3_Numpy_and_Scipy.ipynb

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    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Numpy (<font color='blue'>Num</font>erical <font color='blue'>Py</font>thon) and Scipy (<font color='blue'>Sci</font>entific <font color='blue'>Py</font>thon)"
     ]
    },
    {
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     "collapsed": false,
     "input": [
      "from IPython.display import Image, HTML"
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     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
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     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
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     "input": [
      "%matplotlib inline"
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     "input": [
      "HTML('<iframe src=http://www.numpy.org/ width=900 height=350></iframe>')"
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     "metadata": {},
     "outputs": [
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        "<iframe src=http://www.numpy.org/ width=900 height=350></iframe>"
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    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<hr size=5> \n",
      "# Numpy\n",
      "\n",
      "**Numpy** is the fundamental building block of the so-called **Python Scientific Stack**   \n",
      "\n",
      "* It provides a special data structure, the numpy **ndarray** (*N* - dimensional array) which allows the representation \n",
      "of arrays of numerical values, integers, reals, complex  \n",
      "\n",
      "* Numpy arrays by default can only hold objects of the same type (e.g. cannot mix integers and floats))  \n",
      "\n",
      "* It also provides with random number capability, some convenience simple statistical functions (mean, std, ...) and some essential linear algebra operations "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## 1. Numpy basics "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Constructing an numpy array "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = [1,2,3] # x is a regular python list"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.array(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x"
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     "metadata": {},
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      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 108,
       "text": [
        "array([1, 2, 3])"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.array(range(10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 109
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
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     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 110,
       "text": [
        "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = [[1,2,3],[4,5,6]] # nested lists"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.array(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 112
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    {
     "cell_type": "code",
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     "input": [
      "x"
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     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 113,
       "text": [
        "array([[1, 2, 3],\n",
        "       [4, 5, 6]])"
       ]
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 114,
       "text": [
        "(2, 3)"
       ]
      }
     ],
     "prompt_number": 114
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The shape attribute is a tuple containing the dimensions along each **axis** (0 = rows, 1 = columns, ...)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 115,
       "text": [
        "(2, 3)"
       ]
      }
     ],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.arange(1000).reshape((10,10,10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 116
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x.shape"
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     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 117,
       "text": [
        "(10, 10, 10)"
       ]
      }
     ],
     "prompt_number": 117
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "** Be careful**: keep in mind **references** Vs. **copies** of arrays!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 118,
       "text": [
        "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
       ]
      }
     ],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 120,
       "text": [
        "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a[2] = 99"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 121
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 122,
       "text": [
        "array([ 0,  1, 99,  3,  4,  5,  6,  7,  8,  9])"
       ]
      }
     ],
     "prompt_number": 122
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 123,
       "text": [
        "array([ 0,  1, 99,  3,  4,  5,  6,  7,  8,  9])"
       ]
      }
     ],
     "prompt_number": 123
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     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**x has changed !**, because x = a only means you create a new **reference** pointing to the **same object** as the array `a`  \n",
      "\n",
      "If you want to create a **copy** instead of a reference, you need to do it explicitely"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = a.copy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a[3] = 888"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
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     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 126,
       "text": [
        "array([  0,   1,  99, 888,   4,   5,   6,   7,   8,   9])"
       ]
      }
     ],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 127,
       "text": [
        "array([ 0,  1, 99,  3,  4,  5,  6,  7,  8,  9])"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Numpy array types "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Numpy tries and guess the correct **type** of the numerical values if you pass a list, here x is of type integer 64"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x.dtype"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 128,
       "text": [
        "dtype('int64')"
       ]
      }
     ],
     "prompt_number": 128
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can change that using the method **astype()**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = x.astype(np.float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 129
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x.dtype"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 130,
       "text": [
        "dtype('float64')"
       ]
      }
     ],
     "prompt_number": 130
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 131,
       "text": [
        "array([  0.,   1.,  99.,   3.,   4.,   5.,   6.,   7.,   8.,   9.])"
       ]
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "you cannot mix different types, numpy will cast all values to the safest dtype"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.array([1.0,1,1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 133,
       "text": [
        "array([ 1.,  1.,  1.])"
       ]
      }
     ],
     "prompt_number": 133
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Some convenience functions for creating arrays"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "np.arange(*start*, **stop**, *step*, *dtype=None*)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.arange(100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 134,
       "text": [
        "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
        "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
        "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
        "       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
        "       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
        "       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])"
       ]
      }
     ],
     "prompt_number": 134
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.arange(0,100,10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 135,
       "text": [
        "array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`np.linspace(start, stop, num=50, *endpoint=True*, *retstep=False*)`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.linspace(0,0.5,10, endpoint=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 139,
       "text": [
        "array([ 0.  ,  0.05,  0.1 ,  0.15,  0.2 ,  0.25,  0.3 ,  0.35,  0.4 ,  0.45])"
       ]
      }
     ],
     "prompt_number": 139
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "same as np.arange(0,0.5, 0.05)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.arange(0,0.5,0.05)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 140,
       "text": [
        "array([ 0.  ,  0.05,  0.1 ,  0.15,  0.2 ,  0.25,  0.3 ,  0.35,  0.4 ,  0.45])"
       ]
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`np.ones(shape, dtype=None, order='C')` creates an array of ones, similar function is np.zeros"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.ones((10,12))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 141,
       "text": [
        "array([[ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
        "       [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
        "       [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
        "       [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
        "       [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
        "       [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
        "       [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
        "       [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
        "       [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
        "       [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.]])"
       ]
      }
     ],
     "prompt_number": 141
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`np.empty` can be used to pre-allocate \"empty\" arrays with small memory footprint "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.empty((5,5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 142,
       "text": [
        "array([[ -2.00000000e+000,  -2.68156175e+154,   2.12325858e-314,\n",
        "          2.18209237e-314,   2.12424592e-314],\n",
        "       [  2.18209239e-314,   2.12327588e-314,   2.18144762e-314,\n",
        "          2.12424592e-314,   2.18209242e-314],\n",
        "       [  2.12327588e-314,   2.18121852e-314,   2.12424592e-314,\n",
        "          2.18209244e-314,   2.12327588e-314],\n",
        "       [  2.18145174e-314,   2.12343765e-314,   2.18209247e-314,\n",
        "          2.12327588e-314,   2.18209249e-314],\n",
        "       [  2.12343765e-314,   2.12358380e-314,   2.12358380e-314,\n",
        "          2.12358380e-314,   2.12358380e-314]])"
       ]
      }
     ],
     "prompt_number": 142
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<hr size=5> \n",
      "\n",
      "### 2. Array indexing "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### 1. Simple indexing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = np.arange(25,dtype=float).reshape(5,5) # creating a 2D array, note the dtype"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 143
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[  0.   1.   2.   3.   4.]\n",
        " [  5.   6.   7.   8.   9.]\n",
        " [ 10.  11.  12.  13.  14.]\n",
        " [ 15.  16.  17.  18.  19.]\n",
        " [ 20.  21.  22.  23.  24.]]\n"
       ]
      }
     ],
     "prompt_number": 144
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 145,
       "text": [
        "(5, 5)"
       ]
      }
     ],
     "prompt_number": 145
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b[0,1] # FIRST row, SECOND column, remember, Python indexing is zero-based"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 146,
       "text": [
        "1.0"
       ]
      }
     ],
     "prompt_number": 146
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b[:,0] # first column "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 147,
       "text": [
        "array([  0.,   5.,  10.,  15.,  20.])"
       ]
      }
     ],
     "prompt_number": 147
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b[1,:] # second row"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 148,
       "text": [
        "array([ 5.,  6.,  7.,  8.,  9.])"
       ]
      }
     ],
     "prompt_number": 148
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b[:,2:] # all rows from third colum"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 149,
       "text": [
        "array([[  2.,   3.,   4.],\n",
        "       [  7.,   8.,   9.],\n",
        "       [ 12.,  13.,  14.],\n",
        "       [ 17.,  18.,  19.],\n",
        "       [ 22.,  23.,  24.]])"
       ]
      }
     ],
     "prompt_number": 149
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b[-1,:] # last row"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 150,
       "text": [
        "array([ 20.,  21.,  22.,  23.,  24.])"
       ]
      }
     ],
     "prompt_number": 150
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b[:,::-1] # handy: reverses column order "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 151,
       "text": [
        "array([[  4.,   3.,   2.,   1.,   0.],\n",
        "       [  9.,   8.,   7.,   6.,   5.],\n",
        "       [ 14.,  13.,  12.,  11.,  10.],\n",
        "       [ 19.,  18.,  17.,  16.,  15.],\n",
        "       [ 24.,  23.,  22.,  21.,  20.]])"
       ]
      }
     ],
     "prompt_number": 151
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 152,
       "text": [
        "array([[  0.,   1.,   2.,   3.,   4.],\n",
        "       [  5.,   6.,   7.,   8.,   9.],\n",
        "       [ 10.,  11.,  12.,  13.,  14.],\n",
        "       [ 15.,  16.,  17.,  18.,  19.],\n",
        "       [ 20.,  21.,  22.,  23.,  24.]])"
       ]
      }
     ],
     "prompt_number": 152
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "slices allows to easily select **contiguous** row / colums"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b[0:2,1:3] # first two columns, and columns 2 to 3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 153,
       "text": [
        "array([[ 1.,  2.],\n",
        "       [ 6.,  7.]])"
       ]
      }
     ],
     "prompt_number": 153
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "When you want to select **non-contiguous** (or repeated) rows / columns, you can use the **ix_** construct"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b[np.ix_([0,1,3],[1,1,3])]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 154,
       "text": [
        "array([[  1.,   1.,   3.],\n",
        "       [  6.,   6.,   8.],\n",
        "       [ 16.,  16.,  18.]])"
       ]
      }
     ],
     "prompt_number": 154
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 155,
       "text": [
        "array([[  0.,   1.,   2.,   3.,   4.],\n",
        "       [  5.,   6.,   7.,   8.,   9.],\n",
        "       [ 10.,  11.,  12.,  13.,  14.],\n",
        "       [ 15.,  16.,  17.,  18.,  19.],\n",
        "       [ 20.,  21.,  22.,  23.,  24.]])"
       ]
      }
     ],
     "prompt_number": 155
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### 2. Boolean or conditional indexing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = b[b>10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 156
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 11.  12.  13.  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.]\n"
       ]
      }
     ],
     "prompt_number": 157
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<hr size=5>\n",
      "\n",
      "### Array broadcasting"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### 1. Simple possible case: scalar broadcasting"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.array([0,1,2,3]); print(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0 1 2 3]\n"
       ]
      }
     ],
     "prompt_number": 158
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x + 3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 159,
       "text": [
        "array([3, 4, 5, 6])"
       ]
      }
     ],
     "prompt_number": 159
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<center>\n",
      "\n",
      "![](files/images/broadcast_scalar.png)\n",
      "\n",
      "</center>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### 2. 2D broadcasting "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.arange(12).reshape((3,4))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 160
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 161,
       "text": [
        "array([[ 0,  1,  2,  3],\n",
        "       [ 4,  5,  6,  7],\n",
        "       [ 8,  9, 10, 11]])"
       ]
      }
     ],
     "prompt_number": 161
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = np.arange(3).reshape((3,1)); # or y = np.arange(3)[np.newaxis,:] or y = np.arange(3)[None,:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 162
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 163,
       "text": [
        "(3, 1)"
       ]
      }
     ],
     "prompt_number": 163
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x + y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 164,
       "text": [
        "array([[ 0,  1,  2,  3],\n",
        "       [ 5,  6,  7,  8],\n",
        "       [10, 11, 12, 13]])"
       ]
      }
     ],
     "prompt_number": 164
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<center> \n",
      "\n",
      "![](files/images/broadcast_2D.png)\n",
      "\n",
      "</center>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####  3. 3D broadcasting"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.arange(15).reshape((3, 5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 165
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 166,
       "text": [
        "array([[ 0,  1,  2,  3,  4],\n",
        "       [ 5,  6,  7,  8,  9],\n",
        "       [10, 11, 12, 13, 14]])"
       ]
      }
     ],
     "prompt_number": 166
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = np.ones(8)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 167
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = x[...,np.newaxis] + y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 168
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(z)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[[  1.   1.   1.   1.   1.   1.   1.   1.]\n",
        "  [  2.   2.   2.   2.   2.   2.   2.   2.]\n",
        "  [  3.   3.   3.   3.   3.   3.   3.   3.]\n",
        "  [  4.   4.   4.   4.   4.   4.   4.   4.]\n",
        "  [  5.   5.   5.   5.   5.   5.   5.   5.]]\n",
        "\n",
        " [[  6.   6.   6.   6.   6.   6.   6.   6.]\n",
        "  [  7.   7.   7.   7.   7.   7.   7.   7.]\n",
        "  [  8.   8.   8.   8.   8.   8.   8.   8.]\n",
        "  [  9.   9.   9.   9.   9.   9.   9.   9.]\n",
        "  [ 10.  10.  10.  10.  10.  10.  10.  10.]]\n",
        "\n",
        " [[ 11.  11.  11.  11.  11.  11.  11.  11.]\n",
        "  [ 12.  12.  12.  12.  12.  12.  12.  12.]\n",
        "  [ 13.  13.  13.  13.  13.  13.  13.  13.]\n",
        "  [ 14.  14.  14.  14.  14.  14.  14.  14.]\n",
        "  [ 15.  15.  15.  15.  15.  15.  15.  15.]]]\n"
       ]
      }
     ],
     "prompt_number": 169
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 170,
       "text": [
        "(3, 5, 8)"
       ]
      }
     ],
     "prompt_number": 170
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<center> \n",
      "\n",
      "![](files/images/array_3x5x8.png)\n",
      "\n",
      "</center>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<hr size=3>\n",
      "\n",
      "### Array methods"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We have already seen some array methods above, in this section I just dwelve into their general behavior and present some convenient array methods"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In python, everything is an **object**, numpy ndarrays are no exceptions, and they expose a number of **methods**, which are equivalent to the call \n",
      "the respective numpy function, but can be more convenient to use, and - marginally - faster"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.shape # shape of a (like *size* in matlab)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 171,
       "text": [
        "(10,)"
       ]
      }
     ],
     "prompt_number": 171
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.shape(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 172,
       "text": [
        "(10,)"
       ]
      }
     ],
     "prompt_number": 172
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit a.mean() # The %timeit magic cell function times the execution of an expression"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100000 loops, best of 3: 16.9 \u00b5s per loop\n"
       ]
      }
     ],
     "prompt_number": 173
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit np.mean(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100000 loops, best of 3: 18.1 \u00b5s per loop\n"
       ]
      }
     ],
     "prompt_number": 174
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = np.arange(100,dtype=float).reshape(10,10) # creating a 2D array, note the dtype"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 175
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 176,
       "text": [
        "(10, 10)"
       ]
      }
     ],
     "prompt_number": 176
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 177,
       "text": [
        "array([[  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.],\n",
        "       [ 10.,  11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.],\n",
        "       [ 20.,  21.,  22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.],\n",
        "       [ 30.,  31.,  32.,  33.,  34.,  35.,  36.,  37.,  38.,  39.],\n",
        "       [ 40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,  48.,  49.],\n",
        "       [ 50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.],\n",
        "       [ 60.,  61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.],\n",
        "       [ 70.,  71.,  72.,  73.,  74.,  75.,  76.,  77.,  78.,  79.],\n",
        "       [ 80.,  81.,  82.,  83.,  84.,  85.,  86.,  87.,  88.,  89.],\n",
        "       [ 90.,  91.,  92.,  93.,  94.,  95.,  96.,  97.,  98.,  99.]])"
       ]
      }
     ],
     "prompt_number": 177
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b.sum(0) ### summing over the first axis ('rows')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 178,
       "text": [
        "array([ 450.,  460.,  470.,  480.,  490.,  500.,  510.,  520.,  530.,  540.])"
       ]
      }
     ],
     "prompt_number": 178
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.sum(b, axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 179,
       "text": [
        "array([ 450.,  460.,  470.,  480.,  490.,  500.,  510.,  520.,  530.,  540.])"
       ]
      }
     ],
     "prompt_number": 179
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b[:,0].sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 180,
       "text": [
        "450.0"
       ]
      }
     ],
     "prompt_number": 180
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*swapaxes* method swap the position of 2 axes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = b.swapaxes(1,0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 181
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 182,
       "text": [
        "array([[  0.,  10.,  20.,  30.,  40.,  50.,  60.,  70.,  80.,  90.],\n",
        "       [  1.,  11.,  21.,  31.,  41.,  51.,  61.,  71.,  81.,  91.],\n",
        "       [  2.,  12.,  22.,  32.,  42.,  52.,  62.,  72.,  82.,  92.],\n",
        "       [  3.,  13.,  23.,  33.,  43.,  53.,  63.,  73.,  83.,  93.],\n",
        "       [  4.,  14.,  24.,  34.,  44.,  54.,  64.,  74.,  84.,  94.],\n",
        "       [  5.,  15.,  25.,  35.,  45.,  55.,  65.,  75.,  85.,  95.],\n",
        "       [  6.,  16.,  26.,  36.,  46.,  56.,  66.,  76.,  86.,  96.],\n",
        "       [  7.,  17.,  27.,  37.,  47.,  57.,  67.,  77.,  87.,  97.],\n",
        "       [  8.,  18.,  28.,  38.,  48.,  58.,  68.,  78.,  88.,  98.],\n",
        "       [  9.,  19.,  29.,  39.,  49.,  59.,  69.,  79.,  89.,  99.]])"
       ]
      }
     ],
     "prompt_number": 182
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "here equivalent to *transpose()* on 2D array"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = b.transpose()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 183
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 184,
       "text": [
        "array([[  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.],\n",
        "       [ 10.,  11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.],\n",
        "       [ 20.,  21.,  22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.],\n",
        "       [ 30.,  31.,  32.,  33.,  34.,  35.,  36.,  37.,  38.,  39.],\n",
        "       [ 40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,  48.,  49.],\n",
        "       [ 50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.],\n",
        "       [ 60.,  61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.],\n",
        "       [ 70.,  71.,  72.,  73.,  74.,  75.,  76.,  77.,  78.,  79.],\n",
        "       [ 80.,  81.,  82.,  83.,  84.,  85.,  86.,  87.,  88.,  89.],\n",
        "       [ 90.,  91.,  92.,  93.,  94.,  95.,  96.,  97.,  98.,  99.]])"
       ]
      }
     ],
     "prompt_number": 184
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = np.transpose(b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 185
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 186,
       "text": [
        "array([[  0.,  10.,  20.,  30.,  40.,  50.,  60.,  70.,  80.,  90.],\n",
        "       [  1.,  11.,  21.,  31.,  41.,  51.,  61.,  71.,  81.,  91.],\n",
        "       [  2.,  12.,  22.,  32.,  42.,  52.,  62.,  72.,  82.,  92.],\n",
        "       [  3.,  13.,  23.,  33.,  43.,  53.,  63.,  73.,  83.,  93.],\n",
        "       [  4.,  14.,  24.,  34.,  44.,  54.,  64.,  74.,  84.,  94.],\n",
        "       [  5.,  15.,  25.,  35.,  45.,  55.,  65.,  75.,  85.,  95.],\n",
        "       [  6.,  16.,  26.,  36.,  46.,  56.,  66.,  76.,  86.,  96.],\n",
        "       [  7.,  17.,  27.,  37.,  47.,  57.,  67.,  77.,  87.,  97.],\n",
        "       [  8.,  18.,  28.,  38.,  48.,  58.,  68.,  78.,  88.,  98.],\n",
        "       [  9.,  19.,  29.,  39.,  49.,  59.,  69.,  79.,  89.,  99.]])"
       ]
      }
     ],
     "prompt_number": 186
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### *Missing* values in numpy "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can handle missing values in Numpy in two different ways: \n",
      "    \n",
      "1. By using the np.nan (not a number) special type\n",
      "2. By casting a numpy array into a **Masked array** using the <font color='red'>ma</font> module\n",
      "\n",
      "Using the masked array approach, while somehow more cumbersome, allows more flexibility"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b[5,5] = -999."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 187
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[   0.   10.   20.   30.   40.   50.   60.   70.   80.   90.]\n",
        " [   1.   11.   21.   31.   41.   51.   61.   71.   81.   91.]\n",
        " [   2.   12.   22.   32.   42.   52.   62.   72.   82.   92.]\n",
        " [   3.   13.   23.   33.   43.   53.   63.   73.   83.   93.]\n",
        " [   4.   14.   24.   34.   44.   54.   64.   74.   84.   94.]\n",
        " [   5.   15.   25.   35.   45. -999.   65.   75.   85.   95.]\n",
        " [   6.   16.   26.   36.   46.   56.   66.   76.   86.   96.]\n",
        " [   7.   17.   27.   37.   47.   57.   67.   77.   87.   97.]\n",
        " [   8.   18.   28.   38.   48.   58.   68.   78.   88.   98.]\n",
        " [   9.   19.   29.   39.   49.   59.   69.   79.   89.   99.]]\n"
       ]
      }
     ],
     "prompt_number": 188
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c = b.copy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 189
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c[c == -999.] = np.nan"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 190
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(c)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[  0.  10.  20.  30.  40.  50.  60.  70.  80.  90.]\n",
        " [  1.  11.  21.  31.  41.  51.  61.  71.  81.  91.]\n",
        " [  2.  12.  22.  32.  42.  52.  62.  72.  82.  92.]\n",
        " [  3.  13.  23.  33.  43.  53.  63.  73.  83.  93.]\n",
        " [  4.  14.  24.  34.  44.  54.  64.  74.  84.  94.]\n",
        " [  5.  15.  25.  35.  45.  nan  65.  75.  85.  95.]\n",
        " [  6.  16.  26.  36.  46.  56.  66.  76.  86.  96.]\n",
        " [  7.  17.  27.  37.  47.  57.  67.  77.  87.  97.]\n",
        " [  8.  18.  28.  38.  48.  58.  68.  78.  88.  98.]\n",
        " [  9.  19.  29.  39.  49.  59.  69.  79.  89.  99.]]\n"
       ]
      }
     ],
     "prompt_number": 191
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```nan``` values propagates automatically when using *aggregation* functions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c.mean(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 192,
       "text": [
        "array([ 45.,  46.,  47.,  48.,  49.,  nan,  51.,  52.,  53.,  54.])"
       ]
      }
     ],
     "prompt_number": 192
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Unless you use one of the - few - NaN functions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.nansum(c,axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 193,
       "text": [
        "array([ 450.,  460.,  470.,  480.,  490.,  445.,  510.,  520.,  530.,  540.])"
       ]
      }
     ],
     "prompt_number": 193
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we're gonna see how to use the **`ma` (Masked Arrays) module**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy import ma "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 194
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = ma.masked_values(b, -999.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 195
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0]\n",
        " [1.0 11.0 21.0 31.0 41.0 51.0 61.0 71.0 81.0 91.0]\n",
        " [2.0 12.0 22.0 32.0 42.0 52.0 62.0 72.0 82.0 92.0]\n",
        " [3.0 13.0 23.0 33.0 43.0 53.0 63.0 73.0 83.0 93.0]\n",
        " [4.0 14.0 24.0 34.0 44.0 54.0 64.0 74.0 84.0 94.0]\n",
        " [5.0 15.0 25.0 35.0 45.0 -- 65.0 75.0 85.0 95.0]\n",
        " [6.0 16.0 26.0 36.0 46.0 56.0 66.0 76.0 86.0 96.0]\n",
        " [7.0 17.0 27.0 37.0 47.0 57.0 67.0 77.0 87.0 97.0]\n",
        " [8.0 18.0 28.0 38.0 48.0 58.0 68.0 78.0 88.0 98.0]\n",
        " [9.0 19.0 29.0 39.0 49.0 59.0 69.0 79.0 89.0 99.0]]\n"
       ]
      }
     ],
     "prompt_number": 196
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```b``` has now two new attributes: data, and mask"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(b.data) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[   0.   10.   20.   30.   40.   50.   60.   70.   80.   90.]\n",
        " [   1.   11.   21.   31.   41.   51.   61.   71.   81.   91.]\n",
        " [   2.   12.   22.   32.   42.   52.   62.   72.   82.   92.]\n",
        " [   3.   13.   23.   33.   43.   53.   63.   73.   83.   93.]\n",
        " [   4.   14.   24.   34.   44.   54.   64.   74.   84.   94.]\n",
        " [   5.   15.   25.   35.   45. -999.   65.   75.   85.   95.]\n",
        " [   6.   16.   26.   36.   46.   56.   66.   76.   86.   96.]\n",
        " [   7.   17.   27.   37.   47.   57.   67.   77.   87.   97.]\n",
        " [   8.   18.   28.   38.   48.   58.   68.   78.   88.   98.]\n",
        " [   9.   19.   29.   39.   49.   59.   69.   79.   89.   99.]]\n"
       ]
      }
     ],
     "prompt_number": 197
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(b.mask)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[False False False False False False False False False False]\n",
        " [False False False False False False False False False False]\n",
        " [False False False False False False False False False False]\n",
        " [False False False False False False False False False False]\n",
        " [False False False False False False False False False False]\n",
        " [False False False False False  True False False False False]\n",
        " [False False False False False False False False False False]\n",
        " [False False False False False False False False False False]\n",
        " [False False False False False False False False False False]\n",
        " [False False False False False False False False False False]]\n"
       ]
      }
     ],
     "prompt_number": 198
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b.mean(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 199,
       "text": [
        "masked_array(data = [45.0 46.0 47.0 48.0 49.0 49.44444444444444 51.0 52.0 53.0 54.0],\n",
        "             mask = [False False False False False False False False False False],\n",
        "       fill_value = 1e+20)"
       ]
      }
     ],
     "prompt_number": 199
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c = b.mean(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 200
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 201,
       "text": [
        "(10,)"
       ]
      }
     ],
     "prompt_number": 201
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Masked values are just ignored in calculations "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Other masked array functions allow to set masked values according to some conditions, all (I think) functions exposed for regular numpy arrays are available."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ma."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "SyntaxError",
       "evalue": "invalid syntax (<ipython-input-202-a1280ea86273>, line 1)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-202-a1280ea86273>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    ma.\u001b[0m\n\u001b[0m       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
       ]
      }
     ],
     "prompt_number": 202
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = ma.masked_greater(b, 80)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 203
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 204,
       "text": [
        "masked_array(data =\n",
        " [[0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 --]\n",
        " [1.0 11.0 21.0 31.0 41.0 51.0 61.0 71.0 -- --]\n",
        " [2.0 12.0 22.0 32.0 42.0 52.0 62.0 72.0 -- --]\n",
        " [3.0 13.0 23.0 33.0 43.0 53.0 63.0 73.0 -- --]\n",
        " [4.0 14.0 24.0 34.0 44.0 54.0 64.0 74.0 -- --]\n",
        " [5.0 15.0 25.0 35.0 45.0 -- 65.0 75.0 -- --]\n",
        " [6.0 16.0 26.0 36.0 46.0 56.0 66.0 76.0 -- --]\n",
        " [7.0 17.0 27.0 37.0 47.0 57.0 67.0 77.0 -- --]\n",
        " [8.0 18.0 28.0 38.0 48.0 58.0 68.0 78.0 -- --]\n",
        " [9.0 19.0 29.0 39.0 49.0 59.0 69.0 79.0 -- --]],\n",
        "             mask =\n",
        " [[False False False False False False False False False  True]\n",
        " [False False False False False False False False  True  True]\n",
        " [False False False False False False False False  True  True]\n",
        " [False False False False False False False False  True  True]\n",
        " [False False False False False False False False  True  True]\n",
        " [False False False False False  True False False  True  True]\n",
        " [False False False False False False False False  True  True]\n",
        " [False False False False False False False False  True  True]\n",
        " [False False False False False False False False  True  True]\n",
        " [False False False False False False False False  True  True]],\n",
        "       fill_value = 1e+20)"
       ]
      }
     ],
     "prompt_number": 204
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b.mean(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 205,
       "text": [
        "masked_array(data = [4.5 14.5 24.5 34.5 44.5 54.44444444444444 64.5 74.5 80.0 --],\n",
        "             mask = [False False False False False False False False False  True],\n",
        "       fill_value = 1e+20)"
       ]
      }
     ],
     "prompt_number": 205
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<hr size=5>\n",
      "### Random numbers"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can generate random numbers coming from a wide range of distributions\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random."
