From 13dfbad44056add17d66f76fc5f1c0aff4f12536 Mon Sep 17 00:00:00 2001
From: Donne Martin <donne.martin@gmail.com>
Date: Tue, 14 Apr 2015 14:19:07 -0400
Subject: [PATCH] Updated notebook to v3.

---
 numpy/numpy.ipynb | 1368 +++++++++++++++++++++++----------------------
 1 file changed, 708 insertions(+), 660 deletions(-)

diff --git a/numpy/numpy.ipynb b/numpy/numpy.ipynb
index 85cc682..02e1e62 100644
--- a/numpy/numpy.ipynb
+++ b/numpy/numpy.ipynb
@@ -1,667 +1,715 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:3968461b55764bd352e164c8cf911ac5515f1616c26827bf2eadf78953616464"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NumPy\n",
+    "\n",
+    "* NumPy Arrays, dtype, and shape\n",
+    "* Common Array Operations\n",
+    "* Reshape and Update In-Place\n",
+    "* Combine Arrays\n",
+    "* Create Sample Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NumPy Arrays, dtypes, and shapes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# NumPy\n",
-      "\n",
-      "* NumPy Arrays, dtype, and shape\n",
-      "* Common Array Operations\n",
-      "* Reshape and Update In-Place\n",
-      "* Combine Arrays\n",
-      "* Create Sample Data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3]\n",
+      "(3,)\n",
+      "int64\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## NumPy Arrays, dtypes, and shapes"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = np.array([1, 2, 3])\n",
-      "print(a)\n",
-      "print(a.shape)\n",
-      "print(a.dtype)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[1 2 3]\n",
-        "(3,)\n",
-        "int64\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = np.array([[0, 2, 4], [1, 3, 5]])\n",
-      "print(b)\n",
-      "print(b.shape)\n",
-      "print(b.dtype)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[0 2 4]\n",
-        " [1 3 5]]\n",
-        "(2, 3)\n",
-        "int64\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "np.zeros(5)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "array([ 0.,  0.,  0.,  0.,  0.])"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "np.ones(shape=(3, 4), dtype=np.int32)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 5,
-       "text": [
-        "array([[1, 1, 1, 1],\n",
-        "       [1, 1, 1, 1],\n",
-        "       [1, 1, 1, 1]], dtype=int32)"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Common Array Operations"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c = b * 0.5\n",
-      "print(c)\n",
-      "print(c.shape)\n",
-      "print(c.dtype)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 0.   1.   2. ]\n",
-        " [ 0.5  1.5  2.5]]\n",
-        "(2, 3)\n",
-        "float64\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d = a + c\n",
-      "print(d)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 1.   3.   5. ]\n",
-        " [ 1.5  3.5  5.5]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d[0]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 8,
-       "text": [
-        "array([ 1.,  3.,  5.])"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d[0, 0]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 9,
-       "text": [
-        "1.0"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d[:, 0]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "array([ 1. ,  1.5])"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d.sum()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 11,
-       "text": [
-        "19.5"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d.mean()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 12,
-       "text": [
-        "3.25"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d.sum(axis=0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 13,
-       "text": [
-        "array([  2.5,   6.5,  10.5])"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d.mean(axis=1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 14,
-       "text": [
-        "array([ 3. ,  3.5])"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Reshape and Update In-Place"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "e = np.arange(12)\n",
-      "print(e)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[ 0  1  2  3  4  5  6  7  8  9 10 11]\n"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# f is a view of contents of e\n",
-      "f = e.reshape(3, 4)\n",
-      "print(f)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 0  1  2  3]\n",
-        " [ 4  5  6  7]\n",
-        " [ 8  9 10 11]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Set last five values of e to zero\n",
-      "e[5:] = 0\n",
-      "print(e)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[0 1 2 3 4 0 0 0 0 0 0 0]\n"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# f is also updated\n",
-      "f"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 18,
-       "text": [
-        "array([[0, 1, 2, 3],\n",
-        "       [4, 0, 0, 0],\n",
-        "       [0, 0, 0, 0]])"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# OWNDATA shows f does not own its data\n",
-      "f.flags"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 19,
-       "text": [
-        "  C_CONTIGUOUS : True\n",
-        "  F_CONTIGUOUS : False\n",
-        "  OWNDATA : False\n",
-        "  WRITEABLE : True\n",
-        "  ALIGNED : True\n",
-        "  UPDATEIFCOPY : False"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Combine Arrays"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 20,
-       "text": [
-        "array([1, 2, 3])"
-       ]
-      }
-     ],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 21,
-       "text": [
-        "array([[0, 2, 4],\n",
-        "       [1, 3, 5]])"
-       ]
-      }
-     ],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 22,
-       "text": [
-        "array([[ 1. ,  3. ,  5. ],\n",
-        "       [ 1.5,  3.5,  5.5]])"
-       ]
-      }
-     ],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "np.concatenate([a, a, a])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 23,
-       "text": [
-        "array([1, 2, 3, 1, 2, 3, 1, 2, 3])"
-       ]
-      }
-     ],
-     "prompt_number": 23
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Use broadcasting when needed to do this automatically\n",
-      "np.vstack([a, b, d])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 24,
-       "text": [
-        "array([[ 1. ,  2. ,  3. ],\n",
-        "       [ 0. ,  2. ,  4. ],\n",
