{ "metadata": { "name": "01_setup_and_introduction" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Introduction to Machine Learning in Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this section we'll go through some preliminary topics, as well as some of the\n", "requirements for this tutorial.\n", "\n", "By the end of this section you should:\n", "\n", "- Know what sort of tasks qualify as Machine Learning problems.\n", "- See some simple examples of machine learning\n", "- Know the basics of creating and manipulating numpy arrays.\n", "- Know the basics of scatter plots in matplotlib." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "What is Machine Learning?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this section we will begin to explore the basic principles of machine learning.\n", "Machine Learning is about building programs with **tunable parameters** (typically an\n", "array of floating point values) that are adjusted automatically so as to improve\n", "their behavior by **adapting to previously seen data.**\n", "\n", "Machine Learning can be considered a subfield of **Artificial Intelligence** since those\n", "algorithms can be seen as building blocks to make computers learn to behave more\n", "intelligently by somehow **generalizing** rather that just storing and retrieving data items\n", "like a database system would do.\n", "\n", "We'll take a look at two very simple machine learning tasks here.\n", "The first is a **classification** task: the figure shows a\n", "collection of two-dimensional data, colored according to two different class\n", "labels. A classification algorithm may be used to draw a dividing boundary\n", "between the two clusters of points:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Start pylab inline mode, so figures will appear in the notebook\n", "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n", "For more information, type 'help(pylab)'.\n" ] } ], "prompt_number": 0 }, { "cell_type": "code", "collapsed": false, "input": [ "# Import the example plot from the figures directory\n", "from figures import plot_sgd_separator\n", "plot_sgd_separator()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/usr/local/lib/python2.7/site-packages/scikits/__init__.py:1: UserWarning: Module argparse was already imported from /usr/local/Cellar/python/2.7.5/Frameworks/Python.framework/Versions/2.7/lib/python2.7/argparse.pyc, but /usr/local/lib/python2.7/site-packages is being added to sys.path\n", " __import__('pkg_resources').declare_namespace(__name__)\n" ] }, { "output_type": "display_data", "png": 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vvwARIUHYuXcfdu3ahe+++04jhAcNGlRjT1ilUtEM2MDpJbSfPHmCJ0+eICoq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\nAAAAAElFTkSuQmCC\n" } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This may seem like a trivial task, but it is a simple version of a very important concept.\n", "By drawing this separating line, we have learned a model which can **generalize** to new\n", "data: if you were to drop another point onto the plane which is unlabeled, this algorithm\n", "could now **predict** whether it's a blue or a red point.\n", "\n", "If you'd like to see the source code used to generate this, you can either open the\n", "code in the `figures` directory, or you can load the code using the `%load` magic command:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%load figures/sgd_separator.py" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next simple task we'll look at is a **regression** task: a simple best-fit line\n", "to a set of data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from figures import plot_linear_regression\n", "plot_linear_regression()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": 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} ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, this is an example of fitting a model to data, such that the model can make\n", "generalizations about new data. The model has been **learned** from the training\n", "data, and can be used to predict the result of test data:\n", "here, we might be given an x-value, and the model would\n", "allow us to predict the y value. Again, this might seem like a trivial problem,\n", "but it is a basic example of a type of operation that is fundamental to\n", "machine learning tasks." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Numpy Arrays" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Manipulating `numpy` arrays is an important part of doing machine learning\n", "(or, really, any type of scientific computation) in python. This will likely\n", "be review for most: we'll quickly go through some of the most important features." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "\n", "# Generating a random array\n", "X = np.random.random((3, 5)) # a 3 x 5 array\n", "\n", "print X" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0.24982919 0.48378 0.34611791 0.93056363 0.85692952]\n", " [ 0.93917333 0.39970643 0.71586001 0.36298654 0.85002866]\n", " [ 0.7843741 0.78904057 0.05889152 0.70738888 0.69578127]]\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# Accessing elements\n", "\n", "# get a single element\n", "print X[0, 0]\n", "\n", "# get a row\n", "print X[1]\n", "\n", "# get a column\n", "print X[:, 1]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.249829192873\n", "[ 0.93917333 0.39970643 0.71586001 0.36298654 0.85002866]\n", "[ 0.48378 0.39970643 0.78904057]\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# Transposing an array\n", "print X.T" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0.24982919 0.93917333 0.7843741 ]\n", " [ 0.48378 0.39970643 0.78904057]\n", " [ 0.34611791 0.71586001 0.05889152]\n", " [ 0.93056363 0.36298654 0.70738888]\n", " [ 0.85692952 0.85002866 0.69578127]]\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# Turning a row vector into a column vector\n", "y = np.linspace(0, 12, 5)\n", "print y" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0. 3. 6. 9. 12.]\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# make into a column vector\n", "print y[:, np.newaxis]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0.]\n", " [ 3.]\n", " [ 6.]\n", " [ 9.]\n", " [ 12.]]