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If you want more distributions, and more flexibility, you need to look into scipy.stats.distributions (see the statistical modelling notebook)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Example 1: Normal distribution**   \n",
      "\n",
      "$$P(x) = \\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp\\left[-\\frac{(x-\\mu)^2}{2\\sigma^2}\\right]$$\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.random.normal(0,1,1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 209
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.random.standard_normal(1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 210
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_ = plt.hist(x, color='0.6',normed=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SExORlJQEAMjKysLx48dRW1uL5cuXD6jf2NiIq1ev4umnn8bYsWMRERGBzz//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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10720d090>"
       ]
      }
     ],
     "prompt_number": 211
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Example 2: Poisson distribution**    \n",
      "\n",
      "\\begin{equation*}\n",
      "P\\left( x \\right) = \\frac{{e^{ - \\lambda } \\lambda ^x }}{{x!}}\n",
      "\\end{equation*}"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "poiss = np.random.poisson(lam=4, size=(10000))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 212
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#a = np.array([len(np.where(poiss==x)[0]) for x in np.unique(poiss)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 213
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.array([sum(poiss==x) for x in np.unique(poiss)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 214
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, ax = plt.subplots()\n",
      "ax.plot(np.unique(poiss), a/10000., '-', color='grey')\n",
      "ax.plot(np.unique(poiss), a/10000., 'o', color='purple', markersize=10)\n",
      "ax.set_title(\"Poisson Empirical PMF, $\\lambda = 4$\", fontsize=20)\n",
      "ax.set_xlim(-1,17)\n",
      "ax.set_ylim(0,0.25)\n",
      "[l.set_fontsize(16) for l in ax.xaxis.get_ticklabels()]\n",
      "[l.set_fontsize(16) for l in ax.yaxis.get_ticklabels()];"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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i4sJ1KD0ikUuggoq1jQoqSOTs3Uqkb/hyntsSvuWcCgJPqNVqXLlyhTdTTbuK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\nIYSQH1BBIIQQAoAKAiGEkB/8P184Hcj+1uemAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1074443d0>"
       ]
      }
     ],
     "prompt_number": 215
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math # the math module exposes mathematical functions operating on scalars"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 216
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lam = 4; end = 20 # lambda is a reserved word in python "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 217
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "P = [math.exp(-lam)*lam**x/math.factorial(x) for x in xrange(end)] # note the use of a list comprehension"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 218
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, ax = plt.subplots()\n",
      "ax.plot(P, '-', color='grey')\n",
      "ax.plot(P, 'o', color='purple', markersize=10)\n",
      "ax.set_title(\"Poisson PMF, $\\lambda = 4$\", fontsize=20)\n",
      "ax.set_xlim(-1,17)\n",
      "ax.set_ylim(0,0.25)\n",
      "[l.set_fontsize(16) for l in ax.xaxis.get_ticklabels()]\n",
      "[l.set_fontsize(16) for l in ax.yaxis.get_ticklabels()];"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dQ7B2NIfAX51jzjAM9u7dixkzZtB2JWbE9efcHlljzI2eQyCWYe83bgkEAowa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\n8f+Es95nVwWrzQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10744bfd0>"
       ]
      }
     ],
     "prompt_number": 219
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From Wikipedia (http://en.wikipedia.org/wiki/Poisson_distribution)\n",
      "\n",
      "\n",
      "<!--![Discrete variable](http://upload.wikimedia.org/wikipedia/commons/1/16/Poisson_pmf.svg)-->\n",
      "![Discrete variable](files/images/poisson_pmf.png)\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<hr size=5>\n",
      "# Scipy"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Scipy** can is a library (but can be thought more as a scientific python distribution) that is built on top of Numpy and\n",
      "and provides a large set of standard scientific computing algorithms, in particular: \n",
      "\n",
      "* Special functions ([scipy.special](http://docs.scipy.org/doc/scipy/reference/special.html))\n",
      "* Integration ([scipy.integrate](http://docs.scipy.org/doc/scipy/reference/integrate.html))\n",
      "* Optimization ([scipy.optimize](http://docs.scipy.org/doc/scipy/reference/optimize.html))\n",
      "* Interpolation ([scipy.interpolate](http://docs.scipy.org/doc/scipy/reference/interpolate.html))\n",
      "* Fourier Transforms ([scipy.fftpack](http://docs.scipy.org/doc/scipy/reference/fftpack.html))\n",
      "* Signal Processing ([scipy.signal](http://docs.scipy.org/doc/scipy/reference/signal.html))\n",
      "* Linear Algebra ([scipy.linalg](http://docs.scipy.org/doc/scipy/reference/linalg.html))\n",
      "* Sparse Eigenvalue Problems ([scipy.sparse](http://docs.scipy.org/doc/scipy/reference/sparse.html))\n",
      "* Statistics ([scipy.stats](http://docs.scipy.org/doc/scipy/reference/stats.html))\n",
      "* Multi-dimensional image processing ([scipy.ndimage](http://docs.scipy.org/doc/scipy/reference/ndimage.html))\n",
      "* File IO ([scipy.io](http://docs.scipy.org/doc/scipy/reference/io.html))\n",
      "\n",
      "We'll only have a look at some of these sub-packages, more directly relevant to **analyzing** data\n",
      "   \n",
      "   \n",
      "+ **scipy.stats**: standard continuous and discrete probability distributions (density functions, samplers, ...), various statistical tests, and more descriptive statistics. \n",
      "+ **scipy.interpolate**: 1D and 2D interpolation\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Scipy.stats"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy import stats "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 220
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dir(stats)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 221,
       "text": [
        "['Tester',\n",
        " '__all__',\n",
        " '__builtins__',\n",
        " '__doc__',\n",
        " '__file__',\n",
        " '__name__',\n",
        " '__package__',\n",
        " '__path__',\n",
        " '_binned_statistic',\n",
        " '_rank',\n",
        " '_support',\n",
        " '_tukeylambda_stats',\n",
        " 'absolute_import',\n",
        " 'alpha',\n",
        " 'anderson',\n",
        " 'anglit',\n",
        " 'ansari',\n",
        " 'arcsine',\n",
        " 'bartlett',\n",
        " 'bayes_mvs',\n",
        " 'bernoulli',\n",
        " 'beta',\n",
        " 'betai',\n",
        " 'betaprime',\n",
        " 'binned_statistic',\n",
        " 'binned_statistic_2d',\n",
        " 'binned_statistic_dd',\n",
        " 'binom',\n",
        " 'binom_test',\n",
        " 'boltzmann',\n",
        " 'boxcox',\n",
        " 'boxcox_llf',\n",
        " 'boxcox_normmax',\n",
        " 'boxcox_normplot',\n",
        " 'bradford',\n",
        " 'burr',\n",
        " 'callable',\n",
        " 'cauchy',\n",
        " 'chi',\n",
        " 'chi2',\n",
        " 'chi2_contingency',\n",
        " 'chisqprob',\n",
        " 'chisquare',\n",
        " 'circmean',\n",
        " 'circstd',\n",
        " 'circvar',\n",
        " 'cmedian',\n",
        " 'contingency',\n",
        " 'cosine',\n",
        " 'cumfreq',\n",
        " 'describe',\n",
        " 'dgamma',\n",
        " 'distributions',\n",
        " 'division',\n",
        " 'dlaplace',\n",
        " 'dweibull',\n",
        " 'entropy',\n",
        " 'erlang',\n",
        " 'expon',\n",
        " 'exponpow',\n",
        " 'exponweib',\n",
        " 'f',\n",
        " 'f_oneway',\n",
        " 'f_value',\n",
        " 'f_value_multivariate',\n",
        " 'f_value_wilks_lambda',\n",
        " 'fastsort',\n",
        " 'fatiguelife',\n",
        " 'find_repeats',\n",
        " 'fisher_exact',\n",
        " 'fisk',\n",
        " 'fligner',\n",
        " 'foldcauchy',\n",
        " 'foldnorm',\n",
        " 'fprob',\n",
        " 'frechet_l',\n",
        " 'frechet_r',\n",
        " 'friedmanchisquare',\n",
        " 'futil',\n",
        " 'gamma',\n",
        " 'gausshyper',\n",
        " 'gaussian_kde',\n",
        " 'genexpon',\n",
        " 'genextreme',\n",
        " 'gengamma',\n",
        " 'genhalflogistic',\n",
        " 'genlogistic',\n",
        " 'genpareto',\n",
        " 'geom',\n",
        " 'gilbrat',\n",
        " 'glm',\n",
        " 'gmean',\n",
        " 'gompertz',\n",
        " 'gumbel_l',\n",
        " 'gumbel_r',\n",
        " 'halfcauchy',\n",
        " 'halflogistic',\n",
        " 'halfnorm',\n",
        " 'histogram',\n",
        " 'histogram2',\n",
        " 'hmean',\n",
        " 'hypergeom',\n",
        " 'hypsecant',\n",
        " 'invgamma',\n",
        " 'invgauss',\n",
        " 'invweibull',\n",
        " 'itemfreq',\n",
        " 'jarque_bera',\n",
        " 'johnsonsb',\n",
        " 'johnsonsu',\n",
        " 'kde',\n",
        " 'kendalltau',\n",
        " 'kruskal',\n",
        " 'ks_2samp',\n",
        " 'ksone',\n",
        " 'ksprob',\n",
        " 'kstat',\n",
        " 'kstatvar',\n",
        " 'kstest',\n",
        " 'kstwobign',\n",
        " 'kurtosis',\n",
        " 'kurtosistest',\n",
        " 'laplace',\n",
        " 'levene',\n",
        " 'levy',\n",
        " 'levy_l',\n",
        " 'levy_stable',\n",
        " 'linregress',\n",
        " 'loggamma',\n",
        " 'logistic',\n",
        " 'loglaplace',\n",
        " 'lognorm',\n",
        " 'logser',\n",
        " 'lomax',\n",
        " 'mannwhitneyu',\n",
        " 'maxwell',\n",
        " 'mielke',\n",
        " 'mode',\n",
        " 'moment',\n",
        " 'mood',\n",
        " 'morestats',\n",
        " 'mstats',\n",
        " 'mstats_basic',\n",
        " 'mstats_extras',\n",
        " 'mvn',\n",
        " 'mvsdist',\n",
        " 'nakagami',\n",
        " 'nanmean',\n",
        " 'nanmedian',\n",
        " 'nanstd',\n",
        " 'nbinom',\n",
        " 'ncf',\n",
        " 'nct',\n",
        " 'ncx2',\n",
        " 'norm',\n",
        " 'normaltest',\n",
        " 'np',\n",
        " 'obrientransform',\n",
        " 'oneway',\n",
        " 'pareto',\n",
        " 'pdf_fromgamma',\n",
        " 'pearson3',\n",
        " 'pearsonr',\n",
        " 'percentileofscore',\n",
        " 'planck',\n",
        " 'pointbiserialr',\n",
        " 'poisson',\n",
        " 'power_divergence',\n",
        " 'powerlaw',\n",
        " 'powerlognorm',\n",
        " 'powernorm',\n",
        " 'ppcc_max',\n",
        " 'ppcc_plot',\n",
        " 'print_function',\n",
        " 'probplot',\n",
        " 'randint',\n",
        " 'randwcdf',\n",
        " 'randwppf',\n",
        " 'rankdata',\n",
        " 'ranksums',\n",
        " 'rayleigh',\n",
        " 'rdist',\n",
        " 'recipinvgauss',\n",
        " 'reciprocal',\n",
        " 'relfreq',\n",
        " 'rice',\n",
        " 'rv',\n",
        " 'rv_continuous',\n",
        " 'rv_discrete',\n",
        " 's',\n",
        " 'scoreatpercentile',\n",
        " 'sem',\n",
        " 'semicircular',\n",
        " 'shapiro',\n",
        " 'sigmaclip',\n",
        " 'signaltonoise',\n",
        " 'skellam',\n",
        " 'skew',\n",
        " 'skewtest',\n",
        " 'spearmanr',\n",
        " 'square_of_sums',\n",
        " 'ss',\n",
        " 'statlib',\n",
        " 'stats',\n",
        " 't',\n",
        " 'test',\n",
        " 'threshold',\n",
        " 'tiecorrect',\n",
        " 'tmax',\n",
        " 'tmean',\n",
        " 'tmin',\n",
        " 'triang',\n",
        " 'trim1',\n",
        " 'trim_mean',\n",
        " 'trimboth',\n",
        " 'truncexpon',\n",
        " 'truncnorm',\n",
        " 'tsem',\n",
        " 'tstd',\n",
        " 'ttest_1samp',\n",
        " 'ttest_ind',\n",
        " 'ttest_rel',\n",
        " 'tukeylambda',\n",
        " 'tvar',\n",
        " 'uniform',\n",
        " 'variation',\n",
        " 'vonmises',\n",
        " 'vonmises_cython',\n",
        " 'wald',\n",
        " 'weibull_max',\n",
        " 'weibull_min',\n",
        " 'wilcoxon',\n",
        " 'wrapcauchy',\n",
        " 'zipf',\n",
        " 'zmap',\n",
        " 'zprob',\n",
        " 'zscore']"
       ]
      }
     ],
     "prompt_number": 221
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Statistical tests "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "One commonly used parametric test is the *Student t-test*, which determines whether two independant samples have - statistically - different means, under the assumption that the two samples are normally distributed. The Null hypothesis is that the two samples have **identical** means."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats.ttest_ind?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 222
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = stats.distributions.norm(loc=5, scale=10)\n",
      "Y = stats.distributions.norm(loc=3, scale=10)\n",
      "\n",
      "Xrvs = X.rvs(size=(1000))\n",
      "Yrvs = Y.rvs(size=(1000))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 223
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, ax = plt.subplots(figsize=(6,5))\n",
      "ax.hist(Xrvs,histtype='stepfilled', bins=20, color='coral', alpha=.6, label=r'$\\mu = 5$')\n",
      "ax.hist(Yrvs,histtype='stepfilled', bins=20, color='steelblue', alpha=.6, label=r'$\\mu = 3$')\n",
      "ax.grid(color='b',alpha=.6)\n",
      "ax.legend();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SsBgYQHq7o/T8ktvcAdn5pe870vPrwWJAREQsBkaQ3u4oPb/kNndAdn7p+470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\nRAQWAyIiAosBEREB+P/Yxef/737CbQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10742ff90>"
       ]
      }
     ],
     "prompt_number": 224
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t, p = stats.ttest_ind(Xrvs, Yrvs)\n",
      "print(\"\"\"\\n===========================\n",
      "T statistics = %s ~= %4.2f\n",
      "P-value = %s ~= %8.6f\n",
      "===========================\n",
      "\"\"\" % (t, t, p, p))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "===========================\n",
        "T statistics = 4.5718835021 ~= 4.57\n",
        "P-value = 5.12942295409e-06 ~= 0.000005\n",
        "===========================\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 225
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Correlation (Pearson's R)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import pearsonr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 226
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We're gonna apply that to Time-Series of NINO3.4 and the SOI \n",
      "\n",
      "The NINO indices come from [http://www.cpc.ncep.noaa.gov/data/indices/ersst3b.nino.mth.81-10.ascii](http://www.cpc.ncep.noaa.gov/data/indices/ersst3b.nino.mth.81-10.ascii)\n",
      "\n",
      "The SOI is in the `data` directory"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "from datetime import datetime\n",
      "from dateutil import parser\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 227
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nino_url = \"http://www.cpc.ncep.noaa.gov/data/indices/ersst3b.nino.mth.81-10.ascii\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 228
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# if not working try: \n",
      "#nino = pd.read_table('./data/ersst3b.nino.mth.81-10.ascii', sep='\\s*')\n",
      "nino = pd.read_table(nino_url, sep='\\s*')\n",
      "dates = [datetime(x[0], x[1], 1) for x in zip(nino['YR'].values, nino['MON'].values)]\n",
      "nino.index = dates"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 229
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nino.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>YR</th>\n",
        "      <th>MON</th>\n",
        "      <th>NINO1+2</th>\n",
        "      <th>ANOM</th>\n",
        "      <th>NINO3</th>\n",
        "      <th>ANOM.1</th>\n",
        "      <th>NINO4</th>\n",