-        "       [ 1. ,  3. ,  5. ],\n",
-        "       [ 1. ,  3. ,  5. ],\n",
-        "       [ 1.5,  3.5,  5.5]])"
-       ]
-      }
-     ],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# In machine learning, useful to enrich or \n",
-      "# add new/concatenate features with hstack\n",
-      "np.hstack([b, d])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 25,
-       "text": [
-        "array([[ 0. ,  2. ,  4. ,  1. ,  3. ,  5. ],\n",
-        "       [ 1. ,  3. ,  5. ,  1.5,  3.5,  5.5]])"
-       ]
-      }
-     ],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Create Sample Data"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import pylab as plt\n",
-      "import seaborn\n",
-      "\n",
-      "seaborn.set()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Create evenly spaced numbers over the specified interval\n",
-      "x = np.linspace(0, 2, 10)\n",
-      "plt.plot(x, 'o-');\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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9oogx/gToSnqOQsUYj8YYX899fYbsH50rk51q/GKMPbkvp5H9hT2R4DgTFkJY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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10bc25110>"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "np.random.normal?"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Create sample data, add some noise\n",
-      "x = np.random.uniform(1, 100, 1000)\n",
-      "y = np.log(x) + np.random.normal(0, .3, 1000)\n",
-      "\n",
-      "plt.scatter(x, y)\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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Cv6+vCqNIWorU7j6PtAX95l+L87l25w07Hmi4lV9zxmyJPGdj69eO79SVdmpU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\nqrkAAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10bd30110>"
-       ]
-      }
-     ],
-     "prompt_number": 6
     }
    ],
-   "metadata": {}
+   "source": [
+    "a = np.array([1, 2, 3])\n",
+    "print(a)\n",
+    "print(a.shape)\n",
+    "print(a.dtype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0 2 4]\n",
+      " [1 3 5]]\n",
+      "(2, 3)\n",
+      "int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "b = np.array([[0, 2, 4], [1, 3, 5]])\n",
+    "print(b)\n",
+    "print(b.shape)\n",
+    "print(b.dtype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.,  0.,  0.,  0.,  0.])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.zeros(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 1, 1, 1],\n",
+       "       [1, 1, 1, 1],\n",
+       "       [1, 1, 1, 1]], dtype=int32)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.ones(shape=(3, 4), dtype=np.int32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Common Array Operations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.   1.   2. ]\n",
+      " [ 0.5  1.5  2.5]]\n",
+      "(2, 3)\n",
+      "float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "c = b * 0.5\n",
+    "print(c)\n",
+    "print(c.shape)\n",
+    "print(c.dtype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1.   3.   5. ]\n",
+      " [ 1.5  3.5  5.5]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "d = a + c\n",
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.,  3.,  5.])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d[0, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1. ,  1.5])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d[:, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19.5"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.25"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  2.5,   6.5,  10.5])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d.sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 3. ,  3.5])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d.mean(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reshape and Update In-Place"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0  1  2  3  4  5  6  7  8  9 10 11]\n"
+     ]
+    }
+   ],
+   "source": [
+    "e = np.arange(12)\n",
+    "print(e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0  1  2  3]\n",
+      " [ 4  5  6  7]\n",
+      " [ 8  9 10 11]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# f is a view of contents of e\n",
+    "f = e.reshape(3, 4)\n",
+    "print(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 2 3 4 0 0 0 0 0 0 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set last five values of e to zero\n",
+    "e[5:] = 0\n",
+    "print(e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1, 2, 3],\n",
+       "       [4, 0, 0, 0],\n",
+       "       [0, 0, 0, 0]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# f is also updated\n",
+    "f"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "  C_CONTIGUOUS : True\n",
+       "  F_CONTIGUOUS : False\n",
+       "  OWNDATA : False\n",
+       "  WRITEABLE : True\n",
+       "  ALIGNED : True\n",
+       "  UPDATEIFCOPY : False"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# OWNDATA shows f does not own its data\n",
+    "f.flags"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Combine Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 2, 4],\n",
+       "       [1, 3, 5]])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1. ,  3. ,  5. ],\n",
+       "       [ 1.5,  3.5,  5.5]])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 1, 2, 3, 1, 2, 3])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.concatenate([a, a, a])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1. ,  2. ,  3. ],\n",
+       "       [ 0. ,  2. ,  4. ],\n",
+       "       [ 1. ,  3. ,  5. ],\n",
+       "       [ 1. ,  3. ,  5. ],\n",
+       "       [ 1.5,  3.5,  5.5]])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Use broadcasting when needed to do this automatically\n",
+    "np.vstack([a, b, d])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0. ,  2. ,  4. ,  1. ,  3. ,  5. ],\n",
+       "       [ 1. ,  3. ,  5. ,  1.5,  3.5,  5.5]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# In machine learning, useful to enrich or \n",
+    "# add new/concatenate features with hstack\n",
+    "np.hstack([b, d])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Sample Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "import pylab as plt\n",
+    "import seaborn\n",
+    "\n",