\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is much, much more to know, but these few operations are fundamental to what we'll\n", "do during this tutorial." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Scipy Sparse Matrices" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We won't make very much use of these in this tutorial, but sparse matrices are very nice\n", "in some situations. For example, in some machine learning tasks, especially those associated\n", "with textual analysis, the data may be mostly zeros. Storing all these zeros is very\n", "inefficient. We can create and manipulate sparse matrices as follows:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy import sparse\n", "\n", "# Create a random array with a lot of zeros\n", "X = np.random.random((10, 5))\n", "print X" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0.585518 0.06415763 0.75913686 0.80581087 0.68137401]\n", " [ 0.61971934 0.56481696 0.35755974 0.697473 0.74322261]\n", " [ 0.84875031 0.25221617 0.12119482 0.41220973 0.31692446]\n", " [ 0.74581236 0.82155691 0.14464943 0.33086894 0.05578879]\n", " [ 0.98147051 0.96077721 0.02509871 0.8556486 0.84275739]\n", " [ 0.68719098 0.9262659 0.94879041 0.14042142 0.44292677]\n", " [ 0.69404997 0.64906191 0.06059992 0.87881428 0.32579303]\n", " [ 0.80989958 0.68822441 0.42966414 0.64753776 0.23239696]\n", " [ 0.16881623 0.63786766 0.26436791 0.56446725 0.26133565]\n", " [ 0.75596037 0.51267943 0.19751519 0.77950664 0.33711698]]\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# set the majority of elements to zero\n", "X[X < 0.7] = 0\n", "print X" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0. 0. 0.75913686 0.80581087 0. ]\n", " [ 0. 0. 0. 0. 0.74322261]\n", " [ 0.84875031 0. 0. 0. 0. ]\n", " [ 0.74581236 0.82155691 0. 0. 0. ]\n", " [ 0.98147051 0.96077721 0. 0.8556486 0.84275739]\n", " [ 0. 0.9262659 0.94879041 0. 0. ]\n", " [ 0. 0. 0. 0.87881428 0. ]\n", " [ 0.80989958 0. 0. 0. 0. ]\n", " [ 0. 0. 0. 0. 0. ]\n", " [ 0.75596037 0. 0. 0.77950664 0. ]]\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "# turn X into a csr (Compressed-Sparse-Row) matrix\n", "X_csr = sparse.csr_matrix(X)\n", "print X_csr" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " (0, 2)\t0.759136856415\n", " (0, 3)\t0.805810871534\n", " (1, 4)\t0.743222610991\n", " (2, 0)\t0.848750307269\n", " (3, 0)\t0.745812361726\n", " (3, 1)\t0.821556911739\n", " (4, 0)\t0.98147051221\n", " (4, 1)\t0.96077720626\n", " (4, 3)\t0.855648596065\n", " (4, 4)\t0.842757385091\n", " (5, 1)\t0.926265903612\n", " (5, 2)\t0.948790410975\n", " (6, 3)\t0.878814282633\n", " (7, 0)\t0.809899582584\n", " (9, 0)\t0.755960370379\n", " (9, 3)\t0.779506638739\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# convert the sparse matrix to a dense array\n", "print X_csr.toarray()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0. 0. 0.75913686 0.80581087 0. ]\n", " [ 0. 0. 0. 0. 0.74322261]\n", " [ 0.84875031 0. 0. 0. 0. ]\n", " [ 0.74581236 0.82155691 0. 0. 0. ]\n", " [ 0.98147051 0.96077721 0. 0.8556486 0.84275739]\n", " [ 0. 0.9262659 0.94879041 0. 0. ]\n", " [ 0. 0. 0. 0.87881428 0. ]\n", " [ 0.80989958 0. 0. 0. 0. ]\n", " [ 0. 0. 0. 0. 0. ]\n", " [ 0.75596037 0. 0. 0.77950664 0. ]]\n" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The CSR representation can be very efficient for computations, but it is not\n", "as good for adding elements. For that, the LIL (List-In-List) representation\n", "is better:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Create an empty LIL matrix and add some items\n", "X_lil = sparse.lil_matrix((5, 5))\n", "\n", "for i, j in np.random.randint(0, 5, (15, 2)):\n", " X_lil[i, j] = i + j\n", "\n", "print X_lil" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " (0, 1)\t1.0\n", " (0, 4)\t4.0\n", " (1, 0)\t1.0\n", " (1, 1)\t2.0\n", " (1, 4)\t5.0\n", " (2, 3)\t5.0\n", " (2, 4)\t6.0\n", " (3, 2)\t5.0\n", " (3, 4)\t7.0\n", " (4, 0)\t4.0\n", " (4, 2)\t6.0\n" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "print X_lil.toarray()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0. 1. 0. 0. 4.]\n", " [ 1. 2. 0. 0. 5.]\n", " [ 0. 0. 0. 5. 6.]\n", " [ 0. 0. 5. 0. 7.]\n", " [ 4. 0. 6. 0. 0.]]\n" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Often, once an LIL matrix is created, it is useful to convert it to a CSR format\n", "(many scikit-learn algorithms require CSR or CSC format)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print X_lil.tocsr()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " (0, 1)\t1.0\n", " (0, 4)\t4.0\n", " (1, 0)\t1.0\n", " (1, 1)\t2.0\n", " (1, 4)\t5.0\n", " (2, 3)\t5.0\n", " (2, 4)\t6.0\n", " (3, 2)\t5.0\n", " (3, 4)\t7.0\n", " (4, 0)\t4.0\n", " (4, 2)\t6.0\n" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are several other sparse formats that can be useful for various problems:\n", "\n", "- `CSC` (compressed sparse column)\n", "- `BSR` (block sparse row)\n", "- `COO` (coordinate)\n", "- `DIA` (diagonal)\n", "- `DOK` (dictionary of keys)\n", "\n", "The ``scipy.sparse`` submodule also has a lot of functions for sparse matrices\n", "including linear algebra, sparse solvers, graph algorithms, and much more." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Matplotlib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another important part of machine learning is visualization of data. The most common\n", "tool for this in Python is `matplotlib`. It is an extremely flexible package, but\n", "we will go over some basics here.\n", "\n", "First, something special to IPython notebook. We can turn on the \"IPython inline\" mode,\n", "which will make plots show up inline in the notebook." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n", "For more information, type 'help(pylab)'.