        "      <th>ANOM.2</th>\n",
        "      <th>NINO3.4</th>\n",
        "      <th>ANOM.3</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1950-01-01</th>\n",
        "      <td> 1950</td>\n",
        "      <td> 1</td>\n",
        "      <td> 23.11</td>\n",
        "      <td>-1.57</td>\n",
        "      <td> 23.74</td>\n",
        "      <td>-2.03</td>\n",
        "      <td> 27.03</td>\n",
        "      <td>-1.29</td>\n",
        "      <td> 24.83</td>\n",
        "      <td>-1.84</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1950-02-01</th>\n",
        "      <td> 1950</td>\n",
        "      <td> 2</td>\n",
        "      <td> 24.20</td>\n",
        "      <td>-1.91</td>\n",
        "      <td> 24.92</td>\n",
        "      <td>-1.57</td>\n",
        "      <td> 27.15</td>\n",
        "      <td>-1.02</td>\n",
        "      <td> 25.20</td>\n",
        "      <td>-1.63</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1950-03-01</th>\n",
        "      <td> 1950</td>\n",
        "      <td> 3</td>\n",
        "      <td> 25.37</td>\n",
        "      <td>-1.13</td>\n",
        "      <td> 26.33</td>\n",
        "      <td>-0.91</td>\n",
        "      <td> 27.06</td>\n",
        "      <td>-1.23</td>\n",
        "      <td> 26.03</td>\n",
        "      <td>-1.30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1950-04-01</th>\n",
        "      <td> 1950</td>\n",
        "      <td> 4</td>\n",
        "      <td> 23.86</td>\n",
        "      <td>-1.75</td>\n",
        "      <td> 26.46</td>\n",
        "      <td>-1.10</td>\n",
        "      <td> 27.29</td>\n",
        "      <td>-1.26</td>\n",
        "      <td> 26.36</td>\n",
        "      <td>-1.43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1950-05-01</th>\n",
        "      <td> 1950</td>\n",
        "      <td> 5</td>\n",
        "      <td> 23.03</td>\n",
        "      <td>-1.39</td>\n",
        "      <td> 25.72</td>\n",
        "      <td>-1.52</td>\n",
        "      <td> 27.59</td>\n",
        "      <td>-1.25</td>\n",
        "      <td> 26.19</td>\n",
        "      <td>-1.72</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 230,
       "text": [
        "              YR  MON  NINO1+2  ANOM  NINO3  ANOM.1  NINO4  ANOM.2  NINO3.4  \\\n",
        "1950-01-01  1950    1    23.11 -1.57  23.74   -2.03  27.03   -1.29    24.83   \n",
        "1950-02-01  1950    2    24.20 -1.91  24.92   -1.57  27.15   -1.02    25.20   \n",
        "1950-03-01  1950    3    25.37 -1.13  26.33   -0.91  27.06   -1.23    26.03   \n",
        "1950-04-01  1950    4    23.86 -1.75  26.46   -1.10  27.29   -1.26    26.36   \n",
        "1950-05-01  1950    5    23.03 -1.39  25.72   -1.52  27.59   -1.25    26.19   \n",
        "\n",
        "            ANOM.3  \n",
        "1950-01-01   -1.84  \n",
        "1950-02-01   -1.63  \n",
        "1950-03-01   -1.30  \n",
        "1950-04-01   -1.43  \n",
        "1950-05-01   -1.72  \n",
        "\n",
        "[5 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 230
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "soipath = './data'\n",
      "def get_SOI(soipath, start_date='1950-01-01'):\n",
      "    soi = pd.read_csv(os.path.join(soipath,'NIWA_SOI.csv'), index_col=0)\n",
      "    soi = soi.stack()\n",
      "    soi = soi.dropna()\n",
      "    dates = [parser.parse(\"%s-%s-%s\" % (str(int(x[0])), x[1], \"1\")) for x in soi.index]\n",
      "    soidf = pd.DataFrame(soi.values, index=dates, columns=['SOI'])\n",
      "    soidf = soidf.truncate(before=start_date)\n",
      "    return soidf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 231
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "soi = get_SOI(soipath)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 232
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "soi.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>SOI</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1950-01-01</th>\n",
        "      <td> 0.570492</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1950-02-01</th>\n",
        "      <td> 1.642897</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1950-03-01</th>\n",
        "      <td> 1.745849</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1950-04-01</th>\n",
        "      <td> 1.895316</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1950-05-01</th>\n",
        "      <td> 0.748673</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 233,
       "text": [
        "                 SOI\n",
        "1950-01-01  0.570492\n",
        "1950-02-01  1.642897\n",
        "1950-03-01  1.745849\n",
        "1950-04-01  1.895316\n",
        "1950-05-01  0.748673\n",
        "\n",
        "[5 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 233
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r, p = pearsonr(soi['SOI'], nino['ANOM.3'])\n",
      "\n",
      "print(\"R = {0:<4.2f}, p-value = {1:<4.2f}\".format(r,p))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "operands could not be broadcast together with shapes (768) (771) ",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-234-113fb9f7cc5b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpearsonr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msoi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SOI'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnino\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ANOM.3'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"R = {0:<4.2f}, p-value = {1:<4.2f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/nicolasfauchereau/anaconda/lib/python2.7/site-packages/scipy/stats/stats.pyc\u001b[0m in \u001b[0;36mpearsonr\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m   2510\u001b[0m     \u001b[0mmy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2511\u001b[0m     \u001b[0mxm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mym\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mmx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mmy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2512\u001b[0;31m     \u001b[0mr_num\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxm\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mym\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2513\u001b[0m     \u001b[0mr_den\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxm\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mym\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2514\u001b[0m     \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr_num\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mr_den\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (768) (771) "
       ]
      }
     ],
     "prompt_number": 234
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#df = pd.DataFrame({'SOI':soi['SOI'],'nino':nino['ANOM.3']}, index=nino.index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#df.to_csv('./data/soi_nino.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## scipy.interpolate"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are several interfaces for performing 1D, 2D or N Dimensional interpolation using scipy.\n",
      "\n",
      "For more on that I refer you to \n",
      "\n",
      "+ [http://docs.scipy.org/doc/scipy/reference/interpolate.html](http://docs.scipy.org/doc/scipy/reference/interpolate.html)\n",
      "+ [http://wiki.scipy.org/Cookbook/Interpolation](http://wiki.scipy.org/Cookbook/Interpolation)\n",
      "\n",
      "Here we're going to see one simple example in 1D and 2D using [Radial Basis Functions](http://en.wikipedia.org/wiki/Radial_basis_function) using the default (Multiquadric)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### 1D interpolation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.interpolate import interp1d"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 235
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.linspace(0, 10, 10)\n",
      "y = np.sin(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 236
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(x,y,'ro')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 237,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10c85b6d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ffKNx48bpjjvu0JAhQ3o9Z/PmzXr22We7jS9fvlyJiY5NLabV19crLy8v0tOICmRhkIVB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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c776950>"
       ]
      }
     ],
     "prompt_number": 237
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f1 = interp1d(x, y) # default is linear"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 238
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f2 = interp1d(x, y, kind='cubic') # and we're gonna try cubic interpolation for comparison"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 239
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xnew = np.linspace(0, 10, 50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 240
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ylin = f1(xnew)\n",
      "ycub = f2(xnew)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 241
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, ax = plt.subplots(figsize=(12,8))\n",
      "ax.plot(xnew, ylin, 'b.', ms=8, label='linear')\n",
      "ax.plot(xnew, ycub, 'gx-', ms=10, label='cubic')\n",
      "ax.plot(x,y,'ro-', label='data')\n",
      "ax.legend(loc='lower left')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 242,
       "text": [
        "<matplotlib.legend.Legend at 0x10c837250>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rNO+/jXI5+EePvi+ySa6S9NNp69aFfMoUyA8fhbZTJ90HZWfqXAqXmCfqvyX5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S1MhsynM02+nr46VxkByHJ3J6NpU6x0LLUkeVGymG/fymLn2/Sv27/IniE5IbSRvBD6JkW2EKJY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\nhJ5JkS2EEEIIIYSeSZEthBBCCCGEnv0/QjZv0L76RXgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c84ba90>"
       ]
      }
     ],
     "prompt_number": 242
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 2D interpolation using Radial Basis Functions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.interpolate import Rbf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 243
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# defines a function of X and Y\n",
      "def func(x,y):\n",
      "    return x*np.exp(-x**2-y**2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 244
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random.seed(1) # we 'fix' the generation of random numbers so that we've got consistent results\n",
      "\n",
      "x = np.random.uniform(-2., 2., 100)\n",
      "y = np.random.uniform(-2., 2., 100)\n",
      "\n",
      "z = func(x,y)\n",
      "\n",
      "ti = np.linspace(-2.0, 2.0, 100)\n",
      "\n",
      "XI, YI = np.meshgrid(ti, ti) # meshgrid creates uniform 2D grids from 1D vectors\n",
      "\n",
      "# use RBF\n",
      "rbf = Rbf(x, y, z, epsilon=2) # instantiates the interpolator\n",
      "# you might want to play with the epsilon optional parameter \n",
      "\n",
      "ZI = rbf(XI, YI) # interpolate on grid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 245
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# this is the 'True' field, the result of the function evaluated on a regular grid\n",
      "true_Z = func(XI, YI)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 246
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plot the result\n",
      "f, ax = plt.subplots(figsize=(8,6))\n",
      "im = ax.pcolor(XI, YI, ZI, cmap=plt.get_cmap('RdBu_r'))\n",
      "ax.scatter(x, y, 50, z, cmap=plt.get_cmap('RdBu_r'), edgecolor='.5')\n",
      "ax.set_title('RBF interpolation - multiquadrics')\n",
      "ax.set_xlim(-2, 2)\n",
      "ax.set_ylim(-2, 2)\n",
      "plt.colorbar(im, orientation='vertical', pad=0.06); "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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N+hwpFHn8ZlMeGhoCz/OKC3hqa2slLiLFxcXo6elBZ2env7pGCUK8jVlNmbzQ50ihyOM3\nl6jXX38dmZmZyMvLk61jMpkQGxsr2hcZGekqm86xhSli9u/fH+guUHxEfX19oLtAoUxa/DJT/utf\n/4ra2lr86Ec/UvxVTH8xUygUCoUygs9nyjt27MDRo0fx1FNPjbryMSIiwhUv+SY3t2/OmN1x4uRJ\n9Pf1jb+zFAqFQvGKyMhIkReCP/m/93dBGz6+YC9qtRqrV6/2UY/8j08H5ddffx3Hjh3DU089heTk\n5FHr5+Xl4c0334TdbnfZlSsqKhAdHa0oXff39aFk+XKf9dvfcByHvXs/xcKlyxASMvyC8TyPMydP\nICszw6N75ZN+0AU2IlgfKTVjPQ2j8Dw4jsOhI2VYtnyFpMxisaC64hyWLhlf3mKGV47ANt3gmQCH\nbQh0+x5yhMgo5k+04aH4x7r/GNc57vr0NR/1ZmLw2Vvw5z//GQcOHMDDDz+MkJAQmEwmmEwmUTSp\nnTt3isI9rlixAjqdDi+//DKam5tx/PhxfPDBB9i4caOvujUpuHz5MnJn5rsGZGBYup+/aDFqamoC\n2DPKZIVlWXBOBxwOh6SsuvIC8mfKr9WgUKYSKmZ8n2DDZzPlvXv3AoBo0AWAu+66C1/96lcBDEvT\nwuD7ISEheOKJJ/Daa6/hscceg9FoxG233RbU7lDuJj9Xr7Zj0TLpzJ5hGKjGmRA+0LPfyTD3Huv/\nndK982YWLTyNL5dJLJg3H4cPHsD8hYsQHhEBp9OJi9VVUDPK5h0hfpkNB3qG7acZpaf3ym8zaqX2\ng2QW7Q9U02ztkc8GZU/iMLvLRpSeni6bTH2qoFKp4HDYodF4n8SBMn2JiAjH6pUrcKGyEmazGTzP\nY0ZODpKTk0Y/mEKZIgTjbHc80CxRE8DMmTNRU1mJornzRPuHzGZoxjlTpkxt9Ho9Fi5YEPjZKYUy\nRfnkk0+wa9cumEwmpKWlYevWrcjPzx/1uKtXr+LRRx8FAPztb3/zWX+mryYygcTGxkLFMjhffhZ2\nux08z+NqWytOHD0iivFLmViuX7+Oo2VlOHz4c9RUV4+a9SiY6ezqwsHDZTh4pAwHDx9Be4f/8ylT\nKL5AxTDj+ihRVlaGHTt24M4778Svf/1r5OXlYfv27bh+/bricQ6HAy+++CIKCgp87tpLp2njxFOT\n7vz583H9+nVUnDkFp9OJhIQErF+/XhRUX46JsBtPBtvwWPG078J/nXPnzsHh5FA0fyE0Gg062tvx\n2WefYdWqVdDr9aJ77qtV2hOBO7toXX0D2q9dx6Kly6DRaOB0OlFx7hx6TCbMurlgzI8zcYfDgfLz\nleg3m8EwDNQsi7nFsxEqkzd7VMbaVx/ZZZVszxNib55m9mV/yte7d+/GmjVrsHbtWgDAvffei/Ly\ncuzZswebN2+WPe6NN95AZmYmZs2aJUoz6gvooDyBxMbGSiKYUSae7u5u2O0OFM8f8bVMSExERGQk\nTp06iRUrxpdVazLBcRzqG5uwes1a1z6VSoV5Cxbg8KGDmJGT7crQ5Q+cTif2HTqM+QsWIjomBsCw\nS9fhQ4ewbPFChBnH54NKmfr4a6GXw+FAfX29JLVtcXExamtrZY87c+YMzp49i1/96lc4evSoz/s1\nvX5yUSgAamsvIn92kWS/Xq+H0zm1bLfXrnUiOTnFbVlmZhYam1v82v7FS5dROLvINSADw/d55erV\nOFtR4de2KRQl+vr6wHGcxJPBXVCrm3R3d+OPf/wjHn74YUnOcV9BZ8oeMBFeR/6SqP1xVm6Sad2s\nhz+kb3bb4XCIkqAIYRgWPM+L7ETks1GSs8nHOOYf+V5ItEpyKsdzYGRMJAzLguec3svBXryrnV3d\nyJ9dLNmv0+mGT+OL997Tm+zNdY5RIh7Nrcon8vY0c52aTKuvX3rpJWzYsAG5ubl+a4MOypRpR1JS\nMlqam5CWniHaz/M8nE7HlIrJnhAfj+rDZcidMUNS1thQj2WL3KdV9Rk3Um5OpXtKmVj8JV+Hh4eD\nZVm3oZ6joqLcHlNZWYnq6mq88847AEZSyn7jG9/Afffdh1tuuWXc/aKDMmXakZWdjX2ffoqo6BgY\njUYAw/9c506dRF6edPAKZlQqFVKSElB+9iyK5swByw4rAdVVlYgMN0Iroxj4iuTEBDQ1NiIjM1O0\nf2hoCGqVyq9tU6YG/pr7q9VqZGdno6KiAkuXLnXtP3/+vGhbyG9+8xvR9okTJ/D+++/jF7/4hexA\n7nW/fHIWCiWIYBgGq0pLcfr0KdisNjDMcEjLvJkzJywO+USSn5eHlrY2lH1+EKxKBc7pRHZmOjLS\npKE629rbcamufniVtEqFosJZCLvxw2Us5GZnYd+hI1Cr1UhJTQUA9Pb24vjRoyhd7v6Lj0KZKDZu\n3IiXXnoJubm5yMvLw969e2EymbB+/XoAw6Gh6+rq8OSTTwIAUm+8wze5fPkyGIaR7B8PdFCWIVjs\nyL7q5kTYiXkvrtcbudPTvgttzxqNBkuXLpPUuXkqpda9sTH7A69CZ96om5qUiNSkRIV6PMovVIFn\nVShZsRIqlQpDQ0M4cewYCmbmImGMuc0Z8FizYilqLl3G55cvAQyDUIMea1YshU6r9Y2rz1j/j5Se\nm5/stsJn5xf3KbLfU8DG7M8wmyUlJRgYGMC7774Lk8mE9PR0bNu2zeUlQ4aGdgf1U6ZQKD7HPDSE\nAfMQlgmyrxkMBqwqLcWBzz5DwuqxDcrAcHKNAppAgzJG/L3Qa8OGDdiwYYPbMnehoYWUlpaitLTU\np/2hgzKFQsGluivILyiQ7GcYBuGRERgcHERoKPUppkw8NCHFNMUfcrWv3JzGc5axytLeSM3+wFft\ni12b5OuRblXCqkH1laAkuyrmb+ahkll4xbKq4RCk/o6/7Y/zK8m3Su/YBEjbExIZbArI2ZPJJWoi\nCL4nRJHF4XCgo70dPT3dHh8T6MGXMjnIzkzHpdqLbstMPd008haFMkHQmfIU4XxFBbp7TIiLT4DV\nasHZM2dRVFyMmFipLdDhcODUqZOwWq3DsyCHHenp6cjO8Z9DvDc47Hb09/cjJDTUb1FzKGIiwsPh\nsFrR2NDgcl/iOA6nTp5EdkYa9TOmBAwqX08jJpNkPdaucDxQe7EGap0Bi0tmj+znOBz7/CBKVqxw\nDWw3Hd0/P3gQs+cvhDEszFW/troKl2ovIneGdEHOREXw4jgOZ06fhNVmR1hEFMz9feCcDixcvARa\nrTQXtTskMrTC81CStoXnIc9AfkVMRPIKxRXXY5SsyeOWLpyH6trLOLR/3w3XKQdm5uYgKSHeuxXf\nAUQi+441ipc/pG2yDaVqxHn8JmeLGp2cwimVrylBx9W2q8jMzhHtY1kWs+fOQ3VVJVG3DfHJKaIB\nGQDyZhWgtbU1oHL26ZMnkJyejflLlmNGfgHmLFqKmcXzcKzscMD6FAz40v5eMHMGVpcsQWpSAliG\nxaUr9WhqCex7QZne+DN142RkWs+UpwoqmSw/4RGRqB0cFO1rbW1BftFct/VDQsNgtVigNxh83sfR\nsFmtcDg5RMWIs2iFhIQiIioWXdc73Urx05krDQ2ob2iCRqsF53RCrWKxeOH8cUXp4jgO+z4vQ1ZW\nNlasXAme53H58mUcPHoCq5ctpjI2heJn6KA8BeCcTrf7zYOD0Ov1on06nQ5DQ2Zo3MjBdpvVr2n8\nlOjtNSEyxn1ay/ikZFzvvEYHZQF19Q3oGxhE6dqRWLuDg4PYf+hzbFhbOubBs6KyGsVFRYiLjwcw\nPHvOy8uDTqfDxbp65Odm+6L7FIrHUPl6CsPz4s9Y4Xhe9uNVfwQf5fbkPwAQGRWJax3tkuMulJ/F\nzPxZLlsyAOTlzUQtIWkDgM1mg9PpBKvWuG1Drt+++hhCQjHY3+f2+vt6TQg1Gj06z2j3SnQdN+6L\nO2nWm+tXwuP3jefEn1HqNjQ2oXiOWPEIDQ1FTm4eGhqb3Deq0AbDc2B4Dr19fa4BWUh6ejraO65J\nzzEZPm6uw5PPqM9A7nmQXyTefKl42gaBV9cxVjx9/yYYFTO+T7AxrQblqUpR8Ry0NTfi3OmT6Ors\nRGtzE8oO7Ud6erok4IPeYEBifDzOHD+KIbMZPM+j4+pVnDh8EPMXLAzQFQChRiPMgwOwWa2i/RzH\nobWxHsmpaQHq2eTD6XRCRyggN8nMykJL29Uxn5uRWezDMAwYliaQoEw81KZMmTCsVisGBwdgNIZ5\nvLrYHQzDYMnSZRgcHEBLczO0Wi1WrlotGwwiNy8PScnJqK6ugtVqRWxsLFavuUW2/kSxcPESHC87\njMTUdMTFJ8Jk6kZT3SXMnT+f2jIFsCwLh8PutsxsNg/HlB4jKpaB1WqVuKL1DwxAr/NvRikKhTLF\nB2VfLRj1tZuT3W7HiePHAYaFMTwc/b3VULEsFi5e4tHAKLcSNiQkFHkz80X75NyZDKFGzF+4SNRX\nT6/SXy5SOr0BK9fcgva2NrQ11SMsPBwr19wClmXHlHRCSEtzExquXAGrUoPnnIiIiMDs4jmuwZ5V\nuHqOcIKSawPwXbIKJVmSYRgwACwWi2TNQEX5OcydledWgvTEraq4IA+HjxxG6epS17tos9lwrKwM\nq5bMn3TSplcQKoBXEbU8dSUa7bvCU3eqMUYGm4qRwIJRgh4PU3pQnqyUlZWhoGiuyC2p19SDE8eO\nYtnyFQHsWeBhGAZJKSlISknx2Tnr6y6jr38QC0pWuQbh69c6cKzsSNDe7yUL5uHAwQOYVVCAlNQ0\nWCwWlJ87i6hwI4zjiL4VbjRiYVEBjhw+BDDDgzIDHssXFsOgp4FcKBNPMErQ44EOyhNMb28vQkON\nEj/hiMgosCo1zGYzQkJCAtS7qQfP82hubsbiFaWi/bHxCbje0Q5TTw8ifZScfCLR6/XYsHY1Ll+p\nR9nnB6HVaFCUn4dwH4TDjIoIR+myxeKdwTxDpgQ1dKYc5ExWyfomnZ3XEJeY5LYsNj4BPd1dCAkJ\n8ThYw2iyri8kaX8FjhB+zftKHCOl5v7+foRHuh9007Jy0NhwGRFRUYoRvSRtKNT12/eHcFC88TxY\nhkFeTjbycrLd1wMh0Y41Kpgn5XJwXhzHjjWfsg9yMhMoPccxS9uA+AvKm8hgkzkSGMWn0NXXE4zR\nGIY+k8ltWV+vCUZjmNsyythQq9Ww29wvirLbrNCMI9AGhULxP9Nt9TUdlCeYhIQEdFxtHU6FJ8Bu\nt6O3pxsRkZEB6tnUxGAIwdBgPzg3M7Yrl2omTRIOCoXinunmpzzl5OvJDsMwWLRoEY4e2o/0rBxE\nx8Sg89o1tDY1YmnJskB3b0pSWDwXJw4fwOx5C2EMC4fdZsPFygrExsTI+vtSKJTJQTDOdsdD0A7K\nXV1diImJmdQ2ZDk7bVhEJNauW4/mpia0NTUiKjoaa265BQzDyEaXGkv7ox7rqd3ao1o3z+lFZQHu\nA4W6R+l/lHXTgcioKCxeWoLamiqYzUNQqVjk5s1EVHSM6/6QNmThfSPdpSadz7TA3idx8/GVTVnB\nNuyzqFJOL1yU5FCq5iM3n3HZm8eaiWqM2Z38ZmOm+I2gHZQvXb6CltZWFBcVB7orY4JhGKRnZAAZ\nGQD8t5iKMoxOr0fxvAWB7gaFQvESf6VDnaz4dFCuqqrChx9+iPr6evT09OC73/0uSktLZetfu3YN\nDz/8sGT/448/jjlz5ii2NWf+Apw9dQK9vb2IiIgYb9cpFAqFMglhgtEwPA58OihbrVZkZGRg9erV\nePnllz2W+X76058i48aMEYAkXrMcs2YXo/pCBZYuXTqm/noqWXszh/WVa5HwPGORyD1pU0l09LSr\n/orupYiCSxIpgwtfQXfS9k2UonZJXKCIDgiP9dn3h7skCDJlim5PnsrXnMI5R+vbBMB46vbk9EKu\n9YOSO9rjV/x3UZK2fRQJzC/Rv/wMSwflsTNv3jzMmzcPAPD73//e4+OMRuOYZrs6nQ52u3t3FwqF\nQqEEP4wqOH48+IpJYVN+/vnnYbfbkZiYiI0bN3o8821sqEd6Gs0eRKFQKJSpQUAHZYPBgHvuuQf5\n+flgWRanTp3Ciy++iIceeggrV65UPHagvx/NDfVYt27dBPWWQqFQKBMNtSlPIGFhYdi0aZNrOzs7\nG/39/dhsF2mPAAAgAElEQVS1a9eog/KlmiqUlpa67NY2mw1OpxN6vV7Wlu2N29NEhKccq6uTyN48\nWhsKZcJDfWYL97jm2CHFLKcociHpSiL4kyhSsjcL7cRKITdJyDslfOeU/C3H5VbkqU1ZyW48aphN\npRdkAp66yN6q4EBH3GMlW7Rit71RTEV9U3a7UrTpKtmG/RCeM1jcpahNOcDk5ORg//79o9Zbtmw4\n0EZvby9OnzkDrVYHjUaDwYF+ZGRkICcnx99dpVAoFIqfYcYaEz1ImXSDckNDA6I8zNpjs9lw/Phx\nlKwqhVo9cikXKs6hqakR6ekZCkdTKBRK4Ojo7ETVxUtQqdTgOA4GvQ4L5hRDrR49pzpl6uLTQdli\nsaC9vR3AsOTZ2dmJhoYGGI1GxMbGYufOnairq8OTTz4JADhw4ADUajUyMzNdNuU9e/Zgy5YtHrVX\nVV2FornzRQMyABQWzcHRzw8iNS3d475PhNuTN3K1p22MJhyOVaL2hbvUcJvj95kigwcouT2RnRP+\nxpa4PQnLCIWMFd1jQhIl+wcPpcXxMNaoXZynx/HyZQQT4i7lRSYmkexKvm6M+A0QFSlIvT6TtmVo\n67iGuqarWLVqNdgbM0FTjwn7D5dh3eoVYMj/QF+4S5HnIZis7lLjla9H+wb65JNPsGvXLphMJqSl\npWHr1q3Iz893W7elpQWvvfYaWlpaYDabER0djZKSEtx1112ScWis+HRQrqurw89+9jPX9j/+8Q/8\n4x//wOrVq/Hggw/CZDKho6PDVc4wDN577z10dnaCZVkkJyfjwQcfxIoVniWe7+/rR35htGQ/wzBQ\n+egGUSgUiq+pvnQFpWvWitZAREZFIjs7Gw3NLchKp14lNxnvQi+lQbmsrAw7duzA/fffj/z8fHz8\n8cfYvn07XnjhBcTGxkrqq9VqlJaWIisrCyEhIWhoaMAf//hHOBwO3HPPPePqp6sNn5zlBoWFhXjr\nrbdkyx988EHR9urVq7F69eoxt6dSqWCz2aDVaiVl7rICUSgUSqDheR5qjdbtgtSsrCwcPvw5HZQF\n+NNPeffu3VizZg3Wrl0LALj33ntRXl6OPXv2YPPmzZL6iYmJSExMdG3HxsaisrISFy9e9Fmfgno6\nmZ+fj8rz5Zi3YJFof093t0dRwfy9wpo8zpvIXJ5K1GTzSueRtKlwHvE5xYW+kvo9hR+lRUbp+Qi+\n+BjiPMLLUhFLrDmFldm8glxIXu+Yf+R7s9rZ0xXWHCH8eyqJj9a+p2UyOBxO1DS2ortvEDERRszM\nSIZa7Xmea0YotSrI3hJJVlJ35P4wjDfStmDHaIuSGPZG4hn398lut0Pt7hy+WJk92nmE3QySldnj\nweFwoL6+Hrfffrtof3FxMWpraz06R3t7O8rLy8ccVdIdQT0oR0dHIzzMiJPHyjCzoBA6nR71dZfR\n03UdK1etCnT3KBTKKHSa+nCmph5zZuZi1oxcXOvqwacnLmBhYS5iI8IC3T2/wfA8rDYbdITKV1lZ\niZm52QHq1eRkvDZlOQe6vr4+cByHSCKHfUREBM6fP694zieeeAL19fVwOBxYvXo1vva1r42rj0KC\nelAGgMKCQpjNZly8eBF2uw3p6RkoLprtKr90qRatrW1gVSpwTifSUlORk0sT21MogYbneZypqce6\nkkWuxU6JcTGIj4nCp2WnsGHpnMmXJtNHLJozGwcP7Mf8eQsQGxcLp9OJysoqcHYbYqOjvFKjpjqM\nN4ECJogf/vCHsFgsaGhowBtvvIE33njD4wXKoxH0gzIAhISEuGJuC6XWc+fOQmswYsmKYbs1z/O4\ncvkSKioqUFwcnCkfKZSpwtUuE9KSEl0D8k1YlkVKQiw6e3oRHx0pc3RwExJiwC3Ll6Cqtg7V1ZVg\nAMzIzkRSAp0wkLB+simHh4eDZVmYTCbRfpPJNKpbbkxMDAAgJSUFHMfhlVdewebNmyXv8lgI+kFZ\nzuXGZrOhr38Qi2bPde1jGAY5M/JwsuzwsO1GI2+38oUdebRfu0qRuZTsvWN1bZKeZ2SHd/Zuhcrk\nsT74zc+TobgIRJG5iKrCa2QhbzcmL1JoYybdpUgbtrDYX8GHFKNvKbk9Ce3IY80uRWwrukR5scBy\naNCMsNBwt2XG0BCYzWYwkcZRz8Mr2ZQFMKPYlMV2U/I8CtevZG9W+I7WqFWYU5AnPs+NE5CvEe9p\n1LAJcJeaKqjVamRnZ6OiQpxp8Pz5817ZiDmOA8/z4DjOJ4Py1LPe36CttRUpMn7KiSmpuHr16gT3\niEKhCEmKiUTz1Xa3ZS1XryExZmrOkinewaiYcX2U2LhxIw4cOIB9+/ahpaUFr7/+OkwmE9avXw8A\n2LlzJ5555hlX/UOHDuHYsWNobW1FR0cHysrK8Pe//x3Lli2bnH7KkwmVWg2Lxea2zOGwQxOin+Ae\nUSYDPM/janMT2q+2QqvVImdmPkJCR5+NUXxPqEEHzm7Hta4exMeMyIUdnV1gwMGgk7o6UqYf/kxI\nUVJSgoGBAbz77rswmUxIT0/Htm3bXD7KZGwNlUqF999/H+3t7eB5HnFxcfjiF7+IjRs3+qxPU3ZQ\nTklJwYEDB5CRJV7JyPM8rrY0I/+GX9pN/OH2NJo7kJLbk1IkLk+PGz5WXqJWvmZBPQVXIhJfuECR\nOIn2SYlaHNCLiL4l2LTZbThd9jkS07OQN28JbJYhVF24gPCIcOTlF4hPKrgQ0l2KdNESukiR74q4\njOj3GOVjRRlaye2Jc4ztnIBYlvYi+tdoLC/MxsmLDaisrYPBoMfQkAVhejWWz8oEHIJc6YqytLxL\nkseyL5Rdq5QlcsE9JkoU5WwvopaJ3vGxJrIA/Bdxzo/4y6Z8kw0bNmDDhg1uy8jYGsuXL8fy5cv9\n2p8pOyizLIuc7GycPlaG2XPnQ6fXw2Kx4PzZ05gxY8aUXdVJkafq3Gnkz1sMfciwD7vOEIL8eYtR\nc+4kBvr7YAxzb9+k+A+WZbBkVhZ4nofN4YRWraL/mxQRNHXjFCIjMxNRUVGovHAODrsDao0ac+cU\nIyycfvlOR+x2u2tAFpKdX4S6ixcwZ+HiAPSKAgwvwtRppvTXEYXiEUH7X+BJogMeQFhEBJYuXSZz\nDoVjx7jCeqy5jpVWWHuzotpJ1uXl6wplaW8kaSVpm2Ss+SgUF40qrMZmiKXRN5Uvnuelq29voNHp\nYLfZ4BReqECylqy2VliN7bcf9QpRuxRlaKFkTcjXjNI5CRnc4yQU/kpIoVDGK6w+ZoShI8gHpyR1\nk9I2O/JVKX2lFZJeEDVFt2eMquyYV2YDytG/PIz2NdGwk9BP2Z8E7aBMoXgDwzDgnA63ZV0dbYiO\ni5/gHlEoFE/wZ+zrycj0ulrKtCYpLQNXqipE+2xWC5ovX0RaVk6AekWhUJRgVcy4PsEGnSlTpg1J\naZng+QZUHD0EjU4Hp8MBlmUwd8lynzj9UzzD6eTQ2TcAnUaNKGNIoLtDoUwqptyg7E1kKtFxPnJ7\n8jQr03Ddkb+dnOd2Y2/cnERt+MjerGTPd3phUhRF2/Jixa2KJV2khJmgxPDOkT0qlkdCWgYS0jLg\ndDqhUY2s9HUq3EhyvCZt2kIXKY4oUyldiAJKWZukZSPtS9yeBNuS45yCrEhK7lLkNmFv5j2N4sU5\nUd7Qjq5BK5JiIjFks+O6qRHzs5MQG66c1Y1R+NHEsCrhBlk40s9R3IWErlW8ivhqFN5Hb9ogFiQo\nmX9FLxrPoa2jE+2d12EMCUFOZjpUKvdvk8fuUpIOKCyW8CLal7+hq68plGmASkVdbyaa6pbr0BtC\ncMvMkdgBHMdjz6kqlM7OpKuvb2C12XDo+BmkJKcgd8ZM9Pb24bMjJ1CUn4uk+LhAd2/CmW42Zfpf\nQKFQJoS2ngGsWygO0sKyDBblZ+KjkzXYtHgWtOqxagtTh2NnzqNk6VKEhAxL+2FGI1KSk/Dp/gOI\nj4mGSj29BqlgtAuPh6AflMcTQMrfbk+kOkVKu55H9BK34fRQdibbHKtETfZbVCaRxH0f0ouUtu1E\nf1SCcg3xDyxSk4nMEpxAWtSQUbsEm6S0TbpICZtUelcUv1tGi5LFeej2pBSZSxghiywj5GueqCuS\nrMmoYcLjnPJlapkZT0x4KCKMBuw7dwkbZqe7VzCE0i1LDNysQNonZW5BXW8SUkh6wCpI1AJ3KYn5\nhDyP0OuIIb8QWNjsdqjUGteAPFKXQeGsfFy60oD8GfKLEhXdpQDPo38puUtR/Mr0+slFoVAChlNm\nwB4YssKo1yI7ORbNXf0T3KvJhcVihTHUvX09MjIS/YPmCe5R4GFYZlyfYIMOyhQKZUII16vR1mWS\n7D91sQGzMxKRkxSL5u7pPSiHhoRI8vvepKW1FQlxMRPco8DDqthxfYKN4OsxhUIJSuZnxKO2oRWH\nyi+h09SPpo5u7D1VjfS4CBgNOgxYrDBog96iNi5UKhZhITq0traJ9lssVjQ0NiItKSFAPQsc/kzd\nOBmZ0v8B3mQsUnJ78qpNwd9KNmSyrlK4zLG6OZHlSnZju5O0W/Nu6wHi65Lau31vU1aNskpaaHN2\nki5JCvZmYfYnVmIMFrhZEYVS27zA7Uqxpz5Cwf4scW1ScntyjtiNFW3IAHi7XbZM3BX5MnAcVmZE\n4ZOLV9FyTQOjXoe1xdlQ3ZjNnL3UjEUpEeBtFumxrJJNWTC3IN2GhDZltUb+nBDbnCVvsdDtjGiC\nF95XhfCc0vO6zza1oGAGTlVdxuUrdYiOjsbAwCCsFgtWLZ7v3t7uYXYpwI2NmTLpmNKDMoVCmXys\nyo7DwSvXUZCZApZl0D9kxenaJiSGqqf9TBkYXtS1sGgWHE4n+gcGYUhLhN5gCHS3AgZ1iaJQKBQ/\nYtCosSEvAXXXe3GgtR16tQrzkyIQqtOMfvA0Qq1SISqCZrRTChwzFQnaQVlOJPUmapen8rZS1C4l\ntycluXq4XHCcQt/IMnEkMKJ9BRclbyRqYV3FvpGRyJSifXmhbCuZgkgXKaEsTUbUEkvWXvxzCxMG\nEeckpW6hZwvZb/E7oJT6SiGCFsTSs1LULhArnBmnTVAmlqiFkjVvt4nKSPcpUV1Soh5FsnYHAyA3\nQo3ciLCbHQBvU5DFhTK0knxNlgkla7KfpHwtqCuJfqaQJcrTSGCA59mmyGfMC7+qydfYC0laeF2S\n9j2N9jXBBONirfEQtIMyhUKRh+d51LV2oK2zC6F6HQozkmCg/+2UIGS6ydfT62oplGnAkNWGj49X\nQK1isaI4HzmpiThaXY/LV7sC3TUKhTIKU/q3s1K0LUldYlspapfSqmlPo3QByiushWXSVdzu6wFS\nidoukA+9kaiFZUoRxaQrs30f4YuUq1VEQAC7YKU0GZlLuBrbKfkJKv+blBVIzQwRwkulsBpeerlj\nlP0Ukk5IpG2nWNo+dqEWa+blQ68dlmMjjSFYM78A+05dQGpkiCvGNG8dWeXMO8TytWQ1tlC+JoOA\neBjtyyskErVgW7LCWrBqmpSkhf1RkrYBcd/VWvF5BN+USiuaJWVKiS28WJktkp0liSzgOUpSt6fR\nviYYOlOmUChBi93hhEqlcg3IQubkZqCm9XoAekWhjB2GZcf1CTam9EyZQplu2BwOGNwMyABgDDXA\nbHW4LaNQJiuMTMrKqUrw/YygUCiyhOi06BsccltW33YNqTFhE9wjCoXiDT6dKVdVVeHDDz9EfX09\nenp68N3vfhelpaWKxzQ1NeG1115DXV0djEYj1q1bh69+9aset+mrqF3euD2JMzgRbcjUA7xzexLa\nkaVl8vZmO+GCIrQNK5VJ7M2iNuTLyOM4hQcyVpcolrATsxwZtUvQPmF/ErpIcYIf3LahQVy91gpW\npUZscgZCDTpxGwI7soon7dREX4XtEdfBid4V0jYtiMSllBUKENv7SJcoYbQp3onEKCNqGtuQn5Hs\n2j8wZEVjazvWz0px2ZKFkbMkNmU7aVMeKefJvrmxKdudPM5cM8Pi5MEwDDiOw+wYHWL08l85ilKj\nwB4ssRsLbcOsCjzPj0S+EvRN0d4MiOzIStmeGJZwO1Np3NZzex6hjdkbdynBOyg5p/BxeDHNUoz2\nRb6PAYwENt1syj4dlK1WKzIyMrB69Wq8/PLLoyaRN5vNeOaZZ1BYWIhf/vKXaGlpwSuvvAK9Xo9N\nmzb5smsUCoDhH1vNVWfBgUF4YhocTgdqy08gISERyVkzAt09nzA7MwmVje349OQFhOi1sNrsUIFH\naV7SqP+TvoDjeRxoGcDSlDCEaYcHQifH43BLHwqjoTgwjxWe53H+2iC6LA6oWRYOjkOEVo35GTET\ncs0U/8EGoV14PPj0v2PevHmYN28eAOD3v//9qPUPHz4Mu92Ohx56CBqNBqmpqWhra8Pu3bvpoEzx\nC9fqa6GPiocxLsm1LyQqDp215QiP6oYxMjqAvfMdhRmJKEhPgG3IDLWKBcswotXW/uRSjxUFMQbX\ngAwMr5hfmRaOQ029WJ3i+0H59NV+xEeEYk76SNrDq31DONrQhZKsWJ+3R5k46Ex5AqmtrUV+fj40\nmhH5p7i4GG+99RY6OzsRFxcne6ycSsoruBmRh4hkLvL8onrybZPuUmNNFiGUoXu7u9Bcfxk8zyM5\nIwth0eL74KkkPVwujOjFyZYpSdQO4jhFl6gxytmKEbxYUq4monYJyklp2UmU9fV0IaU4V9JGdHYB\nmq5cwIy5i4fbdAqSTJAuWZKoXe7/Hg+knM0oSN3C6E+kK5OGdwIOJ3hAkuhBJF/bR3OJssmXCSVi\npxMdAw7kZYSAhGUYsAD4ocEbO8RftiKJWCGxBOnaZLNaYbY7kREtzkOcFG5AQ9cABvoHEapVSV2i\nJJHJOPkyoRuYhnCXkvkbUJazvXKXUpCoGbl6AKA0oJHvkbCIJq4IGAEdlE0mE2Jjxb9iIyMjXWVK\ng/JY4XkelRfOo7u7Gyq1GpzDgfDwcMyeMy/gMhfP86g6cwK8Rof43CKAYdDeeBnNVy5j1oKlAe/f\nVEAuZJ9KrQEnExKS4j0c7/6H1vCPWN++xx1mO1KijG7LMmOMaOs3Y0ZMqNtyyuSHzpQnkEAMMufO\nnEZEbDyy8me79nV1duDU8WNYtHTZhPdHSFtTPTTh0YhJyXTtS8zJh6mjDS11tUjLnRm4zk0ROKfT\nrUJit5ih0WpljqJ4Q3Yog5puMwpjxQOh2c7dWNzmWxeXa2YHtIx7V69+qx069fT6Up9q+NvX+JNP\nPsGuXbtgMpmQlpaGrVu3Ij8/323dyspKfPTRR6irq4PZbEZiYiJuvfVWrFmzxmf9CejbGhERAZPJ\nJNp3c/vmjNmX2Gw2DFksSEhOFe2PiUsAWBZm86DP2/SGa22tiE7OkOyPTEiGqaszAD2aekQkpqG7\nsVa0j+d5dF2qQGqO+39EYHgwt1ksXkWJm64kh6jQO2hF9XWzy7zRMWjD5009WBDl+68cs4PHtX6L\nxJTC8zyq2vuQaNTJHEkJBhgVO66PEmVlZdixYwfuvPNO/PrXv0ZeXh62b9+O69fdB9mpra1FRkYG\nfvSjH+E3v/kN1q9fj1dffRWHDx/22fUGdKacl5eHN998E3a73WVXrqioQHR0tMfStTdfkl2d1xCb\nkOy2LCElDe1tbcjKkdobldylSBsmp2DTHtXtiWXl1QOGddmLhXZkJRsyAFgcwrryNmXSbiyyN3sV\nglM57OZYIMNqKtmUNWSYT8EsieN56GKSYLHUo6X8GEKiYsA57LD2mZCcmw9Oo3fdr5uhPZ0OBy6d\nOwuHwwmNXg/b0CCioqKRM2u261mJ3Z7EffP46kfJEiXaloS5FMwSSdcmJbcn65CgjMgKRdqfhTZn\nsq7Qpnzj3VxsBFqGrPj8ygDAMIhSObE6nIfayXgUiZOcHTEKNuVQFkiPMuDTi+2YnxqFWKMepiEb\nTjd3Q81zUNvM4BlGEjpT0aZM9kd4vUr9HmVbKWeYyMasZO/lyYFGKTymgknGG7ux0nmCmN27d2PN\nmjVYu3YtAODee+9FeXk59uzZg82bN0vq33HHHaLtDRs2oLKyEsePH8eKFSt80iefDsoWiwXt7e0A\nhgeuzs5ONDQ0wGg0IjY2Fjt37kRdXR2efPJJAMCKFSvwzjvv4OWXX8add96JtrY2fPDBB7jrrrt8\n2S0XWp0elm6T2zKL2YxQfWB/UWvUatitFmh0etF+p90eyMxpU46IlCyEJ2eAtQyAVamgyy2UlTgv\nnTmK9IK50IeO2Cx72ltxqbICebPnTFSXg5JUA4NUg/DL3D8vcX6EGqe6B7E6KxbV1wZQdbUXoVoV\niuKMqO3ooWsxghx/2ZQdDgfq6+tx++23i/YXFxejtrZW5igpZrNZsjZqPPj0auvq6vDoo4/i0Ucf\nhc1mwz/+8Q88+uijePvttwEMS9MdHR2u+iEhIXjiiSfQ09ODxx57DH/5y19w2223+c0dKjomBt2d\n7ZIFPTzP42pzA5JSUmWOnBgy8wvRdOGUqH88z6Pxwkmk5s4KYM+mHgzDwhAeBV2ofBL5vu5OGKNj\nRQMyAEQlpqCvrxdOcsZKCQgGNYM0vQNH6juRFKbFwpQI6FgG5S1dWJxgCHT3KOOEVbHj+sjR19cH\njuMkplJ3ZlU5Tp8+jQsXLmDdunXjukYhPp0pFxYW4q233pItf/DBByX70tPT8fTTT/usDyJ3JaKM\nB4PZxXNx8vABzJw9B5HRMejrNaGm4hzyZhWAZxjXMWI3F/ksUUoZlKQZpOS3nRwPrT4EOQVFqC8/\ndkOiY8A7HUjMzoPWGO6SrYXysVCeBqTytVCytpIStYeuVWSZUJK2OeTL3G3LlZEStRCyTEvMam2C\ncr2CyUBP/IOS2adEbTIMujo6EJ+a7rbcEB6Ngb4+hEVGEc98tJhOHiLxwxNI1ISUKJSeSV9kkXyt\nVCaRq8USNSesS7xHnN0hWyY6J1GmNAMiy4RyNqsVf20xDhvSVUCigUddhw0NTiBFD+THsGAc1pHv\nAaVIZKMglOjJXnslZwskY1KGFmeCIhausfLStrBHpLvcWDNKKUb7mmAma1KJmpoa/Pa3v8W9996L\nnJwcn5132iWkiI6JxfKVq3DpYg0aL9Ug1BiGpctKoNZNjsUgYZHRKF66Ela7EwAPllVJBkXKxKDV\nG2AxD8BglMaLtlvM0BJmBkpg0bIMCsIn5xc4Zez4S74ODw8Hy7JuFxtHRUUpHltTU4Nf/OIXuPvu\nu7F+/Xqf9mvaDcoAoNFoUTC7WDG+daCZbqHlJiPRyemoP3sUkXGJIrukw26Dw2qBzkClUQolWFGr\n1cjOzkZFRQWWLl3q2n/+/HnRNklVVRWee+45fO1rX8Ott97q+375/IwThK9dU5SidiklpJBGCRv5\nW2m1NVkuXUUtv8JatKKakIetDrEkJyy3klK3oEMWonNCWVpJovZGvlaSsscjXwu3yTYMGnmfWGGk\nMLINO8cDDIuo9FzUnjqMlNwCGMIi0NvZjutNdShctMz1/Hhh0guiDY8Xn4+y+loU0YuQNnmFaFtC\nGVoxohchbTutVtG2UKLmbOL2ncI2iPdISc4mEc6IlORrlV2cmlJYl9WI+8Y6Rr7iGC252lpBvibK\nhG+HkiI82uMWriJXXJlNvLa8QkQ3UZvE6n9pG8H3Y9+fwUM2btyIl156Cbm5ucjLy8PevXthMplc\ns19ycXJlZSV++ctf4gtf+AKWL1/ummWzLIvwcPn1Kd4QtINyMDPY3we7zYqQiEioyFB7QQrX1wln\nVyucPAOVVgc2PkvqghKEhMUmID4+Du0Ndbje0oCw6FjMX7mWruilUCYIf9qUS0pKMDAwgHfffRcm\nkwnp6enYtm2bazU1uTj54MGDsNls+PDDD/Hhhx+69sfFxeGll17ySZ+mxogQJPSbenDxwjlojRFQ\n6fQYqq1GeHgkcgqKAt21ceFoqYHJzqIvNBtgWKgdQ4hrKIcmtQDQSmMgBxsqtQYpuSOBReiATKFM\nHCwZB93HbNiwARs2bHBbRi5OfvDBB90uWPYlwadlBCkOux1VFaeRMmcJEmYUIjY9B2nFS8DpDGi8\nVB3o7o0ZfqgPA1YH+ozJrmAEDrUBVyNngrvqua8fhUKhUKbATFkpE5RSJC6AdG2Sryu1N8tH7eLg\nvqzhci3icgvBEtGIopLS0XjuKGIyxLl8PXV7Im3IErsxJ38ei+BY0jasZFMWbjs7mtBjTIEERgWL\nE7AP2VyDNWljV0IYtYu095KBPoQ2ZdLeLLYxi193YRuke5QwSpjdqXROgFMJ3xUiopfwPSItfELb\noIINebgRYUQvBbuxUkQv0qYssCM7zEOiMqdFfB6h3VhqU5Z3ifKVTVnobyq0bwMAqxl5riqF9lnC\ni4HcFqFgbyZ1EoUETlIbs9CmTH77Ct2OOKIVRuFdEbanFO0LEL9HXkzJAqkN0YQUFL/Q32dCQqo0\nhCcwvPhDKY3kpIbjwJOrUm7Agxn+VgrCy6JQKJOD6TYoT6+rDSB6Qwisg/1uy3jOGZwDMgAuIgHG\nwXZpAc9DDackZy6FQhFzvXcAdVe70G+2jF55GsKw7Lg+wca0mikruTYp1VWK2jVqkokbpOfmo+L0\nCaTPWSIagAdN3TCEGhUTSyi5PZFyNenaJCy3EWVDNqfbvwGxRE22IZK21REIszfDZjXCprvhEsBz\niO6pw0BYMizWEanR6UUQFJXQBYaQr4fs4m2h25M3afpYD+VrnZp0JSPkc4FQwLG+iuglH31K4vYk\ndInyIqKXULIm5Wr7ICFnCyRjsq5QIpZE+/LimQt98yUuUSK3J8IMIYjwpSRtqznlAEGsULL2IjCM\nSD4mzFOSpy9YtNRnsePoxSYkxEQhKtyIqsY2mIesWFWUDZVGJ3seqSSuEO2LULHEUcPIvgXfADYV\nmVaDciDR6vVIzcxC49kyRKVkQaPXw3S1BZzdihlzF8M22aKXeEF/fCGMPQ1gB9vAgwUDDubwNDj0\n4QvusUkAACAASURBVF446voexjYE3twPaA1gjMoReiiUiYTneRypbsIti+dAox4eONMTYmHqH8SR\nynqsmiufRnS6Md3kazooTyBxSamISUhCZ2sTbAM9SMnKHQnhGMyhNBkGQ9FZAHyTqnHccE6wbVWw\nMhr06SKh6e2Ese0ytDmzoTIYRz+eQvEzzV19yE5JdA3IN4kMCwXDqmC12aHTamSOnl7QQTlIuPnd\nTw4BooQUo6z2VarradSu0ZJMCBmWqFlEJ2cCGF4NPZIjWT6RBJkHWSgnK8nVADBkl5eohdvkCmuz\nsEwx2pfy6m9fJKQgJWmyrjhqmAr6jhqYwtLg1Az7SNsM0Rjkk4HL56HLW+QyHwjPoyKaF7ZJXpOe\nkLN5fuSLVfI+yl4VWdHziF6cXSlnMiFtC3ImO4fMojKhDE3K1Q5y9bVgmyzjBdmySPlYXE/5biiu\nvtaMDFAqQr7m7GpBPXGZWj8SwIYns8MR/VHpR7a98oxlvah9o25XnxlZOUluq0SHh6LfbIZeMxJz\nXbiqWhLRSynal69yLweQYLQLj4fpdbWUqQ/nAMdzrgHZBcOi1xAP3uRmURqFMsFEh2hxTSa3e0/f\nAMINNNnJdIUOypQpBWu3wKF2H0XMpgsHN+R+BTyFMpGkRYehrvkqHITS1Ns/CKfDQaVrAQyrGtcn\n2Aha+ZpCcQen0UPrMLst01n7wPooaDyFMh4YhsHymSn47EQ5EuOiER0ehqvXuzFoHsKqwoxAd29y\nEYQD63iYVoOypy5QwNiidgFiNyjShEbmRRZngiKjbQlckpzkcfLZpYQ2ZEDZ7UloN1Y6jrQbC48j\nbcZKWaN4LxaBMQJ779AoWaJENmWtGhE8A5V1AE6dYFEX54TR3AFrwgLgRv+FNmXynDrBdegl7Yn7\n6hS9Kz7yN5e4RAlstaRt1MNMUKQrk8MykgnKPkhkiVKoK432pRTRy4tnLjDskzZllSD7k5Ms0+sE\n9cRfacL+qLmxJ0hRCQcGcpCwKfgXk/ZQwXaYVo8NBSno7DOjv/c68hOjEB6SMFxIZAIba7QvJRcp\nabapSSqcTjOb8rQalCnTg96oHER2XwJYNWyGSKjtZuisfUDqLCBIg7RQpi5x4SGICw8B44VvtDuc\nTg4sE6SRARVg/JyQYrJBB2XK1INhYYqZCdZhhZEbAGeMwUBMJiJ11E5HmXpcaevAldZO6LRaOJwc\nwPNYUpSHEL1ysBTK5CToB2WlRBKjLfBXSjrBi+qRx8mXCWVviVxNRu1yCusqRPQijhNK2xaHvOxM\nbpuJsgFBtK0hIsmAWcFdSnhOp4N0wSAinDnknwKv4J3BCqRlByFfOxzy8rVDdB9ZcPqY4T9tTlEC\nCkAsXxu04l/jdo28aYF8rpzSe+ShektKidKEFMKIXmL5GMJtokzooiSUoAGxZC2J6EUkqHAM2QR1\nxW5XToHpgyPeVXG0L3EZQ/qhCVARz8PB2mXLhG1wWsJdSvCsSJcobxBFFCMLlaRthW3JIiThjJCQ\nbBlWcB0CN6fG9uvoGrDhlpLFrn1Wmw37j53GhiXFUGnE5xG5SEkya4wtWYXfmWY25cl06ykUCoXi\nBZfaOrFgtjj6l06rxZz8GahuaA1Qr3wMqxrfJ8gI+pkyhUKhTFfUGq1bG3JSfCwuNTQGoEe+hwYP\noVAoFEpQ4HS6z/tstdmgnmaD2VQhaGfKnpjqpPZeYltUxsvW5SBfppQlSppdijyP0G4stnc5BNtW\nwm4stGkq2ZABsW1YaEMerutwW488j5Uoc9iFNmXiGkmXGB+4RKlIlyQVmYlI6IYm3wYZnlNrGzmv\ngbhGYeYpu0Z+LcBwm4K+KLwrkq6JFicoJKOH2B4qzRIlcImyyrs2SVyZhGE2CRsy6SLlGBppw2ER\nv0dOQTYVJ+FaN1aXKHZI3IbQjuy0y7tdqUYJ5TlWRCFAycHOqlBGbgvChfIcMaAKB1ji+TNCFynB\nAgydikFf/wDCw8Qx3curL2FWVoqyixThvifORDWJBvQglKDHQ9AOyhQKhTLdWZKfic/OViA7PQ3Z\n6Smw2Gwor74Eo06N6PApknyFDsoUCoVCCQY0ahW+sKgQjR1dOHLyNLQaDWZnpyE81BDorvmM6WZT\nnnKDspJ4pZQJSlpXvh4vkiRJVyahK42y7Clye1JyiSLKlKJ0SdyXRFmixJJgv0XoEkWcR7DtsJPy\ntcAFhYzgpSBfc17I1yKXKLtYZlNrCJcYoSKn8FDVCpHBJNevV3iOkmfl3l1lXJDXIXJ7kpevOaJM\n6AYldGsCxJmhSLnaPkhE9BLIyXZCvhaWkXI154WczIqyRBHua4J3TmUjJXJB1C7J+8e5recO4Zc/\nORA4WKtsmVowkyNNC5JZniD6GlkmcpEiA2aI+kZG6WKRmRCDzISYkeNu/FOQ/w1CVzvJuyq85aT7\n2DRLnxhIptygTKFQKBTPsN34kaDVTOLAOuOVrydBindvoIMyhUKhTDOuXu/GhboWhIQYwPM8LBYL\nivNnIC46MtBdkzLeQdn9AvVJS9AOyjcVRCW50gu1VPHHlFJCClIRE8rZ5GpriezplJdIhVG7bEQj\nQomalKuVVlErrrAmVmY7BCtqpfK1fEQvMqITp7D6WrjNENKycJslypTOw/OEJCjw4TQTq7iFK67N\nGvl7Y9fJR/Ait5UizCkisZGQK4wFEb3s8hG9lFZYK5UJV1cDgG2AlMEFkcGIldFCOVspIQX5brCE\nRK2YkELwPMiIXqJ3jIwopvAlQLbBCiVioky4LYySBohNBiyx+l0iUQvNEIREzasFs1XS1UkgJ0ui\nvYm2VQplABgWXb39qGlqx9pVJa7/D47jcODIMSyePRNhxlBpiD3yPBPIuGNfB9mgTA0FFAqFMo24\ncLkRJYsXiH6wsiyLksULUH6xLoA9owB+mil/8skn2LVrF0wmE9LS0rB161bk5+e7rXvt2jU8/PDD\nkv2PP/445syZ44/uUSgUyqSB53lc6BhAt60XLMuC4XnMyUpCRIh/EkrwDAuNWvrVr9fpFP38AwZd\nfT0+ysrKsGPHDtx///3Iz8/Hxx9/jO3bt+OFF15AbGys7HE//elPkZExktw7NDTU112jUCiUSQXP\n8zjY0I2ZaYmYEx0GALDanTh8sRlzMxMQY/S9axPndLjdz/O8KIHHpIH6KY+P3bt3Y82aNVi7di0A\n4N5770V5eTn27NmDzZs3yx5nNBoREREx7vaVTHjk66ZkCxTajW9W6+q4ipbGK+B4Hjq9ARl5s8Co\nxb9mhSY10k4sdZ9SyCjlYdQuiZ3YTtb10O3JJm5faDe2k/ZmgXsKaVP2JqKX0BZI2o2FNmXS3sxx\n8jZFEqHdcsgm/sUtvDc2nfhfQWirl7q2yWf/8tSEPBqSBPRCVxsiEhQvcLPhbGS0LfksUQ6hTZl0\ncyK2bQOe1ZVmiRLae4koVRK7rUBOJezNnMaziF5KSNctkJmYPLMpK5aR53SIVzXzwm21Fq29Q0iK\niULKjQEZAHQaFdYUZOBAVQPW5qcO79RoR45ziu39jMD+K8mExUjtz4nREWhoakFmeqqo6FJdA9KT\n44cPI9+/AFo6Jdm0pjg+HZQdDgfq6+tx++23i/YXFxejtrZW8djnn38edrsdiYmJ2LhxI5YuXerL\nro2bixfOwsaziJ81DyyrgnWwH+dPliGncD5Cw8f/Y4JCmW5wLIuu9CRYwozQWKyIa2mD2mYf/cAp\nxJVeK1YVpUn2sywDlmXB87zbhBPjYVZWKg6fq0ZPby9m5eWC53lU1VyCw27D0rmFPm3LJ/hZvvbG\n3Gq32/Hqq6+ioaEBLS0tyM/Px1NPPeXT/vj0avv6+sBxHCIjxcvqIyIiYDKZ3B5jMBhwzz334JFH\nHsG2bdtQVFSEF198EZ9//rliW5Xny2G1WBTr+IrB/l6YrXbEZ+eDvfGrTRcahoy5JWioOT8hfaBQ\nphKWUAMa5hdCd82EpBOVCL/Sipa8HPTGRQe6axMKA4CX8/3g4fMBGRg+58p5BUiPi8K58gqcP1+J\n7OS4yTkg+5mb5tY777wTv/71r5GXl4ft27fj+vXrbutzHAetVosvfvGLmD9/vl/6FHCXqLCwMGza\ntMm1nZ2djf7+fuzatQsrV66UPS42KRWnTh5HUkoqMrKy3dYZLYIXr1Qm2G6sq0Vc1kzJ+VmVClCp\nYLXbh/+GWHaWyNUKsicpkSq5PdkU3KXIqF1mDxNLkG5PQsnaQciFDoVoX95E9BIG5CclKqGczRJy\nodQlSl7eUkpsIbw3pOwvkq8lpgX5RCOShBSyPSNQdHMR3ytpQgqB25NdXKbkEiWUnW2DpEsUGf1r\npNyriF4yLoJX52cj9cg5sDfupXZgCMnHzqNlaTH0jddAqNdQaYVRu8ZmIyCjhJHvlTiiGPE+aka+\nKjkN4RIl2CbLGDUx8xdsMw4bcsPVqGruRFFGgqiaw8mB47iR5y50gyPdrMQh7eTLAPCC7bjIMMRF\n5gvKBMeSvwUCaGv2p3ztrblVp9Ph/vvvBwA0NDTAbDb7vE8+nSmHh4eDZVnJrNhkMiEqKsrj8+Tk\n5ODq1avKbUVGYf6ylWhrbYXFMqRYd7xwTqdr0CVhGFb0olMoFGXsoQbo+s2uAfkmDICo+lb0pSYG\npmMBINGoRVe3CVfae1yD4qDVjn0XGjA/PSbAvZsksKrxfWS4aW4tLi4W7ffE3OpPfDooq9VqZGdn\no6KiQrT//PnzyMvL8/g8DQ0NHg/iMwuLUXexxqt+ektSeia6W+ol+3meh9NuhUo9iUPUUSiTDKdW\nA/WQ1W2ZesgCp256/T+tSAmFtbcL+89fwYHzV1B+uRkl2XGIMGhHP3g6wLLj+8gwFnPrROBz+Xrj\nxo146aWXkJubi7y8POzduxcmkwnr168HAOzcuRN1dXV48sknAQAHDhyAWq1GZmYmWJbFqVOnsGfP\nHmzZssWj9kKMRgwN+XemHBWXgKbLtRjovg5j9LBbF89xaK0+i4SMHL+2TaFMNbS9A+jNywCutEjK\n+lMSEN7aEYBeBQ6GYZAXbcBMw4gbKKMd/w+Tti4TqhrboVKrwfM8VAywuCgPei0d7CczPh+US0pK\nMDAwgHfffRcmkwnp6enYtm2by0fZZDKho2Pkn45hGLz33nvo7OwEy7JITk7Ggw8+iBUrVnjUXntL\nM+ITRuQuX/m+C8/D80DR4uW4UnMBLc2XwbMs4OSQkpMHXUSsOMm94Dip7ZFsg3f7N6BsU7YKtkk3\nJyuZJcrDbE9KZTZJCM6RumQWIKeDDEE4YgvjydCBCghteqxa/CXCOeVfW9LtRaUWun2Jpayh/8/e\nm8fGcZ53/N859uTyvu/70EHJlnXYsiw5TuL+EPdA2zQJDARxXDctFLtx/3JTNDViI62AFKgLVEgR\nNKiTwIGdIE2Txskv/imx40S2LluyqJOSeFMUSZFcXrvcY2Z+f1BcvvPM7rsHl9xd8v0ABDjzHvPu\nzOy+M9/nfZ6HCa0ZDJvHxtqb6fmntnHePWfONhY7lKYldCKFtekRlyg27KMllGaIdYmKnfmJhs6k\nbk+sHTlEQnCyNvdgnHt+eacGeBfgra1A0ehEZLe/IA8+twPu6Tn4yQInJ8e1yuIGxGBys6IuUTyX\nLLv5HmPPKy2TGTu+HDKXKQ4aLpNZG0DHzfQjkXued/3NadKW/7895cWtcS8+9tDeyGIx/1IAb589\nj8cP7IYSL3RllPCcmWLNYTZjkC5za7pZl4Vejz/+OB5//PGoZUePHjVtHzlyBEeOHEnpOIGlJQz1\n3cAjj30ipfbJIMkyWrcv2x7YiW8pLOzJAkGyFFzpw3xHI2b374QS1qArMmTfEkrPXM700DYFVwfH\n8ejBfabV2y6nA7s723BtYBQ7WhsyOLokWfNCr+gvA6y5lXXB7enpyahLbsZXX6fKhdPvwTB0HHj4\nkXVxGxAIBOtLfu8g0DsIQ5Ig3ZNXBelBtqlRfxerK8rQOzCcgRGtgXWalIHkza0AMDIygnA4jPn5\neSwtLWFgYAAA0NTUtMZxLpOzk/Le/QegqirX5YQXwWt5m63Ld5+KeQxSkY0dS+PIhi0uUbFdm9i2\nFpcoth2RXamczUbcsrg2cVyiTG5PHFcqLWi25+skg5FJvqayGwdWsqZ9Gg5z6EFe9C9FXd22uG+F\nV7/sNDIaz3xgMUuYslQhPSThEmWSr2lELyaKl0Y+IytJx4voxcrbS9QNT0tSvo4BOynbyXVk3alc\nnJNMI2rJpsxTfPlasa1uh/3me05m8g0rwdguUYaTmDrItZKZe1miC0SZbfpdkRL87qyYQWJJ+uGw\nBlnC8v3Fc8OTtkYUrWTNrQDwL//yLyY/5hdeeAEA8MYbb6RlTDk7KatRAqoLBAKBALDLwMKiD548\nt2l/T28fOhurMzSq1KAPWukmGXMrABw/fnxdx7O10m+kEU0LI7Dk4y4yEQgEgkxwoLMRJ89dwODo\nHRiGgVAojA8vX4cWDqKypCh+B9nEOvkpZys5/7qZLrmQm8iCjUoUDuPKh2cRDGuQHU6E/YtwewpQ\n07EDkiRxV1RTKY/dplI3K5nSFdWJlgFEaqZlCSaksMi+jGStBYh8HSarf1OUr9lk8ApdiUrrMl88\nakdTGIlScxBpUYstUQc5ZZrFDBL7mtPtmFApkZ4rdpuuvmbkVFbKpmU0kQMrZ1tWX5PtIHMP+C3y\nNWNqSZN8zfuuWAxTjMuzpJjHbZKvqWnDbv7BZiV7WqazST/IOdZM5gOzXK04Y6+ipg/0Eucam6Lf\n8e4Nefkn3W5T8f880IUbY1P43ekRKLKMzoZKlJelFpAkrnfAepLBld+ZIOcn5Y3m4pn3kNe0HQWM\nT+Hi1DhGr/egrmsXp+UyhmFADwehBQOYHbqFcCgEm8MJqaIRst25nkMXCARbCFmW0FFfhY76VZfR\nLMyWLCCISTkJ5qbvQnIXwOYy53rOK63ExPjwvTylsZ/qFsaHMTs2gpCsQg8uQdMNhCqXY2o7bl2C\nu7YZaoEIrScQCAQRxJuyIBbTk+NwlkZfJGHPL4Z/fg6qJ7q9Zn7iNqampqHXL79NSwAULQyp/zyC\ntbsQqNkJafQiPPklwsVLIBAI7mGISTm3YaMm8bJCRStnYV2kVkxodqcbC/4FODwFlvrhpUUo9nqz\nnZj5f3JkAFr1TlPyFUlRIVe3Q5kZhlbWjJCzGIG5Gch5RSY7ptXeHDvaFLUbs0nnqW2YV8ZuW+zE\njB2ZukRppC7rzpSUTZmxE8drx9ZVyKp8NmuV1bWLYzfmuLbRiF7mtQHcoSaMZQEha4vU6HVk3KWo\na12IE7XLz9qb6bqB2HZjPzH48m3KiQumCvMwyrcp03arhTL5jOYoXcSGTOoqttVy6j5mts2Tdsw5\nt9j0aV0Hz27MRHhL4vqzZfFsv2w5TaRjLiMTYSbfE7bYpLy1Pu0aKa9txOKdQUu4RD0cgh7ww+5y\nx2gJhCFHfQOW8oogB5fTfxmKDdDSk+Tdpgfh1hYhG4lPhILNiQFg3JaHvvJq3CkoTjydpECQDUjS\n2v5yDDEpJ4Esy2jt3IGJnlPwe+9CC4cwPz6C8ctnUL/9fn7bGE+whhaKyDOqbwqSZ21J3m16EA1z\nvSiYHoJ+9y7qfAOoDw6nMaqFIJdYklWcy6vG4mwANXcnYAuFcLm+GfMOsahQIMhGcla+TvfTPk0q\nwM5hrFtLYVkFuvYVYWKoD76pMTiLSlC973DkLTiWe4zLnYcl/zwkV775OHduQSuuh+y9DcXlgQ4Z\n0A1zRC8iScaMNmUYqJ2/ifPz1QhjWYobDgFFgUXUukdwI1xh6oeVuqnszSaW4Lk9UbmaVzcZn26Z\nkw5To0neGQk9bDO7oKiMJKkTDdQU7cziWsa4kpGbg0b0StjtaQ2YJEsSJYo9r1RavewoRfutm1Dv\n1XEshFC8MIfLja1oHLgbUSWpXJ1M1C52m5aFkjg3MmsyMugbzur7Aw3HyY6Nlils8hQa7c5mfidh\nJXyL+xhzXpVg7IhqlmQZZJuN8KVwXJuoRG36WBZpm6nLi9K1fFTkHOscPCTbyNlJOZMoiorq5uX8\n0IEEjYjl7Tvh/+gUAo5CGMU1yz+sY9eBgB/O8BKQXwqlrG1N48oPzmDU74lMyCt49TzUa7OQoMNI\nszgiSwbqPSHYZQNjPhtmo6fJFWSARUmBc9EXmZBXkACUe6cxW1KMoumZzAxOkBGGJ6ZwY3gcsqJA\n03S01lWhsbo808PiIhZ6CdYFSZZRvvMAAt5J+Cb6l2PptnVDti/HcV4gsYZTwRmcR5+WF7VsQbPD\nKYXgh2PNx1mh2rWEas8MrgzOwB/U0Vqdj45qN86OqcjsyhABAPglBc7AfNQydzCASWf67gVB9nNl\ncAx+XcHhA3shyzIMw0DP9VuYvTGIXe2NUdss+PyYX1hEUVERXJm6X8SkvHWgSShiYRgGwqEgFFWF\nTKRTKm2y25a8u7oBW0EZCguWg53PBcKR+pbkFbzEFjHKAooDHimAacMq/bqkEAKGAoOVCNlVoxaZ\nLRj1fwAIB/1wqQbKjLv4/y6sBmafvjmD8oIF7Kwrw8Xxe3I+I9cls/ralIAhTjv2mtDkFZq2ajul\nEr3BuVa8hCAU0zUncm3C4m0yEb0I7MpgVi7N1wK4mZePyplpS5tpTwHyboxGJF262tgqUa/+b119\nvVpI5epUI3pZ7VPsDpJIgmlHx20LstK++TOquvnnz+DlbGa2qRkm0TJLXSpRJxrRC8mjaTrGZhbx\nsYP7VvuRJOzqasM7pz9AKByGjckTHQgGcfL8ZeTl5aG4qAj9V28iHA7h4J7uFI4uSIYtPSknwtCt\nXoyP3YbicEELBWFTZNRvvx+qjR/6MRN4HeVodl3B9GIe2K+uQwpBVSVoofTZk9oLlnD2qvXHfnIu\nhF1KGEBsm/BGkaeG0eSYhAFgNFQEIPuu2XpihwFVlTDnykOBfzGy36/asGh3onjBl8HRCTaS21Ne\nNNRGj7HQXFeL4Tt30dJQF9n37gc9OHhgP9yu5Yfa9tZmeGdn8d6HPYAjuhq3bog3ZcEKQzevYz6o\noWbXasLrUMCPGx++j679h7MuyIchyRh31eIBjGJ4qQCLugPl6gKKbUu4JTcincvj7LKGxUCM9HDp\nctZNGQPdhbMIazpuTdghSUBb0ShkKR8DRm5lyFkrnbN30FtRgTtGOZw+P5bsDhiGgbahASxlenCC\nDYPG5WfRdd0UF3xyZhYV5WWRCXmFosJCqDY71m5oS5ItNilvrU+bBIZhYGL8DorqWkz7bQ4X8ivr\nMHNnJEMj47OoeNCX1wFPkQMNRQH4nIXoVVugpTk/6qKmosQT/ZnOZsvsCs9Gtw93fcClaRcWwwoW\nQgouTLowP7eIUmkuo2PbaCQAnXMT2LEwjpLpaTQND6FtaBCKyG62pagpLcTw6FjUsoHR26ivLIts\nT87MoaqyMmrd8tK1uWymgiHJa/rLNXL+TZm6MqValz5EhoIBKA5X1LoFlbWYvNGDoqo6rjtMMlGJ\neLZpXplB7db3DjIlFQMKEAiGsPKGrIVp0BPGpkz7CcW2KRu6hhszKva3l+JXF8ZN56692o3xhVVb\nsilLFI1ExEFSYk/sErXrsxmlwkGU2X0447VKbDe9djxUMIW7+rJbGmtTpwFh2HMcpuc/1UxQa4Fj\nYzc40Z5W7g8ZBuzzy3L1Sg3WRYza21N1e0pXlij6uqAwLlL0/Ad1ifnfXOZiM7wRty9qR2e3ad1E\n7c2WtRm8e57an9nIXJa6se3NvDJJXu5TkYDGsnycOt+Dvd3boaoKwpqG85evo7qkECrzplzocWN6\negYVUTJKzXhnId7l1pecn5TXC1W1WRYNrRD0L8LujD5hbxVCuoRLs/n45H3A1OwSfIEwqkrc8C5J\nuDqR2UAluiEh2nIYA5LlAUcgyEbCuo6+GT8CYQP1hQ4UpyHYS0ddBQrn/Hjv3IeAJMEwgK6mGlSV\nmuP115SXoOfUR2hraYLKhK31LwUwvzAPW17hmseSFDn4trsWxKQcA1lRYJMlhANLUMkX4m7/NbTs\nfCBDI8se5oIyTo7Y4bHb4VCBc+NAKJB5S6UkLb8Z6mRitss6JFldfVUUCLKQgfkQBibn0VVViHK7\niluT8/DdncShljLIa1zHUlFcgIri5dj9hhL951+SJDy4ext+8+5JNNTXoaykGGPjExifmMChB7px\ntneDTXdZtnZnvdl0kzIrCK1FVtQNA533PYCLp0/CXVmPgopahPyLuNt/DaWVtVDt0X32eMekZYlK\n1BqRueK5XbEYPKmb7SeKRL1ajwTnN0nbIcwxKz9ohC9WTqXH4CEzK6UNObZ7CN02dA03Zx3YXebD\n+bturL4xG9hTGcCg1hC5DrxzQyVrFt7bdjIJGLhwXKB4kdFomW6SXem9wbgLWVyZYm9by6L/D1jl\nZB52ObYMzSadUMjHt8uxI4Gx/dgssrMRc9sSicvkykTK9NhlXBepKNd4MaRjJKDg4101kX0ljQ5M\nzvvx4fAM9jaso02XccsrzHPh8Yfux+3JKUyM3UZlaTF2tTWs37F5iDdlwQo2uwN7Dn0Mo8ODmL5x\nEXaXC52790JXMu/uI4jNTECFQ9HxYMUi5sMKZEmCxw7cVqrh07eWW5Qgt7jqDeGB5hrL/vJ8Fy6N\neTd0LJIkobaiDLUVZYCcg+E5cxQxKcdBkiSU1TSgrGb1KTEQJ5iEIPPc8dlxx2dHYWEBdACBoApX\n/tZeB5CrSFUlMOorAX8A0s0RIIkFg7nGkgZ4HNEf+pUtFgN6hVxcQb0WxKQs2NT4dXGLbyh2G9Sd\nzUCeC9B1hK8MQJ9K0Q1NkWF7dA+MsbsIXe6H5HFBOdiN8NAdqIN30jvuLKHAJuHuwhLKPNaFXZn3\n/88QW+xhRPxipUCidsN4Nm2uTdnglLG2UBrWkWM3pmVs23h22oTLyFuMOUtU4m84bDvqHiVbgjoO\nogAAIABJREFU7HSJjpVjU0/CJS3b4Nk7+e46rC3W3Cdvm04NkXu1IA/K/u2YP3UZ2swCYFPgvr8D\ncnUZAj234n4O1gvJLkuwH9gB7cwVSAvLWcAMfwDhtz+EdOQ+YGwaUiBoGRvPFk6zhFm3Y7sImkKy\nckNnxgmzyfMP1zV0Fch4t38Cn9hRD4Wxsd8Yn0VNgfNeH5wMUpsR8aYsEAgEySM/0ImZt84CKxNR\nSIPvzFV4HtkFye2A4UsuhZhsV6Ev+C37Q+dvQNnWCOXCjXQMO6uwKxIeKAbevjaCojwnXDYVE/M+\nlOY5saumKH4HmxExKQsEAkGS2FWE/YHVCZnBf/EWnF1NWPrweuL9KTIQjBHQcW4RRt7a/XazlWKH\ngo835mMuEEZQ09FZ44Ga58n0sAQbxJaelNPhvZJM1K71gEqyPCwZjFhp2xIJKkX5muvKk0REL2a1\nJ29syY019rmy5ILnYDU1JN6WRUrioAbH7YbbjjM4wyRf8z9TPJcoSVYiGacoeiAE2NW454mN6KWF\nNRiO6CvlpboK4M50ZBzmsZnFXPaYPJeweFCpe70xdB35NvleXhfDEv0r9Y6z2xQTlS32pry1Pq1A\nIFgXDH8AaqE7apmzqwGBm6NJ9xkeuwuprc6806ZC3dEE3Eq+P0FuImJfCwQCQQoY/WNw7+uC7+y1\nyD61oghqaQH8F24m3V/wygCwswXqo3sgeechuRyAww75ZA+kXHzjE6RGDk6sayHtk/KvfvUr/Oxn\nP4PX60V9fT2eeuopdHV1xaw/NDSE73znO7h16xY8Hg8+8YlP4NOf/nTc42zkd3JDEg4kQTKrgenq\n7FRIJpGEpW0SknWsdqn2EfcYvGQiaVp9zTbldpOMfk6bclb/mg+xPvdxREbuuw29phz5n9wLhMKA\nokCbW8T8b84n1Q8AKPdCKwYv9SF4uR+uAveyjTkYgk2OLVGvhUTPTzKyN7+f9KyiZr+fm34l9jqw\nUXNWoqT1EeS9997Dq6++ij//8z/HN7/5TXR0dOCf//mfcffu3aj1fT4fXn75ZRQXF+PYsWN46qmn\n8H//93/4+c9/ns5hCQSCDSI8MgHfiXPw/fYCfL/5YPmtea0PhoYBLPiBYCg9gxTkFpK0tj8O2Thn\npXVS/vnPf46PfexjeOyxx1BTU4Onn34axcXFeOutt6LW//3vf49QKIQvf/nLqKurw4MPPog/+ZM/\nEZOyQCAQCJaR5LX9ccjGOSttk3I4HEZ/fz927dpl2r9r1y709vZGbdPb24uuri7YbDZT/ZmZGUxO\nTqZraAKBCYcURrVtFkVYAJBdpgmBQGBmvRZ6ZeuclbZJeW5uDrquo6jI7OBeWFgIrzd6IHWv12up\nv7Idq41AkDoGtjvvoMl2F/6QDMm3gO3GAPLhy/TABALBBpOtc1ZGV19LWyxPpiCzdDjvYsifD2/4\nXuCJEDC0VIADReMYMYqgSyITjkCQdWTR6uuNmLPSNikXFBRAlmXL04LX60VxcXHUNtGeSFa26dOI\nQLA2DDgRWp2QI0i4vlCEprxJjNurNnZEhoHrN27izp1xKNBg6Dq62xtQWiCiNwkEKxjrNBFm65yV\ntkcQVVXR0tKCixcvmvb39PSgo6MjapuOjg5cvXoVodDqqsqLFy+ipKQE5eXl3OMlsLAubciSZPrL\nNIosRf7iIUlS5C9VJEUx/SXVVlYif6m2o3+poEgGAkb0290bdsCpmSVs9hzTv2SQpdh/750+C6di\n4GP7d+Pw/j04+MB96Lk5grGp2aSOISlyzD9TPVky/aULRZJi/qXaT3LtVv/WQqLnRpJl01/qx1v7\nfQ0g5e9mrmAYa/uLxUbPWYmSVl3giSeewDvvvIPf/OY3GBkZwX//93/D6/Xik5/8JADgBz/4AV5+\n+eVI/UOHDsHhcOD48eMYHh7G6dOn8dOf/hRPPPFEOoclEEAzJDik6P6lxeoS/Er0aFTrxfTUXeQ5\nVDTXVkf22WwqDu+/H5dvDW/oWASCrUo2zllptSkfPHgQCwsL+PGPfwyv14uGhgZ89atfRVlZGYDl\n1/zx8fFIfbfbjX/8x3/Ed77zHfz93/89PB4P/uiP/gh/+Id/mM5hCQQAJPgNG0pUP6bDLmavgc58\nL4Zt2zZ0NAM3b2Bfe6NlvyRJcLmcCIY12NXN+eYjECTDegZvysY5K+0LvR5//HE8/vjjUcuOHj1q\n2dfQ0ICvf/3r6R5GQqRDiaaS2VoltGRJRoak0jvblkpfrJxGpbVEywBz1KJUJTre2JIZz81QObrc\nE6jV5zERdMOthFHpWsKgVL0s9yY4Hiphp3LNJVmOGVHMMKyRmViZlMrS3ONwBseWUcnYel/z6sb+\n0Uz1+8Abj7VMilqPbtNzkYz0LG/wF9sytjXI5OaOM29+S5b1dlrMtjkre5a1CQTrjoRrgUrcCldA\nURWE8opwRWrGIjZWugaA5rYOXL41YNmv6zoCgSXYxFuyQABgOTztWv5yDTEpC7YcIUPFpJaPOcON\nTEULLiouRggyrvcPR96Y/YEA3jn1AbqjyNoCwVbFMIw1/eUaIkuUQJAhDuzdi/6BQbxzrgeKEYYi\nSdi3oxUFeW5gZjHTwxMIBBlATMopkKjLRjz3KdY2abVTcspYWzDHTgwAMluXlLFtU7XTWsqI/VfG\naqJ6PRxEosjqajvZcnzi6qMkOtbY50bmnONk3aCSobmpEc1NjVDmx+NXjoGsxLY3s9syKZOVxGyx\ndJvKa+y9aieFwSQSKtll3ngSGyvP3kztwtZt5lzR7wp7r3DOMe/8L/fDEScTXHOxFvepXCQXJei1\nICZlgUAgEGQtW2xOFpOyQCAQCLIX8aac47DiEA3PpiVh9E81chevHS3jytemMjlmGZXZeLIbV75l\n5OLlugpTj0jSttW6NFG7QrY1rErWrJQdj0QlabrNK+O6hJFzo3Ika56cnWw0qphwJEqeBErLWImW\n5xLEk33ptp18/hDzvaLSMq3Lg21L29k4x+dJ22xdq7QsxdymdXkmApnjrmY1tTDbPBk6ExI1G2M6\ni+JNbzU23aQsEAgEgs1DLq6gXgtiUhYIBAJB1pLEWsFNgZiUBQKBQJC1bLEX5a01KVPzlsZcbIsp\nkCmj9jXWNsmzIScTgpNvU+a4RFE7MTmIwtiwFJXUVRkXkBApY+zGcpjYmxnXJmrDlVUbYmHIWswy\niskWTPqk9m+FdZ+ylDH2PnoeWTshx95P7cu8+2Hd4NrNWZsmLWPuIzu5VqxLlGq2IdrD5vcT1jYb\n1GPbe6m/lJbEDyp769osNm32f3MZ64ZF7c3stVGIvxY9H+w2rcuzN/Pdznh24xTdpai9mVNm8GzD\nydiNhY15w9hSk7JAIBAIcgux+logEAgEgixBLPTKMXiSdNy2TNxjLUUXdZ60HM9diSdRqymWcV2i\nqCTIk904krBqdyEVDD1xNw9WsrZI0g4XqcuM1UbqmuR7KhcmFtHLrvKlO9M1p25X3JamwZi3eRIl\nQbavfo35rjzkXjHJteb+7UGzqYFVc10WOwxzjslrTSgZN0TT8czHcDGfgx6frWtxl2IGrthiS/tA\n4hI1z82J6wJF61JpmyNDpy2KV7qyTW0gW22hV+5dIYFAIBAINik5/6YsEAgEgs3LFlOvxaS8ApXB\nTQtKLStzmQ2yoJi3MtuyapSVVnnyKZHAWDmVSqtBUpeVbKl8qzNav7Vs9dYwiFzMRvGiEbworOxG\no38l2s4iSVM5m5HTVbvDVGZaYU5XpjNlKl19zOQzptfGxlnhu16YpE6yGt0Umctm/kqbJGoi3yo2\nph1Zbewk91GQkaU1wywo8kxGSlKrr2PL0DyJmpW2bTSiGfP5Zc5qa8B8fthzs7y9el5lO/EGsMU2\nH1jMCey145goLNK2qR7H1EHNIJtg1bS+xWZlMSkLBAKBIGvZWlOymJQFAoFAkMVsNZeo3Nc2BAKB\nQCDYJOTsm/LK0wS1UrJuPxIRPiSD1mU2SBnrLqXI5kJZZ+3G5nbmyEOkT04kKGobZrepTZO1d9pV\nkqWJ9MPaUVViU9SYqE20zGAeT3Wd75LEQl03tDTYlHk2ZDoe6trFukSpxIao2mLb5nnuahb3Kfaa\np+kx1xLdiRO1i40aRW2YrC1UdZm/7uy2umQuC/vDpm2XvnqvaEbiUex4dXlteTZlnksU/Yw25+o2\ntRPTuib3KRr9zM72Y25niuhFbfpkm+/2xGaQSvz6m9rFsSEbnExQ5rINiFKXIFvMpJy7k7JAIBAI\nNj/6FrMqi0lZIBAIBFmLeFPOMXiJJNbUDwPPtSmZZBW8BBVWiXpVSnJQtyctdhmVYTVGltaJ7wor\n59JQdmz0Ld2y0iJx+TotLlFxInqpjIuKRaJmJUlOtCuL+YBTlsw1T9hdKpmIXjQJCCOtWuRTe2z5\nVuFE9KLSrp25d1yW79iqtE1l52ASq3QSl6/Nn8PmWB276uRI9FSuJiYbtq1VhrbFLGPlbIW4S1Fz\ngkmG5rk28SJ6JRHtLV0yNDexhSCt5PykLBAIBILNy1ZbfS0mZYFAIBBkLUK+FggEAoEgSxALvTYR\n1ApiEPuKwVxsg7huSKz/FOeesIbg5GR3orYwRpexE42Gl6XIHmbtnWZ7kstuDoEYNLk9kVCaOmvv\npTZlph7nUVWW3abtMLV3MiEyDS0Jm7LCsSmrxG7I2AapaxevjHUfc9MsSZxQpjTMJnut0uZJQmx4\nrE1RUmOHeVTsxN7pdDBlPlMZ6y4UJrZYjWSJYtcjOMnaBNYWTG3I1DbMw+xOaG7Hhv2kIUF5dmPW\nTmzj2JuX+2XuFRe550xhNmPb7blhNQFIbMhYcl+bwmwmYzdmsNh+qd2a4xK1GUJybgY29aQsEAgE\n60nIkHA56EBQWp40bfMSut06XGnKtCjIDvk6FArh+9//Pk6ePIlgMIju7m4888wzKCkpidlmeHgY\nP/zhDzEwMICJiQl8+tOfxl/8xV/EPZZ4NBIIBIIUCBvAWbkQ20vseKRcwSPlCnYXyjg1r8CfuCgk\niINuGGv6SwevvvoqTp8+jeeffx4vvfQS/H4/jh07Bl2Pne05GAyisrISn/3sZ1FRUWHJZx+LtL0p\np/Ik8c477+Bb3/qWZf9rr70GVeUPLfIByUln1TJe9pp4sP3Qc8lK1haPB+YaUZkzxOmHyuA8+ZR1\ng3IR2TUYNv8asOVUomZdnVgXKGtdItcxYw2HzMeTZGfMfqhrFesiReU6NmqWJUoXOR9miZpTRmRP\n9txQM4DbVEYkSUuWKMREMt1HNBXZar9xXU5M0Z6oiYCRr52xZVeVlKmu1ahdNKIXla8NzpdJYera\nguYfKfZHUTOo7B3bfUyiGb1YaZkjQ/Mkal60r+V+V8/PigvUgqRiQVZR5lThvuf6tXJOr4edeKBM\nRT7TjUsBDpZI6JlXsb8I/ExQRFqWbGwGKY4MTfvkSdKpkkVSthZ73tsQfD4f3n77bRw9ehTd3d0A\ngGeffRZHjx5FT08Pdu/eHbVda2srWltbAQA/+clPEj5e2iblV199FefOncPzzz8Pj8eD733vezh2\n7BiOHTsGmd5gDHa7HcePHzf5ycabkAUCgWA9CUDGJXcpClWgSNbRZ6jw6xLuxzxWps5FWUWp3fpE\n5lKAUBZIroL00NfXB03TTJNvaWkp6urqcP369ZiTcqqk5XFo5Uni85//PLq7u9Hc3Ixnn30Wg4OD\n6Onp4baVJAkFBQUoLCyM/AkEAkEm6XGXYa8ziJ22AOqUEHapfuy0LeECPIl1ICbltJFp+drr9UKW\nZeTn55v2FxYWYnZ2ds39U9LySrqWJ4lgMIgvf/nL0HUdjY2N+NznPoempqaEj02fKljRjcqFvAQV\nFmXRdAyS5J5Rj3SDRNBiNkMkA4aNs/rapicuX5vKwlSSJTIkm5yeK1+DQzhmiUSlXKI1GaZj0OMz\nK6xJP2y/9BjWFdbM+XAQidIRe/U1K1G7bLFXX9uI0mNZfc2aIci9kvBTb5yVsLzV1xKzilexmctY\nOZtK26ozGPnfnmdux5OrqbQcZrZlOzFRMP0Y5N6gK5XZfmUqXzPXRyYmG1aGtnlsMcuo7K04o5+r\nGdmOcpsBB/n+5kk6HAqwpNrglg0UBTXcWVJR5TSPdS4MuGwyZJtslqRBrl0Sq6/ZVduWSGCcRBIW\nswjPZMINa5g5OZuaPdLF66+/HldWfvHFF9fl2DzSMimn+iRRU1ODo0ePorGxEX6/H7/4xS/wta99\nDd/85jdRVVWVjqEJBIJNwGJBPiYbayEbBgxFht3nR83QCKR1+MGeUxwol6M/iBZLGuZ0G9yyhnZb\nCO95VRhFCqrvTczTQeD8vIQjFdmTZSnXSddiLcoTTzyBw4cPc+uUlZWht7cXuq5jfn7eNMd5vV5s\n27Yt7ePiTsrr/STR0dGBjo4O0/YLL7yAX/7yl/jiF7+Ycr8CgWDzMF9ShPmGKrT290G+9wO9kOdG\n37Z2tFzpTfvx8vUgpvU8lMrWJdReQ0HVvdWcsgQcdPjRH3Dj1uLym2SRU8GjFZJFTRGkznot9MrP\nz7e8SEajpaUFiqLgo48+wqFDhwAAU1NTGB0dRWdnZ9rHxZ2UN/pJQpZlNDc3486dOwm3EQgEm5vp\n5lp03LplMgx4Fn0onJvDfFEhSpcW03q8Ei2Afl1BkwHYmIMuGRL8how8ZrKWJaDLbWDFcKa6bRBs\nLtxuNx577DG89tprKCwsjCxkbmpqiqzGBoCXXnoJbW1tePLJJwEA4XAYIyMjAJbNtDMzMxgYGIDT\n6eQqwdxJeaOfJAzDwODgIJqbmxNuQzGZRYjqwYvwZZDKbIQvidiWTPZmS0Sv1f9txC4WInZj9mna\nardksvIQGxrPTszbDnPKaJYoHhL7o0Rtf5r5c+gcmzK7Te3G7Da1N/PdnmJH9KLnkbW/8+z29Dpa\n1gYo7L2CtGCx93FskazdkkabYiN8qUx0LwDQGJtyMi5Q1EWNtQ1TuzHbD81SRu8d1qa80qcuSbAZ\nBqKd1vKJSQy1taBqYiSyj+8SZSNl1Ma+en7ul304G85DqRxGsaRhCirmYMO+vABUpzlLGWurl6id\nmF4r5jqyUetoGci6AZMblELs/+y9wrM3022O21M2ZYVaL/k6GZ566inIsoxXXnkl4vL73HPPmdYt\njY+Po6ysLLI9PT2NF154IbJ94sQJnDhxAtu3b+cqzGmxKaf6JPGjH/0IHR0dqKqqitiUh4eH8aUv\nfSkdwxJkCJcUQK10F4qhY1F2YFQvgQYR4kiQPJJhWMLjrqDLMmRO8Ia14JZ0PCTNY9pQMW8oaHAD\nhXJgXY4l4LNeC72SQVVVPP3003j66adj1jl+/Lhpu6KiAm+88Ubyx0q6RQxSeZLw+Xz49re/Da/X\nC7fbjebmZnz961+POFwLco8qaRqu8AKuLJQgaCgoVJewwzOEXr0GftjjdyAQMEhYflvWZBkKmYDH\namtQMTmBqK/RaaJECqMEYdhIUBzBxrHW1I25Zt1P26ScypPEF77wBXzhC19I6XgriiYvahdda8Gr\ny7twtB9W2lZkIskxEjWNWEQXf+iM7Gcjd56TcbWi8g3PXYomVmCfMjXyo0albhZJYqNtkTI26YbC\nJLwwwsj3LeD8fHlk32zYiTPeSuwtHcc1NMU8HkVOyiUqdtQuO3M+6LlxJVhGTQvUZGGOzGYqSji0\nntUnj0rEStT/AZgkUktEL45LFLuthsyrjampwTyW2LIzlb31JFbpsLI422fjnVHc2taJ+r5+uPxL\n0CUJ4zVVUJ0qihXNJEtbo32xZfTzO8i2PWZdNmoa+z9gTjohOcyTN3VfM8nZ1LVNjn2NTW5QySSS\nSEaGTlc/gjUhQmcJ0ka5fhc3Fwos+3XI8AdlqPYwwuKWEySJKxhE58gAbtdUYVxVISkyKuemUTw5\nk+mhCTYA3stDIuTaL06ujVeQxaiGhqARXaIOGjJU6JwwJAJBbGyahsbx2wAAxSbe2rYS2bDQayPJ\n+UmZyoMyK9fGqWuK8GXQumy92GVUymSlTl0hq51Jzmad2bZI23LsMlZajbf62qXHrstCcz/7me0A\njdrFrL7WGMlxXi1FvWsCNxeLLP0XOsKYsrtgT1DONUnkNCGEElvOthMZ2sPImR4SwcnFWZltZ00L\ndPW1HPuapy+fMumIJ3sy2zR/L7uiWHMFTWV6aHUVsRFnwZQpwhon2pYWSnwVt+UYbEQvGjWNuT68\niF40She7wprK1dYEHUxCCjuVwZm81E66wpqRrznXBjCvlLdK24lF++JF4oq3atrgRP8yHy97HnzW\nklgoF8meMy/IeRYVD8ryNOQr5lWqrXlezNuL0zhjCQQCweYk59+UBdlFv6MFHeUjkINz0PTlOOFe\newnuKiVrX0YpEAi2HEK+FgjWgCFJGLbXY8X7ibeKVyAQCOKx1oVeucamnpQtaiknwhcNUMBG+DKI\nLdhgInxRtyedcZGSSQQvXlJ3GiWKtaM4yLjZMl60r3TBszfLitkWqdKsWeHYtko2MxU1hfFcohRy\nrhz22LbhfMbGSDNBuTntHKYsUTSiV+wobpYsUVL0/wGQhQucpPYwu8Tw7JbUJUdxBpj/iUtQcHXZ\nnS2O6xJ7fBrRS7Ov2pF1zfyTwkb4ovZlaps2jc1OXYKkmGWsbdgS0YyxI1Mbsi2PnivWJSq2uxSN\n2iU5mAhfcSJ6cTNBsW5v1KYrM5+Ld6/Ec5fiRPTKpiheLOJNWSDYAsghPzB1ZzlkYWFFpocjEAgE\nAMSkLNhqGAbck9dhyArmPRWQgwG4+s8jVN8KW7GYnAWCbGOrrb7O2Ul5RcyikqDOyodE9uDVlYi2\nzeagsARbQuwykyRNJCiei5ROJHKNGaxOZD6nEVtm0uyp3cFUolaYsQfDWsy6VC4PErlas8VOSMHD\nJFeSsfGimLmIfMnK0vlOFdL4DSzmV0NzFS6PD0Aorxzy7SuoKCqGbFuWLdlrZ0lAwYnwZblXElxw\nTqVDum2K9sSTSNXYkaioJGuWllOXr2UmGhgvIUU8oiWkWEFhPoeljPlcio3K14wk7Yod0Qwwnx9a\nJttXpW5e1C6eC5SlnBfti0jbPFcmIxlJOlE3qGSihq0zW02+zk4jgkCwThhLvsiEHEGSMFfYjMWR\nW5kZlEAQh+n5RYxMTiMQDGV6KBuOrhtr+ss1cvZNWSBIGsOIuZhFt7uhzY1u8IAEAj7eRT9OXx9B\nWWkJ8vPcONM7DEUGHtrRlnhc9RxHyNcCwWZFkiAZ0WVaJTAPm9uzwQMSCGKj6zreuzaMTxzcC/Xe\nyuy2hhrcmZzG2Wv92L+tJcMjFKwHm3pSpu9EOn2wZJ7AaF3WRcog9mbWRYp1jwLMbk86ySBlo+5C\nzKZOEv/wYCUZjdibqdtPoqjUpsx8jiCx4drV1WNQe3OA2pQ58hFbRu3GLA5yfFqXtSPHc3vSCoph\nX5hA0MMs6jJ0FM0NoHjXgYi9lOcSRUyapnPF+RjJYXF7Yb6q1F2KDd1oJ/ZOZltxkkxQHJsytdtK\npgxO5nOsaIxLVCh2dPO4dmvTMYjdmvmM1G7MukHRDE48dyme25PiMJfxwmOy51iyEXcp6j7FbFvq\nstdVVnFjdBw72pojE/IKVeUluNo3BB3Ssusgx6bMswVnqwsUZavZlDf1pCwQUJTKZuTf7kVw8iqC\nziIoWhCu8Dyq2nda4kYLBJlkcnYB+5qbopZ53E4sBYNwkweLzYgmJmWBYHOj1HTAqYVRoi1CUu2Q\n3fmwOcRXQZBdFHvcuDszi+ryUkvZ4lIADvvWeIjMxcVaayHnf4moWsjKhzot5bhI0bqsixRRqBPO\nIMWTZAG+SxQrqNOFDqnKOVa3p9VtfzC22xOVpINMFC8qbbs4Wau0OJmIzMeXmf/5LlFsRicqdbNZ\nosxRuxQUOFdtyA6VZB5ijkn7pC5RvPshYYGQRnDiuLZYZE9WPg0smcuYaFOKTt2VEr8ePGmZJ4Pr\nSVxzWY59DHabStQKR742uYS5YsvVgDnDlsXtiT3H1ETAXA9LWTJZopTVbUOS0dFQhRMfXkdVWYlp\nUde0dw52VYGsqMu/Usy9EdcFiidZpyiDC9JLzk/KAoFAsBlRFQV72uvx1slzaK6rRoEnD8Nj41hc\nCuCR+7dlengbhlh9LRAIBIKsoKIoH3+wdxtGpmbh9c6gs74CBQUFmR7WhiIWeuUIK+piMuYG7mrs\nJJJVmCvTkF5MGVnuTRVqVgal0b5YNCqDq4mvsDavDOYkACDHYCNz2al8zWzHW21tkq+T+HKx46Zj\no3IyK2dTaZuVrD3EbuxUY8veTtPqayJf8xJSpMt31LL6mo3oRaNEMfIplV3DTLAJ3WyiUMk2Dzm0\n2g9d/axxI3qlRyKXOfI1u60QOytbRldfy1RqZs4dd4U1m4ACMEXmskjSyUTtMq2wZ643gLrqSqaM\n/GzzZGeCSd6mJpMsZast9MqNqyIQCAQCwRYgZ9+UBQKBQLD5EfmUBQKBQCDIEsSknOOwFj1uBikA\nMmOroPZeXgYpNpF9qtG+lncwx7NkfmJtcaSMtePGsS/LnBuaNY3y3KV4LknU3sy1KfPGwnEfi+sS\nxbMpMxG+qN2Y3XZStyfGhmm1IZP7iL1XLFmi2PuIkER2H1O0J4XnZhPbXYralNlt+kNgsemGVmvo\ntjApW7U3r5dNmf381G7Mc5dit3muTLTcYlNm7cikzGSbtrhA0YheTLmsIqxp6B2ZwJxvCeUlRWiu\nKlu+Z3gucrx7JZ4LVKKZoOJlm9pAttqkLGzKAoFAkAHGpmdx4vwNFBQVY9e2DuiSgv/3zCUsLgUy\nPTRBBtl0b8oCgUCQ7ei6jgt9t/H4wQciakprfTXqq8rwuw8u4RP7uzM8wuxhq70p5+ykvHIjy0Q+\nTvX6UdXH5BJFI2qxEZw40b4UnrsU6YhKpOYRUAmQKSPyMZWzZY58yLrvyESSZd2A7Go87+OmAAAg\nAElEQVRsiTqYhHxNSTQhRTz5mi130shczHl1EknUwUrUls/PXBsiJVIzBLtJpaeEE1TEiaBkdmUh\nX1vWJYoTQQq6Wb61yNkMCnHXkZTVSGEWiZiRtnlydbykF7wyVr6mrk1smUW+Zz+/JTlE4oklWFma\nulKZXKJ4cjUQuXZ9dybR1VxvSb9ot9ngyXNjYSmIPBdzHG7UruyUndNFNkzKoVAI3//+93Hy5EkE\ng0F0d3fjmWeeQUlJScw2J06cwLvvvovh4WEAQFNTEz772c+iq6uLe6zcv2ICgUCQYyz4Ayjw5EUt\ny3e74BMSdgRNN9b0lw5effVVnD59Gs8//zxeeukl+P1+HDt2jBtG9sqVK3j44Yfx4osv4hvf+AZq\namrwjW98A3fu3OEeS0zKaSaw5Mfi/ByMLebwnk70hRmEBy4i1H8R4eGrMIJL8RsJBDlETWkhhsYm\no5ZNzHhRlC9ye6+Q6UnZ5/Ph7bffxuc//3l0d3ejubkZzz77LAYHB9HT0xOz3d/+7d/iD/7gD9DU\n1ISamhr81V/9FVwuFy5cuMA9Xs7K17EwJZmgUbo4K6xlMomaJGoiMZlXbS+Xzc96cfniBdhdbths\nDizOzaCsshqNbR2r/ZCxJp68IvGV2UoSN6HMaO8KUTIVpsxmSDAMA9qSD5IsQ1dXZTU7kRlp9J1k\nvxT+0VsI+PwIVXQuRzUKLQGDl1DY1Ak1v/je2DhSM5WoOXmReRG9TO2IaYFeKoVzr6SMxZ7CfFWl\nkLkqK5HSiF68qF1sGZGrIZsfhNhkEQibj8/K2UYSCSh4SDTpBzs+Kq2zEjUtU5OQrxNNLMFrx0ky\nAazKyeXFhbjQN4b5BR/yPe5I+ej4XbgdDtjsJCUjN1kE6/HBWW0dtx+ORL6F6evrg6Zp2L17d2Rf\naWkp6urqcP36ddN+HqFQCKFQCB4P/4Fr003KmSAUCuHih+fQ/eARyExC8sEbVzE62I/axuYMjm7t\n+CZGsDA+Ct3hAQwNctAHT107bAWx7SmpoIcC8M/NIlS9fXWnzYmlml2Qh3pQvONAWo8nEGSSR+/v\nwnuXbgCQkOd2YW7Rh3y3E/t2tGV6aFlFpm3KXq8XsiwjPz/ftL+wsBCzs7MJ9/P666/D6XRi7969\n3Hppm5RPnDiBkydPor+/H36/H8ePH0dZWVncdqdOncIbb7yBiYkJVFZW4nOf+xz279+frmFtCH29\nV9Gy4z7ThAwAje3b0HP63ZyelJdmJjA3Mw256b7IO7lh6Jgb+AhFbbssbxRrITgxjFBxvbVAkhBS\nXNADfsg07rBAkKPYVAVH7utCKKxhKRiCO89tViQEANZvUn799dfxk5/8hFvnxRdfTMuxfvGLX+DE\niRP4p3/6Jzid/N/MtE3KwWAQu3fvxr59+/Dd7343oTa9vb3493//d3zmM5/BgQMHcOrUKfzbv/0b\nXn75ZbS15c7T4sLcPGrbo781KkpuixFzo/2Q6neZ9kmSDNR2YXHkBjwt6XPdMHQNhi16MBRDVmAY\n6ZFGN5KFhQX03eyFrulob2vlrtYUbE1sqgKbqsAQE3JU1mtSfuKJJ3D48GFunbKyMvT29kLXdczP\nz5velr1eL7Zti59C880338QPf/hD/MM//ANaW1vj1k/bjPGpT30KAHDr1q2E27z55pvYuXMn/vRP\n/xQA8Gd/9me4fPky3nzzTXzlK19JqA9qw0tmgZXJ/kwtvkw/PFu0AgM2u4pgYAl2h/UJSNc1pn3i\nGaX4S/B47lIExjZKva5Y+zPNRKXeKzNkJaqdVLK7AE2Dm0yi1Kas81yimCJFAhxV9fAP9CFYbn0g\nc4R98HjyIUkS16asEpsyz97MblsierERpMjx6LbJtYwpunLxAsKhINq7tkGRFfTdvIGrV6/i4MGD\n1nMaL6k8u00UGejM19gS0YuxG1N7r4PZttiUY9tmDWJTZu3WhsazYZPjcyYhiX5Gnk2Z7YdnN+bZ\nokGybXGyPXHdnqgNmbqvMWMw6AN7uqJ28cpStRVvQhtzfn6+RZKORktLCxRFwUcffYRDhw4BAKam\npjA6OorOzk5u25///Of40Y9+hK9+9atx666Q0TN948YN7NplfgvbvXs3ent7MzSi1Gjv3Ib+Kxct\n++dmppCXF93tIVeQY7ydGqaHjfSguvORp+iQF6aYAxmwT95EQVVt+hZRbQDjY7dhs9lw/9798Hjy\n4XK7sX3XblTVNeDKlSuZHl5W4A3q+GgmjCveMAJbLZO9IGHCurGmv7Xidrvx2GOP4bXXXkNPTw/6\n+/vxH//xH2hqakJ396pS+NJLL+EHP/hBZPtnP/sZfvCDH+Bv/uZvUFVVBa/XC6/XC5/Pxz1eRrVV\nr9eLwsJC077CwkJ4vd4MjSg1PPkFKC8vxZWzJ1Hb2gm704k7Q/3wz8/i/gcfzvTw1oSnqBQzs5OQ\nCstN+43xPhTUNqX9eMUd90EduQn/+GXokKFAR2FtE1yllfEbZxHDA33Y9+BBy/7qmlqcOdmXgRFl\nD4Zh4P0pHW6XHW3l+QiENZyd8qPcpqGzIPFc4YKtQaYXegHAU089BVmW8corr0SChzz33HOmF4Xx\n8XHTOqpf/epX0DQNr7zyiqmvI0eO4OjRozGPxZ2UEzWEb9++nVtnPVh5S+NdL4vsTOryLrVJII6h\nLAcDAdwZG4Pd4UBzazvq6hvR13cDc5Mh1NfXo6iYLpWnR2RDQXHChnH0DFkiEbwk85utrMWW4dky\nnRx/Rdqubu1EqOccFn0zMErqYOhhyHeHUFxYhMLSCujx5GpmeIm+DDmbu0xSO402Jlvk5NX/LRI1\nz13KFNGLSttsQg7z+KxJJ6L8L0kxF+3InEhWKyQTtYl1l7LIrjpHvjb1Qfuk8nFsiZg9BnXBiuYi\n1XN3CS1lDtTk3+vHoaA8z44ztxfglWwocUb5WeLJ14lK1Lx2IPK1jcrXTBk9V4xkzZOrASJZc66j\n1bWJ6Ycb7S0J8ZMrkWePXJ0Nk7Kqqnj66afx9NNPx6xz/Phx7nbCx+IVJmoIT5WioiLLW/Hs7CyK\niopS7nMjMAwDF89/gKVAEOXVtfDNzeHGtSto79qO9q4dTL0MDjJNSJKEup17EVycx8zYEGRZgWfb\nbqhR7OeCVWw2G/w+H1xut2m/pmnQeXbXLcBMQMfuKrtl/32VbpwbW8DBmtxeHCkQrAXu3Z+oITxV\nOjo60NPTgz/+4z+O7Lt48WLCBvFMcf3KJRSUVqCtpi6yr665DRfefxeewiI44ix5z0XsefmobFt+\n4AhlwZNrttOxbQfOnzuDBw8djrwxG4aBix+ew/YEVmyuhXnfEq6PTgKGgbaKAhS6s+t+jBUox67I\nCaspgq0DXTy62UnbI+mKEXtsbAwAMDw8jIWFBZSVlUUimLz00ktoa2vDk08+CWB5xfaLL76I//3f\n/8W+fftw5swZXL58GS+//HK6hrUuTE9PYXfHDtM+SZLQ0X0/bl2/iu2778/QyLKT4MIsvIPXI6vW\nJQCFTV2wudfvgS/TuPPy0LmzG6dPvguna9n/1L+4gPb2dlRUrp99/Mz1IWgGsK2pFrIk4erAKLTg\nFB7qrF23YyZLWDNgGIZl4d5cQINLzZ3FfIKNIRvk640kbZPyW2+9hR//+MeR7WPHjgEAjh49iiNH\njgCwGsI7Ojrwla98BW+88QZ++MMfoqqqCn/3d3+3Jh9l9otO3aN4NuZkQnDK1G50j7z8Aiz5fRET\nMLVFW7JGmWzMibtLsTZWmaap4oTkVCzZjhiXKPoZOfZmGzu0OGE1A74FTPdfhbt9TyRUoqFr8PZ+\ngNrte6A64wcDsdiQOTZmS7YnhS0zf34nJ5Qm6/ZEbcjUtUyO8X9pSSmOPPoxBAIB6LqOQpKAwEBs\nG57FbsnLEiWFAQADtyeQ53Jhe/PqBLx/eytujoyj944XHVUcs1A8mzLrokTld5Pd2lwWbYptLZNw\ncdKP3RWr0r5uGDh7ZxEPNxRBsilcFy2ru5QctR5gthtLtE9qU2b75bpWmc+/2abLsSEvDyKxfpIJ\ngSkn1i5uPzwy6PkgJuUU+cxnPoPPfOYz3DrRDN8PPvggHnzwwXQNY0PQ9XDU/Yvzc3C53FHLtip3\n+6/B1bLb9IMoyQpcLbtwd+A6qrruy+DoNgaHwxG/UhroH5vEo/dZTT+ttRV4+4PL/El5A2ksciGg\nGXh7aB6lTgVB3cBcUMd9VflwxQgeIwDuemdx/dYgwrqBhppKNNZW55SboCAxxIqKFCgtLcX47RFU\nMjZlwzDQ23Mee/bl1gPGeqNpOmw0Ny2Wc9IGQ6EoLQSpIstS9EAvkgQlgRXfG0lHqRvtJS7MBjTY\nZAl5djEZ8zjbcw1QbHhg716oqoq+gUGcOHkGH3toL9RNHglMvCnnGDxJOploX/SnjBfta9v2nfjo\nww9w+fYIyqprEQwsYXJsBF3bdsDpWpVjeZmnACpnJ+4uZerW2ql5k/nChjQq5zMSNRkrT9pmzzH9\nwlhcqyzy+jKGYUCWDDjU6D/GVCI2jzu2RG2J9sWRr81uT1QiZ8fCd8mSTFJ3urJEcfywqCvLPYlU\nkhUEgiE47GZZNqxpMAzJ6i7FwsvKhOXIbhFo5iluJqrYblgSgGJ2DRpvciFKi7lMjl3Gyte8KGHg\nu1aZ3JUs5gPGZMaTq2k/XLe31T7HJqdgc7qwa+fqOpa2lmaUlRTjg0vXcGDP7uh9RNvmkUVuUCxa\nmjKP5Qo5PylnAkmScN8DexEIBHDnzhjyiwrQ1fkYJEmCtrXun7h4ikow752EvcgcfCQ0M46CkooM\njWpzsqu9Ee9dvolH7+uKPBwYhoH3L93EzubqDI9OkCo3Bkfx0EMPWfYXFRXBvxTIwIg2FvGmLEgY\nh8OB+samTA8jq6lo6sDihVMIBH2wly1ngApODEHxe1GyS6RiTCeF+R5sa6jFrz+4iny3A5IkYW7R\nj676CpQXegBNmAtyEQOAQt/y7yHTN/pNiJiUc4QVccey9piVnUkhlRZlGDHrxuoT4K/MZhfUanTV\nNEfOTmZltqkXIhdLhrkuq1hT2ZWVrJORtlk1gMrV0XwKu/YexOzEGCZHLgMA6usaUFC2LWGpl8rH\nFNPqa84qaksZG+2LytdMO0siD7rNLqi1RPtKUM5OZtUskU8lJj55VWUpqipL4VsKwDCAPPtyXQNW\nEw13ZIqC6QU/7ngX4HHaUVfginwWGrWLxUhGyk7CFipxInqZ5GuORG2R72k/nHPMSwjCXTWd1Apr\nRgZn2hV4PLg7NYWy0lJTdV3XEdbCScnOCUftEgvIMkbOTsqC3EGSJBRV1qCosibTQ9kyuJ33Vnyn\nED0srOl49/IgivLdqK8ohnfBj7eujOCBhjKU5Yt81hvNjvYW/ObUOTz6yCHY7cuLJg3DwKmz57C9\nI34qwFwnHUklcgkxKQviYhgGdE2DrERP4yjYXLx3fRh7OxtQmLc8AVcUetBWXYZfnbuKT26ry2xq\nuS2Izabi0L77cerMGRhYjqkeCoXQ1daCqvLUwxznCkK+FgjuoWsa+i59iEAgAFm1Qw8FUFhajsrm\n7A6DKkidUFgDIEUm5BVkWcKOpmrcnPCio6IgM4PbwuS5XTjy4N5lDxJJ3lIPx2JS3kTEyxKVaN1k\nbgk55gbfxsxzl5LICLQY9QBAoi5ITMcWpyvW6yqKvfnC2d+jpGUHHHmr4TC9twcx0XcNDR3LmcGs\n7lLp/wLRsVF4dmPZZBum5yp6HwC1E8d2gQI2Jik5awuUSCYwrr2Z7cMgZZLVlcgf8qMgL3qs7LLC\nPFyamoXES0ayTsk2TLZiXrQvXmSyOHZ7XmQu1m5tjagVO/NTom5PlrIY9nZJkvj23njH57GFJvps\nZlNPyoLU8U5NwpZfbJqQAaCophGjF09B1/WYqQkFmSUYCqF3aAzBUAhNVaUoLfAk3DbP6YB30R+1\n7PbUHMoKRcQ6wcYi3pQFAgBT42MoqKyPWubIL8LS4jzc+YUbPKr1wb+4gGvXL0PXNOi6jur6RtQ2\nNGR6WCnRO3gbo5PT2NbaBKfDgVtDI7h4cwhH7tuW0Bu9qshw2e24MzOHquJVmToU1nBjdBJ/cF8r\nYAhnfMHGISblHIPnSBS3bYLJK+g9wYv2xfZj+REkOwwj9kFYOVvmfEqDKmBkB/syS6Vmti49b06n\nCwG/D3ZXHijhwBJcTidsigQbGVsywVNYqTueRM1CI0aybWk37Lmj7RRJwszdSfT1XkXnfftgdzhh\nGAZG+npx9aMPsX33nnv16PFij40XGY4LT8oEzBfSIBLlPfl0bmER4zNzePTAnkjZnh2dmJiawdlr\n/TiwvcXcjpGa2aMf2NGG9y/dRO/oFKqKPZhdXIJ3cQmP7GqH7HBYIngZjKuTxPtFoe5SSfjYWmTp\nWP0kk4CB55LEi8y1lmMwY+UmlqCkenxeP8mQwWhfW21SFvqjICq1TS3wDt+07NfCIRihJdg3Sc7o\nvutXsHP/Idjv2UklSUJ9aydCYQOL8/MZHl1yXL7Rjwd2WBfhVZQWY8GfeOQnSZJwcGcrDmxrhicv\nDx0N1fjkA13wuDYmsYZAsJURk7IgKoqqoqGlFaM9p7G0MAfDMDB/9w5uXzyFrt0PZHp4aSEYCMDh\nckddyVrX1oGhfutDSTYTCoXhckafOGNFhOLhsNtQU1poWYktEGwkhm6s6S/XyHn5WrB+VNbWo7is\nHIM3rmNqZBElZRVoe+SxTbRK04j5WWRJNsmyuUC+x42pmVmUFltt/eFQ9HSjAkG2o+fgxLoWNt2k\nzMu7lGpGKV47XghOi52ajMcUZpN0JJlcsogt2GQLJ52Suuy0wnOfov2s1LW5XOjqNuc8Zuvq5HjU\nbsv3kIo9ufPmfauNPXY7k2sTaac6nQj4F2EYhuX6j/bfQFNLK5Qo6RAt5l9mR8I25DhQ26DEsRuu\nuEjt6GjB2+9/gE889IApVePlG/1oqKmwtmNeng36oYj9lw3lSUNHmsq4i8A4Waoo3BCQHLefZEJH\nykn0k0J2p7hjTdSGbDlGEm5PqYbSzKKMUbzsfpuRTTcpCwTJ0NjajmsfnkbH7r1Q1OWvw/jo0HKg\nlKLiDI8uOew2G/bv6sLbZ86jIM8Np8OOuzNeVJUWo72hBtDF27JAkO2ISVmwJVlcmMdA7xVo4TCC\ngQAun/09bHY7dF1HeUUl7tuXmxmsigvy8YmHHsCifwnBUAi72hq2VPQnweYjF+3CayFnJ+UVNxhe\nBKl47lKJZpRKl7sUhRfRixWPaD+JRgIDzNHAuO5TpMwwSftGzDJ6PMv3Zx3mA2smJqbMItFb243f\nHsHo0CA6du2B3eFEKBTErUsfoaysDA0trff6iX08KuylS7LmwpM9WRepe9cqz+1GHgAYqwYGy/eB\nlZpJlDDqgmSyr9Pv3Eb4LScqw/Ik6Xh9JioRc1yp4krLMqefBMeWslwN5IxkzbLVbMrZeRUEgnXC\nMAwM3rqJHXsfirhB2Wx2dN2/D7dHR6CtU5hIgUCQGoa+tr9cQ0zKgi2Fd+ouSiqqokq6VQ3NGBsZ\nysCoBAJBLAzDWNNfrpGz8nUq8OTsjViZTWFlaZncPOaIXrHbUe/TZFZqm6RtOjhWkSO6N+82p9Gv\n6OrsVOCttgaiS9SRMrYfSYKha5EFXRRFUWEEg5BhXg3PW21Nj8Evo0fkSaL0YiUmrVoUSFZ1TmLV\ntEFfMRh5W7K8fjB3YbrcyJKIq5746uskElLwJGpOv3GjdCU6vi22wlqwypa7KsFgED09PTh96hQG\nBwZy8klKkDolZRWYHh+LWjYxOojq2ujxvgUCQWbQdWNNf7nGlnpTHh4awo2bt9C5oxuNBQUYGx3B\nr0+cwKFHHoFzk4SNFPBRFAUlZWUY7L2KhvauyJvt7cF+uJxO2B0ilKRAkE2sffX12ldihkIhfP/7\n38fJkycRDAbR3d2NZ555BiUlJTHbvP/++/jpT3+K8fFxhMNhVFdX44knnsCRI0e4x9oyk3IoFMKN\nm7fw0OFHI/samppRWVWNs2fO4JHDhzM3OMGG0ta5HaNDA7h05veQZQWGrqOyqgodu+/P9NAEAgEh\nGyblV199FefOncPzzz8Pj8eD733vezh27BiOHTsWM4Vtfn4+Pv3pT6OmpgaqquLcuXP4z//8T+Tn\n52PPnj1R2wCbYFKm2YViuUjduNGL9m3bLfsdTidkRUUoGITNbmf6Zfs0t0nVXYrrkpWM2xOzTT8u\nrx96b/Ijg7HNiEtUEvc4tUenQjzbPK84VgapusYmNDQ2RbYtEdU4Nn2Li9QGuESZ7MYcu6XFFGz2\nrTP3ydaLNwDWbspb0iqlyaacjL0zQZty3AxOprLYkbm49t14NuQEbcPrYkNeA3GzT21ifD4f3n77\nbRw9ehTd3d0AgGeffRZHjx5FT08Pdu/eHbXdzp07Tduf+tSn8Nvf/hbXr1/nTspb5kz7fD548vOj\nljmdLgSCiWfREQgEAsHGoBvGmv7WSl9fHzRNM02+paWlqKurw/Xr1xPqwzAM9PT0YGxsLDKxxyLn\n35QTpbq6GrdHhtHS1mEpm5+fhdttzRssEAgEgsyS6YheXq8Xsiwjn7zUFRYWYnZ2ltvW5/Phr//6\nrxEOhyFJEv7yL//S8gZN2XSTMitXsk9JNTW1uPbrX6O6pg4utzuyf2igH2WlpTHtAst9mrfT4S4V\n9xg8t6cEpe3luuz5IHXZerxEFpbPiISh0ncqUBOF5Rjc5BVsPSLts25fnKhdPDcny/HSpR7SAZm0\nZo4kSoN96dGrLRey5gtOQzo0nnydrmgNScilCUu98aTddEnUKZZtiGSd4HnNJrl6vSbl119/HT/5\nyU+4dV588cU1HcPlcuFf//VfsbS0hJ6eHrz66qsoLCzE3r17Y7bZdJNyLCRJwiOHD+PM6dPQATic\nLvgW5lFWVobu7u40TB0CgYCHYRi4eGMAU7MLUBQF4XAY7Q3VaKgqX3Pf84s+XLoxgGBYgyJL2N7a\niJLC6OYqgQAAnnjiCRyOs8C3rKwMvb290HUd8/Pzprdlr9eLbdu2cdtLkoTKykoAQGNjI0ZGRvDm\nm2+KSXkFu92OQ488gnA4jFAoBKfTKYL1CwQbxO8vXEVzfR127VhecGkYBs5fvoZAMLScxSpFRsbv\n4sbQbezfsxtulxOBYBAffnQZZUUedDQ1pGv4ggyxXr7G+fn5Fkk6Gi0tLVAUBR999BEOHToEAJia\nmsLo6Cg6OzuTOqau69DjBNhJ26R84sQJnDx5Ev39/fD7/Th+/DjKysq4bd555x1861vfsux/7bXX\noMaIupQMsVZmq6pq6T9e8gpzv2yf5rJEV2bTtvT4PBmUFwnMUpfpxyptR+8TINK2NRQYZ2xmqPSd\nDnjCGu8hi5/IInY/8VZbb8iDHSut0iKu1Lr6r2VltmmLFhKjCXMPWKRNrka+jHduAU6nC7XVlatV\nJQl7dm7Dr0+eRmtjPf88xujXMAxc6RvCJw4fjLR32O14aN/9ePv3p9DSUAdVUaL2kZREzRtLirJz\nNsnVcceTQTId4MntduOxxx7Da6+9hsLCwohLVFNTk2nR1ksvvYS2tjY8+eSTAID/+Z//QXt7Oyoq\nKhAKhXD+/Hn87ne/wzPPPMM9Xtom5WAwiN27d2Pfvn347ne/m3A7u92O48ePm058OiZkgUCQPfSP\njqG9Ofpba2lREeYWFlGY70m637szs6iuLI86obc1N2JgZAxtjXVJ9yvIHrIhqcRTTz0FWZbxyiuv\nRIKHPPfcc6b7bnx83PQiurS0hP/6r//C1NQU7HY7amtr8dxzz+HgwYPcY6Vt9vvUpz4FALh161ZS\n7SRJQkFBQbqGsWkIh8O4ef0aZrxeuFwudG7bDpfLZakXDAbRe+0qFhfm4XA40bFtm1hJLsg6VEVB\nKBSOWhYKh6EodDljYmiaBptqi1pms9vgW1xIqV+BgEVVVTz99NN4+umnY9Y5fvy4afvJJ5+MvDUn\ndaykW6SZYDCIL3/5y9B1HY2Njfjc5z6HpqamTA8ro8x6vfjg3Fl07NyNps4dWFyYxwdnz6KxqRH1\nDY2RetNTd3HxowvYtut+FBQVw7e4iAsffoiGhgbUMfUEgkzT3liHM5eu4/B+c9AEXdcxO78Aj9v6\nwJkI5SXFuNL/ETrbmi1lfQPDuH9bK7e9YRgYuTOB/uHbMADUVZajpaF2PdKAC1IkF+NXr4WMTso1\nNTU4evQoGhsb4ff78Ytf/AJf+9rX8M1vfhNVVVVpP16i0b8As70tUfvycp9MHxx3qWhtV/jo/IfY\n/8jHIm8PeZ587D34CE7/7m3U1NRGshxdungR+w49GnHncuflYc9Dh3D29++gpq4eiqJwbc6J2pt5\nNiyem9VGwbO/8+zG5nocd6kkxmJdG5BYa2rP47odWQ6aoC2QmoLZHWTxieX4pi9EHPtzFJwuF0qL\nCnD2o8u4b3snbDYV84s+nL5wCbu3tQNyEncO83kVWUFFSTEuXbuBHZ1tkevYPzgMVZHhcntifn8N\nScK7Z86jorIKDz/8MCRJwuDgEE68dxaPPXwg9tt7qrZgbIAdeRPYkCmZ9lPeaLiTcqJ+XNu3W8NX\nJkJHRwc6OjpM2y+88AJ++ctf4otf/GJKfeY6CwsLcBcURv1BaGrrxMBAP1rb2jHr9aKwuCSqf3V9\ncxuGBwfQ1MJ/SxAINpId7S2YnPbi1IUeGLoBp8OGg/fvhNu1tmQwOztacWtoFG+fPA1VUaFpYVSV\nl+LA7h3cB+qbA0Oor69HM6PMNTU1wuPJw0dXrmNPd2q/a4L0IiZlhkT9uNKFLMtobm7GnTt30tZn\nrhEMBOB0RpfyXC43pmanl+sFA7DHyGzlcDqxMLO4bmMUCFKlvKQI5SVFae+3taEWrQ21SbUZvTOJ\nw488YtlfVlaGy1evpmtogjWSjlCZuQR3Uk7UjytdGIaBwcFBNDdb7UOUFTVnLRDdEtcAAB5vSURB\nVNcrVvQvy7E4fXCTTHDcpSz93Dt+YVERei5djlpndGgALc1NkACUlpTi+rXraIoSNnRsaBDbtm+D\nLFldnUxj5WehiMATUrmy9zqRaAQva7sk3KV4ZRypO21YpEX2KhDXHrZZqsfjSduAWd5ei9SeIglL\nrTxXJoIkKzHvCUlWYkvBqbo5xWlrrpe8e1gi5IpkvZVJm03Z6/XC6/VibGw5gfzw8DAWFhZQVlYG\nj2fZ1YH6cf3oRz9CR0cHqqqqIjbl4eFhfOlLX0rXsHIORVFQWlKEof5baGhelZ+9M9PwL86jqLh4\nuZ6qori4CMP9t1DP1JuaHIeuhSLnXLA2Bvr7MDw0CEW1Qdc0uN0u7Hlgb8qrhQXZg9vpwNz8PArI\ni0cwGExLeFhBehDydYq89dZb+PGPfxzZPnbsGADg6NGjkaTO1I/L5/Ph29/+NrxeL9xuN5qbm/H1\nr38dra1b2xa6s3sXrl27inMnfwtZVaGHNeR53Hjo4UOWetevX8MH7/0WiqJC1zQUFhZi/4MPZWjk\nm4tbN3rhDwRx4JFHI/tmZ2Zw8ne/w+FHH43ZTpAbdG9rx2/fP4NHDz8C+720rZqm4ffvvY+99/Ez\n+Qg2jq02KUtGpsOlpMCvf/1rHHz4YQDpk0tTtVvwWiVzL8W6DIZhwOCIkvEOwRtDopc+GXFys8jX\nMAz87t138CAzIa/Qe+USaqqqUF5RYWnLW31tSdHLniwjzupndpueZKaM324NiSQ4oQFzVb6GJGPR\n58MHF69AN5bvD0PXsGt7J0ruKVKx2qU8zk0gX598/xQ+/vGPr7mfRPj1r3+Nv/mfuTX18Z9/VrBh\n400HGfdTXiuWZDopTgrJuEuZjk+22VbxbI2JuE9JksS1E8ab+LljMNnUOX0klxYqYWJbSVMnqYmX\nbUe2/b5F5BcURq1b39yCgd5rkUk54/BCcKbrGJwLZLE/s8SJ82s+RhrugiRdjvLyPDj80P6k+klq\nYstAuEyWtNmQhS16w8j5SVkgWA9Umw2hYDBqWWBpCXa7Y4NHJBBsTXJQzF0T4vFHIIiCw+FAcGkJ\n4bA1NOTNa1fw/7d35sFNXdcf/0qWvMiWJXmTV7xgy8Z4iTHGBmyLNfmVbmm6DElghtJkOmwDmelM\naWiG1HRSt0kITIdmpi0T0pSUZSjZaEpKMGAbbAcItgFjAcYG22C86Fky2qX3+yOxsGxJ1v6ezP3M\neAZdvXvv0dFD591z7zknOyeHAakIhCcP2kJ79RdszLiV8kSPUCDCpabMP+Hf0/XyNnxq8hj2cPWe\ndNXN7c6Y0+GP88vuuKin6/fUvHlorj+DOUVPISY2DnqdDtfbWpEkldokvAhISkYb9+HkfWPHX8hE\n9yUjqSNDAvDc7/I+rZ+yXbl1re8zc01kJrqrSZpNAoEAAIiOjoZcvgSdnZ3ouakAn89DQcFciEWk\ngAqBQPAPxCgTCE7g8fkoKCThMQQCU9AWM9MiBJQZbZTZdjJ7Mq6e1PbUte3OuO64Nv2SwcpHuOui\ntvbz8NT21Gt9oxy3ClTYzOnk1LCT+Vj8lU5PIFy9vghl8tUck5iJLuuJEKNMIBAIBAJLeNKMMjsf\njTzAYrFAq9XC4k5cJIFAIBAILCLoV8o0TePSpUtQq8cQIRBAp9MiPCwMZWVlJD8xgcAAQyNK9PT2\nIyw0FDlZ6Qj7NoUlgeAJtPnJWikHvVFubm5GSlo6Cp+SWtuUyhHU19dDLpfb7BX6a495Iv4In2Ji\nv9npHK518wqDXo/BwYcICwtHbFyc0881EXf2f32V/cv5HK5eOLlMk7M9ZCfXOrvJnfSbbl/SlY9h\nsVhw9kILxDExyM7Lh06jxflLbUhJjIcsa/rKb75AqzdgVK2GSChEuIPSpg6ZwfvGNE3DYDCAz+c/\nrsHO0j3kyTxp7uugNsp6vR5GkwkJUqlNu0QSgxAeHydPnkRSUhKKiopc/lEnMAtN0/j60kXoDUZI\nk5JBjQ7iansbCouKEevD2t0E33PxSjsKCgqt35MwKgryBDkaG88jKWEMQj9WLjOaTDj/1dfgh4ZC\nEhOLrp5eGI0GLCorBY8X1D9zXkHTNL6+0gpKpUKEQAC9Toew0FAsmF+KEB4xymwkqO/WoaEhJEgT\n7b6XkZkFlWoU4eHhuHLlCkpKSgIs3cyGpmmYzWaEhDiuSesJV9taEZeYDGlSsrUtS5aLpvo6VFbJ\nwefzfTYXwbeMaXV2H5zmzStBe+vXqCj13//BhuaLmDd/gU39d9XoKBqbL0K+uMJv87Kdry5dQmJy\nCgpL5lnblMoRNF64gOqqagYlcx1ilIMIgUCABwODdt8bG1NDIBAgOSUV53u6rQZkIr7K/jURX2QC\nm4ynru1vZJgwhxvG01nWsOvXrmJwcAihYeEwGg2ICAvFvPm+2cOnKAqygmKbNi6XizkFxbiluIG5\nBe7FDPvC7T3dCL4Kg3KGTWauya7tiW7IKe85ucmduS8njeOK+9TR9x8REQG90eS70J1JqNRqREWL\nbAwyAESLRIiIjIJ6TAOh0M1VuqffqY8+oy90ZTAYoNMbkJScYtMukcQgXBAJlUqF6GiSCIdtBIf/\nwgESiQQjI0MwTzoIQNM07vb0WG9GSUwsRkdHmRBxxtHe1gp+RCTKq+QoWVCOBYurkJ6Ti/MNDV6P\nbbFYwOfbPxQkiY3DKEW+QzZjMk7NEw4Ag4ODEEUL7b7nCwaHRyBNTLL7XmJiIoaUI36bm80MDQ9D\nmmjfk5iamob+/vsBlsgzaIvZq79gI6iNMgCUzZ+P+jOn0d97DyaTCQ8HBnC27jQKJuwjax89QkRE\nBMOSBj9msxlKahRp6Rk27dEiMYRiCYaHh7wan8vlwmi0X5mJUo5ASJ7qWU1aahKuX79u02YymXDl\nyhXky/xXwEMkFEI5Yt/wjoyMQCT03wMBmxFERGBsbMzue2NjY4gQBMdvIjHKQYZYLMbKlSuh12px\n4tOPoVSOoLJajri4eADfunB0WmKUfYBqdBSS2Fi776XMmoX7/f1ezxEdLcTQ4EObNpqmcaO9Fbl5\neV6PT/AfOVmZ4MKCuro6tLa2ormlBefOncOC0qf8ehYgLjYGAw/uw2g02rQbDAYMDQ4iRiLx29xs\nRiwWQzk8bNeT2H2nC7PS0hiSzD0sFrNXf8FGUO8pj8PlcpE3Jw/CaCE6Om4gLi4eIrEY9/v7cPum\nApWVldOO4atwKRu5ptmX8kW6TncqUTmXZdKcdmQPDw+HVqOx21/zrTfCnZAsezxVMg8tzU3o67mD\nxJQ0aDUa3O+9i8LCAoSFeVfD2K1wKa9mYhB3wqVsrnNjv9kJc3JzkSeTQaVWI5TPR0S4G9+ZF/uo\nFWXzcaauDrPS0xEfH4/Bhw9x9+5dLCqf72S+4N83no75pfNx9vRp5M2dC6lUipGREVxrb0NRQSGJ\nSGEpM8Ioj5OSkoL4+HgoFAr03LkNaUICnn76ab/cfAaDAZcvX4ZOrweHwwEHQGFhISQz+KlcEBkJ\n7aMxmExG8HiPVz40TePOzU5UVcu9noPD4aC8YiG0Wi0e3L8PsTASc5YvJz8gQQSHw4FofKvBWby1\nDxFGRWLl0mr03X+A+733IJGIsXJp9RN/34jFIqxYvgy3bt3GpZ5uiKJFWFJdHVRRDMHogvaGGWWU\nASA0NBQFBQV+ncNkM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UjwxRdf2L2+oaEBRqMRmzZtQmpqKioqKvDDH/4Qn332mV/k81be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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c80e3d0>"