+    "seaborn.set()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DPfSffqKbbyjS/HlB6iwBAEiYKYxrJe2XJGvtr4wxt4/aVyXpbWvtBUkyxrwp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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10bac8d50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create evenly spaced numbers over the specified interval\n",
+    "x = np.linspace(0, 2, 10)\n",
+    "plt.plot(x, 'o-');\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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U1BsIuUfV++uKxVJJVlaaj6FITh6OxVLiM5aamhtJTw/ijjtsTWYNGw31CGA2qpcorn0I\niMVmm9Wsp+xP7lLMtemXgBkz4rHZZhES8joQh/BmjYSHf89vfjMHNfwNlUA6Yu33YaALUI0Icb8C\nBBIe/gQZGWuIijqmfA6iDjsAsd58C+XlZ/Hmm++SnDycnJxPlYQxMc7//Gcq+fnlfucpaRqZ1CWR\nnCZao6bUnnN2dPKVdyawt3Ribu477N79BYMGhWMydcNkMjFliiYv2ZpGB3pECDYd4fHdjWpI1eQq\nQTGaJ3gbNTVjWLduAxs31nnuZ3T0cjIzC3G5TOTk/AWHYxgOx/2kpvrWBZvNZrZufQCr9SmP9KMQ\n5bgd+AclJWnExa3HavWdh+bt1yK8y7kABAbO5vjxy4E7aG3DhdYqmHk3vairm0FiYhZDhvSjoGA5\npaUiGzsiYiHnnXcR1dW9EQb1U+ABtEQsgC+BmWjZ1Gncc88GJk2KZcIEp07Jywk8odtvPDbbNIKC\neuhC5pKTQRpkieQ04dtJ6OS645yMgW/JkPszFE6nU1cyMw2bbQ1wJwCFhZpSU3MGvXl+gwhJ9zNk\nGCcnD2fJkoU4HLcY9hZlOpq6VmnpBP7whwJMpm44HJm0dJ9DQ0MpKZlETMxsHI5ohDH+fwgP3Vff\nWZ1HcvJw1q9/g7Ky/WihXTh+fA5iPfmOVszViPqiU1b2KRERn1NVJbK0fV8CNNSOUPAOffpkMWTI\nJUAE6em3IxK93gQmAPMRQh7XILziHn7HkJ29DYCUlJtISnIyePA8jhwx7nPiRCNHjhxB1EmrL1DQ\nr1/zJVoS/0iDLJF0CpzAFkpKPmm3Z9teA99eQ56Xt0NXMrMNfSMEb6Umb11k9WHvb64JCUOZN2+R\n0mM3jpCQLJKSUj2fq95sTIxRf3nIkH7YbC3cpBYIDQ3lvfems2LFFp5+ehbHjj0DdCUkZAEJCal+\nX0zMZjPx8WHKvfDmNwh96j97xukdHfB+GQJ0LzpPAk4slr8wceJVpKSIY12uBiIinvMYalhFfv6P\nvP32OsrLJwHwzTfLlWQ1EKH/PyCS1uYCTrp0ycLtnoP47c1BhNohPPw58vOD2LXrAUD8HuLiQjhy\n5Dn0RhdeoKLiOerq5gDHEKpim4Ay7rtvsFw/bgfSIEskpwktlJuMUGRKxWaL5eDBkw9dtwa9zKJe\nAMLbkHdEGNxXmvLPgJMlSzKZOHGwoddvfn65Yoy1cGx+vvHFQhjOCTpBjTGsW/e+rjOT2WD8mgqZ\nq+M6cuQnPvjgKwICAsjKSiMoKIhjx7IQWdpQV/eQQbaytraWmTOzAdF+UHAjwuOcqvx9NfA4sI3E\nxCys1gE+0QFvneglSzIZONBMWdlARD2xqgo2D5NJzFW/v8h+jgLuZteuLWg62CJCMHr0Gl2Y/e8I\nectG4BXc7r+iJX/NQHjPiVRXT6G6+i1lPzMlJWn07PkUor5aTQxzAf2BYKqrr0XUb+9QzvU4QUH/\naPb3IPGPNMgSyWlCDeVOnbrI0MigvaFrf2u1CQlj/Hqjvl2GvAUg8LOfMGw5OWPIzy/H5Wpg6NCv\nKS/PQXhHWhekESNWk5x8q+ccwuP7P+AzRBi3EViHw/EU6emwcaN2XpE1PBx4X9n/Ilwu3zpa/4Ia\nY7FYnmfixL6kpGjGz1+oWTvuTmANItEJ3nnnGYYPP4Z4PMYo96TBc93a2lqGDMmhrm4WANu3ZzFl\nSm/lHOGItdpAhPHqSnj4+2RlPUJ+fjl5eTsM34MW1dDuh8MBYq32BGJ991LgRkpKPsHlchmSvIRn\nW+Tzvans3VvFmjWTmTo1C5utK8JwrgMu876biBC9CbG+nKacV+QClJTU6L7fmxHZ16qnfCMWy6uG\naEVa2njq66V0ZluRBlkiOY2YzWas1gEnHWpVz2WUYRxDaupmjzHdsEGEME2mbrhcDYbwtnjQ2oFA\nLJZSEhJEuNJfGFyssT4FiJZ7GRnd2Lt3kZLUtUFJ6rrL80AWa6Em1OxcWAn0QkvKgpKSZGJiliiy\nk6Po2vVZTpx4EmEQVvL228dISqpVXgRcgBuTqRvJycN9xuhwPIzJVOAxuk1599pxxnB7ff1MCgs3\nK2MVLxtWa57Hq545M1sxxpoHv3HjNDTpyAhE9jLAKi67zKz7HoxRAZerARHmVZOt1C5PEcAPiFpi\ngGew2SazZ89CvJXAhLfaQHj4R1RXf4swpgDLaWxsJC9vB4MGhWOzJSJkNecgXgD04Wctgc37vBbL\nCzgcWcrYioCjiNplIelpteaRk3MX+fkFynxCyM7eTmzsUBm2biPSIEskp5m2ZiG3ljfffNdgqEpL\nJ1BaKgxNRMRziAe/ybN/WNgOamoW4nDEkpoqwub+EBm14riyskmMHVvA669PM+xjNps9BrmsTC2z\n0bJ4hWGI0x1RrNOA3qYYY23/8vL1xMS8qewDYl32PJYsWUhq6gC/42xJdnPnzgqa7ogUiKpUFR8/\nn6ioS8nNfQfowldffeOz94kTjYqX+H+IUqwi5ZNxfPPNLPbuNXrB6elgt7+B291Fdx/mI7Kd3wf0\ncpYgwuCv4nBcZVg7Hjr0dXr3riEgoIL09ETGjs2muvpZwEJw8P+w29Ox26F79wzE2vFVyvm0cjJR\nKjUFeAkRvWhg6NBl3HbbbzCZCnC5+pKeblbGEgvU0bNnIddeO5+oqEiSkkRk48iRI6xcuZfq6uuA\n4Vit7e1ydeYiDbJEcpppfxayEW8DJLr9GI2uamjEA/2vCG9JlMdUVflmInu/LAhv6f52zNLI5Zd/\nzU8/6ZOSylDDo/75Bw7HIjQP0gTE43DEk539HFFRb1BWJpKZ9JnI/pLckpOHK/dpOqoHrA+3i3Ds\n7Yha4o/ZudOB3f4AYAP+BMQSEPAUjY2iPCogYB4VFfMA+M1vHqG+fr6SYQ0hIQtITIxi714QspRa\nVKCs7AK0EHIKwgA/D1gQQh0qTkSOgbhXbncWmZkbADcFBSbs9unANjZtelVZ9zbTrdtk6utf81zr\n2LG/IkLpHwMLEWvJXYFvCQ//nj/96W3gQvbuXaRkaycBKNGFLrr768RsXsjhw29gt8PevVksXVrl\naVohkrtuROty1Xy7SIkRaZAlkk5Aa+pPW0qu8jZATudfEUb3cUTdbikwS3dEFGqCzpVXHqWqyvcl\nQNVvnjkzC4C5c5O555632uTNR0VFYrcbDV5ERC/27atCNCMYgPAMlwF9EOu1zyjbxP6BgV9z/Lh6\nRr3esmiUcOWV85U63EsMa8f+MN6nuxHqVvuBpzn//M/4+usohAEUxvfw4RxlnOoLSyONjVcSEnIH\nF14YxscfP41Yd32T779fATjp0eMhIiIaSUq6lrvuupGtW1dSUnKen9Ho66kBHiY2di4VFT+yb18B\nMFrZrs23unqGJ8GrtPSPqAb92DGtqURDw61+rhUIPIp40VCbQIynurorQUHGnAWn08ntt6+jvDwF\nKKZvXwcZGW+xd+8+bDatH3J1tapdrV/6KKKlLlcS/0ilLonkF0D7Gzy4gCWIh+ZTCENTh/ACRyvb\nRT2vxeIrQ+mt33zPPe965BJVNamWvPmUlJu4+uofEUbgWXr0+IrCwguoqHgOITW5C+EFBijjSVTG\nbUc81I9w/HiwEnatA/b4XMNu/x022ywKCmo923x7DS9VkqL0GthmZf6DsVjMSu1ub0Q99Tblv3GA\nWj6kakXfRl3dWr788gJlu977Deann17i44/PJSPjD6SmbiYnZwyZmUcN9zg8/CPAVx2spOQw+/Zd\ngfjeblPG0BR6g66+YGxHy/hWJTBXI7xx6Nnz74jQdNNNIFas2KIY43XALezb9zQrVx5gyJDWNcoA\ntcuV1LJuCwEZGRmn8vwZR440tLzXr5QePboj5y/n3xGsXl3MypXJiIduAPv3D6RPnyIuvfQCVq8u\nZs+eL0lIGMru3evYv38g0IjZPIfjx4cgPD1xHAxENBboDvweaCQkZAEffvgItbVXY7HM5bHHvuep\np27BbDb7ve6FF75LWtooBg++mMBA/0E2de5Op5MVKzazdet+jh59AhiGy/UBwnDYEKHhGxD1tjPR\nxjkMYZBDga+BZ2lsHExg4LOcOPEo8DeEMW9EvFz8AXCzf/9BvvyygFGjxFqp03mAc84pIDl5P19+\n2ciqVX/ks8+GEBKykGPHooFGevacC7zPd98No7JyPIGBizhx4p+IxKrrES8x5yIMYy1aNnIAx48P\nIyzsYY4e7YcoAwpQ7kAjIvlpP/v3j6V3721MmhRHUtIl7N//Cpdd9j6jR4fz97+PB14DrlaOmc3R\no9cA2j0XcpzzgWuBRiyWF1i4MIErrggnP38ttbU3el23EviXco83ExS0GJdLLWtbRWRkD8LDP+bA\ngcFAI1ZrNnPmjDZ8l088sZJDhwIMc/3hhyjOOuv/ERb2Jfv3X6lcayewV/ldqd/FLfTrt5gtW/7M\n2Wef7ff38WunR4/uc9pznAxZSyS/UFwul2HNeP36NxgzJpjevUXo1uWKJCOjm58jLycq6gDx8RvY\nvfs/2GzTUKUUHY4nKCt7jrKyz5R9T+Av8ak1tclOp5Nx49ZTWtoHITqhhjUfQXjLM3TbbvQzzq+V\n66sZzeUcP64qYYmQ6AUX/D8OHFii7P8mkILdHsu33y7H7W6krGw8UMx7723g8OF4hFEdSV3deM4/\nP43a2uMcPnwpkK2c43WOH78QyFD+nguMIyjoEY4cuQL4EO+17mHDLiQq6ijPP+9PdrMYgGXL9pCU\nNEzJthbLBl9//QZDh+ZRXh6u3I8fgEGIZhB6zMCVwLNYLPVs3fqA5357C6T07DmX+vqvcbleBMwE\nB39KcPAJjhxZjDDak9mzJ4gnn1xNfPx6du/+QokKGAkPP5e9e0tRoycqhYWN9O37DbGxc/nkkwYc\nDnW+f0EofnUFtvHQQ/0JDQ31Oa+kebq43e5TeX73d9/Vn8rzd2p69QpGzl/OvyPQErbSALF2GxcX\nrMgiqolOqxFrjWC1irrelBS7ovgk1m8jIhYyaVIEKSk3YTabyc7exowZ8bpzrEQ8uP+kXHklwcEH\nqK9P91w3J8dYTmW1+gqZ9OoVzLPPrlfOvQ0YhWZ8GxCZyEt02+oQyUbpyt9XISQnX1X2DQYKfc4T\nHz8fu/3/EB5sLMZrvA3UA0mIGmFV7WsFwtirxiQHYTzfR4TIX/Q6z7PExjr58MPv+e9/eyNC2uJ+\ndu8+h4qKyYSGhlJbW0tMzGIcjmsQLxhrEaH3rsCdJCYa682hjoiIV3QJUdnKWN9CeOJqwlsOcAfx\n8c/z8ssP+Lz8iOu+omS/38jQobn07n2QgAATAwb0JjPzS+W+FiOS56YxcOCTnH3275v8Dmtra7nq\nqtf48ccQtBKulYhQfjHQSFTUQUWh7BPlO1B/Mzm89FIYycn++zefCfTqFdyl5b18kWvIEskvAH+d\ngETLQBVjY/mSkjRmzswmNzeezMweJCZmkZGxhkmTIgzHGddZtwDno4W4TUAa9fUDGTRoGpmZG5rt\nQATiQX7vvS9y553PcOTIj8pVRqJvoRgRsZCZM68iMHCusq0O4QWfQIRmNyM8YNGNKCjoUWW/6wgJ\nyUK/zv3cc39WOhL5djmCzxHrq2pPZHVOf0KU/6h/H4doCjFKmb83V1BYOJuffgpFlB+pCUvP0q/f\nIY8RCw0NZeLE3yG6I/1D2e/PwG/xv1ZbbOiQJMb4PqLO+KByHzYDx4G/ERUV6TcSkZ9frtSF3wYE\nU14+kWuvHcSiRZPZuPHfCGMs1oJFVv1LHD9+rNmWmKGhoXzwwWQGDvwSsd78DKBPSvsXZWUpmEwm\noqIGYPzNjEfrECVpCzJkLZGcZvTh34