dLMvNpTwBXj/22K+/\n80JmLkWq2F4NXR/Dtk5Rd+mlfcmJW2+1VkXHy7e+BYWFcP31MCW2w9FFxuriwkI+OPlk5g/yO2XC\ntHUCq7VTZSW8+qp1f8ECK2nhcBKnDQ2xj02iRskJSUrOBWFXPN/MiIiIJLCKri46AwHSXa5gCyfn\n4kgsWzpF4h67amJlQQEPlpezZN06Wvx+0gyDmTEa/ikiIsnvx/v380xDAw+WlydEciIabZ2m2Rfk\n1rS28lR9PYUeD2dMmYInUT8TO50OQhdynXMO5ORATQ2sXQtLl8YnttZW+N//tW5/+cvxiUFkjI63\nFx8NNGHaOoE1pyYnB375S+v+Mcf0f95JTjiVE42N8Nhj8OlPw6xZsYtTxiRBX8VEREREZCyclk7H\nZGbiti8MBCsnxrByc7wc8vl4oq4OgG/OnMnM9HQeKC8H4CM5OZojISIiYZtjv85t7m7kIR7Cy/AX\n6Xz4uJM7uZVb8THyauP1rOf/8f/4K38NK549dlunsVROlNkXHN9qaeHTW7dyzsaNzH/3Xe6vqqLD\n7x/1ecfNhg3WhcQrruh7LC0NLrwQAN8zz/DH+npu37ePQKxbNu/bZ20LC61ZGCITiNPWqdbni/3P\nVrQVF0Nm5uDzJsD6GYa+5MRtt8GDD8JNN8UuRhkzVU6IiIiITEAD500AfKm0lJNzcjghAT+IB4Br\nS0rY3NHBmXZ7hWtKSpibnh68yCQiIhKOWfbFuVe8G6nhZQIE+ApfGXJ/Dx7e5328eGmggVJKhz3/\nAQ6wlrVkMHIlRKffT63dXmUslROXFhby1bIyKrq7ae3tZVtnJ5VeL1/bvZvTc3M5OTd31OceF4cO\nQXc39Pb2f/zSS+EPf8D1979z1QUX4DNNrikuZl4sqzqd5MScObH7miJhqOjq4icHDrCysJCLnHkK\nESr2eDAAP3CopyfYEi6pbd1qbQcmJ5zKCbstLK+9Zm03b45NXBIVSk6IiIjIhNDh9/N8YyP1Ph9f\nmTEj3uHE3Yft7UDfvAmAE3JyOCEnJ14hDas4NZUHFy7ENM1+s3zOyMuLY1QiIpKMitOsSx1NXoN8\n8rmSK4fd38CgiCKqqOIQh0ZMTrTQAkAeI79GOS2d8lJSmGoPtR6N7JQU7rMrCgG6/X6eqKtjXWtr\n4iUmwEpOABQV9X/81FOhoICUxkaOBjYCmzs64pOcmDs3dl9TJAy/rKnhkZoa3m9v58L8/OB74gaf\nD59pUujxkDpCK7cUl4tpHg91PT3UeL3Jn5zw+SAjA9zuoZMTTU19reQk6aitk4iIiEwIHX4/V2zd\nyld378ZrD1GezAarnEgGhto3iYjIGFWnbgLA68vhO3yHAkZegVyEdRH9EIdG3NdJTkxh5EHKFXZL\np2i3VEx3u7mhtJRHjzoqqueNGmdA7cDkhMsFZ5wBwBI7geG8Z4mZvXutrSonJMF8fcYM0l0u1ra1\n8drhw8HH762qomz1am7Zsyes85Ta1WMTYih2aiq8+CJs2QJTp/Z/zqkuaWyE+vq+xw1DyYokouSE\niIiIJL2AaVLk8ZDpcmEClfaFgMmqy+8PzpdItuSEiIjIWNWmWS09ery5zDPnBR/fz35+z+9Zzeoj\njokkOXEY66JhWMkJ+/U4ppUBicCpnHB6wodavhyAxXYf+c2xTk6ockISVHFqKtdNnw7AHfv3Bx8/\n1NMDQGGY1VfTnaHYEyE54bATLv2EJify8uCJJ6z7pgkhyR1JbEpOiIiISNJ7sq6O3FWr6LQrJvZO\n8uTE1s5OAkBBSgolyV7KLSIiEoEAAQ6mfgiAN2DQGjIsehe7+AN/4BVeOeK40VROhNPWKRrDsGOp\n2+/n4epqPmhrwxzLyuOh2joBfPSjACxevx6ALaqcEAm6ZeZMUgyD1w4fe/lhjwAAIABJREFUZn1b\nGwANo0xO1Hi94xNkonDaOnV3g98PZ57ZlxCtqYlfXBIRJSdEREQk6VV7vbSHXHzYN8mTE6HzJtQm\nSUREJpNKKul2t+BJsS7KHQy5ODdcUmG8KyfGMgw7lja0t/PlXbs4f9Om4Xc0TXj8cfjNbwZ/frjK\niZISWLiQ8tpaAPZ0dY0tERKJ5mZosb4PlJyQRDQrPZ0L7Ivub9mr/yNNTszLyGBBRgaZbvf4BJko\nsrKstk9gzZ0AKLVnBlVXxycmiZiSEyIiIpL0qgeULE/mygnTNPmjfUFALZ1ERGSy2cpWAPJSe4H+\nbU2GSyqEm5wIEKCVVutrhFM5kWRtnda0Wn+3pbm5Qy9w6O2Fb30Lbr8dvv99q6XKQMNVTgCccQZz\nGxp4fe1atp96ahQiD5PT0qmkxBqyKxJngyXmTsjOBvrmsTjJiaIwkxPfmT2bXaedxjdmzoxSlAnK\nMPoSoM6cG7stlionkoeSEyIiIpL0nFWRc+yWCZO5cuKnlZU839REqmHwLyUl8Q5HREQkprroIpNM\nStKsi3ihlRMHOAAMn5xopJEAgSHP3047fqxqzVxyh40lYJrBBRPRHog9XkKTE4PyeuHmm+Gpp/oe\nC+mND1hVFUMNxHaccQYev5+znnuOmWlpsav0VEsnSTCnrF/PJzZuZFdnZ/AxZ4HRwOREuJUTk4rT\n2mlg5cTBg/GJRyKWEu8ARERERMaq2r7wcHNZGSdkZ7NwAq6E+2N9Pc83NbGyoIDLhvig/1pzM/+3\nogKA+8vLOTEnJ5YhioiIxN1lXMYlXMLnUrfxIQ3ByonVrGYTVquiwSoe8snnX/gXiijCZOgWQx48\nXMu1dNKJh+EvFFZ7vfhMkxTDYMZgw1wT0IjJiR/+EP7+d6uVytSpUFdnJSdOOqlvn7Y2cCpWhkpO\nnHYaeDxW65V9+2I3nNqpnFByQhJAtdfL+vZ2DPonHpzkxJaODnoDARqVnBjawOSEKieSjionRERE\nJOk5bZ0+OmUK50ydyowkWZ0YiXfb2ni8tpa3nD7JA9T7fPyfrVsJAJ8vKeF65425iIjIJOPGzcy0\nTKCvcmIBC4LPp5I66DH/xD+xjGW4GbpPewYZrGQlV3DFiHFU2FUTc9LTSXEl/uWXg14vlV4vLuDk\noRY4vPGGtf3Zz+Css6zblZX993FaOuXkwFBJmcxM+MhHrNurVo0p7ogoOSEJ5B37ff1J2dlMDUk8\nLMjIIM0w6AwE2NDejjNZr0DJiSM5yYmBbZ1UOZE0Ev/VUURERGQYftOkxr7wUJZ65MWGiWK2/eF+\n/xAtq15obORQTw+LMjJ4qLxcg7BFRGRSK7XfEziVE0UUUUABAItYFJMYgvMmxnnRxJ6uLp5taODD\n9vYxnWe73VZmQUYG2SmDNNrw+cAeYs1pp8GsWdbtgW2dRpo34Vi+3No++ijcfTe89BLs3AlD/T1u\nuglWroQ9e8L42wzBSU7EqlJDZBg19u+n+QOqvlNcLo6xqyf+YQ/FnuJ2k5oESc6YK7B+rwcrJ8rK\nrK2SE0lD39UiIiKS1Op9PvxYb2pKJnByYqR5Go291uDPU3JzyXAPveJTRERkMii1k/qhMyfu4R4e\n4RHyyY9JDBV2cmLghcdou7eqiks3b+a39fVjOk+l/W81a6hkSksLLFli9XQvKIDZs63HDxzov1+4\nyYlzz7UG2h44AD//OdxwA3ziE9bXeOyxI/ffvh02b+47f6RMU5UTklAODTPo+jgnOdHcDKil05AG\nJiecyom6OvD7Bz9GEopmToiIiEhSc1ZEFqemJkXLhNGabV8oGKpyosn+cJM/2EpHERGRSWZg5QRA\njv0nVpy2TuNdOTHLTsRUDvEeIVzO8TOHasVUVATPPtt3f+ZMazswOTHSMGzHwoVWtcSaNbBpE2zZ\nYs2gaGmB4uIj959iDzK352JErLHRmodhGH2JFZE4OuRUdg2ywGpJdjbU1bHKbv2k5MQQ8uwZQo2N\n1raoCFJSoLcX6uv7khWSsPTpVURERJKaMwy7LEkGTY6Wk5xo6u2lrbeXnAFJiCa7ciJfH1xERET6\nVU6YphmXdofBtk7jXDnhVDocCKkSGY0q+/iwh3c7F/jr66GrC5y/p1PZUFg48jkWLLD+C9XRYV1c\nHMhJTthtbiLmVE1Mnz70LAyRGJqelsbxWVmDJjCdodgdgQCg5MSQ7H8fXn/d2rrdVnKzutoaiq3k\nRMKbuMsLRUREZFJwkhOlE7ilE0BuSgpT7Q/qg1VPqHJCRESkj9Pq0WeawQR+LB3y+djS0QGMf1un\nYOVESHLi3dbWYFupcDnHD1k5MdCUKdbQa+g/FDvctk5DycoaPHngJCfsleQRc+ZlzJgxuuNFouwH\nc+bwwSmncE1JyRHPOckJh5ITQ/jUp+CSS+CBB/oe01DspKLkhIiIiCS1yVI5AcPPnVDlhIiISJ80\nlyt4Me/gKCsKKqjgJ/yEdayL+NhvVVTQEQhwfFYWiwdcZIw2J5lQ5fXiN022dHTw0Q0b+KcPP4zo\nPBEnJ0LbI4W2dhpFcsI0TfymOfxOY01ORFLRIRJnJampFIQsOhqs9dNIdnR28kRtLbX2z/b+7m7K\n3nmHe0KTickuPR3uvRcuvrjvMQ3FTipKToiIiEhSq7Z7tU7k5ESH349pmn1zJwa5yKLKCRERmcz2\nsId66jHpu8A92NyJSLzDO6xmNU/xVL/zjuTtlhYes1fp/2LhQtzj3FJqeloabqDXNKn1+Xi2oQE/\nsKWzE6/T8iQMTlunmZHMyBhs7kSEyYnvVlQwddUqfjbSBdOxJiecnvTOAF2RBGYYhjV3wjaayolr\ntm3j89u387rdCs1jGCzNzeVH+/fTEoeKsphR5URSUXJCREREkpqzGrJsArd1WrxuHbmrVgUTEKqc\nEBER6e9e7uUGbuB93g8+Fjp3YjQu5mJSSWUXu/gQqwrhWZ7lNV7Dy+Dn7A0EuHHnTgC+UFLCMueC\n+jhyGwb/fdRRPL9kCVNTUnjeuQhP+EOyO/x+mu33EmHPnIC+yon9+/seC3cgts1lGLT4/cEZHUNy\nBt+ONjnhxKXKCUkSoa2dRpOcOC03F4C1bW0ATE9NZXtnJ4d7e/nlRL5wX1pqbWtq4huHhEXJCRER\nEUlqA9s6eQMBTl2/nqK336ZtAqwI8psm1V4v7X4/p9h9nTVzIgGYJnzve3DLLbBunXVfRETiooce\narAuQs1iVvDxwSonNre380Rt7cgthIA88vg4HwfgCZ7gv/lv7jr8Ej9u+x0B+ioSDvf08JMDB/jS\njh2cu3Ejmzo6yE9J4T/nzYvK3y+oqgqGqAL5bEkJFxYU0BUIsKa1Nfj4YNWWg3GSGDluN1MieS/h\nVE44VQ9+f1+FQpjJifl2pcaekRIp0aqcUHJCksRYkxOn2p8d3rV/JxiGwTftn9n7qqvpiaCyKqmU\nl8MZZ8DixfGORMKg5ISIiIgkNaetk7M6Ms3lYndXFw09PewNc7VgIqvz+egxTVzAUnv108DKiZ5A\ngFa/H1DlRMxUV8OTT8Kf/gSf/jScfz6sWhXvqEREJqWDHMSPn0wyKaCvZc9glRPfqqjg89u38609\ne4KP3VNZyfkbN/IXZ2V9iEu4BDduKqjgyY63WfXBp1iz6TLSzL7qggeqq/lWRQWP1NTwhn3h/Kfz\n51MYzarOd9+F5cut15v33x9ytxebmgi93DjYgobBVEU6b8IxsHKiudlKUBgG5OeHdQpnYPiIlRP2\n+yBVTshkEZqcKBpNcsL+mdnQ3h5MRFxVXEyxx0OV18v/Oi3YJpqlS+E3v4Gbb453JBIGJSdEREQk\naXX6/Ry2qyNC2zo5b+TX2SXMycwZTlmalhb88D7wQsPhkAqRPFVOxEZzs7VNTYWMDNi5E3760/jG\nJCIySR3Amncwi1kY9M13GFg58cbhw7zQ1ESKYXCD0/YD2NTRwYvNzWxsbz/i3EUUcSM3cgZnQPUl\ngIvungwae/pee3faF9UvzM/nkYULWXPSSVzr9DyPlk2brO3evXD55XDXXTBIhajT0sn5Vwg3OeG8\n34iopRPALLtSpaoKAoG+eRP5+RDmexLn/c2B7u7hV3KPtXJi5Uq48kqYP390x4vE2LFjrJwoz8gg\nLyWF7kCAzR0dgLWQ6+YZMwC4q7ISU9W/EmdKToiIiEjSclZCZrpc/VoQnGF/eH3LHv6WzJw2CzPT\n0phjtz2o7+mh066UgL55E1Pc7nEfuik258LI3Lnwxz9at/fuVXsnEZE4CE1OhAqtnDBNk29XVABw\n/fTplGdmBvdzXl8Hm+kEcA7ncH3v11hXNzX4WFVINYbzWn11cTHXl5YG+7xHldM7vaAAPB54/vkj\nKvb8psnfm5oAON+uWog0ORGsnKiqgssug7/9zbq/Zw+sXg11df0PnD7dSkL4fFBb25eciKA6oSQ1\nlXSXCz9wYLg2VGOdOXHttXDHHUpOSNLISUnh6zNmcElBAQvsJF4kXIYRbAu7NmTR1pdKS8lwufig\nvZ1/TIDPS5LclJwQERGRpOW0dCpLS8MIuSi/3ElOjPbDawIJvViQl5JCjtsNWKsLHcF5E2rpFDvO\n91ZuLjg9xdvaQB/wRERibsjkREjlxMvNzaxpbSXD5eLfnFZENic5Mdx8hifr6mgLWRhQGZqcGG3V\nQSSc5MTNN1uJiX/8A1as6LfLutZWmnp7mZqSwhXTpgHhz5wItnWy/y24+26rfdSNN1r3//xn+Mxn\n4MEH+x+YkmIl6ufOtV4blyyBxx6D/8/ee4fHUd7r359ZrYrV5S7Lcm+4YMDGFIOxaaETIEBMDIQU\nSEhC4CTAOeSENEg4EBIOIRCSAC8l4cSU0JIfvZpiA8a9W26SLduSJcuSVtKWef945tldrXe1RbNN\n+n64fM1Kmt0dCWl35rm/933/13/F/K05DINxuneip2gn7ZxoaZFhAKHf8PsJE3hhxgwcCQ4g/WzM\nGJYcfTRXDhvm/9yg3FyusF4jtNtKENKF+P4FQRAEQchaQsuwNSeWleEAajo62N3Z6Z+czEYmDhjA\nwqFDOamsDMMwGFNQwOq2NrZ3dDDFsnpr54SUYacQLU6Ul0NBAQwfriZGd+yAioqe7ysIgiD0iv/j\n/1jDGm7lVkooYReqjLma6m776ff/PZ2d/Hz7dkBNDA8POS+I5pwwTZMHd+8GwGkYeEzTL0j4TDPx\nvoZ4sJ6fysqIk//HlZayYvZsalwufz59ws6J4AGPbduUkwLAioPpxmuvqY4JTYhoEgvjBwxgXXt7\nbOKE1wutrWBNhAtCNrK+rY0Gt5sJAwZQmcTXjrn67yaE40tLeaS+ntVW3JMgpAtxTgiCIAiCkLXo\nC+mqkMLJUqeTI4uLAViSZe6JJreb723axFtWp8F5gwfz96lTub6qCoDRYaY7xTmRBvTvlb7g05nb\nuhBUEARBSApevPyDf7CWtSxmMZ10Uk89cLhzYlhuLgbgBT5uaaHA4eDm6urDHtPvnOjowBdmIv+j\nlhZWt7UxwOFgoTVtrKOcDGDDnDm8f9RRyXVOaHEiqCsjFMMwmFlczEXl5Yz++c/VcXZ24o3BZaC/\nH//3UF8f+OKbb/YsTtgQKRlTKXZBgep6gsSjnQQhQ3igro55K1b4hc9Uo6+VVoXp2hGEVCLihCAI\ngiAIWct6a9JnUlButCZbo53+s6aGB3fv5hsbNoRdTAg33TkyP5+vDh3KfJ3FLCSfUHFizBi1FXFC\nEAQhqWghAuAjPuIAB6iiigoqKKf7+6DT4WBY0ADDtZWVYSeUq/LyyDcMRuTl+QX/YB6sqwNg4dCh\nzLBci3pAwjAMxg4YwMnl5TgdcS6x1NTAzTerbU90dkJDg7odS9H2G28w4qWXcHo8eEyTPTFEO3Vz\nf3i9qmMi6PH84kQYcccOJlrixPr29p537G3vhCBkCPut15ohaRoumlZUhAHsdbvZZ0XlCkI6EHFC\nEARBEISsZa11ATvNWigIRosT2eScWNnayl+tTOmdnZ28ZpVaBjPaWlQJjmlYUFHB01OncltIhraQ\nRII7JwC+8hW47z644IL0HZMgCEI/oIbAQv5kJlNEEX/gD/yFv4TdX/dO5BkGt4waFXYfp8NB68kn\ns/2EExgc4sbc1dHBYqvk+fqqKn/sUW2MXQ498thj8Oyz8Le/9byfLqHOzwer6LpHGhrIMU1GWi7M\naL0ThzweDlp9GtX5+arP4U9/gquvVjt8/jns26duh3NO2MCR1rncymhT3HooIIvO7wQhHOkWJ4py\ncvyOJYl2EtKJiBOCIAiCIGQlPtNknXUiPS2Mc+Ik6+J1ZWsrB61OhkzGNE1u3LIFH1BgTV7+OYzN\nO1outpAidPG1nuA89li48MKAg0LIHNatg1deSfdRCIJgE1qcOJVTuYVbKEWJxDnkhN1/lPW++a3K\nysM6qoKJ5Hq4r7YWj2lySlkZs0pK/