       ]
      }
     ],
     "prompt_number": 247
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plot the difference between the 'true' field and the interpolated \n",
      "f, ax = plt.subplots(figsize=(8,6))\n",
      "im = ax.pcolor(XI, YI, ZI - true_Z, cmap=plt.get_cmap('RdBu_r'), vmin=-1e-3, vmax=1e-3)\n",
      "ax.set_xlim(-2, 2)\n",
      "ax.set_ylim(-2, 2)\n",
      "plt.colorbar(im, orientation='vertical', pad=0.06); "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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P/8DXv/51zJ49G04N7pHgeTcJIYQEHa2qAPr6+uBwOFymnh9vmvoxHMe5pB97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\nQgghHuj/ABvAHLOvsa/AAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c862610>"
       ]
      }
     ],
     "prompt_number": 248
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Curve fitting with scipy.optimize\n",
      "\n",
      "In this case you want to fit noisy, e.g. experimental, data to **a specific function**, with *unknown parameters*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.optimize import curve_fit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 249
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The algorithm uses the [Levenberg-Marquardt](http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm) algorithm to perform non-linear least-square optimization"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def fitFunc(t, a, b, c):\n",
      "    \"\"\" \n",
      "    defines the function\n",
      "    takes 3 parameters: a, b and c\n",
      "    \"\"\"\n",
      "    return a*np.exp(-b*t) + c"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 250
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "curve_fit?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 251
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### defines the evaluation domain\n",
      "t = np.linspace(0,4,50)\n",
      "\n",
      "### defines the paramaters \n",
      "a = 5.0\n",
      "b = 1.5\n",
      "c = 0.5\n",
      "\n",
      "### create the response\n",
      "temp = fitFunc(t, a, b, c)\n",
      "\n",
      "### add some noise to simulate \"real world observations\" such as experimental data\n",
      "noisy = temp + 0.4 * np.random.normal(size=len(temp)) \n",
      "\n",
      "### use curve_fit to estimate the parameters and the covariance matrices\n",
      "fitParams, fitCovariances = curve_fit(fitFunc, t, noisy)\n",
      "\n",
      "afit, bfit, cfit = tuple(fitParams)\n",
      "\n",
      "print(\"\\nEstimated parameters\\na: {0:<4.2f}, b: {1:<4.2f}, c: {2:<4.2f}\\n\\n\".format(afit, bfit, cfit))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Estimated parameters\n",
        "a: 5.03, b: 1.64, c: 0.64\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 252
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, ax = plt.subplots(figsize=(8,8))\n",
      "ax.set_ylabel(u'Temperature (\\xb0C)', fontsize = 16)\n",
      "ax.set_xlabel('time (s)', fontsize = 16)\n",
      "ax.set_xlim(0,4.1)\n",
      "# plot the data as red circles with vertical errorbars\n",
      "ax.errorbar(t, noisy, fmt = 'ro', yerr = 0.2)\n",
      "# now plot the best fit curve \n",
      "\n",
      "ax.plot(t, fitFunc(t, afit, bfit, cfit),'k-', lw=2)\n",
      "\n",
      "# and plot the +- 1 sigma curves\n",
      "# (the square root of the diagonal covariance matrix  \n",
      "# element is the uncertainty on the fit parameter.)\n",
      "\n",
      "sigma_a, sigma_b, sigma_c = np.sqrt(fitCovariances[0,0]), \\\n",
      "np.sqrt(fitCovariances[1,1]), \\\n",
      "np.sqrt(fitCovariances[2,2])\n",
      "\n",
      "ax.plot(t, fitFunc(t, afit + sigma_a, bfit - sigma_b, cfit + sigma_c), 'b-')\n",
      "ax.plot(t, fitFunc(t, afit - sigma_a, bfit + sigma_b, cfit - sigma_c), 'b-');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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yWH0evC2YMWMGtWrV4sUXXzT7elHfwf/6qwODB5fj6NHsZ8QDfP7550yePJnO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\nNAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11123f110>"
       ]
      }
     ],
     "prompt_number": 253
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Getting data in and out of Python"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Python itself has a very good support for IO and dealing with ascii files \n",
      "\n",
      "    f = open('filename','r') \n",
      "    out = f.readlines() \n",
      "    f.close() \n",
      "    "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open('./data/ascii_table.txt', 'r')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 254
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "out = f.readlines()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 255
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 256
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "out"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 257,
       "text": [
        "['   0    1    2    3    4    5    6    7    8    9\\n',\n",
        " '  10   11   12   13   14   15   16   17   18   19\\n',\n",
        " '  20   21   22   23   24   25   26   27   28   29\\n',\n",
        " '  30   31   32   33   34   35   36   37   38   39\\n',\n",
        " '  40   41   42   43   44   45   46   47   48   49\\n',\n",
        " '  50   51   52   53   54   55   56   57   58   59\\n',\n",
        " '  60   61   62   63   64   65   66   67   68   69\\n',\n",
        " '  70   71   72   73   74   75   76   77   78   79\\n',\n",
        " '  80   81   82   83   84   85   86   87   88   89\\n',\n",
        " '  90   91   92   93   94   95   96   97   98   99\\n']"
       ]
      }
     ],
     "prompt_number": 257
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "out = [map(np.int, x.split()) for x in out]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 258
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "map("
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "out"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 260,
       "text": [
        "[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
        " [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
        " [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
        " [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
        " [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
        " [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
        " [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],\n",
        " [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],\n",
        " [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
        " [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]]"
       ]
      }
     ],
     "prompt_number": 260
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "out = np.array(out)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 261
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(out)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0  1  2  3  4  5  6  7  8  9]\n",
        " [10 11 12 13 14 15 16 17 18 19]\n",
        " [20 21 22 23 24 25 26 27 28 29]\n",
        " [30 31 32 33 34 35 36 37 38 39]\n",
        " [40 41 42 43 44 45 46 47 48 49]\n",
        " [50 51 52 53 54 55 56 57 58 59]\n",
        " [60 61 62 63 64 65 66 67 68 69]\n",
        " [70 71 72 73 74 75 76 77 78 79]\n",
        " [80 81 82 83 84 85 86 87 88 89]\n",
        " [90 91 92 93 94 95 96 97 98 99]]\n"
       ]
      }
     ],
     "prompt_number": 262
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "out.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 263,
       "text": [
        "(10, 10)"
       ]
      }
     ],
     "prompt_number": 263
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### text (tab or space delimited) ASCII in Numpy "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.genfromtxt("
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "SyntaxError",
       "evalue": "unexpected EOF while parsing (<ipython-input-264-22645fd25a39>, line 1)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-264-22645fd25a39>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    np.genfromtxt(\u001b[0m\n\u001b[0m                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected EOF while parsing\n"
       ]
      }
     ],
     "prompt_number": 264
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.genfromtxt('./data/ascii_table.txt', dtype=int) # can handle missing values "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 265
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.genfromtxt("
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 267,
       "text": [
        "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],\n",
        "       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
        "       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
        "       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
        "       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
        "       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
        "       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],\n",
        "       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],\n",
        "       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
        "       [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])"
       ]
      }
     ],
     "prompt_number": 267
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#np.loadtxt() ### each row must have the same number of values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 268
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.loadtxt('./data/ascii_table.txt', dtype=np.int)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 269
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 270,
       "text": [
        "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],\n",
        "       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
        "       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
        "       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
        "       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
        "       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
        "       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],\n",
        "       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],\n",
        "       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
        "       [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])"
       ]
      }
     ],
     "prompt_number": 270
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**NOTE**: For reading all csv, tabular format files including xls, xlsx, etc. the most convenient way is to use the special IO functions of [Pandas](http://pandas.pydata.org), which we have seen for the SOI / NINO example, and will see in more details in the pandas notebook. All these functions will load these formats directly into Pandas DataFrames, which are a very convenient data structure for interacting with spreadsheet-like data."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### NetCDF "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "NetCDF stands for [Network Common Data form](http://www.unidata.ucar.edu/software/netcdf/), it is used largely in the atmosphere and ocean sciences to store mostly gridded datasets."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There is one set of functions that comes with Scipy which is available from the IO module, with which you can read and create NetCDF files easily."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.io.netcdf import NetCDFFile"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 271
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we are reading SSTs (for the Southern Hemisphere) from the Hadley SST (available [here]())"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc = NetCDFFile('data/Hadley_SST.nc', 'r')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 272
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc.variables.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 273,
       "text": [
        "['lat', 'time_bnds', 'lon', 'sst', 'time']"
       ]
      }
     ],
     "prompt_number": 273
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lat = nc.variables['lat'][:]\n",
      "lon = nc.variables['lon'][:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 274
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lat"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 275,
       "text": [
        "array([  9.5,   8.5,   7.5,   6.5,   5.5,   4.5,   3.5,   2.5,   1.5,\n",
        "         0.5,  -0.5,  -1.5,  -2.5,  -3.5,  -4.5,  -5.5,  -6.5,  -7.5,\n",
        "        -8.5,  -9.5, -10.5, -11.5, -12.5, -13.5, -14.5, -15.5, -16.5,\n",
        "       -17.5, -18.5, -19.5, -20.5, -21.5, -22.5, -23.5, -24.5, -25.5,\n",
        "       -26.5, -27.5, -28.5, -29.5, -30.5, -31.5, -32.5, -33.5, -34.5,\n",
        "       -35.5, -36.5, -37.5, -38.5, -39.5, -40.5, -41.5, -42.5, -43.5,\n",
        "       -44.5, -45.5, -46.5, -47.5, -48.5, -49.5, -50.5, -51.5, -52.5,\n",
        "       -53.5, -54.5, -55.5, -56.5, -57.5, -58.5, -59.5, -60.5, -61.5,\n",
        "       -62.5, -63.5, -64.5, -65.5, -66.5, -67.5, -68.5, -69.5, -70.5,\n",
        "       -71.5, -72.5, -73.5, -74.5, -75.5, -76.5, -77.5, -78.5, -79.5,\n",
        "       -80.5, -81.5, -82.5, -83.5, -84.5, -85.5, -86.5, -87.5, -88.5, -89.5], dtype=float32)"
       ]
      }
     ],
     "prompt_number": 275
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sst = nc.variables['sst'][:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 276
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mval = nc.variables['sst'].missing_value"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 277
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mval"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 278,
       "text": [
        "-1e+30"
       ]
      }
     ],
     "prompt_number": 278
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc.close() # don't forget to close"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 279
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mval"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 280,
       "text": [
        "-1e+30"
       ]
      }
     ],
     "prompt_number": 280
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sst.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 281,
       "text": [
        "(480, 100, 360)"
       ]
      }
     ],
     "prompt_number": 281
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sst = ma.masked_values(sst, mval)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 282
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, ax = plt.subplots(figsize=(12,6))\n",
      "im = ax.imshow(sst[0,:,:], interpolation='nearest', aspect='auto')\n",
      "ax.set_title('SSTs')\n",
      "cb = plt.colorbar(im);\n",
      "cb.set_label(u'\\xb0C', rotation=0)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Eqqutvx/JHoOrXxdDl087VrOth1B1W1cCLg4/h4Jm/bPZrHGGss3L2jTbnL4/dbOVg6DH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\nCRMmYNKkS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/e+9z3U19cjlUphzZo1mDFjBpLJJB5//HH09PTge9/7HiKRset5MDg4iH37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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c85ed50>"
       ]
      }
     ],