SEoaxbt9MTStRLSnpnYhtLknxVkWy2Kzl4cLOnLeGyZfs8\ndbz6mtw1axLJzV3P2rX/YO/eC/AOU0Ige/fGEBR0iJQU/3NwuVw+ClbBwc8wZMhidu9+EOGB/oWe\nPb/j8ssv4NNPj3L8+AyE1/sFsEg50xyEd6kZnvT0GzxZxQkJqeTna+VhAPHxYfz2t9+xd28W1dUz\nAIiOXsE33/yP6uqm7vpHaCpcxWh9kB9AE/gATZHKxOHDsxEvDJcD/wYe5+OPzdxxh3YvxcuOvn5Y\n5BBYrdkpGO/VAAAgAElEQVTMnZvMnj1PKo0rbsRiKcXh8C71chEWNo+amoVe57BjMvk3cv7aQqrL\nGXv3xuCbyT2bQ4cmNXVjPISGhrJ+/ZP8/vfZ1Ner2flZiHX9WajheJPJ5HOsv22SlpEh61OIDNn+\nOuffWm3n1szfu3Y4OPgZ6uvPBSYCMHToa9x2Wy9MJpPfa/lqRE9AhJ6fAYYiHvzzlXCm0VAsWFDg\nR3rSiQjZaiVHIlknFejKggUFJCQM9QnNRkUd47e//Q67Pd1wjYEDHyI+/mrs9nIOHDBx+PDTCCM4\nD1GCswPvMHTPntM5fHghIAxZU5nc3vcuOlpTIktIGMrdd+dTXn4WwgNei7HO+A5EGHucMhatBhu+\nZeDA2XTt2pW9e7PQWhXWIcLsqjiJKDFS74vxXqYBom574sS+JCVdy/jxhYqGN0REPEdBwe3cc8+7\nPvtCF9LTxxruicUym/fem+6jPpaQMNRnWSI6egW33nqOrsvTHPRdqISB34BYD1d/I0t4770JBmlV\nVedcn2Mgjt0E3OIZE+CznFJcfGZLZ7Y3ZC09ZMkZT1uaJ7SmM5J6vuBgc4vygd61w/X1MxFhSrGm\nW15+FuXlY5u8lt5rTklx8uCD87Dbe6AKfkAODsfvae6funEMJmAqAwdOAbpQUTESuBVVz9nlaiA1\ndbPSztCJWIe9lrKyGAYNmuVz7oqK3lRXf0Nd3cvKFtWIpaM1RjAyffowj0fcnEiK971T2y2mpY0i\nO3sb5eWTEQbJDuwiJKSQuro0NM3u8Yhkqt9jFAaxM3bsNUyYMIY77tBqrsVLzhw0w3Y3oub2ZkOX\nrjVrEiksLKK+3klCwl3k55fz6KPLKS3VXlaqqqZjt28w1HhnZaURGhqK0+lk40ajGIuayOX9+1uy\n5EklXN2IWlOuvpRoXIAQHBFrweHhz1Fd/Ru038jzpKb2BkTLRWMDEP865/oxgX+t8DPZILcXaZAl\nZzRtbT3oT/0pN1c0c/d3PjW5Kj+/HGhrtySjAEZrmttHRQ3Abtd7V+OBArR13LuVcTUn6GEmJWWU\nTtFK1SzOBkIMzRDgOeWYHM47z0xg4GyPSpUwdpdTV5eAPyMm1nEbEB6qSOaKiFhIUtLd7c7QVWuM\ntTCuE/gP8DJ1dcKzE2MvRIT5LwLOVrYXIkqG+vHBBx9y//1q848sbLYrEd6kNy5gFTbbNA4e1KQi\nJ0+O5b///U7XVMM3hFtW9hkbN9Z5sq737HmerVvvIjQ01MvAaZ6rr253tHI2MyKprQGTSa+wdh5w\nL3qDfcUVR6iufkw3poeBPN3vdhNauRNoLy6XEx7+EffcczEpKROafDGUtB+Z1CU5o/H2Dr2TW1rD\n0qX/8oh0+DtfTMwrTQp6eItXBAQ8hcgAbkDUsZ48FssuxD/127FYZnuSs9QHqvcYRJeooeTl7SAm\n5izi4+eRmJhFTs4Y3dqgKoTRiMiiPpeioq4cP/4EwsN/FJHQFORnRMKICX9gNJdffoCgoIlAAVVV\nD5KaurlF0ROn04nL5cJieRIRSm4gJGQBNts0ZsyIx26vITp6OfAKWnlVDEINbDUiTB5LQMAniBra\n1UANIrP5Nj788CycTidms5lFiyZjtR5ChOdzPPcpLGyeMheRgCZezrZ7xiiaanRHGPuHEOuvqlDH\nKhobjxt+Kw7Hw8TEvOK5blraKNLSRrXwAnejTmykDotltudlZM2aRBITK5T9VIN9C926dfc5y969\n1bqx+PPThCJXly4Bfj6TdBRSGOQUIoUxOv/89+z5kqKiSPTCCjff/DmDB1/sd/9LL70Am+1lamuj\nEMboBWprwzj33EP8/vf9/Z6vttaJkIfUBD3U8wcGBpKYeAl9+hRx9tn5fPLJdEQIVe0A9CWqAEZ0\n9ArmzBnTpBiH0+lk3rzdHDjwkeGYDRv+yIUXvsvNN3/FwoV3EhU1gOPHj3sERa64IpykpMs499xN\nnHOOnWHDwnj22U9YufJO/v73K6isrOKzzyaye7eNxx+/XhEfOQvoi1iH/QNCFOND4Brl2iOBl4Fg\nzj57Aw0N1yj3axHQExEG/4KQkEL2738al+s2oBwYzP79v/PcI6fT6RnnpZdeQGBgoCcKkZNzF7W1\nN2KxLGb48O18+OEjiLXOAA4cGMx9931FTc1HHDo0Rvk+AoFqxPqxELtwu69l4MAnOH58H0ePPunZ\nXlsb5RmD9h29y/XXBzBy5GeMHv0FUVE9KS7+A5rudCMOx1skJV3JunU7ycl5h0OH1DKn7gipynsQ\nGcj1XHbZUSorh3v9Vo7Sp09Vs7+/0tK3POIvVutq1q//A717b+Wrr4qoqvoLxcUDKCn5G0lJAxgz\nZojX/tm88kqSQUDGas3m+uvPp7h4gDKWvohoirfwSndqa6MoLq6mtHQ3iYmXtCgMc6YihUEkknbQ\nXKclf2vLZrOZyy+vp6rqr0A0okZ2DWVlnzJpUlwzzRicCK/SpbSo09CH+2w2tatON0RilXFtEPDb\n3xiEd15Wdo/PMaGhoT46xf56HIvw6TRstpfQHsYganxfoaRkAjNnvk5c3CXExPzA4sXpXtnAD6Pv\noSvOcQsmUzEibP4vRHaumR49nmTkyO7Y7fquTncjwsZdKCn5hISEoYZ6Z3U5wV+7xZCQaYisXzVz\nGkymbqxb9zhDhmRRVyeypgMDt3L8+G2G+2+x9CY5+VLS02kSfyFZp9PJsmWaKAesxuF4gpiY+cq6\nrreXb0bUGX+OeClYRliYr5iIy1XY5HfcdCOSLlRVPYgqs1laehe5uZuYNCnW7/7e2wDdunVXhOdf\nQFjYJmpqXsRYthXY7p7dkuaRBlnyi6ctSVneNPWAa25tOSCgG1o7QSdwLg7Hbk+oUT1fcLCZa6/9\nA7feOpeqqkBE03kzBQXLSUlx+ozTt4zJichCBhEuLWzlerdxPdEbf+vgM2dmUVIyDchDK/lZCBxG\nyFqOBBYpmtZibXzKlKvxDbCJHrr6cqHvv1c1s2NRX0oiImq49tox2O36Y50IY/UINlusUiL0FN5r\n6P7KfPbuHYnWvzgei+V5XK7BmM1mdu9O9SROpadP5YYbnlGS5wBWcfBgGElJwwyJVK1pmmE2m5k4\nsS/p6ZsRj1KRca21pxS9nLX+xKuVfcbQvfs07PZFQC3du0/j2LFRwO1EReVSUBDgycZuKadB5Z//\n/Aj4EU38JIeysn1MmhTr92XC3zbjevmfADM1NTcrbSXVl45FQMslU5L2IUPWpxAZtjn181cN58qV\nyRQVRVJS8jd+/HEfFRVVnhBnSwQGBjJ48MUGbeamtKMHD76YQ4e+V8J7LoRc4+18++0tlJa+RWLi\nJZjNZgYPvpioqH7cfvvbfPTRowi95reAK9i//ypD2Fo/DjV8PWxYPf/611YaGlKBiwkOXkjXrt9Q\nXDzN75jAXzjTV6MY/IfpL7vsfT77rA59OFckMR0DdgPfABMM1w4Jyed//9vB0aPDEN50DmBi4MBX\n+fbbhxFSiiDENhYjwtkXAyUMHXqcWbPuMIw3LGwWR49moIWNf8RbH/r66z8G3BQX78IYUu2BqA/u\nR1jYi3z9dQbFxQMoLX2LO+8cSGLitdx882AKCj4A9vOf/xwBqoAEDhwYyP79r3P99eczcuQnjB79\nJXPmiC5LK1du5Y03NnHw4P+4/PK+hlB/REQYn3yyD4fjS2pr/wh0wWJ5gdracYgQdSBwCYMGPcqh\nQ25E0wwzoluUG7GM0YPGxutITLQzYUI9l1zSnVWr/tjkd+z9ey8tfYsxYy4kI8NuCLnDFURG7iQ+\n/ho/v3j/BAYGUlNTR1FRHFoYvgvTplVTXZ1Hbe0xhIe/jqiocubOvUWGrJugvSFrmdQl+UXjnURV\nWjqB9PSgNnZEahspKSOVhKEt6DvteCeEZWdv12Ukb0OsnW5u9tyq5xIUdLYixiDOXV8/k8JC32Qc\n72NVNa/MzA3ExQWTl7fD5x74S+LKykqjZ89CP2cNRHi6xT6f2O2/p6bmMUT50iblXiSQnDyCESPW\noalwpQPfItr+bQJcXH11f894MzPXk5iYxbBhPb2ucCOiXEdLhAK3UtJzByI8XoQozalEhLu3UVOj\nGibtO9F3yyosvBrhTauh9bXYbLNITx/LsmX7SEgQ2dTjxq0nPX2s8tlPjB37N8aNW+9J0BsyJIf0\n9Dgcjvs9yXJbt96F1fqW7t7msW7d40pSmMhWt1hewKjdbcZqHUBa2qgWBTX8JQ3OnJnN4cNxPvtG\nRV2K0+kkO3sb2dnbWvVvwfjbEEliH3zwFQ7Hk6hKYDCe+PiwdvXsljRPuwxyZGTkeZGRkf+NjIzs\n39EDkkhOnkDamzGt4s9oqa3kzGYza9eO1WWwNofark9k9UIlUVFLDW3pWv/Q/BF9lm909Aqf8wiR\nEBd2ew3p6XHMmBHI8OELePVVm+f8/mQ4Q0NDsVrPN5xf37JPdD3Sf7YKsV4bjFbP2hWrNZuUlJvY\nsuUu4uPnIYQ0nkfU8DpQlavATXb2NnJzt2O3H8Zmm4XdPp3u3ed5rhER8QrCK1cN7zhMpm4kJw8n\nKmoVIkJxlK5dX0RkR4+ie3ffF4eSkk+YOvU1nSGL0c1lC3pZTYfjYazWZ3nwwcVK2FiTg9y1q86w\nTaxL7wCCcTjmYjKZPCVLS5YUGdpTxsWFkJiYRWbmeh+jbbG84HkJaO531zyjEFrYmjxpUtKwFlt2\nev/29C9JFssSHI6nFLGXNejXxKUS16mhzWvIkZGRJuB14KeOH45E0ja8k6i0tcvW0dT6c3Nry+r+\nWVlpHDyoXtuJxTIfl2uwrmxlJAsWzFZENLROR6NGrTYoIvlbF/ad1wsIEQ4TasLW6NFBBsUmfQKU\nWLt8E5hAVdUtZGTkAInYbFqtrPca4jXXXE5hYS1CSONfwOOId3b9Pc3nggvsHDiQiGiAkA00Ehv7\nEyNGNHruk/+a6IcRxvkuFi9epniyIIxjIxDMsWPTgfuAW+jV6yx6916vqI8Z13VF+Y3wcE+cqFWO\nN3Ps2DOGRCm1FMro4ZuBcSQmZtHY2IjdbpSvrKm5Ebvd38vWPj/bfFHrkL/7rt7n+z14cCUpKWZy\ncsYQEzMbh8OKw3E/qana9+I/aQvl775JiOJ3+BYlJcnAJiyWUrZufcBLc9y3jr25PAmTqZsis6qv\nRRZ13K1ZX5e0j/YkdT2LeO19vIPHIpG0Gf0DTHiGxygr04QsmntweD+QNmzQpBdV49ya7OR169az\ndGkVDsdTpKdDQcFy1q4dS69ewQwcGITDYbzuq6++z6RJt2I2m/0mWKkPzTVrElmxYg3r1+/kiy+O\nc/SomoEtErZycmYrSU96xSb1AZqGpvgF4oFa1Gx2bErKTdjtaykrO4FYQ3wYuBDhfZoRHlIdBw6s\nVI54CngMMFNU9Ffg37hcLpKShrF+/Q527/6Pn7vej+7dn6GmZpHP2MS8zIgmBl3Ztet+MjPXM3r0\nW9hsZfTqdR4//FDL7Nl5lJbOavL4mpqBwGbCwrYrRj8YrQa5DwBRUQfIykojJcWOXsVKa5tYglG9\n63ngafTiKmbzHJzOmTT3W2vq+wX8JqypdcfNib80nTVdpGyb3qpwcnO/PX8kJlZgtTb6vCRIOo42\nGeTIyMg04LvKysptkZGRjyNbekg6Ad7ykU15F974k14sLd0M3OI3u9XfA0w0Ouii69ojzvPgg/MZ\nM+ZqrrrKgt2uf7CvoqYmhry8Hc2WjDidTlas2Mwzz1TidL4M1NKly5O43XMBoWjlcGhSjJpiU3MI\nHWK9zKMes9lMbm48VutSnfeajTBSf8YY3gXhsW8CRtPQcBGFhakUFsIzzzxDff00YDghIVrJkcXy\nAgMG/EBh4QXKcVqJkgi45SNqkR9GtEiMxeVq4Pnn/0td3SL27oXCwqdobBzkZ27e2d1mamrGIPwH\ntY3hCdRHltt9gnXrdiplYj8h+h8PRRjj19GiEYXAJ5x//kd8/bUZkSUtIhSzZkUSFKQawZ/PSLU2\na7