\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "# When you run %pylab inline, the following import happens automatically\n", "# but it's often useful to be explicit:\n", "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "# plotting a line\n", "x = np.linspace(0, 10, 100)\n", "plt.plot(x, np.sin(x))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", 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} ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "# scatter-plot points\n", "x = np.random.normal(size=500)\n", "y = np.random.normal(size=500)\n", "plt.scatter(x, y)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 21, "text": [ "" ] }, { "output_type": "display_data", "png": 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8FMVvjIws4z3u8Xj45ZdfcuzYsVy0aFGeVnXnz5+nwRBAzc6dQeBLWq2BTEhI\n8Gv38suv6KtuCwEHgWD9iSBLpigCv/u8b0vNNp1CIJGK0oSTJr3FefM+00P+FxGYT1kO54wZMxgc\nHEWHoxKt1sBrZkvUFG1vnznclCRjDmXYqlVnZkd+ksAgSpKvX3saDQaTV8lduHCBERGlaDT2oeZx\nQ2qbor8RqKuf5/26ws6gVrA5gsBCaiXkkpi1SWqzBfPYsWO5vv6klma2Ro1GtNubUFF6U1FC/Hzu\nXS4Xf/31V65bt+6a7pUJCQmsUqUeVTWGqlqCDRu2Em6KhZjbZkI5fPiwUOC3mIMHD9JuD6UkTSQw\nn4pSjm+/nR2E06PHE1TVGjQYnqeqVuaAAc/keuwZM2bQZoujFipuIFCKQUFFr9q2QoU6BN7RV9lN\nCKzyUYr1CLysv07XFfpKn+Pz2abNQ6xVqwWzqwORwAeUZV879WkqSokcRYoTExP5ySefUJajmF38\neBZLlKiYQ84335xMRWlI4CKBJFqtDWgyOamZQBJoNg9i48b3+vXZtGkTGzRorLd7jEAQgcrUzEtm\nAjIlyUmDwUSLJYAmk51OZ30ajU5arRVpMj1LVa3IQYOG5fra+34HinIvs1MBLGfx4tp5paamsn79\nlnra29qMji7L48eP5xijd++BtFj6UTPvZNJme5gvvvhqnmUR3B3kRnfelpqYI0eO9L6Oi4tDXFzc\n7Zj2X0VMTAw2b47HyJGTcOnSr+je/WX06NEdALB//3589dVSpKYeAGBHcvJLmDUrBsOHP4NixYrd\ncOyEhAS4XLEA3oJWfeYC0tPLXbXt6dN/A+gALfNfWwAvASgDIBU22wWo6lykpy+Cy3UJqmrBpUsb\n4Ha3AgCYzRtRqlQUEhMvA0j3GTUNqalJALrq78MBtMCePXvQqFEjAMCWLVvQunUHeDyRyMi4AqOx\nHGQ5HLLswXfffZdDzmeeeQoHDhzGJ59odTk7deqOrl2fR//+fXDp0lk0bNgMCxfO9bbfuXMnWrZs\nj/T0hwBYASwFsBNACWj1PbsC2AmL5X8YP74pnn76aZw4cQKHDx9G6dKlsXnzZuzfvx+VK0/E/fff\nf8Nr/k/OnDmDtLTqyK78E4sLF7SshePGjcfWrTIyMvYAMCIlZST69x+KZcu+8Btjx44/kJHxMgAD\nAAPS0jpj69Yv8yyL4M4QHx+P+Pj4vHW6GXcKsQK/s/zyyy90OGrlsMP6hqhfj61bt+rh7msJnKLV\n+hg7duzh3VXrAAAgAElEQVR21bbNmz9Ak+lFfaV4liZTUdpsQXQ6Izlu3ESmpqbyt99+4549e7ho\n0SI6HBFU1TYMCGjB4sXL8+zZs1y6dCllOYLaBuA7VJQQFilSlMC3uvwXqKoxfvlpoqLKEPjCe1xR\nSnHOnDlMT0+/7rllZmZeN+dLFq1bdyLwnj7+It3047snEE7gBCVpJF988aVcXde8sHbtWipKNIG9\nBNJpsfyP997bmbt27aLFUoTAdB9ZfuU999TIMYYWNdpf/25ctNm68IUXXrnpsgpuD7nRnUKB3wL2\n7t3LL7/8klu3bs1Xf7fbzU2bNnHVqlU57NBXIykpiUWKFKMkfUTgPCXpXUZE3JMnv/Zvv/2WkZFl\nqKpF+OCDj10zOvDvv/9m+fI1KcvhtFjsHDLk+ava2wcPfo6KUoJ2+/20WBx86qmnmZSU5D2+YsUK\nduzYnV26/B83b97MjRs30uEIp9NZhzZbKIcOzS744HK5KEkGauHsmhKT5X784IMPcn1+NyI2thmB\nFfr4f+h28KP6+3j9/W4qSgmuXLnylmwQTpv2IRUl0K/yULVqDQk8Qi2EP4WAm0bjQD78cK8c/S9e\nvMhKlerQbi9DVS3FevVaXNNeLrj7EQr8DvDRRzMpy2F0ODpSUaI5fPhreeqfmZnJ1q07UlXL0Ols\nwiJFonOVuGr37t2sWLEOZdnJ0qWrs1SpSnQ4wtmyZYcCpZW9Gm63m8ePH/erbblnzx5WqVKfqlqE\n5cvXos0WSSCBWb7XVmvADW8oCQkJXL9+vZ8Hxvr16/nxxx8zNLS4j438LFX1nqvmcMkvr78+iYpS\nj1qE6F6azdE0m510OGrQaAygwWCkLDs5dux41qvXggaDiQ5HGBcsWJhjrBMnTnDOnDn86quv8hwc\n5vF4/HJ5O52RukyPEShCIJyhoSV5/vz5q/bPzMzk9u3buXPnTuGFUsi5LQr8kUceYWRkJC0WC6Oj\no/1cz3IrxL+FxMREWq0OAvt1RXOOshyRp7SjmpdFHLOK9UrSdNao0STX/U+ePEm7PZSaB8YJmkxD\nWb16w/ycTq7RshwWoyRNJ3CakvQmJSmYQJrPijmcJ06cuOYYHo+HL774Ks1mmUajhZ07d+ezzw6n\nqpaiovSizRZNmy2YDkcV2mzBfOGFVzljxgwOG/YCFyxYkOMpwOPxMCEhIYf55NKlS/ztt9946tQp\nv8/dbjeHDn2RAQFhDAyM5Msvv8YlS5Zw6dKlvHjxIl0uFz0eD+vXb6W7J6YT+I2yHOZ3I/ntt98Y\nEBBGu70L7famrFChlt+Tx/X4/PP5DAyMpNFoYfPm7XnhwgU2btxWr3fpIbCbslw2RxCS4N/JbVuB\nF1SIfwt//vknVbWkn+3U6WzOFStW5HqM559/kcAYnzEOMygoKtf9R48eTau1KjXvEA8BN81me65M\nMXlhx44d/OGHH3jq1CmuW7eOqlqD/jbjKGa78X3GsLASOWpa+jJr1qdU1WoE/iZwhTZbexqNTmpV\nfTTPFKs1iD/88AOPHj3Kli0foKI0JzCWilKV1arVY/fu/fjtt9/yxIkTrFixNs1mO81mhW+//S5J\nzYfcbg+lw1GNNlsQJ09+76qyxMfH6+1q0GYL5vjxb5LUbgoGg4nZ+cVJoBeNRivbtu2suwI2IfAp\ns3OddOHrr9+4ov2vv/5KRcnyN79Ci+V/bNmyA0+cOMGYmKpUlGhaLA4OHjxMBP78RxAK/DaTlpam\nV0r/Rv8D3kRFCeHJkydzPcaCBQt0RXaRWgWZlxkX1y5XfUePHk+rNZpADwLlCDyjr8JtN9zsyw0u\nl4unTp1i//5DqCjRdDpbUFFCGB0dQ80nPCtI5hLN5kDabA5arYEMDy/J7du3k9SeUu6772FaLAqD\ngrJzsD/0UE99UzNLMf5CgyGMvjcFh6Myt23bxo0bN9JuL+d9StGulULgaVosYYyMLOmzav2IBkMo\nY2Pj9ILCP+l9jlCWw3OYp9xuN53OcB97+AkqSlGv/IGBkdRSCWRFrMYS+Iiy3IHDhr3EyMiyBPbo\nx/cRKEHAwJCQYly9evU1r+2kSZNoNj/tc74JtFrt3ut+6NAhnjt3rsDfoaDwIBT4HWDz5s0MDo6i\nzVaEihLEpUuX5qm/x+Phk08OpcUSQEWJYpky1XN1Azh79iytVie14JMs5RJCm604R4++8QrwWiQn\nJ3P//v1cvXo1g4OjaLE4CBRldiTkT9SSY/0fgVoEhlOSSvOJJ55iZmYmz50752eL7dDhUVqtvXT7\n+G+0WMIYHl6SWnDQ414FJknv0WgM1lfxqwiMZWBgJK9cucKVK1fS6Wzqo+w81NLkhhJoTy0z4Vhq\n3iRFCXymr4oDCKz2uSG05zfffON3vufPn6fF4vS7cQQEdOZ7773HL774gr17P66Xmmuq3zSK6jev\n11i3bms+8sjjtFp76jezUtQCgFwEVtBuD71mgq9Zs2ZRVVsy2w98HcPCSub7e7saH374MaOiyjMs\n7B4OHz5C2MjvcoQCv0O43W6ePn26QIVlz507x8OHD1/X7ODLH3/8wYCAMn6Kx2qN5YQJE/Itww8/\n/EBVLUK7/R5qEZjDqeX86P4P5WnSFdZ8Aj0oSYH8/PPPSWob3HPnzuWyZcvocrl0+/zfPv2fI9CB\nmqmkJIEmVJSudDjC+fbbb1OS7NTqUJZg1ar1mZaWxoSEBAYHR1GSplHb4HtBV85ZKWpP6ko8llqk\nZNZcMwi01l8fo9kckiOvd/YK/AfvCtxkCtILSFQgEECLpSK14J6slfhGAgF86KHuTExMZIsWD9Bg\nMOsy+JrTWnPZsmU5rvPPP//Mhx/uxaCg4rTZatJqHUBZDr1uSoG8smjRIr2Y8kYCe6godTlmTP5/\nG4Jbj1Dg/yHS0tJ0T41Z1PJkL6bDEZ7vormXLl3S61OuoZb/5H1qkYkr9VXnEWZFQmreERn6SrMn\ngXocNWoU16xZQ1UNod3elXZ7TTZu3IZFi5Yj8KOP8m9FLQNgJoHvaDaXYP/+/Xny5EnGxbXTK/mQ\ngIuy3I7Dhj3HuXPnctasWYyNbUqHI1JPUlXaR1km6zKq9DfLTNPPIZZAEA0G5aqr0J9++olWawDN\n5kiaTDaazeHMfrL5ST/fcn7KWZJK+xVpPnz4MLWydVnX6QpluTh/++03v7nWrFlDWQ6llk73HVos\nQRwyZMgNc6nklUceeVw//yyZ17Jixfo3dQ7BzUUo8NuAy+XKs6/tgQMH+OOPP17zcTq/7Ny5k6VK\nVaYkGRgZGcMNGzbkuu93333HyMjSlGUn27TpxHXr1jEgoAKBxtRC7BtQSyf7NYEJBKyU5WiGhBTX\nQ88DqVXCaUBFKc9FixaxWLEKBL7zKmBVbcp+/fpRSxb1JIF79dfbqAXOVCNQnwEB4dy9ezejoytQ\nS1mbSGAItSyHDqpqJ6pqOXbv3o8ej4crV66kwWCnZrdO0JV5bX21rRCYSC3DoF2XZzOBYzQarTly\nhbjdbrZu3ZGy3IzABFos99BkauenrLXr4Jvw6gBttiA/d80RI0bRaKxLraJ9XwJlGBZWmhs3buSn\nn37KzZs3kyRbtepE7akma+zpvP/+rjfnB+HDgAFDaDC85DPPXNar1+qmzyO4eQgFfot59933abEo\nNBqtjI1tnKuKLaNGjdcryzehohTh4sVL8jzvmTNnePLkyWt6I+TW7EKSGRkZugdEKDX78DlaLP3Y\npMm9NBpVavUzsyrvvEqjsQQVJYrDhg3nwYMHGRd3Hy2WB/RVdAcCdj755FB6PB590/C8V2mYTMM4\nbtw4BgcXpebXPIvaRqusK+/a1IoUt2PNmk3ZoUM3mkyD9JtILwLLdWVYl0AiVbUM161bR1KLZHQ6\nw2k0BlAz8WTZkt+ilga2ub4iX6nfEJ6jJDn5wQcz/K7Hli1bqKoxzN4g3aDfnI7p778jEEGTKZQW\nSzCdzjjKcgg//PATv3F69HiC2lPLEgJPERhBhyOKilKcqvoYFaUYR458nU2btqd/0q05bNWqcx5/\nETfm8OHDDAyMpMn0JCXpRSpKSI5cM4K7C6HAbyFa6HMxAn8RcNNkeo6NG7e9bp9du3bpIeRZj+Ob\nqShBTEtLy9WcGRkZ7NjxUVqtgbTZQtigQat811P0eDx85ZVRNJmsNBgslKTSzE6VeoUmk5W1a8fp\nK9cs5bKJERHlvIWBjx49qofgZyk7D1W1snfl37hxW5pMz+k3gENUlOKMj4/n77//zsjIGN3Nz06D\nwUbNLLGMWjX5xlSUMJ47d45lylTVV/Zu7xya+eJbWq21OWLECO+NzOVysX37R/4h8y8EStFkCmaN\nGjX1FblCzXSzkTZbIM+ePeu9LpoLYSy16j6dCbxLs7kILRYHjcZSBGSazTLffnsKDx8+zFWrVvHw\n4cM5ru/MmbNoNpfSzyuOgJOSpDI75/op2mzBnDHjQ/13tJjAOzQag9m8+f23RLkeO3aMY8aM5auv\njsh1mgXBnUMo8FvIhAkT/pFv+gJtNsd1+2i5Qe736eOh2RzIOnVa8b77uuSo07hp0ya+9957XLRo\nEd1uN8eNm0hFaUUtpDqTVmsP9uv3VL7k/+KLL/Sq86d0BfwYNU8SEthGpzOc77zzLmW5CbVq8i5a\nrX3ZvXs/7xhHjhzRswhmUssOuJt2e1X+/PPPJMnTp0+zRo1GNJlstFgUTpky1dvX4/Fw3rx5VJQY\nfXU8yue67KLBEMgXXniJa9eupSwXY3YYvZtaWLtmxjAYivCBBx72KvEZMz6iotTSV/5pNJk60mAI\noCQ9Qs0zxamv5LM8USpxx44dfuekbZw+p6+Mq9PpjOLJkyf5+++/8+zZszx58uQNN6gTExNpMqnM\nTq97nFoK3oM+c1fjb7/9xvnzF7BChdq6GWgMgbeoKGFcuXJlvr5bwb8DocBvIXPmzKGqNvVRLMsZ\nHV3+un3+/PNPfcMqK1LzSWo20q8IfEBVDfFuXk2dOo2KEkWb7X+022uwY8duvP/+RwjM9VF0a1il\nSqN8yT9w4NME3vAZaw+BIjSZhnmr1bhcLnbp0osWi5OyHMZatZr6BQRlZmayTJnKlKSiumLU7OH/\nLEZw5cqVq5p1evX6H7Walq9RK+WWJcsqAoE0GnsxNLQ4a9VqSs0885X+v0LNPe8UNRNJgDdYKikp\nib169aPRaKXRaGVERGlqJoyssb8lkBV09CMDAkL9nmJmzZpFWe7o0/4MTSaZHo+HK1asoN0eQpst\nlA5HmF+yrX+yf/9+3XvH13YeqytoD7M2mVeuXMkff/yRXbr0IjDJp+3nbNDg3muOL/j3IxT4LSQj\nI4ONG7eh3V6TdntXqmrIdQM1svj441m02Zy022MoSUEE1vv80Y7gkCHDmJ6eTotFpWaeIYE0Kko5\ndu3aXfcx1uy7JtNwPvRQz3zJP3HiJNpsnZltK/6EJUpU5vjx47lx40a/tmfOnOHx48eZlJTkDU13\nuVxs0eIBWq1lqdmtNb9wg+F11q/vvzmWkpJyVW+PF154mSbTAGp2bZma2WI8tUIJnQgMotXakxMn\nTqTdHkGgGIH7qG1m+irGYuzZsyc/++xz2mxOqmpxOp0R/PHHH3X3v7d82m6iJAVSliPocITl+M4+\n+eQTKkpXn/YXaTLZePbsWb2k3Vr985UMCAi7pgkrOTmZAQGhzA4c2kmrNYhBQZE0Gq0MCSnGypXr\n0G6vRIejES2WEGpujlnzLmNsbDOS5KFDhzh9+nTOmTOHV65cydf3nUVmZiYTEhJENGchQCjwW4zL\n5eKyZcs4d+5cHjp0KNf9Ll26xH379rFkyWrMLlGmbRI+88xzPHfunB5M4vE51pzjxo1j5cp1GRBQ\nnQ5HPRYrVi7fnixXrlxh5cp1abc3ZEBAZwYEhHH9+vX89ttvOWrUKHbq9Ahbt76X77//Ps+cOcMm\nTdrSaLTSZLLy5ZdH8fvvv6csVyXwkr6CzpLzOM1mJ6OiKjE2tiFLlapASTLSaLTwnXf8Q9fPnDlD\npzOUWtGEPQQG6zeD1gRmE+hGk2koX3/9dYaFxeiKPorapuIVfb5EAsGMiiqnF2bepX++lE5nBE0m\nOzWXwpXUqsFXZ+fOj/DYsWNXTTO7YsUK3VY9jsAPtFqbsnfvAdywYQOdzjrUvFzGEXiaslzcz/zy\nT1avXs2AgFDa7fdQlgM5f76Ws+XKlSucMGEibbYHqD3B7af2BBNMYCmBH6ko5fjhhx9z06ZNVNUQ\nyvL/UVXbMiamCi9dupSv7/yjj2bSatX2HUqXrpan36zg9iMU+F3O9Okf6jbgBQSmUFW1wBKPx0On\ns6i+Gk2n5jcdyEqVanPKlCkcM2YMV61aVeDVWFpaGhctWsTPPvuMW7ZsYUTEPbRaK1Oz1QZS8/yo\nw4CAKFosj1OzdZ+iolRk2bIVqJVMm02gPjW7PKmZRCKpeV80oZZH+1kCdWgwBHHChAl+rntxcfcS\neJNAR2qblZHUfLXLEBhIRdGuSdOm99NoHK/f1CoQqEqt+k91Ao8wLKw0nc57/VbmihJFVQ2iZl+v\nSaAcTaYg7yYsqZlcnn76eTZqdD/793+KgYER+jk8SqA2zWYnT58+zaNHj9JqDSZQnlqqgkkEIjh6\n9OvXvcbJycnct29fjpW6Zj7Kyj/egsBkai6ajShJkXzssR7cs2cPAwNLUosk1fZMLJbuHDt2XJ6/\nay3newS18H4PDYaJrFixTp7HEdw+hAK/xSQlJbFv30EsW7Y227TpxL/++ivPY8yb9xlbtHiQHTs+\n5pc//L77OhMoS8BIoDi1CEcnbbYnaLfXYYsW7fPkLngjunT5PxqNL+sr4JK68u5IYCg1T4o/fJTj\nJEpSCWq+0N9Ty1cdQc07RKHmSZKsvz6h90mn5g0SxpiYKrxw4QJPnz5Nk0khUIlAO72Ni0A3Ag5W\nqdKQkydP5uOPD2Tv3k8wPLwkHY5qNJtDqLkE9iDwBo3GWuzbdyAVJZLAaX2+7ZRlJ5csWUJVDaHT\nWZeyHMKxYyd6z9ntdrNu3ea02boRWESLpRcNhmD9RqWdq9NZ3+uq+NBDXaltuGZdh31U1eB8Xe/p\n02dQURpQq6VZjNlRpCQwjn369NOjVosS2O1z7A0OGDAkz/NNmzaNstzXZ5xMSpLhpv6GBDcXocBv\nMc2atdP/+H+hwTCBoaHF/XJkF4TZs+dQUSoTOEwtWZNMLahF++Oz22tyyZK8+5CTWo3Fffv2+W1I\n1q7dUlfGJamtwNsR+JKa2SKY2cEmHmrFfYdQC18PohZKH0tJMjM7rP6Urvh9zUBNCQymxTKAAwc+\nw7Vr19LhqKvfoD7zabeaklSMvXr1os0WQc2G/RxNpgDOnj2bI0eOpsVSjFqAURmaTCU4YcKbHDFi\nLGU5gk5nKypKCBcu/JKklpZg/fr1Odz99u3bR0UpTn8Plyhmh9Gfp8kUxAMHDpAkp06dSqu1j4+c\nl2gy2fJlT3a73ezRox+tVicNhiACr+jXKpGqWovt23egwfActc3dLrqi/4uKUjpfIfZLly6l3V6N\n2Sl+1zMwMDLP4whuH0KB30ISEhJoNtuZ7QNNBgS0vmn5KzweD199dTSt1gAajVZ9JZ5dkUZVe/Hj\njz/26+Nyufjss8MZHFyM4eExOYJUSHor31gsxWgyqZw06S2S5PPPv0JZbkegv76yzvpD9xAoTpst\niAEBnaiq9fXjHaglr3qNQAUajSGsVCmWWjBOG2rmgCj9eAI17w+V2iP8ArZu3ZmHDx+mzVaEwEBq\nPtdufb6h1CI6wwm8yuwCxs8yLKwEW7fuzOziDn8TGMxSpSry3Llz3LNnD5ctW8ajR4/e8Brv3bvX\nR4EnUNtEDdFvYF0IlKDJFM25c+eSzEoXHEIt58tu2myd2alT9wJ9z2fPnuXPP//MyMjSNBgiKUl2\n1qzZmM8++7x+7leoPeFYKUk2jh07np999hlnzJjBgwcP5noet9vNjh27UVUrMCDgISpKyFXzsgju\nHoQCv4UkJSXRZJKZnZXPw4CAely+fPlNncfj8dDj8bB69YY0Gl+hZmb4hYoSmqNQxIgRY/RK7PsJ\nbKGilOK3337rPe5yuehwRFDL0kdqPskOrlmzhmlpaezY8VF9Fe17Y/LQZKrMr776ip9//jkXL17M\nNm0eoGYyyVLyCTSbHXziif7UzB9jqK3gm+jK0KL/7yRQjrLcmGPHTuDw4SNoMGTdnBzUVv/VCVSk\n0VhUvwG01ZXqTwTG02AIYKdO3ShJr+nnGUmgC43GB1ikSDTr1WtGg8FMi8XBkSNHX/faut1u1qnT\njBbLo9RMR49T2+x8nJo5aC2ByezXb5C3z4YNG1i1akMWLVqODzzQhfPmzeOuXbtyjH3+/Hm2bv0g\nVbUIo6LKXfV3cenSJTZv3l7PMS7r120+FaU2+/YdQEUJofbk8z0VpQpfe20My5WLpd3eiorSm6oa\n4vW5z+1vafXq1Zw/f36+zH2C24tQ4LeYxx8fqNsxP6bV2pPly8fmuYTWp5/OZunSsSxVqhrffnvK\nNR/HT548yZo1m9JgMDIwMPKqK/3y5evS3y1xGh99tI/3+JkzZyhJAT7HSeBe2u0hfPzxgbx06RLX\nr1+v24G7UjOpDCFg9/N20ZI9xfqM4aGqluSmTZv0kPwHCAyiZgO/l1pCpzXUgoWqs1SpKnr61Kr6\nCjqZZvMDtNsjaLEE0WiUaTKVY/bG6EpdUdsJyDQabQTM1DZIx/nI8Ty1Dc4r+nVwMiamBp9//pVr\nfi9JSUl85JGeNBiimW3ucVNLBTuDVusDHD8+Z9a+CRPeoqJE0uHoSFmO4JtvTvE73rBha91F8hS1\n8HuFb7892a9Np07dabH0JjCC2kZv1nlso9MZwdmzZ7Np03asWbM5p0yZyjfeeIM228M+cn7JChVu\nvBE5d+48NmjQli1adOTatWtv2F5wdyAU+C3G7XZz6tQP+NBDvTh8+KtMTEzMU/+vv/6GilJCV26/\nUFEqXtXs8c85r0W9eq3oG+hjNL7AJ598xns8MzOTkiRTy+9BAmd1JdiIVuvjjI1tzB9++IEOR2Nq\nkYgtCDxBRYnyW7EtXryYmjlkKoFjlKTXWLJkJWZmZvLChQvs1asX4+KasWzZmtTMEsHUkmEFESjB\nkiWrs2vX3vTPjreZMTGx7Nq1Jw0GC7WshlnHMggY9FV9A2p7AmcoSVWoBUNltVtAzT6fQs01cQCB\nxZTlB3jvvZ2ued127dpFVS3FbBOVi1pu8Yo0GOw5koIdP36cNlswszdoj9FmC/Le5NLT0/V0shk+\nsj1Gs9nudx2Dg7M2L1+nZkYitULKJSlJZaiqJdm6dUevu6NmVvG9Yf3JkJCS1/29zJw5i4pyDzWT\n1kwqSig3bdp03T6CuwOhwO9y2rV7lFpCJ7f+x76MtWq1yPd4GzZsoKKE0GB4jmbzEwwKKspjx475\ntenatbu+km2kK6kIajmz3VSUYty0aRMdjnACXxC4TIPhbUZHl/ULHS9SJJpahr+6BCJpMIRx1qxZ\nOeQZPHioruj36gpnL7NzkZgpSf/nVUaS9AFLl65BWa5JLRIzitoGbtbKuji1LIO+6WE/p8FQnFou\n8b+pmUEGUnO7rOuzUk2j1Rp4zeLObreb9eq1oM3WVb8JdKDmbeImMI9lytTwa69dI98nENJqLedN\nFevxeGi1BhA4wOwVfQPKcg0uXrzYO05YWGlqG8XH9O/hVWp7CCOZ5bkjy6347rua//zy5cupKKX0\ncZNptXZn1669r/ubqFy5IbOrC2leLL17D7huH8HdQW50pwEF5IcffkD58uVRpkwZTJw4saDD/acI\nCJABfAHArv8bDVm25Hu8Bg0a4Ndf4/HaawEYM+Ye7N69BcWKFfNrM2/eLPTu3R1W604AKQBeANAF\ngAtkJkJCQvDjj0tRsuRomM3hqFz5K8THL4fJZAIAnD9/HhcuJACYDOAAgFawWNojNTUVAJCcnIwe\nPZ5AREQZLFu2EgZDJIDy+uzlARQF8A6AIyB/gMHQFKr6KGT5ZRw9egipqXsA9ALQFUAlADKAaQAq\nA0gF8DyAYQBWQpLeR2goYTIVg8VSBtWr2wHMBdAOwJUc12f79u2oU6cFihevjM6dH8ORI0cAAAaD\nAT/9tARDhpRB6dKTAFwAsBTAuwAm4M8/D2HevHneccqWLYvk5D8BrNY/WYn09BM4ePAgAECSJEyY\nMA5AAwDDAdyntzuNkiVLesepWrU8gH4AXgZQAsBHAP4E0FlvYUFq6n34/fd9AIC2bdti7NhnIMu1\nYTQGoVmzVHz00ZQc5+mLJEkA3D6fuGAwSNftIyhEFOQO4XK5GBMTw8OHDzMjI4PVqlXLsbFWwCn+\n1bz55pvUaiaeoFYo90G2afPgbZk7MzOTNWs2oc32KIHPKMsPsHnzdjd0ieva9f+oJb1yU3MXbEqz\nOdCb37pduy60WLrqq+0PqW3O/aav/rbqq/8T1NzmGhOwcsyYMQwICKMWhUhqyabCCLxESbIy2w86\niVpqWCc1m/grlOU2rFWrKZOTk1mkSFF9pV6S2sZnP2omlA6MjW1Es9mhP/FsJ9CWBoODEye+7Xd+\ny5Yto9UaQy0qVKWWEnYegVB+8MEH3mtnMKj6uTj1dpX4wgvDSWppFt55ZwqrVatHg8FCm60CZTmC\nL700ym+u0aPH0WJpQy174kIaDK8yJCSGJtNw/ekhhYrShNOmTffr5/F4cl0Obf78BXq2w9kEplBR\nQvwCmQR3L7nRnQXSrr/88gvbtGnjfT9+/HiOH+9ff1Eo8GvTr98gahF4WY+3OxkdXeG2zX/lyhU+\n//zLvPfeh/nqq6NvmNY2MTGRZnMYtYIIWTJ/yBo1GpPUTBFGo4XZxY2zfL9lSlJx3XTipOYfXppa\nCtVXGBQUwYCAKn4mCc0LRKbR6PjH5x2oebVkVbpx02is5lPCbDW1sPwmlKQQVqrUgJ07P6JnBnzU\nZ0cfdMcAACAASURBVJzL1IpSRHoLFpNasQ2