IXRu+wQJ7ZsUdtosS7BfROxRChZLovRVtFttN4J/b2UO50U\nO52q5HrBAvj5z2HqVPD51I5FRYH3PZvRETN1XV00hnGv+BFxQugj7LfcCukSJyAgCkq0k5BORJwQ\nBEEQBCEr2dHRQbvPR55hMMGa+gmmMj+f8QUFmMBHWXAB+8+GBt5tbqbA4eCF6dMBeKWx0V/6rRkd\nlIstpIgnn4QTT4TNmwOfa2lR2wglg0IGccMN8IMfKJFCEISsR4sTR3BETPv/bPRobq6u5s6xY+N+\nria3mz9bjsZbLddFsHMiXD9FXOjoJOs5IqK/3kPfRDd27gTiFyfCdmacfnrg9siRtvRLhKPU6WSs\ndY7To3tCOxb1kIAgZCl+50SIWyuV6Ji6PumcaG+Hf/8bnn463UciREHECUEQBEEQspJ1VqTT5MLC\niNOOJ1vTfZneO+H2+fixtUBxc3U1Xxo4kHllZXiBR0IWLLRzYndXF516klFILrffrhaG7rwz8LnQ\nzgkhMzFN/yIdq1en91gEQeg1JqZfnBhLbGLDUSUl3D1+POUJTCc/uHs3rV4vM4qKOMuKU6rMy8MA\n3KbZu5z2lhaw4qK6lU+HQ58LxNI3Af7+o9GWgyKaONGtbyKUM84I3K6qiu35E2Sm5Z7oUZyQzgmh\nD+AzTb9DaHA6nRN9uRS7tRW+9z347/+GLHDR92dEnBAEQRAEIStZa034TA0T6aTJlt6JNW1tbOvo\noCwnxz+ZeZ01Hfkva+pRMzg3l0JLjBH3RIoJnirTE5siTmQ2TU2g40E2bkzvsQiCYAs/5+dcz/WM\nInx/hF24vF7ut0qgb6muxrAcA7kOB5XWpHOvop2CC6f37Qu8VoUjXnEi1DkR5Th3WecTYcWJadMC\nz5ukvgnNzFh6J/T7rnYwxspTT8HDD8OuXQkenSDYR5PHg9e6nU5xQjsn1ra34+2tE8zCa5rcsnUr\nL1niaNoYNAhyclQsnRaChYxExAlBEARBELISLU6EK8PW6N6JZS0tdGWwy6DGWhSYUlhIUY7KzL54\n8GCenzaNJUcf3W1fwzD85XVbXa7UHmh/J9ihI7FO2YEucAXYtCl9xyEIgi0YGIxnPGdwBrkkd0Hv\n8fp69rndjMrP5/KhQ7t9TS/i2yZOmGbPi2fBnROaxkblCLMEFD+HDsGBA4BNsU6GARdfrG7PmtXj\n4/QWv3Oip4iZRDsnHn8c7rpLxAkhI9B9E6U5OeRHcICngnEDBlDocNDh87HFpuuKDw8e5J5du/hu\nus+7cnJAv3ZHc6cJaUXECUEQBEEQspJYxImJAwZQ4XTSaZqsyeAs1RrrYmB8UHdGQU4OFw0ZQm6Y\nCxbdsbFZxInkE/wz1jnbHo9a/AERJzKdvXsDtzdsSN9xCIKQVbR7vdxpuQ9+VF192HuxLsWu7Y04\nocuwNT31ToTrnPj97+GCC2Dx4u776ig7uosTZg9T0T3GOgHcdBO8+iqcf37kY7QBLU6sa2vDHWmo\nRL/vxts5oZ2ogwcneHSCYB/+vok0uiYAcgyD6TaXYmsxdHdXF7tjeI3s9Plo93qj7pcQw4apbfCw\nipBxiDghCIIgCELW4TNN1ludEz2JE4ZhcIx1ofu5XkzOQLQDYlyYYu9wTLT2s2vCSeiB4ElWfYHl\n9cKNN8I11wSKOYXMJPj/X2MjpDtiQBCErOC+2lpqOzsZlZ/PtWGilGx3TkBs4kTwsQwZorahr2tW\n3wTFxVRffz0A7T6fP98+HPr70KLLYeTkwOTJSSvD1owpKKAkJ4cu02SDdZ53GEcdBddfD+eeG/sD\nu90q5g9U1IsgpIFmt9u/WJ8JZdgaHe20yqZBrrqg18Vo119dPh/TP/2Uoz/7jI5kCBTDh6ttKp0T\npglXXw3z50s3ToyIOCEIgiAIQtaxo6ODdp+PPMNgfKQLaYtZJSVAZosTOtZpXJTvRTNBxInUEbq4\nDZCfDz/8oSrKTvPEmxCFYOcESLSTIAhR2dfVxV2W++DX48ZRYMUtBqPjj57fv59BS5Zw1sqV8T+R\ndk7oxfJIi2ft7QGXQLA4oR0AoXFQ2jlx2mkUXHUVw63Fz0i9E6ZpBsSJSM6JFOEwDI6M1jtx1FFw\n881wzjmxP7AWJhwOqKjo1TFevX498774gvfidW4I/Z7TVq5k6rJl7O7szBjnBARKsVfb5JyosyKr\nIPr11/r2dra4XGxyufi3FUdnK9o5EXo+mEzeew/ef18JxS+8kLrnzWJEnBAEQRAEIevQkU6TCwtx\nRslp9YsTNp1wJ4NwsU49ERzr1OR289fdu3lZJsKTQ7ANXH7G2UeojV9KsQVB6IFWj4dL167lkNfL\nrOJiFoZ0TWj0In5NRwcHPB4Oxjvx29kZ6D446SS1jSROaNdESUl3t54WJyI5J0apwvDR1rFG6p2o\n7+qi1evFAYxKszgBMfZOxIv+GQ0c2L0/KgE+bmnhg4MH8dlUHiz0D9w+H8tbWzno9fLPhgZ/50Qm\niBNJdU5Euf4KFkT+lgwBIR3OiQcfDNz+v/9TTgqhR0ScEARBEAQh61gbQ6STRosTq1pbM7IU2+Pz\n+acZY3VO6Fin7R0dbGhv59ubNvG9zZuTdoz9mqIimDlT3Xa5oKcLt44O+Pa34UtfCkRACelFixN6\nUlZ6JwRB6IGzVq3ifSuG47fjx+OIEGMU6jCI23GwY4eKCCwuDrzHRIp1CleGDYFYp0jOidGj1cY6\ntzhMnOjogN27/QMfEwYMUC6Rq6+GK66A9evj+55swi9O2DlUosWJXvZNeHw+tlk/x4kxDpQIAsC+\noFi1f+7fn1HOCS1ObOvo4JDH0+vHC+7i+SyKcyJYEHmlsZHmHuLnEkILzKlyTnz2GXz6qXJW5+Wp\n887Vq1Pz3FmMiBOCIAiCIGQd/jLswsKo+44rKKDc6aTLNP33yyR2dXbiMU3yDYMRMS5ujMjPp8Dh\nwGOarLAu3gc6nck8zP7LvHnKkq0XIXS0Uzjy82HpUhUdpCdihfSixYmLLlLC0Vlnpfd4BEHIaC60\nFq+PKCxkfg/xP6HdDHGLE3l5SgC44IKA6BDNOREqTgQ7J4Inc0OdE5HEiVtugfnzWWPF3U0rKlKC\nydKl8PHHEOPAhN0kVZzoZd/EDuucrcDhiPmcTRAA9gZFHb3b3MwmyzWdCZ0Tg/PyGGEdx2obrpWC\nnRP1UUqxg0u4u0yT5+x2KafaOfGnP6ntxRcHzjkXL07Nc2cxIk4IgiAIgpBVdPl8rNHiRAzOiUwv\nxdZ9E2MHDIg4oRmKwzD80U5LW1oAGJgBk1d9Gr2g0dNFk2H4F4PYvj3phyTEgJ6UO/dcuO02WLAg\nvccjCEJGc31VFY9NnswnxxzT437D8/JYOXs2Z1gCRtzixJgxcOed6t8JJ8C//gWPPBJ+32jiRGcn\n6AW+rq7A/iHOie2h4sSyZeB2s9aKu5teVKSEjc5OJbbr97MUM72oCAM1aV5vlwtRDxb00jmhu77G\nFxTEfM4mCNBdnPACr1v9CpngnACYal1TRSyijxGPz0d9SGRVT9df2jlxgXWe/ZTdDodUdk5s3Ahv\nvaWuB669Fi6/XDnjZs1K/nNnOSJOCIIgCIKQNbR4PJy9ahVfWBfhsYgTEIh2imYtTgdbrQvdWCOd\nNH5xwvqexDmRZCJle4diLQb5J1eF9GGagbiTCLnxgiBkD3dxF3dzN3uIEH9kA0U5OXy9spLSKO+p\nOYbBkcXFtFpdE6FOirgoK4OpU6G8PPzXjzsOrr/+cHG1sFBFD0Lgta62Fnw+5fazYp/0+UVNsDjR\n2elfrFtrxbhMKyoKTPjOmgVhisBTQVFOjj8yybbeCZtinTZbC7cTY3DuCkIwweIEgA6azRRxYrL1\nN7epl+LEXrcbH5ADXDpkCPPLy8mL0PPS0NXFHuvn8utx4wB4r7mZ2gj9OAmhnRNtbQERN1k8+qja\nnnUWjBunhOcXXlDuXaFH5CpWEARBEISs4Z3mZt5ubgYgzzBiXtDP5FJsXYY9Ls7sYi1O6AkncU4k\nmVjFiTFj1HbbtqQejhADzc1qihgC2eyCIGQlXrx8zud48PB1vp7uw/Ezs7gYj2kyPpkRSMcfr/6F\nY8gQtejW0KAWw8aOVY6IffvU9C4w3jpf2OpyYZomhmH4eyxMYK3lyJgG8Pe/q8f9xjeS9/3EwKyS\nEja5XPxz/36+NHBg7x/QZufEBOmbEOJkr9WlMKOoqFt0UqaIE5MswW2j9TueKDrSqTI/nz9OmtTj\nvvrnMLaggGlFRZxcVsYHBw/y9L593GyXc6uoCEpK4NAhJchabvqksHy52l52mdqKuypmxDkhCIIg\nCELWcOHgwdxtTdbMKS3FGWESJ5TZGVyKrScZ413YCC1iFOdEkokl1glgwgS1lYLy9BNchi3Z4IKQ\ncXTSyTa2cYADUfetpx4PHgooYDC9W2C2k4cmTWLZrFkcZZ1npBy92K6dE4ahBItp0/y7jCkowAG0\nB8Wt6F6k2oEDaSksxOn1Mumll9QC3vjxaY/A+86IEQA8Wl/P9l5ugD7yAAAgAElEQVQulgK2dU70\nVXHigNvNtzdu5GOrDF6wH+2c+NLAgYwOOifJhM4JgMmWONFb54QWJ6pi+L50pNORlgPsa1YE03P6\n9cwudLRTMnsn3O5ApGsUUUY4HBEnBEEQBEHIKn5cXc2So49m8dSpMd8nk0uxe+ucMIBXZszgSm1b\nFpJDrM4JfUGyaVP3glIh9TgccOqpcOKJ6T4SQRDCsIMd/Af/wa3cGnXfXajF9JGMxCHLGAFieG/K\nczgYZQ1A6ChJamsBWDN3LgCT6uvJe/BB9bVvfUu9fqaReeXlnF5Rgds0uXPnzt4/oE3Oic3Wz++9\n5mYmLl3Kj7Zs6e2RZQSP7tnDX/fs4dd2/KyFsGhxYlhuLl8O+j3MFOeEjnXa4nLh7cX5a60WJ2IY\nCtFl2Edaboa5paUA/rJw20iFOLFjB3g8yqkR2g8UjRdegCuvhLq65BxbFiDv6oIgCIIgZBWGYTC3\nrIzKOCahM7kUe6vlnEi0c8JpGHypoiLm/g0hQfSFZGMjPPkk/OEP4Uuvx49XizrNzdGFDCG5TJyo\nCmYfeCDdRyIIQhhaaAGggQb+yl973LcWtZg+kpFJP66sIpo4sWMH3HQTEyynhJ78p7oaLryQtaec\nAsC0ujpoalLOggzJR/+FFZP42J49AVFF89prcPfdsHSpirV69NGeC29tcE54fD62WedsYwoK2OJy\nBX6eWY7uL9thZ9a/0I19VqzTsLw8LrKiJstycihMU7dLKNUFBeQbBl2myfZe/B5o58TIGK7TVoc4\nJ0Zb10JNHg8HrS4cW7CrFPt//xeefjr817RQOX58/HFOjz4KS5bA7bf37viyGBEnBEEQBEHoF2Ri\n70ST202zdfIdr3NiZH4++YaB2zTZaV0ICElk2jT46ldh3jyVyf2730G4CcOCAtA5uZs2pfYYBUEQ\nsggtTgC8xmv4iBy7GOycEILQfTqRYlA8HnjhBcZv3AgEBiI4+WS47z4WnXceL3d18cM33lCfv/rq\njInBO7GsjLMGDsQL3LFjR/cvvvkmPPQQfP45/OIX8KtfwX33hX8g07TFObGrsxO3aZJvGBxvTXjX\n9pHzr2Ut6m9xp4gTScPvnMjLY15ZGf8zbhx/njw5zUcVIMcw/EXvvYl2qrO+z2jOCa9pssYSJ2ZY\nA2QlTieDrJhaW4Uy7S7vjXNi40b1GnPbbSrCKRQd56rjXePB6gDi7bfVa1s/RMQJQRAEQRD6BVqc\nWJ5BzgndNzE8Ly/uySmHYfhLLvvK5F5Gc+yx8JvfwOWXg85kL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Kd0OicynXXr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wWLkSXn655/v88IfKQWFNMobF4wks9meyc6KgAKwLs24dHKkgnHMC1EL8BReo29nunrjuusDH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\nnp8VIWLYubORe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1aoyoxARI2eEHFCEIS+xjC6WzslJqrletLImjURO7y2UsohBwOXCTM6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\nIZaxrNd2p04dxHWbH2Xkxo+IT07wr