q5kr6W8HfsokWTpSE+1bjd7lb/179///f69+//9/79+7/bv3//7/v371/av3//3zZzjETys3H0\n6FH3kiUb3UuWbHQfPXq0xf2XLNnohgY3uJX/jrlho+fPS5ZsbHH/JUs2upOTn/Zznnw3NLivu+4N\nt8WS4Qab8t9rbvjBc+7vv//e3a/f48pnP7hHjFju/v77790jRiz3nEM77w9umOeGfPdFFz3m89kl\nlyxQrv2DG2a64THlz8fc8KobXleOEeN66aUN7iVLNrq///57z3176SWbG+qU+7BROX6de+zYv7ov\nvPBuP/N83A0b/Gy3KcevcY8bN8/90ks298KFee5evWZ7xgBL3fCDOyzscTc87bX9b+4RI5a7x42b\n5+fcG9ywXPnzMWVec5V7U+fnexDnE//XrpGUNFd37qNusLl79ZrohjXK9qPKdcQx55wzz3M/R4xY\n3qrf2NGjR5Xv8pjhuKa2dzTN/ZtoaQxt/fckMdAm26r+1+5uT5GRke8C91ZWVn7enL3/NXb7aS2/\n1m5HreXnnL93x6Lw8DkMGnQ2UVGXGloY+j8mTdmieVf6bkhN7a92IsrNfYf09J8wdhPSzpORsYac\nnI89bfes1jzWrBGeij4EbrEILeP8/HJmzIhHZGarnZCcCA/1M0SrwAYl6UaEWqOilnLTTWZWrnyH\nr78ejNaR6Cm6dnVw4sTFaLXTILxJEQ0QXuxUwEx4+ONUV1+Kvp61b98P+f77XorX+xZaXW0OItt5\nMZo8pXruDNRkr/DwLPr0OZfS0j54d5wKC7uP++8fxlNPpRi2x8fP5+WXH2Dq1New2WZ5nbsIkWy2\nhbCwAi66aCC7dkUo29ahrXcvAqYAXbnggglKqN14jUOHelFWdoFyDw+wbNko0tP/xvvvH6amZjjC\ni1cTA68jMXERVuuAFuvd9b/9pvIUTqZ+Xs/JnKejxuCNfPbJbk+SMwz9w8TlatCVGL1JdfWlVFen\nYrdra7r+OjLFxQUTF7cBoFXrz3FxIfTuncWQIf1IShqjXL8LV1/9I7t2Pavs9RB6ZaOgoCDee2+6\nsm+RJ7yZnb3NR3FK7fUrUBtC3I5oH5iKWCcV2yZO7IvJVIDL1cDbb3chM3M8QlVLb/T+wokTs/Gv\niy2y0UVIWRi56upgjGHp8Zx99v3s2zdLubdutNKtE8r/9wDLERnREBCQTmPjVYgXipFUV19BdXUs\nqoqUnkceudlvxu611w7EbDaTlZXG9u1a2LtLl7m43TMQyWaHqal5gUce2URCwlGWLXsVh+M+RDi8\nDK2F5CZ69uzBgQPGa1x11cVs2fITanKY272UiRO3UVYmJLt69pzK4cO16F9Ohgzp12Z1qqbWhDui\nIUNbm6OcijFIOo52G+TKysobOnIgEklb8H4QCYF9VdmqN3qjVFo6wZOs4nQ6yc3dzrJle5Q1WDNW\nq3iIpaTQ5Pqz9/X+/e/nePvt/ykt/kQv3vj4w9jtj6H30iyWFzzdelr74DOu391OUNCDHDmyHM1I\n3o3FMpuUFJG8s3TpRsrLL0AYPH8Rr2jEGquWkOSbja6KnAz2ObqqSi1V2o4mDgLCw32UKVPiOHr0\nCIsXT+TIkUtobLwQ8RKBcs0Q5c++HadSUsRLT1NrnaGhoQalrQEDIsjMfBe9MhYImUzRS3gTAG+/\nfT7l5V0R69+pVFSMVHpNCyMdHb0Ck+kcXRkTlJdPRJ8Id/jwjYjaW33y2AY/9/f00dbELEnnRnrI\nkl8k/vSMLZbZSljYf7TIaFTHovbmLSlJY+rUrGZDkd7Xq6qaTlWV9vAuLZ1AZuYGvvtOKz/p1etd\nUlOvanIOTSXdqFmzubnr2b37CxobL/KSmISJE4XhXLq0kIUL/wnMR3jlK/CVa7wdeBfRICAD0bzC\nhSpUERKygLq6/mjdmzSjKbS4FyrbvIU74JFHriElZSQjRqxQXhpAK2MyK+dJB75HeJq3Exb2qHKc\n9tKjz5SHYKVrktuT8f7669MA8R2+885aJcy8hSFDvmLp0m+oqroWGIXV+hY5OWOw2+0YQ+km6uun\nkpiofs9jW1Gj7vt4lPW3klOJVOqS/GqYOHEwmZlHCQ+vwJ+AhrfKkTAWol2ezXblSat7mUwmRVRB\n1IJ+9908MjLuavKc/gQ61Frn3Nx3WLZsn0cwIzj4GcN8kpKGeZSkDh9+HuGVNyI0iD8nIEAN3d7O\nkCGr6NmzDPg7Yl13rLLfJhITs9i9O5XExEp1VAjPU3wmvM5QZZsbvXKW1ZpNUtIwpk59TVezKkQ0\n1PsqiEYY+yKgmEcesXo0lvX3Ijl5OBs31pGefjvp6WNJT/+JGTNG+dw/rQY5loqKH6iqmo2IiKyj\npCSZRx9dqvQgDsC3wYOGtwhHdPQKoqL2e/4eFXVAUWRrq0jHz0f7hUQknRHpIUtOKacqacSfd6l6\nXElJtTz88BL27JnA4MEWXnjhHsxms9JlaRPiZz8SrauN1gihqZBfcvJwlizRd/bJAY6jKk8Joz9W\nEVUw+dSY5uauVyQffQVI/CeO/RaRmGUCGqmvPxd17dbtbuTNN/9uCLeK8QvhBojmySePEBQkuj4l\nJ98BwIMPvuIjgqGOYdGiyTqRk65Yrd+yaJEIx9vtbygeaReuvro7CQkbMJlMJCSMUcRIBvj5hkQZ\nUnT0CtzuRmVt/joslvmAJp6ixzsK4a92Oi9vh2Hex46lo9Ug3w3k8/77KPfhFmABIrELQkJe8jTH\nUNdajTW9Y5VxqH8f5/X3zld/25KQiOSXhTTIklPGySacNEdTD6La2lpuvjmH6uqLgDkcOAA1NctZ\ntSqWgoIf0NY2cwgPP8igQQ3Kuq8qfLHFU6cLGF4mtm69S1FXigbuIDh4IfX14vpff/0ZDz74ClFR\nl+K7jutk2bJ9ihfp/z6oLy47d+6lpGQQohfyjQjDsx2YiGqEysoaOHLkISDZ6zplwDeEh9dgMl3s\nk9F76FAvxIvEONQkMZstloMHjcZJDRvn5e0gIWGoQRUrIGC5J2tdJKXdiVh71mqtw8Of46GH+tHY\nWOAxcrm565V7IMRTNm5s22/B5WpQrvcJWua5L2FhW6mpeU33+WPA/QwcGEhFxWL8rbV6v3y19Hc9\np+qFsy3IxKxfD+0ue2olsuzpDJ7/+vU7uO8+/cPTt5SoI3E6nYwYsVBpf2csr0lMzPIpn8nM3EBS\n0jBiYt5UsnPVTGZRqtOrVyi7d98LiKSttWtVD2oHJSWfYLNNBt4H/gn0Q4SBYejQpXTp4qasbBLg\nJCzsYWpq4tGEMIz3QXtxuROhGaxm9areXTHCIGpjP//8VKW8yagwJTKDX0KfrKYaT1FK1Qio2s/G\ne2EymTw62EIwA6UM6jr0UYUFCwpITh6ueNy/U+51I2prxczMozzxxJ2G3752/aZ/C/7L0MYRFZVL\nly5aS0JRpiUy2cX6t/izxfICqannk5GRbLgO2ElM/NTn+z/Z36L3C6f+fp/p//bl/GXZk+QMQt9I\nQU3+cbkaFGPsS2Njo9/tKSl2HI5+CCXYZ1Af2KJsSsuwLS2dQG7uBiZNiiUtbRQuVwM229sI49kF\n/QtAeflEMjLW8Mc/FvLCC5/jcLymXE0kkampG+ocSko+UXoRvwP0Qi0XgscIC7uPKVNGsGXLco+m\nM6zi66/fAOYiwthaxvHhwwkIqUjNC0xOHq54loGIl4KBPvdBZJ0/pfxNTcqC6upeytzU8d+Oy+Vi\n3Lj1lJZORyST3UJLPZiPHPkR8YIRAEwGgnz28ZZBhR6YTEW4XGGkp2ua2HV1MzzJWQkJqeTnq2pZ\n4v5436uoqGNeuuMtq1a1xvOVGc6SjkYaZMkpIy1tJKtWNf8QbM2Dz3sfMApqCAMSh8XyKqIsZwP6\nEOrQoa9z8GCYYVt09ApcrrMoK+uOaN8Xr3yuCnp8hCh58YyCtWt3YDKZlDF0QavX9f1nlJPzMQ8/\nHK1LdnIispSzCA+v58iRQSQlrVU80XiEPPwXCEM8HLXP8iOP3MykSbFMmODkwQfnKx6pOsYnleOE\nlxwRsZCqqgcNY965s4IlS3Z5SrzEHON9JC31MpyaNjRo69igllvBIN067kz0WtD+vuPa2loWLtwH\nzFK2ZNG37/9wuaJ91pL9hV+zs33rl9V2heAbUl67diy5uRvYvfs/DBnSj5SU29q01noql1okkuaQ\nIetTiAzbBPPf/37XpMFtLuTX3D5xcSEGj0lTb7pZKX16AqHitJUpU67HZDIp+2th1fj4D4Cu2O3T\n0SsxifDwNcATiOzg8Qhj+hKq0IQYQzDp6bejGdvVaEpdInycnPwqeXnTUcVKRKYxaApXazCqSqnq\nWsITtVjm89570z2Z1yIcr28g0UB8/DwCAkwMGXIJSUnDlCSrNMBJcPAiRV2rGChVxh9EYmIWWVlp\n5OeXA2J9VpuLej83KX82hsrV0LYx/FwHLCYx8YRH71j/27/33hf9qG09C8zw+51705RC2qkykK0J\nr7c0Lvlv/4yfvwxZSzofzSWctCbk52+fXr3mN3m9iRMHK2HORuBmz7qoMhrUsKrd3oXw8Pcwrtnm\n0LPnPzh8uAjR7vBNhGH6GGHMtDHExW3AatWyknv2/IrDhwvQyoa6cs01kVRXr6Sk5DyEMfZuZTce\n4V1eircnCpuYOHGwx+jk5e1QvFhjjfDLL08xGCbVC9y5s0J52VinXPsW5Vr3YbUOIDQ01LCGvXGj\nFsmIjl7BrbeeAwiVMxH+dXoypBMShrJhgz4svJaoqFAWLRrXBiMZQHNZ7XpOdSaxvwhMa5AZzpKO\nRhpkSafDWxLTlxMYW+OJ5B+98pPeq46KeoPoaL0BWQ3cRXW1C28lJqu1mn37DlFRYUYY6u2o66ne\n6B/GCQn36bxT4S1NmjSehIR6RY/Zu9yoDJFFfRnCezV+brGUkpIy3esYY7ehiRP7+u3YlJY2Slkz\nLhdQ0rMAACAASURBVMb4IiDEU5KTtfNqEqIhxMWtV4Q4tOS1W289h9Gj88jJ+caQIb1qVSzr1mlh\n4aSkGC+jFuy5hrf8pVhL1ofWW+ZUZRI31VKztV2SZIazpCORBlly2vBXS5yQMMbwgIyOXk5U1BtK\nxrLYJyrqMuz2OFTDBCEkJi7yhEu9NaLLyiaRmbmePn2ysNmuRBg1M/5+/oWFZvr2vYCzz36KH3/8\nC3Az3bq9TUOD9wtAD59j9TrXak202exi0aLJfPDBs1RXP6rsuRqRRJaJEOq4gYiI56iqEobSYnmB\nrVsfaKYx/c2Glw9/DBnSD5utFC0hS6D3uptaMgDvphdP+tRV5+cXMGlSLJMm+TdqxcXjPdfUy182\nNjbyzTeh7NoVRGt6Vp9qmmqp2V7PV33BCQ42Exs7VHrMkjYhDbLkZ8Ff8pa/kJ+/HsWZmRsYO1bb\nB2Djxrfa1KvVZOqmiF/YKCnpCtQRHr6TLl0+9RhCYWgfYt++NfTtu5cbb5xPQEAAgwZdR0ZGAlqi\n0zhgk6G71Lx5i6irE2vMBw+uJCXFOG+XqwLvjGih8WwGujJpUoTSKMIF9CU/v9xHQKQpI+Hv3qak\n3ER+/iF27dISrqKjV5CSMtZzT5paMhB/1suSNt9r2d95srOLGDtWC/+GhoYa5C+1yMKYFpP6Tgft\n8XxbkxMhkTSHNMiSDkVvHKZMifVs0xuvJUsymThxsEdgoqUH3+7d//HRmW7Og2nK81a7O8XE5LFy\n5T6lvraBoKAHOHJkNFr28nj27XuGBx+83NOQYuvWtwxrrGVlX1NS8ntEOHuHYoyNhm3SJKHxLDKt\nK4DvMHrZlyAkGpcCYT41wGp2L9Ck0aqtrVXqqEVC2JIlzzNxYl9SUm5i/fo/kpv7DmVlYs39qqss\n5OZu12WKt5YbaSmTui2o37nT6VQyzf8PgPXr17JuXevXoTtClKMpPfH2IMugJCeLzLI+hZxpmYbe\nHsKIEbksXXozM2dmK6HiEYiSJOE+Rkcv59Zbz/E0EFCziUWNq1jvDQ7OUjKFzU1mYbfUazYhYaiy\nvivGFR4+k+rqAeiTueAcIEn5ewPwNJmZ/T1ylwkJQ8nPL/cxmlrTBd9s5OXLP+Crr4Yh3nsvB5YC\nVwNw9tkf8Oij/TCZTBQU/OARvTCWXonzbNxY59frairzGjZjtR7yCj8bRUesVrFW6r3ubTwmTTnn\nakRjir+TmFjhE43wl21cXDye+noXzbF06Ualj7T2PWRm9mDSpLhmjzNe8+S90Y5S22ptdvaZwJn2\n7POmvVnWARkZGR08FAMZR474S8o5M+jRoztn0vxXry5m5UpVJSmA