tQIVCrXBFVvvlVJSiJLVUwtomZzq19LCan/7w4cPp\n8XjYtm1nynJLAlNps7VgrVpNrpq+NSUlhXXqNKPdXpEORz0WLVqaW7ZsYenS1Wi3x9BmC2Xnzt0L\nHGyzaNEitm3bhQ8+2D1HxkvB3UtudKepIKv3kydP+j2iR0dHY/PmzTnajRw50vs6Li4OcXFxBZn2\nX0OxYhGwWn9DejoBSAB+Q0REhPd4cnIyBg58FqtWrUF4eDimT38DdevWvWnzq6qKiRPH5rr9uHGT\n4HYHAlgOoA4ADwyG73DPPZEYNuwFREVFgDQCOAUgBgABmGG1BqN+/dKIjz8PYA6ACtBMC18DmI2M\njF+QkbFV7xcJ4AyAswAUuN1uaKaFfgC2AlgPzSQQrUtlgNtdHIAK4AEAzfTPp4BshokTX8KTT74I\nl2sggD0+Z3MegBUGQ0Ps2bMH1atXBwDs3LkTstwUSUk/AvC1MBqQnp4OADh37hyAQAAWAGEAPADM\nKFq0KPbv34+1azcjNfUgACvS0vpiz54Y/PHHH0hLS0ONGjW85ihZlrFhw0ps2bIF6enpqFWrFux2\nO/74YwsOHjwIWZZRokQJ3QySfzp06IAOHToUaAzBrSc+Ph7x8fF561SQO8RXX33Fvn37et/PnTuX\ngwYN8mtTwCn+1SQmJrJs2eq025tTVR9hQECY3+Ntu3Zd9I213QTm0W4PvWrxgFuFx+Ph/7N33uFR\nVN8bf2dn6+xmk5BeqCHUUENoUkIJEKQJKKGK0jsISm9SBUQUsdEFFb4UfyIqoChdQKWLQhBBQHoP\n6dn398edbDaGEkIglPk8D89Dptw5O5ucuXPuOe85ceIEjx07phaCdKDQLSlKoCqBEpRlTypKeQLj\nKUne6vYgikXO1mqYwaQqL3ZzmdFepMj9Jt3c6quLq54URSvB6my5MoU4VRGK/pImAmZKkjsNhj4U\nBT6r1PP6M7Mk7dcEfNir1wB6egZTpBUWpUhlnKXO8EdTp/PhL7/84vzMv/zyi1rcU0e1by7Fgq4f\nCxUSb0cXL15UZ+jTKfS+e1GSvPj9999zz549aiu69AVUByWpABUllDZbCVasWPOOGjZpaWl87705\nbNiwNV99tTfPnDnzSL5njceT7PjOB/KuP//8c6YQyuTJk7N0Qdcc+N25desWly9fzkWLFvHUqVPO\n7ampqVnCEYryMj/55JNHYldSUhIbNnyBZrMvFSWI4eG1OHnyVCpKLQq9kQ00GOqreeXXKFITyxHY\nRxF/bqM67/UUudCtmLnx8F4C3jQYBjA4uBhv3LjBiIha1OtrUOh+v8yMVEIHRXodGBJSlps3b2Z0\ndGvViQapTvlfitL+rhR51V4EerFdu66MjGxIEacuoT4EFPWh4E1JMjM1NZU//vgj+/YdxNGjx7Jb\nt360WIIoUiw9CHjRZHLjn3/+6bw/I0aMoU7nRSCQshzMunWfZ1paGpOSkli4cBj1+hEE9lKS3qCI\n1ycSSKPZ3I5vvDHytvd84MA3qCgRBD6nLL9OT88gXrp0Kde+U4fDwY8+msuaNZuwefN2Wleex5yH\n7sBTUlJYpEgR/v3330xKStIWMR+A2NhYdujQjdHRL3HhwsVMS0uj2ezGjFQ6B63Whly6dGmuXzs1\nNTVL/Hv8+Em0WJ5nusCUyfQqO3fuxQ4dutFgsNJotLNKlTpUlPQCmG2qA/+JIld7AoGhLg77jOpE\n2xN4k4AXAwND2b17P2d63/Xr1/nSS53p41OYnp5B6vH+BN6kXt+PNWuKBgfx8fE8cOAAq1WrRyFo\nlV99YISpTrc7gblUlIL84osvqCj5CPyi2rFfdfRCttVicednn31GRQkkMJU6XVPq9T4MCSnPrl27\nce7cuVy5ciUvXryY5b5t2bKFM2bM4LJlyzLFqc+cOcPGjV9kgQJhdHPLr87gM1IfGzV6MctYDoeD\nRqPCDDEuEmjIMmXCc01wasqU6bRawwisoiS9Q5vNh7Gxsbkytkbu89AdOClyU4sVK8aQkBBOnpxV\nWlNz4Pfmn3/+odWajyIroxUtllBOm/Y2p06dQUUJJTCdJlN7hoSUfWAJWVccDgd79OjrFKEqWTLC\nWYwUHf0SRfZFujP5kaVLP0dSlIBfunSJycnJDAkpq5b4/0GxsNeeIr+8A4EXXM7/iUJZr7bqQMdQ\nUYL53XffZbFrwoTJNBjKUpTKHyZQhP7+hdisWQxjYjrS0zOQbm4lVM10K4WAlolADGW5KO32QAYH\nl+SCBQu5d+9e2u1hFEJgy9WZf2kCL1NRCvCDDz5icHAJihDLdtX2zynK10vy/fc/eKB73LVrX5pM\nr1Bk7STRYmnG0aOzlvinpaVRrzdTLIim37MYGo3FOHfu3AeyIX18d3c/AkOYrkEvy4M4btz4e5+s\nkSc8EgeeG0Y867Rv30F95Z9IISYVxHz5gkmSX375JXv1GsCJEyfdttJz69atrF69EcuUqcGpU2dk\nO72MJN977z2KlLt/1Zn2i6xQQbRoq127ATMLTPVnaGjF24zxPnU6D0qSOwE7y5atynLlnqOHR35K\nko2S1Iiy/BpNJm+azZ4UWinp0qnvOotKfv/9d3bs2J1NmsRQlj2YkVJIAl9Qp/OniEf7M6Pa9Bx1\nOn8aDLVV59uBgImybGTJkpV48uRJXr58mXq9mzozb0kRzrAyOLgUGzVqwbNnz9LLqwBFcUwTdTbv\np471DUuUqHLH++dwOPjNN99w2rRp/Oqrr26bgnn9+nWGh9eiogTTYvFnvXpN75jt88orvSi01ddT\nrCEEEBjKwYPf4Pnz5zOpR94PDoeDLVq0oySVpKhcLUDRQHkQx4+/u16MRt6hOfAnBHf3/OoMlRRq\nfTVoNFru6Yz379+vCh59SmAjFSWcY8ZMyPZ1w8OrE5jp4igPUKfzJElGRbVUZ6olKQSmirJUqaqZ\nzr9+/TotFg+KRdazBPpRlu3OqsybN2/y/fff5+DBg7lu3TqWL18rUzhBlodw4MDX+ccff9Bm86Yk\nTVKdtLfqQNPtGk+RTx5IQGJGh3oSaEt//4IsUKAUdToPikVPB3W6iSxRohJbtWpPSVIoqh2pPqxs\nBD6gXv86AwKKsGvXPjSbq1CkOH6nHtuBQCTDwsRbx4ULF9iwYUt6egazTJnq3Lt3L/v3f51Wa0nq\n9YNotZZh166ZF/DTSUtL49GjR3n8+PFMTj41NZXXr193bktJSWHBgqUoFljbEPiNilKWxYpVoMnk\nQYPBypdf7nFfD2lSPOSt1mLMEB87ScBMq9VbC6E8xmgO/AnB0zM/RbjgK9V5vUSdrgQbNGhx1/jn\n8OEjKUkjXZzZfvr7h97zeikpKTxy5AirVatBkSmSnjExj3q9N0myf/8hNBi6EthJYDtleaRTOjU1\nNZVTpkxneHgdyrIPRaFOAQodkiACCkNDy3HNmjX09AymwRBEg8GdTZq0ptnsRUkaRb2+t7PUf+DA\n1ylJo1w+h2hqLLRMuqvO+08KSVobM9QUrxAIodlclF26dKHF8orLGKkU+imd1IeQay55JYr+k4Np\nMIRy1qxZrF27HoHMWt+AgVOnvsVZs2axYMFSNBheo1iTWEg3Nz+aTB7MCHncoMXi79QOvxeif6cb\n9XqFRYqUcUrDnj59miEhZWm1FqDJ5MHQ0Ao0mV6maDJxg4ryHGfPnsNLly5x+vTpHDNm7D1zu7/8\n8kva7c+7fDYHdTr7XZsya+Q9mgN/Qujd+zWazXXVV/d0oalk2mwRXL169R3PGzNmHGV5gMsf5nYW\nKFD6rtc6e/YsixWrQKu1II3GfBTx6GoU8Wor+/YdyF9++YWzZs1iQEBh2mzVabfXo59fYaeuSo8e\n/akoNVVHOooija8hRfXkjxSVli9QpOGNdzpEvb4ke/bsyREjRvHNNyfw9OnTJMk+fQYxs0jTdnUW\naqYII7Rz2VeSIoskQr2eEHgqUqSU6twLUrQq26k6/rPqcd+p529U7fVWrzmAiuLFadOm0WqNVh9m\nqQTGUacz02j0oMHQmSLWnjHzt1ob0GzOn+nBYLeHZ2kIfTsOHjxIRfFl+tuCJM1kaGh55/6UlBQe\nO3aM586dY9Gi4epnySicat68LQMCitBofJmSNIKK4su1a9fe8XqnT59WdczXErhFnW4qCxcOu++Z\nvMajRXPgTwjJycns12+IOmPMULCzWLpzzpw5dzzvxIkTtNv9qNONIfAxFaUQ586df9drRUe3pl7/\nhuqo4mg2V2OBAiEMDS3HyZOnctas2VSUQFVvujgbNGjGNWvWOOPvGYttl1ycSjRFD8tBLtvOqw74\nH5dtY6nXu3PGjFmZbNq9ezcNBk+K2PY6itDNdIrFycLMSCe8RLHIWJmi4UKo6sy9qdeXo9A330cR\nx3ajeCvYQrFA6U9R2WmjWG/40mmXTvcG+/QZyOLFK9JsbkHR6SecohlFaQpJXROFeqOY3et0oXR3\n96MkfUShCb+Q+fIF37FLvSsLFiyg1eoaInJQlo2ZeoWm07BhS+p0U5zHmUztGBlZjwaD69vCOoaE\nlL/NlTLYvHkzAwKKUpaNLFu2+iOtJ9DIGZoDf8KoUKEmZXmsOtM7RIvFj3v27OG3337Lzp17cuDA\n17OoCx47dozduvXliy92zlY3oMDA4szcY3Emu3fvR1LErI1GGzNSF2/SYglmdHQrlixZla1adeS/\n//6bxYGbzS1VCdgmLuPuoAiDvK/+nEDgOQJv02i0Mi4ujrGxsZw1axY//vhjzp49W9Uhr0RRLNSD\nIiRzk0A+1bEGqM43kmJ2/ZXqPMdR9A9NDwXNpXgDCKRozrCGQHVKUkmazd60WgMJ/Oxi69vs0qUP\n4+LiOHjwYBoM+SkWddPDNHb1YVSMQEWKmb4bCxQozpCQMjQaFRYrVpH79+/Pcr+vXr3Kzp17sUyZ\nGmzT5hVeuHCB69evp81WWr0nJPALrdZ8t10EPX78OH19C9Jur0s3t3CGhVVh8+YtmfFmQwJ/0Ne3\nSA5/6zQeVzQH/oRx6tQplilTlTqdgWaznZ9+uoSLFi1WxYjepSwPZr58QQ9UoRcZ2cRlRpdMi6UB\n3333PZLkyZMn1XzojDiyTudHWe5GYCsNhtdZpEgYu3XrQ4vlOYoYdQTNZg/WqlVLnQ1XoNAS96co\nqlHUGW0hiirLVFosflyzZg0VxYuyXJ06XQhNJm9GRUWpnd+NFJonlynCFVVYp04dyrI3gRcpZuQN\nM81gxcMiPYf6DfV6+SlywwMp0ueGEmhKSTJSrw+iSCtcT4slgBs3biRJ/vDDD3R3r/mfsYNUB16D\nokL0X4o4vSfd3f1umyNOirWCChVq0GjsSuAnGgyDWLRoOSYkJLB160602UrSZnuJFos3V626c6js\n2rVr/Oabb/j999/z4sWLNJvtFOG2zQT+IlCbnTp1y/HvhMbjiebAn1ASEhKcs7Hg4JLMiIuTen1P\nTpw4Kcdj//333wwICKHdHkGrNYT16zdzNgxISUmhv38RChXBNGZkhKQ5nZnNVobbtm1TW5eVUY+N\noQhtTFAdp42SlI8mk50+PgVU5/c+gURK0jssUKAEK1asTaABRV74Coo+mO4UbdjSm0XEUbQY86DJ\n5EfgPQrNbA91Npw+Sz5F0aGnJ0UFpweBYQRC6eMTSIOhLcXsfRDFoqg7RVWmhfnzl+CKFSuc9+fq\n1avMly+IkvSJOu5oAlbqdOmt4a65OPc+NJlKc/ny5be91xs3bqTRGJjp/rm5leXOnTvpcDi4ceNG\nLl26lEeOHMn297dv3z7a7aXVh09JCtkBf5rN7mzQ4IU7Pkw0njyy4zsfWA9cI/cxm81OAaOkpEQA\nns59aWn5kJiYdN9jnjhxAv37D8Ybb4yFm5sVt27th9GYgEGDesBgMAAAfv31V3Ts2Ao+PpMhSUYY\nja9BCEelqqM4kJBwDTqdDr/8sgMOx2YIkanPARQHUBbALgApIN+DTmfGV18tw6ZN6+Dn9zZ0OitK\nlPgcGzd+jUuXLgHYBGANgNYA3gZQEUBvAJUAjAbgAb2+N7y8gpGUtBhAPwBvqte8ASAcQH8ANQEM\nBrAQQCEAhwFMgSS9iNatm8PhWAuheR6pXm8shOa2GcHBwWjdurXzPnl4eGDz5nUIC1sIs7kcChX6\nEmvXLsfp039Clk0A/lKPJIBjkCSHU5zKlW3btqFJk9ZITk7MdP/IROj1ekiShPDwcFy