     "prompt_number": 283
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And the [netCDF4](http://netcdf4-python.googlecode.com/svn/trunk/docs/netCDF4-module.html) standalone module, which is installed as part of the Anaconda distribution, and has more functionalities, including: \n",
      "\n",
      "+ support of NetCDF 4 (and NetCDF 3).\n",
      "+ support for reading multiple files datasets as a single dataset.\n",
      "+ can create files that are readable by HDF5 clients."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from netCDF4 import Dataset"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 284
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc = Dataset('data/Hadley_SST.nc', 'r')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 285
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sst = nc.variables['sst'][:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 286
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sst = ma.masked_values(sst, mval)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 287
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 288
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, ax = plt.subplots(figsize=(12,6))\n",
      "im = ax.imshow(sst[0,:,:], interpolation='nearest', aspect='auto')\n",
      "ax.set_title('SSTs')\n",
      "cb = plt.colorbar(im);\n",
      "cb.set_label(u'\\xb0C', rotation=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Eqqutvx/JHoOrXxdDl087VrOth1B1W1cCLg4/h4Jm/bPZrHGGss3L2jTbnL4/dbOVg6DH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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10fa541d0>"
       ]
      }
     ],
     "prompt_number": 289
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A more recent project is the IRIS module, developped by the Met. Office in the UK: \n",
      "    \n",
      "[http://scitools.org.uk/iris/index.html](http://scitools.org.uk/iris/index.html)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Matlab files"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.io.matlab import loadmat, savemat, whosmat "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 290
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We first create a matlab file containing one variable"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.random.randn(10,10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 291
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "savemat('data/random_2darray.mat', {'xmat':x})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 292
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Then inspect its content with `whosmat`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "whosmat('data/random_2darray.mat')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 293,
       "text": [
        "[('xmat', (10, 10), 'double')]"
       ]
      }
     ],
     "prompt_number": 293
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And read it, accessing the saved variable as a numpy arrray using a dict type syntax"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "matfile = loadmat('data/random_2darray.mat')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 294
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = matfile['xmat'] # like a dictionnary"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 295
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 296,
       "text": [
        "array([[  6.02319280e-01,   4.20282204e-01,   8.10951673e-01,\n",
        "          1.04444209e+00,  -4.00878192e-01,   8.24005618e-01,\n",
        "         -5.62305431e-01,   1.95487808e+00,  -1.33195167e+00,\n",
        "         -1.76068856e+00],\n",
        "       [ -1.65072127e+00,  -8.90555584e-01,  -1.11911540e+00,\n",
        "          1.95607890e+00,  -3.26499498e-01,  -1.34267579e+00,\n",
        "          1.11438298e+00,  -5.86523939e-01,  -1.23685338e+00,\n",
        "          8.75838928e-01],\n",
        "       [  6.23362177e-01,  -4.34956683e-01,   1.40754000e+00,\n",
        "          1.29101580e-01,   1.61694960e+00,   5.02740882e-01,\n",
        "          1.55880554e+00,   1.09402696e-01,  -1.21974440e+00,\n",
        "          2.44936865e+00],\n",
        "       [ -5.45774168e-01,  -1.98837863e-01,  -7.00398505e-01,\n",
        "         -2.03394449e-01,   2.42669441e-01,   2.01830179e-01,\n",
        "          6.61020288e-01,   1.79215821e+00,  -1.20464572e-01,\n",
        "         -1.23312074e+00],\n",
        "       [ -1.18231813e+00,  -6.65754518e-01,  -1.67419581e+00,\n",
        "          8.25029824e-01,  -4.98213564e-01,  -3.10984978e-01,\n",
        "         -1.89148284e-03,  -1.39662042e+00,  -8.61316361e-01,\n",
        "          6.74711526e-01],\n",
        "       [  6.18539131e-01,  -4.43171931e-01,   1.81053491e+00,\n",
        "         -1.30572692e+00,  -3.44987210e-01,  -2.30839743e-01,\n",
        "         -2.79308500e+00,   1.93752881e+00,   3.66332015e-01,\n",
        "         -1.04458938e+00],\n",
        "       [  2.05117344e+00,   5.85662000e-01,   4.29526140e-01,\n",
        "         -6.06998398e-01,   1.06222724e-01,  -1.52568032e+00,\n",
        "          7.95026094e-01,  -3.74438319e-01,   1.34048197e-01,\n",
        "          1.20205486e+00],\n",
        "       [  2.84748111e-01,   2.62467445e-01,   2.76499305e-01,\n",
        "         -7.33271604e-01,   8.36004719e-01,   1.54335911e+00,\n",
        "          7.58805660e-01,   8.84908814e-01,  -8.77281519e-01,\n",
        "         -8.67787223e-01],\n",
        "       [ -1.44087602e+00,   1.23225307e+00,  -2.54179868e-01,\n",
        "          1.39984394e+00,  -7.81911683e-01,  -4.37508983e-01,\n",
        "          9.54250872e-02,   9.21450069e-01,   6.07501958e-02,\n",
        "          2.11124755e-01],\n",
        "       [  1.65275673e-02,   1.77187720e-01,  -1.11647002e+00,\n",
        "          8.09271010e-02,  -1.86578994e-01,  -5.68244809e-02,\n",
        "          4.92336556e-01,  -6.80678141e-01,  -8.45080274e-02,\n",
        "         -2.97361883e-01]])"
       ]
      }
     ],
     "prompt_number": 296
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For matlab files containing matlab **structures**, the syntax is marginally more complicated "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "whosmat('./data/clusters_monthly.mat')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 297,
       "text": [
        "[('clusm', (1, 1), 'struct')]"
       ]
      }
     ],
     "prompt_number": 297
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "matfile = loadmat('./data/clusters_monthly.mat', struct_as_record=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 298
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "matfile.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 299,
       "text": [
        "['clusm', '__version__', '__header__', '__globals__']"
       ]
      }
     ],
     "prompt_number": 299
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clusm = matfile['clusm'][0][0] ### corresponds to the (1,1) shape as indicated by whosmat"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 300
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(clusm)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 301,
       "text": [
        "scipy.io.matlab.mio5_params.mat_struct"
       ]
      }
     ],
     "prompt_number": 301
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### inspect the attributes of clusm\n",
      "dir(clusm)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 302,
       "text": [
        "['__class__',\n",
        " '__delattr__',\n",
        " '__dict__',\n",
        " '__doc__',\n",
        " '__format__',\n",
        " '__getattribute__',\n",
        " '__hash__',\n",
        " '__init__',\n",
        " '__module__',\n",
        " '__new__',\n",
        " '__reduce__',\n",
        " '__reduce_ex__',\n",
        " '__repr__',\n",
        " '__setattr__',\n",
        " '__sizeof__',\n",
        " '__slotnames__',\n",
        " '__str__',\n",
        " '__subclasshook__',\n",
        " '__weakref__',\n",
        " '_fieldnames',\n",
        " 'data',\n",
        " 'name',\n",
        " 'regimes',\n",
        " 'regname',\n",
        " 'time']"
       ]
      }
     ],
     "prompt_number": 302
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clusm._fieldnames"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 303,
       "text": [
        "['time', 'data', 'name', 'regimes', 'regname']"
       ]
      }
     ],
     "prompt_number": 303
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = clusm.time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 304
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 305,
       "text": [
        "array([[1948,    1],\n",
        "       [1948,    2],\n",
        "       [1948,    3],\n",
        "       ..., \n",
        "       [2012,    3],\n",
        "       [2012,    4],\n",
        "       [2012,    5]], dtype=uint16)"
       ]
      }
     ],
     "prompt_number": 305
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = clusm.data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 306
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data[0,:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 307,
       "text": [
        "array([ 0.11290323,  0.0483871 ,  0.09677419,  0.19354839,  0.0483871 ,\n",
        "        0.0483871 ,  0.0483871 ,  0.12903226,  0.03225806,  0.0483871 ,\n",
        "        0.17741935,  0.01612903])"
       ]
      }
     ],
     "prompt_number": 307
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**IMPORTANT**: A note on **matlab dates and time**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Matlab's **datenum** representation is the number of days since midnight on **January 1st, 0 AD.**  Python's datetime handling, (throught the datetime.fromordinal function) assumes time is the number of days since midnight on **January 1st, 1 AD.**  So the duration of year 0 separates the time representation returned by python's fromordinal function and the time representation used by matlab. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "matlab_datenum = 731965.04835648148  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 308
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following snippet is from Conrad Lee at [https://gist.github.com/conradlee/4366296](https://gist.github.com/conradlee/4366296)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from datetime import datetime, timedelta\n",
      " \n",
      "python_datetime = datetime.fromordinal(int(matlab_datenum)) \\\n",
      "+ timedelta(days=matlab_datenum%1) - timedelta(days = 366) ## year 0 was a leap year"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 309
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "python_datetime"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 310,
       "text": [
        "datetime.datetime(2004, 1, 19, 1, 9, 38)"
       ]
      }
     ],
     "prompt_number": 310
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(python_datetime)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2004-01-19 01:09:38\n"
       ]
      }
     ],
     "prompt_number": 311
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Another - easiest - way to do it is to use the module [netcdftime](http://netcdf4-python.googlecode.com/svn/trunk/docs/netcdftime.netcdftime-module.html) (installed with anaconda), which has a ```num2date``` function, and to specify the origin, as well as the calendar (ISO 8601 Proleptic Gregorian calendar, which allows for Year 0), you can convert a entire vector (numpy array) of Matlab datenums that way."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from netcdftime import num2date"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 312
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "date_proleptic = num2date(matlab_datenum, units='days since 000-01-00 00:00:00', \\\n",
      "                          calendar='proleptic_gregorian')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 313
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "date_proleptic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 314,
       "text": [
        "datetime.datetime(2004, 1, 19, 1, 9, 38)"
       ]
      }
     ],
     "prompt_number": 314
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num2date?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 315
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Other file formats\n",
      "\n",
      "**HDF5** \n",
      "\n",
      "Please See [http://www.h5py.org/docs/intro/quick.html](http://www.h5py.org/docs/intro/quick.html)\n",
      "\n",
      "\n",
      "**IDL**: \n",
      "    \n",
      "    from scipy.io import idl\n",
      "    idl.readsav()\n",
      "    \n",
      "**Fortran files**: \n",
      "\n",
      "    from scipy.io import FortranFile\n",
      "    f = FortranFile()\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    To create an unformatted sequential Fortran file:\n",
      "    \n",
      "    >>> from scipy.io import FortranFile\n",
      "    >>> f = FortranFile('test.unf', 'w')\n",
      "    >>> f.write_record(np.array([1,2,3,4,5],dtype=np.int32))\n",
      "    >>> f.write_record(np.linspace(0,1,20).reshape((5,-1)))\n",
      "    >>> f.close()\n",
      "    \n",
      "    To read this file:\n",
      "    \n",
      "    >>> from scipy.io import FortranFile\n",
      "    >>> f = FortranFile('test.unf', 'r')\n",
      "    >>> print(f.read_ints(dtype=np.int32))\n",
      "    [1 2 3 4 5]\n",
      "    >>> print(f.read_reals(dtype=np.float).reshape((5,-1)))\n",
      "    [[ 0.          0.05263158  0.10526316  0.15789474]\n",
      "     [ 0.21052632  0.26315789  0.31578947  0.36842105]\n",
      "     [ 0.42105263  0.47368421  0.52631579  0.57894737]\n",
      "     [ 0.63157895  0.68421053  0.73684211  0.78947368]\n",
      "     [ 0.84210526  0.89473684  0.94736842  1.        ]]\n",
      "    >>> f.close()\n",
      "    "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext load_style"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The load_style extension is already loaded. To reload it, use:\n",
        "  %reload_ext load_style\n"
       ]
      }
     ],
     "prompt_number": 316
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_style talk.css"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<style>\n",
        "\n",
        ".rendered_html {\n",
        "    font-family: \"proxima-nova\", helvetica;\n",
        "    font-size: 130%;\n",
        "    line-height: 1.5;\n",
        "}\n",
        "\n",
        ".rendered_html h1 {\n",
        "    margin: 0.25em 0em 0.5em;\n",
        "    color: #015C9C;\n",
        "    text-align: center;\n",
        "    line-height: 1.2; \n",
        "    page-break-before: always;\n",
        "}\n",
        "\n",
        ".rendered_html h2 {\n",
        "    margin: 1.1em 0em 0.5em;\n",
        "    color: #26465D;\n",
        "    line-height: 1.2;\n",
        "}\n",
        "\n",
        ".rendered_html h3 {\n",
        "    margin: 1.1em 0em 0.5em;\n",
        "    color: #002845;\n",
        "    line-height: 1.2;\n",
        "}\n",
        "\n",
        ".rendered_html li {\n",
        "    line-height: 1.5; \n",
        "}\n",
        "\n",
        "/*.prompt {\n",
        "    font-size: 120%; \n",
        "}*/\n",
        "\n",
        ".CodeMirror-lines {\n",
        "    font-size: 110%; \n",
        "}\n",
        "\n",
        "/*.output_area {\n",
        "    font-size: 120%; \n",
        "}*/\n",
        "\n",
        "/*#notebook {\n",
        "    background-image: url('files/images/witewall_3.png');\n",
        "}*/\n",
        "\n",
        "h1.bigtitle {\n",
        "    margin: 4cm 1cm 4cm 1cm;\n",
        "    font-size: 300%;\n",
        "}\n",
        "\n",
        "h3.point {\n",
        "    font-size: 200%;\n",
        "    text-align: center;\n",
        "    margin: 2em 0em 2em 0em;\n",
        "    #26465D\n",
        "}\n",
        "\n",
        ".logo {\n",
        "    margin: 20px 0 20px 0;\n",
        "}\n",
        "\n",
        "a.anchor-link {\n",
        "    display: none;\n",
        "}\n",
        "\n",
        "h1.title { \n",
        "    font-size: 250%;\n",
        "}\n",
        "\n",
        "</style>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x1112663d0>"
       ]
      }
     ],
     "prompt_number": 317
    }
   ],
   "metadata": {}
  }
 ]
}