6uqLlezdxxDe4KuIh+9ZQAD7\n919JcXE1RUVxlJa+xZgxF7Jq1Tts2rSDo0c/Ad6noWEaIkEogP37B9KnTxGDB18MCO9w5MhFrFsX\nTlHRTZSWriMx8RKOHz9uMMYzZ2ZTVHQxcBHQnR9+2A084BknXIEoOxqD8Hiz6dv3v3zzTTArV95J\nUVEku3atoV8/E7t3f8E779zrmYMwtP/GbP5/HD9+HdCI1ZqNxQI22wmER3wx8BrwZ+AdwsLK2Lnz\nAW644WoqKqpYufJOr7G8A/QHGjnnHDtFRVM8n+vvwerVxaxbF67c2wDlW2gEqti/fyx9+hTx0UfV\nyndSjGgqoZ3nwgvfZc6c0fTpU8TNN3/OnDmjMZvNBAYGkph4CVVVi/jss3ogETgbuIgJE75nyJBI\nw/eu7q8/T1jYOS3+9pcsKaCyUpsbXME55xRw663Nh8jB97fm/dtoC4GBgQwefDGDB19MYGD7g4aX\nXnoBpaVvsX//QNTfwZw5o5s8p9PpZPXqYvbs+ZJLL73gpK7d2TjTnn3e9OjRfU57jvv1/AIkPxut\n9yiKdcIYAFMR66h6wY1ARPlLMqNGvUJV1aMIjeYc4HOEp+h/DCJUq6pL5VJSkkxubiEbN9Z7PKd5\n87Koq5umnEdVyor0c8ZQ1Nrbnj2riIzsRlGR1sSgtHQCpaWbEeIWei/WSVjY59TUzAI2Y7GUkJPz\nADNnZiv76jWVi4B0amoa2Ly5gLS03k3cN9GYwWrNVpKzmtgNEOHkFcBvlb9/glbP3DJNLRmYzWay\nstLYs+dNHI4xtJSA1dY1V6fTyYcf/uSzfciQS1p9jtZc4+dcn9av84ukLik+Imkbsv2ipE2oD5IZ\nM+J92hV6t4Lr16/c5/iwsO2ez8U66kjlk2LFGOtb+A3Fu92fvumDZuwbEWHjxZSVVRpaLIpSmx1o\n7Ra3INZyl+nGsRBhMMuALhw+/DBFRT/6mX2gbmxbgAYslvnU1DyJ8OJvw+GYS35+easNi/c9i4hY\nSHz8B2RmbmDNmkSSkq7FYvF/D5KThxMVtQqhGhar/NfTsJ92/uvQt6RsqU2f0+kkNXUzDsf9iBeN\n2eTkjOkwg5GXt0Npm5htmHtKysjmD1Roqe1gc7/TU4n6YjJ5cmyz98q7FahYb26pP7Pk1470kCVt\nornEFe9M4EmTZnDLLcaEmZycx8jPL1DWYo8pbfkaCAsrpqbGu1Y3kL59D3LvvRuURCR/HoQTTQUr\nloqK9CZGLjo5DRy4hW7dLuZf/wpCbWcojGkJQhvaBNgQBmweoJ5PbdEoSEysYMiQo5SVmXE4fK+W\nkjKSLVuy+cc/xPqoWAufirenaTabyckZw6OPzuf99/9LVdUzVFWFcujQG7hcW8jJ+RiHYzp671uf\neR0fH0ZZmV61bAKJiVmedV6n06mUYy1i0KC+mEzN3UsN4/d8Gw7HGPLzO3o91IzWJ9nFpEkRrTb4\nLYlytDXBqjN0bZJIpEGWdCj60GVoaLDfh6b6eVJSrdLO0EpNzZMEBz9Dff1M5UzZwH/o0iWApKRh\nhIaGGq6jZccaVbAcjr9gsWh9i0U3oHsQiUmpVFTEEh7+LMLLVcUrGoCHgJsQ3vbniLCvE/gr0EDf\nvuewb594ebBas8nKSlMSoqajFylRja3T6aRPn28ZNGgacXG/o2vXC7DbZxEefi7PPTfJkHwmzqMa\n/lzgD5SVdaesTG2v+CLQD4fjCfLziwxGxWTybUNotQ7wGGN9WFS0WbzltBobfVMQrb685bpqf3SU\nKMfpCB93ZHa35NeDzLI+hfzaMg2dTie5ue8ofW21EpimHl4tzd83K7WOgQNnUFFxPuJd8TEALJbn\nee+9CT7XcDqdigqWd1vF9YbsaJHl7a2lrF/LFtrKwcEnqK+/DKN6l9B1zsioIyhIiIEkJw8nL2+H\nbuzC+1YzkJ1OJ0OG5CilUE4CAhbQ2DhbOV8OUVHHPOU9/jJzRZh+Or7j/c4nC9k7Kz06egVr145t\n8tytzfo9Wf1of9+9t+Hzl2XfUbRl/KciO7o1//Z/zV75r+3Z11aklrXklKD3aLRyHycWy2yllrgj\nPAknatMHi+U8KioGoe+x63BEkpv7jscQ6R9k/trqJSWN8TROMJvNWK0D/CRGlSKyqkGEowdQXx/L\noEHT2Lv3Nu+dCQrq0cwD2gzcgtXaiNlsZurU16irU18A/n979x4fVXXuf/wDMjCiQY4Uta1HiFV3\nb0iLkSRW0HqBg8GUiMBojUSL1mo9Xn8BxcNBFDVU0eCxXlBITC8BKoMJ11BtpfoLQbxEWtv903YS\n2yrKCUZCYZOA+f2x9s5cMgkh5jJJvu/XKy/JZC57JTHPrLWe9TxlbjAOl+esqFhPcfGWVp4vXqnO\nAZhZ+OqoWx3H4aOP3sc0a/g6jY3xy3weKW9JuKjoBbZvf79Dkq3iFX257LLOORbUE+pMq+ymxFJS\nl7QoMjFm7typbqvCQ0ASodACfD7fYbv0FBSUUVBQFjehxiQlPYNZTp4AZFBZ6ef441cDtZi9YXP7\n4sV/pra2tlmyzsyZGygsnMSiRaUsWlRKYeEksrNLyM0dQG7uAC6/vJh9+/ZGJUaNHPkI8GVMVvV6\nd0wmOE+fPo60tOcIJ3w9T2rqP5slQB0uqaitYp8nOflR5s8/OeYafoGX/Ba5RO1lmldXPwjkAp9R\nUZEdNeuKfG7TcrK+xZ9HPGvX1hEMzmHu3KldlhjVUbyA5+U3tKSjfpYiX5SWrDtRT162aWk52CTg\nmI5JrS3rOY5DdnYpr7xiioC0VLhh6dJ1cVop/oz+/f/E558XErkkPGxYKTfffB7z519BS8uL8YpN\nmKIf/0Fy8kJmzvwWb775HiUlo9yvv4U5nuSPKoxRVPRSRD/di9rUp9m7T21tLWedVeDuhzuY2et/\nuY96PmrJOt7zeK9fUfEXKiv3Ul09L+r6vMfFX+5ez6JFh6I6OTVf4TDbAJs2Xdlsbz7SF1nKbX3J\nOgfo/DaKR6Kjl4978v/7HUHj15K1dJDwH84z43w1fEa2tSSU4uItbjBuTxnBb/P559/2rgYvi7qm\nJoPHH7/Pva15MhPA9u3vE33+dzpwI7CFUOgsli+vprraC46FwK0kJz/QbPnda5wQK/YPd7zxDB06\nlDvuOIX583+GKXhxE1DC6NEvM336OLKzfxD1Bz9y6TLePuv1169z91kPH7ySk7dGdXPynrugoMwN\nxl7y221MnDivqd9yV0jkZWQtH0siUECWZsJ7fYeI7L/r9cn1+Uqb/TFtS0/Z8vJ3m80+THvF2E5K\n3vGiQuBLRGZR19TcQ3LyPEKhBUDz7NSUlNMj9osdYCWw3P08j+rqYwnv0X6J0aPns2rVXa3OFCPH\n2Fo2buT3wCSV5RJ+Y5DJD3/Yr9U/+t6qhHkjdAjwt7rPGpupG69LVGtCobRW97I7IxNYgU+kZQrI\n0gqv/+76uDWMPS31lF23rohXXrkcU7axgmDwdnbuDEYFsTff/Bsmo3gzpgHDTYSrc00HriO2V/DM\nmd9i8OBSN5gPobh4S0SXowt58cWn2Lbt34E/YY4zeUExF7N8XArUAdlUVmYwc2bbjrm0drY13sx2\n3Lhl/OEPXq3l1oNZ+PFz3Fu8qmItp3k0n3E2z0T3BALjefLJ8HEwsy99OeEOVm15/sSZ0Yr0RgrI\n0kz0zKg/6emftNresKWesmvWXMZppz3sVrLKwCtvWVy8uSmIhUIfYpoeXI0pxpGPCZwwYsQSqqu/\nRmyBDp/vGAKB8XFnqwD9+/sIV66KLHPp+T3mbG/4en/60/s56igfKSmnN1WLasueommXGD+DeMmS\n9Vx6aduCWezjzTWvJz39k1YDeVtnnH6/n02brnTPfacBl5OeXnzYGa9mtCJdRwFZmumomVFR0e/d\nYBwdZBoaGli6dB3PPvs2odBETCa1N1O7nszMB0hNtXjxxeOprvYSo+ZjSmmaXsSt9fI1nZPCx4xM\nJvUlwCLgaAYM+DsHD64HJuLVoy4pOQa4nWAQXnxxKf36NTZrgxgIjGf16ueazvzC85SUHCA7O37m\nsamI1bZSkPG0tirRHkOHDuWVV+5w32hs1oxXJMEoy7oT9dZMQ2+vdN++f7lLzrBz5zC2bfsx4JXI\nnMQllzzJ++/fS2SWbnLyPZxwwoiI9oVLMcvHZinVK2wRXXjDPNYE1v8lNfUAmZnDmmVnL1pkAnLs\n4wYPzmbfvi8BZzFoUBUHDkQmdU0HHiRcNtN7rcjCIXvIysonPf2b7Nu3l/nzh2Ley14I9GfRotKI\nGXtO0/egLS0II7+niZqB3B699Xe/rTT+Pj9+ZVlL5wsHDq+/rllKTkp6iPnzixk8+BgCgSyKi7fw\n/vt3EZkUNmzY/cyc+S3mz7+ScPCbBbwIbGDYsJd4/nlTnevVV3cAmZGvjCkeciEVFReQmbmO9PT4\nCUexiUiFhQ+zZs02ysvfJRiMnLFfjXkjcEFrIwZWEgzOIRjEPc98GdFlN1teVWhrQNZ+rYgoIEuL\n4p3NDC8Vl2HO+prgVlc3m8rKPJ5++lYcx6G8/F3Mr9dleM0Dvvc98PkGxXmlwUAGNTWTWLXqBbd9\nYmSNaAdYgikpiXv7MW4lqdVN54Wh5cDm7YPGVuwaOXIwJ5zwIdu2hTO9x4591l2yrsd0dQqPMxS6\nrcUs7y+636r9WpG+TQFZ4mrpiE/bH+dlC3vLwksoKbmZjz76JcnJcwmFzsHMTFdiZtCm+MfKlX+g\nsjIPMwM1e84moIeTsCJLSK5du4fycjN7Xbu29WuM3QMeOfIRysr+E7/fT1HRb9m+PY/Ro0fi8/0b\nAJMmFfPCC/+XHTv6Ed5vDmd5m+fUTFZEOoYCssTVUtJUOAM7QOT54SFDFrFgQcA9RxtZmONqTGGO\nB4BXef31Y4GFAIwc+TDDhx/N66/XYwLzTCorMzAz4R8DQzHJWH9qdn0+n8+9xisws3UoLw9QVLTO\nnWHHPytsaj2btosnnjik6XafbyApKadH1eseMiSfPXsed1/Re2OxEp+vtbrWIiLto4AsRyS8JLyZ\nffsG8uabDwAwatRXycxcTSgUr7rXZGAL8D6RXYyqqu7guutWc/LJ+TElOm/DtD28C3gSGBnVmtFb\nJl62bD3wc0yrRIDlFBdvZ8eODLzCGpFnhYuLt0RVq6qoqKeo6IWoAG4C7yFgi9utKfKNxWLgRny+\nls/uioi0lwKyxNValaZ4pR4XLjwByCG2ule4AMXtwH80ex2fz9dCN6ZU4HFMAPdTV7eHrKw80tO/\nyZQpkygqeonHH98CPEU4aOawY8eJmKXwBZhjUq0lbEFFxZ8pLz8LM8u+EBN4Wwq4X2/T2V0RkfZQ\nQO7DWiuoHy85Ckzzhu3b33f3Wgewffv7lJffipkBQ2R1L7PUfDOwkoyMkzj77H0UFDxMVZWptRwZ\n5ONXkepHuKCHaaMYXRDk6DijagRWAfcCMGRIHlOmzGwaY+SbjNTUpbzzztGYZXEwbyQux9TrPoej\njrqPQ4fucb8fC5kz51SuvVZ7xiLSOY74HLJlWUdhDo+egfnrd4Nt2803+QydQ07Q8ccmbbXUjSny\n/tOmrXRbMEZ2UpqBOf40GRPQbgEgKSmPujoLGMgpp7zDqFH1/PGPDtXV3wWqGDmylrKy/2yqIV1b\nW8vEiU+4VaQuIC3tVzQ2HqKi4jr3+grcNwiR55MdTJ3qL7vX8xFwknst4TPFCxeubmpbOGXK2KZe\nyQ0NDc3OMicn38OsWd9l+/b3CQYj32iMY9GizUe0d5zIP//O1pfHDhq/xt9155AnA5/btn2uZVnn\nYTJ0prTnxaX7tFaXuaX7V1SchDkbHLmvutn97z3AGOA2jj76UzZuvIM33vg7ixe/RlXVqXzwwVWY\nmtZbgdupqnqRVate5brrJgPxqkhNdV/3cNnMRxGudf0QJjt7PZGFO0xFsPuA6CSvgoKyZs82a9YY\nrrsuA5+vjGDQH/Hc9U1lMkVEOkPLletbYNv2i5gUWICRwKcdeUGSmEwjh+2t3KM/MBV4jP37L+CC\nC1by+9+/SVXVUMx+8irM0vACoACY7rZKNLPvgoKypiYRXkP5eA3mo5vJbwS8NxU+YDZQ4b7OBOAX\njBixiFBobtN9zBuPLXGey7SU9OpYBwLjSUt7rulrpkxmDY4Tv0ymiMgX1tjY2K6PM844o+CMM874\n7Iwzzri4lftJgtq/f3/jeec91wgHGuFA43nnPde4f//+Fu+/ZEmwEfY0QvgxsLQRPmscPnye+7VG\n9+NAIwQb4f5GWON+1Md8fUXj9