4cAFLlizF\ntm3bsvU9BgcHIzn5jHqvD0OIeyUiMfEH/PRTEJo2jcnWOBpPCY/DU0TjzgwZMkJtVPwzgeVUFO9M\n8qfZ4eTJk3R396dON0ydxfpRNMvdSEURucDz5i2gogTSaBxAq7UWq1Wrz44du1Es5kVT6Gi/QKPR\ni7///rs6401ymY0+r86kUylixFcJTGHv3gOddly9epVz587l7NmzGRPTmWKR8qbLGI0oOr4PouiI\nM5ZGYxvKshczx6ynUIRo8lMU56Tn0JdjRsz9JkUDZTvLlq1IkXFSgyIenj7OCPr6FuG1a9fYs+cA\nVqvWiH36vMZz586xZMlwKsrz1Otfo8Xiy5UrV/HTT5dQr/emCBFFEwhiwYIlb1sdK9rJraToD9qU\nwBLq9a0YEVHbmf9dsGBJmkwxlKTRVJQAfv75F9n6PhcsWEyLxVsNY9kJfMD0kJcsG++7L6vG40l2\nfKfmwB9zUlNTOXLkeBYtGs4KFWrzxx9/vO8xRo0a8590w59UZ0fabO24cOFCNa562OkIbLbK7NGj\nJ/X6yhTaJe0JVGXt2o3ZokV7ithyjOpYp1GEWnaqYYw6BEhJGsbXXnuDpFDwCwoKpaK0pNncjYri\nxYCAUAr5gG8IjKTIGvmWIhwTTJGR8jJ1uiCKYqJN6kPCxp49e7Jjx47U6QIpdE6OUsTf81GIZnlR\n5HX/qlaJWilSDF2bPr/NYsXKsWzZajQaXyXwKXW6crTZfGgyVWXGoug2+vmJRcKtW7eySZOmLFeu\nMvv0GcTLly/f9p4LLZe/KYpnJhGoyPLlI1izZjQLFizD8PCaNJubu9iyw3mN7HDixAmOGzdOVYJM\nD9Ecp9GoaOmBTwmaA9cgSQ4ZMpQZfRZJoQdSgkAKbbaKXL16NXU6PTOa+pJWaycOGzaMspyPIm86\nP4EQhoSUYZkyNVRH25dihl5WdZhBqgN9i8AourlliCUNHz6KBkN3FxsWMzw8kiNHjmelSvXo5xdK\nkapnIyBTyAr8QaPRh2ZzE4qK0aoE6lCnMznTGnv16qvOQv0I1KUs+1OkY+53uVYX9Z+n+uDZQpHD\n7kl392DqdH4U8fsCFKXmb1HkjqdrwVykxeJx13u8a9cuduzYnR06dOOOHTvYvHk7Go1dKN5S/qLF\nUoBubn7U6WYS2ENZfpkiDTL9IbGGJpONW7duzfb3mpyczIiISCpKAzUfvCBnznwv578oGo8VmgPX\nIJmuce1DUca+jUAYdbpqtFqrs06d55mamsqIiEjq9YMp0vJEaGXMmDG0WLpTFOZsoxCsktiz50BV\npzyZQBxl+TkCLVRHtJZARdpsvpmEqjp37kkRvkl3qr+yUKGymew8d+4cn3/+RVqtwZQkG61WT06Y\nMIk2W7rA1AkaDP0YERGZ6bwlS5bSzS2IklSUstyLIrySrg2eqjr+ZQQaU7xJRFDkkrtTvF2sppjx\nd3axbyuBEAIXaDJ1YtOmMXe8vzt27FAlDWYQeIcmkxdXrFjBOnWaUJYNNJls7NKlG+32KJfxUylS\nIVdQhGT8KMsxVJRCHD58bLa/28TERH7yySccN248f/jhh2yfp/H4oznwJ5BTp05x8eLFXLFiRa7G\nMn/44QeGh9dhsWIR7N69N2fMmMEvvvjC2W3+/PnzrFGjEY1GhX5+Rfjdd9/xm2++oaKUptDlyEfA\nj3q9J0+fPs26dZvQaLTTYLCyfv2mtFi8CLxL0Rk+nEZjZ5rNnpwxYwbPnz/PlStXqsqKRwhcosXS\nhL17v+a0LzExkUWKhNFgGEJgHSWpFCXJQl/fwpwyZSpLlqxMT88gNmrUKkumxb59+9RUyzjVOZ4j\nYKLZ3JGyXJEiYyWFogrTnSLk04SiGCjdoQ6kyFxJ//koJclOi8WdTZu2ua2QWDpNm7ZlRhyaBObT\nZPLjmTNnmJycTIfDwQ0bNtDNrZLLjPsqZdmihpFcZWQv0mz21gptNDQH/iSxevVqVqpUl7KcjxZL\nHdpskSxRIpw3b958ZDbcunUrU/zU4XCwQoVqFPre8QTSaDB0YYcO3ehwOHjp0iWnQt727dtZrlwV\nynIt1UnNJuBJSSpNRfHiV1+t4fTpM2mzedNotLJt21czqfL9/PPPdHMrp54bQ5HrfYHANiqKH3/9\n9dc72t2lS3eKGDmd/3Q6P/bt25cfffQRAwOL0s2tGo3GYNpsvoyMjGKNGpE0mVyrIb9lhs7KXlos\nddm37+Bs3beoqFbMUEik+qYTxtatOzmPEXr51Wk2v0jgfVosEWzW7CX+8MMPtNsz9wPNbkm+xtON\n5sAfQ1JSUjh9+kw2adKWgwcP47Vr17h8+f/UGeTnBOZRLOJto8nUhlOnvvXQbTpz5gzLlq1GWTbS\nZLJlKsdv2bITRcZKuoPZzmLFIrKMIZr5NlZnskfVz5DeeHgXFcXztqXi6fz666+02YqpoQUPZpSt\nk3r9a1k6PaXz7bff0mIpRBGzXqk+aN4j4E8PjwBeuXKFZ8+epb9/CPV60RJOUYpy5Mix9PAIoE43\nicD/UVGqsGXLNixVqirz5y/Ffv2GOPPj78X//d//UZZ9KTJcvqFYLB3DMmVqZDouLi6OEyZMYqVK\ntanXW2m3V6bFkk/VKVmhPry+oiy7sVChMuzVa5CWUfIMoznwh8DZs2c5f/58Llq0iFeuXLnv89u2\nfZWKEkngUxqNr7B48YqsWDGSoiw83Um+S+AVAtMzpeE9LCpXrktZHkmRzfAnFSWQ3333HWfMmMHa\ntevRZErXBSf1+lFs1qxtljE+/ngeTaZCFAuFHxKon2lWabUW4F9//XVHG1JTU1m1al2aza0pim62\nMb34RVGa8OOPPyYpqhJ79RrI6tWj2bJlWxYoUIyihdpOioVZA4UOipVACL/88ksuXryYVmtjF3uO\n0WJx59GjR9m6dSfWqPE83357FlNTUzlw4FDq9SbKsolRUc0zpQimpKTcUR0yJqY9JSmYohDpUxqN\n7dm9e/8sx8XGxtJicX247abJ5EY/v8LU6fSUJCtFxehums3N2Lp1x/v9OjWeEjQHnsscOXKEHh4B\nVJQYWq0t6O9fmP/++2+2z7927RoNBiszYrUOurlVYWhoBDMkUknRdDeGilKCq1ateiif5bfffmP9\n+i0YHl6XkmSkaz620diHNpsnjcbOlKSB1OncabEUp91emfnzF3eqCLpSs2YTigbDZoo4s5Ui3i3S\nFm02byYkJPDmzZt86aXO9PQMYuHCZbl+/XrnGPHx8Rw9ejwrVapJg8GLev0gKkoTlihRkbdu3WJK\nSgrLlq2mdrhZQ9FwIoCiojM9lW45hZ7KQQJ2Dhs2jB9++OF/pGZvUq83ZUm3W7hwERWlAkXD5SSa\nzW35yiu9mZiYyBdf7ERZNlKvN3PgwDey6JYkJyfzhRfa0WCwU5I8COjp5ubLRYsWZzruu+++o7v7\nfx9uBXns2DHOmTOHitLRZd+N29qp8WygOfBc5vnnX6JON93l1X4we/TIOsu6E5cuXaLRaKer4qDd\nXofDhg1Te0supShNt1KWjRw3Lucl83fjyJEj6mv7hxS51qWYkYGRQoOhInW6Bi6OZBzNZn+Ghlbi\n9Okzbyu61KRJjPrm0Ep9e5hFkd6XnwaDnd9//z1JslmzGJpMbdUZ6LdUFG8eOnQo01jLli2n0Wil\nLJvp7u7j1Ltev349zeYiLs46lSJUU5ZCZKqh+nN60U8Tjho1isePH1czWZYSOECzuTVbtGiX5TN0\n6NCdwByXz/0LixQpz0GDhqn9QW8SuEBFqcQPP/w407lJSUkkyaioFmpOeRyBfVSUQG7bts153PHj\nx2mxeLs83DbRZvNmfHw8P/30U1qtroJgJ2ky2W57vzWefjQHnsuUL1+bwA8uf2BL2bhxm2yf73A4\nWLduE5rNMQR+oiyPpb9/EV6/fp1ffvkl69V7gdHRL3Ljxo0PddY1YcJEyrKr9OsfBGy02drSZqug\nKhamP6j2UGSgLCKwjlZrWU6dOiPLmPv27VMfCn0ocq1NBIKo14dz5syZ3Lt3Lxs0aKnO9q86r20y\n9eY777zjHEeEGLwpZGFJYCEDAkLUVEhPilxtVwceSBG2GUhR2fmNuu8WdboCztS6nTt3skKFWgwK\nKslXXunNW7duOb+TdAc5evQ4mkwdmZ4pIkmzWatWY5YuXZ1COCr9fi1i8+btSYoUQl/fQpQkmUFB\nobRYPCk0yNMXU4dy4sSJme7VvHkLaTZ70G4vTZvNmxs2bCApOhwVKFCCRmN3Ah9QUUpx7NjM52o8\nOzxUB/6///2PpUqVok6n42+//fZARjwpDB8+looSRSFodI6KUolz5nx4X2PExcWxZ8+BLFOmBps3\nb5dFHjanHDlyhNu3b79ruls6U6ZMocHQ28Uh7WO+fAW4ePFifvPNN2r6YH6KVm2RFDHm9GN3M3/+\n2zeN+PPPP1m58nOU5SD1PB/qdB6cOXOmOgOeo86Q9zIjvv0858/PWDRdvnw57XbXZsikyeTBkiUr\nqw+RahRZKv9HoQxYl0AzGo35OGDAQBoMntTrG9NgKMiYmFcyOeis92E6LRYPGgwWtmnTmefPn2dI\nSFkaDFWo1zekxSJkA8SD522nPQZDX/brN5hXr16l3e5HsX7hILBMrfrckOnzffTRR1mufenSJe7b\nty/L93X58mUOHTqSHTp042effa7Nvp9hHqoD/+OPP3jkyBFGRkY+Mw48OTmZHTt2o15vosFgj32D\nKwAAIABJREFU4aBBQ/P8D0x0lh9Ai8WPdnsEPT0D7/p9kOnaKH7U6cYT+JSKUpwzZryT6ZiOHTtT\nZIOUpCg0SXeom1m4cLk7jp2Wlsby5Z+jJDWmaLDwnepU2zhn1GLWPIo6XXMGBBRlXFwcT506xQoV\nalJUYVopOvL8QGA/zWY7PT2DKUrTL1PkTUdRyMbepCxX4pAhQ3j8+HH+/vvvXLZsGbds2cJ//vmH\nFSvWoiwb6ONTkOvWrXPauWLFCipKMQo51qu0WJqxa9e+LFSoFPX6KALdabGUZ9++g7lr1y7abPlo\nsTSkzRbN4OBivHDhArdv305398qZHjYWSwGazV40m3vQaq3PsLAqd82+0dC4E48khPIsOfB00tLS\nHpuFpW+//ZZWaylmyJx+xsKFw+55XmxsLDt27M7GjdtwyZKlmfbFx8fTZLIRiKXI7rBTVCzOp07n\nzwULFt51bKvVi0IzOz2MMIiyHOHi6BaqTvhFWiz5OX/+ApYuXZmSNJqiycFPaljERrPZk8uWLWd0\ndGsaDP3U8MnrFJ16ZtBsfpFBQUVpNrvTas1PDw9/bt++nSRZqlQEZXkcRWrhj1QUb2cmzMsv/7cy\n9Dd6exdRM4TSi20uU5YNtNv96OZWnUajH+vUiea1a9dIimYaZrMP03XLgbM0mTy4efNmzp49m0uW\nLHloaYBXr15l69adGBBQjJUr1+PBgwcfynU08o7s+M6sOpgPgXHjxjn/HxkZicjIyEdx2YeGTvf4\nqPAeOXIEqal1AbirW17AP/90vud5RYsWxaeffnzbfVevXoVOpwDwBtAIQFMI6dffUapUEF555e7j\nK4obbt36B0AAAMBoPAVJOoKkpLFwOIoCmARgPIAhSEjYgTFjuuD8+RMgdwKQIGRfGwK4ie7di6JN\nm5dQr15dREe3xv79djgcqWjRojl8fU/Bbg/Be+/9hMTELRASq9+gUaMWaNCgAf744wDIXeqYdSDL\ndbFz504UKVIEQUE+MBgOIiUl3eoDuHTpMoAi6vEAoCAtzYgbN+ZAyM/exO7dVbFjxw5ER0cjJCQE\nfft2x5w5FUBWgE63F2+88QZq1aqFWrVq3fM7eBAaN34Rv/1WGMnJX+Lcua2oUSMKsbEH4OPj81Cv\nq/Hw2LRpEzZt2nR/J93Nu9evX59hYWFZ/q1Zs8Z5zLM4A3+c2LBhA63WUGa0OJvHokXv3h/xXqSm\npjIwsChFcwRXxbzTNBqt9wwbLVmylIoSSEkaR6OxHW02P5YqVZVFi5ZnoUJlKCRW02e5B+jrG6LO\n+NMzM66qYRYPenkVzqTxcfXq1UwVnEuXLqXZXCtTGEMIa71OoTVyVN2WTIulNBcvFml9ly5dUhUD\n61M0TbaoIZlACk2T7TSZWlIIYyW6xON789133yUpwld9+gym0ehBs7ko8+UL5p9//vlA9z47ZKSj\nZoiP2e1NHlrKqUbekB3fqYVQHgHx8fG8fPnyQ4uXDxkykiaTJ93cStHbu0CuvE4fPnyY3t6BzBCp\nIkVneXO2wkebN2/m0KHDGRxchHr9SwTWUZa7s1Ch0qpuylICm6kolTly5Dh+/PE8Go2+FD0sA9Tr\nHiOwhorizd9//z3LNURTZx813JKuJbJfjaHfougo5EehRFiaen0ATSYvVq5cm3XqNKFe70FgKsXi\n6jIKSdwDFP003dm9e38WLVpe7c5DAudotRbhTz/9RJJcs2YNrdbSTM+qkaQ5DAur+sD3/l4kJCSo\nfUkvqnal0WaLyCQepvHk88gc+N10Kp51Bz5ixDgaDBYajW6sUKHGQ2t5debMGe7fvz9XFsxSUlK4\ncuVKTpkyhVarNyXpPQJbabE0Ytu2r2Y5Pi0tjbt37+amTZsyabccPXqUBoO/y0zRQZ2uCOfMmcMq\nVaJYqlQ1Tpz4lvOBsGvXLvbq1UtNNbziMuvtxVmzMneyv3XrFuvWbUxJGkFgMoUCYW0KPRNXjZPZ\n6nijVTvOq069HUVGi+vMPT/FouYGenoG0uFw8PDhw/T1LUSbrShNJneOHj3BacOUKVOo1w9xOf8K\nTSY3kkIcbOTIUWzUqAmHDh16XwVf2WHw4OG0WssSeJtmc0uWK1fdmYuu8XTwUB346tWrGRwcTLPZ\nTD8/PzZq1CjHRjytrF69mlZrSdVppNFgGMDo6NZ5bdZdSUlJYc2aDWm1VqHZHEOj0Z1hYdVYsmRV\nvvba8CxOIikpiZGRjWm1htJur0o/v8I8fvw4SaqdezyZUbiURiA/Z8+efVcbRGpehp63ojThvHnz\nnPuvXr3KkJAylOVCFKmFpGhGMYmS5KHmkb9LYBCNRn9Kkuk/jjpGDZP4UGS2kMAOAmba7TVos/k4\nZ9mkUEr8448/eOHChUx2iu+3LIEbTA9flShRiRcuXFB7gXamWPz1osXiwcOHDz/gt5OBw+Hg559/\nzp49+3PatOlapstTyCOZgeeGEU8ropHCRBfHcZz58uV/aNdLTEzkX3/9ddsWX9ll+fLltFjCKbrb\n+BJQaDJ53vH4d96ZRYulEYVcK6nTvcXatZ8nSV65coWS5EZRnfk/AqKDzhdf3L112CefzFPz0N+k\nydSGISFlMs3shw4dqTZLWEyR5nhYdcTVaTSWZPfu3anT2ajTtaDJVJ2y7E5RYk+KRsWBBH6jyEJR\naLeH02r14gcffMAffviB58+fz9a9cjgcfOWV3rRY/Gm3h9PLKz8PHjzIyZOnUKfr5PK9/0QgP8uV\nq8IFCxY8kji5xpNPdnzn45NOkUfcvHkTkydPQa9eA7By5cpcHbtQofywWLZBNAYGgK0ICsqfq9dI\nZ+fOnfD3L4yyZevA2zsIS5d+nqNxzp8/j8TE6wCaADgH4G8kJdmwZs2a2x5/+PAxJCQ0BCASmhyO\n5xEbewwA4OnpiXLlykCSzkI0HL4JRUm+Z4ZGt25dsGbNQrzxRgImTYrA3r3bYbPZnPv//vtfJCdX\nAdARwKsA6gAIA1ADBoMR33//MxyOT+BwfImkpG2Q5Towm7vCza0UgGKQpAoAbsJs3oLatWth/fr3\ncfLkEfTq1Qv16tWDr69vtu6VJElYsGAO9u3bjPXr38fff/+OsLAwxMXdgsNRwOVIfwBxOHDgBvr1\n+wkVK9bA2rVrcfbsWTRtGoMiRcqjRYv2OH/+fLauq6Hh5HF4iuQV8fHxLFGiIk2mGAJvU1FK5Grp\ncmJiIqtWrUebrQLt9ia02/24Z8+eXBs/neTkZHp4BDCjYe8hyrJ7jl7Z9+7dS5GR8a/LDHIkx44d\n6zwmISGBQ4aMYPXq0XzuuUhaLFUpdEIc1Os7s2DBkuzTZxD37NnDCxcuMCqqBd3dA1iiRIRT1+S/\nnDp1ihERdWgwWBgUVCxTCOO/fPLJXJpMJSiaBTeg6O9ZixbL86xSpS59fAozI/uEBCazT58B3L9/\nP3fv3s2mTWMYFvYc+/QZfNfQQ1paGidPnsbQ0EosX75WthcJd+3aRaMxH4Hv1LeDMuq/FNWerfT0\nDGThwqWp1w8n8CsNhiEMDS3/wG9QGk8P2fGdz7QDX7FiBW0218KN0zQYLLlapJOSksINGzZw1apV\nPHfuXK6N68o///xDs9nvP3HeWqxXLzpH4wktlE+Znn6nKDW5cOFCkiJsUL9+M1osLQmsoV7fhzab\nP00mT5rNvpQkKyVpLIE3aTR6cMSIEfdcuHU4HCxZshJleawaT/6WVqs3T548edvjDx48SL3eTiHG\ntYY6XVFWrVqL7777LhMTE9m6dScajZ0p+lGepKKEZkp9zS4TJkylooRTtFdbTYvF11kkdC/WrFlD\nD4+CFNK2Bup0XVy+m0RKkkw3t9Iuv3sOSlIgTSYvGo02vvPO3dcJNJ5+NAd+DxYvXkybrU2mPyxZ\nNj5xq/kircxKITxFikVTf7q5+eRovD179tDd3Z/u7lG02UqwQYMWTh3sf//9lyZTPtU5pmeWlGJ0\ndHM2aNCCwNsu93MudboQenvnv6MzJkWs3Gh0c3FmpJtbSy5fvvy2xw8fPpKSNNzlOr8wOLikc/+1\na9dYt25T6nQGGgwWTp487Y7XvnnzJidPnsJevQZwxYoVmfYVKlSWwC6X60xhnz6D7udWMi0tjbt2\n7aKi+BM4RCCNsjySpUtH0GotyIwF3kR1zeEogb+pKEHctWvXfV1L4+kiO77zkVRiPq7Uq1cPkvQ6\nRHw2AibTVNSq1RhGozGvTbsvzGYz2rdvjcWLIwFUBnAIQFO4uW3O0XgVKlTAsWMHsXv3bri7u6Na\ntWrO6lNJkrIc73CYsHHjRXh4XIKoWEzHBw5HKK5erYzhw9/EZ5/Nu+31bDYbyDQAJwEUApAC8ii8\nvLycx1y6dAkpKSnw9/eHJEmQpFSQ6XtTM9nl7u6OjRvXIDk5GXq9/o6Vs4mJiYiIiMTffxdFUlIE\nFi8eg0OHjmDcuJEAAJPJBOCq83id7goUxXTXe/dfdDodKleujA8/nIYePaojOTkBZcpUxtdfr0bn\nzr2xY0dzJCQ0AzAXgA1AJwDVQdbH3r17cf78eezYsRMFC+bHq6+++sT9bmo8ZB6Hp0hesmfPHoaH\nRzIwsDjbtevKGzdu5LVJOeLKlSv08ytEvb4egT5UlAAuW3b7GeyD4HA4GBXVnEZjUwpFwN5qDPoy\nJUlPRSlE4EcCWygEqRYQWMXatZveddyZM9+jouSnwTCQNls1NmjQgmlpaUxJSWHr1h1pNNppMuVj\njRoNuHfvXtpsPpSkaQSWUlGK8v33P7jvz7Jy5cr/hNDOUK83O982VqxYSYslgMA71OlG0G73c6ZI\n5gSHw5GpijQpKYnTps1gy5YdKLRh3lLDNS0py75s374zrdZiBMbRYmnAatXqOZtQazz9ZMd3PlMO\n/MSJE5w2bRrfeustnjhxIq/NyXUuX77Mt956i8OGjeDWrVtzZcz4+PgsawIJCQls3rw1dbqCBPpS\niDldoiwbOW/efHp5hVCS/Cg0xS9QUarzrbfevue1tmzZwunTp/Pzzz93yqxOnTqdilKPorIyhSZT\nB7Zr9yo/+ugjRke3YsOGrbl06Wd3HHPv3r2sWjWKhQqVZdeu/Zw64CS5cOFCms2tXEIkSZRlI/fv\n38/58+fz66+/5vr169m5cy/26/cajx07lsO7eHeWLVtGi8W15VsCAb1abZm+mJxKm61CJkVFjacb\nzYG7cPjwYbq5+dJg6EWDoRfd3HxztbDiQfjxxx9ZqVJdlihRhRMmTM3RIur169cZGxubaYb3IJw6\ndYphYVUpy0aazXYuWvRppv03b95kgQIlVF3xhVSUys4u7qmpqezRoz/1ejMNBgtbtGjD9u27snPn\nnpmycBISEjhnzoesW7cFX3ihAw8cOMC1a9fSzc2bsmxiwYIlGRnZxGVBNZGiPN6ddntdWiy+HDFi\nHDt16sFKleqxV69BmfLFT506RTc3X/Wc32g2t2KzZjFOG2vWbEBRubmAwCHq9e1Yrlx1KooPrdZO\ntNkqsn79Znfsg5lbrFq1ijZbHZc3gStq/N7KjOYVpN3ePEucXuPpRXPgLrRs2UF95RZ/DJI0jS1b\n5n3D2N9++42K4kOhxbGFihLB0aPfvK8xPvjgY5pMdlqthZgvX9BdpQ2yi9DRHqs6kN+pKP5Zxr10\n6RIHDHidzZu35wcffJRF6yUtLY0bNmygovgSeIfAVCqKN3fv3s2hQ0erJe4FCCynJL1Ni8VT1UnZ\noTqzT2i1+tBk6kTgH4qinRAKzZI2BI5Skuw0GPoSWEeTqT2rVq3ntGP+/Pm0Wtu5zGxvUZaNTElJ\n4ddff02braK6SBlJIISy7E673Ve9Pgmk0Gar/NBFouLi4likSJjaiWcBFaUKe/UayIoVa9JgGEDg\nJIHP6Obmm+sl+RqPL5oDd0E03V3t8se8irVq3T0u+yh4/fVhBMa62LWPgYHFs33+oUOHaLH4UQg/\nkcD/6ONT4IGEs9LS0qjTyczINCHN5h58//3373usWrWaEFji8vneYdWqtWi1hhEoRuAXl4dqc5pM\nmRv+mkxeLF68ImXZn8AolxBDJIEhBEJdZq6ptFgCnHHqJUuWqB2U0sc7TaNRocPhUJ27a7VkGnU6\nAyVJl+lzWyzd7+tzHz9+nJGRTRgUVIJNm8Zku6rzypUrfO21oXzhhY6cM+dDpqWl8cKFC2zYsCU9\nPAJZsmTlO+bQazydZMd3PjNZKG3aPI89e97ErVulAABW6wS89FKPPLYKMJmMkOVrSEsv1sS1+8o0\nOHjwIPT6mgBC1C0v4vr1rrh69Sry5cuXI5t0Oh3sdl9cu/YLgOcApECv34OAgAb3PVZSUjIAu8sW\nO86evYRbt14BMA8Z2ttin8PxG4A4iIyMowCSsHv3TyhSpBwuX26rHmcG0AzAOgApLuenAXBAp9Ph\nxo0bmD79AyQk/A6gA4CqUJSPMHjwMEiShOrVq8PhGArgZwDhkOWJCAuLgCzrsX//RKSljQNwGMBX\nqF69Z7Y+a1xcHKpVq4eLF3vA4ZiC8+cXIjLyeRw8uBOyLN/1XE9PT7z99tRM23x8fLBu3apsXVvj\nGeVxeIo8ChwOB8eMmUBPzyB6eARyzJgJed4OjRQLq+7u/tTpRhB4n4qSn4sXf3rvE1VEjnEBZnSF\n2UabzeuBipGGDx9Lg8Gmxodb0GIplykX/H5YvPhTKkoIgXUEvqKiBLFLly40m1+gyBkvRWAVgVlU\nFC/Wr/88jUZ/ms31aLH48ZNPRL/M+vVbUK8f6zIDr0q93swCBUrSZOpM4H+0WFqwbt0mdDgc7NVr\noLr9MoExaq5600zf+ejRo6nT2Qjo6O8fypMnT/L06dMsV+456nQGKopHlm5Fd2PTpk2026u4zOod\nVJRgZxcgDY37ITu+85lx4I8zf/31F3v3Hsh27brym2++ue/zhwwZSUUJoLt7HVqt3g+kC71582Za\nrYUptKaPEOhNL6+gB3ogLFiwkGXK1GD58rW5cuVKxsfHs3z552izRdBkKk9Z9mbt2o05ceJkKoov\nLZY2NJmK8vnnWzsd7qlTp1iwYEm6uZWixRLIevWa8Pr167x+/ToHDHid9eq9wBEjxjpbmFWt2pDA\nWhdnujpTKuPPP/9Mi8WXwNcEdlNRqnPYsDHO/YmJiff9gN+9ezdttmLMKJm/SZPJU4tba+QIzYHn\nEZcvX2aDBi/QYvFgUFCxR5L69fvvv3PDhg23dRYXL17kzJkzOWnSJB46dOiu43z00Ue0WFzLvlMp\nSbpczz9OSkrit99+yxUrVvDcuXNMTk6myeRGUa0oFhyt1lBu2bLFeU5iYiL37t3LI0eO3NO59uw5\ngCbTK+oibCrN5vYcOPAN5/5Bg16nkHrNWHsICipxX5/h/PnzPH78uPPNJC0tjbVrR9NiiVbfKKqz\nffsu2R7vf//7Hzt16sHhw0fx0qVL92WLhuDMmTP8/vvveeTIkbw25YHRHHgeUatWNA2GXgQuENhA\nRfHmH3/8kSe2nD17lr6+BWkydaReP4iK4s3Nmzff8fhNmzbRai3CjBZtKxgUFHrH4x0OB+fOnceO\nHbtz/PiJdxRiOnr0KJs2jWF4eF2OGvVmpgdCfHw8a9SIImDOtIDp5taKy5Yty9Hnvn79OsuXf45W\nawit1iKsVKl2phTDUaPGUJb7uVxvA0NCKjj3x8XFsU2bV+jhEciCBUvz22+/zfSZu3XrR5PJnYoS\nzGLFKjgfnElJSZw58x126dKHn3zySbbfXKZMmU5FKU5gNg2G7gwOLuZsnqyRPf7v/76ionjR3T2S\nFosvx42bnNcmPRCaA88DUlNTqdPp6dpHUVFE4UleMHToCOr1fVwc1TKWL1/rrue8/vooms1etNsr\n0MMjgL/88ssdj+3ZcwAVJYLAHJpMbVi8eAWuWLEiU8bEuXPn6OkZSJ1uKoH1VJR67Ny5p3P/oEHD\naDK1okgTfF/NKvmViuLN2NjYHH/21NRU7t+/nwcOHMgSv//6669pNHoSqEigPxUlgCtWrHTub9Wq\nI83mNgROqDb7cN++fSRFdovVGk7gGoUC43DWr9+CpCgcWrx4Mbdt23ZftiqKp0smEWm1tuD8+fNz\n/NmfNRITE9V7mK5dc46KEsD9+/fntWk5RnPgeYDD4VB/kdJDAQ7abLVyPJN8UF59tTdFDna6A/+V\nhQqVved5J06c4O7du+8qLXDz5k3q9Ram94QUpfU2KkojKkpBdu/en2R6PraraNiVTL01q1VrpMai\n/yQQRsBEnU7hqlWrc+cm/IetW7dSUbwJTCMwlXq9nXPmzMl0jMXirr5BCZsNhgGcNk2IYg0cOITA\nFJfPE0tv70J89905VJQA2mztaLUW4cCBQ7Nlj8PhoMFgoWsbObP51Sw2adyZ06dPq+m0GW9w7u5N\n+OWXX+a1aTlGc+B5xLx5C6gogZTl16koDVixYs0cKRz+8MMPLFq0AvPly8+YmFcyhQCyy9q1a6ko\nBSk60JykxVKfgwYNu+9xbsfly5dVFcEUNdbsOgO6Qas1hFu3buWiRYtotb7g8sd1jgaDxRnHfvXV\n3jQY+qgzbwcNhi7s0aN/rth4O6KiWlJUZ6bb8z6bNWub6RgvrwIEdru8RTV3vkV9+OGHanm/UBKU\npPcYHh6pxvD/Vs+5SkUJvOeaQzoxMa/QYmlC4FcCC2m1ej+Q7sqzRkpKiqqJn75w/SctFh8ePXo0\nr03LMZoDz0O2bt3KSZMmce7cuTkqbz98+LA6S1xD4C+aTDFs3rztvU+8DR999Al9fArRbvdj9+79\nmZycnKNx/ovD4eBzzzVQ0/U2UTSCcI1ht+HSpUt5+fJlVWjrDQKfU1EqZ1pQvHTpEosWLUc3t4p0\nc6vA4sUr8MqVKyTJAwcOsH//wezX7zVnCONBEUVdK1xsXcqoqFaZjvn00yVUlEBK0miaTK1ptwey\nYcNWfP/9D5iUlMT69ZvRZgulu/tz9PYuwPXr19NqLfSfGWAdbtiwIVs2JSQksE+fwSxcuByrVKl/\n17CVxu3Zvn073d39abMVodnsznnzFuaxRQ/GQ3fgQ4YMYYkSJVi2bFm+8MILt110eVYd+IMya9Ys\nmky9XBzCNRqNSl6blYXr16+zffuuLFiwjNqFZr5q7x+0WHydM9AzZ86wS5c+bNiwNd97b06WLJLE\nxERu3ryZW7ZscT7wfv31V1qt3gTGERhPRfHOFY3szz//gopSmMB6At9SUQpw5cqs5fJbtmzhkCFv\n0G73piz3JrCEilKZ/foNYVpaGnfv3s0ff/yR169fZ1JSEr298xP4TH2T2ESr1Ztnz559YHs1sk98\nfDyPHDnyVCwAP3QHvmHDBmccc+jQoRw6NGvMT3PgOWP+/PlUlOeZUSZ+gHa7b16bdVcOHjxIf/8i\nNJt9aDK5ccGCRQ80XrNmbQnMdnmIfcDGjV/KFVsXLVrMsLDnWKZMDX722ed3PO6zzz6j1fq8iw0X\nqdebblvUtHfvXgYEhFCvN9Nu9+X333+fK7ZqPJtkx3c+UCl9VFSU8/9VqlTBqlVa2W9u0aZNG0yZ\n8i5On45BYmJpKMo8TJ8+Ka/NuithYWE4ffooLly4AE9PT5jN5gcaLy4uAYCPyxYf3LqV8EBjpvPy\ny53w8sud7nlcamoqAIvLFjMoJj5Zji1fvjzOnIlFXFwcbDbbbZtfaGjkJrmmhbJgwQK0bdv2tvvG\njRvn/H9kZCQiIyNz67JPLVarFXv2bMPcuXNx8eJlREUtQt26dfParHsiyzICAgJyZawuXV7Czp0j\nER/vD0CCogxHly5jc2Xs7NKoUSMYjcORkDAdDkc4LJYZqFYtCh079oBeL2PgwB4IDw8HAFy4cAFt\n2nTBrl3b4O0dgMWL56BOnTqP1F6NJ5dNmzZh06ZN93WOxNtNJVyIiorCuXPnsmyfPHkymjZtCgCY\nNGkS9uzZc9sZuCRJt52taGhkh08+mYfp0z8ESXTtGgOLxQSSaNWqFfLnz/9IbIiNjcWgQaNw+vQ5\nhIbmx9q1G5CYOAZAIhTlLWze/B0qVaqEiIhI7NtXEampIwDsgtXaGQcP7kbhwoUfiZ0aTxfZ8Z33\ndOD3YtGiRZg7dy42btx421dmzYFr5AaxsbGIiKiFxMTGACSYTGuxe/dmFC9ePFvnHzhwAKtXfwmL\nxYxOnTrl+C3hueeisWPHywBi1C0z0abNISxcOAdubh5IS4sHIJQHbbZ2+OCDaHTs2DFH19J4tsmO\n77x9t9dssm7dOkyfPh1fffXVA8c7NTTuxsiRk3DzZl8kJc1HUtI83Lz5GoYNm5Ctc7ds2YJq1eph\nwoQEjB59HGFhETh9+nSO7EhOTgFgddliQ3JyKkwmE2TZANGYGRDStsfg4eGRo+toaGSHB3Lg/fr1\nQ1xcHKKiolChQgX07t07t+zSeMZISEjAqFHj0aRJW4wdOwGJiYmZ9l+8eBUORwnnz2QJXLhwJVtj\nv/baOMTHz4bDMRUpKR/j+vUYvP32ezmys3//zlCUgQC+BbAaijIOPXt2gE6nw4wZ06AokZDlN2C1\nRqFsWQ9ER0fn6DoaGtnhgRYxY2Njc8sOjWcYh8OB+vWbY88eOxITW+DHH1fhp59aoGjRIli+fBmM\nRjPq1q0Jq3Uybt0qD0AHRZmIVq3aZ2v869dvACjk/DktrRAuXz6YI1s7duwAh8OBd9+dDr1exqhR\nH6FBA9Hool+/3ihTphR27NiBgICO6NChA/T6Z6ZnikYe8MAx8HteQIuBa9yDgwcPolq15rh16yjE\nnCIVer0/9PqKSExcDOAKFKU5oqPDsWHDTyCJ3r17YsqU8dDp7v0SOXLkeMyatRHx8fMAXIWitMHy\n5e+jSZMmD/mTaWjknOz4Tm16oJHnpKamQpJMSF/8A2SkpQGpqZMABAAIQHz8QFith3DjxoX7Hv/N\nN0chISERn34aBZPJjPHjx2TbecfGxuLNN6fjypUbaNu2KTp0yN6sX0PjUaA5cI08JyxDzTz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} ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "# showing images\n", "x = np.linspace(1, 12, 100)\n", "y = x[:, np.newaxis]\n", "\n", "im = y * np.sin(x) * np.cos(y)\n", "print im.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(100, 100)\n" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "# imshow - note that origin is at the top-left by default!\n", "plt.imshow(im)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 23, "text": [ "" ] }, { "output_type": "display_data", "png": 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AAvxywz45bAM2kvNIfszaqyQrr4XqDLkysswBOSHnM7w84v2Wzr6cZrSSmeeC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\nnq+vvx+ynrLHcKkEZELVep4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xIhyBcMF4rgVfg54L/qjuSuFjrTJQcwUZtcdjckih9vZU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WAAAg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\nU6jnGXAhr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} ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "# Contour plot - note that origin here is at the bottom-left by default!\n", "plt.contour(im)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 24, "text": [ "" ] }, { "output_type": "display_data", "png": 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PstC3SvUfixR2FnGX+VavEYdd5L1HRC7IFLnleJG5r4nUVvnXlr94PCJblom8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\nchIAkYmJhEVHU9vE2RGjVOz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n+F7/YHg8Ku/Lv1bZ37lWk/pmdNaUe4kZWqJiwRah/tEm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\nIWyCHVDwTjmMNTGooF+0hP99U23eNSIWzyfgHAw2kxQDIOZS8Jg/yoh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} ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "# 3D plotting\n", "from mpl_toolkits.mplot3d import Axes3D\n", "ax = plt.axes(projection='3d')\n", "xgrid, ygrid = np.meshgrid(x, y.ravel())\n", "ax.plot_surface(xgrid, ygrid, im, cmap=plt.cm.jet, cstride=2, rstride=2, linewidth=0)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 25, "text": [ "" ] }, { "output_type": "display_data", "png": 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DG1qBPHCCqnuatr85wpgDuUGT9pxHYsSBBOiYh3CgPABHRZJksYYT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\n/uVf0traypEjRzh9+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} ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many, many more plot types available. One useful way to explore these is by\n", "looking at the matplotlib gallery: http://matplotlib.org/gallery.html\n", "\n", "You can test these examples out easily in the notebook: simply copy the ``Source Code``\n", "link on each page, and put it in a notebook using the ``%load`` magic.\n", "For example:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%load http://matplotlib.org/mpl_examples/pylab_examples/ellipse_collection.py" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 } ], "metadata": {} } ] }