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HQRrFunROMHHohItwVBEKJGRwccOKDuDxniPI9+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x0Wxct6jYToVCEQm6rkflnEhIMPHTnxYB8KtfFdPW5uue+OILsS4k45+8Ccc5cfiwKJ5O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\nVw7HLOLJ5dK5+OI1nHXWxzz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LobGxFuLigpsJ2xiHOMSDPEgttWiIi4Z1rAvoPhJ+/k9H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\nolIBb1UUBEERW1CBoIRLMARCQMM1gZCs8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d5627f0>"
      ]
     },
     "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 cumulative sum of all series\n",
    "p.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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B84CnV2+RuUKPAj5R1ef9pDlK0KcK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\nzMzMzMzMDID/B0y+wMVncZ/yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11efe0940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = df[:10].plot(kind='bar') # First 10 arrays as bar plots  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11eb29a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = df.boxplot() # Boxplot for each of the 4 series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x105f86a20>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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Q958NI7PSEFaBgRU+IE0jp5E6oyGPgPwpn7BweN4poYb8Q1+Eq5nox1GTiuRZ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\nMCA1MDAgNjEzIDYzNSA1NTAgNjM1IDYxNSAzNTIgNjM1IDYzN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D9Qz059g//NWsfPdHUtu1tyE9Zj7ljCx4ydMbUhsAgOZxpgYAAAAtN+X4xTnh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Cjo31GD99YGwtlGwHnlzXe0PjGEvi4BPrHG1qAIB/5qnI++RVaPrLE7bEy7v6CmScPsuWWOlO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\n+PtIPfPrpsSzUtVLG2GEnd+l8TmpG6h8fr3TZRDRUXjLTgUgEorhm8Ih4X2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e3c5cf8>"
      ]
     },
     "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 Examples](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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