On3Ny5ZEmwcN+4p9+v1h70O79qffHJtYyDwYJznXRP1+fTp9ze7\nz5NPrm16rk8//bQxEHiwMRB4sPHTTz+NGfNq9/nWNsL+Zo8VEWlBu+Jqu5O6bNvOsSxrNlBhWdY3\nbNveH+9+fXwfIWHGHy+Bq6jo0ogl4Uupq2tosdSj45hjRCZhazOwH/gHyckPcOWVp7NwYUvLyRMx\n3ZsuibjdYcCAt1i5cgErV0L4qJGfV165iscfb3np3DN16ngyMsZSVRXZ7OFRzJnnsNGjv8bf/x6d\nLZ6RkcWuXXUR++gmyay6Ora/8efE7kfX1Tlt/pkm0s+/q/XlsYPGr/EnHf5OcRzxkrVlWdmWZd3l\nfrof+Nz9kATlBZ7c3ExyczOZMSOI4zhxl4S95eOCgjIcx2n6vKGh3l3C7Q9cDISA0YRCdzN48GBS\nUp4mcnkX3gJuwATxsZgzvN7X7+HgQa+Zgw+zB/1S0/WablBr+fGPH2Pp0nUtLhP7/X5WrpzK/Pkr\nGD36VjIyPmPs2CIil6CnTfsekycPISsrj4ULX4gKuNH76NHL2RB/STsQGP/FfyAiInG0Z4b8G6DA\nsqxXMH/JbrFt+0DHXpZ0pLYmcMVmXq9e/Zyb6Ww6M6WmPkNGxn2sW3cQuBsTbE1d6dWrZ7Bs2QqC\nwXJOPvl4PvnkS7z++mCgntTUz2hsHMy2bSWAzfHH17F7d+xVNgD1pKUtY/XqOt54YzeQRjB4AaWl\nv2Tlyqlxk7ocx6Gw8ENCoXwqK801elnVU6ZMYubMDU3j2blzOdnZbf++qeGDiHSlIw7I7tL0jE64\nFukArZ0tDndM2s+rr77T7D6xgdssBW8gXNnqOjIyFhDdqtDUlfb7/dx44xRuvNEk3NfW1jJv3mL2\n7dvHmDGnA/Dxx3+huno2u3c7DBmSx549uYBpuXjppcfh85Wyb9/RzJ//mfsaAEVs3XolxcXRR44c\nx6Go6LcsXlxOTc0jUdc4dap5s1FQUNbqG5HWip941PBBRLqKCoP0Ii01hAgExvPCC89EnSEuKaml\npGQCweCvY84fO3i1oWFc5LMDJfzud7tibtvI9u3vkZ3tRC17m5npTcAKSkoC7v29veIk9uy5ntGj\nb+XUU7/MggUBNmwwbxDefPOvQGSLRK/BRLxxnog52tQ+mgGLSCI54j1kSVwt7Yn6/X4yM4cRbiPo\n7dtuido3nTJlLEOG5GOODE0gKSmfs88OAXsw1bMGsW/fw5gCIN5tGQSDczjvvGXU1tYCUFT0WzdY\nPoFZTIndKzbBvbIyn2DwVr73vRVN+9umclb0nnFy8v8lEBjftJ9tGlhcg3k/eYF7PWafNzn50aZ9\n3rbsAcfbRxcR6Q6aIfdK3tJ0g3t+mKZKVa1Zs2ZbVEOFurrZZGSsoL5+DpWVEzGF2bxM68eIbBQR\nCt3GxInz2LTpJpYurQLuxGRWF2JmuV6wayC6x3BZ1GtWVd1BcvI9hEL3A6ZF4qZNNwNEzP69X9sL\ngV9hyl2uJzl5K5s23dQUWDUDFpGeRDPkXiQQGE9q6jOYmesEIIPS0s9wHIcpU8YycuTDRGdCj+PY\nY+9n3759LF26jldeeavZcxYW/onKynxMcP0YU9SjP/D1ZvcNhdKYPbuAqqo7iZ4Vb8RL2Fq4cD9Z\nWTtaHcesWWNYtKiURYtK2bJlFkOHDo2Z/U/EBPr+wOUkJz/AwoUOr7xyR1MpTo9mwCLSU2iG3It4\nS9MVFeH6zFu3XktR0WrWrt1DVdVPMUlaW4HvABvZu3cw8+d7e7zvAc8CPwLM7DRc5QpMVaw1DBt2\nJzfcMJpf/jLcKCLcECJesH2HrKwd5OffgN/vJzvbYedOL5lqXFSCl6mWdbiZrB+Y3tT9KRC4Q8FW\nRHo8BeReJt7S9Pbt71Fe7vUb/gEwCViDCcC5hAPubOBFzJnhQ3zrW/+iqio20L1HTc1CXn7515SW\nXk5m5jy3IcTlJCc/yYIFAT766Dm2br0SU7u6grPPPpH8/B/GXUpuaKinoeEUKivzSEk5vcVg3Dwj\nurgpwIuI9AZasu5l4iUypaScFnMvB3gH+HacZ3gLszecy5/+lOQugUcuc98BJFFensOGDe+wadNN\nJCdvBX5HKHQj11//O5YuvZCRI5/ANGZYwFFHNQ+afr+fQGA8a9fWMX/+FQSDc1i7dk+z+9XW1vLj\nHz/GLbc8xTPPfL9pKbu1zlQiIj2RZsi9jN/vp7BwEnfe+QCh0E6OP/5LNDSczsiRkcvLD2H6BR/C\nZChf5d6+ALiLcILVTdTX38CoUTs45ZTjWbfuLsLJWcaaNdvcZe0tmKztAPPm5VNVNYfIZfPi4lIC\ngfFRZ6QPV7CktraWlJRC9uyZA8BLL+WxffvMZvvEIiK9gQJyL1NbW8vFF+dTXf0l4HF27IB16wo5\n5RQHKCVcyhLCGdPrycx8m/r6g2zc+DImm/oAUMWHHxby4YcQCj3E2Wc/x+uvm1rR6ekFTJkyiTvv\nXAq8D9zuPmchhw4danZdDQ31zc5IT57cer3X2bML3GBsAvaePbnMnp3H00/f2u7vj4hIotKSdS/i\nOA4TJ/6K6upxhI8kmUznDz74LsnJFZg61BcAeYTP7m5l9OhTePfdwZhs6gzgbUyQNc+xd+9sTjih\npmnJuLDQlKUsKTkr6n5wNWPGnNps2Rz6NTsjDf06rVZ0bE1uEZFEpxlyL1JcvIVQ6EZMQQ4wx4PC\nS8yjRg0mFNqA+bH/FJMx/UdCofu47z6I7LoEqc2e/80332PgwIGkpJzGqlWvugG2rNn9Bg8+hhUr\nJrFu3Wbq6hwCgayopg0en8/X6jnhvLwcXnopnIE9ZMgi8vJyDvt9aKlimfacRSSRaYbci5giICsw\ns+NLMEeR9gDPk5r6T1JTLff2C4HXMBnTXpnK2K5LF2CKf3gJXYv46KOlBINzmDv3Xzz99HuY5LAL\niayU5c1y/X4/N9yQ0XT+NzbZLC1tGQ0N9RQXbyEQGN+0bxw5qx06dCjbt88kKyuPrKy27x8frouT\niEgi0gy5V+mHqYB1CBNYv8Q3vnEbV111EdnZPwCgpCSypvXBOM9hui7BSvc+84FjgFsAb8/3aj74\nYD3wACY57HKSk+9h1qwxLR5bij7q1EBJySHmzr0cMDNYbwk8dlY7dOhQ7RmLSJ+gGXIvYs4gO5il\n54NAP/btS2batHMpLt5CcfEWJk1KIly20qt45c2CFwP/An4G/NP97xzgW8RmVxupwGbgZWbN+i7X\nXZfR6rKwVzXL5/O5LR3DM9jZsws6bFarPsYi0hNphtyDxbZaDATG88QT91Jd/XXM0jRUV+8ipgSf\nEQAAGgxJREFULe1Bdu/OByA5eTEmaPswQTaTYcPupKbmPOBE4M/Af7uvcD+wllGjfs/RR3/Itm2z\n3Nufx8ykr3Gfox6fr7TzB9xGqmEtIj2RZsg9lJe45HVJmjEjCMDo0ccS29Vp9+6vNn0eCt1GcvJC\nwhnWi91+wlOBEzDB2HvsXGAHgcD3+c1vppGZ+QBm1jwE86vTn/bMQOPNYPPycjp0Vqsa1iLS02iG\n3EPFK6pxyy15Ldw7uhHErFnfaZrRNjR8h7lzW36dkSMPkp19EX6/n3PPHUVJSab7mg6wPqpGdVu1\nNIPVrFZE+jIF5F4kGDwTuIBjj32QvXvnuLcud/9bDzgcf/wcios/Z+rUc7j22kkArF0bv9FDcvKj\nbNr0n02BMbqedH/S0z9pdz1pbwZ7uNtERPqKfo2NjZ35/I27dtV15vMntOHDk+is8YfP2ua4tzxP\nuO/wHiAf2IcphQkjRsxn504/Bw7c696/kLFj9/Ob3wRwHIfZswsAWLAgwIYN7wA0HV+Kfd3IfevW\ngnFnjr8n6Mvj78tjB41f40/q157HaYbcQ0Uu8b766g5KSu4gnAntxyxTDwAeJzOzARhCdXVkK8Wr\n2bZtPcuWbWTTpv1uNyjYubP1IhqaxYqIdA4ldfVgXnAcMyYZ+Dnh40sFmNaKk4Fcduw4mkOH4p05\nhscf/72KaIiIJADNkHsob+m4oaGB5cv/jincsQGoAL4J/B+82XAodBuW9d+Y/eQc9xmeBz6hpmZi\nV1+6iIjEoYDcA8Tu2wJRtZpNcQ8f8ANgEsOG/YSamhmYTOiXgP1s2/YZcAWwHvgLcCowDLiQ5OTF\nhEK3AbjHjbK6amgiIuJSQE5w8RolTJ48JOrIk6lBvRnTpQnS0r7Mrl1PsW3b0ZgzybB79y6g2P38\nEswMeTrp6cUUFl7JmjXtO250JEleIiLSMgXkBBfvvPFJJ8U7b9yAya5+kHXrUjjllFeBRYSD9o+A\nde7HDkaN+ieBwDFNtadzciYccXBVVyURkY6jgNwDjR49grffDi8zp6UtY9iwD1i3zgbuBl7mgw8O\nAbWYhhAvYQL2vzDL2HexYwfs3bsY+C3Tpp3LqlWv8eyzbxMKzQX8bQquLRUnSU//pru0ntTiY0VE\nJJqyrBNcvLaFGzbUuX2PN5CcPI/nn89g4MCjgRuBVZgl6UcYMOARTCLXBCCD44+vILKsZih0G3Pn\nDiQlpZC5c6cSCt3nPv5Qu7Otg8Ezm0p5Oo7TEd8CEZE+QQE5wXnnjRcufIGsrDxOOGEnFRXTgS3A\n54RCKcyeXcDo0SOBl4FsvIB78OAC4CtNn+/efWGcV3iPPXtmE65ffRXhnsiti32zYPalJ+Idnyoo\naNvziIiIlqx7jLVr65qKd0AeZqb7cyCNYPAG3nprEccfv5vduy8gXGt6I/AO8DkwEDiHkSMfpqrq\nDvd5fgFYcV6toSnburV95cjiJOXl7xIM3kr8No0iInI4Kp3ZiTqqfFxBQRm5uV5TBzDJW48A92AC\n7xLA1J/2++/HcW4CSvAyrM2xqBkkJT3GGWf4eeONU4HteGU1hwxZElW/eubMk/D5BgFQUlLj9i6G\n9PSW95VjS3mmpxfw8stXU1fX8IXH31P15fKBfXnsoPFr/Cqd2Ye8jAnGPqAME4xNsHacuYwefSuV\nlfnEHouqq5vNG29swJxXzgA2kpW1g7y8nKZjT1OmXMnMmRsoL7/SfWwhcAjwu/vKpXFLZ7bUrakv\nB2QRkSOhPeQeIHavdtCgslbvf+qpX27lqwcxR59eAi4gJeU01qzZ1vQ6a9ZsiyqlaYJ52/aC1YNY\nRKT9FJATjOM4FBSUUVBQ1pSl7Pf7KSycRFZWHllZebz22o0kJy/GBOhxDBiwAC9Yjx37LKNHnxLx\ndS/Zahxjxz5LUtI7mKzrCSQl5fPii/9Lbm5mU2Z0Q0O8GW0DUO/uK4/vgu+CiEjfoyXrBNJSoQ3A\nXUYOd2QqKbmMzMx5hEJnc/DgcEwda/jznz9m27Y7AYdhw35CWtpXOPvs0xk8eDMNDf/Gtm3X4S1l\n19XNZtu2DUSeI548+QXS05c37QWnpS3j0kuPw+crPeIqXiIi0nYKyAkkXqENb0829vZ58/Lcc8Nl\nwDWYY1BQV3cHpl71B9TUPMW6dbB793JWrJjUpnPFPt/AmL3gqQrCIiJdQAG5x6sHVgAzMMle9wN7\ngXBSlxfYA4HxBIPRs9/GxkNUVNQD4cYS6nksItL1jjggW5blA5YBI4BBwP22bZd29IX1RbEBM7Lz\nUuztCxYEePvt/yIUGgzchqmwlY2p0rUYcxzK1/TcDQ0NcTKhpwI0y4wWEZGu154Z8g+BXbZtZ1uW\n9W/A24ACcgdo6egQ0HR7Q0MDDQ2DyMxc7S5Zrye6QheYAD3f/QB4FDil6TViZ7+aDYuIdL/2BORV\nwG/cf/fHnKORDtLScrHf7ycQGO8mfZ0IXIkJwBOBezEz40hjgDWYtowXdO5Fi4jIF9buSl2WZSUB\nLwLP2LZd3MLdOrUMWF/iOA7XXPMYxcV3YBK5JhCeEX/AgAE/d2tXQ1LSg/h8f2f37tOB2wEYN66A\nkpJpFBe/BkBOzoVanhYR6RztqtTVroBsWda/A6uBJ2zbLmjlriqd2QHjdxyHadNWUlFRC3wbOA/z\n7b8KgBEj7qa6+muYRhIAH5KR8SHr1s0jHLTrSU6e5y5zt14Gs6OofF7fHX9fHjto/Bp/+0pnHnFh\nEMuyTsSt13iYYCwdpKjot1RUDMKUyLwEs2MwmeTkecyfX8yQIXuBWZiSmD8AfsQ//lHT7HlCoXS8\nClztba8oIiKdoz2Vuu4GjgPmWZb1O/dDa5+dxHEcVq78AzAcU1PalLMcPXo+mzbdxMaN+9ixY1Kz\nx2VlpUWV20xOfhTtJYuIJK4jTuqybfsW4JZOuBaJ4VXuMo0iHGABMBa4gOnTx7Nq1Wts3fojTKAu\nwlvCTktbxrXXTuXaa8NHmkzTiF/HPVIlIiLdT4VBEkS8vsPhyl2HMMnt9wKQlPQQ06blMHt2gfto\nPybrej2jR5excuW9TXvDkRnbLR2pEhGR7qeAnABaq2FtvETkOeO6utmsWrWalJTTCAaXAzmY3YdP\nmD59XIuBVhW4REQSl7o9JYDoGtY+yssD3HLLUzQ0NJCa+gym21K0Z599m8zMFJKS/olpLLGBpKR/\nMm3auV178SIi0iEUkBOOA6wkGJzD3LlT6dfvKObP/4xhw+4j3E6xkFBoDFdd9Sh1dXfiZVfX1c1t\n6m0sIiI9iwJyAggExkdkRG8EZuLNlrduvZbBg4/l9tu/gZkJrwc+B7LcZK8VmCAuIiI9mQJyAvBq\nWC9aVEpm5tvNvl5e/i7Qj9TUf7i3XIsXsOFqTBCvdzOnx3fVZYuISAdSQE4QXq3qjz8eDhTiLU8P\nGrSQYPDWpuXrzMw3Yx7pMHp0GVlZeRQWTsLv9+M4DgUFZRQUlOE4mj2LiPQEyrJOIMXFW6iouB5z\nzOlFoJQDB57AzITL2Lr1JObO9bFrl9eK0WHIkCVUVuZTWQk7dy6nsHASM2duaJaxrSNOIiKJTTPk\nhONglqDLgEvd236FaSaRwSOP/I1nnvk+ixaVkpWVz549s4kshzlt2oMxGdsqkSki0hMoICeQSZPO\nxO9/GMgAfg58CtxH+AyyD8f5b6666lEAUlJOb/YclZVfPezraElbRCTxKCAniNraWs4//yEc5x7C\nCVs5wLHN7ltZOZHc3ExKSmpIS3uO8HGo54GbMGU0zW2xiV5eEZLc3ExyczOZMSOooCwikgAUkBOA\n4zhMnPgramoy43z1EP36/RfRQXci4KOi4jouvfQ4d/k6D5gBDMUro5mVldds/7h5ERItaYuIJAIl\ndSWA4uIthEK3E9skAvKBb3H33Z/x7rt5/O1vH1FZmYepXW34fAPJyZlAIDCenTu95hH9SU//hPz8\nG5TMJSLSQyggJ5Rwkwj4C3Ar0J/jjivl6acDOI7D9Om/ZOvWawHT1SkQmGoe6Z5lPlzziEBgPMHg\ncnV9EhFJMArICSA6SPbH738Lx7kDM9MNB0zHcdi586/Az4AzaGw8FPU8bWke0dbALSIiXUsBOUFM\nnpzE8OH3A/0ZM+Y0fL51+HwDmwKmt89cVfWA+4giKiqyKSpaj89nukB5bRsPR12fREQSjwJyNwu3\nXrwCqAW+QkkJpKb+g1WrpjcF2PA+s8995FXAep599m1CofsAFQEREenJFJC7WTjreS0m2F4CQEVF\nIcuWrWfwYHPsqaGhvtljhw17mVBoIV6QNhnTpZr9ioj0QDr21M0aGrxexzaRXZ7gah5//I2m88Kl\npZ+5vZHN8afk5Ee5+eaziMy4FhGRnksz5G7kOA4lJTWYZhKnNft6Tc2FeLPfrVuvZeHC1Uyd6iVj\nmUzrTZuUMS0i0hsoIHej6GYSG4BHgNsBGDnyEaqqfhp1f5/P12w5WhnTIiK9gwJyQvADk4AGRo26\nmeTkLzNmzKls2PA8FRXXAS3PfpUxLSLSOyggd6Pw+eMAsBKYwY4de9ixYyYlJZCW9hwLF74QdfxJ\nRER6JyV1dSOvSEdWVj4moWsLkYldW7de21QaU8FYRKR3U0DuZn6/n/T0b3b3ZYiISDdTQE4AgcB4\n0tOXA+MwGdfxWyeKiEjvpT3kbuI4TlPbw0BgvJstvZmGhmOA1fh8Pu0bi4j0IQrI3SBcLvMaIFzy\n0suW9oJ1cfGWNtenFhGRnk1L1t0gXC7TJG+Vl+dwyy1P4TgOtbW1nHfeI+TmDiA3dwIzZgRxHKe7\nL1lERDqZZsgJIhg8kw8/XMnHH++hquo+99YiyssDFBdv1lljEZFeTjPkbhBO4qp3P34BTKSi4qtU\nVd1JuJ71VcDL3XehIiLSZRSQu4Hf76ewcBLDhv0EUzLzSlpqEpGcvFWZ1iIifYACcjdZs2YbNTWP\nATWYH0M9I0b8kbS054js6LRp001K6hIR6QO0h9yt/JjZ8Waggeuv/xrZ2RdFNIu4VsFYRKSP0Ay5\nm4T3kfsDF5Oe/gnZ2Rc1NYtQuUwRkb7lC82QLctKBR6ybfv7HXQ9fYZXx1qtE0VEBL5AQLYsKxeT\nBry34y6nb1HrRBER8XyRGfL7wGVAUQddS68XWS5zypSxrFmzDUDVuEREpP0B2bbt1ZZljezAa+nV\nYstl3n9/Hnv23AL4m0pnKiiLiPRd/RobG9v9YDcg/9q27fQW7tL+J+9lnnpqHT/5yQRMwQ8wR5s2\nAxlAPYHAYpYvv1VBWUSk5+vXngd1+rGnXbvqOvslEtbw4UlN46+ra70edXHxN6iufr5XzZQjx98X\n9eXx9+Wxg8av8Se163EdcexJs+A2iC2XOWTIIkz/43DpzPLynKY9ZhER6Vu+0AzZtu0q4JyOuZTe\nLfaY05QpM5k9O59g8EzCpTPru/MSRUSkG6lSVxeKPeaUn38DO3cGKS83pTPT0wsIBLK67wJFRKTb\nKCB3IxUHERERjwJyF3Ich6Ki37J9+/ukpJxOdvaFKg4iIiKAAnKXcRyHadNWUlExCJhDMAilpc+x\ncuVUzYpFRETNJbpKUdFvqajYBXwMrAMOsXXrtcqqFhERQDPkLuE4Ds8881fgZGCme2sBMKPbrklE\nRBKLZshdoLh4C9XVozDB2Od+zGTEiAUEAuO79+JERCQhKCB3K337RUTEUEToAoHAeFJT/wEU4lXq\ngkKqq++hqOil7r04ERFJCArIXcDv97Nq1XRGjdoG/AxYD2QDfrZvf697L05ERBKCAnIX8fv9BALf\nB04ELsF8658nJeW07r0wERFJCArIXSg7+yJSUw9gZsjrSU09QHb2Rd19WSIikgB07KmLZWYO4ytf\n2UFKymlkZ09XURAREQEUkLtMuFLXycCZvPnmDsDMmhWURURES9ZdxFTqGoTZP76E6uqTmDt3ADNm\nBHEcp7svT0REupkCchepqLCJLgxyNfBXystzVD5TREQUkLuC4zi8886/4nzl611+LSIikpgUkLtA\ncfEWqqrmYepXe4VBCoALSE8vUPlMERFRUlfX8WOKgWwG9pOZ+Q/OPXczgUCWkrpEREQz5K4QCIwn\nPX055tt9Menpu/mf/7mZnJwJCsYiIgJohtwl/H4/K1ZkUVxcCsCUKZOaErkCgfEKyiIiooDcVfx+\nPzk5E3AchxkzgpSXXwNAMLicFSu0bC0i0tdpybqLFRdvcYOxOf6kY08iIgIKyCIiIglBAbmLhRO8\nzPEnHXsSERHQHnKXi03w0rEnEREBBeRu4SV4iYiIeLRkLSIikgA0Q+4CjuPo3LGIiLRKAbmT6dyx\niIi0hZasO5nOHYuISFsoIHeyhoaG7r4EERHpARSQO5HjOJSU1ACFeOeOx459loaGegoKynAcp5uv\nUEREEoX2kDtRQcFLVFRcDxzCtF1s4OOP/8bcuQ8C2k8WEZEwzZC7WHV1KtpPFhGRWArInSgn50JS\nU58BfgFMADKATwEtVYuISLQjXrK2LKs/8HPgTOAAMMu27b929IX1Bn6/n8zMYVRUTMXMigFygPXA\nJW4d66xuuz4REUkc7dlDngIMtG37HMuyUoFH3NskDp/P1+y2rKwdpKcfUh1rERFp0p4l6+8BGwFs\n264AUjr0inqZeN2d8vNvICdngoKxiIg0ac8MeQiwJ+LzQ5Zl9bdt+/MOuqZeRd2dRESkLfo1NjYe\n0QMsy3oE2Grb9ir387/btv3vLdz9yJ5cRESk5+vXnge1Z4b8GnApsMqyrDTgndbuvGtXXXuuq1cY\nPjxJ49f4u/syukVfHjto/Bp/Urse156AHAQutizrNffza9r1yiIiItLkiAOybduNwE864VpERET6\nLBUGERERSQAKyCIiIglAAVlERCQBKCCLiIgkAAVkERGRBKCALCIikgAUkEVERBKAArKIiEgCUEAW\nERFJAArIIiIiCUABWUREJAEoIIuIiCQABWQREZEEoIAsIiKSABSQRUREEoACsoiISAJQQBYREUkA\nCsgiIiIJQAFZREQkASggi4iIJAAFZBERkQSggCwiIpIAFJBFREQSgAKyiIhIAlBAFhERSQAKyCIi\nIglAAVlERCQBKCCLiIgkAAVkERGRBKCALCIikgAUkEVERBKAArKIiEgCUEAWERFJAArIIiIiCUAB\nWUREJAEoIIuIiCSAdgdky7KyLMv6ZUdejIiISF81oD0PsiwrH5gAvNWxlyMiItI3tXeG/BrwE6Bf\nB16LiIhIn9XqDNmyrB8Bt8bcnGPb9krLss7vtKsSERHpY/o1Nja264FuQP6xbdtXdOgViYiI9EHK\nshYREUkAXyQgN7ofIiIi8gW1e8laREREOo6WrEVERBKAArKIiEgCUEAWERFJAArIIiIiCaBdpTNb\nY1nWccAvgCRgIHC7bdtbLctKAx4DDgJltm0v6OjXThSWZfUHfg6cCRwAZtm2/dfuvarOZVmWD1gG\njAAGAfcDfwYKgM+BPwI32bbda7MILcs6AXgDuBAz5gL6ztjvAi4FfMD/YKr5FdAHxu/+//4scAZm\nvNcBh+jl47csKxV4yLbt71uWdRpxxmtZ1nXA9Zi/+/fbtr2u2y64g8WM/zvAEszP/QBwtW3bnxzp\n+DtjhnwbsNm27fOBHOAJ9/angCts2z4XSHUH0FtNAQbatn0OMAd4pJuvpyv8ENhl2/Z44D8wP/dH\ngLvd2/oBP+jG6+tU7huSp4F/Yca6mL4z9vOBdPf3/XzgVPrQzx5T1/8Y92/bAuABevn4LcvKBZZi\n3nxDnN93y7JOAm4GzgEmAg9aljWwO663o8UZ/2PAT23b/j6wGphtWdaJHOH4OyMgPwo84/7bB+y3\nLCsJE6BC7u2bgIs64bUTxfeAjQC2bVcAKd17OV1iFTDP/Xd/oAEYY9v2Fve2DfTun/nPgCeBj9zP\n+9LYJwA7LMtaA5QCJcBZfWj8+4HjLMvqBxwH1NP7x/8+cBnhfgbxft/PBl6zbbvBtu097mPO7PIr\n7Ryx4w/Ytv2O+28f5ndiLEc4/i8UkC3L+pFlWTsiP4DTbNt23HdHRcBdmF/SPREPrXNv662GED3e\nQ+6yVq9l2/a/bNve6775WgXcQ/Tv11566c/csqwczOpAmXtTP6Ibr/TasbuGA2cBlwM3AL+ib43/\nNcAP/AWzSrKEXj5+27ZXY5ZhPZHj9f6+DwE+i3N7jxc7ftu2dwJYlnUOcBNmYnrE4/9Ce8i2bT8H\nPBd7u2VZo4BfA3fYtv0Hy7KGYPaUPUOA2i/y2gluD9Hj7W/b9ufddTFdxbKsf8cs1zxh2/avLcta\nFPHlJHrvz/waoNGyrIuA7wCFmCDl6c1jB/hf4M+2bR8E/p9lWQ7w1Yiv9/bx52JmQnMtyzoZ+B1m\nluTp7eMHs3fs8f6+x/4dTAI+7cqL6kqWZc0A7gYusW27xrKsIx5/h8/aLMv6JmaGdIVt25sA3Ol6\nvWVZp7rLOhOALa08TU/3GnAJgJvM9k7rd+/53P2SMiDXtu0C9+a3LMs6z/33JHrpz9y27fNs2z7f\n3T96G7ga2NgXxu56FZM3gGVZXwEGAy/1ofEfQ3hF7FPMRKdP/O5HiDfebcA4y7IGucm+38AkfPU6\nlmVdhZkZn2/bdpV78xGPv8OzrDEJDQOBJZZlAdTatp2FWcr6JXAUsMm27dc74bUTRRC42LKs19zP\nr+nOi+kid2OWY+ZZluXtJd+C+T0YCLwL/Ka7Lq6LNQJ3AEv7wtht215nWdZ4y7K2Yd7k3whU0UfG\nj8kfWG5Z1h8wM+O7MNn2fWH8XuZ4s993N8t6CfAHzO/F3bZt13fTdXaWRnc7Mh+oBla7ce/3tm3f\ne6TjVy1rERGRBNCrE41ERER6CgVkERGRBKCALCIikgAUkEVERBKAArKIiEgCUEAWERFJAArIIiIi\nCeD/A+OBLyXsDdF8AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10bc3a250>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create sample data, add some noise\n",
+    "x = np.random.uniform(1, 100, 1000)\n",
+    "y = np.log(x) + np.random.normal(0, .3, 1000)\n",
+    "\n",
+    "plt.scatter(x, y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}